performance.py

'''
Example 9 for using yfinance
Calculate annual, trailing, cumumlative, and CAGR returns for multiple stocks.
* The start date can be an arbitrary date.  The default is the current date.
* annual return is displayed from the default current day, or an arbitrary given
  day (except for Feb 29 for leap year)
  For leap years, use Feb 28 to replace Feb 29 as simplification & approximation
* trailing, cumumlative returns are currently displayed from the month boundary (last day of Month)
  prior to the given date.
* However, trailing, cumumlative returns can be displayed
  from any date, which can be not at the month boundary (last day of Month),
  by minor change of setting calculation_end_date_for_others_str = calculation_end_date_str.
  prior to the given date in the function "calculation_response(message, history)"
Author: Gang Luo

yfinance References:
  code:      https://github.com/ranaroussi/yfinance
  project:   https://pypi.org/project/yfinance/
  Guide:     https://algotrading101.com/learn/yfinance-guide/


Revision history:
2025-02.23.1444: fixing issues of missing "Adj Close" in yf.download and   yf.Ticker("AAPL"),
   caused by (https://github.com/ranaroussi/yfinance/issues/2283) which is introduced by
   yfinance version 0.2.54 (released on Feb 18, 2025 ).
2025-02.23.1655:  further fix for the issues from  (https://github.com/ranaroussi/yfinance/issues/2283).
   The  "Adj Close" column is missing from yf.download since  yf.download default changed
   from auto_adjust=False to auto_adjust=True. When auto_adjust=True,  column Close is actually Adj Close and
   Adj Close column does not exist any more.
   The  "Adj Close" column is also missing from using  ticker = yf.Ticker("AAPL") data = ticker.history(period="1y")

   The fixes 1: In order to fix the issue in the function stock_prices_df, auto_adjust=False is used explicitly in download function, to get back the Adj Close column.

   The fixes 2: The function "get_yearly_single_stock_data" in part 6 is broken duo to the missing "Adj Close" column
     from ticker = yf.Ticker() and ticker.history().  Add auto_adjust=False into ticker.history(..., auto_adjust=False)
     for fixing the issue.   However, after the fix, the following line in the part 6 has an error:
     complete_history = complete_history.merge(dld_history, how='left', left_index=True, right_index=True)
     The root cause is that Columns of dld_history is of MultiIndex(,  names=['Price', 'Ticker']). However, each price column
     such as 'Close','AdjClose' has only single level with Ticker being column index name.
     Dropping the column MultiIndex level ('Ticker') fixed the issue (dld_history.columns = dld_history.columns.droplevel(1) )

     print("\n===== DataFrame Structure Information for debug =====")
     print("Index Levels:", dld_history.index.names)   # Shows the index levels
     print("Index:", dld_history.index)                # Shows the actual index
     print("Columns:", dld_history.columns)            # Shows column names
     print("Data Types:\n", dld_history.dtypes)        # Shows data types of each column
     print("Shape (Rows, Columns):", dld_history.shape)  # Shows the shape of the DataFrame
2025-02.23.2000: Add the test cases for unit testing of part 1,2,3,4
                 Comment out part 5 which is not used, for better performance.
2025-02.23.2040: Fix the date errors
2025-02.24.2200: updated unit test cases
2025-02.25.0027: make output fonts smaller than the default font size using font 14 by
  using css in gradio.app, provided by deepseek R1
2025-02.25.0028:   years_list = [1, 2, 3, 4,5,6,7,8,9,10,11,12,13,14,15, 20, 25, 30, 40, 50, 60]
'''

script_version = 'version: (2025-02.25.0028)'
import gradio as gr
import yfinance as yf
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import pytz
DEBUG_ENABLED = True
#==============================================================================

print_yearly_total_return = True
num_years_calculation=52   # total years for calculation

# Define a list of years to calculate the trailing returns, cumulative returns, and so on
# remove the row of current year row since it is not a full year.
#years_list = [1, 2, 3, 5, 10, 15, 20, 25, 30, 40, 50, 60]
years_list = [1, 2, 3, 4,5,6,7,8,9,10,11,12,13,14,15, 20, 25, 30, 40, 50, 60]

