Evolutionary Parameter Tuning Engine (Python) To solve the p...

Evolutionary Parameter Tuning Engine (Python)

To solve the problem of market stagnation in HFT (High-Frequency Trading), I architected a Genetic Algorithm that evolves the bot's configuration in real-time.

Instead of static settings, the system spawns a population of 40 different trading 'personalities'. It forces them to compete in a stochastic simulation based on recent market data.

The Process:

Selection: The top 10% performing configurations are isolated.

Crossover: Their traits (RSI thresholds, Risk multipliers) are spliced together.

Mutation: Random variations are introduced to discover new local optima.

As shown in the terminal log, the system automatically evolved a strategy that increased efficiency from 263.3 to 492.8 in 15 generations, injecting a high-frequency aggressive configuration into the live bot.