AttributeError: 'int' object has no attribute 'to_pydatetime'

Rajesh

Hi ,
I am feeding this information to

data = bt.feeds.PandasData(dataname=ohlc_TCS)

error

AttributeError: 'int' object has no attribute 'to_pydatetime'

do i have to manually reset the any parameter, can some one help me on this.

from_date = "01-10-2020"
to_date = "05-01-2021"

# 
ohlc_TCS = fetchOHLCExtended("TCS",from_date, to_date, '5minute')
ohlc_TCS.info()

from_date = "01-10-2020"
to_date = "05-01-2021"

# 
cerebro = bt.Cerebro()
cerebro.broker.set_cash(100000)
cerebro.broker.setcommission(commission=0.001)

data = bt.feeds.PandasData(dataname=ohlc_TCS)
cerebro.adddata(data)
cerebro.addstrategy(macdStrategy)


print('Starting Portfolio Value : %0.2f' % cerebro.broker.getvalue())
cerebro.run()
cerebro.plot()

print('Final Portfolio Value : %0.2f' % cerebro.broker.getvalue())

rajanprabu

@rajesh

Dont make new/more threads with similar/follow up questions. It will clutter the forum. Just follow up on the same thread.

Rajesh

@rajanprabu


from kiteconnect import KiteConnect
import pandas as pd
import datetime as dt
import os
import time
import numpy as np
import backtrader as bt
from cerebro_stra import *
cwd = os.chdir("D:\\Rajesh2025\\46. Algos_trading\\strategy")

#generate trading session
access_token = open("access_token.txt",'r').read()
key_secret = open("api_keys.txt",'r').read().split()
kite = KiteConnect(api_key=key_secret[0])
kite.set_access_token(access_token)

#get dump of all NSE instruments
instrument_dump = kite.instruments("NSE")
instrument_df = pd.DataFrame(instrument_dump)

def instrumentLookup(instrument_df,symbol):
    """Looks up instrument token for a given script from instrument dump"""
    try:
        return instrument_df[instrument_df.tradingsymbol==symbol].instrument_token.values[0]
    except:
        return -1


# Below is only for 5 minutes interveral    
def fetchOHLCExtended(ticker,from_date,to_date,interval):
    """extracts historical data and outputs in the form of dataframe
       inception date string format - dd-mm-yyyy"""
    instrument = instrumentLookup(instrument_df,ticker)
    from_date = dt.datetime.strptime(from_date, "%d-%m-%Y")
    to_date = dt.datetime.strptime(to_date, "%d-%m-%Y")
    data = pd.DataFrame(columns=['date', 'open', 'high', 'low', 'close', 'volume'])
    while True:
        if (to_date.date() - from_date.date() ) <= dt.timedelta(100):
            data = data.append(pd.DataFrame(kite.historical_data(instrument,from_date,to_date,interval)),ignore_index=True)
            break
        else:
            to_date = from_date + dt.timedelta(100)
            data = data.append(pd.DataFrame(kite.historical_data(instrument,from_date,to_date,interval)),ignore_index=True)
            from_date = to_date
    data.set_index("date")
    return data

i got above code from online tutorial,

Rajesh

@rajesh
compared to earlier, context is changed little bit, hence posted in different thread

rajanprabu

@rajesh

As requested in other thread please post few lines from ohlc_TCS. Without seeing that its not easy to understand the problem.

Rajesh

@rajanprabu
Hi @rajanprabu , this is complete code for ohlc_TCS

from kiteconnect import KiteConnect
import pandas as pd
import datetime as dt
import os
import time
import numpy as np
import backtrader as bt
from cerebro_stra import *
cwd = os.chdir(".....\\strategy")

#generate trading session
access_token = open("access_token.txt",'r').read()
key_secret = open("api_keys.txt",'r').read().split()
kite = KiteConnect(api_key=key_secret[0])
kite.set_access_token(access_token)

#get dump of all NSE instruments
instrument_dump = kite.instruments("NSE")
instrument_df = pd.DataFrame(instrument_dump)

def instrumentLookup(instrument_df,symbol):
    """Looks up instrument token for a given script from instrument dump"""
    try:
        return instrument_df[instrument_df.tradingsymbol==symbol].instrument_token.values[0]
    except:
        return -1


