Freelance service at MQL5.com: Westgee chart bot

Description

Specification

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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from 1 to 14 day(s)

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