Python And Machine Learning |top| | Algorithmic Trading A-z With

if prediction == 1 and not position: submit_order("AAPL", side="buy", qty=10) elif prediction == 0 and position: submit_order("AAPL", side="sell", qty=position)

data["target_reg"] = future_returns 9. Baseline: Rule-based Strategy Mean reversion with Z-score algorithmic trading a-z with python and machine learning

for train_idx, test_idx in tscv.split(X): X_train, X_test = X.iloc[train_idx], X.iloc[test_idx] y_train, y_test = y.iloc[train_idx], y.iloc[test_idx] model.fit(X_train, y_train) print(model.score(X_test, y_test)) from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense, Dropout Create 3D sequences: (samples, timesteps, features) def create_sequences(data, seq_len=20): X_seq, y_seq = [], [] for i in range(len(data)-seq_len): X_seq.append(data[i:i+seq_len]) y_seq.append(data[i+seq_len]) # next price return np.array(X_seq), np.array(y_seq) if prediction == 1 and not position: submit_order("AAPL",