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QuantStart是由一个理论扎实、实战经验丰富的英国团队维护的量化投资网站，上面有关于量化投资的各种话题。
从算法交易到机器学习，从如何回测到实盘交易系统，从数学理论到编程技巧，从基础知识入门到职业规划，应有尽有。
石川博士曾在他的推文里说过，他认为 quantstart.com 比任何一本书都更加适合作为量化投资入门以及进阶的材料。这些高质量的文章（以及文章中涉及的一些代码）全部免费刊登在网站上供全世界的网友浏览 —— “高质量” + “免费”，知识的分享就应该这样。
在过去的七年时间里，QuantStart团队、著名的量化金融学者、研究人员、行业专家撰写了200+篇量化交易的知识分享文章。
这些文章可概括地划分为量化交易、数学金融学、计算机金融学和择业指导四大类。
小闪汉化了这200+篇的文集列表，以供想要深入了解量化投资的读者更详细地了解到在量化投资领域所需要的知识储备、操作指导、实务技能和职业建议。
因微信不允许添加外部链接，想要详细阅读文章的朋友可点击"阅读原文"查看文集列表下的每一篇文章。
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[h1]Quantitative Trading[/h1]量化交易篇Getting Started with Quantitative Trading
量化交易入门- Beginner's Guide to Quantitative Trading量化交易入门指南
- Can Algorithmic Traders Still Succeed at the Retail Level?算法交易员在零售层面仍能保持成功吗？
- Top 5 Essential Beginner Books for Algorithmic Trading算法交易初学者必读的5本书
- Building a Quantitative Trading Infrastructure构建量化交易框架
- Installing a Desktop Algorithmic Trading Research Environment using Ubuntu Linux and Python使用Ubuntu Linux和Python安装桌面算法交易研究环境
- Securities Master Databases for Algorithmic Trading算法交易中的证券主数据库
- Securities Master Database with MySQL and PythonMySQL和Python实现的证券主数据库
- Downloading Historical Futures Data From Quandl从Quandl下载期货历史数据
- Research Backtesting Environments in Python with pandas使用Python中的Pandas数据分析工具研究回测环境
- Continuous Futures Contracts for Backtesting Purposes连续期货合约的回溯测试
- Downloading Historical Intraday US Equities From DTN IQFeed with Python使用Python从DTN IQFeed下载美股历史数据
[h2]Backtesting[/h2]回测- Successful Backtesting of Algorithmic Trading Strategies - Part I成功的算法交易策略回测-1
- Successful Backtesting of Algorithmic Trading Strategies - Part II成功的算法交易策略回测-2
- Best Programming Language for Algorithmic Trading Systems?算法交易系统的最佳编程语言
- Event-Driven Backtesting with Python - Part I用Python实现基于事件驱动的回测-1
- Event-Driven Backtesting with Python - Part II用Python实现基于事件驱动的回测-2
- Event-Driven Backtesting with Python - Part III用Python实现基于事件驱动的回测-3
- Event-Driven Backtesting with Python - Part IV用Python实现基于事件驱动的回测-4
- Event-Driven Backtesting with Python - Part V用Python实现基于事件驱动的回测-5
- Event-Driven Backtesting with Python - Part VI用Python实现基于事件驱动的回测-6
- Event-Driven Backtesting with Python - Part VII用Python实现基于事件驱动的回测-7
- Event-Driven Backtesting with Python - Part VIII用Python实现基于事件驱动的回测-8
- Should You Build Your Own Backtester?你需要搭建你自己的回测系统吗？
- Backtesting Systematic Trading Strategies in Python: Considerations and Open Source Frameworks用Python构建策略回测系统的注意事项和开源架构
- Risk and Performance Measurement风险与绩效评估
- Sharpe Ratio for Algorithmic Trading Performance Measurement使用夏普比率评估算法交易绩效
- Money Management via the Kelly Criterion资金管理之凯利公示
- Value at Risk (VaR) for Algorithmic Trading Risk Management - Part I算法交易风险管理之风险价值VAR-1
- Annualised Rolling Sharpe Ratio in QSTraderQSTrader（回测模拟引擎）中的滚动夏普年化比率
[h2]Automated Execution[/h2]自动执行- Interactive Brokers Demo Account Signup Tutorial盈透证券模拟账户注册教程
- Using Python, IBPy and the Interactive Brokers API to Automate Trades用Python、IBPy和盈透证券API实现自动化交易
- Choosing a Platform for Backtesting and Automated Execution如何选择回测和自动执行系统
