Stationary distribution of AMZN stock
You can use Markov Chains and stationary (steady-state) distributions to understand a system's tendencies and forecast the likelihood of a given event. Suppose you are trying to understand the tendencies of Amazon's stock (ticker AMZN). Using the daily stock data, build a simple Markov Chain and calculate the steady-state distribution.
You will want to think through the following when drafing your solution:
- Define the Markov Chain's events (e.g. open price on day X is higher than open price on day Y)
- Based on the event, you will need to calculate the probability of each event
- Calculate the steady-state distribution, you can do this by using linear algebra or creating a simulation
To help get you started, the code below installs yfinance to your Colab runtime and loads in the historical stock data. You can also make a copy of this Google Colab notebook to get started!
# import yahoo finance !pip install yfinance # import packages import logging import sys import pandas as pd import datetime from time import sleep, strftime, time import math import yfinance as yf # get dataframe -- the timeframe you analyze is up to you! data = yf.download("AMZN", start="2020-01-02", end="2020-06-01").reset_index()
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