Hidden Markov Model - Create Indicator & use in strategy

Arjun Whabi

Hi I'm trying to create a hidden markov model. I'd create the model as an indicator.
The model requires to be fitted to the data first, using the fit function. Which requires 'period' number of data points to be present.
I have created the indicator with a basic strategy template but I keep getting the error below.
I'd really appreciate if any one can help me with the code, whether its placed correctly. There's plenty of documentation for simple indicators but there's limited guidance in the documentation for any advanced models.

from future import (absolute_import, division, print_function, unicode_literals)

import datetime # For datetime objects
import os.path # To manage paths
import sys # To find out the script name (in argv[0])
import backtrader as bt
import numpy as np
from hmmlearn import hmm

Create Indicator

class HMM(bt.Indicator):
lines = ('q', 'p')
params = (('n', 500), ('states', 2) ,('period',100)) # n iterations and number of states

def __init__(self):
     self.addminperiod(self.params.period)
     #self._dlast = self.data(-1)  # get previous day value

def next(self):
    r = self.data.get(ago = 0, size = self.params.period)
    ret = bt.ind.PctChange(self.data, period=1)
    mdl2 = hmm.GaussianHMM(n_components=2, n_mix=1, covariance_type='diag', covars_prior=0.01,
                               algorithm='viterbi',n_iter=100)

    # Fits the model till the t-1 observation
    mdl2.fit(self.data0,lengths = None)
    # print("Model Score : " , score  )
    #score = mdl2.score(ret, lengths=None)

    self.lines.q = q = (mdl2.predict(ret, lengths=None))

    a = (mdl2.transmat_)
    a0j = a[0, :]  # State 0 to 0, or 0 to 1
    a1j = a[1, :]  # State 1 to 0, or 1 to 1

        # print("Trans Probaility:  " , a)
        # print("If Current State =0, then TransProb = ", a0j)
        # print("If Current State =1, then TransProb = ",a1j)
    q0 = np.where(a0j == a0j.max())
    q1 = np.where(a1j == a1j.max())
        # If Current predicted state is 0, then q0[0] Returns the next State Value with highest Trans Probability
    if q[-1] == 0:
        self.lines.p = q0[0]
        print("Q=0, thus Next Predicted State  ", self.lines.p)
            # If CUrrent predicted state is 1, then q1[1] Returns the next State Value with highest Trans Probability
    if q[-1] == 1:
        self.lines.p = q1[0]
        print("Q=0, thus Next Predicted State  ", self.lines.p)

Create a Stratey

class TestStrategy(bt.Strategy):
params = {'n': 500, 'states': 2 , 'period': 500}

def log(self, txt, dt=None):
    ''' Logging function for this strategy'''
    dt = dt or self.datas[0].datetime.date(0)
    print('%s, %s' % (dt.isoformat(), txt))

def __init__(self):
    # Keep a reference to the "close" line in the data[0] dataseries
    self.dataclose = self.datas[0].close

    # Add an indicator
    self.indicator = HMM(500, self.data0, 100)



def next(self):
    # Simply log the closing price of the series from the reference
    self.log('Close, %.2f' % self.dataclose[0])

    if self.dataclose[0] < self.dataclose[-1]:
        # current close less than previous close

        if self.dataclose[-1] < self.dataclose[-2]:
            # previous close less than the previous close

            # BUY, BUY, BUY!!! (with all possible default parameters)
            self.log('BUY CREATE, %.2f' % self.dataclose[0])
            self.buy()

if name == 'main':
# Create a cerebro entity
cerebro = bt.Cerebro()

# Add a strategy
cerebro.addstrategy(TestStrategy)

# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, 'D:/data/GLD.txt')

# Create a Data Feed
data = bt.feeds.GenericCSVData(dataname=datapath ,
    fromdate=datetime.datetime(2005, 1, 1), todate=datetime.datetime(2015, 12, 31),
    nullvalue=0.0, headers=True,dtformat=('%m/%d/%Y'),tmformat=('%H.%M'),seperator=",",datetime=0,time=-1,
    open=2,high=3,low=4,close=5,volume=6,openinterest=7 # reverse=True,
)

# Add the Data Feed to Cerebro
cerebro.adddata(data)

# Set our desired cash start
cerebro.broker.setcash(100000.0)

# Print out the starting conditions
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
#print('Close: %.2f' % self.dataclose())
# Run over everything
cerebro.run()

# Print out the final result
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

Results in the following Error:

D:\WinPython-3670\backtrader\Scripts\python.exe D:/WinPython-3670/backtrader/HMM_indicator.py
Starting Portfolio Value: 100000.00
Traceback (most recent call last):
File "D:/WinPython-3670/backtrader/HMM_indicator.py", line 113, in <module>
cerebro.run()
File "D:\WinPython-3670\backtrader\backtrader\cerebro.py", line 1127, in run
runstrat = self.runstrategies(iterstrat)
File "D:\WinPython-3670\backtrader\backtrader\cerebro.py", line 1217, in runstrategies
strat = stratcls(*sargs, **skwargs)
File "D:\WinPython-3670\backtrader\backtrader\metabase.py", line 88, in call
_obj, args, kwargs = cls.doinit(_obj, *args, **kwargs)
File "D:\WinPython-3670\backtrader\backtrader\metabase.py", line 78, in doinit
_obj.init(*args, **kwargs)
File "D:/WinPython-3670/backtrader/HMM_indicator.py", line 66, in init
self.indicator = HMM(500, self.data0, 100)
File "D:\WinPython-3670\backtrader\backtrader\indicator.py", line 53, in call
return super(MetaIndicator, cls).call(*args, **kwargs)
File "D:\WinPython-3670\backtrader\backtrader\metabase.py", line 88, in call
_obj, args, kwargs = cls.doinit(_obj, *args, **kwargs)
File "D:\WinPython-3670\backtrader\backtrader\metabase.py", line 78, in doinit
_obj.init(*args, **kwargs)
TypeError: init() takes 1 positional argument but 2 were given

