def _start(self, app_context, **kwargs):
self.qty = self.get_stg_config_value("qty", 1)
self.threshold = self.get_stg_config_value("threshold", 1)
self.xiv = app_context.ref_data_mgr.get_inst('XIV', 'SMART')
self.vxx = app_context.ref_data_mgr.get_inst('VXX', 'SMART')
self.vxv = app_context.ref_data_mgr.get_inst('VXV', 'SMART')
self.vix = app_context.ref_data_mgr.get_inst(
'VIX', 'SMART') # TODO: Review index
instruments = [self.vxx, self.xiv, self.vix]
self.vix_bar = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.vix.get_symbol()) # non tradable
self.vxv_bar = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.vxv.get_symbol()) # non tradable
self.xiv_bar = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.xiv.get_symbol())
self.vxx_bar = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.vxx.get_symbol())
self.vix_strm = BehaviorSubject(0)
self.vxv_strm = BehaviorSubject(0)
self.ratio_strm = rx.Observable \
.zip(self.vix_strm, self.vxv_strm, lambda x, y: x / y) \
.subscribe(self.on_ratio)
super(VixVxvRatio, self)._start(app_context, **kwargs)
class VixVxvRatio(Strategy):
def __init__(self, stg_id: str, stg_cls: str, state: StrategyState = None):
super(VixVxvRatio, self).__init__(stg_id=stg_id,
stg_cls=stg_cls,
state=state)
self.day_count = 0
self.order = None
def _start(self, app_context: Context) -> None:
self.qty = self._get_stg_config("qty", 1)
self.threshold = self._get_stg_config("threshold", 1)
self.xiv = app_context.ref_data_mgr.get_inst('XIV', 'SMART')
self.vxx = app_context.ref_data_mgr.get_inst('VXX', 'SMART')
self.vxv = app_context.ref_data_mgr.get_inst('VXV', 'SMART')
self.vix = app_context.ref_data_mgr.get_inst(
'VIX', 'SMART') # TODO: Review index
instruments = [self.vxx, self.xiv, self.vix]
self.vix_bar = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.vix.get_symbol()) # non tradable
self.vxv_bar = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.vxv.get_symbol()) # non tradable
self.xiv_bar = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.xiv.get_symbol())
self.vxx_bar = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.vxx.get_symbol())
self.vix_strm = BehaviorSubject(0)
self.vxv_strm = BehaviorSubject(0)
self.ratio_strm = rx.Observable \
.zip(self.vix_strm, self.vxv_strm, lambda x, y: x / y) \
.subscribe(self.on_ratio)
super(VixVxvRatio, self)._start(app_context)
def _stop(self):
super(VixVxvRatio, self)._stop()
def on_bar(self, bar):
if bar.inst_id == self.vix.id():
self.vix_strm.on_next(bar.close)
elif bar.inst_id == self.vxv.id():
self.vxv_strm.on_next(bar.close)
def on_ratio(self, ratio):
# what is order is not filled and there is signal again?
if self.order is None:
# long XIV at the close when VIX index closed below the VXV index
# long XIV when ratio < 0.92
if ratio < self.threshold[0]:
# logger.info("%s,B,%.2f" % (bar.timestamp, bar.close))
self.order = self.market_order(inst_id=self.xiv.id(),
action=Buy,
qty=self.qty)
# long VXX when ratio > 1.08
elif ratio > self.threshold[1]:
# logger.info("%s,B,%.2f" % (bar.timestamp, bar.close))
self.order = self.market_order(inst_id=self.vxx,
action=Buy,
qty=self.qty)
class VixVxvRatio(Strategy):
def __init__(self, stg_id=None, stg_configs=None):
super(VixVxvRatio, self).__init__(stg_id=stg_id, stg_configs=stg_configs)
self.day_count = 0
self.order = None
def _start(self, app_context, **kwargs):
self.qty = self.get_stg_config_value("qty", 1)
self.threshold = self.get_stg_config_value("threshold", 1)
self.xiv = app_context.ref_data_mgr.get_inst('XIV', 'SMART')
self.vxx = app_context.ref_data_mgr.get_inst('VXX', 'SMART')
self.vxv = app_context.ref_data_mgr.get_inst('VXV', 'SMART')
self.vix = app_context.ref_data_mgr.get_inst('VIX', 'SMART') # TODO: Review index
instruments = [self.vxx, self.xiv, self.vix]
self.vix_bar = app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % self.vix.get_symbol()) # non tradable
self.vxv_bar = app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % self.vxv.get_symbol()) # non tradable
self.xiv_bar = app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % self.xiv.get_symbol())
self.vxx_bar = app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % self.vxx.get_symbol())
self.vix_strm = BehaviorSubject(0)
self.vxv_strm = BehaviorSubject(0)
self.ratio_strm = rx.Observable \
.zip(self.vix_strm, self.vxv_strm, lambda x, y: x / y) \
.subscribe(self.on_ratio)
super(VixVxvRatio, self)._start(app_context, **kwargs)
def _stop(self):
super(VixVxvRatio, self)._stop()
def on_bar(self, bar):
if bar.inst_id == self.vix.id():
self.vix_strm.on_next(bar.close)
elif bar.inst_id == self.vxv.id():
self.vxv_strm.on_next(bar.close)
def on_ratio(self, ratio):
