66 lines
1.8 KiB
Python
Executable File
66 lines
1.8 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
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from rating import rating_model
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import pandas as pd
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from tqdm import tqdm
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async def save_similar_stocks(symbol, data):
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with open(f"json/similar-stocks/{symbol}.json", 'w') as file:
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ujson.dump(data, file)
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query_template = """
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SELECT
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quote, stock_peers
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FROM
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stocks
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WHERE
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symbol = ?
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"""
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async def run():
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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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for ticker in tqdm(stocks_symbols):
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filtered_df = []
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df = pd.read_sql_query(query_template, con, params=(ticker,))
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try:
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df = ujson.loads(df['stock_peers'].iloc[0])
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except:
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df = []
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if len(df) > 0:
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df = [stock for stock in df if stock in stocks_symbols]
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for symbol in df:
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try:
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df = pd.read_sql_query(query_template, con, params=(symbol,))
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df_dict = df.to_dict()
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quote_dict = eval(df_dict['quote'][0])[0]
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filtered_df.append(quote_dict) # Add the modified result to the combined list
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except:
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pass
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filtered_df = [
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{
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"symbol": entry["symbol"],
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"name": entry["name"],
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"marketCap": entry["marketCap"],
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"avgVolume": entry["avgVolume"]
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}
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for entry in filtered_df
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]
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sorted_df = sorted(filtered_df, key=lambda x: x['marketCap'], reverse=True)
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await save_similar_stocks(ticker, sorted_df)
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try:
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con = sqlite3.connect('stocks.db')
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asyncio.run(run())
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con.close()
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except Exception as e:
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print(e) |