def __init__(self, time_series=None, inputs=None, input_keys=None, desc="Bollinger Bands", length=14, num_std=3):
super(SMA, self).__init__(time_series=time_series, inputs=inputs, input_keys=input_keys, desc=desc,
length=length, num_std=num_std)
self.length = self.get_int_config("length", 14)
self.num_std = self.get_int_config("num_std", 3)
self.__sma = SMA(inputs=inputs, length=self.length)
self.__std_dev = STD(inputs=inputs, length=self.length)
def __init__(self, input=None, length=14, desc="Average True Range"):
self.length = int(length)
self.__prev_close = None
self.__value = None
self.__average = SMA(input, self.length)
super(ATR,
self).__init__(Indicator.get_name(ATR.__name__, input, length),
input, ['high', 'low', 'close'], desc)
def test_name(self):
bar = self.app_context.inst_data_mgr.get_series("bar")
sma = SMA(inputs=bar, input_keys='close', length=3)
self.assertEquals("SMA(bar[close],length=3)", sma.name)
sma2 = SMA(inputs=sma, input_keys='value', length=10)
self.assertEquals("SMA(SMA(bar[close],length=3)[value],length=10)",
sma2.name)
def __init__(self, time_series=None, inputs=None, input_keys=['high', 'low', 'close'], desc="Average True Range",
length=14):
super(ATR, self).__init__(time_series=time_series, inputs=inputs, input_keys=input_keys, desc=desc,
length=length)
self.length = self.get_int_config("length", 14)
self.__prev_close = None
self.__value = None
self.__average = SMA(inputs=inputs, length=self.length)
def _start(self, app_context, **kwargs):
self.qty = self.get_stg_config_value("qty", 1)
self.bar = app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % self.app_context.app_config.instrument_ids[0])
self.sma_fast = SMA(self.bar, 'close', 10)
self.sma_fast.start(app_context)
self.sma_slow = SMA(self.bar, 'close', 25)
self.sma_slow.start(app_context)
super(SMAStrategy, self)._start(app_context, **kwargs)
def __init__(self,
input=None,
input_key=None,
length=14,
num_std=3,
desc="Bollinger Bands"):
self.length = int(length)
self.num_std = int(num_std)
self.__sma = SMA(input, self.length)
self.__std_dev = STD(input, self.length)
super(BB, self).__init__(
Indicator.get_name(BB.__name__, input, input_key, length, num_std),
input, input_key, desc)
def _start(self, app_context: Context) -> None:
self.instruments = app_context.config.get_app_config("instrumentIds")
self.qty = self._get_stg_config("qty", default=1)
self.bar = self.app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.instruments[0])
self.sma_fast = SMA(self.bar, 'close', 10)
self.sma_fast.start(app_context)
self.sma_slow = SMA(self.bar, 'close', 25)
self.sma_slow.start(app_context)
super(SMAStrategy, self)._start(app_context)
def _start(self, app_context, **kwargs):
self.qty = self.get_stg_config_value("qty", 1)
self.bar = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" %
self.app_context.app_config.instrument_ids[0])
self.sma_fast = SMA(self.bar, 'close', 10)
self.sma_fast.start(app_context)
self.sma_slow = SMA(self.bar, 'close', 25)
self.sma_slow.start(app_context)
super(SMAStrategy, self)._start(app_context, **kwargs)
