class RSRS(IndicatorAbstract):
def __init__(self,
_N: int = 30,
_use_key: typing.List[str] = ['highprice', 'lowprice'],
_idx_key: str = 'time',
_ret_key: str = 'rsrs'):
"""
_N: sample length
"""
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key)
self.N = _N
self.high_buf = deque(maxlen=self.N)
self.low_buf = deque(maxlen=self.N)
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
self.high_buf.append(_data_struct[self.use_key[0]][-1])
self.low_buf.append(_data_struct[self.use_key[1]][-1])
if len(self.high_buf) >= self.N and len(self.low_buf) >= self.N:
x = np.arange(self.N)
high_beta = linregress(x, self.high_buf)[0]
low_beta = linregress(x, self.low_buf)[0]
self.data.addDict({
self.idx_key: index_value,
self.ret_key: high_beta - low_beta,
})
class SharpRate(IndicatorAbstract):
def __init__(
self,
_period: int,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'sharprate',
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key)
self.last_price = None
self.period = _period
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
price_value = _data_struct[self.use_key][0]
if self.last_price is not None:
chg_rate = price_value / self.last_price - 1
self.buf.append(chg_rate)
buf_std = statistics.pstdev(self.buf)
if buf_std != 0:
self.data.addDict({
self.idx_key:
index_value,
self.ret_key:
statistics.mean(self.buf) / buf_std,
})
self.last_price = price_value
class LogReturn(IndicatorAbstract):
def __init__(self,
_skip_period: int = 1,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'logreturn'):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key)
self.skip_period = _skip_period
self.buf = deque(maxlen=self.skip_period)
def _addOne(self, _data_struct: DataStruct):
index = _data_struct.index()[0]
value = _data_struct[self.use_key][0]
if len(self.buf) >= self.skip_period:
last_value = self.buf.popleft()
chg_rate = math.log(value / last_value)
self.data.addDict({
self.idx_key: index,
self.ret_key: chg_rate,
})
self.buf.append(value)
class EFF(IndicatorAbstract):
def __init__(self,
_period: int,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'eff'):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key)
self.period = _period
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
self.buf.append(_data_struct.getColumn(self.use_key)[0])
if len(self.buf) == self.period:
buf_list = list(self.buf)
tmp = 0.0
for a, b in zip(buf_list[:-1], buf_list[1:]):
tmp += abs(b - a)
self.data.addDict({
self.idx_key: index_value,
self.ret_key: (self.buf[-1] - self.buf[0]) / tmp,
})
class BIAS(IndicatorAbstract):
"""
rolling ma
"""
def __init__(self,
_period: int,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'bias'):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key)
self.period = _period
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
price = _data_struct[self.use_key][0]
self.buf.append(price)
price_mean = statistics.mean(self.buf)
self.data.addDict({
self.idx_key:
index_value,
self.ret_key: (price - price_mean) / price_mean * 100,
})
class EMA(IndicatorAbstract):
def __init__(
self, _period: int, _use_key: str='closeprice',
_idx_key: str = 'time', _ret_key: str = 'ema'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.period = _period
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
tmp_value = _data_struct[self.use_key][0]
if len(self) > 0:
last_ret = self.getLastData().toDict()[self.ret_key]
tmp_value = (tmp_value - last_ret) / self.period + last_ret
self.data.addDict({
self.idx_key: index_value,
self.ret_key: tmp_value,
})
class Kurtosis(IndicatorAbstract):
def __init__(
self, _period: int, _use_key: str = 'closeprice',
_idx_key: str = 'time', _ret_key: str = 'kurtosis'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.period = _period
self.last_price = None
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index = _data_struct.index()[0]
price = _data_struct[self.use_key][0]
if self.last_price:
self.buf.append(math.log(price / self.last_price))
if len(self.buf) >= self.period:
self.data.addDict({
self.idx_key: index,
self.ret_key: kurtosis(self.buf),
})
self.last_price = price
class ATRConstStop(StopIndicatorAbstract):
def __init__(
self,
_price_data: DataStruct,
_atr_data: DataStruct,
_stop_type: int,
_rate: float = 3,
_price_use_key: str = 'closeprice',
_atr_use_key: str = 'atr',
_idx_key: str = 'time',
_ret_key: str = 'stopprice',
):
super().__init__()
assert len(_price_data) == 1
assert len(_atr_data) == 1
self.stop_type = _stop_type
self.rate = _rate
self.price_use_key = _price_use_key
self.atr_use_key = _atr_use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key,
)
price_value = _price_data[self.price_use_key][0]
atr_value = _atr_data[self.atr_use_key][0]
if self.stop_type == SignalType.LONG:
self.stopprice = price_value - self.rate * atr_value
elif self.stop_type == SignalType.SHORT:
self.stopprice = price_value + self.rate * atr_value
else:
raise Exception('unknown type')
self._addOne(_price_data)
def _addOne(
self, _price_data: DataStruct,
):
self.data.addDict({
self.idx_key: _price_data.index()[0],
self.ret_key: self.stopprice,
})
def _isStop(self, _data_struct: DataStruct):
price = _data_struct.toDict()[self.price_use_key]
stop_price = self.data[self.ret_key][-1]
if self.stop_type == SignalType.LONG:
if price < stop_price:
self.is_stop = True
elif self.stop_type == SignalType.SHORT:
if price > stop_price:
self.is_stop = True
else:
raise Exception('unknown type')
class STD(IndicatorAbstract):
def __init__(
self, _period: int, _use_key: str = 'closeprice',
_idx_key: str = 'time', _ret_key: str = 'std'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.period = _period
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
self.buf.append(_data_struct.getColumn(self.use_key)[0])
self.data.addDict({
self.idx_key: index_value,
self.ret_key: statistics.pstdev(self.buf),
})
class Diff(IndicatorAbstract):
def __init__(self,
_use_key: str,
_init_value: float = None,
_idx_key: str = 'time',
_ret_key: str = 'diff'):
"""
:param _use_key:
