القائمة
Run the AgentMatrix alpha pipeline from alpha_resource.md through internal validation, strategy packaging, external simulation, and live-readiness gates.
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
通过 HTTP API 查询/拉取 A 股量化数据. 25 张 CH 表 (33 因子/月频/日 K/分钟 K/财务/行业/市值/停牌/ETF/交易日历/行业指数/交易所指数/指数权重/行业成分/交易所成分/L1 快照), 4 个本地 parquet (market_cap/pe/pb/ps), 33 个月度因子 (41.9 万行, 76 月), 加 RQData 异步拉取 (alpha101 等因子). 支持 JSON / NDJSON 流式 / Parquet 二进制 / CSV 四种下载格式. 触发场景: 用户要求查 ROE/PE/PB/动量等因子, 拉日 K 线, 读 parquet 文件, 探索数据源, 查 API 端点, 因子 IC 评估, 触发 RQData 拉新因子数据 (alpha001-101 等), 下载大数据表为 Parquet.
Standardizes factor reproduction, truth validation, and proof export in factor_lab. Invoke when building or reviewing Alpha101, Alpha191, Alpha158, Barra, or paper-derived factor workflows.
Find, deduplicate, review, and normalize factor research sources into alpha_resource.md for AgentMatrix factor_lab.
Builds and validates Alpha101 factors in factor_lab. Invoke when reproducing Alpha101, exporting proof artifacts, or preparing intern/agent research runs.
| name | agentmatrix-alpha-pipeline |
| description | Run the AgentMatrix alpha pipeline from alpha_resource.md through internal validation, strategy packaging, external simulation, and live-readiness gates. |
Use this skill when the task is to run or review the full alpha research pipeline in agentmatrix-research.
alpha_resource.md.FactorResearchSpec, implements the factor, and runs internal validation.strategy_candidate only when data quality and red-flag checks are acceptable.live_ready promotion.Check amazingdata:
python -m research_core.factor_lab.cli check-amazingdata
Run internal validation:
python -m research_core.factor_lab.cli run-factor-research --factor-set wq101 --data-source amazingdata --start 2023-01-01 --end 2025-12-31 --universe csi800 --max-symbols 300
Build strategy signals:
python -m research_core.strategy_engine.cli build-alpha-strategy --validated-run runtime/factor_lab/jobs/<job_id>.json --rebalance-frequency daily --top-n 50
Package external simulation:
python -m research_core.backtest_adapter.cli package-external-sim --engine gm --strategy <strategy_id> --signal-path runtime/strategy_engine/<strategy_id>/target_weights.csv --start 2023-01-01 --end 2025-12-31
live_ready without external simulation evidence and explicit human approval.ods_security_status_daily; use --universe all only when index membership is intentionally not enforced.[start, end] window.--as-of only for a single-snapshot smoke export.