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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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.
PyBroker Indicator Creator
Overview
Write fast, correct PyBroker indicators by registering vectorized NumPy/Numba functions with pybroker.indicator, wiring them into strategy executions and models, and keeping every value free of lookahead bias. Covers the built-in indicator factories and vector helpers, custom Numba @njit kernels, wrapping third-party technical analysis libraries such as TA-Lib and pandas-ta, standalone computation with IndicatorSet, hyperparam-driven indicators, and multi-timeframe interval indicators.
Workflow
Extract the indicator spec: formula or source library, input fields (OHLCV or registered custom columns), lookback lengths, fixed kwargs versus hyperparam parameterization, interval/timeframe needs, consumers (execution functions, models, or standalone DataFrame output), and desired output file/notebook.
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 indicator work, also read references/indicator-patterns.md.
Build a complete runnable indicator surface:
start scripts with pybroker.disable_progress_bar() and pybroker.enable_data_source_cache("<name>"), adding pybroker.enable_indicator_cache("<name>") (or pybroker.enable_caches) when indicator computation is expensive
prefer built-ins first: highest, lowest, and returns at top level, then the factories in the pybroker.indicator module (such as atr, adx, macd, close_minus_ma), then the vectorized helpers (highv, lowv, sumv, returnv, cross, atr)
write custom functions as fn(bar_data, **kwargs) over NumPy arrays — never pandas — that return a full-length one-dimensional array with NaN warmup bars, JIT-compiling explicit loops with a nested Numba @njit kernel
wrap third-party TA libraries (TA-Lib, pandas-ta, ta, tulipy, finta) at the wrapper boundary only, padding outputs to full length and registering one indicator per output column
register with pybroker.indicator(name, fn, **kwargs), then attach with Strategy.add_execution(..., indicators=[...]) and read with ctx.indicator("name"), or compute standalone with ind(df) / IndicatorSet
pass pybroker.hyperparam values as indicator kwargs for parameter search, and bind multi-timeframe indicators with add_execution(..., indicators=ind.intervals("weekly")), read with ctx.interval("weekly").indicator("name") (timeframe= is then required on backtest/walkforward)
Validate the produced code as far as the environment allows. At minimum, run syntax checks for created Python files. When practical, compute the indicators on a small local DataFrame and check output length, NaN warmup, and the bump-last-bar lookahead test from references/indicator-patterns.md.
Implementation Rules
Treat PyBroker as a backtesting framework, not a source of financial advice. Make indicator assumptions explicit and avoid performance claims that are not supported by a produced backtest.
Use completed historical bar data only. An indicator value at bar i may depend only on inputs at index i and earlier: no centered or forward-shifted windows, no normalization over the full series, and no negative indexing into full-length arrays inside kernels (a negative index silently wraps to the end of the series — the future).
An indicator function receives a BarData argument plus its registered kwargs and must return a one-dimensional array with one value per input bar. Left-pad warmup bars with NaN and never return a shortened array (pad libraries such as tulipy that drop warmup rows); a returned pd.Series is converted automatically.
Prefer built-ins before custom code: highest, lowest, and returns at top level, and the factories in the pybroker.indicator module (atr, adx, macd, stochastic, close_minus_ma, laguerre_rsi, and more). Watch the name collision: top-level pybroker.atr is the vectorized function atr(high, low, close, lookback), while the factory is pybroker.indicator.atr(name, lookback).
Indicator names must not contain @ (reserved for interval-suffixed names such as sma_20@weekly), and re-registering a name silently overwrites the previous indicator.
Never use pandas to implement indicator or execution logic. Write indicator logic with vectorized NumPy over BarData arrays, prefer the vectorized helpers (highv, lowv, sumv, returnv, cross, atr) when they fit, and JIT-compile explicit loops with a nested Numba @njit kernel that takes plain NumPy arrays — BarData cannot cross the @njit boundary. Never construct a pd.Series or pd.DataFrame and never call pandas methods such as .rolling, .ewm, .shift, or .apply inside an indicator function or a per-bar execution function; the only sanctioned pandas is the third-party wrapper boundary in the next rule.
Common Deliverables
Standalone .py script that registers indicators and computes or backtests them.
Wrapper modules that register TA-Lib, pandas-ta, ta, tulipy, or finta outputs as PyBroker indicators.
Conversion of a pandas-based indicator into vectorized NumPy/Numba.
Debugging notes and patches for Numba compile errors, output-length mismatches, and lookahead leaks.
Notebook-ready PyBroker indicator cells.
