103 lines
4.0 KiB
Python
103 lines
4.0 KiB
Python
import orjson
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import sqlite3
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import asyncio
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import aiohttp
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from tqdm import tqdm
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# Load stock screener data
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with open(f"json/stock-screener/data.json", 'rb') as file:
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stock_screener_data = orjson.loads(file.read())
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stock_screener_data_dict = {item['symbol']: item for item in stock_screener_data}
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async def save_json(cap_category, data):
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"""Save data to JSON file for specific market cap category."""
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with open(f"json/market-cap/list/{cap_category}.json", 'wb') as file: # Note: 'wb' for binary write
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file.write(orjson.dumps(data))
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async def get_quote_data(symbol):
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"""Get quote data for a symbol from JSON file"""
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try:
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with open(f"json/quote/{symbol}.json", 'r') as file:
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return orjson.loads(file.read())
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except FileNotFoundError:
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return None
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async def process_market_cap_category(cursor, category, condition):
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"""Process stocks for a specific market cap category"""
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base_query = """
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SELECT DISTINCT s.symbol, s.name, s.exchangeShortName, s.marketCap
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FROM stocks s
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WHERE {}
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"""
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full_query = base_query.format(condition)
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cursor.execute(full_query)
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raw_data = cursor.fetchall()
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result_list = []
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for row in raw_data:
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symbol = row[0]
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quote_data = await get_quote_data(symbol)
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if quote_data:
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item = {
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'symbol': symbol,
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'name': row[1],
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'price': quote_data.get('price'),
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'changesPercentage': quote_data.get('changesPercentage'),
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'marketCap': quote_data.get('marketCap'),
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'revenue': None,
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}
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# Add screener data if available
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if symbol in stock_screener_data_dict:
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item['revenue'] = stock_screener_data_dict[symbol].get('revenue')
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result_list.append(item)
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# Sort by market cap and save
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sorted_result = sorted(result_list, key=lambda x: x['marketCap'] if x['marketCap'] else 0, reverse=True)
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# Add rank to each item
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for rank, item in enumerate(sorted_result, 1):
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item['rank'] = rank
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await save_json(category, sorted_result)
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print(f"Processed and saved {len(sorted_result)} stocks for {category}")
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return sorted_result
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async def run():
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"""Main function to run the analysis for all market cap categories"""
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conditions = {
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'mega-cap-stocks': "marketCap >= 200e9 AND (exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX')",
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'large-cap-stocks': "marketCap < 200e9 AND marketCap >= 10e9 AND (exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX')",
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'mid-cap-stocks': "marketCap < 10e9 AND marketCap >= 2e9 AND (exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX')",
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'small-cap-stocks': "marketCap < 2e9 AND marketCap >= 300e6 AND (exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX')",
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'micro-cap-stocks': "marketCap < 300e6 AND marketCap >= 50e6 AND (exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX')",
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'nano-cap-stocks': "marketCap < 50e6 AND (exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX')"
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}
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try:
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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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# Process each market cap category
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for category, condition in conditions.items():
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await process_market_cap_category(cursor, category, condition)
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await asyncio.sleep(1) # Small delay between categories
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except Exception as e:
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print(e)
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raise
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finally:
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con.close()
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if __name__ == "__main__":
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try:
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loop = asyncio.get_event_loop()
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loop.run_until_complete(run())
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except Exception as e:
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print(e)
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finally:
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loop.close() |