القائمة
Evaluate cross-sectional factors with IC, Rank IC, ICIR, decay, and turnover in open-xquant; use for stock selection and multi-asset ranking factors.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use when open-xquant artifacts must be checked for version/run/final traceability before comparison, report review, migration, or final selection.
Compile an open-xquant strategy spec preview and audit that material SPEC execution semantics are preserved in compiled_plan.json before backtests.
Audit strategy_idea_brief.json and the brainstorm conversation before strategy_spec.yaml construction begins.
Audit strategy_spec.yaml field provenance before backtests.
Author workspace-local OpenXQuant custom components from component_request.json, with tests, manifest hashing, catalog refresh, and no global SDK mutation.
Guide a user through the open-xquant pre-spec strategy idea workflow and produce strategy_idea_brief.json before any strategy_spec.yaml work begins.
| name | evaluate-cross-sectional |
| description | Evaluate cross-sectional factors with IC, Rank IC, ICIR, decay, and turnover in open-xquant; use for stock selection and multi-asset ranking factors. |
Use this when the factor ranks assets on the same date.
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)
Use thresholds as heuristics, not rules:
0.03: promising0.01 and 0.03: weak but worth inspecting0.1: unstableInclude: