The QuantConnect Strategy Library is a collection of tutorials that show how to build trading algorithms. I contributed the following tutorials:

- Gaussian Naive Bayes Model
- Gradient Boosting Model
- Using News Sentiment to Predict Price Direction of Drug Manufacturers
- Intraday Arbitrage Between Index ETFs
- Momentum in Mutual Fund Returns
- Ichimoku Clouds in the Energy Sector
- Intraday ETF Momentum
- Residual Momentum

I participated in several QuantConnect YouTube videos.

Value and momentum factors are commonly researched in the literature. What makes them interesting factors is they are negatively correlated, yet they are both profitable. In this video, I demonstrate how to implement a strategy that targets both factors across a universe of US Equities. Additionally, I share some source code that shows how to apply the strategy across multiple asset classes. The strategy in this video is based on "Value and Momentum Everywhere" (Asness, et al., 2012).

To view the strategy code, see the Meetup post in the QuantConnect forum.

In this video, I share 3 strategies:

- Applying the married put Option strategy (strategy code)
- Creating a long-short portfolio using the Machine Learning ranking dataset from Brain (strategy code)
- Tracking the number of mentions on r/WallStreetBets to time trades in volatile Equities (strategy code)

If you have a portfolio of strategies, how much should you allocate to each strategy? In this presentation, I demonstrate how to optimize the weights in a portfolio of crowd-sourced strategies to maximize the Sortino ratio of the portfolio.

To clone the optimization code, see the Alpha Streams Portfolio Optimization Notebook post in the QuantConnect forum.