:param _init_value: if init_value set, the first ret will be complated, else 0
:param _idx_key:
:param _ret_key:
"""
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key)
self.last_value = _init_value
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
cur_value = _data_struct[self.use_key][0]
if self.last_value is not None:
diff_value = cur_value - self.last_value
self.data.addDict({
self.idx_key: index_value,
self.ret_key: diff_value,
})
self.last_value = cur_value
class EFF(IndicatorAbstract):
def __init__(
self, _period: int, _use_key: str = 'closeprice',
_idx_key: str = 'time', _ret_key: str = 'eff'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.period = _period
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
self.buf.append(_data_struct.getColumn(self.use_key)[0])
if len(self.buf) == self.period:
buf_list = list(self.buf)
tmp = 0.0
for a, b in zip(buf_list[:-1], buf_list[1:]):
tmp += abs(b - a)
self.data.addDict({
self.idx_key: index_value,
self.ret_key: (self.buf[-1] - self.buf[0]) / tmp,
})
class BIAS(IndicatorAbstract):
"""
rolling ma
"""
def __init__(
self, _period: int, _use_key: str = 'closeprice',
_idx_key: str = 'time', _ret_key: str = 'bias'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.period = _period
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
price = _data_struct[self.use_key][0]
self.buf.append(price)
price_mean = statistics.mean(self.buf)
self.data.addDict({
self.idx_key: index_value,
self.ret_key: (price - price_mean) / price_mean * 100,
})
class Kurtosis(IndicatorAbstract):
def __init__(self,
_period: int,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'kurtosis'):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key)
self.period = _period
self.last_price = None
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index = _data_struct.index()[0]
price = _data_struct[self.use_key][0]
if self.last_price:
self.buf.append(math.log(price / self.last_price))
if len(self.buf) >= self.period:
self.data.addDict({
self.idx_key: index,
self.ret_key: kurtosis(self.buf),
})
self.last_price = price
class LogReturn(IndicatorAbstract):
def __init__(
self,
_skip_period: int = 1,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'logreturn'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.skip_period = _skip_period
self.buf = deque(maxlen=self.skip_period)
def _addOne(self, _data_struct: DataStruct):
index = _data_struct.index()[0]
value = _data_struct[self.use_key][0]
if len(self.buf) >= self.skip_period:
last_value = self.buf.popleft()
chg_rate = math.log(value / last_value)
self.data.addDict({
self.idx_key: index,
self.ret_key: chg_rate,
})
self.buf.append(value)
def getSettlementData(self):
tmp = DataStruct(['tradingday', 'fund', 'commission', 'margin'],
'tradingday')
for d in self.settlement_record:
tmp.addDict(d)
return tmp
class MAX(IndicatorAbstract):
"""
rolling simple ma
"""
def __init__(
self, _period: int, _use_key: str = 'closeprice',
_idx_key: str = 'time', _ret_key: str = 'max'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.period = _period
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
self.buf.append(_data_struct.getColumn(self.use_key)[0])
self.data.addDict({
self.idx_key: index_value,
self.ret_key: max(self.buf),
})
class SharpRate(IndicatorAbstract):
def __init__(
self, _period: int, _use_key: str = 'closeprice',
_idx_key: str = 'time', _ret_key: str = 'sharprate',
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.last_price = None
self.period = _period
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
price_value = _data_struct[self.use_key][0]
if self.last_price is not None:
chg_rate = price_value / self.last_price - 1
self.buf.append(chg_rate)
buf_std = statistics.pstdev(self.buf)
if buf_std != 0:
self.data.addDict({
self.idx_key: index_value,
self.ret_key: statistics.mean(self.buf) / buf_std,
})
self.last_price = price_value
class FastVolatility(IndicatorAbstract):
def __init__(
self, _period: int,
_factor: int = 1,
_smooth: int = 1,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'volatility',
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.last_price = None
self.period = _period
self.factor = math.sqrt(_factor)
self.smooth = _smooth
self.buf = deque(maxlen=self.period)
self.mean = 0.0
self.sum_of_pow = 0.0
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
price_value = _data_struct[self.use_key][0]
if self.last_price is not None:
chg_rate = price_value / self.last_price - 1
if len(self.buf) >= self.period:
last_value = self.buf.popleft()
self.buf.append(chg_rate)
self.sum_of_pow += chg_rate ** 2
self.sum_of_pow -= last_value ** 2
self.mean += (chg_rate - last_value) / self.period
else:
n = len(self.buf)
self.buf.append(chg_rate)
self.sum_of_pow += chg_rate ** 2
self.mean = (self.mean * n + chg_rate) / len(self.buf)
var = self.sum_of_pow / len(self.buf) - self.mean ** 2
std_value = math.sqrt(max(0.0, var)) * self.factor
if self.smooth > 1 and len(self.data):
last_std_value = self.data[self.ret_key][-1]
std_value = (
(self.smooth - 1) * last_std_value + std_value
) / self.smooth
self.data.addDict({
self.idx_key: index_value,
self.ret_key: std_value,
})
self.last_price = price_value
def settlement(self, _backtest_key) -> DataStruct:
settlement_list = self.fetchSettlementRecords(_backtest_key)
keys = ['tradingday', 'type', 'fund', 'commission', 'margin']
ret = DataStruct(keys, 'tradingday')
for d in settlement_list:
ret.addDict(d)
return ret
def getSignalData(self):
tmp = DataStruct(
['strategy', 'tradingday', 'datetime', 'symbol', 'strength'],
'datetime')
for d in self.signal_record:
tmp.addDict(d)
return tmp
class FastVolatility(IndicatorAbstract):
def __init__(
self,
_period: int,
_factor: int = 1,
_smooth: int = 1,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'volatility',
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key)
self.last_price = None
self.period = _period
self.factor = math.sqrt(_factor)
self.smooth = _smooth
self.buf = deque(maxlen=self.period)
self.mean = 0.0
self.sum_of_pow = 0.0
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