# Below is only for 5 minutes interveral    
def fetchOHLCExtended(ticker,from_date,to_date,interval):
    """extracts historical data and outputs in the form of dataframe
       inception date string format - dd-mm-yyyy"""
    instrument = instrumentLookup(instrument_df,ticker)
    from_date = dt.datetime.strptime(from_date, "%d-%m-%Y")
    to_date = dt.datetime.strptime(to_date, "%d-%m-%Y")
    data = pd.DataFrame(columns=['date', 'open', 'high', 'low', 'close', 'volume'])
    while True:
        if (to_date.date() - from_date.date() ) <= dt.timedelta(100):
            data = data.append(pd.DataFrame(kite.historical_data(instrument,from_date,to_date,interval)),ignore_index=True)
            break
        else:
            to_date = from_date + dt.timedelta(100)
            data = data.append(pd.DataFrame(kite.historical_data(instrument,from_date,to_date,interval)),ignore_index=True)
            from_date = to_date
    data.set_index("date")
    return data



from_date = "01-10-2020"
to_date = "05-01-2021"

# 
ohlc_TCS = fetchOHLCExtended("TCS",from_date, to_date, '5minute')
ohlc_TCS.info()



ohlc_TCS['date'] = pd.to_datetime(ohlc_TCS['date'], format = "%m/%d/%Y %H:%M:%S %z" )
ohlc_TCS.info()



cerebro = bt.Cerebro()
cerebro.broker.set_cash(100000)
cerebro.broker.setcommission(commission=0.001)

data = bt.feeds.PandasData(dataname=ohlc_TCS)
cerebro.adddata(data)
cerebro.addstrategy(macdStrategy)


print('Starting Portfolio Value : %0.2f' % cerebro.broker.getvalue())
cerebro.run()
cerebro.plot()

print('Final Portfolio Value : %0.2f' % cerebro.broker.getvalue())

Below is the code for strategy, is slightly tweaked from strategy code from backtrader site.

import math
import backtrader as bt


from backtrader.indicators import EMA
import argparse
import datetime
import os.path
import time
import sys

import backtrader as bt
import backtrader.feeds as btfeeds
import backtrader.indicators as btind


class macdStrategy(bt.Strategy):
    lines = ('macd', 'signal', 'histo',)
    params = (('period_me1', 12), ('period_me2', 26), ('period_signal', 9),)
    
    def log(self,txt, dt = None):
        dt = dt or self.datas[0].datatime.data(0)
        print('%s, %s' % (dt.isoformat(), txt))
     
    def notify_order(self, order):
        if order.status in [order.Submitted, order.Accepted]:
            self.log('ORDER ACCEPTED/SUBMITTED', dt=order.created.dt)
            self.order = order
            return
        
        if order.status in [order.Expired]:
            self.log('BUY EXPIRED')
        
        elif order.status in [order.Completed]:
            if order.isbuy():
                self.log(
                     'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                    (order.executed.price,
                     order.executed.value,
                     order.executed.comm))
                
                self.buyprice = order.executed.price
                self.buycomm = order.executed.comm
            elif order.issell():
                self.log(
                     'SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
                    (order.executed.price,
                     order.executed.value,
                     order.executed.comm))
                
        elif order.status in [order.Canceled, order.Margin, order.Rejected]:
            self.log('Order Canceled/Margin/Rejected') 
            
            
        # Sentinel to None: new orders allowed 
        self.order = None
            
    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

        me1 = EMA(self.data, period=self.p.period_me1)
        me2 = EMA(self.data, period=self.p.period_me2)
        self.lines.macd = me1 - me2
        self.lines.signal = EMA(self.l.macd, period=self.p.period_signal)
        self.lines.histo = self.l.macd - self.l.signal      
        self.crossover = btind.CrossOver(self.lines.macd, self.lines.signal)
        
        
    def next(self):
        self.log('Close, %.2f' % self.dataclose[0])
        
        # Check if an order is pending ... if yes, we cannot send a 2nd one
        if self.order:
            return
        
        # Check if we are in the market
        if not self.position:
            if self.crossover > 0:
                self.log('BUY CREATE,  %.2f' %self.dataclose[0])
                self.order = self.buy()
            
        else:
            # Already in the market ... we might sell
            if self.crossover < 0:
                self.log('SELL CREATE,  %.2f' %self.dataclose[0])
                self.order = self.sell()

i have one more api code for kite connect, if above code is not enough, i can supply that one as well.

