Full Code of white07S/TradingPatternScanner for AI

Repository: white07S/TradingPatternScanner
Branch: main
Commit: 57e5396c4c54
Files: 20
Total size: 60.3 KB

Directory structure:
gitextract_b1d9ea39/

├── .github/
│   └── workflows/
│       ├── python-ci.yml
│       └── python-publish.yml
├── .gitignore
├── LICENSE.md
├── MANIFEST.in
├── README.md
├── docs/
│   ├── Makefile
│   ├── make.bat
│   └── source/
│       ├── conf.py
│       └── index.rst
├── pyproject.toml
├── requirements.txt
├── tests/
│   ├── __init__.py
│   └── test_hs.py
├── tradingpatterns/
│   ├── __init__.py
│   ├── analysis.py
│   ├── hard_data.py
│   ├── tradingpatterns.py
│   └── tradingpatterns_tech.py
└── update_docs.sh

================================================
FILE CONTENTS
================================================

================================================
FILE: .github/workflows/python-ci.yml
================================================
name: Python CI

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main

jobs:
  build:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: [3.7, 3.8, 3.9]

    steps:
    - name: Checkout repository
      uses: actions/checkout@v2

    - name: Set up Python ${{ matrix.python-version }}
      uses: actions/setup-python@v2
      with:
        python-version: ${{ matrix.python-version }}

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt

    - name: Run tests
      run: |
        python -m unittest discover


================================================
FILE: .github/workflows/python-publish.yml
================================================
name: Upload Python Package to PYPI

on:
  pull_request:
    branches: main

permissions:
  contents: write
  pull-requests: write

jobs:
  release-please:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
        with:
          fetch-depth: 0
      - uses: actions/setup-python@v4
        with:
          python-version: "3.10"
      - name: Install poetry
        run: python -m pip install poetry
      - name: Update patch version of package
        id: versionUpdate
        run: |
          poetry version patch
          echo "::set-output version=$(poetry version -s)"
      - name: Commit and push changes
        run: |
          git config --global user.name "GitHub Actions"
          git config --global user.email "github-actions@users.noreply.github.com"
          git checkout -b release-${{ steps.versionUpdate.outputs.version }}
          git add .
          git commit -m "${{ steps.versionUpdate.outputs.version }} release"
          git push origin release-${{ steps.versionUpdate.outputs.version }}
      - name: Create pull request
        uses: peter-evans/create-pull-request@v3
        with:
          title: Release ${{ steps.versionUpdate.outputs.version }}
          branch: release-${{ steps.versionUpdate.outputs.version }}
          base: main
      - name: Build and upload python package
        env:
          username: ${{ secrets.PYPI_USERNAME }}
          password: ${{ secrets.PYPI_PASSWORD }}
        run: |
          poetry publish --build -u ${username} -p ${password}}


================================================
FILE: .gitignore
================================================
*.py[cod]

# C extensions
*.so

# pycharm
.idea/
.idea

# Packages
*.egg
*.egg-info
build
eggs
parts
bin
var
sdist
develop-eggs
.installed.cfg
lib
lib64

# Installer logs
pip-log.txt

# Unit test / coverage reports
.coverage
.tox
nosetests.xml

# Complexity
output/*.html
output/*/index.html

# Sphinx
docs/_build

# Cookiecutter
output/

# Build
dist/
venv/

================================================
FILE: LICENSE.md
================================================
Copyright (c) 2017, Preetam Sharma
All rights reserved.

#TradingPatternScanner

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

You are free to:

- Share: copy and redistribute the material in any medium or format.

Under the following terms:

- Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You must do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. The credit should be given in the following format: "Original work by [Preetam Sharma] is licensed under CC BY-NC-SA 4.0 and can be found at (https://github.com/white07S/TradingPatternScanner)."

- NonCommercial: You may not use the material for commercial purposes. This includes but is not limited to, selling the material, or using it to promote a product or service.


For more information, see the [Creative Commons website](https://creativecommons.org/licenses/by-nc-sa/4.0/) and the [legal code of the license](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).



================================================
FILE: MANIFEST.in
================================================
include README.md
include requirements.txt

================================================
FILE: README.md
================================================
# TradingPatternScanner
![Python CI](https://github.com/white07S/TradingPatternScanner/actions/workflows/python-ci.yml/badge.svg)

#### Author: Preetam Sharma

Overview
--------

Trading Pattern Scanner Identifies complex patterns like head and shoulder, wedge and many more.

## New Enhancements
Four new features for pattern detection have been added:

1. **Basic Head-Shoulder Detection**: The initial unfiltered version of pattern detection. It uses a rolling window to track high and low points, then identifies Head and Shoulder and Inverse Head and Shoulder patterns.
2. **Head-Shoulder Detection with Savitzky-Golay Filter**: This feature uses the Savitzky-Golay filter to reduce noise and improve pattern detection. It also considers the height of the "Head" or "Inverse Head" to avoid false pattern recognition.
3. **Head-Shoulder Detection with Kalman Filter**: This feature utilizes the Kalman Filter, a recursive filter that estimates the state of a system in real time. It's particularly suitable for financial data due to its inherent noise and uncertainties.
4. **Head-Shoulder Detection with Wavelet Denoising**: This final feature applies wavelet denoising to eliminate noise while preserving key features in the data. It makes pattern detection more robust and reliable, especially in the presence of high-frequency noise.

These enhancements provide more accurate pattern detection for your financial analysis needs.

## Analysis
Each method has been rigorously tested and analysed on **synthetic data that closely mirrors real-world financial data**. However, it's important to note that synthetic data is not an exact representation of the real-world, and the performance of each algorithm may vary in a live setting. Therefore, users are encouraged to test each algorithm against their own datasets and pick the one that best suits their needs. 
- Accuracy for head_shoulder_pattern_window: **78.50%**
- Accuracy for head_shoulder_pattern_filter: **78.50%**
- Accuracy for head_shoulder_pattern_kf: **73.50%**
- Accuracy for head_shoulder_pattern_wavelet: **84.50%**

![Analysis](https://github.com/white07S/TradingPatternScanner/blob/main/docs/images/heatmap.png)

## Heatmap Interpretation

* For instance, let's consider a cell in the 2nd row and 2nd column. the score is 10, it means that a significant number of instances were correctly identified as "Head and Shoulder" pattern (abbreviated as HS).

* On the contrary, a dark cell outside this diagonal indicates a high number of misclassifications. For example, a dark cell at the intersection of "HS" row and "I-HS" column would mean that a large number of instances were true "HS" but were incorrectly predicted as "Inverse Head and Shoulder" (abbreviated as I-HS) by the scanner.

