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Strategy2_ROC_KC_UpDownTrend.py
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Strategy2_ROC_KC_UpDownTrend.py
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import backtrader as bt
from collections import defaultdict # для списков в словарях
import functions
import talib as ta
import numpy as np
import random
class NtimestrueOk(bt.Indicator):
lines = ('ntimestrue',)
params = dict(period=10)
plotinfo = dict(plot=True, subplot=True, plotname='Ntimestrue', )
def __init__(self):
self.l.ntimestrue = bt.indicators.AllN(self.data1, period=self.p.period)
class Ntimestrue(bt.Indicator):
lines = ('ntimestrue',)
params = dict(period=10)
plotinfo = dict(plot=True, subplot=True, plotname='Ntimestrue', )
def __init__(self):
#self.l.ntimestrue = bt.indicators.AllN(self.data, period=self.p.period)
pass
def next(self):
print("hi", self.data[0], self.data1[0]) # -200 => 1
if self.data1[0]:
self.l.ntimestrue[0] = 1.0
else:
self.l.ntimestrue[0] = 0.0
#self.l.ntimestrue[0] = random.randint(0, 1)
class And3(bt.Indicator):
lines = ('and3',)
params = dict(data2=1, data3=1)
plotinfo = dict(plot=True)
def __init__(self):
self.l.and3 = bt.And(self.data, self.p.data2, self.p.data3)
#self.l.and3 = bt.And(bt.And(self.data, self.p.data2), self.p.data3)
class OverUnder(bt.Indicator):
lines = ('overunder',)
params = dict(data2=20)
plotinfo = dict(plot=True)
def __init__(self):
self.l.overunder = self.data > self.p.data2 # данные над data2 == 1
class UnderOver(bt.Indicator):
lines = ('underover',)
params = dict(data2=20)
plotinfo = dict(plot=True)
def __init__(self):
self.l.underover = self.data < self.p.data2 # данные под data2 == 1
class UpDownTrend(bt.Indicator):
lines = ('trend',)
params = dict(period=20, )
plotinfo = dict(plot=True)
def __init__(self):
y1 = self.data
y2 = self.data(-self.p.period)
#self.l.trend = cond = bt.Cmp(y1, y2) # => 1 если y1 > y2 => 0 если y1 == y2 => -1 иначе
self.l.trend = cond = y1 > y2 # => 1 если y1 > y2
class KC(bt.Indicator):
lines = ('mid', 'top', 'bot',)
params = dict(multiplier=2.0, period=20, movav=bt.indicators.MovAv.EMA, atr=bt.indicators.AverageTrueRange, )
plotinfo = dict(subplot=False)
plotlines = dict(
mid=dict(ls='--'),
top=dict(_samecolor=False),
bot=dict(_samecolor=False),
)
def __init__(self):
self.lines.mid = ma = self.p.movav(self.data, period=self.p.period)
atr = self.p.atr(self.data, period=self.p.period)
stddev = self.p.multiplier * atr
self.lines.top = ma + stddev
self.lines.bot = ma - stddev
class OverUnderMovAv(bt.Indicator):
lines = ('overunder',)
params = dict(period=20, movav=bt.indicators.MovAv.EMA)
def __init__(self):
movav = self.p.movav(self.data, period=self.p.period)
self.l.overunder = bt.Cmp(self.data, movav) # данные над sma => 1
class OverUnderMovAvMovAv(bt.Indicator):
lines = ('overunder',)
params = dict(period=20, period2=25, movav=bt.indicators.MovAv.EMA)
def __init__(self):
movav = self.p.movav(self.data, period=self.p.period)
movav2 = self.p.movav(self.data, period=self.p.period2)
self.l.overunder = bt.Cmp(movav, movav2) # => 1 если EMA > EMA2
class Condition1(bt.Indicator):
lines = ('overunder',)
params = dict(period=20, period2=25, movav=bt.indicators.MovAv.EMA)
def __init__(self):