price_value = _data_struct[self.use_key][0]
if self.last_price is not None:
chg_rate = price_value / self.last_price - 1
if len(self.buf) >= self.period:
last_value = self.buf.popleft()
self.buf.append(chg_rate)
self.sum_of_pow += chg_rate**2
self.sum_of_pow -= last_value**2
self.mean += (chg_rate - last_value) / self.period
else:
n = len(self.buf)
self.buf.append(chg_rate)
self.sum_of_pow += chg_rate**2
self.mean = (self.mean * n + chg_rate) / len(self.buf)
std_value = math.sqrt(self.sum_of_pow / len(self.buf) -
self.mean**2) * self.factor
if self.smooth > 1 and len(self.data):
last_std_value = self.data[self.ret_key][-1]
std_value = ((self.smooth - 1) * last_std_value +
std_value) / self.smooth
self.data.addDict({
self.idx_key: index_value,
self.ret_key: std_value,
})
self.last_price = price_value
class RateConstStop(StopIndicatorAbstract):
def __init__(
self,
_data: DataStruct,
_stop_type: int,
_stop_rate: float = 0.05,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'stopprice',
):
super().__init__()
assert len(_data) == 1
self.stop_type = _stop_type
self.stop_rate = _stop_rate
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
price = _data[self.use_key][0]
if self.stop_type == SignalType.LONG:
stop_price = price * (1 - self.stop_rate)
elif self.stop_type == SignalType.SHORT:
stop_price = price * (1 + self.stop_rate)
else:
raise Exception('unknown type')
time = _data.index()[0]
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key,
[[time, stop_price]]
)
def _addOne(self, _data_struct: DataStruct):
stop_price = self.data[self.ret_key][-1]
time = _data_struct.index()[0]
self.data.addDict({
self.idx_key: time,
self.ret_key: stop_price,
})
def _isStop(self, _data_struct: DataStruct):
price = _data_struct[self.use_key][0]
stop_price = self.data[self.ret_key][-1]
if self.stop_type == SignalType.LONG:
if price < stop_price:
self.is_stop = True
elif self.stop_type == SignalType.SHORT:
if price > stop_price:
self.is_stop = True
else:
raise Exception('unknown type')
def settlement(self, _backtest_key) -> DataStruct:
settlement_list = self.fetchSettlementRecords(_backtest_key)
keys = [
'tradingday', 'type',
'fund', 'commission', 'margin'
]
ret = DataStruct(keys, 'tradingday')
for d in settlement_list:
ret.addDict(d)
return ret
def getFillData(self):
tmp = DataStruct([
'strategy', 'tradingday', 'datetime', 'symbol', 'index', 'action',
'direction', 'price', 'quantity', 'commission'
], 'datetime')
for d in self.fill_record:
d = copy(d)
d['action'] = ActionType.toStr(d['action'])
d['direction'] = DirectionType.toStr(d['direction'])
tmp.addDict(d)
return tmp
class AdaKalman(IndicatorAbstract):
def __init__(
self, _ada_period: int=30,
_init_x: float = 0.0, _init_P: float =1.0,
_init_R: float = 0.1 ** 2, _init_Q: float = 0.01 ** 2,
_use_key: str = 'closeprice',
_idx_key: str = 'time', _ret_key: str = 'kalman'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.ada_period = _ada_period
self.value_std = FastSTD(_ada_period, _use_key=self.use_key)
self.x_std = FastSTD(_ada_period, _use_key='x')
self.R = _init_R
self.Q = _init_Q
self.x = _init_x
self.P = _init_P
def _addOne(self, _data_struct: DataStruct):
index = _data_struct.index()[0]
value = _data_struct[self.use_key][0]
self.value_std.addOne(_data_struct)
if len(self.value_std) > 1:
self.R = self.value_std.getLastData()['std'][0] ** 2
if len(self.x_std) > 1:
self.Q = self.x_std.getLastData()['std'][0] ** 2
# predict
# self.x += 0.0 # x assume not changed
self.P += self.Q
# update
k = self.P / (self.P + self.R)
x_diff_value = k * (value - self.x)
self.x += x_diff_value
self.P = (1 - k) * self.P
self.data.addDict({
self.idx_key: index,
self.ret_key: self.x
})
class RateConstStop(StopIndicatorAbstract):
def __init__(
self,
_data: DataStruct,
_stop_type: int,
_stop_rate: float = 0.05,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'stopprice',
):
super().__init__()
assert len(_data) == 1
self.stop_type = _stop_type
self.stop_rate = _stop_rate
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
price = _data[self.use_key][0]
if self.stop_type == SignalType.LONG:
stop_price = price * (1 - self.stop_rate)
elif self.stop_type == SignalType.SHORT:
stop_price = price * (1 + self.stop_rate)
else:
raise Exception('unknown type')
time = _data.index()[0]
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key,
[[time, stop_price]])
def _addOne(self, _data_struct: DataStruct):
stop_price = self.data[self.ret_key][-1]
time = _data_struct.index()[0]
self.data.addDict({
self.idx_key: time,
self.ret_key: stop_price,
})
def _isStop(self, _data_struct: DataStruct):
price = _data_struct[self.use_key][0]
stop_price = self.data[self.ret_key][-1]
if self.stop_type == SignalType.LONG:
if price < stop_price:
self.is_stop = True
elif self.stop_type == SignalType.SHORT:
if price > stop_price:
self.is_stop = True
else:
raise Exception('unknown type')
def getOrderData(self):
tmp = DataStruct([
'strategy', 'tradingday', 'datetime', 'symbol', 'index',
'order_type', 'action', 'direction', 'quantity', 'price'
], 'datetime')
for d in self.order_record:
d = copy(d)
d['order_type'] = OrderType.toStr(d['order_type'])
d['action'] = ActionType.toStr(d['action'])
d['direction'] = DirectionType.toStr(d['direction'])
tmp.addDict(d)
return tmp
class CCI(IndicatorAbstract):
"""
rolling ma
"""
def __init__(
self, _period: int, _constant: float = 0.15,
_close_key: str = 'closeprice',
_high_key: str = 'highprice',
_low_key: str = 'lowprice',
_idx_key: str = 'time', _ret_key: str = 'cci',
):
super().__init__()
self.close_key = _close_key
self.high_key = _high_key
self.low_key = _low_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.period = _period
self.constant = _constant
self.tp_buf = deque(maxlen=self.period)
self.dev_buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
close_price = _data_struct[self.close_key][0]
high_price = _data_struct[self.high_key][0]
low_price = _data_struct[self.low_key][0]
tp = (close_price + high_price + low_price) / 3
if len(self.tp_buf) == 0:
