Full Code of mtamer/python-rsi for AI

Repository: mtamer/python-rsi
Branch: master
Commit: 406e9f5223ea
Files: 7
Total size: 15.2 KB

Directory structure:
gitextract_1_kn1di5/

├── .gitignore
├── .vscode/
│   └── settings.json
├── README.md
└── src/
    ├── main.py
    ├── requirements.txt
    ├── stock.py
    └── stocks.txt

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FILE CONTENTS
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FILE: .gitignore
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FILE: .vscode/settings.json
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{
    "python.pythonPath": "/usr/bin/python3"
}

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FILE: README.md
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# python-rsi

RSI (Relative Strength Index) written in Python

![Python RSI Image](https://github.com/mtamer/python-rsi/blob/master/src/images/example.png)

## About

Relative Strength Index written in Python. The whole point of this application is to be able to come up with a list of as many different types of stocks (stock tickers) that you want to screen and see if it meets the Relative Strength criteria. A combination of the RSI and the 20 and 200 day Moving Average (MA) tend to be strong and popular indicators to determine the future behavior of a stock.

At the end of pulling stock data, a table is shown displaying metrics of the stocks pulled along with a link to view the chart.

![Stock Data Table](https://github.com/mtamer/python-rsi/blob/master/src/images/pulled-data-table.png)

## To Install

1. Clone repo: `git clone https://github.com/mtamer/python-rsi.git`
2. `cd src`
3. `pip install -r requirements.txt`
4. Look inside the `main.py` file and put all the stocks you want to monitor in there or inside of `stocks.txt`
5. To run: `python main.py`
6. Enjoy!

## Disclaimer

This repository is only for educational purposes. The owner and contributors of this repository are not responsible for how you use this code and the gains/losses you accumulate.


================================================
FILE: src/main.py
================================================
#!/usr/bin/env python3

import datetime
import random
import time
from urllib.request import urlopen

import matplotlib
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import numpy as np
import pandas_datareader.data as web
import pylab
from mplfinance.original_flavor import candlestick_ohlc
from pandas.core.common import flatten
from tabulate import tabulate

from stock import Stock

matplotlib.rcParams.update({'font.size': 9})

stocks = []

# If stocks array is empty, pull stock list from stocks.txt file
stocks = stocks if len(stocks) > 0 else [
    line.rstrip() for line in open("stocks.txt", "r")]

# Time frame you want to pull data from
start = datetime.datetime.now()-datetime.timedelta(days=365)
end = datetime.datetime.now()

if __name__ == "__main__":

    # Array of moving averages you want to get
    MAarr = [20, 200]

    allData = []

    for ticker in stocks:

        try:
            data = []

            print("Pulling data for " + ticker)

            stock = Stock(ticker, start, end)

            # Append data to array
            data.append(ticker.upper())

            data.append(stock.closes[-1])

            for MA in MAarr:
                computedSMA = stock.SMA(period=MA)
                # print(computedSMA)
                data.append(computedSMA[-1])

            currentRsi = float("{:.2f}".format(stock.rsi[-1]))

            if currentRsi > 70:
                data.append(str(currentRsi) + " 🔥")
            elif currentRsi < 30:
                data.append(str(currentRsi) + " 🧊")
            else:
                data.append(currentRsi)

            chartLink = "https://finance.yahoo.com/quote/" + ticker + "/chart?p=" + ticker

            data.append(chartLink)

            allData.append(data)

            # Shows chart only if current RSI is greater than or less than 70 or 30 respectively
            if currentRsi < 30 or currentRsi > 70:

                stock.graph(MAarr)

        except Exception as e:
            print('Error: ', str(e))

    print(tabulate(allData, headers=flatten([
        'Stock', 'Price', [str(x) + " MA" for x in MAarr], "RSI", "chart"])))


================================================
FILE: src/requirements.txt
================================================
astroid==2.4.2
autopep8==1.5.3
certifi==2020.4.5.2
chardet==3.0.4
colorama==0.4.3
cycler==0.10.0
idna==2.9
isort==4.3.21
kiwisolver==1.2.0
lazy-object-proxy==1.4.3
lxml==4.5.1
matplotlib==3.2.2
mccabe==0.6.1
mplfinance==0.12.5a3
numpy==1.18.5
pandas==1.0.5
pandas-datareader==0.8.1
pycodestyle==2.6.0
pylint==2.5.3
pyparsing==2.4.7
python-dateutil==2.8.1
pytz==2020.1
requests==2.24.0
six==1.15.0
tabulate==0.8.7 
toml==0.10.1
typed-ast==1.4.1
urllib3==1.25.9
wrapt==1.12.1


