Top10cryptoIndex

# This Python script fetches real-time cryptocurrency data from CoinMarketCap and CryptoCompare APIs.
# It calculates various indices (market cap index, volume index, total index) based on the retrieved data.
# The script then generates various visualizations (pie charts, bar charts) to analyze the distribution of these indices among the top cryptocurrencies.
# Finally, it exports the results to CSV and HTML files for further analysis.

# Start with:
# !pip install requests pandas urllib3 matplotlib logging tenacity cryptocompare pycoinmarketcap tabulate
# Then copy and paste or run the code below:
import requests
import pandas as pd
from urllib.parse import urlencode
from datetime import datetime
import matplotlib.pyplot as plt
import logging
from tenacity import retry, stop_after_attempt, wait_fixed

# API configuration (Replace with your own API Keys)

# To use these APIs, you will need to obtain your own API keys from the following services:

# 1. CoinMarketCap
#   - Visit https://pro.coinmarketcap.com/account/signup
#   - Create a free account or choose a paid plan depending on your usage needs.
#   - Once registered, navigate to your API Keys section in your account settings.
#   - Generate a new API Key and copy it for later use.

CMC_API_URL = "https://pro-api.coinmarketcap.com/v1/cryptocurrency/listings/latest"
CMC_API_KEY = "YOUR_COINMARKETCAP_API_KEY"  # Replace with your actual key
CMC_HEADERS = {
    "Accepts": "application/json",
    "X-CMC_PRO_API_KEY": CMC_API_KEY,
}
CMC_PARAMS = {"limit": 100}

# 2. CryptoCompare
#   - Visit https://www.cryptocompare.com/
#   - Navigate to your profile settings and locate the API Keys section.
#   - Generate a new API Key and copy it for later use.

CRYPTOCOMPARE_API_URL = "https://min-api.cryptocompare.com/data/top/mktcapfull"
CRYPTOCOMPARE_API_KEY = "YOUR_CRYPTOCOMPARE_API_KEY"  # Replace with your actual key
CRYPTOCOMPARE_HEADERS = {
    "Authorization": f"Apikey {CRYPTOCOMPARE_API_KEY}",
}


# Function to retrieve top coins from CryptoCompare and format the data
def get_top_coins_cc(reliable_coins):
    """Fetches top cryptocurrencies from CryptoCompare API and formats the data.

    Args:
        reliable_coins (list): A list of reliable cryptocurrency symbols.

    Returns:
        pandas.DataFrame: A DataFrame containing formatted cryptocurrency data.
    """
    try:
        # Send a GET request to the CryptoCompare API
        response = requests.get(f"{CRYPTOCOMPARE_API_URL}?limit=100&tsym=USD", headers=CRYPTOCOMPARE_HEADERS)
        response.raise_for_status()
        data = response.json()

        # Filter coins based on the provided reliable_coins list and format the data
        coins = [format_cc_data(coin) for coin in data["Data"] if coin["CoinInfo"]["Name"] in reliable_coins and format_cc_data(coin) is not None]
        return pd.DataFrame(coins)
    except requests.exceptions.RequestException as e:
        logging.error(f"Error fetching data from CryptoCompare API: {e}")
        return None

# Function to format cryptocurrency data
def format_cc_data(coin):
    """Formats cryptocurrency data from CryptoCompare API.

    Args:
        coin (dict): A dictionary containing cryptocurrency data from the API.

    Returns:
        dict: A dictionary containing formatted cryptocurrency data.
    """
    try:
        # Extract and format price, volume, and market cap
        price = float(coin["RAW"]["USD"]["PRICE"])
        volume = float(coin["RAW"]["USD"]["TOTALVOLUME24H"])
        market_cap = float(coin["RAW"]["USD"]["MKTCAP"])
        # ... (rounding logic)

        return {
            "Nom": coin["CoinInfo"]["FullName"],
            "Symbole": coin["CoinInfo"]["Name"],
            "Prix USD": price,
            "Volume 24h USD": round(volume, 2),
            "Capitalisation boursière USD": round(market_cap, 2),
        }
    except ValueError as e:
        print(f"Warning: Invalid data format for {coin['CoinInfo']['Name']}: {e}")
    except KeyError as e:
        print(f"Warning: Missing data for {coin['CoinInfo']['Name']}: {e}")

# Calculate indices
def calculate_indices(df):
    """Calculates indices for a given DataFrame of cryptocurrency data.

    Args:
        df (pandas.DataFrame): The DataFrame containing cryptocurrency data.