def __init__(self, input=None, input_key=None, length=14, num_std=3, desc="Bollinger Bands"):
self.length = int(length)
self.num_std = int(num_std)
self.__sma = SMA(input, self.length)
self.__std_dev = STD(input, self.length)
super(BB, self).__init__(Indicator.get_name(BB.__name__, input, input_key, length, num_std), input, input_key,
desc)
def __init__(self, input=None, length=14, desc="Average True Range"):
self.length = int(length)
self.__prev_close = None
self.__value = None
self.__average = SMA(input, self.length)
super(ATR, self).__init__(Indicator.get_name(ATR.__name__, input, length), input, ['high', 'low', 'close'],
desc)
def test_nan_before_size(self):
bar = self.app_context.inst_data_mgr.get_series("bar")
bar.start(self.app_context)
sma = SMA(inputs=bar, input_keys='close', length=3)
sma.start(self.app_context)
t1 = 1
t2 = t1 + 3
t3 = t2 + 3
bar.add(timestamp=t1, data={"close": 2.0, "open": 0})
self.assertEquals([{'value': np.nan}], sma.get_data())
bar.add(timestamp=t2, data={"close": 2.4, "open": 1.4})
self.assertEquals([{
'value': np.nan
}, {
'value': np.nan
}], sma.get_data())
bar.add(timestamp=t3, data={"close": 2.8, "open": 1.8})
self.assertEquals([{
'value': np.nan
}, {
'value': np.nan
}, {
'value': 2.4
}], sma.get_data())
def test_indicator(self, name, serializer):
self.app_context = ApplicationContext()
bar = self.app_context.inst_data_mgr.get_series("bar")
sma = SMA(bar.name, 'close', 1, missing_value=0)
bar.start(self.app_context)
sma.start(self.app_context)
t1 = datetime.datetime.now()
t2 = t1 + datetime.timedelta(0, 3)
t3 = t2 + datetime.timedelta(0, 3)
bar.add({"timestamp": t1, "close": 2.0, "open": 0})
bar.add({"timestamp": t2, "close": 2.4, "open": 1.4})
bar.add({"timestamp": t3, "close": 2.8, "open": 1.8})
SerializerTest.ser_deser(name, serializer, sma)
class ATR(Indicator):
__slots__ = (
'length',
'__prev_close',
'__value',
'__average',
)
def __init__(self, input=None, length=14, desc="Average True Range"):
self.length = int(length)
self.__prev_close = None
self.__value = None
self.__average = SMA(input, self.length)
super(ATR,
self).__init__(Indicator.get_name(ATR.__name__, input, length),
input, ['high', 'low', 'close'], desc)
def on_update(self, data):
sma_input = {}
sma_input['timestamp'] = data['timestamp']
high = data['high']
low = data['low']
close = data['close']
if self.__prev_close is None:
tr = high - low
else:
tr1 = high - low
tr2 = abs(high - self.__prev_close)
tr3 = abs(low - self.__prev_close)
tr = max(max(tr1, tr2), tr3)
self.__prev_close = close
sma_input[Indicator.VALUE] = tr
self.__average.add(sma_input)
result = {}
result['timestamp'] = data['timestamp']
result[Indicator.VALUE] = self.__average.now(Indicator.VALUE)
self.add(result)
def test_nan_before_size(self):
bar = self.app_context.inst_data_mgr.get_series("bar")
bar.start(self.app_context)
sma = SMA(bar, 'close', 3)
sma.start(self.app_context)
t1 = datetime.datetime.now()
t2 = t1 + datetime.timedelta(0, 3)
t3 = t2 + datetime.timedelta(0, 3)
bar.add({"timestamp": t1, "close": 2.0, "open": 0})
self.assertEquals([{"name": "'SMA('bar',close,3)'",
"timestamp": t1, 'value': np.nan}],
sma.get_data())
