Thanks a lot
I need to be careful )

@ab_trader thanks, but I do not benefit from this because the prices are different and I want to buy a stock that day at the same price

2018-02-20, BUY CREATE AAPL, price = 171.85, qty = 26
2018-02-20, BUY COMPLETE, 178.27
I want to buy and complete stock with a price 171.85, not 178.27

For instance,
2018-02-20, BUY CREATE AAPL, price = 171.85
2018-02-21, BUY COMPLETE, 178.13
171.85 and 178.13 are the close price of each day.
It is daily trading. Complete operation is performed the next day, in 2018-02-21.
I want to create and complete the operation in 2018-02-20.
I mean that the action was created and completed at a price of 171.85.
Is it possible to perform such an operation?
In short, I want to buy and sell shares in the market the same day when a signal comes to me, and not the next day as in the document

import datetime  # For datetime objects
import os  # To manage paths
import sys  # To find out the script name (in argv[0])

# Import the backtrader platform
import backtrader as bt
import scipy.stats

    
# Create a Stratey
class TestStrategy(bt.Strategy):

    lines = ('correlation',)
    params = dict(
        period20=20,
        period10=10,
        stake=10,
        qty1=0,
        qty2=0,
        status=0,
        portfolio_value=10000,
    )

    
    def log(self, txt, dt=None):
        ''' Logging function for this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))

    
    def notify_order(self, order):
        if order.status in [bt.Order.Submitted, bt.Order.Accepted]:
            return  # Await further notifications

        if order.status == order.Completed:
            if order.isbuy():
                buytxt = 'BUY COMPLETE, %.2f' % order.executed.price
                self.log(buytxt)
            else:
                selltxt = 'SELL COMPLETE, %.2f' % order.executed.price
                self.log(selltxt)
            
        elif order.status in [order.Expired, order.Canceled, order.Margin]:
            self.log('%s ,' % order.Status[order.status])
            pass  # Simply log

        # Allow new orders
        self.orderid = None
    
    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        
        self.orderid = None
        self.qty1 = self.p.qty1
        self.qty2 = self.p.qty2
        self.status = self.p.status
        self.portfolio_value = self.p.portfolio_value
        self.profit_fix = 30
        self.profit = 0 
        self.cash_aapl = 0
        self.cash_msft = 0
        self.init_close1 = 0
        self.init_close2 = 0
        
        
    def next(self):
        if self.orderid:
            return
        print('*'*20)
        print('Status is ', self.status)

        c10, p = scipy.stats.pearsonr(self.data0.get(size=self.p.period10),
                                     self.data1.get(size=self.p.period10))

        c20, p = scipy.stats.pearsonr(self.data0.get(size=self.p.period20),
                                     self.data1.get(size=self.p.period20))
        c10, c20 = round(c10, 2), round(c20, 2)
        self.log('Close, {}'.format(self.datas[0].close[0]))
        self.log('Close, {}'.format(self.datas[1].close[0]))    
        print('corr10: {}, corr20: {}'.format(c10, c20))
        # Step 2: Check conditions for LONG & place the order
        # Checking the condition for LONG

        
        if self.status == 1:
            self.cash_aapl = round((self.data0.close[0] - self.init_close1)*self.qty1,2)
            self.cash_msft = round((self.init_close2 - self.data1.close[0])*self.qty2,2)
            self.profit = round((self.cash_aapl + self.cash_msft),2)
            
            print('Profit stock1: {}'.format(self.cash_aapl))
            print('Profit stock2: {}'.format(self.cash_msft))
            print('Total profit: {}'.format(self.profit))
               
        
        if (c10 > 0.7 and c20 > 0.7) and (self.status != 1):

            # Calculating the number of shares for each stock
            value = 0.45 * self.portfolio_value  # Divide the cash equally
            x = int(value / (self.data0.close))  # Find the number of shares for Stock1
            y = int(value / (self.data1.close))  # Find the number of shares for Stock2

            
            print('portfolio_value: {}'.format(self.portfolio_value))
            
            # Place the order
            self.log('BUY CREATE %s, price = %.2f, qty = %d' % ("AAPL", self.data0.close[0], x))
            self.buy(data=self.data0, size=(x))  

            self.log('SELL CREATE %s, price = %.2f, qty = %d' % ("MSFT", self.data1.close[0], y))
            self.sell(data=self.data1, size=(y))  

