Why favorable R:R?

BigMark1972 Thanks for you input. Here is some feedback….
For discretionary traders expectancy is the probability or strength of belief that a particular trading action based on past experience and price data will lead to a positive outcome and trading profits. There is no calculation for discretionary expectancy. In this kind of discretionary trading system the trader is completely in charge and free to assign what ever level of expectancy they desire to the trading processes. The discretionary bear flag trading method you mention appears to be a good example of a discretionary traders expectancy.

However for strategy (programmed code) traders the expectancy is far different and more riggorous. The expectancy determination is built into the formula:
Expectancy = (Probability of Win * Average Win) - (Probability of Loss * Average Loss)

Discretionary traders at their option can use the expectancy formula. Strategy traders are required to use the expectancy formula because they have no input in to how a strategy performs.

 

I actually agree with much of what you say. A lot of info is people pulling statistics out of their rear end.

"I, like other traders for the past 15 years of trading systems have been trying to reconcile the bewildering vast number of statistics that are spewed from guru’s books and articles on trading."

Which I wouldn't do. I found in a related field that it's best to develop my own approach. My trading strategy, for example, is something I developed based off my trading philosophy. I like to read, find nice ideas, and some of these just click and become fundamental in my mind.

In sportsbetting I used to test for statistical significance, but I dropped that after I found it's better to judge the methods than the results. This has continued to my trading, and I have no intention of testing my results. I'm certainly not one of those "+ev traders" you talking about. I'm completely aware of probabilities and expectency, but I prefer sound logic to determine my trading, rather than backtesting.

"1. Performance measurements must be consistent with each test.
2. What you apply in testing (optimization) must be verified in production (live trading).
3. Performance must be integrated between all components to be effective (integrated with other parts of trade management)."

I agree with these rules, and I think anyone backtesting or testing over a large sample should following these. If the method can't be exactly replicated in live trading, there's not much point in testing.

"Guru rule 1 from one web site: “…Use a fixed fractional position sizing… For example, you might risk 2% of your account equity on each trade (the "2% rule"). …”

Guru rule 2 from another web site “…we cannot risk more than 6% of our trading account when we enter multiple positions at the same time …”

Guru rule 3 from another web site “…You should not exceed 8% over all losses in month. In other words, the most you can lose in month or total trading account loss is 8%...”

The first rule is understandable. Most people won't be able to judge the quality of the opportunity, so trading a fixed amount is fine. The "2% rule" is a bit blind though, as I think people should understand the underlying mathematics before deciding size. 2% is too high for most anyway, since even 2% leads to large swings, and most aren't profitable anyway so they shouldn't be trading in the first place.

The second two rules, for me these are just stats pulled out of their rear end. Multiple positions should be considered, but 6% is a meaningless figure. Losing 8% in a month is also ridiculous, especially for those that follow the 2% Rule (you could easily lose 8% in a day!).

"Then there is expected value. When I optimize a typical trading system I get 200 profitable settings. When I dump the trade data to my database and SQL it all 200 have a positive expected value? However when I forward test only 8 settings are profitable? So expected value has no predictive quality and is not consistent across testing (same result last 8 years)."

This is where we differ. I don't think your conclusion here is correct. We're dealing with uncertainty, so a strategy that yields profitable results over a sample isn't necessarily +ev, even if our numbers suggest it is. This is why I use the terms "perceived ev" and "true ev". A system that tests well might have perceived +ev, but it may not actually be profitable, and therefore the true ev may be negative.

This is quite important to understand for people who backtest systems, as blindly trading systems that have tested well is not a profitable strategy. If you tested 100 systems, 10 may come back profitable, but that doesn't mean they're actually +ev. Chances are they are not, and this is why there must always be strong underlying logic. The test for a system with solid underlying logic is much stronger than just testing random systems.

"When the forward tested strategy settings live trades are examined, guess what? The expected value from testing has no relationship or correlation to actual results."

That's because the expected value over testing is not equal to the true expected value, and then there's variance to consider. I've had a profitable (sportsbetting) system experience completely different results over two statistically significant samples.

"Expected value doesn’t work for me. Expected value like many other guru statistics does not correlate (because it constantly changes as you note) to building better positive trading results for me. It is a simplistic general statistic."

Whether it is useful or not depends on how it is used, but where we differ here is mainly in our interpretation of ev. You're thinking of it as a statistic generated by testing over a sample. To me, that is just perceived ev, since it may very well be incorrect. If the perceived ev is incorrect, then naturally there must be a value that is correct, and I call this the true expected value. This is what is important as it exists for every trade, but unfortunately we never know what it is. This is the joke in my original point.

 

Hello Mr J Yes do I believe as you stated that we have found much more common ground than our original postings led us to believe. One paragraph you wrote particularly resonated with my heart strings of trading

“This is quite important to understand for people who backtest systems, as blindly trading systems that have tested well is not a profitable strategy. If you tested 100 systems, 10 may come back profitable, but that doesn't mean they're actually +ev. Chances are they are not, and this is why there must always be strong underlying logic. The test for a system with solid underlying logic is much stronger than just testing random systems.”

Trade management for programmed system strategies is the process of finding what factors during testing induce positive trade cash flow during performance and metric reviews during live trading. With the performance reviews telling the trader how they are making profits or losses and metric reviews compare the tested optimization statistics against the actual result statistics.

