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update options cron job

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This commit is contained in:
MuslemRahimi 2024-09-16 00:09:17 +02:00
parent 42d8dba973
commit df63523185
1 changed files with 56 additions and 28 deletions
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84
app/cron_options_gex.py
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@ -24,7 +24,7 @@ def save_json(symbol, data, file_path,filename=None):
# Define the keys to keep
keys_to_keep = {'time', 'sentiment', 'option_activity_type', 'price', 'underlying_price', 'cost_basis', 'strike_price', 'date', 'date_expiration', 'open_interest', 'put_call', 'volume'}
keys_to_keep = {'time', 'sentiment', 'execution_estimate','option_activity_type', 'price', 'underlying_price', 'cost_basis', 'strike_price', 'date', 'date_expiration', 'open_interest', 'put_call', 'volume'}
def filter_data(item):
# Filter the item to keep only the specified keys and format fields
@ -33,6 +33,7 @@ def filter_data(item):
filtered_item['sentiment'] = filtered_item['sentiment'].capitalize()
filtered_item['underlying_price'] = round(float(filtered_item['underlying_price']), 2)
filtered_item['put_call'] = 'Calls' if filtered_item['put_call'] == 'CALL' else 'Puts'
filtered_item['execution_estimate'] = filtered_item['execution_estimate'].replace('_',' ').title()
return filtered_item
@ -64,8 +65,10 @@ def gamma(S, K, T, r, sigma):
except ZeroDivisionError:
return 0
def compute_gex(option_data, r=0.05, sigma=0.2):
def compute_gex_and_dex(option_data, r=0.05, sigma=0.2):
"""
Compute GEX (Gamma Exposure) and DEX (Delta Exposure) for the given option data.
"""
timestamp = datetime.strptime(option_data['date'], "%Y-%m-%d")
try:
@ -75,32 +78,39 @@ def compute_gex(option_data, r=0.05, sigma=0.2):
expiration_date = datetime.strptime(option_data['date_expiration'], "%Y-%m-%d")
T = (expiration_date - timestamp).days / 365.0
if T < 0:
return 0, timestamp.date()
return 0, 0, timestamp.date() # return 0 for both GEX and DEX if T is negative
elif T == 0:
T = 1 #Consider 0DTE options
T = 1 # Consider 0DTE options
option_type = option_data['put_call']
delta_value = delta(S, K, T, r, sigma, option_type)
gamma_value = gamma(S, K, T, r, sigma)
notional = size * S
gex = gamma_value * size * int(option_data['volume']) * S #gamma_value * notional * delta_value
# Calculate GEX (Gamma Exposure)
gex = gamma_value * size * int(option_data['volume']) * S # gamma_value * notional
# Calculate DEX (Delta Exposure)
dex = delta_value * size * S # delta_value * notional
return gex, timestamp.date()
return gex, dex, timestamp.date()
except:
return 0, timestamp.date()
return 0, 0, timestamp.date()
def compute_daily_gex(option_data_list, volatility):
gex_data = []
def compute_daily_gex_and_dex(option_data_list, volatility):
gex_dex_data = []
for option_data in option_data_list:
gex, trade_date = compute_gex(option_data, sigma=volatility)
if gex != 0:
gex_data.append({'date': trade_date, 'gex': gex})
gex, dex, trade_date = compute_gex_and_dex(option_data, sigma=volatility)
if gex != 0 or dex != 0:
gex_dex_data.append({'date': trade_date, 'gex': gex, 'dex': dex})
gex_df = pd.DataFrame(gex_data)
daily_gex = gex_df.groupby('date')['gex'].sum().reset_index()
daily_gex['gex'] = round(daily_gex['gex'], 0)
daily_gex['date'] = daily_gex['date'].astype(str)
return daily_gex
gex_dex_df = pd.DataFrame(gex_dex_data)
daily_gex_dex = gex_dex_df.groupby('date').agg({'gex': 'sum', 'dex': 'sum'}).reset_index()
daily_gex_dex['gex'] = round(daily_gex_dex['gex'], 0)
daily_gex_dex['dex'] = round(daily_gex_dex['dex'], 0)
daily_gex_dex['date'] = daily_gex_dex['date'].astype(str)
return daily_gex_dex
def calculate_otm_percentage(option_data_list):