rajanprabu

@rajesh

Not the code.. I mean the few lines of data that you are feeding to backtrader ( values of ohlc_TCS in your case ) like below

timestamp,open,high,low,close,volume,oi
2019-12-02 09:15:00,12140.00,12161.80,12127.95,12134.65,4277,440100
2019-12-02 09:16:00,12132.80,12138.35,12129.75,12138.35,1875,440100
2019-12-02 09:17:00,12138.80,12139.65,12137.55,12137.55,600,442350
2019-12-02 09:18:00,12137.40,12137.40,12129.00,12137.35,2550,442350
2019-12-02 09:19:00,12136.95,12142.75,12133.50,12142.75,2250,442350
2019-12-02 09:20:00,12143.00,12150.00,12142.90,12146.05,2475,442800
2019-12-02 09:21:00,12145.05,12148.40,12141.15,12141.15,900,442800
2019-12-02 09:22:00,12141.05,12149.30,12138.55,12145.50,1875,442800

Rajesh

@rajanprabu
i am sorry, here is the data

date				open	high	low	close	volume
2020-10-01 09:15:00+05:30	2510	2524	2507	2513.15	231516
2020-10-01 09:20:00+05:30	2514.85	2517.95	2495.9	2502.6	122766
2020-10-01 09:25:00+05:30	2502.1	2504.85	2497	2503.95	132321
2020-10-01 09:30:00+05:30	2502.9	2504	2496	2499.05	45690
2020-10-01 09:35:00+05:30	2498.4	2503	2495.7	2500.05	53982
2020-10-01 09:40:00+05:30	2500.9	2509.8	2497.3	2507.5	61781
2020-10-01 09:45:00+05:30	2507.5	2511.95	2503.7	2504.95	60255
2020-10-01 09:50:00+05:30	2504.95	2507.2	2500	2502.4	39571
2020-10-01 09:55:00+05:30	2502.95	2502.95	2492.35	2495	72469
2020-10-01 10:00:00+05:30	2495	2500	2494	2498	33873
2020-10-01 10:05:00+05:30	2498.2	2503	2496	2501.8	36952
2020-10-01 10:10:00+05:30	2501.8	2503.8	2498	2500.9	36154
2020-10-01 10:15:00+05:30	2500.9	2506	2500	2505.1	46938
2020-10-01 10:20:00+05:30	2505.85	2507.65	2502.1	2502.65	24609
2020-10-01 10:25:00+05:30	2503.75	2504.4	2499.15	2503.5	25443
2020-10-01 10:30:00+05:30	2503	2504	2501	2503.15	11733
2020-10-01 10:35:00+05:30	2503.1	2505.9	2501.55	2502.5	17242
2020-10-01 10:40:00+05:30	2502.5	2505.15	2501.65	2501.9	19095
2020-10-01 10:45:00+05:30	2501.9	2505	2499	2504.7	32404
2020-10-01 10:50:00+05:30	2504.85	2505.2	2501.4	2502.4	12070

just for additional info, here is the screen shot

!

rajanprabu

@rajesh

BT processes them using datetime.datetime.strptime so it can't take the additional +5:30. One had to process the date time format before..

df  =  pd.read_csv(data_file, header=0, sep='\t', parse_dates=True)
df = df.iloc[:, 0:6] 
df.columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume' ]
df['timestamp'] = pd.to_datetime(df['timestamp']).dt.tz_convert(None) 
df.set_index('timestamp', inplace=True)  

data = bt.feeds.PandasData(
    dataname=df,
    timeframe=bt.TimeFrame.Minutes,
    compression=5,
    tz='Asia/Kolkata', 
)

Rajesh

@rajanprabu

i removed timezone information by below code.

ohlc_TCS['date'] = pd.to_datetime(ohlc_TCS['date'], format = "%Y/%m/%d %H:%M:%S" )
ohlc_TCS.info()

ohlc_TCS['date'] = ohlc_TCS['date'].astype(str).str[:-6]
ohlc_TCS.info()

cerebro = bt.Cerebro()
cerebro.broker.set_cash(100000)
cerebro.broker.setcommission(commission=0.001)

data = bt.feeds.PandasData(
    dataname=ohlc_TCS, 
   )
cerebro.adddata(data)
cerebro.addstrategy(macdStrategy)

but after running this, i am getting attribute error, may i know where i making mistake

print('Starting Portfolio Value : %0.2f' % cerebro.broker.getvalue())
cerebro.run()
# cerebro.plot()

print('Final Portfolio Value : %0.2f' % cerebro.broker.getvalue())

