HeThink — Technology for Human Freedom

Convertible Bond Quant Strategy: Alpha Selection, CCB Pricing, and Futures Hedging

2026-04-22T10:12:00.000Z

As traditional dual-low strategies gradually lose their effectiveness, what else can be done in convertible bond investing? This article introduces a complete quantitative framework for convertible bonds: using equity multi-factor Alpha to drive security selection, using the CCB pricing model as the strategic hub linking returns and risk control, and finally using stock index futures to hedge Beta.

AMM Quant Deep Dive 02: A Derivatives Pricing Model for Uniswap V3

2026-04-21T17:22:01.000Z

In the discussion in the previous article, “AMM Quant Deep Dive 01”, we used Ito’s lemma to show that the core cost borne by an LP is LVR (Loss-Versus-Rebalancing), which takes the form \(\frac{\sigma^2}{8} V dt\) under the constant-product formula. That result revealed the essence of the “volatility tax,” but to hedge risk more precisely, we still need a tighter pricing framework.

Drawing on recent research by Hou, Singh, Echenim, and others, this article reviews the static structural prototype of LP positions and discusses their risk and value characterization from two angles: absolute valuation under boundary stopping times, and marginal cost under a continuous-installment perspective.

The Rise and Fall of an Obscure On-Chain LP Strategy: Clouds Break, the Moon Appears, and Flowers Cast Shadows

2026-04-07T08:31:59.000Z

This is the full story of a Solana on-chain strategy, from the investment thesis to Kalman-filter-based hedge modeling, convex optimization for the optimal portfolio, and aggressive leverage through looping loans, ending with half the profit given back after the bull market tide receded.

10,000x Faster: The Ultimate High-Performance Normal Integration Solution, with Code

2026-01-22T06:24:19.000Z

In derivatives pricing and large-scale risk backtesting, where every millisecond matters, traditional normal-distribution integration often becomes both ugly code and a performance bottleneck. This article introduces a fully vectorized numerical algorithm that lets you exploit the parallelism of modern CPUs, GPUs, and even TPUs to break through that bottleneck.

[AMM Quant Deep Dive 01] Understanding the Economic Essence of AMMs: From Impermanent Loss (IL) to Loss-Versus-Rebalancing (LVR)

2025-12-07T15:57:02.000Z

Many people treat providing liquidity (LP) on on-chain exchanges (DEXs) such as Uniswap as a passive form of “yield management” or “mining.” That is an extremely dangerous misunderstanding. From the perspective of market microstructure, an AMM is fundamentally an adverse-selection game arena defined by an algorithm.

This article is the first entry in the [AMM Quant Deep Dive] series. We will abandon the traditional retail-investor perspective, start from macro market equilibrium, strip away the disguise of “impermanent loss (IL)” through rigorous mathematical derivation, and introduce the true core risk metric for professional AMM market making: Loss-Versus-Rebalancing (LVR).

From Chatterjee's Correlation to Tau-Star: Completeness and Power in Independence Testing

2025-11-12T14:18:36.000Z

In the earlier article Three Measures for Sequence Correlation, I introduced Chatterjee’s correlation coefficient, which can detect nonlinear and non-monotonic associations between variables. Its form is simple, computation is fast, and it is especially suitable for screening input features for nonlinear machine learning models. New research, however, shows that its detection efficiency is inadequate for some non-functional dependence structures. This article first reviews and expands on the concrete mechanics and key properties of Chatterjee’s \(\xi\). It then introduces an alternative: the equally consistent, distribution-free independence test statistic \(\tau^*\).

Kuru: Building a Next-Generation DEX on a Next-Generation Blockchain

2025-09-29T18:42:23.000Z

In the crypto world, the decentralized exchange (DEX) is a beautiful ideal: it allows anyone to custody and trade their own assets securely, without permission and without trusting a third party. But on the road to realizing that ideal, the space has long faced a painful trade-off.

With the Dust of the Market: Notes on the Practice of the Generalized Liquidity Provider

2025-07-08T14:16:04.000Z

When things reach an impasse, change becomes possible; through change comes flow; through flow comes endurance.

The techniques of markets change without cease. High-frequency market making chases light at the microsecond scale; on-chain protocols build pools of liquidity with mathematics; spot-futures arbitrage searches across different time horizons for the pull of equilibrium. There are countless methods.

And yet the Great Way is simple, and all methods return to one origin. There is a path that does not cling to technique but wanders through principle, turning complexity back into simplicity: the practice of the “generalized liquidity provider” (LP). Those who follow it eventually see that market making and arbitrage are merely two sides of the same coin, each rooted in the other.

These notes are a record of that practice. What they seek is not a bag of tricks, but a way that runs through the whole system: a unified underlying framework for every form of liquidity provision. Only then can one build durable understanding in a market that never stands still, and ultimately move in harmony with it.

Research Report on Technical Implementation Paths for Low-Latency Trading in China's A-Share Market in 2025

2025-06-02T11:43:50.000Z

This report systematically reviews the key links in the low-latency trading stack for China’s A-share market in 2025 and beyond, from exchanges to brokers to the client side. Achieving extreme low latency requires coordinated optimization across trading units, ultra-fast broker counters, low-latency brokerage and market data services, networks, algorithms, and hardware. New regulatory rules also raise the bar for both technical implementation and compliance. By combining the latest technology and policy trends, the report evaluates the latency characteristics of different approaches and provides a reference for market participants.

