Complex Arbitrage in Rust
A six-part build-up of multi-leg crypto arbitrage - from negative-cycle detection to the linear algebra, copulas, and machine learning behind it, ending in low-latency Rust execution.
Graph Algorithms for Arbitrage Detection: From Bellman-Ford to RICH
How negative cycles, multi-asset graphs, and the RICH algorithm identify arbitrage opportunities in the deep cryptocurrency market with sub-millisecond precision.
Futures-Spot Arbitrage: From Cash-and-Carry to DeFi-CeFi
How funding rates, basis, and the convergence of decentralized and centralized markets create risk-free opportunities for capital in the crypto market.
Matrices, Tensors, and Tropical Algebra: Linear Algebra for Arbitrage Detection
How the matrix of exchange rates, eigenvalues, tropical algebra, and tensor decompositions turn cryptocurrency market chaos into clear arbitrage signals.
Vine Copulas for Arbitrage: Modeling High-Dimensional Dependencies
How to use Vine Copulas to identify hidden dependencies between dozens of crypto assets and build robust, high-dimensional statistical arbitrage strategies.
GNN, Transformers, and RL for Arbitrage: When Neural Networks Learn to Trade
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.
Complex Arbitrage Execution in Rust: From Nanoseconds to Atomic Multi-Legs
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.
