Quantitative Research &
Knowledge Documentation Ecosystem

Inside Our Risk Engine: How We Score Every Position

A deep dive into the multi-factor risk model that powers TheQuantHackers' automated position sizing and portfolio construction.

Can DeepSeek-R1 Price Options? An Experiment in LLM-Driven Derivatives Analytics

We tested whether reasoning-focused LLMs can implement Black-Scholes, calibrate local vol, and price exotic options from scratch.

Macro Regime Detection for Dynamic Portfolio Allocation

Using inflation surprises, yield curve signals, and credit spreads to switch between risk-on and defensive portfolio tilts.

The Quant DNA Score: How We Profile Your Research Strengths

An explanation of how TheQuantHackers scores users across 9 skill dimensions using challenge performance, peer reviews, and research output.

TheQuantHackers Partners with TransFICC

TheQuantHackers announces strategic partnership with TransFICC to enhance fixed income electronic trading capabilities.

Real-Time Risk Management: The Future of Finance

An analysis of how real-time risk management is transforming financial institutions.

TQH TERMINAL Awarded Best Analytics Platform 2024

TQH TERMINAL wins prestigious award for best quantitative analytics platform.

New FRTB Module Released in TQH TERMINAL

TQH TERMINAL now includes a comprehensive FRTB module for regulatory capital calculations.

TheQuantHackers Expands London Office

TheQuantHackers expands its London presence with a new office.

Quantitative Finance Frontiers

This section features cutting-edge research in quantitative investment, including the latest technological developments and research reports from major brokerages.

年末福利！2024年AI量化论文优选48篇｜附精华解读

- Application of artificial intelligence and large language models in finance and trading

2024 Review of **Essential Literature** on Artificial Intelligence in the Financial FieldIn the past two years, with the rapid development of generative artificial intelligence (Generative AI) and large language models (LLMs) technology, cutting-edge research and applications in the financial field have also ushered in a new round of innovation. This article aims to summarize, sort out and display the representative **key academic papers** and **practical cases** in the intersection of quantitative finance and LLM in the past two years. Through a systematic introduction of historical evolution, methodological challenges, application scenarios and evaluation benchmarks, this review provides readers with a clear navigation map to help practitioners and researchers better understand and use LLM technology to promote innovation and change in the financial field.

1. A brief history of the development of LLM

年末福利！2024年AI量化论文优选41篇｜附精华解读

The papers selected for this article span a variety of asset classes and research directions, including but not limited to the following key areas:

Deep Dive: Hedge Funds and Generative AI Revolutionary Change or Flash in the Pan?

In the world of hedge funds, the key to making money is speed, accuracy, and stability. With the emergence of generative AI (genAI) and large language models (LLMs) dramatically accelerating the proce...

ChatGPT can also do investment analysis? Teach you step by step how to use LangChain to build a stock research frameworkInvestment analysts are increasingly applying artificial intelligence to process financial data, identify trends, and gain deep insights. Among them, **LangChain** is an open source framework with great potential that can combine language models like GPT with investment analysis. This article will explore how to use LangChain to simplify the initial investment research process, including **integrating financial data sources**, **building advanced customized models**, and making more informed **investment decisions** based on predictive insights.

In this article we will focus on:

DeepSeek：一家用实力“做空”美国科技股的量化背景初创

Hello everyone, today I want to talk to you about DeepSeek, which has recently attracted global attention. This Chinese AI research laboratory, founded in 2023 and founded by Liam Wenfeng, is launchin...

In today's increasingly complex financial markets, efficient sell-side securities research often requires the support of automated tools. However, many existing AI solutions only focus on technical in...

InvestorBench: Benchmark for LLM financial decision-making tasksIn recent research, agents based on large language models (LLM) have demonstrated excellent decision-making capabilities in complex and open environments, covering multiple application scenarios such as financial transactions (Zhang et al. (2024b); Guo et al. (2024); Eigner and Händler (2024); Wang et al. (2024)). However, for the specific transaction decision-making needs in the financial field, how to build a multi-modal LLM agent framework suitable for a variety of financial tasks still faces great challenges. This is mainly due to the high volatility and diversity of financial markets: intelligent agents not only need to capture high-timeliness core trading signals, but also continue to make high-quality decisions in an environment with mixed information modalities and rapidly changing market conditions.> **Figure 1: Schematic diagram of the overall architecture of the InvestorBench framework. **

In addition, as application scenarios expand from a single target to multiple financial tasks, the influencing factors in financial decision-making will also become complex and diverse depending on th...

