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"The modern-day trading cannot be imagined without automated trading systems usually called Expert Advisors or robots. Most, if not all, of them feature a clear, hard-coded trading strategy and money management system. Their main advantage is a rigid algorithm excluding the human factor. However, this advantage is also their main drawback since trading robots lack flexibility. Regardless of the market conditions, an Expert Advisor always applies the same trading strategy with the same strictly categorized parameters. In other words, the system always acts rigidly: average trend > enter the market with one lot, strong trend > enter the market with two lots. No deviations!"



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Fig.5 Balance line before and after the signal correction

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Contents

• 1. Structure of DBN
• 2. Preparation and selection of data
  
  • 2.1. Input variables
  • 2.2. Output variables
  • 2.3. Initial data frame
    
    • 2.3.1. Deleting highly correlated variables
  • 2.4. Selection of the most important variables
• 3. Experimental part.
  
  • 3.1. Building models
    
    • 3.1.1. Brief description of the "darch" package
    • 3.1.2. Building the DBN model. Parameters.
  • 3.2. Formation of training and testing samples.
    
    • 3.2.1. Balancing classes and pre-processing.
    • 3.2.2. Coding the target variable
  • 3.3. Training the model
    
    • 3.3.1. Pre-training
    • 3.3.2. Fine-tuning
  • 3.4. Testing the model. Мetrics.
    
    • 3.4.1. Decoding predictions.
    • 3.4.2. Improving the prediction results
      
      • Calibration
      • Smoothing with a Markov chain model
      • Correcting predicted signals on the theoretical balance curve
    • 3.4.3. Metrics
• 4. Structure of the Expert Advisor
  
  • 4.1. Description of the Expert Advisor's operation
  • 4.2. Self-control. Self-training
• Installation and launching
• Ways and methods of improving qualitative indicators.
• Conclusion

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Table of Contents

• Introduction
• CStrategyList Strategy Manager
• Loading Strategies from an XML list. A Portfolio of Strategies
• Managing Strategies Using a Custom Panel
• Expert Advisors Trading in a Group
• Analyzing Expert Advisor Operation in the Strategy Tester
• Conclusion

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Contents

• Introduction
• 1. How evolutionary algorithms first appeared
• 2. Evolutionary algorithms (methods)
• 3. Genetic algorithms (GA)
  
  • 3.1. Field of application.
  • 3.2. Problems being solved
  • 3.3. Classic GA
  • 3.4. Search strategies
  • 3.5. Difference from the classic search of optimum
  • 3.6. Terminology of GA
  
  
• 4. Advantages of GA
• 5. Disadvantages of GA
• 6. Experimental part
  
  • 6.1. Search for the best combination of predictors
    
    • with tabuSearch
    
    
  • 6.2. Search for the best parameters of TS:
    
    • with rgenoud (Genetic Optimization Using Derivatives)
    • with SOMA (Self-Organising Migrating Algorithm)
    • with GenSA (Generalized Simulated Annealing)
    
    
  
  
• 7. Ways and methods of improving qualitative characteristics
• Conclusion

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Contents

• Introduction
• The Slider Control
  
  • Developing a Class for Creating the Slider Control
  • Testing the Slider Control
  
  
• The Dual Slider Control
  
  • Developing a Class for Creating the Dual Slider Control
  • Testing the Dual Slider Control
  
  
• Conclusion

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Table of Contents

• Introduction
• An overview of classes used by the strategy generator
• An overview of the signal module, the concept of a pattern
• A module of signals. First use
• Writing the first strategy based on CSignalMACD
• An adapter of signals
• Combining multiple signals in a trading strategy code
• Conclusion

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Table of Contents

• Introduction
• 1. The LifeHack Ticks indicator
• 2. The LifeHack Balance Equity indicator
• 3. The LifeHack Drawdown Load indicator
• Conclusion

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Contents

• Introduction
• Tabs control
• Developing a class for creating the Tabs control
• Testing the Tabs Control
• Conclusion

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The article provides the results of testing a simple trading strategy in three modes: "1 minute OHLC" using only Open, High, Low and Close prices of minute bars; detailed modeling in "Every tick" mode, as well as the most accurate "Every tick based on real ticks" mode applying actual historical data.

Comparing the results allows us to assess the quality in various modes, as well as helps us to use the tester more efficiently in order to receive results faster. "1 minute OHLC" mode allows receiving quick estimated test results, "Every tick" mode is closer to reality, while testing on real ticks is most accurate but time-consuming. Keep in mind that errors in a trading robot's logic may affect the number of trading operations making the strategy test results more susceptible to a selected test mode.

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