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ticker_data_graphs.py
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ticker_data_graphs.py
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from bs4 import BeautifulSoup
import requests
from pprint import pprint
import yfinance as yf
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objects as go
from plotly.subplots import make_subplots
def get_headers():
return {"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.122 Safari/537.36"}
def get_sp_tickers():
# Get sp500 ticker and sector
url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies'
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
table = soup.find('table', {'class': 'wikitable sortable'})
rows = table.find_all('tr')[1:] # skip the header row
sp500 = []
for row in rows:
cells = row.find_all('td')
ticker = cells[0].text.strip()
company = cells[1].text.strip()
sector = cells[3].text.strip()
sp500.append({'ticker': ticker, 'company': company, 'sector': sector})
return sp500
def get_sp500_stocks(sp500):
sp500_stocks = []
for stock in sp500:
# try:
print(stock['ticker'])
price = get_stock_price2(stock['ticker'])
print(price)
data = get_historical(stock['ticker'])
technical_data, prices = add_technical_indicators(data)
print(prices.head())
print(prices.shape)
prices_mod = prices[['MA20', 'MA50', 'RSI', 'MACD', 'UpperBand', 'LowerBand',]].iloc[-1, :]
print(prices_mod)
sp500_stocks.append((stock['ticker'], stock['sector'], price))
plot_technical_indicators(prices, stock['ticker'])
# except:
#print((f"There was an issue with {stock['ticker']}."))
return sp500_stocks
def get_stock_price2(ticker):
stock = yf.Ticker(ticker)
info = stock.info
try:
if 'currentPrice' in info:
price = info['currentPrice'] # use currentPrice or regularMarketPrice
return price
else:
print(f"Current price not available for {ticker}")
return None
except:
print(f'Current price not available for {ticker}')
return price
def get_historical(ticker):
stock = yf.Ticker(ticker)
history = stock.history(start='2019-01-01', end='2023-09-24')
return history
def add_technical_indicators(data):
# get historical stock prices
prices = data
if len(prices) < 20:
return
# calculate 20-day moving average
prices['MA20'] = prices['Close'].rolling(window=20).mean()
# calculate 50-day moving average
prices['MA50'] = prices['Close'].rolling(window=50).mean()
# calculate relative strength index (RSI)
delta = prices['Close'].diff()
gain = delta.where(delta > 0, 0)
loss = -delta.where(delta < 0, 0)
avg_gain = gain.rolling(window=14).mean()
avg_loss = loss.rolling(window=14).mean()
rs = avg_gain/avg_loss
prices['RSI'] = 100 - (100/(1 + rs))
# calculating moving average convergence divergence (MACD)
exp1 = prices['Close'].ewm(span=12, adjust=False).mean()
exp2 = prices['Close'].ewm(span=26, adjust=False).mean()
macd = exp1 - exp2
signal = macd.ewm(span=9, adjust=False).mean()
prices['MACD'] = macd - signal
prices['signal'] = signal
# calculate Bollinger Bands
prices['MA20'] = prices['Close'].rolling(window=20).mean()
prices['20STD'] = prices['Close'].rolling(window=20).std()
prices['UpperBand'] = prices['MA20'] + (prices['20STD'] * 2)
prices['LowerBand'] = prices['MA20'] - (prices['20STD'] * 2)
# Features for deep learning model
train_data_aux = prices[['Close', 'MA20', 'MA50', 'RSI', 'MACD', 'UpperBand', 'LowerBand']].dropna()
return train_data_aux, prices
def plot_technical_indicators(prices, ticker):
fig, ax = plt.subplots(4, 1, figsize=(16, 10), dpi=100)
# Plot 20-day and 50-day moving averages
ax[0].plot(prices.index, prices['Close'], label='Close Price')
ax[0].plot(prices.index, prices['Close'], label='Close Price')
ax[0].plot(prices.index, prices['MA20'], label='20-day MA')
ax[0].plot(prices.index, prices['MA50'], label='50-day MA')
ax[0].fill_between(prices.index, prices['MA20'], prices['MA50'], alpha=0.35, color='gray', label='Moving Averages')
ax[0].set_title(f'{ticker}: Moving Averages')
ax[0].set_xlabel('Date')
ax[0].set_ylabel('Price')
ax[0].legend()
# Set a dark theme
# template = "plotly_dark"
# # Create a subplot figure
# fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=[
# f'{ticker}: Moving Averages',
# f'{ticker}: Bollinger Bands'
# ])
# # Plot 20-day and 50-day moving averages
# fig.add_trace(go.Scatter(x=prices.index, y=prices['Close'], mode='lines', name='Close Price'), row=1, col=1)
# fig.add_trace(go.Scatter(x=prices.index, y=prices['MA20'], mode='lines', name='20-day MA'), row=1, col=1)
# fig.add_trace(go.Scatter(x=prices.index, y=prices['MA50'], mode='lines', name='50-day MA'), row=1, col=1)
# fig.add_trace(go.Scatter(x=prices.index, y=prices['MA20'], fill='tonexty', fillcolor='rgba(128,128,128,0.3)', name='Moving Averages'), row=1, col=1)
# # Plot Bollinger Bands
# fig.add_trace(go.Scatter(x=prices.index, y=prices['Close'], mode='lines', name='Close Price'), row=2, col=1)
# fig.add_trace(go.Scatter(x=prices.index, y=prices['UpperBand'], mode='lines', name='Upper Band'), row=2, col=1)
# fig.add_trace(go.Scatter(x=prices.index, y=prices['LowerBand'], mode='lines', name='Lower Band'), row=2, col=1)
# fig.add_trace(go.Scatter(x=prices.index, y=prices['UpperBand'], fill='tonexty', fillcolor='rgba(128,128,128,0.3)', name='Bollinger Bands'), row=2, col=1)
# # Update layout for better appearance
# fig.update_layout(
# title=f'{ticker} Technical Indicators',
# xaxis_rangeslider_visible=False,
# template=template,
# height=800,
# showlegend=False,
# paper_bgcolor='rgba(0,0,0,0)',
# plot_bgcolor='rgba(0,0,0,0)',
# )
# # Show the figure
# fig.show()
# Plot RSI
ax[1].plot(prices.index, prices['RSI'], label='RSI')
ax[1].axhline(y=70, color='r', linestyle='--', label='Overbought')
ax[1].axhline(y=30, color='g', linestyle='--', label='Oversold')
ax[1].set_title(f'{ticker}: RSI')
ax[1].set_xlabel('Date')
ax[1].set_ylabel('RSI')
ax[1].legend()
# Plot MACD
ax[2].plot(prices.index, prices['MACD'], label='MACD')
ax[2].plot(prices.index, prices['signal'], label='Signal')
ax[2].set_title(f'{ticker}: MACD')
ax[2].set_xlabel('Date')
ax[2].set_ylabel('MACD')
ax[2].legend()
# Plot Bollinger Bands
ax[3].plot(prices.index, prices['Close'], label='Close Price')
ax[3].plot(prices.index, prices['UpperBand'], label='Upper Band')
ax[3].plot(prices.index, prices['LowerBand'], label='Lower Band')
ax[3].fill_between(prices.index, prices['LowerBand'], prices['UpperBand'], alpha=0.35, color='gray', label='Bollinger Bands')
ax[3].set_title(f'{ticker}: Bollinger Bands')
ax[3].set_xlabel('Date')
ax[3].set_ylabel('Price')
ax[3].legend()
plt.tight_layout()
plt.show()
if __name__ == "__main__":
sp500 = get_sp_tickers()
get_sp500_stocks(sp500)