dev = high_price - low_price
else:
dev = abs(tp - self.tp_buf[-1])
self.tp_buf.append(tp)
self.dev_buf.append(dev)
self.data.addDict({
self.idx_key: index_value,
self.ret_key: (tp - statistics.mean(self.tp_buf)) / (
self.constant * statistics.mean(self.dev_buf)
),
})
class SimMA(IndicatorAbstract):
EMPTY = 0
LONG = 1
SHORT = 2
def __init__(
self, _period: int, _use_key: str = 'closeprice',
_idx_key: str = 'time', _ret_key: str = 'simma'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.value = 1.0
self.last_price = None
self.last_status = self.EMPTY
self.period = _period
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index = _data_struct.index()[0]
price = _data_struct[self.use_key][0]
self.buf.append(price)
ma_value = sum(self.buf) / len(self.buf)
if self.last_status == self.LONG:
self.value *= price / self.last_price
if self.last_status == self.SHORT:
self.value /= price / self.last_price
self.data.addDict({
self.idx_key: index,
self.ret_key: self.value,
})
if price > ma_value:
self.last_status = self.LONG
elif price < ma_value:
self.last_status = self.SHORT
else:
self.last_status = self.EMPTY
self.last_price = price
class CCI(IndicatorAbstract):
"""
rolling ma
"""
def __init__(
self,
_period: int,
_constant: float = 0.15,
_close_key: str = 'closeprice',
_high_key: str = 'highprice',
_low_key: str = 'lowprice',
_idx_key: str = 'time',
_ret_key: str = 'cci',
):
super().__init__()
self.close_key = _close_key
self.high_key = _high_key
self.low_key = _low_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key)
self.period = _period
self.constant = _constant
self.tp_buf = deque(maxlen=self.period)
self.dev_buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
close_price = _data_struct[self.close_key][0]
high_price = _data_struct[self.high_key][0]
low_price = _data_struct[self.low_key][0]
tp = (close_price + high_price + low_price) / 3
if len(self.tp_buf) == 0:
dev = high_price - low_price
else:
dev = abs(tp - self.tp_buf[-1])
self.tp_buf.append(tp)
self.dev_buf.append(dev)
self.data.addDict({
self.idx_key:
index_value,
self.ret_key: (tp - statistics.mean(self.tp_buf)) /
(self.constant * statistics.mean(self.dev_buf)),
})
class ReturnRate(IndicatorAbstract):
def __init__(
self,
_smooth_period: int = 1,
_skip_period: int = 1,
_use_abs: bool=False,
_use_percent: bool=False,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'returnrate'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.skip_period = _skip_period
self.smooth_period = _smooth_period
self.buf = deque(maxlen=self.skip_period)
self.last_rate = None
self.use_abs = _use_abs
self.use_percent = _use_percent
def _addOne(self, _data_struct: DataStruct):
index = _data_struct.index()[0]
value = _data_struct[self.use_key][0]
if len(self.buf) >= self.skip_period:
last_value = self.buf.popleft()
chg_rate = value / last_value - 1
if self.use_abs:
chg_rate = abs(chg_rate)
if self.use_percent:
chg_rate *= 100.0
if self.last_rate is None:
self.last_rate = chg_rate
else:
self.last_rate = (chg_rate - self.last_rate) / \
self.smooth_period + self.last_rate
self.data.addDict({
self.idx_key: index,
self.ret_key: self.last_rate
})
self.buf.append(value)
class FastSTD(IndicatorAbstract):
def __init__(
self, _period: int, _ignore_mean: bool = False,
_use_key: str = 'closeprice',
_idx_key: str = 'time', _ret_key: str = 'std'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.sum_of_pow = 0.0
self.mean = 0.0
self.period = _period
self.ignore_mean = _ignore_mean
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
value = _data_struct[self.use_key][0]
index = _data_struct.index()[0]
if len(self.buf) >= self.period:
last_value = self.buf.popleft()
self.buf.append(value)
self.sum_of_pow += value ** 2
self.sum_of_pow -= last_value ** 2
if not self.ignore_mean:
self.mean += (value - last_value) / self.period
else:
n = len(self.buf)
self.buf.append(value)
self.sum_of_pow += value ** 2
if not self.ignore_mean:
self.mean = (self.mean * n + value) / len(self.buf)
self.data.addDict({
self.idx_key: index,
self.ret_key: math.sqrt(
self.sum_of_pow / len(self.buf) - self.mean ** 2
)
})
def deal(self, _market_event: MarketEvent):
"""
recv ticker data from market supply,
and split into min, the call do_deal(...)
:param _market_event:
:return:
"""
symbol = _market_event.symbol
data = _market_event.data
for period in self.min_periods:
key = symbol + (period, )
try:
spliter = self.split_dict[key]
except KeyError:
spliter = self.split_dict[key] = SplitIntoMinute(period)
flag = False
for d in data:
flag = flag or spliter.addOne(d)
if flag: # gen new bar
if len(spliter.getBarList()) > 1: # has finished bar
last_bar = spliter.getBarList().pop(0)
last_begin_time = spliter.getBarBeginTimeList().pop(0)
last_end_time = spliter.getBarEndTimeList().pop(0)
price_list = last_bar['price']
volume_list = last_bar['amount']
bar_data = DataStruct([
'open', 'high', 'low', 'close', 'volume', 'begin_time',
'end_time'
], 'begin_time')
bar_data.addDict({
'open': price_list[0],
'high': max(price_list),
'low': min(price_list),
'close': price_list[-1],
'volume': sum(volume_list),
'begin_time': last_begin_time,
'end_time': last_end_time
})
self.do_deal(
MarketEvent(_market_event.market_register_key,
_market_event.strategy,
_market_event.symbol, bar_data), period)
def signalToLongShort(
self, _backtest_key: str, _strategy: str = None
) -> typing.Tuple[DataStruct, DataStruct]:
signal_list = self.fetchSignalRecords(_backtest_key, _strategy)
keys = [
'type', 'symbol', 'strategy', 'signal_type',
'tradingday', 'datetime', 'strength'
]
long_ret = DataStruct(keys, 'datetime')
short_ret = DataStruct(keys, 'datetime')
for d in signal_list:
if d['signal_type'] == SignalType.LONG:
long_ret.addDict(d)
if d['signal_type'] == SignalType.SHORT:
short_ret.addDict(d)
return long_ret, short_ret
class Volatility(IndicatorAbstract):
def __init__(
self, _period: int,
_factor: int = 1,
_smooth: int = 1,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'volatility',
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.last_price = None
