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1

Such an approach is done by the systemic investor blogger in his blog Time Series Matching with Dynamic Time Warping.


3

Yes, this is an issue. There will be datamining bias. The best practice is to hold enough of your data out-of-sample to test your models. For example, if you have 10 years of data, use the first 5 years to come up with your models. You could then rank them from best to worst, based on whatever metric you prefer. Then use the second 5 years of data to test ...


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Choose the most robust (or insensitive) strategy. You are right that the best strategy might be overfit. So look at your parameter space and focus on the area where profitability, for example, changes least when you change the parameter value. Here is a 1D example: The most profitable strategy is that single point that unfortunately leaves no room for ...


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What you could do is to apply the methods of portfolio risk analysis. If you buy $n$ stocks with percentages $w_i,i=1,\ldots,n$ then your portfolio return is $r = \sum_{i=1}^n w_i r_i$. Dealing with investment strategies I would not include an expected profit in the VaR calculation and put $\mu=0$ for this reason. To calculate the volatility of your ...


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If the returns are $N(\mu,\Sigma)$ distributed, then $WML\sim N(0,\sigma)$, because the equally-weighted $\mu$'s cancel while $\Sigma=\sqrt{w \Sigma w'}$ with $w=\{1/n...1/n\}$. So your new VaR becomes: $$\mbox{VaR}\left(\alpha\right)_{WML}=\Phi^{-1}\left(\alpha\right)\cdot\sigma$$ Your sampling formula from above remains still valid though, just with ...


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I've analysed numerous strategies, and have never encountered problem similar to yours. Your approach of volatility measurement may be a bit deviating from the conventional thinking. Essentially, why would you measure volatility of overlapping returns? It's not sensible. No matter what rolling or walk-forward schemes you are adopting, one can always derive ...



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