[ { "path": ".github/FUNDING.yml", "content": "# These are supported funding model platforms\n\ngithub: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]\npatreon: firmai\nopen_collective: # Replace with a single Open Collective username\nko_fi: # Replace with a single Ko-fi username\ntidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel\ncommunity_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry\nliberapay: # Replace with a single Liberapay username\nissuehunt: # Replace with a single IssueHunt username\notechie: # Replace with a single Otechie username\ncustom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']\n" }, { "path": "README.md", "content": "# Machine Learning and Data Science Applications in Industry\n\n---------\n\n\n## šŸŒŸ We Are Growing!\n\nWe're seeking to collaborate with motivated, independent PhD graduates or doctoral students on approximately seven new projects in 2024. If youā€™re interested in contributing to cutting-edge investment insights and data analysis, please get in touch! This could be in colaboration with a university or as independent study. \n\n![image](https://github.com/user-attachments/assets/da97663a-b63f-4286-94cc-fcd168905109)\n\n\n### šŸš€ About Sov.ai\n\nSov.ai is at the forefront of integrating advanced machine learning techniques with financial data analysis to revolutionize investment strategies. We are working with **three of the top 10** quantitative hedge funds, and with many mid-sized and boutique firms. \n\nOur platform leverages diverse data sources and innovative algorithms to deliver actionable insights that drive smarter investment decisions. \n\nBy joining Sov.ai, you'll be part of a dynamic research team dedicated to pushing the boundaries of what's possible in finance through technology. Before expressing your interest, please be aware that the research will be predominantly challenging and experimental in nature.\n\n\n### šŸ” Research and Project Opportunities\n\nWe offer a wide range of projects that cater to various interests and expertise within machine learning and finance. Some of the exciting recent projects include:\n\n- **Predictive Modeling with GitHub Logs:** Develop models to predict market trends and investment opportunities using GitHub activity and developer data.\n- **Satallite Data Analysis:** Explore non-traditional data sources such as social media sentiment, satellite imagery, or web traffic to enhance financial forecasting.\n- **Data Imputation Techniques:** Investigate new methods for handling missing or incomplete data to improve the robustness and accuracy of our models.\n\nPlease visit [docs.sov.ai](https://docs.sov.ai) for more information on public projects that have made it into the subscription product. If you already have a corporate sponsor, we are also happy to work with them. \n\n### šŸŒ Why Join Sov.ai?\n\n- **Innovative Environment:** Engage with the latest technologies and methodologies in machine learning and finance.\n- **Collaborative Team:** Work alongside a team of experts passionate about driving innovation in investment insights.\n- **Flexible Projects:** Tailor your research to align with your interests and expertise, with the freedom to explore new ideas.\n- **Experienced Researchers:** Experts previously from NYU, Columbia, Oxford-Man Institute, Alan Turing Institute, and Cambridge.\n- **Post Research:** Connect with alumni that has moved on to DRW, Citadel Securities, Virtu Financial, Akuna Capital, HRT.\n\n\n### šŸ¤ How to Apply\n\nIf youā€™re excited about leveraging your expertise in machine learning and finance to drive impactful research and projects, weā€™d love to hear from you! Please reach out to us at [research@sov.ai](mailto:research@sov.ai) with your resume and a brief description of your research interests.\n\nJoin us in shaping the future of investment insights and making a meaningful impact in the world of finance!\n\n\n\n### Admin\n\nHave a look at the newly started [FirmAI Medium](https://medium.com/firmai) publication where we have experts of AI in business, write about their topics of interest. \n\n\nPlease add your tools and notebooks to this [Google Sheet](https://docs.google.com/spreadsheets/d/1pVdV3r4X3k5D1UtKbhMTmjU8mJTZSLAhJzycurgh_o4/edit?usp=sharing). Or simply add it to this subreddit, [r/datascienceproject](https://www.reddit.com/r/datascienceproject/) \n\nHighlight in **YELLOW** to get your package added, you can also just add it yourself with a **pull request**. \n\n

\n \n

\n\n\nA curated list of applied machine learning and data science notebooks and libraries accross different industries. The code in this repository is in Python (primarily using jupyter notebooks) unless otherwise stated. The catalogue is inspired by `awesome-machine-learning`. [r/datascienceproject](https://www.reddit.com/r/datascienceproject/) is a subreddit where you can share all your data science projects. \n\n***Caution:*** This is a work in progress, please contribute, especially if you are a subject expert in any of the industries listed below. If you are a **[**analytical, computational, statistical, quantitive**]** researcher/analyst in field **X** or a field **X** **[**machine learning engineer, data scientist, modeler, programmer**]** then your contribution will be greatly appreciated. \n\n\nIf you want to contribute to this list (please do), send me a pull request or contact me [@dereknow](https://twitter.com/dereknow) or on [linkedin](https://www.linkedin.com/in/snowderek/) or get in contact on the website [FirmAI](https://www.firmai.org).\nAlso, a listed repository should be deprecated if:\n\n* Repository's owner explicitly say that \"this library is not maintained\".\n* Not committed for long time (2~3 years).\n\n
\n\n**Help Needed:** If there is any contributors out there willing to help first populate and then maintain a Python analytics section **in any one of the following sub/industries,** please get in contact with me. Also contact me to add **additional industries**. \n\n
\n\n| | | |\n| -------------------------------- | -------------------------------- | --------------------------------- |\n| [Accommodation & Food](#accommodation) | [Agriculture](#agriculture) | [Banking & Insurance](#bankfin) |\n| [Biotechnological & Life Sciences](#biotech) | [Construction & Engineering](#construction) | [Education & Research](#education) |\n| [Emergency & Relief](#emergency) | [Finance](#finance) | [Manufacturing](#manufacturing) |\n| [Government and Public Works](#public) | [Healthcare](#healthcare) | [Media & Publishing](#media) |\n| [Justice, Law and Regulations](#legal) | [Miscellaneous](#miscellaneous) | [Accounting](#accounting) |\n| [Real Estate, Rental & Leasing](#realestate) | [Utilities](#utilities) | [Wholesale & Retail](#wholesale) |\n\n\n\n## Table of Contents\n\n### Industry Applications\n\n\n- [Accommodation & Food](#accommodation)\n - [Food](#accommodation-food)\n - [Restaurant](#accommodation-rest)\n - [Accommodation](#accommodation-acc)\n- [Accounting](#accounting)\n - [Machine Learning](#accounting-ml)\n - [Analytics](#accounting-analytics)\n - [Textual Analysis](#accounting-text)\n - [Data](#accounting-data)\n - [Research and Articles](#accounting-ra)\n - [Websites](#accounting-web)\n - [Courses](#accounting-course)\n- [Agriculture](#agriculture)\n - [Economics](#agriculture-econ)\n - [Development](#agriculture-dev)\n- [Banking & Insurance](#bankfin)\n - [Consumer Financial](#bankfin-cf)\n - [Management and Operations](#bankfin-mo)\n - [Valuation](#bankfin-value)\n - [Fraud](#bankfin-fraud)\n - [Insurance and Risk](#bankfin-ir)\n - [Physical](#bankfin-ph)\n - [Data](#bankfin-data)\n- [Biotechnological & Life Sciences](#biotech)\n - [General](#biotech-general)\n - [Sequencing](#biotech-seq)\n - [Chemoinformatics and drug discovery](#biotech-chem)\n - [Genomics](#biotech-gene)\n - [Life-sciences](#biotech-life) \n- [Construction & Engineering](#construction)\n - [Construction](#construction-const)\n - [Engineering](#construction-eng)\n - [Material Science](#construction-mat)\n- [Economics](#economics)\n - [General](#economics-general)\n - [Machine Learning](#economics-ml) \n - [Computational](#economics-computational) \n- [Education & Research](#education)\n - [Student](#education-student)\n - [School](#education-school)\n- [Emergency & Relief](#emergency)\n - [Preventative and Reactive](#emergency-prevent)\n - [Crime](#emergency-crime)\n - [Ambulance](#emergency-ambulance)\n - [Disaster Management](#emergency-disaster)\n- [Finance](#finance)\n - [Trading & Investment](#finance-trade)\n - [Data](#finance-data)\n- [Healthcare](#healthcare)\n - [General](#healthcare-general)\n- [Justice, Law and Regulations](#legal)\n - [Tools](#legal-tools)\n - [Policy and Regulatory](#legal-pr)\n - [Judicial](#legal-judicial)\n- [Manufacturing](#manufacturing)\n - [General](#manufacturing-general)\n - [Maintenance](#manufacturing-maintenance)\n - [Failure](#manufacturing-fail)\n - [Quality](#manufacturing-quality)\n- [Media & Publishing](#media)\n - [Marketing](#media-marketing)\n- [Miscellaneous](#miscellaneous)\n - [Art](#miscellaneous-art)\n - [Tourism](#miscellaneous-tour)\n- [Physics](#physics)\n - [General](#physics-general)\n - [Machine Learning](#physics-ml)\n- [Government and Public Works](#public)\n - [Social Policies](#public-social)\n - [Election Analysis](#public-elect)\n - [Disaster Management](#public-dis)\n - [Politics](#public-poli)\n - [Charities](#public-charity)\n- [Real Estate, Rental & Leasing](#realestate)\n - [Real Estate](#realestate-real)\n - [Rental & Leasing](#realestate-rental)\n- [Utilities](#utilities)\n - [Electricity](#utilities-elect)\n - [Coal, Oil & Gas](#utilities-coal)\n - [Water & Pollution](#utilities-water)\n - [Transportation](#utilities-transport)\n- [Wholesale & Retail](#wholesale)\n - [Wholesale](#wholesale-whole)\n - [Retail](#wholesale-retail)\n \n\n\n\n## ML/DS Career Section for Industry Machine Learning\nSee [data-science-career repo](https://github.com/firmai/data-science-career) for more. \n\n### Platforms:\n1. [Triplebyte](https://triplebyte.com/a/Nosq7GM/d) - Take a quiz. Get offers from multiple top tech companies at once (now have a machine learning track).\n1. [Toptal](https://www.toptal.com/) - Developers seeking to gain entry into the Toptal community are put through a battery of personality and technical tests.\n1. [Hired](https://hired.com/) - Hired matches employers with qualified candidates through a combination of in-house algorithms and online support.\n1. [Kaggle](https://www.kaggle.com/jobs) - Scalable Path is a premium talent matching service.\n\n### Reviews:\n\n- [Glassdoor](https://www.glassdoor.com/index.htm) - Best employee narratives.\n- [Indeed](https://www.indeed.com/) - Best coverage.\n- [Kununu](https://www.kununu.com/us) - Best well-rounded infromation.\n- [Comparably](https://www.comparably.com/) - Best comparison functionality. \n- [InHerSight](https://www.inhersight.com/) - Best female-friendly perspective.\n\n\n## Accommodation & Food\n\n\n**Food**\n\n- [RobotChef](https://github.com/bschreck/robo-chef) - Refining recipes based on user reviews.\n- [Food Amenities](https://github.com/Ankushr785/Food-amenities-demand-prediction) - Predicting the demand for food amenities using neural networks \n- [Recipe Cuisine and Rating](https://github.com/catherhuang/FP3-recipe) - Predict the rating and type of cuisine from a list of ingredients. \n- [Food Classification](https://github.com/stratospark/food-101-keras) - Classification using Keras. \n- [Image to Recipe](https://github.com/Murgio/Food-Recipe-CNN) - Translate an image to a recipe using deep learning. \n- [Calorie Estimation](https://github.com/jubins/DeepLearning-Food-Image-Recognition-And-Calorie-Estimation) - Estimate calories from photos of food. \n- [Fine Food Reviews](https://github.com/Architectshwet/Amazon-Fine-Food-Reviews) - Sentiment analysis on Amazon Fine Food Reviews. \n\n\n**Restaurant** \n\n- [Restaurant Violation](https://github.com/nd1/DC_RestaurantViolationForecasting) - Food inspection violation forecasting. \n- [Restaurant Success](https://github.com/alifier/Restaurant_success_model) - Predict whether a restaurant is going to fail. \n- [Predict Michelin](https://github.com/josephofiowa/dc-michelin-challenge/tree/master/submissions) - Predict the likelihood that restaurant is a Michelin restaurant. \n- [Restaurant Inspection](https://github.com/gzsuyu/Data-Analysis-NYC-Restaurant-Inspection-Data) - An inspection analysis to see if cleanliness is related to rating.\n- [Sales](https://github.com/ayeright/sales-forecast-lstm) - Restaurant sales forecasting with LSTM.\n- [Visitor Forecasting](https://github.com/anki1909/Recruit-Restaurant-Visitor-Forecasting) - Reservation and visitation number prediction. \n- [Restaurant Profit](https://github.com/everAspiring/RegressionAnalysis) - Restaurant regression analysis. \n- [Competition](https://github.com/klin90/missinglink) - Restaurant competitiveness analysis.\n- [Business Analysis](https://github.com/nvodoor/RBA) - Restaurant business analysis project. \n- [Location Recommendation](https://github.com/sanatasy/Restaurant_Risk) - Restaurant location recommendation tool and analysis. \n- [Closure, Rating and Recommendation](https://github.com/Lolonon/Restaurant-Analytical-Solution) - Three prediction tasks using Yelp data. \n- [Anti-recommender](https://github.com/Myau5x/anti-recommender) - Find restaurants you donā€™t want to attend. \n- [Menu Analysis](https://github.com/bzjin/menus) - Deeper analysis of restaurants through their menus. \n- [Menu Recommendation](https://github.com/rphaneendra/Menu-Similarity) - NLP to recommend restaurants with similar menus. \n- [Food Price](https://gist.github.com/analyticsindiamagazine/f9b2ba171a0eef9ad396ce6f1b83bbbc) - Predict food cost. \n- [Automated Restaurant Report](https://github.com/firmai/interactive-corporate-report) - Automated machine learning company report.\n\n\n**Accommodation** \n\n- [Peer-to-Peer Housing](https://github.com/rochiecuevas/shared_accommodations) - The effect of peer to peer rentals on housing. \n- [Roommate Recommendation](https://github.com/SiddheshAcharekar/Liveright) - A system for students seeking roommates. \n- [Room Allocation](https://github.com/nus-usp/room-allocation) - Room allocation process. \n- [Dynamic Pricing](https://github.com/marcotav/hotels) - Hotel dynamic pricing calculations. \n- [Hotel Similarity](https://github.com/Montclair-State-University-Info368/Assignment-6) - Compare brands that directly compete\n- [Hotel Reviews](https://github.com/EliadProject/Hotels-Data-Science) - Cluster hotel reviews. \n- [Predict Prices](https://github.com/morenobcn/capstone_hotels_arcpy) - Predict hotel room rates. \n- [Hotels vs Airbnb](https://github.com/morenobcn/hotels_vs_airbnb_proof_of_concept) - Comparing the two approaches. \n- [Hotel Improvement](https://github.com/argha48/smarthotels) - Analyse reviews to suggest hotel improvements. \n- [Orders](https://github.com/Hasan330/Order-Cancellation-Prediction-Model) - Order cancellation prediction for hotels. \n- [Fake Reviews](https://github.com/danielmachinelearning/HotelSpamDetection) - Identify whether reviews are fake/spam. \n- [Reverse Image Lodging](https://github.com/starfoe/Eye-bnb) - Find your preferred lodging by uploading an image. \n\n\n\n\n## Accounting\n\n\n#### Machine Learning\n* [Chart of Account Prediction](https://github.com/agdgovsg/ml-coa-charging ) - Using labeled data to suggest the account name for every transaction.\n* [Accounting Anomalies](https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_CoLab.ipynb) - Using deep-learning frameworks to identify accounting anomalies.\n* [Financial Statement Anomalies](https://github.com/rameshcalamur/fin-stmt-anom) - Detecting anomalies before filing, using R.\n* [Useful Life Prediction (FirmAI)](http://www.firmai.org/documents/Aged%20Debtors/) - Predict the useful life of assets using sensor observations and feature engineering.\n* [AI Applied to XBRL](https://github.com/Niels-Peter/XBRL-AI) - Standardized representation of XBRL into AI and Machine learning.\n \n\n#### Analytics\n\n* [Forensic Accounting](https://github.com/mschermann/forensic_accounting) - Collection of case studies on forensic accounting using data analysis. On the lookout for more data to practise forensic accounting, *please get in [touch](https://github.com/mschermann/)* \n* [General Ledger (FirmAI)](http://www.firmai.org/documents/General%20Ledger/) - Data processing over a general ledger as exported through an accounting system.\n* [Bullet Graph (FirmAI)](http://www.firmai.org/documents/Bullet-Graph-Article/) - Bullet graph visualisation helpful for tracking sales, commission and other performance.\n* [Aged Debtors (FirmAI)](http://www.firmai.org/documents/Aged%20Debtors/) - Example analysis to invetigate aged debtors.\n* [Automated FS XBRL](https://github.com/CharlesHoffmanCPA/charleshoffmanCPA.github.io) - XML Language, however, possibly port analysis into Python.\n\n\n#### Textual Analysis\n\n* [Financial Sentiment Analysis](https://github.com/EricHe98/Financial-Statements-Text-Analysis) - Sentiment, distance and proportion analysis for trading signals.\n* [Extensive NLP](https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb) - Comprehensive NLP techniques for accounting research.\n\n\n#### Data, Parsing and APIs\n\n* [EDGAR](https://github.com/TiesdeKok/UW_Python_Camp/blob/master/Materials/Session_5/EDGAR_walkthrough.ipynb) - A walk-through in how to obtain EDGAR data. \n* [PyEDGAR](https://github.com/gaulinmp/pyedgar) - A library for downloading, caching, and accessing EDGAR filings.\n* [IRS](http://social-metrics.org/sox/) - Acessing and parsing IRS filings.\n* [Financial Corporate](http://raw.rutgers.edu/Corporate%20Financial%20Data.html) - Rutgers corporate financial datasets.\n* [Non-financial Corporate](http://raw.rutgers.edu/Non-Financial%20Corporate%20Data.html) - Rutgers non-financial corporate dataset.\n* [PDF Parsing](https://github.com/danshorstein/python4cpas/blob/master/03_parsing_pdf_files/AR%20Aging%20-%20working.ipynb) - Extracting useful data from PDF documents. \n* [PDF Tabel to Excel](https://github.com/danshorstein/ficpa_article) - How to output an excel file from a PDF.\n\n\n#### Research And Articles\n\n* [Understanding Accounting Analytics](http://social-metrics.org/accountinganalytics/) - An article that tackles the importance of accounting analytics.\n* [VLFeat](http://www.vlfeat.org/) - VLFeat is an open and portable library of computer vision algorithms, which has Matlab toolbox.\n\n\n#### Websites\n\n* [Rutgers Raw](http://raw.rutgers.edu/) - Good digital accounting research from Rutgers.\n\n\n#### Courses\n\n* [Computer Augmented Accounting](https://www.youtube.com/playlist?list=PLauepKFT6DK8TaNaq_SqZW4LIDJhCkZe2) - A video series from Rutgers University looking at the use of computation to improve accounting.\n* [Accounting in a Digital Era](https://www.youtube.com/playlist?list=PLauepKFT6DK8_Xun584UQPPsg1grYkWw0) - Another series by Rutgers investigating the effects the digital age will have on accounting.\n\n\n\n## Agriculture\n\n\n**Economics**\n\n- [Prices](https://github.com/deadskull7/Agricultural-Price-Prediction-and-Visualization-on-Android-App) - Agricultural price prediction. \n- [Prices 2](https://github.com/Vipul115/Statistical-Time-Series-Analysis-on-Agricultural-Commodity-Prices) - Agricultural price prediction. \n- [Yield](https://github.com/DFS-UCU/UkrainianAgriculture) - Agricultural analysis looking at crop yields in Ukraine. \n- [Recovery](https://github.com/vicelab/slaer) - Strategic land use for agriculture and ecosystem recovery\n- [MPR](https://github.com/gumballhead/mpr) - Mandatory Price Reporting data from the USDA's Agricultural Marketing Service.\n\n\n**Development**\n\n- [Segmentation](https://github.com/chrieke/InstanceSegmentation_Sentinel2) - Agricultural field parcel segmentation using satellite images. \n- [Water Table](https://github.com/jfzhang95/LSTM-water-table-depth-prediction) - Predicting water table depth in agricultural areas. \n- [Assistant](https://github.com/surajmall/Agriculture-Assistant/tree/master/models) - Notebooks from agricultural assistant. \n- [Eco-evolutionary](https://github.com/tecoevo/agriculture) - Eco-evolutionary dynamics. \n- [Diseases](https://github.com/gauravmunjal13/Agriculture) - Identification of crop diseases and pests using Deep Learning framework from the images.\n- [Irrigation and Pest Prediction](https://github.com/divyam3897/agriculture) - Analyse irrigation and predict pest likelihood. \n\n\n\n\n## Banking & Insurance\n\n\n#### Consumer Finance\n\n- [Loan Acceptance](https://github.com/Paresh3189/Bankruptcy-Prediction-Growth-Modelling) - Classification and time-series analysis for loan acceptance.\n- [Predict Loan Repayment](https://github.com/Featuretools/predict-loan-repayment) - Predict whether a loan will be repaid using automated feature engineering.\n- [Loan Eligibility Ranking](https://github.com/RealRadOne/Gyani-The-Loan-Eligibility-Predictor) - System to help the banks check if a customer is eligible for a given loan.\n- [Home Credit Default (FirmAI)](http://www.firmai.org/documents/Aggregator/#each-time-step-takes-30-seconds) - Predict home credit default.\n- [Mortgage Analytics](https://github.com/abuchowdhury/Mortgage_Bank_Loan_Analtsics/blob/master/Mortgage%20Bank%20Loan%20Analytics.ipynb) - Extensive mortgage loan analytics.\n- [Credit Approval](https://github.com/IBM-Cloud-DevFest-2018/Data-Science-for-Banking/blob/master/02-CreditCardApprovalModel/CreditCardApprovalModel.ipynb) - A system for credit card approval.\n- [Loan Risk](https://github.com/Brett777/Predict-Risk) - Predictive model to help to reduce charge-offs and losses of loans.\n- [Amortisation Schedule (FirmAI)](http://www.firmai.org/documents/Amortization%20Schedule/) - Simple amortisation schedule in python for personal use.\n\n\n\n#### Management and Operation\n\n- [Credit Card](https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/03_ipy_notebooks/clv_prediction.ipynb) - Estimate the CLV of credit card customers.\n- [Survival Analysis](https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/01_code/01_02_clv_survival/Survival_Analysis.py) - Perform a survival analysis of customers.\n- [Next Transaction](https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/01_code/01_02_clv_survival/Customer_NextTransaction_Prediction.py) - Deep learning model to predict the transaction amount and days to next transaction.\n- [Credit Card Churn](https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/01_code/01_02_clv_survival/Customer_NextTransaction_Prediction.py) - Predicting credit card customer churn.\n- [Bank of England Minutes](https://github.com/sekhansen/mpc_minutes_demo/blob/master/information_retrieval.ipynb) - Textual analysis over bank minutes.\n- [CEO](https://github.com/kaumaron/Data_Science/tree/master/CEO_Compensation) - Analysis of CEO compensation. \n\n\n#### Valuation\n\n- [Zillow Prediction](https://github.com/eswar3/Zillow-prediction-models) - Zillow valuation prediction as performed on Kaggle.\n- [Real Estate](https://github.com/denadai2/real-estate-neighborhood-prediction) - Predicting real estate prices from the urban environment.\n- [Used Car](https://nbviewer.jupyter.org/github/albahnsen/PracticalMachineLearningClass/blob/master/exercises/P1-UsedVehiclePricePrediction.ipynb) - Used vehicle price prediction.\n\n\n\n#### Fraud\n\n- [XGBoost](https://github.com/KSpiliop/Fraud_Detection) - Fraud Detection by tuning XGBoost hyper-parameters with Simulated Annealing\n- [Fraud Detection Loan in R](https://github.com/longtng/frauddetectionproject/blob/master/A%20Consideration%20Point%20of%20%20Fraud%20Detection%20in%20Bank%20Loans%20Project%20Code.ipynb) - Fraud detection in bank loans.\n- [AML Finance Due Diligence](https://github.com/Michaels72/AML-Due-Diligence/blob/master/AML_Finance_DD.ipynb) - Search news articles to do finance AML DD.\n- [Credit Card Fraud](https://github.com/am-aditya/Artificial-Intelligence-for-Banking/blob/master/03_ipy_notebooks/fraud_detection.ipynb) - Detecting credit card fraud.\n\n\n#### Insurance and Risk\n\n\n- [Car Damage Detective](https://github.com/neokt/car-damage-detective) - Assessing car damage with convolution neural networks for a personal auto *claims.*\n- [Medical Insurance Claims](https://github.com/roshank1605A04/Insurance-Claim-Prediction/blob/master/InsuranceClaim.ipynb) - Predicting medical insurance claims. \n- [Claim Denial](https://github.com/slegroux/claimdenial/blob/master/Claim%20Denial.ipynb) - Predicting insurance claim denial\n- [Claim Fraud](https://github.com/rshea3/alpha-insurance) - Predictive models to determine which automobile claims are fraudulent. \n- [Claims Anomalies](https://github.com/dchannah/fraudhacker) - Anomaly detection system for medical insurance claims data.\n- [Actuarial Sciences (R)](https://github.com/JSchelldorfer/ActuarialDataScience) - A range of actuarial tools in R.\n- [Bank Failure](https://github.com/Shomona/Bank-Failure-Prediction/blob/master/Bank.ipynb) - Predicting bank failure.\n- [Risk Management](https://github.com/andrey-lukyanov/Risk-Management) - Finance risk engagement course resources.\n- [VaR GaN](https://github.com/hamaadshah/market_risk_gan_keras) - Estimate Value-at-Risk for market risk management using Keras and TensorFlow.\n- [Compliance](https://github.com/SaiBiswas/Bank-Grievance-Compliance-Management/blob/master/The%20Main%20File.ipynb) - Bank Grievance Compliance Management. \n- [Stress Testing](https://github.com/apbecker/Systemic_Risk/blob/master/Generalized.ipynb) - ECB stress testing.\n- [Stress Testing Techniques](https://github.com/kaitai/stress-testing-with-jupyter/blob/master/Playing%20with%20financial%20data%20and%20Python%203.ipynb) - A notebook with various stress testing exercises. \n- [Reverse Stress Test](https://github.com/arcadynovosyolov/reverse_stress_testing/blob/master/reverse_stress_testing.ipynb) - Given a portfolio and a predefined loss size, determine which factors stress (scenarios) would lead to that loss\n- [BoE stress test](https://github.com/VankatPetr/BoE_stress_test/blob/master/BoE_stress_test_5Y_cummulative_imparment_charge.ipynb)- Stress test results and plotting. \n- [Recovery](https://github.com/hkacmaz/Bankin_Recovery/blob/master/Banking_Recovery.ipynb) - Recovery of money owed. \n- [Quality Control](https://github.com/mick-zhang/Quality-Control-for-Banking-using-LDA-and-LDA-Mallet) - Quality control for banking using LDA\n\n\n\n#### Physical\n\n* [Bank Note Fraud Detection](https://github.com/apoorv-goel/Bank-Note-Authentication-Using-DNN-Tensorflow-Classifier-and-RandomForest) - Bank Note Authentication Using DNN Tensorflow Classifier and RandomForest.\n* [ATM Surveillance](https://github.com/ShreyaGupta08/InfosysHack) - ATM Surveillance in banks use case.\n\n\n\n## Biotechnological & Life Sciences\n\n\n**General**\n\n- [Programming](https://github.com/burkesquires/python_biologist) - Python Programming for Biologists\n- [Introduction DL](https://colab.research.google.com/drive/17E4h5aAOioh5DiTo7MZg4hpL6Z_0FyWr) - A Primer on Deep Learning in Genomics\n- [Pose](https://github.com/talmo/leap) - Estimating animal poses using DL.\n- [Privacy](https://github.com/greenelab/SPRINT_gan) - Privacy preserving NNs for clinical data sharing. \n- [Population Genetics](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004845) - DL for population genetic inference. \n- [Bioinformatics Course](https://github.com/ricket-sjtu/bioinformatics) - Course materials for Computational *Biology*and Bioinformatics\n- [Applied Stats](https://github.com/waldronlab/AppStatBio) - Applied Statistics for High-Throughput *Biology*\n- [Scripts](https://github.com/mingzhangyang/Mybiotools) - Python scripts for biologists. \n- [Molecular NN](https://github.com/mitmedialab/Evolutron) - A mini-framework to build and train neural networks for molecular *biology*.\n- [Systems Biology Simulations](https://github.com/hallba/WritingSimulators) - Systems *biology* practical on writing simulators with F# and Z3\n- [Cell Movement](https://github.com/jrieke/lstm-biology) - LSTM to predict biological cell movement.\n- [Deepchem](https://github.com/deepchem/deepchem) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology\n\n\n**Sequencing** \n\n- [DNA, RNA and Protein Sequencing](https://github.com/ehsanasgari/Deep-Proteomics) - Anew representation for biological sequences using DL.\n- [CNN Sequencing](https://github.com/budach/pysster) - A toolbox for learning motifs from DNA/RNA sequence data using convolutional neural networks\n- [NLP Sequencing](https://github.com/hussius/deeplearning-biology) - Language transfer learning model for genomics\n\n\n**Chemoinformatics and drug discovery**\n\n- [Novel Molecules](https://github.com/HIPS/neural-fingerprint) - A convolutional net that can learn features.\n- [Automating Chemical Design](https://github.com/aspuru-guzik-group/chemical_vae) - Generate new molecules for efficient exploration.\n- [GAN drug Discovery](https://github.com/gablg1/ORGAN) - A method that combines generative models with reinforcement learning.\n- [RL](https://github.com/MarcusOlivecrona/REINVENT) - generating compounds predicted to be active against a biological target.\n- [One-shot learning](https://github.com/deepchem/deepchem) - Python library that aims to make the use of machine-learning in drug discovery straightforward and convenient.\n\n\n**Genomics**\n\n- [Jupyter Genomics](https://github.com/ucsd-ccbb/jupyter-genomics) - Collection of computation biology and bioinformatics notebooks. \n- [Variant calling](https://github.com/google/deepvariant) - Correctly identify variations from the reference genome in an individual's DNA.\n- [Gene Expression Graphs](https://github.com/mila-iqia/gene-graph-conv) - Using convolutions on an image. \n- [Autoencoding Expression](https://github.com/greenelab/adage) - Extracting relevant patterns from large sets of gene expression data\n- [Gene Expression Inference](https://github.com/uci-cbcl/D-GEX) - Predict the expression of specified target genes from a panel of about 1,000 pre-selected ā€œlandmark genesā€. \n- [Plant Genomics](https://github.com/widdowquinn/Teaching-EMBL-Plant-Path-Genomics) - Presentation and example material for *Plant* and Pathogen Genomics\n\n\n**Life-sciences**\n\n- [Plants Disease](https://github.com/viritaromero/Plant-diseases-classifier) - App that detects diseases in *plants* using a deep learning model.\n- [Leaf Identification](https://github.com/AayushG159/Plant-Leaf-Identification) - Identification of *plants* through *plant* leaves on the basis of their shape, color and texture.