QuantStart量化交易文集
Over the last seven years more than 200 quantitative finance articles have been written by members of the QuantStart team, prominent quant finance academics, researchers and industry professionals.
在过去七年中，QuantStart一共发表了200多篇量化金融文章，这些文章的作者包括QS团队成员、优秀的量化金融学者、研究人员和行业专家。
The articles are broadly categorised into Quantitative Trading, Mathematical Finance, Computational Finance and Careers Guidance.
这些文章大体分为量化交易,金融数学,计算金融和职业指导。
Quantitative Trading 量化交易
Getting Started with Quantitative Trading 量化交易起步
- Beginner\’s Guide to Quantitative Trading 量化交易入门指南
- Can Algorithmic Traders Still Succeed at the Retail Level? 算法交易商仍能在零售层面取得成功吗？
- 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 搭建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 Python
- 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研究回测环境
- Continuous Futures Contracts for Backtesting Purposes 用于回测的连续期货合约
- Downloading Historical Intraday US Equities From DTN IQFeed with Python
Backtesting 回测
- Successful Backtesting of Algorithmic Trading Strategies – Part I
- Successful Backtesting of Algorithmic Trading Strategies – Part II
- Best Programming Language for Algorithmic Trading Systems?
- Event-Driven Backtesting with Python – Part I
- Event-Driven Backtesting with Python – Part II
- Event-Driven Backtesting with Python – Part III
- Event-Driven Backtesting with Python – Part IV
- Event-Driven Backtesting with Python – Part V
- Event-Driven Backtesting with Python – Part VI
- Event-Driven Backtesting with Python – Part VII
- Event-Driven Backtesting with Python – Part VIII
- Should You Build Your Own Backtester?
- Backtesting Systematic Trading Strategies in Python: Considerations and Open Source Frameworks
Risk and Performance Measurement 风险与性能度量
- Sharpe Ratio for Algorithmic Trading Performance Measurement
- Money Management via the Kelly Criterion
- Value at Risk (VaR) for Algorithmic Trading Risk Management – Part I
- Annualised Rolling Sharpe Ratio in QSTrader
Automated Execution 自动执行
- Interactive Brokers Demo Account Signup Tutorial
- Using Python, IBPy and the Interactive Brokers API to Automate Trades
- Choosing a Platform for Backtesting and Automated Execution
Quantitative Trading Strategies 量化交易策略
- How to Identify Algorithmic Trading Strategies
- Backtesting a Moving Average Crossover in Python with pandas
- Backtesting a Forecasting Strategy for the S&P500 in Python with pandas
- Backtesting An Intraday Mean Reversion Pairs Strategy Between SPY And IWM
- ARIMA+GARCH Trading Strategy on the S&P500 Stock Market Index Using R
- Kalman Filter-Based Pairs Trading Strategy In QSTrader
- Monthly Rebalancing of ETFs with Fixed Initial Weights in QSTrader
- Strategic and Equal Weighted ETF Portfolios in QSTrader
- Aluminum Smelting Cointegration Strategy in QSTrader
- Sentiment Analysis Trading Strategy via Sentdex Data in QSTrader
- Market Regime Detection using Hidden Markov Models in QSTrader
Quant Funds and Institutional Management 量化基金和机构管理
Talks and Interviews 对话与访谈
- My Interview Over At OneStepRemoved.com
- My Talk At The London Financial Python User Group
- My Chat With Traders Interview with Aaron Fifield
- When Should You Build Your Own Backtester? – QuantCon NYC, April 2016 talk
QSTrader QS交易员
- Announcing the QuantStart Advanced Trading Infrastructure Article Series
- Advanced Trading Infrastructure – Position Class
- Advanced Trading Infrastructure – Portfolio Class
- Advanced Trading Infrastructure – Portfolio Handler Class
Forex Trading Diary 外汇交易日记
- Forex Trading Diary #1 – Automated Forex Trading with the OANDA API
- Forex Trading Diary #2 – Adding a Portfolio to the OANDA Automated Trading System
- Forex Trading Diary #3 – Open Sourcing the Forex Trading System
- Forex Trading Diary #4 – Adding a Backtesting Capability
- Forex Trading Diary #5 – Trading Multiple Currency Pairs
- Forex Trading Diary #6 – Multi-Day Trading and Plotting Results
- Forex Trading Diary #7 – New Backtest Interface
Careers Advice 职业咨询
Life as a Quant 宽客人生
- Understanding How to Become a Quantitative Analyst
- What are the Different Types of Quantitative Analysts?
- My Experiences as a Quantitative Developer in a Hedge Fund
- A Day in the Life of a Quantitative Developer
- Careers in Quantitative Finance
- What are the Career Paths in Systematic Trading?
- Setting up an Algorithmic Trading Business
Undergraduates 大学生
- What Classes Should You Take To Become a Quantitative Analyst?
- Why Study for a Mathematical Finance PhD?
