Kazanskyi

df_15_val.reset_index(inplace = True)
ypred_15 = pd.Series(ypred_15)
df_15_val = df_15_val.join(ypred_15.rename('15d_forecast'), how = 'left')

df_15_val["True_Forecasted"] = df_15_val.future_15dprice_class.astype(str) + " " + df_15_val["15d_forecast"].astype(str)

df_15_val[["future_15dprice_change", "True_Forecasted"]].groupby("True_Forecasted").agg({'future_15dprice_change': ['mean', 'median','min', 'max',]})

Kazanskyi

for j, i in enumerate(targets_all):
    remove_redundant_list = list(targets_all)
    remove_redundant_list.remove(i)
    
    new_column_classification = i + "_classification"
    # first let's create a new column with the 150days prediction classifier: 0 when price dropped and 1 when it's increased.
    df_compact_reserve.loc[df_compact_reserve[i]<0.01, new_column_classification] = 0
    df_compact_reserve.loc[df_compact_reserve[i]>=0.01, new_column_classification] = 1

    #let's join prediction result to the test dataframe and get indexes

Kazanskyi

target_vars = ["future_15dprice_change", "future_30dprice_change", "future_60dprice_change", "future_90dprice_change", "future_120dprice_change", "future_150dprice_change"]

min_15d_threshold = 0.038
min_30d_threshold = 0.059
min_60d_threshold = 0.093
min_90d_threshold = 0.122
min_120d_threshold = 0.148
min_150d_threshold = 0.173
list_of_thresholds = [min_15d_threshold,min_30d_threshold,min_60d_threshold,min_90d_threshold,min_120d_threshold,min_150d_threshold]

Kazanskyi

import os
import pandas as pd
import numpy as np
from datetime import date
import VIX
import new_earnings
import tiingo_data as tii
import US_bond
import crisis_dataset

Kazanskyi

import time
from datetime import date
import pandas as pd
import numpy as np
from selenium import webdriver
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.select import Select
import io
  

Kazanskyi

import datetime
from datetime import date
import pandas as pd
import numpy as np

def get_dates():
    last_date = date.today()
    historical_days = 1450
    historical_date = last_date-datetime.timedelta(days=historical_days)
    

Kazanskyi

import time
import datetime
from datetime import date
from selenium import webdriver
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.common.keys import Keys
import pandas as pd
import os

def download_tables(last_date = date.today(), historical_days = 1450):

Kazanskyi

import pandas as pd
import datetime
from datetime import date

def get_vix(last_date = date.today(), historical_days = 1450):
    historical_date = last_date-datetime.timedelta(days=historical_days)
    
    url="https://cdn.cboe.com/api/global/us_indices/daily_prices/VIX_History.csv"
    response=pd.read_csv(url)
    

Kazanskyi

import pandas as pd
import numpy as np
import tiingo_data as tii
from datetime import date

print("Please enter the company's symbol: ")  
symbol = input()

#Get all companies sector, industry, location, etc
metadata = tii.fetch_metadata()

Kazanskyi

import pandas as pd #data manipulation and analysis package
import numpy as np
import datetime
from datetime import date
import requests

#Enter TIINGO
print("Enter 40 signs tiingo API: ")  
Tiingo_API = input()