AttributeError: 'int' object has no attribute 'to_pydatetime'

rajanprabu

@rajesh

My solution was tested on what you posted

date				open	high	low	close	volume
2020-10-01 09:15:00+05:30	2510	2524	2507	2513.15	231516
2020-10-01 09:20:00+05:30	2514.85	2517.95	2495.9	2502.6	122766
2020-10-01 09:25:00+05:30	2502.1	2504.85	2497	2503.95	132321
2020-10-01 09:30:00+05:30	2502.9	2504	2496	2499.05	45690
2020-10-01 09:35:00+05:30	2498.4	2503	2495.7	2500.05	53982
2020-10-01 09:40:00+05:30	2500.9	2509.8	2497.3	2507.5	61781
2020-10-01 09:45:00+05:30	2507.5	2511.95	2503.7	2504.95	60255

Now in the screenshot have an index column.. BT expects Date to be on the index column by default unless specifically stated.
Just make date as your dataframe index

ohlc_TCS['date'] = pd.to_datetime(ohlc_TCS['date'], format = "%Y/%m/%d %H:%M:%S" )
ohlc_TCS.info()

ohlc_TCS['date'] = ohlc_TCS['date'].astype(str).str[:-6]
ohlc_TCS.info()

### Just add this line
ohlc_TCS.set_index('date', inplace=True)  

cerebro = bt.Cerebro()
cerebro.broker.set_cash(100000)
cerebro.broker.setcommission(commission=0.001)

data = bt.feeds.PandasData(
    dataname=ohlc_TCS, 
   )
cerebro.adddata(data)
cerebro.addstrategy(macdStrategy)

Rajesh

@rajanprabu
i used your solution, i am getting below error message, i am missing some thing ?

ohlc_TCS['date'] = pd.to_datetime(ohlc_TCS['date'], format = "%Y/%m/%d %H:%M:%S" )
ohlc_TCS.info()

ohlc_TCS['date'] = ohlc_TCS['date'].astype(str).str[:-6]
ohlc_TCS.info()

### Just add this line
ohlc_TCS.set_index('date', inplace=True)  

cerebro = bt.Cerebro()
cerebro.broker.set_cash(100000)
cerebro.broker.setcommission(commission=0.001)

data = bt.feeds.PandasData(
    dataname=ohlc_TCS, 
   )
cerebro.adddata(data)
cerebro.addstrategy(macdStrategy)

TypeError: super(type, obj): obj must be an instance or subtype of type

rajanprabu

@rajesh

ohlc_TCS['date'] = pd.to_datetime(ohlc_TCS['date'], format = "%Y/%m/%d %H:%M:%S" )
ohlc_TCS.info()

ohlc_TCS['date'] = ohlc_TCS['date'].astype(str).str[:-6]
ohlc_TCS.info()

### Just add this line
ohlc_TCS.set_index('date', inplace=True)  
ohlc_TCS = ohlc_TCS.iloc[:, 0:6]  
ohlc_TCS.columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume' ]      

cerebro = bt.Cerebro()
cerebro.broker.set_cash(100000)
cerebro.broker.setcommission(commission=0.001)

data = bt.feeds.PandasData(
    dataname=ohlc_TCS, 
   )
cerebro.adddata(data)
cerebro.addstrategy(macdStrategy)

Please give it a try with this and let me know.. It really hard to bug fix without sample data at hand. If this doesn't work please upload your some lines of your data. Not a screenshot.

