100 lines
4.2 KiB
Python
100 lines
4.2 KiB
Python
import os
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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 tqdm import tqdm
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from dotenv import load_dotenv
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load_dotenv()
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api_key = os.getenv('FMP_API_KEY')
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# Configurations
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include_current_quarter = False
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max_concurrent_requests = 100 # Limit concurrent requests
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async def fetch_data(session, url, symbol, attempt=0):
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try:
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async with session.get(url) as response:
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if response.status == 200:
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data = await response.json()
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return data
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else:
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print(f"Error fetching data for {symbol}: HTTP {response.status}")
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return None
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except Exception as e:
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print(f"Exception during fetching data for {symbol}: {e}")
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return None
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async def save_json(symbol, period, data_type, data):
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os.makedirs(f"json/financial-statements/{data_type}/{period}/", exist_ok=True)
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with open(f"json/financial-statements/{data_type}/{period}/{symbol}.json", 'w') as file:
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ujson.dump(data, file)
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async def get_financial_statements(session, symbol, semaphore, request_counter):
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base_url = "https://financialmodelingprep.com/api/v3"
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periods = ['quarter', 'annual']
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financial_data_types = ['key-metrics', 'income-statement', 'balance-sheet-statement', 'cash-flow-statement', 'ratios']
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growth_data_types = ['income-statement-growth', 'balance-sheet-statement-growth', 'cash-flow-statement-growth']
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async with semaphore:
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for period in periods:
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# Fetch regular financial statements
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for data_type in financial_data_types:
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url = f"{base_url}/{data_type}/{symbol}?period={period}&apikey={api_key}"
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data = await fetch_data(session, url, symbol)
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if data:
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await save_json(symbol, period, data_type, data)
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request_counter[0] += 1 # Increment the request counter
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if request_counter[0] >= 500:
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await asyncio.sleep(60) # Pause for 60 seconds
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request_counter[0] = 0 # Reset the request counter after the pause
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# Fetch financial statement growth data
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for growth_type in growth_data_types:
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growth_url = f"{base_url}/{growth_type}/{symbol}?period={period}&apikey={api_key}"
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growth_data = await fetch_data(session, growth_url, symbol)
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if growth_data:
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await save_json(symbol, period, growth_type, growth_data)
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request_counter[0] += 1 # Increment the request counter
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if request_counter[0] >= 500:
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await asyncio.sleep(60) # Pause for 60 seconds
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request_counter[0] = 0 # Reset the request counter after the pause
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# Fetch owner earnings data
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owner_earnings_url = f"https://financialmodelingprep.com/api/v4/owner_earnings?symbol={symbol}&apikey={api_key}"
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owner_earnings_data = await fetch_data(session, owner_earnings_url, symbol)
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if owner_earnings_data:
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await save_json(symbol, 'quarter', 'owner-earnings', owner_earnings_data)
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request_counter[0] += 1 # Increment the request counter
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if request_counter[0] >= 500:
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await asyncio.sleep(60) # Pause for 60 seconds
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request_counter[0] = 0 # Reset the request counter after the pause
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async def run():
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con = sqlite3.connect('stocks.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 symbol NOT LIKE '%.%'")
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symbols = [row[0] for row in cursor.fetchall()]
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con.close()
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semaphore = asyncio.Semaphore(max_concurrent_requests)
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request_counter = [0] # Using a list to keep a mutable counter across async tasks
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async with aiohttp.ClientSession() as session:
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tasks = []
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for symbol in tqdm(symbols):
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task = asyncio.create_task(get_financial_statements(session, symbol, semaphore, request_counter))
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tasks.append(task)
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await asyncio.gather(*tasks)
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if __name__ == "__main__":
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asyncio.run(run())
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