cameronShadmehry

# Necessary Libraries
import yfinance as yf, pandas as pd, shutil, os, time, glob, smtplib, ssl
from get_all_tickers import get_tickers as gt

cameronShadmehry

# These two lines remove the Stocks folder and then recreate it in order to remove old stocks.
shutil.rmtree("<Your Path>\\Daily_Stock_Report\\Stocks\\")
os.mkdir("<Your Path>\\Daily_Stock_Report\\Stocks\\")
# Holds the amount of API calls we executed
Amount_of_API_Calls = 0
# This while loop is reponsible for storing the historical data for each ticker in our list. Note that yahoo finance sometimes incurs json.decode errors and because of this we are sleeping for 2 seconds after each iteration, also if a call fails we are going to try to execute it again. Also, do not make more than 2,000 calls per hour or 48,000 calls per day or Yahoo Finance may block your IP. The clause "(Amount_of_API_Calls < 1800)" below will stop the loop from making too many calls to the yfinance API.Prepare for this loop to take some time. It is pausing for 2 seconds after importing each stock.
Stock_Failure = 0  # Used to make sure we don't waste too many API calls on one Stock ticker that could be having issues
Stocks_Not_Imported 

cameronShadmehry

# List of the stocks we are interested in analyzing. At the time of writing this, it narrows the list of stocks down to 44. If you have a list of your own you would like to use just create a new list instead of using this, for example: tickers = ["FB", "AMZN", ...] 
tickers = gt.get_tickers_filtered(mktcap_min=150000, mktcap_max=10000000)
# Check that the amount of tickers isn't more than 1800
print("The amount of stocks chosen to observe: " + str(len(tickers)))

cameronShadmehry

# OBV Analysis, feel free to replace this section with your own analysis -------------------------------------------------------------------------
list_files = (glob.glob("<Your Path>\\Daily_Stock_Report\\Stocks\\*.csv")) # Creates a list of all csv filenames in the stocks folder
new_data = [] #  This will be a 2D array to hold our stock name and OBV score
interval = 0  # Used for iteration
while interval < len(list_files):
    Data = pd.read_csv(list_files[interval]).tail(10)  # Gets the last 10 days of trading for the current stock in iteration
    pos_move = []  # List of days that the stock price increased
    neg_move = []  # List of days that the stock price increased
    OBV_Value = 0  # Sets the initial OBV_Value to zero
    count = 0

cameronShadmehry

Analysis = pd.read_csv("<Your Path>\\Daily_Stock_Report\\OBV_Ranked.csv")  # Read in the ranked stocks
top10 = Analysis.head(10)  # I want to see the 10 stocks in my analysis with the highest OBV values
bottom10 = Analysis.tail(10)  # I also want to see the 10 stocks in my analysis with the lowest OBV values
# This is where we write the body of our email. Add the top 10 and bottom 10 dataframes to include the results of your analysis
Body_of_Email = """\
Subject: Daily Stock Report

Your highest ranked OBV stocks of the day:

""" + top10.to_string(index=False) + """\

cameronShadmehry

# Necessary Libraries
import yfinance as yf, pandas as pd, shutil, os, time, glob
import numpy as np
import requests
from get_all_tickers import get_tickers as gt
from statistics import mean

cameronShadmehry

# If you have a list of your own you would like to use just create a new list instead of using this, for example: tickers = ["FB", "AMZN", ...] 
tickers = gt.get_tickers_filtered(mktcap_min=5000, mktcap_max=10000000)
# Check that the amount of tickers isn't more than 2000
print("The amount of stocks chosen to observe: " + str(len(tickers)))
# These two lines remove the Stocks folder and then recreate it in order to remove old stocks. Make sure you have created a Stocks Folder the first time you run this.
shutil.rmtree("<Your Path>\\SMA_Analysis\\Stocks\\")
os.mkdir("<Your Path>\\SMA_Analysis\\Stocks\\")
# Holds the amount of API calls we executed
Amount_of_API_Calls = 0
# This while loop is reponsible for storing the historical data for each ticker in our list. Note that yahoo finance sometimes incurs json.decode errors and because of this we are sleeping for 2

cameronShadmehry

# Get the path for each stock file in a list
list_files = (glob.glob("<Your Path>\\SMA_Analysis\\Stocks\\*.csv"))
# You can use this line to limit the analysis to a portion of the stocks in the "stocks folder"
# list_files = list_files[:100]
# Create the dataframe that we will be adding the final analysis of each stock to
Compare_Stocks = pd.DataFrame(columns=["Company", "Days_Observed", "Crosses", "True_Positive", "False_Positive", "True_Negative", "False_Negative", "Sensitivity", 
"Specificity", "Accuracy", "TPR", "FPR"])
# While loop to cycle through the stock paths
count = 0
for stock in list_files:

cameronShadmehry

Compare_Stocks = pd.read_csv("<Your Path>\\SMA_Analysis\\All_Stocks.csv") # Read in the All_Stocks data to a dataframe
# Delete companies that don't have enough crosses observed. I am using 50 crosses as my cuttoff:
Not_Enough_Records = []  
Row = 0
while Row < (len(Compare_Stocks)):
    if Compare_Stocks.iloc[Row, 2] < 50:
        Not_Enough_Records.append(Row)  
    Row += 1
Compare_Stocks = Compare_Stocks.drop(Not_Enough_Records)  # Remove records that do not have enough crosses for us to observe
Avg_Accuracy = []  # List to hold the accuracy of each stock

cameronShadmehry

list_files = (glob.glob("<Your path>\\Stocks\\*.csv")) # Creates a list of all csv filenames in the stocks folder
new_data = [] #  This will be a 2D array to hold our stock name and OBV score