Building a humble buy and sell decision engine for me in Python

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import pandas as pd
import yfinance as yf
import numpy as np

start_date = pd.to_datetime('2020-01-01')
end_date = pd.to_datetime('2024-01-01')

goog_data = yf.download('GOOG', start_date, end_date)

goog_data_signal = pd.DataFrame(index=goog_data.index)
goog_data_signal['price'] = goog_data['Adj Close']
goog_data_signal['daily_difference'] = goog_data_signal['price'].diff()
goog_data_signal['signal'] = 0.0
goog_data_signal['signal'] = np.where(goog_data_signal['daily_difference'] > 0, 1.0, 0.0)
goog_data_signal['positions'] = goog_data_signal['signal'].diff()

goog_data_signal.tail(10)

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import pandas as pd
import yfinance as yf
import numpy as np
import matplotlib.pyplot as plt

start_date = pd.to_datetime('2020-01-01')
end_date = pd.to_datetime('2024-01-01')

goog_data = yf.download('GOOG', start_date, end_date)

goog_data_signal = pd.DataFrame(index=goog_data.index)
goog_data_signal['price'] = goog_data['Adj Close']
goog_data_signal['daily_difference'] = goog_data_signal['price'].diff()
goog_data_signal['signal'] = 0.0
goog_data_signal['signal'] = np.where(goog_data_signal['daily_difference'] > 0, 1.0, 0.0)
goog_data_signal['positions'] = goog_data_signal['signal'].diff()

fig = plt.figure()
ax1 = fig.add_subplot(111, ylabel='Google price in $')

goog_data_signal['price'].plot(ax=ax1, color='r', lw=2.)
ax1.plot(goog_data_signal.loc[goog_data_signal.positions == 1.0].index, 
         goog_data_signal.price[goog_data_signal.positions == 1.0],
         '^', markersize=5, color='m')
ax1.plot(goog_data_signal.loc[goog_data_signal.positions == -1.0].index,
         goog_data_signal.price[goog_data_signal.positions == -1.0],
         'v', markersize=5, color='k')
plt.show()

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import pandas as pd
import yfinance as yf
import numpy as np
import matplotlib.pyplot as plt

start_date = pd.to_datetime('2021-01-01')
end_date = pd.to_datetime('2024-01-01')

goog_data = yf.download('GOOG', start_date, end_date)

goog_data_signal = pd.DataFrame(index=goog_data.index)
goog_data_signal['price'] = goog_data['Adj Close']
goog_data_signal['daily_difference'] = goog_data_signal['price'].diff()
goog_data_signal['signal'] = 0.0
goog_data_signal['signal'] = np.where(goog_data_signal['daily_difference'] > 0, 1.0, 0.0)
goog_data_signal['positions'] = goog_data_signal['signal'].diff()


initial_capital = float(1000.0)

positions = pd.DataFrame(index=goog_data_signal.index).fillna(0.0)
portfolio = pd.DataFrame(index=goog_data_signal.index).fillna(0.0)

positions['GOOG'] = goog_data_signal['signal']
portfolio['positions'] = (positions.multiply(goog_data_signal['price'], axis=0))
portfolio['cash'] = initial_capital - (positions.diff().multiply(goog_data_signal['price'], axis=0)).cumsum()
portfolio['total'] = portfolio['positions'] + portfolio['cash']

# portfolio.tail(100)

portfolio.plot()
plt.show()

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EMA = ( P - EMAp ) * K + EMAp

Where:

P = Price for the current period
EMAp = the Exponential moving Average for the previous period
K = the smoothing constant, equal to 2 / (n + 1)
n = the number of periods in a simple moving average roughly approximated by the EMA

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import pandas as pd
import yfinance as yf

start_date = pd.to_datetime('2021-01-01')
end_date = pd.to_datetime('2024-01-01')

goog_data = yf.download('GOOG', start_date, end_date)
close = goog_data['Close']

'''
EMA = ( P - EMAp ) * K + EMAp

Where:

P = Price for the current period
EMAp = the Exponential moving Average for the previous period
K = the smoothing constant, equal to 2 / (n + 1)
n = the number of periods in a simple moving average roughly approximated by the EMA
'''

num_periods = 20 # number of days over which to average
K = 2 / (num_periods + 1) # smoothing constant
ema_p = 0

ema_values = [] # to hold computed EMA values

for close_price in close:
  if (ema_p == 0):
    ema_p = close_price
  else:
    ema_p = (close_price - ema_p) * K + ema_p

  ema_values.append(ema_p)

goog_data = goog_data.assign(ClosePrice=pd.Series(close, index=goog_data.index))
goog_data = goog_data.assign(Exponential20DayMovingAverage=pd.Series(ema_values, index=goog_data.index))

close_price = goog_data['ClosePrice']
ema = goog_data['Exponential20DayMovingAverage']

import matplotlib.pyplot as plt

fig = plt.figure()
ax1 = fig.add_subplot(111, ylabel='Google price in $')
close_price.plot(ax=ax1, color='g', lw=2., legend=True)
ema.plot(ax=ax1, color='b', lw=2., legend=True)
plt.show()

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import pandas as pd
import yfinance as yf
import numpy as np

start_date = pd.to_datetime('2023-07-01')
end_date = pd.to_datetime('2024-01-01')

goog_data = yf.download('GOOG', start_date, end_date)
close = goog_data['Close']

'''
EMA = ( P - EMAp ) * K + EMAp

Where:

P = Price for the current period
EMAp = the Exponential moving Average for the previous period
K = the smoothing constant, equal to 2 / (n + 1)
n = the number of periods in a simple moving average roughly approximated by the EMA
'''

num_periods = 20 # number of days over which to average
K = 2 / (num_periods + 1) # smoothing constant
ema_p = 0

ema_values = [] # to hold computed EMA values

for close_price in close:
  if (ema_p == 0):
    ema_p = close_price
  else:
    ema_p = (close_price - ema_p) * K + ema_p

  ema_values.append(ema_p)

goog_data = goog_data.assign(ClosePrice=pd.Series(close, index=goog_data.index))
goog_data = goog_data.assign(Ema20=pd.Series(ema_values, index=goog_data.index))
goog_data['Signal'] = np.where(goog_data['ClosePrice'] > goog_data['Ema20'], 1.0, 0.0)
goog_data['Position'] = goog_data['Signal'].diff()

initial_capital = float(1000.0)

positions = pd.DataFrame(index=goog_data.index).fillna(0.0)
portfolio = pd.DataFrame(index=goog_data.index).fillna(0.0)

positions['GOOG'] = goog_data['Signal']
portfolio['Signal'] = goog_data['Signal']
portfolio['Price'] = goog_data['ClosePrice']
portfolio['Positions'] = (positions.multiply(goog_data['ClosePrice'], axis=0))
portfolio['Cash'] = initial_capital - (positions.diff().multiply(goog_data['ClosePrice'], axis=0)).cumsum()
portfolio['Total'] = portfolio['Positions'] + portfolio['Cash']

portfolio.tail(100)
# goog_data.tail(100)

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# MACD Strategy
import pandas as pd
import pandas_datareader as pdr
import matplotlib.pyplot as plt
from datetime import datetime
import yfinance as yf

# Fetch stock data
def fetch_stock_data(ticker, start, end):
    return yf.download(ticker, start, end)

# Calculate MACD
def calculate_macd(data, short_window=12, long_window=26, signal_window=9):
    data['EMA_short'] = data['Close'].ewm(span=short_window, adjust=False).mean()
    data['EMA_long'] = data['Close'].ewm(span=long_window, adjust=False).mean()
    data['MACD'] = data['EMA_short'] - data['EMA_long']
    data['Signal_Line'] = data['MACD'].ewm(span=signal_window, adjust=False).mean()

# Generate trading signals (buy=1 , sell=-1, do nothing=0)
def generate_signals(data):
    data['Signal'] = 0
    data['Signal'][data['MACD'] > data['Signal_Line']] = 1
    data['Signal'][data['MACD'] < data['Signal_Line']] = -1
    data['Position'] = data['Signal'].diff()

# Backtesting the strategy
def backtest_strategy(data):
    initial_capital= float(100000.0)
    positions = pd.DataFrame(index=data.index).fillna(0.0)
    portfolio = pd.DataFrame(index=data.index).fillna(0.0)
    
    # Buy a 100 shares
    positions['Stock'] = 100*data['Signal']  
    portfolio['positions'] = (positions.multiply(data['Close'], axis=0))
    portfolio['cash'] = initial_capital - (positions.diff().multiply(data['Close'], axis=0)).cumsum()
    portfolio['total'] = portfolio['positions'] + portfolio['cash']

    # Calculate daily returns
    portfolio['returns'] = portfolio['total'].pct_change()

    return portfolio

# Calculate the Sharpe Ratio
def calculate_sharpe_ratio(portfolio):
    # Assuming risk-free rate = 0 for simplicity
    risk_free_rate = 0
    sharpe_ratio = (portfolio['returns'].mean() - risk_free_rate) / portfolio['returns'].std()
    # Annualize the Sharpe ratio
    sharpe_ratio_annualized = (252**0.5) * sharpe_ratio
    return sharpe_ratio_annualized

# Main execution function
def run_strategy(ticker):
    start_date = '2023-06-09'
    end_date = datetime.now().strftime('%Y-%m-%d')

    data = fetch_stock_data(ticker, start_date, end_date)
    calculate_macd(data)
    generate_signals(data)

    portfolio = backtest_strategy(data)
    sharpe_ratio = calculate_sharpe_ratio(portfolio)

    # Plot the results
    plt.figure(figsize=(10,6))
    plt.plot(data['Close'], label='Close Price', alpha=0.5)
    plt.plot(data['EMA_short'], label='12-day EMA', alpha=0.5)
    plt.plot(data['EMA_long'], label='26-day EMA', alpha=0.5)
    plt.plot(data['MACD'], label='MACD', alpha=0.5)
    plt.plot(data['Signal_Line'], label='Signal Line', alpha=0.5)
    plt.scatter(data.index, data['Position'], label='Buy Signal', marker='^', color='green', alpha=1)
    plt.scatter(data.index, data['Position'], label='Sell Signal', marker='v', color='red', alpha=1)
    plt.title(f'MACD Strategy: {ticker}')
    plt.legend()
    plt.show()

    plt.figure(figsize=(10,6))
    plt.plot(portfolio['total'], label='Portfolio Value')
    plt.title('Portfolio Performance')
    plt.legend()
    plt.show()

    print(f"Sharpe Ratio: {sharpe_ratio}")

# Run the strategy for a given stock
run_strategy('AAPL')