Tag Archives: Machine learning
Validation of trading strategies is important for minimizing probability of Type-I error, or false discoveries. Below is an example of how to quickly validate strategies developed with DLPAL S on correlated but more importantly, anti-correlated securities.
Engineering of features with economic value, also known as attributes, predictors or alpha factors, is the first step in the extraction of market alpha. The example in this article shows proper classification of DJIA stocks in the weekly timeframe has … Continue reading
This article shows how to develop and execute a long/short equity strategy for Dow 30 stocks in the weekly timeframe with DLPAL LS software.
We evaluate the performance of two new clusters implemented in DLPAL LS for feature generation. We find that the new clusters can lead to increased performance. Especially one of these new clusters appears quite promising.
A reference to idiosyncratic trading strategies was made in a market commentary by Neal Berger, the President of Eagle’s View Asset Management. In this article we attempt to clarify what these idiosyncratic strategies are.
In the past trading psychology played an important role in controlling the emotional factors that affected profitability. Nowadays, anyone talking about a need for proper trading psychology has fallen behind in the era of algo trading and machine learning. If … Continue reading