            
            self.qty1 = x  # The new open position quantity for Stock1 is x shares
            self.qty2 = y  # The new open position quantity for Stock2 is y shares
            self.init_close1 = self.data0.close[0]
            self.init_close2 = self.data1.close[0]
            self.status = 1  # The current status is "long the spread"
            
        elif (self.profit > self.profit_fix) and self.status == 1:
            print('profit: {}, profit_fix: {}'.format(self.profit, self.profit_fix))
            # Placing the order
            self.log('SELL CREATE %s, price = %.2f, qty = %d' % ("AAPL", self.data0.close[0], self.qty1))
            self.sell(data=self.data0, size=(self.qty1))  
            
            self.log('BUY CREATE %s, price = %.2f, qty = %d' % ("MSFT", self.data1.close[0], self.qty2))
            self.buy(data=self.data1, size=(self.qty2))  

            # Updating the counters with new value
            self.portfolio_value+=self.profit
            self.qty1, self.qty2 = 0, 0
            
            self.cash_aapl, self.cash_msft, self.profit = 0, 0, 0
            
            self.status = 0 

    def stop(self):
        print('==================================================')
        print('Starting Value - %.2f' % self.broker.startingcash)
        print('Ending   Value - %.2f' % self.broker.getvalue())
        print('==================================================')
        
        
if __name__ == '__main__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(TestStrategy)
    
    
    def read_data(file_name):    
        datapath = os.path.abspath(os.getcwd() + '/' + file_name)
        return bt.feeds.YahooFinanceCSVData(
                dataname=datapath,
                # Do not pass values before this date
                #fromdate=datetime.datetime(2018, 1, 1),
                # Do not pass values before this date
                #todate=datetime.datetime(2019, 1, 1),
                # Do not pass values after this date
                reverse=False)
    
    # Create a Data Feed
    data1 = read_data('aapl_1819.csv')
    cerebro.adddata(data1, name='AAPL')
    
    data2 = read_data('msft_1819.csv')
    cerebro.adddata(data2, 'MSFT')
    
    # Set our desired cash start
    cerebro.broker.setcash(100000.0)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

Output:

Status is  0
2018-02-20, Close, 171.85
2018-02-20, Close, 92.72
corr10: 0.9, corr20: 0.73
portfolio_value: 10000
2018-02-20, BUY CREATE AAPL, price = 171.85, qty = 26
2018-02-20, SELL CREATE MSFT, price = 92.72, qty = 48
2018-02-21, BUY COMPLETE, 178.13
2018-02-21, SELL COMPLETE, 95.61
********************
Status is  1
2018-02-21, Close, 171.07
2018-02-21, Close, 91.49
corr10: 0.93, corr20: 0.76
Profit stock1: -20.28
Profit stock2: 59.04
Total profit: 38.76
profit: 38.76, profit_fix: 30
2018-02-21, SELL CREATE AAPL, price = 171.07, qty = 26
2018-02-21, BUY CREATE MSFT, price = 91.49, qty = 48
2018-02-22, SELL COMPLETE, 177.95
2018-02-22, BUY COMPLETE, 94.96

@ab_trader but I just want to check my algorithm on paper trading

@backtrader How to check algorithm for history data with minutes timeframe?

@ab_trader said in problem with live trading(delayed data):

Buy subscription for market data from Interactive Brokers.

Is there another way to get permission? )

I am new to backtrader
I want to reciev data with timeframe=bt.TimeFrame.Minutes

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os.path  # To manage paths
import sys  # To find out the script name (in argv[0])

# Import the backtrader platform
import backtrader as bt


# Create a Stratey
class TestStrategy(bt.Strategy):

    def log(self, txt, dt=None):
        ''' Logging function fot this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close

    def next(self):
        # Simply log the closing price of the series from the reference
        self.log('Close, %.2f' % self.dataclose[0])

        if self.dataclose[0] < self.dataclose[-1]:
            # current close less than previous close

            if self.dataclose[-1] < self.dataclose[-2]:
                # previous close less than the previous close

                # BUY, BUY, BUY!!! (with all possible default parameters)
                self.log('BUY CREATE, %.2f' % self.dataclose[0])
                self.buy()


if __name__ == '__main__':
    # Create a cerebro entity
    cerebro = bt.Cerebro()

    # Add a strategy
    cerebro.addstrategy(TestStrategy)

    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere

    # Create a Data Feed
    data = bt.feeds.YahooFinanceData(
        dataname='AAPL',
        # Do not pass values before this date
        fromdate=datetime.datetime(2016, 1, 1),
        # Do not pass values before this date
        todate=datetime.datetime(2016, 2, 1),
        timeframe = bt.TimeFrame.Minutes,
        # Do not pass values after this date
        reverse=False)

    # Add the Data Feed to Cerebro
    cerebro.adddata(data)

    # Set our desired cash start
    cerebro.broker.setcash(100000.0)

    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    # Run over everything
    cerebro.run()

    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())

I get an error.What is the error?

