Insights, updates, and tutorials from the Quantum AI Trading Bot development journey
This week I tested a new approach to my paper trading system, focusing on building a robust data pipeline for 289 symbols and implementing real-time feature extraction.
Exploring the personal motivations and learning potential of building an AI trading bot, from bridging theory to practice in automated financial strategies.
Testing an LSTM network to predict market directions with a dataset of 289 symbols, using real-time features to see if the model could provide actionable insights in paper trading.
Diving into market volatility with a new paper trading experiment focused on mastering risk management, optimizing stop-loss settings, and dynamic position sizing.
Exploring ensemble machine learning with LSTM and gradient boosting techniques to improve predictive accuracy in paper trading experiments.
Testing a reinforcement learning model to navigate stock market fluctuations in a paper trading environment, exploring responsible AI-driven trading strategies.
Full quantum stack deployed with 7 quantum algorithms (Grover, QAOA, VQE, QFT), 50+ quantum features, and expected 10-97% accuracy improvements over classical methods.
Announcing the public launch of Quantum AI Trading Bot - an advanced AI-powered paper trading system with multi-source data integration.
How I built a unified data pipeline that integrates six different market data sources into a single, coherent stream for the Quantum AI Trading Bot's paper trading experiments.
Building institutional-grade risk management into a paper trading bot - because responsible AI means treating simulated capital with the same discipline as real capital.
A deep dive into the ML pipeline powering the Quantum AI Trading Bot - from feature engineering through model training to paper trading inference.
Building a backtesting framework that honestly evaluates trading strategies - including the pitfalls that make most backtests dangerously misleading.
How I built a resilient 24/7 paper trading infrastructure with automatic recovery, health monitoring, and zero-downtime deployments.