Resources
references/wiki-index.md: start here for topic routing across the bundled references.
references/wiki-11-configuring-parallelization.md: worker counts, parallel indicators and model training, and the Ray backend.
references/wiki-15-multiple-time-intervals.md: interval types, compressing bars, and multi-timeframe strategies.
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_types.pyi: generated type stubs for enums, BarData, Portfolio, order/trade/position records, and evaluation result types.
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/indicator-patterns.md: load when writing nontrivial indicator code; vectorization patterns, third-party library recipes, session hygiene, and the validation checklist.
assets/indicator_template.py: copy and adapt when creating a new standalone indicator script.
Wrap third-party TA libraries at the wrapper boundary only: NumPy-native libraries (TA-Lib, tulipy) consume BarData arrays directly, while pandas-based libraries (pandas-ta, ta, finta) get a minimal pd.Series/pd.DataFrame built from BarData arrays — the only sanctioned pandas in indicator code. Register one indicator per output column for multi-output functions (the talib.MACD tuple, pandas-ta DataFrames). None of these libraries is a PyBroker dependency: state the required pip install and never assume one is importable.
Compute indicators standalone with ind(df) on a single-symbol DataFrame (returns a date-indexed pd.Series) or with IndicatorSet for multi-symbol frames (requires a symbol column; output columns are symbol, date, then sorted indicator names). IndicatorSet never uses the disk cache.
For parameter search, pass pybroker.hyperparam(name, default=..., low=..., high=..., step=...) objects as indicator kwargs and run strategy.optimize(...); override standalone computation with ind(df, hyperparams={...}). Hyperparam-driven indicators are never disk-cached.
For multi-timeframe indicators, do not pass an interval to indicator(); bind the registered indicator with ind.intervals("weekly") when passing it to add_execution(indicators=...), and read it with ctx.interval("weekly").indicator("name") over completed compressed bars. Binding is exhaustive: include "base" (e.g. ind.intervals("base", "weekly")) to keep the base-timeframe variant; unbound indicators default to base. The bound interval is available through ctx.interval without declaring it in intervals= (which provides bars only). backtest/walkforward then require timeframe=, and each interval must be strictly coarser than the base timeframe.
Register non-OHLCV columns with pybroker.register_columns before an indicator reads them; they appear as BarData attributes and are None when the input data lacks them, so guard for that.
In execution functions read values with ctx.indicator("name") (arrays are truncated to completed bars; pass a symbol for another symbol's values) and guard lookbacks with ctx.bars or warmup=.
Start generated scripts with pybroker.disable_progress_bar() so progress output does not flood agent context, and pybroker.enable_data_source_cache("<name>") so repeated runs do not refetch data; add pybroker.disable_logging() when running many backtests, such as parameter optimization.
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 indicator fired, 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. That position never becomes a Trade, so trade_count, win_rate, total_pnl and every other trade-level metric silently exclude it while its P&L sits in unrealized_pnl — an easy way to under-report an indicator's hit rate. Set exit_on_last_bar=True whenever trade statistics are reported. calc_bootstrap is a backtest/walkforward parameter defaulting to False, not a StrategyConfig field; pass calc_bootstrap=True to populate result.bootstrap with BCa confidence intervals for profit factor and Sharpe plus percentile bounds on max drawdown, which is what to reach for when comparing indicator variants with error bars instead of point estimates.
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. Do not replace the metrics_df print outright: the default JSON payload (trades plus orders) is usually larger than the metrics table.
If a Numba @njit function fails to compile or raises a cryptic error such as a TypingError, re-run once with the NUMBA_DISABLE_JIT=1 environment variable to get a readable Python traceback, fix the underlying code, then remove the variable so the backtest runs compiled. Never leave JIT disabled in the final script.
Debug indicator failures serially before parallelizing: exceptions surface raw (there is no error handling on the indicator compute path), and under parallel_indicators=True they arrive wrapped in joblib worker tracebacks, so reproduce with the default serial path first.
Self-test novel indicator logic for lookahead with the bump-last-bar check in references/indicator-patterns.md: recompute after changing only the final input bar and assert every earlier output is unchanged.
If exact API names, constructor parameters, or methods matter, read references/api-public-surface.md.
For exact type signatures — indicator(), Indicator, IndicatorSet, the vector helpers, and the cache and parallel functions in references/pybroker_model.pyi; BarData fields and the column/indicator scopes in references/pybroker_types.pyi — read the matching references/pybroker_*.pyi stub.
If the user wants a standalone file, copy and adapt assets/indicator_template.py.