================================================
FILE: src/stock.py
================================================
#!/usr/bin/env python3

import datetime
import random
from urllib.request import urlopen

import matplotlib
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import numpy as np
import pandas_datareader.data as web
import pylab
from mplfinance.original_flavor import candlestick_ohlc

matplotlib.rcParams.update({'font.size': 9})


class Stock:

    ticker = None
    dates = None
    closes = None
    highs = None
    lows = None
    opens = None
    volumes = None
    rsi = None

    def __init__(self, ticker, start, end=datetime.datetime.now()):
        self.ticker = ticker

        """
        Different sources for pulling data can be found here:
        https://readthedocs.org/projects/pandas-datareader/downloads/pdf/latest/
        """

        stockData = web.DataReader(ticker, 'yahoo', start, end)

        self.dates = [mdates.date2num(d) for d in stockData.index]
        self.closes = stockData['Close']
        self.highs = stockData['High']
        self.lows = stockData['Low']
        self.opens = stockData['Open']
        self.volumes = stockData['Volume']

        self.rsi = self.RSI(self.closes)

    def RSI(self, prices, n=14):
        deltas = np.diff(prices)
        seed = deltas[:n+1]
        up = seed[seed >= 0].sum()/n
        down = -seed[seed < 0].sum()/n
        rs = up/down
        rsi = np.zeros_like(prices)
        rsi[:n] = 100. - 100./(1.+rs)

        for i in range(n, len(prices)):
            delta = deltas[i-1]  # The diff is 1 shorter

            if delta > 0:
                upval = delta
                downval = 0.
            else:
                upval = 0.
                downval = -delta

            up = (up*(n-1) + upval)/n
            down = (down*(n-1) + downval)/n

            rs = up/down
            rsi[i] = 100. - 100./(1.+rs)

        return rsi

    def SMA(self, period, values=None):

        values = self.closes if values is None else values

        """
        Simple Moving Average. Periods are the time frame. For example, a period of 50 would be a 50 day
        moving average. Values are usually the stock closes but can be passed any values
        """

        weigths = np.repeat(1.0, period)/period
        smas = np.convolve(values, weigths, 'valid')
        return smas  # as a numpy array

    def EMA(self, period, values=None):

        values = self.closes if values is None else values

        """
        Exponential Moving Average. Periods are the time frame. For example, a period of 50 would be a 50 day
        moving average. Values are usually the stock closes but can be passed any values
        """

        weights = np.exp(np.linspace(-1., 0., period))
        weights /= weights.sum()
        a = np.convolve(values, weights, mode='full')[:len(values)]
        a[:period] = a[period]
        return a

    def MACD(self, x, slow=26, fast=12):
        """
        Compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
        return value is emaslow, emafast, macd which are len(x) arrays
        """

        emaslow = self.EMA(slow, x)
        emafast = self.EMA(fast, x)
        return emaslow, emafast, emafast - emaslow

    def graph(self, movingAverageArr=[]):
        try:

            x = 0
            y = len(self.dates)
            newAr = []
            while x < y:
                appendLine = self.dates[x], self.opens[x], self.closes[x], self.highs[x], self.lows[x], self.volumes[x]
                newAr.append(appendLine)
                x += 1

            # Fix this
            SP = len(self.dates[200-1:])

            fig = plt.figure(facecolor='#07000d')

            ax1 = plt.subplot2grid(
                (6, 4), (1, 0), rowspan=4, colspan=4, facecolor='#07000d')
            candlestick_ohlc(ax1, newAr[-SP:], width=.6,
                             colorup='#53c156', colordown='#ff1717')

            for MA in movingAverageArr:

                computedSMA = self.SMA(MA, self.closes)

                # Used to generate random hex color to put on graph
                def r(): return random.randint(0, 255)
                randomColor = '#%02X%02X%02X' % (r(), r(), r())

                maLabel = str(MA) + ' SMA'
                ax1.plot(self.dates[-SP:], computedSMA[-SP:], randomColor,
                         label=maLabel, linewidth=1.5)

            ax1.grid(False, color='w')
            ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
            ax1.xaxis.set_major_formatter(
                mdates.DateFormatter('%Y-%m-%d'))
            ax1.yaxis.label.set_color("w")
            ax1.spines['bottom'].set_color("#5998ff")
            ax1.spines['top'].set_color("#5998ff")
            ax1.spines['left'].set_color("#5998ff")
            ax1.spines['right'].set_color("#5998ff")
            ax1.tick_params(axis='y', colors='w')
            plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
            ax1.tick_params(axis='x', colors='w')
            plt.ylabel('Stock price and Volume')

