Krzysiaczek99
Well-known member
- Messages
- 430
- Likes
- 1
summary
Hello,
As I have a lot of requests what actually was done in this thread would like to summarize this thread i.e. what was done during this almost 6 years by me.
- Initial TradeFX system was extended and redesigned. The BUY orders were added, bugs and future leaks removed and proper out of sample testing implemented. Than additionl scripts were designed to combine separate BUY and SELL signals as an ensemble trading signal. The evaluation of the system was done using portfolio of 8 symbols i.e. 1min data EURUSD, GBPUSD, AUDUSD, XAUUSD, GBPJPY, AUDJPY and SP500 on daily basis, than all daily results from all symbols were combined in one result. Measures used for evaluation was a mix of Machine Learning standard measures (Precision, Accuracy, Recall) and strategy performance measures like, Profit, Profit Factor, Number of pairs symbol/algo profitable or per trade profit.
- On the MT4 side, the EA was redesigned and adapted to handle multiple symbols so in total 8 EAs were running in pararell, each of them trading different symbol and communicating with different MATLAB engine.
- In total around 21 different ML algorithms were integrated and tried to find out the best performing ones for HF financial data. Algorithms were integrated from Theano, Weka and different MATLAB toolboxes. Algorithms tried were:
LIBSVM, LIBLINEAR DBN, RBM, TDBN, PegasosLR, PegasosSVM, Ridor, Ripper, J48, Tlogistic_cg, Tlogistgic_sgd, Slogistic, SDAE, LMT, CHIRP, LDKL, ELM, SVMPerf, ISSVM, DCSVM.
- Different preprocessing methods were tried like rebinning of data, filtering of data by Ehlers filters, different feature selection and scaling methods.
- Different strategies which were creating different input data and labels were also created and tested.
- Different retraining periods (every 24, 12, 8 and 4h) and different lengths of training data was also checked.
- Differen Portfolios were created using QuantAnalyzer which dramatically was improving performance however it was not possible to test it in 'Walk Forward' way due to limits in QuantAnalyzer design.
As a conclusion I can say that thats financial data seems to be very strong non stationary which cause very high variance of output and non stable performance, for some weeks this system was very profitable for some not (with portfolio equal weighted).
For sure it is possible to make portfolio optimization (Markowitz way) which would improve the performance however i didn't have a platform to properly test it, maybe when I will find time I will integrate portfolio optimization MATLAB solution with the system and measure possible improvement of performance.
Krzysztof
Hello,
As I have a lot of requests what actually was done in this thread would like to summarize this thread i.e. what was done during this almost 6 years by me.
- Initial TradeFX system was extended and redesigned. The BUY orders were added, bugs and future leaks removed and proper out of sample testing implemented. Than additionl scripts were designed to combine separate BUY and SELL signals as an ensemble trading signal. The evaluation of the system was done using portfolio of 8 symbols i.e. 1min data EURUSD, GBPUSD, AUDUSD, XAUUSD, GBPJPY, AUDJPY and SP500 on daily basis, than all daily results from all symbols were combined in one result. Measures used for evaluation was a mix of Machine Learning standard measures (Precision, Accuracy, Recall) and strategy performance measures like, Profit, Profit Factor, Number of pairs symbol/algo profitable or per trade profit.
- On the MT4 side, the EA was redesigned and adapted to handle multiple symbols so in total 8 EAs were running in pararell, each of them trading different symbol and communicating with different MATLAB engine.
- In total around 21 different ML algorithms were integrated and tried to find out the best performing ones for HF financial data. Algorithms were integrated from Theano, Weka and different MATLAB toolboxes. Algorithms tried were:
LIBSVM, LIBLINEAR DBN, RBM, TDBN, PegasosLR, PegasosSVM, Ridor, Ripper, J48, Tlogistic_cg, Tlogistgic_sgd, Slogistic, SDAE, LMT, CHIRP, LDKL, ELM, SVMPerf, ISSVM, DCSVM.
- Different preprocessing methods were tried like rebinning of data, filtering of data by Ehlers filters, different feature selection and scaling methods.
- Different strategies which were creating different input data and labels were also created and tested.
- Different retraining periods (every 24, 12, 8 and 4h) and different lengths of training data was also checked.
- Differen Portfolios were created using QuantAnalyzer which dramatically was improving performance however it was not possible to test it in 'Walk Forward' way due to limits in QuantAnalyzer design.
As a conclusion I can say that thats financial data seems to be very strong non stationary which cause very high variance of output and non stable performance, for some weeks this system was very profitable for some not (with portfolio equal weighted).
For sure it is possible to make portfolio optimization (Markowitz way) which would improve the performance however i didn't have a platform to properly test it, maybe when I will find time I will integrate portfolio optimization MATLAB solution with the system and measure possible improvement of performance.
Krzysztof