Quantocracy’s Daily Wrap for 02/02/2017

The narrative of Smart Beta products is that factors are becoming an investment commodity. Factors are not commodities, but unique expressions of investment themes. One Value strategy can be very different from another, and can lead to very different results. There are many places that factor portfolios can differ. The difficulty for asset allocators is in identifying how factor strategies differ

I have created a quantitative trading strategy that incorporates a simple machine learning model to trade SPY as part of my ongoing research in quantitative trading. The focus here was not on creating a strategy with alpha but rather to develop a framework both in my mind and in code to develop more advanced models in the future. 1. Does SPY Exhibit Short-Term Mean Reversion or Momentum? Examining

The QuantStart team are very happy to announce that the full version of Advanced Algorithmic Trading has now been released. This brings the total number of pages to 517. To access the full version customers simply need to follow the download link received in the original pre-order purchase email. If the download email has been misplaced then please email support@quantstart.com and the link will be

Well, I was midway through a formal book review on Larry and Andrews new book, Your Complete Guide to Factor-Based Investing, when I noticed that the team over at GestaltU already wrote the review I was going to write great job and I encourage everyone to read it. larry factor book So we wont rehash what has already been said about Larry and Andrews book, instead, Ill bullet

The fastest way to test the profitability of a trading model generating signals is to do a simple backtest (which means no hindsight biases i.e at least 1 period of timeframe lag from signal even if you timeframe is in milliseconds) using historical time series. actual returns = absolute return (no hindsight biases to signal) transaction cost spillage Spillage really matters when the trade