- Why a Masters in Finance Won\’t Make You a Quant Trader
- Best Undergraduate Degree Course For Becoming A Quant?
- The Top 5 UK Universities For Becoming A Quant
- How to Learn Advanced Mathematics Without Heading to University – Part 1
- How to Learn Advanced Mathematics Without Heading to University – Part 2
- How to Learn Advanced Mathematics Without Heading to University – Part 3
Postgraduates 研究生
- Junior Quant Jobs – Beginning a Career in Financial Engineering after a PhD
- How To Get A Quant Job Once You Have A PhD
- Getting a Job in a Top Tier Quant Hedge Fund
- How to Get a Job at a High Frequency Trading Firm
- Which Programming Language Should You Learn To Get A Quant Developer Job?
Career Changers 转行
- Can You Still Become a Quant in Your Thirties?
- Self-Study Plan for Becoming a Quantitative Trader – Part I
- Self-Study Plan for Becoming a Quantitative Trader – Part II
- Self-Study Plan for Becoming a Quantitative Developer
- Self-Study Plan for Becoming a Quantitative Analyst
- Mailbag: Can You Get A Job In HFT Without A Degree?
- Quant Finance Career Skills – What Are Employers Looking For?
Quant Reading Lists 量化阅读列表
- Quant Reading List Derivative Pricing
- Quant Reading List C++ Programming
- Quant Reading List Numerical Methods
- Quant Reading List Python Programming
- 5 Important But Not So Common Books A Quant Should Read Before Applying for a Job
- 5 Top Books for Acing a Quantitative Analyst Interview
- Top 5 Finite Difference Methods books for Quant Analysts
- Top 5 Essential Beginner C++ Books for Financial Engineers
- Quantitative Finance Reading List
- Top 10 Essential Resources for Learning Financial Econometrics
- Free Quantitative Finance Resources
- Top 5 Essential Books for Python Machine Learning
Mathematics 数学
Linear Algebra 线性代数
- Scalars, Vectors, Matrices and Tensors – Linear Algebra for Deep Learning (Part 1)
- Matrix Algebra – Linear Algebra for Deep Learning (Part 2)
Bayesian Statistics 贝叶斯统计
- Bayesian Statistics: A Beginner\’s Guide
- Bayesian Inference of a Binomial Proportion – The Analytical Approach
- Markov Chain Monte Carlo for Bayesian Inference – The Metropolis Algorithm
- Bayesian Linear Regression Models with PyMC3
Machine Learning 机器学习
- Basics of Statistical Mean Reversion Testing
- Basics of Statistical Mean Reversion Testing – Part II
- Forecasting Financial Time Series – Part I
- Beginner\’s Guide to Statistical Machine Learning – Part I
- Support Vector Machines: A Guide for Beginners
- Supervised Learning for Document Classification with Scikit-Learn
- The Bias-Variance Tradeoff in Statistical Machine Learning – The Regression Setting
- Using Cross-Validation to Optimise a Machine Learning Method – The Regression Setting
- Beginner\’s Guide to Unsupervised Learning
- Beginner\’s Guide to Decision Trees for Supervised Machine Learning
- Maximum Likelihood Estimation for Linear Regression
- Bootstrap Aggregation, Random Forests and Boosted Trees
- K-Means Clustering of Daily OHLC Bar Data
Rough Path Theory 拉夫路径理论
- Rough Path Theory and Signatures Applied To Quantitative Finance – Part 1
- Rough Path Theory and Signatures Applied To Quantitative Finance – Part 2
- Rough Path Theory and Signatures Applied To Quantitative Finance – Part 3
- Rough Path Theory and Signatures Applied To Quantitative Finance – Part 4
Deep Learning 深度学习
- Deep Learning with Theano – Part 1: Logistic Regression
- What is Deep Learning?
- Should You Buy or Rent a GPU-Based Deep Learning Machine for Quant Trading Research?