## Abbreviations
The abbreviations used in the heatmap and the code are as follows:

* **HS** - Head and Shoulder pattern
* **I-HS** - Inverse Head and Shoulder pattern


Installation / Usage
--------------------

Install using pip:

    $ pip install tradingpattern

    
# TradingPatternScanner

# Trading patterns:
* **Head and Shoulder and inverse Head and Shoulder**: These patterns indicate a potential reversal in the market, with the "head" being the highest point, and the "shoulders" being the points on either side at a slightly lower level.
* **Multiple top and bottom**: These patterns indicate a range-bound market, with multiple highs and lows forming a horizontal range.
* **Horizontal support and resistance**: These patterns indicate key levels at which the market has previously struggled to break through.
* **Ascending and Descending Triangle pattern**: These patterns indicate a potential breakout in the market, with the upper trendline being resistance and the lower trendline being support.
* **Wedge up and down**: These patterns indicate a potential reversal in the market, with the trendlines converging towards each other.
* **Channel up and down**: These patterns indicate a strong trend in the market, with price moving within a well-defined upper and lower trendline.
* **Double top and bottom**: These patterns indicate a potential reversal in the market, with the market hitting a high or low twice and then reversing.
* **Trend line support and resistance**: These patterns indicate key levels at which the market is likely to experience support or resistance based on historical price action.
* **Finding Higher-High and Lower-Low**

# Designed for fast performance:
* **Uses only Pandas as Numpy, no other external libraries**: This approach helps to keep the library lightweight and fast.
* **Uses the concept of vectorization**: This approach helps to improve performance by processing large amounts of data at once, rather than iterating over each individual data point.

# New and Unique:
* **No other python** library exists for such task currently: This library is new and unique, as it aims to provide an all-in-one solution for identifying various trading patterns.


### Lets check if its works for simplicity I used finviz and checked the pattern with the respective stock.

* Head and Shoulder:
![Head and Shoulder](https://user-images.githubusercontent.com/58583011/212490681-6dfca525-cd2e-4c87-830a-655ac9294a8a.png)

We can see that it finds out that we have inverse head and shoulder pattern in the stock on 9th Januray 2023 in 1 day interval. Lets match with Finviz.
![Finviz](https://user-images.githubusercontent.com/58583011/212490765-220182a5-e637-4f83-9a65-3031b7c99fee.png)

* We can see that Finviz also detects on 9th Januray 2023 in 1 day interval.
* You can adjust the window size to your liking. A smaller window size will be more sensitive to detecting patterns, but it will also increase the chances of false positives. A larger window size will be less sensitive to detecting patterns, but it will also decrease the chances of false positives.

# Future add-ons:
* **Request your favourite pattern to get added in the list**: The library is open for suggestions for adding new patterns.
* **Work on visualization and plotting**: The library can be extended to include visualization and plotting features to help users better understand the patterns identified.
* **Add unit testing**: The library can be extended to include unit testing to ensure that the code is working as expected and to catch any bugs early on.



================================================
FILE: docs/Makefile
================================================
# Makefile for Sphinx documentation
#

# You can set these variables from the command line.
SPHINXOPTS    =
SPHINXBUILD   = sphinx-build
PAPER         =
BUILDDIR      = build

# User-friendly check for sphinx-build
ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
	$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don\'t have Sphinx installed, grab it from http://sphinx-doc.org/)
endif

# Internal variables.
PAPEROPT_a4     = -D latex_paper_size=a4
PAPEROPT_letter = -D latex_paper_size=letter
ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
# the i18n builder cannot share the environment and doctrees with the others
I18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source

.PHONY: help
help:
	@echo "Please use \`make <target>' where <target> is one of"
	@echo "  html       to make standalone HTML files"
	@echo "  dirhtml    to make HTML files named index.html in directories"
	@echo "  singlehtml to make a single large HTML file"
	@echo "  pickle     to make pickle files"
	@echo "  json       to make JSON files"
	@echo "  htmlhelp   to make HTML files and a HTML help project"
	@echo "  qthelp     to make HTML files and a qthelp project"
	@echo "  applehelp  to make an Apple Help Book"
	@echo "  devhelp    to make HTML files and a Devhelp project"
	@echo "  epub       to make an epub"
	@echo "  epub3      to make an epub3"
	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
	@echo "  latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
	@echo "  text       to make text files"
	@echo "  man        to make manual pages"
	@echo "  texinfo    to make Texinfo files"
	@echo "  info       to make Texinfo files and run them through makeinfo"
	@echo "  gettext    to make PO message catalogs"
	@echo "  changes    to make an overview of all changed/added/deprecated items"
	@echo "  xml        to make Docutils-native XML files"
	@echo "  pseudoxml  to make pseudoxml-XML files for display purposes"
	@echo "  linkcheck  to check all external links for integrity"
	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
	@echo "  coverage   to run coverage check of the documentation (if enabled)"
	@echo "  dummy      to check syntax errors of document sources"

.PHONY: clean
clean:
	rm -rf $(BUILDDIR)/*

.PHONY: html
html:
	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
	@echo
	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."

.PHONY: dirhtml
dirhtml:
	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
	@echo
	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."

.PHONY: singlehtml
singlehtml:
	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
	@echo
	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."

.PHONY: pickle
pickle:
	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
	@echo
	@echo "Build finished; now you can process the pickle files."

.PHONY: json
json:
	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
	@echo
	@echo "Build finished; now you can process the JSON files."

.PHONY: htmlhelp
htmlhelp:
	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
	@echo
	@echo "Build finished; now you can run HTML Help Workshop with the" \
	      ".hhp project file in $(BUILDDIR)/htmlhelp."

.PHONY: qthelp
qthelp:
	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
	@echo
	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/twitterpandas.qhcp"
	@echo "To view the help file:"
	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/twitterpandas.qhc"

.PHONY: applehelp
applehelp:
	$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
	@echo
	@echo "Build finished. The help book is in $(BUILDDIR)/applehelp."
	@echo "N.B. You won't be able to view it unless you put it in" \
	      "~/Library/Documentation/Help or install it in your application" \
	      "bundle."

.PHONY: devhelp
devhelp:
	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
	@echo
	@echo "Build finished."
	@echo "To view the help file:"
	@echo "# mkdir -p $$HOME/.local/share/devhelp/twitterpandas"
	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/twitterpandas"
	@echo "# devhelp"

.PHONY: epub
epub:
	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
	@echo
	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."

.PHONY: epub3
epub3:
	$(SPHINXBUILD) -b epub3 $(ALLSPHINXOPTS) $(BUILDDIR)/epub3
	@echo
	@echo "Build finished. The epub3 file is in $(BUILDDIR)/epub3."

.PHONY: latex
latex:
	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
	@echo
	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
	@echo "Run \`make' in that directory to run these through (pdf)latex" \
	      "(use \`make latexpdf' here to do that automatically)."

.PHONY: latexpdf
latexpdf:
	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
	@echo "Running LaTeX files through pdflatex..."
	$(MAKE) -C $(BUILDDIR)/latex all-pdf
	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."

.PHONY: latexpdfja
latexpdfja:
	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
	@echo "Running LaTeX files through platex and dvipdfmx..."
	$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."

