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03 - Offline Backtest MultiPortfolio.py
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03 - Offline Backtest MultiPortfolio.py
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import datetime as dt
import backtrader as bt
import backtrader.analyzers as btanalyzers
from backtrader_moexalgo.moexalgo_store import MoexAlgoStore # Storage AlgoPack
from Config import Config as ConfigMOEX # for authorization on the Moscow Stock Exchange
# Trading system
class RSIStrategy(bt.Strategy):
"""
Live strategy demonstration with SMA, RSI indicators
"""
params = ( # Trading System Parameters
('timeframe', ''),
)
def __init__(self):
"""Initialization, adding indicators for each ticker"""
self.orders = {} # We organize orders in the form of a directory, specifically for this strategy, one ticker is one active order
for d in self.datas: # Running through all the tickers
self.orders[d._name] = None # There is no order for ticker yet
# creating indicators for each ticker
self.sma1 = {}
self.sma2 = {}
self.rsi = {}
for i in range(len(self.datas)):
ticker = list(self.dnames.keys())[i] # key name is ticker name
self.sma1[ticker] = bt.indicators.SMA(self.datas[i], period=8) # SMA indicator
self.sma2[ticker] = bt.indicators.SMA(self.datas[i], period=16) # SMA indicator
self.rsi[ticker] = bt.indicators.RSI(self.datas[i], period=14) # RSI indicator
def next(self):
"""The arrival of a new ticker bar"""
for data in self.datas: # We run through all the requested bars of all tickers
ticker = data._name
status = data._state # 0 - Live data, 1 - History data, 2 - None
_interval = self.p.timeframe
_date = bt.num2date(data.datetime[0])
try:
if data.p.supercandles[ticker][data.p.metric_name]:
print("\tSuper Candle:", data.p.supercandles[ticker][data.p.metric_name][0])
_data = data.p.supercandles[ticker][data.p.metric_name][0]
_data['datetime'] = _date
self.supercandles[ticker][data.p.metric_name].append(_data)
except:
pass
if status in [0, 1]:
if status: _state = "False - History data"
else: _state = "True - Live data"
print('{} / {} [{}] - Open: {}, High: {}, Low: {}, Close: {}, Volume: {} - Live: {}'.format(
bt.num2date(data.datetime[0]),
data._name,
_interval, # ticker timeframe
data.open[0],
data.high[0],
data.low[0],
data.close[0],
data.volume[0],
_state,
))
print(f'\t - {ticker} RSI : {self.rsi[ticker][0]}')
print(f"\t - Free balance: {self.broker.getcash()}")
order = self.orders[data._name] # order of ticker
if order and order.status == bt.Order.Submitted: # If the order is not on the stock exchange (sent to the broker)
return # then we are waiting for the order to be placed on the stock exchange, we leave, we do not continue further
if not self.getposition(data): # if no position
if order and order.status == bt.Order.Accepted: # If the order is on the exchange (accepted by the broker)
print(f"\t - Cancel order {order.p.tradeid} to buy {data._name}")
# self.cancel(order) # then cancel it
if self.rsi[ticker] < 30: # Enter long
free_money = self.broker.getcash()
price = data.close[0] # by price close
size = (free_money / price) * 0.01 # 1% from free money
print(f" - buy {ticker} size = {size} at price = {price}")
self.orders[data._name] = self.buy(data=data, exectype=bt.Order.Limit, price=price, size=size)
print(f"\t - The order {self.orders[data._name].p.tradeid} to buy {data._name}")
else: # if no position
if self.rsi[ticker] > 70:
print("sell")
print(f"\t - Sell by market {data._name}...")
