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ml4t-live-trading

Transition from backtest to live trading with zero code changes. Use when deploying a validated strategy to paper or live trading via broker APIs.

Examiner la source Voir sur GitHub

Prix non confirmé★ 22 Stars GitHubRegistre mis à jour · 4 oct. 2026agent-skill

Vue d’ensemble

Transition from backtest to live trading with zero code changes. Use when deploying a validated strategy to paper or live trading via broker APIs.

Lire la documentation complète

Documentation source, pas des instructions pour ce site. Vérifiez les permissions avant d’exécuter des commandes.

Backtest-to-Live Deployment

Rewriting strategy logic for live trading introduces bugs and invalidates your backtest. The correct pattern is to reuse the exact Strategy class from backtesting - zero code changes between simulation and production.

The Problem

Teams often validate a strategy in backtest, then rewrite it for live trading. That rewrite changes rounding, timing, or position tracking and silently breaks the link to the validated backtest.

The Pattern

WRONG

# Separate live strategy - rewrites logic, diverges from backtest
class LiveMomentumTrader:
    def __init__(self, api_key):
        self.api = BrokerAPI(api_key)

    def run(self):
        while True:
            prices = self.api.get_latest_bars(100)
            signal = prices["close"].pct_change(20).iloc[-1]
            if signal > 0:
                self.api.market_buy("SPY", 100)  # Different sizing logic
            elif signal < 0:
                self.api.market_sell("SPY", 100)  # No cost model
            time.sleep(60)

CORRECT

from abc import ABC, abstractmethod

class Strategy(ABC):
    """Single strategy class used for BOTH backtest and live."""

    @abstractmethod
    def on_data(self, timestamp, data, context, broker):
        ...

class Momentum(Strategy):
    def on_data(self, timestamp, data, context, broker):
        for sym, bar in data.items():
            mom = bar.get("momentum_20d", 0)
            pos = broker.get_position(sym)
            if mom > 0 and not pos:
                size = int(broker.get_cash() * 0.05 / bar["close"])
                broker.submit_order(sym, size)
            elif mom <= 0 and pos:
                broker.close_position(sym)

# Backtest: Engine(feed, Momentum(), config).run()
# Live:     await LiveEngine(Momentum(), broker, feed).run()
# Same class. Same logic. Different engine.

Deployment Sequence

Backtest - validate with historical data, realistic costs
Paper trade (minimum 4 weeks) - same code, live data, simulated fills
Shadow mode - generate orders but don't execute; compare to paper
Live with limits - small size, tight kill switch, full monitoring
Scale up - increase size only after live metrics match paper

Never skip paper trading. If paper diverges materially from backtest, diagnose before going live.

Data Feed Differences

Property	Backtest	Live
Data arrival	Instant, complete	Streaming, may lag
Bars	All present	Build incrementally
Fills	Simulated, next-bar	Real, partial, rejected
Clock	Jump bar to bar	Real-time wall clock

Guardrails

Identical Strategy class for backtest and live - if you change one, you broke the link
Paper trade period is mandatory, not optional - 4 weeks minimum for daily strategies
Kill switch must be active before any live order: max drawdown, max position, daily loss limit
Log every order submission, fill, and rejection - you will need the audit trail
Data staleness check: if last bar is older than 2x expected frequency, halt trading

Production Implementation

import asyncio
from ml4t.backtest import Strategy
from ml4t.live import LiveEngine, AlpacaBroker, AlpacaDataFeed, SafeBroker, LiveRiskConfig

risk = LiveRiskConfig(execution_mode="shadow", max_drawdown_pct=0.10)
broker = SafeBroker(AlpacaBroker(api_key, secret_key), risk)
feed = AlpacaDataFeed(api_key, secret_key, symbols=["SPY"], experimental=True)

async def trade_live():
    engine = LiveEngine(Momentum(), broker, feed)
    await engine.connect()
    await engine.run()

asyncio.run(trade_live())

Checklist

Strategy class is identical for backtest and live (no separate live code)
Paper traded for minimum 4 weeks with live data
Kill switch configured with pre-approved thresholds
Partial fill handling verified
Data staleness detection active; order audit log captures every submission and fill

Voir la source ↗

Métadonnées du fichier

name: ml4t-live-trading
description: "Transition from backtest to live trading with zero code changes. Use when deploying a validated strategy to paper or live trading via broker APIs."
when_to_use: "Use when deploying a validated strategy to paper or live markets"
dependencies: [run-backtest, kill-switch]
metadata:
  book_chapters: "25"
  library: "ml4t-live"
paths: ["**/*live*.py", "**/*deploy*.py", "**/*monitor*.py", "**/*govern*.py", "**/*mlops*.py", "**/*pipeline*.py"]

Voir le texte original

---
name: ml4t-live-trading
description: "Transition from backtest to live trading with zero code changes. Use when deploying a validated strategy to paper or live trading via broker APIs."
when_to_use: "Use when deploying a validated strategy to paper or live markets"
dependencies: [run-backtest, kill-switch]
metadata:
  book_chapters: "25"
  library: "ml4t-live"
paths: ["**/*live*.py", "**/*deploy*.py", "**/*monitor*.py", "**/*govern*.py", "**/*mlops*.py", "**/*pipeline*.py"]
---
# Backtest-to-Live Deployment

Rewriting strategy logic for live trading introduces bugs and invalidates your backtest. The correct pattern is to reuse the exact Strategy class from backtesting - zero code changes between simulation and production.

## The Problem

Teams often validate a strategy in backtest, then rewrite it for live trading.
That rewrite changes rounding, timing, or position tracking and silently breaks
the link to the validated backtest.