self.period = _period
self.factor = math.sqrt(_factor)
self.smooth = _smooth
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
price_value = _data_struct[self.use_key][0]
if self.last_price is not None:
chg_rate = price_value / self.last_price - 1
self.buf.append(chg_rate)
std_value = statistics.pstdev(self.buf) * self.factor
if self.smooth > 1 and len(self.data):
last_std_value = self.data[self.ret_key][-1]
std_value = (
(self.smooth - 1) * last_std_value + std_value
) / self.smooth
self.data.addDict({
self.idx_key: index_value,
self.ret_key: std_value,
})
self.last_price = price_value
class FastSTD(IndicatorAbstract):
def __init__(self,
_period: int,
_ignore_mean: bool = False,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'std'):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key)
self.sum_of_pow = 0.0
self.mean = 0.0
self.period = _period
self.ignore_mean = _ignore_mean
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
value = _data_struct[self.use_key][0]
index = _data_struct.index()[0]
if len(self.buf) >= self.period:
last_value = self.buf.popleft()
self.buf.append(value)
self.sum_of_pow += value**2
self.sum_of_pow -= last_value**2
if not self.ignore_mean:
self.mean += (value - last_value) / self.period
else:
n = len(self.buf)
self.buf.append(value)
self.sum_of_pow += value**2
if not self.ignore_mean:
self.mean = (self.mean * n + value) / len(self.buf)
self.data.addDict({
self.idx_key:
index,
self.ret_key:
math.sqrt(self.sum_of_pow / len(self.buf) - self.mean**2)
})
class ReturnRate(IndicatorAbstract):
def __init__(self,
_smooth_period: int = 1,
_skip_period: int = 1,
_use_abs: bool = False,
_use_percent: bool = False,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'returnrate'):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key)
self.skip_period = _skip_period
self.smooth_period = _smooth_period
self.buf = deque(maxlen=self.skip_period)
self.last_rate = None
self.use_abs = _use_abs
self.use_percent = _use_percent
def _addOne(self, _data_struct: DataStruct):
index = _data_struct.index()[0]
value = _data_struct[self.use_key][0]
if len(self.buf) >= self.skip_period:
last_value = self.buf.popleft()
chg_rate = value / last_value - 1
if self.use_abs:
chg_rate = abs(chg_rate)
if self.use_percent:
chg_rate *= 100.0
if self.last_rate is None:
self.last_rate = chg_rate
else:
self.last_rate = (chg_rate - self.last_rate) / \
self.smooth_period + self.last_rate
self.data.addDict({
self.idx_key: index,
self.ret_key: self.last_rate
})
self.buf.append(value)
def fillToBuySell(
self,
_backtest_key: str,
_strategy: str = None) -> typing.Tuple[DataStruct, DataStruct]:
fill_list = self.fetchFillRecords(_backtest_key, _strategy)
keys = [
'type', 'index', 'symbol', 'tradingday', 'datetime', 'quantity',
'action', 'direction', 'price', 'commission', 'strategy'
]
buy_ret = DataStruct(keys, 'datetime')
sell_ret = DataStruct(keys, 'datetime')
for d in fill_list:
if d['direction'] == DirectionType.BUY:
buy_ret.addDict(d)
if d['direction'] == DirectionType.SELL:
sell_ret.addDict(d)
return buy_ret, sell_ret
def signalToLongShort(
self,
_backtest_key: str,
_strategy: str = None) -> typing.Tuple[DataStruct, DataStruct]:
signal_list = self.fetchSignalRecords(_backtest_key, _strategy)
keys = [
'type', 'symbol', 'strategy', 'signal_type', 'tradingday',
'datetime', 'strength'
]
long_ret = DataStruct(keys, 'datetime')
short_ret = DataStruct(keys, 'datetime')
for d in signal_list:
if d['signal_type'] == SignalType.LONG:
long_ret.addDict(d)
if d['signal_type'] == SignalType.SHORT:
short_ret.addDict(d)
return long_ret, short_ret
def fillToBuySell(
self, _backtest_key: str, _strategy: str = None
) -> typing.Tuple[DataStruct, DataStruct]:
fill_list = self.fetchFillRecords(_backtest_key, _strategy)
keys = [
'type', 'index', 'symbol', 'tradingday',
'datetime', 'quantity', 'action', 'direction',
'price', 'commission', 'strategy'
]
buy_ret = DataStruct(keys, 'datetime')
sell_ret = DataStruct(keys, 'datetime')
for d in fill_list:
if d['direction'] == DirectionType.BUY:
buy_ret.addDict(d)
if d['direction'] == DirectionType.SELL:
sell_ret.addDict(d)
return buy_ret, sell_ret
class ATR(IndicatorAbstract):
def __init__(
self, _period: int,
_high_key: str = 'highprice',
_low_key: str = 'lowprice',
_close_key: str = 'closeprice',
_idx_key: str = 'time', _ret_key: str = 'atr'
):
super().__init__()
self.high_key = _high_key
self.low_key = _low_key
self.close_key = _close_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.period = _period
self.last_atr = None
self.last_close_price = None
def _addOne(self, _data_struct: DataStruct):
if self.last_close_price is not None:
index_value = _data_struct.index()[0]
tr_value = max(
_data_struct[self.high_key][0], self.last_close_price
) - min(
_data_struct[self.low_key][0], self.last_close_price
)
if self.last_atr is None:
self.last_atr = tr_value
else:
self.last_atr = (tr_value - self.last_atr) / self.period + self.last_atr
self.data.addDict({
self.idx_key: index_value,
self.ret_key: self.last_atr,
})
self.last_close_price = _data_struct[self.close_key][0]
class LowBar(BarIndicatorAbstract):
def __init__(self,
_use_key: str,
_idx_key: str = 'time',
_ret_key: str = 'low'):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key)
def _addOne(self,
_data_struct: DataStruct,
_idx: typing.Union[str, datetime] = None):
self.data.addDict({
self.idx_key: _idx,
self.ret_key: min(_data_struct[self.use_key])
})
class RSI(IndicatorAbstract):
def __init__(
self, _period: int, _use_key: str = 'closeprice',
_idx_key: str = 'time', _ret_key: str = 'rsi'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.period = _period
self.gain_buf = deque(maxlen=self.period)
self.loss_buf = deque(maxlen=self.period)
self.last_price = None
def _addOne(self, _data_struct: DataStruct):
price = _data_struct[self.use_key][0]
if self.last_price is not None:
price_diff = price - self.last_price
if price_diff >= 0:
self.gain_buf.append(price_diff)
self.loss_buf.append(0)
else:
self.gain_buf.append(0)
self.loss_buf.append(-price_diff)
