Zero records while loading Data from CSV

[KK]

Tried to use both GenericCSVData, and PandasData methods, both resulting zero records.
GenericCSVData
csv data as

while checking len(data) it shows ZERO records

i tried to use PandasData method
defined

and used this following code

I checked the dataframe it has the data as shown here but when i use length of the records it shows ZERO again,

Appreciated if anyone could let me know where the issue is...

[vladisld]

@kk Please do not post the code as image. Mind the top of the page:

"For code/output blocks: Use ``` (aka backtick or grave accent) in a single line before and after the block. See: http://commonmark.org/help/"

For better support it usually required to copy/past part/all of your code to test locally, which is not feasible to do with images.

[KK]

@vladisld got it.... i will take care of that next time onwards

[KK]

@vladisld here is the complete code (its same as Momentum Strategy Article)

import os
os.getcwd()
os.chdir("C:\KK\Data\sectors")

from numpy import cumsum, log, polyfit, sqrt, std, subtract
import numpy as np
import backtrader as bt

def Momentum_func(the_array):
r = np.log(the_array)
slope, _, rvalue, _, _ = linregress(np.arrange(len(r)), r)
annulized = (1 + slope) ** 252
return annulized * (rvalue ** 2)

class Momentum(bt.ind.OperationN):
lines = ('trend',)
params = dict(period = 50)
func = Momentum_func

import collections

class ClenowStrategy(bt.Strategy):
params = dict(
momentum = Momentum,
mom_period = 90,
movav = bt.ind.SMA,
idx_period = 200,
stock_period= 100,
reserve = 0.05,
selcperc = 0.01,
)

def log(self, arg):
    print('{} {}'.format(self.datetime.date(), arg))
    

def __init__(self):
    self.inds = collections.defaultdict(dict)
    self.stocks = self.datas[1:]
    self.d_with_len = []
    
    #for fixed size positioning
    self.selnum = int(len(self.datas) * self.p.selcperc)
    self.perctarget = (1.0 - self.p.reserve)/self.selnum
    
    self.add_timer(
        when = Timer.SESSION_START,
        weekdays = [self.p.rebal_weekday],
        weekcarry = True,
    )
    
    self.idx_mav = self.p.movav(self.data0, period=self.p.idx_period)
    
    for d in self.stocks:
        self.inds[d]['mom'] = self.p.momentum(d, period = self.mom_period)
        self.inds[d]['mav'] = self.p.movav(d, period = self.stock_period)
        self.inds[d]['vol'] = self.p.volatr(d, period=self.p.vol_period)


def prenext(self):
    self.d_with_len = [d for d in self.datas if len(d)]
    self.next()

def nextstart(self):
    self.d_with_len = self.datas
    self(next)

def next(self):
    notify_timer()
    
def notify_timer(self, timer, when, *args, **kwargs):
    self.rankings = list(filter(lambda d: len(d) > self.p.stock_period, self.stocks))
    self.rankings.sort(key=lambda d: self.inds[d]["mom"][0])
    num_stocks = len(self.rankings)
    
    # sell stocks based on criteria
    for i, d in enumerate(self.rankings):
        if self.getposition(self.data).size:
            if i > num_stocks * self.p.selcperc or d < self.inds[d]["mav"]:
                self.close(d)
    
    if self.spy < self.idx_mav:
        return
    
    # buy stocks with remaining cash
    for i, d in enumerate(self.rankings[:int(num_stocks * self.p.selcperc)]):
        cash = self.broker.get_cash()
        value = self.broker.get_value()
        if cash <= 0:
            break
        if not self.getposition(self.data).size:
            size = value * self.perctarget / self.inds[d]["vol"]
            self.buy(d, size=size)

def stop(self):
    print('Final Portfolio Value %.2f' %(cerebro.broker.getvalue()))

import backtrader.feeds as btfeed

class PandasData(btfeed.PandasData):
params = (
('dtformat', '%Y-%m-%d'),
('datetime', None),
('time', None),
('open', 'Open'),
('high', 'High'),
('low', 'Low'),
('close', 'Close'),
('volume', 'Volume'),
('openinterest', None),
)

import pandas as pd
import datetime

fromdate = datetime.datetime(2011, 1, 1)
todate = datetime.datetime(2021, 5, 31)

if name == "main":
cerebro = bt.Cerebro(stdstats=False)
cerebro.broker.set_coc(True)