KeyError                                  Traceback (most recent call last)
<ipython-input-6-0bc68b8d700a> in <module>()
     68 
     69     # Run over everything
---> 70     cerebro.run()
     71 
     72     # Print out the final result

~\Anaconda3\envs\env_zipline\lib\site-packages\backtrader\cerebro.py in run(self, **kwargs)
   1125             # let's skip process "spawning"
   1126             for iterstrat in iterstrats:
-> 1127                 runstrat = self.runstrategies(iterstrat)
   1128                 self.runstrats.append(runstrat)
   1129                 if self._dooptimize:

~\Anaconda3\envs\env_zipline\lib\site-packages\backtrader\cerebro.py in runstrategies(self, iterstrat, predata)
   1208                 if self._exactbars < 1:  # datas can be full length
   1209                     data.extend(size=self.params.lookahead)
-> 1210                 data._start()
   1211                 if self._dopreload:
   1212                     data.preload()

~\Anaconda3\envs\env_zipline\lib\site-packages\backtrader\feed.py in _start(self)
    201 
    202     def _start(self):
--> 203         self.start()
    204 
    205         if not self._started:

~\Anaconda3\envs\env_zipline\lib\site-packages\backtrader\feeds\yahoo.py in start(self)
    347 
    348     def start(self):
--> 349         self.start_v7()
    350 
    351         # Prepared a "path" file -  CSV Parser can take over

~\Anaconda3\envs\env_zipline\lib\site-packages\backtrader\feeds\yahoo.py in start_v7(self)
    319         }
    320 
--> 321         urlargs.append('interval={}'.format(intervals[self.p.timeframe]))
    322         urlargs.append('events=history')
    323         urlargs.append('crumb={}'.format(crumb))

@backtrader Thanks. You helped me a lot

----------------------------------------
2016-02-16, BUY CREATE LABU in long , 25.4, SMA: 23.916
2016-02-16, Number of stocks: 1850.0
2016-02-17, BUY EXECUTED, Price: 28.60, Cost: 52910.00, Comm 0.00
2016-02-17, Cash 47090.00, Value 100000.00
----------------------------------------
2016-02-17, SELL CREATE LABU in long, 28.60, SMA: 24.13, DIFF: 12.60
2016-02-17, Number of stocks 1850.00
2016-02-18, SELL EXECUTED, Price: 25.68, Profit 47508.00, Comm 0.00
2016-02-18, OPERATION PROFIT, GROSS -5402.00, NET -5402.00, SUM -5402.00
----------------------------------------
2016-02-18, BUY CREATE LABU in long , 25.68, SMA: 24.016
2016-02-18, Number of stocks: 1731.0
2016-02-19, BUY EXECUTED, Price: 26.96, Cost: 46667.76, Comm 0.00
2016-02-19, Cash 47930.24, Value 94598.00
----------------------------------------
2016-02-19, BUY CREATE TQQQ in long , 13.8, SMA: 12.718
2016-02-19, Number of stocks: 1632.0
2016-02-22, BUY EXECUTED, Price: 14.45, Cost: 23582.40, Comm 0.00
2016-02-22, Cash 24347.84, Value 96052.04
2016-02-22, Cash 24347.84, Value 96052.04
----------------------------------------
2016-02-22, SELL CREATE LABU in long, 27.80, SMA: 24.14, DIFF: 8.26
2016-02-22, Number of stocks 1632.00
2016-02-23, SELL EXECUTED, Price: 24.20, Profit 39494.40, Comm 0.00
2016-02-23, Cash 63842.24, Value 88645.40
----------------------------------------
2016-02-23, SELL CREATE LABU in long, 24.20, SMA: 24.47, DIFF: 2320.00
2016-02-23, Number of stocks 1632.00
2016-02-24, SELL EXECUTED, Price: 24.76, Profit 40408.32, Comm 0.00
2016-02-24, OPERATION PROFIT, GROSS -4722.12, NET -4722.12, SUM -10124.12
2016-02-24, Cash 104250.56, Value 89369.96

The problem was with the size of stocks.

