Predicting Bitcoin Prices with Python: A Comparative Analysis of ARIMA, Linear Regression, and Naive Models Using Technical Indicators and Moving Averages
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ASSIGNMENT INSTRUCTIONS:
– Coding language Python
– The data set is from Yahoo Finance (Take the last five years in both ML models)
– the rest of the needed libraries to be used in the project is Pandas, Scikit-Learn, Keras, and TensorFlow
– Business problem or business question of the project is: predicting bitcoin prices with the least error to maximize returns and mitigate risk
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There are two different ML model selections, and I am not sure which one to go with, and I would like to further discuss it with the selected tutor:
The first two models are:
– ARIMA
– Use technical indicators features in linear regression
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The second two models are:
– ARIMA
– Naive model using moving average with length one
My price is t-1 and say this is my prediction (which will be today’s price)
Without knowing the past data just use today’s price to predict tomorrow
———–
What matters most here is to compare the results, analyze them, and highlight the areas where you could improve. Showcase where the prediction matches the real data and where it’s different. Backing it up with a literature review of course (there will be attached guidelines which have further details)
———–
Put the code in the appendix and save it in Jupiter notebook and include hashtags where you explain what you did in each step save it as a pdf
When writing the code in Python make sure to add a hashtag before the code where you explain what are you doing at this step briefly for example:
—-> # Download the Bitcoin price data from Yahoo Finance
—–>start_date = ‘2016-04-05’
—–> end_date = ‘2023-04-05’
Merge the coding file with the report and save them both in one pdf
if you wanted to mention the code in the report just write refer to page 2 in the appendix
———
I will attach the guidelines file as well.
HOW TO WORK ON THIS ASSIGNMENT (EXAMPLE ESSAY / DRAFT)
Kindly note,
In this project, we use Python machine-learning techniques to forecast Bitcoin prices. We will use the most recent five years of data from Yahoo Finance for both machine learning models in this project’s data set.
Scikit-Learn, Keras, Tensorflow, and Pandas are the libraries utilized in this project. Our goal is to predict Bitcoin prices with the least amount of error possible to maximize returns and reduce risk.
To examine and identify areas for improvement, we will compare the output of our two chosen machine-learning models. The first two models are ARIMA and use linear regression with technical indicator features. The next two models are a naive moving average model with length one and an ARIMA model. The latter forecast prices without knowledge of historical data by using today’s prices.
We will highlight the instances where the prediction and the actual data agree and when they don’t. We will do a literature study to support our analysis, and we will adhere to the additional requirements that are provided.
The code will be saved in a Jupyter Notebook and added as an appendix. To succinctly describe what we are doing in each stage, we will use hashtags. For instance, # From Yahoo Finance, download the Bitcoin price information. We’ll combine the report and the coding file into one PDF and save it. Refer to page 2 in the appendix if the code is mentioned in the report by writing “refer to page 2 in the appendix.”
I appreciate your reading.
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