Rajesh

@rajanprabu

Hope you can use below data, thanks in advance for help

date      open               high	     low	 close     volume
0	2020-10-01 09:15:00+05:30	2510	2524	2507	2513.15	231516
1	2020-10-01 09:20:00+05:30	2514.85	2517.95	2495.9	2502.6	122766
2	2020-10-01 09:25:00+05:30	2502.1	2504.85	2497	2503.95	132321
3	2020-10-01 09:30:00+05:30	2502.9	2504	2496	2499.05	45690
4	2020-10-01 09:35:00+05:30	2498.4	2503	2495.7	2500.05	53982
5	2020-10-01 09:40:00+05:30	2500.9	2509.8	2497.3	2507.5	61781
6	2020-10-01 09:45:00+05:30	2507.5	2511.95	2503.7	2504.95	60255
7	2020-10-01 09:50:00+05:30	2504.95	2507.2	2500	2502.4	39571
8	2020-10-01 09:55:00+05:30	2502.95	2502.95	2492.35	2495	72469
9	2020-10-01 10:00:00+05:30	2495	2500	2494	2498	33873
10	2020-10-01 10:05:00+05:30	2498.2	2503	2496	2501.8	36952
11	2020-10-01 10:10:00+05:30	2501.8	2503.8	2498	2500.9	36154
12	2020-10-01 10:15:00+05:30	2500.9	2506	2500	2505.1	46938
13	2020-10-01 10:20:00+05:30	2505.85	2507.65	2502.1	2502.65	24609
14	2020-10-01 10:25:00+05:30	2503.75	2504.4	2499.15	2503.5	25443
15	2020-10-01 10:30:00+05:30	2503	2504	2501	2503.15	11733
16	2020-10-01 10:35:00+05:30	2503.1	2505.9	2501.55	2502.5	17242
17	2020-10-01 10:40:00+05:30	2502.5	2505.15	2501.65	2501.9	19095
18	2020-10-01 10:45:00+05:30	2501.9	2505	2499	2504.7	32404
19	2020-10-01 10:50:00+05:30	2504.85	2505.2	2501.4	2502.4	12070
20	2020-10-01 10:55:00+05:30	2504.1	2504.15	2502	2502.4	8106
21	2020-10-01 11:00:00+05:30	2502.4	2503.95	2500	2500.55	16986
22	2020-10-01 11:05:00+05:30	2501	2502.8	2499.75	2501.6	19452
23	2020-10-01 11:10:00+05:30	2501.6	2505	2500.1	2504.55	22629
24	2020-10-01 11:15:00+05:30	2504.55	2509	2504	2504.85	44211
25	2020-10-01 11:20:00+05:30	2504.85	2508	2504.1	2507.95	19798
26	2020-10-01 11:25:00+05:30	2507.95	2507.95	2505	2506.95	16231
27	2020-10-01 11:30:00+05:30	2506.5	2511	2506.5	2509.9	57648
28	2020-10-01 11:35:00+05:30	2510	2510	2506	2507.2	32086
29	2020-10-01 11:40:00+05:30	2507	2507.4	2502.3	2505.25	36883
30	2020-10-01 11:45:00+05:30	2505.9	2507	2502.8	2507	16419
31	2020-10-01 11:50:00+05:30	2507	2509	2506.45	2507.5	13789
32	2020-10-01 11:55:00+05:30	2508.6	2508.95	2505.9	2507	11581
33	2020-10-01 12:00:00+05:30	2506.8	2508.85	2506	2508.15	9224
34	2020-10-01 12:05:00+05:30	2508.8	2510	2507.75	2508.3	14697
35	2020-10-01 12:10:00+05:30	2508.85	2509	2500.6	2500.6	19791
36	2020-10-01 12:15:00+05:30	2500.6	2504.5	2500.6	2501.6	18619
37	2020-10-01 12:20:00+05:30	2501.6	2501.85	2497	2499	51028
38	2020-10-01 12:25:00+05:30	2499.1	2500	2496	2498	29806
39	2020-10-01 12:30:00+05:30	2498	2499	2494.05	2498.25	26769
40	2020-10-01 12:35:00+05:30	2498.25	2502.65	2498.25	2500.5	12196
41	2020-10-01 12:40:00+05:30	2500.5	2502.85	2500	2501.15	18210
42	2020-10-01 12:45:00+05:30	2501.15	2502	2499.6	2501.75	8930
43	2020-10-01 12:50:00+05:30	2501.75	2503.55	2500	2501.25	11219
44	2020-10-01 12:55:00+05:30	2501.25	2502.2	2497	2497.5	14306
45	2020-10-01 13:00:00+05:30	2497.65	2501	2496.25	2499.15	11898
46	2020-10-01 13:05:00+05:30	2499.1	2502.2	2498.2	2500.5	10122
47	2020-10-01 13:10:00+05:30	2500.5	2504.9	2500.15	2503.15	17223
48	2020-10-01 13:15:00+05:30	2502.55	2505	2502	2502.2	14267
49	2020-10-01 13:20:00+05:30	2502.2	2503	2500.05	2500.6	12051
50	2020-10-01 13:25:00+05:30	2500.6	2503.9	2500.15	2500.25	8655

rajanprabu

@rajesh

Since your tabs are not in order.. used delim_whitespace=True this time.

data_file = 'example given above'

df  =  pd.read_csv(data_file, header=0, delim_whitespace=True,  parse_dates=True)
df['date'] = pd.to_datetime(df['date']).dt.tz_convert(None) 
df.set_index('date', inplace=True)  

data = bt.feeds.PandasData(
    dataname=df,
    timeframe=bt.TimeFrame.Minutes,
    compression=5,
    tz='Asia/Kolkata', 
)

I have tested this and this works fine.. Hope this helps.

