Populate stock predictions in django database with models

Question

I have a code which outputs the prediction of stocks. I've been trying to figure out how to populate the predictions into the database. Every time the code runs, it should modify the database with the new predictions.

This is my 'predictions.py':

import yfinance as yf
import numpy as np # Processing data as arrays
import pandas as pd # Used to create a dataframe that holds all data
from keras.models import load_model # Used to load existing models
import datetime
import joblib
from Dashboards.models import Predictions


def models_loader(folder, name, days = [1, 5, 30]):
    model = []
    for i in days:
        model.append(load_model(folder+'/'+ name + '_' + str(i) + '.h5'))
    return model

days = [1,5,14,30,90] #Day intervals
scaler = joblib.load('scaler.sav')

models = models_loader('ML Model','Model', days) #Load models 

has_Run = False
companies = ['TSLA', 'AAPL','SIRI','GGB','PLUG']

while True:
    print("Has started")
    time = datetime.datetime.today()
    schedule = datetime.time(17,0,0)
    if time.hour == schedule.hour and has_Run==False: #change second time hour to schedule hour
        last = datetime.date.today() 
        td = datetime.timedelta(100)
        start = last - td
    
        for symbols in companies:
            print(symbols)
            predINV= []
            data = yf.download(symbols,start = start, end = last)
            data = data.filter(['Close'])
            if len(data.index) > 59:
                INPUT = data.values
                INPUT = INPUT[-60:]
                scaled_input = scaler.fit_transform(INPUT)
                scaled_input = np.reshape(scaled_input, (-1,60,1))
                for i in range(len(days)):
                    pred = models[i].predict(scaled_input) 
                    predINV.append(scaler.inverse_transform(pred))
                    predINV[i] = predINV[i].round(decimals = 2)
                    print(f'Day {days[i]}: {predINV[i]}')

                    pred_to_db = Predictions(symbol=symbols, day1=predINV[0], day5=predINV[1], day14=predINV[2], day30=predINV[3], day90=predINV[4])
                    pred_to_db.save()

                    stocks = Predictions.objects.all()

                    for stock in stocks:
                    
                        stock.symbol = symbols
                        stock.day1 = predINV[0]
                        stock.day5 = predINV[1]
                        stock.day14 = predINV[2]
                        stock.day30 = predINV[3]
                        stock.day90 = predINV[4]

                    stock.save(update_fields=['symbol', 'day1', 'day5', 'day14', 'day30', 'day90'])
        
            del data 
            has_Run = True  

      elif has_Run==True and time.hour != schedule.hour:
         has_Run=False

(This is what I want to populate to DB. It should populate all the data and every time the code runs modify the rows with the new values.) Output of 'predictions.py' looks like this:

Has started
TSLA
[*********************100%***********************]  1 of 1 completed
Day 1: [[836.88]]
Day 5: [[836.88]]
Day 14: [[836.07]]
Day 30: [[835.21]]
Day 90: [[827.17]]
AAPL
[*********************100%***********************]  1 of 1 completed
Day 1: [[163.32]]
Day 5: [[163.32]]
Day 14: [[163.69]]
Day 30: [[163.57]]
Day 90: [[163.04]]
SIRI
[*********************100%***********************]  1 of 1 completed
Day 1: [[6.17]]
Day 5: [[6.17]]
Day 14: [[6.16]]
Day 30: [[6.15]]
Day 90: [[6.12]]
....

This is my 'models.py':

class Predictions(models.Model):
  '''Predictions Table to maintain stock predictions'''
  symbol = models.CharField(default='', max_length=1)
  day1 = models.DecimalField(max_digits=10, decimal_places=2, default=0.00)
  day5 = models.DecimalField(max_digits=10, decimal_places=2, default=0.00)
  day14 = models.DecimalField(max_digits=10, decimal_places=2, default=0.00)
  day30 = models.DecimalField(max_digits=10, decimal_places=2, default=0.00)
  day90 = models.DecimalField(max_digits=10, decimal_places=2, default=0.00)