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Ipython notebooks for math and finance tutorials
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ARIMA + GARCH to model SPX returns.ipynb
Covariance, Correlation and Confidence Intervals.ipynb
Expected Value and Standard Deviation.ipynb
Integration, Cointegration, and Stationarity.ipynb
Long-Short Strategies using Ranking.ipynb
Mean reversion.ipynb
Measuring Momentum.ipynb
Momentum Strategies.ipynb
Overfitting.ipynb not needed/to be deleted Feb 27, 2017
Pairs Trading.ipynb
README.md
Random Variables.ipynb
Simple ML Strategies to generate Trading Signal.ipynb
Time Series Analysis - 1.ipynb
Time Series Analysis - 2.ipynb
Time Series Analysis - 3.ipynb
Time Series Analysis - 4.ipynb
Trading Strategy Model Selection Pitfalls.ipynb
download.png

README.md

Tutorials

IPython notebooks for trading and math tutorials. You can view these here in github, or you can download and run locally on your computer. You will need to have the auquanToolbox installed to be able to run them.

For instructions on how to install the toolbox, visit here

Contents:

Trading Strategies:

  1. Mean Reversion Basics
  2. Momentum Strategy Basics
  3. How to measure momentum
  4. Model Selection Pitfalls
  5. Avoid Overfitting
  6. Pairs Trading
  7. Long-Short Strategies using Ranking

Math

  1. Random Variables
  2. Expected Value and Standard Deviation
  3. Covariance, Correlation and Confidence Intervals
  4. Stationarity, Integration and CoIntegration

Time Series Analysis

  1. Part 1 - Stationarity, Auto Correlation, White Noise and Random Walks
  2. Part 2 - AR and MA models
  3. Part 3 - ARMA and ARIMA models
  4. Part 4 - ARCH and GARCH models
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