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ml4t

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ml4t-run-backtest

Event-driven backtesting with realistic order execution, position tracking, and performance measurement. Use when simulating a trading strategy on historical data.

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Prix non confirmé★ 20 Stars GitHubRegistre mis à jour · 28 sept. 2026agent-skill

Vue d’ensemble

Event-driven backtesting with realistic order execution, position tracking, and performance measurement. Use when simulating a trading strategy on historical data.

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Event-Driven Backtesting

Vectorized backtests hide execution reality. Event-driven simulation processes each bar sequentially, submitting orders that fill at future prices - the only way to model what actually happens when you trade.

The Problem

Vectorized positions * returns backtests assume instant fills at known prices. In reality, you decide to trade on bar t but fill at bar t+1. Ignoring this inflates Sharpe by 0.3-0.5 or more for daily strategies. The faster the signal, the larger the gap.

The Pattern

WRONG
# Vectorized: signal and fill use the SAME bar's price
signals = compute_signal(prices)          # uses close[t]
positions = np.where(signals > 0, 1, 0)  # no shift!
returns = prices.pct_change()
strategy_returns = positions * returns    # lookahead: traded at price used to decide
sharpe = strategy_returns.mean() / strategy_returns.std() * np.sqrt(252)
CORRECT
import numpy as np

def event_backtest(prices: np.ndarray, signal_fn, cost_bps: float = 10):
    """Minimal event-driven backtest: decide on bar t, fill on bar t+1."""
    n = len(prices)
    cash, shares = 100_000.0, 0
    equity = np.zeros(n)

    for t in range(1, n):
        # Fill yesterday's order at today's open
        target = signal_fn(prices[:t])  # can only see past
        current_shares = shares
        trade = target - current_shares
        if trade != 0:
            fill_price = prices[t]  # next bar (simulating open)
            cost = abs(trade * fill_price) * cost_bps / 10_000
            cash -= trade * fill_price + cost
            shares += trade
        equity[t] = cash + shares * prices[t]

    returns = np.diff(equity[1:]) / equity[1:-1]
    sharpe = returns.mean() / returns.std() * np.sqrt(252)
    return equity, sharpe

Key Execution Rules

  1. Signal on bar t, fill on bar t+1 - never fill at the price you used to decide
  2. Track cash and positions explicitly - position * price = equity, not magic
  3. Deduct costs per trade - commission + spread + slippage on every fill
  4. No fractional knowledge - signal_fn(prices[:t]) sees only past bars

Guardrails

  • Fill at SAME_BAR close is optimistic - prefer next-bar open for daily strategies (close-to-open gap is 50-100 bps on equities)
  • Any Sharpe above 2.0 on daily data warrants checking for fill-timing bugs
  • Position sizing must respect available cash (no implicit margin)
  • Watch for survivorship bias in the universe - delisted symbols vanish from data

Production Implementation

ml4t-backtest provides a validated event-driven engine:

from ml4t.backtest import (
    Strategy, Engine, DataFeed, BacktestConfig,
)

class Momentum(Strategy):
    def on_data(self, timestamp, data, context, broker):
        for sym, bar in data.items():
            if bar["signals"].get("momentum", 0) > 0 and not broker.get_position(sym):
                size = int(broker.get_cash() * 0.1 / bar["close"])
                broker.submit_order(sym, size)

feed = DataFeed(prices_df=prices, signals_df=signals)
config = BacktestConfig(commission_rate=0.001, slippage_rate=0.001)
result = Engine(feed, Momentum(), config).run()
print(f"Sharpe: {result.metrics['sharpe']:.2f}  MaxDD: {result.metrics['max_drawdown']:.1%}")

Checklist

  • Orders fill at a future bar, not the decision bar
  • Signal function sees only past data (prices[:t])
  • Commission and slippage deducted on every fill
  • Cash balance tracked - no implicit leverage
  • Sharpe < 2.0 on daily data (or justified)
Voir la source ↗
Métadonnées du fichier
name: ml4t-run-backtest
description: "Event-driven backtesting with realistic order execution, position tracking, and performance measurement. Use when simulating a trading strategy on historical data."
when_to_use: "Use when simulating a strategy bar-by-bar with fills, positions, and costs"
dependencies: [cost-model]
metadata:
  book_chapters: "16"
  library: "ml4t-backtest"
paths: ["**/*backtest*.py", "**/*strategy*.py", "**/*engine*.py", "**/*broker*.py", "**/*cost*.py", "**/*regime*.py", "**/*tearsheet*.py"]
Voir le texte original
---
name: ml4t-run-backtest
description: "Event-driven backtesting with realistic order execution, position tracking, and performance measurement. Use when simulating a trading strategy on historical data."
when_to_use: "Use when simulating a strategy bar-by-bar with fills, positions, and costs"
dependencies: [cost-model]
metadata:
  book_chapters: "16"
  library: "ml4t-backtest"
paths: ["**/*backtest*.py", "**/*strategy*.py", "**/*engine*.py", "**/*broker*.py", "**/*cost*.py", "**/*regime*.py", "**/*tearsheet*.py"]
---
# Event-Driven Backtesting

Vectorized backtests hide execution reality. Event-driven simulation processes each bar sequentially, submitting orders that fill at future prices - the only way to model what actually happens when you trade.

