Data visualization of RSI with stock prices

RSI_stock.py

import yfinance as yf
import plotly.graph_objects as go
import plotly.express as px

ge = yf.Ticker('GE')
old = ge.history(start="2020-01-01",  end="2021-03-10")
old.reset_index(inplace=True)

def computeRSI (data, time_window):
    diff = data.diff(1).dropna()        # diff in one field(one day)

    #this preservers dimensions off diff values
    up_chg = 0 * diff
    down_chg = 0 * diff
    
    # up change is equal to the positive difference, otherwise equal to zero
    up_chg[diff > 0] = diff[ diff>0 ]
    
    # down change is equal to negative deifference, otherwise equal to zero
    down_chg[diff < 0] = diff[ diff < 0 ]
    
    # check pandas documentation for ewm
    # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.ewm.html
    # values are related to exponential decay
    # we set com=time_window-1 so we get decay alpha=1/time_window
    up_chg_avg   = up_chg.ewm(com=time_window-1 , min_periods=time_window).mean()
    down_chg_avg = down_chg.ewm(com=time_window-1 , min_periods=time_window).mean()
    
    rs = abs(up_chg_avg/down_chg_avg)
    rsi = 100 - 100/(1+rs)
    return rsi

old['RSI'] = computeRSI(old['Close'], 14)

print(old.head())
print(old.tail())

fig1 = px.line(old, x="Date", y="RSI", title='GE Stock Prices')

fig2 = go.Candlestick(x=old['Date'],open=old['Open'],high=old['High'],low=old['Low'],close=old['Close'])
# fig2.update_layout(title="GE Stock Prices", yaxis_title='GE Stock', )

fig1.add_trace(fig2)
fig1.show()