from ta.utils import dropna
from ta.volatility import BollingerBands
from ta.trend import EMAIndicator
from ta.trend import SMAIndicator
from ta.trend import MACD
from ta.momentum import RSIIndicator
from db.model import *
from download import *
from db.dao.equity_dao import StocksDao
import traceback
if __name__ == '__main__':
exchange='HEX'
dao = StocksDao()
equity_ref_df = dao.get_all_stocks(exchange)
eod_df = dao.get_all_stocks_prices(exchange)
# Calac data
last_entry_df = dao.get_all_stocks_prices_max_entry_date(exchange)
last_entry_dict = dict(zip(last_entry_df['equity_id'].tolist(), last_entry_df['last_entry'].tolist()))
for idx1, row1 in equity_ref_df.iterrows():
equity_id = row1['equity_id']
ticker = row1['local_code']
try:
insert_list = list()
# get all the prices for equity
ticker_eod_df = eod_df[eod_df['equity_id'] == equity_id]
ticker_eod_df = ticker_eod_df.sort_values(by=['trading_date'])
df = dropna(ticker_eod_df)
from db.model import *
from download import *
from db.dao.equity_dao import StocksDao
import traceback
import PyDSWS
import datetime as dt
from constants import *
if __name__ == '__main__':
exchange = NASDAQ
# start_date = '20160101'
start_date = (dt.datetime.now() - dt.timedelta(days=5)).strftime('%Y%m%d')
end_date = dt.date.today().strftime('%Y%m%d')
dao = StocksDao()
equity_ref_df = dao.get_all_stocks(exchange)
conn = PyDSWS.Datastream(username='******', password='******')
for idx1, row1 in equity_ref_df.iterrows():
ticker = row1['ds_code']
equity_id = row1['equity_id']
try:
insert_list = list()
data = conn.get_data(tickers=ticker,
fields=['RECMED', 'RECNO', 'RECCON'],
start=start_date,
end=end_date)
data.fillna(0, inplace=True)
data_df = data[ticker]
from db.model import *
from download import *
from db.dao.equity_dao import StocksDao
import traceback
if __name__ == '__main__':
exchange = 'SGX'
trade_date = '2021-05-25'
dao = StocksDao()
stock_info_df = dao.get_stock_info_by_date(exchange, trade_date)
stock_info_df['range'] = (
(stock_info_df['adj_close'] - stock_info_df['ema_200']) /
stock_info_df['ema_200']) * 100
report_df = stock_info_df[(stock_info_df['range'] <= 10)
& (stock_info_df['anr_med'] <= 3.0)]
report_df['reco'] = report_df['rsi_14'].apply(lambda x: 'HOT-BUYS'
if x <= 40 else 'COLD-BUY')
report_df = report_df.sort_values(
by=['reco', 'anr_med', 'anr_count', 'range'], ascending=False)
report_df.to_csv('reco_{}_{}.csv'.format(exchange, trade_date))
print(report_df)
print('Done')