Is 75% of successful forecasts on close price enough for profitable trading?

[userque]

The reason why I opened this post was to find out if any forum member was using that kind of information to trade.

Yes.

And to find out if traders were actually using mathematical approaches, intuition or both of them.

I use mathematical and intuitive approaches. The intuitions may be relied upon until such time that additional testing/research can confirm/reject those hypotheses. At that point, the intuitive parameters are replaced with the mathematical results of the testing/research.

 

[gon]

Yes.



I use mathematical and intuitive approaches. The intuitions may be relied upon until such time that additional testing/research can confirm/reject those hypotheses. At that point, the intuitive parameters are replaced with the mathematical results of the testing/research.

Indeed, when I was trying the model what I got was a better intuitive knowledge of price action.

 

[ironchef]

You are conflating the accuracy of the median prediction with the mean prediction that takes into account the whole distribution. As an extreme example, a median expectation of a late night stroll through Mogadishu is "nothing happened", but would you be willing to take that walk without knowing the mean expectation?

Let me see if I understand what you are saying: 75% probability of prediction is not the same as positive profit expectancy? In other words, just like selling OTM options, one has usually > 80% win probability but the 20% that you lose can wipe you out?

 

[ironchef]

Hi.

Indeed, the starting point of all this was thinking in price like an object with a certain mass subject to a series of dynamics. So I started to apply velocity, acceleration, force and other concepts taken precisely from physics and calculus.

But, the $64 dolar question, the information that a classificator like this one provides could be used as a predictor within a bigger model, just another feature else.

In my case, I got a few lessons:
- Technical indicators and price/volume history matters.
- Even 2-bar SMA is too smoothed to provide an accurate estimation of actual price change. This is important, because getting quite good accuracy with the 2-daySMA is not an easy task, and I found that I could not get a direct profit from it after struggling for a few weeks.
- Comparing the bars and other data to the model predictions provides a mathematical confirmation of what each type of bar and price dynamics should represent, not just the theories and opinions of authors that do not provide one sole proof about what they say or sell.

But, what do we want to maximize? Profits?

Then if profit is the function to maximize and loss the function to minimize, then price, volume or banana is not the target item we should be studying ultimately.

If you want to maximize profit, you need to provide the market information as input data, also any prediction or classification if you want to. But the target should be profit.

And to map market to profit you need a set of intermediate layers. I think that here is where strategy comes. For this, if I would have time to go ahead in the future, I would be for strategy definition and parameter optimization more than purely thinking about a stochastic approach. This reminds me to unsupervised reinforcement learning, but again, maybe more simple approaches to identify the different price areas to detect breakouts, trend reversals, gaps, supports and resistance levels, etc... could be more than sufficient.

So, imo if you already have a good system, just write it and optimize it. But, for anyone not being a programmer knowledgeable at statistics I would discourage of trying it.

The reason why I opened this post was to find out if any forum member was using that kind of information to trade. And to find out if traders were actually using mathematical approaches, intuition or both of them.

Thank you.

I read the two articles you posted prior. Very interesting, I used to try modeling using similar parameters listed by the papers but did not (and did not know how to) apply machine learning. Basically I used multi parameters regression to try forecast. It didn't work very well for me.

Question: Do you have any suggestion how I can learn Support Vector Machine learning, or are there software package that can do SVM?

Regards,

 

[sle]

Let me see if I understand what you are saying: 75% probability of prediction is not the same as positive profit expectancy? In other words, just like selling OTM options, one has usually > 80% win probability but the 20% that you lose can wipe you out?

Exactly. It does not have to be options, there are a lot of strategies on linear instruments that are asymmetric by nature. Let's say you're an HFT trader and are making markets in a stock X. You don't really try to do anything sophisticated, simply buy for the bid and right away try sell at the offer. The median result of each trade will be the bid/ask spread (let's say 1 cent), but once in a blue moon when you are sitting on the bid, news come out and the stock moves $1 down.

 

[gon]

Thank you.

I read the two articles you posted prior. Very interesting, I used to try modeling using similar parameters listed by the papers but did not (and did not know how to) apply machine learning. Basically I used multi parameters regression to try forecast. It didn't work very well for me.

Question: Do you have any suggestion how I can learn Support Vector Machine learning, or are there software package that can do SVM?

Regards,

I use this:
http://scikit-learn.org/stable/modules/svm.html

 

[Kevin Schmit]

Are you making a one day ahead forecast, up or down, of a two day moving average? If so, your null is 75% accuracy, your model is adding nothing. Just guess up if today's close is above MA2 and guess down if below. You'll be right 75% of the time. If the math of why is beyond you, here is a simulation that illustrates the point:

> require(quantmod)
> a <-data.frame(rnorm(10001))
> b <- cbind(a,c(rollmean(a$rnorm,2),0))
> colnames(b) <- c('Returns','MA2')
> (sum(sign(b$Returns * b$MA)+1)-1)/20000
[1] 0.7533

 

[gon]

Are you making a one day ahead forecast, up or down, of a two day moving average? If so, your null is 75% accuracy, your model is adding nothing. Just guess up if today's close is above MA2 and guess down if below. You'll be right 75% of the time. If the math of why is beyond you, here is a simulation that illustrates the point:

> require(quantmod)
> a <-data.frame(rnorm(10001))
> b <- cbind(a,c(rollmean(a$rnorm,2),0))
> colnames(b) <- c('Returns','MA2')
> (sum(sign(b$Returns * b$MA)+1)-1)/20000
[1] 0.7533

Totally agree.