Felipe Queis, a senior full-stack engineer, uses TimescaleDB to power his crypto trading bot, which has achieved impressive results, including a success rate of 61.5% and a cumulative gross result of approximately 487%. He was initially drawn to cryptocurrencies after a friend's servers were infected with ransomware, and he started creating a simple Moving Average Convergence Divergence (MACD) crossover bot. However, he soon realized that his bot needed significant improvements and started working on it in his spare time. Felipe's project has evolved into a sophisticated system with technical indicators, sentiment analysis powered by machine learning, and a capital management system. He uses TimescaleDB to store and process the large amounts of data generated by his bot, which includes real-time aggregations, high ingestion rates, and efficient storage and compression. With TimescaleDB, Felipe's query response time is in the milliseconds, even with huge datasets. He has successfully integrated TimescaleDB into his existing technology stack, including Node.js, TensorFlow, and Cote, without adding significant maintenance overhead. Felipe's experience showcases the power of time-series data to fuel real-world decisions, and he has become an evangelist for the use of Time-scaleDB in the developer community.