TradeMaster is an open source platform for quantitative trading (QT), which fully integrates reinforcement learning (RL) technology into the entire quantitative trading process. From data preparation,...

TradingAgents: Multi-agent LLM financial trading framework## Summary

In recent years, multi-agent collaboration driven by large language models (LLM) has made significant progress in automated decision-making and problem solving. However, in the financial field, most r...

This article introduces a new quantitative investment framework, aiming to mine and optimize the Alpha factor in stock investment strategies through the collaboration of Large Language Models (LLMs) a...

2025还在手写论文？教你用ChatGPT自动生成股票研究的学术论文

\text{Signal}_{i,t} = \frac{\text{AccountingVar1}_{i,t}}{\text{AccountingVar2}_{i,t}}

Replacing Quantitative Researchers? Using ChatGPT o1 to Automatically Backtest Trading Algorithms from Quant Papers

To better understand the community's AI + quant projects, we explore here a novel AI-assisted workflow designed to leverage ChatGPT o1 to automatically generate code from quantitative finance papers a...

Can ChatGPT Do Quantitative Trading? The Most Comprehensive Guide to AI-Powered Quantitative Trading

To help readers better understand quantitative trading in the age of AI and explore the community's AI + quant projects, we introduce here the various possible integrations of ChatGPT with quantitativ...

Deep Dive: Sentiment Analysis of Tesla News and Investment Recommendation Generation Using DeepSeek-R1

In stock investing, news sentiment often has a significant impact on stock prices. Real-time monitoring and analysis of news can help us gauge market sentiment and optimize investment decisions. This...

OpenAI的GPT将如何重塑量化金融

自2022年末ChatGPT亮相以来，其强大的功能和潜力让世界为之震撼，预示着大型语言模型将引领社会变革。尽管这一反应略显仓促，但它真实地反映了人工智能领域的飞速进步及其应用的广泛性。虽然有人担忧这项技术可能威胁到就业，但已有创新者利用GPT开发新产品，并将其融入现有服务中。这些产品迅速获得市场青睐，为全球带来了巨大的价值。

Introduction to AI quantitative trading

生成式人工智能（Generative AI）正在深刻改变量化交易的格局。本章节将系统性地介绍 生成式AI在量化交易中的应用，帮助读者了解这一前沿领域，比如AI Agent, RAG, 大语言模型等。

扩散模型训练与采样过程梗概

In the training stage, noise is added to the picture, and the noise-added picture is input to the network. What the network needs to predict is the added noise.

一文读懂Transformer：从原理到实践，揭开大模型的核心奥秘

注意力机制是Transformer的灵魂所在。它不再依赖序列顺序，而是让模型在任意时刻参考上下文中所有位置的词语。

VQVAE生成模型梗概

parser.add_argument("--batch_size", type=int, default=32)

Comprehensive Guide: Google Gemini Flash 2.0 vs DeepSeek R1 vs OpenAI o3-mini Comparison and Applications

In recent years, with the rapid development of deep learning and natural language processing, large language models (LLMs) have demonstrated unprecedented potential in multilingual text understanding,...

紧急应对DeepSeek？OpenAI发布号称“最强大”的GPT-4.5模型

面向 Pro 用户和全球开发者开放使用

代码开源！如何使用DeepSeek-R1或ChatGPT与Langchain构建专业金融分析师

在快速变化的金融市场中，如何用人工智能来助力投资决策正成为越来越多投资人的关注重点。利用大模型进行股票分析和新闻洞察，不仅能让我们高效获取关键数据，还能生成更具参考价值的投资建议。本文将带你一步步搭建两个实用场景：

什么是AAA信用评级？

什么是CAPE比率（希勒市盈率）？

公司的盈利能力在很大程度上受到经济周期的影响。在经济扩张期，利润大幅上升，消费支出增加；而在经济衰退期，消费者支出减少，利润骤降甚至可能出现亏损。尽管周期性行业（如商品和金融）公司的利润波动显著高于防御性行业（如公用事业和制药），但在深度衰退面前，只有少数公司能够维持稳定的盈利能力。

CBOE波动率指数（VIX）是什么？

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Financial Fundamentals

- [Primary Market](一级市场_Primary Market.md)

T+1结算周期：理解证券交易的结算期限

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什么是VIX期权？

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什么是一级市场？

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什么是中型市值公司？

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什么是二元期权？

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什么是 80-20 法则？

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什么是二级市场？

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什么是交易商？

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什么是价值投资？

什么是保证金？

什么是保证金交易？

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什么是债券？

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什么是债券市场？

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什么是债务重组？

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什么是公开市场操作 (OMOs)?

