Strategy

Summary:

Develop an MT5 autotrader that:

• Trades using a “Buy Low, Sell High” strategy based on mean reversion.
• Uses the 21-day Bollinger Bands (with the 21-day SMA as the centerline) to identify overbought/oversold conditions.
• Executes trades at a fixed size of 0.01 units per trade.
• Sets stop-loss levels using the ATR (21) to adapt to market volatility.
• Incorporates risk management, trade confirmation, and performance logging to ensure disciplined execution.

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Strategy Overview:

• Core Strategy:
  The system implements a “Buy Low, Sell High” approach using mean reversion principles. The algorithm will seek to identify when the price is near its lower or upper bounds relative to historical averages and then enter trades accordingly.

• Timeframe & Indicators:

  • Bollinger Bands:
    • Period: 21 days
    • The 21-day Simple Moving Average (SMA) forms the centerline.
    • Upper and lower bands are calculated based on standard deviation (a standard multiplier, e.g., 2, can be used unless specified otherwise).
    • Use the position of the price relative to these bands to gauge potential reversal zones.
  • ATR (21):
    • Period: 21 days
    • This indicator will be used to dynamically determine the stop-loss distance based on current market volatility.

Entry Criteria:

1. Buy Signal (Long Entry):
   • Condition: Price is trading at or near the lower Bollinger Band, indicating potential oversold conditions relative to the 21-day average.
   • Additional Confirmation:
     • Look for a divergence in momentum (if available) or a clear signal of price reversal.
     • Price should have historically demonstrated a rebound when near this band.
2. Sell Signal (Short Entry):
   • Condition: Price is trading at or near the upper Bollinger Band, indicating potential overbought conditions relative to the 21-day average.
   • Additional Confirmation:
     • Similar to the long side, include confirmation from price action that a reversal or pullback is likely.

Order Execution & Position Sizing:

• Unit Size:
  • All trades will be executed with a fixed size of 0.01 units per trade.
• Trade Frequency:
  • The system should limit the number of simultaneous open positions to reduce exposure and adhere to the “quality over quantity” principle.
• Scaling and Re-entries:
  • If the trade conditions reoccur and confirmation is maintained, the system may consider additional entries (still at 0.01 units each) but should remain conservative to avoid overtrading.

Stop-Loss Calculation:

• ATR (21) Based Stop-Loss:
  • For each trade, calculate the current ATR (with a period of 21 days).
  • Use a multiplier (e.g., 1x or 1.5x ATR) as a risk-adjusted stop-loss distance from the entry price.
  • The stop-loss will be placed:
    • For a long trade: below the entry price by the computed ATR value.
    • For a short trade: above the entry price by the computed ATR value.
  • The exact multiplier can be adjusted during optimization to suit the volatility profile of the instrument.

Trade Management & Exit Criteria:

• Take-Profit & Trailing Stops:
  • Define a take-profit mechanism based on a favorable risk-reward ratio (preferably 1:1 or higher).
  • Consider implementing a trailing stop feature (also ATR-based) that adjusts as the trade moves favorably.
• Manual Override / Safety Checks:
  • Include logic to prevent entries during extremely low or high volatility periods if it deviates from historical behavior.
  • Incorporate filters (e.g., market news or high-impact events) to temporarily halt trading.

Other Considerations:

• Backtesting and Optimization:

  • The autotrader should include parameters for the ATR multiplier and Bollinger Bands deviation multiplier to be optimized based on historical data.
  • Ensure that the strategy is tested on multiple instruments to confirm its robustness.

• Logging and Reporting:

  • The system must log each trade, including entry conditions, stop-loss levels (based on ATR), take-profit levels, and exit reasons.
  • Regular performance reports (e.g., daily/weekly summaries) should be generated for ongoing evaluation and refinement.