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alpaca data store live backfill_start sometimes not working reliably
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I tried to understand the documentation on the Alpaca Data Store and followed the example:
alpaca-backtrader-api.Problems in live mode:
- on backfill_start=True at minute data resolution, appereantly datastore attempts to download the complete minute history ---> this sometimes causes REST API timouts or freezing when launching
- tickdata and bardata must be added separately: just cerebro.resampledata(data0...) does not work in live mode (as suggested in the documentations)
What I tried so far but did not solve my problems:
- 'backfill_from' with an additional DataFeed
- 'backfill_start' = False : causes error because indicators cannot be calculated
See example code to reproduce (c&p):
import alpaca_backtrader_api import backtrader as bt from datetime import datetime import logging import matplotlib import logging import pandas as pd import numpy as np import pdb import pytz import pendulum import pickle from alpaca_keys_paper import * #======global settings IS_BACKTEST = True IS_LIVE = False BACKTEST_CASH = 100000 TIMEFRAME = bt.TimeFrame.Minutes FROMDATE = datetime( 2017, 1, 3) TODATE = datetime( 2017, 1, 4) COMPRESSION=1 # minutes CHEAT_ON_CLOSE = True LOGGING = True SLIPPAGE = 0.0001 SYMB = 'SPY' #======params======= class StrategyStoploss(bt.Strategy): params = dict( ) def __init__(self): self.live_bars = False self.datas_mapping = {} self.dt_previous_bar = 0 self.rsi = bt.indicators.RSI_SMA(self.datas[0].close, period=14) self.rsi2 = bt.indicators.RSI_SMA(self.datas[0].close, period=21) self.last_bar = 0 self.startdate = pendulum.now('UTC').to_iso8601_string().replace(':','-').split('.')[0] def log(self,txt,*args): if LOGGING: # can be deactivated for wrapper optimizer dt = self.data.datetime[0] dt = bt.num2date(dt) if IS_BACKTEST: print("_dt=" + dt.isoformat(),f"portfolio={'%.0f' % self.broker.getvalue()}", txt, *args) # only print portfolio value in BACKTEST, otherwise API call limit reached else: print("_dt=" + dt.isoformat(), txt, *args) def notify_store(self, msg, *args, **kwargs): super().notify_store(msg, *args, **kwargs) self.log(msg) def stop(self): print('==================================================') print('Starting Value - %.2f' % self.broker.startingcash) print('Ending Value - %.2f' % self.broker.getvalue()) print('==================================================') def calculate_size(self,money_available,last_price,size_down_on_frac=0.98): frac = np.trunc(money_available / last_price) whole = np.trunc(frac) if (whole / frac) >= size_down_on_frac: whole = whole - 1 return whole def notify_data(self, data, status, *args, **kwargs): super().notify_data(data, status, *args, **kwargs) print('event=notify_data', data._getstatusname(status), *args) if data._getstatusname(status) == "LIVE": self.live_bars = True # workaround: introduce datas_mapping dictionary to access data by symbol name for num in range(len(self.datas)): sym_name = self.datas[num]._name if not sym_name in self.datas_mapping.keys(): self.datas_mapping[sym_name] = f"self.datas[{num}]" def datas_get(self,symbol): return eval(self.datas_mapping[symbol]) def __bt_to_pandas__(self, btdata, length): get = lambda mydata: mydata.get(ago=0, size=length) fields = { 'open': get(btdata.open), 'high': get(btdata.high), 'low': get(btdata.low), 'close': get(btdata.close), 'volume': get(btdata.volume) } time = [btdata.num2date(x) for x in get(btdata.datetime)] return pd.DataFrame(data=fields, index=time) def notify_order(self, order): if order.getstatusname() not in ['Completed','Canceled']: return # ordertype buy='buy' if order.isbuy() else ''; sell='sell' if order.issell() else '' side=buy+sell # logging the order self.log("event=order",f"type={order.getordername()}",f"side={side}",f"price={'%.2f' % order.executed.price}",f"size={str(int(order.executed.size))}",f"margin={str(order.executed.margin)}",f"status={str(order.getstatusname())}",f"ref={str(order.ref)}") # if order not completed do nothing #if not order.status == order.Completed: #return def notify_trade(self, trade): ttype='long' if trade.long==True else 'short' asset = trade.getdataname() price = trade.price size = trade.size status = trade.status side = 'buy' if size > 0 else 'sell' if size == 0: side = 'unclear' self.log("event=trade",f"type={ttype}",f"asset={asset}",f"price={'%.2f' % price}",f"size={str(size)}",f"commission={str(trade.commission)}",f"tradeid={str(trade.tradeid)}",f"ref={str(trade.ref)}",f"status={str(status)}") # on buy order save buy price if side=='buy': self.buy_prices[asset] = price if side=='sell' : self.sell_prices[asset] = price def next(self): # Return 0 if no live bars if not self.live_bars and not IS_BACKTEST: # only run code if we have live bars (today's bars). # ignore if we are backtesting return # data databar = self.datas_get(SYMB + '_bar') datatick = self.datas_get(SYMB) # assign prices for tickdata and bardata p_open_tick = datatick.open[0] p_close_tick = datatick.close[0] p_open_bar = databar.open[0] p_close_bar = databar.close[0] # check if new bar is reached with the new tick (live!) new_bar = False if databar.datetime[0] > self.last_bar: self.last_bar = databar.datetime[0] new_bar = True self.log("event=new_bar",f"asset={SYMB}",f"=======================================") # logging info if new_bar: self.log("event=tick",f"asset={SYMB}",f"open={str(p_open_tick)}",f"close={str(p_close_tick)}") self.log("event=bar",f"asset={SYMB}",f"open={str(p_open_bar)}",f"close={str(p_close_bar)}") cerebro = bt.Cerebro() cerebro.addstrategy(StrategyStoploss) store = alpaca_backtrader_api.AlpacaStore( key_id=ALPACA_API_KEY, secret_key=ALPACA_SECRET_KEY, paper=not IS_LIVE, usePolygon=False ) #===========configuring broker if IS_BACKTEST: cerebro.broker.set_slippage_perc(SLIPPAGE,slip_open=True,slip_limit=True,slip_match=True,slip_out=False) cerebro.broker.set_coc(CHEAT_ON_CLOSE) cerebro.broker.setcash(BACKTEST_CASH) else: broker = store.getbroker() cerebro.setbroker(broker) #===========configuring data DataFactory = store.getdata # or use alpaca_backtrader_api.AlpacaData # in backtest, data is minute-bar data # in live, tickdata is fed via websocket! next() is called by tick, so data need to be resampled to higher resolution! if IS_BACKTEST: data0 = DataFactory(dataname=SYMB,historical=True,fromdate=FROMDATE,todate=TODATE,timeframe=TIMEFRAME,compression=COMPRESSION) else: data0 = DataFactory(dataname=SYMB,historical=False,timeframe=TIMEFRAME,compression=1,backfill_start=True,backfill=True,qcheck=0.9) cerebro.adddata(data0,name=SYMB) # tickdata in live mode cerebro.resampledata(data0,name=SYMB + '_bar',timeframe=TIMEFRAME,compression=COMPRESSION) # bardata in live mode # risk cerebro.addobserver(bt.observers.DrawDown) cerebro.broker.RETURNS = [] cerebro.broker.BENCHMARK = [] results=cerebro.run() # timezone not needed, everything is in UTC
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anyone has some ideas how to make the minute data work in a scalable way?