.PHONY: text
text:
	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
	@echo
	@echo "Build finished. The text files are in $(BUILDDIR)/text."

.PHONY: man
man:
	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
	@echo
	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."

.PHONY: texinfo
texinfo:
	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
	@echo
	@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
	@echo "Run \`make' in that directory to run these through makeinfo" \
	      "(use \`make info' here to do that automatically)."

.PHONY: info
info:
	$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
	@echo "Running Texinfo files through makeinfo..."
	make -C $(BUILDDIR)/texinfo info
	@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."

.PHONY: gettext
gettext:
	$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
	@echo
	@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."

.PHONY: changes
changes:
	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
	@echo
	@echo "The overview file is in $(BUILDDIR)/changes."

.PHONY: linkcheck
linkcheck:
	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
	@echo
	@echo "Link check complete; look for any errors in the above output " \
	      "or in $(BUILDDIR)/linkcheck/output.txt."

.PHONY: doctest
doctest:
	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
	@echo "Testing of doctests in the sources finished, look at the " \
	      "results in $(BUILDDIR)/doctest/output.txt."

.PHONY: coverage
coverage:
	$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage
	@echo "Testing of coverage in the sources finished, look at the " \
	      "results in $(BUILDDIR)/coverage/python.txt."

.PHONY: xml
xml:
	$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
	@echo
	@echo "Build finished. The XML files are in $(BUILDDIR)/xml."

.PHONY: pseudoxml
pseudoxml:
	$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
	@echo
	@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."

.PHONY: dummy
dummy:
	$(SPHINXBUILD) -b dummy $(ALLSPHINXOPTS) $(BUILDDIR)/dummy
	@echo
	@echo "Build finished. Dummy builder generates no files."


================================================
FILE: docs/make.bat
================================================
@ECHO OFF

REM Command file for Sphinx documentation

if "%SPHINXBUILD%" == "" (
	set SPHINXBUILD=sphinx-build
)
set BUILDDIR=build
set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source
set I18NSPHINXOPTS=%SPHINXOPTS% source
if NOT "%PAPER%" == "" (
	set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS%
	set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS%
)

if "%1" == "" goto help

if "%1" == "help" (
	:help
	echo.Please use `make ^<target^>` where ^<target^> is one of
	echo.  html       to make standalone HTML files
	echo.  dirhtml    to make HTML files named index.html in directories
	echo.  singlehtml to make a single large HTML file
	echo.  pickle     to make pickle files
	echo.  json       to make JSON files
	echo.  htmlhelp   to make HTML files and a HTML help project
	echo.  qthelp     to make HTML files and a qthelp project
	echo.  devhelp    to make HTML files and a Devhelp project
	echo.  epub       to make an epub
	echo.  epub3      to make an epub3
	echo.  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter
	echo.  text       to make text files
	echo.  man        to make manual pages
	echo.  texinfo    to make Texinfo files
	echo.  gettext    to make PO message catalogs
	echo.  changes    to make an overview over all changed/added/deprecated items
	echo.  xml        to make Docutils-native XML files
	echo.  pseudoxml  to make pseudoxml-XML files for display purposes
	echo.  linkcheck  to check all external links for integrity
	echo.  doctest    to run all doctests embedded in the documentation if enabled
	echo.  coverage   to run coverage check of the documentation if enabled
	echo.  dummy      to check syntax errors of document sources
	goto end
)

if "%1" == "clean" (
	for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i
	del /q /s %BUILDDIR%\*
	goto end
)


REM Check if sphinx-build is available and fallback to Python version if any
%SPHINXBUILD% 1>NUL 2>NUL
if errorlevel 9009 goto sphinx_python
goto sphinx_ok

:sphinx_python

set SPHINXBUILD=python -m sphinx.__init__
%SPHINXBUILD% 2> nul
if errorlevel 9009 (
	echo.
	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
	echo.installed, then set the SPHINXBUILD environment variable to point
	echo.to the full path of the 'sphinx-build' executable. Alternatively you
	echo.may add the Sphinx directory to PATH.
	echo.
	echo.If you don't have Sphinx installed, grab it from
	echo.http://sphinx-doc.org/
	exit /b 1
)

:sphinx_ok


if "%1" == "html" (
	%SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished. The HTML pages are in %BUILDDIR%/html.
	goto end
)

if "%1" == "dirhtml" (
	%SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml.
	goto end
)

if "%1" == "singlehtml" (
	%SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml.
	goto end
)

if "%1" == "pickle" (
	%SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished; now you can process the pickle files.
	goto end
)

if "%1" == "json" (
	%SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished; now you can process the JSON files.
	goto end
)

if "%1" == "htmlhelp" (
	%SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished; now you can run HTML Help Workshop with the ^
.hhp project file in %BUILDDIR%/htmlhelp.
	goto end
)

if "%1" == "qthelp" (
	%SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished; now you can run "qcollectiongenerator" with the ^
.qhcp project file in %BUILDDIR%/qthelp, like this:
	echo.^> qcollectiongenerator %BUILDDIR%\qthelp\twitterpandas.qhcp
	echo.To view the help file:
	echo.^> assistant -collectionFile %BUILDDIR%\qthelp\twitterpandas.ghc
	goto end
)

if "%1" == "devhelp" (
	%SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished.
	goto end
)

if "%1" == "epub" (
	%SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished. The epub file is in %BUILDDIR%/epub.
	goto end
)

if "%1" == "epub3" (
	%SPHINXBUILD% -b epub3 %ALLSPHINXOPTS% %BUILDDIR%/epub3
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished. The epub3 file is in %BUILDDIR%/epub3.
	goto end
)

if "%1" == "latex" (
	%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished; the LaTeX files are in %BUILDDIR%/latex.
	goto end
)

if "%1" == "latexpdf" (
	%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
	cd %BUILDDIR%/latex
	make all-pdf
	cd %~dp0
	echo.
	echo.Build finished; the PDF files are in %BUILDDIR%/latex.
	goto end
)

if "%1" == "latexpdfja" (
	%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
	cd %BUILDDIR%/latex
	make all-pdf-ja
	cd %~dp0
	echo.
	echo.Build finished; the PDF files are in %BUILDDIR%/latex.
	goto end
)

if "%1" == "text" (
	%SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished. The text files are in %BUILDDIR%/text.
	goto end
)

if "%1" == "man" (
	%SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished. The manual pages are in %BUILDDIR%/man.
	goto end
)

if "%1" == "texinfo" (
	%SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo.
	goto end
)

if "%1" == "gettext" (
	%SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished. The message catalogs are in %BUILDDIR%/locale.
	goto end
)

if "%1" == "changes" (
	%SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes
	if errorlevel 1 exit /b 1
	echo.
	echo.The overview file is in %BUILDDIR%/changes.
	goto end
)