[h2]Quantitative Trading Strategies[/h2]量化交易策略- How to Identify Algorithmic Trading Strategies如何甄选算法交易策略？
- Backtesting a Moving Average Crossover in Python with pandas用Python中的Pandas回测移动平均交叉策略
- Backtesting a Forecasting Strategy for the S&P500 in Python with pandas用Python中的Pandas回测基于标准普尔S&P500的预测策略
- Backtesting An Intraday Mean Reversion Pairs Strategy Between SPY And IWM运动SPY和IWM的日内均值回归配对策略的回测
- ARIMA+GARCH Trading Strategy>Kalman Filter-Based Pairs Trading Strategy In QSTraderARMA+GARCH交易策略和卡尔曼滤波器模型在QSTrader系统中的应用
- Monthly Rebalancing of ETFs with Fixed Initial Weights in QSTraderETFs固定初始权重值每月再平衡在QSTrade的应用
- Strategic and Equal Weighted ETF Portfolios in QSTraderStrategic型和Equal Weighted型ETF投资组合在QSTrader中的应用
- Aluminum Smelting Cointegration Strategy in QSTrader炼铝业协整策略在QSTrader的应用
- Sentiment Analysis Trading Strategy via Sentdex Data in QSTrader基于Sentdex（http://sentdex.com，是一种情感算法工具）的情绪分析交易策略在QSTrader中的应用
- Market Regime Detection using Hidden Markov Models in QSTraderQSTrader系统中基于隐马尔可夫模型的市场状态监测
[h2]Quant Funds and Institutional Management[/h2]量化基金和机构管理- What are the Different Types of Quant Funds?量化基金有哪些不同的类型？
[h2]Talks and Interviews[/h2]对话与访谈- My Interview Over At OneStepRemoved.comOneStepRemoved.com采访
- My Talk At The London Financial Python User GroupThe London Financial Python User Group演讲
- My Chat With Traders Interview with Aaron Fifield与交易员的聊天Aaron Fifield访谈录
- When Should You Build Your Own Backtester? - QuantCon NYC, April 2016 talk什么时候应该建立你自己的回测器？-QuantCon纽约，2016年4月演讲
Careers Advice
择业指导[h2]Life as a Quant[/h2]投身宽客事业- Understanding How to Become a Quantitative Analyst
了解如何成为量化分析师？
- What are the Different Types of Quantitative Analysts?
量化分析师有哪些不同的类型？
- My Experiences as a Quantitative Developer in a Hedge Fund
我在对冲基金团队担任量化交易工程师的那些事
- A Day in the Life of a Quantitative Developer
量化交易工程师的一天
- Careers in Quantitative Finance
量化金融的职业发展
- What are the Career Paths in Systematic Trading?
自动化交易职业生涯规划
- Setting up an Algorithmic Trading Business
开设算法交易业务
[h2]Undergraduates[/h2]本科生
- What Classes Should You Take To Become a Quantitative Analyst?
成为量化分析师需要接收哪些课程的培训？
- Why Study for a Mathematical Finance PhD?
为什么要深造数学金融博士？
- Why a Masters in Finance Won't Make You a Quant Trader
为什么金融硕士不足以让你成为量化交易员？
- Best Undergraduate Degree Course For Becoming A Quant?
成为量化专家的最佳本科专业是？
- The Top 5 UK Universities For Becoming A Quant
做宽客首选的Top5 英国名校
- How to Learn Advanced Mathematics Without Heading to University - Part 1
如何不在高校也能学习高数-1
- How to Learn Advanced Mathematics Without Heading to University - Part 2
如何不在高校也能学习高数-2
- How to Learn Advanced Mathematics Without Heading to University - Part 3
如何不在高校也能学习高数-3
[h2]Postgraduates[/h2]研究生
- Junior Quant Jobs - Beginning a Career in Financial Engineering after a PhD
初级量化工作——博士毕业后如何在金融工程领域开启你的职业生涯
- How To Get A Quant Job Once You Have A PhD
获得博士学位后如何获得一份量化工作
- Getting a Job in a Top Tier Quant Hedge Fund
如何加入顶级量化对冲基金公司
- How to Get a Job at a High Frequency Trading Firm
如何加入高频交易公司
- Which Programming Language Should You Learn To Get A Quant Developer Job?
