SPY Buy/Sell Points by Bollinger Bands & RSI

import yfinance as yf
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.arima.model import ARIMA as arima
import ta
import seaborn as sb
import pandas as pd
from datetime import datetime as dt
from datetime import timedelta

end_time = dt.now()
start_time = end_time - timedelta(days=365*2)
tickers = 'SPY, MSFT, META, NVDA, AMD'
popular5 = yf.download(tickers, start = start_time, end = end_time)

def calculate_indicators(historical_close):
    historical_close_ma20 = historical_close.rolling(20).mean()
    std = historical_close.rolling(20).std()
    upper = historical_close_ma20 + 2* std
    lower = historical_close_ma20 - 2*std
    rsi14 = ta.momentum.rsi(historical_close, 14)
    # put indicators in a dataframe
    df = pd.concat([historical_close, upper, lower, rsi14], axis=1)
    df.columns = ['close','upper','lower','rsi']
    df['signal']='no action'
    df.loc[(df['close'] < df['lower']) & (df['rsi']<30), 'signal'] = 'buy'
    df.loc[(df.close>df.upper) & (df.rsi > 70), 'signal'] = 'sell'
    # print(df.query('signal == "buy"').shape[0])
    # print(df.query('signal == "sell"').shape[0])
    # visualize
    fig = plt.figure(figsize = (15,6))
    ax = fig.add_axes([0.1,0.1,0.9,0.9])
    ax.plot(historical_close.index, historical_close.values, label = 'daily close')
    ax.plot(historical_close_ma20.index, historical_close_ma20.values)
    ax.plot(upper.index, upper.values, label='upper Bollinger')
    ax.plot(lower.index, lower.values, label='lower Bollinger')

    ax2 = ax.twinx()
    ax2.plot(rsi14, alpha=0.3)
    ax2.axhline(y=70, ls='--', alpha=0.5, c='r')
    ax2.axhline(y=30, ls='--', alpha=0.5, c='g')
    ax.set_title(f'{symbol} Prices & Indicators', fontsize=25)
    ax2.set_ylabel('RSI', fontsize=15)
    ax.set_ylabel('Price', fontsize=15)
    ax.xaxis.set_tick_params(labelsize=15)
    ax.yaxis.set_tick_params(labelsize=15)
    ax2.yaxis.set_tick_params(labelsize=15)
    ax.legend()
    # draw arrow
    signal_colors = {'buy':'green', 'sell':'red'}
#    signal_offsets = {'buy':pd.Timedelta(days=-40), 'sell':pd.Timedelta(days=40)}
    y_offsets = {'buy':-20, 'sell':20}
    signal_offsets = {'buy':pd.Timedelta(days=-40), 'sell':pd.Timedelta(days=40)}
    for signal, color in signal_colors.items():
        signal_data = df[df['signal']==signal]
        ax.scatter(signal_data.index, signal_data.close, marker='o', s=90, c=color, label=signal)
        time_deltas = signal_data.replace(signal_offsets)
        y_ends = signal_data.close - 1
        for i, row in signal_data.iterrows():
            x_end = i
#            x_start = x_end + time_deltas['signal'].loc[i]
            x_start = x_end + pd.Timedelta(days=-40)
            y_start = y_ends[i] + y_offsets[signal]
            y_end = y_ends[i]
            ax.annotate(signal_data.signal[i], xy=(x_end, y_end), xytext=(x_start, y_start), arrowprops=
                        dict(facecolor=signal_colors[row['signal']], shrink=0.1, lw=2))

symbol = 'SPY'
ts = popular5['Close'][symbol]
calculate_indicators(ts)

returns = ts.pct_change()*100
plot_pacf(returns[1:], zero=False, auto_ylims=True);

position=False
portfolio_value = 1.0
for index, row in df_tsla.iterrows():
    if not position and row.signal == 'buy':
        position = True
        entry = row.close
    if position:
        if (row.signal == 'sell' or row.close < entry*0.9):
            position = False
            exit = row.close
            portfolio_value *= (exit - entry)/entry + 1

print(f'return = {portfolio_value}')