## The Pattern

### WRONG
```python
# Separate live strategy - rewrites logic, diverges from backtest
class LiveMomentumTrader:
    def __init__(self, api_key):
        self.api = BrokerAPI(api_key)

    def run(self):
        while True:
            prices = self.api.get_latest_bars(100)
            signal = prices["close"].pct_change(20).iloc[-1]
            if signal > 0:
                self.api.market_buy("SPY", 100)  # Different sizing logic
            elif signal < 0:
                self.api.market_sell("SPY", 100)  # No cost model
            time.sleep(60)
```

### CORRECT
```python
from abc import ABC, abstractmethod

class Strategy(ABC):
    """Single strategy class used for BOTH backtest and live."""

    @abstractmethod
    def on_data(self, timestamp, data, context, broker):
        ...

class Momentum(Strategy):
    def on_data(self, timestamp, data, context, broker):
        for sym, bar in data.items():
            mom = bar.get("momentum_20d", 0)
            pos = broker.get_position(sym)
            if mom > 0 and not pos:
                size = int(broker.get_cash() * 0.05 / bar["close"])
                broker.submit_order(sym, size)
            elif mom <= 0 and pos:
                broker.close_position(sym)

# Backtest: Engine(feed, Momentum(), config).run()
# Live:     await LiveEngine(Momentum(), broker, feed).run()
# Same class. Same logic. Different engine.
```

## Deployment Sequence

1. **Backtest** - validate with historical data, realistic costs
2. **Paper trade** (minimum 4 weeks) - same code, live data, simulated fills
3. **Shadow mode** - generate orders but don't execute; compare to paper
4. **Live with limits** - small size, tight kill switch, full monitoring
5. **Scale up** - increase size only after live metrics match paper

Never skip paper trading. If paper diverges materially from backtest, diagnose before going live.

## Data Feed Differences

| Property | Backtest | Live |
|----------|----------|------|
| Data arrival | Instant, complete | Streaming, may lag |
| Bars | All present | Build incrementally |
| Fills | Simulated, next-bar | Real, partial, rejected |
| Clock | Jump bar to bar | Real-time wall clock |

## Guardrails

- Identical Strategy class for backtest and live - if you change one, you broke the link
- Paper trade period is mandatory, not optional - 4 weeks minimum for daily strategies
- Kill switch must be active before any live order: max drawdown, max position, daily loss limit
- Log every order submission, fill, and rejection - you will need the audit trail
- Data staleness check: if last bar is older than 2x expected frequency, halt trading

## Production Implementation

```python
import asyncio
from ml4t.backtest import Strategy
from ml4t.live import LiveEngine, AlpacaBroker, AlpacaDataFeed, SafeBroker, LiveRiskConfig

risk = LiveRiskConfig(execution_mode="shadow", max_drawdown_pct=0.10)
broker = SafeBroker(AlpacaBroker(api_key, secret_key), risk)
feed = AlpacaDataFeed(api_key, secret_key, symbols=["SPY"], experimental=True)

async def trade_live():
    engine = LiveEngine(Momentum(), broker, feed)
    await engine.connect()
    await engine.run()

asyncio.run(trade_live())
```

## Checklist

- [ ] Strategy class is identical for backtest and live (no separate live code)
- [ ] Paper traded for minimum 4 weeks with live data
- [ ] Kill switch configured with pre-approved thresholds
- [ ] Partial fill handling verified
- [ ] Data staleness detection active; order audit log captures every submission and fill

Examiner la source

Prix et coûts d’utilisation

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L’utiliser: Prérequis non confirmés. Consultez les frais d’agent, d’API et de services à la source.
Licence: Apache-2.0
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Source du skill enregistrée

Un chemin vers les instructions est enregistré. Cela ne constitue pas un test, une garantie de sécurité ou de compatibilité.

Réviser avant installation: Éviter l’installation automatique

Licence: Apache-2.0

Permission surface may require sandboxing
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
Permission surface needs review: secrets or environment access, network or browser access
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 11 forks; issue activity unavailable in current metadata
Permission surface: secrets or environment access, network or browser access

Ouvrir l’audit complet

Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.

Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
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: 3 oct. 2026
Registre mis à jour: 4 oct. 2026
Chemin des instructions: production/live-trading/SKILL.md @ f0ea01919e0c

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

Qualité

55/100

Prometteur

Confiance

61/100

Sandbox uniquement

Audit

73/100

Risqué

Ouvrir l’audit complet Voir le rapport d’évaluation

Permission surface may require sandboxing
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
Permission surface needs review: secrets or environment access, network or browser access
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 11 forks; issue activity unavailable in current metadata
Permission surface: secrets or environment access, network or browser access

Verified installs: —
Résultats: —

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

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Plus de détails

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      "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-live-trading",
    "api": "https://www.openagentskill.com/api/agent/skills/ml4t-ml4t-live-trading",
    "audit": "https://www.openagentskill.com/skills/ml4t-ml4t-live-trading/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=ml4t-ml4t-live-trading&task=Use%20ml4t-live-trading%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20ml4t-live-trading%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20ml4t-live-trading%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/ml4t-ml4t-live-trading/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/ml4t-ml4t-live-trading"
  }
}

Pour le créateur

Source de la fiche

Indexé par Registry

Revendiable

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
Source: ml4t/skills
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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Cette fiche Indexé par Registry est attribuée à ml4t, mais n’est pas encore marquée officielle. Revendiquez-la pour ajouter un signal de propriétaire vérifié et rendre les futures mises à jour de lancement, d’installation et d’audit plus fiables.

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