# what is order is not filled and there is signal again?
if self.order is None:
# long XIV at the close when VIX index closed below the VXV index
# long XIV when ratio < 0.92
if ratio < self.threshold[0]:
# logger.info("%s,B,%.2f" % (bar.timestamp, bar.close))
self.order = self.market_order(inst_id=self.xiv.id(),
action=OrdAction.BUY,
qty=self.qty)
# long VXX when ratio > 1.08
elif ratio > self.threshold[1]:
# logger.info("%s,B,%.2f" % (bar.timestamp, bar.close))
self.order = self.market_order(inst_id=self.vxx, action=OrdAction.BUY, qty=self.qty)
def publish_value(self, initial_value, selector=None):
"""Returns an observable sequence that is the result of invoking the
selector on a connectable observable sequence that shares a single
subscription to the underlying sequence and starts with initial_value.
This operator is a specialization of Multicast using a BehaviorSubject.
Example:
res = source.publish_value(42)
res = source.publish_value(42, lambda x: x.map(lambda y: y * y))
Keyword arguments:
initial_value -- {Mixed} Initial value received by observers upon
subscription.
selector -- {Function} [Optional] Optional selector function which can
use the multicasted source sequence as many times as needed, without
causing multiple subscriptions to the source sequence. Subscribers
to the given source will receive immediately receive the initial
value, followed by all notifications of the source from the time of
the subscription on.
Returns {Observable} An observable sequence that contains the elements
of a sequence produced by multicasting the source sequence within a
selector function.
"""
if selector:
def subject_selector():
return BehaviorSubject(initial_value)
return self.multicast(subject_selector=subject_selector,
selector=selector)
else:
return self.multicast(BehaviorSubject(initial_value))
def _publish_value(initial_value: Any, mapper: Mapper = None) -> Callable[[Observable], Observable]:
if mapper:
def subject_factory(scheduler):
return BehaviorSubject(initial_value)
return ops.multicast(subject_factory=subject_factory, mapper=mapper)
return ops.multicast(BehaviorSubject(initial_value))
def _start(self, app_context, **kwargs):
self.ou_params = self.get_stg_config_value("ou_params", 1)
self.gamma = self.get_stg_config_value("gamma", 1)
self.bar_0 = app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % self.app_context.app_config.instrument_ids[0])
self.bar_0.start(app_context)
self.bar_1 = app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % self.app_context.app_config.instrument_ids[1])
self.bar_1.start(app_context)
self.instruments = self.app_context.app_config.instrument_ids
self.log_spot_0 = BehaviorSubject(0)
self.log_spot_1 = BehaviorSubject(0)
self.spread_stream = rx.Observable \
.zip(self.log_spot_0, self.log_spot_1, lambda x, y: [x, y, x - y]) \
.subscribe(self.rebalance)
super(PairTradingWithOUSpread, self)._start(app_context, **kwargs)
class ServerState:
db_data = None
monitor_data = None
bc_monitorSub = BehaviorSubject(None)
db_monitorSub = BehaviorSubject(None)
bc_monitor = Observable
db_monitor = Observable
def set_db_data(self, data):
self.db_data = data
self.bc_monitor.map(self.set_db_data(self.db_data)).publish()
def get_db_data(self):
return self.db_datas
def setMonitor(self, d):
self.monitor_data = d
def getMonitor(self):
return self.monitor_data
# select method will use to get observerable
def select(self, observer_name):
if observer_name == BC_MONITOR_SUB:
return self.bc_monitorSub
elif observer_name == DB_MONITOR_SUB:
return self.db_monitorSub
else:
