Cryptocurrency Price Prediction with Machine Learning

You must have heard or invested in any cryptocurrency once in your life. It is a digital medium of exchange that is encrypted and decentralized. Many people use cryptocurrencies as a form of investing because it gives great returns even in a short period. Bitcoin, Ethereum, and Binance Coin are among the popular cryptocurrencies today. If you want to know how to predict the future prices of any cryptocurrency with machine learning, this article is for you. In this article, I will walk you through the task of cryptocurrency price prediction with machine learning using Python.

Cryptocurrency Price Prediction with Machine Learning

Predicting the price of cryptocurrencies is one of the popular case studies in the data science community. The prices of stocks and cryptocurrencies don’t just depend on the number of people who buy or sell them. Today, the change in the prices of these investments also depends on the changes in the financial policies of the government regarding any cryptocurrency. The feelings of people towards a particular cryptocurrency or personality who directly or indirectly endorse a cryptocurrency also result in a huge buying and selling of a particular cryptocurrency, resulting in a change in prices.

In short, buying and selling result in a change in the price of any cryptocurrency, but buying and selling trends depend on many factors. Using machine learning for cryptocurrency price prediction can only work in situations where prices change due to historical prices that people see before buying and selling their cryptocurrency. So, in the section below, I will take you through how you can predict the bitcoin prices (which is one of the most popular cryptocurrencies) for the next 30 days.

Cryptocurrency Price Prediction using Python

I’ll start the task of Cryptocurrency price prediction by importing the necessary Python libraries and the dataset we need. For this task, I will collect the latest Bitcoin prices data from Yahoo Finance, using the yfinance API. This will help you collect the latest data each time you run this code:

import pandas as pd
import yfinance as yf
import datetime
from datetime import date, timedelta
today = date.today()

d1 = today.strftime("%Y-%m-%d")
end_date = d1
d2 = date.today() - timedelta(days=730)
d2 = d2.strftime("%Y-%m-%d")
start_date = d2

data = yf.download('BTC-USD', 
                      start=start_date, 
                      end=end_date, 
                      progress=False)
data["Date"] = data.index
data = data[["Date", "Open", "High", "Low", "Close", "Adj Close", "Volume"]]
data.reset_index(drop=True, inplace=True)
print(data.head())
        Date         Open         High  ...        Close    Adj Close       Volume
0 2019-12-28  7289.031250  7399.041016  ...  7317.990234  7317.990234  21365673026
1 2019-12-29  7317.647461  7513.948242  ...  7422.652832  7422.652832  22445257702
2 2019-12-30  7420.272949  7454.824219  ...  7292.995117  7292.995117  22874131672
3 2019-12-31  7294.438965  7335.290039  ...  7193.599121  7193.599121  21167946112
4 2020-01-01  7194.892090  7254.330566  ...  7200.174316  7200.174316  18565664997

[5 rows x 7 columns]

In the above code, I have collected the latest data of Bitcoin prices for the past 730 days, and then I have prepared it for any data science task. Now, let’s have a look at the shape of this dataset to see if we are working with 730 rows or not:

data.shape
(731, 7)

So the dataset contains 731 rows, where the first row contains the names of each column. Now let’s visualize the change in bitcoin prices till today by using a candlestick chart:

import plotly.graph_objects as go
figure = go.Figure(data=[go.Candlestick(x=data["Date"],
                                        open=data["Open"], 
                                        high=data["High"],
                                        low=data["Low"], 
                                        close=data["Close"])])
figure.update_layout(title = "Bitcoin Price Analysis", 
                     xaxis_rangeslider_visible=False)
figure.show()
Cryptocurrency Price Prediction with Machine Learning

The Close column in the dataset contains the values we need to predict. So, let’s have a look at the correlation of all the columns in the data concerning the Close column:

correlation = data.corr()
print(correlation["Close"].sort_values(ascending=False))
Adj Close    1.000000
Close        1.000000
High         0.998933
Low          0.998740
Open         0.997557
Volume       0.334698
Name: Close, dtype: float64

Cryptocurrency Price Prediction Model

Predicting the future prices of cryptocurrency is based on the problem of Time series analysis. The AutoTS library in Python is one of the best libraries for time series analysis. So here I will be using the AutoTS library to predict the bitcoin prices for the next 30 days:

from autots import AutoTS
model = AutoTS(forecast_length=30, frequency='infer', ensemble='simple')
model = model.fit(data, date_col='Date', value_col='Close', id_col=None)
prediction = model.predict()
forecast = prediction.forecast
print(forecast)
                   Close
2021-12-28  57865.012345
2021-12-29  54259.592685
2021-12-30  53794.634938
2021-12-31  54365.964301
2022-01-01  55371.531945
2022-01-02  57220.503886
2022-01-03  57132.487546
2022-01-04  58021.727065
2022-01-05  58376.081818
2022-01-06  59931.323291
2022-01-07  60168.816716
2022-01-08  60617.974204
2022-01-09  58785.722512
2022-01-10  55180.302852
2022-01-11  54715.345105
2022-01-12  55286.674468
2022-01-13  56292.242112
2022-01-14  58141.214053
2022-01-15  58053.197713
2022-01-16  58942.437232
2022-01-17  59296.791985
2022-01-18  60852.033458
2022-01-19  61089.526883
2022-01-20  61538.684371
2022-01-21  59706.432679
2022-01-22  56101.013019
2022-01-23  55636.055272
2022-01-24  56207.384635
2022-01-25  57212.952279
2022-01-26  59061.924220

So this is how you can use machine learning to predict the price of any cryptocurrency by using the Python programming language.

Summary

Buying and selling result in a change in the price of any cryptocurrency, but buying and selling trends depend on many factors. Using machine learning for cryptocurrency price prediction can only work in situations where prices change due to historical prices that people see before buying and selling their cryptocurrency. I hope you liked this article on cryptocurrency price prediction with machine learning using Python. Feel free to ask your valuable questions in the comments section below.

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Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
Articles: 1207

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