EMA and SMA of Stock with Rolling days of 250

SMA_EMA_250.py

import numpy as np
from datetime import datetime
import pandas as pd
import plotly.graph_objs as go


'''
Sample CSV File
Ticker,Date,Open,High,Low,Close,Volume
SBIN,2024-04-24,778.50,778.50,770.20,773.10,7842615
SBIN,2024-04-25,770.65,814.40,769.65,812.70,36732976
SBIN,2024-04-26,815.90,816.90,798.90,801.30,14964358
'''

FileName = "C:\\Fibotrader\\csvexport\\{}.csv".format("SBIN")
df = pd.read_csv(FileName, index_col="Date", parse_dates=True, usecols=['Date', 'Close'], na_values=['nan'])

percentage_change = np.diff(df['Close']) / df['Close'][:-1]
df['PerChange'] = np.concatenate(([0], percentage_change))  

sma_250 = df['PerChange'].rolling(window=250).mean()
sma_250 = sma_250*250

df['SMA250'] = np.concatenate(([0], sma_250.loc[df.index[:-1]]))  

ema_250 = df['PerChange'].ewm(span=250, min_periods=0, adjust=False).mean()
ema_250 = ema_250 * 250

df['EMA250'] = np.concatenate(([0], ema_250.loc[df.index[:-1]]))  


fig = go.Figure()

fig.add_trace(go.Scatter(x=df.index, y=df['EMA250'], mode='lines', name='EMA250'))
fig.add_trace(go.Scatter(x=df.index, y=df['SMA250'], mode='lines', name='SMA250'))

fig.update_layout(
    title="SMA250 and EMA250",
    xaxis_title="Date",
    yaxis_title="EMA&SMA's",
    xaxis_rangeslider_visible=True
)

fig.show()

Very nicely put, sir.