# print observer_name + ' is not recognize observable.'
return None
# dispatch method will use to update data on the observer
def dispatch(self, observer_name, payload):
if observer_name == BC_MONITOR_SUB:
self.bc_monitorSub.on_next(payload)
return payload
elif observer_name == DB_MONITOR_SUB:
self.db_monitorSub.on_next(payload)
return payload
else:
# print observer_name + ' is not recognize observable.'
return None
def main(inputs, starting_floor):
floor_C = BehaviorSubject(starting_floor)
inputs.floor_changed_S.subscribe(floor_C)
calls_C = BehaviorSubject(set())
calls_S = inputs.called_S \
.map(lambda call: call.floor) \
.scan(lambda called_floors, floor: called_floors | set([floor]), set())
calls_S.subscribe(calls_C)
selections_C = BehaviorSubject(set())
inputs.floor_selected_S \
.scan(lambda selections, floor: selections | set([floor]), set()) \
.subscribe(selections_C)
resume_S = inputs.tick_S \
.with_latest_from(selections_C, lambda _, selections: first(selections)) \
.filter(cmp(not_, is_none))
# TODO (TS 2016-05-16) Don't fire if motor is already moving.
motor_started_S = inputs.called_S \
.map(lambda call: call.floor) \
.merge(inputs.floor_selected_S, resume_S) \
.with_latest_from(floor_C, determine_direction)
stop_for_call_S = inputs.floor_changed_S \
.with_latest_from(calls_C, arrived_on_called_floor) \
.filter(identity) \
.map(always(None))
stop_for_selection_S = inputs.floor_changed_S \
.with_latest_from(selections_C, arrived_on_selected_floor) \
.filter(identity) \
.map(always(None))
motor_stopped_S = Observable.merge(stop_for_call_S, stop_for_selection_S)
motor_C = BehaviorSubject(None)
motor_change_S = Observable.merge(motor_started_S, motor_stopped_S)
motor_change_S.subscribe(motor_C)
return motor_C
def _start(self, app_context: Context) -> None:
self.ou_params = self._get_stg_config("ou_params", default=1)
self.gamma = self._get_stg_config("gamma", default=1)
self.instruments = app_context.config.get_app_config("instrumentIds")
self.bar_0 = self.app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.instruments[0])
self.bar_1 = self.app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.instruments[1])
self.bar_0.start(app_context)
self.bar_1.start(app_context)
self.log_spot_0 = BehaviorSubject(0)
self.log_spot_1 = BehaviorSubject(0)
self.spread_stream = rx.Observable \
.zip(self.log_spot_0, self.log_spot_1, lambda x, y: [x, y, x - y]) \
.subscribe(self.rebalance)
super(PairTradingWithOUSpread, self)._start(app_context)
class Player:
def __init__(self, id, name, ws_subject, size, pos):
self.id = id
self.ws_subject = ws_subject
self.name = name
self.size = BehaviorSubject(size)
self.pos = BehaviorSubject(pos)
self.dir = BehaviorSubject(None)
ws_subject.to_observable().map(lambda d: json.loads(d.data)).filter(lambda m: m["t"] == "d").subscribe(self.dir)
self.dir.subscribe(lambda d: print(d))
def partial_data(self):
return {"id": self.id, "size": self.size.value, "pos": self.pos.value}
def full_data(self):
data = self.partial_data()
data["name"] = self.name
return data
def __init__(self, id, name, ws_subject, size, pos):
self.id = id
self.ws_subject = ws_subject
self.name = name
self.size = BehaviorSubject(size)
self.pos = BehaviorSubject(pos)
self.dir = BehaviorSubject(None)
ws_subject.to_observable().map(lambda d: json.loads(d.data)).filter(lambda m: m["t"] == "d").subscribe(self.dir)
self.dir.subscribe(lambda d: print(d))
def _start(self, app_context, **kwargs):
self.qty = self.get_stg_config_value("qty", 1)
self.threshold = self.get_stg_config_value("threshold", 1)
self.xiv = app_context.ref_data_mgr.get_inst('XIV', 'SMART')
self.vxx = app_context.ref_data_mgr.get_inst('VXX', 'SMART')
self.vxv = app_context.ref_data_mgr.get_inst('VXV', 'SMART')
