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ml4t-rl-execution

Reinforcement learning for trade execution and hedging. Use when optimizing execution algorithms or dynamic hedging policies.

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价格未确认★ 20 GitHub Stars目录更新于 · 2026年9月28日agent-skill

概览

Reinforcement learning for trade execution and hedging. Use when optimizing execution algorithms or dynamic hedging policies.

展开完整说明

以下为来源文档，不是本网站的操作指令。执行命令前请先核实权限。

RL for Trade Execution

Fixed execution schedules (TWAP, VWAP) ignore real-time market conditions. An RL agent adapts its execution rate based on order book state, reducing implementation shortfall.

The Problem

Executing a large order at a fixed rate creates predictable market impact. A 100K-share TWAP sell ignores favorable liquidity bursts and pushes through thin books. The result: 20-50 bps of avoidable shortfall on institutional orders, compounding across thousands of trades per year.

The Pattern

Model execution as a finite-horizon MDP. State: remaining shares, time left, volume, spread. Action: execution rate. Reward: negative implementation shortfall.

WRONG

# Static TWAP - ignores market conditions entirely
def twap_execute(total_shares: int, n_slices: int) -> list[int]:
    base = total_shares // n_slices
    remainder = total_shares % n_slices
    return [base + (1 if i < remainder else 0) for i in range(n_slices)]

schedule = twap_execute(100_000, 20)  # Same size every slice, blind to liquidity

CORRECT

import gymnasium as gym
import numpy as np
from gymnasium import spaces

class ExecutionEnv(gym.Env):
    """Agent decides what fraction of remaining shares to execute each step."""
    def __init__(self, total_shares=100_000, n_steps=20):
        super().__init__()
        self.total_shares, self.n_steps = total_shares, n_steps
        self.observation_space = spaces.Box(0, 1, shape=(4,), dtype=np.float32)
        self.action_space = spaces.Box(0, 1, shape=(1,), dtype=np.float32)

    def reset(self, seed=None, options=None):
        super().reset(seed=seed)
        self.remaining, self.step_idx = self.total_shares, 0
        self.arrival_price = 100.0
        return self._obs(), {}

    def step(self, action):
        shares = int(np.clip(action[0], 0, 1) * self.remaining)
        impact = 0.0001 * (shares / 5000)  # Linear market impact
        exec_price = self.arrival_price * (1 + impact)
        shortfall = (exec_price - self.arrival_price) / self.arrival_price
        reward = -abs(shortfall) * shares / self.total_shares
        self.remaining -= shares
        self.step_idx += 1
        done = self.step_idx >= self.n_steps or self.remaining <= 0
        if done and self.remaining > 0:
            reward -= 0.01  # Non-completion penalty
        return self._obs(), reward, done, False, {}

    def _obs(self):
        return np.array([
            self.remaining / self.total_shares, self.step_idx / self.n_steps,
            np.random.uniform(0.01, 0.05),  # spread
            np.random.uniform(0.3, 1.0),    # volume ratio
        ], dtype=np.float32)

Guardrails

Non-completion penalty is mandatory - without it the agent learns zero-trade is optimal
Normalize all state features - raw share counts and prices break learning
Validate against TWAP baseline - if RL underperforms TWAP, the environment is misconfigured
Use square-root impact for large orders - linear impact underestimates cost at scale
Episode = one parent order - do not mix multiple orders into one episode

Production Implementation

ml4t-backtest provides execution simulation with realistic market impact:

from ml4t.backtest import BacktestConfig, CommissionType, Engine
from ml4t.backtest.config import SlippageType
from ml4t.backtest.execution.impact import SquareRootImpact
from ml4t.backtest.execution.limits import VolumeParticipationLimit

config = BacktestConfig(
    commission_type=CommissionType.PER_SHARE,
    commission_per_share=0.005,
    slippage_type=SlippageType.VOLUME_BASED,
    slippage_rate=0.001,
)
env_engine = Engine(
    feed,
    strategy,
    config,
    market_impact_model=SquareRootImpact(volatility=0.02),
    execution_limits=VolumeParticipationLimit(max_participation=0.05),
)