Time Series Analysis 时间序列分析
- Beginner\’s Guide to Time Series Analysis
- Serial Correlation in Time Series Analysis
- White Noise and Random Walks in Time Series Analysis
- Autoregressive Moving Average ARMA(p, q) Models for Time Series Analysis – Part 1
- Autoregressive Moving Average ARMA(p, q) Models for Time Series Analysis – Part 2
- Autoregressive Moving Average ARMA(p, q) Models for Time Series Analysis – Part 3
- Autoregressive Integrated Moving Average ARIMA(p, d, q) Models for Time Series Analysis
- Generalised Autoregressive Conditional Heteroskedasticity GARCH(p, q) Models for Time Series Analysis
- State Space Models and the Kalman Filter
- Dynamic Hedge Ratio Between ETF Pairs Using the Kalman Filter
- Cointegrated Time Series Analysis for Mean Reversion Trading with R
- Cointegrated Augmented Dickey Fuller Test for Pairs Trading Evaluation in R
- Johansen Test for Cointegrating Time Series Analysis in R
- Hidden Markov Models – An Introduction
- Hidden Markov Models for Regime Detection using R
Derivatives Pricing 衍生品定价
The Binomial Model 二叉树模型
- Introduction to Option Pricing with Binomial Trees
- Hedging the sale of a Call Option with a Two-State Tree
- Risk Neutral Pricing of a Call Option with a Two-State Tree
- Replication Pricing of a Call Option with a One-Step Binomial Tree
- Multinomial Trees and Incomplete Markets
- Pricing a Call Option with Two Time-Step Binomial Trees
- Pricing a Call Option with Multi-Step Binomial Trees
- Derivative Pricing with a Normal Model via a Multi-Step Binomial Tree
- Risk Neutral Pricing of a Call Option with Binomial Trees with Non-Zero Interest Rates
Stochastic Calculus 随机计算
- Introduction to Stochastic Calculus
- The Markov and Martingale Properties
- Brownian Motion and the Wiener Process
- Stochastic Differential Equations
- Geometric Brownian Motion
- Ito\’s Lemma
- Deriving the Black-Scholes Equation
Numerical PDE 偏微分方程
- Derivative Approximation via Finite Difference Methods
- Solving the Diffusion Equation Explicitly
- Crank-Nicholson Implicit Scheme
- Tridiagonal Matrix Solver via Thomas Algorithm
Black-Scholes Model 布莱克-舒尔斯期权定价模型
- Derivatives Pricing I: Pricing under the Black-Scholes model
- Derivatives Pricing II: Volatility Is Rough
C++ Implementation C++实现
C++ Language C++语言
- C++ Virtual Destructors: How to Avoid Memory Leaks
- Passing By Reference To Const in C++
- Mathematical Constants in C++
- STL Containers and Auto_ptrs – Why They Don\’t Mix
- Function Objects (“Functors”) in C++ – Part 1
- C++ Standard Template Library Part I – Containers
- C++ Standard Template Library Part II – Iterators
- C++ Standard Template Library Part III – Algorithms
- What\’s New in the C++11 Standard Template Library?
Numerical Methods in C++
- Tridiagonal Matrix Algorithm (“Thomas Algorithm”) in C++
- Matrix Classes in C++ – The Header File
- Matrix Classes in C++ – The Source File
- Statistical Distributions in C++
- Random Number Generation via Linear Congruential Generators in C++
- Eigen Library for Matrix Algebra in C++
Derivatives Pricing with C++
- European vanilla option pricing with C++ and analytic formulae
- European vanilla option pricing with C++ via Monte Carlo methods
- Digital option pricing with C++ via Monte Carlo methods
- Double digital option pricing with C++ via Monte Carlo methods
- Asian option pricing with C++ via Monte Carlo Methods
- Floating Strike Lookback Option Pricing with C++ via Analytic Formulae
- C++ Explicit Euler Finite Difference Method for Black Scholes
- Generating Correlated Asset Paths in C++ via Monte Carlo
- Implied Volatility in C++ using Template Functions and Interval Bisection
- Implied Volatility in C++ using Template Functions and Newton-Raphson
- Heston Stochastic Volatility Model with Euler Discretisation in C++
- Jump-Diffusion Models for European Options Pricing in C++
- Calculating the Greeks with Finite Difference and Monte Carlo Methods in C++
GPU/CUDA Programming in C++
- Installing Nvidia CUDA on Mac OSX for GPU-Based Parallel Computing
- Vector Addition “Hello World!” Example with CUDA on Mac OSX
- Installing Nvidia CUDA on Ubuntu 14.04 for Linux GPU Computing
- dev_array: A Useful Array Class for CUDA
- Monte Carlo Simulations In CUDA – Barrier Option Pricing
- Matrix-Matrix Multiplication on the GPU with Nvidia CUDA
Python Implementation Python实现
- Options Pricing in Python
- European Vanilla Call-Put Option Pricing with Python
- LU Decomposition in Python and NumPy
- Cholesky Decomposition in Python and NumPy
- QR Decomposition with Python and NumPy
- Jacobi Method in Python and NumPy
- Parallelising Python with Threading and Multiprocessing
- Quick-Start Python Quantitative Research Environment on Ubuntu 14.04
- Easy Multi-Platform Installation of a Scientific Python Stack Using Anaconda
Quantstart
- QuantStart: 2014 in Review
- Announcement: Speaking at QuantCon in April 2016
- How to Write a Great Quant Blog
- QuantStart April 2016 News
- Advanced Algorithmic Trading and QSTrader Updates
- Advanced Algorithmic Trading and QSTrader – Second Update
- QuantStart Events in October and November 2016
- QuantStart New York City October 2016 Trip Report
- Advanced Algorithmic Trading and QSTrader – Fourth Update
- QuantStart Gets a Makeover
- QuantStart Singapore November 2016 Trip Report
- Advanced Algorithmic Trading and QSTrader – Fifth Update
- QuantStart Upcoming Content Survey 2017
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