\n- [Crop Analysis](https://github.com/openalea/eartrack) - An imaging library to detect and track future position of ears on maize *plants*\n- [Seedlings](https://github.com/mfsatya/PlantSeedlings-Classification) - *Plant* Seedlings Classification from kaggle competition\n- [Plant Stress](https://github.com/Planteome/ontology-of-plant-stress) - An ontology containing plant stresses; biotic and abiotic.\n- [Animal Hierarchy](https://github.com/sacul-git/hierarpy) - Package for calculating *animal* dominance hierarchies.\n- [Animal Identification](https://github.com/A7med01/Deep-learning-for-Animal-Identification) - Deep learning for animal identification. \n- [Species](https://github.com/NomaanAhmed/BigData_AnimalSpeciesAnalysis) - Big Data analysis of different species of *animals*\n- [Animal Vocalisations](https://github.com/timsainb/AVGN) - A generative network for animal vocalizations\n- [Evolutionary](https://github.com/hardmaru/estool) - Evolution Strategies Tool\n- [Glaciers](https://github.com/OGGM/oggm-edu) - Educational material about glaciers.\n\n\n\n## Construction & Engineering\n\n\n**Construction**\n\n- [DL Architecture](https://github.com/carolineh101/deep-learning-architecture) - Deep learning classifier and image generator for building architecture.\n- [Construction Materials](https://github.com/damontallen/Construction-materials) - A course on construction materials. \n- [Bad Actor Risk Model](https://github.com/dariusmehri/Social-Network-Bad-Actor-Risk-Tool) - Risk model to improve construction related building safety\n- [Inspectors](https://github.com/dariusmehri/Tracking-Inspectors-with-Euclidean-Distance-Algorithm) - Determine the assigned inspections. \n- [Corrupt Social Interactions](https://github.com/dariusmehri/Social-Network-Analysis-to-Expose-Corruption) - Uncover potential corrupt social interactions between an industry member and the staff at the DOB\n- [Risk Construction](https://github.com/dariusmehri/Risk-Screening-Tool-to-Predict-Accidents-at-Construction-Sites) - Identify high risk construction. \n- [Facade Risk](https://github.com/dariusmehri/Algorithm-for-Finding-Buildings-with-Facade-Risk) - A risk model to predict unsafe facades.\n- [Staff Levels](https://github.com/dariusmehri/Predicting-Staff-Levels-for-Front-line-Workers) - Predicting staff levels for front line workers. \n- [Injuries](https://github.com/dariusmehri/Topic-Modeling-and-Analysis-of-Building-Related-Injuries) - Building related injuries topic modelling. \n- [Building Violations](https://github.com/dariusmehri/Predictive-Analysis-of-Building-Violations) - Predictive analysis of building violations. \n- [Productivity](https://github.com/dariusmehri/Inspection-Productivity-Analysis-and-Visualization-with-Tableau) - Productivity analysis and inspection with Tableau. \n\n\n**Engineering:**\n\n- [Structural Analysis](https://github.com/ritchie46/anaStruct) - 2D Structural Analysis in Python. \n- [Structural Engineering](https://github.com/buddyd16/Structural-Engineering) - Structural engineering modules.\n- [Nusa](https://github.com/JorgeDeLosSantos/nusa) - Structural analysis using the finite element method.\n- [StructPy](https://github.com/BrianChevalier/StructPy) - Structural Analysis Library for Python based on the direct stiffness method\n- [Aileron](https://github.com/albiboni/AileronSimulation) - Structural analysis of the aileron of a Boeing 737\n- [Vibration](https://github.com/vibrationtoolbox/vibration_toolbox) - Educational vibration programs. \n- [Civil](https://github.com/ebrahimraeyat/Civil) - Collection of civil engineering tools in FreeCAD\n- [GEstimator](https://github.com/manuvarkey/GEstimator) - Simple civil estimation software\n- [Fatpack](https://github.com/Gunnstein/fatpack) - Functions and classes for fatigue analysis of data series.\n- [Pysteel](https://github.com/yajnab/pySteel) - Automated design of different steel structure \n- [Structural Uncertainty](https://github.com/davidsteinar/structural-uncertainty) - Quantifying structural uncertainty with deep learning.\n- [Pymech](https://github.com/jellespijker/pymech) - A Python module for mechanical engineers\n- [Aerospace Engineering](https://github.com/AlvaroMenduina/Jupyter_Notebooks/tree/master/Introduction_Aerospace_Engineering) - Astrodynamics and Statistics\n- [Interactive Quantum Chemistry](https://github.com/psi4/psi4numpy) - Combining Psi4 and Numpy for education and development.\n- [Chemical and Process Engineering](https://github.com/CAChemE/learn) - Various resources.\n- [PyTherm](https://github.com/iurisegtovich/PyTherm-applied-thermodynamics) - Applied Thermodynamics \n- [Aerogami](https://github.com/kshitizkhanal7/Aerogami) - Aerodynamics using planes.\n- [Electro geophysics](https://github.com/geoscixyz/em-apps) - Interactive applications for electromagnetics in geophysics\n- [Graph Signal](https://github.com/mdeff/pygsp_tutorial_graphsip) - Graph signal processing tutorial.\n- [Mechanical Vibrations](https://github.com/DocVaughan/MCHE485---Mechanical-Vibrations) - Mechanical Vibrations at the Univsersity of Louisiana.\n- [Process Dynamics](https://github.com/OpenChemE/CHBE356) - Process Dynamics and Control\n- [Battery Life Cycle](https://github.com/rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation) - Data driven prediction of batter life cycle. \n- [Wind Energy](https://github.com/DTUWindEnergy/Python4WindEnergy) - Python for wind energy\n- [Energy Use](https://github.com/openeemeter/eemeter/blob/master/scripts/tutorial.ipynb) - Standard methods for calculating normalized metered energy consumption\n- [Nuclear Radiation](https://github.com/HitarthiShah/Radiation-Data-Analysis) - How people are affected by radiations emitted by nuclear power plants\n\n\n\n**Material Science**\n\n\n- [Python Materials Genomics](https://github.com/materialsproject/pymatgen/) - Robust material analysis code used in a well-established project.\n- [Materials Mining](https://github.com/dchannah/materials_mining) - Scripts for simulations and analysis of materials.\n- [Emmet](https://github.com/materialsproject/emmet) - Build databases of material properties. \n- [Megnet](https://github.com/materialsvirtuallab/megnet) - Graph networks as a ML framework for Molecules and Crystals\n- [Atomate](https://github.com/hackingmaterials/atomate) - Pre-built workflows for computational material science.\n- [Bylaws Compliance](https://github.com/Mehranov/UnderstandingAndPredictingPropertyMaintenanceFines/blob/master/Assignment4_complete.ipynb) - Predicting property fines. \n- [Asphalt Binder](https://github.com/sierraporta/asphalt_binder) - Construction materials, free energy and chemical composition of asphalt binder.\n- [Steel](https://github.com/hbutsuak95/Quality-Optimization-of-Steel) - Optimisation of steel.\n- [Awesome Materials Informatics](https://github.com/tilde-lab/awesome-materials-informatics) - Curated list of known efforts in materials informatics.\n\n\n\n## Economics\n\n\n**General**\n\n- [Trading Economics API](https://github.com/tradingeconomics/tradingeconomics) - Information for 196 countries.\n- [Development Economics](https://github.com/jhconning/Dev-II/tree/master/notebooks) - Development microeconomics are written mostly as interactive jupyter notebooks\n- [Applied Econ & Fin](https://github.com/lnsongxf/Applied_Computational_Economics_and_Finance/blob/master/Chapter05.ipynb) - Applied Computational Economics and Finance\n- [Macroeconomics](https://github.com/jlperla/ECON407_2018) - Topics in macroeconomics with notebook examples. \n\n\n**Machine Learning**\n\n- [EconML](https://github.com/microsoft/EconML) - Automated Learning and Intelligence for Causation and *Economics.*\n- [Auctions](https://github.com/saisrivatsan/deep-opt-auctions) - Optimal auctions using deep learning. \n\n\n**Computational**\n\n- [Quant Econ](https://github.com/jstac/quantecon_nyu_2016) - Quantitative economics course by NYU\n- [Computational](https://github.com/zhentaoshi/econ5170) - Computational methods in economics. \n- [Computational 2](https://github.com/QuantEcon/columbia_mini_course) - Small course in computational economics. \n- [Econometric Theory](https://github.com/jstac/econometrics/tree/master/notebooks) - Notebooks of A Primer on Econometric theory. \n\n\n## Education & Research\n\n\n**Student**\n\n- [Student Performance](https://github.com/roshank1605A04/Education-Process-Mining) - Mining student performance using machine learning. \n- [Student Performance 2](https://github.com/janzaib-masood/Educational-Data-Mining) - Student exam performance.\n- [Student Performance 3](https://github.com/RohithYogi/Student-Performance-Prediction) - Student achievement in secondary education.\n- [Student Performance 4](https://github.com/roshank1605A04/Students-Performance-Analytics) - Students Performance Evaluation using Feature Engineering\n- [Student Intervention](https://github.com/eloyekunle/student_intervention/blob/master/student_intervention.ipynb) - Building a student intervention system. \n- [Student Enrolment](https://github.com/arrahman17/Learning-Analytics-Project-) - Student enrolment and performance analysis. \n- [Academic Performance](https://github.com/janzaib-masood/Educational-Data-Mining) - Explore the demographic and family features that have an impact a student's academic performance. \n- [Grade Analysis](https://github.com/kaumaron/Data_Science/tree/master/Grade_Analysis) - Student achievement analysis. \n\n\n**School**\n\n- [School Choice](https://github.com/nprapps/school-choice) - Data analysis for education's school choice.\n- [School Budgets and Priorities](https://github.com/tullyvelte/SchoolPerformanceDataAnalysis) - Helping the school board and mayor make strategic decisions regarding future school budgets and priorities\n- [School Performance](https://github.com/bradleyrobinson/School-Performance) - Data analysis practice using data from data.utah.gov on school performance.\n- [School Performance 2](https://github.com/vtyeh/pandas-challenge) - Using pandas to analyze school and student performance within a district\n- [School Performance 3](https://github.com/benattix/philly-schools) - Philadelphia School Performance\n- [School Performance 4](https://github.com/adrianakopf/NJPublicSchools) - NJ School Performance\n- [School Closure](https://github.com/whugue/school-closure) - Identify schools at risk for closure by performance and other characteristics.\n- [School Budgets](https://github.com/datacamp/course-resources-ml-with-experts-budgets/blob/master/notebooks/1.0-full-model.ipynb) - Tools and techniques for school budgeting. \n- [School Budgets](https://github.com/nymarya/school-budgets-for-education/tree/master/notebooks) - Same as a above, datacamp. \n- [PyCity](https://github.com/JonathanREB/Budget_SchoolsAnalysis/blob/master/PyCitySchools_starter.ipynb) - School analysis. \n- [PyCity 2](https://github.com/1davegalloway/SchoolDistrictAnalysis) - School budget vs school results. \n- [Budget NLP](https://github.com/jinsonfernandez/NLP_School-Budget-Project) - NLP classification for budget resources. \n- [Budget NLP 2](https://github.com/DivyaMadhu/School-Budget-Prediction) - Further classification exercise. \n- [Budget NLP 3](https://github.com/sushant2811/SchoolBudgetData/blob/master/SchoolBudgetData.ipynb) - Budget classification. \n- [Survey Analysis](https://github.com/kaumaron/Data_Science/tree/master/Education) - Education survey analysis. \n\n\n## Emergency & Police\n\n\n**Preventative and Reactive**\n\n- [Emergency Mapping](https://github.com/aeronetlab/emergency-mapping) - Detection of destroyed houses in California \n- [Emergency Room](https://github.com/roshetty/Supporting-Emergency-Room-Decision-Making-with-Relevant-Scientific-Literature) - Supporting em*ergency r*oom decision making\n- [Emergency Readmission](https://github.com/mesgarpour/T-CARER) - Adjusted Risk of *Emergency* Readmission. \n- [Forest Fire](https://github.com/LeadingIndiaAI/Forest-Fire-Detection-through-UAV-imagery-using-CNNs) - Forest fire detection through UAV imagery using CNNs\n- [Emergency Response](https://github.com/sky-t/hack-or-emergency-response) - Emergency response analysis. \n- [Emergency Transportation](https://github.com/bayesimpact/bayeshack-transportation-ems) - Transportation prompt on *emergency* services\n- [Emergency Dispatch](https://github.com/jamesypeng/Smarter-Emergency-Dispatch) - Reducing response times with predictive modeling, optimization, and automation\n- [Emergency Calls](https://github.com/analystiu/LICT-Project-Emergency-911-Calls) - Emergency calls analysis project. \n- [Calls Data Analysis](https://github.com/tanoybhattacharya/911-Data-Analysis) - 911 data analysis. \n- [Emergency Response](https://github.com/amunategui/Leak-At-Chemical-Factory-RL) - Chemical factory RL. \n\n\n**Crime**\n\n- [Crime Classification](https://github.com/datadesk/lapd-crime-classification-analysis) - Times analysis of serious assaults misclassified by LAPD.\n- [Article Tagging](https://github.com/chicago-justice-project/article-tagging) - Natural Language Processing of Chicago news article\n- [Crime Analysis](https://github.com/chrisPiemonte/crime-analysis) - Association Rule Mining from Spatial Data for *Crime* Analysis\n- [Chicago Crimes](https://github.com/search?o=desc&q=crime+language%3A%22Jupyter+Notebook%22+NOT+%22taxi%22+NOT+%22baseline%22&s=stars&type=Repositories) - Exploring public Chicago *crimes* data set in Python\n- [Graph Analytics](https://github.com/pedrohserrano/graph-analytics-nederlands) - The Hague Crimes.\n- [Crime Prediction](https://github.com/vikram-bhati/PAASBAAN-crime-prediction) - *Crime* classification, analysis & prediction in Indore city.\n- [Crime Prediction](https://github.com/tina31726/Crime-Prediction) - Developed predictive models for *crime* rate.\n- [Crime Review](https://github.com/felzek/Crime-Review-Data-Analysis) - Crime review data analysis. \n- [Crime Trends](https://github.com/benjaminsingleton/crime-trends) - The *Crime* Trends Analysis Tool analyses *crime* trends and surfaces problematic *crime* conditions\n- [Crime Analytics](https://github.com/cmenguy/crime-analytics) - Analysis of *crime* data in Seattle and San Francisco.\n\n\n**Ambulance:**\n\n- [Ambulance Analysis](https://github.com/kaiareyes/ambulance) - An investigation of Local Government Area ambulance time variation in Victoria.\n- [Site Location](https://github.com/ankitkariryaa/ambulanceSiteLocation) - Ambulance site locations. \n- [Dispatching](https://github.com/DimaStoyanov/Ambulance-Dispatching) - Applying game theory and discrete event simulation to find optimal solution for ambulance dispatching\n- [Ambulance Allocation](https://github.com/scngo/SD-ambulance-allocation) - Time series analysis of ambulance dispatches in the City of San Diego.\n- [Response Time](https://github.com/nonsignificantp/ambulance-response-time) - An analysis on the improvements of ambulance response time.\n- [Optimal Routing](https://github.com/aditink/EMSRouting) - Project to find optimal routing of ambulances in Ithaca.\n- [Crash Analysis](https://github.com/ArpitVora/Maryland_Crash) - Predicting the probability of accidents on a given segment on a given time.\n\n\n\n**Disaster Management**\n\n- [Conflict Prediction](https://github.com/Polichinel/Master_Thesis) - Notebooks on conflict prediction.\n- [Burglary Prediction](https://github.com/Polichinel/Master_Thesis) - Spatio-Temporal Modelling for burglary prediction.\n- [Predicting Disease Outbreak](https://github.com/ab-bh/Disease-Outbreak-Prediction/blob/master/Disease%20Outbreak%20Prediction.ipynb) - Machine Learning implementation based on multiple classifier algorithm implementations.\n- [Road accident prediction](https://github.com/leportella/federal-road-accidents) - Prediction on type of victims on federal road accidents in Brazil.\n- [Text Mining](https://github.com/rajaswa/Disaster-Management-) - Disaster Management using Text mining.\n- [Twitter and disasters](https://github.com/paultopia/concrete_NLP_tutorial/blob/master/NLP_notebook.ipynb) - Try to correctly predict whether tweets that are about disasters.\n- [Flood Risk](https://github.com/arijitsaha/FloodRisk) - Impact of catastrophic flood events.\n- [Fire Prediction](https://github.com/Senkichi/The_Catastrophe_Coefficient) - We used 4 different algorithms to predict the likelihood of future fires.\n\n\n\n## Finance\n\n\n**Trading and Investment** \n- For **more** see [financial-machine-learning](https://github.com/firmai/financial-machine-learning)\n- For **asset management** see [financial-machine-learning](https://github.com/firmai/machine-learning-asset-management)\n- [Deep Portfolio](https://github.com/DLColumbia/DL_forFinance) - Deep learning for finance Predict volume of bonds.\n- [AI Trading](https://github.com/borisbanushev/stockpredictionai/blob/master/readme2.md) - Modern AI trading techniques.\n- [Corporate Bonds](https://github.com/ishank011/gs-quantify-bond-prediction) - Predicting the buying and selling volume of the corporate bonds.\n- [Simulation](https://github.com/chenbowen184/Computational_Finance) - Investigating simulations as part of computational finance.\n- [Industry Clustering](https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries) - Project to cluster industries according to financial attributes.\n- [Financial Modeling](https://github.com/MiyainNYC/Financial-Modeling/tree/master/codes) - HFT trading and implied volatility modeling.\n- [Trend Following](http://inseaddataanalytics.github.io/INSEADAnalytics/ExerciseSet2.html) - A futures trend following portfolio investment strategy.\n- [Financial Statement Sentiment](https://github.com/MAydogdu/TextualAnalysis) - Extracting sentiment from financial statements using neural networks.\n- [Applied Corporate Finance](https://github.com/chenbowen184/Data_Science_in_Applied_Corporate_Finance) - Studies the empirical behaviors in stock market.\n- [Market Crash Prediction](https://github.com/sarachmax/MarketCrashes_Prediction/blob/master/LPPL_Comparasion.ipynb) - Predicting market crashes using an LPPL model.\n- [NLP Finance Papers](https://github.com/chenbowen184/Research_Documents_Curation_with_NLP) - Curating quantitative finance papers using machine learning.\n- [ARIMA-LTSM Hybrid](https://github.com/imhgchoi/Corr_Prediction_ARIMA_LSTM_Hybrid) - Hybrid model to predict future price correlation coefficients of two assets\n- [Basic Investments](https://github.com/SeanMcOwen/FinanceAndPython.com-Investments) - Basic investment tools in python.\n- [Basic Derivatives](https://github.com/SeanMcOwen/FinanceAndPython.com-Derivatives) - Basic forward contracts and hedging.\n- [Basic Finance](https://github.com/SeanMcOwen/FinanceAndPython.com-BasicFinance) - Source code notebooks basic finance applications.\n- [Advanced Pricing ML](https://github.com/jjakimoto/finance_ml) - Additional implementation of Advances in Financial Machine Learning (Book)\n- [Options and Regression](https://github.com/aluo417/Financial-Engineering-Projects) - Financial engineering project for option pricing techniques. \n- [Quant Notebooks](https://github.com/LongOnly/Quantitative-Notebooks) - Educational notebooks on quant finance, algorithmic trading and investment strategy. \n- [Forecasting Challenge](https://github.com/bukosabino/financial-forecasting-challenge-gresearch) - Financial forecasting challenge by G-Research (Hedge Fund)\n- [XGboost](https://github.com/firmai?after=Y3Vyc29yOnYyOpK5MjAxOS0wNS0wMlQwNToyMzoyMSswMTowMM4KBjIV&tab=stars) - A trading algorithm using XgBoost\n- [Research Paper Trading](https://github.com/rawillis98/alpaca) - A strategy implementation based on a paper using Alpaca Markets. \n- [Various](https://github.com/arcadynovosyolov/finance) - Options, Allocation, Simulation \n- [ML & RL NYU](https://github.com/joelowj/Machine-Learning-and-Reinforcement-Learning-in-Finance) - Machine Learning and Reinforcement Learning in Finance. \n\n\n\n**Data**\n\n- [Datastream](https://github.com/mbravidor/PyDSout) - Datastrem from Thomson Reuters accessible through Python. \n- [AlphaVantage](http://twopirllc) - API wrapper to simplify the process of acquiring free financial data.\n- [FSA](https://github.com/duncangh/FSA)- A project to transfer SEC Edgar Filingsā€™ financial data to custom financial statement analysis models. \n- [TradeConnector](https://github.com/tradeasystems/tradeasystems_connector) - A layer to connect with market data providers. \n- [Employee Count SEC Filings](https://github.com/healthgradient/sec_employee_information_extraction) - Extraction to get the exact employee count values for companies from SEC filings.\n- [SEC Parsing](https://github.com/healthgradient/sec-doc-info-extraction/blob/master/classify_sections_containing_relevant_information.ipynb) - NLP to find and extract specific information from long, unstructured documents\n- [Open Edgar](https://github.com/LexPredict/openedgar) - OpenEDGAR (openedgar.io)\n- [Rating Industries](http://www.ratingshistory.info/) - Histories from multiple agencies converted to CSV format\n\n**Personal Papers**\n\n- [Financial Machine Learning Regulation](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3371902)\n- [Predicting Restaurant Facility Closures](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420490)\n- [Predicting Corporate Bankruptcies](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420889)\n- [Predicting Earnings Surprises](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420722) \n- [Machine Learning in Asset Management](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420952) \n\n\n\n## Healthcare\n\n\n**General**\n\n- [zEpid](https://github.com/pzivich/zEpid) - Epidemiology analysis package.\n- [Python For Epidemiologists](https://github.com/pzivich/Python-for-Epidemiologists) - Tutorial to introduce epidemiology analysis in Python. \n- [Prescription Compliance](https://github.com/rjhere/Prescription-compliance-prediction) - An analysis of prescription and medical compliance \n- [Respiratory Disease](https://github.com/alistairwallace97/olympian-biotech) - Tracking respiratory diseases in Olympic athletes\n- [Bubonic Plague](https://github.com/callysto/curriculum-notebooks/blob/master/Humanities/BubonicPlague/bubonic-plague-and-SIR-model.ipynb) - Bubonic plague and SIR model. \n\n\n\n\n## Justics, Law & Regulations\n\n\n\n#### Tools\n- [LexPredict](https://github.com/LexPredict/lexpredict-contraxsuite) - Software package and library.\n- [AI Para-legal](https://github.com/davidawad/lobe) - Lobe is the world's first AI paralegal.\n- [Legal Entity Detection](https://github.com/hockeyjudson/Legal-Entity-Detection/blob/master/Dataset_conv.ipynb) - NER For Legal Documents.\n- [Legal Case Summarisation](https://github.com/Law-AI/summarization) - Implementation of different summarisation algorithms applied to legal case judgements.\n- [Legal Documents Google Scholar](https://github.com/GirrajMaheshwari/Web-scrapping-/blob/master/Google_scholar%2BExtract%2Bcase%2Bdocument.ipynb) - Using Google scholar to extract cases programatically.\n- [Chat Bot](https://github.com/akarazeev/LegalTech) - Chat-bot and email notifications.\n- [Congress API](https://github.com/propublica/congress-api-docs) - ProPublica congress API access. \n- [Data Generator GDPR](https://github.com/toningega/Data_Generator) - Dummy data generator for GDPR compliance\n- [Blackstone](https://github.com/ICLRandD/Blackstone) - spaCy pipeline and model for NLP on unstructured legal text.\n\n\n\n#### Policy and Regulatory\n- [GDPR scores](https://github.com/erickjtorres/AI_LegalDoc_Hackathon) - Predicting GDPR Scores for Legal Documents.\n- [Driving Factors FINRA](https://github.com/siddhantmaharana/text-analysis-on-FINRA-docs) - Identify the driving factors that influence the FINRA arbitration decisions.\n- [Securities Bias Correction](https://github.com/davidsontheath/bias_corrected_estimators/blob/master/bias_corrected_estimators.ipynb) - Bias-Corrected Estimation of Price Impact in Securities Litigation.\n- [Public Firm to Legal Decision](https://github.com/anshu3769/FirmEmbeddings) - Embed public firms based on their reaction to legal decisions.\n- [Night Life Regulation](https://github.com/Kevin-McIsaac/Nightlife) - Australian nightlife and its regulation and policing\n- [Comments](https://github.com/ProximaDas/nlp-govt-regulations) - Public comments on government regulations. \n- [Clustering](https://github.com/philxchen/Clustering-Canadian-regulations) - Clustering Canadian regulations. \n- [Environment](https://github.com/ds-modules/EEP-147) - Regulation of Energy and the Environment\n- [Risk](https://github.com/vsub21/systemic-risk-dashboard) - Systematic risk of various financial regulations.\n- [FINRA Compliance](https://github.com/raymond180/FINRA_TRACE) - Topic modelling on compliance. \n\n\n\n#### Judicial Applied\n- [Supreme Court Prediction](https://github.com/davidmasse/US-supreme-court-prediction) - Predicting the ideological direction of Supreme Court decisions: ensemble vs. unified case-based model.\n- [Supreme Court Topic Modeling](https://github.com/AccelAI/AI-Law-Minicourse/tree/master/Supreme_Court_Topic_Modeling) - Multiple steps necessary to implement topic modeling on supreme court decisions.\n- [Judge Opinion](https://github.com/GirrajMaheshwari/Legal-Analytics-project---Court-misclassification) - Using text mining and machine learning to analyze judgesā€™ opinions for a particular concern.\n- [ML Law Matching](https://github.com/whs2k/GPO-AI) - A machine learning law match maker.\n- [Bert Multi-label Classification](https://github.com/brightmart/sentiment_analysis_fine_grain) - Fine Grained Sentiment Analysis from AI.\n- [Some Computational AI Course](https://www.youtube.com/channel/UC5UHm2J9pbEZmWl97z_0hZw) - Video series Law MIT.\n- [Financial Machine Learning Regulation (Paper)](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3371902)\n\n\n\n## Manufacturing\n\n\n**General**\n\n- [Green Manufacturing](https://github.com/Danila89/kaggle_mercedes) - Mercedes-Benz Greener *Manufacturing* competition on Kaggle. \n- [Semiconductor Manufacturing](https://github.com/Meena-Mani/SECOM_class_imbalance) - Semicondutor *manufacturing* process line data analysis. \n- [Smart Manufacturing](https://github.com/usnistgov/modelmeth) - Shared work of a modelling Methodology.\n- [Bosch Manufacturing](https://github.com/han-yan-ds/Kaggle-Bosch) - Bosch manufacturing project, Kaggle. \n\n\n**Maintenance**\n\n- [Predictive Maintenance](https://github.com/Azure/lstms_for_predictive_maintenance) 1 - Predict remaining useful life of aircraft engines\n- [Predictive Maintenance 2](https://github.com/Samimust/predictive-maintenance) - Time-To-Failure (TTF) or Remaining Useful Life (RUL) \n- [Manufacturing Maintenance](https://github.com/m-hoff/maintsim) - Simulation of maintenance in *manufacturing* systems. \n\n\n**Failure**\n\n- [Predictive Analytics](https://github.com/IBM/iot-predictive-analytics) - Method for Predicting failures in Equipment using Sensor data.\n- [Detecting Defects](https://github.com/roshank1605A04/SECOM-Detecting-Defected-Items) - Anomaly detection for defective semiconductors\n- [Defect Detection](https://github.com/jorgehas/smart-defect-inspection) - Smart defect detection for pill manufacturing. \n- [Manufacturing Failures](https://github.com/aayushmudgal/Reducing-Manufacturing-Failures) - Reducing manufacturing failures. \n- [Manufacturing Anomalies](https://github.com/mohan-mj/Manufacturing-Line-I4.0) - Intelligent anomaly detection for *manufacturing* line. \n\n\n**Quality**\n\n- [Quality Control](https://github.com/buzz11/productionFailures) - Bosh failure of quality control. \n- [Manufacturing Quality](https://github.com/limberc/tianchi-IMQF) - Intelligent *Manufacturing* Quality Forecast \n- [Auto Manufacturing](https://github.com/trentwoodbury/ManufacturingAuctionRegression) - Regression Case Study Project on *Manufacturing* Auction Sale Data.\n\n\n\n## Media & Publishing\n\n\n**Marketing**\n\n- [Video Popularity](https://github.com/andrei-rizoiu/hip-popularity) - HIP model for predicting the popularity of videos.\n- [YouTube transcriber](https://github.com/hathix/youtube-transcriber) - Automatically transcribe YouTube videos. \n- [Marketing Analytics](https://github.com/byukan/Marketing-Data-Science) - Marketing analytics case studies. \n- [Algorithmic Marketing](https://github.com/ikatsov/algorithmic-examples) - Models from Introduction to Algorithmic Marketing book\n- [Marketing Scripts](https://github.com/HowardNTUST/Marketing-Data-Science-Application) - Marketing data science applications. \n- [Social Mining](https://github.com/mikhailklassen/Mining-the-Social-Web-3rd-Edition/tree/master/notebooks) - Mining the social web. \n\n\n\n## Miscellaneous\n\n\n**Art**\n\n- [Painting Forensics](https://github.com/ivan-bilan/Painting_Forensics) - Analysing paintings to find out their year of creation. \n\n\n\n**Tourism**\n- [Flickr](https://github.com/xiaofei6677/TourismFlickrMiner) - Metadata mining tool for tourism research. \n- [Fashion](https://github.com/khanhnamle1994/fashion-recommendation) **-** A clothing retrieval and visual recommendation model for fashion images\n\n\n\n\n## Physics\n\n\n\n**General**\n\n- [Gamma-hadron Reconstruction](https://github.com/fvisconti/gammas_machine_learning) - Tools used in Gamma-ray ground based astronomy. \n- [Curriculum](https://github.com/callysto/curriculum-notebooks/tree/master/Physics) - Newtonian notebooks. \n- [Interaction Networks](https://github.com/higgsfield/interaction_network_pytorch) - Interaction Networks for Learning about Objects, Relations and *Physics.* \n- [Particle Physics](https://github.com/hep-lbdl/adversarial-jets) - Training, generation, and analysis code for learning Particle *Physics*\n- [Computational Physics](https://github.com/ernestyalumni/CompPhys) - A computational physics repository. \n- [Medical Physics](https://github.com/robmarkcole/Useful-python-for-medical-physics) - Useful python for medical physics. \n- [Medical Physics 2](https://github.com/pymedphys/pymedphys) - A common, core Python package for Medical *Physics*\n- [Flow Physics](https://github.com/FPAL-Stanford-University/FloATPy) - Flow *Physics* and Aeroacoustics Toolbox with Python\n\n\n\n**Machine Learning**\n\n- [Physics ML and Stats](https://github.com/dkirkby/MachineLearningStatistics) - Machine learning and statistics for physicists\n- [High Energy](https://github.com/arogozhnikov/hep_ml) - Machine Learning for High Energy *Physics*.\n- [High Energy GAN](https://github.com/hep-lbdl/CaloGAN) - Generative Adversarial Networks for High Energy *Physics.* \n- [Neural Networks](https://github.com/GiggleLiu/marburg) - P*hysics* meets neural networks\n\n\n\n\n## Government and Public Works\n\n\n#### Social Policies\n- [Triage](https://github.com/dssg/triage) - General Purpose Risk Modeling and Prediction Toolkit for Policy and Social Good Problems.