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When my data has 10000 points, It will be very slow when I want to view the details of the plot (zoom and drag), but it is easy to zoom and drag the plot drawn by MATLAB. Does Python have a high-performance drawing tool like matlab's plot

@dasch said in data feeds with different start date:

you could provide some code to see what issue you have, its better than just guesswork what your issues may be.

The trade just happened, but no plot.
I think it is too little data in C98, if I change to another data not so little (with BTC data 90% of the length), then the graph will show normal. There should be some problem with the logic of plot.
You can try to reproduce this whit data feeds of different start time. But need to override the prenext method to make trade happen when there is only one data feeds have valid data

@run-out There is no buy-sell plot in the second symbol(C98USDT), I don't know why.

@davidavr how about the backtest speed of LEAN

https://www.backtrader.com/blog/posts/2016-10-29-strategy-selection/strategy-selection/
Two strategies have different parameters, How to pass the parameter to the strategy selection

@backtrader said in optimization on Strategy Selection example:

You then need to have 5 optimizations (the sample shows 2) and the StFetcher instantiating the appropriate strategy with the appropriate parameters. In the example idx is used as the key to decide which strategy to return. The same idx can be used to decide which strategy to instantiate and with which parameters.

Can you give me an example code? thank you

There is no buy-sell triangles in symbol C98's figure. But the cash grows which means there are trades in C98

@dehati_paul said in How to speed up almost 100 times when add data and preload data?:

es only when my data classes are derived from bt.feeds.PandasData, but when the classes are derived from bt.feeds.PandasDirectData (to speed up first time loading) I get the following error. Any insights?
_pickle.PicklingError: Can't pickle <class 'pandas.core.frame.Pandas'>: attribute lookup Pa

I got the same error. Have you fixed it?

two strategies have the same next() method

    def next(self):
        for i,d in enumerate(self.datas):
            pos = self.getposition(d)
            if pos.size:
                if self.inds[d]['signal'] < 0:
                    self.close(data=d)
                    print(self.p.name)

            elif self.inds[d]['signal'] > 0:
                self.buy(data=d)
        pass

when I use these codes, the strategy will do short selling making the cash higher than the total account value. But I don't want to short, so how to make each strategy have their own position

@the-world I solved this error by modifying the code using the pickle in the cerebro.py

            if self.p.optdatas and self._dopreload and self._dorunonce:
                if self.p.load_my_data:
                    begin_time = time.time()
                    self.datas = self.load_my_data_from_pickle()
                    end_time = time.time()
                    print("every time pre_load from pkl consume time :{}".format(end_time - begin_time))
                else:
                    begin_time = time.time()
                    for data in self.datas:
                        data.reset()
                        if self._exactbars < 1:  # datas can be full length
                            data.extend(size=self.params.lookahead)
                        data._start()
                        if self._dopreload:
                            data.preload()
                    end_time = time.time()
                    print("every time pre_load from raw consume time :{}".format(end_time-begin_time))

When I using optstrategy, It turns out errors.

How to get the TimeReturn of one data, When there are multiple data feeds，

In the document

@run-out Thanks for your suggestion. I need the coins in the same portfolio, but I will tune the params separately using the optimize.

when there are multiple assets, how to plot the profit of each asset rather than the total value of the broker.

And how to make the trades results of different data have different legends.

When I backtest on one coin data(such as BTC), it costs 7s. When I add the other data(such as ETH), it cost 14s. How to improve it? There are hundreds of symbols.

import time
from datetime import datetime,timedelta
from zipfile import ZipFile
from backtrader_plotting import Bokeh
from backtrader_plotting.schemes import Tradimo
import numpy as np
import os
import pandas as pd
import backtrader as bt
def data_kline_load(symbol,interval,start,end):
    dirpath = 'D:\\Seafile\\trading\\binance-public-data-master\\python\\data\\spot\\daily\\klines\\{}\\{}\\'.format(symbol,interval)
    time = start
    data=pd.DataFrame(columns=['Open','High','Low','Close','Volume','uVolume'])
    while(time!=end+timedelta(days=1)):
        filename = symbol+'-{}-'.format(interval)+time.strftime('%Y-%m-%d')
        filepath = dirpath+filename+'.zip'
        if(os.path.exists(filepath)==False):
            time = time + timedelta(days=1)
            continue
        #print(filepath)
        z = ZipFile(filepath)
        f = z.open(filename+'.csv')
        df = pd.read_csv(f,header=None)
        df.index = pd.to_datetime(df[0],unit='ms')
        z.close()
        f.close()
        df = df[[1,2,3,4,5]]
        df.columns = ['Open','High','Low','Close','Volume']
        data = pd.concat([data,df],axis=0)
        time = time + timedelta(days=1)
    print(symbol)
    print(data.shape[0])
    return data

class BinancePdData(bt.feeds.PandasData):
    params = (
        ('datetime', None),
        ('dtformat',('%Y-%m-%d %H:%M:%S')),
        ('open', -1),
        ('high', -1),
        ('low', -1),
        ('close', -1),
        ('volume', -1),
        ('uvolume',-1),
        ('openinterest', -1),
    )