## The Problem

Vectorized `positions * returns` backtests assume instant fills at known prices. In reality, you decide to trade on bar _t_ but fill at bar _t+1_. Ignoring this inflates Sharpe by 0.3-0.5 or more for daily strategies. The faster the signal, the larger the gap.

## The Pattern

### WRONG
```python
# Vectorized: signal and fill use the SAME bar's price
signals = compute_signal(prices)          # uses close[t]
positions = np.where(signals > 0, 1, 0)  # no shift!
returns = prices.pct_change()
strategy_returns = positions * returns    # lookahead: traded at price used to decide
sharpe = strategy_returns.mean() / strategy_returns.std() * np.sqrt(252)
```

### CORRECT
```python
import numpy as np

def event_backtest(prices: np.ndarray, signal_fn, cost_bps: float = 10):
    """Minimal event-driven backtest: decide on bar t, fill on bar t+1."""
    n = len(prices)
    cash, shares = 100_000.0, 0
    equity = np.zeros(n)

    for t in range(1, n):
        # Fill yesterday's order at today's open
        target = signal_fn(prices[:t])  # can only see past
        current_shares = shares
        trade = target - current_shares
        if trade != 0:
            fill_price = prices[t]  # next bar (simulating open)
            cost = abs(trade * fill_price) * cost_bps / 10_000
            cash -= trade * fill_price + cost
            shares += trade
        equity[t] = cash + shares * prices[t]

    returns = np.diff(equity[1:]) / equity[1:-1]
    sharpe = returns.mean() / returns.std() * np.sqrt(252)
    return equity, sharpe
```

## Key Execution Rules

1. **Signal on bar _t_, fill on bar _t+1_** - never fill at the price you used to decide
2. **Track cash and positions explicitly** - position * price = equity, not magic
3. **Deduct costs per trade** - commission + spread + slippage on every fill
4. **No fractional knowledge** - `signal_fn(prices[:t])` sees only past bars

## Guardrails

- Fill at `SAME_BAR` close is optimistic - prefer next-bar open for daily strategies (close-to-open gap is 50-100 bps on equities)
- Any Sharpe above 2.0 on daily data warrants checking for fill-timing bugs
- Position sizing must respect available cash (no implicit margin)
- Watch for survivorship bias in the universe - delisted symbols vanish from data

## Production Implementation

`ml4t-backtest` provides a validated event-driven engine:

```python
from ml4t.backtest import (
    Strategy, Engine, DataFeed, BacktestConfig,
)

class Momentum(Strategy):
    def on_data(self, timestamp, data, context, broker):
        for sym, bar in data.items():
            if bar["signals"].get("momentum", 0) > 0 and not broker.get_position(sym):
                size = int(broker.get_cash() * 0.1 / bar["close"])
                broker.submit_order(sym, size)

feed = DataFeed(prices_df=prices, signals_df=signals)
config = BacktestConfig(commission_rate=0.001, slippage_rate=0.001)
result = Engine(feed, Momentum(), config).run()
print(f"Sharpe: {result.metrics['sharpe']:.2f}  MaxDD: {result.metrics['max_drawdown']:.1%}")
```

## Checklist

- [ ] Orders fill at a future bar, not the decision bar
- [ ] Signal function sees only past data (`prices[:t]`)
- [ ] Commission and slippage deducted on every fill
- [ ] Cash balance tracked - no implicit leverage
- [ ] Sharpe < 2.0 on daily data (or justified)

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Licence: 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
  • L’approbation de revue IA est absente
  • 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. 1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
  2. 2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
  3. 3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

RépertoriéContrôle statique

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source
ml4t/skills
Licence
Apache-2.0
Version
Unknown
Dernier push GitHub
27 sept. 2026
Registre mis à jour
28 sept. 2026
Chemin des instructions
backtest/run-backtest/SKILL.md @ f0ea01919e0c

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

54/100

Revue nécessaire

Confiance

66/100

Sandbox uniquement

Audit

75/100

Risqué

Ouvrir l’audit completVoir le rapport d’évaluation
  • 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
  • L’approbation de revue IA est absente
  • 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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      "task": "Use ml4t-run-backtest 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-run-backtest",
    "api": "https://www.openagentskill.com/api/agent/skills/ml4t-ml4t-run-backtest",
    "audit": "https://www.openagentskill.com/skills/ml4t-ml4t-run-backtest/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=ml4t-ml4t-run-backtest&task=Use%20ml4t-run-backtest%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20ml4t-run-backtest%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20ml4t-run-backtest%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/ml4t-ml4t-run-backtest/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/ml4t-ml4t-run-backtest"
  }
}

Pour le créateur

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Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.

Créateur
ml4t
Indexé par
Index communautaire OpenAgentSkill

L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.

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