movav = self.p.movav(self.data, period=self.p.period)
movav2 = self.p.movav(self.data, period=self.p.period2)
cond1 = bt.Cmp(self.data, movav) # данные над sma => 1
cond2 = bt.Cmp(movav, movav2) # => 1 если EMA > EMA2
self.l.overunder = ((cond2 == 1) == cond1) # => 1 если cond2 == cond1 == 1
class TestStrategy01(bt.Strategy):
"""
- Отображает статус подключения
- При приходе нового бара отображает его цены/объем
- Отображает статус перехода к новым барам
"""
params = ( # Параметры торговой системы
('name', ''), # Название торговой системы
('symbols', ''), # Список торгуемых тикеров. По умолчанию торгуем все тикеры
('Percent', 20),
('lots', ''), # лоты
# ('my_log', ''), # лог
)
def __init__(self):
"""Инициализация торговой системы"""
self.isLive = False # Сначала будут приходить исторические данные
# To keep track of pending orders
self.order = None
self.orders = defaultdict(list)
self.dataclose = None
print(self.p.lots)
self.sma_all1 = defaultdict(list)
self.sma_all2 = defaultdict(list)
self.ema_all1 = defaultdict(list)
self.ema_all2 = defaultdict(list)
self.close_under_ema_all1 = defaultdict(list)
self.ema_all1_over_ema_all2 = defaultdict(list)
self.cond1 = defaultdict(list)
self.test1 = defaultdict(list)
self.macd = defaultdict(list)
self.bbands = defaultdict(list)
self.close_over_middle = defaultdict(list)
self.crossover = defaultdict(list)
self.crossover_80 = defaultdict(list)
self.crossover_20 = defaultdict(list)
self.crossover_DK = defaultdict(list)
self.stoch = defaultdict(list)
self.price_buy = defaultdict(list)
self.size_buy = defaultdict(list)
self.first_buy = defaultdict(list)
self.my_logs = []
self.ema_all1 = defaultdict(list)
self.ema_all2 = defaultdict(list)
self.close_under_ema_all10 = defaultdict(list)
self.roc = defaultdict(list)
self.kc = defaultdict(list)
self.trend = defaultdict(list)
self.roc_over_0 = defaultdict(list)
self.close_over_kc_top = defaultdict(list)
self.and3 = defaultdict(list)
self.enter_long = defaultdict(list)
self.close_long = defaultdict(list)
for i in range(len(self.datas)):
ticker = list(self.dnames.keys())[i] # key name is ticker name
self.ema_all1[ticker] = bt.indicators.ExponentialMovingAverage(self.datas[i], period=8)
# self.ema_all2[ticker] = bt.indicators.ExponentialMovingAverage(self.datas[i], period=16)
# self.close_under_ema_all10[ticker] = OverUnder(self.ema_all1[ticker].lines.ema, data2=self.ema_all2[ticker].lines.ema)
self.roc[ticker] = bt.indicators.RateOfChange100(self.datas[i], period=100)
self.kc[ticker] = KC(self.datas[i], period=200, multiplier=3.0)
self.trend[ticker] = UpDownTrend(self.kc[ticker].lines.top, period=20)
self.roc_over_0[ticker] = OverUnder(self.roc[ticker].lines.roc100, data2=0.0)
self.close_over_kc_top[ticker] = OverUnder(self.datas[i].close, data2=self.kc[ticker].lines.top)
self.and3[ticker] = And3(self.trend[ticker].lines.trend,
data2=self.roc_over_0[ticker].lines.overunder,
data3=self.close_over_kc_top[ticker].lines.overunder)
self.enter_long[ticker] = NtimestrueOk(self.datas[i], self.and3[ticker].lines.and3, period=10)
# self.enter_long[ticker] = bt.indicators.CrossUp(self.ema_all1[ticker].lines.ema,
# self.kc[ticker].lines.bot)
#self.close_long[ticker] = UnderOver(self.datas[i].close, data2=self.kc[ticker].lines.bot)