if "%1" == "linkcheck" (
	%SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck
	if errorlevel 1 exit /b 1
	echo.
	echo.Link check complete; look for any errors in the above output ^
or in %BUILDDIR%/linkcheck/output.txt.
	goto end
)

if "%1" == "doctest" (
	%SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest
	if errorlevel 1 exit /b 1
	echo.
	echo.Testing of doctests in the sources finished, look at the ^
results in %BUILDDIR%/doctest/output.txt.
	goto end
)

if "%1" == "coverage" (
	%SPHINXBUILD% -b coverage %ALLSPHINXOPTS% %BUILDDIR%/coverage
	if errorlevel 1 exit /b 1
	echo.
	echo.Testing of coverage in the sources finished, look at the ^
results in %BUILDDIR%/coverage/python.txt.
	goto end
)

if "%1" == "xml" (
	%SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished. The XML files are in %BUILDDIR%/xml.
	goto end
)

if "%1" == "pseudoxml" (
	%SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml.
	goto end
)

if "%1" == "dummy" (
	%SPHINXBUILD% -b dummy %ALLSPHINXOPTS% %BUILDDIR%/dummy
	if errorlevel 1 exit /b 1
	echo.
	echo.Build finished. Dummy builder generates no files.
	goto end
)

:end


================================================
FILE: docs/source/conf.py
================================================
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# patternscanner documentation build configuration file, created by
# cookiecutter pipproject
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.

import sys
import os

# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
sys.path.insert(0, os.path.abspath('../..'))

# -- General configuration ------------------------------------------------

# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'

# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
    'sphinx.ext.autodoc',
]

# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']

# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'

# The encoding of source files.
#source_encoding = 'utf-8-sig'

# The master toctree document.
master_doc = 'index'

# General information about the project.
project = 'patternscanner'
copyright = '2016, Preetam Sharma'
author = 'Preetam Sharma'

# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '0.0.1'
# The full version, including alpha/beta/rc tags.
release = '0.0.1'

# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None

# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = []

# The reST default role (used for this markup: `text`) to use for all
# documents.
#default_role = None

# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True

# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True

# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False

# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'

# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []

# If true, keep warnings as "system message" paragraphs in the built documents.
#keep_warnings = False

# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = False


# -- Options for HTML output ----------------------------------------------

# The theme to use for HTML and HTML Help pages.  See the documentation for
# a list of builtin themes.
html_theme = 'sphinx_rtd_theme'

# Theme options are theme-specific and customize the look and feel of a theme
# further.  For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}

# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []

# The name for this set of Sphinx documents.
# "<project> v<release> documentation" by default.
#html_title = 'patternscanner v0.0.1'

# A shorter title for the navigation bar.  Default is the same as html_title.
#html_short_title = None

# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None

# The name of an image file (relative to this directory) to use as a favicon of
# the docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None

# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']

# Add any extra paths that contain custom files (such as robots.txt or
# .htaccess) here, relative to this directory. These files are copied
# directly to the root of the documentation.
#html_extra_path = []

# If not None, a 'Last updated on:' timestamp is inserted at every page
# bottom, using the given strftime format.
# The empty string is equivalent to '%b %d, %Y'.
#html_last_updated_fmt = None

# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True

# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}

# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}

# If false, no module index is generated.
#html_domain_indices = True

# If false, no index is generated.
#html_use_index = True

# If true, the index is split into individual pages for each letter.
#html_split_index = False

# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True

# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True

# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True

# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it.  The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''

# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None

# Language to be used for generating the HTML full-text search index.
# Sphinx supports the following languages:
#   'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja'
#   'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr', 'zh'
#html_search_language = 'en'

# A dictionary with options for the search language support, empty by default.
# 'ja' uses this config value.
# 'zh' user can custom change `jieba` dictionary path.
#html_search_options = {'type': 'default'}

# The name of a javascript file (relative to the configuration directory) that
# implements a search results scorer. If empty, the default will be used.
#html_search_scorer = 'scorer.js'

# Output file base name for HTML help builder.
htmlhelp_basename = 'patternscannerdoc'

# -- Options for LaTeX output ---------------------------------------------

latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',

# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',

# Additional stuff for the LaTeX preamble.
#'preamble': '',

# Latex figure (float) alignment
#'figure_align': 'htbp',
}

# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
#  author, documentclass [howto, manual, or own class]).
latex_documents = [
    (master_doc, 'patternscanner.tex', 'patternscanner Documentation',
     'Preetam Sharma', 'manual'),
]

# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None

# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False

# If true, show page references after internal links.
#latex_show_pagerefs = False

# If true, show URL addresses after external links.
#latex_show_urls = False

# Documents to append as an appendix to all manuals.
#latex_appendices = []

# If false, no module index is generated.
#latex_domain_indices = True


# -- Options for manual page output ---------------------------------------

# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
    (master_doc, 'patternscanner', 'patternscanner Documentation',
     [author], 1)
]

# If true, show URL addresses after external links.
#man_show_urls = False


# -- Options for Texinfo output -------------------------------------------

# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
#  dir menu entry, description, category)
texinfo_documents = [
    (master_doc, 'patternscanner', 'patternscanner Documentation',
     author, 'patternscanner', 'One line description of project.',
     'Miscellaneous'),
]

# Documents to append as an appendix to all manuals.
#texinfo_appendices = []

# If false, no module index is generated.
#texinfo_domain_indices = True

# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'

# If true, do not generate a @detailmenu in the "Top" node's menu.
#texinfo_no_detailmenu = False


================================================
FILE: docs/source/index.rst
================================================
Welcome to patternscanner's documentation!
=========================================

Contents:

.. toctree::
   :maxdepth: 2



Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`



================================================
FILE: pyproject.toml
================================================
[tool.poetry]
name = "tradingpattern"
version = "0.0.5"
repository = "https://github.com/white07S/TradingPatternScanner"
description = "Trading Pattern Scanner Identifies complex patterns like head and shoulder, wedge and many more."
authors = ["Preetam Sharma"]
license = "CC BY-NC-SA 4.0"
readme = "README.md"
packages = [
    { include = "tradingpatterns" }
]

[tool.poetry.dependencies]
python = "^3.10"
numpy = "^1.24.2"
pandas = "^2.0.0"

[tool.poetry.group.dev.dependencies]
coverage = "^7.2.2"
nose = "^1.3.7"
Sphinx = "^6.1.3"
sphinx-rtd-theme-citus = "^0.5.25"

[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

================================================
FILE: requirements.txt
================================================
numpy
pandas


================================================
FILE: tests/__init__.py
================================================


