“Indicateur de résistance relative python” Réponses codées

Indicateur de résistance relative python

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()
Sleepy Skimmer

Indicateur de résistance relative python

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()
Sleepy Skimmer

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