Rajesh

@rajanprabu
the above code didn't work, i am trying in different ways, thanks for trying

Rajesh

@rajesh
i am using following csv file

date	open	high	low	close	volume
01-10-2020 03:45	2510	2524	2507	2513.15	231516
01-10-2020 03:50	2514.85	2517.95	2495.9	2502.6	122766
01-10-2020 03:55	2502.1	2504.85	2497	2503.95	132321
01-10-2020 04:00	2502.9	2504	2496	2499.05	45690
01-10-2020 04:05	2498.4	2503	2495.7	2500.05	53982
01-10-2020 04:10	2500.9	2509.8	2497.3	2507.5	61781
01-10-2020 04:15	2507.5	2511.95	2503.7	2504.95	60255
01-10-2020 04:20	2504.95	2507.2	2500	2502.4	39571
01-10-2020 04:25	2502.95	2502.95	2492.35	2495	72469
01-10-2020 04:30	2495	2500	2494	2498	33873
01-10-2020 04:35	2498.2	2503	2496	2501.8	36952
01-10-2020 04:40	2501.8	2503.8	2498	2500.9	36154
01-10-2020 04:45	2500.9	2506	2500	2505.1	46938
01-10-2020 04:50	2505.85	2507.65	2502.1	2502.65	24609
01-10-2020 04:55	2503.75	2504.4	2499.15	2503.5	25443
01-10-2020 05:00	2503	2504	2501	2503.15	11733
01-10-2020 05:05	2503.1	2505.9	2501.55	2502.5	17242
01-10-2020 05:10	2502.5	2505.15	2501.65	2501.9	19095
01-10-2020 05:15	2501.9	2505	2499	2504.7	32404
01-10-2020 05:20	2504.85	2505.2	2501.4	2502.4	12070
01-10-2020 05:25	2504.1	2504.15	2502	2502.4	8106

i am using below code to run strategy.

data_file = '...\\datafile.csv'

df  =  pd.read_csv(data_file, header=0)

df["date"] = pd.to_datetime(df["date"]).dt.tz_convert(None)

df.drop(df.iloc[ :, 0:1,], inplace = True, axis = 1)

df.set_index("date", inplace = True) 
df.to_csv("datafile_clean.csv")

# df.iloc[0:3, :]
# df.to_csv('tcs4.csv')


cdata = btfeeds.GenericCSVData(
    dataname="datafile_clean.csv",
    fromdate = dt.datetime(2020, 10,1),
    dtformat="%Y-%m-%d %H:%M",
    compression = 1,
    datatime = 0 ,
    open=1,
    high=2,
    low=3,
    close=4,
    volume=5,
   )

cerebro = bt.Cerebro()

cerebro.adddata(cdata)
cerebro.addstrategy(macdStrategy)


print('Starting Portfolio Value : %0.2f' % cerebro.broker.getvalue())
cerebro.run()
# cerebro.plot()

print('Final Portfolio Value : %0.2f' % cerebro.broker.getvalue())

and i am getting below error:

ValueError: unconverted data remains: :00

from the past few days i am trying to resolve this, can any one help on this

Rajesh

@rajesh
i figured out the issue and solved the issue, now i got new error, can some one look into it.

df  =  pd.read_csv(data_file, header=0)

df["date"] = pd.to_datetime(df["date"]).dt.tz_convert(None)

df.drop(df.iloc[ :, 0:1,], inplace = True, axis = 1)

df.set_index("date", inplace = True) 
df.to_csv("datafile_clean.csv")

# df.iloc[0:3, :]
# df.to_csv('tcs4.csv')


cdata = btfeeds.GenericCSVData(
    dataname="datafile_clean.csv",
    fromdate = dt.datetime(2020, 10,1),
    dtformat="%Y-%m-%d %H:%M:%S",
    datatime = 0 ,
    open=1,
    high=2,
    low=3,
    close=4,
    volume=5,
    reverse = True
   )


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

    # Add a strategy
    cerebro.adddata(cdata)
    cerebro.addstrategy(macdStrategy)
    print('Starting Portfolio Value : %0.2f' % cerebro.broker.getvalue())
    cerebro.run()
    # cerebro.plot()
    print('Final Portfolio Value : %0.2f' % cerebro.broker.getvalue())

TypeError: super(type, obj): obj must be an instance or subtype of type