add otm percentage
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MuslemRahimi
parent
8797e61495
commit
b146a93ddb
@ -83,15 +83,35 @@ def compute_daily_gex(option_data_list, volatility):
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daily_gex['date'] = daily_gex['date'].astype(str)
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return daily_gex
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def summarize_option_chain(option_data_list):
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def calculate_otm_percentage(option_data_list):
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otm_count = 0
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total_options = len(option_data_list)
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for option_data in option_data_list:
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strike_price = float(option_data['strike_price'])
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put_call = option_data['put_call']
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stock_price = float(option_data['stock_price']) # Get stock price for this option
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# Check if the option is out-of-the-money
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if (put_call == 'CALL' and strike_price > stock_price) or (put_call == 'PUT' and strike_price < stock_price):
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otm_count += 1
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if total_options > 0:
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return (otm_count / total_options) * 100
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else:
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return 0
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def summarize_option_chain_with_otm(option_data_list, df_price):
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summary_data = []
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for option_data in option_data_list:
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try:
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date = datetime.strptime(option_data['date'], "%Y-%m-%d").date()
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expiration_date = datetime.strptime(option_data['date_expiration'], "%Y-%m-%d").date()
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open_interest = int(option_data.get('open_interest', 0))
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volume = int(option_data.get('volume', 0))
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price = float(option_data.get('price', 0))
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strike_price = float(option_data.get('strike_price', 0))
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put_call = option_data.get('put_call', 'CALL')
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sentiment = option_data.get('sentiment', 'NEUTRAL')
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@ -102,6 +122,17 @@ def summarize_option_chain(option_data_list):
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except (TypeError, ValueError):
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premium = 0
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# Determine the stock price based on expiration date
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if expiration_date > date.today():
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stock_price = df_price['close'].iloc[-1] # Latest stock price
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else:
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# Get the stock price on the option's date
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stock_price_row = df_price[df_price['date'] == str(date)]
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if not stock_price_row.empty:
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stock_price = stock_price_row['close'].values[0]
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else:
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continue # Skip this option if the price isn't available for the date
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# Calculate Bull/Bear/Neutral premiums based on sentiment
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if sentiment == 'BULLISH':
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bull_premium = premium
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@ -116,6 +147,7 @@ def summarize_option_chain(option_data_list):
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bear_premium = 0
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neutral_premium = premium
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# Append option data for later summarization
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summary_data.append({
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'date': date,
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'open_interest': open_interest,
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@ -123,38 +155,54 @@ def summarize_option_chain(option_data_list):
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'p_vol': volume if put_call == 'PUT' else 0,
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'bull_premium': bull_premium,
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'bear_premium': bear_premium,
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'neutral_premium': neutral_premium
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'neutral_premium': neutral_premium,
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'put_call': put_call,
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'strike_price': strike_price,
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'stock_price': stock_price
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})
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except:
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pass
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except Exception as e:
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print(f"Error processing option data: {e}")
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continue
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# Summarize by date
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df_summary = pd.DataFrame(summary_data)
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# Calculate OTM percentage for each day
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def calculate_daily_otm(df):
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return calculate_otm_percentage(df.to_dict('records')) # Pass the day's options for OTM calculation
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# Apply OTM percentage calculation for each day
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daily_summary = df_summary.groupby('date').agg(
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total_oi=('open_interest', 'sum'),
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total_bull_prem=('bull_premium', 'sum'),
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total_bear_prem=('bear_premium', 'sum'),
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total_neutral_prem=('neutral_premium', 'sum'),
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c_vol=('c_vol', 'sum'),
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p_vol=('p_vol', 'sum')
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p_vol=('p_vol', 'sum'),
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).reset_index()
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# Calculate Bull/Bear ratio
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try:
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daily_summary['bear_ratio'] = round(daily_summary['total_bear_prem'] / (daily_summary['total_bull_prem']+daily_summary['total_bear_prem']+daily_summary['total_neutral_prem']) * 100, 2)
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daily_summary['bull_ratio'] = round(daily_summary['total_bull_prem'] / (daily_summary['total_bull_prem']+daily_summary['total_bear_prem']+daily_summary['total_neutral_prem']) * 100, 2)
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daily_summary['neutral_ratio'] = round(daily_summary['total_neutral_prem'] / (daily_summary['total_bull_prem']+daily_summary['total_bear_prem']+daily_summary['total_neutral_prem']) * 100, 2)
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except:
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daily_summary['bear_ratio'] = None
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daily_summary['bull_ratio'] = None
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daily_summary['neutral_ratio'] = None
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# Calculate OTM percentage for each date and assign it to the daily_summary
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daily_summary['otm_ratio'] = df_summary.groupby('date').apply(lambda df: round(calculate_otm_percentage(df.to_dict('records')), 1)).values
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daily_summary['total_volume'] = round(daily_summary['c_vol'] + daily_summary['p_vol'],2)
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daily_summary['total_neutral_prem'] = round(daily_summary['total_neutral_prem'],2)
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# Calculate Bull/Bear/Neutral ratios
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try:
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total_prem = daily_summary['total_bull_prem'] + daily_summary['total_bear_prem'] + daily_summary['total_neutral_prem']
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daily_summary['bull_ratio'] = round(daily_summary['total_bull_prem'] / total_prem * 100, 2)
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daily_summary['bear_ratio'] = round(daily_summary['total_bear_prem'] / total_prem * 100, 2)
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daily_summary['neutral_ratio'] = round(daily_summary['total_neutral_prem'] / total_prem * 100, 2)
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except ZeroDivisionError:
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daily_summary['bull_ratio'] = None
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daily_summary['bear_ratio'] = None
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daily_summary['neutral_ratio'] = None
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# Calculate total volume (call + put) and format other fields
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daily_summary['total_volume'] = round(daily_summary['c_vol'] + daily_summary['p_vol'], 2)
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daily_summary['total_neutral_prem'] = round(daily_summary['total_neutral_prem'], 2)
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daily_summary['date'] = daily_summary['date'].astype(str)
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daily_summary = daily_summary.sort_values(by='date', ascending=False)
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# Return the summarized dataframe
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return daily_summary
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@ -203,7 +251,7 @@ query_template = """
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"""
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# Process each symbol
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for ticker in total_symbols:
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for ticker in ['GME']: #total_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 if ticker in stock_symbols else etf_con, params=(start_date_str, end_date_str)).round(2)
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@ -212,11 +260,12 @@ for ticker in total_symbols:
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volatility = calculate_volatility(df_price)
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ticker_data = get_data(ticker)
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daily_option_chain = summarize_option_chain(ticker_data)
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daily_option_chain = summarize_option_chain_with_otm(ticker_data, df_price)
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daily_option_chain = daily_option_chain.merge(df_price[['date', 'changesPercentage']], on='date', how='inner')
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if not daily_option_chain.empty:
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save_json(ticker, daily_option_chain.to_dict('records'), 'json/options-chain/companies')
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daily_gex = compute_daily_gex(ticker_data, volatility)
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daily_gex = daily_gex.merge(df_price[['date', 'close']], on='date', how='inner')
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if not daily_gex.empty:
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