@backtrader said in Problem with multiple stocks.:

you only have SELL orders. How do you expect a trade to be closed if you don't buy what you have sold?
Sorry, output was not correct.
Correct output:

2016-02-16, BUY CREATE LABU in long , 25.4, SMA: 23.916
2016-02-16, Number of stocks: 1850.0
2016-02-17, BUY EXECUTED, Price: 28.60, Cost: 52910.00, Comm 0.00
2016-02-17, Cash 47090.00, Value 100000.00
2016-02-17, SELL CREATE LABU in long, 28.60, SMA: 24.13, DIFF: 12.60
2016-02-18, SELL EXECUTED, Price: 25.68, Profit 47508.00, Comm 0.00
2016-02-18, OPERATION PROFIT, GROSS -5402.00, NET -5402.00, SUM -5402.00
2016-02-18, BUY CREATE LABU in long , 25.68, SMA: 24.016
2016-02-18, Number of stocks: 1731.0
2016-02-19, BUY EXECUTED, Price: 26.96, Cost: 46667.76, Comm 0.00
2016-02-19, Cash 47930.24, Value 94598.00
2016-02-19, BUY CREATE TQQQ in long , 13.8, SMA: 12.718
2016-02-19, Number of stocks: 1632.0
2016-02-22, BUY EXECUTED, Price: 14.45, Cost: 23582.40, Comm 0.00
2016-02-22, Cash 24347.84, Value 96052.04
2016-02-22, Cash 24347.84, Value 96052.04
2016-02-22, SELL CREATE LABU in long, 27.80, SMA: 24.14, DIFF: 8.26
2016-02-23, SELL EXECUTED, Price: 24.20, Profit 39494.40, Comm 0.00
2016-02-23, Cash 63842.24, Value 88645.40
2016-02-23, SELL CREATE LABU in long, 24.20, SMA: 24.47, DIFF: 2320.00
2016-02-24, SELL EXECUTED, Price: 24.76, Profit 40408.32, Comm 0.00
2016-02-24, OPERATION PROFIT, GROSS -4722.12, NET -4722.12, SUM -10124.12
2016-02-24, Cash 104250.56, Value 89369.96
2016-02-24, SELL CREATE LABU in long, 24.76, SMA: 24.83, DIFF: 2376.00
2016-02-25, SELL EXECUTED, Price: 24.24, Profit 39559.68, Comm 0.00
2016-02-25, Cash 143810.24, Value 90787.28
2016-02-25, SELL CREATE LABU in long, 24.24, SMA: 25.12, DIFF: 2324.00
2016-02-26, SELL EXECUTED, Price: 25.52, Profit 41648.64, Comm 0.00
2016-02-26, Cash 185458.88, Value 86687.12
2016-02-26, SELL CREATE LABU in long, 25.52, SMA: 25.59, DIFF: 2452.00
2016-02-29, SELL EXECUTED, Price: 23.52, Profit 38384.64, Comm 0.00
2016-02-29, Cash 223843.52, Value 95660.96
2016-02-29, SELL CREATE LABU in long, 23.52, SMA: 25.67, DIFF: 2252.00
Start capital: 100000.00
Final Portfolio Value: 95660.96

I have both buy and sell orders.
This code will work correctly if I use only one stock. If I use for several stocks, there is a problem with sell orders. Above it can be seen that until the stock TQQQ occurs, the LABU is trading successfully. When two stocks are combined, an error occurs.
I realized that after the SELL the trade does not close, I can not understand why it is re-executed. What do I need to fix in the code?

