https://www.cnblogs.com/bitquant/p/QuantStart.html
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.
在過去七年中，QuantStart一共發表了200多篇量化金融文章，這些文章的作者包括QS團隊成員、優秀的量化金融學者、研究人員和行業專家。
The articles are broadly categorised into Quantitative Trading, Mathematical Finance, Computational Finance and Careers Guidance.
這些文章大體分為量化交易,金融數學,計算金融和職業指導。
Quantitative Trading 量化交易
Getting Started with Quantitative Trading 量化交易起步
- Beginner's Guide to Quantitative Trading 量化交易入門指南
- Can Algorithmic Traders Still Succeed at the Retail Level? 算法交易商仍能在零售層面取得成功嗎？
- Top 5 Essential Beginner Books for Algorithmic Trading 算法交易初學者必讀的5本書
Building a Quantitative Trading Infrastructure 構建量化交易框架
- Installing a Desktop Algorithmic Trading Research Environment using Ubuntu Linux and Python 搭建桌面算法交易研究環境
- Securities Master Databases for Algorithmic Trading 算法交易中的證券主數據庫
- Securities Master Database with MySQL and Python
- Downloading Historical Futures Data From Quandl 從Quandl下載期貨歷史數據
- Research Backtesting Environments in Python with pandas 使用Python中的pandas研究回測環境
- Continuous Futures Contracts for Backtesting Purposes 用於回測的連續期貨合約
- Downloading Historical Intraday US Equities From DTN IQFeed with Python
Backtesting 回測
- 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 風險與性能度量
- 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 自動執行
- 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 量化基金和機構管理
Talks and Interviews 對話與訪談
- 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交易員
- 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 外匯交易日記
- 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 職業咨詢
Life as a Quant 寬客人生
- 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 大學生
- 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 轉行
- 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 量化閱讀列表
- 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 數學
Linear Algebra 線性代數
- Scalars, Vectors, Matrices and Tensors - Linear Algebra for Deep Learning (Part 1)
- Matrix Algebra - Linear Algebra for Deep Learning (Part 2)
Bayesian Statistics 貝葉斯統計
- 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 機器學習
- 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 拉夫路徑理論
- 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 深度學習
- 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 時間序列分析
- 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 衍生品定價
The Binomial Model 二叉樹模型
- 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 隨機計算
- 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 布萊克-舒爾斯期權定價模型
- Derivatives Pricing I: Pricing under the Black-Scholes model
- Derivatives Pricing II: Volatility Is Rough
C++ Implementation C++實現
C++ Language C++語言
- 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實現
- 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