If you mean from this that a system with solid underlying logic shows during a live trading performance review that real trades have current +ev or as I called it positive cash flow and that going forward we can anticipate a continuation of positive cash flow for next segment of live trading – than yes that kind of +ev I understand and have used for years. Positive (Expected) cash flow is an important stat I use in my trading today because it is a business statistic and not a predictive statistic. Positive (Expected) cash flow with a business context gives me an indication of the predicted cash flow for the next live trading segment.

The main problem with talking about live trading is there is no accepted business language to describe business trading results. You state:

“That's because the expected value over testing is not equal to the true expected value, and then there's variance to consider. I've had a profitable (sportsbetting) system experience completely different results over two statistically significant samples.”

When you state “True expected value” does this mean in a live trading performance review where live swing trades on a 30 minute intraday chart for the last 6 weeks made profits that were in line with the positive (expected) cash flow from the last live performance review?

What I am reading in all trading books and articles today has an absence of an acceptable business language to describe business trading results. We have each built our own languages for trading results and this is what we are conversing about. For the last 20 years every book I see on trading that describes risk and money management has failed to present adequate trading terminology and procedures that transfer from testing strategies to live trading. Ask any one of these authors “What combination of money management factors in testing a strategy normally produce positive cash flow in live trading?” In one seminar when I stood up and asked an expert money management author this question their face turned red and they babbled “…Traders need to test to find this out for themselves…”

Then you wrote a very important paragraph about testing. In it you said:
“This is where we differ. I don't think your conclusion here is correct. We're dealing with uncertainty, so a strategy that yields profitable results over a sample isn't necessarily +ev, even if our numbers suggest it is. This is why I use the terms "perceived ev" and "true ev". A system that tests well might have perceived +ev, but it may not actually be profitable, and therefore the true ev may be negative.”

I agree you that this is where are methods differ in obtaining positive results in live trading. You have methods in involving expectancy during testing that correlate to “true expectancy” in live trading. I have never used expectancy in any form in testing since it has had no predictive value whatever to produce positive cash flow in live trades. That is the crux of my method, a strategies description must state “What trade management, coupled with statistical measures and market conditions are necessary in the testing arena to apply to this strategy to gain a high probability of success in live trading.”

One strategy I currently trade has ½% position sizing, it likes statistical high volatility and markets where VIX is high. Based on these factors it continues to produce consistent positive cash flow at each performance and metric review. But in no shape or form can I relate the concept of expectancy to this strategy - except maybe as continuous positive cash flow during reviews.

Again trade management for programmed system strategies is the process of finding what factors during testing induce positive trade cash flow during performance and metric reviews during live trading. This means traders must some how “quantify” with hard cold facts or numbers those items that are characteristics of their strategies to produce profits.

What I do during testing is verify, when these strategy conditions appear to be met, that the current performance measurements of the strategy look the same as past live trading. Then I integrate in trade management components (position sizing etc…) and test again. This also must look the same as past live trading. In this testing what I’m comparing is the forward out-of-sample test data to the past live results. If these comparisons are met I live trade. In live trading I do the first performance and metric reviews to verify present results are in-line past live trading and that trade statistics from optimized tests are in-line with live trading. At this point I compute expectancy to measure positive cash flow in the future.

Please write soon... I must stop writing as my adrenaline rush is fading……

 

"If you mean from this that a system with solid underlying logic shows during a live trading performance review that real trades have current +ev or as I called it positive cash flow and that going forward we can anticipate a continuation of positive cash flow for next segment of live trading – than yes that kind of +ev I understand and have used for years."

Yes, that's roughly it.

"The main problem with talking about live trading is there is no accepted business language to describe business trading results. You state:"

That might be true. I have a gambling background and the concept of "perceived ev" and "true ev" would be understood, even though the terms are unofficial. It's simply to distinguish between what we think our edge is, and what our edge truly is, because these two amounts are rarely the same. Think of it as "perceived ev" being the return that we think we will get, while "true ev" is the return we will actually get.

"When you state “True expected value” does this mean in a live trading performance review where live swing trades on a 30 minute intraday chart for the last 6 weeks made profits that were in line with the positive (expected) cash flow from the last live performance review?"

Yes, if variance didn't exist, then the "true ev" would be perfectly in line with the results of live trading.

"I agree you that this is where are methods differ in obtaining positive results in live trading. You have methods in involving expectancy during testing that correlate to “true expectancy” in live trading."

It's not something I use, it's just something that exists while I trade. Either my trades are profitable (positive expected value) or unprofitable (negative expected value). The only way I use it is that I trade when I think I have an edge, and in my interpretation of ev, +ev is the same as having an edge.

"What I do during testing is verify, when these strategy conditions appear to be met, that the current performance measurements of the strategy look the same as past live trading. Then I integrate in trade management components (position sizing etc…) and test again. This also must look the same as past live trading. In this testing what I’m comparing is the forward out-of-sample test data to the past live results. If these comparisons are met I live trade. In live trading I do the first performance and metric reviews to verify present results are in-line past live trading and that trade statistics from optimized tests are in-line with live trading. At this point I compute expectancy to measure positive cash flow in the future."

This sounds like proper testing. Many people who backtest systems simply aren't doing it correctly, which is why the system fails in real trading. The systems most people come up with often have a random hypothesis tested over the data it was formed on!

Like I said a few posts ago, I think we do agree in a lot of areas, we just have different interpretations of a concept that is not significant enough in the trading world.