'''
spy = bt.feeds.YahooFinanceData(dataname='^NSEI',
                             fromdate=fromdate,
                             todate=todate,
                             plot=False)
cerebro.adddata(spy)  # add S&P 500 Index
'''
directory = "C:\\KK\\Data\\sectors"

for filename in os.listdir(directory):
    datapath = os.path.join(directory, filename)
    dataframe = pd.read_csv(datapath,
                            parse_dates=True,
                            index_col=0)

    df = PandasData(dataname=dataframe)
    print('Stock: {} - Records {:.2f}'.format(filename, len(df)))
    if len(df) > 100:
        cerebro.adddata(df, name = filename)  
    
     
'''
for filename in os.listdir(directory):
    f = os.path.join(directory, filename)
    if(os.path.isfile(f)):
        data = bt.feeds.GenericCSVData(
            dataname = f,
            datetime = 0, time = -1, open = 1, high = 2, low = 3, close = 4, volume = 5, openinterest = -1,
            nullvalue = 0.0, dtformat = ('%d-%m-%Y'),
            fromdate = fromdate, todate = todate
        )
        print('Stock: {} - Records {:.2f}'.format(f, len(data)))
        if len(data) > 100:
            cerebro.adddata(data, name = filename)  
 '''
    
#set desired cash 
cerebro.broker.setcash(500000.0)
cerebro.broker.setcommission(0.001) #set commission 

cerebro.addobserver(bt.observers.Value)
cerebro.addanalyzer(bt.analyzers.SharpeRatio, riskfreerate=0.0)
cerebro.addanalyzer(bt.analyzers.Returns)
cerebro.addanalyzer(bt.analyzers.DrawDown)
cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio') 

cerebro.addstrategy(ClenowStrategy)
'''
stop = 5
cerebro.optstrategy(Strategy,
                #momentum_period=range(50, 300, 50),
                num_positions=range(1, 4))

'''
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

results = cerebro.run()

[KK]

@vladisld posted complete code, but looks like some issue the way i put the entire code, so it formed multiple blocks, sorry for it. Unable to delete it after posting

[KK]

Inline code

import os

os.getcwd()
os.chdir("C:\\KK\\Data\\sectors")

from numpy import cumsum, log, polyfit, sqrt, std, subtract
import numpy as np
import backtrader as bt

def Momentum_func(the_array):
    r = np.log(the_array)
    slope, _, rvalue, _, _ = linregress(np.arrange(len(r)), r)
    annulized = (1 + slope) ** 252
    return annulized * (rvalue ** 2)

class Momentum(bt.ind.OperationN):
    lines = ('trend',)
    params = dict(period = 50)
    func = Momentum_func

import collections

class ClenowStrategy(bt.Strategy):
    params = dict(
        momentum = Momentum,
        mom_period = 90,
        movav = bt.ind.SMA,
        idx_period = 200,
        stock_period= 100,
        reserve = 0.05,
        selcperc = 0.01,
    )
   
    def __init__(self):
        self.inds = collections.defaultdict(dict)
        self.stocks = self.datas[1:]
        self.d_with_len = []
        
        #for fixed size positioning
        self.selnum = int(len(self.datas) * self.p.selcperc)
        self.perctarget = (1.0 - self.p.reserve)/self.selnum
        
        self.add_timer(
            when = Timer.SESSION_START,
            weekdays = [self.p.rebal_weekday],
            weekcarry = True,
        )
        
        self.idx_mav = self.p.movav(self.data0, period=self.p.idx_period)
        
        for d in self.stocks:
            self.inds[d]['mom'] = self.p.momentum(d, period = self.mom_period)
            self.inds[d]['mav'] = self.p.movav(d, period = self.stock_period)
            self.inds[d]['vol'] = self.p.volatr(d, period=self.p.vol_period)

    
    def prenext(self):
        self.d_with_len = [d for d in self.datas if len(d)]
        self.next()
    
    def nextstart(self):
        self.d_with_len = self.datas
        self(next)
    
    def next(self):
        notify_timer()
        
    def notify_timer(self, timer, when, *args, **kwargs):
        self.rankings = list(filter(lambda d: len(d) > self.p.stock_period, self.stocks))
        self.rankings.sort(key=lambda d: self.inds[d]["mom"][0])
        num_stocks = len(self.rankings)
        