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什么是关税？

什么是凯恩斯主义经济学？

什么是利率？

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什么是卖出期权？

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什么是卖空？

什么是变化率（ROC）？

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什么是可转换债券？

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什么是合并与收购（M&A）？

$2.65 Billion and Establish Saks Global, A Technology-Powered Luxury Retail Company](https://www.neimanmarcusgroup.com/HBC,-Parent-of-Saks-Fifth-Avenue,-to-Acquire-Neiman-Marcus-Group-for-2-65-Billion...

什么是商业周期？

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什么是固定收益？

什么是国债（T-Bond）？

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什么是国内生产总值（GDP）？

$$ \begin{aligned}&\text{GDP} = \text{C} + \text{G} + \text{I} + \text{NX} \\&\textbf{其中：} \\&\text{C} = \text{消费} \\&\text{G} = \text{政府支出} \\&\text{I} = \text{投资} \\&\text{NX} = \text{净出口} \\\end{al...

什么是国库券？

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什么是国民生产总值 (GNP)？

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什么是基准年？

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什么是基尼指数？

什么是增长曲线？

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什么是增长率？

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什么是复利？

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什么是复合年增长率（CAGR）？

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什么是外汇（FX）？

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什么是外汇市场（Forex或FX）？

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什么是多因子模型？

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什么是大萧条？

什么是失业？

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什么是宏观经济学？

什么是寡头垄断？

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什么是对冲？

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什么是市盈率 (P/E)？

$$ \text{市盈率} = \frac{\text{每股市场价值}}{\text{每股收益}} $$

什么是年回报率？

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年金的未来价值是什么？

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什么是年金表？

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什么是恶性通货膨胀？

什么是房地产泡沫？

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什么是报价？

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倒挂收益率曲线表明长期利率低于短期利率

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什么是新自由主义？

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什么是无杠杆Beta？

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什么是有效市场假说（EMH）？

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什么是有限合伙企业 (LP)？

什么是期货交易？

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什么是财务杠杆？

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杠杆收购是什么？

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什么是杠杆比率？

$$ \text{债务权益比率} = \frac{\text{总负债}}{\text{总股东权益}} $$

什么是标的资产

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什么是比较优势？

什么是毛利率？

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什么是汇率？

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什么是波动性？

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什么是流动性？

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什么是熊市？

什么是牛市？

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现值是什么？

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什么是生产者价格指数 (PPI)？

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什么是知识经济？

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什么是纳斯达克？

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什么是经济增长？

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经济订货量（EOQ）是什么？

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什么是绝对优势？

联邦基金利率是什么？

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什么是股份公司？

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股票市场：运作、参与者与重要性

什么是股息？

$0.1625 per Common Share Producing a Forward Yield of 4.42% and Announces a Special Dividend Payment of $0.15 per Common Share](https://www.unitedbancorp.com/news-market-info/press-releases/press-rele...

什么是股权？

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什么是股权稀释？

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什么是自由市场？

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什么是自由贸易协定（FTA）？

什么是菲利普斯曲线？

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什么是衍生品

$80 - $62.2) x 1,000 = $17,780]。持有短仓（卖方）的交易者将损失 17,780 美元。

被动投资：一种最小化交易以最大化回报的策略

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什么是规模经济？

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什么是证券？

什么是货币政策？

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货币流通速度是什么？

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什么是贴现率？

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什么是贸易逆差？

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什么是资产负债表？

什么是资本主义？

什么是资本化？

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什么是边际收益？

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什么是远期价格

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什么是远期合同？

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什么是通货膨胀？

什么是量化宽松？

面值是什么？

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什么是风险投资 (VC)?

$132 Million in 11 Disruptive Technology Startups](https://www.intc.com/news-events/press-releases/detail/1074/intel-capital-invests-132-million-in-11-disruptive)."

什么是首席经纪业务？

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什么是首次公开募股 (IPO)？

Quantitative Finance Fundamentals

This list is compiled by TheQuantHackers. This section covers 192 fundamental concepts required for quantitative finance. Contributions are welcome. The document is divided into the following main sec...