            maLeg = plt.legend(loc=9, ncol=2, prop={'size': 7},
                               fancybox=True, borderaxespad=0.)
            maLeg.get_frame().set_alpha(0.4)
            textEd = pylab.gca().get_legend().get_texts()
            pylab.setp(textEd[0:5], color='w')

            volumeMin = 0

            ax0 = plt.subplot2grid(
                (6, 4), (0, 0), sharex=ax1, rowspan=1, colspan=4, facecolor='#07000d')

            rsiCol = '#c1f9f7'
            posCol = '#386d13'
            negCol = '#8f2020'

            ax0.plot(self.dates[-SP:], self.rsi[-SP:],
                     rsiCol, linewidth=1.5)
            ax0.axhline(70, color=negCol)
            ax0.axhline(30, color=posCol)
            ax0.fill_between(self.dates[-SP:], self.rsi[-SP:], 70, where=(self.rsi[-SP:]
                                                                          >= 70), facecolor=negCol, edgecolor=negCol, alpha=0.5)
            ax0.fill_between(self.dates[-SP:], self.rsi[-SP:], 30, where=(self.rsi[-SP:]
                                                                          <= 30), facecolor=posCol, edgecolor=posCol, alpha=0.5)
            ax0.set_yticks([30, 70])
            ax0.yaxis.label.set_color("w")
            ax0.spines['bottom'].set_color("#5998ff")
            ax0.spines['top'].set_color("#5998ff")
            ax0.spines['left'].set_color("#5998ff")
            ax0.spines['right'].set_color("#5998ff")
            ax0.tick_params(axis='y', colors='w')
            ax0.tick_params(axis='x', colors='w')
            plt.ylabel('RSI')

            ax1v = ax1.twinx()
            ax1v.fill_between(self.dates[-SP:], volumeMin,
                              self.volumes[-SP:], facecolor='#00ffe8', alpha=.4)
            ax1v.axes.yaxis.set_ticklabels([])
            ax1v.grid(False)

            # Edit this to 3, so it's a bit larger
            ax1v.set_ylim(0, 3*self.volumes.max())
            ax1v.spines['bottom'].set_color("#5998ff")
            ax1v.spines['top'].set_color("#5998ff")
            ax1v.spines['left'].set_color("#5998ff")
            ax1v.spines['right'].set_color("#5998ff")
            ax1v.tick_params(axis='x', colors='w')
            ax1v.tick_params(axis='y', colors='w')
            ax2 = plt.subplot2grid(
                (6, 4), (5, 0), sharex=ax1, rowspan=1, colspan=4, facecolor='#07000d')
            fillcolor = '#00ffe8'
            nslow = 26
            nfast = 12
            nema = 9
            emaslow, emafast, macd = self.MACD(self.closes)
            ema9 = self.EMA(nema, macd)
            ax2.plot(self.dates[-SP:], macd[-SP:],
                     color='#4ee6fd', lw=2)
            ax2.plot(self.dates[-SP:], ema9[-SP:],
                     color='#e1edf9', lw=1)
            ax2.fill_between(self.dates[-SP:], macd[-SP:]-ema9[-SP:], 0,
                             alpha=0.5, facecolor=fillcolor, edgecolor=fillcolor)

            plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
            ax2.spines['bottom'].set_color("#5998ff")
            ax2.spines['top'].set_color("#5998ff")
            ax2.spines['left'].set_color("#5998ff")
            ax2.spines['right'].set_color("#5998ff")
            ax2.tick_params(axis='x', colors='w')
            ax2.tick_params(axis='y', colors='w')
            plt.ylabel('MACD', color='w')
            ax2.yaxis.set_major_locator(
                mticker.MaxNLocator(nbins=5, prune='upper'))
            for label in ax2.xaxis.get_ticklabels():
                label.set_rotation(45)

            plt.suptitle(self.ticker.upper(), color='w')

            plt.setp(ax0.get_xticklabels(), visible=False)
            plt.setp(ax1.get_xticklabels(), visible=False)

            # ax1.annotate('Look Here!', (date[-1], Av1[-1]),
            #              xytext=(0.8, 0.9), textcoords='axes fraction',
            #              arrowprops=dict(facecolor='white', shrink=0.05),
            #              fontsize=14, color='w',
            #              horizontalalignment='right', verticalalignment='bottom')

            plt.subplots_adjust(left=.09, bottom=.14,
                                right=.94, top=.95, wspace=.20, hspace=0)

            plt.show()
            fig.savefig('example.png', facecolor=fig.get_facecolor())

        except Exception as e:
            print('Error graphing data: ', str(e))


================================================
FILE: src/stocks.txt
================================================
baba
roku
msft
amzn
tqqq
nvda
vgt
twtr
dis
t
snap
tsla