        # sell stocks based on criteria
        for i, d in enumerate(self.rankings):
            if self.getposition(self.data).size:
                if i > num_stocks * self.p.selcperc or d < self.inds[d]["mav"]:
                    self.close(d)
        
        if self.spy < self.idx_mav:
            return
        
        # buy stocks with remaining cash
        for i, d in enumerate(self.rankings[:int(num_stocks * self.p.selcperc)]):
            cash = self.broker.get_cash()
            value = self.broker.get_value()
            if cash <= 0:
                break
            if not self.getposition(self.data).size:
                size = value * self.perctarget / self.inds[d]["vol"]
                self.buy(d, size=size)
    
import backtrader.feeds as btfeed


class PandasData(btfeed.PandasData):
    params = (
        ('dtformat', '%Y-%m-%d'),
        ('datetime', None),
        ('time', None),
        ('open', 'Open'),
        ('high', 'High'),
        ('low', 'Low'),
        ('close', 'Close'),
        ('volume', 'Volume'),
        ('openinterest', None),
    )
    

import pandas as pd
import datetime

fromdate = datetime.datetime(2011, 1, 1)
todate = datetime.datetime(2021, 5, 31)


if __name__ == "__main__":
    cerebro = bt.Cerebro(stdstats=False)
    cerebro.broker.set_coc(True)
    
   
    spy = bt.feeds.YahooFinanceData(dataname='^NSEI',
                                 fromdate=fromdate,
                                 todate=todate,
                                 plot=False)
    cerebro.adddata(spy)  # add S&P 500 Index
    
    directory = "C:\\KK\\Data\\sectors"
    
    for filename in os.listdir(directory):
        datapath = os.path.join(directory, filename)
        dataframe = pd.read_csv(datapath,
                                parse_dates=True,
                                index_col=0)

        df = PandasData(dataname=dataframe)
        print('Stock: {} - Records {:.2f}'.format(filename, len(df)))
        if len(df) > 100:
            cerebro.adddata(df, name = filename)  
        
         
    '''
    for filename in os.listdir(directory):
        f = os.path.join(directory, filename)
        if(os.path.isfile(f)):
            data = bt.feeds.GenericCSVData(
                dataname = f,
                datetime = 0, time = -1, open = 1, high = 2, low = 3, close = 4, volume = 5, openinterest = -1,
                nullvalue = 0.0, dtformat = ('%d-%m-%Y'),
                fromdate = fromdate, todate = todate
            )
            print('Stock: {} - Records {:.2f}'.format(f, len(data)))
            if len(data) > 100:
                cerebro.adddata(data, name = filename)  
     '''
        
    #set desired cash 
    cerebro.broker.setcash(500000.0)
    cerebro.broker.setcommission(0.001) #set commission 
    
    cerebro.addobserver(bt.observers.Value)
    cerebro.addanalyzer(bt.analyzers.SharpeRatio, riskfreerate=0.0)
    cerebro.addanalyzer(bt.analyzers.Returns)
    cerebro.addanalyzer(bt.analyzers.DrawDown)
    cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio') 

    cerebro.addstrategy(ClenowStrategy)
    '''
    stop = 5
    cerebro.optstrategy(Strategy,
                    #momentum_period=range(50, 300, 50),
                    num_positions=range(1, 4))
    
    '''
    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

    results = cerebro.run()
    
    print(f"Sharpe: {results[0].analyzers.sharperatio.get_analysis()['sharperatio']:.2f}")
    print(f"Norm. Annual Return: {results[0].analyzers.returns.get_analysis()['rnorm100']:.2f}%")
    print(f"Max Drawdown: {results[0].analyzers.drawdown.get_analysis()['max']['drawdown']:.2f}%")

[vladisld]

@kk said in Zero records while loading Data from CSV:

data = bt.feeds.GenericCSVData(...

Few points:

• AFAIU, creating the datafeed, being it GenericCSVData or PandasData will not load the actual data ( it may differ for other data feeds) . Data is loaded during the Cerebro engine run. It could be loaded all in once using 'preload' if Cerebro preload parameter was set to 'True' (default) or on the go.

• len(datafeed) returns the length of the iterated part of the datafeed - even if the datafeed's data was fully pre-loaded. So even after datafeed.preload() method is called - len(datafeed) will still return zero ( since it hasn't been iterated yet )

• one may use datafeed.buflen() to get the actual number of loaded bars. Once again buflen method will return zero upon data feed creation - since data was not attempted to be loaded at this point - at least for CSV and Pandas data feeds.

Please correct me if you know/experience otherwise

[dasch]

remove index_col=.. from read_csv.

[KK]

@dasch yes it worked with pandas data, thanks for the help. also other issue as @vladisld mentioned i was checking the length of the records was also issue as data was not loaded

[KK]

@vladisld you are right, data is not loaded at this point of time, so checking the length will always gives zero and data is not loading. i tried with buflen() still the issue same. Thanks for the help