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edtechre

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• 10 hours ago last updated

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   Pybroker Optimize · edtechre bundle

   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.
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   Pybroker Model Trainer · edtechre bundle

   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.
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   Pybroker Multi Interval · edtechre bundle

   Build, wire, and debug multi-timeframe PyBroker strategies using the bundled PyBroker wiki references generated from the local docs. Use when an agent needs to trade a base timeframe with confirmation from coarser weekly or monthly bars, compress bars into higher time intervals, declare compressed bars with the intervals parameter of add_execution, read completed higher-timeframe bars with ctx.interval and IntervalContext, bind indicators to intervals with Indicator.intervals, train models per interval with ModelSource.intervals, choose interval formats such as every-n-bars ints, duration strings like 5m or 1h, or calendar strings like weekly and monthly, pass timeframe to backtest, walkforward, or optimize, compress OHLCV bars standalone with compress_bars, guard warmup while interval arrays are still empty, or debug interval errors such as undeclared intervals, missing timeframe, or intervals not strictly coarser than the base data.
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   Pybroker Strategy Creator · edtechre bundle

   Create, adapt, review, and debug PyBroker algorithmic trading strategy and backtest code using the bundled PyBroker wiki references generated from the local docs. Use when an agent needs to turn trading rules into PyBroker Strategy/ExecContext logic, add indicators, models, stops, ranking, rotation, position sizing, rebalancing, custom data sources, walkforward analysis, bootstrap metrics, parameter optimization, multiple time intervals, slippage modeling, margin trading, parallelization, or dynamic symbol selection, or to answer PyBroker usage questions.
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   Pybroker Indicator Creator · edtechre bundle

   Write, register, and debug PyBroker indicators using the bundled PyBroker wiki references generated from the local docs. Use when an agent needs to write custom indicator functions with pybroker.indicator, vectorize indicator logic with NumPy and Numba @njit kernels, wrap third-party technical analysis libraries such as TA-Lib, pandas-ta, ta, tulipy, or finta, use the built-in indicator factories and vector helpers, compute indicators standalone with IndicatorSet, parameterize indicators with hyperparams for optimization, compute indicators on multiple time intervals, feed custom data columns into indicators, cache indicator computations, or debug Numba compilation errors and parallel indicator failures.
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   Pybroker Rotational Trading · edtechre bundle

   Build ranked-signal and rotational PyBroker strategies using the bundled PyBroker wiki references generated from the local docs. Use when an agent needs to rank symbols with ctx.long_score or ctx.short_score, cap positions with Strategy.set_max_long_positions or set_max_short_positions, rotate a portfolio into its top-ranked symbols with Strategy.enable_rotation and a worst_rank_held hold band, write a custom rotation sizer over RotationContext long_ranks and short_ranks, choose between ranked-cap prioritization and full rotation, carry stops and fill prices into rotation orders, handle unrankable NaN scores or long/short overlap, screen a dynamic universe with a SymbolSelector before ranking, search position caps or worst_rank_held as hyperparams, migrate deprecated StrategyConfig.max_long_positions code, or debug rotation errors such as worst_rank_held below a position cap or a sizer without rotation enabled.
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   Dependency Migration Triage · edtechre bundle

   Rigorously triage a single Dependabot (or Renovate) dependency version-bump PR: pull the real changelog/release notes across the full old->new version range (not the PyPI summary blurb), always read the actual source diff between the two tags as well - a changelog records what changed and can never record what stayed the same, which is what most "we are unaffected" arguments actually rest on - map both against actual usage sites in this codebase, write a migration plan that distinguishes API-signature changes from conceptual/behavioral ones (a version bump can quietly change defaults or strictness with no signature change at all), verify whether existing tests would actually catch a regression in that exact spot or merely execute the line with stale data, write a regression test that fails on the old behavior when a real issue is found, fix what's fixable, and open a PR mirroring the migration that links back to the originating Dependabot PR. Use this whenever the user wants to review, assess, migrate, or avo
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