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- Introduction to Stochastic Calculus
- The Markov and Martingale Properties
- Quant Reading List Derivative Pricing
- Quant Reading List C++ Programming
- Quant Reading List Numerical Methods
- Quant Reading List Python Programming
- Building a Raspberry Pi Cluster for QSTrader using SLURM - Part 1
- Brownian Motion and the Wiener Process
- Stochastic Differential Equations
- Geometric Brownian Motion
- Ito's Lemma
- Deriving the Black-Scholes Equation
- Junior Quant Jobs Beginning a career in Financial Engineering after a PhD
- Derivative Approximation via Finite Difference Methods
- Solving the Diffusion Equation Explicitly
- Crank-Nicholson Implicit Scheme
- Tridiagonal Matrix Solver via Thomas Algorithm
- Options Pricing in Python
- European Vanilla Call-Put Option Pricing with Python
- 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
- Understanding How to Become a Quantitative Analyst
- 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
- Quantitative Finance Reading List
- What are the Different Types of Quantitative Analysts?
- C++ Virtual Destructors: How to Avoid Memory Leaks
- What Classes Should You Take To Become a Quantitative Analyst?
- Why Study for a Mathematical Finance PhD?
- Passing By Reference To Const in C++
- My Experiences as a Quantitative Developer in a Hedge Fund
- Mathematical Constants in C++
- Which Programming Language Should You Learn To Get A Quant Developer Job?
- STL Containers and Auto_ptrs - Why They Don't Mix
- LU Decomposition in Python and NumPy
- Cholesky Decomposition in Python and NumPy
- QR Decomposition with Python and NumPy
- Jacobi Method in Python and NumPy
- 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
- Tridiagonal Matrix Algorithm ("Thomas Algorithm") in C++
- Matrix Classes in C++ - The Header File
- Matrix Classes in C++ - The Source File
- C++ Standard Template Library Part I - Containers
- Can You Still Become a Quant in Your Thirties?
- Self-Study Plan for Becoming a Quantitative Developer
- Asian option pricing with C++ via Monte Carlo Methods
- Self-Study Plan for Becoming a Quantitative Analyst
- Risk Neutral Pricing of a Call Option with Binomial Trees with Non-Zero Interest Rates
- Beginner's Guide to Quantitative Trading
- Floating Strike Lookback Option Pricing with C++ via Analytic Formulae
- Statistical Distributions in C++
- Function Objects ("Functors") in C++ - Part 1
- Random Number Generation via Linear Congruential Generators in C++
- How to Identify Algorithmic Trading Strategies
- Successful Backtesting of Algorithmic Trading Strategies - Part I
- Can Algorithmic Traders Still Succeed at the Retail Level?
- Successful Backtesting of Algorithmic Trading Strategies - Part II
- C++ Explicit Euler Finite Difference Method for Black Scholes
- Securities Master Databases for Algorithmic Trading
- Securities Master Database with MySQL and Python
- C++ Standard Template Library Part II - Iterators
- Sharpe Ratio for Algorithmic Trading Performance Measurement
- Top 5 Essential Beginner Books for Algorithmic Trading
- Implied Volatility in C++ using Template Functions and Interval Bisection
- Generating Correlated Asset Paths in C++ via Monte Carlo
- Interactive Brokers Demo Account Signup Tutorial
- Top 10 Essential Resources for Learning Financial Econometrics
- C++ Standard Template Library Part III - Algorithms
- Best Programming Language for Algorithmic Trading Systems?
- What's New in the C++11 Standard Template Library?
