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bugfixing for bid ask midpoint ratio

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This commit is contained in:
MuslemRahimi 2024-09-16 19:27:56 +02:00
parent afb0271e2a
commit 1479cc127c
1 changed files with 13 additions and 10 deletions
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23
app/cron_options_gex.py
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@ -222,17 +222,23 @@ def get_historical_option_data(option_data_list, df_price):
# Calculate total volume # Calculate total volume
daily_summary['total_volume'] = daily_summary['c_vol'] + daily_summary['p_vol'] daily_summary['total_volume'] = daily_summary['c_vol'] + daily_summary['p_vol']
# Calculate bid/ask/midpoint ratios
# Calculate bid/ask/midpoint ratios # Calculate bid/ask/midpoint ratios
try: try:
daily_summary['bid_ratio'] = round(daily_summary['bid_vol'] / daily_summary['total_volume'] * 100, 2) if daily_summary['total_volume'] > 0:
daily_summary['ask_ratio'] = round(daily_summary['ask_vol'] / daily_summary['total_volume'] * 100, 2) daily_summary['bid_ratio'] = round(daily_summary['bid_vol'] / daily_summary['total_volume'] * 100, 2)
daily_summary['midpoint_ratio'] = round(daily_summary['midpoint_vol'] / daily_summary['total_volume'] * 100, 2) daily_summary['ask_ratio'] = round(daily_summary['ask_vol'] / daily_summary['total_volume'] * 100, 2)
except ZeroDivisionError: daily_summary['midpoint_ratio'] = round(daily_summary['midpoint_vol'] / daily_summary['total_volume'] * 100, 2)
else:
daily_summary['bid_ratio'] = None
daily_summary['ask_ratio'] = None
daily_summary['midpoint_ratio'] = None
except:
daily_summary['bid_ratio'] = None daily_summary['bid_ratio'] = None
daily_summary['ask_ratio'] = None daily_summary['ask_ratio'] = None
daily_summary['midpoint_ratio'] = None daily_summary['midpoint_ratio'] = None
# Calculate OTM percentage for each date and assign it to the daily_summary # Calculate OTM percentage for each date and assign it to the daily_summary
daily_summary['otm_ratio'] = df_summary.groupby('date').apply(lambda df: round(calculate_otm_percentage(df.to_dict('records')), 1)).values daily_summary['otm_ratio'] = df_summary.groupby('date').apply(lambda df: round(calculate_otm_percentage(df.to_dict('records')), 1)).values
@ -242,7 +248,7 @@ def get_historical_option_data(option_data_list, df_price):
daily_summary['bull_ratio'] = round(daily_summary['total_bull_prem'] / total_prem * 100, 2) daily_summary['bull_ratio'] = round(daily_summary['total_bull_prem'] / total_prem * 100, 2)
daily_summary['bear_ratio'] = round(daily_summary['total_bear_prem'] / total_prem * 100, 2) daily_summary['bear_ratio'] = round(daily_summary['total_bear_prem'] / total_prem * 100, 2)
daily_summary['neutral_ratio'] = round(daily_summary['total_neutral_prem'] / total_prem * 100, 2) daily_summary['neutral_ratio'] = round(daily_summary['total_neutral_prem'] / total_prem * 100, 2)
except ZeroDivisionError: except:
daily_summary['bull_ratio'] = None daily_summary['bull_ratio'] = None
daily_summary['bear_ratio'] = None daily_summary['bear_ratio'] = None
daily_summary['neutral_ratio'] = None daily_summary['neutral_ratio'] = None
@ -355,7 +361,7 @@ etf_cursor.execute("PRAGMA journal_mode = wal")
etf_cursor.execute("SELECT DISTINCT symbol FROM etfs") etf_cursor.execute("SELECT DISTINCT symbol FROM etfs")
etf_symbols = [row[0] for row in etf_cursor.fetchall()] etf_symbols = [row[0] for row in etf_cursor.fetchall()]
total_symbols = stock_symbols + etf_symbols total_symbols = ['NUVL'] #stock_symbols + etf_symbols
query_template = """ query_template = """
SELECT date, close,change_percent SELECT date, close,change_percent
@ -373,7 +379,6 @@ for ticker in total_symbols:
volatility = calculate_volatility(df_price) volatility = calculate_volatility(df_price)
ticker_data = get_data(ticker) ticker_data = get_data(ticker)
# Group ticker_data by 'date' and collect all items for each date # Group ticker_data by 'date' and collect all items for each date
grouped_history = defaultdict(list) grouped_history = defaultdict(list)
for item in ticker_data: for item in ticker_data:
@ -394,11 +399,9 @@ for ticker in total_symbols:
except: except:
pass pass
daily_historical_option_data = get_historical_option_data(ticker_data, df_price) daily_historical_option_data = get_historical_option_data(ticker_data, df_price)
daily_historical_option_data = daily_historical_option_data.merge(df_price[['date', 'changesPercentage']], on='date', how='inner') daily_historical_option_data = daily_historical_option_data.merge(df_price[['date', 'changesPercentage']], on='date', how='inner')
# Add "history" column containing all filtered items with the same date # Add "history" column containing all filtered items with the same date
#daily_historical_option_data['history'] = daily_historical_option_data['date'].apply(lambda x: grouped_history.get(x, [])) #daily_historical_option_data['history'] = daily_historical_option_data['date'].apply(lambda x: grouped_history.get(x, []))