Multiple Data Feeds (3555 csv files) on a single strategy
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I am new here, just wondering if i could do such performance with backtrader.
I have 3555 good csv files laying there.here is my codes:
# -*- coding:utf-8 -*- # env python 3.6 # 从兴趣开始学习Python # 我是pepCoder & pepTrader # email: mike_leigh@qq.com # Blog: https://me.csdn.net/weixin_44736043 # Date: 2020/5/4 10:49 from __future__ import (absolute_import, division, print_function, unicode_literals) import datetime import os.path import sys import pandas as pd import backtrader as bt # Create a Strategy class TestStrategy(bt.Strategy): params = ( ('maperiod', 30), ) 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 self.order = None self.buyprice = None self.buycomm = None # Add a MovingAverageSimple indicator self.sma = bt.indicators.SimpleMovingAverage( self.datas[0], period=self.params.maperiod) # Indicators for the plotting show bt.indicators.ExponentialMovingAverage(self.datas[0], period=90) bt.indicators.WeightedMovingAverage(self.datas[0], period=200).subplot = True # bt.indicators.StochasticSlow(self.datas[0]) bt.indicators.MACDHisto(self.datas[0]) rsi = bt.indicators.RSI(self.datas[0]) bt.indicators.SmoothedMovingAverage(rsi, period=10) bt.indicators.ATR(self.datas[0]).plot = False 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, 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 else: # Sell self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' % (order.executed.price, order.executed.value, order.executed.comm)) self.bar_executed = len(self) elif order.status in [order.Canceled, order.Margin, order.Rejected]: self.log('Order Canceled/Margin/Rejected') self.order = None def notify_trade(self, trade): if not trade.isclosed: return self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm)) def next(self): # Simply log the closing price of the series from the reference 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: # Not yet ... we MIGHT BUY if ... if self.dataclose[0] > self.sma[0]: # BUY, BUY, BUY!!! (with all possible default parameters) self.log('BUY CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.buy() else: if self.dataclose[0] < self.sma[0]: # SELL, SELL, SELL!!! (with all possible default parameters) self.log('SELL CREATE, %.2f' % self.dataclose[0]) # Keep track of the created order to avoid a 2nd order self.order = self.sell() if __name__ == '__main__': cerebro = bt.Cerebro() cerebro.addstrategy(TestStrategy) root_path = '.\\__stock__' for root, dirs, files in os.walk(root_path): for csv in files: if csv.endswith('.csv'): csv_path = os.path.join(root, csv) df = pd.read_csv("{}".format(csv_path), encoding='gbk', index_col=0, parse_dates=True) df = df.sort_values(by=['日期'], ascending=True) df = df.loc[:, ['日期', '开盘价', '收盘价', '最高价', '最低价', '成交量']] df = df.rename(columns={ '开盘价': 'open', '收盘价': 'close', '最高价': 'high', '最低价': 'low', '成交量': 'volume'}) df['openinterest'] = 0 data = bt.feeds.PandasData(dataname = df, fromdate = datetime.datetime(2015, 1, 1), todate = datetime.datetime(2020, 4, 15)) cerebro.adddata(data) cerebro.broker.setcash(1000000.0) # Add a FixedSize sizer according to the stake cerebro.addsizer(bt.sizers.FixedSize, stake=10) # Set the commission - 0.1% ... divide by 100 to remove the % cerebro.broker.setcommission(commission=0.001) print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue()) cerebro.run() print('Final Portfolio value: %.2f' % cerebro.broker.getvalue()) cerebro.plot()I know it looks hilarious, plz give me hint where I should be heading. lolz
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run your code and you will see if it is possible or not.
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it runs separately, one by one each.
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The way your strategy is implemented assumes only a single data feed is present. To be more exact, the strategy is working only with the first available data feed (it creates all the indicators for the data[0], and the next method only tracks the data[0] close price), despite multiple data feeds available.
Please take a look at:
https://www.backtrader.com/blog/posts/2017-04-09-multi-example/multi-example/ -
Do you wish to run your algorithm on all stocks at the same time or are you trying to run your algorithm for each stock independently?
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@run-out ok, the codes I presented is just a practice work. Im trying to get to know BTr and get a hold of it. here is what im trying to deal with. Im trying to set up a way to find MA combo pattern, and then run all 3555 csv files to match the pattern, then trigger trades. This is the reason why i have 3555 csv files on my HDD. =D
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Running multiple data feeds is a bit tricker. You have to cycle through them in next.
Here is a good example/article.