# Set the stock tickers list
tickers_lists = [["qqq","hxq.to","spy", "vfv.to","xiu.to", "xbb.to","xcb.to","xhb.to"], #0  checking ETF
    ["qqq","spy", "vfv.to", "vgg.to", "zlu.to", "xiu.to","zlb.to","vdy.to", "xfn.to", "ry.to", "td.to", "na.to",
      "slf.to", "gwo.to", "bce.to", "t.to", "rci-b.to", "enb.to", "trp.to","cp.to"], #1 main monitoring list
    ["xiu.to", "xfn.to", "na.to","ry.to", "bmo.to","bns.to", "td.to", "cm.to", "cwb.to",
      "slf.to", "gwo.to", "bce.to", "t.to", "rci-b.to", "enb.to", "trp.to", "vdy.to","xdv.to","cdz.to","xdiv.to", "zeb.to"],  #2   financial  ETF & stocks
    ["spy","qqq","tqqq","mags","msft","AAPL","goog","AMZN","NVDA","meta","tsla","BRK-A","shop.to","hxq.to"],   #3  US mega stocks + risky shopfy
    ["^DJI","dia","^GSPC","spy","voo","ivv", "tpu-u.to","vfv.to", "zsp.to","hxs.to","tpu.to","xus.to", "xsp.to",
      "^IXIC","^ndx", "qqq","hxq.to","^GSPTSE","xic.to","xiu.to", "HXT.TO", "TTP.TO","ZCN.TO", "xfn.to", "xit.to"], #4   indexes and index ETFs
    ["dia","^DJI","^GSPC","spy","vfv.to", "zsp.to","hxs.to","xus.to", "xsp.to",
      "^IXIC","qqq","hxq.to","^GSPTSE","xic.to","xiu.to", "HXT.TO", "xfn.to"], #5   indexes and typical index ETFs
    ["^IXIC","^ndx","ONEQ","CIBR","QQJG", "qqq", "tqqq", "spy", "vfv.to", "HXQ.to", "ZQQ.to", "XQQ.to", "QQC.to", "ZNQ.TO",
         "xiu.to", "xit.to"],  #6   Nasdaq ETF and TSX IT ETF
    ["qqq","tqqq","sqqq", "QLD", "spy", "spxu", "upro", "sso", "spxl","tecl"], #7 leveraged ETFs
    ["^IXIC","^DJI","^GSPC","^GSPTSE"], #8 testing
    ["vfv.to","spy"] #9 testing
]

#==============================================================================
# Part 1:
#  retrieve daily adjusted close prices of a list of tickers from yahoo finance
#  Generate the year-end adjusted close prices
#  return year-end adjusted close prices, and  daily adjusted close prices
def stock_prices_df(tickers_list, end_date_str):
    tickers_list_upper = [ticker.upper() for ticker in tickers_list]
    tickers_str = ", ".join(tickers_list_upper)
    try:
        '''
        'try' statement for handlingy the exception error  for yf.download
        '''
        # Download the historical data,  see 2025-02.23.1655 revision note
        data = yf.download(tickers_str, period="max", auto_adjust=False) # default changed to auto_adjust=True at yfinance version 0.2.54,
                                                                         # when auto_adjust=True,  Close = Adj Close and Adj Close does not exist
    except:
        return  pd.DataFrame()
    else:
        data_adj_close = data['Adj Close']

    # Filter out rows with dates newer than calculation_end_date
    data_adj_close = data_adj_close[data_adj_close.index <= end_date_str]
    #print("\nDebug- stock_prices_df\n", data_adj_close)

    # Rearrange columns based on the order in tickers_list_upper
    if len(tickers_list)>1:
        data_adj_close = data_adj_close.reindex(columns=tickers_list_upper)

    # needed this when having only a single ticker in the ticker list
    if len(tickers_list_upper)==1:
        data_adj_close = pd.DataFrame(data_adj_close)
        data_adj_close.rename(columns={'Adj Close': tickers_list_upper[0]}, inplace=True)

    data_adj_close.columns = map(str.lower, data_adj_close.columns) # must after  pd.DataFrame(data_adj_close)
    # data_adj_close_year_end = data_adj_close.resample('A').ffill().round(2) # must before index changed to date
    data_adj_close_year_end = data_adj_close.resample('YE').ffill().round(2) # must before index changed to date

    data_adj_close.index=data_adj_close.index.date
    data_adj_close_year_end.index=data_adj_close_year_end.index.date

    last_date = data_adj_close_year_end.index[-1]
    data_adj_close_year_end = data_adj_close_year_end.rename(index={last_date: end_date_str})
    #print("\nstock_prices_df\n", end_date_str, "\n", data_adj_close_year_end)
    return data_adj_close_year_end, data_adj_close

