Register, train, wire, and debug machine learning models for PyBroker backtests using the bundled PyBroker wiki references generated from the local docs. Use when an agent needs to register a model with pybroker.model, write train_fn/predict_fn code for scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, Keras, statsmodels (ARIMA/SARIMAX), or arch models, build ensembles or regime models, run walkforward analysis, build time-series or lagged-feature models, train pooled multi-symbol models, load pretrained models, cache data and trained models, or prevent lookahead leakage in model-driven backtests.
Register, train, wire, and debug machine learning models for PyBroker backtests using the bundled PyBroker wiki references generated from the local docs. Use when an agent needs to register a model with pybroker.model, write train_fn/predict_fn code for scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, Keras, statsmodels (ARIMA/SARIMAX), or arch models, build ensembles or regime models, run walkforward analysis, build time-series or lagged-feature models, train pooled multi-symbol models, load pretrained models, cache data and trained models, or prevent lookahead leakage in model-driven backtests.
PyBroker Model Trainer
Overview
Wire machine learning models into PyBroker backtests by registering training and prediction functions with pybroker.model, feeding them indicator features, and evaluating them with walkforward analysis while keeping the train/test flow free of lookahead leakage. Covers per-symbol, pooled multi-symbol, per-bar time-series, lagged-feature, and pretrained models across common libraries such as scikit-learn, XGBoost, and arch.
Workflow
Extract the modeling spec: library, prediction target and horizon, features (indicators, lagged columns, custom columns), per-symbol vs pooled training, vectorized vs per-bar prediction, walkforward windows and lookahead, caching, and whether the model is trainable or pretrained.
Ask only for missing blockers. If details are absent but noncritical, make conservative assumptions and state them in the final answer or code comments.
Read references/wiki-index.md to choose the smallest relevant wiki page. For nontrivial model work, also read references/model-training-patterns.md.
Build a complete runnable model surface:
define feature indicators with pybroker.indicator or built-ins, and register any non-OHLCV data columns with pybroker.register_columns
write a train_fn that builds the target from train data only and returns the model, or (model, input_cols) to pin prediction columns
register the model with pybroker.model(name, train_fn, ...), choosing indicators, lags/lag_cols, per_bar, pooled, pretrained, input_data_fn, and predict_fn as needed
consume predictions in an execution function via ctx.preds(name) and pass the model source to Strategy.add_execution(models=...)
run strategy.walkforward(windows, train_size, lookahead) for evaluation, or backtest(train_size=...) for a single train/test pass
Validate the produced code as far as the environment allows. At minimum, run syntax checks for created Python files. Run a small local-data walkforward when the repo and data make that practical.
Implementation Rules
Treat PyBroker as a backtesting framework, not a source of financial advice. Make modeling assumptions explicit and avoid performance claims that are not supported by the produced backtest, including model fit metrics.
Use completed historical bar data only. Do not use future prices, future indicator values, or shuffled time series outside the supported train-split shuffle. An indicator value at bar i may depend only on inputs at index i and earlier: no backward shifts such as shift(-1) outside the sanctioned train_fn target, and no negative indexing into full-length arrays inside indicator functions (a negative index silently wraps to the end of the series — the future).
Set lookahead to the number of bars ahead of the prediction target (default 1 for next-bar targets). Walkforward holds out lookahead bars between each train and test split, so an understated value leaks train-adjacent bars into testing.
When registering with lags, the current bar's value is the first feature of each lag block, so the training target must be the next bar's value (for example fit(lag_train[:-1], target[1:])). The training fn must accept lag_train and lag_test keyword arguments.
Keep feature data out-of-band: never widen or mutate the input DataFrame. Work on a .copy() inside train_fn when adding a target column.
An input_data_fn must return exactly one row per bar. A vectorized predict_fn must return one prediction per input row; for classifiers, slice a single predict_proba column. With per_bar=True, predict_fn is required, receives rows up to and including the current bar, must return a scalar, and cannot be combined with pooled=True.
A pooled train_fn receives a sorted symbols tuple and combined frames with a symbol column. Build targets with per-symbol operations such as groupby("symbol")[col].shift(-1) so labels never cross a symbol boundary, and return (model, input_cols) to keep symbol out of model input.
To train a model on a longer time interval, bind it with model_source.intervals("weekly") when passing it to add_execution(models=...); binding is exhaustive, so the base-timeframe model is trained only when is included (e.g. ), and the bound interval is available through without declaring it in (which provides bars only). For interval-bound models, is measured in that interval's compressed bars, and predictions are read with . is then required on /.
Common Deliverables
Standalone .py walkforward backtest script with a trained model.
train_fn/predict_fn pairs for a user's chosen library.
Conversion of an existing single-symbol model to pooled multi-symbol training.
Debugging notes and patches for leaking targets, misaligned predictions, or invalid pybroker.model registrations.
Notebook-ready PyBroker model training cells.
Resources
references/wiki-index.md: start here for topic routing across the bundled references.
references/wiki-06-training-a-model.md: model registration, train/backtest flow, model caching, and walkforward analysis.
references/wiki-16-time-series-models.md: GARCH with per_bar=True and Random Forest on lagged returns with lags/lag_cols.
references/wiki-17-multi-symbol-models.md: pooled multi-symbol training with pooled=True.
references/model-training-patterns.md: load when writing nontrivial train/predict code; library recipes, session hygiene, and the leakage checklist.
references/api-public-surface.md: generated public API signatures and first docstring sentences from local source.
references/pybroker_model.pyi: generated type stubs for model(), indicator(), vector helpers, data sources, and top-level module functions.
references/pybroker_context.pyi: generated type stubs for ExecContext (including its writable order/stop attributes), IntervalContext, RotationContext, ExecResult, and the slippage models.
references/pybroker_strategy.pyi: generated type stubs for Strategy, StrategyConfig, TestResult, and the optimization types.
references/pybroker_types.pyi: generated type stubs for enums, BarData, Portfolio, order/trade/position records, and evaluation result types.
assets/model_training_template.py: copy and adapt when creating a new standalone model training script.