\n- [World Bank Poverty I](https://github.com/worldbank/ML-classification-algorithms-poverty/tree/master/notebooks) - A comparative assessment of machine learning classification algorithms applied to poverty prediction.\n- [World Bank Poverty II](https://github.com/avsolatorio/world-bank-pover-t-tests-solution) - Repository for the World Bank Pover-t Test Competition Solution Overseas Company Land Ownership .\n- [Overseas Company Land Ownership](https://github.com/Global-Witness/overseas-companies-land-ownership/blob/master/overseas_companies_land_ownership_analysis.ipynb) - Identifying foreign ownership in the UK.\n- [CFPB](https://github.com/MAydogdu/ConsumerFinancialProtectionBureau/blob/master/CFPB_Complaints_2017September.ipynb) - Consumer Finances Protection Bureau complaints analysis.\n- [Cannabis Legalisation Effect](https://github.com/tslindner/Effects-of-Cannabis-Legalization) - Effects of cannabis legalization on crime.\n- [Public Credit Card](https://github.com/dmodjeska/barnet_transactions/blob/master/Barnet_Transactions_Analysis.ipynb) - Identification of potential fraud for council credit cards. [Data](https://open.barnet.gov.uk/dataset/corporate-credit-card-transaction-2016-17)\n- [Recidivism Prediction](https://github.com/shayanray/GlassBox/tree/master/mlPredictor) - Transparency and audibility to recidivism risk assessment \n- [Household Poverty](https://github.com/Featuretools/predict-household-poverty) - Predict poverty in households in Costa Rica. \n- [NLP Public Policy](https://github.com/ancilcrayton/nlp_public_policy) - An example of an NLP use-case in public policy. \n- [World Food Production](https://github.com/roshank1605A04/World-Food-Production) - Comparing Top food and feed Producers around the globe.\n- [Tax Inequality](https://github.com/DataScienceForGood/TaxationInequality) - Data project around taxation and inequality in Basel Stadt.\n- [Sheriff Compliance](https://github.com/austinbrian/sheriffs) - Compliance to ICE requests.\n- [Apps Detection](https://github.com/MengchuanFu/Suspecious-Apps-Detection) - Suspicious app detection for kids. \n- [Social Assistance](https://github.com/farkhondehm/Social-Assistance) - Trending information on social assistance\n- [Computational Social Science](https://github.com/abjer/sds/tree/master/material) - Social data science summer school course. \n- [Liquor and Crime](https://github.com/bhaveshgoyal/safeLiquor) - Effect of liquor licenses issued on the crime rate.\n- [Animal Placement Kennels](https://github.com/austinpetsalive/distemper-outbreak) - Optimising animal placement in shelters.\n- [Staffing Wall](https://github.com/ryanschaub/The-U.S.-Mexican-Border-Wall-and-Staffing-A-Statistical-Approach-) - Independent exploration project on U.S. Mexican Border wall\n- [Worker Fatalities](https://github.com/zischwartz/workerfatalities) - Worker Fatalities and Catastrophes Map from OSHA data\n\n\n**Charities**\n\n- [Census Data API](https://github.com/johnfwhitesell/CensusPull/blob/master/Census_ACS5_Pull.ipynb) - Pull variables from the 5-year American Community Survey.\n- [Philantropic Giving](https://github.com/datakind/datadive-gates92y-proj3-form990) - Work done by numerous DataKind volunteers on harnessing Form 990 data\n- [Charity Recommender](https://github.com/Chris-Manna/charity_recommender) - NYC *Charity* Collaborative Recommender System on an Implicit DataSet.\n- [Donor Identification](https://github.com/gouravaich/finding-donors-for-charity) - A machine learning project in which we need to find donors for *charity.*\n- [US Charities](https://github.com/staceb/charities_in_the_united_states) - Charity exploration and machine learning. \n- [Charity Effectiveness](https://github.com/LauraChen/02-Metis-Web-Scraping) - Scraping online data about *charities* to understand effectiveness\n\n\n#### Election Analysis\n- [Election Analysis](https://github.com/1jinwoo/DeepWave/blob/master/DR_Random_Forest.ipynb) - Election Analysis and Prediction Models\n- [American Election Causal](https://github.com/Akesari12/LS123_Data_Prediction_Law_Spring-2019/blob/master/labs/OLS%20for%20Causal%20Inference/OLS_Causal_Inference_solution.ipynb) - Using ANES data with causal inference models.\n- [Campaign Finance and Election Results](https://github.com/sfbrigade/datasci-campaign-finance/blob/master/notebooks/ML%20Campaign%20Finance%20and%20Election%20Results%20Example.ipynb) - Investigating the relation between campaign finance and subsequent election results.\n- [Voting System](https://github.com/nealmcb/pr_voting_methods) - Proportional representation voting methods. \n- [President Vote](https://github.com/austinbrian/portfolio/blob/master/tax_votes/president_counties.ipynb) - Vote by income level analysis.. \n\n\n**Politics**\n\n- [Congressional politics](https://github.com/kaumaron/Data_Science/tree/master/Congressional_Partisanship) - House and senate congressional partisanship. \n- [Politico](https://github.com/okfn-brasil/perfil-politico) - A platform for profiling public figures in Brazilian *politics.*\n- [Bots](https://github.com/ParticipaPY/politic-bots) - Tools and algorithms to analyze Paraguayan Tweets in times of election\n- [Gerrymander tests](https://github.com/PrincetonUniversity/gerrymandertests) - Lots of metrics for quantifying gerrymandering.\n- [Sentiment](https://github.com/JulianMar11/SentimentPoliticalCompass) - Analyse newspapers with respect to their *political* conviction using entity sentiments of party representatives. \n- [DL Politics](https://github.com/muntisa/Deep-Politics) - Prediction of Spanish *Political* Affinity with Deep Neural Nets: Socialist vs People's Party\n- [PAC Money](https://github.com/edmundooo/more-money-more-problems) - Effects of PAC money on US *politics*.\n- [Power Networks](https://github.com/abhiagar90/power_networks) - Constructing a watchdog for Indian corporate and *political* networks\n- [Elite](https://github.com/philippschmalen/Project_tsds) - Political elite in the US.\n- [Debate Analysis](https://github.com/kkirchhoff01/DebateAnalysis) - Program to analyze *political* debates.\n- [Political Affiliation](https://github.com/davidjwiner/political_affiliation_prediction) - Political affiliation prediction using twitter metadata. \n- [Political Ads](https://github.com/philiplbean/facebook_political_ads) - Investigation into Facebook *Political* Ads and Targeting\n- [Political Identity](https://github.com/pgromano/Political-Identity-Analysis) - Multi-axial *political* model.\n- [YT Politics](https://github.com/kmunger/YT_descriptive) - Mapping *Politics* on YouTube\n- [Political Ideology](https://github.com/albertwebson/Political-Vector-Projector) - Unsupervised learning of *political* ideology by word vector projections\n\n\n\n## Real Estate, Rental & Leasing\n\n\n**Real Estate**\n\n- [Finding Donuts](https://github.com/GretelDePaepe/FindingDonuts) - Finding real estate opportunities by predicting transforming neighbourhoods. \n- [Neighbourhood](https://github.com/denadai2/real-estate-neighborhood-prediction) - Predicting real estate prices from the urban environment.\n- [Real Estate Classification](https://github.com/Sardhendu/PropertyClassification) - Classifying the type of property given Real Estate, satellite and Street view Images\n- [Recommender](https://github.com/hyattsaleh15/RealStateRecommender) - This tools aims to recommend a user the top 5 real estate properties that matches their search.\n- [House Price](https://github.com/Shreyas3108/house-price-prediction) - Predicting *house* prices using Linear Regression and GBR\n- [House Price Portland](https://github.com/girishkuniyal/Predict-housing-prices-in-Portland) - Predict housing prices in Portland. \n- [Zillow Prediction](https://github.com/eswar3/Zillow-prediction-models) - Zillow valuation prediction as performed on Kaggle.\n- [Real Estate](https://github.com/denadai2/real-estate-neighborhood-prediction) - Predicting real estate prices from the urban environment.\n\n\n**Rental & Leasing**\n\n- [Analysing Rentals](https://github.com/ual/rental-listings) - Analyzing and visualizing rental listings data. \n- [Interest Prediction](https://github.com/mratsim/Apartment-Interest-Prediction) - Predict people interest in renting specific NYC apartments.\n- [Housing Uni vs Non-Uni](https://github.com/5x12/pwc_europe_data_analytics_hackathon) - The effect on university lodging after the GFC.\n- [Predict Household Poverty](https://github.com/Featuretools/predict-household-poverty) - Predict the poverty of households in Costa Rica using automated feature engineering.\n- [Airbnb public analytics competition](http://inseaddataanalytics.github.io/INSEADAnalytics/groupprojects/AirbnbReport2016Jan.html): - Now strategic management. \n\n\n\n## Utilities\n\n\n**Electricity**\n\n- [Electricity Price](https://github.com/luqmanhakim/research-on-sp-wholesale/blob/master/research-on-sp-wholesale-plan.ipynb) - Electricity price comparison Singapore. \n- [Electricity-Coal Correlation](https://github.com/richardddli/state_electricity_rates) - Determining the correlation between state electricity rates and coal generation over the past decade.\n- [Electricity Capacity](https://github.com/datadesk/california-electricity-capacity-analysis) - A Los Angeles Times analysis of California's costly power glut.\n- [Electricity Systems](https://github.com/PyPSA/WHOBS) - Optimal Wind+Hydrogen+Other+Battery+Solar (WHOBS) *electricity* systems for European countries.\n- [Load Disaggregation](https://github.com/pipette/Electricity-load-disaggregation) - Smart meter load disaggregation with Hidden Markov Models\n- [Price Forecasting](https://github.com/farwacheema/DA-electricity-price-forecasting) - Forecasting Day-Ahead *electricity* prices in the German bidding zone with deep neural networks.\n- [Carbon Index](https://github.com/gschivley/carbon-index) - Calculation of *electricity* COā‚‚ intensity at national, state, and NERC regions from 2001-present.\n- [Demand Forecasting](https://github.com/hvantil/ElectricityDemandForecasting) - *Electricity* demand forecasting for Austin.\n- [Electricity Consumption](https://github.com/un-modelling/Electricity_Consumption_Surveys) - Estimating *Electricity* Consumption from Household Surveys\n- [Household power consumption](https://github.com/amirrezaeian/Individual-household-electric-power-consumption-Data-Set-) - Individual household power consumption LSTM.\n- [Electricity French Distribution](http://inseaddataanalytics.github.io/INSEADAnalytics/groupprojects/group_energy.html) - An analysis of electricity data provided by the French Distribution Network (RTE)\n- [Renewable Power Plants](https://github.com/Open-Power-System-Data/renewable_power_plants) - Time series of cumulated installed capacity.\n- [ Wind Farm Flow](https://github.com/FUSED-Wind/FUSED-Wake) - A repository of wind plant flow models connected to FUSED-Wind.\n- [Power Plant](https://github.com/YungChunLu/UCI-Power-Plant) - The dataset contains 9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011).\n\n\n\n\n**Coal, Oil & Gas**\n\n- [Coal Phase Out](https://github.com/samarthiith/DE_CoalPhaseOut) - Generation adequacy issues with Germanyā€™s coal phaseout. \n- [Coal Prediction](https://github.com/Jean-njoroge/coal-exploratory/tree/master/notebooks) - Predicting coal production. \n- [Oil & Gas](https://github.com/sdasadia/Oil-Natural-Gas-Price-Prediction) - Oil & *Natural* *Gas* price prediction using ARIMA & Neural Networks\n- [Gas Formula](https://github.com/cep-kse/natural_gas_formula) - Calculating potential economic effect of price indexation formula.\n- [Demand Prediction](https://github.com/victorpena1/Natural-Gas-Demand-Prediction) - Natural gas demand prediction.\n- [Consumption Forecasting](https://github.com/williamadams1/natural-gas-consumption-forecasting) - Natural gas consumption forecasting. \n- [Gas Trade](https://github.com/bahuisman/NatGasModel) - World Model for *Natural* *Gas* Trade.\n\n\n**Water & Pollution**\n\n- [Safe Water](https://github.com/codeforboston/safe-water) - Predict health-based drinking water violations in the United States.\n- [Hydrology Data](https://github.com/mroberge/hydrofunctions) - A suite of convenience functions for exploring water data in Python.\n- [Water Observatory](https://github.com/sentinel-hub/water-observatory-backend) - Monitoring water levels of lakes and reservoirs using satellite imagery.\n- [Water Pipelines](https://github.com/wassname/pipe-segmentation) - Using machine learning to find water pipelines in aerial images. \n- [Water Modelling](https://github.com/awracms/awra_cms_older) - Australian Water Resource Assessment (AWRA) Community Modelling System.\n- [Drought Restrictions](https://github.com/datadesk/california-ccscore-analysis) - A Los Angeles Times analysis of water usage after the state eased drought restrictions\n- [Flood Prediction](https://github.com/cadrev/lstm-flood-prediction) - Applying LSTM on river water level data\n- [Sewage Overflow](https://github.com/jesseanddd/sewer-overflow) - Insights into the sanitary sewage overflow (SSO). - This has been removed\n- [Water Accounting](https://github.com/johnpfay/USWaterAccounting) - Assembles water budget data for the US from existing data source\n- [Air Quality Prediction](https://github.com/txytju/air-quality-prediction) - Predict air quality(aq) in Beijing and London in the next 48 hours.\n\n\n**Transportation**\n\n- [Transdim](https://github.com/xinychen/transdim) - Creating accurate and efficient solutions for the spatio-temporal traffic data imputation and prediction tasks.\n- [Transport Recommendation](https://github.com/AlanConstantine/KDD-Cup-2019-CAMMTR) - Context-Aware Multi-Modal Transportation Recommendation \n- [Transport Data](https://github.com/CityofToronto/bdit_data-sources) - Data and notebooks for Toronto transport. \n- [Transport Demand](https://github.com/pawelmorawiecki/traffic_jam_Nairobi) - Predicting demand for public transportation in Nairobi.\n- [Demand Estimation](https://github.com/Lemma1/DPFE) - Implementation of dynamic origin-destination demand estimation.\n- [Congestion Analysis](https://github.com/hackoregon/transportation-congestion-analysis) - Transportation systems analysis\n- [TS Analysis](https://github.com/nishanthgampa/Time-Series-Analysis-on-Transportation-Data) - Time series analysis on transportation data.\n- [Network Graph Subway](https://github.com/fangshulin/Vulnerability-Analysis-for-Transportation-Networks) - Vulnerability analysis for transportation networks. - Have been taken down\n- [Transportation Inefficiencies](https://github.com/akpen/Stockholm-0.1) - Quantifying the inefficiencies of Transportation Networks\n- [Train Optimisation](https://github.com/crowdAI/train-schedule-optimisation-challenge-starter-kit) - Train schedule optimisation \n- [Traffic Prediction](https://github.com/mratsim/McKinsey-SmartCities-Traffic-Prediction) - multi attention recurrent neural networks for time-series (city traffic) \n- [Predict Crashes](https://github.com/Data4Democracy/crash-model) - Crash prediction modelling application that leverages multiple data sources\n- [AI Supply chain](https://github.com/llSourcell/AI_Supply_Chain) - Supply chain optimisation system. \n- [Transfer Learning Flight Delay](https://github.com/cavaunpeu/flight-delays) - Using variation encoders in Keras to predict flight delay. \n- [Replenishment](https://github.com/pratishthakapoor/RetailReplenishement/tree/master/Code) - Retail replenishment code for supply chain management.\n\n\n\n## Wholesale & Retail\n\n\n**Wholesale**\n\n- [Customer Analysis](https://github.com/kralmachine/WholesaleCustomerAnalysis) - Wholesale customer analysis.\n- [Distribution](https://github.com/Semionn/JB-wholesale-distribution-analysis) - JB wholesale distribution analysis. \n- [Clustering](https://github.com/prakhardogra921/Clustering-Analysis-on-customers-of-a-wholesale-distributor) - Unsupervised learning techniques are applied on product spending data collected for customers \n- [Market Basket Analysis](https://github.com/tstreamDOTh/Instacart-Market-Basket-Analysis) - Instacart public dataset to report which products are often shopped together.\n\n\n**Retail**\n\n- [Retail Analysis](https://github.com/SarahMestiri/online-retail-case) - Studying Online *Retail* Dataset and getting insights from it.\n- [Online Insights](https://github.com/roshank1605A04/Online-Retail-Transactions-of-UK) - Analyzing the Online Transactions in UK\n- [Retail Use-case](https://github.com/IBM-DSE/CyberShop-Analytics) - Notebooks & Data for CyberShop *Retail* Use Case\n- [Dwell Time](https://github.com/arvindkarir/retail) - Customer dwell time and other analysis. \n- [Retail Cohort](https://github.com/finnqiao/cohort_online_retail) - Cohort analysis. \n\n\n\n\n" }, { "path": "assets/Earnings.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"Earnings.ipynb\",\n \"provenance\": []\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.13\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python [conda env:py_27_talib]\",\n \"language\": \"python\",\n \"name\": \"conda-env-py_27_talib-py\"\n }\n },\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"OBAcb0g45S7m\",\n \"outputId\": \"01a5f4c3-d370-4dae-fb28-e28bcea58275\"\n },\n \"source\": [\n \"## The data for earnings have not been uploaded for trading sensitivity. \\n\",\n \"## Library Packages \\n\",\n \"%run libraries.py \\n\",\n \"from __future__ import division\\n\",\n \"from tsfresh import extract_features\\n\",\n \"\\n\",\n \"# Settings \\n\",\n \"pd.set_option('display.max_columns', None)\\n\",\n \"pd.set_option('display.max_rows', None)\\n\",\n \"seed = 7\\n\",\n \"np.random.seed(seed)\\n\",\n \"\\n\",\n \"import warnings\\n\",\n \"warnings.filterwarnings('ignore')\\n\",\n \"\\n\",\n \"\\n\",\n \"np.set_printoptions(threshold=np.nan)\\n\",\n \"\\n\",\n \"!free -h\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \" total used free shared buffers cached\\r\\n\",\n \"Mem: 125G 20G 105G 12M 1.2G 15G\\r\\n\",\n \"-/+ buffers/cache: 3.7G 122G\\r\\n\",\n \"Swap: 15G 1.6G 14G\\r\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"PvpF3dqy5S7u\"\n },\n \"source\": [\n \"# Clean and filter Analysts\\n\",\n \"\\n\",\n \"analyst = pd.read_csv(\\\"analysts.csv\\\")\\n\",\n \"analyst_1 = analyst.rename(columns={\\\"OFTIC\\\":\\\"ticker\\\", \\\"FPEDATS\\\":\\\"qtrdat\\\", \\\"ANNDATS\\\":\\\"anndat\\\", \\\"ANNTIMS\\\":\\\"anntim\\\",\\n\",\n \" \\\"VALUE\\\":\\\"estimate\\\"})\\n\",\n \"analyst_1[\\\"iters\\\"] = analyst_1['ticker']+analyst_1['qtrdat'].astype(str)\\n\",\n \"print(analyst_1.shape, len(analyst_1[\\\"ticker\\\"].unique()),len(pd.unique(analyst_1[\\\"iters\\\"])))\\n\",\n \"# Wow it removes a lot of values, I would not have expected, read to make sure. \\n\",\n \"analyst_1 = analyst_1.drop_duplicates(subset=[\\\"ESTIMATOR\\\",\\\"ANALYS\\\",\\\"qtrdat\\\",\\\"ticker\\\"],keep='last')\\n\",\n \"print(analyst_1.shape, len(analyst_1[\\\"ticker\\\"].unique()),len(pd.unique(analyst_1[\\\"iters\\\"])))\\n\",\n \"\\n\",\n \"print(analyst_1.shape, len(analyst_1[\\\"ticker\\\"].unique()),len(pd.unique(analyst_1[\\\"iters\\\"])))\\n\",\n \"analyst_1 = analyst_1[analyst_1[\\\"anndat\\\"]39]\\n\",\n \"# del analyst_1\\n\",\n \"\\n\",\n \"analyst_2 = analyst_2[analyst_2[\\\"ticker\\\"].isin(ticks.index.values)].reset_index(drop=True)\\n\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"K0CTHPU35S7x\"\n },\n \"source\": [\n \"analyst_2.head()\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"W6cS0_RO5S71\"\n },\n \"source\": [\n \"# Creation of big summary information to serve as target\\n\",\n \"\\n\",\n \"big = analyst_2[[\\\"estimate\\\",\\\"ticker\\\",\\\"ANNDATS_ACT\\\"]].groupby([\\\"ticker\\\",\\\"ANNDATS_ACT\\\"]).mean().reset_index()\\n\",\n \"big[\\\"est_std\\\"] = analyst_2[[\\\"estimate\\\",\\\"ticker\\\",\\\"ANNDATS_ACT\\\"]].groupby([\\\"ticker\\\",\\\"ANNDATS_ACT\\\"]).std().reset_index()[\\\"estimate\\\"]\\n\",\n \"big[\\\"est_count\\\"] = analyst_2[[\\\"estimate\\\",\\\"ticker\\\",\\\"ANNDATS_ACT\\\"]].groupby([\\\"ticker\\\",\\\"ANNDATS_ACT\\\"]).count().reset_index()[\\\"estimate\\\"]\\n\",\n \"big = big.rename(columns={\\\"estimate\\\":\\\"est_avg\\\"})\\n\",\n \"\\n\",\n \"# Add some extra info \\n\",\n \"\\n\",\n \"big[\\\"est_std\\\"] = big[\\\"est_std\\\"].fillna(method=\\\"ffill\\\")\\n\",\n \"big = pd.merge(big, analyst_2[[\\\"ticker\\\",\\\"ANNDATS_ACT\\\",\\\"qtrdat\\\",\\\"ACTUAL\\\"]].drop_duplicates(keep=\\\"last\\\"), on=[\\\"ticker\\\",\\\"ANNDATS_ACT\\\"],how=\\\"left\\\")\\n\",\n \"print(big.shape)\\n\",\n \"big.head()\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"gnDXvL6-5S75\"\n },\n \"source\": [\n \"# Clean Daily\\n\",\n \"\\n\",\n \"daily = pd.read_csv(\\\"daily.csv\\\")\\n\",\n \"daily = daily.rename(columns={\\\"GVKEY\\\":\\\"gvkey\\\", \\\"tic\\\":\\\"ticker\\\",\\n\",\n \" \\\"cusip\\\":\\\"cusip\\\",\\\"conm\\\":\\\"coyn\\\", \\\"cshtrd\\\":\\\"volume\\\",\\n\",\n \" \\\"prccd\\\":\\\"close\\\",\\\"prchd\\\":\\\"high\\\",\\\"prcld\\\":\\\"low\\\",\\n\",\n \" \\\"prcod\\\":\\\"open\\\",\\\"prcstd\\\":\\\"prcstd\\\",\\\"idbflag\\\":\\\"idbflag\\\",\\n\",\n \" \\\"spcseccd\\\":\\\"spcseccd\\\",\\\"ipodate\\\":\\\"ipodate\\\"}) \\n\",\n \"\\n\",\n \"daily = daily.drop(\\\"iid\\\",axis=1)\\n\",\n \"daily = daily.sort_values([\\\"gvkey\\\",\\\"datadate\\\"]\\n\",\n \" )\\n\",\n \"\\n\",\n \"daily['datadate'] = pd.to_datetime(daily['datadate'].astype(str), format='%Y%m%d')\\n\",\n \"print(daily.shape)\\n\",\n \"daily.head()\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"ONc_c9V45S78\"\n },\n \"source\": [\n \"d_a = pd.merge(daily,big, left_on=[\\\"ticker\\\",\\\"datadate\\\"], right_on=[\\\"ticker\\\",\\\"ANNDATS_ACT\\\"],how=\\\"left\\\")\\n\",\n \"d_a = d_a.reset_index(drop=True)\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"GUhMZnug5S7_\"\n },\n \"source\": [\n \"d_a[\\\"ANNDATS_ACT\\\"] = d_a[[\\\"ANNDATS_ACT\\\",\\\"gvkey\\\"]].groupby([\\\"gvkey\\\"]).bfill()\\n\",\n \"\\n\",\n \"d_a[\\\"est_avg\\\"] = d_a[[\\\"est_avg\\\",\\\"gvkey\\\"]].groupby([\\\"gvkey\\\"]).bfill()\\n\",\n \"d_a[\\\"est_std\\\"] = d_a[[\\\"est_std\\\",\\\"gvkey\\\"]].groupby([\\\"gvkey\\\"]).bfill()\\n\",\n \"d_a[\\\"est_count\\\"] = d_a[[\\\"est_count\\\",\\\"gvkey\\\"]].groupby([\\\"gvkey\\\"]).bfill()\\n\",\n \"d_a[\\\"ACTUAL\\\"] = d_a[[\\\"ACTUAL\\\",\\\"gvkey\\\"]].groupby([\\\"gvkey\\\"]).bfill()\\n\",\n \"d_a[\\\"qtrdat\\\"] = d_a[[\\\"qtrdat\\\",\\\"gvkey\\\"]].groupby([\\\"gvkey\\\"]).bfill()\\n\",\n \"\\n\",\n \"d_a = d_a[~d_a[\\\"ANNDATS_ACT\\\"].isnull()].reset_index(drop=True)\\n\",\n \"d_a[\\\"id\\\"] = d_a[\\\"gvkey\\\"].astype(str) + d_a[\\\"ANNDATS_ACT\\\"].astype(str)\\n\",\n \"print(d_a.shape)\\n\",\n \"\\n\",\n \"d_a = d_a.sort_values([\\\"gvkey\\\",\\\"ANNDATS_ACT\\\",\\\"datadate\\\"],ascending=[True,False,False])\\n\",\n \"d_a[\\\"counter\\\"] = d_a[[\\\"id\\\",\\\"gvkey\\\"]].groupby('id',sort=False).cumcount()\\n\",\n \"\\n\",\n \"\\n\",\n \"d_a.head()\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"YDwXGuaF5S8C\"\n },\n \"source\": [\n \"# 6 million instead of 19 million -> This makes sense. \\n\",\n \"d_a = d_a[d_a[\\\"counter\\\"]<50].reset_index(drop=True)\\n\",\n \"\\n\",\n \"\\n\",\n \"lower = d_a[\\\"id\\\"].value_counts()\\n\",\n \"lower = pd.DataFrame(lower)\\n\",\n \"lower = lower[lower[\\\"id\\\"]<50]\\n\",\n \"lower.index.values\\n\",\n \"\\n\",\n \"d_a = d_a[~d_a[\\\"id\\\"].isin(list(lower.index.values))].reset_index(drop=True)\\n\",\n \"\\n\",\n \"#You see I have kind-off dropped mention of the earnings date as it does not carry much sig..\\n\",\n \"\\n\",\n \"d_a = d_a.sort_values([\\\"gvkey\\\",\\\"ANNDATS_ACT\\\",\\\"datadate\\\"])\\n\",\n \"d_a= d_a[~d_a[\\\"ACTUAL\\\"].isnull()].reset_index(drop=True)\\n\",\n \"d_a.head()\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"vpp3x9u-5S8F\"\n },\n \"source\": [\n \"d_a.shape\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"QVYsekfp5S8J\"\n },\n \"source\": [\n \"# Combining Ratio and Quarter\\n\",\n \"# 1984\\n\",\n \"\\n\",\n \"ratio = pd.read_csv(\\\"ratio.csv\\\")\\n\",\n \"ratio = ratio[ratio[\\\"public_date\\\"]>19810000].reset_index(drop=True)\\n\",\n \"\\n\",\n \"quarter = pd.read_csv(\\\"quarter.csv\\\")\\n\",\n \"quarter = quarter[quarter[\\\"datadate\\\"]>19810000]\\n\",\n \"\\n\",\n \"# Removing everything but last\\n\",\n \"\\n\",\n \"ratio = ratio.drop_duplicates(subset=[\\\"gvkey\\\",\\\"qdate\\\"],keep=\\\"last\\\")\\n\",\n \"ratio = ratio.sort_values([\\\"gvkey\\\",\\\"public_date\\\"])\\n\",\n \"ratio = pd.merge(quarter, ratio, left_on=[\\\"gvkey\\\",\\\"datadate\\\"], right_on=[\\\"gvkey\\\",\\\"qdate\\\"],how=\\\"left\\\")\\n\",\n \"\\n\",\n \"ratio = ratio[~ratio[\\\"qdate\\\"].isnull()].reset_index(drop=True)\\n\",\n \"ratio = ratio.groupby([\\\"gvkey\\\"]).ffill() # The whole thing might perform better without ffill.\\n\",\n \"ratio = ratio.dropna(how=\\\"all\\\",axis=1)\\n\",\n \"ratio.head()\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"344rPVtk5S8N\"\n },\n \"source\": [\n \"# Removes columns that do not change\\n\",\n \"\\n\",\n \"df = ratio \\n\",\n \"print(df.shape)\\n\",\n \"for col in df.columns:\\n\",\n \" if len(df[col].unique()) ==1:\\n\",\n \" print(col,\\\": \\\",list(df[col].unique())[:2])\\n\",\n \" df.drop(col,inplace=True,axis=1)\\n\",\n \"print(df.shape)\\n\",\n \"\\n\",\n \"# Creating dummies for small object uniques\\n\",\n \"\\n\",\n \"list_dummies =[]\\n\",\n \"for col in df.columns:\\n\",\n \" if (len(df[col].unique()) <15):\\n\",\n \" list_dummies.append(col)\\n\",\n \" print(col)\\n\",\n \"temp_list= list_dummies\\n\",\n \"list_dummies.remove(\\\"fyr\\\") # removing time feautures\\n\",\n \"list_dummies.remove(\\\"fyrc\\\") \\n\",\n \"list_dummies.append(\\\"state\\\") # add state features \\n\",\n \"df_edit = pd.get_dummies(df, columns = list_dummies) # This automatically release o features\\n\",\n \"\\n\",\n \"# Binarise slightly empty columns\\n\",\n \"df = df_edit.copy()\\n\",\n \"this =[]\\n\",\n \"for col in df.columns:\\n\",\n \" if df[col].dtype != \\\"object\\\":\\n\",\n \" is_null = df[col].isnull().astype(int).sum()\\n\",\n \" if (is_null/df.shape[0]) >0.70: # if more than 70% is null binarise\\n\",\n \" print(col)\\n\",\n \" this.append(col)\\n\",\n \" df[col] = df[col].astype(float)\\n\",\n \" df[col] = df[col].apply(lambda x: 0 if (np.isnan(x)) else 1)\\n\",\n \"df = pd.get_dummies(df, columns = this) \"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"12B4Ogec5S8Q\"\n },\n \"source\": [\n \"#df = df.fillna(value=0)\\n\",\n \"#df.to_csv(\\\"dframe.csv\\\", index_col = False)\\n\",\n \"\\n\",\n \"df = df.fillna(value=0)\\n\",\n \"df.to_csv(\\\"df.csv\\\", index_col = False)\\n\",\n \"#df = pd.read_csv(\\\"df.csv\\\")\\n\",\n \"print(df.shape)\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"YljEcXY55S8T\"\n },\n \"source\": [\n \"d_a = d_a.fillna(value=0)\\n\",\n \"d_a.to_csv(\\\"d_a.csv\\\", index_col = False)\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"Nj0Ur8hC5S8W\"\n },\n \"source\": [\n \"print(d_a.shape)\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"WhMbZHDQ5S8Z\"\n },\n \"source\": [\n \"d_a.head()\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"mY9QWS1p5S8b\"\n },\n \"source\": [\n \"analyst_4[\\\"est_all\\\"] = analyst_4[\\\"estimate\\\"]\\n\",\n \"analyst_4 = analyst_4[[\\\"est_all\\\",\\\"ticker\\\",\\\"anndat\\\"]]\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"wxrUJHb25S8e\"\n },\n \"source\": [\n \"free_space = pd.merge(d_a, analyst_4 , left_on=[\\\"datadate\\\",\\\"ticker\\\"], right_on=[\\\"ticker\\\",\\\"anndat\\\"],how=\\\"left\\\")\\n\",\n \"free_space = free_space[[\\\"gvkey\\\",\\\"datadate\\\",\\\"cusip\\\",\\\"ticker_x\\\",\\\"volume\\\",\\\"close\\\",\\n\",\n \" \\\"high\\\",\\\"low\\\",\\\"open\\\",\\\"ANNDATS_ACT\\\",\\\"est_avg\\\",\\\"est_std\\\",\\n\",\n \" \\\"est_count\\\",\\\"ACTUAL\\\",\\\"id\\\",\\\"counter\\\",\\\"est_all\\\",\\\"qtrdat\\\"]]\\n\",\n \"free_space = free_space.rename(columns={\\\"ANNDATS_ACT\\\":\\\"anndat\\\",\\\"ticker_x\\\":\\\"ticker\\\"})\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"7LgKu2jg5S8h\"\n },\n \"source\": [\n \"free_space[\\\"est_all\\\"] = free_space[\\\"est_all\\\"].fillna(value=0)\\n\",\n \"free_space = free_space.ffill()\\n\",\n \"free_space = free_space.fillna(value=0)\\n\",\n \"free_space.head()\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"tQZQuPiD5S8j\"\n },\n \"source\": [\n \"runner = free_space[[\\\"id\\\",\\\"ACTUAL\\\",\\\"ticker\\\", \\\"est_avg\\\",\\\"est_all\\\",\\\"qtrdat\\\",\\\"counter\\\",\\\"datadate\\\",\\\"volume\\\",\\\"close\\\",\\\"high\\\",\\\"low\\\",\\\"open\\\"]]\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"DB3camy55S8n\"\n },\n \"source\": [\n \"tech = runner.copy()\\n\",\n \"tech = tech.reset_index(drop=True)\\n\",\n \"\\n\",\n \"df = tech\\n\",\n \"\\n\",\n \"good = MA(df, 10, \\\"close\\\")\\n\",\n \"good = EMA(good, 10, \\\"close\\\")\\n\",\n \"good = ROC(good, 10, \\\"close\\\")\\n\",\n \"good = MOM(good, 10, \\\"close\\\")\\n\",\n \"good = ATR(good, 10)\\n\",\n \"good = BBANDS(good, 10, \\\"close\\\")\\n\",\n \"good = TRIX(good, 10)\\n\",\n \"good = ADX(good, 8, 6)\\n\",\n \"good = Vortex(good, 8)\\n\",\n \"good = KST(good,5,7,10,2,2,2,5,3)\\n\",\n \"good = RSI(good, 15)\\n\",\n \"good = TSI(good, 7,4)\\n\",\n \"good = MFI(good, 20)\\n\",\n \"good = OBV(good, 10)\\n\",\n \"good = FORCE(good, 10)\\n\",\n \"good = EOM(good, 20)\\n\",\n \"good = COPP(good,10)\\n\",\n \"good = KELCH(good, 10)\\n\",\n \"\\n\",\n \"good = DONCH(good, 10)\\n\",\n \" \\n\",\n \"good = MA(good, 5, \\\"close\\\")\\n\",\n \"good = EMA(good, 5, \\\"close\\\")\\n\",\n \"good = ROC(good, 5, \\\"close\\\")\\n\",\n \"good = ROC(good, 4, \\\"close\\\")\\n\",\n \"good = MOM(good, 5, \\\"close\\\")\\n\",\n \"good = ATR(good, 5)\\n\",\n \"good = BBANDS(good, 5, \\\"close\\\")\\n\",\n \"good = TRIX(good, 4)\\n\",\n \"good = ADX(good, 4, 3)\\n\",\n \"good = Vortex(good, 4)\\n\",\n \"good = KST(good,3,5,8,2,2,2,5,3)\\n\",\n \"good = RSI(good, 5)\\n\",\n \"good = TSI(good, 6,3)\\n\",\n \"good = Chaikin(good)\\n\",\n \"good = MFI(good, 8)\\n\",\n \"good = OBV(good, 4)\\n\",\n \"good = FORCE(good, 8)\\n\",\n \"good = EOM(good, 6)\\n\",\n \"good = COPP(good,3)\\n\",\n \"good = KELCH(good, 5)\\n\",\n \"good = DONCH(good, 8)\\n\",\n \"\\n\",\n \"good = MA(good, 3, \\\"close\\\")\\n\",\n \"good = EMA(good, 3, \\\"close\\\")\\n\",\n \"good = ROC(good, 3, \\\"close\\\")\\n\",\n \"good = MOM(good, 3, \\\"close\\\")\\n\",\n \"good = ATR(good, 3)\\n\",\n \"good = BBANDS(good, 4, \\\"close\\\")\\n\",\n \"good = TRIX(good, 3)\\n\",\n \"good = ADX(good, 3, 3)\\n\",\n \"good = Vortex(good, 3)\\n\",\n \"good = KST(good,3,4,5,2,2,2,5,3)\\n\",\n \"good = RSI(good, 3)\\n\",\n \"good = TSI(good, 4,3)\\n\",\n \"good = TSI(good, 3,2)\\n\",\n \"good = MFI(good, 4)\\n\",\n \"good = OBV(good, 3)\\n\",\n \"good = FORCE(good, 4)\\n\",\n \"good = EOM(good, 5)\\n\",\n \"good = KELCH(good, 4)\\n\",\n \"good = DONCH(good, 5)\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"h4kGnx-O5S8t\"\n },\n \"source\": [\n \"good.head()\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"nZ-EUeFV5S8w\"\n },\n \"source\": [\n \"good.shape\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"Iu8dfKqs5S80\"\n },\n \"source\": [\n \"good.to_csv(\\\"good.csv\\\")\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"Zbokg_r55S83\"\n },\n \"source\": [\n \"### Start here\\n\",\n \"good = pd.read_csv(\\\"good.csv\\\")\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"VsQ_D20v5S85\"\n },\n \"source\": [\n \"ticky = good[\\\"ticker\\\"].unique()\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"90ZXkQoW5S88\"\n },\n \"source\": [\n \"actuals = pd.read_csv(\\\"actuals.csv\\\")\\n\",\n \"\\n\",\n \"actuals = actuals[actuals[\\\"TICKER\\\"].isin(ticky)].reset_index(drop=True)\\n\",\n \"\\n\",\n \"# I think you get the last 4 years data and then chuck it in the model and then delete the last4 years.\\n\",\n \"# Remember to pick up all the past VALUES too \\n\",\n \"# Take 10 years and do a double wave if you still can not prevail. !! \\n\",\n \"# I work with half a terabyte data. \\n\",\n \"\\n\",\n \"actuals[\\\"p4\\\"] = actuals[\\\"VALUE\\\"].shift(4) # this is one of the most important ones\\n\",\n \"actuals[\\\"p8\\\"] = actuals[\\\"VALUE\\\"].shift(8)\\n\",\n \"actuals[\\\"p12\\\"] = actuals[\\\"VALUE\\\"].shift(12)\\n\",\n \"actuals[\\\"p16\\\"] = actuals[\\\"VALUE\\\"].shift(16)\\n\",\n \"\\n\",\n \"actuals[\\\"actual_roll_3\\\"] = pd.rolling_mean(actuals[\\\"VALUE\\\"], 4, 4).reset_index(drop=True).shift(1)\\n\",\n \"actuals[\\\"actual_roll_5\\\"] = pd.rolling_mean(actuals[\\\"VALUE\\\"], 8, 8).reset_index(drop=True).shift(1)\\n\",\n \"actuals[\\\"actual_roll_10\\\"] = pd.rolling_mean(actuals[\\\"VALUE\\\"], 12, 12).reset_index(drop=True).shift(1)\\n\",\n \"actuals[\\\"actual_roll_20\\\"] = pd.rolling_mean(actuals[\\\"VALUE\\\"], 16, 16).reset_index(drop=True).shift(1)\\n\",\n \"\\n\",\n \"\\n\",\n \"actuals[\\\"actual_p_roll\\\"] = (actuals[\\\"p4\\\"] + actuals[\\\"p8\\\"] + actuals[\\\"p12\\\"] + actuals[\\\"p16\\\"])/4\\n\",\n \"\\n\",\n \"actuals[\\\"actual_p_roll_w\\\"] = (actuals[\\\"p4\\\"]*.45 + actuals[\\\"p8\\\"]*.25 + actuals[\\\"p12\\\"]*.15 + actuals[\\\"p16\\\"]*.15)/4\\n\",\n \"\\n\",\n \"actuals[\\\"p1\\\"] = actuals[\\\"VALUE\\\"].shift(1)\\n\",\n \"actuals[\\\"p2\\\"] = actuals[\\\"VALUE\\\"].shift(2)\\n\",\n \"actuals[\\\"p3\\\"] = actuals[\\\"VALUE\\\"].shift(3)\\n\",\n \"\\n\",\n \"actuals[\\\"diff1\\\"] = actuals[\\\"VALUE\\\"]-actuals[\\\"p1\\\"]\\n\",\n \"actuals[\\\"diff2\\\"] = actuals[\\\"p1\\\"]-actuals[\\\"p2\\\"]\\n\",\n \"actuals[\\\"diff4\\\"] = actuals[\\\"VALUE\\\"] - actuals[\\\"p4\\\"]\\n\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"IfLLBHvP5S8_\",\n \"outputId\": \"5da7e0ca-57de-4a22-877e-5183cef1f946\"\n },\n \"source\": [\n \"actuals = actuals.drop_duplicates(subset=[\\\"PENDS\\\",\\\"TICKER\\\"], keep=\\\"last\\\").reset_index(drop=True)\\n\",\n \"actuals = actuals.drop_duplicates(subset=[\\\"PENDS\\\",\\\"CUSIP\\\"], keep=\\\"last\\\").reset_index(drop=True)\\n\",\n \"\\n\",\n \"ticks = actuals[[\\\"PENDS\\\",\\\"TICKER\\\"]].groupby(\\\"TICKER\\\").count()\\n\",\n \"ticks = ticks[ticks[\\\"PENDS\\\"]>39]\\n\",\n \"\\n\",\n \"actuals = actuals[actuals[\\\"TICKER\\\"].isin(ticks.index.values)].reset_index(drop=True)\\n\",\n \"actuals = actuals.sort_values([\\\"TICKER\\\",\\\"PENDS\\\"],ascending=[True,False])\\n\",\n \"actuals[\\\"counter\\\"] = actuals[[\\\"TICKER\\\",\\\"PENDS\\\"]].groupby('TICKER',sort=False).cumcount()\\n\",\n \"\\n\",\n \"actuals = actuals.sort_values([\\\"CUSIP\\\",\\\"ANNDATS\\\"],ascending=[True,True]).reset_index(drop=True)\\n\",\n \"actuals.shape\\n\",\n \"\\n\",\n \"actuals.head()\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