otm_count = 0
@ -134,6 +144,7 @@ def get_historical_option_data(option_data_list, df_price):
strike_price = float(option_data.get('strike_price', 0))
put_call = option_data.get('put_call', 'CALL')
sentiment = option_data.get('sentiment', 'NEUTRAL')
execution_estimate = option_data.get('execution_estimate', 'UNKNOWN')
# Safely convert premium to float, default to 0 if missing or invalid
try:
@ -166,6 +177,12 @@ def get_historical_option_data(option_data_list, df_price):
bear_premium = 0
neutral_premium = premium
# Categorize volume based on execution_estimate
bid_vol = volume if "bid" in execution_estimate.lower() else 0
ask_vol = volume if "ask" in execution_estimate.lower() else 0
midpoint_vol = volume if "midpoint" in execution_estimate.lower() else 0
# Append option data for later summarization
summary_data.append({
'date': date,
@ -175,6 +192,9 @@ def get_historical_option_data(option_data_list, df_price):
'bull_premium': bull_premium,
'bear_premium': bear_premium,
'neutral_premium': neutral_premium,
'bid_vol': bid_vol,
'ask_vol': ask_vol,
'midpoint_vol': midpoint_vol,
'put_call': put_call,
'strike_price': strike_price,
'stock_price': stock_price
@ -187,10 +207,6 @@ def get_historical_option_data(option_data_list, df_price):
# Summarize by date
df_summary = pd.DataFrame(summary_data)
# Calculate OTM percentage for each day
def calculate_daily_otm(df):
return calculate_otm_percentage(df.to_dict('records')) # Pass the day's options for OTM calculation
# Apply OTM percentage calculation for each day
daily_summary = df_summary.groupby('date').agg(
total_oi=('open_interest', 'sum'),
@ -199,12 +215,27 @@ def get_historical_option_data(option_data_list, df_price):
total_neutral_prem=('neutral_premium', 'sum'),
c_vol=('c_vol', 'sum'),
p_vol=('p_vol', 'sum'),
bid_vol=('bid_vol', 'sum'),
ask_vol=('ask_vol', 'sum'),
midpoint_vol=('midpoint_vol', 'sum')
).reset_index()
# Calculate total volume
daily_summary['total_volume'] = daily_summary['c_vol'] + daily_summary['p_vol']
# Calculate bid/ask/midpoint ratios
try:
daily_summary['bid_ratio'] = round(daily_summary['bid_vol'] / daily_summary['total_volume'] * 100, 2)
daily_summary['ask_ratio'] = round(daily_summary['ask_vol'] / daily_summary['total_volume'] * 100, 2)
daily_summary['midpoint_ratio'] = round(daily_summary['midpoint_vol'] / daily_summary['total_volume'] * 100, 2)
except ZeroDivisionError:
daily_summary['bid_ratio'] = None
daily_summary['ask_ratio'] = None
daily_summary['midpoint_ratio'] = None
# 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
# Calculate Bull/Bear/Neutral ratios
try:
total_prem = daily_summary['total_bull_prem'] + daily_summary['total_bear_prem'] + daily_summary['total_neutral_prem']
@ -216,12 +247,10 @@ def get_historical_option_data(option_data_list, df_price):
daily_summary['bear_ratio'] = None
daily_summary['neutral_ratio'] = None
# Calculate total volume (call + put) and format other fields
daily_summary['total_volume'] = round(daily_summary['c_vol'] + daily_summary['p_vol'], 2)
# Format other fields
daily_summary['total_neutral_prem'] = round(daily_summary['total_neutral_prem'], 2)
daily_summary['date'] = daily_summary['date'].astype(str)
daily_summary = daily_summary.sort_values(by='date', ascending=False)
# Return the summarized dataframe
return daily_summary
@ -382,8 +411,7 @@ for ticker in total_symbols:
save_json(ticker, option_chain_data.to_dict('records'), 'json/options-chain/companies')
daily_gex = compute_daily_gex(ticker_data, volatility)
daily_gex = compute_daily_gex_and_dex(ticker_data, volatility)
daily_gex = daily_gex.merge(df_price[['date', 'close']], on='date', how='inner')
if not daily_gex.empty:
save_json(ticker, daily_gex.to_dict('records'), 'json/options-gex/companies')