utils/plotting.py

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
from pandas.plotting import register_matplotlib_converters

register_matplotlib_converters()

def plot_stock_data_with_signals(data):
 # First, ensure the DataFrame's index is in datetime format.
 data.index = pd.to_datetime(data.index, errors='coerce')

 # Create a new figure and set the size.
 plt.figure(figsize=(14, 7))

 # Plotting stock 'Close' prices
 plt.plot(data.index, data['Close'], label='Close Price', color='black', lw=2)

 # Check and plot EMAs if they exist
 if 'EMA_20' in data.columns and 'EMA_50' in data.columns:
 plt.plot(data.index, data['EMA_20'], label='EMA 20', color='blue', lw=1.5)
 plt.plot(data.index, data['EMA_50'], label='EMA 50', color='red', lw=1.5)

 # Check and plot Bollinger Bands if they exist
 if 'BB_Upper' in data.columns and 'BB_Lower' in data.columns:
 plt.fill_between(data.index, data['BB_Lower'], data['BB_Upper'], color='grey', alpha=0.1, label='Bollinger Bands')

 # Highlight buy/sell signals if they exist
 if 'Combined_Signal' in data.columns:
 buy_signals = data[data['Combined_Signal'] == 'buy']
 sell_signals = data[data['Combined_Signal'] == 'sell']
 plt.scatter(buy_signals.index, buy_signals['Close'], label='Buy Signal', marker='^', color='green', alpha=1)
 plt.scatter(sell_signals.index, sell_signals['Close'], label='Sell Signal', marker='v', color='red', alpha=1)

 plt.title('Stock Price with Buy/Sell Signals')
 plt.xlabel('Date')
 plt.ylabel('Price')
 plt.legend()

 # Improve the x-axis date format
 plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
 plt.gca().xaxis.set_major_locator(mdates.MonthLocator())
 plt.gcf().autofmt_xdate() # Rotation

 plt.show()