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目标函数设计：你优 化的那个指标，正悄悄替你选好了策略
- 02证明多时间框架回测中没有look-ahead：扰动未来，证明过去看不到它
- 03前视偏差：一根 K 线的错误如何从纯噪声中凭空制造出 15 的夏普比率
- 04Walk-Forward 优化：唯一诚实的策略测试方法
- +7 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回测引擎速度阶梯：笔记本 CPU 上 298 倍提速，PnL 精确到最后一笔交易
- 02框架税：当你的回测库比手写的 pandas 循环还慢
- 03聚合 Parquet 缓存：如何将多时间框架回测加速数百倍
- 04双轴参数空间：为什么你的大部分参数扫描几乎是免费的
- +7 more
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套利检测的图算法：从 Bellman-Ford 到 RICH
- 02期货-现货套利：从期现套利到 DeFi-CeFi
- 03矩阵、张量与热带代数：用于套利检测的线性代数
- 04套利中的 Vine Copulas：高维依赖关系建模
- +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.
- 01CCXT：WebSocket 订单簿方法实际工作原理
- 02算法交易中的订单类型：从追价限价单到虚拟订单
- 03墙内排队：订单簿密集区的挂单位置分析
- 04算法交易的K线类型与聚合方法
- +8 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马科维茨投资组合理论之加密货币篇：从零到英雄
- 0212种投资组合优化算法比较：HRP、Black-Litterman、NCO及其他
- 03我们的内部算法揭秘：HRP + 多空 + 基于Hull-White的CVaR
- 04Copula模型：加密投资组合联合风险建模
- +1 more
Risk & Position Sizing
Build position sizes from drawdown math, realistic trading costs, and uncertainty in backtest results before applying Kelly.
- 01亏损与盈利的不对称性：正在摧毁你账户的数学原理
- 02滑点曲线,而非滑点常数:经得起实盘检验的成本模型
- 03蒙特卡洛自助法：如何用10行代码获取回测的置信区间
- 04策略的凯利公式：如何确定仓位大小并分配资本
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配对交易中的距离法：Rust实现与 分析
- 02加密货币市场的统计套利与配对交易：从协整到卡尔曼滤波
- 03信号相关性：需要监控多少个交易对
- 04统计套利中均值回归与动量策略的动态结合：数学基础与实践实现
- +1 more
Deep Learning for Markets
Neural forecasting for crypto — transformers, diffusion models, and foundation models, and how conformal prediction keeps their uncertainty honest.
- 01用于多时间跨度组合预测的时序融合 Transformer
- 02扩散模型对抗加密货币无政府状态：为什么DDPM比你的占星师更能预测比特币崩盘
- 03Kronos：让K线图说Transformer语言的基础模型
- 04用于风险感知仓位管理的保形预测
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基于代理型AI的投资组合管理革命
- 02TradingAgents：模拟对冲基金的多智能体AI交易框架
- 03AI4Finance Foundation：FinGPT、FinRL和FinRobot量化交易生态系统
- 04AI Hedge Fund：AI分析师投票决定交易的多智能体基金
- +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.
- 01QuestDB 算法交易实战：读懂市场语言的架构设计
- 02QuestDB 算法交易实战：改变游戏规则的 SQL 扩展
- 03QuestDB 算法交易实战：从订单簿到生产架构
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算法交易系统中的数据通信：技术综述
- 02Aeron：驱动半个HFT行业的消息传递系统揭秘
- 03ZigBolt：为什么我们用 Zig 从零打造了自己的 Aeron，实现了每条消息 20 纳秒延迟
- 04使用 FAST/FIX 开发简单 C++ 剥头皮交易机器人：分步指南
Execution & Market Making
From TWAP/VWAP and Almgren-Chriss to TCA, slippage models, and the Avellaneda-Stoikov market maker — how to turn a signal into fills without paying the spread twice.
- 01TWAP、VWAP与POV对比：如何选择执行基准（以及何时 它们会对你撒谎）
- 02去掉玄乎其辞的 Almgren-Chriss：一个下午就能实现的最优执行模型
- 03切片内部:调度器与交易所之间的子订单战术
- 04加密市场的智能订单路由：一笔订单，十二个场所，没有 NBBO
- +7 more
Onchain & DeFi Quant
Quantitative DeFi from first principles — MEV and sandwich attacks, atomic arbitrage, liquidation cascades on lending protocols, and LP profitability on Uniswap v3.
- 01MEV 剖析：三明治攻击、抢先交易与内存池的黑暗森林
- 02MEV 供应链：PBS、MEV-Boost 与区块市场
- 03链上套利：原子周期、闪电贷和必须赢得的拍卖
- 04链上清算：Aave 与 Compound 的机制，以及围绕它们的机器人生意
- +5 more
Causality & Lead-Lag in Markets
Who moves whom — Granger causality, transfer entropy, PCMCI discovery, and the causal ML toolbox (double ML, causal forests, synthetic control) applied to crypto markets.
- 01Toda-Yamamoto vs Differenced Granger: Does the BTC Lead-Lag Survive?
- 02Transfer Entropy: Which Way Does Information Flow Between Crypto Assets?
- 03PCMCI: Causal Discovery in Multivariate Crypto Time Series
- 04交易中异质处理效应的因果森林
- +3 more
Volatility Modeling & Regimes
Forecast volatility with the GARCH family — asymmetry and leverage effects, dynamic correlations, vol targeting — then detect market regimes with HMMs and trade adaptively.
- 01GARCH(1,1)：预测加密货币波动率
- 02非对称与厚尾GARCH：EGARCH、GJR与Student-t
- 03DCC-GARCH：配对交易与组合风险的动态相关性
- 04波动率目标化与基于GARCH预测的交易
- +1 more
Modern ML for Trading
The full gradient-boosting-to-neural-operator toolkit — ensembles, AutoML, probabilistic forecasting, distillation and pruning for latency, plus physics-informed architectures like Neural ODEs and Fourier operators.
- 01用于收益方向预测的 XGBoost ：类别不平衡与决策阈值
- 02集成方法：结合弱学习者以获得稳健的 Alpha
- 03用于系统化交易流程的 AutoML
- 04Multi-Task Learning for Simultaneous Price, Volume, and Volatility Prediction
- +11 more