index_value = _data_struct.index()[0]
gain_mean = statistics.mean(self.gain_buf)
loss_mean = max(statistics.mean(self.loss_buf), 0.01)
self.data.addDict({
self.idx_key: index_value,
self.ret_key: 100 - 100 / (1 + gain_mean / loss_mean),
})
self.last_price = price
class OpenBar(BarIndicatorAbstract):
def __init__(
self, _use_key: str, _idx_key: str = 'time', _ret_key: str = 'open'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
def _addOne(
self, _data_struct: DataStruct,
_idx: typing.Union[str, datetime] = None
):
self.data.addDict({
self.idx_key: _idx,
self.ret_key: _data_struct[self.use_key][0]
})
class STD(IndicatorAbstract):
def __init__(self,
_period: int,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'std'):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key)
self.period = _period
self.buf = deque(maxlen=self.period)
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
self.buf.append(_data_struct.getColumn(self.use_key)[0])
self.data.addDict({
self.idx_key: index_value,
self.ret_key: statistics.pstdev(self.buf),
})
class Kalman(IndicatorAbstract):
def __init__(
self, _init_x: float = 0.0, _init_P: float = 1.0,
_R: float = 0.1 ** 2, _Q: float = 0.01 ** 2,
_use_key: str = 'closeprice',
_idx_key: str = 'time', _ret_key: str = 'kalman'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.x = _init_x
self.P = _init_P
self.R = _R
self.Q = _Q
def _addOne(self, _data_struct: DataStruct):
index = _data_struct.index()[0]
value = _data_struct[self.use_key][0]
self.P += self.Q
k = self.P / (self.P + self.R)
self.x += k * (value - self.x)
self.P = (1 - k) * self.P
self.data.addDict({
self.idx_key: index,
self.ret_key: self.x
})
class FastMA(IndicatorAbstract):
"""
rolling ma
"""
def __init__(self,
_period: int,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'ma'):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct([self.idx_key, self.ret_key], self.idx_key)
self.period = _period
self.buf = deque(maxlen=self.period)
self.mean = 0.0
def _addOne(self, _data_struct: DataStruct):
index = _data_struct.index()[0]
value = _data_struct[self.use_key][0]
if len(self.buf) >= self.period:
last_value = self.buf.popleft()
self.buf.append(value)
self.mean += (value - last_value) / self.period
else:
n = len(self.buf)
self.buf.append(value)
self.mean = (self.mean * n + value) / len(self.buf)
self.data.addDict({
self.idx_key: index,
self.ret_key: self.mean,
})
class ATRTrailingStop(StopIndicatorAbstract):
def __init__(
self,
_price_data: DataStruct,
_atr_data: DataStruct,
_stop_type: int,
_rate: float = 3,
_price_use_key: str = 'closeprice',
_atr_use_key: str = 'atr',
_idx_key: str = 'time',
_ret_key: str = 'stopprice',
):
super().__init__()
assert len(_price_data) == 1
assert len(_atr_data) == 1
self.stop_type = _stop_type
self.rate = _rate
self.price_use_key = _price_use_key
self.atr_use_key = _atr_use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key,
)
self.best_price = _price_data[self.price_use_key][0]
self._addOne(_price_data, _atr_data)
def get_stop_price(self, _price, _atr) -> float:
if self.stop_type == SignalType.LONG:
self.best_price = max(self.best_price, _price)
return self.best_price - self.rate * _atr
elif self.stop_type == SignalType.SHORT:
self.best_price = min(self.best_price, _price)
return self.best_price + self.rate * _atr
else:
raise Exception('unknown type')
def addOne(
self, _data_struct: DataStruct,
_atr_data: DataStruct = None,
) -> bool:
if not self.is_stop:
assert len(_data_struct) == 1
self._addOne(_data_struct, _atr_data)
self._isStop(_data_struct)
return self.is_stop
def _addOne(
self, _price_data: DataStruct,
_atr_data: DataStruct = None,
):
assert len(_atr_data) == 1
price = _price_data.toDict()[self.price_use_key]
atr = _atr_data.toDict()[self.atr_use_key]
price_time = _price_data.index()[0]
atr_time = _atr_data.index()[0]
assert price_time == atr_time
self.data.addDict({
self.idx_key: price_time,
self.ret_key: self.get_stop_price(price, atr),
})
def _isStop(self, _data_struct: DataStruct):
price = _data_struct.toDict()[self.price_use_key]
stop_price = self.data[self.ret_key][-1]
if self.stop_type == SignalType.LONG:
if price < stop_price:
self.is_stop = True
elif self.stop_type == SignalType.SHORT:
if price > stop_price:
self.is_stop = True
else:
raise Exception('unknown type')
class ARMAGARCH(IndicatorAbstract):
def __init__(
self,
_fit_period: int = 60,
_fit_begin: int = 252,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: typing.Tuple[str] = ('mean', 'std'),
):
super().__init__()
# fitting control
self.fit_count = 0
self.fit_period = _fit_period
self.fit_begin = _fit_begin
self.model = ARMAIGARCHModel(_use_mu=False)
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key[0], self.ret_key[1]],
self.idx_key
)
# log return buf
self.last_price = None
self.return_buf = []
# model params
self.phi = None
self.theta = None
self.alpha = None
self.beta = None
self.const = None
self.new_mean = None
self.new_info = None
self.new_var = None
def _addOne(self, _data_struct: DataStruct):
index = _data_struct.index()[0]
price = _data_struct[self.use_key][0]
if self.last_price is not None:
rate = math.log(price) - math.log(self.last_price)
if self.new_mean is not None:
self.new_info = rate - self.new_mean
self.return_buf.append(rate)
self.fit_count += 1 # retrain model
if self.fit_count > self.fit_period and \
len(self.return_buf) >= self.fit_begin:
self.model.fit(self.return_buf)
self.phi = self.model.getPhis()[0]
self.theta = self.model.getThetas()[0]
self.alpha = self.model.getAlphas()[0]
self.beta = self.model.getBetas()[0]
self.const = self.model.getConst()[0]
self.new_info = self.model.latent_arma_arr[-1]
self.new_var = self.model.latent_garch_arr[-1]
self.fit_count = 0
if self.new_info is not None: # predict value
self.new_mean = self.phi * rate + self.theta * self.new_info
self.new_var = self.alpha * self.new_info ** 2 + \
self.beta * self.new_var + self.const
self.data.addDict({
self.idx_key: index,
self.ret_key[0]: self.new_mean,
self.ret_key[1]: math.sqrt(self.new_var),
