No excecution of orders during tutorial SMAcrossover

censorship

I started recently with python so excuse me for not being the brightest light atm.
However i've been trying alot and reading alot and i can't figure out the problem.
I cut off almost all my code to find the essence of the problem.

So now i'm trying a basic strategy found online.
Its just the SMA crossover thing and i'm importing data with pandas.
I think the data is getting imported good, i can print values out correctly during the cerebo run and i also can plot the graph in the end. Also the SMA's are visible where i can see them crossing, however the orders don't get triggered upon that. No errors are given.
I've been trying putting other triggers for the buys/sells but they also don't work.

import matplotlib
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
import pandas as pd
import numpy as np
import datetime
import seaborn as sns
from sklearn.model_selection import train_test_split
import sys
import json
import pandas
import numpy as np
from sklearn.cluster import MeanShift, estimate_bandwidth
from datetime import datetime
import matplotlib.pyplot as plt
from __future__ import (absolute_import, division, print_function,unicode_literals)
import datetime
import backtrader as bt
import backtrader.feeds as btfeed
import backtrader.feeds as btfeeds
import backtrader.indicators as btind
from datetime import datetime
import datetime
import os.path  # To manage paths
import argparse
from __future__ import (absolute_import, division, print_function,unicode_literals)
################## 
path_to_market_data = '/Users/Lenovo/Desktop/machinelearning/'
symbol = 'Bitfinex_BTCUSD_d2kort.csv'
filename = path_to_market_data + symbol
pd.options.display.float_format = '${:,.2f}'.format
daily = pd.read_csv(filename, parse_dates=['Date'], index_col=['Date'])
del daily['Unix Timestamp']
del daily['Symbol']
daily["Volume"] = pd.to_numeric(daily["Volume"],errors='coerce')
print(daily)


class PandasData(btfeed.DataBase):
    params = (('datetime', None),('open', -1),('high', -1),('low', -1),('close', -1),('volume', -1))

#data = bt.feeds.PandasData(dataname=daily)
    
class CandlesticksBW(bt.Strategy):
    
    def __init__(self):
        self.sma = btind.SimpleMovingAverage(period=15)
        self.sma2 = btind.SimpleMovingAverage(period=2)
        self.dataclose = self.data.close
        self.dataopen = self.data.open
        self.test = self.sma>self.sma2
    def next(self):
        if self.sma>self.sma2:
            self.buy()
        if self.sma < self.sma2:
            self.sell()

def runstrat():
    args = parse_args()
    # Create a cerebro entity
    cerebro = bt.Cerebro()
    startcash = 10000
    # Add a strategy
    cerebro.addstrategy(CandlesticksBW)
    # Get a pandas dataframe
    datapath = filename
    # Simulate the header row isn't there if noheaders requested
    skiprows = 0 if args.noheaders else 0
    header = None if args.noheaders else 0
    dataframe = pandas.read_csv(datapath,skiprows=skiprows,header=header,parse_dates=True,index_col=0)
    if not args.noprint:
        print('--------------------------------------------------')
        print(daily)
        print('--------------------------------------------------')
    # Pass it to the backtrader datafeed and add it to the cerebro
    data = bt.feeds.PandasData(dataname=daily)
    cerebro.adddata(data)
    # Set our desired cash start
    cerebro.broker.setcash(startcash)
    # Set the commission
    cerebro.broker.setcommission(commission=0.0005)
    # Add a sizer
    #cerebro.addsizer(bt.sizers.PercentSizer, percents=50)
    # Run over everything
    cerebro.run()
    # Print out the starting conditions
    print('Starting Portfolio Value: %.2f' % startcash)
    # Get final portfolio Value
    portvalue = cerebro.broker.getvalue()
    pnl = portvalue - startcash
    # Print out the final result
    print('Final Portfolio Value: ${}'.format(portvalue))
    print('P/L: ${}'.format(pnl))
    # Plot the result
    cerebro.plot(style='bar')
    print(data.close[0])
    if data.close[0]>15:
        print('closegroterdan15')


def parse_args():
    parser = argparse.ArgumentParser(description='Pandas test script')
    parser.add_argument('--noheaders', action='store_true', default=False,required=False,help='Do not use header rows')
    parser.add_argument('--noprint', action='store_true', default=False,help='Print the dataframe')
    return parser.parse_args()

if __name__ == '__main__':
    runstrat()

censorship

Apologies, found it after all ;)
Had to say which kind of orders i want to place.
Since it was already happened and a market order couldn't work.
This can be closed or removed !

censorship

Okay another question :

I have a function that calculates values
It reads the Close prices from a csv file with pandas.
It uses a library to calculate.
Then i get different pricepoints as return in an array.

Now i want to use this in backtrader and i really wonder how i can implement this since i'm new to backtrader.

It uses all the data from the past with samples of 50.
So first 50 candles will be empty.
From there different values will be calculated.
And i need to be able to access them afcorse.

How can i fix this the most easy way ?

vladisld

Seems to me like a classic case for the indicator. Take a look at: https://www.backtrader.com/docu/inddev/

censorship

@vladisld

I have been reading altoa dn the indicator seemed the solution indeed.
However in the examples, the lines always contain only 1 value for each next.
In my usecase i will need to be able to save more values matching the last close.
And this is with 1 calculation.
So thats where i started wondering if it would work or not.
Thanks in advance !

censorship

One step closer

Normalyl i'm putting values in my calculation like this : ( i use a panda dataframe and convert to numpy array)
[2000]
[3000]
[1000]

But the lines are giving :
[2000,3000,1000]

Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

When using reshape he says no attribute found reshape
Probably there is an easy fix for this but can't find it in all the posts here nor online.

vladisld

@censorship said in No excecution of orders during tutorial SMAcrossover:

the lines always contain only 1 value for each next.
In my usecase i will need to be able to save more values matching the last close

Indicator's next method will indeed be called for each new bar of the underlying data. However, by properly defining the minimum period of the indicator, it is ensured that previous minimum period bars of the underlying data are available and you may safely perform your calculations.

If you need to pre-calculate all the indicator values for all the available bars of the underlying data - you may provide the implementation of the once method - where all the underlying data bars are pre-loaded.

@censorship said in No excecution of orders during tutorial SMAcrossover:

i use a panda dataframe and convert to numpy array

Not sure you need pandas to implement a simple indicator at all.

censorship

@vladisld

Okay thanks for the advice, i think i understand that.

Just one problem, i'm using a machine learning function.
And that wants data in a numpy array format like this;
(2D numpy array i think)

[1000]
[2000]
[3000]

But the lines in backtrader are working different :
[1000 2000 3000 4000]
(1D array i think)
So i wonder if there is a way to convert this too a numpy array on which the algorithm is working.