القائمة
Use when tasks need strategy-agnostic OHLCV indicators, math utilities, generic factor examples, regime slicing, resampling, or label makers.
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استنادا إلى تصنيف SOC المهني
Use when tasks need factor diagnostics, IC/grouped return analysis, attribution, robustness checks, or time-series distribution and stationarity checks.
Use when tasks need vectorized strategy execution, portfolio weighting, portfolio-level filters, transaction cost helpers, exit A/B analysis, overlay metrics, or multi-strategy return blending.
Use when tasks need optional PyCaret model training, ML factor generation, inference wrappers, feature importance, or sparse LASSO weight generation.
Use when tasks need PandaData/PandaAI market data, reference data, adjustment factors, futures tick downloads, or symbol conversion.
Use when tasks need HTML reports, Markdown strategy reports, PNG chart helpers, or report files under the research reports directory.
Use when tasks need reusable research pipeline templates, factor screening, parameter sensitivity sweeps, or strategy comparison.
| name | compute |
| description | Use when tasks need strategy-agnostic OHLCV indicators, math utilities, generic factor examples, regime slicing, resampling, or label makers. |
Compute derived values from OHLCV data. This skill is organized in reusable, strategy-agnostic layers:
| Layer | Module | Contents |
|---|---|---|
| Utils | skills.compute.utils | Math primitives: safe_divide, rolling_zscore, calculate_atr, clip_outliers, etc. |
| Indicators | skills.compute.indicators | 41 public technical indicators: rsi, trend_score, er, supertrend, etc. |
Also: label makers (label_maker.py), compact generic factor examples, and the Factor wrapper.
Shared market-structure helpers used by strategy domains:
skills.compute.resample.resample_to_5m(df_1m) — A-share 1m eob OHLCV to 5m eob bars without crossing lunch break; keeps 09:31-11:30 and 13:01-15:00, removes zero-volume rows, and preserves OHLCV aggregation.skills.compute.regime.split_by_regime(df, regimes=None) — lithium-cycle date slicing for DatetimeIndex or MultiIndex inputs.Strategy-specific factors and feature engineering live in strategies/, not here.
pandas, numpy; some indicators use statsmodels.from skills.compute.indicators import trend_score, rsi, erfrom skills.compute.utils import safe_divide, calculate_atrfrom skills.compute.wrappers import Factorfrom skills.compute.resample import resample_to_5mfrom skills.compute.regime import split_by_regimeskills.compute.utils)from skills.compute.utils import safe_divide, rolling_zscore, calculate_atr, clip_outliers, round_away_from_zero
6 public functions: safe_divide, rolling_zscore, rolling_regression_vectorized, calculate_atr, clip_outliers, round_away_from_zero, plus private helpers _weighted_polyfit_coefficients, _rolling_linear_regression, _scalar_kalman_smoother.
skills.compute.indicators)from skills.compute.indicators import trend_score, rsi, er, supertrend
41 public functions organized by category:
roc, ma, daily_return, ma_cross, price_above_ma, momentum_acceleration, momentum_weighted, bias_momentum, mom_skip, high_vol_oddsrsrs, rsrs_v1–v3, rsrs_norm, trend_score, trend_score_v2, trend_score_v2_skip, supertrend, donchian_channelma_vol, ma_vol_ratio, orb, orb_relvol, stand_orb_relvoler, er_enhanced, er_adaptive, er_directionalcci, slowkdj, williams_r, rsi, rsi_divergencebollinger_reversal, mean_reversion, price_drawdownatr_stop, volatility_regime, volatility_inv, fund_premium_rateFactor wrapper (skills.compute.wrappers)from skills.compute.wrappers import Factor
from skills.compute.indicators import trend_score_v2
__init__(func: Callable, **params) — binds callable and defaults; name from func.__name__ and params.calculate(data: pd.DataFrame) -> pd.Series — groupby('symbol') then func(group, **params); MultiIndex (symbol, eob), NaNs dropped.cal_df(data) -> pd.DataFrame — (eob, symbol) column layout.Contract for func: first argument is a single-symbol DataFrame indexed by eob with OHLCV columns as required.
skills.compute.ts_factor_examples: four single-symbol examples: momentum, volatility, trend slope, mean-reversion z-score.skills.compute.cs_factor_examples: four panel examples with MultiIndex (symbol, eob): momentum score, volatility score, trend score, mean-reversion score.from strategies.cross_sectional.exits import (
gap_down_filter,
vol_spike_filter,
drawdown_from_high_filter,
)
Panel-level filters return a MultiIndex Series; use with ExitFilterConfig and condition in the cross-sectional backtester (see strategies/cross_sectional/STRATEGY.md).
1. Panel factor
import pandas as pd
from skills.compute.wrappers import Factor
from skills.compute.indicators import roc, trend_score_v2
f = Factor(trend_score_v2, period=24)
scores = f.calculate(data) # data: MultiIndex (symbol, eob)
f2 = Factor(roc, period=60)
roc_df = f2.cal_df(data)
2. Direct single-symbol call
Slice one symbol, index by eob → roc(sym_df, period=20).
3. Generic time-series example via wrapper
from skills.compute.ts_factor_examples import ts_momentum
from skills.compute.wrappers import Factor
ja = Factor(ts_momentum, lookback=60)
s = ja.calculate(panel)
4. Exit filter in a backtest
Pass {'func': drawdown_from_high_filter, 'kwargs': {...}, 'condition': lambda x: x < 0.1} in exit_filters on ModularBacktester (see strategy domain doc).
skills.compute.label_maker)from skills.compute.label_maker import ForwardReturnLabelMaker, TripleBarrierLabelMaker
Public supervised-learning labels:
ForwardReturnLabelMaker: forward-return threshold labels.TripleBarrierLabelMaker: AFML-style volatility-scaled triple-barrier labels.Momentum/trend, volume, mean reversion, generic factor examples, exit filters (strategies.cross_sectional.exits), and label makers.