Deep dives into AI trading, market analysis, and the future of DeFi.
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How funding rate arbitrage works across crypto exchanges, why rates differ on Binance, Bybit, OKX and dYdX, and how to build a monitoring and execution system to extract profit from these discrepancies.
Deep dive into QuestDB's time-series SQL extensions: SAMPLE BY, ASOF JOIN, HORIZON JOIN, WINDOW JOIN, LATEST ON, and real-world trading query patterns.
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.