UwjikUkC

1# This Python script fetches real-time cryptocurrency data from CoinMarketCap and CryptoCompare APIs.
2# It calculates various indices (market cap index, volume index, total index) based on the retrieved data.
3# The script then generates various visualizations (pie charts, bar charts) to analyze the distribution of these indices among the top cryptocurrencies.
4# Finally, it exports the results to CSV and HTML files for further analysis.
5
6# Start with:
7# !pip install requests pandas urllib3 matplotlib logging tenacity cryptocompare pycoinmarketcap tabulate
8# Then copy and paste or run the code below:
9import requests
10import pandas as pd
11from urllib.parse import urlencode
12from datetime import datetime
13import matplotlib.pyplot as plt
14import logging
15from tenacity import retry, stop_after_attempt, wait_fixed
16
17# API configuration (Replace with your own API Keys)
18
19# To use these APIs, you will need to obtain your own API keys from the following services:
20
21# 1. CoinMarketCap
22#   - Visit https://pro.coinmarketcap.com/account/signup
23#   - Create a free account or choose a paid plan depending on your usage needs.
24#   - Once registered, navigate to your API Keys section in your account settings.
25#   - Generate a new API Key and copy it for later use.
26
27CMC_API_URL = "https://pro-api.coinmarketcap.com/v1/cryptocurrency/listings/latest"
28CMC_API_KEY = "YOUR_COINMARKETCAP_API_KEY"  # Replace with your actual key
29CMC_HEADERS = {
30    "Accepts": "application/json",
31    "X-CMC_PRO_API_KEY": CMC_API_KEY,
32}
33CMC_PARAMS = {"limit": 100}
34
35# 2. CryptoCompare
36#   - Visit https://www.cryptocompare.com/
37#   - Navigate to your profile settings and locate the API Keys section.
38#   - Generate a new API Key and copy it for later use.
39
40CRYPTOCOMPARE_API_URL = "https://min-api.cryptocompare.com/data/top/mktcapfull"
41CRYPTOCOMPARE_API_KEY = "YOUR_CRYPTOCOMPARE_API_KEY"  # Replace with your actual key
42CRYPTOCOMPARE_HEADERS = {
43    "Authorization": f"Apikey {CRYPTOCOMPARE_API_KEY}",
44}
45
46
47# Function to retrieve top coins from CryptoCompare and format the data
48def get_top_coins_cc(reliable_coins):
49    """Fetches top cryptocurrencies from CryptoCompare API and formats the data.
50
51    Args:
52        reliable_coins (list): A list of reliable cryptocurrency symbols.
53
54    Returns:
55        pandas.DataFrame: A DataFrame containing formatted cryptocurrency data.
56    """
57    try:
58        # Send a GET request to the CryptoCompare API
59        response = requests.get(f"{CRYPTOCOMPARE_API_URL}?limit=100&tsym=USD", headers=CRYPTOCOMPARE_HEADERS)
60        response.raise_for_status()
61        data = response.json()
62
63        # Filter coins based on the provided reliable_coins list and format the data
64        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]
65        return pd.DataFrame(coins)
66    except requests.exceptions.RequestException as e:
67        logging.error(f"Error fetching data from CryptoCompare API: {e}")
68        return None
69
70# Function to format cryptocurrency data
71def format_cc_data(coin):
72    """Formats cryptocurrency data from CryptoCompare API.
73
74    Args:
75        coin (dict): A dictionary containing cryptocurrency data from the API.
76
77    Returns:
78        dict: A dictionary containing formatted cryptocurrency data.
79    """
80    try:
81        # Extract and format price, volume, and market cap
82        price = float(coin["RAW"]["USD"]["PRICE"])
83        volume = float(coin["RAW"]["USD"]["TOTALVOLUME24H"])
84        market_cap = float(coin["RAW"]["USD"]["MKTCAP"])
85        # ... (rounding logic)
86
87        return {
88            "Nom": coin["CoinInfo"]["FullName"],
89            "Symbole": coin["CoinInfo"]["Name"],
90            "Prix USD": price,
91            "Volume 24h USD": round(volume, 2),
92            "Capitalisation boursière USD": round(market_cap, 2),
93        }
94    except ValueError as e:
95        print(f"Warning: Invalid data format for {coin['CoinInfo']['Name']}: {e}")
96    except KeyError as e:
97        print(f"Warning: Missing data for {coin['CoinInfo']['Name']}: {e}")
98
99# Calculate indices
100def calculate_indices(df):
101    """Calculates indices for a given DataFrame of cryptocurrency data.