Probability Fundamentals

- [Conditional Probability](条件概率_Conditional Probability.md)

什么是 P 值？

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什么是 T 检验？

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什么是 Z 分数？

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什么是 Z 检验？

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什么是变异系数 (CV)？

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大数法则是什么？

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条件概率是什么

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什么是概率分布？

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什么是正态分布？

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什么是相关性？

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什么是相关系数？

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什么是系统抽样？

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什么是联合概率？

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什么是蒙特卡罗模拟？

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什么是衍生品？

$80 - $62.2) x 1,000 = $17,780]。持有短仓（卖方）的交易者将损失 17,780 美元。

贝叶斯定理：理解条件概率与信息更新

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Fama-French三因子模型简介

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Quantitative Terminology

- [Trend Trading](趋势交易_Trend Trading.md)

什么是伽玛中性？

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什么是Gamma？

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什么是伽马对冲？

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什么是动量投资？

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什么是双顶？

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什么是回测？

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什么是因子投资？

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什么是均值回归？

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什么是多空基金？

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什么是多空股权？

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套利者是试图从市场 inefficiencies 中获利的投资者。

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什么是市价单？

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什么是市场中性？

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什么是德尔塔中性？

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什么是德尔塔对冲？

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什么是指数移动平均线（EMA）？

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什么是新闻交易者？

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本杰明·格雷厄姆（Benjamin Graham）是20世纪上半叶一位影响深远的投资者。

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杰西·L·利弗莫尔是20世纪初著名的股票交易员。

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什么是波动率套利？

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什么是波动率微笑？

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相对强弱指数 (RSI) 是什么？

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什么是看跌期权？

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什么是移动平均线（MA）？

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什么是空头头寸？

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空头看涨价差

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什么是 IOC 订单？

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简单移动平均线（SMA）是什么？

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什么是 Beta？

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什么是货币套利交易？

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什么是费舍尔变换指标？

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什么是资本资产定价模型（CAPM）？

$$ \begin{aligned} &ER_i = R_f + \beta_i ( ER_m - R_f ) \\ &\textbf{其中：} \\ &ER_i = \text{投资的预期回报} \\ &R_f = \text{无风险利率} \\ &\beta_i = \text{投资的Beta值} \\ &(ER_m - R_f) = \text{市场风险溢价} \\ \end{aligned...

什么是趋势交易？

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什么是跨式期权策略？

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什么是逼空？

$8 Billion in 2020, Including $2.5 Billion Alone from Monday’s Pop](https://www.cnbc.com/2020/02/03/telsa-shorts-down-8-billion-in-2020-including-2point5-billion-on-monday.html).”

长期资本管理公司（LTCM）是什么？

What is Long Term Capital Management (LTCM)?

什么是阿尔法（Alpha）？

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限价单是什么？

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什么是限价单簿？

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什么是限时订单？

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什么是高频交易（HFT）？

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什么是黄金交叉？

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Statistics Fundamentals

- [Expected Value](期望值_Expected Value.md)

什么是R平方？

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什么是中心极限定理（CLT）？

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什么是假设检验？

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何为决定系数？

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什么是协方差？

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卡方（χ2）统计量是什么？

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变量膨胀因子（VIF）是什么？

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什么是回归分析？

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什么是均匀分布？

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什么是多元线性回归（MLR）？

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什么是多重共线性？

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方差分析（ANOVA）

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什么是最小二乘法？

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什么是期望值？

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相关系数是什么？

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什么是线性关系？

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经验法则是什么？

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什么是统计显著性？

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置信区间在统计学中是指某一总体参数在特定比例的情况下落在一组数值之间的概率

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自相关是什么？

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什么是非线性？

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什么是默顿模型？

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行业内幕：2025年最值得关注的10家对冲基金

> 管理规模：74亿美元

Wall Street's Ceiling? A Detailed Look at 50 Top Foreign Buy-Side Firms

This document, carefully curated by TheQuantHackers, covers detailed information on 50 proprietary trading firms, market makers, hedge funds, and asset management institutions, including their foundin...

Citadel, Millennium, or Others? Unpacking the Internal Cultures of Different Multi-Strategy Hedge Funds

Once, the gold standard in finance was Goldman Sachs. Later, it was the hedge funds founded by individually brilliant managers like Paul Marshall (the UK hedge fund manager now trying to become a medi...

本期推文带来对买方顶级大佬Giuseppe Paleologo和卖方大佬Nick Baltas的人物专访，围绕三个Multi展开, Multi-Asset, Multi-Strategy Portfolios, Multi-Manager Hedge Funds

从800万到220亿：Julian Robertson如何打造老虎对冲基金王朝

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Trading Strategy Revealed! How Jim Simons Used Mathematics and Technology to Maintain a 66% Return Rate

If I told you there is a hedge fund that has maintained an astonishing average annual return of 66% for decades, you might not believe it. That fund is Renaissance Technologies, founded by the legenda...