})
self.last_price = price
class SAR(IndicatorAbstract):
BEGIN = 0
RISING = 1
FALLING = -1
def __init__(
self,
_init_step: float = 0.02,
_max_step: float = 0.2,
_close_key: str = 'closeprice',
_high_key: str = 'highprice',
_low_key: str = 'lowprice',
_idx_key: str = 'time',
_ret_key: str = 'sar',
):
super().__init__()
self.close_key = _close_key
self.high_key = _high_key
self.low_key = _low_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.init_step = _init_step
self.max_step = _max_step
self.status = self.BEGIN
self.ep = None
self.step = None
self.sar = None
def _addOne(self, _data_struct: DataStruct):
index_value = _data_struct.index()[0]
close_price = _data_struct[self.close_key][0]
high_price = _data_struct[self.high_key][0]
low_price = _data_struct[self.low_key][0]
if self.status == self.RISING: # last status if rising
if low_price < self.sar: # if break sar, change status
self.status = self.FALLING
self.sar = self.ep # new sar if the ep of last status
self.ep = low_price
self.step = self.init_step
else: # else continue rising
self.sar += self.step * (self.ep - self.sar)
if high_price > self.ep:
self.ep = high_price
self.step = min(self.max_step, self.step + self.init_step)
elif self.status == self.FALLING:
if high_price > self.sar: # if break sar, change status
self.status = self.RISING
self.sar = self.ep
self.ep = high_price
self.step = self.init_step
else: # else continue falling
self.sar -= self.step * (self.sar - self.ep)
if low_price < self.ep:
self.ep = low_price
self.step = min(self.max_step, self.step + self.init_step)
else: # just begin
if close_price >= (high_price + low_price) / 2:
# close price greater than mid of high and low
self.status = self.RISING
self.ep = high_price
self.step = self.init_step
self.sar = low_price
else:
self.status = self.FALLING
self.ep = low_price
self.step = self.init_step
self.sar = high_price
self.data.addDict({
self.idx_key: index_value,
self.ret_key: self.sar,
})
class VolatilityTrailingStop(StopIndicatorAbstract):
def __init__(
self,
_price_data: DataStruct,
_volatility_data: DataStruct,
_stop_type: int,
_rate: float = 4,
_price_use_key: str = 'closeprice',
_volatility_use_key: str = 'volatility',
_idx_key: str = 'time',
_ret_key: str = 'stopprice',
):
super().__init__()
assert len(_price_data) == 1
assert len(_volatility_data) == 1
self.stop_type = _stop_type
self.rate = _rate
self.price_use_key = _price_use_key
self.volatility_use_key = _volatility_use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key,
)
self._addOne(_price_data, _volatility_data)
def get_stop_price(self, _price, _volatility):
if len(self.data):
last_stop_price = self.data[self.ret_key][-1]
if self.stop_type == SignalType.LONG:
return max(_price * (1 - self.rate * _volatility), last_stop_price)
elif self.stop_type == SignalType.SHORT:
return min(_price * (1 + self.rate * _volatility), last_stop_price)
else:
raise Exception('unknown type')
else:
if self.stop_type == SignalType.LONG:
return _price * (1 - self.rate * _volatility)
elif self.stop_type == SignalType.SHORT:
return _price * (1 + self.rate * _volatility)
else:
raise Exception('unknown type')
def addOne(
self, _data_struct: DataStruct,
_volatility_data: DataStruct = None,
) -> bool:
if not self.is_stop:
assert len(_data_struct) == 1
self._addOne(_data_struct, _volatility_data)
self._isStop(_data_struct)
return self.is_stop
def _addOne(
self, _price_data: DataStruct,
_volatility_data: DataStruct = None,
):
assert len(_volatility_data) == 1
price = _price_data.toDict()[self.price_use_key]
volatility = _volatility_data.toDict()[self.volatility_use_key]
price_time = _price_data.index()[0]
volatility_time = _volatility_data.index()[0]
assert price_time == volatility_time
self.data.addDict({
self.idx_key: price_time,
self.ret_key: self.get_stop_price(price, volatility),
})
def _isStop(self, _data_struct: DataStruct):
price = _data_struct.toDict()[self.price_use_key]
stop_price = self.data[self.ret_key][-1]
if self.stop_type == SignalType.LONG:
if price < stop_price:
self.is_stop = True
elif self.stop_type == SignalType.SHORT:
if price > stop_price:
self.is_stop = True
else:
raise Exception('unknown type')
class GARCH(IndicatorAbstract):
def __init__(
self,
_fit_period: int = 60,
_fit_begin: int = 252,
_factor: int = 1,
_smooth_period: int = 1,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'predict',
):
super().__init__()
# fitting control
self.fit_count = 0
self.fit_period = _fit_period
self.fit_begin = _fit_begin
# scale the volatility
self.factor = math.sqrt(_factor)
self.smooth_period = _smooth_period
self.model = IGARCHModel(_use_mu=False)
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
# log return buf
self.last_price = None
self.return_buf = []
# model params
self.alpha = None
self.beta = None
self.const = None
self.latent = None
def _addOne(self, _data_struct: DataStruct):
index = _data_struct.index()[0]
price = _data_struct[self.use_key][0]
if self.last_price is not None:
rate = math.log(price) - math.log(self.last_price)
self.return_buf.append(rate)
self.fit_count += 1
if self.fit_count > self.fit_period and \
len(self.return_buf) >= self.fit_begin:
# retrain model and reset sigma2
self.model.fit(self.return_buf)
# print(
# self.model.getAlphas(), self.model.getBetas(),
# self.model.getConst(), self.model.getMu()
# )
self.alpha = self.model.getAlphas()[0]
self.beta = self.model.getBetas()[0]
self.const = self.model.getConst()[0]
self.latent = self.model.latent_arr[-1]
self.fit_count = 0
if self.latent is not None: # predict value
self.latent = self.alpha * rate * rate + \
self.beta * self.latent + self.const
predict = math.sqrt(self.latent)
predict *= self.factor
if self.smooth_period > 1 and len(self.data): # smooth
last_value = self.data[self.ret_key][-1]
predict = (predict - last_value) / \
self.smooth_period + last_value
self.data.addDict({
self.idx_key: index,
self.ret_key: predict,
})
self.last_price = price
class Plunge(IndicatorAbstract):
def __init__(
self,
_fast_ema_period: int = 50,
