137 lines
3.9 KiB
Python
Executable File
137 lines
3.9 KiB
Python
Executable File
import pandas as pd
|
|
from datetime import datetime
|
|
#import yfinance as yf
|
|
import numpy as np
|
|
import ujson
|
|
import asyncio
|
|
import sqlite3
|
|
from tqdm import tqdm
|
|
|
|
|
|
|
|
async def save_json(symbol, data):
|
|
with open(f"json/var/{symbol}.json", 'w') as file:
|
|
ujson.dump(data, file)
|
|
|
|
# Define risk rating scale
|
|
def assign_risk_rating(var):
|
|
if var >= 25: # This threshold can be adjusted based on your specific criteria
|
|
return 1
|
|
elif var >= 20:
|
|
return 2
|
|
elif var >= 15:
|
|
return 3
|
|
elif var >= 10:
|
|
return 4
|
|
elif var >= 8:
|
|
return 5
|
|
elif var >= 6:
|
|
return 6
|
|
elif var >= 4:
|
|
return 7
|
|
elif var >= 2:
|
|
return 8
|
|
elif var >= 1:
|
|
return 9
|
|
else:
|
|
return 10
|
|
|
|
def compute_var(df):
|
|
# Calculate daily returns
|
|
df['Returns'] = df['close'].pct_change()
|
|
df = df.dropna()
|
|
# Calculate VaR at 95% confidence level
|
|
confidence_level = 0.95
|
|
var = abs(np.percentile(df['Returns'], 100 * (1 - confidence_level)))
|
|
var_N_days = round(var * np.sqrt(5)*100,2) # N days
|
|
|
|
# Assign risk rating
|
|
risk_rating = assign_risk_rating(var_N_days)
|
|
outlook = 'Neutral'
|
|
if risk_rating < 5:
|
|
outlook = 'Risky'
|
|
elif risk_rating > 5:
|
|
outlook = 'Minimum Risk'
|
|
|
|
return {'rating': risk_rating, 'var': -var_N_days, 'outlook': outlook}
|
|
|
|
#print(f"The Value at a 95% confidence level is: {var_N_days}%")
|
|
#print(f"The risk rating based on the Value at Risk is: {risk_rating}")
|
|
|
|
async def run():
|
|
start_date = "2015-01-01"
|
|
end_date = datetime.today().strftime("%Y-%m-%d")
|
|
|
|
con = sqlite3.connect('stocks.db')
|
|
etf_con = sqlite3.connect('etf.db')
|
|
crypto_con = sqlite3.connect('crypto.db')
|
|
|
|
cursor = con.cursor()
|
|
cursor.execute("PRAGMA journal_mode = wal")
|
|
cursor.execute("SELECT DISTINCT symbol FROM stocks")
|
|
stocks_symbols = [row[0] for row in cursor.fetchall()]
|
|
|
|
etf_cursor = etf_con.cursor()
|
|
etf_cursor.execute("PRAGMA journal_mode = wal")
|
|
etf_cursor.execute("SELECT DISTINCT symbol FROM etfs")
|
|
etf_symbols = [row[0] for row in etf_cursor.fetchall()]
|
|
|
|
crypto_cursor = crypto_con.cursor()
|
|
crypto_cursor.execute("PRAGMA journal_mode = wal")
|
|
crypto_cursor.execute("SELECT DISTINCT symbol FROM cryptos")
|
|
crypto_symbols = [row[0] for row in crypto_cursor.fetchall()]
|
|
|
|
total_symbols = stocks_symbols + etf_symbols + crypto_symbols
|
|
|
|
for symbol in tqdm(total_symbols):
|
|
if symbol in etf_symbols: # Fixed variable name from symbols to symbol
|
|
query_con = etf_con
|
|
elif symbol in crypto_symbols: # Fixed variable name from symbols to symbol
|
|
query_con = crypto_con
|
|
elif symbol in stocks_symbols: # Fixed variable name from symbols to symbol
|
|
query_con = con
|
|
|
|
query_template = """
|
|
SELECT
|
|
date, open, high, low, close, volume
|
|
FROM
|
|
"{symbol}"
|
|
WHERE
|
|
date BETWEEN ? AND ?
|
|
"""
|
|
query = query_template.format(symbol=symbol)
|
|
df = pd.read_sql_query(query, query_con, params=(start_date, end_date))
|
|
|
|
try:
|
|
res_dict = compute_var(df)
|
|
|
|
await save_json(symbol, res_dict)
|
|
except Exception as e:
|
|
print(e)
|
|
|
|
|
|
con.close()
|
|
etf_con.close()
|
|
crypto_con.close()
|
|
|
|
try:
|
|
asyncio.run(run())
|
|
except Exception as e:
|
|
print(e)
|
|
|
|
#Test mode
|
|
'''
|
|
|
|
# Download data
|
|
ticker = 'TCON'
|
|
start_date = datetime(2015, 1, 1)
|
|
end_date = datetime.today()
|
|
|
|
df = yf.download(ticker, start=start_date, end=end_date, interval="1d")
|
|
df = df.reset_index()
|
|
df = df[['Date', 'Close']]
|
|
|
|
# Calculate daily returns
|
|
df['Returns'] = df['Close'].pct_change()
|
|
df = df.dropna()
|
|
''' |