'''
pip install pandas,beautifulsoup4,finance-datareader -y
'''
import pandas as pd
import FinanceDataReader as fdr
from datetime import datetime
from dateutil.relativedelta import relativedelta
def get_stock_code(name):
'''이름을 입력하면 코드를 리턴'''
df = fdr.StockListing('KRX')
stock_code = df[df['Name'] == name]['Code'].to_string(index = False)
return stock_code
if __name__ == '__main__':
before_one_week = (datetime.now() - relativedelta(years = 3)).strftime('%Y-%m-%d')
df_exchange_rate = fdr.DataReader(symbol = 'USD/KRW', start = before_one_week)[['Close']]
df_kospi = fdr.DataReader(symbol = 'KS11', start = before_one_week)[['Close']]
df_lg_energy = fdr.DataReader(symbol = get_stock_code('LG에너지솔루션'), start = before_one_week)[['Close']]
df_samsung_sdi = fdr.DataReader(symbol = get_stock_code('삼성SDI'), start = before_one_week)[['Close']]
df_echoprobm = fdr.DataReader(symbol = get_stock_code('에코프로비엠'), start = before_one_week)[['Close']]
df_lnf = fdr.DataReader(symbol = get_stock_code('엘앤에프'), start = before_one_week)[['Close']]
df_ski_tech = fdr.DataReader(symbol = get_stock_code('SK아이이테크놀로지'), start = before_one_week)[['Close']]
moving_average_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 60, 120, 200]
result_dict = {}
for day in moving_average_list:
result_dict[f'avg_{day}'] = [
int(df_exchange_rate.tail(day).sum() / day),
int(df_kospi.tail(day).sum() / day),
int(df_lg_energy.tail(day).sum() / day),
int(df_samsung_sdi.tail(day).sum() / day),
int(df_echoprobm.tail(day).sum() / day),
int(df_lnf.tail(day).sum() / day),
int(df_ski_tech.tail(day).sum() / day)]
df_result = pd.DataFrame(result_dict).T
df_result.columns = ['USD/KRW', 'KOSPI', 'LG_ENSOL', 'SS_SDI', 'ECHOPROBM', 'LNF', 'SKI_TECH']
for column_name in df_result.columns:
df_result[f'{column_name}_per'] = round(df_result[column_name] / df_result[column_name].iloc[0], 3) * 100
print(df_result)
