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strategy.py
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strategy.py
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from btgym import BTgymEnv, BTgymBaseStrategy, BTgymDataset
import numpy as np
import backtrader.indicators as btind
import backtrader.talib as talib
import datetime
class MyStrategy(BTgymBaseStrategy):
def __init__(self, **kwargs):
self.previous_cash = None
self.dim_time = self.p.state_shape[list(self.p.state_shape.keys())[0]].shape[0]
BTgymBaseStrategy.__init__(self,**kwargs)
"""
Example subclass of BTgym inner computation startegy,
overrides default get_state() and get_reward() methods.
"""
def set_datalines(self):
self.indicators = []
period = self.dim_time
self.indicators.append(btind.ExponentialMovingAverage(
self.datas[0],
period=15
))
self.indicators.append(btind.MACDHisto(
self.datas[0]
))
self.indicators.append(btind.RSI(
self.datas[0],
period=15
))
self.indicators.append(btind.BollingerBandsPct(
self.datas[0]
))
# self.indicators.append(btind.MACDHisto(
# self.datas[0]
# ))
# self.indicators.append(btind.RSI(
# self.datas[0],
# period=15
# ))
# self.indicators.append(btind.BollingerBandsPct(
# self.datas[0]
# ))
# self.indicators.append(talib.ADOSC(
# self.datas[0].high,self.datas[0].low,self.datas[0].close,self.datas[0].volume
# ))
#
# print("Set Lines:")
def _get_raw_state(self):
"""
Default state observation composer.
Returns:
and updates time-embedded environment state observation as [n,4] numpy matrix, where:
4 - number of signal features == state_shape[1],
n - time-embedding length == state_shape[0] == <set by user>.
Note:
`self.raw_state` is used to render environment `human` mode and should not be modified.
"""
self.raw_state = np.row_stack(
(
np.frombuffer(self.data.open.get(size=self.dim_time)),
np.frombuffer(self.data.high.get(size=self.dim_time)),
np.frombuffer(self.data.low.get(size=self.dim_time)),
np.frombuffer(self.data.close.get(size=self.dim_time)),
)
).T
return self.raw_state
def get_state(self):
X = self.raw_state
self.state['raw_state'] = X
self.state.pop('indicator_states', None)
if True:
for indicator in self.indicators:
if 'indicator_states' not in self.state:
self.state['indicator_states'] = np.row_stack((np.frombuffer(indicator.get(size=self.dim_time)),)).T
else:
self.state['indicator_states'] = np.concatenate((self.state['indicator_states'],np.row_stack((np.frombuffer(indicator.get(size=self.dim_time)),)).T),axis=1)
self.state['indicator_states'][np.isnan(self.state['indicator_states'])] = 1
newarr = []
for a in np.nditer(self.state['indicator_states'][:,2]):
if(a <= 30):
newarr.append(-1)
elif a >= 70:
newarr.append(1)
else:
newarr.append(0)
self.state['indicator_states'][:,2] = newarr
#self.state['indicator_states'][:,3] = self.state['indicator_states'][:,3] /100
return self.state
def get_reward(self):
"""
Default reward estimator.
Computes `dummy` reward as log utility of current to initial portfolio value ratio.
Same principles as for state composer apply.
Returns:
reward scalar, float
"""
if self.previous_cash == None:
self.previous_cash = self.env.broker.startingcash
reward = float(np.log(self.stats.broker.value[0] / self.previous_cash))
self.previous_cash = self.stats.broker.value[0]
return reward
# return float(np.log(self.stats.broker.value[0] / (self.env.broker.startingcash + (0.3 * self.env.broker.startingcash))))
import numpy as np
# The above could be sent to an independent module
import backtrader as bt
from agent import Agent
HOLD = 0
BUY = 1
SELL = 2
STOP = 3
class DeepLearningStrategy(bt.Strategy):
params = dict(
smaperiod=5,
trade=False,
stake=10,
exectype=bt.Order.Market,
stopafter=0,
valid=None,
cancel=0,
donotsell=False,
stoptrail=False,
stoptraillimit=False,
trailamount=None,
trailpercent=None,
limitoffset=None,
oca=False,
bracket=False,
)
def __init__(self):
# To control operation entries
self.orderid = list()
self.order = None
self.counttostop = 0
self.datastatus = 0
self.state = np.empty([4, 8])
self.stateFilled = 0
self.agent = Agent(8, 4)
# Create SMA on 2nd data
self.ema = bt.indicators.MovAv.EMA(self.data, period=self.p.smaperiod)
#self.ema = bt.indicators.MovAv.SMA(self.data, period=self.p.smaperiod)
self.macd = btind.MACD(self.data)
self.williamad = btind.WilliamsAD(self.data)
self.bollinger = btind.BollingerBands(self.data,period=self.p.smaperiod )
self.currentOrder = None
print('--------------------------------------------------')
print('Strategy Created')
print('--------------------------------------------------')
def notify_data(self, data, status, *args, **kwargs):
print('*' * 5, 'DATA NOTIF:', data._getstatusname(status),self.p.stopafter, args)
if status == data.LIVE:
self.counttostop = self.p.stopafter
self.datastatus = 1
def notify_store(self, msg, *args, **kwargs):
print('*' * 5, 'STORE NOTIF:', msg)
def notify_order(self, order):
if order.status in [order.Completed, order.Cancelled, order.Rejected]:
self.order = None
print('-' * 50, 'ORDER BEGIN', datetime.datetime.now())
print(order)
print('-' * 50, 'ORDER END')
def notify_trade(self, trade):
print('-' * 50, 'TRADE BEGIN', datetime.datetime.now())
print(trade)
print('-' * 50, 'TRADE END')
def prenext(self):
self.next(frompre=True)
def next(self, frompre=False):
for i in range(0,len(self.state)):
try:
self.state[i] = self.state[i +1]
except IndexError:
self.state[i][0] = self.data.open[0]
self.state[i][1] = self.data.high[0]
self.state[i][2] = self.data.low[0]
self.state[i][3] = self.data.close[0]
self.state[i][4] = self.ema[0]
self.state[i][5] = self.macd[0]
self.state[i][6] = self.williamad[0]
self.state[i][7] = self.bollinger[0]
if self.stateFilled == self.p.smaperiod:
if self.datastatus == 1:
exectype = self.p.exectype if not self.p.oca else bt.Order.Limit
close = self.data0.close[0]
price = round(close * 0.90, 2)
action = self.agent.act(self.state)
if (self.currentOrder != action and action != HOLD) or action == STOP:
for i in range(len(self.orderid)):
self.cancel(self.orderid[i])
self.close(self.orderid[i])
del self.orderid[i]
self.order = None
print("Cancel Order")
if action == BUY:
self.order = self.buy(size=self.p.stake,
exectype=exectype,
price=price,
valid=self.p.valid,
transmit=not self.p.bracket)
print("Buy order:{}".format(self.broker.getcash()))
self.orderid.append(self.order)
elif action == SELL:
self.order = self.sell(size=self.p.stake,
exectype=exectype,
price=price,
valid=self.p.valid,
transmit=not self.p.bracket)
print("Sell order:{}".format(self.broker.getcash()))
self.orderid.append(self.order)
if not self.currentOrder:
self.currentOrder = action
else:
self.stateFilled += 1
def start(self):
if self.data0.contractdetails is not None:
print('Timezone from ContractDetails: {}'.format(
self.data0.contractdetails.m_timeZoneId))
header = ['Datetime', 'Open', 'High', 'Low', 'Close', 'Volume',
'OpenInterest', 'SMA']
print(', '.join(header))
self.done = False