def next(self):
prices = self.data.Close
if len(self.data.Volume) > 19:
last_price = prices[-1]
if self.buy_price == 0 and Ticker.hasSignalByBollingerBandsV1(prices) == 1:
self.buy_price = last_price
self.buy()
if self.buy_price != 0 and Ticker.hasSignalByBollingerBandsV1(prices) == -1:
self.position.close()
self.buy_price = 0
def next(self):
prices = self.data.Close
if len(self.data.Volume) > 14:
last_price = prices[-1]
if self.buy_price == 0 and Ticker.isPriceUpTrendByRSI12D(
prices) == True:
self.buy_price = last_price
self.buy()
# if self.buy_price != 0 and (TakeProfit.takeProfit(5, last_price, self.buy_price) or CutLoss.shouldCutLossByPercent(8, last_price, self.buy_price)):
if self.buy_price != 0 and Ticker.isPriceUpTrendByRSI12D(
prices) == False:
self.position.close()
self.buy_price = 0
def next(self):
prices = self.data.Close
opens = self.data.Open
volumes = self.data.Volume
if len(self.data.Volume) > 5:
last_price = prices[-1]
# 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:
# print(self.buy_price * (1 - STOPLOSS))
# print(last_price)
# print(self.trailing_price * (1 - STOPLOSS))
# print('------------------------')
if TakeProfit.takeProfit(
15, last_price,
self.buy_price) or last_price < self.buy_price * (
1 - STOPLOSS
) or last_price < self.trailing_price * (1 - STOPLOSS):
print(self.data.Date[-1])
self.position.close()
self.buy_price = 0
self.trailing_price = 0
def next(self):
prices = self.data.avgHL
if len(self.data.Volume) > 22:
last_price = prices[-1]
if self.buy_price == 0 and Ticker.isCounterTrendV2(prices):
self.buy_price = last_price
self.buy()
if self.buy_price != 0 and (
_sr.takeProfitByPercent(5, last_price, self.buy_price) or
_sr.shouldCutLossByPercent(8, last_price, self.buy_price)):
self.position.close()
self.buy_price = 0
def next(self):
prices = self.data.Close
if len(self.data.Volume) > 66:
last_price = prices[-1]
if self.buy_price == 0 and Ticker.isStockOut(prices):
self.buy_price = last_price
self.buy()
if self.buy_price != 0 and (TakeProfit.takeProfit(
5, last_price, self.buy_price)
or CutLoss.shouldCutLossByPercent(
8, last_price, self.buy_price)):
self.position.close()
self.buy_price = 0
def next(self):
prices = self.data.Close
opens = self.data.Open
volumes = self.data.Volume
if len(self.data.Volume) > 5:
last_price = prices[-1]
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 and (TakeProfit.takeProfit(
5, last_price, self.buy_price)
or CutLoss.shouldCutLossByPercent(
8, last_price, self.buy_price)):
self.position.close()
self.buy_price = 0
def getResult(ticker_id, np_data):
# rand_from = int(np_data.size / 4)
# rand_to = int(np_data.size / 3)
# look_back = random.randint(rand_from,rand_to) # days
# price_data = np_data[:, close_col_index]
# print(price_data)
# print(len(price_data))
# return
f = open("tracking-history.log", "w+")
close_col_index = 4
date_col_index = 0
sold = True
test_from = 10
test_to = len(np_data) - 5
index = 0
sold_price = 0
commission = 0
train_price = np.array([])
for data in np_data:
index = index + 1
train_price = np.append(train_price, data[close_col_index])
if test_from < index < test_to:
copy_data = np_data.copy()
test_prices = copy_data[index:]
curr_price = data[close_col_index]
cut_loss_price = curr_price * 0.96
if Ticker.isStockOut(train_price):
buy_log = "Mua co phieu " + ticker_id + " o gia: " + str(curr_price) + " ngay " + data[
date_col_index] + "\n"
f.write(buy_log)
# f.write("--" + str(index) + "\n")
for np_test_price in test_prices:
test_price = np_test_price[close_col_index]
if test_price > curr_price:
cut_loss_price = test_price * 0.96
if test_price < cut_loss_price: # Co cut loss
sold_price = test_price
commission = sold_price - curr_price
sold_log = "Ban co phieu " + ticker_id + " o gia: " + str(sold_price) + " ngay " + \
np_test_price[date_col_index] + "\r\n"
f.write(sold_log)
# f.write("---" + str(index) + "\n")
break
f.close()
print("Commission: " + str(commission))
