first 100 trades training lab
by nowrich
A structured 100-trade simulated practice curriculum with risk limits, journal templates, and progression gates.
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Works with the AI tools you already use
See it in action
You say
Build a 100-trade practice lab for a mean-reversion strategy on BTCUSD. Use a 1% risk per trade limit and include a progression gate at trade 25 based on profit factor.
Your agent does
BTCUSD Mean-Reversion Lab
Setup Rules
- Entry: Price touches lower Bollinger Band (20,2) and RSI < 30.
- Stop: 1.5 ATR below entry.
- Target: 2.0R or Mean touch.
Risk Limits
- Max Trade Risk: 1% balance.
- Daily Stop: 3%.
Phase 1 Gate (Trade 25)
- Profit Factor > 1.2
- Discipline > 4/5
What you get
About this skill
The problem
Traders often fail because they lack a disciplined process, jumping into live markets without testing their strategy or emotional control. Raw practice without structure leads to repetitive mistakes and a lack of objective data to improve performance.
What it does
- Generates a complete simulated trading curriculum with strict entry, exit, and invalidation rules.
- Establishes hard risk limits including per-trade caps, daily loss stops, and maximum drawdown gates.
- Defines a multi-stage progression system that requires meeting specific win rate and discipline metrics to advance.
- Creates a structured journaling framework with pre-trade checklists, emotional tracking, and R-multiple analysis.
- Sets up a mandatory review cadence for daily, weekly, and milestone-based performance audits.
Why this beats prompting it yourself
This skill provides a pre-configured architecture for professional-grade trade logging and risk management that standard prompts miss. It enforces specific mathematical position sizing and progression gates, ensuring you build a data-backed trading plan rather than a simple list of trades.
Use cases
- Building a repeatable trading process before risking real capital in live markets.
- Backtesting a new technical strategy through 100 disciplined, simulated iterations.
- Creating an accountability framework for student traders or internal trading desks.
- Developing emotional awareness by tracking psychological states alongside trade outcomes.
Known limitations
Requires the user to manually capture and organize chart screenshots. Effectiveness depends on the user's honesty in reporting simulated slippage and discipline scores.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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