Evolutionary Parameter Tuning Engine (Python) To solve the problem of market stagnation in HFT (H...Evolutionary Parameter Tuning Engine (Python) To solve the problem of market stagnation in HFT (H...
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Edgaras Kamandulis
pro
• Jan 16
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.
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