TICKERCUSIPOFTICCNAMEPENDSMEASUREPDICITYANNDATSANNTIMSACTDATSACTTIMSVALUECURR_ACTUSFIRMp4p8p12p16actual_roll_3actual_roll_5actual_roll_10actual_roll_20actual_p_rollactual_p_roll_wp1p2p3diff1diff2diff4counter
0AIR00036110AIRAAR CORP19830228EPSQTR198303250:00:00198303250:00:000.0444USD10.20000.220.220.210.1800000.1975000.2125000.2156250.21250.0523750.17000.18000.1700-0.1256-0.0100-0.1556133
1AIR00036110AIRAAR CORP19830531EPSQTR198307280:00:00198307280:00:000.0740USD10.17000.220.230.250.1411000.1755500.1978670.2052750.21750.0508750.04440.17000.18000.0296-0.1256-0.0960132
2AIR00036110AIRAAR CORP19831130EPSQTR198312210:00:00198312210:00:000.0592USD10.18000.210.280.220.1171000.1573000.1848670.1942750.22250.0521250.07400.04440.1700-0.01480.0296-0.1208131
3AIR00036110AIRAAR CORP19840229EPSQTR198403290:00:00198403290:00:000.0859USD10.17000.210.240.220.0869000.1384500.1664670.1842250.21000.0495000.05920.07400.04440.0267-0.0148-0.0841130
4AIR00036110AIRAAR CORP19840831EPSQTR198409270:00:00198409270:00:000.1125USD10.04440.200.220.220.0658750.1229380.1536250.1758440.17110.0339950.08590.05920.07400.02660.02670.0681129
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" TICKER CUSIP OFTIC CNAME PENDS MEASURE PDICITY ANNDATS \\\\\\n\",\n \"0 AIR 00036110 AIR AAR CORP 19830228 EPS QTR 19830325 \\n\",\n \"1 AIR 00036110 AIR AAR CORP 19830531 EPS QTR 19830728 \\n\",\n \"2 AIR 00036110 AIR AAR CORP 19831130 EPS QTR 19831221 \\n\",\n \"3 AIR 00036110 AIR AAR CORP 19840229 EPS QTR 19840329 \\n\",\n \"4 AIR 00036110 AIR AAR CORP 19840831 EPS QTR 19840927 \\n\",\n \"\\n\",\n \" ANNTIMS ACTDATS ACTTIMS VALUE CURR_ACT USFIRM p4 p8 p12 \\\\\\n\",\n \"0 0:00:00 19830325 0:00:00 0.0444 USD 1 0.2000 0.22 0.22 \\n\",\n \"1 0:00:00 19830728 0:00:00 0.0740 USD 1 0.1700 0.22 0.23 \\n\",\n \"2 0:00:00 19831221 0:00:00 0.0592 USD 1 0.1800 0.21 0.28 \\n\",\n \"3 0:00:00 19840329 0:00:00 0.0859 USD 1 0.1700 0.21 0.24 \\n\",\n \"4 0:00:00 19840927 0:00:00 0.1125 USD 1 0.0444 0.20 0.22 \\n\",\n \"\\n\",\n \" p16 actual_roll_3 actual_roll_5 actual_roll_10 actual_roll_20 \\\\\\n\",\n \"0 0.21 0.180000 0.197500 0.212500 0.215625 \\n\",\n \"1 0.25 0.141100 0.175550 0.197867 0.205275 \\n\",\n \"2 0.22 0.117100 0.157300 0.184867 0.194275 \\n\",\n \"3 0.22 0.086900 0.138450 0.166467 0.184225 \\n\",\n \"4 0.22 0.065875 0.122938 0.153625 0.175844 \\n\",\n \"\\n\",\n \" actual_p_roll actual_p_roll_w p1 p2 p3 diff1 diff2 \\\\\\n\",\n \"0 0.2125 0.052375 0.1700 0.1800 0.1700 -0.1256 -0.0100 \\n\",\n \"1 0.2175 0.050875 0.0444 0.1700 0.1800 0.0296 -0.1256 \\n\",\n \"2 0.2225 0.052125 0.0740 0.0444 0.1700 -0.0148 0.0296 \\n\",\n \"3 0.2100 0.049500 0.0592 0.0740 0.0444 0.0267 -0.0148 \\n\",\n \"4 0.1711 0.033995 0.0859 0.0592 0.0740 0.0266 0.0267 \\n\",\n \"\\n\",\n \" diff4 counter \\n\",\n \"0 -0.1556 133 \\n\",\n \"1 -0.0960 132 \\n\",\n \"2 -0.1208 131 \\n\",\n \"3 -0.0841 130 \\n\",\n \"4 0.0681 129 \"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 11\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"bufEAXoo5S9C\",\n \"outputId\": \"6e85462a-fc1d-4272-9ec8-aec935cce9cb\"\n },\n \"source\": [\n \"analyst = pd.read_csv(\\\"analysts.csv\\\")\\n\",\n \"\\n\",\n \"analyst = analyst.rename(columns={\\\"OFTIC\\\":\\\"ticker\\\", \\\"FPEDATS\\\":\\\"qtrdat\\\", \\\"ANNDATS\\\":\\\"anndat\\\", \\\"ANNTIMS\\\":\\\"anntim\\\",\\n\",\n \" \\\"VALUE\\\":\\\"estimate\\\"})\\n\",\n \"\\n\",\n \"big = analyst[[\\\"estimate\\\",\\\"ticker\\\",\\\"ANNDATS_ACT\\\"]].groupby([\\\"ticker\\\",\\\"ANNDATS_ACT\\\"]).mean().reset_index()\\n\",\n \"big[\\\"est_std\\\"] = analyst[[\\\"estimate\\\",\\\"ticker\\\",\\\"ANNDATS_ACT\\\"]].groupby([\\\"ticker\\\",\\\"ANNDATS_ACT\\\"]).std().reset_index()[\\\"estimate\\\"]\\n\",\n \"big[\\\"est_count\\\"] = analyst[[\\\"estimate\\\",\\\"ticker\\\",\\\"ANNDATS_ACT\\\"]].groupby([\\\"ticker\\\",\\\"ANNDATS_ACT\\\"]).count().reset_index()[\\\"estimate\\\"]\\n\",\n \"big = big.rename(columns={\\\"estimate\\\":\\\"est_avg\\\"})\\n\",\n \"\\n\",\n \"# Add some extra info \\n\",\n \"\\n\",\n \"big[\\\"est_std\\\"] = big[\\\"est_std\\\"].fillna(method=\\\"ffill\\\")\\n\",\n \"big = pd.merge(big, analyst[[\\\"ticker\\\",\\\"ANNDATS_ACT\\\",\\\"qtrdat\\\",\\\"ACTUAL\\\"]].drop_duplicates(keep=\\\"last\\\"), on=[\\\"ticker\\\",\\\"ANNDATS_ACT\\\"],how=\\\"left\\\")\\n\",\n \"\\n\",\n \"big.head()\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
tickerANNDATS_ACTest_avgest_stdest_countqtrdatACTUAL
0134820000516.01.2150001.7889802199906300.0020
137591619980813.00.0206001.7889801199806300.0244
237591619981210.00.0346430.0182297199809300.0569
337591619981210.00.0346430.0182297199809300.0238
437591619990210.00.0094000.0182291199812310.0038
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" ticker ANNDATS_ACT est_avg est_std est_count qtrdat ACTUAL\\n\",\n \"0 1348 20000516.0 1.215000 1.788980 2 19990630 0.0020\\n\",\n \"1 375916 19980813.0 0.020600 1.788980 1 19980630 0.0244\\n\",\n \"2 375916 19981210.0 0.034643 0.018229 7 19980930 0.0569\\n\",\n \"3 375916 19981210.0 0.034643 0.018229 7 19980930 0.0238\\n\",\n \"4 375916 19990210.0 0.009400 0.018229 1 19981231 0.0038\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 12\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"NlHZu9CO5S9G\"\n },\n \"source\": [\n \"actuals.reset_index(drop=True,inplace=True)\\n\",\n \"actuals = pd.merge(actuals, big, left_on=[\\\"TICKER\\\",\\\"PENDS\\\"], right_on=[\\\"ticker\\\",\\\"qtrdat\\\"],how=\\\"left\\\")\\n\",\n \"\\n\",\n \"actuals = actuals.drop_duplicates(subset=[\\\"PENDS\\\",\\\"TICKER\\\"], keep=\\\"last\\\").reset_index(drop=True)\\n\",\n \"actuals = actuals.drop_duplicates(subset=[\\\"PENDS\\\",\\\"CUSIP\\\"], keep=\\\"last\\\").reset_index(drop=True)\\n\",\n \"\\n\",\n \"\\n\",\n \"actuals[\\\"e_p4\\\"] = actuals[\\\"est_avg\\\"].shift(4) # this is one of the most important ones\\n\",\n \"actuals[\\\"e_p8\\\"] = actuals[\\\"est_avg\\\"].shift(8)\\n\",\n \"actuals[\\\"e_p12\\\"] = actuals[\\\"est_avg\\\"].shift(12)\\n\",\n \"actuals[\\\"e_p16\\\"] = actuals[\\\"est_avg\\\"].shift(16)\\n\",\n \"\\n\",\n \"actuals[\\\"e_actual_roll_3\\\"] = pd.rolling_mean(actuals[\\\"est_avg\\\"], 4, 4).reset_index(drop=True).shift(1)\\n\",\n \"actuals[\\\"e_actual_roll_5\\\"] = pd.rolling_mean(actuals[\\\"est_avg\\\"], 8, 8).reset_index(drop=True).shift(1)\\n\",\n \"actuals[\\\"e_actual_roll_10\\\"] = pd.rolling_mean(actuals[\\\"est_avg\\\"], 12, 12).reset_index(drop=True).shift(1)\\n\",\n \"actuals[\\\"e_actual_roll_20\\\"] = pd.rolling_mean(actuals[\\\"est_avg\\\"], 16, 16).reset_index(drop=True).shift(1)\\n\",\n \"\\n\",\n \"\\n\",\n \"actuals[\\\"e_actual_p_roll\\\"] = (actuals[\\\"e_p4\\\"] + actuals[\\\"e_p8\\\"] + actuals[\\\"e_p12\\\"] + actuals[\\\"e_p16\\\"])/4\\n\",\n \"\\n\",\n \"actuals[\\\"e_actual_p_roll_w\\\"] = (actuals[\\\"e_p4\\\"]*.45 + actuals[\\\"e_p8\\\"]*.25 + actuals[\\\"e_p12\\\"]*.15 + actuals[\\\"e_p16\\\"]*.15)/4\\n\",\n \"\\n\",\n \"actuals[\\\"e_p1\\\"] = actuals[\\\"est_avg\\\"].shift(1)\\n\",\n \"actuals[\\\"e_p2\\\"] = actuals[\\\"est_avg\\\"].shift(2)\\n\",\n \"actuals[\\\"e_p3\\\"] = actuals[\\\"est_avg\\\"].shift(3)\\n\",\n \"\\n\",\n \"actuals[\\\"e_diff1\\\"] = actuals[\\\"est_avg\\\"]-actuals[\\\"e_p1\\\"]\\n\",\n \"actuals[\\\"e_diff2\\\"] = actuals[\\\"e_p1\\\"]-actuals[\\\"e_p2\\\"]\\n\",\n \"actuals[\\\"e_diff4\\\"] = actuals[\\\"est_avg\\\"] - actuals[\\\"e_p4\\\"]\\n\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"I6L60eUV5S9I\"\n },\n \"source\": [\n \"actuals[\\\"d_e_p4\\\"] = actuals[\\\"e_p4\\\"] - actuals[\\\"p4\\\"]\\n\",\n \"actuals[\\\"d_e_p8\\\"] = actuals[\\\"e_p8\\\"] - actuals[\\\"p8\\\"]\\n\",\n \"actuals[\\\"d_e_p12\\\"] = actuals[\\\"e_p12\\\"] - actuals[\\\"p12\\\"]\\n\",\n \"actuals[\\\"d_e_p16\\\"] = actuals[\\\"e_p16\\\"] - actuals[\\\"p16\\\"]\\n\",\n \"\\n\",\n \"actuals[\\\"d_e_actual_roll_3\\\"] = actuals[\\\"e_actual_roll_3\\\"] - actuals[\\\"actual_roll_3\\\"]\\n\",\n \"actuals[\\\"d_e_actual_roll_5\\\"] = actuals[\\\"e_actual_roll_5\\\"] - actuals[\\\"actual_roll_5\\\"]\\n\",\n \"actuals[\\\"d_e_actual_roll_10\\\"] = actuals[\\\"e_actual_roll_10\\\"] - actuals[\\\"actual_roll_10\\\"]\\n\",\n \"actuals[\\\"d_e_actual_roll_20\\\"] = actuals[\\\"e_actual_roll_20\\\"] - actuals[\\\"actual_roll_20\\\"]\\n\",\n \"\\n\",\n \"\\n\",\n \"actuals[\\\"d_e_actual_p_roll\\\"] = actuals[\\\"e_actual_p_roll\\\"] - actuals[\\\"actual_p_roll\\\"]\\n\",\n \"\\n\",\n \"actuals[\\\"d_e_actual_p_roll_w\\\"] = actuals[\\\"e_actual_p_roll_w\\\"] - actuals[\\\"actual_p_roll_w\\\"] \\n\",\n \"\\n\",\n \"actuals[\\\"d_e_p1\\\"] = actuals[\\\"e_p1\\\"] - actuals[\\\"p1\\\"]\\n\",\n \"actuals[\\\"d_e_p2\\\"] = actuals[\\\"e_p2\\\"] - actuals[\\\"p2\\\"]\\n\",\n \"actuals[\\\"d_e_p3\\\"] = actuals[\\\"e_p3\\\"] - actuals[\\\"p3\\\"]\\n\",\n \"\\n\",\n \"actuals[\\\"d_e_diff1\\\"] = actuals[\\\"e_diff1\\\"] - actuals[\\\"diff1\\\"]\\n\",\n \"actuals[\\\"d_e_diff2\\\"] = actuals[\\\"e_diff2\\\"] - actuals[\\\"diff2\\\"] \\n\",\n \"actuals[\\\"d_e_diff4\\\"] = actuals[\\\"e_diff4\\\"] - actuals[\\\"diff4\\\"]\\n\",\n \"\\n\",\n \"actuals[\\\"target\\\"] = (actuals[\\\"VALUE\\\"]-actuals[\\\"est_avg\\\"])/actuals[\\\"est_avg\\\"]\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"b0vxyYb95S9L\",\n \"outputId\": \"b6e05b69-0eac-4e43-a8af-d3a6de910676\"\n },\n \"source\": [\n \"actuals.fillna(method=\\\"ffill\\\",inplace=True)\\n\",\n \"actuals.fillna(value=0,inplace=True)\\n\",\n \"\\n\",\n \"max_count = actuals[[\\\"TICKER\\\",\\\"counter\\\"]].groupby('TICKER',sort=False).max().reset_index()\\n\",\n \"max_count[\\\"counter\\\"] = max_count[\\\"counter\\\"] - 24\\n\",\n \"\\n\",\n \"actuals = pd.merge(actuals, max_count, on=\\\"TICKER\\\", how=\\\"left\\\")\\n\",\n \"actuals[\\\"flag\\\"] = actuals[\\\"counter_x\\\"] == actuals[\\\"counter_y\\\"]\\n\",\n \"\\n\",\n \"actuals[\\\"flag\\\"].loc[actuals[\\\"flag\\\"]== False] = None\\n\",\n \"actuals[\\\"flag\\\"].loc[actuals[\\\"flag\\\"]== True] = 1\\n\",\n \"\\n\",\n \"actuals[\\\"flag\\\"] = actuals[[\\\"flag\\\",\\\"TICKER\\\"]].groupby([\\\"TICKER\\\"]).bfill()\\n\",\n \"\\n\",\n \"actuals = actuals[actuals[\\\"flag\\\"]!=1].reset_index(drop=True)\\n\",\n \"\\n\",\n \"actuals.head(10)\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
TICKERCUSIPOFTICCNAMEPENDSMEASUREPDICITYANNDATSANNTIMSACTDATSACTTIMSVALUECURR_ACTUSFIRMp4p8p12p16actual_roll_3actual_roll_5actual_roll_10actual_roll_20actual_p_rollactual_p_roll_wp1p2p3diff1diff2diff4counter_xtickerANNDATS_ACTest_avgest_stdest_countqtrdatACTUALe_p4e_p8e_p12e_p16e_actual_roll_3e_actual_roll_5e_actual_roll_10e_actual_roll_20e_actual_p_rolle_actual_p_roll_we_p1e_p2e_p3e_diff1e_diff2e_diff4d_e_p4d_e_p8d_e_p12d_e_p16d_e_actual_roll_3d_e_actual_roll_5d_e_actual_roll_10d_e_actual_roll_20d_e_actual_p_rolld_e_actual_p_roll_wd_e_p1d_e_p2d_e_p3d_e_diff1d_e_diff2d_e_diff4targetcounter_yflag
0AIR00036110AIRAAR CORP19900228EPSQTR199003070:00:00199003070:00:000.2531USD10.25310.21980.16650.14190.2781000.2606250.2364750.2157870.1953250.0537760.27980.27310.3064-0.02670.00670.0000108AIR19900307.00.3064450.01263311.019900228.00.25310.2638200.2148500.1705200.1332330.4399610.4062060.3634350.3293370.1956060.0544990.2997800.2811400.9151030.0066650.0186400.0426250.010720-0.0049500.004020-0.0086670.1618610.1455810.1269600.1135500.0002810.0007220.0199800.0080400.6087030.0333650.0119400.042625-0.174078109NaN
1AIR00036110AIRAAR CORP19900531EPSQTR199006210:00:00199006210:00:000.2598USD10.30640.27310.18650.15990.2781000.2647880.2436920.2227370.2314750.0645290.25310.27980.27310.0067-0.0267-0.0466107AIR19900621.00.8226580.41538553.019900531.00.25980.9151030.7825410.5450000.4596500.4506170.4176550.3747620.3401630.6755740.1895320.3064450.2997800.2811400.5162130.006665-0.0924450.6087030.5094410.3585000.2997500.1725170.1528680.1310700.1174260.4440990.1250040.0533450.0199800.0080400.5095130.033365-0.045845-0.684195109NaN
2AIR00036110AIRAAR CORP19900831EPSQTR199009190:00:00199009190:00:000.2798USD10.27310.23320.19320.14660.2664500.2631250.2498000.2289810.2115250.0580410.25980.25310.27980.02000.00670.0067106AIR19900919.00.2905280.02121318.019900831.00.27980.2811400.2376110.1812000.1532000.4275060.4226700.3979000.3628510.2132880.0590190.8226580.3064450.299780-0.5321310.5162130.0093880.0080400.004411-0.0120000.0066000.1610560.1595450.1481000.1338700.0017630.0009780.5628580.0533450.019980-0.5521310.5095130.002688-0.036925109NaN
3AIR00036110AIRAAR CORP19901130EPSQTR199012240:00:00199012240:00:000.0933USD10.27980.24650.20650.16650.2681250.2689500.2570170.2373060.2248250.0608710.27980.25980.2531-0.18650.0200-0.1865105AIR19901224.00.2591240.05591429.019901130.00.09330.2997800.2548000.2148500.1621000.4298530.4292840.4070110.3714340.2328830.0637860.2905280.8226580.306445-0.031404-0.532131-0.0406560.0199800.0083000.008350-0.0044000.1617280.1603340.1499940.1341280.0080580.0029150.0107280.5628580.0533450.155096-0.5521310.145844-0.639941109NaN
4AIR00036110AIRAAR CORP19910228EPSQTR199103190:00:00199103190:00:000.1665USD10.25310.25310.21980.16650.2215000.2498000.2475830.2327310.2231250.0587790.09330.27980.25980.0732-0.1865-0.0866104AIR19910319.00.2388620.08228121.019910228.00.16650.3064450.2638200.2148500.1705200.4196890.4298250.4107000.3774980.2389090.0654150.2591240.2905280.822658-0.020262-0.031404-0.0675840.0533450.010720-0.0049500.0040200.1981890.1800250.1631170.1447670.0157840.0066360.1658240.0107280.562858-0.0934620.1550960.019016-0.302945109NaN
5AIR00036110AIRAAR CORP19910531EPSQTR199106220:00:00199106220:00:000.1665USD10.25980.30640.27310.18650.1998500.2389750.2431420.2327310.2564500.0656120.16650.09330.27980.00000.0732-0.0933103AIR19910622.00.7197420.38131448.019910531.00.16650.8226580.9151030.7825410.5450000.4027930.4267050.4127010.3817700.7663260.1995260.2388620.2591240.2905280.480880-0.020262-0.1029170.5628580.6087030.5094410.3585000.2029430.1877300.1695590.1490380.5098760.1339130.0723620.1658240.0107280.480880-0.093462-0.009617-0.768667109NaN
6AIR00036110AIRAAR CORP19910831EPSQTR199109210:00:00199109210:00:000.1466USD10.27980.27310.23320.19320.1765250.2214880.2342580.2314810.2448250.0645360.16650.16650.0933-0.01990.0000-0.1332102AIR19910921.00.1443330.0101913.019910831.00.14660.2905280.2811400.2376110.1812000.3770640.4022850.4074680.3926910.2476200.0659610.7197420.2388620.259124-0.5754080.480880-0.1461940.0107280.0080400.004411-0.0120000.2005390.1807970.1732090.1612100.0027950.0014250.5532420.0723620.165824-0.5555080.480880-0.0129940.015704109NaN
7AIR00036110AIRAAR CORP19911130EPSQTR199112180:00:00199112180:00:000.1132USD10.09330.27980.24650.20650.1432250.2056750.2270420.2285690.2065250.0449710.14660.16650.1665-0.0334-0.01990.0199101AIR19911218.00.1625400.0180175.019911130.00.11320.2591240.2997800.2548000.2148500.3405150.3851840.3996950.3903870.2571390.0655000.1443330.7197420.2388620.018207-0.575408-0.0965840.1658240.0199800.0083000.0083500.1972900.1795090.1726530.1618180.0506140.020528-0.0022670.5532420.0723620.051607-0.555508-0.116484-0.303556109NaN
8AIR00036110AIRAAR CORP19920229EPSQTR199203180:00:00199203180:00:000.1199USD10.16650.25310.25310.21980.1482000.1848500.2159330.2227370.2231250.0522840.11320.14660.16650.0067-0.0334-0.0466100AIR19920318.00.1647270.04162811.019920229.00.11990.2388620.3064450.2638200.2148500.3163690.3680290.3920060.3871170.2559940.0639750.1625400.1443330.7197420.0021870.018207-0.0741350.0723620.0533450.010720-0.0049500.1681690.1831790.1760730.1643800.0328690.0116910.049340-0.0022670.553242-0.0045130.051607-0.027535-0.272130109NaN
9AIR00036110AIRAAR CORP19920531EPSQTR199206200:00:00199206200:00:000.1732USD10.16650.25980.30640.27310.1365500.1682000.2048330.2164940.2514500.0567000.11990.11320.14660.05330.00670.006799AIR19920620.00.4394710.24300835.019920531.00.17320.7197420.8226580.9151030.7825410.2978360.3503140.3837490.3839850.8100110.1960490.1647270.1625400.1443330.2747440.002187-0.2802700.5532420.5628580.6087030.5094410.1612860.1821140.1789150.1674910.5585610.1393490.0448270.049340-0.0022670.221444-0.004513-0.286970-0.605890109NaN
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" TICKER CUSIP OFTIC CNAME PENDS MEASURE PDICITY ANNDATS \\\\\\n\",\n \"0 AIR 00036110 AIR AAR CORP 19900228 EPS QTR 19900307 \\n\",\n \"1 AIR 00036110 AIR AAR CORP 19900531 EPS QTR 19900621 \\n\",\n \"2 AIR 00036110 AIR AAR CORP 19900831 EPS QTR 19900919 \\n\",\n \"3 AIR 00036110 AIR AAR CORP 19901130 EPS QTR 19901224 \\n\",\n \"4 AIR 00036110 AIR AAR CORP 19910228 EPS QTR 19910319 \\n\",\n \"5 AIR 00036110 AIR AAR CORP 19910531 EPS QTR 19910622 \\n\",\n \"6 AIR 00036110 AIR AAR CORP 19910831 EPS QTR 19910921 \\n\",\n \"7 AIR 00036110 AIR AAR CORP 19911130 EPS QTR 19911218 \\n\",\n \"8 AIR 00036110 AIR AAR CORP 19920229 EPS QTR 19920318 \\n\",\n \"9 AIR 00036110 AIR AAR CORP 19920531 EPS QTR 19920620 \\n\",\n \"\\n\",\n \" ANNTIMS ACTDATS ACTTIMS VALUE CURR_ACT USFIRM p4 p8 \\\\\\n\",\n \"0 0:00:00 19900307 0:00:00 0.2531 USD 1 0.2531 0.2198 \\n\",\n \"1 0:00:00 19900621 0:00:00 0.2598 USD 1 0.3064 0.2731 \\n\",\n \"2 0:00:00 19900919 0:00:00 0.2798 USD 1 0.2731 0.2332 \\n\",\n \"3 0:00:00 19901224 0:00:00 0.0933 USD 1 0.2798 0.2465 \\n\",\n \"4 0:00:00 19910319 0:00:00 0.1665 USD 1 0.2531 0.2531 \\n\",\n \"5 0:00:00 19910622 0:00:00 0.1665 USD 1 0.2598 0.3064 \\n\",\n \"6 0:00:00 19910921 0:00:00 0.1466 USD 1 0.2798 0.2731 \\n\",\n \"7 0:00:00 19911218 0:00:00 0.1132 USD 1 0.0933 0.2798 \\n\",\n \"8 0:00:00 19920318 0:00:00 0.1199 USD 1 0.1665 0.2531 \\n\",\n \"9 0:00:00 19920620 0:00:00 0.1732 USD 1 0.1665 0.2598 \\n\",\n \"\\n\",\n \" p12 p16 actual_roll_3 actual_roll_5 actual_roll_10 \\\\\\n\",\n \"0 0.1665 0.1419 0.278100 0.260625 0.236475 \\n\",\n \"1 0.1865 0.1599 0.278100 0.264788 0.243692 \\n\",\n \"2 0.1932 0.1466 0.266450 0.263125 0.249800 \\n\",\n \"3 0.2065 0.1665 0.268125 0.268950 0.257017 \\n\",\n \"4 0.2198 0.1665 0.221500 0.249800 0.247583 \\n\",\n \"5 0.2731 0.1865 0.199850 0.238975 0.243142 \\n\",\n \"6 0.2332 0.1932 0.176525 0.221488 0.234258 \\n\",\n \"7 0.2465 0.2065 0.143225 0.205675 0.227042 \\n\",\n \"8 0.2531 0.2198 0.148200 0.184850 0.215933 \\n\",\n \"9 0.3064 0.2731 0.136550 0.168200 0.204833 \\n\",\n \"\\n\",\n \" actual_roll_20 actual_p_roll actual_p_roll_w p1 p2 p3 \\\\\\n\",\n \"0 0.215787 0.195325 0.053776 0.2798 0.2731 0.3064 \\n\",\n \"1 0.222737 0.231475 0.064529 0.2531 0.2798 0.2731 \\n\",\n \"2 0.228981 0.211525 0.058041 0.2598 0.2531 0.2798 \\n\",\n \"3 0.237306 0.224825 0.060871 0.2798 0.2598 0.2531 \\n\",\n \"4 0.232731 0.223125 0.058779 0.0933 0.2798 0.2598 \\n\",\n \"5 0.232731 0.256450 0.065612 0.1665 0.0933 0.2798 \\n\",\n \"6 0.231481 0.244825 0.064536 0.1665 0.1665 0.0933 \\n\",\n \"7 0.228569 0.206525 0.044971 0.1466 0.1665 0.1665 \\n\",\n \"8 0.222737 0.223125 0.052284 0.1132 0.1466 0.1665 \\n\",\n \"9 0.216494 0.251450 0.056700 0.1199 0.1132 0.1466 \\n\",\n \"\\n\",\n \" diff1 diff2 diff4 counter_x ticker ANNDATS_ACT est_avg est_std \\\\\\n\",\n \"0 -0.0267 0.0067 0.0000 108 AIR 19900307.0 0.306445 0.012633 \\n\",\n \"1 0.0067 -0.0267 -0.0466 107 AIR 19900621.0 0.822658 0.415385 \\n\",\n \"2 0.0200 0.0067 0.0067 106 AIR 19900919.0 0.290528 0.021213 \\n\",\n \"3 -0.1865 0.0200 -0.1865 105 AIR 19901224.0 0.259124 0.055914 \\n\",\n \"4 0.0732 -0.1865 -0.0866 104 AIR 19910319.0 0.238862 0.082281 \\n\",\n \"5 0.0000 0.0732 -0.0933 103 AIR 19910622.0 0.719742 0.381314 \\n\",\n \"6 -0.0199 0.0000 -0.1332 102 AIR 19910921.0 0.144333 0.010191 \\n\",\n \"7 -0.0334 -0.0199 0.0199 101 AIR 19911218.0 0.162540 0.018017 \\n\",\n \"8 0.0067 -0.0334 -0.0466 100 AIR 19920318.0 0.164727 0.041628 \\n\",\n \"9 0.0533 0.0067 0.0067 99 AIR 19920620.0 0.439471 0.243008 \\n\",\n \"\\n\",\n \" est_count qtrdat ACTUAL e_p4 e_p8 e_p12 e_p16 \\\\\\n\",\n \"0 11.0 19900228.0 0.2531 0.263820 0.214850 0.170520 0.133233 \\n\",\n \"1 53.0 19900531.0 0.2598 0.915103 0.782541 0.545000 0.459650 \\n\",\n \"2 18.0 19900831.0 0.2798 0.281140 0.237611 0.181200 0.153200 \\n\",\n \"3 29.0 19901130.0 0.0933 0.299780 0.254800 0.214850 0.162100 \\n\",\n \"4 21.0 19910228.0 0.1665 0.306445 0.263820 0.214850 0.170520 \\n\",\n \"5 48.0 19910531.0 0.1665 0.822658 0.915103 0.782541 0.545000 \\n\",\n \"6 3.0 19910831.0 0.1466 0.290528 0.281140 0.237611 0.181200 \\n\",\n \"7 5.0 19911130.0 0.1132 0.259124 0.299780 0.254800 0.214850 \\n\",\n \"8 11.0 19920229.0 0.1199 0.238862 0.306445 0.263820 0.214850 \\n\",\n \"9 35.0 19920531.0 0.1732 0.719742 0.822658 0.915103 0.782541 \\n\",\n \"\\n\",\n \" e_actual_roll_3 e_actual_roll_5 e_actual_roll_10 e_actual_roll_20 \\\\\\n\",\n \"0 0.439961 0.406206 0.363435 0.329337 \\n\",\n \"1 0.450617 0.417655 0.374762 0.340163 \\n\",\n \"2 0.427506 0.422670 0.397900 0.362851 \\n\",\n \"3 0.429853 0.429284 0.407011 0.371434 \\n\",\n \"4 0.419689 0.429825 0.410700 0.377498 \\n\",\n \"5 0.402793 0.426705 0.412701 0.381770 \\n\",\n \"6 0.377064 0.402285 0.407468 0.392691 \\n\",\n \"7 0.340515 0.385184 0.399695 0.390387 \\n\",\n \"8 0.316369 0.368029 0.392006 0.387117 \\n\",\n \"9 0.297836 0.350314 0.383749 0.383985 \\n\",\n \"\\n\",\n \" e_actual_p_roll e_actual_p_roll_w e_p1 e_p2 e_p3 e_diff1 \\\\\\n\",\n \"0 0.195606 0.054499 0.299780 0.281140 0.915103 0.006665 \\n\",\n \"1 0.675574 0.189532 0.306445 0.299780 0.281140 0.516213 \\n\",\n \"2 0.213288 0.059019 0.822658 0.306445 0.299780 -0.532131 \\n\",\n \"3 0.232883 0.063786 0.290528 0.822658 0.306445 -0.031404 \\n\",\n \"4 0.238909 0.065415 0.259124 0.290528 0.822658 -0.020262 \\n\",\n \"5 0.766326 0.199526 0.238862 0.259124 0.290528 0.480880 \\n\",\n \"6 0.247620 0.065961 0.719742 0.238862 0.259124 -0.575408 \\n\",\n \"7 0.257139 0.065500 0.144333 0.719742 0.238862 0.018207 \\n\",\n \"8 0.255994 0.063975 0.162540 0.144333 0.719742 0.002187 \\n\",\n \"9 0.810011 0.196049 0.164727 0.162540 0.144333 0.274744 \\n\",\n \"\\n\",\n \" e_diff2 e_diff4 d_e_p4 d_e_p8 d_e_p12 d_e_p16 \\\\\\n\",\n \"0 0.018640 0.042625 0.010720 -0.004950 0.004020 -0.008667 \\n\",\n \"1 0.006665 -0.092445 0.608703 0.509441 0.358500 0.299750 \\n\",\n \"2 0.516213 0.009388 0.008040 0.004411 -0.012000 0.006600 \\n\",\n \"3 -0.532131 -0.040656 0.019980 0.008300 0.008350 -0.004400 \\n\",\n \"4 -0.031404 -0.067584 0.053345 0.010720 -0.004950 0.004020 \\n\",\n \"5 -0.020262 -0.102917 0.562858 0.608703 0.509441 0.358500 \\n\",\n \"6 0.480880 -0.146194 0.010728 0.008040 0.004411 -0.012000 \\n\",\n \"7 -0.575408 -0.096584 0.165824 0.019980 0.008300 0.008350 \\n\",\n \"8 0.018207 -0.074135 0.072362 0.053345 0.010720 -0.004950 \\n\",\n \"9 0.002187 -0.280270 0.553242 0.562858 0.608703 0.509441 \\n\",\n \"\\n\",\n \" d_e_actual_roll_3 d_e_actual_roll_5 d_e_actual_roll_10 \\\\\\n\",\n \"0 0.161861 0.145581 0.126960 \\n\",\n \"1 0.172517 0.152868 0.131070 \\n\",\n \"2 0.161056 0.159545 0.148100 \\n\",\n \"3 0.161728 0.160334 0.149994 \\n\",\n \"4 0.198189 0.180025 0.163117 \\n\",\n \"5 0.202943 0.187730 0.169559 \\n\",\n \"6 0.200539 0.180797 0.173209 \\n\",\n \"7 0.197290 0.179509 0.172653 \\n\",\n \"8 0.168169 0.183179 0.176073 \\n\",\n \"9 0.161286 0.182114 0.178915 \\n\",\n \"\\n\",\n \" d_e_actual_roll_20 d_e_actual_p_roll d_e_actual_p_roll_w d_e_p1 \\\\\\n\",\n \"0 0.113550 0.000281 0.000722 0.019980 \\n\",\n \"1 0.117426 0.444099 0.125004 0.053345 \\n\",\n \"2 0.133870 0.001763 0.000978 0.562858 \\n\",\n \"3 0.134128 0.008058 0.002915 0.010728 \\n\",\n \"4 0.144767 0.015784 0.006636 0.165824 \\n\",\n \"5 0.149038 0.509876 0.133913 0.072362 \\n\",\n \"6 0.161210 0.002795 0.001425 0.553242 \\n\",\n \"7 0.161818 0.050614 0.020528 -0.002267 \\n\",\n \"8 0.164380 0.032869 0.011691 0.049340 \\n\",\n \"9 0.167491 0.558561 0.139349 0.044827 \\n\",\n \"\\n\",\n \" d_e_p2 d_e_p3 d_e_diff1 d_e_diff2 d_e_diff4 target counter_y \\\\\\n\",\n \"0 0.008040 0.608703 0.033365 0.011940 0.042625 -0.174078 109 \\n\",\n \"1 0.019980 0.008040 0.509513 0.033365 -0.045845 -0.684195 109 \\n\",\n \"2 0.053345 0.019980 -0.552131 0.509513 0.002688 -0.036925 109 \\n\",\n \"3 0.562858 0.053345 0.155096 -0.552131 0.145844 -0.639941 109 \\n\",\n \"4 0.010728 0.562858 -0.093462 0.155096 0.019016 -0.302945 109 \\n\",\n \"5 0.165824 0.010728 0.480880 -0.093462 -0.009617 -0.768667 109 \\n\",\n \"6 0.072362 0.165824 -0.555508 0.480880 -0.012994 0.015704 109 \\n\",\n \"7 0.553242 0.072362 0.051607 -0.555508 -0.116484 -0.303556 109 \\n\",\n \"8 -0.002267 0.553242 -0.004513 0.051607 -0.027535 -0.272130 109 \\n\",\n \"9 0.049340 -0.002267 0.221444 -0.004513 -0.286970 -0.605890 109 \\n\",\n \"\\n\",\n \" flag \\n\",\n \"0 NaN \\n\",\n \"1 NaN \\n\",\n \"2 NaN \\n\",\n \"3 NaN \\n\",\n \"4 NaN \\n\",\n \"5 NaN \\n\",\n \"6 NaN \\n\",\n \"7 NaN \\n\",\n \"8 NaN \\n\",\n \"9 NaN \"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 15\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"8erih34g5S9P\",\n \"outputId\": \"836ec191-f5da-4fcf-9e63-07616d7adfa0\"\n },\n \"source\": [\n \"# So start here. \\n\",\n \"# You can potentially go back and remove the 16 and 12's actuals if nothing happens.\\n\",\n \"\\n\",\n \"actuals.shape\\n\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"(87698, 73)\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 16\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"vDYnqhlg5S9T\"\n },\n \"source\": [\n \"appender = pd.DataFrame()\\n\",\n \"actuals = actuals.fillna(value=0)\\n\",\n \"actuals.reset_index(drop=True,inplace=True)\\n\",\n \"#df_comp = actuals.ix[:600,:]\\n\",\n \"\\n\",\n \"df_comp = actuals\\n\",\n \"df_comp[\\\"id\\\"] = 1\\n\",\n \"df_comp[\\\"last\\\"] = 0 \\n\",\n \"for i in range(len(df_comp)):\\n\",\n \" df_test_1 = df_comp.ix[-1+i:7+i].copy() # Have to use copy whenver you do .ix\\n\",\n \" df_test_1[\\\"id\\\"].ix[-1+i:7+i] = i\\n\",\n \" df_test_1[\\\"last\\\"].ix[7+i] = 1\\n\",\n \" appender = pd.concat([appender,df_test_1],axis=0)\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"Vbl7x0GO5S9V\",\n \"outputId\": \"1c9587fd-ec04-443e-e2b2-f1b899ec340c\"\n },\n \"source\": [\n \"1+1\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"2\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 18\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"jbxJEoY55S9Z\"\n },\n \"source\": [\n \"appender.to_csv(\\\"appender.csv\\\")\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"a3QsR0F65S9c\",\n \"outputId\": \"279b2fb9-5e5e-4777-bf92-acef5137bd45\"\n },\n \"source\": [\n \"test = appender[[\\\"target\\\",\\\"id\\\"]].ix[appender[\\\"last\\\"]==1]\\n\",\n \"test.set_index(\\\"id\\\",inplace=True)\\n\",\n \"test[\\\"target\\\"].fillna(method=\\\"bfill\\\",inplace=True) # For the target opisite order applies\\n\",\n \"test[\\\"target\\\"].fillna(method=\\\"Ffill\\\",inplace=True)\\n\",\n \"test.head()\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
target
id
0-0.303556
1-0.272130
2-0.605890
3-0.107239
4-0.542627