bar.add({"timestamp": t2, "close": 2.4, "open": 1.4})
self.assertEquals([{"name": "'SMA('bar',close,3)'", "timestamp": t1, 'value': np.nan},
{"name": "'SMA('bar',close,3)'", "timestamp": t2, 'value': np.nan}],
sma.get_data())
bar.add({"timestamp": t3, "close": 2.8, "open": 1.8})
self.assertEquals([{"name": "'SMA('bar',close,3)'", "timestamp": t1, 'value': np.nan},
{"name": "'SMA('bar',close,3)'", "timestamp": t2, 'value': np.nan},
{"name": "'SMA('bar',close,3)'", "timestamp": t3, 'value': 2.4}],
sma.get_data())
class ATR(Indicator):
__slots__ = (
'length',
'__prev_close',
'__value',
'__average',
)
def __init__(self, input=None, length=14, desc="Average True Range"):
self.length = int(length)
self.__prev_close = None
self.__value = None
self.__average = SMA(input, self.length)
super(ATR, self).__init__(Indicator.get_name(ATR.__name__, input, length), input, ['high', 'low', 'close'],
desc)
def on_update(self, data):
sma_input = {}
sma_input['timestamp'] = data['timestamp']
high = data['high']
low = data['low']
close = data['close']
if self.__prev_close is None:
tr = high - low
else:
tr1 = high - low
tr2 = abs(high - self.__prev_close)
tr3 = abs(low - self.__prev_close)
tr = max(max(tr1, tr2), tr3)
self.__prev_close = close
sma_input[Indicator.VALUE] = tr
self.__average.add(sma_input)
result = {}
result['timestamp'] = data['timestamp']
result[Indicator.VALUE] = self.__average.now(Indicator.VALUE)
self.add(result)
class ATR(Indicator):
__slots__ = (
'length',
'__prev_close',
'__value',
'__average',
)
def __init__(self, time_series=None, inputs=None, input_keys=['high', 'low', 'close'], desc="Average True Range",
length=14):
super(ATR, self).__init__(time_series=time_series, inputs=inputs, input_keys=input_keys, desc=desc,
length=length)
self.length = self.get_int_config("length", 14)
self.__prev_close = None
self.__value = None
self.__average = SMA(inputs=inputs, length=self.length)
def _process_update(self, source: str, timestamp: int, data: Dict[str, float]):
sma_input = {}
high = data['high']
low = data['low']
close = data['close']
if self.__prev_close is None:
tr = high - low
else:
tr1 = high - low
tr2 = abs(high - self.__prev_close)
tr3 = abs(low - self.__prev_close)
tr = max(max(tr1, tr2), tr3)
self.__prev_close = close
sma_input[Indicator.VALUE] = tr
self.__average.add(timestamp=timestamp, data=sma_input)
result = {}
# result['timestamp'] = data['timestamp']
result[Indicator.VALUE] = self.__average.now(Indicator.VALUE)
self.add(timestamp=timestamp, data=result)
def test_indicator(self, name, datastore):
self.app_context = ApplicationContext()
bar = self.app_context.inst_data_mgr.get_series("bar")
sma = SMA(bar.name, 'close', 1, missing_value=0)
t1 = datetime.now()
t2 = t1 + timedelta(0, 3)
t3 = t2 + timedelta(0, 3)
bar.add({"timestamp": t1, "close": 2.0, "open": 0})
bar.add({"timestamp": t2, "close": 2.4, "open": 1.4})
bar.add({"timestamp": t3, "close": 2.8, "open": 1.8})
DataStoreTest.save_load(name, sma, datastore,
datastore.save_time_series, 'time_series')
class BB(Indicator):
UPPER = 'uppper'
LOWER = 'lower'
__slots__ = (
'length',
'num_std'