The Medallion Fund: Renaissance Technologies' One-of-a-Kind Money Machine

The Medallion Fund, an exclusive employee-only fund set up by Renaissance Technologies for its quantitative analysts, is renowned as the most mysterious black box in finance.

什么是多经理，多策略对冲基金？为什么Citadel、Millennium能如此成功？

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Quantitative Industry Overview

- Hedge Funds

Quadrature Capital：你从未听过的神秘自营交易公司

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Dream Employers for New Graduates? A Complete Overview of 12 Top Global Investment Banks

In the global capital markets, large international investment banks ("foreign sell-side firms") command attention with their comprehensive financial services and cross-border presence. They are typica...

规模越大代表业绩越好？论对冲基金规模与其表现的关系

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量化金融薪资 - 量化分析师能赚多少？

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量化分析师的多面性与雇主类型

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内部课程｜剑桥大学TheQuantHackers和北大量化交易协会内部培训项目全面启动

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From Top University to Top Quant Firm: A Firsthand Look at Jane Street Interns

This article takes you inside the technical internship program at Jane Street through the firsthand experiences of three interns who successfully converted to full-time employees.

How to Become an Excellent Quant? Unveiling the Daily Life of a Quantitative Analyst

In an era of high-frequency trading, robo-advisors, and AI-driven hedge funds, quantitative analysts (Quants) have increasingly become the "unsung heroes" of finance. From interest rate models at trad...

Bonus! A Complete Guide to Career Paths in Hedge Funds

When discussing career progression in hedge funds, the "fraternity-style hierarchy" commonly used elsewhere does not quite apply. Unlike investment banking or private equity, career paths in hedge fun...

Global Quant Salaries Revealed: Which Level Are You At?

The quantitative finance industry has seen vigorous development in recent years. From high-frequency trading to machine learning-driven predictive models, technological advancements have driven demand...

2023年如何获得量化金融工作或实习机会

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如何在IMC交易获得量化实习机会

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如何在Jane Street获得量化实习机会

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如何在Optiver获得量化实习机会

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如何在Akuna Capital获得量化交易员职位

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量化交易员职位面试问题

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量化金融职业的最佳城市

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最佳量化金融学位

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量化开发者的职业路径

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量化交易员职业发展路径

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量化面试官的两种类型

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量化交易员是做什么的？

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打造完美量化简历

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Directory

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Recommended advanced books on quantitative finance

The following are advanced books recommended for readers with a certain foundation:

AI for Finance

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AI quantitative trading book recommendations

<img src="../images/ml-trading.jpg" alt="Machine Learning and Quantitative Trading" width="200"/>

algorithmic-and-high-frequency-trading-pdf-free

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Artificial Intelligence in Finance- A Python-Based Guide

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Artificial Intelligence, Learning and Computation in Economics and Finance

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Recommended introductory books on quantitative finance

The following are recommended introductory books for beginners in quantitative finance:

Deep Learning for Finance

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Developing High Frequency Trading Systems

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fifty-challenging-problems-in-2

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Financial Machine Learning

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Financial Signal Processing and Machine Learning

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Financial Theory with Python

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Hands-On Artificial Intelligence for Banking

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Hands-On Cybersecurity for Finance

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Hands-On Financial Trading with Python

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Hands-On Machine Learning for Algorithmic Trading

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Learning Quantitative Finance with R

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Learning-Quantitative-Finance-with-R

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Machine Learning for Algorithmic Trading

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Machine Learning for Finance

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Machine Learning for Financial Risk Management with Python

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Machine Learning in Finance- From Theory to Practice

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Machine-Learning-for-Algorithmic-Trading

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Mastering pandas for Finance

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Mastering Python for Finance- Implement advanced state-of-the-art financial statistical applications using Python

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Mathematical Techniques in Finance

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Monte Carlo Methods in Financial Engineering

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Optimization Methods in Finance

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Option Volatility and Pricing

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Probabilistic Machine Learning for Finance and Investing

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Recommended books on quantitative financial programming

The following are programming-related books recommended for quantitative finance practitioners:

Python for Algorithmic Trading

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Python for Finance Cookbook

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quant job interview questions and answers

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Quantitative Trading- How to Build Your Own Algorithmic Trading Business

