im getting this error:
- I: No timezone found, symbol may be delisted
Traceback (most recent call last):
  File "D:\Final Year Project\proj_aaru\vis.py", line 24, in <module>
    ma20 = talib.SMA(df['Close'], timeperiod=20)
  File "C:\Users\nitin\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\talib\__init__.py", line 64, in wrapper
    result = func(*_args, **_kwds)
  File "talib/_func.pxi", line 4538, in talib._ta_lib.SMA
  File "talib/_func.pxi", line 68, in talib._ta_lib.check_begidx1
Exception: inputs are all NaN

#code
import yfinance as yf
import talib
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Define the symbols for which you want to fetch data
symbols = ['^NSEI', 'ICICIBANK.NS']

# Define the start and end dates
start_date = '2022-01-01'
end_date = '2022-12-31'

# Download the OHLCV data for each symbol
dfs = {}
for symbol in symbols:
    dfs[symbol] = yf.download(symbol, start=start_date, end=end_date)

# Create a dictionary to store the technical indicators for each symbol
ti_dict = {}

# Calculate technical indicators for each symbol and store in ti_dict
for symbol, df in dfs.items():
    ma20 = talib.SMA(df['Close'], timeperiod=20)
    ma50 = talib.SMA(df['Close'], timeperiod=50)
    upper_band, middle_band, lower_band = talib.BBANDS(df['Close'], timeperiod=20)
    plus_di = talib.PLUS_DI(df['High'], df['Low'], df['Close'], timeperiod=14)
    minus_di = talib.MINUS_DI(df['High'], df['Low'], df['Close'], timeperiod=14)
    adx = talib.ADX(df['High'], df['Low'], df['Close'], timeperiod=14)
    slowk, slowd = talib.STOCH(df['High'], df['Low'], df['Close'], fastk_period=14, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
    fastk, fastd = talib.STOCHF(df['High'], df['Low'], df['Close'], fastk_period=14, fastd_period=3, fastd_matype=0)
    rsi = talib.RSI(df['Close'], timeperiod=14)
    st_10_3 = talib.STOCHRSI(df['Close'], timeperiod=10, fastk_period=3, fastd_period=3, fastd_matype=0)

    # Create a dataframe for the technical indicators
    ti_df = pd.DataFrame({
        'MA20': ma20,
        'MA50': ma50,
        'Upper Band': upper_band,
        'Middle Band': middle_band,
        'Lower Band': lower_band,
        'Plus DI': plus_di,
        'Minus DI': minus_di,
        'ADX': adx,
        'SlowK': slowk,
        'SlowD': slowd,
        'FastK': fastk,
        'FastD': fastd,
        'RSI': rsi,
        'StochRSI': st_10_3
    })

    # Store the dataframe in the ti_dict dictionary
    ti_dict[symbol] = ti_df

# Create a dictionary to store the correlation matrices for each symbol
corr_dict = {}

# Create a heatmap of the correlation matrix for each symbol and store in corr_dict
for symbol, ti_df in ti_dict.items():
    corr_matrix = ti_df.corr()
    corr_dict[symbol] = corr_matrix
    sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')
    plt.title(f'Correlation Matrix for {symbol}')
    plt.show()