'__sma',
'__std_dev',
)
def __init__(self,
input=None,
input_key=None,
length=14,
num_std=3,
desc="Bollinger Bands"):
self.length = int(length)
self.num_std = int(num_std)
self.__sma = SMA(input, self.length)
self.__std_dev = STD(input, self.length)
super(BB, self).__init__(
Indicator.get_name(BB.__name__, input, input_key, length, num_std),
input, input_key, desc)
def on_update(self, data):
result = {}
result['timestamp'] = data['timestamp']
sma = self.__sma.now(self.input_keys[0])
std = self.__std_dev.now(self.input_keys[0])
if not np.isnan(sma):
upper = sma + std * self.num_std
lower = sma - std * self.num_std
result[BB.UPPER] = upper
result[BB.LOWER] = lower
result[Indicator.VALUE] = sma
else:
result[BB.UPPER] = np.nan
result[BB.LOWER] = np.nan
result[Indicator.VALUE] = np.nan
self.add(result)
class BB(Indicator):
UPPER = 'uppper'
LOWER = 'lower'
__slots__ = (
'length',
'num_std'
'__sma',
'__std_dev',
)
def __init__(self, input=None, input_key=None, length=14, num_std=3, desc="Bollinger Bands"):
self.length = int(length)
self.num_std = int(num_std)
self.__sma = SMA(input, self.length)
self.__std_dev = STD(input, self.length)
super(BB, self).__init__(Indicator.get_name(BB.__name__, input, input_key, length, num_std), input, input_key,
desc)
def on_update(self, data):
result = {}
result['timestamp'] = data['timestamp']
sma = self.__sma.now(self.input_keys[0])
std = self.__std_dev.now(self.input_keys[0])
if not np.isnan(sma):
upper = sma + std * self.num_std
lower = sma - std * self.num_std
result[BB.UPPER] = upper
result[BB.LOWER] = lower
result[Indicator.VALUE] = sma
else:
result[BB.UPPER] = np.nan
result[BB.LOWER] = np.nan
result[Indicator.VALUE] = np.nan
self.add(result)
class BB(Indicator):
UPPER = 'uppper'
LOWER = 'lower'
__slots__ = (
'length',
'num_std'
'__sma',
'__std_dev',
)
def __init__(self, time_series=None, inputs=None, input_keys=None, desc="Bollinger Bands", length=14, num_std=3):
super(SMA, self).__init__(time_series=time_series, inputs=inputs, input_keys=input_keys, desc=desc,
length=length, num_std=num_std)
self.length = self.get_int_config("length", 14)
self.num_std = self.get_int_config("num_std", 3)
self.__sma = SMA(inputs=inputs, length=self.length)
self.__std_dev = STD(inputs=inputs, length=self.length)
def _process_update(self, source: str, timestamp: int, data: Dict[str, float]):
result = {}
sma = self.__sma.now(self.input_keys[0])
std = self.__std_dev.now(self.input_keys[0])
if not np.isnan(sma):
upper = sma + std * self.num_std
lower = sma - std * self.num_std
result[BB.UPPER] = upper
result[BB.LOWER] = lower
result[Indicator.VALUE] = sma
else:
result[BB.UPPER] = np.nan
result[BB.LOWER] = np.nan
result[Indicator.VALUE] = np.nan
self.add(timestamp=timestamp, data=result)
def test_save_and_load_indicator(self):
app_context = self.new_app_context()
bar = app_context.inst_data_mgr.get_series("bar")
bar.start(app_context)
sma = SMA(bar, input_key='close', length=3)
sma.start(app_context)
t1 = 0
t2 = t1 + 1
t3 = t2 + 1
t4 = t3 + 1
t5 = t4 + 1
bar.add({"timestamp": t1, "close": 2.0, "open": 0})
self.assertTrue(math.isnan(sma.now('value')))
