What we're about

Purpose
Study tools used in:
Computational Investing
Technical Investment Strategies
Machine Learning for Investment Strategies

Who should join:
Anyone interested in developing and using Data Science and Machine Learning for Investment Strategies

What we will do:
Invite practitioners from disciplines using technical analysis, data science and Machine Learning for Investment in stock market.

References:

1. https://www.udacity.com/course/machine-learning-for-trading--ud501

-- Updated version of: https://pe.gatech.edu/courses/computational-investing-part-1

2. http://ta-lib.org/

3. https://github.com/rochars/trade

4. http://www.zipline.io/

5. https://github.com/jeffrey-liang/quantitative

6. https://github.com/pmorissette/bt

7. https://github.com/backtrader/backtrader

8. https://github.com/gbeced/pyalgotrade

9. https://github.com/femtotrader/pandas_talib

10. https://github.com/cuemacro/finmarketpy

Books:

1. What Hedge Funds Really Do: An Introduction to Portfolio Management Kindle Editionby Philip J. Romero (Author), Tucker Balch (Author)

2. Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments: Developing Predictive-Model-Based Trading Systems Using TSSBby David Aronson and Timothy Masters | Jun 1, 2013

3. Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Riskby Richard C. Grinold and Ronald N. Kahn | Oct 26, 1999

4. Applied Quantitative Methods for Trading and Investmentby Christian L. Dunis, Jason Laws, et al. | Oct 24, 2003

5. Foundations for Scientific Investing: Capital Markets Intuition and Critical Thinking Skills, by Timothy Crack

Upcoming events

No upcoming events

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