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Westgee chart bot

MQL5 インディケータ エキスパート 外国為替 売買ロボット/指標のデバッグ ストラテジーの最適化 Python インターネットデータ収集

指定

import pandas as pd
import numpy as np

def generate_signals(data):
    signals = pd.DataFrame(index=data.index)
    signals['price'] = data['Close']
    
    # Calculate moving averages
    signals['short_ma'] = data['Close'].rolling(window=20, min_periods=1).mean()
    signals['long_ma'] = data['Close'].rolling(window=50, min_periods=1).mean()
    
    # Generate buy signals
    signals['buy_signal'] = np.where(signals['short_ma'] > signals['long_ma'], 1, 0)
    
    # Generate sell signals
    signals['sell_signal'] = np.where(signals['short_ma'] < signals['long_ma'], -1, 0)
    
    return signals

def backtest_strategy(signals, initial_capital=10000):
    positions = pd.DataFrame(index=signals.index).fillna(0.0)
    positions['asset'] = 1000 * signals['buy_signal'] + -1000 * signals['sell_signal']
    
    # Calculate portfolio value
    portfolio = positions.multiply(signals['price'], axis=0)
    pos_diff = positions.diff()
    portfolio['holdings'] = (positions.multiply(signals['price'], axis=0)).sum(axis=1)
    portfolio['cash'] = initial_capital - (pos_diff.multiply(signals['price'], axis=0)).sum(axis=1).cumsum()
    portfolio['total'] = portfolio['cash'] + portfolio['holdings']
    portfolio['returns'] = portfolio['total'].pct_change()
    
    return portfolio

# Example usage:
# Assuming 'data' is a DataFrame containing historical price data with 'Date' as index and 'Close' as a column
# signals = generate_signals(data)
# portfolio = backtest_strategy(signals)
# print(portfolio.tail())

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