bar.add({"timestamp": t2, "close": 2.4, "open": 1.4})
self.assertTrue(math.isnan(sma.now('value')))
bar.add({"timestamp": t3, "close": 2.8, "open": 1.8})
self.assertEquals(2.4, sma.now('value'))
app_context.stop()
## restart...:
app_context = self.new_app_context()
bar_new = app_context.inst_data_mgr.get_series("bar")
bar_new.start(app_context)
sma_new = app_context.inst_data_mgr.get_series(sma.id())
sma_new.start(app_context)
bar_new.add({"timestamp": t4, "close": 3.2, "open": 2.2})
self.assertEquals(2.8, sma_new.now('value'))
bar_new.add({"timestamp": t5, "close": 3.6, "open": 2.6})
self.assertEquals(3.2, sma_new.now('value'))
self.assertTrue(math.isnan(sma_new.get_by_idx(0, 'value')))
self.assertTrue(math.isnan(sma_new.get_by_idx(1, 'value')))
self.assertEquals(2.4, sma_new.get_by_idx(2, 'value'))
self.assertEquals(2.8, sma_new.get_by_idx(3, 'value'))
self.assertEquals(3.2, sma_new.get_by_idx(4, 'value'))
self.assertTrue(math.isnan(sma_new.get_by_time(t1, 'value')))
self.assertTrue(math.isnan(sma_new.get_by_time(t2, 'value')))
self.assertEquals(2.4, sma_new.get_by_time(t3, 'value'))
self.assertEquals(2.8, sma_new.get_by_time(t4, 'value'))
self.assertTrue(math.isnan(sma_new.get_by_time(t1, 'value')))
self.assertTrue(math.isnan(sma_new.get_by_time(t1, 'value')))
old_bar_dict = bar.get_data_dict(['close'])
old_sma_dict = sma.get_data_dict(['value'])
new_bar_dict = bar_new.get_data_dict(['close'])
new_sma_dict = sma_new.get_data_dict(['value'])
self.assertEquals(3, len(old_bar_dict))
self.assertEquals(3, len(old_sma_dict))
self.assertEquals(5, len(new_bar_dict))
self.assertEquals(5, len(new_sma_dict))
self.assertEquals(2.0, new_bar_dict['0'])
self.assertTrue(math.isnan(new_sma_dict['0']))
self.assertEquals(2.4, new_bar_dict['1'])
self.assertTrue(math.isnan(new_sma_dict['1']))
self.assertEquals(2.8, new_bar_dict['2'])
self.assertEquals(2.4, new_sma_dict['2'])
self.assertEquals(3.2, new_bar_dict['3'])
self.assertEquals(2.8, new_sma_dict['3'])
self.assertEquals(3.6, new_bar_dict['4'])
self.assertEquals(3.2, new_sma_dict['4'])
db = app_context.get_ref_data_store()
app_context.stop()
db.remove_database()
class SMAStrategy(Strategy):
def __init__(self, stg_id=None, stg_configs=None):
super(SMAStrategy, self).__init__(stg_id=stg_id, stg_configs=stg_configs)
self.buy_order = None
def _start(self, app_context, **kwargs):
self.qty = self.get_stg_config_value("qty", 1)
self.bar = app_context.inst_data_mgr.get_series("Bar.%s.Time.86400" % self.app_context.app_config.instrument_ids[0])
self.sma_fast = SMA(self.bar, 'close', 10)
self.sma_fast.start(app_context)
self.sma_slow = SMA(self.bar, 'close', 25)
self.sma_slow.start(app_context)
super(SMAStrategy, self)._start(app_context, **kwargs)
def _stop(self):
super(SMAStrategy, self)._stop()
def on_bar(self, bar):
if self.buy_order is None and self.sma_fast.now('value') > self.sma_slow.now('value'):
self.buy_order = self.market_order(inst_id=bar.inst_id, action=OrdAction.BUY, qty=self.qty)
logger.info("%s,B,%s,%s,%.2f,%.2f,%.2f" % (
bar.timestamp, self.buy_order.cl_id, self.buy_order.cl_ord_id, bar.close, self.sma_fast.now('value'),
