# price is increased
if last_price > self.trailing_price:
self.trailing_price = last_price
if self.buy_price == 0 and Ticker.isFollowTrendingV2(
prices, volumes, opens[-1], 2.5):
self.buy_price = last_price
self.buy()
if self.buy_price != 0:
if _sr.takeProfitByPercent(
15, last_price,
self.buy_price) or last_price < self.buy_price * (
1 - STOPLOSS
) or last_price < self.trailing_price * (1 - STOPLOSS):
self.position.close()
self.buy_price = 0
self.trailing_price = 0
ticker_id = 'HBC'
DATA_PATH = os.path.abspath('../../vn-stock-data/VNX/')
ticker = _af.get_pricing_by_path(DATA_PATH + '/' + ticker_id + '.csv',
'2010-01-01', '2020-10-05')
bt = Backtest(ticker, FollowTheTrend, commission=.005, exclusive_orders=False)
stats = bt.run()
# bt.plot()
print(stats)
# print(stats['_trades'])
# new_file = path+"/result_"+ticker_id+".csv"
# stats['_trades'].to_csv(new_file, index=False)
self.data.LowerShadow)
# if np.datetime64(self.data.Date[-1]) == np.datetime64('2018-07-12T00:00:00.000000000'):
# t4 = jModel.isMorningStarsPattern(self.data.Open, self.data.Close, self.data.High, self.data.Low, self.data.Body, self.data.Height, self.data.UpShadow, self.data.LowerShadow)
# print('today - isMorningStarsPattern - ' + str(t4))
# print(jModel.isBlackCandlestick(self.data.Open[-3], self.data.Close[-3]))
# print(jModel.isBodyOver45(self.data.Body[-3], self.data.Height[-3]))
# print(jModel.isBodyLess35(self.data.Body[-2], self.data.Height[-2]))
# print(jModel.isWhiteCandlestick(self.data.Open[-1], self.data.Close[-1]))
# exit()
if hasBuySignal is not False and self.orderPending is False:
self.buy()
self.orderPending = True
if hasSellSignal is not False and self.orderPending is True:
self.position.close()
self.orderPending = False
DATA_PATH = os.path.abspath('../../vn-stock-data/VNX/')
ticker_id = 'VRE'
ticker_data = _af.get_pricing_by_path(DATA_PATH + '/' + ticker_id + '.csv',
'2018-01-01')
new_data = jModel.convertToJapanCandle(ticker_data)
bt = Backtest(ticker_data, Harami, commission=.005, exclusive_orders=False)
stats = bt.run()
print(stats)
# print(stats['_trades'])
bt.plot()
import os
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
METHOD_MODULE_PATH = os.path.abspath('..')
sys.path.insert(1, METHOD_MODULE_PATH)
import method.SaleRules as _sr
import method.algofuncs as _af
DATA_PATH = os.path.abspath('../vn-stock-data/VNX/')
ticker_data = _af.get_pricing_by_path(DATA_PATH + '/' + 'KOS.csv',
start_date='2020-10-01')
mmi_week = _af.MMI(ticker_data["Close"], 5)
plt.figure()
plt.subplot(211)
plt.plot(ticker_data["Close"], label='Close Price', color='black')
plt.subplot(212)
plt.plot(mmi_week, label='MMI Week')
plt.legend()
plt.show()
# plt.figure()
# plt.subplot(211)
# Vol = _af.getVolatility(ticker_data["Close"], 252)
# # Vol2 = ticker_data["High"] - ticker_data["Low"]
# plt.plot(ticker_data["Close"], label='Close Price', color='black')
# plt.plot(ticker_data["High"], label='High Price')
# plt.plot(ticker_data["Low"], label='Low Price')
