direct forecasts for the SPY and based on forecasts computed by aggregating stock level signals of the ETFs components were tested and both showed very good results, registering Total Returns close to and above the SPY, Sharpe Ratios significantly greater than. Stock charts are like snow flakes. Actual Positive 192 226, when model m03 predicted negative returns, it was right 192 times and wrong 205 times with an accuracy of under. Amazingly, using this mechanical technique, you could have made money during this period by being a bull or a bear. Here we focus on ways of constructing long only SPY trading strategies (the SPY is an ETF that tracks the performance of the S P 500 index) by 1) using the predictions generated by our algorithm specifically for this ETF and 2) aggregating the signals. Model m02 adds the daily return of SPY on day t-2 as a predictor variable, and. I decided to treat this problem as a classification problem to keep things simple I am only interested in predicting the direction of future returns (positive or negative) and not the magnitude. The interpretation of model m01 is that the sign of the daily return (positive or negative) of SPY on day t is the response variable and the predictor variable is the daily return of SPY on day t-1. To do this we sum the weights of the components for which our predictions are long and those for which they are short and divide by the total sum of the weights, which gives the weighted percentage of stocks within the ETF in each direction.
But lets take a closer look at model m03 which has an accuracy of 54.3. The machine learning method I decided on using is logistic regression which is a simple learning method that is often used before more flexible learning methods. Here is the SPY closing price with a mapped trading signal color gradient. SPY Trading Strategies using a Direct and an Aggregated S P 500 Forecast. Between April 20, 2007 and February 18, 2011 (a 1,000 day period) the strategy generated 206 trades and.37 return. Yesterday was a rather frustrating trading day. The following table gives the performance of the I Know First SPY trading strategies using the 1 month forecast for the period 08/18/2015 01/31/2018 without including transaction or spread costs versus the performance of the benchmark, the long only SPY. These can easily be integrated into investment selection processes and, combined with the appropriate strategy, be translated into portfolios with outstanding statistics for all types of investors. I could run the strategy through the optimizer and produce much more impressive results, but I dont recommend that. Combination of Direct and Aggregation, we combine the two previous approaches into one portfolio which results in an improved strategy in terms of risk adjusted return. The smoother has a slight negative slope which confirms the weak evidence for short-term mean reversion. This post on StackExchange helped me understand this concept more clearly.
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