Update app.py

app.py

@@ -11,11 +11,11 @@ from selenium.webdriver.common.by import By
 from selenium.webdriver.support.ui import WebDriverWait
 from selenium.webdriver.support import expected_conditions as EC
 
-def get_target_date(experations):
+def get_target_date(experations, min_days, max_days):
     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)
+    start_date = today + timedelta(days=min_days)
+    end_date = today + timedelta(days=max_days)
     filtered_dates = [date for date in experations_dates if start_date <= date <= end_date]
     return filtered_dates[0] if filtered_dates else None
 
@@ -36,7 +36,14 @@ def calculate_option_value(row, stock_price, risk_free_rate, maturity):
 def main():
     st.title("Options Analysis App")
 
+    # User inputs
     stock = st.text_input("Enter stock symbol (e.g., AAPL):", "AAPL")
+    option_type = st.selectbox("Option type:", ["Calls", "Puts"])
+    min_days = st.number_input("Minimum days to expiration:", value=60, min_value=0, max_value=365)
+    max_days = st.number_input("Maximum days to expiration:", value=80, min_value=0, max_value=365)
+    num_strikes = st.number_input("Number of strikes to display (above and below current price):", value=3, min_value=1, max_value=10)
+    use_custom_risk_free_rate = st.checkbox("Use custom risk-free rate")
+    custom_risk_free_rate = st.number_input("Custom risk-free rate (%):", value=4.0, min_value=0.0, max_value=20.0, disabled=not use_custom_risk_free_rate)
 
     if st.button("Analyze Options"):
         try:
@@ -44,9 +51,12 @@ def main():
             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}")
+            if use_custom_risk_free_rate:
+                risk_free_rate = custom_risk_free_rate / 100
+            else:
+                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:.2%}")
 
             # Set up Selenium with headless Chrome
             chrome_options = Options()
@@ -65,10 +75,10 @@ def main():
             experations_btn.click()
 
             experations = driver.find_elements(By.XPATH, '//span[contains(@class, "C($linkColor)")]')
-            target_date_dt = get_target_date(experations)
+            target_date_dt = get_target_date(experations, min_days, max_days)
 
             if not target_date_dt:
-                st.error("Could not find a suitable expiration date. Please try again.")
+                st.error(f"Could not find a suitable expiration date between {min_days} and {max_days} days from now. Please try different date ranges.")
                 driver.quit()
                 return
 
@@ -99,37 +109,47 @@ def main():
                 driver.quit()
                 return
 
-            calls = [i.text.split() for i in tables[0].find_elements(By.TAG_NAME,'tr')]
+            table_index = 0 if option_type == "Calls" else 1
+            options = [i.text.split() for i in tables[table_index].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)
+            options_df = pd.DataFrame(options[1:], columns=columns_names)
 
-            calls_df['Strike'] = calls_df['Strike'].astype(float)
+            options_df['Strike'] = options_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]
+            lower = options_df[options_df['Strike'] < stock_price].tail(num_strikes)[::-1]
+            higher = options_df[options_df['Strike'] > stock_price].head(num_strikes)[::-1]
 
-            calls_df = pd.concat([higher, lower])
-            calls_df.reset_index(drop=True, inplace=True)
+            options_df = pd.concat([higher, lower])
+            options_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}")
+            options_df["IV"] = options_df["Implied Volatility"].str.strip("%").astype(float)
+
+            if option_type == "Calls":
+                options_df[['BSM Price','Delta', 'Theta','Vega',"Gamma",'Rho']] = options_df[['Strike','IV']].apply(
+                    lambda row: calculate_option_value(row, stock_price, risk_free_rate, maturity), 
+                    axis=1, 
+                    result_type='expand'
+                )
+            else:
+                # For puts, we need to adjust the calculation
+                options_df[['BSM Price','Delta', 'Theta','Vega',"Gamma",'Rho']] = options_df[['Strike','IV']].apply(
+                    lambda row: calculate_option_value(row, stock_price, risk_free_rate, maturity), 
+                    axis=1, 
+                    result_type='expand'
+                )
+                options_df['Delta'] = options_df['Delta'] - 1  # Adjust delta for puts
+
+            options_df.drop(columns=["Date","Time","ET","Change","% Change","Implied Volatility"], inplace=True)
+
+            options_df['Ask'] = options_df['Ask'].astype(float)
+            options_df["Under"] = np.where(options_df['Ask'] < options_df['BSM Price'], True, False)
+
+            st.write(f"Analysis for {stock} {option_type.lower()} expiring on {target_date_str}")
             st.write(f"Current stock price: ${stock_price}")
-            st.dataframe(calls_df)
+            st.dataframe(options_df)
 
         except Exception as e:
             st.error(f"An error occurred: {str(e)}")