xingwudao/open-xquant/evaluate-cross-sectional

Claude Code and agent skill by xingwudao/open-xquant

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Evaluate cross-sectional factors with IC, Rank IC, ICIR, decay, and turnover in open-xquant; use for stock selection and multi-asset ranking factors.

Install

npx vskill@latest install xingwudao/open-xquant/evaluate-cross-sectional

SKILL.md

Evaluate Cross-Sectional Factor

Use this when the factor ranks assets on the same date.

Minimal SDK Pattern

python
import pandas as pd
from oxq.data.market import LocalMarketDataProvider
from oxq.factor_eval.metrics import (
    compute_decay,
    compute_ic,
    compute_icir,
    compute_rank_ic,
    compute_turnover,
)
symbols = ["SPY", "QQQ", "IWM"]
market = LocalMarketDataProvider(data_dir="/path/to/parquet")
factor_df = pd.DataFrame()
prices_df = pd.DataFrame()
for sym in symbols:
    bars = market.get_bars(sym, "2018-01-01", "2024-12-31")
    prices_df[sym] = bars["close"]
    factor_df[sym] = bars["close"].pct_change(20)
factor_df = factor_df.dropna()
prices_df = prices_df.loc[factor_df.index]
forward_returns = prices_df.pct_change(1).shift(-1).dropna()
common = factor_df.index.intersection(forward_returns.index)
factor_df = factor_df.loc[common]
forward_returns = forward_returns.loc[common]
ic = compute_ic(factor=factor_df, forward_returns=forward_returns)
rank_ic = compute_rank_ic(factor=factor_df, forward_returns=forward_returns)
icir = compute_icir(ic["mean"], ic["std"])
decay = compute_decay(factor=factor_df, prices=prices_df, horizons=[1, 3, 5, 10, 20])
turnover = compute_turnover(factor=factor_df)

Review Checklist

  • enough symbols for cross-sectional claims
  • no forward-return leakage
  • factor and returns aligned on common dates
  • NaN rows removed deliberately
  • multiple horizons checked
  • turnover considered with likely trading cost

Interpretation

Use thresholds as heuristics, not rules:

  • IC mean above 0.03: promising
  • IC mean between 0.01 and 0.03: weak but worth inspecting
  • ICIR below 0.1: unstable
  • high turnover: likely cost-sensitive even with positive IC
  • fast decay: signal may require execution assumptions the backtest cannot meet

Report Shape

Include:

  • factor name and formula
  • symbols and date range
  • number of dates and symbols
  • horizon results
  • IC mean, IC std, ICIR, Rank IC
  • turnover
  • decay pattern
  • pass, weak, or fail conclusion with caveats

Red Lines

  • Do not use cross-sectional IC as primary evidence for fewer than 10 symbols.
  • Do not report IC mean without IC std or ICIR.
  • Do not ignore turnover and decay.

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

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