Collections
Curated reading paths through the blog, ordered from basics to advanced.
Backtesting Without Fooling Yourself
A step-by-step path from what your backtest really optimizes to proving an edge survives overfitting, multiple testing, and live execution. Read top to bottom — each part builds on the last.
- 01 Objective-Function Design: The Metric You Optimize Secretly Picks Your Strategy
- 02 Walk-Forward Optimization: The Only Honest Strategy Test
- 03 Plateau Analysis: How to Distinguish a Robust Optimum from Overfitting
- 04 Monte Carlo Bootstrap: How to Get Confidence Intervals for a Backtest in 10 Lines of Code
- + 3 more
High-Performance Backtest Engines
How to build a backtest engine that runs hundreds of times faster without changing a single PnL number — data layout, caching, adaptive resolution, and architecture, from first speedups to production internals.
- 01 The Backtest Speed Ladder: 298x on a Laptop CPU, Identical PnL to the Last Trade
- 02 Aggregated Parquet Cache: How to Speed Up Multi-Timeframe Backtests by Hundreds of Times
- 03 Adaptive Drill-Down: Backtest with Variable Granularity from Minutes to Raw Trades
- 04 The IPC Tax: Put the Backtest Engine Behind a Socket and Lose 13% — Almost None of It to the Socket
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.
- 01 Graph Algorithms for Arbitrage Detection: From Bellman-Ford to RICH
- 02 Futures-Spot Arbitrage: From Cash-and-Carry to DeFi-CeFi
- 03 Matrices, Tensors, and Tropical Algebra: Linear Algebra for Arbitrage Detection
- 04 Vine Copulas for Arbitrage: Modeling High-Dimensional Dependencies
- + 2 more
Order Book & Market Microstructure
How the order book really works — accessing the data, reading queue position, rebuilding bars from order flow, and modeling it with deep learning and Hawkes processes.
- 01 CCXT: How WebSocket Orderbook Methods Really Work
- 02 Order Types in Algorithmic Trading: From Limit with Chasing to Virtual Orders
- 03 Queue Inside the Wall: Analyzing Order Position in Order Book Density
- 04 Bar Types and Aggregation Methods for Algorithmic Trading
- + 2 more
Portfolio Construction & Risk
From Markowitz to production HRP + CVaR: how to allocate across crypto assets, model tail dependence with copulas, and size positions without blowing up.
- 01 Markowitz Portfolio Theory for Crypto: From Zero to Hero
- 02 12 Portfolio Optimization Algorithms, Compared: HRP, Black-Litterman, NCO and Beyond
- 03 Inside Our House Algorithm: HRP + Long/Short + CVaR with Hull-White
- 04 Copula Models for Joint Risk Modeling in Crypto Portfolios
- + 1 more
Statistical Arbitrage & Pairs Trading
Trade the spread between correlated assets — from the distance approach to cointegration and Kalman filters, then dynamically combining mean reversion with momentum.
- 01 Distance Approach in Pairs Trading: Implementation and Analysis with Rust
- 02 Statistical Arbitrage and Pairs Trading in Crypto Markets: From Cointegration to the Kalman Filter
- 03 Dynamically Combining Mean Reversion and Momentum Strategies in Statistical Arbitrage: Mathematical Foundations and Practical Implementation
Deep Learning for Markets
Neural forecasting for crypto — transformers, diffusion models, and foundation models, and how conformal prediction keeps their uncertainty honest.
- 01 Temporal Fusion Transformers for Multi-Horizon Portfolio Forecasting
- 02 Diffusion Models vs Cryptocurrency Anarchy: Why DDPM Can Predict Bitcoin Crashes Better Than Your Astrologist
- 03 Kronos: A Foundation Model That Teaches Candlestick Charts to Speak Transformer Language
- 04 Conformal Prediction for Risk-Aware Position Sizing
AI Agents for Trading
The agentic-AI stack for markets — multi-agent frameworks, open-source hedge funds, and LLMs that mine alpha from earnings calls.
- 01 Revolution in Investment Portfolio Management with Agentic AI
- 02 TradingAgents: Multi-Agent AI Framework That Models a Hedge Fund
- 03 AI4Finance Foundation: The FinGPT, FinRL, and FinRobot Ecosystem for Algo-Trading
- 04 AI Hedge Fund: A Multi-Agent Fund Where AI Analysts Vote on Trades
- + 1 more
QuestDB for Algorithmic Trading
Stand up a time-series stack for trading on QuestDB — from architecture to the SQL that matters, to a production deployment.
- 01 QuestDB for Algorithmic Trading: Architecture That Speaks the Language of Markets
- 02 QuestDB for Algorithmic Trading: SQL Extensions That Change the Game
- 03 QuestDB for Algorithmic Trading: From Order Books to Production Architecture
Low-Latency Trading Infrastructure
The plumbing under an HFT stack — how components talk (WebSocket, FIX, gRPC, Aeron), messaging on Aeron and Zig, and a C++ FIX/FAST scalper.
- 01 Data Communication in Algo Trading Systems: A Technology Overview
- 02 Aeron: Inside the Messaging System That Powers Half of the HFT Industry
- 03 ZigBolt: Why We Built Our Own Aeron in Zig and Hit 20 Nanoseconds Per Message
- 04 Developing a Simple C++ Scalper Using FAST/FIX: Step-by-Step Guide