125 lines
4.4 KiB
Python
Executable File
125 lines
4.4 KiB
Python
Executable File
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, time
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import pandas as pd
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from GetStartEndDate import GetStartEndDate
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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('FMP_API_KEY')
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async def save_price_data(symbol, data):
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with open(f"json/one-day-price/{symbol}.json", 'w') as file:
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ujson.dump(data, file)
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async def fetch_and_save_symbols_data(symbols):
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tasks = []
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for symbol in symbols:
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task = asyncio.create_task(get_todays_data(symbol))
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tasks.append(task)
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responses = await asyncio.gather(*tasks)
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for symbol, response in zip(symbols, responses):
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if len(response) > 0:
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await save_price_data(symbol, response)
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async def get_todays_data(ticker):
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start_date_1d, end_date_1d = GetStartEndDate().run()
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current_weekday = end_date_1d.weekday()
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start_date = start_date_1d.strftime("%Y-%m-%d")
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end_date = end_date_1d.strftime("%Y-%m-%d")
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url = f"https://financialmodelingprep.com/api/v3/historical-chart/1min/{ticker}?from={start_date}&to={end_date}&apikey={api_key}"
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df_1d = pd.DataFrame()
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current_date = start_date_1d
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target_time = time(9,30)
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extract_date = current_date.strftime('%Y-%m-%d')
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async with aiohttp.ClientSession() as session:
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responses = await asyncio.gather(session.get(url))
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for response in responses:
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try:
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json_data = await response.json()
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df_1d = pd.DataFrame(json_data).iloc[::-1].reset_index(drop=True)
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df_1d = df_1d.drop(['volume'], axis=1)
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df_1d = df_1d.round(2).rename(columns={"date": "time"})
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try:
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with open(f"json/quote/{ticker}.json", 'r') as file:
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res = ujson.load(file)
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df_1d.loc[df_1d.index[0], 'close'] = res['previousClose']
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except:
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pass
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if current_weekday == 5 or current_weekday == 6:
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pass
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else:
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if current_date.time() < target_time:
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pass
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else:
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end_time = pd.to_datetime(f'{extract_date} 16:00:00')
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new_index = pd.date_range(start=df_1d['time'].iloc[-1], end=end_time, freq='1min')
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remaining_df = pd.DataFrame(index=new_index, columns=['open', 'high', 'low','close'])
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remaining_df = remaining_df.reset_index().rename(columns={"index": "time"})
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remaining_df['time'] = remaining_df['time'].dt.strftime('%Y-%m-%d %H:%M:%S')
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remainind_df = remaining_df.set_index('time')
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df_1d = pd.concat([df_1d, remaining_df[1::]], ignore_index=True)
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#To-do FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
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df_1d = ujson.loads(df_1d.to_json(orient="records"))
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except Exception as e:
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print(e)
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df_1d = []
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res = df_1d
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return res
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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")
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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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con.close()
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etf_con.close()
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index_symbols = ['^SPX','^VIX']
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total_symbols = stocks_symbols + etf_symbols + index_symbols
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total_symbols = sorted(total_symbols, key=lambda x: '.' in x)
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chunk_size = 1000
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for i in range(0, len(total_symbols), chunk_size):
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symbols_chunk = total_symbols[i:i+chunk_size]
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await fetch_and_save_symbols_data(symbols_chunk)
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print('sleeping...')
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await asyncio.sleep(60) # Wait for 60 seconds between chunks
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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) |