成为一位量化交易工程师你需要学会哪些开发语言
[h2]Career Changers[/h2]转行
- Can You Still Become a Quant in Your Thirties?
30岁是否还能成为一位宽客？
- Self-Study Plan for Becoming a Quantitative Trader - Part I
成为量化交易员的自学计划--1
- Self-Study Plan for Becoming a Quantitative Trader - Part II
成为量化交易员的自学计划--2
- Self-Study Plan for Becoming a Quantitative Developer
成为量化开发人员的自学计划
- Self-Study Plan for Becoming a Quantitative Analyst
成为量化分析师的自学计划
- Mailbag: Can You Get A Job In HFT Without A Degree?
没有学位能加入高频交易公司吗？
- Quant Finance Career Skills - What Are Employers Looking For?
雇主需要的量化金融从业技能
[h1]Quant Reading Lists[/h1]宽客阅读清单
- Quant Reading List Derivative Pricing
宽客阅读清单—衍生品定价
- Quant Reading List C++ Programming
宽客阅读清单—C++编程
- Quant Reading List Numerical Methods
宽客阅读清单—数值计算方法
- Quant Reading List Python Programming
宽客阅读清单—Python编程
- 5 Important But Not So Common Books A Quant Should Read Before Applying for a Job
5本重要但不常见的书籍——宽客在应聘前需要看的书
- 5 Top Books for Acing a Quantitative Analyst Interview
5本面试量化分析师要看的书
- Top 5 Finite Difference Methods books for Quant Analysts
量化分析师必看的5本有限差分法
- Top 5 Essential Beginner C++ Books for Financial Engineers
5本金融工程师必看的C++入门书
- Quantitative Finance Reading List
定量金融阅读清单
- Top 10 Essential Resources for Learning Financial Econometrics
金融统计学必看的10本书
- Free Quantitative Finance Resources
免费的定量金融资源
- Top 5 Essential Books for Python Machine Learning
最适合Python学习的5本书
Mathematics数学理论[h2]Linear Algebra[/h2]线性代数- Scalars, Vectors, Matrices and Tensors - Linear Algebra for Deep Learning (Part 1)
标量，向量，矩阵和张量-深度学习的线性代数（第1部分）
- Matrix Algebra - Linear Algebra for Deep Learning (Part 2)
矩阵代数-深度学习的线性代数（第2部分）
[h1]Bayesian Statistics[/h1]贝叶斯统计
- Bayesian Statistics: A Beginner's Guide
贝叶斯统计：新手指南
- Bayesian Inference of a Binomial Proportion - The Analytical Approach
贝叶斯二项式分布—分析方法
- Markov Chain Monte Carlo for Bayesian Inference - The Metropolis Algorithm
贝叶斯理论之马尔可夫链蒙特卡洛—Metropolis算法
- Bayesian Linear Regression Models with PyMC3
用PyMC3实现贝叶斯线性回归模型
[h1]Machine Learning[/h1]机器学习
- Basics of Statistical Mean Reversion Testing
统计均值回归测试基础
- Basics of Statistical Mean Reversion Testing - Part II
统计均值回归测试基础——第二篇
- Forecasting Financial Time Series - Part I
金融时间序列分析预测—第一篇
- Beginner's Guide to Statistical Machine Learning - Part I
统计机器学习入门指南——第一部分
- Support Vector Machines: A Guide for Beginners
支持向量机初学者指南
- Supervised Learning for Document Classification with Scikit-Learn
基于Scikit学习法的文献分类监督学习
- The Bias-Variance Tradeoff in Statistical Machine Learning - The Regression Setting
统计机器学习中的偏差-方差权衡-回归设置
- Using Cross-Validation to Optimise a Machine Learning Method - The Regression Setting
使用交叉验证优化机器学习方法-回归设置
- Beginner's Guide to Unsupervised Learning
无监督学习入门指南
- Beginner's Guide to Decision Trees for Supervised Machine Learning
监督学习之决策树入门指南
- Maximum Likelihood Estimation for Linear Regression
线性回归——最大似然法
- Bootstrap Aggregation, Random Forests and Boosted Trees
自助聚焦、随机森林和增强树
- K-Means Clustering of Daily OHLC Bar Data
每日bar数据k-均值聚类分析应用
[h2]Rough Path Theory[/h2][h2]粗糙路径理论[/h2]- Rough Path Theory and Signatures Applied To Quantitative Finance - Part 1
应用于量化金融的粗糙路径理论和特征—第一部分
- Rough Path Theory and Signatures Applied To Quantitative Finance - Part 2
应用于量化金融的粗糙路径理论和特征—第二部分
- Rough Path Theory and Signatures Applied To Quantitative Finance - Part 3
应用于量化金融的粗糙路径理论和特征—第三部分
- Rough Path Theory and Signatures Applied To Quantitative Finance - Part 4
应用于量化金融的粗糙路径理论和特征—第四部分
[h1]Deep Learning[/h1]深度学习
- Deep Learning with Theano - Part 1: Logistic Regression
用Theano进行深度学习.第1部分：逻辑回归
- What is Deep Learning?
