Coding and Visualizing a Stock Price crossing a Simple Moving Average in Python

main.py

import yfinance as yf
import pandas as pd
import plotly.graph_objs as go
from plotly.subplots import make_subplots

#Get Historical Stock Data from AAPL
stock_data = yf.download(tickers='AAPL', interval='1d', period='3y')

#Calculate an SMA
stock_data['SMA50'] = stock_data['Close'].rolling(window=50).mean()

#Determine when the stock price crosses above / below the SMA
stock_data['Crossings'] = 0 #cross below = -1, cross above = 1
cross_below_indexes = (stock_data['Close'].shift(1) > stock_data['SMA50'].shift(1)) & (stock_data['Close'] < stock_data['SMA50'])
cross_above_indexes = (stock_data['Close'].shift(1) < stock_data['SMA50'].shift(1)) & (stock_data['Close'] > stock_data['SMA50'])
stock_data.loc[cross_below_indexes, 'Crossings'] = -1
stock_data.loc[cross_above_indexes, 'Crossings'] = 1

#use plotly to create graph
fig = make_subplots(rows=1, cols=1, shared_xaxes=True)

# Add the stock price and SMA50 lines
stock_price_line = go.Scatter(x=stock_data.index, y=stock_data['Close'], mode='lines', name='Stock Price', line=dict(color='blue'))
sma50_line = go.Scatter(x=stock_data.index, y=stock_data['SMA50'], mode='lines', name='SMA50', line=dict(color='orange'))
fig.add_trace(stock_price_line)
fig.add_trace(sma50_line)

# Add markers for crossings
cross_below = go.Scatter(x=stock_data[stock_data['Crossings'] == -1].index, y=stock_data[stock_data['Crossings'] == -1]['SMA50'],
    mode='markers', name='Price Crosses Below SMA50', marker=dict(color='red', size=10))
fig.add_trace(cross_below)

# Add markers for crossings above SMA50
cross_above = go.Scatter(x=stock_data[stock_data['Crossings'] == 1].index, y=stock_data[stock_data['Crossings'] == 1]['SMA50'],
    mode='markers', name='Price Crosses Above SMA50', marker=dict(color='green', size=10))
fig.add_trace(cross_above)


# Customize the layout
fig.update_layout(title='AAPL Stock Price and SMA50 Crossings',
    xaxis_rangeslider_visible=False, xaxis_title='Date',
    yaxis_title='Price', showlegend=True)

# Show the chart
fig.show()