================================================
FILE: tests/test_hs.py
================================================
from tradingpatterns.hard_data import generate_sample_df_with_pattern
from tradingpatterns.tradingpatterns import detect_head_shoulder

def test_detect_head_shoulder():
    # Generate data with head and shoulder pattern
    df_head_shoulder = generate_sample_df_with_pattern("Head and Shoulder")
    df_inv_shoulder = generate_sample_df_with_pattern("Inverse Head and Shoulder")
    df_with_detection = detect_head_shoulder(df_head_shoulder)
    df_with_inv_detection = detect_head_shoulder(df_inv_shoulder)
    assert "Head and Shoulder" in df_with_detection['head_shoulder_pattern'].values
    assert "Inverse Head and Shoulder" in df_with_inv_detection['head_shoulder_pattern'].values




================================================
FILE: tradingpatterns/__init__.py
================================================


================================================
FILE: tradingpatterns/analysis.py
================================================
import matplotlib.pyplot as plt
import tradingpatterns_tech
import hard_data
from sklearn.metrics import accuracy_score
import pandas as pd
import seaborn as sns
from collections import Counter

def main():
    df = hard_data.generate_data_head_shoulder(10)

    df = detect_and_rename(df, 'window', 3)
    df = detect_and_rename(df, 'filter', 3, 0.01, 1)
    df = detect_and_rename(df, 'kf', 3)
    df = detect_and_rename(df, 'wavelet', 3)

    algorithms = [
        'head_shoulder_pattern_window', 
        'head_shoulder_pattern_filter', 
        'head_shoulder_pattern_kf', 
        'head_shoulder_pattern_wavelet'
    ]

    true_labels = ['Head and Shoulder']*10 + ['Inverse Head and Shoulder']*10 + ['No Pattern']*(len(df)-20)

    print_algorithm_accuracies(algorithms, true_labels, df)

    patterns = ['Head and Shoulder', 'Inverse Head and Shoulder']
    ground_truth_counts = Counter(true_labels)
    predicted_counts = {alg: Counter(df[alg].fillna('No Pattern')) for alg in algorithms}

    df_counts = pd.DataFrame(index=patterns)
    df_counts['Ground Truth'] = [ground_truth_counts[pattern] for pattern in patterns]

    for alg in algorithms:
        df_counts[alg] = [predicted_counts[alg][pattern] for pattern in patterns]

    plot_heatmap(df_counts)
    plt.savefig('heatmap.png')

def detect_and_rename(df, method, window, threshold=None, time_delay=None):
    if method == 'filter':
        df = tradingpatterns_tech.detect_head_shoulder_filter(df, window, threshold, time_delay)
    elif method == 'kf':
        df = tradingpatterns_tech.detect_head_shoulder_kf(df, window)
    elif method == 'wavelet':
        df = tradingpatterns_tech.detect_head_shoulder_wavelet(df, window)
    else:
        df = tradingpatterns_tech.detect_head_shoulder(df, window)

    try:
        df.rename(columns={'head_shoulder_pattern': f'head_shoulder_pattern_{method}'}, inplace=True)
    except KeyError:
        print(f"The 'head_shoulder_pattern' column was not found. It seems the function 'detect_head_shoulder_{method}' failed to generate the required column.")
        exit(1)
        
    return df

def print_algorithm_accuracies(algorithms, true_labels, df):
    for alg in algorithms:
        try:
            predicted_labels = df[alg].fillna('No Pattern').tolist()
            accuracy = accuracy_score(true_labels, predicted_labels)
            print(f'Accuracy for {alg}: {accuracy*100:.2f}%')
        except KeyError:
            print(f"The column {alg} was not found in the dataframe.")
            
def plot_heatmap(df_counts):
    sns.heatmap(df_counts, annot=True, fmt="d", cmap="YlGnBu", yticklabels=['HS', 'I-HS'])
    plt.title("Pattern Counts: Ground Truth vs. Predicted")
    plt.tight_layout()

if __name__ == "__main__":
    main()


================================================
FILE: tradingpatterns/hard_data.py
================================================
import pandas as pd
def generate_sample_df_with_pattern(pattern):
    date_rng = pd.date_range(start='1/1/2020', end='1/10/2020', freq='D')
    data = {'date': date_rng}
    if pattern == 'Head and Shoulder':
        data['Open'] = [90, 85, 80, 90, 85, 80, 75, 80, 85, 90]
        data['High'] = [95, 90, 85, 95, 90, 85, 80, 85, 90, 95]
        data['Low'] = [80, 75, 70, 80, 75, 70, 65, 70, 75, 80]
        data['Close'] = [90, 85, 80, 90, 85, 80, 75, 80, 85, 90]
        data['Volume'] = [1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000]
    elif pattern == 'Inverse Head and Shoulder':
        data['Open'] = [20, 25, 30, 20, 25, 30, 35, 30, 25, 20]
        data['High'] = [25, 30, 35, 25, 30, 35, 40, 35, 30, 25]
        data['Low'] = [15, 20, 25, 15, 20, 25, 30, 25, 20, 15]
        data['Close'] = [20, 25, 30, 20, 25, 30, 35, 30, 25, 20]
        data['Volume'] = [1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000]
    elif pattern == "Double Top" or "Double Bottom" or "Ascending Triangle" or "Descending Triangle":
        data['High'] = [95, 90, 85, 95, 90, 85, 80, 85, 90, 95]
        data['Low'] = [80, 75, 70, 80, 75, 70, 65, 70, 75, 80]
        df = pd.DataFrame(data)
        df.iloc[3:5,1] =100
        df.iloc[6:8,1] =70
        df.iloc[6:9,2] =70
    df = pd.DataFrame(data)
    return df


import pandas as pd
import numpy as np
import random
from datetime import datetime, timedelta

# Function to generate random OHLCV data
def generate_random_data(length):
    close_values = np.random.randint(100, 200, length).tolist()
    return {
        'Open': [value - random.randint(0, 10) for value in close_values],
        'High': [value + random.randint(0, 10) for value in close_values],
        'Low': [value - random.randint(0, 10) for value in close_values],
        'Close': close_values,
        'Volume': np.random.randint(1000, 2000, length).tolist(),
    }

# Function to inject head and shoulders and inverse head and shoulders patterns
def inject_patterns(data):
    shoulder_height = random.randint(120, 140)
    head_height = random.randint(150, 170)
    inv_shoulder_depth = random.randint(60, 80)
    inv_head_depth = random.randint(40, 60)
    
    # Left Shoulder
    data['High'][3] = shoulder_height
    data['Close'][3] = shoulder_height - random.randint(0, 5)
    
    # Head
    data['High'][5] = head_height
    data['Close'][5] = head_height - random.randint(0, 5)
    
    # Right Shoulder
    data['High'][7] = shoulder_height
    data['Close'][7] = shoulder_height - random.randint(0, 5)
    
    # Left Inverse Shoulder
    data['Low'][13] = inv_shoulder_depth
    data['Close'][13] = inv_shoulder_depth + random.randint(0, 5)
    
    # Inverse Head
    data['Low'][15] = inv_head_depth
    data['Close'][15] = inv_head_depth + random.randint(0, 5)
    
    # Right Inverse Shoulder
    data['Low'][17] = inv_shoulder_depth
    data['Close'][17] = inv_shoulder_depth + random.randint(0, 5)
    
    return data

def generate_data_head_shoulder(n):
    # Start date
    start_date = datetime.now()

