Trading Bot Logic Scaffold

    1

    Converts trading strategies into modular bot logic with mandatory risk management and state machine controls.

    $15

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    Trading Bot Logic Scaffold

    Example session with this skill installed

    Generate a Python bot for a 15-minute EMA crossover strategy. Risk 1% per trade, max 3 concurrent trades, and stop trading if daily loss hits 3%. No trades during the NYSE open volatility.

    • Read your context and instructions
    • Compiled the trading bot logic

    I have scaffolded your EMA Crossover bot with a 4-module architecture:

    1. Strategy: EMA(9) crosses EMA(21).
    2. Risk: pos_size = account_balance * 0.01 / stop_loss_dist.
    3. Filter: if 09:30 <= current_time <= 09:45: return False.
    4. State: Logic moves from IDLE to EVALUATING on cross.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem

    Amateur trading bots often fail because they focus exclusively on entry signals while ignoring risk management. Developers waste hours debugging race conditions, double-firing trades, and liquidations caused by missing state controls or news events.

    What it does

    • Translates plain-English trading strategies into strict, modular If/Then logic modules.
    • Enforces a mandatory Risk Management Engine calculating position sizing based on account percentage and drawdown limits.
    • Implements Environmental Filters to block trades during high-impact news or high-slippage periods.
    • Generates a robust State Machine (IDLE, EVALUATING, IN_TRADE, COOLDOWN) to prevent logic loops and redundant executions.

    Frameworks & tools

    Python, Pine Script (TradingView), and MQL4/MQL5 (MetaTrader).

    Why this beats prompting it yourself

    Standard LLM prompts often produce "happy path" code that lacks defensive programming. This skill forces the inclusion of professional-grade risk management and state transitions that most developers forget to specify, ensuring capital preservation is baked into the architecture.

    Use cases

    • Converting a manual price action strategy into a structured Python bot.
    • Adding hard drawdown limits and session filters to existing TradingView scripts.
    • Building a modular execution layer that separates entry alpha from risk logic.
    • Scaffolding a state-controlled bot for MetaTrader that handles API disconnects gracefully.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    • Passed all security checks, Safe to install

    Listed1 month ago

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