self.close_long[ticker] = UnderOver(self.ema_all1[ticker].lines.ema, data2=self.kc[ticker].lines.mid)
# self.close_long[ticker] = bt.indicators.CrossDown(self.ema_all1[ticker].lines.ema,
# self.kc[ticker].lines.top)
# self.sma_all1[ticker] = bt.indicators.SMA(self.datas[i], period=64)
# self.sma_all2[ticker] = bt.indicators.SMA(self.datas[i], period=128)
# self.ema_all1[ticker] = bt.indicators.ExponentialMovingAverage(self.datas[i], period=11)
# self.ema_all2[ticker] = bt.indicators.ExponentialMovingAverage(self.datas[i], period=30)
# #self.close_under_ema_all1[ticker] = bt.ind.Cmp(self.ema_all1[ticker], self.datas[i].close)
# self.close_under_ema_all1[ticker] = OverUnderMovAv(self.datas[i].close, period=21)
#
# self.ema_all1_over_ema_all2[ticker] = OverUnderMovAvMovAv(self.datas[i].close, period=11, period2=30)
#
# self.cond1[ticker] = Condition1(self.datas[i].close, period=21, period2=30)
#
# # self.test1[ticker] = ((self.ema_all1_over_ema_all2[ticker] == 1) == self.close_under_ema_all1[ticker])
#
# self.stoch[ticker] = bt.indicators.Stochastic(self.datas[i], period=21, period_dfast=7, period_dslow=7)
# self.crossover_80[ticker] = bt.ind.CrossOver(self.stoch[ticker].lines.percD, 80)
# self.crossover_20[ticker] = bt.ind.CrossOver(self.stoch[ticker].lines.percD, 20)
# self.crossover_DK[ticker] = bt.ind.CrossOver(self.stoch[ticker].lines.percD, self.stoch[ticker].lines.percK)
#
# self.macd[ticker] = bt.indicators.MACD(self.datas[i], period_me1=8, period_me2=16, period_signal=9)
# self.bbands[ticker] = bt.indicators.BollingerBands(self.datas[i], period=20)
# self.close_over_middle[ticker] = OverUnder(self.datas[i].close, data2=self.bbands[ticker].lines.mid)
# self.crossover[ticker] = bt.ind.CrossOver(self.ema_all1[ticker], self.ema_all2[ticker])
# #self.crossover[ticker] = bt.ind.UpDayBool(self.sma_all1[ticker], self.sma_all2[ticker])
def log(self, txt, dt=None):
"""Вывод строки с датой на консоль"""
dt = bt.num2date(
self.datas[0].datetime[0]) if dt is None else dt # Заданная дата или дата последнего бара первого тикера ТС
print(f'{dt.strftime("%d.%m.%Y %H:%M")}, {txt}') # Выводим дату и время с заданным текстом на консоль
def log_csv(self, ticker=None, signal=None, signal_price=None, order=None, order_price=None,
size=None, status=None, cost=None, comm=None, amount=None, pnl=None, dt=None):
"""Собираем логи для csv файла"""
tradedate = bt.num2date(self.datas[0].datetime[0]) if dt is None else dt # Заданная дата или дата последнего бара первого тикера ТС
depo = f"{self.cerebro.broker.get_cash():.2f}"
amount = f"{(self.cerebro.broker.get_value()):.2f}" # - (self.cerebro.broker.get_cash()):.2f}"
strategy_name = self.p.name
info = ""
if order == "BUY" and float(cost) < 0: info = "Warning"
self.my_logs.append([tradedate, ticker, signal, signal_price, order, order_price, size, status,
cost, comm, pnl, amount, depo, strategy_name, info])
def next(self):
"""
Приход нового бара тикера
"""
if self.p.name != '': # Если указали название торговой системы, то будем ждать прихода всех баров
lastdatetimes = [bt.num2date(data.datetime[0]) for data in self.datas] # Дата и время последнего бара каждого тикера
if lastdatetimes.count(lastdatetimes[0]) != len(lastdatetimes): # Если дата и время последних баров не идентичны
