joelowj / awesome-algorithmic-trading Goto Github PK

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

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

• 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

• 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

• 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

• 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

• Advanced Risk and Portfolio Management

• Certificate in Quantitative Finance

• The Python Quants Group

• Quantor

• Quantopian

• QuantConnect

• QuantNet

• QuantStart

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