self.vix = app_context.ref_data_mgr.get_inst('VIX', 'SMART') # TODO: Review index
instruments = [self.vxx, self.xiv, self.vix]
self.vix_bar = app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % self.vix.get_symbol()) # non tradable
self.vxv_bar = app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % self.vxv.get_symbol()) # non tradable
self.xiv_bar = app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % self.xiv.get_symbol())
self.vxx_bar = app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % self.vxx.get_symbol())
self.vix_strm = BehaviorSubject(0)
self.vxv_strm = BehaviorSubject(0)
self.ratio_strm = rx.Observable \
.zip(self.vix_strm, self.vxv_strm, lambda x, y: x / y) \
.subscribe(self.on_ratio)
super(VixVxvRatio, self)._start(app_context, **kwargs)
def _start(self, app_context, **kwargs):
self.ou_params = self.get_stg_config_value("ou_params", 1)
self.gamma = self.get_stg_config_value("gamma", 1)
self.bar_0 = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" %
self.app_context.app_config.instrument_ids[0])
self.bar_0.start(app_context)
self.bar_1 = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" %
self.app_context.app_config.instrument_ids[1])
self.bar_1.start(app_context)
self.instruments = self.app_context.app_config.instrument_ids
self.log_spot_0 = BehaviorSubject(0)
self.log_spot_1 = BehaviorSubject(0)
self.spread_stream = rx.Observable \
.zip(self.log_spot_0, self.log_spot_1, lambda x, y: [x, y, x - y]) \
.subscribe(self.rebalance)
super(PairTradingWithOUSpread, self)._start(app_context, **kwargs)
class SerialHelper:
ser = serial.Serial('/dev/ttyACM0', 115200)
def __init__(self):
self.messages = BehaviorSubject("")
self.read()
# def read(self, recieved):
# while self.ser.in_waiting:
# recieved(self.ser.readline())
#
def read(self):
self.threadObj = threading.Thread(target=worker, args=(self,))
self.threadObj.start()
def setMessage(self, message):
self.messages.on_next(message)
def write(self, message):
self.ser.write(message);
def __init__(self, new_players, exiting_players):
self._new_players = []
self._players = []
self._removed_players = []
new_players.subscribe(self.add_player)
exiting_players.subscribe(self.remove_player)
# streams api
self.new_players_broadcast = Subject()
self.removed_players_broadcast = Subject()
self.collisions = Subject()
self.players = BehaviorSubject([])
self.players_count = self.players \
.map(lambda ps: len(ps))
def __init__(self, parent=None):
super().__init__(parent)
self._commandSubject = BehaviorSubject(kine.Command.arc(0, 0))
def subject_factory(scheduler):
return BehaviorSubject(initial_value)
class Integrator:
def __init__(self, new_players, exiting_players):
self._new_players = []
self._players = []
self._removed_players = []
new_players.subscribe(self.add_player)
exiting_players.subscribe(self.remove_player)
# streams api
self.new_players_broadcast = Subject()
self.removed_players_broadcast = Subject()
self.collisions = Subject()
self.players = BehaviorSubject([])
self.players_count = self.players \
.map(lambda ps: len(ps))
def add_player(self, player):
self._new_players.append(player)
def remove_player(self, player):
self._removed_players.append(player)
def broadcast(self, message):
for p in self._players:
p.ws_subject.on_next(message)
def update(self, dt):
if len(self._new_players):
self._do_add_players()
self._integrate_speed(dt)
self._collide()
if len(self._removed_players):
self._do_remove_players()
def _collide(self):
for a, b in combinations(self._players, 2):
posa = a.pos.value
posb = b.pos.value
sizea = a.size.value
sizeb = b.size.value
distance = math.sqrt((posa["x"] - posb["x"]) ** 2 + (posa["y"] - posb["y"]) ** 2)
