189 lines
8.4 KiB
Python
189 lines
8.4 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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import pandas as pd
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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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query_etf_holding = f"SELECT holding from etfs WHERE symbol = ?"
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async def save_json(category, data, category_type='market-cap'):
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with open(f"json/{category_type}/list/{category}.json", 'wb') as file:
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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_category(cursor, category, condition, category_type='market-cap'):
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"""
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Process stocks for a specific category (market cap or sector)
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Args:
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cursor: Database cursor
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category: Category name
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condition: SQL WHERE condition
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category_type: Either 'market-cap' or 'sector'
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"""
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base_query = """
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SELECT DISTINCT s.symbol, s.name, s.exchangeShortName, s.marketCap, s.sector
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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': round(quote_data.get('price'),2),
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'changesPercentage': round(quote_data.get('changesPercentage'),2),
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'marketCap': quote_data.get('marketCap'),
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'sector': row[4], # Include sector information
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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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if item['marketCap'] > 0:
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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, category_type)
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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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def get_etf_holding(etf_symbols, etf_con):
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quote_cache = {}
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for ticker in tqdm(etf_symbols):
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res = []
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df = pd.read_sql_query(query_etf_holding, etf_con, params=(ticker,))
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try:
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# Load holdings data from the SQL query result
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data = orjson.loads(df['holding'].iloc[0])
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res = [{key: item[key] for key in ('asset', 'weightPercentage', 'sharesNumber')} for item in data]
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for item in res:
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asset = item['asset']
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# Check if the asset data is already in the cache
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if asset in quote_cache:
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quote_data = quote_cache[asset]
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else:
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# Load the quote data from file if not in cache
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try:
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with open(f"json/quote/{asset}.json") as file:
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quote_data = orjson.loads(file.read())
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quote_cache[asset] = quote_data # Cache the loaded data
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except:
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quote_data = None
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# Assign price and changesPercentage if available, otherwise set to None
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item['price'] = round(quote_data.get('price'), 2) if quote_data else None
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item['changesPercentage'] = round(quote_data.get('changesPercentage'), 2) if quote_data else None
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item['name'] = quote_data.get('name') if quote_data else None
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except Exception as e:
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print(e)
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res = []
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# Save results to a file if there's data to write
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if res:
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with open(f"json/etf/holding/{ticker}.json", 'wb') as file:
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file.write(orjson.dumps(res))
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async def run():
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"""Main function to run the analysis for all categories"""
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market_cap_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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sector_conditions = {
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'financial': "(exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX') AND (sector = 'Financials' OR sector = 'Financial Services')",
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'healthcare': "(exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX') AND (sector = 'Healthcare')",
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'technology': "(exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX') AND (sector = 'Technology')",
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'industrials': "(exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX') AND (sector = 'Industrials')",
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'consumer-cyclical': "(exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX') AND (sector = 'Consumer Cyclical')",
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'real-estate': "(exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX') AND (sector = 'Real Estate')",
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'basic-materials': "(exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX') AND (sector = 'Basic Materials')",
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'communication-services': "(exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX') AND (sector = 'Communication Services')",
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'energy': "(exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX') AND (sector = 'Energy')",
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'consumer-defensive': "(exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX') AND (sector = 'Consumer Defensive')",
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'utilities': "(exchangeShortName = 'NYSE' OR exchangeShortName = 'NASDAQ' OR exchangeShortName = 'AMEX') AND (sector = 'Utilities')"
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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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etf_con = sqlite3.connect('etf.db')
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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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# Process market cap categories
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for category, condition in market_cap_conditions.items():
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await process_category(cursor, category, condition, 'market-cap')
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await asyncio.sleep(1) # Small delay between categories
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# Process sector categories
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for category, condition in sector_conditions.items():
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await process_category(cursor, category, condition, 'sector')
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await asyncio.sleep(1) # Small delay between categories
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get_etf_holding(etf_symbols, etf_con)
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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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etf_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() |