什么是深度学习？
- Should You Buy or Rent a GPU-Based Deep Learning Machine for Quant Trading Research?
你应该买还是租一台基于GPU的深度学习机用于定量交易研究？
[h1]Time Series Analysis[/h1]时间序列分析
- Beginner's Guide to Time Series Analysis
时间序列分析指南
- Serial Correlation in Time Series Analysis
时间序列的序列相关性
- White Noise and Random Walks in Time Series Analysis
时间序列中的白噪声和随机游动
- Autoregressive Moving Average ARMA(p, q) Models for Time Series Analysis - Part 1
时间序列分析——自回归滑动平均(ARMA)模型(p, q) ——第一部分
- Autoregressive Moving Average ARMA(p, q) Models for Time Series Analysis - Part 2
时间序列分析——自回归滑动平均(ARMA)模型(p, q) ——第二部分
- Autoregressive Moving Average ARMA(p, q) Models for Time Series Analysis - Part 3
时间序列分析——自回归滑动平均(ARMA)模型(p, q) ——第三部分
- Autoregressive Integrated Moving Average ARIMA(p, d, q) Models for Time Series Analysis
时间序列分析——自回归移动平均(p, d, q) 模型
- Generalised Autoregressive Conditional Heteroskedasticity GARCH(p, q) Models for Time Series Analysis
时间序列分析——自回归条件异方差模型
- State Space Models and the Kalman Filter
状态空间模型与卡尔曼滤波
- Dynamic Hedge Ratio Between ETF Pairs Using the Kalman Filter
基于卡尔曼滤波对ETF的动态对冲比率研究
- Cointegrated Time Series Analysis for Mean Reversion Trading with R
基于R语言的协整时间序列分析——均值回归交易
- Cointegrated Augmented Dickey Fuller Test for Pairs Trading Evaluation in R
基于R语言对配对交易进行协整Dickey Fuller测试
- Johansen Test for Cointegrating Time Series Analysis in R
基于R语言的时间序列分析之Johansen协整检验
- Hidden Markov Models - An Introduction
隐马尔可夫模型-简介
- Hidden Markov Models for Regime Detection using R
基于R语言的隐式马科夫模型市场探测应用
[h1]Derivatives Pricing[/h1]衍生品定价
- [h2]The Binomial Model[/h2]二叉树模型
- Introduction to Option Pricing with Binomial Trees
二叉树期权定价方法介绍
- Hedging the sale of a Call Option with a Two-State Tree
用二叉树模型对冲看涨期权
- Risk Neutral Pricing of a Call Option with a Two-State Tree
用二叉树模型对看涨期权进行风险定价
- Replication Pricing of a Call Option with a One-Step Binomial Tree
用一步二叉树模型对看涨期权模拟定价
- Multinomial Trees and Incomplete Markets
多元树和不完全信息
- Pricing a Call Option with Two Time-Step Binomial Trees
用两步二叉树模型对看涨期权进行定价
- Pricing a Call Option with Multi-Step Binomial Trees
用多步二叉树模型对看涨期权进行定价
- Derivative Pricing with a Normal Model via a Multi-Step Binomial Tree
用多步二叉树模型对正态模型户进行金融衍生品定价
- Risk Neutral Pricing of a Call Option with Binomial Trees with Non-Zero Interest Rates
用非零利率二叉树模型对看涨期权进行风险中性定价
[h2]Stochastic Calculus[/h2]随机计算
- Introduction to Stochastic Calculus
随机计算导论
- The Markov and Martingale Properties
马尔可夫与鞅性质
- Brownian Motion and the Wiener Process
布朗运动与维纳过程
- Stochastic Differential Equations
随机微分方程
- Geometric Brownian Motion
几何布朗运动
- Ito's Lemma
伊腾法则
- Deriving the Black-Scholes Equation
布莱克-斯科尔斯方程推导
[h2]Numerical PDE[/h2]偏微分方程
- Derivative Approximation via Finite Difference Methods
有限差分法进行导数逼近求解