return # то еще не пришли все новые бары. Ждем дальше, выходим
#print(self.p.name)
for data in self.datas: # Пробегаемся по всем запрошенным тикерам
ticker = data._dataname
if self.p.symbols == '' or ticker in self.p.symbols: # Если торгуем все тикеры или данный тикер
self.log(f'{ticker} - {bt.TimeFrame.Names[data.p.timeframe]} {data.p.compression} - Open={data.open[0]:.2f}, High={data.high[0]:.2f}, Low={data.low[0]:.2f}, Close={data.close[0]:.2f}, Volume={data.volume[0]:.0f}',
bt.num2date(data.datetime[0]))
_close = data.close[0] # текущий close
_low = data.low[0] # текущий low
_high = data.high[0] # текущий high
_open = data.open[0]
_oc2 = (_open + _close) / 2
_volume = data.volume # ссылка на Объемы # print(volume[0])
#print(bool(self.trend[ticker]), bool(self.close_over_kc_top[ticker]), bool(self.roc_over_0[ticker]), bool(self.and3[ticker]))
print(bool(self.enter_long[ticker]))
#print(bool(self.cond1[ticker]), bool(self.test1[ticker]), self.cond1[ticker] == self.test1[ticker])
#if self.cond1[ticker] != self.test1[ticker]: print("ERROR***")
# # https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
# # pip install TA_Lib-0.4.24-cp39-cp39-win_amd64.whl
# _ticker = ticker
# _current_close = data.close[0]
# _idx = data.line.idx
# _dd = data.close.array
# _kk = np.array(_dd)
# # print(_dd, _kk)
# _sma1 = ta.SMA(_kk, timeperiod=50); _sma2 = ta.SMA(_kk, timeperiod=100)
# print("[", ticker, _sma1[_idx], _sma2[_idx], "]")
# условие на покупку
if not self.orders[ticker]:
if self.enter_long[ticker]: #
lot = self.p.lots[ticker]
percent = 3 # сколько % от депозита использовать на сделку
depo = self.cerebro.broker.get_cash()
ticker_price = _close
size = functions.calc_size(depo=depo, lot=lot, percent=percent, ticker_price=ticker_price)
self.log(f"BUY CREATE [{ticker}] {self.data.close[0]:.2f}")
self.log_csv(ticker=ticker, signal='BUY', signal_price=_close, size=size)
if type(self.first_buy[ticker]) == list:
self.first_buy[ticker] = True
self.buy(data=data, exectype=bt.Order.Market, size=size)
# if not self.first_buy[ticker]:
# self.buy(data=data, exectype=bt.Order.Market, size=size)
self.first_buy[ticker] = False
self.orders[ticker] = True
profit_percent = 1
ratio_profit = 5 # 1/3 => 1%*3=3%
stop_loss_percent = 5
# условие на продажу
if self.orders[ticker] and self.price_buy[ticker]:
# print(f"_close={_close} self.price_buy[ticker]={self.price_buy[ticker]} take_profit={self.price_buy[ticker]*(1+profit_percent*ratio_profit/100)} stop-loss={self.price_buy[ticker]*(1-profit_percent/100)}")
size = self.size_buy[ticker]
# условие на продажу stop-loss %
if _close <= self.price_buy[ticker] * (1 - stop_loss_percent / 100):
self.log(f"SELL STOP LOSS CREATE [{ticker}] {self.data.close[0]:.2f}")
self.log_csv(ticker=ticker, signal='STOP LOSS', signal_price=_close, size=size)
self.sell(data=data, exectype=bt.Order.Market, size=size)
self.orders[ticker] = False
self.first_buy[ticker] = True
# условие на продажу take-profit
if self.close_long[ticker] and self.orders[ticker]: #
self.log(f"SELL CREATE [{ticker}] {self.data.close[0]:.2f}")
self.log_csv(ticker=ticker, signal='SELL', signal_price=_close, size=size)
self.sell(data=data, exectype=bt.Order.Market, size=size)
