How to Create A Bitcoin Price Prediction Model In Python

Cryptocurrencies, particularly Bitcoin, have gained significant attention in recent years due to their volatile nature and potential for substantial returns. In this tutorial, we’ll walk through creating a Bitcoin price prediction model using Python. We’ll leverage historical price data and a simple linear regression algorithm to predict future prices. Additionally, we’ll explore potential enhancements to improve the model’s accuracy.

Getting Started

Before we dive into the code, ensure you have Python installed on your system along with the necessary libraries. We’ll be using pandas for data manipulation, yfinance for fetching historical price data, and scikit-learn for building our regression model.

You’ll need to make sure you have these libraries installed in your dev environment. You can run the following commands in your terminal.

pip install pandas
pip install yfinance
pip install scikit-learn

Now let’s add in our imports for our script. You can name the file anything you want, we named ours “bitcoin_prediction.py”.

import pandas as pd
import yfinance as yf
from sklearn.linear_model import LinearRegression

Data Acquisition

The first step is to download historical price data for Bitcoin. We’ll use Yahoo Finance’s API through the yfinance library to fetch this data.

The library offers an easy-to-use API that enables users to retrieve historical market data for a specific time period, specific financial instruments (e.g., stocks, cryptocurrencies), and at various intervals (daily, weekly, monthly). It also provides functionalities to fetch data for multiple instruments simultaneously and supports data adjustments like dividends and stock splits.

Overall, yfinance is a valuable tool for financial analysis, algorithmic trading, and building predictive models by providing access to historical market data directly within Python code, eliminating the need for manual data scraping or using other APIs.

Here is the relevant code:

symbol = "BTC-USD"
start_date = pd.Timestamp.today() - pd.Timedelta(days=365*10)
end_date = pd.Timestamp.today()
df = yf.download(symbol, start=start_date, end=end_date)

Here, we’re fetching data for the past 10 years using the symbol “BTC-USD”. Adjust the start_date and end_date as needed. Additionally, you can change the symbol pair to a different coin entirely if you so choose.

Data Processing

Next, we’ll preprocess the data to prepare it for model training. For simplicity, we’ll use the opening, high, and low prices as features and the closing price as the target variable.

This is a crucial step for creating our Bitcoin price prediction model because it allows us to define the parameters on which we can train the model on.

Here is that relevant code:

X = df[['Open', 'High', 'Low']]
y = df['Close']

Next comes the fun part… We’re going to take the data we got from the earlier steps and use that to train our model so that it can make accurate price predictions.

training the Model

We’ll use a linear regression model for our prediction task. This model assumes a linear relationship between the input features and the target variable.

Linear regression assumes that there exists a linear relationship between the input features and the target variable. In other words, it assumes that changes in the target variable are directly proportional to changes in the input features. This assumption means that the relationship between the variables can be represented by a straight line in a two-dimensional space or a plane in a higher-dimensional space.

In practical terms, when we say we’re using linear regression for prediction and assuming a linear relationship, it implies that we believe the target variable (e.g., Bitcoin price) can be reasonably approximated by a linear combination of the input features (e.g., opening, high, low prices). While this assumption might not always hold true in real-world scenarios, linear regression can still provide valuable insights and predictions, especially when the relationship between variables is approximately linear or when used in conjunction with other techniques for feature engineering and model evaluation.

Now here is the code for this:

model = LinearRegression()
model.fit(X, y)

Output the Predictions

Now that we have a trained model, let’s make predictions for future Bitcoin prices. This next part of code ties everything together and prints out the price prediction based on the last 10 years of Bitcoin price data.

last_row = df.tail(1)
X_pred = last_row[['Open', 'High', 'Low']]
date_pred = last_row.index[0] + pd.Timedelta(days=1) # predict the next day's price
y_pred = model.predict(X_pred)
print('Predicted price on', date_pred.strftime('%Y-%m-%d'), ':', y_pred[0])

In this snippet, we’re predicting the price for the next day using the last available data point.

Here is the full code:

import pandas as pd
import yfinance as yf
from sklearn.linear_model import LinearRegression

# Download historical price data
symbol = "BTC-USD"
start_date = pd.Timestamp.today() - pd.Timedelta(days=365*10)
end_date = pd.Timestamp.today()
df = yf.download(symbol, start=start_date, end=end_date)

# Preprocess data
X = df[['Open', 'High', 'Low']]
y = df['Close']

# Train model
model = LinearRegression()
model.fit(X, y)

# Make predictions
last_row = df.tail(1)
X_pred = last_row[['Open', 'High', 'Low']]
date_pred = last_row.index[0] + pd.Timedelta(days=1) # predict the next day's price
y_pred = model.predict(X_pred)
print('Predicted price on', date_pred.strftime('%Y-%m-%d'), ':', y_pred[0])

Improvements & Considerations

While our initial model provides a basic prediction, there are several ways we can enhance its performance:

1. Feature Engineering: Include additional relevant features such as trading volume, moving averages, or sentiment analysis from news articles or social media.
2. Advanced Models: Experiment with more sophisticated machine learning algorithms such as decision trees, random forests, or neural networks.
3. Hyperparameter Tuning: Optimize the parameters of the chosen model to improve its accuracy.
4. Time Series Analysis: Utilize techniques specifically designed for time series data, such as ARIMA or LSTM models.
5. Regularization: Apply regularization techniques like Ridge or Lasso regression to prevent overfitting.

Wrapping Up

In this tutorial, we’ve learned how to create a simple Bitcoin price prediction model using Python. We fetched historical price data, trained a linear regression model, and made predictions for future prices. Additionally, we explored ways to enhance the model’s accuracy by incorporating advanced techniques and additional features. Remember, while predictive modeling can provide valuable insights, it’s essential to exercise caution and consider various factors before making financial decisions based on model predictions.