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" target\\n\",\n \"id \\n\",\n \"0 -0.303556\\n\",\n \"1 -0.272130\\n\",\n \"2 -0.605890\\n\",\n \"3 -0.107239\\n\",\n \"4 -0.542627\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 20\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"v-DQJQy85S9g\",\n \"outputId\": \"6ef163bc-5959-4cba-e381-802ad82b7ba4\"\n },\n \"source\": [\n \"appender.shape\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"(789253, 75)\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 21\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"GmJr9emC5S9l\",\n \"outputId\": \"a263d17a-a572-4dc0-fbad-1572af336a26\"\n },\n \"source\": [\n \"appender = appender[appender[\\\"id\\\"].isin(test.index)]\\n\",\n \"appender.tail(10)\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
TICKERCUSIPOFTICCNAMEPENDSMEASUREPDICITYANNDATSANNTIMSACTDATSACTTIMSVALUECURR_ACTUSFIRMp4p8p12p16actual_roll_3actual_roll_5actual_roll_10actual_roll_20actual_p_rollactual_p_roll_wp1p2p3diff1diff2diff4counter_xtickerANNDATS_ACTest_avgest_stdest_countqtrdatACTUALe_p4e_p8e_p12e_p16e_actual_roll_3e_actual_roll_5e_actual_roll_10e_actual_roll_20e_actual_p_rolle_actual_p_roll_we_p1e_p2e_p3e_diff1e_diff2e_diff4d_e_p4d_e_p8d_e_p12d_e_p16d_e_actual_roll_3d_e_actual_roll_5d_e_actual_roll_10d_e_actual_roll_20d_e_actual_p_rolld_e_actual_p_roll_wd_e_p1d_e_p2d_e_p3d_e_diff1d_e_diff2d_e_diff4targetcounter_yflagidlast
87696LULUY2573F10FLEXFLEX LTD20070430EPSQTR200706155:32:002007090510:00:000.0250USD10.01-0.020.040.100-0.0075-0.025-0.005833-0.0131250.005000.0010000.1400.140.02-0.160.120.0339XCO20060331.0-0.3509500.4404572.020051231.00.3164-0.35095-0.350950.0340.0340.3806270.3618110.3443550.3356070.2935010.077014-0.35095-0.35095-0.350950.0415520.0415520.038433-1.02095-0.330950.024-0.5461.0806270.7205610.4118550.2443570.4085010.1725149.694-1.290950.0908-0.0184481.2815520.048433-1.901553160876891
87689TSLY2573F10FLEXFLEX LTD20060331EPSQTR2006051514:51:002007012917:21:000.3164USD11.161.120.950.8750.37750.3550.3733330.3668751.026250.2689370.6300.940.67-0.160.360.0340XCO20060331.0-0.3509500.4404572.020051231.00.31640.034000.034000.0340.0340.3806270.3618110.3443550.3356070.2935010.077014-0.35095-0.350950.034000.0415520.0415520.038433-0.092500.064000.024-0.5461.0806270.7205610.4118550.2443570.4085010.1725149.694-1.290950.0908-0.0184481.2815520.048433-1.901553160876900
87690EDUY2573F10FLEXFLEX LTD20060531EPSQTR200607159:12:00200610099:36:000.3164USD11.160.230.260.3500.37750.3550.3733330.3668751.026250.2689370.6300.940.67-0.160.360.0342XCO20060331.0-0.3509500.4404572.020051231.00.31640.034000.034000.0340.0340.3806270.3618110.3443550.3356070.2935010.077014-0.35095-0.35095-0.350950.0415520.0415520.038433-0.092500.064000.024-0.5461.0806270.7205610.4118550.2443570.4085010.1725149.694-1.290950.0908-0.0184481.2815520.048433-1.901553220876900
87691TSLY2573F10FLEXFLEX LTD20060630EPSQTR2006081514:51:002007012917:22:000.3164USD10.670.640.550.4600.37750.3550.3733330.3668750.580000.1532500.6300.940.94-0.160.360.0339XCO20060331.0-0.3509500.4404572.020051231.00.3164-0.350950.034000.0340.0340.3806270.3618110.3443550.3356070.2935010.077014-0.35095-0.35095-0.350950.0415520.0415520.038433-1.020950.064000.024-0.5461.0806270.7205610.4118550.2443570.4085010.1725149.694-1.290950.0908-0.0184481.2815520.048433-1.901553160876900
87692EDUY2573F10FLEXFLEX LTD20060831EPSQTR200610173:29:00200610177:50:340.1825USD10.670.380.340.4800.37750.3550.3733330.3668750.580000.1532500.6300.940.94-0.160.360.0341XCO20060331.0-0.3509500.4404572.020051231.00.3164-0.350950.034000.0340.0340.3806270.3618110.3443550.3356070.2935010.077014-0.35095-0.35095-0.350950.0415520.0415520.038433-1.020950.064000.024-0.5461.0806270.7205610.4118550.2443570.4085010.1725149.694-1.290950.0908-0.0184481.2815520.048433-1.901553220876900
87693YGEY2573F10FLEXFLEX LTD20061231EPSQTR200702159:30:002007071810:01:540.1825CNY1-0.20-0.02-0.04-0.060-0.0075-0.025-0.005833-0.013125-0.08000-0.0275000.1400.020.01-0.160.120.0339XCO20060331.0-0.3509500.4404572.020051231.00.3164-0.350950.034000.0340.0340.3806270.3618110.3443550.3356070.2935010.077014-0.35095-0.35095-0.350950.0415520.0415520.038433-1.020950.064000.024-0.5461.0806270.7205610.4118550.2443570.4085010.1725149.694-1.290950.0908-0.0184481.2815520.048433-1.901553150876900
87694LULUY2573F10FLEXFLEX LTD20070131EPSQTR200703157:28:002007072712:04:000.1825USD1-0.20-0.040.010.000-0.0075-0.025-0.005833-0.013125-0.08000-0.0275000.1400.020.01-0.160.120.0340XCO20060331.0-0.3509500.4404572.020051231.00.3164-0.350950.034000.0340.0340.3806270.3618110.3443550.3356070.2935010.077014-0.35095-0.35095-0.350950.0415520.0415520.038433-1.020950.064000.024-0.5461.0806270.7205610.4118550.2443570.4085010.1725149.694-1.290950.0908-0.0184481.2815520.048433-1.901553160876900
87695YGEY2573F10FLEXFLEX LTD20070331EPSQTR200705159:30:002007071810:03:330.1825CNY10.01-0.020.04-0.010-0.0075-0.025-0.005833-0.0131250.005000.0010000.1400.140.02-0.160.120.0338XCO20060331.0-0.3509500.4404572.020051231.00.3164-0.35095-0.350950.0340.0340.3806270.3618110.3443550.3356070.2935010.077014-0.35095-0.35095-0.350950.0415520.0415520.038433-1.02095-0.330950.024-0.5461.0806270.7205610.4118550.2443570.4085010.1725149.694-1.290950.0908-0.0184481.2815520.048433-1.901553150876900
87696LULUY2573F10FLEXFLEX LTD20070430EPSQTR200706155:32:002007090510:00:000.0250USD10.01-0.020.040.100-0.0075-0.025-0.005833-0.0131250.005000.0010000.1400.140.02-0.160.120.0339XCO20060331.0-0.3509500.4404572.020051231.00.3164-0.35095-0.350950.0340.0340.3806270.3618110.3443550.3356070.2935010.077014-0.35095-0.35095-0.350950.0415520.0415520.038433-1.02095-0.330950.024-0.5461.0806270.7205610.4118550.2443570.4085010.1725149.694-1.290950.0908-0.0184481.2815520.048433-1.901553160876900
87697LULUY2573F10FLEXFLEX LTD20070731EPSQTR2007091016:05:002007091016:48:000.0350USD10.01-0.02-0.040.060-0.0075-0.025-0.005833-0.0131250.005000.0010000.0250.140.020.010.120.0338LULU20070910.00.0164290.0024407.020070731.00.0350-0.35095-0.350950.0340.0340.3806270.3618110.3443550.3356070.2935010.077014-0.35095-0.35095-0.350950.0415520.0415520.038433-1.02095-0.330950.024-0.5461.0806270.7205610.4118550.2443570.4085010.1725149.694-1.290950.0908-0.0184481.2815520.0484331.130435160876901
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" TICKER CUSIP OFTIC CNAME PENDS MEASURE PDICITY ANNDATS \\\\\\n\",\n \"87696 LULU Y2573F10 FLEX FLEX LTD 20070430 EPS QTR 20070615 \\n\",\n \"87689 TSL Y2573F10 FLEX FLEX LTD 20060331 EPS QTR 20060515 \\n\",\n \"87690 EDU Y2573F10 FLEX FLEX LTD 20060531 EPS QTR 20060715 \\n\",\n \"87691 TSL Y2573F10 FLEX FLEX LTD 20060630 EPS QTR 20060815 \\n\",\n \"87692 EDU Y2573F10 FLEX FLEX LTD 20060831 EPS QTR 20061017 \\n\",\n \"87693 YGE Y2573F10 FLEX FLEX LTD 20061231 EPS QTR 20070215 \\n\",\n \"87694 LULU Y2573F10 FLEX FLEX LTD 20070131 EPS QTR 20070315 \\n\",\n \"87695 YGE Y2573F10 FLEX FLEX LTD 20070331 EPS QTR 20070515 \\n\",\n \"87696 LULU Y2573F10 FLEX FLEX LTD 20070430 EPS QTR 20070615 \\n\",\n \"87697 LULU Y2573F10 FLEX FLEX LTD 20070731 EPS QTR 20070910 \\n\",\n \"\\n\",\n \" ANNTIMS ACTDATS ACTTIMS VALUE CURR_ACT USFIRM p4 p8 \\\\\\n\",\n \"87696 5:32:00 20070905 10:00:00 0.0250 USD 1 0.01 -0.02 \\n\",\n \"87689 14:51:00 20070129 17:21:00 0.3164 USD 1 1.16 1.12 \\n\",\n \"87690 9:12:00 20061009 9:36:00 0.3164 USD 1 1.16 0.23 \\n\",\n \"87691 14:51:00 20070129 17:22:00 0.3164 USD 1 0.67 0.64 \\n\",\n \"87692 3:29:00 20061017 7:50:34 0.1825 USD 1 0.67 0.38 \\n\",\n \"87693 9:30:00 20070718 10:01:54 0.1825 CNY 1 -0.20 -0.02 \\n\",\n \"87694 7:28:00 20070727 12:04:00 0.1825 USD 1 -0.20 -0.04 \\n\",\n \"87695 9:30:00 20070718 10:03:33 0.1825 CNY 1 0.01 -0.02 \\n\",\n \"87696 5:32:00 20070905 10:00:00 0.0250 USD 1 0.01 -0.02 \\n\",\n \"87697 16:05:00 20070910 16:48:00 0.0350 USD 1 0.01 -0.02 \\n\",\n \"\\n\",\n \" p12 p16 actual_roll_3 actual_roll_5 actual_roll_10 \\\\\\n\",\n \"87696 0.04 0.100 -0.0075 -0.025 -0.005833 \\n\",\n \"87689 0.95 0.875 0.3775 0.355 0.373333 \\n\",\n \"87690 0.26 0.350 0.3775 0.355 0.373333 \\n\",\n \"87691 0.55 0.460 0.3775 0.355 0.373333 \\n\",\n \"87692 0.34 0.480 0.3775 0.355 0.373333 \\n\",\n \"87693 -0.04 -0.060 -0.0075 -0.025 -0.005833 \\n\",\n \"87694 0.01 0.000 -0.0075 -0.025 -0.005833 \\n\",\n \"87695 0.04 -0.010 -0.0075 -0.025 -0.005833 \\n\",\n \"87696 0.04 0.100 -0.0075 -0.025 -0.005833 \\n\",\n \"87697 -0.04 0.060 -0.0075 -0.025 -0.005833 \\n\",\n \"\\n\",\n \" actual_roll_20 actual_p_roll actual_p_roll_w p1 p2 p3 \\\\\\n\",\n \"87696 -0.013125 0.00500 0.001000 0.140 0.14 0.02 \\n\",\n \"87689 0.366875 1.02625 0.268937 0.630 0.94 0.67 \\n\",\n \"87690 0.366875 1.02625 0.268937 0.630 0.94 0.67 \\n\",\n \"87691 0.366875 0.58000 0.153250 0.630 0.94 0.94 \\n\",\n \"87692 0.366875 0.58000 0.153250 0.630 0.94 0.94 \\n\",\n \"87693 -0.013125 -0.08000 -0.027500 0.140 0.02 0.01 \\n\",\n \"87694 -0.013125 -0.08000 -0.027500 0.140 0.02 0.01 \\n\",\n \"87695 -0.013125 0.00500 0.001000 0.140 0.14 0.02 \\n\",\n \"87696 -0.013125 0.00500 0.001000 0.140 0.14 0.02 \\n\",\n \"87697 -0.013125 0.00500 0.001000 0.025 0.14 0.02 \\n\",\n \"\\n\",\n \" diff1 diff2 diff4 counter_x ticker ANNDATS_ACT est_avg est_std \\\\\\n\",\n \"87696 -0.16 0.12 0.03 39 XCO 20060331.0 -0.350950 0.440457 \\n\",\n \"87689 -0.16 0.36 0.03 40 XCO 20060331.0 -0.350950 0.440457 \\n\",\n \"87690 -0.16 0.36 0.03 42 XCO 20060331.0 -0.350950 0.440457 \\n\",\n \"87691 -0.16 0.36 0.03 39 XCO 20060331.0 -0.350950 0.440457 \\n\",\n \"87692 -0.16 0.36 0.03 41 XCO 20060331.0 -0.350950 0.440457 \\n\",\n \"87693 -0.16 0.12 0.03 39 XCO 20060331.0 -0.350950 0.440457 \\n\",\n \"87694 -0.16 0.12 0.03 40 XCO 20060331.0 -0.350950 0.440457 \\n\",\n \"87695 -0.16 0.12 0.03 38 XCO 20060331.0 -0.350950 0.440457 \\n\",\n \"87696 -0.16 0.12 0.03 39 XCO 20060331.0 -0.350950 0.440457 \\n\",\n \"87697 0.01 0.12 0.03 38 LULU 20070910.0 0.016429 0.002440 \\n\",\n \"\\n\",\n \" est_count qtrdat ACTUAL e_p4 e_p8 e_p12 e_p16 \\\\\\n\",\n \"87696 2.0 20051231.0 0.3164 -0.35095 -0.35095 0.034 0.034 \\n\",\n \"87689 2.0 20051231.0 0.3164 0.03400 0.03400 0.034 0.034 \\n\",\n \"87690 2.0 20051231.0 0.3164 0.03400 0.03400 0.034 0.034 \\n\",\n \"87691 2.0 20051231.0 0.3164 -0.35095 0.03400 0.034 0.034 \\n\",\n \"87692 2.0 20051231.0 0.3164 -0.35095 0.03400 0.034 0.034 \\n\",\n \"87693 2.0 20051231.0 0.3164 -0.35095 0.03400 0.034 0.034 \\n\",\n \"87694 2.0 20051231.0 0.3164 -0.35095 0.03400 0.034 0.034 \\n\",\n \"87695 2.0 20051231.0 0.3164 -0.35095 -0.35095 0.034 0.034 \\n\",\n \"87696 2.0 20051231.0 0.3164 -0.35095 -0.35095 0.034 0.034 \\n\",\n \"87697 7.0 20070731.0 0.0350 -0.35095 -0.35095 0.034 0.034 \\n\",\n \"\\n\",\n \" e_actual_roll_3 e_actual_roll_5 e_actual_roll_10 e_actual_roll_20 \\\\\\n\",\n \"87696 0.380627 0.361811 0.344355 0.335607 \\n\",\n \"87689 0.380627 0.361811 0.344355 0.335607 \\n\",\n \"87690 0.380627 0.361811 0.344355 0.335607 \\n\",\n \"87691 0.380627 0.361811 0.344355 0.335607 \\n\",\n \"87692 0.380627 0.361811 0.344355 0.335607 \\n\",\n \"87693 0.380627 0.361811 0.344355 0.335607 \\n\",\n \"87694 0.380627 0.361811 0.344355 0.335607 \\n\",\n \"87695 0.380627 0.361811 0.344355 0.335607 \\n\",\n \"87696 0.380627 0.361811 0.344355 0.335607 \\n\",\n \"87697 0.380627 0.361811 0.344355 0.335607 \\n\",\n \"\\n\",\n \" e_actual_p_roll e_actual_p_roll_w e_p1 e_p2 e_p3 \\\\\\n\",\n \"87696 0.293501 0.077014 -0.35095 -0.35095 -0.35095 \\n\",\n \"87689 0.293501 0.077014 -0.35095 -0.35095 0.03400 \\n\",\n \"87690 0.293501 0.077014 -0.35095 -0.35095 -0.35095 \\n\",\n \"87691 0.293501 0.077014 -0.35095 -0.35095 -0.35095 \\n\",\n \"87692 0.293501 0.077014 -0.35095 -0.35095 -0.35095 \\n\",\n \"87693 0.293501 0.077014 -0.35095 -0.35095 -0.35095 \\n\",\n \"87694 0.293501 0.077014 -0.35095 -0.35095 -0.35095 \\n\",\n \"87695 0.293501 0.077014 -0.35095 -0.35095 -0.35095 \\n\",\n \"87696 0.293501 0.077014 -0.35095 -0.35095 -0.35095 \\n\",\n \"87697 0.293501 0.077014 -0.35095 -0.35095 -0.35095 \\n\",\n \"\\n\",\n \" e_diff1 e_diff2 e_diff4 d_e_p4 d_e_p8 d_e_p12 d_e_p16 \\\\\\n\",\n \"87696 0.041552 0.041552 0.038433 -1.02095 -0.33095 0.024 -0.546 \\n\",\n \"87689 0.041552 0.041552 0.038433 -0.09250 0.06400 0.024 -0.546 \\n\",\n \"87690 0.041552 0.041552 0.038433 -0.09250 0.06400 0.024 -0.546 \\n\",\n \"87691 0.041552 0.041552 0.038433 -1.02095 0.06400 0.024 -0.546 \\n\",\n \"87692 0.041552 0.041552 0.038433 -1.02095 0.06400 0.024 -0.546 \\n\",\n \"87693 0.041552 0.041552 0.038433 -1.02095 0.06400 0.024 -0.546 \\n\",\n \"87694 0.041552 0.041552 0.038433 -1.02095 0.06400 0.024 -0.546 \\n\",\n \"87695 0.041552 0.041552 0.038433 -1.02095 -0.33095 0.024 -0.546 \\n\",\n \"87696 0.041552 0.041552 0.038433 -1.02095 -0.33095 0.024 -0.546 \\n\",\n \"87697 0.041552 0.041552 0.038433 -1.02095 -0.33095 0.024 -0.546 \\n\",\n \"\\n\",\n \" d_e_actual_roll_3 d_e_actual_roll_5 d_e_actual_roll_10 \\\\\\n\",\n \"87696 1.080627 0.720561 0.411855 \\n\",\n \"87689 1.080627 0.720561 0.411855 \\n\",\n \"87690 1.080627 0.720561 0.411855 \\n\",\n \"87691 1.080627 0.720561 0.411855 \\n\",\n \"87692 1.080627 0.720561 0.411855 \\n\",\n \"87693 1.080627 0.720561 0.411855 \\n\",\n \"87694 1.080627 0.720561 0.411855 \\n\",\n \"87695 1.080627 0.720561 0.411855 \\n\",\n \"87696 1.080627 0.720561 0.411855 \\n\",\n \"87697 1.080627 0.720561 0.411855 \\n\",\n \"\\n\",\n \" d_e_actual_roll_20 d_e_actual_p_roll d_e_actual_p_roll_w d_e_p1 \\\\\\n\",\n \"87696 0.244357 0.408501 0.172514 9.694 \\n\",\n \"87689 0.244357 0.408501 0.172514 9.694 \\n\",\n \"87690 0.244357 0.408501 0.172514 9.694 \\n\",\n \"87691 0.244357 0.408501 0.172514 9.694 \\n\",\n \"87692 0.244357 0.408501 0.172514 9.694 \\n\",\n \"87693 0.244357 0.408501 0.172514 9.694 \\n\",\n \"87694 0.244357 0.408501 0.172514 9.694 \\n\",\n \"87695 0.244357 0.408501 0.172514 9.694 \\n\",\n \"87696 0.244357 0.408501 0.172514 9.694 \\n\",\n \"87697 0.244357 0.408501 0.172514 9.694 \\n\",\n \"\\n\",\n \" d_e_p2 d_e_p3 d_e_diff1 d_e_diff2 d_e_diff4 target counter_y \\\\\\n\",\n \"87696 -1.29095 0.0908 -0.018448 1.281552 0.048433 -1.901553 16 \\n\",\n \"87689 -1.29095 0.0908 -0.018448 1.281552 0.048433 -1.901553 16 \\n\",\n \"87690 -1.29095 0.0908 -0.018448 1.281552 0.048433 -1.901553 22 \\n\",\n \"87691 -1.29095 0.0908 -0.018448 1.281552 0.048433 -1.901553 16 \\n\",\n \"87692 -1.29095 0.0908 -0.018448 1.281552 0.048433 -1.901553 22 \\n\",\n \"87693 -1.29095 0.0908 -0.018448 1.281552 0.048433 -1.901553 15 \\n\",\n \"87694 -1.29095 0.0908 -0.018448 1.281552 0.048433 -1.901553 16 \\n\",\n \"87695 -1.29095 0.0908 -0.018448 1.281552 0.048433 -1.901553 15 \\n\",\n \"87696 -1.29095 0.0908 -0.018448 1.281552 0.048433 -1.901553 16 \\n\",\n \"87697 -1.29095 0.0908 -0.018448 1.281552 0.048433 1.130435 16 \\n\",\n \"\\n\",\n \" flag id last \\n\",\n \"87696 0 87689 1 \\n\",\n \"87689 0 87690 0 \\n\",\n \"87690 0 87690 0 \\n\",\n \"87691 0 87690 0 \\n\",\n \"87692 0 87690 0 \\n\",\n \"87693 0 87690 0 \\n\",\n \"87694 0 87690 0 \\n\",\n \"87695 0 87690 0 \\n\",\n \"87696 0 87690 0 \\n\",\n \"87697 0 87690 1 \"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 22\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"HFQvhq085S9q\"\n },\n \"source\": [\n \"feat_df = appender.drop([\\\"TICKER\\\",\\\"CUSIP\\\",\\\"OFTIC\\\",\\\"CNAME\\\",\\\"MEASURE\\\",\\\"PDICITY\\\",\\\"ANNDATS\\\",\\\"ANNTIMS\\\",\\\"ACTDATS\\\",\\n\",\n \" \\\"ACTTIMS\\\",\\\"VALUE\\\",\\\"CURR_ACT\\\",\\\"USFIRM\\\",\\\"target\\\",\\\"ticker\\\",\\\"ANNDATS_ACT\\\",\\\"qtrdat\\\",\\\"ACTUAL\\\",\\n\",\n \" \\\"counter_y\\\",\\\"flag\\\",\\\"last\\\"],axis=1)\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"-inzjtkN5S9s\"\n },\n \"source\": [\n \"appender.reset_index(drop=True,inplace=True)\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"9oHzfV6N5S9v\",\n \"outputId\": \"0ed7ca23-ce44-4937-f418-8aee7d7b87ba\"\n },\n \"source\": [\n \"# Start Here Tonight.\\n\",\n \"\\n\",\n \"from tsfresh import select_features\\n\",\n \"#from tsfresh.utilities.dataframe_functions import impute\\n\",\n \"from tsfresh import extract_relevant_features # Remember it is binary do it works diffrent with y. \\n\",\n \"from tsfresh import extract_features\\n\",\n \"\\n\",\n \"features_filtered_direct = extract_features(feat_df,column_id=\\\"id\\\", column_sort=\\\"PENDS\\\");\\n\",\n \"\\n\",\n \"features_filtered_direct = features_filtered_direct.replace([np.inf, -np.inf], np.nan)\\n\",\n \"features_filtered_direct.fillna(method=\\\"ffill\\\",inplace=True) # no use if all items are zero\\n\",\n \"features_filtered_direct.fillna(value=0,inplace=True)\\n\",\n \"features_filtered_direct.dropna(how=\\\"any\\\",axis=1,inplace=True)\\n\",\n \"\\n\",\n \"test.fillna(value=0,inplace=True)\\n\",\n \"y = test[\\\"target\\\"]\\n\",\n \"\\n\",\n \"features_filtered = select_features(features_filtered_direct, y)\\n\",\n \"features_filtered.reset_index(inplace=True)\\n\",\n \"features_filtered.rename(columns={\\\"index\\\":\\\"id\\\"},inplace=True)\\n\",\n \"\\n\",\n \"features_filtered.to_csv(\\\"features.csv\\\")\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Feature Extraction: 100%|ā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆ| 52/52 [01:05<00:00, 1.26s/it]\\n\",\n \"Feature Selection: 0%| | 0/11544 [00:00\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
Unnamed: 0idACTUALtickerest_avgest_allqtrdatcounterdatadatevolumeclosehighlowopenMA_10EMA_10ROC_10Momentum_10ATR_10BollingerB_10Bollinger%b_10Trix_10ADX_8_6Vortex_8KST_5_7_10_2_2_2_5_3RSI_15TSI_7_4MFI_20OBV_10Force_10EoM_20Copp_10KelChM_10KelChU_10KelChD_10Donchian_10MA_5EMA_5ROC_5ROC_4Momentum_5ATR_5BollingerB_5Bollinger%b_5Trix_4ADX_4_3Vortex_4KST_3_5_8_2_2_2_5_3RSI_5TSI_6_3ChaikinMFI_8OBV_4Force_8EoM_6Copp_3KelChM_5KelChU_5KelChD_5Donchian_8MA_3EMA_3ROC_3Momentum_3ATR_3BollingerB_4Bollinger%b_4Trix_3ADX_3_3Vortex_3KST_3_4_5_2_2_2_5_3RSI_3TSI_4_3TSI_3_2MFI_4OBV_3Force_4EoM_5KelChM_4KelChU_4KelChD_4Donchian_5
01968350100001984-07-24 00:00:000.47SMP0.480.019840630.0491984-05-1411800.019.37519.75019.3750.020.890520.347423-0.135816-2.23520.7973220.2406650.198564-0.0004420.324168-0.661741-1.1818780.402400-0.2988420.40-57444.2205912.785-0.0000010.01141520.89566721.81516719.9761673.35019.82520.002287-0.030765-0.018490-1.51520.4285250.0916650.252373-0.0101790.219372-0.224269-1.5227910.404300-0.3140547279.5226870.125-71142.00523586.280-0.000006-0.02370919.79400020.85300018.7350003.36019.79833319.781909-0.006410-0.36520.1613900.1038570.301064-0.0108100.200458-0.111111-1.1376380.465050-0.313426-0.2975700.25-18597.666667148541.565-0.00000519.73000020.65875018.8012503.430
11968351100001984-07-24 00:00:000.47SMP0.480.019840630.0481984-05-153700.019.37519.50019.3750.020.586020.170619-0.143836-3.04520.5614450.2357480.250469-0.0012210.371729-0.646667-1.5984040.402400-0.3214080.35-71985.8431525.220-0.000002-0.02147520.61900021.47600019.7620003.63019.70219.793191-0.018490-0.006410-0.61520.1190160.0976440.330022-0.0103950.445348-0.175272-1.8000770.404300-0.341541-6444.9738940.125-13948.25548447.155-0.000005-0.04295919.66733320.43533318.8993333.36019.75666719.578454-0.055799-0.12519.8306950.1126940.356932-0.0107590.4870740.112648-0.9556590.465050-0.351966-0.3740510.2570164.33333382152.375-0.00000519.64583320.34083318.9508333.360
21968352100001984-07-24 00:00:000.47SMP0.480.019840630.0471984-05-1617300.018.62519.25018.6250.020.185519.889597-0.163297-4.00520.3230000.2221610.152019-0.0021520.443784-0.713325-2.3523010.329198-0.4159400.35-90981.4622200.780-0.000002-0.06653920.24233321.13483319.3498333.63019.47919.403794-0.044872-0.092349-1.11519.8293440.1390880.184789-0.0122050.634240-0.141768-2.2903320.197036-0.468010-15800.9066870.12548298.25154638.105-0.000007-0.08700919.48333320.16433318.8023333.27019.12500019.101727-0.038710-1.89519.5403470.1606240.228657-0.0140700.693493-1.121287-1.2947380.113155-0.524753-0.6538250.25-9700.000000217862.750-0.00000819.51333320.07708318.9495833.670
31968353100001984-07-24 00:00:000.47SMP0.480.019840630.0461984-05-1770300.018.25018.75018.0000.019.784519.591488-0.154310-4.01020.0370000.2038180.119461-0.0031860.495251-0.728316-2.6214970.280586-0.4976880.35-78631.1495247.030-0.000002-0.11546419.86000020.73050018.9895003.36019.22919.019196-0.110624-0.058065-1.25019.4695630.1811250.218909-0.0142230.728687-1.223827-2.2046120.120085-0.565516-20143.4028280.125-24850.0055125.840-0.000008-0.10156919.27733319.87833318.6763333.43018.75000018.675864-0.058065-1.12519.1451740.1190080.208333-0.0169090.796703-1.307692-1.3003660.050043-0.644741-0.7875560.00-29200.000000345024.110-0.00000919.02083319.48958318.5520833.180
41968354100001984-07-24 00:00:000.47SMP0.480.019840630.0451984-05-1817300.018.50018.50018.2500.019.476519.393036-0.114409-3.08019.7575460.1761930.215441-0.0040990.559638-0.486903-2.7248960.280586-0.4697010.40-69938.0161179.480-0.000002-0.15221019.51700020.33050018.7035003.36018.82518.846131-0.045161-0.045161-2.02019.1463750.1104730.343725-0.0139570.820703-1.133333-2.0271310.120085-0.498462-7128.0540220.250-17575.00351686.190-0.000007-0.07672118.90000019.32500018.4750003.49018.45833318.587932-0.006711-0.87518.8225870.1036250.403175-0.0147050.880381-1.071429-1.2015470.050043-0.520602-0.4659900.25-23433.333333-4812.500-0.00001018.75000019.18750018.3125002.150
51968355100001984-07-24 00:00:000.47SMP0.480.019840630.0441984-05-2124600.017.75018.62517.7500.019.162519.094302-0.112056-3.14019.5516280.1790380.088291-0.0050690.611722-0.459159-2.7532040.243080-0.5295990.35-63279.9209064.340-0.000003-0.19107319.20116719.99316718.4091673.43018.50018.480754-0.083871-0.046980-1.62518.9725830.1282000.183772-0.0148780.874316-0.857143-1.8306980.070514-0.573936-14487.4064180.250-23725.00406308.250-0.000009-0.07554918.60833319.13333318.0833334.00018.16666718.168966-0.027397-0.87518.7237930.0846690.156782-0.0160410.926581-0.800000-1.3153500.017970-0.619516-0.6709280.25-25866.666667-33962.500-0.00000718.40625019.03125017.7812502.040
61968356100001984-07-24 00:00:000.47SMP0.480.019840630.0431984-05-2212500.018.00018.12517.6250.018.963518.895338-0.088146-1.99019.2922410.1845780.224733-0.0058890.661022-0.458002-2.4770270.234137-0.4834020.35-36696.8479253.690-0.000003-0.20922818.98783319.67183318.3038333.49018.22518.320503-0.033557-0.013699-1.37518.6900560.0785610.342853-0.0140450.914363-0.894737-1.7528170.061062-0.488318-6365.1045420.250-16275.00380679.000-0.000008-0.05813718.30833318.90833317.7083334.00018.08333318.084483-0.027027-0.25018.4243970.0712270.403175-0.0136340.956385-0.615385-1.1810660.013609-0.478225-0.3880730.251733.3333333000.000-0.00000918.17708318.77083317.5833332.135
71968357100001984-07-24 00:00:000.47SMP0.480.019840630.0421984-05-2333300.017.37518.00017.3750.018.727018.618913-0.108974-2.36519.0572880.2045340.147026-0.0067130.711051-0.949203-2.4082860.215977-0.5245600.35-17149.3462298.375-0.000003-0.22839818.77083319.41133318.1303334.00017.97518.005335-0.047945-0.060811-1.25018.4600370.0971590.156441-0.0143540.945301-0.611111-1.8527810.043549-0.547339-15047.7897250.125-7025.00594382.985-0.000008-0.07003718.05833318.65833317.4583333.87017.70833317.729741-0.021127-1.12518.2121980.1054080.218537-0.0144720.975405-0.687500-1.4053610.006906-0.566993-0.5986040.25-15133.33333332375.000-0.00000617.98958318.55208317.4270832.760
81968358100001984-07-24 00:00:000.47SMP0.480.019840630.0411984-05-2410800.017.00017.87517.0000.018.477018.324565-0.171540-2.50018.8423270.2283390.149919-0.0075490.760449-0.842105-2.4642310.190630-0.5795670.308399.3638715.000-0.000005-0.24403818.56366719.17666717.9506674.00017.72517.670223-0.081081-0.042254-1.25018.2650250.1298830.185080-0.0152460.966894-0.652174-2.0071380.026469-0.618921-10072.7255070.125-14050.002375.000-0.000008-0.08392317.85000018.47500017.2250003.67017.45833317.364871-0.055556-0.75018.0435990.0998220.196429-0.0161690.986627-0.750000-1.3265400.002787-0.662538-0.7412970.00-10533.3333339750.000-0.00000917.70833318.42708316.9895831.750
91968359100001984-07-24 00:00:000.47SMP0.480.019840630.0401984-05-2536000.016.87517.25016.5000.018.112518.061008-0.129032-3.64518.5528130.1958270.151105-0.0083240.795734-0.906977-2.5394360.161710-0.6190840.25-17430.0679037.985-0.000005-0.26029718.22083318.79583317.6458334.00017.40017.405149-0.049296-0.062500-1.62517.9266830.1101740.226139-0.0155010.977691-0.954545-2.1966680.014833-0.664755-3685.4060080.125-16900.00-80750.000-0.000009-0.08760017.54166718.26666716.8166673.27517.08333317.119935-0.028777-1.12517.6468000.1167210.283494-0.0161080.992237-0.888889-1.4357210.001076-0.719111-0.8066550.00-26700.000000-9975.000-0.00001117.41666718.10416716.7291671.750
101968360100001984-07-24 00:00:000.47SMP0.480.019840630.0391984-05-298400.016.87516.87516.5000.017.862517.845370-0.129032-2.50018.2477560.1886750.206992-0.0089590.677935-0.900000-2.4340730.161710-0.6418410.20-16250.08500.000-0.000006-0.26281917.94583318.52083317.3708334.00017.22517.228433-0.062500-0.028777-0.87517.5761220.1112930.317426-0.0146020.509451-0.904762-1.9292110.014833-0.6876671935.3525150.125-20025.0015575.000-0.000011-0.06186517.28333317.90833316.6583332.87016.91666716.997468-0.007353-0.50017.2609000.0555730.334915-0.0139260.644618-1.000000-1.1575640.001076-0.746218-0.8305860.00-15600.0000004612.500-0.00001117.12500017.78125016.4687501.625
111968361100001984-07-24 00:00:000.47SMP0.480.019840630.0381984-05-3030600.017.12517.25016.3750.017.637517.714394-0.080537-2.25018.0663460.1537990.311069-0.0093440.593793-0.609756-2.1504860.270169-0.5363590.20-13190.0-60525.000-0.000006-0.25829217.69583318.34583317.0458333.96517.05017.193955-0.0143880.007353-0.87517.4674150.0490710.589642-0.0119670.275331-0.608696-1.5247670.434403-0.5003748340.6307730.250-4050.0044662.500-0.000009-0.01984917.08333317.78333316.3833332.76016.95833317.0612340.0148150.12517.2554500.0282110.826396-0.0090580.470808-0.625000-0.7711700.648499-0.417151-0.0771540.25-1800.000000675.000-0.00000816.95833317.67708316.2395831.375
121968362100001984-07-24 00:00:000.47SMP0.480.019840630.0371984-05-314600.017.12517.25016.8750.017.487517.607231-0.061644-1.50017.9179190.1364240.348054-0.0095070.483218-0.500000-1.8307770.270169-0.4730960.20-11460.019050.000-0.000005-0.25605517.52083318.14583316.8958334.39017.00017.1709700.0073530.014815-0.25017.3949430.0294120.750000-0.0090970.313635-0.315789-1.1294380.434403-0.4057683736.0660510.250-1350.0017462.500-0.0000030.00489016.98333317.63333316.3333333.01017.04166717.0931170.0148150.25017.2527250.0339620.716506-0.0051170.5468470.230769-0.4665130.648499-0.2658950.1216540.5010200.000000-775.000-0.00000316.90625017.50000016.3125001.625
131968363100001984-07-24 00:00:000.47SMP0.480.019840630.0361984-06-0129000.017.37517.37517.1250.017.400017.565007-0.060811-0.87517.8192070.1225160.488273-0.0094040.399858-0.405405-1.3257540.309088-0.2956290.25-1530.036137.500-0.000005-0.23698517.41666717.99166716.8416673.90017.07517.2389800.0296300.0296300.37517.3882950.0489990.858569-0.0056780.5331610.400000-0.7196480.571328-0.13641910252.1320540.37514900.00-1650.000-0.0000020.02455916.98333317.50833316.4583333.13517.20833317.2340580.0145990.50017.3138620.0476790.806186-0.0007980.7235590.750000-0.0316490.8114430.1385550.6202360.7519866.666667-3500.0000.00000117.01041717.47916716.5416672.125
141968364100001984-07-24 00:00:000.47SMP0.480.019840630.0351984-06-048500.017.75017.75017.1250.017.325017.5986420.000000-0.75017.8066240.0914660.768198-0.0089900.368940-0.184211-0.6816300.415882-0.0207010.30-2410.06600.000-0.000004-0.21651717.32916717.94166716.7166673.12017.25017.4093200.0518520.0364960.87517.5088640.0766880.877964-0.0015820.6745430.705882-0.2352700.7948350.2171295463.0561410.50017025.001000.0000.0000030.04877617.11666717.61666716.6166672.37517.41666717.4920290.0364960.62517.5319310.0681200.8438560.0042410.8283221.0000000.3639100.9501360.5367850.8675461.0012500.00000087.5000.00000617.20833317.73958316.6770831.625
151968365100001984-07-24 00:00:000.47SMP0.480.019840630.0341984-06-05121300.017.62517.87517.6250.017.312517.603435-0.020833-0.12517.8190560.0884950.703973-0.0084230.4365880.028571-0.2967450.4483650.0614140.35-12080.0-12087.500-0.000004-0.18819317.29583317.84583316.7458333.13517.40017.4812130.0291970.0291970.75017.6309090.0655270.6973380.0008670.8078751.2500000.0718080.8372600.246140-38292.7527450.625-20950.0022000.0000.0000050.04618117.30833317.78333316.8333332.50017.58333317.5585150.0143880.50017.7034660.0634660.6409340.0053850.9063621.2222220.6097570.9665420.4600700.5315971.00-27933.33333345350.0000.00000817.40625017.78125017.0312501.625
161968366100001984-07-24 00:00:000.47SMP0.480.019840630.0331984-06-0616100.018.00018.50017.8750.017.312517.6755370.0359710.00017.9428640.0884950.948741-0.0076490.4849090.3428570.1238320.5813870.2384590.40-11720.00.000-0.000003-0.15646717.31666717.87916716.7541673.38517.57517.6541420.0510950.0359710.87517.9206060.0768660.8145990.0034160.8745401.1250000.4975670.9362030.421300-17189.8788580.750-16925.005300.0000.0000090.04811817.55000017.97500017.1250002.75017.79166717.7792570.0140850.62518.1017330.0588460.8002400.0074860.9453821.0714290.8094570.9922010.6061380.7043871.00-32233.33333310062.5000.00001117.66666718.10416717.2291671.500
171968367100001984-07-24 00:00:000.47SMP0.480.019840630.0321984-06-076500.018.50018.50018.1250.017.425017.8254400.0882351.12518.0441610.1233821.000015-0.0066180.4607720.6969700.7054960.5813870.4440910.45-7740.0-30150.000-0.000002-0.11624617.39583317.93333316.8583333.76017.85017.9360950.0647480.0422541.37518.1137370.0958130.8800580.0066910.6415710.9444441.0232290.9362030.614764-1852.3505540.875-22550.00-47937.5000.0000100.07000917.80833318.23333317.3833332.25018.04166718.1396290.0496450.75018.3008660.0861410.8432180.0112550.5876171.0769231.0677930.9922010.7668870.8626531.00-32900.000000-25312.5000.00000817.93750018.40625017.4687501.000
181968368100001984-07-24 00:00:000.47SMP0.480.019840630.0311984-06-0852100.018.00018.50018.0000.017.525017.8571780.0666671.00018.1270410.1238520.718843-0.0056500.4435310.7352940.9398350.5469340.2893740.45-11870.041300.000-0.000002-0.08185417.48333317.98333316.9833333.25017.97517.9573970.0140850.0212770.62518.2424920.0746390.5186340.0066730.5250860.7647061.1126360.7350890.319037-14054.9661970.875-37700.0049162.5000.0000070.04564317.98333318.45833317.5083332.25018.16666718.0698140.0000000.37518.4004330.0796470.4782400.0083050.4087340.5333330.8977450.6149260.3091920.1304260.75-9833.33333310900.0000.00000818.09375018.53125017.6562501.375
191968369100001984-07-24 00:00:000.47SMP0.480.019840630.0301984-06-114800.018.00018.12518.0000.017.637517.8831460.0666671.12518.1266700.1153080.678243-0.0047660.3614090.7142860.9226160.5469340.2239920.40-8270.0-35100.000-0.000003-0.05485017.60000018.03750017.1625003.00018.02517.9715980.0212770.0000000.25018.2033280.0690700.4799200.0055260.3628570.3125000.9524510.7350890.218439-2719.2211240.750-7375.00-22575.0000.0000060.00930818.08333318.45833317.7083332.25018.16666718.034907-0.0270270.00018.2627170.0551720.3750000.0049880.460442-0.2222220.5597330.6149260.173112-0.0182850.50-15200.000000-43687.5000.00000418.17708318.58333317.7708331.500
