Making some modifications on my article "How I will trade using MACD as indicator"

 This is in reference to my previous post(How I will trade using MACD as indicator). Based on the video, to use the MACD, it should be also checked against the 200 Exponential Moving Average indicator. For details, pls watch the said video on the article which I made as reference. I believe in his formula becasue it is based on actual historical data and also I had made the same observation as he did. 

So in this article, I modified the program accordingly to include the 200-EMA in the plot and I also use matplotlib to make it more presentable. Here is the output:

Other changes made to the program:

1. Generate Buy/Sell Signals
2. Red Triangle - Indicates a Buy Signal
3. Blue Inverted Triangle - Indicates a Sell Signal
4. Criteria for a Buy Signal :  close price is above the 200ema and the macd and signal crosses below the zero line and the histogram is positive
5. Criteria for a Sell Signal :  close price is below the 200ema and the macd and signal crosses above the zero line and the histogram is negative
6. Prints Buy/Signal and the Date at the command prompt

And here is the code:

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import yfinance as yf import matplotlib.pyplot as plt import numpy as np # Retrieve historical data of AAPL from Yahoo Finance data = yf.download('AAPL', period='1y') # Calculate 200-day EMA data['EMA_200'] = data['Close'].ewm(span=200, adjust=False).mean() # Calculate MACD data['ema_12'] = data['Close'].ewm(span=12, adjust=False).mean() data['ema_26'] = data['Close'].ewm(span=26, adjust=False).mean() data['MACD'] = data['ema_12'] - data['ema_26'] data['Signal'] = data['MACD'].ewm(span=9, adjust=False).mean() data['Histogram'] = data['MACD'] - data['Signal'] # Define colors based on histogram values and trends colors = np.where((data['Histogram'] > 0) & (data['Histogram'].diff() > 0), 'green', # Increasing and positive np.where((data['Histogram'] > 0) & (data['Histogram'].diff() <= 0), 'lightgreen', # Decreasing and positive np.where((data['Histogram'] < 0) & (data['Histogram'].diff() < 0), 'red', # Decreasing and negative 'lightcoral'))) # Increasing and negative # Find the indices where conditions are met for the blue dot blue_dot_indices = np.where( (data['Close'] < data['EMA_200']) & (data['MACD'] > 0) & (data['Signal'] > 0) & (data['Histogram'] < 0) )[0] # Find the indices where conditions are met for the red dot red_dot_indices = np.where( (data['Close'] > data['EMA_200']) & (data['MACD'] < 0) & (data['Signal'] < 0) & (data['Histogram'] > 0) )[0] # Plot the closing price, EMA 200, MACD line, Signal line, and Histogram plt.figure(figsize=(12, 8)) plt.subplot(2, 1, 1) plt.plot(data.index, data['Close'], label='Close') plt.plot(data.index, data['EMA_200'], label='EMA 200') plt.scatter(data.index[blue_dot_indices], data['Close'].iloc[blue_dot_indices], marker='v', c='blue', s=50, label='Sell Signal') plt.scatter(data.index[red_dot_indices], data['Close'].iloc[red_dot_indices], marker='^', c='red', s=50, label='Buy Signal') plt.title('AAPL Stock Price') plt.xlabel('Date') plt.ylabel('Price') plt.legend() plt.subplot(2, 1, 2) plt.plot(data.index, data['MACD'], label='MACD') plt.plot(data.index, data['Signal'], label='Signal') plt.bar(data.index, data['Histogram'], label='Histogram', color=colors, alpha=0.8, width=0.8) plt.title('MACD') plt.xlabel('Date') plt.ylabel('MACD') plt.legend() # Adjust line thickness and magnify the histogram ax = plt.gca() ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['bottom'].set_linewidth(2.0) ax.spines['left'].set_linewidth(2.0) ax.bar(data.index, data['Histogram'], color=colors, alpha=0.8, width=0.8) ax.margins(0, 0.2) # Print "Buy" and "Sell" labels for idx in blue_dot_indices: print('Sell', data.index[idx].date()) for idx in red_dot_indices: print('Buy', data.index[idx].date()) plt.tight_layout() plt.show()