Checklist

Environment has both time pressure and execution cost in the reward
State is normalized (fractions, ratios) not raw values
Non-completion is penalized (agent must finish the order)
Trained agent beats TWAP baseline on test episodes
Action space bounded (cannot execute more than remaining shares)

查看来源 ↗

文件元数据

name: ml4t-rl-execution
description: "Reinforcement learning for trade execution and hedging. Use when optimizing execution algorithms or dynamic hedging policies."
when_to_use: "Use when building adaptive execution strategies that minimize market impact beyond static TWAP/VWAP schedules"
dependencies: [run-backtest, cost-model]
metadata:
  book_chapters: "21"
  library: "ml4t-backtest"
paths: ["**/*backtest*.py", "**/*strategy*.py", "**/*engine*.py", "**/*execution*.py", "**/*rl*.py", "**/*impact*.py"]

查看原始文本

---
name: ml4t-rl-execution
description: "Reinforcement learning for trade execution and hedging. Use when optimizing execution algorithms or dynamic hedging policies."
when_to_use: "Use when building adaptive execution strategies that minimize market impact beyond static TWAP/VWAP schedules"
dependencies: [run-backtest, cost-model]
metadata:
  book_chapters: "21"
  library: "ml4t-backtest"
paths: ["**/*backtest*.py", "**/*strategy*.py", "**/*engine*.py", "**/*execution*.py", "**/*rl*.py", "**/*impact*.py"]
---
# RL for Trade Execution

Fixed execution schedules (TWAP, VWAP) ignore real-time market conditions. An RL agent adapts its execution rate based on order book state, reducing implementation shortfall.

## The Problem

Executing a large order at a fixed rate creates predictable market impact. A 100K-share TWAP sell ignores favorable liquidity bursts and pushes through thin books. The result: 20-50 bps of avoidable shortfall on institutional orders, compounding across thousands of trades per year.

## The Pattern

Model execution as a finite-horizon MDP. State: remaining shares, time left, volume, spread. Action: execution rate. Reward: negative implementation shortfall.

### WRONG

```python
# Static TWAP - ignores market conditions entirely
def twap_execute(total_shares: int, n_slices: int) -> list[int]:
    base = total_shares // n_slices
    remainder = total_shares % n_slices
    return [base + (1 if i < remainder else 0) for i in range(n_slices)]

schedule = twap_execute(100_000, 20)  # Same size every slice, blind to liquidity
```

### CORRECT

```python
import gymnasium as gym
import numpy as np
from gymnasium import spaces

class ExecutionEnv(gym.Env):
    """Agent decides what fraction of remaining shares to execute each step."""
    def __init__(self, total_shares=100_000, n_steps=20):
        super().__init__()
        self.total_shares, self.n_steps = total_shares, n_steps
        self.observation_space = spaces.Box(0, 1, shape=(4,), dtype=np.float32)
        self.action_space = spaces.Box(0, 1, shape=(1,), dtype=np.float32)

    def reset(self, seed=None, options=None):
        super().reset(seed=seed)
        self.remaining, self.step_idx = self.total_shares, 0
        self.arrival_price = 100.0
        return self._obs(), {}

    def step(self, action):
        shares = int(np.clip(action[0], 0, 1) * self.remaining)
        impact = 0.0001 * (shares / 5000)  # Linear market impact
        exec_price = self.arrival_price * (1 + impact)
        shortfall = (exec_price - self.arrival_price) / self.arrival_price
        reward = -abs(shortfall) * shares / self.total_shares
        self.remaining -= shares
        self.step_idx += 1
        done = self.step_idx >= self.n_steps or self.remaining <= 0
        if done and self.remaining > 0:
            reward -= 0.01  # Non-completion penalty
        return self._obs(), reward, done, False, {}

    def _obs(self):
        return np.array([
            self.remaining / self.total_shares, self.step_idx / self.n_steps,
            np.random.uniform(0.01, 0.05),  # spread
            np.random.uniform(0.3, 1.0),    # volume ratio
        ], dtype=np.float32)
```