    # Dataframe for storing data
    df = pd.DataFrame()

    # Generate n Head and Shoulders patterns
    for i in range(n):
        data = generate_random_data(20)  # 20 data points are needed for one full pattern
        data = inject_patterns(data)
        
        temp_df = pd.DataFrame(data)
        temp_df['Datetime'] = pd.date_range(start=start_date, periods=20, freq='D')  # Adjust the frequency accordingly
        start_date += timedelta(days=20)  # Adjust the timedelta accordingly
        
        df = df.append(temp_df, ignore_index=True)

    df = df[['Datetime', 'Open', 'High', 'Low', 'Close', 'Volume']]

    return df


================================================
FILE: tradingpatterns/tradingpatterns.py
================================================
import pandas as pd
import numpy as np


def detect_head_shoulder(df, window=3):
# Define the rolling window
    roll_window = window
    # Create a rolling window for High and Low
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()
    # Create a boolean mask for Head and Shoulder pattern
    mask_head_shoulder = ((df['high_roll_max'] > df['High'].shift(1)) & (df['high_roll_max'] > df['High'].shift(-1)) & (df['High'] < df['High'].shift(1)) & (df['High'] < df['High'].shift(-1)))
    # Create a boolean mask for Inverse Head and Shoulder pattern
    mask_inv_head_shoulder = ((df['low_roll_min'] < df['Low'].shift(1)) & (df['low_roll_min'] < df['Low'].shift(-1)) & (df['Low'] > df['Low'].shift(1)) & (df['Low'] > df['Low'].shift(-1)))
    # Create a new column for Head and Shoulder and its inverse pattern and populate it using the boolean masks
    df['head_shoulder_pattern'] = np.nan
    df.loc[mask_head_shoulder, 'head_shoulder_pattern'] = 'Head and Shoulder'
    df.loc[mask_inv_head_shoulder, 'head_shoulder_pattern'] = 'Inverse Head and Shoulder'
    return df 
    # return not df['head_shoulder_pattern'].isna().any().item()

def detect_multiple_tops_bottoms(df, window=3):
# Define the rolling window
    roll_window = window
    # Create a rolling window for High and Low
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()
    df['close_roll_max'] = df['Close'].rolling(window=roll_window).max()
    df['close_roll_min'] = df['Close'].rolling(window=roll_window).min()
    # Create a boolean mask for multiple top pattern
    mask_top = (df['high_roll_max'] >= df['High'].shift(1)) & (df['close_roll_max'] < df['Close'].shift(1))
    # Create a boolean mask for multiple bottom pattern
    mask_bottom = (df['low_roll_min'] <= df['Low'].shift(1)) & (df['close_roll_min'] > df['Close'].shift(1))
    # Create a new column for multiple top bottom pattern and populate it using the boolean masks
    df['multiple_top_bottom_pattern'] = np.nan
    df.loc[mask_top, 'multiple_top_bottom_pattern'] = 'Multiple Top'
    df.loc[mask_bottom, 'multiple_top_bottom_pattern'] = 'Multiple Bottom'
    return df

def calculate_support_resistance(df, window=3):
# Define the rolling window
    roll_window = window
    # Set the number of standard deviation
    std_dev = 2
    # Create a rolling window for High and Low
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()
    # Calculate the mean and standard deviation for High and Low
    mean_high = df['High'].rolling(window=roll_window).mean()
    std_high = df['High'].rolling(window=roll_window).std()
    mean_low = df['Low'].rolling(window=roll_window).mean()
    std_low = df['Low'].rolling(window=roll_window).std()
    # Create a new column for support and resistance
    df['support'] = mean_low - std_dev * std_low
    df['resistance'] = mean_high + std_dev * std_high
    return df
def detect_triangle_pattern(df, window=3):
    # Define the rolling window
    roll_window = window
    # Create a rolling window for High and Low
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()
    # Create a boolean mask for ascending triangle pattern
    mask_asc = (df['high_roll_max'] >= df['High'].shift(1)) & (df['low_roll_min'] <= df['Low'].shift(1)) & (df['Close'] > df['Close'].shift(1))
    # Create a boolean mask for descending triangle pattern
    mask_desc = (df['high_roll_max'] <= df['High'].shift(1)) & (df['low_roll_min'] >= df['Low'].shift(1)) & (df['Close'] < df['Close'].shift(1))
    # Create a new column for triangle pattern and populate it using the boolean masks
    df['triangle_pattern'] = np.nan
    df.loc[mask_asc, 'triangle_pattern'] = 'Ascending Triangle'
    df.loc[mask_desc, 'triangle_pattern'] = 'Descending Triangle'
    return df

def detect_wedge(df, window=3):
    # Define the rolling window
    roll_window = window
    # Create a rolling window for High and Low
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()
    df['trend_high'] = df['High'].rolling(window=roll_window).apply(lambda x: 1 if (x[-1]-x[0])>0 else -1 if (x[-1]-x[0])<0 else 0)
    df['trend_low'] = df['Low'].rolling(window=roll_window).apply(lambda x: 1 if (x[-1]-x[0])>0 else -1 if (x[-1]-x[0])<0 else 0)
    # Create a boolean mask for Wedge Up pattern
    mask_wedge_up = (df['high_roll_max'] >= df['High'].shift(1)) & (df['low_roll_min'] <= df['Low'].shift(1)) & (df['trend_high'] == 1) & (df['trend_low'] == 1)
    # Create a boolean mask for Wedge Down pattern
        # Create a boolean mask for Wedge Down pattern
    mask_wedge_down = (df['high_roll_max'] <= df['High'].shift(1)) & (df['low_roll_min'] >= df['Low'].shift(1)) & (df['trend_high'] == -1) & (df['trend_low'] == -1)
    # Create a new column for Wedge Up and Wedge Down pattern and populate it using the boolean masks
    df['wedge_pattern'] = np.nan
    df.loc[mask_wedge_up, 'wedge_pattern'] = 'Wedge Up'
    df.loc[mask_wedge_down, 'wedge_pattern'] = 'Wedge Down'
    return df
def detect_channel(df, window=3):
    # Define the rolling window
    roll_window = window
    # Define a factor to check for the range of channel
    channel_range = 0.1
    # Create a rolling window for High and Low
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()
    df['trend_high'] = df['High'].rolling(window=roll_window).apply(lambda x: 1 if (x[-1]-x[0])>0 else -1 if (x[-1]-x[0])<0 else 0)
    df['trend_low'] = df['Low'].rolling(window=roll_window).apply(lambda x: 1 if (x[-1]-x[0])>0 else -1 if (x[-1]-x[0])<0 else 0)
    # Create a boolean mask for Channel Up pattern
    mask_channel_up = (df['high_roll_max'] >= df['High'].shift(1)) & (df['low_roll_min'] <= df['Low'].shift(1)) & (df['high_roll_max'] - df['low_roll_min'] <= channel_range * (df['high_roll_max'] + df['low_roll_min'])/2) & (df['trend_high'] == 1) & (df['trend_low'] == 1)
    # Create a boolean mask for Channel Down pattern
    mask_channel_down = (df['high_roll_max'] <= df['High'].shift(1)) & (df['low_roll_min'] >= df['Low'].shift(1)) & (df['high_roll_max'] - df['low_roll_min'] <= channel_range * (df['high_roll_max'] + df['low_roll_min'])/2) & (df['trend_high'] == -1) & (df['trend_low'] == -1)
    # Create a new column for Channel Up and Channel Down pattern and populate it using the boolean masks
    df['channel_pattern'] = np.nan
    df.loc[mask_channel_up, 'channel_pattern'] = 'Channel Up'
    df.loc[mask_channel_down, 'channel_pattern'] = 'Channel Down'
    return df