102
103    Args:
104        df (pandas.DataFrame): The DataFrame containing cryptocurrency data.
105
106    Returns:
107        pandas.DataFrame: The DataFrame with additional columns for indices.
108    """
109    total_market_cap = df["Capitalisation boursière USD"].sum()
110    total_volume = df["Volume 24h USD"].sum()
111    df["Indice MC"] = round(df["Capitalisation boursière USD"] / total_market_cap, 4)
112    df["Indice V"] = round(df["Volume 24h USD"] / total_volume, 4)
113    df["Indice Total"] = round((0.7 * df["Indice MC"]) + (0.3 * df["Indice V"]), 4)
114    return df
115
116# Fonction to record indexces historical
117historical_data = []
118
119# Main execution
120def main():
121    # Define a list of reliable coins
122    reliable_coins = ["BTC", "ETH", "USDT", "USDC", "BNB", "XRP", "BUSD", "ADA", "SOL", "TRON", "AVAX", "TON"] # Example list, replace with your desired coins
123
124    # Retrieve data from CryptoCompare, passing the reliable_coins list
125    top_coins_df = get_top_coins_cc(reliable_coins)
126
127    if top_coins_df is None or top_coins_df.empty:
128        print("No data available.")
129        return
130
131    # Calculate indices and add to historical data
132    top_coins_df = calculate_indices(top_coins_df)
133    top_coins_df = top_coins_df.sort_values("Indice Total", ascending=False)
134
135    # Add current data to historical data
136    current_datetime = datetime.now().strftime("%Y%m%d_%H%M%S")
137    historical_data.append(top_coins_df[["Nom", "Symbole", "Indice V", "Indice Total"]])
138
139    # Export results to CSV and HTML
140    top_coins_df.to_csv(f"top_coins_{current_datetime}.csv", index=False)
141    top_coins_df.to_html(f"top_coins_{current_datetime}.html", index=False)
142
143    # Display top 10 coins by Total Index
144    print("*****************************************************************")
145    print("🔝 Top 10 cryptocurrencies by Total Index:")
146    print(top_coins_df[["Nom", "Symbole", "Prix USD", "Indice Total"]].head(10).to_markdown(index=False))
147    print("*****************************************************************")
148
149    # Create a pie chart for Total Index distribution
150    top_10 = top_coins_df.head(10)
151    plt.figure(figsize=(5, 5))
152    plt.pie(
153        top_10["Indice Total"],
154        labels=top_10["Nom"],
155        autopct=lambda p: f'{p:.1f}%',
156        startangle=140,
157        colors=plt.cm.tab20.colors[:10]
158    )
159    plt.title("Total Index Distribution (Top 10 Cryptos)")
160    plt.axis("equal")
161    plt.show()
162
163    # Display top 10 coins by Volume Index
164    #sorted_by_v = top_coins_df.sort_values("Indice V", ascending=False)
165    sorted_by_v = top_coins_df.sort_values("Volume 24h USD", ascending=False)  # Sort by Volume 24h USD
166
167    print("*****************************************************************")
168    print("🔝 Top 10 cryptocurrencies by 24h Volume Index:")
169    print(sorted_by_v[["Nom", "Symbole", "Prix USD", "Indice V", "Volume 24h USD"]].head(10).to_markdown(index=False))
170    print("*****************************************************************")
171
172    # Create a horizontal bar chart for Volume Index
173    plt.figure(figsize=(6, 4))
174    plt.barh(sorted_by_v["Nom"].head(10), sorted_by_v["Indice V"].head(10), color="teal")
175    plt.xlabel("Volume Index (24h)")
176    plt.ylabel("Cryptocurrencies")
177    plt.title("Top 10 Cryptocurrencies by 24h Volume Index")
178    plt.gca().invert_yaxis()  # Invert y-axis to display top coins at the top
179    plt.show()
180
181if __name__ == "__main__":
182    main()