\\n\",\n \"\"\n ],\n \"text/plain\": [\n \" Unnamed: 0 id ACTUAL ticker est_avg est_all \\\\\\n\",\n \"0 1968350 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"1 1968351 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"2 1968352 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"3 1968353 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"4 1968354 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"5 1968355 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"6 1968356 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"7 1968357 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"8 1968358 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"9 1968359 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"10 1968360 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"11 1968361 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"12 1968362 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"13 1968363 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"14 1968364 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"15 1968365 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"16 1968366 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"17 1968367 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"18 1968368 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"19 1968369 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"\\n\",\n \" qtrdat counter datadate volume close high low open \\\\\\n\",\n \"0 19840630.0 49 1984-05-14 11800.0 19.375 19.750 19.375 0.0 \\n\",\n \"1 19840630.0 48 1984-05-15 3700.0 19.375 19.500 19.375 0.0 \\n\",\n \"2 19840630.0 47 1984-05-16 17300.0 18.625 19.250 18.625 0.0 \\n\",\n \"3 19840630.0 46 1984-05-17 70300.0 18.250 18.750 18.000 0.0 \\n\",\n \"4 19840630.0 45 1984-05-18 17300.0 18.500 18.500 18.250 0.0 \\n\",\n \"5 19840630.0 44 1984-05-21 24600.0 17.750 18.625 17.750 0.0 \\n\",\n \"6 19840630.0 43 1984-05-22 12500.0 18.000 18.125 17.625 0.0 \\n\",\n \"7 19840630.0 42 1984-05-23 33300.0 17.375 18.000 17.375 0.0 \\n\",\n \"8 19840630.0 41 1984-05-24 10800.0 17.000 17.875 17.000 0.0 \\n\",\n \"9 19840630.0 40 1984-05-25 36000.0 16.875 17.250 16.500 0.0 \\n\",\n \"10 19840630.0 39 1984-05-29 8400.0 16.875 16.875 16.500 0.0 \\n\",\n \"11 19840630.0 38 1984-05-30 30600.0 17.125 17.250 16.375 0.0 \\n\",\n \"12 19840630.0 37 1984-05-31 4600.0 17.125 17.250 16.875 0.0 \\n\",\n \"13 19840630.0 36 1984-06-01 29000.0 17.375 17.375 17.125 0.0 \\n\",\n \"14 19840630.0 35 1984-06-04 8500.0 17.750 17.750 17.125 0.0 \\n\",\n \"15 19840630.0 34 1984-06-05 121300.0 17.625 17.875 17.625 0.0 \\n\",\n \"16 19840630.0 33 1984-06-06 16100.0 18.000 18.500 17.875 0.0 \\n\",\n \"17 19840630.0 32 1984-06-07 6500.0 18.500 18.500 18.125 0.0 \\n\",\n \"18 19840630.0 31 1984-06-08 52100.0 18.000 18.500 18.000 0.0 \\n\",\n \"19 19840630.0 30 1984-06-11 4800.0 18.000 18.125 18.000 0.0 \\n\",\n \"\\n\",\n \" MA_10 EMA_10 ROC_10 Momentum_10 ATR_10 BollingerB_10 \\\\\\n\",\n \"0 20.8905 20.347423 -0.135816 -2.235 20.797322 0.240665 \\n\",\n \"1 20.5860 20.170619 -0.143836 -3.045 20.561445 0.235748 \\n\",\n \"2 20.1855 19.889597 -0.163297 -4.005 20.323000 0.222161 \\n\",\n \"3 19.7845 19.591488 -0.154310 -4.010 20.037000 0.203818 \\n\",\n \"4 19.4765 19.393036 -0.114409 -3.080 19.757546 0.176193 \\n\",\n \"5 19.1625 19.094302 -0.112056 -3.140 19.551628 0.179038 \\n\",\n \"6 18.9635 18.895338 -0.088146 -1.990 19.292241 0.184578 \\n\",\n \"7 18.7270 18.618913 -0.108974 -2.365 19.057288 0.204534 \\n\",\n \"8 18.4770 18.324565 -0.171540 -2.500 18.842327 0.228339 \\n\",\n \"9 18.1125 18.061008 -0.129032 -3.645 18.552813 0.195827 \\n\",\n \"10 17.8625 17.845370 -0.129032 -2.500 18.247756 0.188675 \\n\",\n \"11 17.6375 17.714394 -0.080537 -2.250 18.066346 0.153799 \\n\",\n \"12 17.4875 17.607231 -0.061644 -1.500 17.917919 0.136424 \\n\",\n \"13 17.4000 17.565007 -0.060811 -0.875 17.819207 0.122516 \\n\",\n \"14 17.3250 17.598642 0.000000 -0.750 17.806624 0.091466 \\n\",\n \"15 17.3125 17.603435 -0.020833 -0.125 17.819056 0.088495 \\n\",\n \"16 17.3125 17.675537 0.035971 0.000 17.942864 0.088495 \\n\",\n \"17 17.4250 17.825440 0.088235 1.125 18.044161 0.123382 \\n\",\n \"18 17.5250 17.857178 0.066667 1.000 18.127041 0.123852 \\n\",\n \"19 17.6375 17.883146 0.066667 1.125 18.126670 0.115308 \\n\",\n \"\\n\",\n \" Bollinger%b_10 Trix_10 ADX_8_6 Vortex_8 KST_5_7_10_2_2_2_5_3 \\\\\\n\",\n \"0 0.198564 -0.000442 0.324168 -0.661741 -1.181878 \\n\",\n \"1 0.250469 -0.001221 0.371729 -0.646667 -1.598404 \\n\",\n \"2 0.152019 -0.002152 0.443784 -0.713325 -2.352301 \\n\",\n \"3 0.119461 -0.003186 0.495251 -0.728316 -2.621497 \\n\",\n \"4 0.215441 -0.004099 0.559638 -0.486903 -2.724896 \\n\",\n \"5 0.088291 -0.005069 0.611722 -0.459159 -2.753204 \\n\",\n \"6 0.224733 -0.005889 0.661022 -0.458002 -2.477027 \\n\",\n \"7 0.147026 -0.006713 0.711051 -0.949203 -2.408286 \\n\",\n \"8 0.149919 -0.007549 0.760449 -0.842105 -2.464231 \\n\",\n \"9 0.151105 -0.008324 0.795734 -0.906977 -2.539436 \\n\",\n \"10 0.206992 -0.008959 0.677935 -0.900000 -2.434073 \\n\",\n \"11 0.311069 -0.009344 0.593793 -0.609756 -2.150486 \\n\",\n \"12 0.348054 -0.009507 0.483218 -0.500000 -1.830777 \\n\",\n \"13 0.488273 -0.009404 0.399858 -0.405405 -1.325754 \\n\",\n \"14 0.768198 -0.008990 0.368940 -0.184211 -0.681630 \\n\",\n \"15 0.703973 -0.008423 0.436588 0.028571 -0.296745 \\n\",\n \"16 0.948741 -0.007649 0.484909 0.342857 0.123832 \\n\",\n \"17 1.000015 -0.006618 0.460772 0.696970 0.705496 \\n\",\n \"18 0.718843 -0.005650 0.443531 0.735294 0.939835 \\n\",\n \"19 0.678243 -0.004766 0.361409 0.714286 0.922616 \\n\",\n \"\\n\",\n \" RSI_15 TSI_7_4 MFI_20 OBV_10 Force_10 EoM_20 Copp_10 \\\\\\n\",\n \"0 0.402400 -0.298842 0.40 -57444.2 205912.785 -0.000001 0.011415 \\n\",\n \"1 0.402400 -0.321408 0.35 -71985.8 431525.220 -0.000002 -0.021475 \\n\",\n \"2 0.329198 -0.415940 0.35 -90981.4 622200.780 -0.000002 -0.066539 \\n\",\n \"3 0.280586 -0.497688 0.35 -78631.1 495247.030 -0.000002 -0.115464 \\n\",\n \"4 0.280586 -0.469701 0.40 -69938.0 161179.480 -0.000002 -0.152210 \\n\",\n \"5 0.243080 -0.529599 0.35 -63279.9 209064.340 -0.000003 -0.191073 \\n\",\n \"6 0.234137 -0.483402 0.35 -36696.8 479253.690 -0.000003 -0.209228 \\n\",\n \"7 0.215977 -0.524560 0.35 -17149.3 462298.375 -0.000003 -0.228398 \\n\",\n \"8 0.190630 -0.579567 0.30 8399.3 638715.000 -0.000005 -0.244038 \\n\",\n \"9 0.161710 -0.619084 0.25 -17430.0 679037.985 -0.000005 -0.260297 \\n\",\n \"10 0.161710 -0.641841 0.20 -16250.0 8500.000 -0.000006 -0.262819 \\n\",\n \"11 0.270169 -0.536359 0.20 -13190.0 -60525.000 -0.000006 -0.258292 \\n\",\n \"12 0.270169 -0.473096 0.20 -11460.0 19050.000 -0.000005 -0.256055 \\n\",\n \"13 0.309088 -0.295629 0.25 -1530.0 36137.500 -0.000005 -0.236985 \\n\",\n \"14 0.415882 -0.020701 0.30 -2410.0 6600.000 -0.000004 -0.216517 \\n\",\n \"15 0.448365 0.061414 0.35 -12080.0 -12087.500 -0.000004 -0.188193 \\n\",\n \"16 0.581387 0.238459 0.40 -11720.0 0.000 -0.000003 -0.156467 \\n\",\n \"17 0.581387 0.444091 0.45 -7740.0 -30150.000 -0.000002 -0.116246 \\n\",\n \"18 0.546934 0.289374 0.45 -11870.0 41300.000 -0.000002 -0.081854 \\n\",\n \"19 0.546934 0.223992 0.40 -8270.0 -35100.000 -0.000003 -0.054850 \\n\",\n \"\\n\",\n \" KelChM_10 KelChU_10 KelChD_10 Donchian_10 MA_5 EMA_5 ROC_5 \\\\\\n\",\n \"0 20.895667 21.815167 19.976167 3.350 19.825 20.002287 -0.030765 \\n\",\n \"1 20.619000 21.476000 19.762000 3.630 19.702 19.793191 -0.018490 \\n\",\n \"2 20.242333 21.134833 19.349833 3.630 19.479 19.403794 -0.044872 \\n\",\n \"3 19.860000 20.730500 18.989500 3.360 19.229 19.019196 -0.110624 \\n\",\n \"4 19.517000 20.330500 18.703500 3.360 18.825 18.846131 -0.045161 \\n\",\n \"5 19.201167 19.993167 18.409167 3.430 18.500 18.480754 -0.083871 \\n\",\n \"6 18.987833 19.671833 18.303833 3.490 18.225 18.320503 -0.033557 \\n\",\n \"7 18.770833 19.411333 18.130333 4.000 17.975 18.005335 -0.047945 \\n\",\n \"8 18.563667 19.176667 17.950667 4.000 17.725 17.670223 -0.081081 \\n\",\n \"9 18.220833 18.795833 17.645833 4.000 17.400 17.405149 -0.049296 \\n\",\n \"10 17.945833 18.520833 17.370833 4.000 17.225 17.228433 -0.062500 \\n\",\n \"11 17.695833 18.345833 17.045833 3.965 17.050 17.193955 -0.014388 \\n\",\n \"12 17.520833 18.145833 16.895833 4.390 17.000 17.170970 0.007353 \\n\",\n \"13 17.416667 17.991667 16.841667 3.900 17.075 17.238980 0.029630 \\n\",\n \"14 17.329167 17.941667 16.716667 3.120 17.250 17.409320 0.051852 \\n\",\n \"15 17.295833 17.845833 16.745833 3.135 17.400 17.481213 0.029197 \\n\",\n \"16 17.316667 17.879167 16.754167 3.385 17.575 17.654142 0.051095 \\n\",\n \"17 17.395833 17.933333 16.858333 3.760 17.850 17.936095 0.064748 \\n\",\n \"18 17.483333 17.983333 16.983333 3.250 17.975 17.957397 0.014085 \\n\",\n \"19 17.600000 18.037500 17.162500 3.000 18.025 17.971598 0.021277 \\n\",\n \"\\n\",\n \" ROC_4 Momentum_5 ATR_5 BollingerB_5 Bollinger%b_5 Trix_4 \\\\\\n\",\n \"0 -0.018490 -1.515 20.428525 0.091665 0.252373 -0.010179 \\n\",\n \"1 -0.006410 -0.615 20.119016 0.097644 0.330022 -0.010395 \\n\",\n \"2 -0.092349 -1.115 19.829344 0.139088 0.184789 -0.012205 \\n\",\n \"3 -0.058065 -1.250 19.469563 0.181125 0.218909 -0.014223 \\n\",\n \"4 -0.045161 -2.020 19.146375 0.110473 0.343725 -0.013957 \\n\",\n \"5 -0.046980 -1.625 18.972583 0.128200 0.183772 -0.014878 \\n\",\n \"6 -0.013699 -1.375 18.690056 0.078561 0.342853 -0.014045 \\n\",\n \"7 -0.060811 -1.250 18.460037 0.097159 0.156441 -0.014354 \\n\",\n \"8 -0.042254 -1.250 18.265025 0.129883 0.185080 -0.015246 \\n\",\n \"9 -0.062500 -1.625 17.926683 0.110174 0.226139 -0.015501 \\n\",\n \"10 -0.028777 -0.875 17.576122 0.111293 0.317426 -0.014602 \\n\",\n \"11 0.007353 -0.875 17.467415 0.049071 0.589642 -0.011967 \\n\",\n \"12 0.014815 -0.250 17.394943 0.029412 0.750000 -0.009097 \\n\",\n \"13 0.029630 0.375 17.388295 0.048999 0.858569 -0.005678 \\n\",\n \"14 0.036496 0.875 17.508864 0.076688 0.877964 -0.001582 \\n\",\n \"15 0.029197 0.750 17.630909 0.065527 0.697338 0.000867 \\n\",\n \"16 0.035971 0.875 17.920606 0.076866 0.814599 0.003416 \\n\",\n \"17 0.042254 1.375 18.113737 0.095813 0.880058 0.006691 \\n\",\n \"18 0.021277 0.625 18.242492 0.074639 0.518634 0.006673 \\n\",\n \"19 0.000000 0.250 18.203328 0.069070 0.479920 0.005526 \\n\",\n \"\\n\",\n \" ADX_4_3 Vortex_4 KST_3_5_8_2_2_2_5_3 RSI_5 TSI_6_3 Chaikin \\\\\\n\",\n \"0 0.219372 -0.224269 -1.522791 0.404300 -0.314054 7279.522687 \\n\",\n \"1 0.445348 -0.175272 -1.800077 0.404300 -0.341541 -6444.973894 \\n\",\n \"2 0.634240 -0.141768 -2.290332 0.197036 -0.468010 -15800.906687 \\n\",\n \"3 0.728687 -1.223827 -2.204612 0.120085 -0.565516 -20143.402828 \\n\",\n \"4 0.820703 -1.133333 -2.027131 0.120085 -0.498462 -7128.054022 \\n\",\n \"5 0.874316 -0.857143 -1.830698 0.070514 -0.573936 -14487.406418 \\n\",\n \"6 0.914363 -0.894737 -1.752817 0.061062 -0.488318 -6365.104542 \\n\",\n \"7 0.945301 -0.611111 -1.852781 0.043549 -0.547339 -15047.789725 \\n\",\n \"8 0.966894 -0.652174 -2.007138 0.026469 -0.618921 -10072.725507 \\n\",\n \"9 0.977691 -0.954545 -2.196668 0.014833 -0.664755 -3685.406008 \\n\",\n \"10 0.509451 -0.904762 -1.929211 0.014833 -0.687667 1935.352515 \\n\",\n \"11 0.275331 -0.608696 -1.524767 0.434403 -0.500374 8340.630773 \\n\",\n \"12 0.313635 -0.315789 -1.129438 0.434403 -0.405768 3736.066051 \\n\",\n \"13 0.533161 0.400000 -0.719648 0.571328 -0.136419 10252.132054 \\n\",\n \"14 0.674543 0.705882 -0.235270 0.794835 0.217129 5463.056141 \\n\",\n \"15 0.807875 1.250000 0.071808 0.837260 0.246140 -38292.752745 \\n\",\n \"16 0.874540 1.125000 0.497567 0.936203 0.421300 -17189.878858 \\n\",\n \"17 0.641571 0.944444 1.023229 0.936203 0.614764 -1852.350554 \\n\",\n \"18 0.525086 0.764706 1.112636 0.735089 0.319037 -14054.966197 \\n\",\n \"19 0.362857 0.312500 0.952451 0.735089 0.218439 -2719.221124 \\n\",\n \"\\n\",\n \" MFI_8 OBV_4 Force_8 EoM_6 Copp_3 KelChM_5 KelChU_5 \\\\\\n\",\n \"0 0.125 -71142.00 523586.280 -0.000006 -0.023709 19.794000 20.853000 \\n\",\n \"1 0.125 -13948.25 548447.155 -0.000005 -0.042959 19.667333 20.435333 \\n\",\n \"2 0.125 48298.25 154638.105 -0.000007 -0.087009 19.483333 20.164333 \\n\",\n \"3 0.125 -24850.00 55125.840 -0.000008 -0.101569 19.277333 19.878333 \\n\",\n \"4 0.250 -17575.00 351686.190 -0.000007 -0.076721 18.900000 19.325000 \\n\",\n \"5 0.250 -23725.00 406308.250 -0.000009 -0.075549 18.608333 19.133333 \\n\",\n \"6 0.250 -16275.00 380679.000 -0.000008 -0.058137 18.308333 18.908333 \\n\",\n \"7 0.125 -7025.00 594382.985 -0.000008 -0.070037 18.058333 18.658333 \\n\",\n \"8 0.125 -14050.00 2375.000 -0.000008 -0.083923 17.850000 18.475000 \\n\",\n \"9 0.125 -16900.00 -80750.000 -0.000009 -0.087600 17.541667 18.266667 \\n\",\n \"10 0.125 -20025.00 15575.000 -0.000011 -0.061865 17.283333 17.908333 \\n\",\n \"11 0.250 -4050.00 44662.500 -0.000009 -0.019849 17.083333 17.783333 \\n\",\n \"12 0.250 -1350.00 17462.500 -0.000003 0.004890 16.983333 17.633333 \\n\",\n \"13 0.375 14900.00 -1650.000 -0.000002 0.024559 16.983333 17.508333 \\n\",\n \"14 0.500 17025.00 1000.000 0.000003 0.048776 17.116667 17.616667 \\n\",\n \"15 0.625 -20950.00 22000.000 0.000005 0.046181 17.308333 17.783333 \\n\",\n \"16 0.750 -16925.00 5300.000 0.000009 0.048118 17.550000 17.975000 \\n\",\n \"17 0.875 -22550.00 -47937.500 0.000010 0.070009 17.808333 18.233333 \\n\",\n \"18 0.875 -37700.00 49162.500 0.000007 0.045643 17.983333 18.458333 \\n\",\n \"19 0.750 -7375.00 -22575.000 0.000006 0.009308 18.083333 18.458333 \\n\",\n \"\\n\",\n \" KelChD_5 Donchian_8 MA_3 EMA_3 ROC_3 Momentum_3 \\\\\\n\",\n \"0 18.735000 3.360 19.798333 19.781909 -0.006410 -0.365 \\n\",\n \"1 18.899333 3.360 19.756667 19.578454 -0.055799 -0.125 \\n\",\n \"2 18.802333 3.270 19.125000 19.101727 -0.038710 -1.895 \\n\",\n \"3 18.676333 3.430 18.750000 18.675864 -0.058065 -1.125 \\n\",\n \"4 18.475000 3.490 18.458333 18.587932 -0.006711 -0.875 \\n\",\n \"5 18.083333 4.000 18.166667 18.168966 -0.027397 -0.875 \\n\",\n \"6 17.708333 4.000 18.083333 18.084483 -0.027027 -0.250 \\n\",\n \"7 17.458333 3.870 17.708333 17.729741 -0.021127 -1.125 \\n\",\n \"8 17.225000 3.670 17.458333 17.364871 -0.055556 -0.750 \\n\",\n \"9 16.816667 3.275 17.083333 17.119935 -0.028777 -1.125 \\n\",\n \"10 16.658333 2.870 16.916667 16.997468 -0.007353 -0.500 \\n\",\n \"11 16.383333 2.760 16.958333 17.061234 0.014815 0.125 \\n\",\n \"12 16.333333 3.010 17.041667 17.093117 0.014815 0.250 \\n\",\n \"13 16.458333 3.135 17.208333 17.234058 0.014599 0.500 \\n\",\n \"14 16.616667 2.375 17.416667 17.492029 0.036496 0.625 \\n\",\n \"15 16.833333 2.500 17.583333 17.558515 0.014388 0.500 \\n\",\n \"16 17.125000 2.750 17.791667 17.779257 0.014085 0.625 \\n\",\n \"17 17.383333 2.250 18.041667 18.139629 0.049645 0.750 \\n\",\n \"18 17.508333 2.250 18.166667 18.069814 0.000000 0.375 \\n\",\n \"19 17.708333 2.250 18.166667 18.034907 -0.027027 0.000 \\n\",\n \"\\n\",\n \" ATR_3 BollingerB_4 Bollinger%b_4 Trix_3 ADX_3_3 Vortex_3 \\\\\\n\",\n \"0 20.161390 0.103857 0.301064 -0.010810 0.200458 -0.111111 \\n\",\n \"1 19.830695 0.112694 0.356932 -0.010759 0.487074 0.112648 \\n\",\n \"2 19.540347 0.160624 0.228657 -0.014070 0.693493 -1.121287 \\n\",\n \"3 19.145174 0.119008 0.208333 -0.016909 0.796703 -1.307692 \\n\",\n \"4 18.822587 0.103625 0.403175 -0.014705 0.880381 -1.071429 \\n\",\n \"5 18.723793 0.084669 0.156782 -0.016041 0.926581 -0.800000 \\n\",\n \"6 18.424397 0.071227 0.403175 -0.013634 0.956385 -0.615385 \\n\",\n \"7 18.212198 0.105408 0.218537 -0.014472 0.975405 -0.687500 \\n\",\n \"8 18.043599 0.099822 0.196429 -0.016169 0.986627 -0.750000 \\n\",\n \"9 17.646800 0.116721 0.283494 -0.016108 0.992237 -0.888889 \\n\",\n \"10 17.260900 0.055573 0.334915 -0.013926 0.644618 -1.000000 \\n\",\n \"11 17.255450 0.028211 0.826396 -0.009058 0.470808 -0.625000 \\n\",\n \"12 17.252725 0.033962 0.716506 -0.005117 0.546847 0.230769 \\n\",\n \"13 17.313862 0.047679 0.806186 -0.000798 0.723559 0.750000 \\n\",\n \"14 17.531931 0.068120 0.843856 0.004241 0.828322 1.000000 \\n\",\n \"15 17.703466 0.063466 0.640934 0.005385 0.906362 1.222222 \\n\",\n \"16 18.101733 0.058846 0.800240 0.007486 0.945382 1.071429 \\n\",\n \"17 18.300866 0.086141 0.843218 0.011255 0.587617 1.076923 \\n\",\n \"18 18.400433 0.079647 0.478240 0.008305 0.408734 0.533333 \\n\",\n \"19 18.262717 0.055172 0.375000 0.004988 0.460442 -0.222222 \\n\",\n \"\\n\",\n \" KST_3_4_5_2_2_2_5_3 RSI_3 TSI_4_3 TSI_3_2 MFI_4 OBV_3 \\\\\\n\",\n \"0 -1.137638 0.465050 -0.313426 -0.297570 0.25 -18597.666667 \\n\",\n \"1 -0.955659 0.465050 -0.351966 -0.374051 0.25 70164.333333 \\n\",\n \"2 -1.294738 0.113155 -0.524753 -0.653825 0.25 -9700.000000 \\n\",\n \"3 -1.300366 0.050043 -0.644741 -0.787556 0.00 -29200.000000 \\n\",\n \"4 -1.201547 0.050043 -0.520602 -0.465990 0.25 -23433.333333 \\n\",\n \"5 -1.315350 0.017970 -0.619516 -0.670928 0.25 -25866.666667 \\n\",\n \"6 -1.181066 0.013609 -0.478225 -0.388073 0.25 1733.333333 \\n\",\n \"7 -1.405361 0.006906 -0.566993 -0.598604 0.25 -15133.333333 \\n\",\n \"8 -1.326540 0.002787 -0.662538 -0.741297 0.00 -10533.333333 \\n\",\n \"9 -1.435721 0.001076 -0.719111 -0.806655 0.00 -26700.000000 \\n\",\n \"10 -1.157564 0.001076 -0.746218 -0.830586 0.00 -15600.000000 \\n\",\n \"11 -0.771170 0.648499 -0.417151 -0.077154 0.25 -1800.000000 \\n\",\n \"12 -0.466513 0.648499 -0.265895 0.121654 0.50 10200.000000 \\n\",\n \"13 -0.031649 0.811443 0.138555 0.620236 0.75 19866.666667 \\n\",\n \"14 0.363910 0.950136 0.536785 0.867546 1.00 12500.000000 \\n\",\n \"15 0.609757 0.966542 0.460070 0.531597 1.00 -27933.333333 \\n\",\n \"16 0.809457 0.992201 0.606138 0.704387 1.00 -32233.333333 \\n\",\n \"17 1.067793 0.992201 0.766887 0.862653 1.00 -32900.000000 \\n\",\n \"18 0.897745 0.614926 0.309192 0.130426 0.75 -9833.333333 \\n\",\n \"19 0.559733 0.614926 0.173112 -0.018285 0.50 -15200.000000 \\n\",\n \"\\n\",\n \" Force_4 EoM_5 KelChM_4 KelChU_4 KelChD_4 Donchian_5 \\n\",\n \"0 148541.565 -0.000005 19.730000 20.658750 18.801250 3.430 \\n\",\n \"1 82152.375 -0.000005 19.645833 20.340833 18.950833 3.360 \\n\",\n \"2 217862.750 -0.000008 19.513333 20.077083 18.949583 3.670 \\n\",\n \"3 345024.110 -0.000009 19.020833 19.489583 18.552083 3.180 \\n\",\n \"4 -4812.500 -0.000010 18.750000 19.187500 18.312500 2.150 \\n\",\n \"5 -33962.500 -0.000007 18.406250 19.031250 17.781250 2.040 \\n\",\n \"6 3000.000 -0.000009 18.177083 18.770833 17.583333 2.135 \\n\",\n \"7 32375.000 -0.000006 17.989583 18.552083 17.427083 2.760 \\n\",\n \"8 9750.000 -0.000009 17.708333 18.427083 16.989583 1.750 \\n\",\n \"9 -9975.000 -0.000011 17.416667 18.104167 16.729167 1.750 \\n\",\n \"10 4612.500 -0.000011 17.125000 17.781250 16.468750 1.625 \\n\",\n \"11 675.000 -0.000008 16.958333 17.677083 16.239583 1.375 \\n\",\n \"12 -775.000 -0.000003 16.906250 17.500000 16.312500 1.625 \\n\",\n \"13 -3500.000 0.000001 17.010417 17.479167 16.541667 2.125 \\n\",\n \"14 87.500 0.000006 17.208333 17.739583 16.677083 1.625 \\n\",\n \"15 45350.000 0.000008 17.406250 17.781250 17.031250 1.625 \\n\",\n \"16 10062.500 0.000011 17.666667 18.104167 17.229167 1.500 \\n\",\n \"17 -25312.500 0.000008 17.937500 18.406250 17.468750 1.000 \\n\",\n \"18 10900.000 0.000008 18.093750 18.531250 17.656250 1.375 \\n\",\n \"19 -43687.500 0.000004 18.177083 18.583333 17.770833 1.500 \"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 22\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"scrolled\": true,\n \"id\": \"KqZhVw1x5S-S\",\n \"outputId\": \"3ad459cd-80c7-4804-e13a-cfb0e469cfff\"\n },\n \"source\": [\n \"good[\\\"target\\\"] = (good[\\\"ACTUAL\\\"]-good[\\\"est_avg\\\"])/good[\\\"ACTUAL\\\"]\\n\",\n \"good[\\\"close_ret\\\"] = good['close'].pct_change()\\n\",\n \"good[\\\"close_three\\\"] = (good['close'] - good['close'].shift(3))/good['close'].shift(3)\\n\",\n \"good[\\\"close_five\\\"] = (good['close'] - good['close'].shift(5))/good['close'].shift(5)\\n\",\n \"good[\\\"close_ten\\\"] = (good['close'] - good['close'].shift(10))/good['close'].shift(10)\\n\",\n \"good[\\\"close_twenty\\\"] = (good['close'] - good['close'].shift(20))/good['close'].shift(20)\\n\",\n \"\\n\",\n \"good.head()\\n\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
Unnamed: 0idACTUALtickerest_avgest_allqtrdatcounterdatadatevolumeclosehighlowopenMA_10EMA_10ROC_10Momentum_10ATR_10BollingerB_10Bollinger%b_10Trix_10ADX_8_6Vortex_8KST_5_7_10_2_2_2_5_3RSI_15TSI_7_4MFI_20OBV_10Force_10EoM_20Copp_10KelChM_10KelChU_10KelChD_10Donchian_10MA_5EMA_5ROC_5ROC_4Momentum_5ATR_5BollingerB_5Bollinger%b_5Trix_4ADX_4_3Vortex_4KST_3_5_8_2_2_2_5_3RSI_5TSI_6_3ChaikinMFI_8OBV_4Force_8EoM_6Copp_3KelChM_5KelChU_5KelChD_5Donchian_8MA_3EMA_3ROC_3Momentum_3ATR_3BollingerB_4Bollinger%b_4Trix_3ADX_3_3Vortex_3KST_3_4_5_2_2_2_5_3RSI_3TSI_4_3TSI_3_2MFI_4OBV_3Force_4EoM_5KelChM_4KelChU_4KelChD_4Donchian_5targetclose_retclose_threeclose_fiveclose_tenclose_twenty
01968350100001984-07-24 00:00:000.47SMP0.480.019840630.0491984-05-1411800.019.37519.7519.3750.020.890520.347423-0.135816-2.23520.7973220.2406650.198564-0.0004420.324168-0.661741-1.1818780.402400-0.2988420.40-57444.2205912.785-0.0000010.01141520.89566721.81516719.9761673.3519.82520.002287-0.030765-0.018490-1.51520.4285250.0916650.252373-0.0101790.219372-0.224269-1.5227910.404300-0.3140547279.5226870.125-71142.00523586.280-0.000006-0.02370919.79400020.85300018.7350003.3619.79833319.781909-0.006410-0.36520.1613900.1038570.301064-0.0108100.200458-0.111111-1.1376380.465050-0.313426-0.2975700.25-18597.666667148541.565-0.00000519.73000020.65875018.8012503.43-0.021277NaNNaNNaNNaNNaN
11968351100001984-07-24 00:00:000.47SMP0.480.019840630.0481984-05-153700.019.37519.5019.3750.020.586020.170619-0.143836-3.04520.5614450.2357480.250469-0.0012210.371729-0.646667-1.5984040.402400-0.3214080.35-71985.8431525.220-0.000002-0.02147520.61900021.47600019.7620003.6319.70219.793191-0.018490-0.006410-0.61520.1190160.0976440.330022-0.0103950.445348-0.175272-1.8000770.404300-0.341541-6444.9738940.125-13948.25548447.155-0.000005-0.04295919.66733320.43533318.8993333.3619.75666719.578454-0.055799-0.12519.8306950.1126940.356932-0.0107590.4870740.112648-0.9556590.465050-0.351966-0.3740510.2570164.33333382152.375-0.00000519.64583320.34083318.9508333.36-0.0212770.000000NaNNaNNaNNaN
21968352100001984-07-24 00:00:000.47SMP0.480.019840630.0471984-05-1617300.018.62519.2518.6250.020.185519.889597-0.163297-4.00520.3230000.2221610.152019-0.0021520.443784-0.713325-2.3523010.329198-0.4159400.35-90981.4622200.780-0.000002-0.06653920.24233321.13483319.3498333.6319.47919.403794-0.044872-0.092349-1.11519.8293440.1390880.184789-0.0122050.634240-0.141768-2.2903320.197036-0.468010-15800.9066870.12548298.25154638.105-0.000007-0.08700919.48333320.16433318.8023333.2719.12500019.101727-0.038710-1.89519.5403470.1606240.228657-0.0140700.693493-1.121287-1.2947380.113155-0.524753-0.6538250.25-9700.000000217862.750-0.00000819.51333320.07708318.9495833.67-0.021277-0.038710NaNNaNNaNNaN
31968353100001984-07-24 00:00:000.47SMP0.480.019840630.0461984-05-1770300.018.25018.7518.0000.019.784519.591488-0.154310-4.01020.0370000.2038180.119461-0.0031860.495251-0.728316-2.6214970.280586-0.4976880.35-78631.1495247.030-0.000002-0.11546419.86000020.73050018.9895003.3619.22919.019196-0.110624-0.058065-1.25019.4695630.1811250.218909-0.0142230.728687-1.223827-2.2046120.120085-0.565516-20143.4028280.125-24850.0055125.840-0.000008-0.10156919.27733319.87833318.6763333.4318.75000018.675864-0.058065-1.12519.1451740.1190080.208333-0.0169090.796703-1.307692-1.3003660.050043-0.644741-0.7875560.00-29200.000000345024.110-0.00000919.02083319.48958318.5520833.18-0.021277-0.020134-0.058065NaNNaNNaN
41968354100001984-07-24 00:00:000.47SMP0.480.019840630.0451984-05-1817300.018.50018.5018.2500.019.476519.393036-0.114409-3.08019.7575460.1761930.215441-0.0040990.559638-0.486903-2.7248960.280586-0.4697010.40-69938.0161179.480-0.000002-0.15221019.51700020.33050018.7035003.3618.82518.846131-0.045161-0.045161-2.02019.1463750.1104730.343725-0.0139570.820703-1.133333-2.0271310.120085-0.498462-7128.0540220.250-17575.00351686.190-0.000007-0.07672118.90000019.32500018.4750003.4918.45833318.587932-0.006711-0.87518.8225870.1036250.403175-0.0147050.880381-1.071429-1.2015470.050043-0.520602-0.4659900.25-23433.333333-4812.500-0.00001018.75000019.18750018.3125002.15-0.0212770.013699-0.045161NaNNaNNaN
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Unnamed: 0 id ACTUAL ticker est_avg est_all \\\\\\n\",\n \"0 1968350 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"1 1968351 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"2 1968352 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"3 1968353 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"4 1968354 100001984-07-24 00:00:00 0.47 SMP 0.48 0.0 \\n\",\n \"\\n\",\n \" qtrdat counter datadate volume close high low open \\\\\\n\",\n \"0 19840630.0 49 1984-05-14 11800.0 19.375 19.75 19.375 0.0 \\n\",\n \"1 19840630.0 48 1984-05-15 3700.0 19.375 19.50 19.375 0.0 \\n\",\n \"2 19840630.0 47 1984-05-16 17300.0 18.625 19.25 18.625 0.0 \\n\",\n \"3 19840630.0 46 1984-05-17 70300.0 18.250 18.75 18.000 0.0 \\n\",\n \"4 19840630.0 45 1984-05-18 17300.0 18.500 18.50 18.250 0.0 \\n\",\n \"\\n\",\n \" MA_10 EMA_10 ROC_10 Momentum_10 ATR_10 BollingerB_10 \\\\\\n\",\n \"0 20.8905 20.347423 -0.135816 -2.235 20.797322 0.240665 \\n\",\n \"1 20.5860 20.170619 -0.143836 -3.045 20.561445 0.235748 \\n\",\n \"2 20.1855 19.889597 -0.163297 -4.005 20.323000 0.222161 \\n\",\n \"3 19.7845 19.591488 -0.154310 -4.010 20.037000 0.203818 \\n\",\n \"4 19.4765 19.393036 -0.114409 -3.080 19.757546 0.176193 \\n\",\n \"\\n\",\n \" Bollinger%b_10 Trix_10 ADX_8_6 Vortex_8 KST_5_7_10_2_2_2_5_3 \\\\\\n\",\n \"0 0.198564 -0.000442 0.324168 -0.661741 -1.181878 \\n\",\n \"1 0.250469 -0.001221 0.371729 -0.646667 -1.598404 \\n\",\n \"2 0.152019 -0.002152 0.443784 -0.713325 -2.352301 \\n\",\n \"3 0.119461 -0.003186 0.495251 -0.728316 -2.621497 \\n\",\n \"4 0.215441 -0.004099 0.559638 -0.486903 -2.724896 \\n\",\n \"\\n\",\n \" RSI_15 TSI_7_4 MFI_20 OBV_10 Force_10 EoM_20 Copp_10 \\\\\\n\",\n \"0 0.402400 -0.298842 0.40 -57444.2 205912.785 -0.000001 0.011415 \\n\",\n \"1 0.402400 -0.321408 0.35 -71985.8 431525.220 -0.000002 -0.021475 \\n\",\n \"2 0.329198 -0.415940 0.35 -90981.4 622200.780 -0.000002 -0.066539 \\n\",\n \"3 0.280586 -0.497688 0.35 -78631.1 495247.030 -0.000002 -0.115464 \\n\",\n \"4 0.280586 -0.469701 0.40 -69938.0 161179.480 -0.000002 -0.152210 \\n\",\n \"\\n\",\n \" KelChM_10 KelChU_10 KelChD_10 Donchian_10 MA_5 EMA_5 ROC_5 \\\\\\n\",\n \"0 20.895667 21.815167 19.976167 3.35 19.825 20.002287 -0.030765 \\n\",\n \"1 20.619000 21.476000 19.762000 3.63 19.702 19.793191 -0.018490 \\n\",\n \"2 20.242333 21.134833 19.349833 3.63 19.479 19.403794 -0.044872 \\n\",\n \"3 19.860000 20.730500 18.989500 3.36 19.229 19.019196 -0.110624 \\n\",\n \"4 19.517000 20.330500 18.703500 3.36 18.825 18.846131 -0.045161 \\n\",\n \"\\n\",\n \" ROC_4 Momentum_5 ATR_5 BollingerB_5 Bollinger%b_5 Trix_4 \\\\\\n\",\n \"0 -0.018490 -1.515 20.428525 0.091665 0.252373 -0.010179 \\n\",\n \"1 -0.006410 -0.615 20.119016 0.097644 0.330022 -0.010395 \\n\",\n \"2 -0.092349 -1.115 19.829344 0.139088 0.184789 -0.012205 \\n\",\n \"3 -0.058065 -1.250 19.469563 0.181125 0.218909 -0.014223 \\n\",\n \"4 -0.045161 -2.020 19.146375 0.110473 0.343725 -0.013957 \\n\",\n \"\\n\",\n \" ADX_4_3 Vortex_4 KST_3_5_8_2_2_2_5_3 RSI_5 TSI_6_3 Chaikin \\\\\\n\",\n \"0 0.219372 -0.224269 -1.522791 0.404300 -0.314054 7279.522687 \\n\",\n \"1 0.445348 -0.175272 -1.800077 0.404300 -0.341541 -6444.973894 \\n\",\n \"2 0.634240 -0.141768 -2.290332 0.197036 -0.468010 -15800.906687 \\n\",\n \"3 0.728687 -1.223827 -2.204612 0.120085 -0.565516 -20143.402828 \\n\",\n \"4 0.820703 -1.133333 -2.027131 0.120085 -0.498462 -7128.054022 \\n\",\n \"\\n\",\n \" MFI_8 OBV_4 Force_8 EoM_6 Copp_3 KelChM_5 KelChU_5 \\\\\\n\",\n \"0 0.125 -71142.00 523586.280 -0.000006 -0.023709 19.794000 20.853000 \\n\",\n \"1 0.125 -13948.25 548447.155 -0.000005 -0.042959 19.667333 20.435333 \\n\",\n \"2 0.125 48298.25 154638.105 -0.000007 -0.087009 19.483333 20.164333 \\n\",\n \"3 0.125 -24850.00 55125.840 -0.000008 -0.101569 19.277333 19.878333 \\n\",\n \"4 0.250 -17575.00 351686.190 -0.000007 -0.076721 18.900000 19.325000 \\n\",\n \"\\n\",\n \" KelChD_5 Donchian_8 MA_3 EMA_3 ROC_3 Momentum_3 \\\\\\n\",\n \"0 18.735000 3.36 19.798333 19.781909 -0.006410 -0.365 \\n\",\n \"1 18.899333 3.36 19.756667 19.578454 -0.055799 -0.125 \\n\",\n \"2 18.802333 3.27 19.125000 19.101727 -0.038710 -1.895 \\n\",\n \"3 18.676333 3.43 18.750000 18.675864 -0.058065 -1.125 \\n\",\n \"4 18.475000 3.49 18.458333 18.587932 -0.006711 -0.875 \\n\",\n \"\\n\",\n \" ATR_3 BollingerB_4 Bollinger%b_4 Trix_3 ADX_3_3 Vortex_3 \\\\\\n\",\n \"0 20.161390 0.103857 0.301064 -0.010810 0.200458 -0.111111 \\n\",\n \"1 19.830695 0.112694 0.356932 -0.010759 0.487074 0.112648 \\n\",\n \"2 19.540347 0.160624 0.228657 -0.014070 0.693493 -1.121287 \\n\",\n \"3 19.145174 0.119008 0.208333 -0.016909 0.796703 -1.307692 \\n\",\n \"4 18.822587 0.103625 0.403175 -0.014705 0.880381 -1.071429 \\n\",\n \"\\n\",\n \" KST_3_4_5_2_2_2_5_3 RSI_3 TSI_4_3 TSI_3_2 MFI_4 OBV_3 \\\\\\n\",\n \"0 -1.137638 0.465050 -0.313426 -0.297570 0.25 -18597.666667 \\n\",\n \"1 -0.955659 0.465050 -0.351966 -0.374051 0.25 70164.333333 \\n\",\n \"2 -1.294738 0.113155 -0.524753 -0.653825 0.25 -9700.000000 \\n\",\n \"3 -1.300366 0.050043 -0.644741 -0.787556 0.00 -29200.000000 \\n\",\n \"4 -1.201547 0.050043 -0.520602 -0.465990 0.25 -23433.333333 \\n\",\n \"\\n\",\n \" Force_4 EoM_5 KelChM_4 KelChU_4 KelChD_4 Donchian_5 \\\\\\n\",\n \"0 148541.565 -0.000005 19.730000 20.658750 18.801250 3.43 \\n\",\n \"1 82152.375 -0.000005 19.645833 20.340833 18.950833 3.36 \\n\",\n \"2 217862.750 -0.000008 19.513333 20.077083 18.949583 3.67 \\n\",\n \"3 345024.110 -0.000009 19.020833 19.489583 18.552083 3.18 \\n\",\n \"4 -4812.500 -0.000010 18.750000 19.187500 18.312500 2.15 \\n\",\n \"\\n\",\n \" target close_ret close_three close_five close_ten close_twenty \\n\",\n \"0 -0.021277 NaN NaN NaN NaN NaN \\n\",\n \"1 -0.021277 0.000000 NaN NaN NaN NaN \\n\",\n \"2 -0.021277 -0.038710 NaN NaN NaN NaN \\n\",\n \"3 -0.021277 -0.020134 -0.058065 NaN NaN NaN \\n\",\n \"4 -0.021277 0.013699 -0.045161 NaN NaN NaN \"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 23\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"X5Ilo7Dq5S-a\"\n },\n \"source\": [\n \"good = good.ix[:,1:]\\n\",\n \"\\n\",\n \"good = good.drop([\\\"ACTUAL\\\"],axis=1)\\n\",\n \"good = good[good[\\\"counter\\\"]<25]\\n\",\n \"good = good.ffill()\\n\",\n \"good = good.fillna(value=0)\\n\",\n \"# I don't actually know if I should make all of my features percentage change. \"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"WnchgBKs5S-c\",\n \"outputId\": \"f9c05052-335e-4c29-b6a0-b8815f77c273\"\n },\n \"source\": [\n \"good.head(30)\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
