compute

name	compute
description	Use when tasks need strategy-agnostic OHLCV indicators, math utilities, generic factor examples, regime slicing, resampling, or label makers.

Description

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.

Prerequisites

• Python: pandas, numpy; some indicators use statsmodels.
• Imports:
  • from skills.compute.indicators import trend_score, rsi, er
  • from skills.compute.utils import safe_divide, calculate_atr
  • from skills.compute.wrappers import Factor
  • from skills.compute.resample import resample_to_5m
  • from skills.compute.regime import split_by_regime

API Reference

Math Utilities (skills.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.

Universal Indicators (skills.compute.indicators)

from skills.compute.indicators import trend_score, rsi, er, supertrend

41 public functions organized by category:

• Price/Momentum: roc, ma, daily_return, ma_cross, price_above_ma, momentum_acceleration, momentum_weighted, bias_momentum, mom_skip, high_vol_odds
• Trend: rsrs, rsrs_v1–v3, rsrs_norm, trend_score, trend_score_v2, trend_score_v2_skip, supertrend, donchian_channel
• Volume: ma_vol, ma_vol_ratio, orb, orb_relvol, stand_orb_relvol
• Efficiency: er, er_enhanced, er_adaptive, er_directional
• Oscillators: cci, slowkdj, williams_r, rsi, rsi_divergence
• Mean-reversion: bollinger_reversal, mean_reversion, price_drawdown
• Volatility/Risk: atr_stop, volatility_regime, volatility_inv, fund_premium_rate

Factor 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.

Generic factor examples

• 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.

Exit / risk filters (cross-sectional package)

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).

Recipes

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).

Label Generation (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.

Factor categories (illustrative)

Momentum/trend, volume, mean reversion, generic factor examples, exit filters (strategies.cross_sectional.exits), and label makers.