20230410A.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
from datetime import datetime, timedelta

# Set the seed for reproducibility
np.random.seed(300)

# Dates
start_date = datetime(2023, 1, 1)
end_date = datetime(2023, 1, 31)
num_days = (end_date - start_date).days + 1

# Starting stock price
start_price = 100

# Generate 31 random percentage changes in price
daily_changes = np.random.normal(0, 0.03, size=num_days+1)
high_values = np.random.normal(0, 0.5, size=num_days)
low_values = np.random.normal(0, 0.5, size=num_days)

# Calculate the daily prices
daily_prices = start_price * np.cumprod(1 + daily_changes)

open_prices = daily_prices[:-1]
close_prices = daily_prices[1:]
high_prices = np.maximum(open_prices, close_prices) + abs(high_values)
low_prices = np.minimum(open_prices, close_prices) - abs(low_values)
volume = np.random.normal(1000000, 500000, 31)

# Create a DataFrame to store the simulated data
data = pd.DataFrame({
    'Open': open_prices,
    'High': high_prices,
    'Low': low_prices,
    'Close': close_prices,
    'Volume': volume
})

data["python_date"] = [start_date.date() + timedelta(days=x) for x in range(num_days)]