Process finished with exit code 1

ab_trader

I might be wrong, but you call your indicator with three arguments:

self.indicator = HMM(500, self.data0, 100)

but based on your implementation it doesn't need any arguments:

class HMM(bt.Indicator):

def __init__(self):
     self.addminperiod(self.params.period)

You need to describe these arguments in the __init()__.

backtrader

The poster is trying to assign a value to the declared params, which are barely readable due to the lack of formattting.

self.myindicator = MyIndicator(n=xxxx)

Because each parameter has a name.

ab_trader

@backtrader I see, missed during first look.

It was a chat here some time ago. I am not able to find now, did you remove it?

Arjun Whabi

@backtrader Hi What should I do ? Any help would be greatly appreciated ?
Would you like me to clean up the code and report ?

I 've searched the community forum and web...I cannot understand which params need need to be initiated/declared under which section?

every article has the user doing it differently.

backtrader

@arjun-whabi said in Hidden Markov Model - Create Indicator & use in strategy:

There's plenty of documentation for simple indicators but there's limited guidance in the documentation for any advanced models.

Sorry, but the trees are not letting you see the forest. This has nothing to do with advanced, simple or rocket science models. It's about the arguments you are passing and how you are passing them.

It's clear in the error message

@arjun-whabi said in Hidden Markov Model - Create Indicator & use in strategy:

TypeError: __init__() takes 1 positional argument but 2 were given

@arjun-whabi said in Hidden Markov Model - Create Indicator & use in strategy:

I cannot understand which params need need to be initiated/declared under which section?

You apparently already understand it.

@arjun-whabi said in Hidden Markov Model - Create Indicator & use in strategy:

params = (('n', 500), ('states', 2) ,('period',100))  # n iterations and number of states

But you are expecting magic

@arjun-whabi said in Hidden Markov Model - Create Indicator & use in strategy:

    # Add an indicator
    self.indicator = HMM(500, self.data0, 100)

How should the indicator know what the usage of 500 and 100 is and to what has to be assigned to?

My suggestion. Read this:

Docs - Platform Concepts - Parameters

And probably also this section within the same page

Docs - Platform Concepts - Data Feeds - Passing them around

Arjun Whabi

@backtrader thanks for the help and the links.
I made the mistake that I was not naming the params in the strategy section. Once I named self.p.n , self.p.states and so on it worked.

I'm stuck again in the below bt.indicator section:
I'm required to convert the price to an array before fitting it to the model.

class HMM(bt.Indicator):
lines = ('q', 'p')
params = (('n', 500), ('states', 2) ,('period',500)) # n iterations and number of states

def __init__(self):
     self.addminperiod(self.params.period)

def next(self):
    r = np.array(self.data.get( size=self.params.period))
    print(' type r : ' , type(r))
    print(' len r : ' , len(r))
    print(' r shape: ', r.shape)
    ret = r.reshape(-1,1)

    print(ret)

    mdl2 = hmm.GaussianHMM(n_components=self.p.states, covariance_type='diag', covars_prior=0.01,
                           algorithm='viterbi', n_iter=self.p.n)

    # Fits the model 
    mdl2.fit(ret,lengths = None)

it throws the following error:

Traceback (most recent call last):
File "D:/WinPython-3670/backtrader/HMM_indicator.py", line 117, in <module>
cerebro.run()
File "D:\WinPython-3670\backtrader\backtrader\cerebro.py", line 1127, in run
runstrat = self.runstrategies(iterstrat)
File "D:\WinPython-3670\backtrader\backtrader\cerebro.py", line 1293, in runstrategies
self._runonce(runstrats)
File "D:\WinPython-3670\backtrader\backtrader\cerebro.py", line 1652, in _runonce
strat._once()
File "D:\WinPython-3670\backtrader\backtrader\lineiterator.py", line 297, in _once
indicator._once()
File "D:\WinPython-3670\backtrader\backtrader\lineiterator.py", line 317, in _once
self.oncestart(self._minperiod - 1, self._minperiod)
File "D:\WinPython-3670\backtrader\backtrader\indicator.py", line 124, in oncestart_via_nextstart
self.nextstart()
File "D:\WinPython-3670\backtrader\backtrader\lineiterator.py", line 347, in nextstart
self.next()
File "D:/WinPython-3670/backtrader/HMM_indicator.py", line 40, in next
self.lines.q = q = (mdl2.predict(ret, lengths=None))
File "D:\WinPython-3670\backtrader\backtrader\lineseries.py", line 79, in set
value = value(0)
TypeError: 'numpy.ndarray' object is not callable

Any suggestions ?

Arjun Whabi

@backtrader Sorry just moving over from TradeStation platform to python so I'm not the best with the language. Appreciate your help a lot

backtrader

You should take 2 (or 3) steps back and develop a simple indicator.

From the documentation - Docs - Indicator Development (you should read the entire doc)

    def next(self):
        self.lines.dummyline[0] = max(0.0, self.params.value)

Now look for that in your code.