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Expand Up @@ -17,17 +17,17 @@ This repository is a collection of research notebooks and tutorials using the Qu

### Research 2 Production Notebook Series

- [Mean Reversion](https://github.com/QuantConnect/Research/blob/master/Research2Production/01%20Mean%20Reversion.ipynb)
- [Random Forest Regression](https://github.com/QuantConnect/Research/blob/master/Research2Production/02%20Random%20Forest%20Regression.ipynb)
- [Uncorrelated Assets](https://github.com/QuantConnect/Research/blob/master/Research2Production/03%20Uncorrelated%20Assets.ipynb)
- [Kalman Filters and Pairs Trading](https://github.com/QuantConnect/Research/blob/master/Research2Production/04%20Kalman%20Filters%20and%20Pairs%20Trading.ipynb)
- [Stationary Processes and Z-Scores](https://github.com/QuantConnect/Research/blob/master/Research2Production/05%20Stationary%20Processes%20and%20Z-Scores.ipynb)
- [Principal Component Analysis](https://github.com/QuantConnect/Research/blob/master/Research2Production/06%20Principal%20Component%20Analysis.ipynb)
- [Hidden Markov Models](https://github.com/QuantConnect/Research/blob/master/Research2Production/07%20Hidden%20Markov%20Models.ipynb)
- [Long Short-Term Memory](https://github.com/QuantConnect/Research/blob/master/Research2Production/08%20Long%20Short-Term%20Memory.ipynb)
- Mean Reversion [C#](https://github.com/QuantConnect/Research/blob/master/Research2Production/CSharp/01%20Mean%20Reversion%20CSharp.ipynb) | [Python](https://github.com/QuantConnect/Research/blob/master/Research2Production/Python/01%20Mean%20Reversion.ipynb)
- Random Forest Regression [Python](https://github.com/QuantConnect/Research/blob/master/Research2Production/Python/02%20Random%20Forest%20Regression.ipynb)
- Uncorrelated Assets [C#](https://github.com/QuantConnect/Research/blob/master/Research2Production/CSharp/03%20Uncorrelated%20Assets%20CSharp.ipynb) | [Python](https://github.com/QuantConnect/Research/blob/master/Research2Production/Python/03%20Uncorrelated%20Assets.ipynb)
- Kalman Filters and Pairs Trading [C#](https://github.com/QuantConnect/Research/blob/master/Research2Production/CSharp/04%20Kalman%20Filters%20and%20Pairs%20Trading%20CSharp.ipynb) | [Python](https://github.com/QuantConnect/Research/blob/master/Research2Production/Python/04%20Kalman%20Filters%20and%20Pairs%20Trading.ipynb)
- Stationary Processes and Z-Scores [C#](https://github.com/QuantConnect/Research/blob/master/Research2Production/CSharp/05%20Stationary%20Processes%20and%20Z-Scores%20CSharp.ipynb) | [Python](https://github.com/QuantConnect/Research/blob/master/Research2Production/Python/05%20Stationary%20Processes%20and%20Z-Scores.ipynb)
- Principal Component Analysis [Python](https://github.com/QuantConnect/Research/blob/master/Research2Production/Python/06%20Principle%20Compenent%20Analysis.ipynb)
- Hidden Markov Models [Python](https://github.com/QuantConnect/Research/blob/master/Research2Production/Python/07%20Hidden%20Markov%20Models.ipynb)
- Long Short-Term Memory [Python](https://github.com/QuantConnect/Research/blob/master/Research2Production/Python/08%20Long%20Short-Term%20Memory.ipynb)

### Analysis Examples
- [Fudamental Factor Analysis](https://github.com/QuantConnect/Research/blob/master/Analysis/01%20Fudamental%20Factor%20Analysis.ipynb): This research applies MorningStar fundamental data to demonstrate how to select the effective factors for long/short strategies.
- [Fudamental Factor Analysis](https://github.com/QuantConnect/Research/blob/master/Analysis/01%20Fundamental%20Factor%20Analysis.ipynb): This research applies MorningStar fundamental data to demonstrate how to select the effective factors for long/short strategies.

- [Kalman Filter Based Pairs Trading](https://github.com/QuantConnect/Research/blob/master/Analysis/02%20Kalman%20Filter%20Based%20Pairs%20Trading.ipynb): This research demonstrates the basic principle of pairs trading and introduces the concepts of cointegration and Kalman Filter for pairs trading.

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