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MQL5 Experts Forex Trading robot/indicator debugging Strategy optimization Statistics and mathematics Strategy modules Python Panels and dialog boxes C# Stocks Custom graphics Futures MySQL Product Design JavaScript Options Java Collection of data on the internet SQL HTML PHP Data mining Uploading data to a website Text writing OpenCL ALGLIB PostgreSQL Linux R RegExp

Specification

Act as a professional Quantitative Developer and Risk Manager. I want to build a systematic trading strategy rulebook that prioritizes capital preservation and statistical edge over raw performance. 

Please generate a structured trading strategy using the following framework:

1. ASSET CLASS & TIMEFRAME:
- Asset: [e.g., Apple (AAPL), Bitcoin (BTC), or EUR/USD]
- Timeframe: [e.g., 5-minute, 1-hour, Daily]

2. CORE THESIS:
- Explain the market inefficiency or behavioral anomaly this strategy is trying to exploit (e.g., mean reversion after an overextended move, or momentum breakout).

3. SYSTEMATIC LOGIC (No Ambiguity):
- Precise Entry Conditions: What exact mathematical or technical indicator conditions must be met to trigger a BUY or SELL signal? (Include specific lookback periods).
- Precise Exit Conditions: What technical triggers signal that the move is over?

4. DEEP RISK MANAGEMENT (Crucial):
- Stop-Loss: How is the invalidation point calculated? (e.g., Average True Range (ATR) multiplier, or fixed percentage).
- Take-Profit: What is the Risk-to-Reward ratio per trade? (Minimum 1:2 recommended).
- Position Sizing: Use a fixed-fractional model where no single trade risks more than 1% of the total account equity. Show the formula for calculating share/contract size based on the distance to the stop-loss.

5. FAILSALES & SYSTEMIC RISK:
- Define a daily maximum drawdown limit (e.g., if the account loses 3% in a single day, the bot completely halts all trading).
- How should the system handle high-impact macroeconomic news events?

Please output this strategy as a clean, step-by-step specification sheet, followed by a Python code snippet using `pandas` and `numpy` to backtest the logic.

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Project information

Budget
500+ USD