idtickerest_avgest_allqtrdatcounterdatadatevolumeclosehighlowopenMA_10EMA_10ROC_10Momentum_10ATR_10BollingerB_10Bollinger%b_10Trix_10ADX_8_6Vortex_8KST_5_7_10_2_2_2_5_3RSI_15TSI_7_4MFI_20OBV_10Force_10EoM_20Copp_10KelChM_10KelChU_10KelChD_10Donchian_10MA_5EMA_5ROC_5ROC_4Momentum_5ATR_5BollingerB_5Bollinger%b_5Trix_4ADX_4_3Vortex_4KST_3_5_8_2_2_2_5_3RSI_5TSI_6_3ChaikinMFI_8OBV_4Force_8EoM_6Copp_3KelChM_5KelChU_5KelChD_5Donchian_8MA_3EMA_3ROC_3Momentum_3ATR_3BollingerB_4Bollinger%b_4Trix_3ADX_3_3Vortex_3KST_3_4_5_2_2_2_5_3RSI_3TSI_4_3TSI_3_2MFI_4OBV_3Force_4EoM_5KelChM_4KelChU_4KelChD_4Donchian_5targetclose_retclose_threeclose_fiveclose_tenclose_twenty
25100001984-07-24 00:00:00SMP0.480.019840630.0241984-06-1925600.018.00018.25017.8750.017.912517.8373860.0000000.37518.0501050.0756470.564574-0.0020190.277868-1.666667e-010.3402830.5372980.1136710.45-8140.0-35887.54.445812e-080.03138617.93750018.33750017.5375002.12517.75017.8809080.0212770.0285710.12518.0764920.0821260.6714990.0009790.4502862.666667e-010.0943020.6644970.1580832699.5337030.250-11500.0-9550.0-2.437302e-070.04174117.75000018.20000017.3000002.12517.87500017.9399990.0359710.50018.1291050.0929080.6324140.0028320.5664830.5384620.1223410.7609900.2118170.2782140.50-13700.0000007500.01.012419e-0517.75000018.21875017.2812500.875-0.021277-0.0136990.0285710.0069930.0212770.014085
26100001984-07-24 00:00:00SMP0.480.019840630.0231984-06-20107500.018.00018.25018.0000.017.912517.866952-0.0270270.00018.0864500.0756470.564574-0.0016090.200808-9.090909e-020.1939830.5372980.1256450.50-9750.00.06.767256e-070.03221917.93333318.29583317.5708332.12517.82517.9206060.0285710.0359710.37518.1343280.0832110.6179850.0016940.2956795.333333e-010.1026290.6644970.158011-30503.8735160.375-10275.00.08.461050e-060.03200717.81666718.24166717.3916671.62518.08333317.969999-0.0136990.62518.1895520.0833810.5627910.0031070.4391300.9166670.3229830.7609900.1971560.2183080.75-6933.33333351300.01.182737e-0517.87500018.34375017.4062501.000-0.0212770.0000000.0359710.0212770.0000000.000000
27100001984-07-24 00:00:00SMP0.480.019840630.0221984-06-21113100.017.87518.25017.8750.017.850017.868416-0.006944-0.62518.1161860.0602220.523257-0.0012780.2295952.368476e-15-0.0417810.4939630.0768700.50-21710.0-66625.08.423214e-070.02149917.89583318.25833317.5333332.12517.90017.9054040.028777-0.0205480.37518.1728860.0723030.4806830.0014360.4606686.666667e-01-0.1332260.4669320.073776-43095.8243310.375-33475.0-13537.59.821601e-060.00225717.90000018.35000017.4500001.37517.95833317.922500-0.006944-0.37518.2197760.0348920.2516500.0017280.6117350.5000000.1222770.3441120.061402-0.0689320.75-46233.33333346400.01.369919e-0518.02083318.48958317.5520830.875-0.021277-0.006944-0.0205480.021429-0.0337840.028777
28100001984-07-24 00:00:00SMP0.480.019840630.0211984-06-2229300.018.12518.37517.6250.017.862517.9150670.0069440.12518.1632430.0625220.735048-0.0009280.2501563.448276e-020.1751930.4170750.1463210.55-13570.0-2850.01.775061e-060.02401117.88333318.27083317.4958332.12518.05017.978603-0.0068490.0069440.75018.2402570.0315840.6315590.0018640.5431632.142857e-010.1165870.2468000.179203-5235.3353320.500-27350.07250.01.114935e-050.00807318.02500018.55000017.5000001.37518.00000018.0237500.0069440.12518.2973880.0226800.8061860.0023670.698037-0.0909090.2792230.1078300.2224240.3168680.75-27933.333333-3062.51.407196e-0518.04166718.47916717.6041671.000-0.0212770.0139860.0069440.0431650.0069440.066176
29100001984-07-24 00:00:00SMP0.480.019840630.0201984-06-259700.018.25018.37518.0000.017.887517.9759640.0209790.25018.2017450.0677680.799041-0.0005500.2648432.068966e-010.2167960.4170750.2285600.60-12600.01225.02.723434e-060.02469617.90000018.31250017.4875002.12518.05018.0690680.0138890.0138890.00018.2851710.0315840.8508230.0026150.5844101.428571e-010.3114330.2468000.2909758650.1982280.625-18525.02562.51.293475e-050.02147018.07500018.50000017.6500001.00018.08333318.1368750.0209790.25018.3361940.0357370.7904740.0035090.7411890.0833330.3823360.1078300.3770700.5480280.75-24700.000000-3975.01.849822e-0618.08333318.52083317.6458331.000-0.0212770.0068970.0138890.0000000.0138890.081481
30100001984-07-24 00:00:00SMP0.480.019840630.0191984-06-2611000.018.00018.25018.0000.017.900017.9803340.0212770.12518.2105180.0681680.581954-0.0002700.4314232.758621e-010.1616330.4170750.1306200.60-13670.01337.53.070939e-060.01656317.91666718.34166717.4916671.62518.05018.0460460.0000000.0069930.00018.2734480.0315840.4122940.0021070.780024-2.030122e-150.3921870.2468000.1174549015.4742400.625-21275.03050.01.304776e-060.01078318.08333318.48333317.6833331.25018.12500018.068437-0.0068970.12518.2930970.0357370.4031750.0019570.8671400.0909090.2777460.1078300.086397-0.0684500.509333.333333-0.08.333096e-0718.08333318.52083317.6458331.000-0.021277-0.0136990.0069930.0000000.0069930.066667
31100001984-07-24 00:00:00SMP0.480.019840630.0181984-06-2738200.016.87518.00016.6250.017.825017.779364-0.035714-0.75018.1722420.099129-0.037640-0.0004450.550409-5.405405e-02-0.4659260.183095-0.3128010.55-16930.0-24450.01.429934e-06-0.00554917.85833318.38333317.3333331.37517.82517.655697-0.055944-0.068966-1.12518.1822980.1232310.067511-0.0026350.877831-5.454545e-01-0.3757710.025310-0.4468904110.6060240.625-2550.0-8950.0-4.179866e-06-0.06676217.90000018.52500017.2750001.25017.70833317.471719-0.075342-1.25018.1465490.1422100.129902-0.0071950.930115-0.687500-0.5437750.003455-0.589053-0.7893420.50-13166.66666774900.0-5.044909e-0617.87500018.56250017.1875001.000-0.021277-0.062500-0.068966-0.062500-0.042553-0.014599
32100001984-07-24 00:00:00SMP0.480.019840630.0171984-06-2829100.017.37517.37516.6250.017.812517.7058440.000000-0.12518.0272890.1018550.258860-0.0006830.534904-3.611111e-01-0.4375860.183095-0.2545120.50-13530.0-3025.08.205955e-09-0.01215217.81250018.38750017.2375001.37517.72517.562131-0.041379-0.047945-0.50017.9131990.1314060.349731-0.0046820.609905-7.272727e-01-0.4995900.025310-0.29925618724.1792230.500-2600.0-21262.5-5.546445e-06-0.07471517.72500018.42500017.0250001.25017.41666717.423359-0.034722-0.87517.7607740.1408950.399327-0.0084140.549894-1.000000-0.8010140.003455-0.335592-0.2977870.25-6700.000000150.0-6.614288e-0617.64583318.33333316.9583331.000-0.0212770.029630-0.047945-0.027972-0.0071430.014599
33100001984-07-24 00:00:00SMP0.480.019840630.0161984-06-297200.017.50017.62517.2500.017.825017.668418-0.0410960.12517.9541460.0991290.316070-0.0008890.439784-2.777778e-01-0.4002460.279133-0.1902150.50-10780.0-1637.51.066710e-06-0.01244917.81666718.39166717.2416671.25017.60017.541421-0.041096-0.027778-0.62517.8171330.1228510.453750-0.0046940.367700-5.454545e-01-0.4423820.287058-0.19476914504.8065120.500-3225.09200.0-1.714164e-06-0.03272817.60833318.23333316.9833331.00017.25000017.4616800.037037-0.50017.6928870.1060030.533813-0.0061190.454712-0.550000-0.6254270.415163-0.186311-0.0708170.25-633.3333331875.0-1.737031e-0617.45833318.14583316.7708330.750-0.0212770.007194-0.027778-0.0344830.0071940.007194
34100001984-07-24 00:00:00SMP0.480.019840630.0151984-07-0222300.017.87517.87517.5000.017.787517.705978-0.006944-0.37517.9397560.0937460.552474-0.0009290.434526-1.891892e-01-0.0720090.364515-0.0414240.50-9030.0-6562.55.875735e-07-0.01134217.79583318.32083317.2708331.25017.52517.652614-0.0069440.059259-0.37517.8364220.1016750.696425-0.0026100.383055-3.043478e-01-0.1028710.4917780.01079217791.8989010.5005100.010650.0-7.468531e-070.02765417.51666718.14166716.8916671.00017.58333317.6683400.0287771.00017.7839440.0949100.783744-0.0010930.4389910.500000-0.0743040.6797650.0893500.3330500.5019533.333333-1412.5-2.345966e-0617.37500018.09375016.6562500.750-0.0212770.0214290.059259-0.020548-0.0205480.007042
35100001984-07-24 00:00:00SMP0.480.019840630.0141984-07-0315200.017.62517.75017.1250.017.750017.691255-0.020833-0.37517.9052550.0929530.424239-0.0009520.430771-2.500000e-01-0.3137960.302993-0.0391550.45-7990.03900.04.138999e-08-0.01176917.74166718.29166717.1916671.25017.45017.6434090.0444440.014388-0.37517.8076140.0849990.617985-0.0015300.3907331.111111e-01-0.3268190.298756-0.00885013325.5367970.50010850.024475.0-3.668239e-060.02459317.40000018.10000016.7000001.12517.66666717.6466700.0071430.25017.7669720.0485350.5365960.0001390.4311310.583333-0.0609950.2883650.0350640.0846140.504766.666667-17250.0-4.117796e-0617.45833317.98958316.9270831.750-0.021277-0.0139860.014388-0.020833-0.0208330.000000
36100001984-07-24 00:00:00SMP0.480.019840630.0131984-07-0559200.017.50017.62517.5000.017.700017.656481-0.020979-0.50017.8542990.0924520.377780-0.0010010.490467-2.000000e-01-0.3405670.302993-0.0742590.45-13910.024150.0-7.945980e-07-0.01337217.68750018.22500017.1500001.25017.57517.5956060.0071940.0000000.62517.7467430.0431460.401093-0.0014030.5849446.923077e-01-0.3027040.298756-0.072351-11451.2055910.500-11225.0-18687.5-3.387508e-060.00180717.47500017.92500017.0250001.12517.66666717.573335-0.0209790.00017.6959860.0401200.323223-0.0006280.6447290.200000-0.0970010.288365-0.071533-0.1549190.75-17366.6666673762.51.784140e-0617.56250017.93750017.1875001.750-0.021277-0.0070920.0000000.037037-0.027778-0.027778
37100001984-07-24 00:00:00SMP0.480.019840630.0121984-07-0624000.017.25017.50017.0000.017.637517.582575-0.048276-0.62517.7898810.0967830.272995-0.0011380.533108-4.166667e-01-0.5740440.234166-0.1671710.40-5000.055687.5-1.480696e-06-0.02726117.61250018.16250017.0625001.12517.55017.480404-0.014286-0.034965-0.12517.6644950.0517550.169711-0.0023450.682049-2.142857e-01-0.6122740.137202-0.212063-1709.7633320.375-19025.0-14300.04.017141e-07-0.02721717.50000017.90000017.1000001.12517.45833317.411667-0.021277-0.62517.5979930.0592650.199760-0.0029580.751528-0.636364-0.2857030.070836-0.282129-0.5031540.50-32800.000000-4200.02.092882e-0617.51041717.91666717.1041671.750-0.021277-0.014286-0.034965-0.007194-0.034965-0.067568
38100001984-07-24 00:00:00SMP0.480.019840630.0111984-07-0927800.017.00017.37517.0000.017.525017.476653-0.068493-1.12517.7144480.0986500.196328-0.0013900.577452-4.054054e-01-0.7020180.234166-0.2847900.40-10710.01687.5-1.492859e-06-0.04369017.52083318.03333317.0083331.12517.45017.320269-0.048951-0.035461-0.50017.5679970.0774170.166895-0.0039510.764316-5.714286e-01-0.6286280.137202-0.369535-6414.6493780.375-31550.0-16800.01.603555e-06-0.04562517.43333317.83333317.0333331.75017.25000017.205834-0.028571-0.62517.4864960.0639240.189946-0.0057400.835387-0.500000-0.3898100.070836-0.492462-0.7362230.25-37000.000000-4812.5-2.633025e-0617.35416717.76041716.9479171.625-0.021277-0.014493-0.035461-0.028571-0.062069-0.055556
39100001984-07-24 00:00:00SMP0.480.019840630.0101984-07-1011600.017.12517.12516.8750.017.412517.412716-0.048611-1.12517.6072760.0834990.302259-0.0016320.639604-1.785714e-01-0.8706220.217997-0.2861340.40-10520.0-2137.5-1.450766e-06-0.05579317.40416717.90416716.9041671.12517.30017.255180-0.028369-0.021429-0.75017.4203310.0599320.331215-0.0044410.851798-7.000000e-01-0.6281490.105202-0.3386664780.1380920.375-24850.0-6650.0-2.867679e-06-0.03715017.29166717.66666716.9166671.75017.12500017.165417-0.007246-0.37517.3057480.0495920.390211-0.0056060.906415-1.000000-0.3552000.040377-0.405950-0.4251780.25-13400.0000001800.0-4.282022e-0617.23958317.55208316.9270831.375-0.0212770.007353-0.021429-0.041958-0.061644-0.048611
40100001984-07-24 00:00:00SMP0.480.019840630.091984-07-1126600.016.62516.87516.5000.017.275017.269495-0.014815-1.37517.4741350.0871520.068263-0.0020080.708782-1.851852e-01-1.0099560.176266-0.4128960.40-12080.0-21450.09.331235e-07-0.07225217.26250017.77500016.7500001.75017.10017.045120-0.050000-0.036232-1.00017.2385540.0756850.132983-0.0060700.914563-1.000000e+00-0.7506210.051329-0.4973832413.1446550.375-16700.01875.0-4.302609e-06-0.04772017.12500017.45000016.8000001.75016.91666716.895208-0.022059-0.62517.0903740.0635370.152817-0.0080780.949892-0.900000-0.6932080.011279-0.597322-0.7072960.00-14266.66666728525.0-3.107210e-0617.02083317.39583316.6458331.250-0.021277-0.029197-0.036232-0.056738-0.076389-0.069930
41100001984-07-24 00:00:00SMP0.480.019840630.081984-07-1230200.016.25016.50015.8750.017.212517.084132-0.064748-0.62517.2970190.1129620.004980-0.0025420.758195-6.666667e-01-1.3162910.129168-0.5387720.40-11280.05000.08.342662e-07-0.08465817.16666717.60416716.7291671.75016.85016.780080-0.057971-0.044118-1.25016.9923690.0970650.133149-0.0083940.945946-1.062500e+00-1.0353680.022511-0.6344145968.4826740.250-18250.0-28750.0-4.313955e-06-0.07146616.85833317.28333316.4333331.75016.66666716.572604-0.051095-0.75016.7951870.0943960.183772-0.0114070.971631-1.230769-1.0101850.003315-0.738204-0.8440050.00-15066.666667-6200.0-5.229534e-0616.76041717.16666716.3541670.875-0.021277-0.022556-0.044118-0.071429-0.037037-0.078014
42100001984-07-24 00:00:00SMP0.480.019840630.071984-07-1362300.016.37516.37516.1250.017.112516.955199-0.064286-1.00017.1293790.1280660.163476-0.0030610.727514-7.931034e-01-1.3384390.129168-0.5276020.45-7960.0-33200.01.325123e-06-0.09841017.08333317.47083316.6958331.75016.67516.645053-0.036765-0.043796-0.87516.7865800.0914420.303252-0.0090990.706332-1.000000e+00-1.0882850.022511-0.57959124781.2588930.2504275.0-60000.0-4.316144e-06-0.06515016.66666717.04166716.2916671.75016.41666716.473802-0.015038-0.75016.5850940.0932790.358675-0.0109300.622732-0.923077-1.0843700.003315-0.625159-0.5877140.251833.333333-21562.5-3.877290e-0616.55208316.92708316.1770830.875-0.0212770.007692-0.043796-0.050725-0.057554-0.064286
43100001984-07-24 00:00:00SMP0.480.019840630.061984-07-1626500.016.25016.50016.1250.016.987516.826981-0.090909-1.25017.0149470.1390620.187807-0.0035540.705598-6.923077e-01-1.2724710.185985-0.5441780.45-11330.0-24125.01.542528e-06-0.11118116.96666717.35416716.5791671.75016.52516.513369-0.051095-0.022556-0.75016.6910530.0892460.313533-0.0091140.586525-6.875000e-01-1.1736920.219663-0.5859097591.2195690.250-5250.0-15537.5-3.083669e-06-0.04385316.50000016.87500016.1250001.75016.29166716.3619010.000000-0.37516.5425470.0431820.323223-0.0097960.448283-0.545455-0.9103310.363083-0.620687-0.6006970.251866.666667-13037.5-3.531788e-0616.36458316.77083315.9583330.875-0.021277-0.007634-0.022556-0.044118-0.071429-0.064748
44100001984-07-24 00:00:00SMP0.480.019840630.051984-07-1710500.016.25016.37516.1250.016.825016.722075-0.078014-1.62516.8985930.1285480.234143-0.0039710.689945-7.777778e-01-1.2159100.185985-0.5534880.40-13560.019175.0-1.949846e-06-0.11422916.81666717.19166716.4416671.75016.35016.425579-0.0225560.000000-0.87516.5857020.0398730.346607-0.0084090.526621-5.384615e-01-1.1783930.219663-0.5889822679.4259450.1251400.060875.0-3.191172e-06-0.02574316.34166716.71666715.9666671.62516.29166716.305951-0.0076340.00016.4587730.0153550.375000-0.0079420.3610580.142857-0.7076720.363083-0.618612-0.6051730.2511933.3333336037.5-3.021217e-0616.26041716.63541715.8854171.125-0.0212770.0000000.000000-0.051095-0.090909-0.109589
45100001984-07-24 00:00:00SMP0.480.019840630.041984-07-1814500.016.25016.25016.1250.016.687516.636243-0.071429-1.37516.7806670.1166870.275321-0.0042880.678764-7.083333e-01-1.3516730.185985-0.5593310.35-12040.0962.5-2.159891e-06-0.11941716.68750017.01250016.3625001.75016.27516.3670530.000000-0.007634-0.37516.4738010.0137390.388197-0.0072660.496669-3.552714e-15-1.0893260.219663-0.5907125040.1080130.1258950.09500.0-2.607480e-06-0.01668816.25000016.57500015.9250001.37516.25000016.2779750.000000-0.12516.3543870.0153550.375000-0.0059350.317446-0.166667-0.5585960.363083-0.617429-0.6073060.25-8833.333333-0.0-2.247867e-0616.26041716.51041716.0104171.625-0.0212770.000000-0.007634-0.022556-0.078014-0.097222
46100001984-07-24 00:00:00SMP0.480.019840630.031984-07-1928400.016.25016.37516.0000.016.562516.566017-0.057971-1.25016.7069090.0988520.309130-0.0045010.552333-6.666667e-01-1.2427620.185985-0.5632570.30-6120.038500.0-2.167158e-06-0.12367916.55416716.90416716.2041671.75016.27516.328035-0.0076340.0000000.00016.4408680.0137390.388197-0.0059670.503272-1.111111e-01-0.9018700.219663-0.5917653946.1347970.125-6625.0-450.0-1.873222e-06-0.00834416.25000016.52500015.9750001.25016.25000016.2639880.0000000.00016.3646930.000000-inf-0.0041700.564719-0.333333-0.3694160.363083-0.616676-0.6084770.000.0000004237.5-1.783304e-0716.23958316.52083315.9583331.500-0.0212770.0000000.0000000.000000-0.071429-0.097222
47100001984-07-24 00:00:00SMP0.480.019840630.021984-07-2027200.016.25016.50016.1250.016.462516.508560-0.044118-1.00016.6692890.0823100.343177-0.0046120.585010-4.400000e-01-1.0800680.267514-0.5660160.35-3720.0-3200.0-2.070630e-06-0.12826116.45833316.79583316.1208331.75016.25016.3020230.0000000.000000-0.12516.4605780.000000inf-0.0047090.612833-3.157968e-15-0.7567730.613899-0.592437-2757.1089250.2500.0-13650.01.386156e-07-0.00417216.25000016.55000015.9500001.37516.25000016.2569940.0000000.00016.4323470.000000-inf-0.0027960.6664920.142857-0.2438550.905996-0.616161-0.6091700.250.0000000.01.161782e-0716.23958316.52083315.9583331.250-0.0212770.0000000.000000-0.007634-0.057971-0.090909
48100001984-07-24 00:00:00SMP0.480.019840630.011984-07-2316100.015.62516.12515.6250.016.325016.347912-0.087591-1.37516.5703280.0915430.031595-0.0048480.657948-5.200000e-01-1.3012790.183498-0.7388930.30-2550.016087.5-2.669986e-06-0.14027916.32500016.67500015.9750001.62516.12516.076349-0.038462-0.038462-0.62516.3487190.0693350.052786-0.0060310.760763-5.000000e-01-0.9118350.152284-0.826403-7486.5775140.250-4025.010500.0-2.167678e-06-0.04054816.15000016.47500015.8250002.00016.04166715.940997-0.038462-0.62516.2786730.0776700.125000-0.0065920.801206-0.454545-0.4751850.115867-0.914285-0.9807130.25-5366.666667-3500.0-2.778100e-0616.12500016.46875015.7812501.000-0.021277-0.038462-0.038462-0.038462-0.080882-0.137931
74100001984-10-24 00:00:00SMP0.360.019840930.0241984-09-2019300.017.62517.62517.3750.017.675017.770690-0.013986-0.25017.9000380.0381870.425920-0.0025140.356807-1.363636e-01-0.3570070.381952-0.1201610.30-15280.02525.0-1.722731e-06-0.09666117.67083317.98333317.3583332.62517.60017.6628260.021739-0.007042-0.12517.7635200.0466810.530429-0.0013060.5246037.692308e-02-0.1935550.372342-0.0640377750.6602660.500-5825.02912.52.475090e-070.00413317.58333317.98333317.1833331.62517.66666717.6556690.000000-0.12517.7271360.0163210.2834940.0000720.593703-0.250000-0.0033720.343752-0.008903-0.0273100.50-16100.000000-37650.02.538478e-0717.63541717.97916717.2916670.750-0.200000-0.007042-0.007042-0.007042-0.013986-0.129630
75100001984-10-24 00:00:00SMP0.360.019840930.0231984-09-2178100.018.25018.50017.7500.017.712517.8578370.0281690.37518.0091220.0549471.052271-0.0023320.3593501.481481e-010.1656930.6247880.1609160.30-7470.014850.0-1.559217e-06-0.07274717.68750018.03750017.3375002.50017.80017.8585510.0281690.0354611.00018.0090140.0582480.9340190.0012790.5047884.000000e-010.2923480.8548530.28642511148.6203740.5007450.030700.01.211860e-060.03388117.74166718.16666717.3166671.00017.87500017.9528340.0281690.62518.1135680.0668200.8675740.0042500.5326750.4615380.3767750.9206320.4191350.6098380.5025833.33333326550.01.715301e-0617.76041718.16666717.3541670.750-0.2000000.0354610.0354610.0579710.020979-0.104294
76100001984-10-24 00:00:00SMP0.360.019840930.0221984-09-2419300.017.75018.50017.6250.017.712517.8382300.0000000.00018.0983720.0549470.538531-0.0021690.3611661.515152e-010.1127260.5871250.0540030.30-5140.0-0.0-1.810930e-06-0.06204517.71250018.11250017.3125002.00017.80017.8223670.0070920.0000000.00018.1726760.0582480.4517760.0014830.4948802.500000e-010.1531680.7339480.078625-957.7437620.50014550.01987.59.571595e-070.02048717.80000018.30000017.3000000.75017.87500017.8514170.0070920.00018.3067840.0621330.4154400.0027840.5021600.2352940.2812700.7358230.086706-0.0052530.5013166.666667-3550.03.985914e-0717.84375018.40625017.2812500.750-0.200000-0.0273970.0000000.0000000.000000-0.112500
77100001984-10-24 00:00:00SMP0.360.019840930.0211984-09-2514200.017.87518.00017.6250.017.725017.8449160.0141840.12518.0804870.0561030.650840-0.0019770.4078861.624098e-150.2107370.5871250.0534600.30-3720.0-312.5-1.060255e-06-0.04948417.72083318.13333317.3083332.00017.85017.8399120.0070420.0141840.25018.1151170.0538810.5259940.0014570.623798-1.421085e-150.2691890.7339480.06869315.2553200.37513425.0-2750.0-7.681926e-070.00706217.84166718.36666717.3166671.12517.95833317.863209-0.0205480.25018.1533920.0604260.5000000.0018870.6762450.1764710.2674650.7358230.0706180.0219850.2524333.333333-562.5-9.218311e-0717.87500018.43750017.3125000.750-0.2000000.0070420.0141840.0141840.007042-0.071429
78100001984-10-24 00:00:00SMP0.360.019840930.0201984-09-2640200.016.87517.62516.2500.017.650017.668567-0.049296-0.75017.9976710.083183-0.027864-0.0021060.441257-2.325581e-01-0.4745140.314630-0.2363830.30-7400.0-27600.0-2.301924e-06-0.05839517.65000018.20000017.1000001.62517.67517.518274-0.042553-0.075342-0.87517.9517450.1142100.103698-0.0023030.688257-3.666667e-01-0.3999970.163083-0.313180-2222.9231470.3758200.029812.5-5.756273e-06-0.05878817.68333318.40833316.9583331.12517.50000017.369104-0.049296-1.37517.8891960.1315830.150894-0.0057450.763288-0.826087-0.4328880.074835-0.408312-0.6090150.25-15100.000000-15675.0-7.158196e-0617.71875018.56250016.8750000.750-0.200000-0.055944-0.075342-0.049296-0.042553-0.129032
79100001984-10-24 00:00:00SMP0.360.019840930.0191984-09-2729200.016.75016.87516.3750.017.550017.501555-0.028986-1.00017.7935490.1050660.066139-0.0024300.465094-4.285714e-01-0.6555480.314630-0.3644040.30-13940.07000.0-2.569475e-06-0.06826517.54583318.13333316.9583331.37517.50017.262183-0.082192-0.056338-0.87517.5928300.1498300.213961-0.0058620.720487-8.888889e-01-0.6687040.163083-0.4493172812.5628780.250-18625.0-4200.0-6.857001e-06-0.08903217.50833318.28333316.7333331.12517.16666717.059552-0.062937-1.00017.3820980.1344330.258311-0.0106210.806809-1.150000-0.8481550.074835-0.552498-0.7251380.00-18400.00000073350.0-7.742650e-0617.34375018.12500016.5625001.125-0.200000-0.007407-0.056338-0.049645-0.056338-0.135484
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" id ticker est_avg est_all qtrdat counter \\\\\\n\",\n \"25 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 24 \\n\",\n \"26 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 23 \\n\",\n \"27 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 22 \\n\",\n \"28 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 21 \\n\",\n \"29 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 20 \\n\",\n \"30 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 19 \\n\",\n \"31 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 18 \\n\",\n \"32 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 17 \\n\",\n \"33 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 16 \\n\",\n \"34 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 15 \\n\",\n \"35 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 14 \\n\",\n \"36 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 13 \\n\",\n \"37 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 12 \\n\",\n \"38 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 11 \\n\",\n \"39 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 10 \\n\",\n \"40 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 9 \\n\",\n \"41 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 8 \\n\",\n \"42 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 7 \\n\",\n \"43 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 6 \\n\",\n \"44 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 5 \\n\",\n \"45 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 4 \\n\",\n \"46 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 3 \\n\",\n \"47 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 2 \\n\",\n \"48 100001984-07-24 00:00:00 SMP 0.48 0.0 19840630.0 1 \\n\",\n \"74 100001984-10-24 00:00:00 SMP 0.36 0.0 19840930.0 24 \\n\",\n \"75 100001984-10-24 00:00:00 SMP 0.36 0.0 19840930.0 23 \\n\",\n \"76 100001984-10-24 00:00:00 SMP 0.36 0.0 19840930.0 22 \\n\",\n \"77 100001984-10-24 00:00:00 SMP 0.36 0.0 19840930.0 21 \\n\",\n \"78 100001984-10-24 00:00:00 SMP 0.36 0.0 19840930.0 20 \\n\",\n \"79 100001984-10-24 00:00:00 SMP 0.36 0.0 19840930.0 19 \\n\",\n \"\\n\",\n \" datadate volume close high low open MA_10 EMA_10 \\\\\\n\",\n \"25 1984-06-19 25600.0 18.000 18.250 17.875 0.0 17.9125 17.837386 \\n\",\n \"26 1984-06-20 107500.0 18.000 18.250 18.000 0.0 17.9125 17.866952 \\n\",\n \"27 1984-06-21 113100.0 17.875 18.250 17.875 0.0 17.8500 17.868416 \\n\",\n \"28 1984-06-22 29300.0 18.125 18.375 17.625 0.0 17.8625 17.915067 \\n\",\n \"29 1984-06-25 9700.0 18.250 18.375 18.000 0.0 17.8875 17.975964 \\n\",\n \"30 1984-06-26 11000.0 18.000 18.250 18.000 0.0 17.9000 17.980334 \\n\",\n \"31 1984-06-27 38200.0 16.875 18.000 16.625 0.0 17.8250 17.779364 \\n\",\n \"32 1984-06-28 29100.0 17.375 17.375 16.625 0.0 17.8125 17.705844 \\n\",\n \"33 1984-06-29 7200.0 17.500 17.625 17.250 0.0 17.8250 17.668418 \\n\",\n \"34 1984-07-02 22300.0 17.875 17.875 17.500 0.0 17.7875 17.705978 \\n\",\n \"35 1984-07-03 15200.0 17.625 17.750 17.125 0.0 17.7500 17.691255 \\n\",\n \"36 1984-07-05 59200.0 17.500 17.625 17.500 0.0 17.7000 17.656481 \\n\",\n \"37 1984-07-06 24000.0 17.250 17.500 17.000 0.0 17.6375 17.582575 \\n\",\n \"38 1984-07-09 27800.0 17.000 17.375 17.000 0.0 17.5250 17.476653 \\n\",\n \"39 1984-07-10 11600.0 17.125 17.125 16.875 0.0 17.4125 17.412716 \\n\",\n \"40 1984-07-11 26600.0 16.625 16.875 16.500 0.0 17.2750 17.269495 \\n\",\n \"41 1984-07-12 30200.0 16.250 16.500 15.875 0.0 17.2125 17.084132 \\n\",\n \"42 1984-07-13 62300.0 16.375 16.375 16.125 0.0 17.1125 16.955199 \\n\",\n \"43 1984-07-16 26500.0 16.250 16.500 16.125 0.0 16.9875 16.826981 \\n\",\n \"44 1984-07-17 10500.0 16.250 16.375 16.125 0.0 16.8250 16.722075 \\n\",\n \"45 1984-07-18 14500.0 16.250 16.250 16.125 0.0 16.6875 16.636243 \\n\",\n \"46 1984-07-19 28400.0 16.250 16.375 16.000 0.0 16.5625 16.566017 \\n\",\n \"47 1984-07-20 27200.0 16.250 16.500 16.125 0.0 16.4625 16.508560 \\n\",\n \"48 1984-07-23 16100.0 15.625 16.125 15.625 0.0 16.3250 16.347912 \\n\",\n \"74 1984-09-20 19300.0 17.625 17.625 17.375 0.0 17.6750 17.770690 \\n\",\n \"75 1984-09-21 78100.0 18.250 18.500 17.750 0.0 17.7125 17.857837 \\n\",\n \"76 1984-09-24 19300.0 17.750 18.500 17.625 0.0 17.7125 17.838230 \\n\",\n \"77 1984-09-25 14200.0 17.875 18.000 17.625 0.0 17.7250 17.844916 \\n\",\n \"78 1984-09-26 40200.0 16.875 17.625 16.250 0.0 17.6500 17.668567 \\n\",\n \"79 1984-09-27 29200.0 16.750 16.875 16.375 0.0 17.5500 17.501555 \\n\",\n \"\\n\",\n \" ROC_10 Momentum_10 ATR_10 BollingerB_10 Bollinger%b_10 Trix_10 \\\\\\n\",\n \"25 0.000000 0.375 18.050105 0.075647 0.564574 -0.002019 \\n\",\n \"26 -0.027027 0.000 18.086450 0.075647 0.564574 -0.001609 \\n\",\n \"27 -0.006944 -0.625 18.116186 0.060222 0.523257 -0.001278 \\n\",\n \"28 0.006944 0.125 18.163243 0.062522 0.735048 -0.000928 \\n\",\n \"29 0.020979 0.250 18.201745 0.067768 0.799041 -0.000550 \\n\",\n \"30 0.021277 0.125 18.210518 0.068168 0.581954 -0.000270 \\n\",\n \"31 -0.035714 -0.750 18.172242 0.099129 -0.037640 -0.000445 \\n\",\n \"32 0.000000 -0.125 18.027289 0.101855 0.258860 -0.000683 \\n\",\n \"33 -0.041096 0.125 17.954146 0.099129 0.316070 -0.000889 \\n\",\n \"34 -0.006944 -0.375 17.939756 0.093746 0.552474 -0.000929 \\n\",\n \"35 -0.020833 -0.375 17.905255 0.092953 0.424239 -0.000952 \\n\",\n \"36 -0.020979 -0.500 17.854299 0.092452 0.377780 -0.001001 \\n\",\n \"37 -0.048276 -0.625 17.789881 0.096783 0.272995 -0.001138 \\n\",\n \"38 -0.068493 -1.125 17.714448 0.098650 0.196328 -0.001390 \\n\",\n \"39 -0.048611 -1.125 17.607276 0.083499 0.302259 -0.001632 \\n\",\n \"40 -0.014815 -1.375 17.474135 0.087152 0.068263 -0.002008 \\n\",\n \"41 -0.064748 -0.625 17.297019 0.112962 0.004980 -0.002542 \\n\",\n \"42 -0.064286 -1.000 17.129379 0.128066 0.163476 -0.003061 \\n\",\n \"43 -0.090909 -1.250 17.014947 0.139062 0.187807 -0.003554 \\n\",\n \"44 -0.078014 -1.625 16.898593 0.128548 0.234143 -0.003971 \\n\",\n \"45 -0.071429 -1.375 16.780667 0.116687 0.275321 -0.004288 \\n\",\n \"46 -0.057971 -1.250 16.706909 0.098852 0.309130 -0.004501 \\n\",\n \"47 -0.044118 -1.000 16.669289 0.082310 0.343177 -0.004612 \\n\",\n \"48 -0.087591 -1.375 16.570328 0.091543 0.031595 -0.004848 \\n\",\n \"74 -0.013986 -0.250 17.900038 0.038187 0.425920 -0.002514 \\n\",\n \"75 0.028169 0.375 18.009122 0.054947 1.052271 -0.002332 \\n\",\n \"76 0.000000 0.000 18.098372 0.054947 0.538531 -0.002169 \\n\",\n \"77 0.014184 0.125 18.080487 0.056103 0.650840 -0.001977 \\n\",\n \"78 -0.049296 -0.750 17.997671 0.083183 -0.027864 -0.002106 \\n\",\n \"79 -0.028986 -1.000 17.793549 0.105066 0.066139 -0.002430 \\n\",\n \"\\n\",\n \" ADX_8_6 Vortex_8 KST_5_7_10_2_2_2_5_3 RSI_15 TSI_7_4 MFI_20 \\\\\\n\",\n \"25 0.277868 -1.666667e-01 0.340283 0.537298 0.113671 0.45 \\n\",\n \"26 0.200808 -9.090909e-02 0.193983 0.537298 0.125645 0.50 \\n\",\n \"27 0.229595 2.368476e-15 -0.041781 0.493963 0.076870 0.50 \\n\",\n \"28 0.250156 3.448276e-02 0.175193 0.417075 0.146321 0.55 \\n\",\n \"29 0.264843 2.068966e-01 0.216796 0.417075 0.228560 0.60 \\n\",\n \"30 0.431423 2.758621e-01 0.161633 0.417075 0.130620 0.60 \\n\",\n \"31 0.550409 -5.405405e-02 -0.465926 0.183095 -0.312801 0.55 \\n\",\n \"32 0.534904 -3.611111e-01 -0.437586 0.183095 -0.254512 0.50 \\n\",\n \"33 0.439784 -2.777778e-01 -0.400246 0.279133 -0.190215 0.50 \\n\",\n \"34 0.434526 -1.891892e-01 -0.072009 0.364515 -0.041424 0.50 \\n\",\n \"35 0.430771 -2.500000e-01 -0.313796 0.302993 -0.039155 0.45 \\n\",\n \"36 0.490467 -2.000000e-01 -0.340567 0.302993 -0.074259 0.45 \\n\",\n \"37 0.533108 -4.166667e-01 -0.574044 0.234166 -0.167171 0.40 \\n\",\n \"38 0.577452 -4.054054e-01 -0.702018 0.234166 -0.284790 0.40 \\n\",\n \"39 0.639604 -1.785714e-01 -0.870622 0.217997 -0.286134 0.40 \\n\",\n \"40 0.708782 -1.851852e-01 -1.009956 0.176266 -0.412896 0.40 \\n\",\n \"41 0.758195 -6.666667e-01 -1.316291 0.129168 -0.538772 0.40 \\n\",\n \"42 0.727514 -7.931034e-01 -1.338439 0.129168 -0.527602 0.45 \\n\",\n \"43 0.705598 -6.923077e-01 -1.272471 0.185985 -0.544178 0.45 \\n\",\n \"44 0.689945 -7.777778e-01 -1.215910 0.185985 -0.553488 0.40 \\n\",\n \"45 0.678764 -7.083333e-01 -1.351673 0.185985 -0.559331 0.35 \\n\",\n \"46 0.552333 -6.666667e-01 -1.242762 0.185985 -0.563257 0.30 \\n\",\n \"47 0.585010 -4.400000e-01 -1.080068 0.267514 -0.566016 0.35 \\n\",\n \"48 0.657948 -5.200000e-01 -1.301279 0.183498 -0.738893 0.30 \\n\",\n \"74 0.356807 -1.363636e-01 -0.357007 0.381952 -0.120161 0.30 \\n\",\n \"75 0.359350 1.481481e-01 0.165693 0.624788 0.160916 0.30 \\n\",\n \"76 0.361166 1.515152e-01 0.112726 0.587125 0.054003 0.30 \\n\",\n \"77 0.407886 1.624098e-15 0.210737 0.587125 0.053460 0.30 \\n\",\n \"78 0.441257 -2.325581e-01 -0.474514 0.314630 -0.236383 0.30 \\n\",\n \"79 0.465094 -4.285714e-01 -0.655548 0.314630 -0.364404 0.30 \\n\",\n \"\\n\",\n \" OBV_10 Force_10 EoM_20 Copp_10 KelChM_10 KelChU_10 \\\\\\n\",\n \"25 -8140.0 -35887.5 4.445812e-08 0.031386 17.937500 18.337500 \\n\",\n \"26 -9750.0 0.0 6.767256e-07 0.032219 17.933333 18.295833 \\n\",\n \"27 -21710.0 -66625.0 8.423214e-07 0.021499 17.895833 18.258333 \\n\",\n \"28 -13570.0 -2850.0 1.775061e-06 0.024011 17.883333 18.270833 \\n\",\n \"29 -12600.0 1225.0 2.723434e-06 0.024696 17.900000 18.312500 \\n\",\n \"30 -13670.0 1337.5 3.070939e-06 0.016563 17.916667 18.341667 \\n\",\n \"31 -16930.0 -24450.0 1.429934e-06 -0.005549 17.858333 18.383333 \\n\",\n \"32 -13530.0 -3025.0 8.205955e-09 -0.012152 17.812500 18.387500 \\n\",\n \"33 -10780.0 -1637.5 1.066710e-06 -0.012449 17.816667 18.391667 \\n\",\n \"34 -9030.0 -6562.5 5.875735e-07 -0.011342 17.795833 18.320833 \\n\",\n \"35 -7990.0 3900.0 4.138999e-08 -0.011769 17.741667 18.291667 \\n\",\n \"36 -13910.0 24150.0 -7.945980e-07 -0.013372 17.687500 18.225000 \\n\",\n \"37 -5000.0 55687.5 -1.480696e-06 -0.027261 17.612500 18.162500 \\n\",\n \"38 -10710.0 1687.5 -1.492859e-06 -0.043690 17.520833 18.033333 \\n\",\n \"39 -10520.0 -2137.5 -1.450766e-06 -0.055793 17.404167 17.904167 \\n\",\n \"40 -12080.0 -21450.0 9.331235e-07 -0.072252 17.262500 17.775000 \\n\",\n \"41 -11280.0 5000.0 8.342662e-07 -0.084658 17.166667 17.604167 \\n\",\n \"42 -7960.0 -33200.0 1.325123e-06 -0.098410 17.083333 17.470833 \\n\",\n \"43 -11330.0 -24125.0 1.542528e-06 -0.111181 16.966667 17.354167 \\n\",\n \"44 -13560.0 19175.0 -1.949846e-06 -0.114229 16.816667 17.191667 \\n\",\n \"45 -12040.0 962.5 -2.159891e-06 -0.119417 16.687500 17.012500 \\n\",\n \"46 -6120.0 38500.0 -2.167158e-06 -0.123679 16.554167 16.904167 \\n\",\n \"47 -3720.0 -3200.0 -2.070630e-06 -0.128261 16.458333 16.795833 \\n\",\n \"48 -2550.0 16087.5 -2.669986e-06 -0.140279 16.325000 16.675000 \\n\",\n \"74 -15280.0 2525.0 -1.722731e-06 -0.096661 17.670833 17.983333 \\n\",\n \"75 -7470.0 14850.0 -1.559217e-06 -0.072747 17.687500 18.037500 \\n\",\n \"76 -5140.0 -0.0 -1.810930e-06 -0.062045 17.712500 18.112500 \\n\",\n \"77 -3720.0 -312.5 -1.060255e-06 -0.049484 17.720833 18.133333 \\n\",\n \"78 -7400.0 -27600.0 -2.301924e-06 -0.058395 17.650000 18.200000 \\n\",\n \"79 -13940.0 7000.0 -2.569475e-06 -0.068265 17.545833 18.133333 \\n\",\n \"\\n\",\n \" KelChD_10 Donchian_10 MA_5 EMA_5 ROC_5 ROC_4 Momentum_5 \\\\\\n\",\n \"25 17.537500 2.125 17.750 17.880908 0.021277 0.028571 0.125 \\n\",\n \"26 17.570833 2.125 17.825 17.920606 0.028571 0.035971 0.375 \\n\",\n \"27 17.533333 2.125 17.900 17.905404 0.028777 -0.020548 0.375 \\n\",\n \"28 17.495833 2.125 18.050 17.978603 -0.006849 0.006944 0.750 \\n\",\n \"29 17.487500 2.125 18.050 18.069068 0.013889 0.013889 0.000 \\n\",\n \"30 17.491667 1.625 18.050 18.046046 0.000000 0.006993 0.000 \\n\",\n \"31 17.333333 1.375 17.825 17.655697 -0.055944 -0.068966 -1.125 \\n\",\n \"32 17.237500 1.375 17.725 17.562131 -0.041379 -0.047945 -0.500 \\n\",\n \"33 17.241667 1.250 17.600 17.541421 -0.041096 -0.027778 -0.625 \\n\",\n \"34 17.270833 1.250 17.525 17.652614 -0.006944 0.059259 -0.375 \\n\",\n \"35 17.191667 1.250 17.450 17.643409 0.044444 0.014388 -0.375 \\n\",\n \"36 17.150000 1.250 17.575 17.595606 0.007194 0.000000 0.625 \\n\",\n \"37 17.062500 1.125 17.550 17.480404 -0.014286 -0.034965 -0.125 \\n\",\n \"38 17.008333 1.125 17.450 17.320269 -0.048951 -0.035461 -0.500 \\n\",\n \"39 16.904167 1.125 17.300 17.255180 -0.028369 -0.021429 -0.750 \\n\",\n \"40 16.750000 1.750 17.100 17.045120 -0.050000 -0.036232 -1.000 \\n\",\n \"41 16.729167 1.750 16.850 16.780080 -0.057971 -0.044118 -1.250 \\n\",\n \"42 16.695833 1.750 16.675 16.645053 -0.036765 -0.043796 -0.875 \\n\",\n \"43 16.579167 1.750 16.525 16.513369 -0.051095 -0.022556 -0.750 \\n\",\n \"44 16.441667 1.750 16.350 16.425579 -0.022556 0.000000 -0.875 \\n\",\n \"45 16.362500 1.750 16.275 16.367053 0.000000 -0.007634 -0.375 \\n\",\n \"46 16.204167 1.750 16.275 16.328035 -0.007634 0.000000 0.000 \\n\",\n \"47 16.120833 1.750 16.250 16.302023 0.000000 0.000000 -0.125 \\n\",\n \"48 15.975000 1.625 16.125 16.076349 -0.038462 -0.038462 -0.625 \\n\",\n \"74 17.358333 2.625 17.600 17.662826 0.021739 -0.007042 -0.125 \\n\",\n \"75 17.337500 2.500 17.800 17.858551 0.028169 0.035461 1.000 \\n\",\n \"76 17.312500 2.000 17.800 17.822367 0.007092 0.000000 0.000 \\n\",\n \"77 17.308333 2.000 17.850 17.839912 0.007042 0.014184 0.250 \\n\",\n \"78 17.100000 1.625 17.675 17.518274 -0.042553 -0.075342 -0.875 \\n\",\n \"79 16.958333 1.375 17.500 17.262183 -0.082192 -0.056338 -0.875 \\n\",\n \"\\n\",\n \" ATR_5 BollingerB_5 Bollinger%b_5 Trix_4 ADX_4_3 Vortex_4 \\\\\\n\",\n \"25 18.076492 0.082126 0.671499 0.000979 0.450286 2.666667e-01 \\n\",\n \"26 18.134328 0.083211 0.617985 0.001694 0.295679 5.333333e-01 \\n\",\n \"27 18.172886 0.072303 0.480683 0.001436 0.460668 6.666667e-01 \\n\",\n \"28 18.240257 0.031584 0.631559 0.001864 0.543163 2.142857e-01 \\n\",\n \"29 18.285171 0.031584 0.850823 0.002615 0.584410 1.428571e-01 \\n\",\n \"30 18.273448 0.031584 0.412294 0.002107 0.780024 -2.030122e-15 \\n\",\n \"31 18.182298 0.123231 0.067511 -0.002635 0.877831 -5.454545e-01 \\n\",\n \"32 17.913199 0.131406 0.349731 -0.004682 0.609905 -7.272727e-01 \\n\",\n \"33 17.817133 0.122851 0.453750 -0.004694 0.367700 -5.454545e-01 \\n\",\n \"34 17.836422 0.101675 0.696425 -0.002610 0.383055 -3.043478e-01 \\n\",\n \"35 17.807614 0.084999 0.617985 -0.001530 0.390733 1.111111e-01 \\n\",\n \"36 17.746743 0.043146 0.401093 -0.001403 0.584944 6.923077e-01 \\n\",\n \"37 17.664495 0.051755 0.169711 -0.002345 0.682049 -2.142857e-01 \\n\",\n \"38 17.567997 0.077417 0.166895 -0.003951 0.764316 -5.714286e-01 \\n\",\n \"39 17.420331 0.059932 0.331215 -0.004441 0.851798 -7.000000e-01 \\n\",\n \"40 17.238554 0.075685 0.132983 -0.006070 0.914563 -1.000000e+00 \\n\",\n \"41 16.992369 0.097065 0.133149 -0.008394 0.945946 -1.062500e+00 \\n\",\n \"42 16.786580 0.091442 0.303252 -0.009099 0.706332 -1.000000e+00 \\n\",\n \"43 16.691053 0.089246 0.313533 -0.009114 0.586525 -6.875000e-01 \\n\",\n \"44 16.585702 0.039873 0.346607 -0.008409 0.526621 -5.384615e-01 \\n\",\n \"45 16.473801 0.013739 0.388197 -0.007266 0.496669 -3.552714e-15 \\n\",\n \"46 16.440868 0.013739 0.388197 -0.005967 0.503272 -1.111111e-01 \\n\",\n \"47 16.460578 0.000000 inf -0.004709 0.612833 -3.157968e-15 \\n\",\n \"48 16.348719 0.069335 0.052786 -0.006031 0.760763 -5.000000e-01 \\n\",\n \"74 17.763520 0.046681 0.530429 -0.001306 0.524603 7.692308e-02 \\n\",\n \"75 18.009014 0.058248 0.934019 0.001279 0.504788 4.000000e-01 \\n\",\n \"76 18.172676 0.058248 0.451776 0.001483 0.494880 2.500000e-01 \\n\",\n \"77 18.115117 0.053881 0.525994 0.001457 0.623798 -1.421085e-15 \\n\",\n \"78 17.951745 0.114210 0.103698 -0.002303 0.688257 -3.666667e-01 \\n\",\n \"79 17.592830 0.149830 0.213961 -0.005862 0.720487 -8.888889e-01 \\n\",\n \"\\n\",\n \" KST_3_5_8_2_2_2_5_3 RSI_5 TSI_6_3 Chaikin MFI_8 OBV_4 \\\\\\n\",\n \"25 0.094302 0.664497 0.158083 2699.533703 0.250 -11500.0 \\n\",\n \"26 0.102629 0.664497 0.158011 -30503.873516 0.375 -10275.0 \\n\",\n \"27 -0.133226 0.466932 0.073776 -43095.824331 0.375 -33475.0 \\n\",\n \"28 0.116587 0.246800 0.179203 -5235.335332 0.500 -27350.0 \\n\",\n \"29 0.311433 0.246800 0.290975 8650.198228 0.625 -18525.0 \\n\",\n \"30 0.392187 0.246800 0.117454 9015.474240 0.625 -21275.0 \\n\",\n \"31 -0.375771 0.025310 -0.446890 4110.606024 0.625 -2550.0 \\n\",\n \"32 -0.499590 0.025310 -0.299256 18724.179223 0.500 -2600.0 \\n\",\n \"33 -0.442382 0.287058 -0.194769 14504.806512 0.500 -3225.0 \\n\",\n \"34 -0.102871 0.491778 0.010792 17791.898901 0.500 5100.0 \\n\",\n \"35 -0.326819 0.298756 -0.008850 13325.536797 0.500 10850.0 \\n\",\n \"36 -0.302704 0.298756 -0.072351 -11451.205591 0.500 -11225.0 \\n\",\n \"37 -0.612274 0.137202 -0.212063 -1709.763332 0.375 -19025.0 \\n\",\n \"38 -0.628628 0.137202 -0.369535 -6414.649378 0.375 -31550.0 \\n\",\n \"39 -0.628149 0.105202 -0.338666 4780.138092 0.375 -24850.0 \\n\",\n \"40 -0.750621 0.051329 -0.497383 2413.144655 0.375 -16700.0 \\n\",\n \"41 -1.035368 0.022511 -0.634414 5968.482674 0.250 -18250.0 \\n\",\n \"42 -1.088285 0.022511 -0.579591 24781.258893 0.250 4275.0 \\n\",\n \"43 -1.173692 0.219663 -0.585909 7591.219569 0.250 -5250.0 \\n\",\n \"44 -1.178393 0.219663 -0.588982 2679.425945 0.125 1400.0 \\n\",\n \"45 -1.089326 0.219663 -0.590712 5040.108013 0.125 8950.0 \\n\",\n \"46 -0.901870 0.219663 -0.591765 3946.134797 0.125 -6625.0 \\n\",\n \"47 -0.756773 0.613899 -0.592437 -2757.108925 0.250 0.0 \\n\",\n \"48 -0.911835 0.152284 -0.826403 -7486.577514 0.250 -4025.0 \\n\",\n \"74 -0.193555 0.372342 -0.064037 7750.660266 0.500 -5825.0 \\n\",\n \"75 0.292348 0.854853 0.286425 11148.620374 0.500 7450.0 \\n\",\n \"76 0.153168 0.733948 0.078625 -957.743762 0.500 14550.0 \\n\",\n \"77 0.269189 0.733948 0.068693 15.255320 0.375 13425.0 \\n\",\n \"78 -0.399997 0.163083 -0.313180 -2222.923147 0.375 8200.0 \\n\",\n \"79 -0.668704 0.163083 -0.449317 2812.562878 0.250 -18625.0 \\n\",\n \"\\n\",\n \" Force_8 EoM_6 Copp_3 KelChM_5 KelChU_5 KelChD_5 \\\\\\n\",\n \"25 -9550.0 -2.437302e-07 0.041741 17.750000 18.200000 17.300000 \\n\",\n \"26 0.0 8.461050e-06 0.032007 17.816667 18.241667 17.391667 \\n\",\n \"27 -13537.5 9.821601e-06 0.002257 17.900000 18.350000 17.450000 \\n\",\n \"28 7250.0 1.114935e-05 0.008073 18.025000 18.550000 17.500000 \\n\",\n \"29 2562.5 1.293475e-05 0.021470 18.075000 18.500000 17.650000 \\n\",\n \"30 3050.0 1.304776e-06 0.010783 18.083333 18.483333 17.683333 \\n\",\n \"31 -8950.0 -4.179866e-06 -0.066762 17.900000 18.525000 17.275000 \\n\",\n \"32 -21262.5 -5.546445e-06 -0.074715 17.725000 18.425000 17.025000 \\n\",\n \"33 9200.0 -1.714164e-06 -0.032728 17.608333 18.233333 16.983333 \\n\",\n \"34 10650.0 -7.468531e-07 0.027654 17.516667 18.141667 16.891667 \\n\",\n \"35 24475.0 -3.668239e-06 0.024593 17.400000 18.100000 16.700000 \\n\",\n \"36 -18687.5 -3.387508e-06 0.001807 17.475000 17.925000 17.025000 \\n\",\n \"37 -14300.0 4.017141e-07 -0.027217 17.500000 17.900000 17.100000 \\n\",\n \"38 -16800.0 1.603555e-06 -0.045625 17.433333 17.833333 17.033333 \\n\",\n \"39 -6650.0 -2.867679e-06 -0.037150 17.291667 17.666667 16.916667 \\n\",\n \"40 1875.0 -4.302609e-06 -0.047720 17.125000 17.450000 16.800000 \\n\",\n \"41 -28750.0 -4.313955e-06 -0.071466 16.858333 17.283333 16.433333 \\n\",\n \"42 -60000.0 -4.316144e-06 -0.065150 16.666667 17.041667 16.291667 \\n\",\n \"43 -15537.5 -3.083669e-06 -0.043853 16.500000 16.875000 16.125000 \\n\",\n \"44 60875.0 -3.191172e-06 -0.025743 16.341667 16.716667 15.966667 \\n\",\n \"45 9500.0 -2.607480e-06 -0.016688 16.250000 16.575000 15.925000 \\n\",\n \"46 -450.0 -1.873222e-06 -0.008344 16.250000 16.525000 15.975000 \\n\",\n \"47 -13650.0 1.386156e-07 -0.004172 16.250000 16.550000 15.950000 \\n\",\n \"48 10500.0 -2.167678e-06 -0.040548 16.150000 16.475000 15.825000 \\n\",\n \"74 2912.5 2.475090e-07 0.004133 17.583333 17.983333 17.183333 \\n\",\n \"75 30700.0 1.211860e-06 0.033881 17.741667 18.166667 17.316667 \\n\",\n \"76 1987.5 9.571595e-07 0.020487 17.800000 18.300000 17.300000 \\n\",\n \"77 -2750.0 -7.681926e-07 0.007062 17.841667 18.366667 17.316667 \\n\",\n \"78 29812.5 -5.756273e-06 -0.058788 17.683333 18.408333 16.958333 \\n\",\n \"79 -4200.0 -6.857001e-06 -0.089032 17.508333 18.283333 16.733333 \\n\",\n \"\\n\",\n \" Donchian_8 MA_3 EMA_3 ROC_3 Momentum_3 ATR_3 \\\\\\n\",\n \"25 2.125 17.875000 17.939999 0.035971 0.500 18.129105 \\n\",\n \"26 1.625 18.083333 17.969999 -0.013699 0.625 18.189552 \\n\",\n \"27 1.375 17.958333 17.922500 -0.006944 -0.375 18.219776 \\n\",\n \"28 1.375 18.000000 18.023750 0.006944 0.125 18.297388 \\n\",\n \"29 1.000 18.083333 18.136875 0.020979 0.250 18.336194 \\n\",\n \"30 1.250 18.125000 18.068437 -0.006897 0.125 18.293097 \\n\",\n \"31 1.250 17.708333 17.471719 -0.075342 -1.250 18.146549 \\n\",\n \"32 1.250 17.416667 17.423359 -0.034722 -0.875 17.760774 \\n\",\n \"33 1.000 17.250000 17.461680 0.037037 -0.500 17.692887 \\n\",\n \"34 1.000 17.583333 17.668340 0.028777 1.000 17.783944 \\n\",\n \"35 1.125 17.666667 17.646670 0.007143 0.250 17.766972 \\n\",\n \"36 1.125 17.666667 17.573335 -0.020979 0.000 17.695986 \\n\",\n \"37 1.125 17.458333 17.411667 -0.021277 -0.625 17.597993 \\n\",\n \"38 1.750 17.250000 17.205834 -0.028571 -0.625 17.486496 \\n\",\n \"39 1.750 17.125000 17.165417 -0.007246 -0.375 17.305748 \\n\",\n \"40 1.750 16.916667 16.895208 -0.022059 -0.625 17.090374 \\n\",\n \"41 1.750 16.666667 16.572604 -0.051095 -0.750 16.795187 \\n\",\n \"42 1.750 16.416667 16.473802 -0.015038 -0.750 16.585094 \\n\",\n \"43 1.750 16.291667 16.361901 0.000000 -0.375 16.542547 \\n\",\n \"44 1.625 16.291667 16.305951 -0.007634 0.000 16.458773 \\n\",\n \"45 1.375 16.250000 16.277975 0.000000 -0.125 16.354387 \\n\",\n \"46 1.250 16.250000 16.263988 0.000000 0.000 16.364693 \\n\",\n \"47 1.375 16.250000 16.256994 0.000000 0.000 16.432347 \\n\",\n \"48 2.000 16.041667 15.940997 -0.038462 -0.625 16.278673 \\n\",\n \"74 1.625 17.666667 17.655669 0.000000 -0.125 17.727136 \\n\",\n \"75 1.000 17.875000 17.952834 0.028169 0.625 18.113568 \\n\",\n \"76 0.750 17.875000 17.851417 0.007092 0.000 18.306784 \\n\",\n \"77 1.125 17.958333 17.863209 -0.020548 0.250 18.153392 \\n\",\n \"78 1.125 17.500000 17.369104 -0.049296 -1.375 17.889196 \\n\",\n \"79 1.125 17.166667 17.059552 -0.062937 -1.000 17.382098 \\n\",\n \"\\n\",\n \" BollingerB_4 Bollinger%b_4 Trix_3 ADX_3_3 Vortex_3 \\\\\\n\",\n \"25 0.092908 0.632414 0.002832 0.566483 0.538462 \\n\",\n \"26 0.083381 0.562791 0.003107 0.439130 0.916667 \\n\",\n \"27 0.034892 0.251650 0.001728 0.611735 0.500000 \\n\",\n \"28 0.022680 0.806186 0.002367 0.698037 -0.090909 \\n\",\n \"29 0.035737 0.790474 0.003509 0.741189 0.083333 \\n\",\n \"30 0.035737 0.403175 0.001957 0.867140 0.090909 \\n\",\n \"31 0.142210 0.129902 -0.007195 0.930115 -0.687500 \\n\",\n \"32 0.140895 0.399327 -0.008414 0.549894 -1.000000 \\n\",\n \"33 0.106003 0.533813 -0.006119 0.454712 -0.550000 \\n\",\n \"34 0.094910 0.783744 -0.001093 0.438991 0.500000 \\n\",\n \"35 0.048535 0.536596 0.000139 0.431131 0.583333 \\n\",\n \"36 0.040120 0.323223 -0.000628 0.644729 0.200000 \\n\",\n \"37 0.059265 0.199760 -0.002958 0.751528 -0.636364 \\n\",\n \"38 0.063924 0.189946 -0.005740 0.835387 -0.500000 \\n\",\n \"39 0.049592 0.390211 -0.005606 0.906415 -1.000000 \\n\",\n \"40 0.063537 0.152817 -0.008078 0.949892 -0.900000 \\n\",\n \"41 0.094396 0.183772 -0.011407 0.971631 -1.230769 \\n\",\n \"42 0.093279 0.358675 -0.010930 0.622732 -0.923077 \\n\",\n \"43 0.043182 0.323223 -0.009796 0.448283 -0.545455 \\n\",\n \"44 0.015355 0.375000 -0.007942 0.361058 0.142857 \\n\",\n \"45 0.015355 0.375000 -0.005935 0.317446 -0.166667 \\n\",\n \"46 0.000000 -inf -0.004170 0.564719 -0.333333 \\n\",\n \"47 0.000000 -inf -0.002796 0.666492 0.142857 \\n\",\n \"48 0.077670 0.125000 -0.006592 0.801206 -0.454545 \\n\",\n \"74 0.016321 0.283494 0.000072 0.593703 -0.250000 \\n\",\n \"75 0.066820 0.867574 0.004250 0.532675 0.461538 \\n\",\n \"76 0.062133 0.415440 0.002784 0.502160 0.235294 \\n\",\n \"77 0.060426 0.500000 0.001887 0.676245 0.176471 \\n\",\n \"78 0.131583 0.150894 -0.005745 0.763288 -0.826087 \\n\",\n \"79 0.134433 0.258311 -0.010621 0.806809 -1.150000 \\n\",\n \"\\n\",\n \" KST_3_4_5_2_2_2_5_3 RSI_3 TSI_4_3 TSI_3_2 MFI_4 OBV_3 \\\\\\n\",\n \"25 0.122341 0.760990 0.211817 0.278214 0.50 -13700.000000 \\n\",\n \"26 0.322983 0.760990 0.197156 0.218308 0.75 -6933.333333 \\n\",\n \"27 0.122277 0.344112 0.061402 -0.068932 0.75 -46233.333333 \\n\",\n \"28 0.279223 0.107830 0.222424 0.316868 0.75 -27933.333333 \\n\",\n \"29 0.382336 0.107830 0.377070 0.548028 0.75 -24700.000000 \\n\",\n \"30 0.277746 0.107830 0.086397 -0.068450 0.50 9333.333333 \\n\",\n \"31 -0.543775 0.003455 -0.589053 -0.789342 0.50 -13166.666667 \\n\",\n \"32 -0.801014 0.003455 -0.335592 -0.297787 0.25 -6700.000000 \\n\",\n \"33 -0.625427 0.415163 -0.186311 -0.070817 0.25 -633.333333 \\n\",\n \"34 -0.074304 0.679765 0.089350 0.333050 0.50 19533.333333 \\n\",\n \"35 -0.060995 0.288365 0.035064 0.084614 0.50 4766.666667 \\n\",\n \"36 -0.097001 0.288365 -0.071533 -0.154919 0.75 -17366.666667 \\n\",\n \"37 -0.285703 0.070836 -0.282129 -0.503154 0.50 -32800.000000 \\n\",\n \"38 -0.389810 0.070836 -0.492462 -0.736223 0.25 -37000.000000 \\n\",\n \"39 -0.355200 0.040377 -0.405950 -0.425178 0.25 -13400.000000 \\n\",\n \"40 -0.693208 0.011279 -0.597322 -0.707296 0.00 -14266.666667 \\n\",\n \"41 -1.010185 0.003315 -0.738204 -0.844005 0.00 -15066.666667 \\n\",\n \"42 -1.084370 0.003315 -0.625159 -0.587714 0.25 1833.333333 \\n\",\n \"43 -0.910331 0.363083 -0.620687 -0.600697 0.25 1866.666667 \\n\",\n \"44 -0.707672 0.363083 -0.618612 -0.605173 0.25 11933.333333 \\n\",\n \"45 -0.558596 0.363083 -0.617429 -0.607306 0.25 -8833.333333 \\n\",\n \"46 -0.369416 0.363083 -0.616676 -0.608477 0.00 0.000000 \\n\",\n \"47 -0.243855 0.905996 -0.616161 -0.609170 0.25 0.000000 \\n\",\n \"48 -0.475185 0.115867 -0.914285 -0.980713 0.25 -5366.666667 \\n\",\n \"74 -0.003372 0.343752 -0.008903 -0.027310 0.50 -16100.000000 \\n\",\n \"75 0.376775 0.920632 0.419135 0.609838 0.50 25833.333333 \\n\",\n \"76 0.281270 0.735823 0.086706 -0.005253 0.50 13166.666667 \\n\",\n \"77 0.267465 0.735823 0.070618 0.021985 0.25 24333.333333 \\n\",\n \"78 -0.432888 0.074835 -0.408312 -0.609015 0.25 -15100.000000 \\n\",\n \"79 -0.848155 0.074835 -0.552498 -0.725138 0.00 -18400.000000 \\n\",\n \"\\n\",\n \" Force_4 EoM_5 KelChM_4 KelChU_4 KelChD_4 Donchian_5 \\\\\\n\",\n \"25 7500.0 1.012419e-05 17.750000 18.218750 17.281250 0.875 \\n\",\n \"26 51300.0 1.182737e-05 17.875000 18.343750 17.406250 1.000 \\n\",\n \"27 46400.0 1.369919e-05 18.020833 18.489583 17.552083 0.875 \\n\",\n \"28 -3062.5 1.407196e-05 18.041667 18.479167 17.604167 1.000 \\n\",\n \"29 -3975.0 1.849822e-06 18.083333 18.520833 17.645833 1.000 \\n\",\n \"30 -0.0 8.333096e-07 18.083333 18.520833 17.645833 1.000 \\n\",\n \"31 74900.0 -5.044909e-06 17.875000 18.562500 17.187500 1.000 \\n\",\n \"32 150.0 -6.614288e-06 17.645833 18.333333 16.958333 1.000 \\n\",\n \"33 1875.0 -1.737031e-06 17.458333 18.145833 16.770833 0.750 \\n\",\n \"34 -1412.5 -2.345966e-06 17.375000 18.093750 16.656250 0.750 \\n\",\n \"35 -17250.0 -4.117796e-06 17.458333 17.989583 16.927083 1.750 \\n\",\n \"36 3762.5 1.784140e-06 17.562500 17.937500 17.187500 1.750 \\n\",\n \"37 -4200.0 2.092882e-06 17.510417 17.916667 17.104167 1.750 \\n\",\n \"38 -4812.5 -2.633025e-06 17.354167 17.760417 16.947917 1.625 \\n\",\n \"39 1800.0 -4.282022e-06 17.239583 17.552083 16.927083 1.375 \\n\",\n \"40 28525.0 -3.107210e-06 17.020833 17.395833 16.645833 1.250 \\n\",\n \"41 -6200.0 -5.229534e-06 16.760417 17.166667 16.354167 0.875 \\n\",\n \"42 -21562.5 -3.877290e-06 16.552083 16.927083 16.177083 0.875 \\n\",\n \"43 -13037.5 -3.531788e-06 16.364583 16.770833 15.958333 0.875 \\n\",\n \"44 6037.5 -3.021217e-06 16.260417 16.635417 15.885417 1.125 \\n\",\n \"45 -0.0 -2.247867e-06 16.260417 16.510417 16.010417 1.625 \\n\",\n \"46 4237.5 -1.783304e-07 16.239583 16.520833 15.958333 1.500 \\n\",\n \"47 0.0 1.161782e-07 16.239583 16.520833 15.958333 1.250 \\n\",\n \"48 -3500.0 -2.778100e-06 16.125000 16.468750 15.781250 1.000 \\n\",\n \"74 -37650.0 2.538478e-07 17.635417 17.979167 17.291667 0.750 \\n\",\n \"75 26550.0 1.715301e-06 17.760417 18.166667 17.354167 0.750 \\n\",\n \"76 -3550.0 3.985914e-07 17.843750 18.406250 17.281250 0.750 \\n\",\n \"77 -562.5 -9.218311e-07 17.875000 18.437500 17.312500 0.750 \\n\",\n \"78 -15675.0 -7.158196e-06 17.718750 18.562500 16.875000 0.750 \\n\",\n \"79 73350.0 -7.742650e-06 17.343750 18.125000 16.562500 1.125 \\n\",\n \"\\n\",\n \" target close_ret close_three close_five close_ten close_twenty \\n\",\n \"25 -0.021277 -0.013699 0.028571 0.006993 0.021277 0.014085 \\n\",\n \"26 -0.021277 0.000000 0.035971 0.021277 0.000000 0.000000 \\n\",\n \"27 -0.021277 -0.006944 -0.020548 0.021429 -0.033784 0.028777 \\n\",\n \"28 -0.021277 0.013986 0.006944 0.043165 0.006944 0.066176 \\n\",\n \"29 -0.021277 0.006897 0.013889 0.000000 0.013889 0.081481 \\n\",\n \"30 -0.021277 -0.013699 0.006993 0.000000 0.006993 0.066667 \\n\",\n \"31 -0.021277 -0.062500 -0.068966 -0.062500 -0.042553 -0.014599 \\n\",\n \"32 -0.021277 0.029630 -0.047945 -0.027972 -0.007143 0.014599 \\n\",\n \"33 -0.021277 0.007194 -0.027778 -0.034483 0.007194 0.007194 \\n\",\n \"34 -0.021277 0.021429 0.059259 -0.020548 -0.020548 0.007042 \\n\",\n \"35 -0.021277 -0.013986 0.014388 -0.020833 -0.020833 0.000000 \\n\",\n \"36 -0.021277 -0.007092 0.000000 0.037037 -0.027778 -0.027778 \\n\",\n \"37 -0.021277 -0.014286 -0.034965 -0.007194 -0.034965 -0.067568 \\n\",\n \"38 -0.021277 -0.014493 -0.035461 -0.028571 -0.062069 -0.055556 \\n\",\n \"39 -0.021277 0.007353 -0.021429 -0.041958 -0.061644 -0.048611 \\n\",\n \"40 -0.021277 -0.029197 -0.036232 -0.056738 -0.076389 -0.069930 \\n\",\n \"41 -0.021277 -0.022556 -0.044118 -0.071429 -0.037037 -0.078014 \\n\",\n \"42 -0.021277 0.007692 -0.043796 -0.050725 -0.057554 -0.064286 \\n\",\n \"43 -0.021277 -0.007634 -0.022556 -0.044118 -0.071429 -0.064748 \\n\",\n \"44 -0.021277 0.000000 0.000000 -0.051095 -0.090909 -0.109589 \\n\",\n \"45 -0.021277 0.000000 -0.007634 -0.022556 -0.078014 -0.097222 \\n\",\n \"46 -0.021277 0.000000 0.000000 0.000000 -0.071429 -0.097222 \\n\",\n \"47 -0.021277 0.000000 0.000000 -0.007634 -0.057971 -0.090909 \\n\",\n \"48 -0.021277 -0.038462 -0.038462 -0.038462 -0.080882 -0.137931 \\n\",\n \"74 -0.200000 -0.007042 -0.007042 -0.007042 -0.013986 -0.129630 \\n\",\n \"75 -0.200000 0.035461 0.035461 0.057971 0.020979 -0.104294 \\n\",\n \"76 -0.200000 -0.027397 0.000000 0.000000 0.000000 -0.112500 \\n\",\n \"77 -0.200000 0.007042 0.014184 0.014184 0.007042 -0.071429 \\n\",\n \"78 -0.200000 -0.055944 -0.075342 -0.049296 -0.042553 -0.129032 \\n\",\n \"79 -0.200000 -0.007407 -0.056338 -0.049645 -0.056338 -0.135484 \"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 25\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"Z9mj7N305S-f\",\n \"outputId\": \"b0367074-e1a1-4890-dadf-462ef420aac8\"\n },\n \"source\": [\n \"len(good[\\\"id\\\"].unique())\\n\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"95127\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 26\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"qM0x6cot5S-i\"\n },\n \"source\": [\n \"#feature_1 = good.ix[:8000,:]\\n\",\n \"\\n\",\n \"feature_1 = good[good[\\\"ticker\\\"].isin(['AIR', 'ACMR', 'ADCT', 'AGCO', 'AM', 'ASKI', 'ASTA', 'ATMI', 'ABT',\\n\",\n \" 'ANF', 'AXAS'])]\\n\",\n \"\\n\",\n \"feature_1 = feature_1.reset_index(drop=True)\\n\",\n \"\\n\",\n \"feature = feature_1.drop([\\\"ticker\\\",\\\"est_avg\\\",\\\"qtrdat\\\",\\\"target\\\"],axis=1)\\n\",\n \"\\n\",\n \"y = feature_1[[\\\"target\\\",\\\"id\\\"]].ix[feature_1[\\\"counter\\\"]==1]\\n\",\n \"y.set_index(\\\"id\\\",inplace=True)\\n\",\n \"\\n\",\n \"\\n\",\n \"feature= feature[feature[\\\"id\\\"].isin(y.index)]\\n\",\n \"\\n\",\n \"y = y[\\\"target\\\"]\\n\",\n \"y = y.astype(float)\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"yqLLDQpd5S-k\",\n \"outputId\": \"b22b2f67-3fc5-49b4-ac0c-b35b29b47718\"\n },\n \"source\": [\n \"len(y)\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"430\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 28\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"SqMdkoZx5S-p\",\n \"outputId\": \"2efafe92-3e07-4af1-c65f-db9c0bc08ed8\"\n },\n \"source\": [\n \"len(feature[\\\"id\\\"].unique())\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"430\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 29\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"EKt86Tmc5S-s\"\n },\n \"source\": [\n \"feature = feature.replace([np.inf, -np.inf], np.nan)\\n\",\n \"feature.fillna(method=\\\"ffill\\\",inplace=True) # no use if all items are zero\\n\",\n \"feature.fillna(value=0,inplace=True)\\n\",\n \"feature.dropna(how=\\\"any\\\",axis=1,inplace=True)\\n\",\n \"feature = feature.reset_index(drop=True)\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"zKrn2Ts05S-v\",\n \"outputId\": \"87695a79-a1cf-469c-ddbf-9bd0ed5c83fb\"\n },\n \"source\": [\n \"from tsfresh import select_features\\n\",\n \"#from tsfresh.utilities.dataframe_functions import impute\\n\",\n \"from tsfresh import extract_relevant_features # Remember it is binary do it works diffrent with y. \\n\",\n \"from tsfresh import extract_features\\n\",\n \"\\n\",\n \"features_filtered_direct = extract_features(feature,column_id=\\\"id\\\", column_sort=\\\"datadate\\\");\\n\",\n \"\\n\",\n \"features_filtered_direct = features_filtered_direct.replace([np.inf, -np.inf], np.nan)\\n\",\n \"features_filtered_direct.fillna(method=\\\"ffill\\\",inplace=True) # no use if all items are zero\\n\",\n \"features_filtered_direct.fillna(method=\\\"bfill\\\",inplace=True)\\n\",\n \"features_filtered_direct.dropna(how=\\\"any\\\",axis=1,inplace=True)\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Feature Extraction: 100%|ā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆ| 80/80 [07:14<00:00, 1.21s/it] \\n\"\n ],\n \"name\": \"stderr\"\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"RbX9M-mm5S-x\",\n \"outputId\": \"399bc1b3-1a84-46b9-a8e1-50c69def2ab2\"\n },\n \"source\": [\n \"features_filtered = select_features(features_filtered_direct, y)\\n\",\n \"features_filtered.reset_index(inplace=True)\\n\",\n \"features_filtered.rename(columns={\\\"index\\\":\\\"id\\\"},inplace=True)\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"Feature Selection: 0%| | 0/17720 [00:00(full_1[\\\"est_avg_x\\\"].map(lambda x: float(x)/r_5)),1,0)\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"bvYk2F0e5S-_\"\n },\n \"source\": [\n \"full_1 = full_1.reset_index(drop=True)\\n\",\n \"\\n\",\n \"full_1 = full_1.sort_values(\\\"ANNDATS\\\")\\n\",\n \"\\n\",\n \"full_1 = full_1.replace([np.inf, -np.inf], np.nan)\\n\",\n \"full_1.fillna(method=\\\"ffill\\\",inplace=True) # no use if all items are zero\\n\",\n \"full_1.fillna(value=0 ,inplace=True)\\n\",\n \"full_1.dropna(how=\\\"any\\\",axis=1,inplace=True)\\n\",\n \"\\n\",\n \"y = full_1[\\\"beat_5\\\"]\\n\",\n \"X = full_1.drop([\\\"ACTUAL\\\",\\\"target_y\\\",\\\"target_x\\\",\\\"beat_5\\\",\\\"VALUE\\\",\\\"id_x\\\",\\\"est_avg_y\\\",\\\"ticker_x\\\",\\\"datadate\\\",\\\"TICKER\\\",\\\"CUSIP\\\",\\\"OFTIC\\\",\\\"CNAME\\\",\\n\",\n \" \\\"MEASURE\\\",\\\"PDICITY\\\",\\\"ANNDATS\\\",\\\"ANNTIMS\\\",\\\"ACTDATS\\\",\\\"ACTTIMS\\\",\\\"VALUE\\\",\\\"CURR_ACT\\\",\\\"USFIRM\\\",\\n\",\n \" \\\"ticker_y\\\",\\\"ANNDATS_ACT\\\",\\\"ACTUAL\\\",\\\"counter_y\\\",\\\"flag\\\",\\\"last\\\"],axis=1)\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"rts_F84f5S_B\",\n \"outputId\": \"0acde6d7-c833-4bc9-bbf4-e2948d8b5719\"\n },\n \"source\": [\n \"X.shape\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"execute_result\",\n \"data\": {\n \"text/plain\": [\n \"(296, 19973)\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n },\n \"execution_count\": 144\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"7v46C3PA5S_F\"\n },\n \"source\": [\n \"X_train = X.ix[:219,:]\\n\",\n \"#X_train = Standardisation(X_train) Performance without standardisation seems much better\\n\",\n \"y_train = y.ix[:219]\\n\",\n \"X_test = X.ix[220:295,:] \\n\",\n \"#X_test = Standardisation(X_test) So just do both to see what is better, maybe more data better\\n\",\n \"y_test = y.ix[220:295]\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"R6nz4rXr5S_I\",\n \"outputId\": \"72c57ace-419c-4868-f8f4-a34710573a0f\"\n },\n \"source\": [\n \"classifiers = [AdaBoostClassifier(),\\n\",\n \" GradientBoostingClassifier()]\\n\",\n \"\\n\",\n \"for clf in classifiers:\\n\",\n \" clf.fit(X_train, y_train)\\n\",\n \" name = clf.__class__.__name__\\n\",\n \"\\n\",\n \" print(\\\"=\\\"*30)\\n\",\n \" print(name)\\n\",\n \"\\n\",\n \" print('****Results****')\\n\",\n \" y_predict = clf.predict(X_test)\\n\",\n \" print metrics.accuracy_score(y_test, y_predict)\\n\",\n \" acc = accuracy_score(y_test, y_predict)\\n\",\n \"\\n\",\n \" print(\\\"Accuracy: {:.4%}\\\".format(acc))\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \"==============================\\n\",\n \"AdaBoostClassifier\\n\",\n \"****Results****\\n\",\n \"0.578947368421\\n\",\n \"Accuracy: 57.8947%\\n\",\n \"==============================\\n\",\n \"GradientBoostingClassifier\\n\",\n \"****Results****\\n\",\n \"0.552631578947\\n\",\n \"Accuracy: 55.2632%\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"KAcRzIF95S_M\",\n \"outputId\": \"f4020c3f-ca42-4160-e9db-d9adf6bc7b30\"\n },\n \"source\": [\n \"from sklearn.metrics import classification_report\\n\",\n \"from scikitplot import plotters as skplt # more flexible import \\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"# you don't really know wht feature is more important due to multi-collinearity. \\n\",\n \"# It gives about 1% more with all the features in place, I think that is already a good\\n\",\n \"# development. \\n\",\n \"\\n\",\n \"gb = GradientBoostingClassifier()\\n\",\n \"gb.fit(X, y)\\n\",\n \"features_list = X.columns.values.tolist()\\n\",\n \"gb_plt = skplt.plot_feature_importances(gb, feature_names=features_list)\\n\",\n \"gb_plt.set_title('Feature Importance: Gradient Boosting')\\n\",\n \"plt.xticks(rotation=90)\\n\",\n \"plt.show()\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"display_data\",\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAX0AAAIwCAYAAABqV7t+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nbAR6uhfBpgAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"tags\": []\n }\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"X89HRQiR5S_P\",\n \"outputId\": \"76620675-604b-4dc8-ead1-484b154afefa\"\n },\n \"source\": [\n \"# Great all I have left to do is run the big ones and see what happens.\\n\",\n \"\\n\",\n \"gb.fit(X_train, y_train)\\n\",\n \"preds = gb.predict(X_test)\\n\",\n \"target_names = ['Below','beat']\\n\",\n \"print(classification_report(y_test, preds, target_names=target_names))\"\n ],\n \"execution_count\": null,\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"text\": [\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" Below 0.60 0.91 0.72 43\\n\",\n \" beat 0.64 0.21 0.32 33\\n\",\n \"\\n\",\n \"avg / total 0.62 0.61 0.55 76\\n\",\n \"\\n\"\n ],\n \"name\": \"stdout\"\n }\n ]\n },\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"dJTXu_DB5S_U\"\n },\n \"source\": [\n \"# Like normalization, standardization can be useful, and even required in some\\n\",\n \"# machine learning algorithms when your time series data has input values with differing scales.\\n\",\n \"\\n\",\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"\\n\",\n \"def Standardisation(df):\\n\",\n \" listed = list(df)\\n\",\n \" scaler = StandardScaler()\\n\",\n \" scaled = scaler.fit_transform(df)\\n\",\n \" df = pd.DataFrame(scaled)\\n\",\n \" df.columns = listed\\n\",\n \" return df\\n\"\n ],\n \"execution_count\": null,\n \"outputs\": []\n }\n ]\n}" }, { "path": "assets/empty.ipynb", "content": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"empty.ipynb\",\n \"provenance\": []\n },\n \"kernelspec\": {\n \"name\": \"python3\",\n \"display_name\": \"Python 3\"\n }\n },\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"metadata\": {\n \"id\": \"TFSfpbfOfrbg\",\n \"colab_type\": \"code\",\n \"colab\": {}\n },\n \"source\": [\n \"\"\n ],\n \"execution_count\": 0,\n \"outputs\": []\n }\n ]\n}" }, { "path": "assets/first.txt", "content": "\n" } ]