@run-out yes indeed, when the stdev is small, which means that the three MAs are very close, which could be a good buy point or breakup point. I am testing my theory. and thanks for the help with the codes, chief. =)

this is what I have for now, which is working fine outside of btr system. I have to be honest that I am pretty shady with class writing.

    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.sma5 = bt.talib.SMA(self.datas[0], timeperiod=self.p.ma5period)
        self.sma20 = bt.talib.SMA(self.datas[0], timeperiod=self.p.ma20period)
        self.sma60 = bt.talib.SMA(self.datas[0], timeperiod=self.p.ma60period)

        self.stdev = stats.pstdev((self.sma5, self.sma20, self.sma60))

        self.MACDhisto = bt.indicators.MACDHisto(self.datas[0])

DOM

  File "P:/LEIGH PYTHON/Codes/Quant/backtrader_initiation.py", line 67, in __init__
    self.stdev = stats.pstdev((self.sma5, self.sma20, self.sma60))
  File "C:\Users\Mike_Leigh\.conda\envs\LEIGH\lib\statistics.py", line 666, in pstdev
    var = pvariance(data, mu)
  File "C:\Users\Mike_Leigh\.conda\envs\LEIGH\lib\statistics.py", line 637, in pvariance
    T, ss = _ss(data, mu)
  File "C:\Users\Mike_Leigh\.conda\envs\LEIGH\lib\statistics.py", line 535, in _ss
    c = mean(data)
  File "C:\Users\Mike_Leigh\.conda\envs\LEIGH\lib\statistics.py", line 312, in mean
    T, total, count = _sum(data)
  File "C:\Users\Mike_Leigh\.conda\envs\LEIGH\lib\statistics.py", line 148, in _sum
    for n,d in map(_exact_ratio, values):
  File "C:\Users\Mike_Leigh\.conda\envs\LEIGH\lib\statistics.py", line 230, in _exact_ratio
    raise TypeError(msg.format(type(x).__name__))
TypeError: can't convert type 'SMA' to numerator/denominator

would anyone please give me a direction on this? much appreciated!

@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

it runs separately, one by one each.

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

@backtrader thanks chief, I made the same insane mistake lolz. changed to "backtrader_initiation", problem solved.