Deep dives into AI trading, market analysis, and the future of DeFi.
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Materialized views, 2D array order book analytics, and reference architecture for a QuestDB-powered algorithmic trading platform.
Deep dive into QuestDB's three-tier storage architecture — WAL, columnar storage, and Parquet on object storage — and schema design principles for algorithmic trading systems.
We analyze communication technologies at all levels of an algorithmic trading platform: from exchange connectivity protocols (REST, WebSocket, FIX) to internal IPC, message brokers, and data stores.
Why losing 50% requires 100% growth to recover, how volatility drag destroys capital even in sideways markets, and which formulas every algo trader must know for building risk management.
How to squeeze maximum performance out of Rust for multi-leg arbitrage execution: io_uring, lock-free order books, LMAX Disruptor, SIMD, type-state machines, and arena allocators.
How graph neural networks find arbitrage chains in 78 ms, why RL agents show 142% annual returns against 12% for rule-based bots, and how to build an integrated system in Rust.
How the matrix of exchange rates, eigenvalues, tropical algebra, and tensor decompositions turn cryptocurrency market chaos into clear arbitrage signals.