This is a summary of links featured on Quantocracy on Saturday, 09/03/2016. To see our most recent links, visit the Quant Mashup. Read on readers!
Backtesting With Zipline Ii [Koppian Adventures]In this post, we play again little bit around with python and the pandas-library. You may want to read the first part of this series. There we have backtested a simple crossing moving average strategy in pandas. We had a long/slow moving average over the last 40 days and a fast/short moving average over the last 20 days. When the stock price rockets skywards, the short moving average is above the
AllocateSmartly [TrendXplorer]Launched only recently, AllocateSmartly.com tracks the industrys best tactical asset allocation strategies with thorough, up-to-date backtests. As of writing 16 (sub) strategies are tracked and benchmarked on near real-time basis. All of the tracked strategies are both quantitative and systematic, meaning well-defined mathematical rules govern exactly when and what to trade. Among the featured
Possible Addition of NARX Network to Conditional Restricted Boltzmann Machine [Dekalog Blog]It has been over three months since my last post, due to working away from home for some of the summer, a summer holiday and moving home. However, during this time I have continued with my online reading and some new thinking about my conditional restricted boltzmann machine based trading system has developed, namely the use of a nonlinear autoregressive exogenous model in the bottom layer