joelowj/awesome-algorithmic-trading

awesome-algorithmic-trading

A curated list of awesome algorithmic trading tutorials, projects and communities.

Table of Contents

1. Stock Prices
2. Market Mechanics
3. Data Processing
4. Stock Returns
5. Momentum Trading
6. Quant Workflow
7. Outliers & Filtering
8. Regression
9. Time Series Modeling
10. Volatility
11. Pairs Trading & Mean Reversion
12. Breakout Strategy
13. Stock, Indices & Funds
14. ETFs
15. Portfolio Risk & Returns
16. Portfolio Optimization
17. Smart Beta & Portfolio Optimization
18. Factors
19. Factor Models & Factor Types
20. Risk Factor Models - Time Series & Cross Sectional
21. Risk Factor Models with PCA
22. Alpha Factors
23. Alpha Factor Research Methods
24. Advanced Portfolio Optimization
25. Multi-factor Model

Tutorials

• Algorithmic Trading Strategies

• Artificial Intelligence for Trading

• Computational Investing, Part I

• Financial Engineering and Risk Management Part I

• Financial Engineering and Risk Management Part II

• MIT Open Courseware - Analytics of Finance

• MIT Open Courseware - Investments

• MIT Open Courseware - Topics in Mathematics with Applications in Finance

• Machine Learning and Reinforcement Learning in Finance Specialization

• Machin Learning for Trading

• Model a Quantitative Trading Strategy in R

• Trading Strategies in Emerging Markets Specialization

• Time Series with R

• Quantitative Analyst with R

Projects

Articles

• 10 Things to Know About Every Cash Flow Statement

• The 15 Stock Diversification Myth

• The Limitations of Ratio Analysis

• The Right Way and the Wrong Way to Benchmark a Diversified Portfolio

• Performance Measurement: The What, Why, and How of the Investment Management Process

• Utility Theory and Attitude toward Risk (Explained With Diagram)

• The Guide to Diversification

• Diversification: How much is too much?

• Successful Backtesting of Algorithmic Trading Strategies - Part I

• Successful Backtesting of Algorithmic Trading Strategies - Part II

Research Papers

• Are Markets Efficient?

• Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers

• Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about Future Earnings?

• Betting Against Beta

• Momentum

• Separating Winners from Losers Among Low Book-to-Market Stocks Using Financial Statement Analysis

• The 101 Ways to Measure Portfolio Performance

• Does the Composition of the Market Portfolio Really Matter?

• Pairs Trading: Performance of a Relative Value Arbitrage Rule

Books

• Algorithmic Trading and DMA: An introduction to direct access trading strategies

• Building Winning Algorithmic Trading Systems, + Website: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Trading (Wiley Trading)

• Finding Alphas: A Quantitative Approach to Building Trading Strategies

• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

• Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading

• Python for Finance: Analyze Big Financial Data

• Technical Analysis Explained, Fifth Edition: The Successful Investor's Guide to Spotting Investment Trends and Turning Points

• Quantitative Investing: Strategies to exploit stock market anomalies for all investors

Communities

• Advanced Risk and Portfolio Management

• Certificate in Quantitative Finance

• The Python Quants Group

• Quantor

• Quantopian

• QuantConnect

• QuantNet

• QuantStart

License

This work is licensed under a Creative Commons Attribution 4.0 International License.