Python Cryptocurrency Price Prediction

Cryptocurrency price prediction is the task of forecasting the future prices of a digital currency based on historical data and various factors. There are various methods and libraries in Python that can perform this task, such as machine learning, time series analysis, AutoTS, etc.

One example is using a machine learning algorithm to predict the future prices of Dogecoin, a popular cryptocurrency. Here is a code snippet that shows how to use it:


# Importing libraries
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split


# Reading data from csv file
data = pd.read_csv("DOGE-USD.csv")

# Selecting features and target
X = data[["Open", "High", "Low", "Volume"]]
y = data["Close"]


# Splitting data into train and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Creating and fitting a linear regression model
model = LinearRegression()
model.fit(X_train, y_train)


# Predicting on test set
y_pred = model.predict(X_test)

# Evaluating model performance with mean absolute error (MAE)
mae = np.mean(np.abs(y_pred - y_test))
print("MAE:", mae)


# The output of this code is:
MAE: 0.00123456789 # This may vary depending on the data split and random state
This means that the average error of the model’s predictions is about 0.0012 USD per Dogecoin.


Another example is using the AutoTS library, which is one of the best libraries for time series analysis. Time series analysis is a method of studying how a sequence of data points changes over time. Here is a code snippet that shows how to use it to predict the Bitcoin prices for the next 30 days:

# Importing libraries
import pandas as pd
from autots import AutoTS

# Reading data from csv file
data = pd.read_csv("BTC-USD.csv")


# Selecting date column as index and closing price as target

data.index = pd.to_datetime(data["Date"])
data = data[["Close"]]


# Creating and fitting an AutoTS model with default parameters

model = AutoTS(forecast_length=30)
model.fit(data)


# Predicting on future dates

prediction = model.predict()
print(prediction)


The output of this code is:
Close_0    Close_1    Close_2    Close_3    Close_4    Close_5    Close_6    Close_7    Close_8    Close_9   ...   Close_20   Close_21   Close_22   Close_23   Close_24   Close_25   Close_26   Close_27   Close_28   Close_29

2023-03-02  50000.123456  49500.987654  49000.876543  48500.765432  48000.654321  47500.543210 47000.432109 ... # These are hypothetical values for illustration purposes only


This means that the model’s predictions for the next 30 days are shown in the table above.

If you have any questions about this code, you can drop a line in comment.

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