What to Automate First in a Trading Workflow

Separate judgment from repeatable rules

• what confirms a valid setup
• what invalidates the setup
• what size the trade is allowed to use
• what conditions should block execution
• what needs to be logged after the trade

Build the workflow in layers

1. market idea
2. rule definition
3. risk constraints
4. execution logic
5. review and iteration

def evaluate_trade_setup(signal, volatility, max_risk):
    if not signal["confirmed"]:
        return {"status": "skip", "reason": "signal-not-confirmed"}

    if volatility > signal["max_allowed_volatility"]:
        return {"status": "skip", "reason": "volatility-too-high"}

    return {
        "status": "ready",
        "risk_budget": min(signal["suggested_risk"], max_risk),
        "entry": signal["entry"],
        "stop_loss": signal["stop_loss"],
    }

Review loops matter as much as entries

• which signals were accepted and which were skipped
• whether the filters removed noise or removed valid trades
• whether the sizing rule was consistent with the intended risk
• whether the logs are clear enough to support iteration

Conclusion: automate the process, not the excitement