if distance < sizea + sizeb:
self.collisions.on_next((a, b))
def _integrate_speed(self, dt):
for player in self._players:
direction = player.dir.value
if direction is not None \
and direction["x"] != 0 \
and direction["y"] != 0:
position = player.pos.value
speed = 300 * dt
result = {
"x": position["x"] + direction["x"] * speed,
"y": position["y"] + direction["y"] * speed
}
player.pos.on_next(result)
def _do_add_players(self):
self._players.extend(self._new_players)
self.new_players_broadcast.on_next(
(self._players, self._new_players.copy()))
self._new_players.clear()
self.players.on_next(self._players)
def _do_remove_players(self):
for p in self._removed_players:
self._players.remove(p)
self.removed_players_broadcast.on_next(
(self._players, self._removed_players.copy()))
self._removed_players.clear()
self.players.on_next(self._players)
class ServerState():
"""description of class"""
__metaclass__ = Singleton
DEVICE_MODE = 'HBS'
MAC_ADDRESS_HSU = ''
LINK_STATE = ''
NUM_OF_LINKS = 0
monitor_data = None
SwCapabilities = None
def setSwCapabilities(self, s):
self.SwCapabilities = s
def getSwCapabilities(self):
self.SwCapabilities
def setMonitor(self, d):
self.monitor_data = d
def getMonitor(self):
return self.monitor_data
#this stream will hold all hsu devices on the sector of the HBS
hsuDevicesSub = BehaviorSubject(None)
registeredHsusSub = BehaviorSubject({})
#this stream will hold the local monitor of the device
monitorSub = BehaviorSubject(None)
#this stream will hold the local recentEventSub of the device
recentEventSub = BehaviorSubject(None)
#api data stream
systemSub = BehaviorSubject(None)
deviceModeSub = BehaviorSubject(None)
radioSub = BehaviorSubject(None)
networkSub = BehaviorSubject(None)
portsSub = BehaviorSubject(None)
wifiSub = BehaviorSubject(None)
#events streams
onFlushingLogsSub = BehaviorSubject(None)
#dispath method will use to update data on the observer
def dispath(self, observer_name, payload):
if observer_name == consts.MONITOR_SUB:
self.monitorSub.on_next(payload)
return payload
elif observer_name == consts.SYSTEM_SUB:
self.systemSub.on_next(payload)
return payload
#elif observer_name == consts.DEVICE_MODE_SUB:
# self.deviceModeSub.on_next(payload)
# return payload
elif observer_name == consts.RADIO_SUB:
self.radioSub.on_next(payload)
return payload
elif observer_name == consts.PORT_SUB:
self.portsSub.on_next(payload)
return payload
elif observer_name == consts.WIFI_SUB:
self.wifiSub.on_next(payload)
return payload
elif observer_name == consts.HSU_DEVICES_SUB:
self.hsuDevicesSub.on_next(payload)
return payload
elif observer_name == consts.REGISTERED_HSU_SUB:
self.registeredHsusSub.on_next(payload)
return payload
elif observer_name == consts.FLUSH_LOG_SUB:
self.onFlushingLogsSub.on_next(payload)
return payload
else:
print observer_name + ' is not recognize observable.'
return None
#select method will use to get observerable
def select(self, observer_name):
if observer_name == consts.MONITOR_SUB:
return self.monitorSub
elif observer_name == 'network':
return self.networkSub
elif observer_name == consts.SYSTEM_SUB:
return self.systemSub
#elif observer_name == consts.DEVICE_MODE_SUB:
# return self.deviceModeSub
elif observer_name == consts.RADIO_SUB:
return self.radioSub
elif observer_name == consts.PORT_SUB:
return self.portsSub
elif observer_name == consts.WIFI_SUB:
return self.wifiSub
elif observer_name == consts.NETWORK_SUB:
return self.wifiSub
elif observer_name == consts.HSU_DEVICES_SUB:
return self.hsuDevicesSub
elif observer_name == consts.REGISTERED_HSU_SUB:
return self.registeredHsusSub
elif observer_name == consts.FLUSH_LOG_SUB:
return self.onFlushingLogsSub
else:
print observer_name + ' is not recognize observable.'
