Profitable Insights into Financial Markets
Access to high quality, cost effective market data is a continuing problem for retail traders. I was recently told about the ongoing efforts of the startup brokerage “Alpaca”. The gentleman I spoke with said the API gave access to the tick data of thousands of stocks everyday and without cost.
I thought it was too good to be true but recently I took a little bit of time to investigate.
Read MoreIn this post I will be reviewing the course “Mean Reversion Strategies in Python by Dr. E.P. Chan” (<—affiliate link, use discount code LONGSHORT for an additional 5% off at checkout). In mean reversion strategies, the course author focuses on three categories of mean reversion systems. These are pairs trading, index arbitrage, and cross-sectional long-short strategies.
Read MoreMy initial insample performance targets for this portfolio are a sharpe ratio of 1.5 or better, max drawdown less than 15%, and only 1 losing year every ten years. I aim to develop variations of this portfolio that can scale from 100K to several million.
To support this development effort and still stay true to my original goal of sharing my work, I’ve created a subscription tier for premium content. I’m calling this the Take Profits Strategy Subscription.
Over the next 12 months, every month (including February), I will share one new algorithmic strategy with my subscribers. My goal is to have each strategy be fully functional on the Quantconnect platform out of the box. This will allow you to backtest and live trade the strategy immediately if you choose.
The code will be documented in a way that practitioners can build on and customize the strategy. The code documentation will also provide in-depth explanations behind the purpose and function of each component. I will also provide any additional research documents and notebooks, that helped me in the development process. Ultimately, I will pick the best combination of these strategies to implement in the multi-strategy portfolio.
Read MoreThis post focuses on Chapter 3 in the new book Advances in Financial Machine Learning by Marcos Lopez De Prado. In this chapter De Prado demonstrates a workflow for improved return labeling for the purposes of supervised classification models. He introduces multiple concepts but focuses on the Triple-Barrier Labeling method, which incorporates profit-taking, stop-loss, and holding period information, and also meta-labeling which is a technique designed to address several issues.
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