#==============================================================================
# Part 2:  Calculate annual returns at year end, and at any given day (by calculation_end_date_str)
#
# annual return calculation can start at any given day
def get_annual_returns_anyday_df(daily_adj_close_df, calculation_end_date_str):

    calculation_end_date=pd.to_datetime(calculation_end_date_str).tz_localize('America/New_York')

    # Create a DataFrame with a complete date range
    date_range = pd.date_range(start=daily_adj_close_df.index.min(), end=daily_adj_close_df.index.max(), freq='D')

    complete_stock_history = pd.DataFrame(index=date_range)
    # Merge the complete DataFrame with the original stock_history
    complete_stock_history = complete_stock_history.merge(daily_adj_close_df, how='left', left_index=True, right_index=True)
    complete_stock_history = complete_stock_history.ffill()  # fill the newy added rows with previous day value
    '''
    Filter out the rows that matches the month and date of calculation_end_date, which are the ends of
    annual periods from the calculation_end_date.
    '''
    # Filter out rows with dates newer than calculation_end_date
    #filtered_stock_history = complete_stock_history[complete_stock_history.index <= calculation_end_date]
    # note" daily_adj_close_df satisfys daily_adj_close_df.index <= calculation_end_date
    filtered_stock_history = complete_stock_history
    #print(filtered_stock_history)
    target_month=filtered_stock_history.index.max().month
    target_day=filtered_stock_history.index.max().day
    #print("target_month", target_month, "target_day",target_day, "start_year", filtered_stock_history.index.max().year)
    annual_returns = filtered_stock_history[(filtered_stock_history.index.month == target_month)
           & (filtered_stock_history.index.day ==target_day)]
    annual_returns_percent = annual_returns.pct_change().dropna(how='all')

    annual_returns_df = pd.DataFrame(annual_returns_percent)
    #print("\ndebug-annual_returns_df\n", annual_returns_df)
    return annual_returns_df

# annual return calculation can start at year end
def get_annual_returns_year_end_df(data_adj_close_df, calculation_end_date_str):
    annual_returns_percent = data_adj_close_df.pct_change().dropna(how='all')
    return annual_returns_percent

#==============================================================================
# Part 3: calculate the annualized trailing total return from the data generated in step 1 & display
# Define a function to calculate the annualized trailing total return for a given number of years
def get_trailing_return(ticker, data, years):
    # Get the total return values for the last n years
    trailing_data = data[ticker].tail(years)
    # Check if there are empty values within years
    if trailing_data.isna().any():
        return np.nan
    # Check if there are valid total return values for all years
    if len(trailing_data) == years:
        # Convert the percentage strings to numeric values
        trailing_data = trailing_data.astype(str).str.replace('%', '').astype(float)
        """ Calculate the annualized trailing total return using the formula from Investopedia[^1^][1]:
            Annualized Return = [(1 + r1) * (1 + r2) * ... * (1 + rn)]^(1/n) - 1
            Where r1, r2, ..., rn are the total return values for each year                    """
        annualized_trailing_return = (trailing_data + 1).prod() ** (1 / years) - 1

        # Format the result as a percentage with two decimal places
        annualized_trailing_return = annualized_trailing_return * 100
        annualized_trailing_return = annualized_trailing_return.round(2)
        return annualized_trailing_return
    else:
        return np.nan

# Define a function to Loop through the list and print the trailing returns for each num_years
def get_trailing_return_column(ticker, annual_returns_df):
    trailing_return_column = {}
    for num_years in years_list:
        # Check if the ticker data is available in all_tickers_returns_df
        if ticker in annual_returns_df.columns:
            # using data from step 1, avoiding get_annual_returns_df(ticker) for less traffic from yahoo server
            data = annual_returns_df[[ticker]]
            trailing_return = get_trailing_return(ticker, data, num_years)
            trailing_return_column[f"{num_years}-Year"] = trailing_return
        else:
            print(f"Data not available for {ticker}. Skipping.")
            trailing_return_column[f"{num_years}-Year"] = np.nan
    return trailing_return_column

