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final_result_df = pd.DataFrame()

# read from local data
for symbol in os.listdir('price_data/'):      
    df = pd.read_csv(f'price_data/{symbol}')
    if df.empty:
        continue
    df.replace('', np.nan, inplace=True)
    df.dropna(inplace=True)
    df.ta.cdl_pattern(name="all", append=True)

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import pandas as pd
import numpy as np
from datetime import datetime
import yfinance as yf
import math
import matplotlib.pyplot as plt

def Supertrend(df, atr_period, multiplier):
    
    high = df['High']

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# we are using mplfinance to help us visualize the indicator
import mplfinance as mpf

# to make the visualization better by only taking the last 100 rows of data
df = df[-100:]

# extract only ['Open', 'High', 'Close', 'Low'] from df
ohcl = df[['Open', 'High', 'Close', 'Low']]

# add colors for the 'value bar'

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# import required libraries
import pandas as pd
import yfinance as yf
import numpy as np
import math

# get stock prices
df = yf.download('AAPL', start='2020-01-01', threads= False)

# parameter setup

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import requests
from bs4 import BeautifulSoup

def get_stock_list():
  # this is the website we're going to scrape from
  url = "https://www.malaysiastock.biz/Stock-Screener.aspx"
  response = requests.get(url, headers={'User-Agent':'test'})
  soup = BeautifulSoup(response.content, "html.parser")
  table = soup.find(id = "MainContent2_tbAllStock")
  # return the result (only ticker code) in a list

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import email
# you can add this part of code at the end of part 2
# remember: screened_list contains the result of the screening
# configure email and message
msg = email.message_from_string(", ".join(screened_list))
msg['From'] = '[email protected]'
msg['To'] = '[email protected]'
msg['Subject'] = "EMA Bounce Result for Today!"

s = smtplib.SMTP("smtp.gmail.com",587)

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import requests
from bs4 import BeautifulSoup
import yfinance as yf

def get_stock_list():
  # this is the website we're going to scrape from
  url = "https://www.malaysiastock.biz/Stock-Screener.aspx"
  response = requests.get(url, headers={'User-Agent':'test'})
  soup = BeautifulSoup(response.content, "html.parser")
  table = soup.find(id = "MainContent2_tbAllStock")

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# function to check for EMA Bounce
def check_bounce_EMA(df):
  candle1 = df.iloc[-1]
  candle2 = df.iloc[-2]
  cond1 = candle1['EMA18'] > candle1['EMA50'] > candle1['EMA100']
  cond2 = candle1['STOCH_%K(5,3,3)'] <= 30 or candle1['STOCH_%D(5,3,3)'] <= 30
  cond3 = candle2['Low'] < candle2['EMA50'] and \
          candle2['Close'] > candle2['EMA50'] and \
          candle1['Low'] > candle1 ['EMA50']
  return cond1 and cond2 and cond3