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finance-bp-061-finrl

核心功能是基于ZVT框架生成量化策略代码,覆盖数据采集、因子计算、回测至交易执行全流程,支持多智能体集成交易、DRL训练等14类场景,含多种经典与AI策略。用户输入市场(A股/港股/加密货币,美股支持有限)、数据源、策略类型等参数,输出合规可执行的Python代码,严格遵循交易规则避免风险,适合量化研究者快速落地策略。

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热度 16.02
最近更新 2026-04-22 19:56:56
分类:开发依赖:代码执行 / 第三方 API / 内部调用大模型 / 读写文件应用场景:代码生成 / 数据处理 / 自动化脚本 / 财务

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add finance-bp-061-finrl
Metadata
name
finance-bp-061-finrl
description
Crystal skill compiled from finance-bp-061 (backtesting, factor-research on multi-market). Triggers: ensemble trading, multiple agents, graph neural network, portfolio optimization, paper trading, Alpaca API, Alpaca, EIIE.
license
Proprietary. See LICENSE.txt in project root.
compatibility
Designed for Doramagic-host ecosystem (Claude Code / openclaw / Cursor). Requires Python 3.12+ with uv package manager.
metadata
{"version":"v6.1","blueprint_id":"finance-bp-061","compiled_at":"2026-04-22T11:46:44.501076+00:00","capability_markets":"multi-market","capability_activities":"backtesting, factor-research","sop_version":"crystal-compilation-v6.1"}

finance-bp-061-finrl

I help you build quant strategies on A-share with ZVT — from data fetch to backtest, one flow. Just tell me what you want; I'll write the code, you don't have to dig docs. (Heads up: ZVT natively supports A-share, HK, and crypto. US stocks — stockus_nasdaq_AAPL — are half-baked; don't bother for serious work.)

Pipeline

data_collection -> data_storage -> factor_computation -> target_selection -> trading_execution -> visualization

Top Use Cases (14 total)

Ensemble Stock Trading ICAIF 2020 (UC-101)

Executing automated stock trading using an ensemble of multiple DRL agents (A2C, DDPG, PPO, TD3, SAC) to reduce individual agent weakness and improve Triggers: ensemble trading, multiple agents, stock trading

NeurIPS 2018 DRL Training (UC-107)

Training deep reinforcement learning agents (A2C, DDPG, PPO, SAC, TD3) for automated stock trading using the StockTradingEnv environment Triggers: DRL training, stock trading, A2C

NeurIPS 2018 Ensemble Backtesting (UC-108)

Backtesting multiple trained DRL agents against baseline strategies (MVO, DJIA) to evaluate and compare ensemble trading performance Triggers: backtesting, ensemble, DRL agents

For all 14 use cases, see references/USE_CASES.md.

Install

# One-time setup before first use
bash scripts/install.sh

Execute trigger: When user intent matches intent_router.uc_entries[].positive_terms AND user uses action verb (run/execute/跑/执行/backtest/fetch/collect)

What I'll Ask You

  • Target market: A-share (default), HK, or crypto? (US stocks in ZVT are half-baked — stockus_nasdaq_AAPL exists but coverage is thin)
  • Data source / provider: eastmoney (free, no account), joinquant (account+paid), baostock (free, good history), akshare, or qmt (broker)?
  • Strategy type: MACD golden-cross, MA crossover, volume breakout, fundamental screen, or custom factor?
  • Time range: start_timestamp and end_timestamp for backtest period
  • Target entity IDs: specific stocks (stock_sh_600000) or index components (SZ1000)?

Semantic Locks (Fatal)

ID Rule On Violation
SL-01 Execute sell orders before buy orders in every trading cycle halt
SL-02 Trading signals MUST use next-bar execution (no look-ahead) halt
SL-03 Entity IDs MUST follow format entity_type_exchange_code halt
SL-04 DataFrame index MUST be MultiIndex (entity_id, timestamp) halt
SL-05 TradingSignal MUST have EXACTLY ONE of: position_pct, order_money, order_amount halt
SL-06 filter_result column semantics: True=BUY, False=SELL, None/NaN=NO ACTION halt
SL-07 Transformer MUST run BEFORE Accumulator in factor pipeline halt
SL-08 MACD parameters locked: fast=12, slow=26, signal=9 halt

Full lock definitions: references/LOCKS.md

Top Anti-Patterns (15 total)

  • AP-ZVT-183: 除权因子为 inf/NaN 时直接参与乘法导致复权静默失败
  • AP-ZVT-183B: HFQ(后复权)与 QFQ(前复权)K 线表使用错误导致因子计算漂移
  • AP-QLIB-1930: 回测结果与模型无关——共享 dataset 对象导致预测值被首次模型覆盖

All 15 anti-patterns: references/ANTI_PATTERNS.md

Evidence Quality Notice

[QUALITY NOTICE] This crystal was compiled from blueprint finance-bp-061. Evidence verify ratio = 18.9% and audit fail total = 32. Generated results may have uncaptured requirement gaps. Verify critical decisions against source files (LATEST.yaml / LATEST.jsonl).

Reference Files

File Contents When to Load
references/seed.yaml V6+ 全量权威 (source-of-truth) 有行为/决策争议时必读
references/ANTI_PATTERNS.md 15 条跨项目反模式 开始实现前
references/WISDOM.md 跨项目精华借鉴 架构决策时
references/CONSTRAINTS.md domain + fatal 约束 规则冲突时
references/USE_CASES.md 全量 KUC-* 业务场景 需要完整示例时
references/LOCKS.md SL-* + preconditions + hints 生成回测/交易代码前
references/COMPONENTS.md AST 组件地图(按 module 拆分) 查 API 时

Compiled by Doramagic crystal-compilation-v6.1 from finance-bp-061 blueprint at 2026-04-22T11:46:44.501076+00:00. See human_summary.md for non-technical overview.