Autonomous Optimization in Financial Prediction Systems: A Self-Improving Algorithm Architecture for Cryptocurrency Market Analysis

This paper presents a self-improving algorithm architecture for cryptocurrency price prediction, specifically implemented in a Bitcoin market analysis system. The approach integrates three core components: an ensemble-based prediction engine, continuous validation, and autonomous optimization. What makes this interesting is that the system essentially teaches itself - it runs predictions, waits to see if they’re right, then tweaks its own parameters to do better next time. The prediction engine uses enhanced technical indicators that incorporate macroeconomic factors (M2 money supply) and market sentiment with adaptive temporal lags. Traditional RSI gets turbocharged with monetary policy context and real-time news sentiment, improving Sharpe ratios by over 35%. After analyzing over 930 predictions across different time horizons (1h, 6h, 12h, 24h), I found the system hits accuracy rates up to 69.4% for short-term predictions. The multi-objective optimization framework does a decent job balancing accuracy against false positives while keeping confidence levels calibrated. Most importantly, the architecture adapts to different market conditions (trending, ranging, volatile) without any human intervention. This work contributes to autonomous machine learning systems and shows that self- improving algorithms can actually work in the messy reality of financial markets. Index Terms—self-improving algorithms, machine learning, cryptocurrency prediction, autonomous optimization, ensemble methods, adaptive systems, macroeconomic indicators, sentiment analysis, temporal dynamics