Algorithmic Trading A-z With Python And Machine Learning May 2026
future_returns = data["Close"].shift(-5) / data["Close"] - 1 data["target"] = (future_returns > 0.01).astype(int) # 1=up>1%, 0=else For (expected return):
data["target_reg"] = future_returns 9. Baseline: Rule-based Strategy Mean reversion with Z-score algorithmic trading a-z with python and machine learning
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) future_returns = data["Close"]
data["zscore"] = (data["Close"] - data["Close"].rolling(20).mean()) / data["Close"].rolling(20).std() data["signal"] = 0 data.loc[data["zscore"] < -1.5, "signal"] = 1 # Buy data.loc[data["zscore"] > 1.5, "signal"] = -1 # Sell from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import TimeSeriesSplit features = ["rsi", "volatility", "volume_ratio", "ret_lag1", "ret_lag2"] X = data[features].dropna() y = data["target"].dropna() Align indices common_idx = X.index.intersection(y.index) X, y = X.loc[common_idx], y.loc[common_idx] Time series cross-validation tscv = TimeSeriesSplit(n_splits=5) model = RandomForestClassifier(n_estimators=100, max_depth=5) 0.01).astype(int) # 1=up>