Evolutionary Parameter Tuning Engine (Python) To solve the p... by Edgaras KamandulisEvolutionary Parameter Tuning Engine (Python) To solve the p... by Edgaras Kamandulis
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 th...
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.