return None
def getDeviceMode(self):
return self.DEVICE_MODE
def setDeviceMode(self, mode):
self.DEVICE_MODE = mode
def getMacAdressHsu(self):
return self.MAC_ADDRESS_HSU
def setMacAdressHsu(self, address):
self.MAC_ADDRESS_HSU = address
@classmethod
def setNumOfLinks(self, number):
self.NUM_OF_LINKS = number
@classmethod
def getNumOfLinks(self):
return self.NUM_OF_LINKS
def action1(scheduler, state=None):
subject[0] = BehaviorSubject(100)
def __init__(self):
self.messages = BehaviorSubject("")
self.read()
class PairTradingWithOUSpread(Strategy):
"""
This is the baby version that assume the asset we are trading paris
that the spread follows Ornstein-Uhlenbeck mean reverting process with known parameters in advance
in reality this is not true
So for more advanced version the strategy itself should able to call statistical inference logic to
get the appreciation rate and volatility of the asset
So now this class is used as testing purpose
"""
def __init__(self, stg_id=None, stg_configs=None):
"""
:param stg_id:
:param portfolio:
:param instruments:
:param ou_params: a dictionary with k, theta, eta as keys
:param gamma: risk preference
:param trading_config:
:return:
"""
super(PairTradingWithOUSpread, self).__init__(stg_id=stg_id, stg_configs=stg_configs)
self.buy_order = None
def _start(self, app_context, **kwargs):
self.ou_params = self.get_stg_config_value("ou_params", 1)
self.gamma = self.get_stg_config_value("gamma", 1)
self.bar_0 = app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % self.app_context.app_config.instrument_ids[0])
self.bar_0.start(app_context)
self.bar_1 = app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % self.app_context.app_config.instrument_ids[1])
self.bar_1.start(app_context)
self.instruments = self.app_context.app_config.instrument_ids
self.log_spot_0 = BehaviorSubject(0)
self.log_spot_1 = BehaviorSubject(0)
self.spread_stream = rx.Observable \
.zip(self.log_spot_0, self.log_spot_1, lambda x, y: [x, y, x - y]) \
.subscribe(self.rebalance)
super(PairTradingWithOUSpread, self)._start(app_context, **kwargs)
def _stop(self):
super(PairTradingWithOUSpread, self)._stop()
def on_bar(self, bar):
# logger.info("%s,%s,%.2f" % (bar.inst_id, bar.timestamp, bar.close))
if bar.inst_id == self.instruments[0]:
self.log_spot_0.on_next(math.log(bar.close))
elif bar.inst_id == self.instruments[1]:
self.log_spot_1.on_next(math.log(bar.close))
def rebalance(self, spread_triple):
if spread_triple[0] == 0:
return
# we have to rebalance on each bar
k = self.ou_params['k']
eta = self.ou_params['eta']
theta = self.ou_params['theta']
spread = spread_triple[2]
weight = k * (spread - theta) / eta ** 2
portfolio = self.get_portfolio()
allocation_0 = -portfolio.total_equity * weight
allocation_1 = portfolio.total_equity * weight
# TODO: need to check if the portoflio.positions is empty
delta_0 = allocation_0
delta_1 = allocation_1
if self.instruments[0] in portfolio.positions.keys():
delta_0 = allocation_0 - portfolio.positions[self.instruments[0]].current_value()
if self.instruments[1] in portfolio.positions.keys():
delta_1 = allocation_1 - portfolio.positions[self.instruments[1]].current_value()
qty = abs(delta_0) / spread_triple[0] # assume no lot size here
if delta_0 > 0:
self.market_order(inst_id=self.instruments[0], action=OrdAction.BUY, qty=qty)
else:
self.market_order(inst_id=self.instruments[0], action=OrdAction.SELL, qty=qty)
qty = abs(delta_1) / spread_triple[1] # assume no lot size here
if delta_1 > 0:
self.market_order(inst_id=self.instruments[1], action=OrdAction.BUY, qty=qty)
else:
self.market_order(inst_id=self.instruments[1], action=OrdAction.SELL, qty=qty)
def action1(scheduler, state=None):
nonlocal subject
subject = BehaviorSubject(100)
class PairTradingWithOUSpread(Strategy):
"""
This is the baby version that assume the asset we are trading paris
that the spread follows Ornstein-Uhlenbeck mean reverting process with known parameters in advance
in reality this is not true