self.sma_slow.now('value')))
elif self.buy_order is not None and self.sma_fast.now('value') < self.sma_slow.now('value'):
sell_order = self.market_order(inst_id=bar.inst_id, action=OrdAction.SELL, qty=self.qty)
logger.info("%s,S,%s,%s,%.2f,%.2f,%.2f" % (
bar.timestamp, sell_order.cl_id, sell_order.cl_ord_id, bar.close, self.sma_fast.now('value'),
self.sma_slow.now('value')))
self.buy_order = None
class SMAStrategy(Strategy):
def __init__(self, stg_id=None, stg_configs=None):
super(SMAStrategy, self).__init__(stg_id=stg_id,
stg_configs=stg_configs)
self.buy_order = None
def _start(self, app_context, **kwargs):
self.qty = self.get_stg_config_value("qty", 1)
self.bar = app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" %
self.app_context.app_config.instrument_ids[0])
self.sma_fast = SMA(self.bar, 'close', 10)
self.sma_fast.start(app_context)
self.sma_slow = SMA(self.bar, 'close', 25)
self.sma_slow.start(app_context)
super(SMAStrategy, self)._start(app_context, **kwargs)
def _stop(self):
super(SMAStrategy, self)._stop()
def on_bar(self, bar):
if self.buy_order is None and self.sma_fast.now(
'value') > self.sma_slow.now('value'):
self.buy_order = self.market_order(inst_id=bar.inst_id,
action=OrdAction.BUY,
qty=self.qty)
logger.info(
"%s,B,%s,%s,%.2f,%.2f,%.2f" %
(bar.timestamp, self.buy_order.cl_id, self.buy_order.cl_ord_id,
bar.close, self.sma_fast.now('value'),
self.sma_slow.now('value')))
elif self.buy_order is not None and self.sma_fast.now(
'value') < self.sma_slow.now('value'):
sell_order = self.market_order(inst_id=bar.inst_id,
action=OrdAction.SELL,
qty=self.qty)
logger.info("%s,S,%s,%s,%.2f,%.2f,%.2f" %
(bar.timestamp, sell_order.cl_id, sell_order.cl_ord_id,
bar.close, self.sma_fast.now('value'),
self.sma_slow.now('value')))
self.buy_order = None
def test_save_and_load_indicator(self):
app_context = self.create_app_context(
conf={
"Application": {
"createDBAtStart": True,
"deleteDBAtStop": False,
"persistenceMode": "RealTime"
}
})
app_context.start()
bar = app_context.inst_data_mgr.get_series("bar")
bar.start(app_context)
sma = SMA(inputs=bar, input_keys='close', length=3)
sma.start(app_context)
t1 = 0
t2 = t1 + 1
t3 = t2 + 1
t4 = t3 + 1
t5 = t4 + 1
bar.add(timestamp=t1, data={"close": 2.0, "open": 0})
self.assertTrue(math.isnan(sma.now('value')))
bar.add(timestamp=t2, data={"close": 2.4, "open": 1.4})
self.assertTrue(math.isnan(sma.now('value')))
bar.add(timestamp=t3, data={"close": 2.8, "open": 1.8})
self.assertEquals(2.4, sma.now('value'))
app_context.stop()
## restart...:
app_context = self.create_app_context(
conf={
"Application": {
"createDBAtStart": False,
"deleteDBAtStop": True,
"persistenceMode": "RealTime"
}
})
app_context.start()
bar_new = app_context.inst_data_mgr.get_series("bar")
bar_new.start(app_context)
sma_new = app_context.inst_data_mgr.get_series(sma.id())
sma_new.start(app_context)
bar_new.add(timestamp=t4, data={"close": 3.2, "open": 2.2})
self.assertEquals(2.8, sma_new.now('value'))
bar_new.add(timestamp=t5, data={"close": 3.6, "open": 2.6})
self.assertEquals(3.2, sma_new.now('value'))