# Create an empty DataFrame to store all tickers' trailing returns
def get_trailing_return_all(annual_returns_df):
    all_tickers_trailing_returns_df = pd.DataFrame(index=years_list)
    tickers=annual_returns_df.columns.tolist()
    # Loop through each ticker in the list
    for ticker in tickers:
        trailing_returns = get_trailing_return_column(ticker, annual_returns_df)
        # Add the trailing returns to the DataFrame
        all_tickers_trailing_returns_df[ticker] = pd.Series(trailing_returns).values
    return all_tickers_trailing_returns_df

#==============================================================================
# Part 4: calculate the cumulative return from the data (all_tickers_returns_df) generated in part 1 & display
#  Define a function to calculate the cumulative return for a given number of years from a ticker
def get_cumulative_return(ticker, data, years):
    # Calculate the cumulative return
    cumulative_return = (1 + data[ticker]).rolling(window=years).apply(lambda x: x.prod(), raw=True) - 1
    return cumulative_return

# Define a function to Loop through the list and return the cumulative returns for each num_years
def get_cumulative_return_column(ticker, annual_returns_df):
    cumulative_returns = {}
    for years in years_list:
        # Calculate the cumulative return for the given number of years
        cumulative_return = get_cumulative_return(ticker, annual_returns_df, years)
        # Get the last value, which is the cumulative return up to the current year
        cumulative_returns[years] = cumulative_return.iloc[-1]
    return cumulative_returns

def get_cumulative_return_all(annual_returns_df):
    # Create an empty DataFrame with years_list as the index for cumulative  returns
    all_tickers_cumulative_returns_df = pd.DataFrame(index=years_list)
    tickers=annual_returns_df.columns.tolist()
    # Loop through each ticker in the list
    for ticker in tickers:
        cumulative_returns = get_cumulative_return_column(ticker, annual_returns_df)
        # Add the trailing returns to the DataFrame
        all_tickers_cumulative_returns_df[ticker] = pd.Series(cumulative_returns).values
    return all_tickers_cumulative_returns_df

#==============================================================================
# Part 5: calculate the  CAGR (Compound Annual Growth Rate) from the data
# in all_tickers_cumulative_returns_df generated earlier & display
# Define a function to calculate the CAGR from the cumulative value and the years
def calculate_cagr(value, years):
    # Otherwise, calculate the CAGR using the formula
    cagr = (value + 1) ** (1 / np.array(years)) - 1
    #print("debug-cagr\n", cagr, "end")
    return cagr

# Define a function to format the Float64Index values into percentage strings
def format_to_percentage(value):
    # If any element in the value array is not null, format it as a percentage string with two decimal places
    if np.any(pd.notnull(value)):
        return f"{value:.2f}%"
    # Otherwise, return None
    return None

def get_cagr_return_all(all_tickers_cumulative_returns_df):
    # Apply the calculate_cagr function to each column of the DataFrame
    all_tickers_cagrs_df = all_tickers_cumulative_returns_df.apply(lambda x: calculate_cagr(x, x.index), axis=0)
    return all_tickers_cagrs_df

#==============================================================================
# Part 6:
# single ticker's Prices, Returns,Dividends, good for verifying whether "Adj Close" is correct.
'''
    Calculate and display: yearly dividendSum, 'Close' & 'Adj Close' prices,
    Return(by 'Close' price), total return(by 'Adj Close' price),
    CalReturn(total return by 'Close' price and "dividendSum).
    Note: CalReturn from  is expected to be nearly same as  total return,
           when the 'Adj Close' price is correct.
'''
def get_yearly_single_stock_data(ticker):
    stock = yf.Ticker(ticker)
    #-------- mainly for downloading 'Dividends'
    history = stock.history(period="max", auto_adjust=False) # see 2025-02.23.1655 revision note
    dividend_history=history['Dividends']
    dividend_history.index=dividend_history.index.date

