fetchFlashHistory.py

import argparse
import requests
import datetime
import pandas as pd
from dateutil.relativedelta import relativedelta
import os
from dotenv import load_dotenv


def getFlashHistory():
    # Load the API key, ticker, and other settings from the .env file
    load_dotenv()
    api_key = os.getenv('API_KEY')
    ticker = os.getenv('TICKER')
    timeunit = os.getenv('TIMEUNIT')
    timespan = os.getenv('TIMESPAN')
    timeinterval = int(os.getenv('TIMEINTERVAL'))
    intervaltype = os.getenv('INTERVALTYPE')
    
    timeamount = int(os.getenv('TIMEAMOUNT'))

    today = datetime.date.today()

    # Calculate the start date based on the time unit and time amount

    window = today - relativedelta(days=5)


    # Define the API endpoint URL template
    url_template = "https://api.polygon.io/v2/aggs/ticker/{ticker}/range/{multiplier}/{timespan}/{from_date}/{to_date}?apiKey={api_key}"

    # Set the interval based on the time interval and interval type
    multiplier = timeinterval

    # Update the timespan value based on the interval type
    timespan = intervaltype

    # Function to fetch data for a specific date range
    def fetch_data(from_date, to_date):
        #print(timespan, multiplier, from_date, to_date)
        url = url_template.format(ticker=ticker, multiplier=multiplier, timespan=timespan,
                                from_date=from_date, to_date=to_date, api_key=api_key)
        response = requests.get(url)
        data = response.json()
        return data.get('results', [])
    # Fetch data in smaller chunks (e.g., one month at a time)
    all_data = []
    start_date = window
    total_chunks = (today - window).days // 30
    current_chunk = 1

    while start_date < today:
        end_date = start_date + relativedelta(days=30)
        if end_date > today:
            end_date = today
        #print(f"Fetching data for {ticker} from {start_date} to {end_date} ({current_chunk}/{total_chunks})")
        chunk_data = fetch_data(start_date, end_date)
        all_data.extend(chunk_data)
        start_date = end_date
        current_chunk += 1

    # Convert the data to a pandas DataFrame and save it to a CSV file
    df = pd.DataFrame(all_data)
    output_file = f"data/flash_historical_data.csv"
    df.to_csv(output_file, index=False)
    #print(f"Data for {ticker} saved to {output_file}")