model = RandomForestClassifier(n_estimators=100, random_state=42) model.fit(X_train, y_train) X_test = test[features] test['Prediction'] = model.predict(X_test) print(f"Accuracy: {accuracy_score(test['Target'], test['Prediction']):.2f}") I. Advanced: LSTM (Long Short-Term Memory) For price sequences, LSTMs capture long-term dependencies. Using TensorFlow/Keras:
data['Target'] = (data['Returns'].shift(-5) > 0).astype(int) # 1 if price higher in 5 days For regression (predicting exact return):
Combine sentiment score + technicals as features for your model. Instead of predicting price, teach an agent to maximize equity curve . Using Stable-Baselines3 :
import yfinance as yf import pandas as pd data = yf.download('AAPL', start='2020-01-01', end='2024-01-01') data['Returns'] = data['Close'].pct_change() print(data.head())