Building Statistical Arbitrage Portfolios with Correlation-Matrix Clustering: A Graph-Clustering Framework

2025-06-02T07:28:02.000Z

The core of a statistical arbitrage strategy lies in identifying and exploiting temporary deviations in asset prices. Traditional approaches such as pairs trading are well known, but scaling them efficiently to large asset universes while preserving robustness has remained difficult. A recent Oxford research paper, Correlation Matrix Clustering for Statistical Arbitrage Portfolios, proposes an innovative framework: apply graph clustering algorithms to the correlation matrix of stock residual returns, then build mean-reverting statistical arbitrage portfolios inside each cluster. Empirical results show that the method can generate annualized returns above 10% with Sharpe ratios significantly greater than 1.

A Bayesian-Inference Method for Measuring Box Office Outperformance

2025-03-26T07:43:51.000Z

The film industry is a market with exceptionally high uncertainty, and box office performance directly affects both the financial condition and market valuation of production companies. The degree of box office outperformance is a key indicator of how well a movie is received by the market and an important variable in evaluating film-company performance. Traditional box office forecasting methods, however, often rely on static point-estimate models. They struggle to capture the dynamic evolution of box office revenue over time, and they are even less capable of quantifying how far box office results exceed market expectations and with what level of uncertainty. As a result, they are difficult to convert into actionable investment strategies.

A Funding-Rate Arbitrage Strategy for Crypto Perpetual Futures

2024-12-16T10:25:46.000Z

This strategy captures low-risk arbitrage opportunities in the cryptocurrency market through the funding-rate mechanism and staking yield.

Learning to Rank in Brief: From RankNet to LambdaMART

2024-11-18T06:39:52.000Z

This article gives a concise introduction to the background, taxonomy, and evaluation metrics of learning to rank, then reviews several classic algorithms from its development history. The goal is not to cover every technical detail, but to build an intuitive understanding of the core concepts behind ranking, as preparation for practical LTR applications in different scenarios.

Shaputa: A Feature Selection Method That Combines SHAP and Boruta

2024-11-05T06:20:14.000Z

Shaputa is a hybrid feature selection technique that combines SHAP (SHapley Additive exPlanations) with Boruta’s shadow-feature mechanism. By constructing a random baseline for every feature and comparing it with model-derived SHAP importance, Shaputa produces more robust and structure-aware feature selection results in high-dimensional, complex datasets. It is especially useful in nonlinear settings where traditional methods often struggle.

Trends in Large Language Models and Their Applications in Quantitative Investing

2024-08-26T10:33:51.000Z

Large language models have become the hottest and fastest-moving frontier in computing since ChatGPT burst onto the scene at the end of 2022. LLMs have gradually shed the role of mere tools and become independent carriers of intelligence that can handle tasks on their own. In 2024, hundred-billion-parameter models such as Qwen2 and Llama3.1 were released one after another, and the performance of open-source models has moved steadily closer to that of closed-source systems. For quantitative researchers, a necessary question is how to combine LLM technology with financial applications to improve research and investment. This article first draws on Li Mu’s talk to discuss LLM trends and best practices, and then reviews specific application scenarios for LLMs in quantitative investing.

A Brief Discussion of Event-Driven Strategy Testing: Counterfactual Inference, Risk Factor Models, and Convolution Integrals

2024-08-25T11:19:47.000Z

This article discusses how to test and exploit events in quantitative investing, including event factors and event strategies.

Phase Three Quant Work Summary and Next Steps: Complexity and Cost Control

2024-08-23T11:03:07.000Z

Phase three completed end-to-end development and testing of the multi-factor quant system, and the strategy is now preparing to go live. The next stage will focus on strengthening diversified alpha capabilities and exploring diversified strategies.

Thinking One Move Ahead: Multi-Period Portfolio Optimization

2024-06-20T16:58:55.000Z

In the earlier article Portfolio Optimization for Long-Only Multi-Factor Equity Strategies, I gave a fairly complete introduction to how the problem of solving single-period portfolio weights can be transformed into a quadratic program. This article summarizes the key ideas of the Stanford and BlackRock collaboration paper Multi-Period Trading via Convex Optimization. Published in 2017, that paper offered one of the first systematic treatments of multi-period optimization in portfolio management.

Algorithmic Trading Strategies Based on Deep Reinforcement Learning

2024-06-17T10:53:00.000Z

There is broad market demand for algorithmic trading. This article analyzes how to implement algorithmic trading from the perspective of deep reinforcement learning, including policy inputs and outputs, reward functions, and neural-network structure. Compared with traditional methods, DRL has clear advantages in optimal execution across multiple orders and does not require precise mathematical modeling of time-varying market microstructure. The policy is optimized toward a global objective. Finally, the article also considers how intraday trading strategies can be integrated organically with alpha strategies.

Phase Two Quant Work Summary and the Plan Ahead

2024-04-30T08:48:14.000Z

This article reviews the quant work completed in phase two, lays out the tasks of phase three, and looks ahead to phase four.