    #-------- mainly for downloading 'Close','Adj Close'
    dld_history=yf.download(ticker, period="max", auto_adjust=False) # see 2025-02.23.1655 revision note
    dld_history=dld_history[['Close','Adj Close']]
    dld_history.rename(columns={'Adj Close': 'AdjClose'}, inplace=True)
    '''
    note: see 2025-02.23.1655 revision note
          Columns  is of MultiIndex(,  names=['Price', 'Ticker']).  Each price colums such as 'Close','AdjClose'
          has only single sub-column with Ticker is column index name.
           Drop the column MultiIndex level ('Ticker')
    '''
    dld_history.columns = dld_history.columns.droplevel(1) # see 2025-02.23.1655 revision note
    date_range = pd.date_range(start=dld_history.index.min(), end=dld_history.index.max(), freq='D')
    complete_history = pd.DataFrame(index=date_range)

    # Merge the complete DataFrame with the original stock_history
    complete_history = complete_history.merge(dld_history, how='left', left_index=True, right_index=True)
    complete_history[['Close','AdjClose']] = complete_history[['Close','AdjClose']].ffill().round(3)

    # Merge dividend into complete_history
    complete_history = complete_history.merge(dividend_history, how='left', left_index=True, right_index=True)
    # replace all NaN values in the 'Dividends' column with 0.0
    complete_history['Dividends'] = complete_history['Dividends'].fillna(0.0).round(3)

    complete_history['Year']=complete_history.index.year
    complete_history['Date']=complete_history.index
    yearly_data = complete_history.groupby('Year').agg({'Date': 'last', 'Close': 'last', 'AdjClose': 'last','Dividends': 'sum'})
    yearly_data.rename(columns={'Dividends': 'DivSum'}, inplace=True)

    # calculating 'Return' and 'TotalReturn'
    yearly_data['DivRatio']=yearly_data['DivSum'] / yearly_data['Close']
    yearly_data['Return']=yearly_data['Close'].pct_change()
    yearly_data['TotalReturn']=yearly_data['AdjClose'].pct_change()

    '''
    The CalReturn column is the yearly total return calculated from un-adjusted "Close" prices and yearly "dividend sum",
    which is expected to be equal to the total return that is calculated from "AdjClose" prices
    '''
    yearly_data['CalReturn'] = (yearly_data['Close'] + yearly_data['DivSum']) / yearly_data['Close'].shift(1) - 1
    # set the display format
    yearly_data[['DivRatio','Return','TotalReturn','CalReturn']] = yearly_data[['DivRatio','Return','TotalReturn','CalReturn']].mul(100).round(2)
    '''
    #yearly_data[['DivRatio','Return', 'TotalReturn', 'CalReturn']] = yearly_data[['DivRatio','Return', 'TotalReturn', 'CalReturn']].applymap("{:.2f}%".format)
    yearly_data[['DivRatio','Return', 'TotalReturn', 'CalReturn']]= \
     yearly_data[['DivRatio','Return', 'TotalReturn', 'CalReturn']].applymap(lambda x: f"{x:.2f}%" if not pd.isna(x) else "NaN")
    '''
    # Use .applymap() and lambda to format the values as percentage strings only if they are not NaN
    yearly_data[['DivRatio','Return', 'TotalReturn', 'CalReturn']]= \
         yearly_data[['DivRatio','Return', 'TotalReturn', 'CalReturn']].applymap(lambda x: f"{x:.2f}%" if not pd.isna(x) else x)

    # 'Date' column is no longer required
    yearly_data.drop('Date', axis=1, inplace=True)
    return yearly_data

#==============================================================================
# Part 7: utility functions
# get the last trading day of S&P 500 in string format
def get_last_trading_day():
    # Get today's date, use .strftime("%Y-%m-%d") to convert to a string
    today_date_str=datetime.now(pytz.timezone('America/New_York')).date().strftime("%Y-%m-%d")
    stock = yf.Ticker("^GSPC") # S&P 500 (^GSPC) ticker
    #  search and see yfinance_BUG_1 NOTE in this file
    history_df=stock.history(period="max", end=today_date_str)["Close"]
    last_trading_day_str = history_df.index.max().date().strftime("%Y-%m-%d")
    return last_trading_day_str

def str_to_integer(integer_str):
    try:
        integer_number = int(integer_str)
        return integer_number
    except ValueError:
        return -1

