Genetic criterias & genetic algorithm settings

Understanding Criteria for Genetic Bots in Trading

Genetic bots are automated trading systems that utilize genetic algorithms to optimize trading strategies. These bots evolve and improve their performance over time by applying genetic principles such as mutation, crossover, and selection. In this article, we will explore the criteria used to evaluate genetic bots, including Balance, Average Profit Trade, Profit Factor, Profitable Deals, Expected Payoff, Drawdown, Recovery Factor, as well as the parameters involved in the genetic algorithm.

Criteria for Genetic Bots:

1. Balance:

   • Description: Balance represents the highest value of the final account balance achieved by the genetic bot.

   • Importance: It indicates the overall profitability of the trading strategy implemented by the bot.

2. Average Profit Trade:

   • Description: Average Profit Trade is the highest value of the average profit earned per trade executed by the genetic bot.

   • Importance: It reflects the consistency and effectiveness of the bot's trading decisions in generating profitable trades.

3. Profit Factor:

   • Description: Profit Factor represents the highest value of the ratio between the total profit and total loss generated by the genetic bot.

   • Importance: It indicates the bot's ability to generate profits relative to its losses, with higher values indicating a more favorable risk-reward ratio.

4. Profitable Deals:

   • Description: Profitable Deals represent the highest number of trades executed by the genetic bot that resulted in a profit.

   • Importance: It reflects the bot's ability to identify favorable market conditions and execute profitable trades.

5. Expected Payoff:

   • Description: Expected Payoff is the highest value of the average profit per trade multiplied by the win rate (percentage of profitable trades).

   • Importance: It provides an estimate of the expected profitability of the genetic bot's trading strategy.

6. Drawdown:

   • Description: Drawdown measures the largest decline in the bot's account balance or equity during its trading period.

   • Importance: It indicates the bot's ability to manage risk and withstand market fluctuations without incurring significant losses.

7. Recovery Factor:

   • Description: Recovery Factor represents the value obtained by dividing the final account balance by the maximum drawdown.

   • Importance: It assesses the bot's ability to recover from drawdowns and regain profitability.

For each criterion, traders can assign a strength value ranging from 1 to 10, indicating the relative importance of that criterion in the evaluation process. Traders have the flexibility to select one or multiple criteria or even consider all criteria simultaneously to assess the performance of the genetic bot.

Genetic Algorithm Parameters:

Genetic Algorithm Parameters

1. Generation Size:

   • Description: Generation Size refers to the number of individuals or trading strategies in each generation of the genetic algorithm.

   • Range: Acceptable values typically range from 5 to 100.

   • Importance: It affects the diversity and exploration of the population during the evolutionary process.

2. Gene Mutation Probability:

   • Description: Gene Mutation Probability represents the likelihood of a genetic mutation occurring during the genetic algorithm's operation.

   • Range: Acceptable values usually range from 1 to 100 (percentage).

   • Importance: It determines the degree of exploration and randomness in the search for optimal trading strategies.

3. Population Filter:

   • Description: Population Filter represents the percentage of individuals retained from each generation to form the next generation.

   • Range: Acceptable values typically range from 20 to 80 (percentage).

   • Importance: It influences the selection pressure and the preservation of desirable genetic material in the population.

Conclusion:

Criteria such as Balance, Average Profit Trade, Profit Factor, Profitable Deals, Expected Payoff, Drawdown, and Recovery Factor play a crucial role in evaluating the performance of genetic bots. Traders can assign different strengths to these criteria based on their trading objectives and preferences. Additionally, the parameters of the genetic algorithm, including Generation Size, Gene Mutation Probability, and Population Filter, impact the optimization process and the quality of trading strategies generated. By understanding and fine-tuning these criteria and parameters, traders can leverage genetic bots to develop and enhance profitable trading strategies.