Algorithmic Trading A-z With Python- Machine Le...

split_idx = int(len(X_scaled) * 0.8) X_train, X_test = X_scaled[:split_idx], X_scaled[split_idx:] y_train, y_test = y[:split_idx], y[split_idx:]

A model trained on 2021's bull market fails in 2022's bear market. Your model must detect regime changes (e.g., using Hidden Markov Models from hmmlearn).

Strategies based on the assumption that prices will eventually revert to their mean. Algorithmic Trading A-Z with Python- Machine Le...

data = yf.download('AAPL', start='2019-01-01', end='2024-01-01') data = data[['Open', 'High', 'Low', 'Close', 'Volume']]

y_pred = model.predict(X_test) print(f"Accuracy: accuracy_score(y_test, y_pred):.2f") split_idx = int(len(X_scaled) * 0

Using ta library:

import ta
data['rsi'] = ta.momentum.RSIIndicator(data['Close'], window=14).rsi()
data['macd'] = ta.trend.MACD(data['Close']).macd()
data['bb_high'] = ta.volatility.BollingerBands(data['Close']).bollinger_hband()
data['bb_low'] = ta.volatility.BollingerBands(data['Close']).bollinger_lband()
data['volume_ratio'] = data['Volume'] / data['Volume'].rolling(20).mean()
 A model trained on 2021's bull market fails

Add target variable (future return):

data['target'] = data['Close'].shift(-1) / data['Close'] - 1  # next day return

data = yf.download('SPY', start='2015-01-01', end='2023-12-31') data = data[['Open', 'High', 'Low', 'Close', 'Volume']].dropna()