130 lines
4.5 KiB
Python
130 lines
4.5 KiB
Python
import numpy as np
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from scipy.stats import norm
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from datetime import datetime, date, timedelta
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import pandas as pd
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from benzinga import financial_data
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import ujson
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import sqlite3
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import os
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from dotenv import load_dotenv
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# Load API key from environment
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load_dotenv()
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api_key = os.getenv('BENZINGA_API_KEY')
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fin = financial_data.Benzinga(api_key)
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def save_json(symbol, data):
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with open(f'json/options-gex/companies/{symbol}.json', 'w') as file:
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ujson.dump(data, file)
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def calculate_volatility(prices_df):
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prices_df = prices_df.sort_values(by='date')
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prices_df['return'] = prices_df['close'].pct_change()
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returns = prices_df['return'].dropna()
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return returns.std() * np.sqrt(252)
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def black_scholes_d1(S, K, T, r, sigma):
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try:
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if sigma <= 0 or np.sqrt(T) <= 0:
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return 0
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return (np.log(S / K) + (r + 0.5 * sigma**2) * T) / (sigma * np.sqrt(T))
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except ZeroDivisionError:
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return 0
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def black_scholes_d2(S, K, T, r, sigma):
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return black_scholes_d1(S, K, T, r, sigma) - sigma * np.sqrt(T)
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def delta(S, K, T, r, sigma, option_type='CALL'):
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d1 = black_scholes_d1(S, K, T, r, sigma)
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return norm.cdf(d1) if option_type == 'CALL' else norm.cdf(d1) - 1
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def gamma(S, K, T, r, sigma):
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try:
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d1 = black_scholes_d1(S, K, T, r, sigma)
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return norm.pdf(d1) / (S * sigma * np.sqrt(T)) if S > 0 and sigma > 0 and np.sqrt(T) > 0 else 0
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except ZeroDivisionError:
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return 0
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def compute_gex(option_data, r=0.05, sigma=0.2):
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S = float(option_data['underlying_price'])
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K = float(option_data['strike_price'])
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size = float(option_data['open_interest'])
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expiration_date = datetime.strptime(option_data['date_expiration'], "%Y-%m-%d")
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timestamp = datetime.strptime(option_data['date'], "%Y-%m-%d")
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T = (expiration_date - timestamp).days / 365.0
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if T <= 0:
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return 0, timestamp.date()
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option_type = option_data['put_call']
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delta_value = delta(S, K, T, r, sigma, option_type)
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gamma_value = gamma(S, K, T, r, sigma)
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notional = size * S
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gex = gamma_value * notional * delta_value
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return gex, timestamp.date()
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def compute_daily_gex(option_data_list, volatility):
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gex_data = []
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for option_data in option_data_list:
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gex, trade_date = compute_gex(option_data, sigma=volatility)
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if gex != 0:
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gex_data.append({'date': trade_date, 'gex': gex})
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gex_df = pd.DataFrame(gex_data)
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daily_gex = gex_df.groupby('date')['gex'].sum().reset_index()
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daily_gex['gex'] = round(daily_gex['gex'], 0)
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daily_gex['date'] = daily_gex['date'].astype(str)
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return daily_gex
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def get_data(ticker):
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res_list = []
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page = 0
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while True:
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try:
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data = fin.options_activity(date_from=start_date_str, date_to=end_date_str, company_tickers=ticker, page=page, pagesize=1000)
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data = ujson.loads(fin.output(data))['option_activity']
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filtered_data = [{key: value for key, value in item.items() if key not in ['description_extended', 'updated']} for item in data]
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res_list += filtered_data
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page += 1
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except Exception as e:
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print(f"Error retrieving data for {ticker}: {e}")
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break
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return res_list
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# Define date range
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end_date = date.today()
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start_date = end_date - timedelta(180)
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end_date_str = end_date.strftime('%Y-%m-%d')
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start_date_str = start_date.strftime('%Y-%m-%d')
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# Connect to SQLite database
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stock_con = sqlite3.connect('stocks.db')
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stock_cursor = stock_con.cursor()
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stock_cursor.execute("PRAGMA journal_mode = wal")
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stock_cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol NOT LIKE '%.%' AND marketCap >= 1E9")
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stock_symbols = [row[0] for row in stock_cursor.fetchall()]
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query_template = """
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SELECT date, close
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FROM "{ticker}"
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WHERE date BETWEEN ? AND ?
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"""
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# Process each symbol
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for ticker in stock_symbols:
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try:
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query = query_template.format(ticker=ticker)
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df_price = pd.read_sql_query(query, stock_con, params=(start_date_str, end_date_str)).round(2)
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volatility = calculate_volatility(df_price)
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ticker_data = get_data(ticker)
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daily_gex = compute_daily_gex(ticker_data, volatility)
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daily_gex = daily_gex.merge(df_price, on='date', how='inner')
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if not daily_gex.empty:
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save_json(ticker, daily_gex.to_dict('records'))
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except:
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pass
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# Close the database connection
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stock_con.close()
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