# validate the date string
def is_valid_date(date_string):
    try:
        # Attempt to parse the date string
        datetime.strptime(date_string, "%Y-%m-%d")
        return True
    except ValueError:
        # Raised when the date string is not in the expected format
        return False

def date_label_conversion_strip_time(all_tickers_returns_df, calculation_end_date_str):
    all_tickers_returns_df.index=all_tickers_returns_df.index.date
    all_tickers_returns_df.index.name='date'
    # print("debug get_annual_returns_tickers_df", all_tickers_returns_df)
    # Convert calculation_end_date_str to a datetime object, replace the index's mon/day portion of date
    end_date_datetime_obj = datetime.strptime(calculation_end_date_str, "%Y-%m-%d")
    all_tickers_returns_df.index = all_tickers_returns_df.index.map(
      lambda x: x.replace(month=end_date_datetime_obj.month,
      day=end_date_datetime_obj.day))
    return all_tickers_returns_df

#==============================================================================
# Part 8: gradio handling - Input command handling and display in web page

help_info_str="Input Formats:\n  \
            1. ticker list....................Example:  spy vfv.to xiu.to xic.to xfn.to ry.to \n \
            2. One of default ticker list, a number between 1 and 7....Example:   0, or 1, ...,7 \n \
            3. CalculationEndDate as prefix.  Example:   2020-12-31 2 \n \
            .........................................2020-12-31 spy vfv.to xiu.to xic.to xfn.to ry.to \n \
            4. single ticker: Dividend/Close/AdjClose/Return/TotalReturn/CalReturn(by Close/Dividends).  @1 spy \n \
            note: daily adjusted close data are from Yahoo Finance. \n" + script_version

# Main Handling Process
def calculation_response(message):
    # if there is no input, display help information
    if message=="":
        return help_info_str

    tickers=message.split()

    # ******************************************************************************
    #  processing web input parameters
    #  set calculation_end_date_str, and tickers

    #---------------------------------------------------------
    # single stock ticker - detailed information
    if (tickers[0] == "@1"):
        tickers.pop(0) # remove the first string which is "@1"
        if len(tickers)==0:
            ticker = 'spy' # default ticker = spy
        else:
            ticker=tickers[0]
        output_string=f"\n {ticker}\n"
        output_dataframe0=get_yearly_single_stock_data(ticker)
        output_html=output_string + output_dataframe0.to_html()
        return output_html

    #----------------------------------------------------------
    # Get today's date, use .strftime("%Y-%m-%d") to convert to a string
    #calculation_end_date_str=datetime.now(pytz.timezone('America/New_York')).date().strftime("%Y-%m-%d")
    calculation_end_date_str = get_last_trading_day()
    # Check whether the first str is date for calculation end date
    if is_valid_date(tickers[0]):
        calculation_end_date_str = tickers[0] # reset calculation_end_date_str
        tickers.pop(0) # remove the first string which is the date

    #............ For display trailing and cumulative returns at month_boundary_date
    # Assuming calculation_end_date_str contains the date string '2024-01-03'
    calculation_end_date = datetime.strptime(calculation_end_date_str, '%Y-%m-%d')
    # Calculate the first day of the current month
    first_day_of_month = calculation_end_date.replace(day=1)
    # Calculate the last day of the month
    last_day_of_month = (calculation_end_date.replace(day=1) + timedelta(days=32)).replace(day=1) - timedelta(days=1)
    # Calculate the last day of the previous month
    last_day_of_previous_month = first_day_of_month - timedelta(days=1)
    # Check if the original date is the last day of the month
    if (calculation_end_date == last_day_of_month):
        calculation_end_date_month_boundary_date_str=calculation_end_date_str
    else:
        calculation_end_date_month_boundary_date_str=last_day_of_previous_month.strftime('%Y-%m-%d')
    # calculation_end_date_for_others are for trailing and cumulative returns
    calculation_end_date_for_others_str=calculation_end_date_month_boundary_date_str

    '''  Handling Feb 29 of leap years.
    For leap years, to simiplify the calculation,  Feb 28 will be used to replace Feb 29 for
    for calculating returns.
    Therefore, if calculation_end_date_for_others_str is Feb 29, then replace 29 to 28 of calculation_end_date_for_others_str
    '''
    leap_year=False
    if (
        calculation_end_date_for_others_str[-5:] == '02-29'
    ):
        calculation_end_date_for_others_str = calculation_end_date_for_others_str[:-2] + '28'
        leap_year=True
    #................End