    Returns:
        pandas.DataFrame: The DataFrame with additional columns for indices.
    """
    total_market_cap = df["Capitalisation boursière USD"].sum()
    total_volume = df["Volume 24h USD"].sum()
    df["Indice MC"] = round(df["Capitalisation boursière USD"] / total_market_cap, 4)
    df["Indice V"] = round(df["Volume 24h USD"] / total_volume, 4)
    df["Indice Total"] = round((0.7 * df["Indice MC"]) + (0.3 * df["Indice V"]), 4)
    return df

# Fonction to record indexces historical
historical_data = []

# Main execution
def main():
    # Define a list of reliable coins
    reliable_coins = ["BTC", "ETH", "USDT", "USDC", "BNB", "XRP", "BUSD", "ADA", "SOL", "TRON", "AVAX", "TON"] # Example list, replace with your desired coins

    # Retrieve data from CryptoCompare, passing the reliable_coins list
    top_coins_df = get_top_coins_cc(reliable_coins)

    if top_coins_df is None or top_coins_df.empty:
        print("No data available.")
        return

    # Calculate indices and add to historical data
    top_coins_df = calculate_indices(top_coins_df)
    top_coins_df = top_coins_df.sort_values("Indice Total", ascending=False)

    # Add current data to historical data
    current_datetime = datetime.now().strftime("%Y%m%d_%H%M%S")
    historical_data.append(top_coins_df[["Nom", "Symbole", "Indice V", "Indice Total"]])

    # Export results to CSV and HTML
    top_coins_df.to_csv(f"top_coins_{current_datetime}.csv", index=False)
    top_coins_df.to_html(f"top_coins_{current_datetime}.html", index=False)

    # Display top 10 coins by Total Index
    print("*****************************************************************")
    print("🔝 Top 10 cryptocurrencies by Total Index:")
    print(top_coins_df[["Nom", "Symbole", "Prix USD", "Indice Total"]].head(10).to_markdown(index=False))
    print("*****************************************************************")

    # Create a pie chart for Total Index distribution
    top_10 = top_coins_df.head(10)
    plt.figure(figsize=(5, 5))
    plt.pie(
        top_10["Indice Total"],
        labels=top_10["Nom"],
        autopct=lambda p: f'{p:.1f}%',
        startangle=140,
        colors=plt.cm.tab20.colors[:10]
    )
    plt.title("Total Index Distribution (Top 10 Cryptos)")
    plt.axis("equal")
    plt.show()

    # Display top 10 coins by Volume Index
    #sorted_by_v = top_coins_df.sort_values("Indice V", ascending=False)
    sorted_by_v = top_coins_df.sort_values("Volume 24h USD", ascending=False)  # Sort by Volume 24h USD

    print("*****************************************************************")
    print("🔝 Top 10 cryptocurrencies by 24h Volume Index:")
    print(sorted_by_v[["Nom", "Symbole", "Prix USD", "Indice V", "Volume 24h USD"]].head(10).to_markdown(index=False))
    print("*****************************************************************")

    # Create a horizontal bar chart for Volume Index
    plt.figure(figsize=(6, 4))
    plt.barh(sorted_by_v["Nom"].head(10), sorted_by_v["Indice V"].head(10), color="teal")
    plt.xlabel("Volume Index (24h)")
    plt.ylabel("Cryptocurrencies")
    plt.title("Top 10 Cryptocurrencies by 24h Volume Index")
    plt.gca().invert_yaxis()  # Invert y-axis to display top coins at the top
    plt.show()

if __name__ == "__main__":
    main()