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import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
import streamlit as st
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')
# Creating a figure and axes explicitly
fig, ax = plt.subplots(figsize=(14, 7))
# Plotting stock 'Close' prices
ax.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:
ax.plot(data.index, data['EMA_20'], label='EMA 20', color='blue', lw=1.5)
ax.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:
ax.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']
ax.scatter(buy_signals.index, buy_signals['Close'], label='Buy Signal', marker='^', color='green', alpha=1)
ax.scatter(sell_signals.index, sell_signals['Close'], label='Sell Signal', marker='v', color='red', alpha=1)
ax.set_title('Stock Price with Buy/Sell Signals')
ax.set_xlabel('Date')
ax.set_ylabel('Price')
ax.legend()
# Improve the x-axis date format
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax.xaxis.set_major_locator(mdates.MonthLocator())
fig.autofmt_xdate() # Rotation
# Use the figure object in st.pyplot()
st.pyplot(fig)
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