- Eigen Library for Matrix Algebra in C++
- Implied Volatility in C++ using Template Functions and Newton-Raphson
- Free Quantitative Finance Resources
- Heston Stochastic Volatility Model with Euler Discretisation in C++
- Getting a Job in a Top Tier Quant Hedge Fund
- Jump-Diffusion Models for European Options Pricing in C++
- Calculating the Greeks with Finite Difference and Monte Carlo Methods in C++
- Installing a Desktop Algorithmic Trading Research Environment using Ubuntu Linux and Python
- Basics of Statistical Mean Reversion Testing
- How to Get a Job at a High Frequency Trading Firm
- Self-Study Plan for Becoming a Quantitative Trader - Part I
- Why a Masters in Finance Won't Make You a Quant Trader
- My Interview Over At OneStepRemoved.com
- Downloading Historical Futures Data From Quandl
- Self-Study Plan for Becoming a Quantitative Trader - Part II
- Forecasting Financial Time Series - Part I
- Research Backtesting Environments in Python with pandas
- Backtesting a Moving Average Crossover in Python with pandas
- Backtesting a Forecasting Strategy for the S&P500 in Python with pandas
- Continuous Futures Contracts for Backtesting Purposes
- Using Python, IBPy and the Interactive Brokers API to Automate Trades
- Backtesting An Intraday Mean Reversion Pairs Strategy Between SPY And IWM
- Choosing a Platform for Backtesting and Automated Execution
- Event-Driven Backtesting with Python - Part I
- Event-Driven Backtesting with Python - Part II
- Downloading Historical Intraday US Equities From DTN IQFeed with Python
- Event-Driven Backtesting with Python - Part III
- My Talk At The London Financial Python User Group
- Event-Driven Backtesting with Python - Part IV
- Event-Driven Backtesting with Python - Part V
- Event-Driven Backtesting with Python - Part VI
- Beginner's Guide to Statistical Machine Learning - Part I
- Event-Driven Backtesting with Python - Part VII
- Parallelising Python with Threading and Multiprocessing
- Quick-Start Python Quantitative Research Environment on Ubuntu 14.04
- Money Management via the Kelly Criterion
- Top 5 Essential Books for Python Machine Learning
- How To Get A Quant Job Once You Have A PhD
- A Day in the Life of a Quantitative Developer
- Value at Risk (VaR) for Algorithmic Trading Risk Management - Part I
- Basics of Statistical Mean Reversion Testing - Part II
- Easy Multi-Platform Installation of a Scientific Python Stack Using Anaconda
- Installing Nvidia CUDA on Mac OSX for GPU-Based Parallel Computing
- Vector Addition "Hello World!" Example with CUDA on Mac OSX
- Support Vector Machines: A Guide for Beginners
- Event-Driven Backtesting with Python - Part VIII
- Installing Nvidia CUDA on Ubuntu 14.04 for Linux GPU Computing
- dev_array: A Useful Array Class for CUDA
- Bayesian Statistics: A Beginner's Guide
- Monte Carlo Simulations In CUDA - Barrier Option Pricing
- QuantStart: 2014 in Review
- Supervised Learning for Document Classification with Scikit-Learn
- Forex Trading Diary #1 - Automated Forex Trading with the OANDA API
- Forex Trading Diary #2 - Adding a Portfolio to the OANDA Automated Trading System
- The Bias-Variance Tradeoff in Statistical Machine Learning - The Regression Setting
- Forex Trading Diary #3 - Open Sourcing the Forex Trading System
- Using Cross-Validation to Optimise a Machine Learning Method - The Regression Setting
- Best Undergraduate Degree Course For Becoming A Quant?
- Matrix-Matrix Multiplication on the GPU with Nvidia CUDA
- Forex Trading Diary #4 - Adding a Backtesting Capability
- Forex Trading Diary #5 - Trading Multiple Currency Pairs
- The Top 5 UK Universities For Becoming A Quant
- Bayesian Inference of a Binomial Proportion - The Analytical Approach
- Forex Trading Diary #6 - Multi-Day Trading and Plotting Results
- Successful Algorithmic Trading Updated for Python 2.7.x and Python 3.4.x
- Beginner's Guide to Time Series Analysis
- Forex Trading Diary #7 - New Backtest Interface
- 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
- ARIMA+GARCH Trading Strategy on the S&P500 Stock Market Index Using R
- Announcement: Speaking at QuantCon in April 2016
- How to Write a Great Quant Blog
- Announcing the QuantStart Advanced Trading Infrastructure Article Series
- State Space Models and the Kalman Filter
- QuantStart: 2015 In Review
- Advanced Trading Infrastructure - Position Class
- Advanced Trading Infrastructure - Portfolio Class
- Advanced Trading Infrastructure - Portfolio Handler Class
- Careers in Quantitative Finance
- How to Learn Advanced Mathematics Without Heading to University - Part 1
- Markov Chain Monte Carlo for Bayesian Inference - The Metropolis Algorithm
- QuantStart Singapore November 2016 Trip Report
- Bayesian Linear Regression Models with PyMC3
- QuantStart April 2016 News
- Advanced Algorithmic Trading and QSTrader Updates
- How to Learn Advanced Mathematics Without Heading to University - Part 2
- Deep Learning with Theano - Part 1: Logistic Regression
- 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
- Advanced Algorithmic Trading and QSTrader - Second Update
- Mailbag: Can You Get A Job In HFT Without A Degree?
- Beginner's Guide to Unsupervised Learning
- Mailbag: How Do You Move From Quant Developer To Quant Trader?
- Maximum Likelihood Estimation for Linear Regression
- Should You Build Your Own Backtester?
- Beginner's Guide to Decision Trees for Supervised Machine Learning
- Dynamic Hedge Ratio Between ETF Pairs Using the Kalman Filter
- How to Learn Advanced Mathematics Without Heading to University - Part 3
- Hidden Markov Models - An Introduction
- Quant Finance Career Skills - What Are Employers Looking For?
- Kalman Filter-Based Pairs Trading Strategy In QSTrader
- Hidden Markov Models for Regime Detection using R
- QuantStart Events in October and November 2016
- QuantStart New York City October 2016 Trip Report
- Monthly Rebalancing of ETFs with Fixed Initial Weights in QSTrader
- Strategic and Equal Weighted ETF Portfolios in QSTrader
- Advanced Algorithmic Trading and QSTrader - Fourth Update
- QuantStart Gets a Makeover
- Black Friday Weekend - 40% Discount On All Ebooks!