    # Check whether numebr 0, 1, 2, .. is selected for using a default ticker list
    integer_value=str_to_integer(tickers[0])
    if (integer_value >= 0 and integer_value <len(tickers_lists)):
        tickers=tickers_lists[integer_value]

    # if no tickers were set, display help information
    if len(tickers)==0:
        return help_info_str
    tmp_ticker_list=tickers
    tickers = [ticker.lower() for ticker in tmp_ticker_list]

    #*********************************************************************************
    # Calculating year-end prices, Annual, Trailing, Cumulative, and CAGR returns & generating html for display
    #
    # list of year-end prices of stocks
    output_string1= f"\nAdj Close Prices ($) at year-end\n"
    data_adj_close_year_end_df, data_adj_close_df = stock_prices_df(tickers, calculation_end_date_str)
    output_dataframe= data_adj_close_year_end_df
    output_html1=output_string1 + output_dataframe.to_html()
    #print("\ndebug1  output_dataframe\n",  output_string1, output_dataframe)

    #  Annual Total Return
    output_string = f"\nAnnual Total Return (%) as {calculation_end_date_str}\n"
    #output_dataframe = get_annual_returns_tickers_year_boundary_df(tickers, calculation_end_date_str)
    output_dataframe = get_annual_returns_year_end_df(data_adj_close_year_end_df, calculation_end_date_str)
    output_dataframe = output_dataframe.dropna(how='all')
    output_dataframe = output_dataframe.round(4)*100
    #output_dataframe.index=output_dataframe.index.date
    # Assuming your DataFrame is named output_dataframe
    last_date = output_dataframe.index[-1]
    output_dataframe = output_dataframe.rename(index={last_date: calculation_end_date_str})
    # Convert the DataFrame to HTML, Combine the expected string outputs
    output_html2 = output_string + output_dataframe.to_html()
    #print("\ndebug2  output_dataframe\n", output_dataframe)

    # annual_returns  - at any given day, for calculating trailing and cumulative returns, not to be displayed
    calculation_end_date_for_others_str=calculation_end_date_str
    annual_returns_dataframe=get_annual_returns_anyday_df(data_adj_close_df, calculation_end_date_for_others_str)
    #print("\ndebug2-T  get_annual_returns_anyday_df\n", annual_returns_dataframe)
    #print("\ndebug2-T  calculation_end_date_str\n", calculation_end_date_str)
    #print("\ndebug2-T  calculation_end_date_for_others_str\n", calculation_end_date_for_others_str)

    # Trailing Return
    if (leap_year):
        output_string3 = f"\nTrailing Total Return (%) as {calculation_end_date_for_others_str} (leap year: Feb 29 replaced by Feb 28 for approximation)\n"
    else:
        output_string3 = f"\nTrailing Total Return (%) as {calculation_end_date_for_others_str}\n"
    output_dataframe3=get_trailing_return_all(annual_returns_dataframe)
    output_dataframe3 = output_dataframe3.dropna(how='all')
    # Insert an empty to align the ticker symbols with annual return display
    output_dataframe3.insert(0, "-", "      ")
    output_dataframe3.index.name="yrs"
    output_html3=output_string3 + output_dataframe3.to_html()
    #print("\ndebug3\n", output_string3, output_dataframe3)
    # Cumulative Return
    output_string4 = f"\nCumulative Return (%) as {calculation_end_date_for_others_str}\n"
    cumulative_return_all_dataframe=get_cumulative_return_all(annual_returns_dataframe)
    cumulative_return_all_dataframe = cumulative_return_all_dataframe.dropna(how='all')
    output_dataframe4=cumulative_return_all_dataframe.round(4)*100
    output_dataframe4.index.name="yrs"
    output_html4=output_string4 + output_dataframe4.to_html()

