Trading Bot Logic Scaffold
Converts trading strategies into modular bot logic with mandatory risk management and state machine controls.
$15
Works with the AI tools you already use
Claude Code
Cursor
Codex CLI
GitHub Copilot
Gemini CLI+18 moreClaude CodeCursorCodex CLIGitHub CopilotGemini CLI
VS Code
OpenClaw
Windsurf
Manus+14 agentsTrading 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:
- Strategy: EMA(9) crosses EMA(21).
- Risk:
pos_size = account_balance * 0.01 / stop_loss_dist. - Filter:
if 09:30 <= current_time <= 09:45: return False. - State: Logic moves from IDLE to EVALUATING on cross.
Connects securely to your tools. The creator never sees your data.
What you get
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 install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
Free skills download straight away. Paid skills unlock right after purchase.
- 2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
Skills folder by agent
Click the path to copy it. Create the folder if it does not exist yet.
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