DJI Market State Analysis using the Cruz Fitting Algorithm

Based upon the Cruz stochastic fitting algorithm and the colab code implementing a Hidden Markov Model, let’s predict the DJI stock returns from 1970 to 2022 and detect three states such as bull (green), sideways (yellow) and bear (red) markets.

Let’s set the working directory YOURPATH

import os
os.chdir(‘YOURPATH’)
os. getcwd()

and import/install the following libraries

!pip install hmmlearn

Successfully installed hmmlearn-0.2.8

import yfinance as yf
from datetime import date
import numpy as np
import pandas as pd
from hmmlearn import hmm
import matplotlib.pyplot as plt
plt.rcParams[‘figure.figsize’] = [16, 12]

Let’s download the historical dataset

stonks = [‘DJI’]
df = yf.download(stonks)

[*********************100%***********************]  1 of 1 completed

and create the new dataset

df[‘Log’] = np.log(df[‘Close’])
df[‘Returns’] = df[‘Log’].pct_change()
df[‘Range’] = (df[‘High’] / df[‘Low’]) – 1
df.dropna(inplace=True)
data = df[[‘Returns’, ‘Range’]]

data.head()

data.tail()

Let’s fit this dataset using the stochastic model hmm.GaussianHMM

model = hmm.GaussianHMM(n_components=3, covariance_type=’full’)
model.fit(data)

GaussianHMM(covariance_type='full', n_components=3)

model.score(data)

147312.57522273296

Let’s predict the market states

states = model.predict(data)

and plot the state histogram

pd.Series(states).hist()
plt.savefig(“cruzhiststates.png”)

Let’s plot the stock price coloured by the detected 3 states

state1,state2,state3 = [],[],[]
i = 0
for state in states:
if state == 0:
state1.append(df[‘Close’][i])
state2.append(float(‘nan’))
state3.append(float(‘nan’))
if state == 1:
state2.append(df[‘Close’][i])
state1.append(float(‘nan’))
state3.append(float(‘nan’))
if state == 2:
state3.append(df[‘Close’][i])
state2.append(float(‘nan’))
state1.append(float(‘nan’))
i += 1
plt.plot(state1,color=’green’)
plt.plot(state2,color=’yellow’)
plt.plot(state3,color=’red’)
plt.savefig(“cruzplotstates.png”)

Here, the horizontal axis represents the time index in the range [1970-01-05 – 2022-03-02].

The above plot is a stochastic indicator of the underlying market state: bull (green), sideways (yellow) and bear (red) markets.

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