update dashboard

app/cron_dashboard.py

@@ -223,8 +223,8 @@ async def get_recent_earnings(session):
 			pass
 
 	res_list = remove_duplicates(res_list)
-	#res_list.sort(key=lambda x: x['marketCap'], reverse=True)
+	res_list.sort(key=lambda x: x['marketCap'], reverse=True)
-	res_list.sort(key=lambda x: (-parse_time(x['time']).timestamp(), -x['marketCap']))
+	#res_list.sort(key=lambda x: (-parse_time(x['time']).timestamp(), -x['marketCap']))
 	res_list = [{k: v for k, v in d.items() if k != 'marketCap'} for d in res_list]
 	return res_list[0:10]
 

app/support.py

@@ -0,0 +1,228 @@
+import pandas as pd
+import numpy as np
+import ujson
+import matplotlib.pyplot as plt
+from scipy.stats import norm
+from datetime import datetime, date, timedelta
+from benzinga import financial_data
+import os
+from dotenv import load_dotenv
+import seaborn as sns
+import sqlite3
+
+def calculate_volatility(prices_df):
+    prices_df = prices_df.sort_values(by='date')
+    prices_df['return'] = prices_df['close'].pct_change()
+    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)
+stock_con = sqlite3.connect('stocks.db')
+
+
+query_template = """
+    SELECT date, close,change_percent
+    FROM "{ticker}"
+    WHERE date BETWEEN ? AND ?
+"""
+
+
+ticker = 'NVDA'
+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 = df_price.rename(columns={"change_percent": "changesPercentage"})
+
+volatility = calculate_volatility(df_price)
+print(start_date, end_date)
+print('volatility', volatility)
+
+
+def get_data(ticker):
+    res_list = []
+    page = 0
+    while True:
+        try:
+            data = fin.options_activity(date_from=start_date_str, date_to=end_date_str, company_tickers=ticker, page=page, pagesize=1000)
+            data = ujson.loads(fin.output(data))['option_activity']
+            if not data:
+                break  # Stop when no more data is returned
+            filtered_data = [{key: value for key, value in item.items() if key not in ['description_extended', 'updated']} for item in data]
+            res_list += filtered_data
+        except Exception as e:
+            print(e)
+            break
+        page += 1
+    return res_list
+
+
+ticker_data = get_data(ticker)
+ticker_data = [item for item in ticker_data if datetime.strptime(item['date_expiration'], '%Y-%m-%d') >= datetime.now()]
+print(len(ticker_data))
+
+def calculate_option_greeks(S, K, T, r, sigma, option_type='CALL'):
+    """
+    Calculate option Greeks using Black-Scholes formula
+    S: Current stock price
+    K: Strike price
+    T: Time to expiration (in years)
+    r: Risk-free rate
+    sigma: Volatility
+    """
+    if T <= 0:
+        return 0, 0
+    
+    d1 = (np.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (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))
+    else:  # PUT
+        delta = -norm.cdf(-d1)
+        gamma = norm.pdf(d1) / (S * sigma * np.sqrt(T))
+        
+    return delta, gamma
+
+def process_options_data(df):
+    """
+    Process options data and calculate DEX and GEX
+    """
+    # Convert data types
+    df['strike_price'] = pd.to_numeric(df['strike_price'])
+    df['volume'] = pd.to_numeric(df['volume'])
+    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'])
+    
+    # Calculate time to expiration in years
+    df['T'] = (df['date_expiration'] - df['date']).dt.days / 365.0
+    
+    # Parameters for calculations
+    risk_free_rate = 0.05  # Current approximate risk-free rate
+    # Calculate historical volatility (or you could use implied volatility if available)
+    sigma = volatility #df['underlying_price'].pct_change().std() * np.sqrt(252) if len(df) > 30 else 0.3
+    
+    # Calculate Greeks for each option
+    greeks = df.apply(lambda row: calculate_option_greeks(
+        row['underlying_price'],
+        row['strike_price'],
+        row['T'],
+        risk_free_rate,
+        sigma,
+        row['put_call']
+    ), axis=1)
+    
+    df['delta'], df['gamma'] = zip(*greeks)
+    
+    # Calculate DEX (Delta Exposure) and GEX (Gamma Exposure)
+    # Convert volume to float if it's not already
+    df['volume'] = df['volume'].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
+    
+    # Calculate exposures
