user28102875,

There is an issue with MultiIndex in Dataframe, this way we can fix it

    # Drop columns that are not needed
    if isinstance(df.columns, pd.MultiIndex) and df.columns.nlevels > 1:
        df.columns = df.columns.droplevel(1)

Also StochasticOscillator requires High, Low, Close

stoch_rsi = StochasticOscillator(df['High'], df['Low'], df['Close'], window=14, smooth_window=3)

There is your full fixed code (maybe you need another changes for logic, but it works):

import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from ta.trend import EMAIndicator
from ta.momentum import RSIIndicator, StochasticOscillator
from ta.volatility import AverageTrueRange


# Data Retrieval
def get_data(ticker, start, end):
    df = yf.download(ticker, start=start, end=end)
    df.reset_index(inplace=True)  # Reset the index to make it easier to work with
    df['Date'] = df['Date'].dt.date  # Convert to date type if needed
    print("Initial DataFrame head:\n", df.head())  # Print the first few rows
    print("Initial DataFrame shape:", df.shape)  # Print the shape of the DataFrame

    # Drop columns that are not needed
    if isinstance(df.columns, pd.MultiIndex) and df.columns.nlevels > 1:
        df.columns = df.columns.droplevel(1)
    return df


# Indicator Calculation
def calculate_indicators(df):
    # Exponential Moving Averages (EMAs)
    df['EMA8'] = EMAIndicator(df['Close'], window=8).ema_indicator()
    df['EMA14'] = EMAIndicator(df['Close'], window=14).ema_indicator()
    df['EMA50'] = EMAIndicator(df['Close'], window=50).ema_indicator()

    # Print shapes to debug
    print("EMA8 shape:", df['EMA8'].shape)
    print("EMA14 shape:", df['EMA14'].shape)
    print("EMA50 shape:", df['EMA50'].shape)

    # Relative Strength Index (RSI)
    df['RSI14'] = RSIIndicator(df['Close'], window=14).rsi()
    print("RSI14 shape:", df['RSI14'].shape)

    # Stochastic RSI
    stoch_rsi = StochasticOscillator(df['High'], df['Low'], df['Close'], window=14, smooth_window=3)
    df['StochRSI_K'] = stoch_rsi.stoch()
    df['StochRSI_D'] = stoch_rsi.stoch_signal()

    # Print shapes
    print("StochRSI_K shape:", df['StochRSI_K'].shape)
    print("StochRSI_D shape:", df['StochRSI_D'].shape)

    # Average True Range (ATR)
    atr = AverageTrueRange(df['High'], df['Low'], df['Close'], window=14)
    df['ATR14'] = atr.average_true_range()
    print("ATR14 shape:", df['ATR14'].shape)

    # Drop rows with NaN values
    df.dropna(inplace=True)

    return df


# Entry Conditions
def signal_generator(df):
    df['Long'] = (
            (df['Close'] > df['EMA8']) &
            (df['EMA8'] > df['EMA14']) &
            (df['EMA14'] > df['EMA50']) &
            (df['StochRSI_K'] > df['StochRSI_D'])
    )

    df['Short'] = (
            (df['Close'] < df['EMA8']) &
            (df['EMA8'] < df['EMA14']) &
            (df['EMA14'] < df['EMA50']) &
            (df['StochRSI_K'] < df['StochRSI_D'])
    )

    return df


# Position Management
def backtest_strategy(df):
    position = None
    entry_price = 0
    results = []

    for index, row in df.iterrows():
        if position is None:  # No open position
            if row['Long']:
                position = 'long'
                entry_price = row['Close']
                stop_loss = entry_price - (3 * row['ATR14'])
                take_profit = entry_price + (2 * row['ATR14'])

            elif row['Short']:
                position = 'short'
                entry_price = row['Close']
                stop_loss = entry_price + (3 * row['ATR14'])
                take_profit = entry_price - (2 * row['ATR14'])

        elif position == 'long':
            if row['Close'] >= take_profit or row['Close'] <= stop_loss:
                results.append(row['Close'] - entry_price)  # Profit or loss
                position = None  # Close position

        elif position == 'short':
            if row['Close'] <= take_profit or row['Close'] >= stop_loss:
                results.append(entry_price - row['Close'])  # Profit or loss
                position = None  # Close position

    return results


# Performance Metrics
def calculate_performance(results, df):
    df['Daily_Returns'] = df['Close'].pct_change()
    df['Strategy_Returns'] = pd.Series(results).shift(1).fillna(0)  # Align with dates

    df['Cumulative_Strategy'] = (1 + df['Strategy_Returns']).cumprod()
    df['Cumulative_Buy_Hold'] = (1 + df['Daily_Returns']).cumprod()

    return df


# Visualization
def plot_performance(df):
    plt.figure(figsize=(12, 6))
    plt.plot(df['Cumulative_Strategy'], label='Strategy Returns', color='blue')
    plt.plot(df['Cumulative_Buy_Hold'], label='Buy and Hold Returns', color='orange')
    plt.title('Cumulative Returns: Strategy vs. Buy and Hold')
    plt.xlabel('Date')
    plt.ylabel('Cumulative Returns')
    plt.legend()
    plt.grid()
    plt.show()


# Main Execution
ticker = 'IWV'
start_date = '2020-01-01'
end_date = '2024-01-01'

data = get_data(ticker, start_date, end_date)

# Check the DataFrame contents before calculating indicators
print("Data after retrieval:\n", data.head())
print("Data shape after retrieval:", data.shape)

# Now attempt to calculate indicators
data = calculate_indicators(data)
data = signal_generator(data)
results = backtest_strategy(data)
data = calculate_performance(results, data)
plot_performance(data)