def next(self):
prices = self.data.Close
opens = self.data.Open
volumes = self.data.Volume
lows = pd.DataFrame(data=self.data.Low).Low
highs = pd.DataFrame(data=self.data.High).High
if len(prices) > 23:
last_price = prices[-1]
atr_22 = _sr.getATR(22, highs, lows)
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 and (
(_sr.takeProfitByPercent(5, last_price, self.buy_price)
and opens[-1] < prices[-1]
and abs(highs.iloc[-1] - lows.iloc[-1]) > 2 * atr_22) or
_sr.shouldCutLossByPercent(8, last_price, self.buy_price)):
# if self.buy_price != 0 and ((_sr.takeProfitByPercent(5, last_price, self.buy_price) and abs(highs.iloc[-1] - lows.iloc[-1]) > 2*atr_22 ) or _sr.shouldCutLossByPercent(8, last_price, self.buy_price)):
self.position.close()
self.buy_price = 0
def next(self):
prices = self.data.Close
opens = self.data.Open
volumes = self.data.Volume
# self._index = self._index + 1
# print("----------"+str(self._index)+"--------")
if len(self.data.Volume) > 5:
last_5_volumes = volumes[-5::]
last_3_prices = prices[-3::]
# min_volume = min(last_5_volumes)
max_volume = max(last_5_volumes)
last_volume = last_5_volumes[-1]
mean_f = np.mean(volumes[-5:-2])
last_open_price = opens[-1]
last_price = prices[-1]
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 and (TakeProfit.takeProfit(5, last_price, self.buy_price) or CutLoss.shouldCutLossByPercent(8,last_price,self.buy_price)):
self.position.close()
self.buy_price = 0
def next(self):
prices = self.data.Close
if len(self.data.Volume) > 66:
last_price = prices[-1]
if self.buy_price == 0 and Ticker.isStockOut(prices):
self.buy_price = last_price
self.buy()
if self.buy_price != 0 and (TakeProfit.takeProfit(
5, last_price, self.buy_price)
or CutLoss.shouldCutLossByPercent(
8, last_price, self.buy_price)):
self.position.close()
self.buy_price = 0
vn30_ticker = Ticker.getListBlueChips2020()
for ticker in vn30_ticker:
ticker_data = _read_file(ticker + '.csv')
bt = Backtest(ticker_data,
StockBreakOut,
commission=.005,
exclusive_orders=False)
stats = bt.run()
# bt.plot()
# print(stats)
# print(ticker)
# print(stats['Sharpe Ratio'])
# # print(stats['_trades'])
new_file = path + "/result_" + ticker + ".csv"
stats['_trades'].to_csv(new_file, index=False)
if len(self.data.Volume) > 14:
last_price = prices[-1]
if self.buy_price == 0 and Ticker.isPriceUpTrendByRSI12D(
prices) == True:
self.buy_price = last_price
self.buy()
# if self.buy_price != 0 and (TakeProfit.takeProfit(5, last_price, self.buy_price) or CutLoss.shouldCutLossByPercent(8, last_price, self.buy_price)):
if self.buy_price != 0 and Ticker.isPriceUpTrendByRSI12D(
prices) == False:
self.position.close()
self.buy_price = 0
best_return = 0
best_return_ticker = ''
all_ticker = Ticker.getAllTickers(
'/home/zuongthao/GIT/zuongthaotn/backtesting.py/method/VNX.csv')
for ticker in all_ticker:
ticker_data = _read_file(ticker + '.csv')
bt = Backtest(ticker_data,
RSIuptrending,
commission=.005,
exclusive_orders=False)
stats = bt.run()
# bt.plot()
# print(stats)
# exit()
if stats['Return [%]'] > best_return:
best_return_ticker = ticker
# print(ticker)
# print(stats['Sharpe Ratio'])
# # print(stats['_trades'])
def next(self):
prices = self.data.Close
opens = self.data.Open
volumes = self.data.Volume
if len(self.data.Volume) > 5:
last_price = prices[-1]
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 and (TakeProfit.takeProfit(
5, last_price, self.buy_price)
or CutLoss.shouldCutLossByPercent(
8, last_price, self.buy_price)):
self.position.close()
self.buy_price = 0
vn30_ticker = Ticker.getListVN30()
for ticker in vn30_ticker:
BID = _read_file(ticker + '.csv')
bt = Backtest(BID, FollowTheTrend, commission=.005, exclusive_orders=False)
stats = bt.run()
# bt.plot()
print(ticker)
print(stats['Sharpe Ratio'])
# # print(stats['_trades'])
# new_file = path+"/testcases/FollowTheTrend/result_"+ticker+".csv"
# stats['_trades'].to_csv(new_file, index=False)