Hi, I am new at Backtrader.
I have two stocks and use a simple algorithm in the long position
If I buy and sell in that order
self.buy(first_stock)
self.sell(first_stock)
self.buy(second_stock)
self.sell(second_stock)

The code works correctly and after each sell operation notify_trade(self, trade) function is called and print profit etc.
I have problem,when this situation happens
self.buy(first_stock)
self.buy(second_stock)
self.sell(first_stock)
self.sell(second_stock)
In this situation notify_trade(self, trade) function is not called

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

import datetime  # For datetime objects
import os 


# Import the backtrader platform
import backtrader as bt
import numpy as np

# Create a Stratey
class TestStrategy(bt.Strategy):
    params = (
        ('maperiod', 10),
        ('oneplot', True),
        #('order_pct', round(0.95/len(self.datas),2)),
    )

    def log(self, txt, dt=None):
        ''' Logging function fot this strategy'''
        dt = dt or self.datas[0].datetime.date(0)
        print('%s, %s' % (dt.isoformat(), txt))

    def __init__(self):
        
        # Keep a reference to the "close" line in the data[0] dataseries
        self.dataclose = self.datas[0].close

        # To keep track of pending orders and buy price/commission
        self.order = None
        self.buyprice = None
        self.buycomm = None
        
        self.buy_price = [1,1]
        self.entry = None
        self.sum = 0
        self.pos = [None, None]
        self.trading = False
        self.lose = 0
        
        self.order_pct = round(0.95/len(self.datas),2)
        
        # self.number_stake = 100
        # Add a MovingAverageSimple indicator
        self.sma = []
        
        for i, d in enumerate(self.datas):
            self.sma.append(bt.indicators.SimpleMovingAverage(
                self.datas[i], period=self.params.maperiod))    
                
    def next(self):
        for i, d in enumerate(self.datas):
            #self.log('{}, {}'.format(d._name, self.buy_price[i]))
            dt, dn = self.datetime.date(), d._name
            pos = self.getposition(d).size
             
            day_close = d.close[0]
            date = d.datetime.date
            symbol = d._name
            
            if self.order:
                return

            if not pos:
                if (day_close > self.sma[i][0]) :

                    # BUY, BUY, BUY!!! (with all possible default parameters)
                    diff = ((day_close/self.sma[i][0])-1)*100
                    
                    self.log("BUY CREATE {} in long , {}, SMA: {}".format(
                              d._name,
                              day_close, 
                              self.sma[i][0]))
                    
                    # Keep track of the created order to avoid a 2nd order
                    amount_to_invest = (self.order_pct * self.broker.cash)
                    self.size = amount_to_invest // day_close

                    self.order = self.buy(data=d, exectype=bt.Order.Close, size=self.size)

                    self.buy_price[i] = day_close

                    self.log(('Number of stocks: {}').format(self.size))

            else:
                if ((day_close > self.buy_price[i] * 1.06) or
                    (day_close < self.buy_price[i] * 0.97)) :
                    # SELL, SELL, SELL!!! (with all possible default parameters)
                    
                    self.log('SELL CREATE %s in long, %.2f, SMA: %.2f, DIFF: %.2f' % 
                             (d._name,
                              day_close, 
                              self.sma[i][0], 
                              ( (day_close/self.buy_price[i])-1)*100 ))

                    # Keep track of the created order to avoid a 2nd order
                    self.order = self.sell(data=d, exectype=bt.Order.Close, size=self.size)
                    self.buy_price[i] = 1
                        
                    
    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            # Buy/Sell order submitted/accepted to/by broker - Nothing to do
            return

        # Check if an order has been completed
        # Attention: broker could reject order if not enough cash
        if order.status in [order.Completed]:
            # order.executed.value = order.executed.price * self.p.stake
            if order.isbuy():
                self.log(
                    'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                    (order.executed.price,
                     order.executed.price * self.size,
                     order.executed.comm))

                self.buyprice = order.executed.price
                self.buycomm = order.executed.comm
            else:  # Sell
                self.log('SELL EXECUTED, Price: %.2f, Profit %.2f, Comm %.2f' %
                         (order.executed.price,
                          #order.executed.value,
                          order.executed.price * self.size,
                          order.executed.comm))

            self.bar_executed = len(self)
        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log('Order Canceled/Margin/Rejected')

        # Write down: no pending order
        self.order = None

    def notify_trade(self, trade):
        if not trade.isclosed:
            return
        
        self.sum+=trade.pnlcomm
        
        
        self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f, SUM %.2f' %
                 (trade.pnl, trade.pnlcomm, self.sum))
        
    def notify_cashvalue(self,cash, value):
        for i in self.buy_price:
            if i != 1 or self.trading:
                self.log('Cash %.2f, Value %.2f' % (cash, value))


if __name__ == '__main__':
    
    def read_data(file_name):    
        datapath = os.path.abspath(os.getcwd() + '/datas/' + file_name)
        return bt.feeds.YahooFinanceCSVData(
                dataname=datapath,
                # Do not pass values before this date
                fromdate=datetime.datetime(2016, 2, 1),
                # Do not pass values before this date
                todate=datetime.datetime(2016, 3, 1),
                # Do not pass values after this date
                reverse=False)
    
