fix dependencies

README.md

@@ -12,7 +12,9 @@ pinned: false
 # Gameday SPX: An ML Approach to Predicting 🟥 or 🟩
 Given the proliferation of trading styles widely available today to retail investors, there are so many different ways to make money in today's market. This model is an effort to simplify my personal trading style by gathering most of the features that I would consider important, feed them to a model and get an output for whether the day will be green for $SPX (higher than yesterday's close) or red (lower than yesterday's close).
 
-This is specific for $SPX, because I primarily trade this index. Justification for 
+In addition to daily data, I also grab intraday data to emulate how I would trade. 
+
+This is specific for $SPX, because I primarily trade this index and primarily do credit spreads. My style leans toward "this level should hold". 
 
 # TL;DR on WTF
 - The purpose of this project is to predict whether the current day's close will be above the previous day's close (`Target`).

getDailyData.py

@@ -11,8 +11,8 @@ import datetime
 import json
 from pandas.tseries.offsets import BDay
 from sqlalchemy import create_engine
-from dotenv import load_dotenv
-load_dotenv()
+# from dotenv import load_dotenv
+# load_dotenv()
 
 data_start_date = '2018-07-01'
 

getIntraData.py

@@ -6,10 +6,10 @@ import datetime
 from sqlalchemy import create_engine
 import os
 from getDailyData import data_start_date
-from dotenv import load_dotenv
+# from dotenv import load_dotenv
 
 # Load environment variables from the .env file
-load_dotenv()
+# load_dotenv()
 
 def get_intra(periods_30m = 1):
     '''