104 lines
3.5 KiB
Python
104 lines
3.5 KiB
Python
import ujson
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import asyncio
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import aiohttp
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import sqlite3
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from datetime import datetime,timedelta
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from tqdm import tqdm
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import pandas as pd
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import time
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from dotenv import load_dotenv
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import os
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load_dotenv()
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api_key = os.getenv('NASDAQ_API_KEY')
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# Get today's date
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today = datetime.today()
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# Calculate the date six months ago
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dates = [today - timedelta(days=i) for i in range(365)] #six months ago
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date_str = ','.join(date.strftime('%Y-%m-%d') for date in dates)
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async def save_json(symbol, data):
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with open(f"json/implied-volatility/companies/{symbol}.json", 'w') as file:
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ujson.dump(data, file)
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# Function to filter the list
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def filter_past_six_months(data):
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filtered_data = []
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for entry in data:
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entry_date = datetime.strptime(entry['date'], '%Y-%m-%d')
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if entry_date >= six_months_ago:
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filtered_data.append(entry)
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sorted_data = sorted(filtered_data, key=lambda x: datetime.strptime(x['date'], '%Y-%m-%d'))
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return sorted_data
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async def get_data(ticker):
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#ticker_str = ','.join(ticker_list)
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async with aiohttp.ClientSession() as session:
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url = url = f"https://data.nasdaq.com/api/v3/datatables/ORATS/OPT?date={date_str}&ticker={ticker}&api_key={api_key}"
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async with session.get(url) as response:
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if response.status == 200:
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res = await response.json()
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data = res['datatable']['data']
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columns = res['datatable']['columns']
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return data, columns
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else:
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return [], []
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async def run():
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con = sqlite3.connect('stocks.db')
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etf_con = sqlite3.connect('etf.db')
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cursor = con.cursor()
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cursor.execute("PRAGMA journal_mode = wal")
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cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketcap >=10E6 AND symbol NOT LIKE '%.%'")
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stocks_symbols = [row[0] for row in cursor.fetchall()]
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etf_cursor = etf_con.cursor()
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etf_cursor.execute("PRAGMA journal_mode = wal")
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etf_cursor.execute("SELECT DISTINCT symbol FROM etfs")
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etf_symbols = [row[0] for row in etf_cursor.fetchall()]
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total_symbols = stocks_symbols+etf_symbols
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chunk_size = len(total_symbols) // 70 # Divide the list into N chunks
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chunks = [total_symbols[i:i + chunk_size] for i in range(0, len(total_symbols), chunk_size)]
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for ticker in tqdm(total_symbols):
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data, columns = await get_data(ticker)
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filtered_data = []
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for element in tqdm(data):
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# Assuming the number of columns matches the length of each element in `data`
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filtered_data.append({columns[i]["name"]: element[i] for i in range(len(columns))})
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try:
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sorted_data = sorted(filtered_data, key=lambda x: datetime.strptime(x['date'], '%Y-%m-%d'))
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if len(sorted_data) > 0:
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await save_json(ticker, sorted_data)
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except Exception as e:
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print(e)
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'''
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for symbol in chunk:
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try:
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filtered_data = [item for item in transformed_data if symbol == item['ticker']]
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sorted_data = sorted(filtered_data, key=lambda x: datetime.strptime(x['date'], '%Y-%m-%d'))
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if len(sorted_data) > 0:
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await save_json(symbol, sorted_data)
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except Exception as e:
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print(e)
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'''
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con.close()
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etf_con.close()
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try:
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asyncio.run(run())
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except Exception as e:
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print(e) |