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Finrl Rl Trading

核心功能是采用A2C、DDPG等集成强化学习算法,完成数据采集、因子计算、交易执行到可视化的全流程。典型场景包括集成股票交易、DRL模型训练、策略回测对比等。使用时需提供目标市场、数据源、策略类型等信息,输出交易/回测结果及可视化内容。适配Doramagic生态,要求Python3.12+及uv包管理器。

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热度 48.79
最近更新 2026-05-18 11:48:15
分类:数据依赖:代码执行 / 第三方 API / 读写文件 / 内部调用大模型应用场景:数据处理 / 财务 / 报告撰写 / 自动化脚本 ...

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 finrl-rl-trading
Metadata
name
finrl-rl-trading
description
Use ensemble deep reinforcement learning (A2C, DDPG, PPO, TD3, SAC) to execute automated multi-market stock trading with
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":"2026-04-22T13:00:18.884984+00:00","capability_markets":"multi-market","capability_activities":"backtesting, factor-research","sop_version":"crystal-compilation-v6.1"}

FinRL 强化学习交易 (finrl-rl-trading)

Use ensemble deep reinforcement learning (A2C, DDPG, PPO, TD3, SAC) to execute automated multi-market stock tr。

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.

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 (25 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-179: 第三方数据接口超限后异常被吞噬,数据静默缺失
  • 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

All 25 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 25 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 2026-04-22T13:00:18.884984+00:00. See human_summary.md for non-technical overview.