PYTHON

Python Relative Strength Indicator

import yfinance as yf
import matplotlib.pyplot as plt
import datetime as dt
import numpy as np
import pandas as pd


def download_data(stock, start, end):
    data = {}
    ticker = yf.download(stock, start, end)
    data['Price'] = ticker['Adj Close']
    return pd.DataFrame(data)


if __name__ == '__main__':

    start_date = dt.datetime(2015, 1, 1)
    end_date = dt.datetime(2020, 1, 1)

    stock_data = download_data('IBM', start_date, end_date)

    stock_data['Return'] = np.log(stock_data['Price'] / stock_data['Price'].shift(1))
    stock_data['Move'] = stock_data['Price'] - stock_data['Price'].shift(1)

    stock_data['Up'] = np.where(stock_data['Move'] > 0, stock_data['Move'], 0)
    stock_data['Down'] = np.where(stock_data['Move'] < 0, stock_data['Move'], 0)

    stock_data['Average_Gain'] = stock_data['Up'].rolling(14).mean()
    stock_data['Average_Loss'] = stock_data['Down'].abs().rolling(14).mean()

    RS = stock_data['Average_Gain'] / stock_data['Average_Loss']

    stock_data['RSI'] = 100.0 - (100.0 / (1.0 + RS))

    stock_data = stock_data.dropna()

    print(stock_data)
    plt.plot(stock_data['RSI'])
    plt.show()

Python Relative Strength Indicator

import yfinance as yf
import matplotlib.pyplot as plt
import datetime as dt
import numpy as np
import pandas as pd


class MovingAverageRSIStrategy:

    def __init__(self, capital, stock, start, end, short_period, long_period):
        self.data = None
        self.is_long = False
        self.short_period = short_period
        self.long_period = long_period
        self.capital = capital
        self.equity = [capital]
        self.stock = stock
        self.start = start
        self.end = end

    def download_data(self):
        stock_data = {}
        ticker = yf.download(self.stock, self.start, self.end)
        stock_data['Price'] = ticker['Adj Close']
        self.data = pd.DataFrame(stock_data)

    def construct_signals(self):
        self.data['Short_MA'] = self.data['Price'].ewm(span=self.short_period).mean()
        self.data['Long_MA'] = self.data['Price'].ewm(span=self.long_period).mean()
        self.data['Move'] = self.data['Price'] - self.data['Price'].shift(1)
        self.data['Up'] = np.where(self.data['Move'] > 0, self.data['Move'], 0)
        self.data['Down'] = np.where(self.data['Move'] < 0, self.data['Move'], 0)
        self.data['Average_Gain'] = self.data['Up'].rolling(14).mean()
        self.data['Average_Loss'] = self.data['Down'].abs().rolling(14).mean()
        relative_strength = self.data['Average_Gain'] / self.data['Average_Loss']
        self.data['RSI'] = 100.0 - (100.0 / (1.0 + relative_strength))
        self.data = self.data.dropna()

    def plot_signals(self):
        plt.figure(figsize=(12, 6))
        plt.plot(self.data['Price'], label='Stock Price')
        plt.plot(self.data['Short_MA'], label='Short MA', color='blue')
        plt.plot(self.data['Long_MA'], label='Long MA', color='green')
        plt.title('Moving Average (MA) Crossover Trading Strategy With RSI')
        plt.xlabel('Date')
        plt.ylabel('Stock Price')
        plt.show()

    def simulate(self):
        price_when_buy = 0

        for index, row in self.data.iterrows():
            if row['Short_MA'] < row['Long_MA'] and self.is_long:
                self.equity.append(self.capital * row.Price / price_when_buy)
                self.is_long = False
            elif row['Short_MA'] > row['Long_MA'] and not self.is_long 
                    and row['RSI'] < 30:
                price_when_buy = row['Price']
                self.is_long = True

    def plot_equity(self):
        plt.figure(figsize=(12, 6))
        plt.title('Equity Curve')
        plt.plot(self.equity, label='Stock Price', color='green')
        plt.xlabel('Date')
        plt.ylabel('Capital ($)')
        plt.show()

    def show_stats(self):
        print("Profit Of The Trading Strategy: %.2f%%" % (
            (float(self.equity[-1]) - float(self.equity[0])) /
            float(self.equity[0]) * 100))
        print("Actual Capital: %0.2f" % self.equity[-1])
        returns = (self.data['Price'] - self.data['Price'].shift(1)) / self.data['Price'].shift(1)
        ratio = returns.mean() / returns.std() * np.sqrt(252)
        print('Sharpe Ratio: %.2f' % ratio)


if __name__ == '__main__':

    start_date = dt.datetime(2010, 1, 1)
    end_date = dt.datetime(2020, 1, 1)

    model = MovingAverageRSIStrategy(100, 'IBM', start_date, end_date, 40, 150)
    model.download_data()
    model.construct_signals()
    model.plot_signals()
    model.simulate()
    model.plot_equity()
    model.show_stats()