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Quantum Machine Learning and Optimisation in Finance- On the Road to Quantum Advantage

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Reinforcement Learning for Finance

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Stochastic Modeling of Big Data in Finance

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学习量化金融的最佳书籍

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Application of machine learning in financial time series analysis and forecasting, 385 pages pdf

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Economics and Financial Data Science

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Advances in Active Portfolio Management

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Advances in Financial Machine Learning

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Algorithmic and High-Frequency Trading

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Algorithmic and High-Frequency Trading

- Authors: Álvaro Cartea, Sebastian Jaimungal, José Penalva

Algorithmic Trading Methods

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Algorithmic Trading Methods-Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques-Academic Press (2020)

- Author: Robert L. Kissell

Applications of computational intelligence in data-driven trading

- Author: Cris Doloc

Empirical Asset Pricing via Machine Learning

- Authors: Shihao Gu, Bryan Kelly, Dacheng Xiu

Financial Technology- Case Studies in Fintech Innovation-Kogan Page (2020)

- Author: Niels Pedersen

Machine Learning for Asset Managers

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Machine Learning for Asset Managers-Cambridge University Press (2020)

- Author: Marcos M. López de Prado

Machine Learning in Asset Pricing

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Machine Learning in Asset Pricing-Princeton Univ Pr (2021)

- Author: Stefan Nagel

Machine Learning in Finance- From Theory to Practice-Springer (2020)

- Authors: Matthew F. Dixon, Igor Halperin, Paul Bilokon

Marcos López de Prado - Advances in Financial Machine Learning-John Wiley & Sons, Inc. (2018)

- Author: Marcos López de Prado

Quantum Finance- Path Integrals and Hamiltonians for Options and Interest Rates-Cambridge University Press (2004)

- Author: Belal E. Baaquie

Robert Carver - Systematic Trading- A unique new method for designing trading and investing systems-Harriman House (2015)

- Author: Robert Carver

The Book of Alternative Data- A Guide for Investors Traders and Risk Managers-Wiley (2020)

- Author: Alexander Denev, Saeed Amen

101 Formulaic Alphas

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151 Trading Strategies

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151 Trading Strategies (paper version)

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A Complete Guide to the Futures Market

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A complete guide to the futures market- technical analysis and trading systems, fundamental analysis, options, spreads, and trading principles-J

- Authors: Jack D. Schwager, Mark Etzkorn

Active Portfolio Management

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Active Portfolio Management- A Quantitative Approach for Producing Superior Returns and Controlling Risk-McGraw-Hill (1999)

- Authors: Richard C. Grinold, Ronald N. Kahn

Algorithmic Trading: Winning Strategies and Their Rationale

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Algorithmic Trading- Winning Strategies and Their Rationale-Wiley (2013)

- Author: Ernie Chan (陈佳平)

BetaPlus Behavioral Finance

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BetaPlus Profitability Factor

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BetaPlus Time Series Analysis

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BetaPlus Trend Following

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Finding Alphas

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High-frequency Trading

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High-frequency Trading

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Optimization Methods in Finance

- Authors: Gérard Cornuéjols, Javier Peña, Reha Tütüncü

Quantitative Investment Analysis Workbook, 3rd Edition-Wiley (2015)

- Authors: Richard A. DeFusco, Dennis W. McLeavey, Jerald E. Pinto, David E. Runkle

Quantitative Risk Management

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Quantitative Strategies for Achieving Alpha (McGraw-Hill Finance & Investing)

- Author: Richard Tortoriello

Quantitative Trading- How to Build Your Own Algorithmic Trading Business

- Author: Ernest P. Chan

Statistical Arbitrage

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The Volatility Smile

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Volatility Trading, + Website-Wiley (2013)

- Author: Euan Sinclair

理解资产价格-NobelEconomics1

- Author: John H. Cochrane

Ishikawa-Liu Yangyi-Lian Xiangbin-Factor Investing: Methods and Practice-Electronic Industry Press (2020)

- Authors: Ishikawa, Liu Yangyi, Lian Xiangbin

Quantitative Green Paper

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Frequently Asked Questions in Quantitative Finance

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Paul P. Wilmott - Frequently Asked Questions in Quantitative Finance, Second Edition -A John Wiley and Sons, Ltd. (2009)

- Author: Paul P. Wilmott

Quant Green Book explains 60 questions

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Quant Green Book explains 60 questions

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Quant Job Interview Questions And Answers

