Why do i not see a plot when using the following code, arima model?

from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.stattools import acf, pacf
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.seasonal import seasonal_decompose
from datetime import timedelta, datetime
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import refinitiv.data as rd
import warnings
warnings.filterwarnings('ignore')
plt.style.use('dark_background')

rd.open_session()

def get_data(ric, s_date):

spot_df = rd.get_history(

    ric, 'TRDPRC_1', interval='daily', start=s_date, count=10000)

_e_date = datetime.fromtimestamp(datetime.timestamp(spot_df.index[-1]))

e_date = _e_date + timedelta(days=1)

e_date = e_date.strftime('%Y-%m-%d')

_e_date = _e_date + timedelta(days=30)

fce_date = _e_date.strftime('%Y-%m-%d')

decomposition = seasonal_decompose(spot_df.TRDPRC_1, period=12)

trend = decomposition.trend

seasonal = decomposition.seasonal

residual = decomposition.resid

spot_df['spot_1diff'] = spot_df['TRDPRC_1'].diff()

spot_df = spot_df[spot_df['spot_1diff'].notnull()]  # drop null rows

lag_acf = acf(spot_df['spot_1diff'], nlags=50)

lag_pacf = pacf(spot_df['spot_1diff'], nlags=50, method='ols')

new_spot = spot_df['spot_1diff'].resample(

    'D').ffill()  # resample per day and fill the gaps

new_spot = new_spot.bfill()

new_spot = new_spot.astype('float')

arma_model = ARIMA(new_spot, order=(1, 0, 1))

results = arma_model.fit()

residuals = pd.DataFrame(results.resid)

predictions_ARIMA = pd.Series(results.fittedvalues, copy=True)

predictions_ARIMA_cumsum = predictions_ARIMA.cumsum()

predictions_ARIMA_final = pd.Series(

    spot_df['TRDPRC_1'].iloc[0], index=new_spot.index)

predictions_ARIMA_final = predictions_ARIMA_final.add(

    predictions_ARIMA_cumsum, fill_value=0)

new_spot = spot_df.TRDPRC_1.resample(

    'D', label='right').ffill().astype('float')

es_model = ExponentialSmoothing(

    new_spot, trend='add', damped=False, seasonal='mul', seasonal_periods=30)

es_results = es_model.fit()

predicted_values = es_model.predict(

    params=es_results.params, start=e_date, end=fce_date)

preds = pd.DataFrame(index=pd.date_range(

    start=e_date, end=fce_date), data=predicted_values, columns=['TRDPRC_1'])

plt.figure(figsize=(16, 7))

plt.plot(new_spot, label='Actual')

plt.plot(preds, label='Forecast', color='pink')

plt.legend(loc='best')

plt.show()


return preds