1. Download historical - > Training ML model
2. Continue live trade - > Trained model

Tick stream structure:
{
'type': 'PRICE',
'time': '2018-01-12T20:59:01.706352414Z',
'bids': [
{'price': '13227.9', 'liquidity': 25},
{'price': '13227.8', 'liquidity': 25},
{'price': '13227.7', 'liquidity': 50},
{'price': '13227.5', 'liquidity': 50},
{'price': '13227.3', 'liquidity': 50}],
'instrument': 'DE30_EUR',
'status': 'non-tradeable',
'asks': [
{'price': '13229.8', 'liquidity': 50},
{'price': '13229.9', 'liquidity': 50},
{'price': '13230.1', 'liquidity': 50},
{'price': '13230.3', 'liquidity': 50}],
'closeoutAsk': '13230.3',
'closeoutBid': '13227.3',
'tradeable': False}

is it true?

    # Common fields
    self.lines.datetime[0] = parse_date(msg['time'])
    self.lines.volume[0] = 0.0
    self.lines.openinterest[0] = 0.0

    # Put the prices into the bar
    tick = float(msg['asks'][0]['price']) if self.p.useask else float(msg['bids'][0]['price'])
    
    self.lines.open[0] = tick
    self.lines.high[0] = tick
    self.lines.low[0] = tick
    self.lines.close[0] = tick
    self.lines.volume[0] = 0.0
    self.lines.openinterest[0] = 0.0

@matthias-janssen said in Oanda v20 API:

Thank you for contacting OANDA.
Unfortunately that is not possible , our V1 API will be phasing out by the end of the ye

@backtrader

@backtrader

I can not open v1 demo account. Could anyone open?

I can only open v20 account. And This support oandaV20 api. Pls help me.

Use v20 REST API only if you have a v20 account
Use v1 REST API if your account id contains only digits (ie. 2534253) as it is a legacy account

@holicst Did you fix it?

I'm new, Can anyone help me ? /zigzag indicator/
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:zigzag
https://github.com/joshuaulrich/TTR/blob/master/src/zigzag.c
@backtrader @ab_trader

class Zigzag(bt.Indicator):
    
    lines = ('zigzag',)
    plotinfo = dict(subplot=False)
    params = (
        ('change', 40),
        ('use_percent', False),
        ('use_retrace', False),
        ('use_last_ex', False),
    )

    def __init__(self):

        self.reference = {
            'price': None,
            'index': None,
        }
        self.inflection = {
            'price': None,
            'index': None,
        }
        if self.params.use_percent:
            self.change = self.params.change / 100.0
        else:
            self.change = self.params.change
        
        self.reference['price'] = (self.data.high.array[0] + self.data.low.array[0]) / 2
        self.reference['index'] = 0
        self.inflection['price'] = (self.data.high.array[0] + self.data.low.array[0]) / 2
        self.inflection['index'] = 1

        self.extreme_min = 0.0
        self.extreme_max = 0.0
        self.local_min = 0.0
        self.local_max = 0.0        
        self.signal = 0
        self.i = 0
        
   
    def next(self):
        

        self.i = len(self.data.open)-1
        # Initialize all zigzag values to NA 
        self.zigzag.array.append(NAN)
        
        if self.params.use_percent:
            #If % change given (absolute move)      
            self.extreme_min = self.inflection['price'] * (1.0 - self.change)
            self.extreme_max = self.inflection['price'] * (1.0 + self.change)
        else:
            #If $ change given (only absolute moves make sense) 
            self.extreme_min = self.inflection['price'] - self.change
            self.extreme_max = self.inflection['price'] + self.change

        #Find local maximum and minimum

        
        self.local_max = self.inflection['price'] if self.inflection['price'] > self.data.high.array[self.i] else self.data.high.array[self.i]
        self.local_min = self.inflection['price'] if self.inflection['price'] < self.data.low.array[self.i] else self.data.low.array[self.i]
        
        ##Find first trend 
        if self.signal == 0:
            if self.params.use_retrace:
                #Retrace prior move 
                self.signal = 1 if (self.inflection['price'] >= self.reference['price']) else -1
            else:
                #Absolute move
                if self.local_min <= self.extreme_min:
                    #Confirmed Downtrend
                    self.signal = -1


                if self.local_max >= self.extreme_max:
                    #Confirmed Uptrend 
                    self.signal = 1

        ##Downtrend
        if self.signal == -1:
            #New Minimum
            if self.data.low.array[self.i] == self.local_min:
                #Last Extreme
                if self.params.use_last_ex:
                    self.inflection['price'] = self.data.low.array[self.i]
                    self.inflection['index'] = self.i
                else:
                    #First Extreme 
                    if (self.data.low.array[self.i] != self.data.low[-1]):
                        self.inflection['price'] = self.data.low.array[self.i]
                        self.inflection['index'] = self.i

            #Retrace prior move
            if self.params.use_retrace:
                self.extreme_max = self.inflection['price'] +((self.reference['price'] - self.inflection['price']) * self.change)

            #Trend Reversal 
            if self.data.high.array[self.i] >= self.extreme_max:
                self.zigzag[self.reference['index']] = self.reference['price']
                self.reference = copy.copy(self.inflection)
                self.inflection['price'] = self.data.high.array[self.i]
                self.inflection['index'] = self.i
                self.signal = 1


        #Uptrend 
        if self.signal == 1:

            #New Maximum
            if self.data.high.array[self.i] == self.local_max:
                #Last Extreme 
                if self.params.use_last_ex:
                    self.inflection['price'] = self.data.high.array[self.i]
                    self.inflection['index'] = self.i
                else:
                    #First Extreme 
                    if (self.data.high.array[self.i] != self.data.high[-1]):
                        self.inflection['price'] = self.data.high.array[self.i]
                        self.inflection['index'] = self.i

            #Retrace prior move 
            if self.params.use_retrace:
                self.extreme_min = self.inflection['price'] -((self.inflection['price'] - self.reference['price']) * self.change)

            #Trend Reversal
            if (self.data.low.array[self.i] <= self.extreme_min):
                self.zigzag[self.reference['index']] = self.reference['price']
                self.reference = copy.copy(self.inflection)
                self.inflection['price'] = self.data.low.array[self.i]
                self.inflection['index'] = self.i
                self.signal = -1

            self.i = self.i + 1
            
        self.zigzag[self.reference['index']] = self.reference['price']
        self.zigzag[self.inflection['index']] = self.inflection['price']

ok, many thanks @ab_trader

@backtrader @administrators How to update my ML model(example decision tree)after 30 next (bar, cycle)? Pls write example code? :)

What is advantage of backtrader from other platfrom based blotter, table/simply can vectorization operation https://github.com/ranaroussi/qtpylib, https://github.com/mhallsmoore/qsforex etc./?

What do MetaParams, AutoInfoclass and MetaBase do ?