self.assertTrue(math.isnan(sma_new.get_by_idx(0, 'value')))
self.assertTrue(math.isnan(sma_new.get_by_idx(1, 'value')))
self.assertEquals(2.4, sma_new.get_by_idx(2, 'value'))
self.assertEquals(2.8, sma_new.get_by_idx(3, 'value'))
self.assertEquals(3.2, sma_new.get_by_idx(4, 'value'))
self.assertTrue(math.isnan(sma_new.get_by_time(t1, 'value')))
self.assertTrue(math.isnan(sma_new.get_by_time(t2, 'value')))
self.assertEquals(2.4, sma_new.get_by_time(t3, 'value'))
self.assertEquals(2.8, sma_new.get_by_time(t4, 'value'))
self.assertTrue(math.isnan(sma_new.get_by_time(t1, 'value')))
self.assertTrue(math.isnan(sma_new.get_by_time(t1, 'value')))
old_bar_dict = bar.get_data_dict(['close'])
old_sma_dict = sma.get_data_dict(['value'])
new_bar_dict = bar_new.get_data_dict(['close'])
new_sma_dict = sma_new.get_data_dict(['value'])
self.assertEquals(3, len(old_bar_dict))
self.assertEquals(3, len(old_sma_dict))
self.assertEquals(5, len(new_bar_dict))
self.assertEquals(5, len(new_sma_dict))
self.assertEquals(2.0, new_bar_dict[t1])
self.assertTrue(math.isnan(new_sma_dict[t1]))
self.assertEquals(2.4, new_bar_dict[t2])
self.assertTrue(math.isnan(new_sma_dict[t2]))
self.assertEquals(2.8, new_bar_dict[t3])
self.assertEquals(2.4, new_sma_dict[t3])
self.assertEquals(3.2, new_bar_dict[t4])
self.assertEquals(2.8, new_sma_dict[t4])
self.assertEquals(3.6, new_bar_dict[t5])
self.assertEquals(3.2, new_sma_dict[t5])
db = app_context.get_data_store()
app_context.stop()
db.remove_database()
def test_empty_at_initialize(self):
close = self.app_context.inst_data_mgr.get_series("bar")
sma = SMA(close, 'close', 3)
self.assertEquals(0, len(sma.get_data()))
class SMAStrategy(Strategy):
def __init__(self, stg_id: str, stg_cls: str, state: StrategyState = None):
super(SMAStrategy, self).__init__(stg_id=stg_id, stg_cls=stg_cls, state=state)
self.buy_order = None
def _start(self, app_context: Context) -> None:
self.instruments = app_context.config.get_app_config("instrumentIds")
self.qty = self._get_stg_config("qty", default=1)
self.bar = self.app_context.inst_data_mgr.get_series(
"Bar.%s.Time.86400" % self.instruments[0])
self.sma_fast = SMA(self.bar, 'close', 10)
self.sma_fast.start(app_context)
self.sma_slow = SMA(self.bar, 'close', 25)
self.sma_slow.start(app_context)
super(SMAStrategy, self)._start(app_context)
def _stop(self):
super(SMAStrategy, self)._stop()
def on_bar(self, bar):
if self.buy_order is None and self.sma_fast.now('value') > self.sma_slow.now('value'):
self.buy_order = self.market_order(inst_id=bar.inst_id, action=Buy, qty=self.qty)
logger.info("%s,B,%s,%s,%.2f,%.2f,%.2f" % (
bar.timestamp, self.buy_order.cl_id, self.buy_order.cl_ord_id, bar.close, self.sma_fast.now('value'),
self.sma_slow.now('value')))
elif self.buy_order is not None and self.sma_fast.now('value') < self.sma_slow.now('value'):
sell_order = self.market_order(inst_id=bar.inst_id, action=Sell, qty=self.qty)
logger.info("%s,S,%s,%s,%.2f,%.2f,%.2f" % (
bar.timestamp, sell_order.cl_id, sell_order.cl_ord_id, bar.close, self.sma_fast.now('value'),
self.sma_slow.now('value')))
self.buy_order = None