#self.sell(data=data, exectype=bt.Order.Market, size=size)
self.orders[ticker] = False
# if _close>=self.price_buy[ticker]*(1+profit_percent*ratio_profit/100):
# self.log(f"SELL TAKE PROFIT CREATE [{ticker}] {self.data.close[0]:.2f}")
# self.sell(data=data, exectype=bt.Order.Market, size=size)
# self.orders[ticker] = False
# ==========================================================================================================================
# # условие на покупку
# if not self.orders[ticker]:
# if self.sma_all1[ticker] > self.sma_all2[ticker]:
# #print(self.bbands[ticker].lines.top, self.bbands[ticker].lines.mid, self.bbands[ticker].lines.bot)
# self.log(f"BUY CREATE [{ticker}] {self.data.close[0]:.2f}")
# self.buy(data=data, exectype=bt.Order.Market) # , size=size)
# self.orders[ticker] = True
#
# # условие на продажу
# if self.orders[ticker]:
# if self.sma_all1[ticker] < self.sma_all2[ticker]:
# self.log(f"SELL CREATE [{ticker}] {self.data.close[0]:.2f}")
# self.sell(data=data, exectype=bt.Order.Market) # , size=size)
# self.orders[ticker] = False
# # ==========================================================================================================================
# # условие на покупку
# if not self.orders[ticker]:
# if self.sma_all1[ticker] > self.sma_all2[ticker]:
# #print(self.bbands[ticker].lines.top, self.bbands[ticker].lines.mid, self.bbands[ticker].lines.bot)
#
# lot = self.p.lots[ticker]
# percent = 3 # сколько % от депозита использовать на сделку
# depo = self.cerebro.broker.get_cash()
# ticker_price = _close
#
# size = functions.calc_size(depo=depo, lot=lot, percent=percent, ticker_price=ticker_price)
#
# self.log(f"BUY CREATE [{ticker}] {self.data.close[0]:.2f}")
# self.log_csv(ticker=ticker, signal='BUY', signal_price=_close, size=size)
# self.buy(data=data, exectype=bt.Order.Market, size=size)
# self.orders[ticker] = True
#
# profit_percent = 1
# ratio_profit = 5 # 1/3 => 1%*3=3%
# stop_loss_percent = 1
# # условие на продажу
# if self.orders[ticker] and self.price_buy[ticker]:
# # print(f"_close={_close} self.price_buy[ticker]={self.price_buy[ticker]} take_profit={self.price_buy[ticker]*(1+profit_percent*ratio_profit/100)} stop-loss={self.price_buy[ticker]*(1-profit_percent/100)}")
# size = self.size_buy[ticker]
# # условие на продажу stop-loss %
# if _close <= self.price_buy[ticker] * (1 - stop_loss_percent / 100):
# self.log(f"SELL STOP LOSS CREATE [{ticker}] {self.data.close[0]:.2f}")
# self.log_csv(ticker=ticker, signal='STOP LOSS', signal_price=_close, size=size)
# self.sell(data=data, exectype=bt.Order.Market, size=size)
# self.orders[ticker] = False
# # условие на продажу take-profit
# elif self.sma_all1[ticker] < self.sma_all2[ticker]:
# self.log(f"SELL CREATE [{ticker}] {self.data.close[0]:.2f}")
# self.log_csv(ticker=ticker, signal='SELL', signal_price=_close, size=size)
# self.sell(data=data, exectype=bt.Order.Market, size=size)
# self.orders[ticker] = False
# # if _close>=self.price_buy[ticker]*(1+profit_percent*ratio_profit/100):
# # self.log(f"SELL TAKE PROFIT CREATE [{ticker}] {self.data.close[0]:.2f}")
# # self.sell(data=data, exectype=bt.Order.Market, size=size)
# # self.orders[ticker] = False
#
# # ==========================================================================================================================
