Boring Pips MT4






Over-fitting is a common issue in developing Expert Advisors (EAs) where the EA performs exceptionally well in historical backtests but fails to deliver similar results in live trading due to an over-optimization to past data. It often results from the EA being too tailored to historical market conditions, lacking generalizability for future scenarios.

Boring Pips MT4

Live trading performance:

Key Points Regarding Over-fitting:

  1. Common Problem: Many EAs suffer from over-fitting, either because developers are unaware of its existence or intentionally use it to present impressive backtest results.
  2. Avoiding Over-fitting: Traders are advised to avoid using EAs without live trading results for at least 5 months or 300 tracked trades. Live trading results provide insight into the EA’s performance in unforeseen market conditions.
  3. Generalizability: EAs should be designed to have generalizability, allowing them to adapt to different market conditions rather than being overly specific to historical data.
  4. Live Trading Monitoring: Monitoring an EA’s live trading performance is crucial to observe how it performs with data it has never encountered before.

Boring Pips MT4

Boring Pips EA’s Anti-Overfitting Process:

  1. Initial Optimization: The EA undergoes optimization using historical data from 2010 to 2019 to test the initial premise of the trading strategy and extract robust parameter values.
  2. Walk-forward: Parameters from the first stage are tested using entirely new data from 2019 to 2022 to ensure the system’s stability and evaluate its predictive power.
  3. Stress Testing: Robust parameter values undergo stress testing, introducing variables like noise and lag to evaluate the system’s tolerance to random factors.

Live Trading Performance: Boring Pips EA’s live trading performance has been monitored with a real account since October 10, 2022. The performance can be tracked here.

Boring Pips EA Algorithm Overview:

  • Trading Strategies: Combines momentum, supply and demand zones, Fibonacci retracement, and artificial intelligence algorithms.
  • Signal Scanning: Continuous scanning of potential supply and demand zones using an advanced algorithm.
  • Momentum Analysis: Utilizes deep learning algorithms to measure momentum across four timeframes simultaneously.
  • Automated Trading Decisions: Makes trading decisions based on changes in momentum at identified supply and demand zones.
  • Risk Management: Manages trades based on probability distribution rules to maximize exploitation of the trading edge.

Installation and Recommendations:

  • Pairs and Timeframe: Recommended for AUDCAD, AUDNZD, and NZDCAD on the M5 timeframe.
  • Risk Management: Choose an appropriate risk mode (Boring, Low risk, Medium risk, or High risk).
  • Base Balance: Allocate a specific amount of balance for trading.
  • Personalization and Risk Management Settings: Follow detailed instructions for customization and risk management.


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