Tune PyBroker strategy hyperparameters with Optuna-backed search using the bundled PyBroker wiki references generated from the local docs. Use when an agent needs to declare tunable values with pybroker.hyperparam, run Strategy.optimize with grid, TPE, or random samplers, choose n_trials, direction, train_size, or seed, write score functions over TestResult metrics, wire hyperparams into indicator kwargs or ctx.hyperparam via add_execution(hyperparams=...), pass custom Optuna samplers or a supplied study, inspect OptimizeResult, WindowOptimizeResult, or study.trials_dataframe(), run walkforward optimization with windows, pin winning values with backtest(params=...), or debug failed trials and grid explosions.
Instrucciones de origen · Vista previa de solo lectura
name
pybroker-optimize
description
Tune PyBroker strategy hyperparameters with Optuna-backed search using the bundled PyBroker wiki references generated from the local docs. Use when an agent needs to declare tunable values with pybroker.hyperparam, run Strategy.optimize with grid, TPE, or random samplers, choose n_trials, direction, train_size, or seed, write score functions over TestResult metrics, wire hyperparams into indicator kwargs or ctx.hyperparam via add_execution(hyperparams=...), pass custom Optuna samplers or a supplied study, inspect OptimizeResult, WindowOptimizeResult, or study.trials_dataframe(), run walkforward optimization with windows, pin winning values with backtest(params=...), or debug failed trials and grid explosions.
PyBroker Optimizer
Overview
Tune PyBroker strategy hyperparameters with Strategy.optimize by declaring tunable values with pybroker.hyperparam, wiring them into indicators and execution functions, and scoring each candidate combination on a training window before the winning values are replayed on held-out test data. Covers grid, TPE, and random sampling through the integrated Optuna backend, custom Optuna samplers and studies, walkforward optimization across multiple windows, and reading results from OptimizeResult and the underlying optuna.Study.
Workflow
Extract the optimization spec: which values to tune with their low/high/step ranges and numeric types, the score metric and direction, sampler and trial budget, train_size, windows, seed, caching, and whether the strategy uses models (trainable models are unsupported by optimize).
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 optimization work, also read references/optimization-patterns.md.
Build a complete runnable optimization surface:
declare tunable values with pybroker.hyperparam(name, default=..., low=..., high=..., step=...), using low == high to pin a value without searching it
attach each hyperparam where it is consumed: as an indicator keyword argument, through add_execution(..., hyperparams=[...]) read with ctx.hyperparam(name), or into set_max_long_positions/set_max_short_positions/enable_rotation(worst_rank_held=...)
write a score_fn(result: TestResult) -> float over result.metrics with None-guards for metrics that can be undefined
run strategy.optimize(score_fn, sampler=..., n_trials=..., seed=..., train_size=..., windows=...), with trial parallelism configured through pybroker.set_parallel(n_jobs=...)
read OptimizeResult: best_params, best_score, result (the held-out test TestResult), study, and windows; pin winning values into later runs with backtest(params=...) or walkforward(..., params=...)
Validate the produced code as far as the environment allows. At minimum, run syntax checks for created Python files. Run a small-grid optimize on small local data when the repo and data make that practical.
Implementation Rules
Treat PyBroker as a backtesting framework, not a source of financial advice. State assumptions explicitly, and never present the in-sample best_score as expected performance; report the held-out test metrics from OptimizeResult.result.
Use completed historical bar data only. Indicator logic must be lookahead-free: never index a full-length array with a negative index (it silently wraps to the end of the series, the future) and never shift future values backward; a value at bar i may depend only on inputs at index i and earlier. Self-test novel indicator logic with the bump-last-bar check: change only the final input bar and assert every earlier output is unchanged.
A Hyperparam's default, low, high, and step must all share one numeric type (all int or all float; bools are rejected), step must be positive, low cannot exceed high, and high - low must be an exact multiple of step. Candidate values run from low to high inclusive; low == high declares a fixed hyperparam that resolves in backtests and appears in best_params but is excluded from the search.
The search-space key is the hyperparam name, not the consuming keyword: with lookback = pybroker.hyperparam("lookback", ...), pybroker.indicator("sma", sma, period=lookback) searches "lookback".
ctx.hyperparam("name") works only when the same registered Hyperparam object is passed in add_execution(..., hyperparams=[...]); reading an unattached name raises ValueError.
Write score_fn(result: TestResult) -> float over result.metrics fields and guard Optional metrics (lambda r: r.metrics.sharpe if r.metrics.sharpe is not None else 0.0); a None or NaN score marks that trial FAILED instead of aborting the study. Use direction="minimize" for objectives such as drawdown.
Choose the sampler by search-space size: "grid" (the default) exhaustively enumerates every lattice combination and evaluates trials in parallel; "random" also parallelizes; "tpe" adapts to earlier trials and therefore runs sequentially. Any instance is also accepted; it is deep-copied and reseeded per window and must be picklable when .
Common Deliverables
Standalone .py optimization script reporting best_params and held-out test metrics.
Conversion of a hard-coded strategy to pybroker.hyperparam-driven values plus an optimize run.
Walkforward optimization (windows > 1) with per-window parameter reporting.
Optuna study analysis: trials_dataframe() summaries, custom sampler configuration, supplied studies.
Notebook-ready PyBroker optimization cells.
