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

# Example: position sizing based on volatility (Kelly Criterion simplified)
test_data['volatility'] = test_data['returns'].rolling(20).std()
test_data['kelly_fraction'] = (test_data['prediction'] * 0.5) / test_data['volatility']  # dummy
test_data['position_size'] = test_data['kelly_fraction'].clip(0, 0.2)  # max 20% per trade
X_test = test[features]
test['Prediction'] = model.predict(X_test)
print(f"Accuracy: accuracy_score(test['Target'], test['Prediction']):.2f")

Most ML fails in trading due to overfitting (the model memorizes noise). Algorithmic Trading A-Z with Python- Machine Le...

The goal is usually to predict future price direction (Classification) or exact price (Regression). # Example: position sizing based on volatility (Kelly

y_pred = model.predict(X_test) print(f"Accuracy: accuracy_score(y_test, y_pred):.2f") Most ML fails in trading due to overfitting

Never shuffle time series data.

split_idx = int(len(data) * 0.8)
train = data.iloc[:split_idx]
test = data.iloc[split_idx:]

from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
scaler = MinMaxScaler()
scaled = scaler.fit_transform(data[features])