Time Series Prediction Interval - Stock Price Animation Code

5_stock_price.py

!pip install --upgrade pandas
!pip install --upgrade pandas-datareader
!pip install celluloid

from celluloid import Camera as Cam
from IPython.display import HTML, clear_output

import pandas_datareader as pdr
import pandas as pd
from statsmodels.tsa.ar_model import AutoReg

import numpy as np
import seaborn as sb
import matplotlib.pyplot as plt
from matplotlib import rcParams
from cycler import cycler 

shares_df = pdr.DataReader('AAPL', 'yahoo', start='2021-01-01', end='2021-12-31')
data = shares_df['Close']
data = data.asfreq('d')
data = pd.DataFrame(data.interpolate())

model_fit = AutoReg(data, lags=1).fit()
pred = model_fit.get_prediction(start=pd.to_datetime('2022-01-01'),end = pd.to_datetime('2022-03-31'), dynamic=False)
predictions = pd.DataFrame(pred.predicted_mean)

fig, ax = plt.subplots(dpi = 400)
cam = Cam(fig)

rcParams['figure.figsize'] = 15,5
rcParams['axes.spines.top'] = False
rcParams['axes.spines.right'] = False
rcParams['lines.linewidth'] = 2.5
rcParams['axes.prop_cycle'] = cycler(color = ['#000080'])

plt.title("Stock Prices")
plt.xlabel("Date")
plt.ylabel("Price (in $)")
sb.despine(right = True, top = True)

for i in range(1,len(data)):
    sb.lineplot(data = data.iloc[0:i], x = data.index[0:i], y = data.Close[0:i], color = '#000080')
    cam.snap()

for i in range(1,len(data)):
    sb.lineplot(data = data, x = data.index, y = data.Close, color = '#000080')
    cam.snap()
plt.close(fig)

animation = cam.animate(blit=False, interval=15)
HTML(animation.to_html5_video())