def detect_double_top_bottom(df, window=3, threshold=0.05):
    # Define the rolling window
    roll_window = window
    # Define a threshold to check for the range of pattern
    range_threshold = threshold

    # Create a rolling window for High and Low
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()

    # Create a boolean mask for Double Top pattern
    mask_double_top = (df['high_roll_max'] >= df['High'].shift(1)) & (df['high_roll_max'] >= df['High'].shift(-1)) & (df['High'] < df['High'].shift(1)) & (df['High'] < df['High'].shift(-1)) & ((df['High'].shift(1) - df['Low'].shift(1)) <= range_threshold * (df['High'].shift(1) + df['Low'].shift(1))/2) & ((df['High'].shift(-1) - df['Low'].shift(-1)) <= range_threshold * (df['High'].shift(-1) + df['Low'].shift(-1))/2)
    # Create a boolean mask for Double Bottom pattern
    mask_double_bottom = (df['low_roll_min'] <= df['Low'].shift(1)) & (df['low_roll_min'] <= df['Low'].shift(-1)) & (df['Low'] > df['Low'].shift(1)) & (df['Low'] > df['Low'].shift(-1)) & ((df['High'].shift(1) - df['Low'].shift(1)) <= range_threshold * (df['High'].shift(1) + df['Low'].shift(1))/2) & ((df['High'].shift(-1) - df['Low'].shift(-1)) <= range_threshold * (df['High'].shift(-1) + df['Low'].shift(-1))/2)

    # Create a new column for Double Top and Double Bottom pattern and populate it using the boolean masks
    df['double_pattern'] = np.nan
    df.loc[mask_double_top, 'double_pattern'] = 'Double Top'
    df.loc[mask_double_bottom, 'double_pattern'] = 'Double Bottom'

    return df

def detect_trendline(df, window=2):
    # Define the rolling window
    roll_window = window
    # Create new columns for the linear regression slope and y-intercept
    df['slope'] = np.nan
    df['intercept'] = np.nan

    for i in range(window, len(df)):
        x = np.array(range(i-window, i))
        y = df['Close'][i-window:i]
        A = np.vstack([x, np.ones(len(x))]).T
        m, c = np.linalg.lstsq(A, y, rcond=None)[0]
        df.at[df.index[i], 'slope'] = m
        df.at[df.index[i], 'intercept'] = c

    # Create a boolean mask for trendline support
    mask_support = df['slope'] > 0

    # Create a boolean mask for trendline resistance
    mask_resistance = df['slope'] < 0

    # Create new columns for trendline support and resistance
    df['support'] = np.nan
    df['resistance'] = np.nan

    # Populate the new columns using the boolean masks
    df.loc[mask_support, 'support'] = df['Close'] * df['slope'] + df['intercept']
    df.loc[mask_resistance, 'resistance'] = df['Close'] * df['slope'] + df['intercept']

    return df

def find_pivots(df):
    # Calculate differences between consecutive highs and lows
    high_diffs = df['high'].diff()
    low_diffs = df['low'].diff()

    # Find higher high
    higher_high_mask = (high_diffs > 0) & (high_diffs.shift(-1) < 0)
    
    # Find lower low
    lower_low_mask = (low_diffs < 0) & (low_diffs.shift(-1) > 0)

    # Find lower high
    lower_high_mask = (high_diffs < 0) & (high_diffs.shift(-1) > 0)

    # Find higher low
    higher_low_mask = (low_diffs > 0) & (low_diffs.shift(-1) < 0)

    # Create signals column
    df['signal'] = ''
    df.loc[higher_high_mask, 'signal'] = 'HH'
    df.loc[lower_low_mask, 'signal'] = 'LL'
    df.loc[lower_high_mask, 'signal'] = 'LH'
    df.loc[higher_low_mask, 'signal'] = 'HL'

    return df

================================================
FILE: tradingpatterns/tradingpatterns_tech.py
================================================
from scipy.signal import savgol_filter
import numpy as np
import pandas as pd
from pykalman import KalmanFilter
import pywt

'''
Algorithm 1: Basic Head-Shoulder Detection
This algorithm implements a basic detection of the "Head and Shoulder" and "Inverse Head and Shoulder" patterns 
in a data frame. These patterns are significant in financial analysis as they indicate possible market reversals. 
In the code, the algorithm uses a rolling window to track high and low points in the 'High' and 'Low' columns of 
the data frame. It then creates boolean masks to identify where these patterns occur. The identified patterns are 
then added to the data frame in a new 'head_shoulder_pattern' column.
'''

def detect_head_shoulder(df, window=3):
# Define the rolling window
    roll_window = window
    # Create a rolling window for High and Low
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()
    # Create a boolean mask for Head and Shoulder pattern
    mask_head_shoulder = ((df['high_roll_max'] > df['High'].shift(1)) & (df['high_roll_max'] > df['High'].shift(-1)) & (df['High'] < df['High'].shift(1)) & (df['High'] < df['High'].shift(-1)))
    # Create a boolean mask for Inverse Head and Shoulder pattern
    mask_inv_head_shoulder = ((df['low_roll_min'] < df['Low'].shift(1)) & (df['low_roll_min'] < df['Low'].shift(-1)) & (df['Low'] > df['Low'].shift(1)) & (df['Low'] > df['Low'].shift(-1)))
    # Create a new column for Head and Shoulder and its inverse pattern and populate it using the boolean masks
    df['head_shoulder_pattern'] = np.nan
    df.loc[mask_head_shoulder, 'head_shoulder_pattern'] = 'Head and Shoulder'
    df.loc[mask_inv_head_shoulder, 'head_shoulder_pattern'] = 'Inverse Head and Shoulder'
    return df 