## Guardrails

- **Non-completion penalty is mandatory** - without it the agent learns zero-trade is optimal
- **Normalize all state features** - raw share counts and prices break learning
- **Validate against TWAP baseline** - if RL underperforms TWAP, the environment is misconfigured
- **Use square-root impact for large orders** - linear impact underestimates cost at scale
- **Episode = one parent order** - do not mix multiple orders into one episode

## Production Implementation

`ml4t-backtest` provides execution simulation with realistic market impact:

```python
from ml4t.backtest import BacktestConfig, CommissionType, Engine
from ml4t.backtest.config import SlippageType
from ml4t.backtest.execution.impact import SquareRootImpact
from ml4t.backtest.execution.limits import VolumeParticipationLimit

config = BacktestConfig(
    commission_type=CommissionType.PER_SHARE,
    commission_per_share=0.005,
    slippage_type=SlippageType.VOLUME_BASED,
    slippage_rate=0.001,
)
env_engine = Engine(
    feed,
    strategy,
    config,
    market_impact_model=SquareRootImpact(volatility=0.02),
    execution_limits=VolumeParticipationLimit(max_participation=0.05),
)
```

## Checklist

- [ ] Environment has both time pressure and execution cost in the reward
- [ ] State is normalized (fractions, ratios) not raw values
- [ ] Non-completion is penalized (agent must finish the order)
- [ ] Trained agent beats TWAP baseline on test episodes
- [ ] Action space bounded (cannot execute more than remaining shares)

查看并核实来源

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安装前审查: 避免自动安装

许可证: Apache-2.0

Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
缺少 AI 审查批准
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
GitHub adoption: 20 GitHub stars
Stars/forks activity: 20 stars, 11 forks; issue activity unavailable in current metadata
Review status: AI review approval is missing

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1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录静态检查通过

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: ml4t/skills
许可证: Apache-2.0
版本: Unknown
最近 GitHub 推送: 2026年9月27日
目录更新于: 2026年9月28日
技能指令路径: backtest/rl-execution/SKILL.md @ f0ea01919e0c

版本来自目录元数据，使用前请核实来源发布记录。

质量

54/100

需审查

信任

63/100

仅限沙盒

审计

74/100

高风险

打开完整审计查看评估报告

Financial research output is not financial advice; require human review before any live investment decision
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
缺少 AI 审查批准
Financial research output is not financial advice; require human review before any live investment decision.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
GitHub adoption: 20 GitHub stars
Stars/forks activity: 20 stars, 11 forks; issue activity unavailable in current metadata
Review status: AI review approval is missing

Verified installs: —
结果: —

复制不等于安装。安装数需有成功安装回报，不代表全面的质量保证。

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      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "ml4t-ml4t-rl-execution (ml4t-rl-execution)",
      "install_command": "npx skills add ml4t/skills --skill ml4t-rl-execution",
      "risk_summary": "Risky; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "ml4t-ml4t-rl-execution",
      "task": "Use ml4t-rl-execution in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/ml4t-ml4t-rl-execution",
    "api": "https://www.openagentskill.com/api/agent/skills/ml4t-ml4t-rl-execution",
    "audit": "https://www.openagentskill.com/skills/ml4t-ml4t-rl-execution/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=ml4t-ml4t-rl-execution&task=Use%20ml4t-rl-execution%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20ml4t-rl-execution%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20ml4t-rl-execution%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/ml4t-ml4t-rl-execution/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/ml4t-ml4t-rl-execution"
  }
}

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创作者: ml4t
来源: ml4t/skills
收录方: OpenAgentSkill 社区索引

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/ml4t-ml4t-rl-execution?metric=listed&label=Listed)](https://www.openagentskill.com/skills/ml4t-ml4t-rl-execution?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/ml4t-ml4t-rl-execution?metric=trust&label=Trust)](https://www.openagentskill.com/skills/ml4t-ml4t-rl-execution?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/ml4t-ml4t-rl-execution?metric=audit&label=Audit)](https://www.openagentskill.com/skills/ml4t-ml4t-rl-execution/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/ml4t-ml4t-rl-execution?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/ml4t-ml4t-rl-execution?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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