        
    # Create a cerebro entity
    cerebro = bt.Cerebro()

        
    # Add a strategy
    strategy = TestStrategy
    cerebro.addstrategy(strategy)

    # Datas are in a subfolder of the samples. Need to find where the script is
    # because it could have been called from anywhere

    data0 = read_data('labu.csv')
    data1 = read_data('TQQQ.csv')
    data2 = read_data('SOXL.csv')
    
    # Add the Data Feed to Cerebro
    cerebro.adddata(data0, name='LABU')
    
    data1.plotinfo.plotmaster = data0
    cerebro.adddata(data1, name='TQQQ')

#     data2.plotinfo.plotmaster = data0
#     cerebro.adddata(data2, name='data2')

    #cerebro.addobserver(bt.observers.DrawDown)
    #cerebro.adddata(get_data(datapath0))
    # Set our desired cash start
    cerebro.broker.setcash(100000.0)

    # Add a FixedSize sizer according to the stake
    cerebro.addsizer(bt.sizers.FixedSize)

    # Set the commission
    cerebro.broker.setcommission(commission=0.00)

    start_value = cerebro.broker.getvalue()
    # Run over everything
    results = cerebro.run()
    strat = results[0]

    # Print out the starting conditions
    print('Start capital: %.2f' % start_value)
    # Print out the final result
    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
    

Output:

2016-02-17, SELL CREATE labu in long, 28.60, SMA: 24.13, DIFF: 12.60
2016-02-18, SELL EXECUTED, Price: 25.68, Profit 47508.00, Comm 0.00
2016-02-18, OPERATION PROFIT, GROSS -5402.00, NET -5402.00, SUM -5402.00
2016-02-18, BUY CREATE 0 in long , 25.68, SMA: 24.016
2016-02-18, Number of stocks: 1731.0
2016-02-19, BUY EXECUTED, Price: 26.96, Cost: 46667.76, Comm 0.00
2016-02-19, Cash 47930.24, Value 94598.00
2016-02-19, BUY CREATE 1 in long , 13.8, SMA: 12.718
2016-02-19, Number of stocks: 1632.0
2016-02-22, BUY EXECUTED, Price: 14.45, Cost: 23582.40, Comm 0.00
2016-02-22, Cash 24347.84, Value 96052.04
2016-02-22, Cash 24347.84, Value 96052.04
2016-02-22, SELL CREATE labu in long, 27.80, SMA: 24.14, DIFF: 8.26
2016-02-23, SELL EXECUTED, Price: 24.20, Profit 39494.40, Comm 0.00
2016-02-23, Cash 63842.24, Value 88645.40
2016-02-23, SELL CREATE labu in long, 24.20, SMA: 24.47, DIFF: 2320.00
2016-02-24, SELL EXECUTED, Price: 24.76, Profit 40408.32, Comm 0.00
2016-02-24, OPERATION PROFIT, GROSS -4722.12, NET -4722.12, SUM -10124.12
2016-02-24, Cash 104250.56, Value 89369.96
2016-02-24, SELL CREATE labu in long, 24.76, SMA: 24.83, DIFF: 2376.00
2016-02-25, SELL EXECUTED, Price: 24.24, Profit 39559.68, Comm 0.00
2016-02-25, Cash 143810.24, Value 90787.28
2016-02-25, SELL CREATE labu in long, 24.24, SMA: 25.12, DIFF: 2324.00
2016-02-26, SELL EXECUTED, Price: 25.52, Profit 41648.64, Comm 0.00
2016-02-26, Cash 185458.88, Value 86687.12
2016-02-26, SELL CREATE labu in long, 25.52, SMA: 25.59, DIFF: 2452.00
2016-02-29, SELL EXECUTED, Price: 23.52, Profit 38384.64, Comm 0.00
2016-02-29, Cash 223843.52, Value 95660.96
2016-02-29, SELL CREATE labu in long, 23.52, SMA: 25.67, DIFF: 2252.00