Candlestick Chart

import numpy as np
import pandas as pd
import mplfinance as mpf
import matplotlib.pyplot as plt
from datetime import datetime, timedelta

# Generate realistic OHLC data for the last 30 business days
np.random.seed(42)
dates = pd.date_range(end=datetime.today(), periods=30, freq='B')
price = 100 + np.cumsum(np.random.randn(30))  # random walk around 100

# Create Open, High, Low, Close columns
open_prices = price + np.random.randn(30) * 0.5
close_prices = price + np.random.randn(30) * 0.5
high_prices = np.maximum(open_prices, close_prices) + np.abs(np.random.randn(30) * 0.5)
low_prices = np.minimum(open_prices, close_prices) - np.abs(np.random.randn(30) * 0.5)

df = pd.DataFrame({
    'Open': open_prices,
    'High': high_prices,
    'Low': low_prices,
    'Close': close_prices
}, index=dates)

# Plot candlestick chart using mplfinance
fig, ax = mpf.plot(df, type='candle', style='charles',
                   title='Example Candlestick Chart (Last 30 Days)',
                   ylabel='Price ($)',
                   returnfig=True)

# Show the figure
plt.show()
# Add a simple moving average (10 & 20 days)
df['SMA10'] = df['Close'].rolling(window=10).mean()
df['SMA20'] = df['Close'].rolling(window=20).mean()

# Generate random volume data for demonstration
df['Volume'] = np.random.randint(1000, 5000, size=len(df))

# Re‑plot with volume and moving averages
mpf.plot(df,
         type='candle',
         style='charles',
         title='Candlestick Chart with Volume and SMA',
         ylabel='Price ($)',
         volume=True,
         mav=(10, 20),
         returnfig=False)

plt.show()
# END-OF-CODE