November 2024 Summaries

QuestDB is highlighted as a next-generation, open-source database optimized for handling large volumes of market data, particularly suited for tick data, offering high ingestion throughput and efficient SQL analytics. The text explores the potential of scaling trading bots using QuestDB, inspired by Marc van Duyn's article on building a trading bot with the investing-algorithm-framework, an open-source Python library. The framework, which integrates order execution, broker connection, and backtesting, typically uses SQLite or in-memory databases, but the text advocates for QuestDB's use due to its ability to handle complex, high-frequency, multi-market scenarios. By leveraging QuestDB, trading bots can efficiently manage data ingestion and storage, offering advantages like proprietary dataset handling, fault-tolerant architectures, and seamless data visualization for diverse use cases. The piece delves into practical implementations, such as using ccxt for data extraction and Cryptofeed for real-time updates, and demonstrates how QuestDB can be integrated into a trading bot system, ultimately supporting a scalable, modular, and optimized architecture for modern trading needs.

The author recounts their journey of creating a market-making game using a Raspberry Pi Pico microcontroller, initially inspired by their child's interest in a Raspberry Pi. They explore the transition from simple LED projects to an advanced trading game that simulates the dynamics of market-making, including quoting bid and ask prices and managing risk in ETF markets. Through this process, the author delves into hardware and software development, employing tools like PCB design for hardware improvements and refining the software with asynchronous programming and multiprocessing for efficiency. They also discuss challenges like memory management and button debouncing, offering insights into solutions like precompiling and using interrupts. The project culminates in real-time data visualization using QuestDB and Grafana, providing a practical understanding of market-making and highlighting the versatility of the Raspberry Pi Pico and micropython in handling complex tasks. The experience enriches the author's appreciation of hands-on creation and the potential of microcontrollers, 3D printing, and CAD design, opening new perspectives on problem-solving.

QuestDB, an open-source database optimized for market data, is utilized to analyze the French real estate market using the Demande de Valeurs Foncières (DVF) dataset, which includes detailed property transaction records. The analysis reveals significant trends and shifts in the market since COVID-19, such as the impact of rising interest rates and the migration from urban centers like Paris to suburban areas, which is possibly due to the financial and spatial advantages of peripheral regions. Using QuestDB for data ingestion and Grafana for visualization, the study highlights a structural decline in transaction numbers and values, particularly in Paris, while showcasing regional disparities in property prices and transaction volume. The data suggests a reduction in the number of transactions and a decrease in prices, challenging the common belief that real estate prices always rise. The enriched DVF dataset, with geographic coordinates, allows for mapping and further analysis, spotlighting high-value transactions and price per square meter variations across different regions, ultimately reflecting on the wealth gap and illustrating market trends through various visualizations.