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AlphaBacktestExperiment

The keystone "model used as alpha" experiment — train a model, register it, deploy it as DeployedModelAlpha, run a backtest, and persist combined ML + trading metrics under one MLflow parent run.

When to use​

Use AlphaBacktestExperiment whenever you want to answer the question "how does this model perform when its predictions actually drive trades?". The standard Experiment family computes IC / RMSE / MAE in isolation; AlphaBacktestExperiment adds Sharpe / Sortino / hit-rate and links them back to the trained ModelVersion so the Strategy Browser, MLflow UI, and Postgres catalog all converge.

Shape​

ConceptClass / table
Orchestratoralphaswarm.ml.alpha_backtest_experiment::AlphaBacktestExperiment
Combined metricsalphaswarm.ml.alpha_metrics
Combined run rowMLAlphaBacktestRun (Alembic 0025)
Per-bar audit (opt-in)MLPredictionAudit (Alembic 0025)
Celery taskalphaswarm.tasks.ml_tasks.run_alpha_backtest_experiment (queue ml)
RESTPOST /ml/alpha-backtest-runs, GET /ml/alpha-backtest-runs[/{id}/predictions]

Workflow​

Metric vocabulary​

The combined metrics blob persisted on MLAlphaBacktestRun.combined_metrics rolls up:

  • ML-side: ic_spearman, ic_pearson, icir, mae, rmse, hit_rate
  • Trading-side: sharpe, sortino, calmar, max_drawdown, total_return, turnover_adj_sharpe
  • Combined scalar: score = combined_score(ml_metrics, trading_metrics) — default weighting in alphaswarm/ml/alpha_metrics.py prioritises Sharpe (0.45) but also rewards IC / IR / hit-rate so a high-IC model that fails to translate to PnL is penalised.

Calling from code​

from alphaswarm.ml.alpha_backtest_experiment import AlphaBacktestExperiment

experiment = AlphaBacktestExperiment(
    dataset_cfg=dataset_cfg,
    model_cfg=model_cfg,
    strategy_cfg=strategy_cfg,
    backtest_cfg=backtest_cfg,
    run_name="ridge-alpha-backtest",
    train_first=True,
    capture_predictions=True,
)
result = experiment.run()
print(result.combined_metrics)

Calling from REST​

curl -XPOST http://localhost:8000/ml/alpha-backtest-runs \
  -H 'content-type: application/json' \
  -d @configs/ml/alpha_backtest/ridge_alpha_backtest.yaml

The response is a TaskAccepted envelope; subscribe to /chat/stream/{task_id} for progress events.

Where this goes wrong​

  • Forgetting train_first=False when re-using an existing deployment_id will trigger a re-train. Set it explicitly.
  • The combined-metric weights are heuristic — customise them per strategy by passing weights={...} to combined_score.
  • MLPredictionAudit is gated behind ALPHASWARM_ML_PREDICTION_AUDIT_ENABLED; default is false to keep the table small. Enable it for forensic explainability.