    # CAGR Return
    '''
    # following code is fine, but is not needed
    output_string5 = f"\nCompound Annual Growth Rate (CAGR) (%) as {calculation_end_date_for_others_str}\n"
    output_dataframe5=get_cagr_return_all (cumulative_return_all_dataframe)
    output_dataframe5=output_dataframe5.round(4)*100
    output_html5=output_string5 + output_dataframe5.to_html()
    '''
    # print total 1,2,3,4 (not 5)
    output_html = output_html1 + output_html2 + output_html3 + output_html4
    return  output_html

# Custom CSS for font size 14
custom_css = """
    .chatbot-container { 
        font-size: 14px !important; 
        line-height: 1.4 !important;
    }
    .chatbot-container table {
        font-size: 14px !important;
        margin: 2px !important;
        border-collapse: collapse !important;
    }
    .chatbot-container th, .chatbot-container td {
        font-size: 14px !important;
        padding: 5px !important;
        border: 1px solid #ddd !important;
    }
    .chatbot-container pre {
        font-size: 14px !important;
        margin: 4px 0 !important;
    }
    .chatbot-container .message {
        padding: 8px !important;
        margin: 4px 0 !important;
    }
"""
# Gradio Web interface
with gr.Blocks(css=custom_css) as web_block:
    chatbot = gr.Chatbot(height="500px", elem_classes=["chatbot-container"])
    with gr.Row():
        msg = gr.Textbox(show_label=False, scale=2, min_width=380)
        clear = gr.ClearButton([msg, chatbot], scale=0, min_width=50)
    
    def respond(message, chat_history):
        bot_message = calculation_response(message)
        chat_history.append((message, bot_message))
        return "", chat_history

    msg.submit(respond, [msg, chatbot], [msg, chatbot])

web_block.launch()
#web_block.launch(debug=True)

#----------- test cases-----------------

'''
#-------- part 1 stock_prices_df
calculation_end_date_str="2025-02-21"
data_adj_close_year_end_df, data_adj_close_df = stock_prices_df(["SPY", "MSFT"], "2025-02-21")
#print("\nUnit_test  data_adj_close_df data_adj_close_year_end_df\n", data_adj_close_year_end_df, "\ndata_adj_close_df\n",data_adj_close_df)

#tickers = yf.download(["AAPL", "MSFT"], period="1y", auto_adjust=False) # default changed to auto_adjust=True at yfinance version 0.2.54
                                                                         # when auto_adjust=True,  Close = Adj Close and Adj Close does not exist
#print("\nUnit_test  test2\n", tickers)
#tickers = yf.download(["AAPL", "MSFT"], period="1y")
#print("\nUnit_test  test\n", tickers)

#-------- part 2 get_annual_returns_year_end_df
output_dataframe = get_annual_returns_year_end_df(data_adj_close_year_end_df, calculation_end_date_str)
#print("\nUnit_test  get_annual_returns_year_end_df\n", output_dataframe)

# for calculating trailing return
annual_returns_dataframe=get_annual_returns_anyday_df(data_adj_close_df, calculation_end_date_str)
#print("\nUnit_test  get_annual_returns_anyday_df\n", annual_returns_dataframe)

#-------- part 3 get_trailing_return_all
output_dataframe3=get_trailing_return_all(annual_returns_dataframe)
#print("\nUnit_test  get_trailing_return_all\n", output_dataframe3)

#-------- part 4 get_cumulative_return_all
cumulative_return_all_dataframe=get_cumulative_return_all(annual_returns_dataframe)
#print("\nUnit_test  get_cumulative_return_all\n", cumulative_return_all_dataframe)

#-------- part 5 get_cagr_return_all
#output_dataframe5=get_cagr_return_all (cumulative_return_all_dataframe)
#print("\nUnit_test  get_cagr_return_all\n", output_dataframe5)

#-------- part 6 stock_prices_df
#output_dataframe0=get_yearly_single_stock_data("SPY")
#print("\nUnit_test  part 6 test\n", output_dataframe0)

#--------  testing calculation_response
bot_message = calculation_response("8")
#bot_message = calculation_response("SPY MSFT")
#print("\nUnit_test  calculation_response\n", bot_message)

html_content=bot_message
import html2text
text_maker = html2text.HTML2Text()
text_maker.ignore_links = True
plain_text = text_maker.handle(html_content)
print("\nUnit_test  calculation_response\n", plain_text)

#-------- End of unit testing 
print("\n-----Unit_test  End------\n")
'''
print("\n----- End------\n")