- Bootstrap Aggregation, Random Forests and Boosted Trees
- K-Means Clustering of Daily OHLC Bar Data
- Advanced Algorithmic Trading and QSTrader - Fifth Update
- Aluminum Smelting Cointegration Strategy in QSTrader
- Sentiment Analysis Trading Strategy via Sentdex Data in QSTrader
- Advanced Algorithmic Trading - Final Release
- Annualised Rolling Sharpe Ratio in QSTrader
- Market Regime Detection using Hidden Markov Models in QSTrader
- QuantStart Upcoming Content Survey 2017
- What is Deep Learning?
- What are the Career Paths in Systematic Trading?
- Rough Path Theory and Signatures Applied To Quantitative Finance - Part 1
- Setting up an Algorithmic Trading Business
- Rough Path Theory and Signatures Applied To Quantitative Finance - Part 2
- What are the Different Types of Quant Funds?
- Rough Path Theory and Signatures Applied To Quantitative Finance - Part 3
- Scalars, Vectors, Matrices and Tensors - Linear Algebra for Deep Learning (Part 1)
- Rough Path Theory and Signatures Applied To Quantitative Finance - Part 4
- Matrix Algebra - Linear Algebra for Deep Learning (Part 2)
- QSTrader: v0.1.0 Released
- Should You Buy or Rent a GPU-Based Deep Learning Machine for Quant Trading Research?
- Derivatives Pricing I: Pricing under the Black-Scholes model
- Backtesting Systematic Trading Strategies in Python: Considerations and Open Source Frameworks
- Derivatives Pricing II: Volatility Is Rough
- Derivatives Pricing III: Models driven by Lévy processes
- What Alternative Career Paths Exist For Quants?
- High Frequency Trading I: Introduction to Market Microstructure
- Best Operating System For Quant Trading?
- High Frequency Trading II: Limit Order Book
- High Frequency Trading III: Optimal Execution
- Capital Raising for Early Stage Quant Fund Managers - Part I
- QSTrader: A Major Update On Our Progress
- QSTrader: November 2017 Update
- Installing TensorFlow on Ubuntu 16.04 with an Nvidia GPU
- Engineering To Quant Finance - How To Make The Transition
- Hiring a Software Developer to Code Up a Trading Strategy
- Systematic Tactical Asset Allocation: An Introduction
- The 60/40 Benchmark Portfolio
- Generating Synthetic Histories for Backtesting Tactical Asset Allocation Strategies
- How to Learn Advanced Mathematics Without Heading to University - Part 4
- Matrix Inversion - Linear Algebra for Deep Learning (Part 3)
- Sigma Algebras and Probability Spaces
- QuantStart Content Survey 2020
- Periodically Rebalanced Static Allocation 'Buy and Hold' Strategies in QSTrader
- QSTrader: v0.1.1 Released
- QuantStart News - June 2020
- Installing TensorFlow 2.2 on Ubuntu 18.04 with an Nvidia GPU
- Introduction to Artificial Neural Networks and the Perceptron
- Connecting to the Interactive Brokers Native Python API
- QuantStart News - July 2020
- Training the Perceptron with Scikit-Learn and TensorFlow
- QSTrader: Documentation Released
- QuantStart News - August 2020
- Simple versus Advanced Systematic Trading Strategies - Which is Better?
- Understanding Equities Data
- Installing an Algorithmic Trading Research Environment with Python on Windows
- Installing an Algorithmic Trading Research Environment with Python on Mac
- Installing an Algorithmic Trading Research Environment with Python on Linux
- Creating an Algorithmic Trading Prototyping Environment with Jupyter Notebooks and Plotly
- An Introduction to Stooq Pricing Data
- Building a Raspberry Pi Cluster for QSTrader using SLURM - Part 2
- Evaluating Data Coverage with Tiingo
- Building a Raspberry Pi Cluster for QSTrader using SLURM - Part 3
- Geometric Brownian Motion Simulation with Python
- Building a Raspberry Pi Cluster for QSTrader Using SLURM - Part 4
- Building a Raspberry Pi Cluster for QSTrader Using SLURM - Part 5
- QSTrader Asset Class Hierarchy
- QSTrader Fee Model Class Hierarchy
- Candlestick Subplots with Plotly and the AlphaVantage API
- Creating a Returns Series with Polygon's Forex Data
- Calculating Realised Volatility with Polygon Forex data
- Brownian Motion Simulation with Python
- QSTrader v0.2.6 Released
- Creating a Backtesting environment with Docker, Jupyter Notebook and QSTrader.
- Momentum Top N with Docker, Jupyter and QSTrader
- QSTrader v0.3.0 Released
- Python Libraries for Quantitative Trading
- Ornstein-Uhlenbeck Simulation with Python
- Vasicek Model Simulation with Python