# if not self.orders[ticker]:
# if self.sma_all1[ticker] > self.sma_all2[ticker]:
# #print(self.bbands[ticker].lines.top, self.bbands[ticker].lines.mid, self.bbands[ticker].lines.bot)
# self.log(f"BUY CREATE [{ticker}] {self.data.close[0]:.2f}")
# self.buy(data=data, exectype=bt.Order.Market) # , size=size)
# self.orders[ticker] = True
# if not self.orders[ticker]:
# if random.randint(0, 10) > 8:
# #print(self.bbands[ticker].lines.top, self.bbands[ticker].lines.mid, self.bbands[ticker].lines.bot)
# self.log(f"BUY CREATE [{ticker}] {self.data.close[0]:.2f}")
# self.buy(data=data, exectype=bt.Order.Market) # , size=size)
# self.orders[ticker] = True
#
# if self.orders[ticker]:
# if _close < self.bbands[ticker].lines.mid:
# self.log(f"SELL CREATE [{ticker}] {self.data.close[0]:.2f}")
# self.sell(data=data, exectype=bt.Order.Market) # , size=size)
# self.orders[ticker] = False
# if not self.orders[ticker]:
# if self.sma_all1[ticker] > self.sma_all2[ticker]:
# #print(self.bbands[ticker].lines.top, self.bbands[ticker].lines.mid, self.bbands[ticker].lines.bot)
# self.log(f"BUY CREATE [{ticker}] {self.data.close[0]:.2f}")
# self.buy(data=data, exectype=bt.Order.Market) # , size=size)
# self.orders[ticker] = True
#
# if self.orders[ticker]:
# if self.sma_all1[ticker] < self.sma_all2[ticker]:
# self.log(f"SELL CREATE [{ticker}] {self.data.close[0]:.2f}")
# self.sell(data=data, exectype=bt.Order.Market) # , size=size)
# self.orders[ticker] = False
def notify_trade(self, trade):
if not trade.isclosed:
return
self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' %
(trade.pnl, trade.pnlcomm))
def notify_order(self, order):
ticker = order.data._name
size = order.size
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(f"BUY EXECUTED [{order.data._name}], {order.executed.price:.2f} size={size}")
self.log_csv(ticker=ticker, order='BUY', order_price=order.executed.price, size=size,
status=order.getstatusname(order.status), cost=f"{order.executed.value:.2f}",
comm=f"{order.executed.comm:.2f}")
self.price_buy[ticker] = order.executed.price # записываем цену покупки для тикера
self.size_buy[ticker] = size # записываем объем покупки для тикера
elif order.issell():
self.log(f"SELL EXECUTED [{order.data._name}], {order.executed.price:.2f} size={size}")
self.log_csv(ticker=ticker, order='SELL', order_price=order.executed.price, size=size,
status=order.getstatusname(order.status),
cost=f"{order.executed.value + order.executed.pnl:.2f}",
comm=f"{order.executed.comm:.2f}", pnl=f"{order.executed.pnl:.2f}")
self.price_buy.pop(ticker, None) # удаляем цену покупки для тикера
self.size_buy.pop(ticker, None) # удаляем объем покупки для тикера
self.bar_executed = len(self)
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log('Order Canceled/Margin/Rejected')
# Write down: no pending order
self.order = None
def notify_data(self, data, status, *args, **kwargs):
"""Изменение статсуса приходящих баров"""
dataStatus = data._getstatusname(status) # Получаем статус (только при LiveBars=True)
print(f'{data._dataname} - {dataStatus}') # Статус приходит для каждого тикера отдельно
self.isLive = dataStatus == 'LIVE' # В Live режим переходим после перехода первого тикера