124 lines
3.6 KiB
Python
Executable File
124 lines
3.6 KiB
Python
Executable File
import pandas as pd
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from datetime import datetime
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import numpy as np
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import ujson
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import asyncio
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import sqlite3
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from tqdm import tqdm
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async def save_json(symbol, data):
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with open(f"json/var/{symbol}.json", 'w') as file:
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ujson.dump(data, file)
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# Define risk rating scale
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def assign_risk_rating(var):
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if var >= 25:
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return 1
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elif var >= 20:
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return 2
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elif var >= 15:
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return 3
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elif var >= 10:
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return 4
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elif var >= 8:
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return 5
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elif var >= 6:
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return 6
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elif var >= 4:
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return 7
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elif var >= 2:
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return 8
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elif var >= 1:
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return 9
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else:
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return 10
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def compute_var(df):
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# Calculate daily returns
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df['Returns'] = df['close'].pct_change()
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df = df.dropna()
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# Calculate VaR at 95% confidence level
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confidence_level = 0.95
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var = abs(np.percentile(df['Returns'], 100 * (1 - confidence_level)))
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var_N_days = round(var * np.sqrt(5)*100,2) # N days
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return -var_N_days #{'rating': risk_rating, 'var': -var_N_days, 'outlook': outlook}
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async def run():
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start_date = "2015-01-01"
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end_date = datetime.today().strftime("%Y-%m-%d")
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con = sqlite3.connect('stocks.db')
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etf_con = sqlite3.connect('etf.db')
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crypto_con = sqlite3.connect('crypto.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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crypto_cursor = crypto_con.cursor()
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crypto_cursor.execute("PRAGMA journal_mode = wal")
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crypto_cursor.execute("SELECT DISTINCT symbol FROM cryptos")
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crypto_symbols = [row[0] for row in crypto_cursor.fetchall()]
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total_symbols = stocks_symbols + etf_symbols + crypto_symbols
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for symbol in tqdm(total_symbols):
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if symbol in etf_symbols:
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query_con = etf_con
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elif symbol in crypto_symbols:
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query_con = crypto_con
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elif symbol in stocks_symbols:
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query_con = con
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query_template = """
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SELECT
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date, open, high, low, close, volume
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FROM
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"{symbol}"
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WHERE
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date BETWEEN ? AND ?
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"""
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query = query_template.format(symbol=symbol)
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df = pd.read_sql_query(query, query_con, params=(start_date, end_date))
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# Convert date to datetime
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df['date'] = pd.to_datetime(df['date'])
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# Group by year and month
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monthly_groups = df.groupby(df['date'].dt.to_period('M'))
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history = []
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try:
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for period, group in monthly_groups:
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var_data = compute_var(group)
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history.append({'date': str(period), 'var': var_data})
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risk_rating = assign_risk_rating(abs(history[-1]['var']))
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outlook = 'Neutral'
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if risk_rating < 5:
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outlook = 'Risky'
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elif risk_rating > 5:
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outlook = 'Minimum Risk'
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res = {'rating': risk_rating, 'history': history, 'outlook': outlook}
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await save_json(symbol, res)
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except Exception as e:
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print(f"Error processing {symbol}: {e}")
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con.close()
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etf_con.close()
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crypto_con.close()
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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)
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