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10

I highly recommend the Maximum Entropy Bootstrap for time series, implemented by the meboot package in R. In my work, I've stopped using both the block bootstrap and residuals bootstrap in favor of meboot, and I am pleased with the results. Hrishikesh Vinod, the researcher behind meboot, described it in his talk at UseR/2010 last year. The algorithm is ...


7

A block bootstrap makes sense to me. (If the term doesn't make sense to you, I explain it at the end.) In order to pick the block size, I would essentially do a grid search: pick the largest feasible block size pick a smallest reasonable block size pick how many block sizes you feel like testing I'd run the selected bootstraps and see if there was a ...


5

Another possibility is to analyze the equity curve itself so as to go live with the system when good performance is expected and to either reduce risk or just paper trade when performance is expected to be negative. Are a series of positive returns followed by negative returns (i.e. is there mean reversion)? Does a trend-following "meta-system" and/or a ...


4

You could try measuring autocorrelation at varying lags, as described here, and then choose your optimal block size according to the results of this test, i.e. if there is significant autocorrelation up to and including lag 5, your block size should be no larger than 5.


4

Be careful when you optimize the exit parameters (and any other parameter) as you could get better results in backtest that will only be due to over fitting. IF you haven't done that yetn use In and Out sample to verify your improvements. After that you can try to build entry filters. In my experience trend following usually have bigger drawdowns than mean ...


4

I won't give you the answer delivered on a silver platter but hopefully the following will get your started: a) you need to define exactly over which look-back period you aim to derive the maximum drawdown. b) you need to keep track of the max price while you iterate the look-back window. c) you need to keep track of the min price SUBSEQUENT to any NEW ...


3

Zipline, the opensource python backtester, has a batch and iterative implementation for max drawdown. Here is the batch: https://github.com/quantopian/zipline/blob/master/zipline/finance/risk.py#L284 Here is the iterative: https://github.com/quantopian/zipline/blob/master/zipline/finance/risk.py#L578 disclosure: I'm one of the zipline maintainers


3

I concur with Richard here. Generally draw downs are measured on invested capital not raw pnl. Thus if you start out with 1 million xyz currency units and suffer a loss on the first trade of 100,000 xyz then you suffered a 10% draw down. Make sure you understand that going practice is that most draw down calculations apply a moving window which means your ...


2

I like the function b.star in the np package for R to select the block size and pass it to tsboot although I don't have the math background to determine whether this is the best method.


1

Hello people. This is quite a complex problem if you want to solve this in a computationally efficient way for a rolling window. I have gone ahead and written a solution to this in C#. I want to share this as the effort required to replicate this work is quite high. First, here are the results: here we take a simple drawdown implementation and ...


1

(After the clarification, this answer is no longer relevant) Expected maximum drawdown is going to be highly sensitive to your choice of SDE, and to your calibration of it. Therefore you should play with a variety of parameterizations to estimate your model error. So far as efficient computation goes, we can regard this as a payoff very similar to a ...


1

Here you go, http://arxiv.org/pdf/cond-mat/9808295.pdf http://www.cs.rpi.edu/~magdon/talks/mdd_NYU04.pdf http://www.intelligenthedgefundinvesting.com/pubs/rb-kwcmlr.pdf However, you mentioned you make an assumption of the portfolio dynamics. That means you either have historical data available about your portfolio returns and standard deviation or you ...



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