DislikedHello everybody, algoTraderJo, is there any reason for using 2 output units (MLE and MSE) instead of using 1 unit (the MLE/MSE ratio)?Ignored
Algorithmic Quant Trading (Machine Learning + Stat-Arb) 25 replies
Forex with Machine Learning Software Project 1 reply
Machine Learning + Retail Forex = Profitable? (Quant) 1 reply
Potential new machine learning style software. 79 replies
My most recent advancements into machine learning 16 replies
DislikedHello everybody, algoTraderJo, is there any reason for using 2 output units (MLE and MSE) instead of using 1 unit (the MLE/MSE ratio)?Ignored
DislikedI've just found this thread and scanned over it - very, very interesting! I would like to reproduce your results, but with different software than F4. As it uses simple linear regression and hourly bars, I suppose the results should be reproducible with any other platform. My question is - what is your experience with the robustness of the systems? Doe they completely fall apart when the time zone is a little different, such as by 1 hour, or when the weekend starts or end a little sooner or earlier? Also, have you checked if the results still hold...Ignored
Also I want to point out that things like "are the parameters like a broad hill or a peak" in my experience matter very little for real live trading performance when using machine learning algos. In my experience the most important thing is to use lots of data (20 or more years) and for the data mining bias analysis to give a low probability for systems to come from spurious relationships.
I have always failed when I have used little data or when I have attempted to trade without doing a very meticulous data mining bias analysis (as explained before on this thread).
I know that my experience is not everything but I am just telling you what has worked for me (perhaps others use different techniques that work as well).
Disliked{quote} I don't know in my opinion a single output model would be easier to train, especially if you use algorithms like backpropagation, rprop etc....Ignored
Disliked{quote} Easier to train, definitely. But better? Do some experiments and share your findings with us! Perhaps I have always been wrong about using 2 targets!Ignored
DislikedOn E/U with SL=30% ATR from 01/01/2001 to 27/05/2015, changing one parameter at a time:
A B C D
9 110 4 8 147.24%
8 110 4 8 -81.52%
10 110 4 8 -24.50%
9 109 4 8 198.52%
9 111 4 8 131.50%
9 110 3 8 113.25%
9 110 5 8 2.81%
9 110 4 7 192.90%
9 110 4 9 37.78%Ignored
Disliked{quote} Hmm, this looks as if the system, if it's not a random result, has indeed been trained on some seasonal effect. A=9 is the opening time in Europe. When the system is trained on the daily MFE/MAE, 7 or 8 would be the best D value. If we assume that this is the edge of the system, can these two parameters then be eliminated from the parameter space? This would make data mining bias estimates a lot easier.Ignored
DislikedHello everybody, I'm using Machine Learning algorithms for trading since 2010, my current system trading on 1min charts have 8 intsruments EURUSD GBPUSD AUDUSD GBPJPY AUDJPY USDJPY XAUUSD US500, the training size is 20000 bars, retraining every 24h. So my question is: Because of the size of training set and multiple intstruments is data mining bias removed from my results ?? Krzysztof http://www.trade2win.com/boards/trad...-machines.htmlIgnored
Dislikedgood read on data mining bias http://www.priceactionlab.com/Blog/2...a-mining-bias/. Jo, what do you think of "Myth No. 1: Data-mining bias can be measured" here specially the part about generating random data and applying mining to that.Ignored
DislikedOK. From my experience I see that efficiency of my system depends of hard to say what ? There are days and weeks that it work well for all instruments (correlation ??) and days and weeks that it doesn't. Only explanation which comes to my mind is that when it doesn't signal to noise ratio is very low or randomness very high of the market. usually it occurs for all instruments together. Do you think it has something with data mining bias ?? KrzysztofIgnored
DislikedOf course you can measure data-mining bias, this is just basic statistical hypothesis testing, anyone who knows statistics will tell you that.Ignored
QuoteDisliked... such methods essentially rank the performance of an algo with respect to the performance of a large number of algos mined on random data and nothing more than that.
Disliked{quote} Can you explain us how you make this miracle? Machine learning is about infering a model from the data. Per definition you cannot measure how wrong your model is (=data mining bias) since you don't know the real underlying model; not even if there is one to be found. You can always perfectly fit any model to your data by increasing the allowed variance. An extreme example being EUR/USD is always 1.3000, plus or minus 7500 pips. As well as you can always make this variance as low as you want by increasing your tolerance to the bias (aka overfit)....Ignored
DislikedThis is what I mean:
1. curve fitting bias = how my model deviates from the general market model
2. data mining bias = whether the relationships found by my model within the historical data are spurious or notIgnored