- Solving the Diffusion Equation Explicitly
明确求解扩散方程
- Crank-Nicholson Implicit Scheme
Crank-Nicholson隐式方式
- Tridiagonal Matrix Solver via Thomas Algorithm
Thomas求解三对角矩阵
- Black-Scholes Model
布莱克-舒尔斯期权定价模型
- Derivatives Pricing I: Pricing under the Black-Scholes model
衍生产品定价1：Black-Scholes模型下的定价
- Derivatives Pricing II: Volatility Is Rough
衍生品定价2：波动性随机
C++ ImplementationC++执行[h2]C++ Language[/h2]C++编程语言- C++ Virtual Destructors: How to Avoid Memory Leaks
C++之virtual虚析构函数：如何避免内存泄露
- Passing By Reference To Const in C++
C++之使用const 引用传递参数
- Mathematical Constants in C++
C++中的常用数学函数
- STL Containers and Auto_ptrs - Why They Don't Mix
为什么不要混合使用STL容器和Auto_ptrs
- Function Objects ("Functors") in C++ - Part 1
C++之函数对象（仿函数）-第一篇
- C++ Standard Template Library Part I - Containers
C ++标准模板库第一部分-容器
- C++ Standard Template Library Part II - Iterators
C ++标准模板库第二部分-迭代器
- C++ Standard Template Library Part III - Algorithms
C ++标准模板库第三部分-算法
- What's New in the C++11 Standard Template Library?
C ++ 11标准模板库中有哪些新增功能？
[h2]Numerical Methods in C++[/h2]C++数值方法
- Tridiagonal Matrix Algorithm ("Thomas Algorithm") in C++
C++三对角矩阵算法（“托马斯算法”）
- Matrix Classes in C++ - The Header File
C ++矩阵头文件
- Matrix Classes in C++ - The Source File
C ++矩阵源文件
- Statistical Distributions in C++
C++中的统计分布
- Random Number Generation via Linear Congruential Generators in C++
C++线性同余法生成随机数
- Eigen Library for Matrix Algebra in C++
C ++中矩阵代数的特征库
[h2]Derivatives Pricing with C++[/h2]C++衍生品定价- European vanilla option pricing with C++ and analytic formulae使用C++和解析公式对欧式期权进行定价
- European vanilla option pricing with C++ via Monte Carlo methods
基于C++的蒙特卡罗模拟在欧式期权进行模拟定价的应用
- Digital option pricing with C++ via Monte Carlo methods
通过蒙特卡洛方法使用C ++进行二元期权定价
- Double digital option pricing with C++ via Monte Carlo methods
通过蒙特卡洛方法使用C ++进行双二元期权定价
- Asian option pricing with C++ via Monte Carlo Methods
通过蒙特卡洛方法使用C ++进行亚洲期权定价
- Floating Strike Lookback Option Pricing with C++ via Analytic Formulae
通过解析公式使用C ++进行浮动罢工回溯期权定价
- C++ Explicit Euler Finite Difference Method for Black Scholes
Black Scholes模型下的C ++显式Euler有限差分法
- Generating Correlated Asset Paths in C++ via Monte Carlo
通过Monte Carlo在C ++中生成相关的资产路径
- Implied Volatility in C++ using Template Functions and Interval Bisection
区模板函数和二等分区间在隐含波动率的应用
- Implied Volatility in C++ using Template Functions and Newton-Raphson
使用模板函数和Newton-Raphson的C ++中的隐含波动率
- Heston Stochastic Volatility Model with Euler Discretisation in C++
Heston随机波动率模型在Euler方程中的应用
- Jump-Diffusion Models for European Options Pricing in C++
C ++中跳跃扩散模型在欧洲期权定价的应用
- Calculating the Greeks with Finite Difference and Monte Carlo Methods in C++
在C ++中使用有限差分和蒙特卡洛方法计算希腊值
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