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update cron job && hedge fund latest quarter

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This commit is contained in:
MuslemRahimi 2024-10-24 13:36:17 +02:00
parent 5cfb116bf2
commit 50b9bc72a9
3 changed files with 30 additions and 12 deletions
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2
app/create_institute_db.py
View File

@ -62,7 +62,7 @@ crypto_con.close()
load_dotenv()
api_key = os.getenv('FMP_API_KEY')
quarter_date = '2024-06-30'
quarter_date = '2024-09-30'
if os.path.exists("backup_db/institute.db"):

2
app/create_stock_db.py
View File

@ -27,7 +27,7 @@ warnings.filterwarnings("ignore", category=RuntimeWarning, message="invalid valu
start_date = datetime(2015, 1, 1).strftime("%Y-%m-%d")
end_date = datetime.today().strftime("%Y-%m-%d")
quarter_date = '2024-6-30'
quarter_date = '2024-09-30'
if os.path.exists("backup_db/stocks.db"):

38
app/support.py
View File

@ -16,11 +16,24 @@ def calculate_volatility(prices_df):
returns = prices_df['return'].dropna()
return returns.std() * np.sqrt(252)
# Load API key from environment
load_dotenv()
api_key = os.getenv('BENZINGA_API_KEY')
fin = financial_data.Benzinga(api_key)
# Connect to SQLite database
stock_con = sqlite3.connect('stocks.db')
etf_con = sqlite3.connect('etf.db')
stock_cursor = stock_con.cursor()
stock_cursor.execute("PRAGMA journal_mode = wal")
stock_cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol NOT LIKE '%.%' AND marketCap >= 500E6")
stock_symbols = [row[0] for row in stock_cursor.fetchall()]
etf_cursor = etf_con.cursor()
etf_cursor.execute("PRAGMA journal_mode = wal")
etf_cursor.execute("SELECT DISTINCT symbol FROM etfs")
etf_symbols = [row[0] for row in etf_cursor.fetchall()]
query_template = """
@ -30,20 +43,22 @@ query_template = """
"""
ticker = 'NVDA'
ticker = 'SPY'
end_date = date.today()
start_date = end_date - timedelta(1)
end_date_str = end_date.strftime('%Y-%m-%d')
start_date_str = start_date.strftime('%Y-%m-%d')
query = query_template.format(ticker=ticker)
df_price = pd.read_sql_query(query, stock_con, params=('2024-01-01', end_date_str)).round(2)
df_price = pd.read_sql_query(query, stock_con if ticker in stock_symbols else etf_con, params=('2024-01-01', end_date_str)).round(2)
df_price = df_price.rename(columns={"change_percent": "changesPercentage"})
volatility = calculate_volatility(df_price)
print(start_date, end_date)
print('volatility', volatility)
stock_con.close()
etf_con.close()
def get_data(ticker):
res_list = []
@ -80,14 +95,15 @@ def calculate_option_greeks(S, K, T, r, sigma, option_type='CALL'):
return 0, 0
d1 = (np.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * np.sqrt(T))
d2 = d1 - sigma * np.sqrt(T)
#d2 = d1 - sigma * np.sqrt(T)
if option_type == 'CALL':
delta = norm.cdf(d1)
gamma = norm.pdf(d1) / (S * sigma * np.sqrt(T))
#gamma = norm.pdf(d1) / (S * sigma * np.sqrt(T))
else: # PUT
delta = -norm.cdf(-d1)
gamma = norm.pdf(d1) / (S * sigma * np.sqrt(T))
delta = norm.cdf(d1) - 1 #-norm.cdf(-d1)
#gamma = norm.pdf(d1) / (S * sigma * np.sqrt(T))
gamma = norm.pdf(d1) / (S * sigma * np.sqrt(T)) if S > 0 and sigma > 0 and np.sqrt(T) > 0 else 0
return delta, gamma
@ -98,6 +114,7 @@ def process_options_data(df):
# Convert data types
df['strike_price'] = pd.to_numeric(df['strike_price'])
df['volume'] = pd.to_numeric(df['volume'])
df['open_interest'] = pd.to_numeric(df['open_interest'])
df['underlying_price'] = pd.to_numeric(df['underlying_price'])
df['date_expiration'] = pd.to_datetime(df['date_expiration'])
df['date'] = pd.to_datetime(df['date'])
@ -124,18 +141,19 @@ def process_options_data(df):
# Calculate DEX (Delta Exposure) and GEX (Gamma Exposure)
# Convert volume to float if it's not already
df['volume'] = df['volume'].astype(float)
df['volume'] = df['volume'].astype(int)
df['open_interest'] = df['open_interest'].astype(float)
# Get price per contract
df['price'] = pd.to_numeric(df['price'])
# Calculate position values
contract_multiplier = 100 # Standard option contract multiplier
df['position_value'] = df['price'] * df['volume'] * contract_multiplier
df['position_value'] = df['price'] * df['open_interest'] * contract_multiplier
# Calculate exposures
df['dex'] = df['delta'] * df['volume'] * contract_multiplier
df['gex'] = df['gamma'] * df['volume'] * contract_multiplier * df['underlying_price'] * 0.01
df['gex'] = df['gamma'] * df['volume'] * contract_multiplier #df['gamma'] * df['open_interest'] * df['volume'] * df['underlying_price']
df['dex'] = df['delta'] * df['volume'] * contract_multiplier * df['underlying_price'] * 0.01 #df['delta'] * df['volume'] * df['underlying_price'] *0.01
return df