Machine learning in trading: theory, models, practice and algo-trading - page 2811
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Am I the only one who hears the words "algo-trading" and "alcohol-trading" sound almost identical?
kind of makes you wonder
set date
the first 10 lines are price information, if you want to create new features, if not they should be removed from the training.
last line - target
divide the selection in half for traine and test.
on Forest without any tuning I get on new data
Confusion Matrix and Statistics Reference Prediction -1 0 1 -1 2428 453 23 0 597 3295 696 1 14 448 2046 Overall Statistics Accuracy : 0.7769 95% CI : (0.7686, 0.785) No Information Rate : 0.4196 P-Value [Acc > NIR] : < 2.2e-16 Kappa : 0.6567 Mcnemar's Test P-Value : 2.565e-16 Statistics by Class: Class: -1 Class: 0 Class: 1 Sensitivity 0.7989 0.7853 0.7400 Specificity 0.9316 0.7772 0.9361 Pos Pred Value 0.8361 0.7182 0.8158 Neg Pred Value 0.9139 0.8335 0.9040 Prevalence 0.3039 0.4196 0.2765 Detection Rate 0.2428 0.3295 0.2046 Detection Prevalence 0.2904 0.4588 0.2508 Balanced Accuracy 0.8653 0.7812 0.8381
on HGbusta with new chips I got Akurashi 0.83.
I wonder if it is possible to achieve 0.9 Akurasi ?
?? Where did I say that?
Here.
Here.
For me it's about a specific sample that has not been trained without manipulating the data.
Correlation filtering is one simple way to move the training forward.date set
Tried it, it doesn't work, it's all about the signs again.
If you are interested, I'm throwing a multicurrency tester constructor with spread, primitive lot and a hint of opening closing positions with fractional lot.
For the tester to work, you need to prepare a dataframe with ['open', 'spread] columns, and also throw a numpy array of format x (n,2) with forecasts of buy/sell probabilities for each new bar into signal. The tester works from a loop, below is an example of initialising the use of the tester
trading logic and lot can be adjusted in the transcript_sig method of the Symbol object
The results of the test lie in the trade_history_data dictionary , for the overall test and trade_symbol_data of each symbol.
There are lists there, if anyone wants to optimise or change something - welcome).
You have to come up with some fun rewards there to capture the patterns. Otherwise, it will grind to the pseudo-optimum of any ph-i
It's all about q function and critics, an interesting topic....
it was discussed here more than a year ago, when I was writing RL algorithms.
I don't want to come back yet, and I already have a certain mixture of RL + supervised, I switched to author's schemes long ago.It was discussed here more than a year ago, when I was writing RL algorithms.
I don't want to go back yet, and neither do I.