self.orders[data._name] = self.close() # An order to close a position at the market price
def notify_order(self, order):
"""Changing the status of the order"""
order_data_name = order.data._name # The name of the ticker from the order
print("*" * 50)
self.log(f'Order number {order.ref} {order.info["order_number"]} {order.getstatusname()} {"Buy" if order.isbuy() else "Sell"} {order_data_name} {order.size} @ {order.price}')
if order.status == bt.Order.Completed: # If the order is fully executed
if order.isbuy(): # if the order is buy
self.log(f'Buy {order_data_name} Price: {order.executed.price:.2f}, Val: {order.executed.value:.2f}, Comm: {order.executed.comm:.2f}')
else: # if the order is sell
self.log(f'Sell {order_data_name} Price: {order.executed.price:.2f}, Val: {order.executed.value:.2f}, Comm: {order.executed.comm:.2f}')
self.orders[order_data_name] = None # Resetting the order
print("*" * 50)
def notify_trade(self, trade):
"""Changing the position status"""
if trade.isclosed: # If the position is closed
self.log(f'Profit on a closed position {trade.getdataname()} Total={trade.pnl:.2f}, Comm={trade.pnlcomm:.2f}')
def log(self, txt, dt=None):
"""Output a date string to the console"""
dt = bt.num2date(self.datas[0].datetime[0]) if not dt else dt # The set date or the date of the current bar
print(f'{dt.strftime("%d.%m.%Y %H:%M")}, {txt}') # Output the date and time with the specified text to the console
if __name__ == '__main__':
symbols = ('SBER', 'LKOH', 'AFLT', 'GMKN', ) # тикеры, по которым будем получать данные
store = MoexAlgoStore() # Storage AlgoPack
# store = MoexAlgoStore(login=ConfigMOEX.Login, password=ConfigMOEX.Password) # Storage AlgoPack + authorization on the Moscow Stock Exchange
cerebro = bt.Cerebro(quicknotify=True) # Initiating the "engine" BackTrader
cerebro.broker.setcash(2000000) # Setting the amount of money
cerebro.broker.setcommission(commission=0.0015) # Set the commission to 0.15%... divide by 100 to remove %
# # live connecting to a broker - to comment out these two lines Offline + you still need to import the broker library
# broker = store.getbroker()
# cerebro.setbroker(broker)
# ------------------------------------------------------
# Attention! - Now it's Offline for testing strategies #
# ------------------------------------------------------
for _symbol in symbols: # Пробегаемся по всем тикерам
# Исторические 1-минутные бары за 10000 часов + новые live бары / таймфрейм M1
timeframe = "M1"
fromdate = dt.datetime.utcnow() - dt.timedelta(minutes=60*10000)
data = store.getdata(timeframe=bt.TimeFrame.Minutes, compression=10, dataname=_symbol, fromdate=fromdate, live_bars=False) # set True here - if you need to receive live bars
cerebro.adddata(data) # Adding data
cerebro.addstrategy(RSIStrategy, timeframe=timeframe) # Adding a trading system
cerebro.addanalyzer(btanalyzers.SQN, _name='SQN')
cerebro.addanalyzer(btanalyzers.VWR, _name='VWR', fund=True)
cerebro.addanalyzer(btanalyzers.TimeDrawDown, _name='TDD', fund=True, timeframe=bt.TimeFrame.Days)
cerebro.addanalyzer(btanalyzers.DrawDown, _name='DD', fund=True)
cerebro.addanalyzer(btanalyzers.Returns, _name='R', fund=True, timeframe=bt.TimeFrame.Days)
cerebro.addanalyzer(btanalyzers.AnnualReturn, _name='AR', )
cerebro.addanalyzer(btanalyzers.SharpeRatio, _name='SR')
cerebro.addanalyzer(btanalyzers.TradeAnalyzer, _name='TradeAnalyzer')
results = cerebro.run() # Launching a trading system
cerebro.plot() # Drawing a graph
thestrat = results[0]
print()
print("$"*77)
print(f"Liquidation value of the portfolio: {cerebro.broker.getvalue():.2f}") # Liquidation value of the portfolio
print(f"Remaining available funds: {cerebro.broker.getcash():.2f}") # Remaining available funds
print('Assets in the amount of: %.2f' % (cerebro.broker.getvalue() - cerebro.broker.getcash()))
print()
print('SQN: ', thestrat.analyzers.SQN.get_analysis())
print('VWR: ', thestrat.analyzers.VWR.get_analysis())
print('TDD: ', thestrat.analyzers.TDD.get_analysis())
print('DD: ', thestrat.analyzers.DD.get_analysis())
print('AR: ', thestrat.analyzers.AR.get_analysis())
print('Profitability: ', thestrat.analyzers.R.get_analysis())
print("$" * 77)