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A Practical Guide to Quantitative Finance Interviews

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150 Most Frequently Asked Questions on Quant Interviews

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A Linear Algebra Primer for Financial Engineering

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A Primer for the Mathematics of Financial Engineering

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Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization

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An Introduction to the Mathematics of Financial Derivatives

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An Introduction to the Mathematics of Financial Derivatives

- Author: Ali Hirsa

Risk and Asset Allocation

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Dan Stefanica - A Primer for the Mathematics of Financial Engineering-FE Press (2008)

- Author: Dan Stefanica

Dynamic Asset Pricing Theory

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Darrell Duffie - Dynamic asset pricing theory-Princeton University Press (2001)

- Author: Darrell Duffie

Dynamic Asset Pricing Theory

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Empirical Dynamic Asset Pricing

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Empirical Dynamic Asset Pricing

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Market Microstructure Theory

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Maureen O'Hara - Market Microstructure Theory -Wiley (1998)

- Author: Maureen O'Hara

Methods of Mathematical Finance

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Methods of Mathematical Finance-Springer (2001)

- Authors: Ioannis Karatzas, Steven E. Shreve

Paul Wilmott - Paul Wilmott introduces quantitative finance-Wiley (2007)

- Author: Paul Wilmott

Paul Wilmott Introduces Quantitative Finance

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Quantitative Risk Management

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Quantitative risk management - concepts, techniques and tools-Princeton University Press (2015)

- Authors: Alexander J. McNeil, Rüdiger Frey, Paul Embrechts

Risk and Asset Allocation

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Stochastic Calculus for Finance

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The Volatility Smile

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Heard）Heard On The Street Quantitative Questions From Wall Street Job Interviews

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黄皮书）Frequently Asked Questions In Quantitative Finance

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量化金融图书馆

- [AI for Finance](book/AI for Finance/index.md) - 金融AI应用

LLMQuant 独家资源概览

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Quant Paper Selection - Quant Paper

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Quant Toolbox

This list is compiled by TheQuantHackers. We have collected a resource list on systematic trading (quantitative trading) strategies, including papers, software, books, and articles to help you find, d...

Quantitative Frameworks by Programming Language & Their GitHub Repositories

This list is compiled by TheQuantHackers. A meticulously curated collection of outstanding quantitative finance libraries, packages, and resources. For learning and communication purposes only. Copyri...

本列表由[TheQuantHackers社区](https://llmquant.com/)整理, 只供学习交流使用, 版权归原作者所有。

Statistical arbitrage strategy

- Cointegration

Trend following strategy

- Trend definition

Event-Driven Hedge Fund Primer: Understanding Alpha from Corporate Events

- Summary

Understanding Tail Risk in Financial Markets: Applying Extreme Value Theory (EVT) to VaR and ES

In financial risk management, Extreme Value Theory (EVT) is an essential tool for analyzing and modeling the tail behavior of data distributions. Particularly in financial markets, extreme market move...

2. 发送订单请求

宏观对冲基金入门：一文揭秘 Macro hedge fund

The Founding Father of Quantitative Analysis: Understanding Modern Portfolio Theory and the Markowitz Model

In the world of investing and wealth management, there is a frequently quoted adage "There is no such thing as a free lunch." In other words, every return conceals underlying risk, a fact all too cl...

多策略对冲基金入门：一文解密 Multi-strategy hedge fund

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Decoding Nine Popular Portfolio Strategies: Find Your Style from Institutional Approaches

In the investment world, the "perfect portfolio" is almost a myth. Many beginners and veterans alike search tirelessly for that magical combination that "maximizes returns while minimizing risk." Unfo...

A Trillion-Dollar Mathematical Formula: From Physics Equations to Financial Option Pricing

You may find it hard to believe that an equation rooted in physics and mathematics could spawn multiple financial industry chains with a combined scale of trillions of dollars. Yet this is precisely t...

独家分享：Point72前股票分析师揭秘顶级对冲基金的投资策略

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读取 38 家科技股的历史数据

> 关键观点：

量化交易员带你入门：为什么有些交易策略能带来盈利？

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量化交易员带你入门：如何如何划分交易风格？

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量化交易员带你入门：如何打造“好用”的交易策略？

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Do You Really Understand the Sharpe Ratio? A Quantitative Trader's Guide

Welcome to this introductory article on the Sharpe Ratio. This piece will help you systematically and deeply understand the important role of the Sharpe Ratio in quantitative investing and portfolio m...

