Update app.py

app.py

@@ -0,0 +1,109 @@
+import streamlit as st
+import numpy as np
+import pandas as pd
+from datetime import datetime, timedelta
+import mibian
+from yahoofinancials import YahooFinancials
+import matplotlib.pyplot as plt
+from selenium.webdriver import Chrome
+from selenium.webdriver.common.by import By
+import chromedriver_autoinstaller
+
+# Install the correct version of ChromeDriver
+chromedriver_autoinstaller.install()
+
+def get_target_date(experations):
+    today = datetime.today()
+    experations_dates = [datetime.strptime(i.text, '%b %d, %Y') for i in experations if len(i.text)>5 and i.text[-4:]=='2024']
+    start_date = today + timedelta(days=60)
+    end_date = today + timedelta(days=80)
+    filtered_dates = [date for date in experations_dates if start_date <= date <= end_date]
+    return filtered_dates[0]
+
+def calculate_option_value(row, stock_price, risk_free_rate, maturity):
+    strike = row['Strike']
+    volatility = row['IV']
+    
+    option = mibian.BS([stock_price, strike, risk_free_rate, maturity], volatility=volatility)
+    return [
+        option.callPrice,
+        option.callDelta, 
+        option.callTheta,
+        option.vega,
+        option.gamma,
+        option.callRho
+    ]
+
+def main():
+    st.title("Options Analysis App")
+
+    stock = st.text_input("Enter stock symbol (e.g., AAPL):", "AAPL")
+
+    if st.button("Analyze Options"):
+        # Get current date
+        today = datetime.today()
+
+        # Get risk-free rate
+        yahoo_financials_treasuries = YahooFinancials('^TNX')
+        risk_free_rate = round(yahoo_financials_treasuries.get_current_price()/100, 4)
+        st.write(f"Risk-free rate: {risk_free_rate}")
+
+        # Set up Selenium
+        url = f"https://finance.yahoo.com/quote/{stock}/options/?straddle=false"
+        driver = Chrome()
+        driver.get(url)
+
+        experations_btn = driver.find_element(By.XPATH, '/html/body/div[1]/main/section/section/section/article/section[2]/div/div[1]/button').click()
+        experations = driver.find_elements(By.CLASS_NAME, 'itm.svelte-5qjwyh')
+        target_date_dt = get_target_date(experations)
+
+        # Desired date as a string
+        target_date_str = target_date_dt.strftime('%b %d, %Y')
+
+        # Loop through date elements to find the desired date and click on it
+        for element in experations:
+            if element.text == target_date_str:
+                element.click()
+                break
+
+        driver.implicitly_wait(5)
+        tables = driver.find_elements(By.TAG_NAME, "table")
+        stock_price = float(driver.find_element(By.CLASS_NAME, "livePrice").text)
+
+        calls = [i.text.split() for i in tables[0].find_elements(By.TAG_NAME,'tr')]
+
+        columns_names = ['Contract','Date','Time','ET','Strike','Price','Bid','Ask','Change','% Change','Volume','Open Interest','Implied Volatility']
+        calls_df = pd.DataFrame(calls[1:], columns=columns_names)
+
+        calls_df['Strike'] = calls_df['Strike'].astype(float)
+
+        lower = calls_df[calls_df['Strike'] < stock_price].tail(3)[::-1]
+        higher = calls_df[calls_df['Strike'] > stock_price].head(3)[::-1]
+
+        calls_df = pd.concat([higher, lower])
+        calls_df.reset_index(drop=True, inplace=True)
+
+        maturity = (target_date_dt - today).days
+
+        calls_df["IV"] = calls_df["Implied Volatility"].str.strip("%").astype(float)
+
+        calls_df[['BSM Price','Call Delta', 'Call Theta','Vega',"Gamma",'Rho']] = calls_df[['Strike','IV']].apply(
+            lambda row: calculate_option_value(row, stock_price, risk_free_rate, maturity), 
+            axis=1, 
+            result_type='expand'
+        )
+
+        calls_df.drop(columns=["Date","Time","ET","Change","% Change","Implied Volatility"], inplace=True)
+
+        calls_df['Ask'] = calls_df['Ask'].astype(float)
+        calls_df["Under"] = np.where(calls_df['Ask'] < calls_df['BSM Price'], True, False)
+
+        st.write(f"Analysis for {stock} options expiring on {target_date_str}")
+        st.write(f"Current stock price: ${stock_price}")
+        st.dataframe(calls_df)
+
+        # Close the browser
+        driver.quit()
+
+if __name__ == "__main__":
+    main()