# Create a Stratey
class TestStrategy(bt.Strategy):
    params = (
        ('pfast', 7),
        ('pslow', 25),
        ('printlog', False),
    )

    def log(self, txt, dt=None, doprint=False):
        ''' Logging function fot this strategy'''
        if self.params.printlog or doprint:
            dt = dt or self.datas[0].datetime.datetime(0)
            print('%s, %s' % (dt.isoformat(sep=' '), txt),file=self.log_file)

    def __init__(self):
        # Keep a reference to the "close" line in the data[0] dataseries
        self.log_file = open('position_log.txt','w')
        self.inds = dict()

        for i,d in enumerate(self.datas):
            self.inds[d] = dict()
            self.inds[d]['sma1'] = bt.ind.SMA(d.close,period=self.p.pfast)
            self.inds[d]['sma2'] = bt.ind.SMA(d.close,period=self.p.pslow)
            self.inds[d]['cross'] = bt.ind.CrossOver(self.inds[d]['sma1'],self.inds[d]['sma2'],plot = False)



        # To keep track of pending orders and buy price/commission
        self.order = None
        self.buyprice = None
        self.buycomm = None



    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]:
            if order.isbuy():
                self.log(
                    'BUY EXECUTED,Symbol: %s, Price: %.5f, Cost: %.5f, Comm %.5f' %
                    (order.data._name,
                     order.executed.price,
                     order.executed.value,
                     order.executed.comm))


            else:  # Sell
                self.log('SELL EXECUTED,Symbol: %s Price: %.5f, Cost: %.5f, Comm %.5f' %
                         (order.data._name,
                          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')

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

    def notify_trade(self, trade):
        if not trade.isclosed:
            return

        self.log('OPERATION PROFIT, GROSS %.5f, NET %.5f' %
                 (trade.pnl, trade.pnlcomm))

    def next(self):
        for i,d in enumerate(self.datas):
            dt,dn = d.datetime.datetime(0),d._name
            #持仓
            pos = self.getposition(d)

            if not pos.size:#没有持仓
                # if d.close[0]>d.close[-1]*1.01:
                if self.inds[d]['cross'] > 0:
                    self.order = self.buy(data=d)
            else:
                if self.inds[d]['cross']<0:
                    self.order = self.close(data=d)
                    pass


    def stop(self):
        self.log_file.close()


if __name__ == '__main__':
    cerebro = bt.Cerebro()
    cerebro.addstrategy(TestStrategy,pfast=7,pslow=25)
    # cerebro.optstrategy(
    #     TestStrategy,
    #     maperiod1=range(2, 10),maperiod2=range(15,35,2))
    # cerebro.addanalyzer(bt.analyzers.SharpeRatio)

    #symbols = ['BTCUSDT','ADAUSDT','QTUMUSDT','GTOUSDT','DENTUSDT','MTLUSDT','XVSUSDT','XRPUSDT','TCTUSDT','OMGUSDT','ANKRUSDT','ZILUSDT','DOGEUSDT','ETHUSDT','CFXUSDT','BCHUSDT','DOTUSDT','WINUSDT','FILUSDT','UNIUSDT','REEFUSDT','WRXUSDT','LUNAUSDT','DREPUSDT','TRXUSDT','SHIBUSDT','CAKEUSDT','ENJUSDT','BTTUSDT','TFUELUSDT','BNBUSDT','BTSUSDT','NKNUSDT','CHRUSDT','CHZUSDT','WAVESUSDT','RVNUSDT']
    symbols = ['FILUSDT','CHRUSDT']
    interval = '1m'
    start = datetime(2021,7,1)
    end = datetime(2021,7,31)
    for symbol in symbols:
        df = data_kline_load(symbol=symbol,interval=interval,start=start,end=end)
        data = BinancePdData(dataname=df)
        cerebro.adddata(data,name=symbol)
    time1=time.perf_counter()
    cerebro.broker.setcash(10000.0)
    cerebro.addsizer(bt.sizers.PercentSizer,percents=25)
    #cerebro.broker.setcommission(commission=0.001)

    print('Starting Portfolio Value: %.5f' % cerebro.broker.getvalue())
    cerebro.run(maxcpus=16)
    print('Final Portfolio Value: %.5f' % cerebro.broker.getvalue())
    print(time.perf_counter()-time1)
    b = Bokeh(style='bar', scheme=Tradimo())
    cerebro.plot(b)