def test_moving_average_calculation(self):
bar = self.app_context.inst_data_mgr.get_series("bar")
bar.start(self.app_context)
sma = SMA(bar, input_key='close', length=3)
sma.start(self.app_context)
t1 = datetime.datetime.now()
t2 = t1 + datetime.timedelta(0, 3)
t3 = t2 + datetime.timedelta(0, 3)
t4 = t3 + datetime.timedelta(0, 3)
t5 = t4 + datetime.timedelta(0, 3)
bar.add({"timestamp": t1, "close": 2.0, "open": 0})
self.assertTrue(math.isnan(sma.now('value')))
bar.add({"timestamp": t2, "close": 2.4, "open": 1.4})
self.assertTrue(math.isnan(sma.now('value')))
bar.add({"timestamp": t3, "close": 2.8, "open": 1.8})
self.assertEquals(2.4, sma.now('value'))
bar.add({"timestamp": t4, "close": 3.2, "open": 2.2})
self.assertEquals(2.8, sma.now('value'))
bar.add({"timestamp": t5, "close": 3.6, "open": 2.6})
self.assertEquals(3.2, sma.now('value'))
self.assertTrue(math.isnan(sma.get_by_idx(0, 'value')))
self.assertTrue(math.isnan(sma.get_by_idx(1, 'value')))
self.assertEquals(2.4, sma.get_by_idx(2, 'value'))
self.assertEquals(2.8, sma.get_by_idx(3, 'value'))
self.assertEquals(3.2, sma.get_by_idx(4, 'value'))
self.assertTrue(math.isnan(sma.get_by_time(t1, 'value')))
self.assertTrue(math.isnan(sma.get_by_time(t2, 'value')))
self.assertEquals(2.4, sma.get_by_time(t3, 'value'))
self.assertEquals(2.8, sma.get_by_time(t4, 'value'))
self.assertEquals(3.2, sma.get_by_time(t5, 'value'))
def test_get_full_cls_name(self):
from algotrader.technical.ma import SMA
ma = SMA(inputs="null")
self.assertEqual("algotrader.technical.ma.SMA", get_full_cls_name(ma))
self.assertEqual("algotrader.technical.ma.SMA", get_full_cls_name(SMA))
def test_empty_at_initialize(self):
close = self.app_context.inst_data_mgr.get_series("bar")
sma = SMA(inputs=close, input_keys='close', length=3)
self.assertEquals(0, len(sma.get_data()))
def test_moving_average_calculation(self):
bar = self.app_context.inst_data_mgr.get_series("bar")
bar.start(self.app_context)
sma = SMA(inputs=bar, input_keys='close', length=3)
sma.start(self.app_context)
t1 = 1
t2 = t1 + 3
t3 = t2 + 3
t4 = t3 + 3
t5 = t4 + 3
bar.add(data={"timestamp": t1, "close": 2.0, "open": 0})
self.assertTrue(math.isnan(sma.now('value')))
bar.add(data={"timestamp": t2, "close": 2.4, "open": 1.4})
self.assertTrue(math.isnan(sma.now('value')))
bar.add(data={"timestamp": t3, "close": 2.8, "open": 1.8})
self.assertEquals(2.4, sma.now('value'))
bar.add(data={"timestamp": t4, "close": 3.2, "open": 2.2})
self.assertEquals(2.8, sma.now('value'))
bar.add(data={"timestamp": t5, "close": 3.6, "open": 2.6})
self.assertEquals(3.2, sma.now('value'))
self.assertTrue(math.isnan(sma.get_by_idx(0, 'value')))
self.assertTrue(math.isnan(sma.get_by_idx(1, 'value')))
self.assertEquals(2.4, sma.get_by_idx(2, 'value'))
self.assertEquals(2.8, sma.get_by_idx(3, 'value'))
self.assertEquals(3.2, sma.get_by_idx(4, 'value'))
self.assertTrue(math.isnan(sma.get_by_time(t1, 'value')))
self.assertTrue(math.isnan(sma.get_by_time(t2, 'value')))
self.assertEquals(2.4, sma.get_by_time(t3, 'value'))
self.assertEquals(2.8, sma.get_by_time(t4, 'value'))
self.assertEquals(3.2, sma.get_by_time(t5, 'value'))
