add implied volatiltiy
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MuslemRahimi
parent
c280d40167
commit
1df37cf81a
@ -7,6 +7,13 @@ from dotenv import load_dotenv
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from benzinga import financial_data
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from tqdm import tqdm
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from concurrent.futures import ThreadPoolExecutor
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import pandas as pd
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import math
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from scipy.stats import norm
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from scipy.optimize import brentq
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load_dotenv()
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api_key = os.getenv('BENZINGA_API_KEY')
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@ -14,6 +21,28 @@ api_key = os.getenv('BENZINGA_API_KEY')
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fin = financial_data.Benzinga(api_key)
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risk_free_rate = 0.05
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def black_scholes_price(S, K, T, r, sigma, option_type="CALL"):
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d1 = (math.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * math.sqrt(T))
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d2 = d1 - sigma * math.sqrt(T)
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if option_type == "CALL":
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return S * norm.cdf(d1) - K * math.exp(-r * T) * norm.cdf(d2)
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elif option_type == "PUT":
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return K * math.exp(-r * T) * norm.cdf(-d2) - S * norm.cdf(-d1)
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# Implied volatility function
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def implied_volatility(S, K, T, r, market_price, option_type="CALL"):
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def objective_function(sigma):
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return black_scholes_price(S, K, T, r, sigma, option_type) - market_price
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# Use brentq to solve for the implied volatility
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try:
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return brentq(objective_function, 1e-6, 3) # Bounds for volatility
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except ValueError:
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return None # Return None if there's no solution
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def calculate_dte(date_expiration):
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expiration_date = datetime.strptime(date_expiration, "%Y-%m-%d")
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return (expiration_date - datetime.today()).days
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@ -52,7 +81,60 @@ def options_bubble_data(chunk):
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except:
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break
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res_filtered = [{key: value for key, value in item.items() if key in ['ticker','date', 'date_expiration', 'put_call', 'volume', 'open_interest']} for item in res_list]
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res_filtered = [{key: value for key, value in item.items() if key in ['ticker','underlying_price','strike_price','price','date', 'date_expiration', 'put_call', 'volume', 'open_interest']} for item in res_list]
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#================Start computing historical iv60=====================#
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# Convert to DataFrame for easier manipulation
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df = pd.DataFrame(res_filtered)
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# Ensure correct types for dates and numerical fields
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df['date'] = pd.to_datetime(df['date'])
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df['date_expiration'] = pd.to_datetime(df['date_expiration'])
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df['underlying_price'] = pd.to_numeric(df['underlying_price'], errors='coerce')
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df['strike_price'] = pd.to_numeric(df['strike_price'], errors='coerce')
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df['price'] = pd.to_numeric(df['price'], errors='coerce')
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df['volume'] = pd.to_numeric(df['volume'], errors='coerce')
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df['open_interest'] = pd.to_numeric(df['open_interest'], errors='coerce')
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df['days_to_expiration'] = (df['date_expiration'] - df['date']).dt.days
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df_30d = df[(df['days_to_expiration'] >= 40) & (df['days_to_expiration'] <= 80)]
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# Calculate implied volatility for options in the 30-day range
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iv_data = []
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for _, option in df_30d.iterrows():
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S = option['underlying_price']
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K = option['strike_price']
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T = option['days_to_expiration'] / 365
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market_price = option['price']
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option_type = "CALL" if option['put_call'] == "CALL" else "PUT"
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# Check for missing values
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if pd.notna(S) and pd.notna(K) and pd.notna(T) and pd.notna(market_price):
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# Calculate IV
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iv = implied_volatility(S, K, T, risk_free_rate, market_price, option_type)
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if iv is not None:
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iv_data.append({
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"date": option['date'],
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"IV": iv,
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"volume": option['volume']
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})
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# Create a DataFrame with the calculated IV data
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iv_df = pd.DataFrame(iv_data)
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# Calculate daily IV60 by averaging IVs (weighted by volume)
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def calculate_daily_iv60(group):
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weighted_iv = (group["IV"] * group["volume"]).sum() / group["volume"].sum()
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return weighted_iv
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# Group by date and compute daily IV60
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iv60_history = iv_df.groupby("date").apply(calculate_daily_iv60)
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# Fill NaN values using forward fill to carry the last valid IV60 forward
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iv60_history = iv60_history.ffill()
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iv60_history = iv60_history.to_dict()
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iv60_dict = {k.strftime('%Y-%m-%d'): v for k, v in iv60_history.items()}
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#print(iv60_dict)
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#====================================================================#
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for option_type in ['CALL', 'PUT']:
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for item in res_filtered:
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@ -65,31 +147,41 @@ def options_bubble_data(chunk):
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pass
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#Save raw data for each ticker for options page stack bar chart
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result_list = []
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for ticker in chunk:
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try:
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ticker_filtered_data = [entry for entry in res_filtered if entry['ticker'] == ticker]
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if len(ticker_filtered_data) != 0:
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#sum up calls and puts for each day for the plot
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# Sum up calls and puts for each day for the plot
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summed_data = {}
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for entry in ticker_filtered_data:
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volume = int(entry['volume'])
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open_interest = int(entry['open_interest'])
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put_call = entry['put_call']
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date_str = entry['date']
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if entry['date'] not in summed_data:
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summed_data[entry['date']] = {'CALL': {'volume': 0, 'open_interest': 0}, 'PUT': {'volume': 0, 'open_interest': 0}}
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if date_str not in summed_data:
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summed_data[date_str] = {'CALL': {'volume': 0, 'open_interest': 0}, 'PUT': {'volume': 0, 'open_interest': 0}, 'iv60': None}
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summed_data[entry['date']][put_call]['volume'] += volume
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summed_data[entry['date']][put_call]['open_interest'] += open_interest
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result_list = [{'date': date, 'CALL': summed_data[date]['CALL'], 'PUT': summed_data[date]['PUT']} for date in summed_data]
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#reverse the list
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summed_data[date_str][put_call]['volume'] += volume
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summed_data[date_str][put_call]['open_interest'] += open_interest
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if date_str in iv60_dict:
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summed_data[date_str]['iv60'] = round(iv60_dict[date_str]*100,1)
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result_list.extend([{'date': date, 'CALL': summed_data[date]['CALL'], 'PUT': summed_data[date]['PUT'], 'iv60': summed_data[date]['iv60']} for date in summed_data])
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# Reverse the list
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result_list = result_list[::-1]
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with open(f"json/options-flow/company/{ticker}.json", 'w') as file:
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ujson.dump(result_list, file)
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except:
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except Exception as e:
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print(e)
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pass
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#Save bubble data for each ticker for overview page
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for ticker in chunk:
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@ -139,7 +231,7 @@ async def main():
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chunk_size = len(total_symbols) // 2000 # Divide the list into N chunks
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chunks = [total_symbols[i:i + chunk_size] for i in range(0, len(total_symbols), chunk_size)]
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#chunks = [['NVDA']]
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loop = asyncio.get_running_loop()
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with ThreadPoolExecutor(max_workers=4) as executor:
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tasks = [loop.run_in_executor(executor, options_bubble_data, chunk) for chunk in chunks]
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