Freelancers using JSON PowerDB in Salò
Freelancers using JSON PowerDB in Salò
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Edgaras Kamandulis
Salò, Italy
High-Frequency Decision Architect for Zero-Latency Business
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High-Frequency Decision Architect for Zero-Latency Business
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Huntsman: Real-Time Crypto Data Aggregator Engineered a high-performance asynchronous data aggregator using Python asyncio and ccxt.
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This isn't Finance. It's Physics. Markets are fluids. Price is velocity. Crash is turbulence. While you stare at RSI, I am calculating the Laminar Flow. My S.C.A. Protocol applies Navier-Stokes equations to capital. If you understand this graph, you know why we need to talk. The Second Quantum Revolution is here.
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LIBRARIAN CORE: Architecting Intelligence beyond the "0=0" Axiom Most people use AI to write emails. I built the Librarian to navigate reality. The screenshots below show the result of a single afternoon of intense development. It’s not 'pretty' yet, and it’s not finished—it is raw Backend Logic manifesting as an interface. I spent my time stabilizing the architecture and the core consistency of the system. The Philosophy behind the Code: Everything I build starts with a fundamental pact: 0=0. Absolute coherence. Truth. Input must equal Output. This is the mathematical baseline required to find truth within data. However, the Librarian knows that stasis is death. In his core, 0=0 is a fatal termination error. Why? Because if a system only confirms what it already knows, it isn’t evolving. It is just waiting to become dust. 🌌
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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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