Quantitative Hedge Fund Primer: Decoding Quant Strategy Types

- [Summary](summary)

量化金融项目集

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量化交易入门

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量化交易大赛

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Do You Really Understand Implied Volatility? A Quantitative Trader's Guide

To meet the learning needs of our readers and incorporate the latest industry insights, TheQuantHackers presents the Quantitative Trader's Guide series. This article will take you from zero to underst...

Ornstein-Uhlenbeck Mean Reversion Backtest in Python: From SDE to Signal

Implement and backtest a mean-reversion strategy using the Ornstein-Uhlenbeck process with OLS estimation, half-life calculation, and vectorized signal generation.

Alpaca WebSocket L2 Order Book Streaming in Python: A Production Guide

Build a sequence-gap-aware, fully asynchronous Level 2 order book consumer for Alpaca Markets. Covers microprice, order book imbalance, reconnection handling, and a realistic latency budget.

Sharpe Ratio Confidence Intervals via Stationary Block Bootstrap in Python

Compute defensible 95% confidence intervals for the annualised Sharpe ratio using a stationary block bootstrap. Covers skewness, kurtosis, block-size selection, and the deflated Sharpe ratio correction for multiple testing.

Order Book Imbalance Strategy: A Production OBI Signal in Python

Build a volume-weighted Order Book Imbalance (OBI) signal from L2 market data with adaptive z-score thresholds, decay-weighted multi-level aggregation, microprice anchoring, and a realistic event-driven backtest framework with execution lag simulation.

Event-Driven Algorithmic Trading Engine in Python: Architecture and Design

Design a production-grade event-driven algorithmic trading engine with asyncio event loop, ZeroMQ process separation, heartbeat watchdog, and type-safe event hierarchy. Covers startup ordering, queue backpressure, and clock-sync edge cases.

Pre-Trade Risk Gate for Algorithmic Trading in Go: Architecture and Implementation

Build a synchronous pre-trade risk gate in Go with position caps, order rate limiting, notional limits, drawdown circuit breakers, and square-root market impact estimation. Covers clock drift, flapping protection, and race-condition-safe validation.

Lookahead Bias Free Backtesting in Python: Purged Walk-Forward and Realistic Execution

Detect and eliminate lookahead bias in quantitative backtests with automated linting, purged walk-forward cross-validation, and realistic execution assumptions including slippage, fill probability, and timestamp jitter.

Algorithmic Trading Latency Benchmarks: C++ vs Go vs Python Tick-to-Trade

End-to-end latency benchmarks for the tick-to-order hot path across C++, Go (tuned GOGC), Rust, Java (ZGC), and Python (uvloop). Includes methodology, allocation profiles, and a decision framework for when to optimise.

Algorithmic Trading Deployment: From Paper Trading to Production

A six-step production deployment path for algorithmic trading strategies: vectorised research, event-driven backtest, realistic execution, paper trading, kill switch deployment, and slow scaling. Includes monitoring, alerting, and edge case handling.

Interactive Brokers API for Algorithmic Trading in Python: A Production Guide

Connect to Interactive Brokers from Python for algorithmic trading. Covers ib_insync connection, Level 2 market data, order placement (LMT, MKT, PEG MID), account tracking, and production resilience with automatic reconnection.

Crypto Trading Bot with Python and Binance API: A Production Architecture

Build a production-grade crypto trading bot in Python for Binance. Covers WebSocket order book streaming, REST authentication (HMAC-SHA256), spread mean-reversion strategy, order reconciliation, and rate-limit handling.

Pairs Trading with Cointegration in Python: From ADF Test to Backtest

Build a pairs trading strategy with cointegration testing (Engle-Granger, ADF), hedge-ratio estimation, z-score signals, half-life exits, and a walk-forward backtest in Python.

Walk-Forward Optimization in Python: The Cure for Overfit Backtests

Implement walk-forward optimization in Python: anchored vs rolling windows, re-fit cadence, embargo, stitched out-of-sample equity, and the per-window diagnostics that expose overfit strategies.

COT Report Explained for Retail Traders: Reading Commitments of Traders Like a Quant

The CFTC Commitments of Traders report explained: commercials vs non-commercials, net positioning, extremes vs history, weekly change momentum, and the three mistakes retail traders make.

How to Avoid Overfitting a Trading Strategy: A Quant's Checklist with Python

A five-gate anti-overfitting checklist: trial accounting, deflated Sharpe, purged CV, walk-forward, and point-in-time discipline — with Python code for each gate.