ml4t
Indexado en Registry
ml4t-rl-execution
Reinforcement learning for trade execution and hedging. Use when optimizing execution algorithms or dynamic hedging policies.
Resumen
Reinforcement learning for trade execution and hedging. Use when optimizing execution algorithms or dynamic hedging policies.
Leer documentación completa
Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.
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)
Metadatos del archivo
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"]
Ver texto original
---
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)
Revisar el código fuente
Precio y costes de ejecución
- Obtener el skill
- Precio sin confirmar
- Ejecutarlo
- Requisitos sin confirmar. Consulta los costes del agente, API y servicios en la fuente.
- Licencia
- Apache-2.0
- Precio sin confirmar
- No hemos confirmado el precio. Los enlaces existentes al código y a la instalación siguen disponibles.
Obtener gratis no significa ejecutar gratis. El precio no es una evaluación de seguridad. Enviar información de precio →
Fuente del skill registrada
La ruta de instrucciones está registrada. No implica pruebas de ejecución, seguridad ni compatibilidad.
Revisar antes de instalar: Evitar instalación automática
Licencia: 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
- Falta aprobación de revisión por IA
- 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
Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.
Empieza con una tarea pequeña
- 1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
- 2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
- 3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.
Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.
Fuente y notas de uso
IndexadoRevisión estática
Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.
- Repositorio fuente
- ml4t/skills
- Licencia
- Apache-2.0
- Versión
- Unknown
- Último push de GitHub
- 27 sept 2026
- Registro actualizado
- 28 sept 2026
- Ruta de instrucciones
- backtest/rl-execution/SKILL.md @ f0ea01919e0c
Versión declarada en el registro; consulta las versiones de la fuente.
Calidad
54/100
Requiere revisión
Confianza
63/100
Solo sandbox
Auditoría
74/100
Riesgoso
- 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
- Falta aprobación de revisión por IA
- 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
- —
- Resultados
- —
Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.
Acceso para agentes
La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.
Más detalles
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"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"
}
}Para el creador
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- ml4t
- Fuente
- ml4t/skills
- Indexado por
- Índice comunitario de OpenAgentSkill
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