Resources
references/wiki-index.md: start here for topic routing across the bundled references.
references/wiki-12-parameter-optimization.md: declaring hyperparameters, grid search, TPE and other samplers, and walkforward optimization.
references/wiki-11-configuring-parallelization.md: worker counts with set_parallel, parallel indicators, and the Ray backend.
references/wiki-03-evaluating-with-bootstrap-metrics.md: evaluation metrics, bootstrap confidence intervals, and maximum drawdown.
references/optimization-patterns.md: load when writing nontrivial optimization code; score-function recipes, Optuna integration, walkforward windows, and the optimization checklist.
references/api-public-surface.md: generated public API signatures and first docstring sentences from local source.
references/pybroker_strategy.pyi: generated type stubs for Strategy, StrategyConfig, TestResult, and the optimization types.
references/pybroker_context.pyi: generated type stubs for ExecContext (including ctx.hyperparam and its writable order/stop attributes), IntervalContext, RotationContext, ExecResult, and the slippage models.
references/pybroker_model.pyi: generated type stubs for model(), indicator(), vector helpers, data sources, and top-level module functions.
references/pybroker_types.pyi: generated type stubs for enums, BarData, Portfolio, order/trade/position records, and evaluation result types.
assets/optimize_template.py: copy and adapt when creating a new standalone optimization script.
optuna.samplers.BaseSampler
windows > 1
n_trials is required for every sampler except "grid", where it defaults to the full grid size and a smaller value samples that many combinations at random. Heed the grid-explosion warning when grid_size * windows exceeds 1000: coarsen step, fix values with low == high, or switch to "tpe"/"random" with an explicit n_trials.
Pass seed= for reproducible optimization; it seeds the sampler and bootstrap metrics, and window i derives seed + i. Unlike backtest/walkforward (default seed=42), optimize defaults to seed=None, which does not reproduce.
Every trial backtests only the training split (train_size, exclusive of 0 and 1, default 0.5); the winning combination is then replayed once on the held-out test window, which score_fn never sees, producing OptimizeResult.result.
With windows > 1, each walkforward window is tuned by its own study and the per-window winners are replayed into one continuous stitched result with cash and positions carried across window boundaries. best_params, best_score, and study describe the last window only; report per-window values from OptimizeResult.windows (WindowOptimizeResult holds params, study, train_score, and the window dates, but no per-window test result). study= is rejected when windows > 1.
optimize rejects trainable model sources; pretrained models (pybroker.model(..., pretrained=True)) are supported and loaded once per train window, then reused across that window's trials. Tune trainable models inside train_fn with a validation split over the train window instead.
Trial and window parallelism come only from the global pybroker.set_parallel(n_jobs=...); optimize has no n_jobs parameter.
pruner= is passed through to the created Optuna study but never triggers, because each trial is one complete backtest with no intermediate values to report; do not rely on pruning for budget control.
Pin tuned values outside of optimization with strategy.backtest(params={...}) or strategy.walkforward(..., params=...); hyperparam defaults apply when params is omitted.
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.
An indicator returns one full-length one-dimensional array with one value per input bar, warmup left-padded with NaN — never a shortened array.
Keep feature data out-of-band: never widen or mutate the user's input DataFrame.
Enable caching while iterating: pybroker.enable_data_source_cache(name) to skip refetching data, or pybroker.enable_caches(name) to also cache indicators. Call pybroker.disable_progress_bar() in agent-run scripts, and add pybroker.disable_logging() for optimize runs, which backtest once per trial and would otherwise flood context with per-run logs.
StrategyConfig.exit_on_last_bar defaults to False, which leaves the position still open at the end of each trial's window out of trade_count, win_rate, total_pnl and every other trade-level metric, with its P&L stranded in unrealized_pnl. A score_fn that reads realized P&L or trade counts therefore ranks trials on an unclosed book unless exit_on_last_bar=True is set; bar-level scores (sharpe, max_drawdown, profit_factor) come from per-bar market value and are barely affected. optimize scopes this deliberately: each tuning trial liquidates at the end of its own window, while the stitched opt.result uses the whole dataset so it matches an equivalent walkforward() run.
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 signal, 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. A limit price only gates the fill: the order still fills at the fill price, never at the limit.
calc_bootstrap is an optimize/backtest/walkforward parameter defaulting to False, not a StrategyConfig field (the StrategyConfig knob is bootstrap_samples, default 10_000). Only opt.result can carry bootstrap metrics: the per-trial train replays hardcode calc_bootstrap=False, so a score_fn never sees them and cannot rank on a confidence interval. Pass calc_bootstrap=True to populate opt.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). It changes no metrics_df value, so it never alters a score, and it is paid once on the final result rather than per trial.
Report opt.result.metrics_df as the human-readable summary. When structured output is needed (agent parsing, saved report files, downstream tools), use opt.to_json() / opt.to_json_str(): they serialize best_params, a study summary, the held-out test result (with the same include=/max_rows=/symbols= controls as TestResult.to_json), and per-window results including selector-resolved symbols. The in-sample best_score still must never be presented as performance.
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.
Guard lookbacks with ctx.bars or warmup, and set at most one order side per symbol per bar. Use current API only: rank with ctx.long_score/ctx.short_score and cap positions with strategy.set_max_long_positions(n)/set_max_short_positions(n), not the deprecated StrategyConfig fields.
If exact API names, constructor parameters, or methods matter, read references/api-public-surface.md.
For exact type signatures — Strategy.optimize, Hyperparam, OptimizeResult, and WindowOptimizeResult in references/pybroker_strategy.pyi, ctx.hyperparam and the 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/optimize_template.py.
