I tested your idea on one short-term system as following: System sample size: 3666 Average return: -0.42% Using activation/deactivation with last 120 trade results: x>=2 & x<=2.5 average return 0.34% x>=2.5 average return -0.50% This system only trades big cap stocks. Your activation/deactivation strategy can turn a money-loss system into a winning system. That is great. However, when x>=2.5, it does not do well. Do you see any results like that in your testing? Or it is system dependent. Thanks, Jim
Eric; Thank you; brilliant idea if implemented properly. I worked on something similar last year which we did not test properly and discarded; it took your posts to revive the concept. Michael
I'm glad it was able to help you. I keep telling myself that "one of these days" I'm going to start trading options. Currently, I still have my hands full expanding my Nasdaq trading, and will likely pursue NYSE once I run out of Nasdaq ideas. However, once I get around to options, I'll be sure to give you a call. Best of luck, -Eric
Jim, Sorry, I just noticed this post. I have never encountered a situation where the future profits are negative when the past profits are 'too good.' From my experience, better the past returns lead to (on average) better future returns. It may be possible that your sample size with x > 2.5 might not be sufficient to provide statistically valid results. Also, as you point out, it might be a system dependent issue where this is a repeatable issue for your system. Good luck, -Eric
Anyone looking at autocorrelation of returns versus: 1) time scale of returns 2) design space parameters ??
Not me. In fact, I must admit that I haven't a clue what either of those are. Sorry. Care to elaborate on what they are and why you think they may be useful for trading system applications? Thanks, -Eric
http://en.wikipedia.org/wiki/Autocorrelation Autocorrelation of returns is correlation(return[1...t-1],return[2...t]), or in other words the correlation between a series of returns and the same series of returns shifted one period forward or backward. Returns can be per-minute, per-day, or per-trade. It is a simple test to answer the questions: Do returns come in streaks? Do they oscillate? Is there no pattern at all (random walk)? It is particularly interesting to calculate autocorrelation across different time scales, and across the design space of a system. Why? Strong autocorrelating systems on short time scales naturally perform better with stops. So instead of brute force or real money testing different forms of stop losses, profit targets, trailing stops, or MA smoothing rules, look for the root cause of stop loss success⦠AUTOCORRELATION! Even if a systemâs long-term net profitability is zero or negative, a strong positive (trending returns) or negative (oscillatory returns) autocorrelation on certain time scales can be taken advantage of in a system-of-systems design. Methods include activation/deactivation rules, regression, ranking/screens, or trend following rules. A system with autocorrelation close to zero, even if nicely profitable will have volatile returns and will be more dangerous and psychologically painful to trade. You wonât know the system is dead until youâve taken on significant losses⦠when it effectively becomes strongly autocorrelating. Better to trade a strong autocorrelating system-of-system design that will automatically kill a dying system for you before losses accumulate.
If there is no autocorrelation of returns then "trading the equity curve" by following the trend of the recent past will fare just as poorly as trend following in a market with minimal autocorrelation. Well put prophet!