Machine Learning for Trading

Introduction to ML in finance, feature selection, model training, and avoiding overfitting.

ML Applications in Finance

Prediction Tasks

• Return Prediction: Predict next period returns
• Volatility Forecasting: Predict future volatility
• Classification: Buy/sell/hold signals

Feature Engineering

Technical Features

# Create features
data['MA_20'] = data['Close'].rolling(20).mean()
data['RSI'] = calculate_rsi(data['Close'])
data['MACD'] = calculate_macd(data['Close'])

Market Features

# Market microstructure features
data['Bid_Ask_Spread'] = data['Ask'] - data['Bid']
data['Volume_Ratio'] = data['Volume'] / data['Volume'].rolling(20).mean()

Model Training

Train-Test Split

from sklearn.model_selection import train_test_split

# Time series split (important for finance!)
train_size = int(len(data) * 0.7)
X_train = features[:train_size]
X_test = features[train_size:]
y_train = target[:train_size]
y_test = target[train_size:]

Model Selection

from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

model = RandomForestRegressor(n_estimators=100)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)

Avoiding Overfitting

Cross-Validation

from sklearn.model_selection import TimeSeriesSplit

tscv = TimeSeriesSplit(n_splits=5)
for train_idx, test_idx in tscv.split(X):
    # Train and validate
    pass

Regularization

from sklearn.linear_model import Ridge

# Ridge regression with regularization
model = Ridge(alpha=1.0)
model.fit(X_train, y_train)

Key Takeaways

• Feature Engineering: Create meaningful features
• Time Series Split: Use proper train-test split
• Overfitting: Always validate on out-of-sample data
• Regularization: Prevent overfitting

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