simulation and matplot

python
Published

October 12, 2022

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
np.random.seed(10)
size = 100
y_data = np.cumsum(np.random.randn(size) * 100.0)
y_data_2 = np.cumsum(np.random.randn(size))
y_data_3 = np.cumsum(np.random.randn(size) * 100.0)
x = np.linspace(0.0, 10.0, size)
plt.scatter(x,y_data)
<matplotlib.collections.PathCollection at 0x127781370>

price_data = pd.DataFrame(
    np.cumsum(np.random.randn(150, 2).dot([[0.5, 0.8], [0.8, 1.0]]), axis=0) + 100,
    columns=["Security 1", "Security 2"],
    index=pd.date_range(start="01-01-2007", periods=150),
)
symbol = "Security 1"
dates_all = price_data.index.values
final_prices = price_data[symbol].values.flatten()
price_data.index.names=['date']
price_data
Security 1 Security 2
date
2007-01-01 101.081239 101.483726
2007-01-02 100.405215 100.596704
2007-01-03 100.449096 100.569060
2007-01-04 101.446296 102.097565
2007-01-05 100.361914 100.826963
... ... ...
2007-05-26 94.435223 94.219550
2007-05-27 93.879329 93.549650
2007-05-28 95.058473 95.068661
2007-05-29 94.784069 94.489578
2007-05-30 95.480560 95.187888 
plt.figure()
plt.plot(x, y_data)
plt.xlabel("Time")
plt.show()