'''
Algorithm 2: Head-Shoulder Detection with Savitzky-Golay Filter

This algorithm is an improvement of the first one. It first applies the Savitzky-Golay filter to smooth the 'High' and 'Low' columns. 
This filter is used to reduce noise and improve the reliability of pattern detection. 
In addition to the head-shoulder pattern detection of the first algorithm, this algorithm also considers 
the height of the "Head" or "Inverse Head" and introduces a threshold to avoid false pattern 
recognition due to insignificant price changes.
'''
def detect_head_shoulder_filter(df, window=3, threshold=0.01, time_delay=1):
    roll_window = window
    df['High_smooth'] = savgol_filter(df['High'], roll_window, 2) # Apply Savitzky-Golay filter
    df['Low_smooth'] = savgol_filter(df['Low'], roll_window, 2)
    
    df['high_roll_max'] = df['High_smooth'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low_smooth'].rolling(window=roll_window).min()
    
    # Define the height of the head and inverse head
    df['head_height'] = df['high_roll_max'] - df['Low'].rolling(window=roll_window).min()
    df['inv_head_height'] = df['High'].rolling(window=roll_window).max() - df['low_roll_min']
    
    # Define the masks for head and shoulder and inverse head and shoulder
    mask_head_shoulder = ((df['head_height'] > threshold) & (df['high_roll_max'] > df['High_smooth'].shift(time_delay)) & (df['high_roll_max'] > df['High_smooth'].shift(-time_delay)) & (df['High_smooth'] < df['High_smooth'].shift(time_delay)) & (df['High_smooth'] < df['High_smooth'].shift(-time_delay)))
    mask_inv_head_shoulder = ((df['inv_head_height'] > threshold) & (df['low_roll_min'] < df['Low_smooth'].shift(time_delay)) & (df['low_roll_min'] < df['Low_smooth'].shift(-time_delay)) & (df['Low_smooth'] > df['Low_smooth'].shift(time_delay)) & (df['Low_smooth'] > df['Low_smooth'].shift(-time_delay)))
    
    df['head_shoulder_pattern'] = np.nan
    df.loc[mask_head_shoulder, 'head_shoulder_pattern'] = 'Head and Shoulder'
    df.loc[mask_inv_head_shoulder, 'head_shoulder_pattern'] = 'Inverse Head and Shoulder'
    
    return df

'''
Algorithm 3: Head-Shoulder Detection with Kalman Filter

In this algorithm, the Kalman Filter is used to smooth the 'High' and 'Low' columns. 
The Kalman Filter is a recursive filter that estimates the state of a system in real time, 
making it more suitable for financial data with its inherent noise and uncertainties. 
This can potentially improve the accuracy of pattern detection. 
The pattern detection process is similar to Algorithm 1, but it operates on the smoothed data.
'''


def kalman_smooth(series, n_iter=10):
    # Initialize Kalman filter
    kf = KalmanFilter(initial_state_mean=0, n_dim_obs=1)

    # Use the EM algorithm to estimate the best values for the parameters
    kf = kf.em(series, n_iter=n_iter)

    # Use the observed values of the price to get a rolling mean
    state_means, _ = kf.filter(series.values)

    return state_means.flatten()


def detect_head_shoulder_kf(df, window=3):
    roll_window = window
    df['High_smooth'] = kalman_smooth(df['High'])
    df['Low_smooth'] = kalman_smooth(df['Low'])
    
    df['high_roll_max'] = df['High_smooth'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low_smooth'].rolling(window=roll_window).min()
    
    mask_head_shoulder = ((df['high_roll_max'] > df['High_smooth'].shift(1)) & (df['high_roll_max'] > df['High_smooth'].shift(-1)) & (df['High_smooth'] < df['High_smooth'].shift(1)) & (df['High_smooth'] < df['High_smooth'].shift(-1)))
    mask_inv_head_shoulder = ((df['low_roll_min'] < df['Low_smooth'].shift(1)) & (df['low_roll_min'] < df['Low_smooth'].shift(-1)) & (df['Low_smooth'] > df['Low_smooth'].shift(1)) & (df['Low_smooth'] > df['Low_smooth'].shift(-1)))
    
    df['head_shoulder_pattern'] = np.nan
    df.loc[mask_head_shoulder, 'head_shoulder_pattern'] = 'Head and Shoulder'
    df.loc[mask_inv_head_shoulder, 'head_shoulder_pattern'] = 'Inverse Head and Shoulder'
    
    return df

'''
Algorithm 4: Head-Shoulder Detection with Wavelet Denoising

In this algorithm, wavelet denoising is applied to the 'High' and 'Low' columns before 
the pattern detection process. Wavelet denoising is an effective technique for eliminating 
noise while preserving the key features in the data. This can make the pattern detection process 
more robust and reliable, especially in the presence of high frequency noise in the data. 
'''

def wavelet_denoise(series, wavelet='db1', level=1):
    # Perform wavelet decomposition
    coeff = pywt.wavedec(series, wavelet, mode="per")
    # Set detail coefficients for levels > level to zero
    for i in range(1, len(coeff)):
        coeff[i] = pywt.threshold(coeff[i], value=np.std(coeff[i])/2, mode="soft")
    # Perform inverse wavelet transform
    return pywt.waverec(coeff, wavelet, mode="per")


def detect_head_shoulder_wavelet(df, window=3):
    roll_window = window
    df['High_smooth'] = wavelet_denoise(df['High'], 'db1', level=1)
    df['Low_smooth'] = wavelet_denoise(df['Low'], 'db1', level=1)
    
    df['high_roll_max'] = df['High_smooth'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low_smooth'].rolling(window=roll_window).min()
    
    mask_head_shoulder = ((df['high_roll_max'] > df['High_smooth'].shift(1)) & (df['high_roll_max'] > df['High_smooth'].shift(-1)) & (df['High_smooth'] < df['High_smooth'].shift(1)) & (df['High_smooth'] < df['High_smooth'].shift(-1)))
    mask_inv_head_shoulder = ((df['low_roll_min'] < df['Low_smooth'].shift(1)) & (df['low_roll_min'] < df['Low_smooth'].shift(-1)) & (df['Low_smooth'] > df['Low_smooth'].shift(1)) & (df['Low_smooth'] > df['Low_smooth'].shift(-1)))
    
    df['head_shoulder_pattern'] = np.nan
    df.loc[mask_head_shoulder, 'head_shoulder_pattern'] = 'Head and Shoulder'
    df.loc[mask_inv_head_shoulder, 'head_shoulder_pattern'] = 'Inverse Head and Shoulder'
    
    return df


================================================
FILE: update_docs.sh
================================================
#!/usr/bin/env bash

# build the docs
cd docs
make clean
make html
cd ..

# commit and push
git add -A
git commit -m "building and pushing docs"
git push origin master

# switch branches and pull the data we want
git checkout gh-pages
rm -rf .
touch .nojekyll
git checkout master docs/build/html
mv ./docs/build/html/* ./
rm -rf ./docs
git add -A
git commit -m "publishing updated docs..."
git push origin gh-pages

# switch back
git checkout master