1. Data Retrieval
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import requests
import pandas as pd
def fetch_crypto_data(coin_id):
url = f"
https://api.coingecko.com/api/v3/coins/{coin_id}/market_chart"
params = {'vs_currency': 'usd', 'days': '30'}
response = requests.get(url, params=params)
data = response.json()
return pd.DataFrame(data['prices'], columns=['timestamp', 'price'])
# Example of use
bitcoin_data = fetch_crypto_data('bitcoin')
2. Historical Analysis
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def calculate_roi(dataframe):
start_price = dataframe['price'].iloc[0]
end_price = dataframe['price'].iloc[-1]
return (end_price - start_price) / start_price * 100
# Example of use
bitcoin_roi = calculate_roi(bitcoin_data)
3. Forecasting (Simple Example)
pythonCopy code
from statsmodels.tsa.arima_model import ARIMA
def predict_prices(dataframe):
model = ARIMA(dataframe['price'], order=(5,1,0))
model_fit = model.fit(disp=0)
forecast = model_fit.forecast(steps=5)[0]
return forecast
# Example of use
bitcoin_forecast = predict_prices(bitcoin_data)
4. Alerts
Implementing a notification system like sending emails is too complex to detail here.
5. User Interface
Implementing a graphical or command line interface is a project in itself and depends on your specific preferences and needs.
6. Data Saving
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def save_to_csv(dataframe, filename):
dataframe.to_csv(filename, index=False)
# Example of use
save_to_csv(bitcoin_data, 'bitcoin_data.csv')
This script is a starting point. It requires modifications, improvements, and adjustments according to your specific needs, as well as the installation of the necessary Python libraries. Additionally, it is crucial to test and validate each part of the script to ensure its reliability.
