cilipep/ai-agent-with-tslab/walk-forward-validation

def walk_forward(df, config, n_folds=5):
    total = len(df) - config.window
    test_size = total // (n_folds + 1)
    min_train = total // (n_folds + 1)
    equity_parts = []
    for fold in range(n_folds):
        train_end = min_train + fold * test_size
        test_start = train_end
        test_end = min(test_start + test_size, total)
        # Split: train / val / test
        train_indices = list(range(0, train_end - config.window))
        val_split = int(len(train_indices) * 0.85)
        # Train models, optimize threshold on val
        # Evaluate on test
        # Concatenate equity
        part = result["equity"].copy()
        if equity_parts: part = part * equity_parts[-1][-1]
        equity_parts.append(part)
    full_equity = np.concatenate(equity_parts)
    total_return = (full_equity[-1] / full_equity[0] - 1) * 100
    max_dd = ((full_equity / np.maximum.accumulate(full_equity)) - 1).min() * 100
    sharpe = np.mean(returns) / (np.std(returns) + 1e-8) * np.sqrt(365)
    return full_equity, fold_results