So for more advanced version the strategy itself should able to call statistical inference logic to
get the appreciation rate and volatility of the asset
So now this class is used as testing purpose
"""
def __init__(self, stg_id: str, stg_cls: str, state: StrategyState = None):
super(PairTradingWithOUSpread, self).__init__(stg_id=stg_id,
stg_cls=stg_cls,
state=state)
self.buy_order = None
def _start(self, app_context: Context) -> None:
self.ou_params = self._get_stg_config("ou_params", default=1)
self.gamma = self._get_stg_config("gamma", default=1)
self.instruments = app_context.config.get_app_config("instrumentIds")
self.bar_0 = self.app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.instruments[0])
self.bar_1 = self.app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.instruments[1])
self.bar_0.start(app_context)
self.bar_1.start(app_context)
self.log_spot_0 = BehaviorSubject(0)
self.log_spot_1 = BehaviorSubject(0)
self.spread_stream = rx.Observable \
.zip(self.log_spot_0, self.log_spot_1, lambda x, y: [x, y, x - y]) \
.subscribe(self.rebalance)
super(PairTradingWithOUSpread, self)._start(app_context)
def _stop(self):
super(PairTradingWithOUSpread, self)._stop()
def on_bar(self, bar):
# logger.info("%s,%s,%.2f" % (bar.inst_id, bar.timestamp, bar.close))
if bar.inst_id == self.instruments[0]:
self.log_spot_0.on_next(math.log(bar.close))
elif bar.inst_id == self.instruments[1]:
self.log_spot_1.on_next(math.log(bar.close))
def rebalance(self, spread_triple):
if spread_triple[0] == 0:
return
# we have to rebalance on each bar
k = self.ou_params['k']
eta = self.ou_params['eta']
theta = self.ou_params['theta']
spread = spread_triple[2]
weight = k * (spread - theta) / eta**2
portfolio = self.get_portfolio()
allocation_0 = -portfolio.total_equity * weight
allocation_1 = portfolio.total_equity * weight
# TODO: need to check if the portoflio.positions is empty
delta_0 = allocation_0
delta_1 = allocation_1
if self.instruments[0] in portfolio.positions.keys():
delta_0 = allocation_0 - portfolio.positions[
self.instruments[0]].current_value()
if self.instruments[1] in portfolio.positions.keys():
delta_1 = allocation_1 - portfolio.positions[
self.instruments[1]].current_value()
qty = abs(delta_0) / spread_triple[0] # assume no lot size here
if delta_0 > 0:
self.market_order(inst_id=self.instruments[0], action=Buy, qty=qty)
else:
self.market_order(inst_id=self.instruments[0],
action=Sell,
qty=qty)
qty = abs(delta_1) / spread_triple[1] # assume no lot size here
if delta_1 > 0:
self.market_order(inst_id=self.instruments[1], action=Buy, qty=qty)
else:
self.market_order(inst_id=self.instruments[1],
action=Sell,
qty=qty)
def rxValue(self, subj, pred, default): # -> BehaviorSubject:
value = BehaviorSubject(default)
self._rxValues[value] = (subj, pred, default)
self._rxUpdate(subj, pred, default, value)
return value
def __init__(self):
self.data: Subject = Subject()
self.threats: BehaviorSubject = BehaviorSubject(0)
class Backend(QObject):
def __init__(self, parent=None):
super().__init__(parent)
self._commandSubject = BehaviorSubject(kine.Command.arc(0, 0))
@pyqtSlot()
def forward(self):
self._commandSubject.on_next(kine.Command.arc(0.5, 0.0))
@pyqtSlot()
def backward(self):
self._commandSubject.on_next(kine.Command.arc(-0.5, 0.0))
@pyqtSlot()
def left(self):
self._commandSubject.on_next(kine.Command.arc(0.5, 0.5))
@pyqtSlot()
def right(self):
self._commandSubject.on_next(kine.Command.arc(0.5, -0.5))
@pyqtSlot()
def stop(self):
self._commandSubject.on_next(kine.Command.arc(0.0, 0.0))
@pyqtSlot()
def pivotLeft(self):
self._commandSubject.on_next(
kine.Command(velocity=0, angularVelocity=0.3))
@pyqtSlot()
def pivotRight(self):
self._commandSubject.on_next(
kine.Command(velocity=0, angularVelocity=-0.3))
@pyqtSlot()
def toOrigin(self):
self._commandSubject.on_next(
kine.Command.arc_to(self.robot.pose, Vec2.zero(), 0.5))
@property
def commands(self):
return self._commandSubject
def subject_selector():
return BehaviorSubject(initial_value)