CS9_Binance.py

import ccxt
from datetime import datetime, timedelta

# Set up Binance API credentials
binance_api_key = "ENTER YOUR API KEY HERE"
binance_api_secret = "ENTER YOUR API SECRET KEY HERE"

# Read coin data from the spreadsheet
coin_data = []
with open('data.csv', 'r') as file:
    lines = file.readlines()
    headers = lines[0].strip().split(',')
    for line in lines[1:]:
        data = line.strip().split(',')
        coin_data.append(dict(zip(headers, data)))

# Instantiate the Binance API
binance = ccxt.binance({
    'apiKey': binance_api_key,
    'secret': binance_api_secret
})

# Load the market symbols supported by Binance
binance.load_markets()

# Fetch OHLCV data for each coin
for coin in coin_data:
    symbol = coin['Symbol']
    date_str = coin['Date']
    days = int(coin['Days'])

    # Set the start time for data collection
    start_time = datetime.strptime(date_str, "%d/%m/%Y")

    # Set the end time as the current datetime
    end_time = datetime.now()

    # Convert the start and end times to UNIX timestamps
    start_timestamp = int(start_time.timestamp() * 1000)
    end_timestamp = int(end_time.timestamp() * 1000)

    # Set the desired timeframe
    timeframe = '15m'

    # Get the correct symbol for the coin on Binance
    market_symbols = binance.symbols
    if symbol not in market_symbols:
        print(f"Skipping {symbol} - Symbol not found on Binance.")
        continue

    # Create a list to store the OHLCV data
    filtered_data = []

    # Fetch OHLCV data in chunks to cover the desired period
    current_timestamp = start_timestamp
    while current_timestamp <= end_timestamp:
        # Limit the number of data points to fetch in each request to avoid exceeding the API rate limits
        limit = 500

        # Fetch OHLCV data from the Binance API
        ohlcv_data = binance.fetch_ohlcv(symbol, timeframe, since=current_timestamp, limit=limit)

        # Check if OHLCV data is empty
        if len(ohlcv_data) == 0:
            break

        # Extract data from the fetched OHLCV data and filter based on the specified timeframe
        for item in ohlcv_data:
            timestamp = datetime.fromtimestamp(item[0] / 1000)
            if start_time <= timestamp <= end_time:
                filtered_data.append([
                    timestamp,
                    item[1],
                    item[2],
                    item[3],
                    item[4],
                    item[5]
                ])

        # Update the current timestamp to the next available timestamp
        current_timestamp = ohlcv_data[-1][0] + (ohlcv_data[1][0] - ohlcv_data[0][0])

        # Break the loop if we've reached the end of the desired range
        if current_timestamp > end_timestamp:
            break

    # Save the filtered OHLCV data to a CSV file
    csv_filename = f"{symbol.replace('/', '_')}_ohlcv_data.csv"
    with open(csv_filename, 'w') as file:
        file.write("timestamp,open,high,low,close,volume\n")
        for row in filtered_data:
            file.write(",".join(str(value) for value in row) + "\n")

    print(f"OHLCV data for {symbol} saved to {csv_filename}.")