"base"
model_source.intervals("base", "weekly")
ctx.interval
intervals=
lookahead
ctx.interval("...").preds(name)
timeframe=
backtest
walkforward
strategy.optimize supports pretrained models only. Tune trainable models inside train_fn with a search over the train window, or compare registrations across walkforward runs; pybroker.hyperparam is for strategy-level parameters.
Fit scalers, encoders, and any early-stopping validation splits on train data only.
Never use pandas to implement indicator or execution logic: write indicators as vectorized NumPy over BarData arrays (Numba @njit for explicit loops) and read ctx.* NumPy arrays in execution functions — no pd.Series/pd.DataFrame construction and no .rolling/.ewm/.shift/.apply in either. Pandas belongs only at the train_fn/input_data_fn boundary where PyBroker hands you DataFrames; building the target there with shift(-1) on a .copy() stays sanctioned.
Enable caching while iterating: pybroker.enable_data_source_cache(name) to skip refetching data, or pybroker.enable_caches(name) to also cache indicators and trained models.
Call pybroker.disable_progress_bar() in agent-run scripts; progress bar output floods AI token context.
Orders fill at PriceType.MIDDLE — the midpoint of the low and high of the execution bar, which under the default buy_delay/sell_delay of 1 is the bar after the prediction, so PriceType.CLOSE means the next bar's close. Override with ctx.buy_fill_price / ctx.sell_fill_price, which take a PriceType (OPEN, HIGH, LOW, CLOSE, MIDDLE, AVERAGE), a number, or a (symbol, bar_data) callable, and read back as None rather than MIDDLE until set. A limit price only gates the fill: the order still fills at the fill price, never at the limit.
StrategyConfig.exit_on_last_bar defaults to False, which leaves any position still open when the data ends out of trade_count, win_rate, total_pnl and every other trade-level metric, with its P&L stranded in unrealized_pnl. Set exit_on_last_bar=True whenever trade statistics are reported; in walkforward the liquidation fires only on each symbol's true final bar, never at window boundaries.
calc_bootstrap is a walkforward/backtest parameter defaulting to False, not a StrategyConfig field, and it is the natural companion to walkforward analysis: it puts confidence intervals around a model's out-of-sample edge instead of a single point estimate. Pass calc_bootstrap=True to populate result.bootstrap with conf_intervals (BCa — bias corrected and accelerated — intervals for profit factor and Sharpe; 6x2, MultiIndexed on name then conf, columns lower/upper) and drawdown_conf (percentile bounds on max drawdown; 4x2, indexed on conf, columns amount/percent). A profit factor interval whose lower sits below 1 means the edge is not distinguishable from noise. It leaves metrics_df unchanged, stays None under train_only=True, costs roughly bars x StrategyConfig.bootstrap_samples (default 10_000) once for the whole run rather than per window, and needs StrategyConfig.bars_per_year or the Sharpe intervals are per-bar rather than annualized.
Report result.metrics_df as the human-readable summary. When structured output is needed (agent parsing, saved report files, downstream tools), use result.to_json() / result.to_json_str(): the default payload serializes metrics, trades, orders, and bootstrap capped at max_rows=100 rows per table, symbols= filters to specific tickers, and include= opts into portfolio/positions/metrics_df/signals/stops (signals carries model predictions when StrategyConfig(return_signals=True)). Do not replace the metrics_df print outright: the default JSON payload (trades plus orders) is usually larger than the metrics table.
On a Numba compilation or typing error in an @njit indicator, re-run once with the environment variable NUMBA_DISABLE_JIT=1 to get a readable Python traceback, fix the error, then re-run with JIT enabled. Never leave JIT disabled in the final script.
Self-test novel indicator logic for lookahead with the bump-last-bar check: recompute after changing only the final input bar and assert every earlier output is unchanged.
Guard lookbacks with ctx.bars or warmup, and set at most one order side per symbol per bar.
Use ctx.calc_target_shares(target_size) for allocation-based sizing. Use fixed ctx.buy_shares or ctx.sell_shares only when the user asks for fixed share sizing.
Check ctx.long_pos() or ctx.short_pos() before entering or exiting positions. Use ctx.sell_all_shares() and ctx.cover_all_shares() for full exits.
Set entry-time stops on the same bar as the entry order: hold_bars, stop_loss_pct, stop_profit_pct, or stop_trailing_pct.
Rank by model score with ctx.long_score / ctx.short_score and cap positions with strategy.set_max_long_positions(n) / set_max_short_positions(n); the StrategyConfig fields of the same names are deprecated. For score-driven rotation, strategy.enable_rotation(worst_rank_held=...) makes scores drive all trading and ignores order fields set in execution functions.
Use strategy.set_before_exec or strategy.set_after_exec for cross-symbol portfolio logic instead of hiding global state inside a per-symbol execution function.
Optional packages are not PyBroker dependencies: name the required pip install for every model or data-source library the script imports (for example pip install yfinance scikit-learn for the template) and never assume one is importable. When the network or a data-source package is unavailable, validate with a tiny local DataFrame passed to Strategy instead.
If exact API names, constructor parameters, or methods matter, read references/api-public-surface.md.
For exact type signatures — pybroker.model() and train_fn/predict_fn parameter types in references/pybroker_model.pyi, ExecContext prediction access and its writable order/stop attributes in references/pybroker_context.pyi — read the matching references/pybroker_*.pyi stub.
If the user wants a standalone file, copy and adapt assets/model_training_template.py.
