Hi All,
Aron: you got better results by removing your original entries, because your original entries were not better then random and you got more time in the market by using simple random entries (my guess). To All: Thanks for all the thoughts and consideration. To give some more hints and encourage thoughts here is a bit more info. My general idea is to divide a complete trading system into smaller independently testable/optimizable pieces. I'm building a single equity, intraday, automated trading system. To make it simple let's say it consists of a filter (when not to trade) an entry & timing logic (generate buy and short signals) and a trade management logic (initial stop, trailing logic, profit taking exits, etc.) If we accept that the price movements consists of noise and real price movements than the trade management logic's only job is to keep my stops (initial and trailing) out of the noise level, while minimizing initial loss and maximizing profit. It has to accomplish this REGARDLESS OF THE QUALITY OF THE ENTRIES AND FILTERS. If all my entries are bad it has to produce the least amount of loss. If all my entries are excellent it has to collect the most profit. If I run a number of backtest runs with random entries while keeping the settings of trade management logic constant I get a "sample" of what might happen using the settings if my entries are not better then chance. This sample has a distribution of profits, CARs, system drawdowns, etc. All the attributes of a backtest runs or a series of real life trades! If I run similar test with each possible setting (optimization) and compare the samples of each settings, I'm able to select settings that produce the best performance distribution (defined by my objective). So if my trade management logic is up to its job (using the best settings) it has to produce the best distribution of drawdowns and profits of backtest runs with random entries. Similarly, the filter's job is to keep me out of market when trading is not profitable. It's not profitable because there are more noise than real price movement (so initial stop is going to be hit sooner or later) OR because of entering the market in the wrong direction. If using random entries (in random directions) and the filter is bad, the initial stop is hit because of either cause. If the filter is good, the numbers of initial losses are minimized because initial stop is hit if I try to ride the market in wrong direction but noise is appropriately addressed. So if I use random entries and use the same initial loss with no trailing and add the "perfect" filter to it,the filtered system has to provide the smallest loss and the smallest drawdown. By running a number of random backtests for each possible filter settings, I produce a sample of that filter settings. These samples can be somehow compared and the best selected. Any opinion, thoughts or experience is appreciated. I don't really know what the best way of comparing "samples" is. Any idea? Regards, Y
