Editing Algorithmic Trading (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''ML trading strategy with cross-sectional momentum:'''
<syntaxhighlight lang="python">
import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.preprocessing import RobustScaler
import yfinance as yf

def compute_features(prices_df: pd.DataFrame) -> pd.DataFrame:
    """Compute cross-sectional trading features."""
    features = pd.DataFrame(index=prices_df.index)
    # Momentum features (key factors in equity strategies)
    for window in [5, 21, 63, 126, 252]:
        returns = prices_df.pct_change(window)
        features[f'mom_{window}d'] = returns.rank(axis=1, pct=True).stack()
    # Mean-reversion (contrarian at short timescales)
    features['reversal_5d'] = -prices_df.pct_change(5).rank(axis=1, pct=True).stack()
    # Volatility (vol normalization)
    features['vol_21d'] = prices_df.pct_change().rolling(21).std().stack()
    # Volume-price trend
    features['volume_trend'] = (prices_df / prices_df.rolling(20).mean()).stack()
    return features.dropna()

def train_alpha_model(features: pd.DataFrame, returns: pd.Series):
    """Train cross-sectional return predictor."""
    # Target: forward 21-day cross-sectional rank of returns
    y = returns.groupby(level='date').rank(pct=True)
    X = features.reindex(y.index)
    # Time-series cross-validation (never look forward)
    cutoff = int(len(y.unique()) * 0.8)
    dates = sorted(y.index.get_level_values('date').unique())
    train_dates = dates[:cutoff]
    X_train = X[X.index.get_level_values('date').isin(train_dates)]
    y_train = y[y.index.get_level_values('date').isin(train_dates)]
    scaler = RobustScaler()
    model = GradientBoostingRegressor(n_estimators=200, max_depth=3, learning_rate=0.05)
    model.fit(scaler.fit_transform(X_train.fillna(0)), y_train)
    return model, scaler

def backtest_strategy(model, scaler, features, prices, top_n=20):
    """Simulate long-short strategy: long top quintile, short bottom quintile."""
    returns = prices.pct_change()
    preds = pd.Series(model.predict(scaler.transform(features.fillna(0))),
                      index=features.index, name='alpha_score')
    portfolio_returns = []
    for date in sorted(preds.index.get_level_values('date').unique()):
        day_preds = preds.xs(date, level='date').sort_values(ascending=False)
        longs = day_preds.head(top_n).index
        shorts = day_preds.tail(top_n).index
        if date in returns.index:
            long_ret = returns.loc[date, longs].mean()
            short_ret = returns.loc[date, shorts].mean()
            portfolio_returns.append(long_ret - short_ret)
    pnl = pd.Series(portfolio_returns)
    sharpe = pnl.mean() / pnl.std() * np.sqrt(252)
    print(f"Annualized Sharpe: {sharpe:.2f} | Max Drawdown: {(pnl.cumsum() - pnl.cumsum().cummax()).min():.2%}")
    return pnl
</syntaxhighlight>

; Algorithmic trading AI tools
: '''Backtesting''' → Backtrader, Zipline, QuantConnect (cloud), VectorBT
: '''Alternative data''' → Quandl (Nasdaq), Bloomberg Terminal, Refinitiv Eikon
: '''NLP sentiment''' → FinBERT, Bloomberg NLP, Ravenpack, Accern
: '''Execution''' → Alpaca (API broker), Interactive Brokers API, FIX Protocol
: '''Research platforms''' → QuantConnect, Numerai (crowdsourced hedge fund)
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