+    df['dex'] = df['delta'] * df['volume'] * contract_multiplier
+    df['gex'] = df['gamma'] * df['volume'] * contract_multiplier * df['underlying_price'] * 0.01
+    
+    return df
+
+def plot_option_exposure(df, current_price):
+    """
+    Create visualization of DEX and GEX profiles with focused y-axis
+    """
+    plt.style.use('dark_background')
+    fig, ax = plt.subplots(figsize=(12, 8))
+    
+    # Aggregate exposures by strike price
+    dex_by_strike = df.groupby('strike_price')['dex'].sum().reset_index()
+    gex_by_strike = df.groupby('strike_price')['gex'].sum().reset_index()
+    
+    # Filter out strikes with no significant exposure
+    significant_exposure = abs(dex_by_strike['dex']) > abs(dex_by_strike['dex']).max() * 0.01
+    min_strike = dex_by_strike[significant_exposure]['strike_price'].min()
+    max_strike = dex_by_strike[significant_exposure]['strike_price'].max()
+    
+    # Add some padding to the range
+    strike_padding = (max_strike - min_strike) * 0.1
+    y_min = max(min_strike - strike_padding, dex_by_strike['strike_price'].min())
+    y_max = min(max_strike + strike_padding, dex_by_strike['strike_price'].max())
+    
+    # Plot DEX bars
+    positive_dex = dex_by_strike[dex_by_strike['dex'] > 0]
+    negative_dex = dex_by_strike[dex_by_strike['dex'] <= 0]
+    
+    ax.barh(positive_dex['strike_price'], positive_dex['dex'],
+            color='green', alpha=0.7, label='Positive DEX')
+    ax.barh(negative_dex['strike_price'], negative_dex['dex'],
+            color='#964B00', alpha=0.7, label='Negative DEX')
+    
+    # Plot GEX profile
+    ax.plot(gex_by_strike['gex'], gex_by_strike['strike_price'],
+            color='yellow', label='GEX Profile', linewidth=2)
+    
+    # Calculate and plot support/resistance levels
+    significant_gex = gex_by_strike[abs(gex_by_strike['gex']) > abs(gex_by_strike['gex']).mean()]
+    resistance_level = significant_gex[significant_gex['strike_price'] > current_price]['strike_price'].min()
+    support_level = significant_gex[significant_gex['strike_price'] < current_price]['strike_price'].max()
+    
+    # Add reference lines
+    if pd.notna(resistance_level):
+        ax.axhline(y=resistance_level, color='red', linestyle='--', alpha=0.5,
+                   label=f'Call Resistance: {resistance_level:.1f}')
+    if pd.notna(support_level):
+        ax.axhline(y=support_level, color='green', linestyle='--', alpha=0.5,
+                   label=f'Put Support: {support_level:.1f}')
+    ax.axhline(y=current_price, color='white', linestyle='--', alpha=0.5,
+               label=f'Spot Price: {current_price:.1f}')
+    
+    # Calculate and plot HVL
+    hvl = dex_by_strike['strike_price'][abs(dex_by_strike['dex']).idxmin()]
+    ax.axhline(y=hvl, color='gray', linestyle='--', alpha=0.5,
+               label=f'HVL: {hvl:.1f}')
+    
+    # Set y-axis limits to focus on relevant region
+    ax.set_ylim(y_min, y_max)
+    
+    # Customize the plot
+    ax.set_title(f'Net DEX All Expirations for {df["ticker"].iloc[0]}\nTimestamp: {df["date"].iloc[0]}', pad=20)
+    ax.set_xlabel('Exposure (Contract-Adjusted)')
+    ax.set_ylabel('Strike Price')
+    ax.grid(True, alpha=0.2)
+    ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
+    
+    # Format axis
+    ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'{x/1e6:.1f}B' if abs(x) >= 1e6 else f'{x/1e3:.1f}M'))
+    
+    plt.tight_layout()
+    return fig
+    
+# Use the functions with your data
+df = pd.DataFrame(ticker_data)
+processed_df = process_options_data(df)
+current_price = float(df['underlying_price'].iloc[0])
+fig = plot_option_exposure(processed_df, current_price)
+plt.show()
+
+# Print analysis
+print("\nKey Levels Analysis:")
+dex_by_strike = processed_df.groupby('strike_price')['dex'].sum()
+gex_by_strike = processed_df.groupby('strike_price')['gex'].sum()
+
+print(f"Largest DEX Positive: Strike ${dex_by_strike.idxmax():.2f} (${dex_by_strike.max()/1e6:.2f}M)")
+print(f"Largest DEX Negative: Strike ${dex_by_strike.idxmin():.2f} (${dex_by_strike.min()/1e6:.2f}M)")
+print(f"Largest GEX: Strike ${gex_by_strike.idxmax():.2f} (${gex_by_strike.max()/1e6:.2f}M)")
+print(f"Net DEX: ${dex_by_strike.sum()/1e6:.2f}M")
+print(f"Net GEX: ${gex_by_strike.sum()/1e6:.2f}M")