Machine learning + backtrader

Hi,
Do you have on your mind to add any machine learning library in backtrader or any ml sample?
Rgds,
Jj

Not at the moment.

Ok, thanks. In the future if you consider it please let me know. I would like to suggest some ideas

Please suggest some ideas. This is an area I plan to go in near future after getting my head fully around current backtrader capability.

What's stopping you from doing any machine learning? Why cant you just import the libraries, run the models & execute based on the parameters?
There's plenty of examples and ideas on Quantopian in python:
https://www.quantopian.com/posts/machinelearningonquantopianpart3buildinganalgorithm

Great thread. Thanks for sharing.

Hi,
In the first phase the kind of things that i want to learn is the indicators that i should use depend on the asset class
https://www.linkedin.com/pulse/selectingyourindicatorsmachinelearningtadslaff
Finally qlearning Could be the target

At this point machine learning opportunities are endless from both ML feature building and from ML algo selecting points of view. imho implementing and further support of special ML functions/procedures into bt will be just wasting of time.

hi,
I was thinking more in defining a style guide that how to combine bt with the best ML libraries. For example in the regression linear example, the use is not direct .

@junajo10 I maybe we could give it a try if you have an article with an example...

Here, i have found a case that could be a good starting point.
https://www.quantopian.com/posts/582de686358fe9440b0007f2#587e9dae9ea10879ef9ecb16
Kalman filter : Pairs trading

@junajo10 Thanks Junajo10 ! I need some documentation to understand this filter and it is related to ML ?
Do you have any tips ?
Thanks,
Remroc

https://www.quantopian.com/posts/quantopianlectureserieskalmanfilters
https://www.quantstart.com/articles/kalmanfilterbasedpairstradingstrategyinqstrader

From 101 (csv, pandas) to some ML usage and only for trading + code, not just ML + code or trading theory.
https://www.udacity.com/course/machinelearningfortradingud501

thanks guys. really good stuff :thumbsup:

Hi guys,
Here is an implementation of Kalman filter in a quantopian algo :
https://www.quantopian.com/posts/erniechansewaslashewcpairtradewithkalmanfilterLet's tackle this...
Remroc

For me the problem is to migrate this code to backtrader .

@junajo10 said in Machine learning + backtrader:
https://www.quantstart.com/articles/kalmanfilterbasedpairstradingstrategyinqstrader
Here is a version of the
KalmanFilter
described in that article (seems 1to1 identical to the one that implements Ernie Chan's strategy)import numpy as np import pandas as pd class KalmanFilter(bt.Indicator): _mindatas = 2 # needs at least 2 data feeds lines = ('et', 'sqrt_qt',) params = dict( delta=1e4, vt=1e3, ) def __init__(self): self.wt = self.p.delta / (1  self.p.delta) * np.eye(2) self.theta = np.zeros(2) self.P = np.zeros((2, 2)) self.R = None self.d1_prev = self.data1(1) # data1 yesterday's price def next(self): F = np.asarray([self.data0[0], 1.0]).reshape((1, 2)) y = self.d1_prev[0] if self.R is not None: # self.R starts as None, self.C set below self.R = self.C + self.wt else: self.R = np.zeros((2, 2)) yhat = F.dot(self.theta) et = y  yhat # Q_t is the variance of the prediction of observations and hence # \sqrt{Q_t} is the standard deviation of the predictions Qt = F.dot(self.R).dot(F.T) + self.p.vt sqrt_Qt = np.sqrt(Qt) # The posterior value of the states \theta_t is distributed as a # multivariate Gaussian with mean m_t and variancecovariance C_t At = self.R.dot(F.T) / Qt self.theta = self.theta + At.flatten() * et self.C = self.R  At * F.dot(self.R) # Fill the lines self.lines.et[0] = et self.l.sqrt_qt[0] = sqrt_Qt
The filter perse is not directly used in the decision making process. Knowing (from the article) which relations
et
andsqrt_qt
need to have to trigger signals, the behavior can be packed in another indicatorclass KalmanSignals(bt.Indicator): _mindatas = 2 # needs at least 2 data feeds lines = ('long', 'short',) def __init__(self): kf = KalmanFilter(self.data0, self.data1) et, sqrt_qt = kf.lines.et, kf.lines.sqrt_qt self.lines.long = et < 1.0 * sqrt_qt # longexit is et > 1.0 * sqrt_qt ... the opposite of long self.lines.short = et > sqrt_qt # shortexit is et < sqrt_qt ... the opposite of short
And the signals can be easily applied in the strategy
next
method for an easy to follow logicclass St(bt.Strategy): params = ( ) def __init__(self): self.ksig = KalmanSignals(self.data0, self.data1) def next(self): size = self.position.size if not size: if self.ksig.long: self.buy() elif self.ksig.short: self.sell() elif size > 0: if not self.ksig.long: self.close() elif not self.ksig.short: # implicit size < 0 self.close()
The difficult thing here is to select assets for which it makes sense to apply the filter.

Thanks @backtrader .
@backtrader said "The difficult thing here is to select assets for which it makes sense to apply the filter".
For solving thsi problem use these tecniques:
Augmented Dickey Fuller
Hurst exponent
Half Life

@backtrader, add this filter to main package.