_slow_ema_period: int = 100,
_atr_period: int = 20,
_extreme_period: int = 20,
_smooth_period: int = 1,
_high_key: str = 'highprice',
_low_key: str = 'lowprice',
_close_key: str = 'closeprice',
_idx_key: str = 'time', _ret_key: str = 'plunge'
):
super().__init__()
self.high_key = _high_key
self.low_key = _low_key
self.close_key = _close_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.fast_ema_period = _fast_ema_period
self.slow_ema_period = _slow_ema_period
self.fast_ema_value = None
self.slow_ema_value = None
self.atr_buf = deque(maxlen=_atr_period)
self.high_buf = deque(maxlen=_extreme_period)
self.low_buf = deque(maxlen=_extreme_period)
self.ret_buf = deque(maxlen=_smooth_period)
self.last_close_price = None
def _addOne(self, _data_struct: DataStruct):
self.high_buf.append(_data_struct[self.high_key][0])
self.low_buf.append(_data_struct[self.low_key][0])
closeprice = _data_struct[self.close_key][0]
if self.last_close_price is not None:
index_value = _data_struct.index()[0]
# atr
tr_value = max(
_data_struct[self.high_key][0], self.last_close_price
) - min(_data_struct[self.low_key][0], self.last_close_price)
self.atr_buf.append(tr_value)
atr_value = sum(self.atr_buf) / len(self.atr_buf)
# ema
self.fast_ema_value = (closeprice - self.fast_ema_value) / \
self.fast_ema_period + self.fast_ema_value
self.slow_ema_value = (closeprice - self.slow_ema_value) / \
self.slow_ema_period + self.slow_ema_value
# plunge
if self.fast_ema_value > self.slow_ema_value:
plunge_value = (max(self.high_buf) - closeprice) / atr_value
elif self.fast_ema_value < self.slow_ema_value:
plunge_value = (closeprice - min(self.low_buf)) / atr_value
else:
plunge_value = 0.0
self.ret_buf.append(plunge_value)
self.data.addDict({
self.idx_key: index_value,
self.ret_key: sum(self.ret_buf) / len(self.ret_buf),
})
else:
self.fast_ema_value = closeprice
self.slow_ema_value = closeprice
self.last_close_price = closeprice
class ZigZag(IndicatorAbstract):
UNKNOWN = 0
UP = 1
DOWN = 2
def __init__(
self, _threshold: float,
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_ret_key: str = 'zigzag'
):
super().__init__()
self.use_key = _use_key
self.idx_key = _idx_key
self.ret_key = _ret_key
self.data = DataStruct(
[self.idx_key, self.ret_key],
self.idx_key
)
self.threshold = _threshold
self.status = ZigZag.UNKNOWN
self.high_time: typing.Union[str, datetime] = None
self.high_price: float = None
self.low_time: typing.Union[str, datetime] = None
self.low_price: float = None
def _addOne(self, _data_struct: DataStruct):
time: typing.Union[str, datetime] = _data_struct.index()[0]
price: float = _data_struct[self.use_key][0]
if self.status == ZigZag.UNKNOWN:
# update high and low point
if self.high_price is None or price > self.high_price:
self.high_time = time
self.high_price = price
if self.low_price is None or price < self.low_price:
self.low_time = time
self.low_price = price
# if change status
if (price - self.low_price) > self.threshold * self.low_price:
self.status = ZigZag.UP
self.data.addDict({
self.idx_key: self.low_time,
self.ret_key: self.low_price,
})
# reset high
self.high_time = time
self.high_price = price
# clear low
self.low_time = None
self.low_price = None
if (self.high_price - price) > self.threshold * self.high_price:
self.status = ZigZag.DOWN
self.data.addDict({
self.idx_key: self.high_time,
self.ret_key: self.high_price,
})
# reset low
self.low_time = time
self.low_price = price
# clear high
self.high_time = None
self.high_price = None
elif self.status == ZigZag.UP:
if price > self.high_price:
self.high_time = time
self.high_price = price
if (self.high_price - price) > self.threshold * self.high_price:
self.status = ZigZag.DOWN
self.data.addDict({
self.idx_key: self.high_time,
self.ret_key: self.high_price,
})
# reset low
self.low_time = time
self.low_price = price
# clear high
self.high_time = None
self.high_price = None
elif self.status == ZigZag.DOWN:
if price < self.low_price:
self.low_time = time
self.low_price = price
if (price - self.low_price) > self.threshold * self.low_price:
self.status = ZigZag.UP
self.data.addDict({
self.idx_key: self.low_time,
self.ret_key: self.low_price,
})
# reset high
self.high_time = time
self.high_price = price
# clear low
self.low_time = None
self.low_price = None
else:
raise Exception('unknown status')
class StepDrawdownStop(StopIndicatorAbstract):
def __init__(
self,
_data: DataStruct,
_stop_type: int,
_init_stop: float = 0.05,
_profit_thresh: typing.Sequence[float] = (
0.1, 0.2, 0.3, 0.5
),
_stop_thresh: typing.Sequence[float] = (
1.0, 0.5, 0.3, 0.15
),
_use_key: str = 'closeprice',
_idx_key: str = 'time',
_status_key: str = 'status',
_ret_key: str = 'stopprice',
):
super().__init__()
assert len(_data) == 1
self.stop_type = _stop_type
self.status = 0
self.profit_thresh = _profit_thresh
self.stop_thresh = _stop_thresh
self.use_key = _use_key
self.idx_key = _idx_key
self.status_key = _status_key
self.ret_key = _ret_key
self.init_price = _data.toDict()[self.use_key]
if self.stop_type == SignalType.LONG:
self.init_stop_price = self.init_price * (1 - _init_stop)
elif self.stop_type == SignalType.SHORT:
self.init_stop_price = self.init_price * (1 + _init_stop)
else:
raise Exception('unknown type')
self.best_price = self.init_price
self.data = DataStruct(
[self.idx_key, self.status_key, self.ret_key],
self.idx_key,
[[_data.index()[0], self.status, self.init_stop_price]]
)
def _set_best_price(self, _price):
if self.stop_type == SignalType.LONG:
self.best_price = max(self.best_price, _price)
elif self.stop_type == SignalType.SHORT:
self.best_price = min(self.best_price, _price)
else:
raise Exception('unknown type')
def _profit_rate(self, _price) -> float:
if self.stop_type == SignalType.LONG:
return _price / self.init_price - 1.0
elif self.stop_type == SignalType.SHORT:
return 1.0 - _price / self.init_price
else:
raise Exception('unknown type')
def _update_status(self, _profit_rate):
while True:
if self.status < len(self.profit_thresh):
if _profit_rate > self.profit_thresh[self.status]:
self.status += 1
else:
return
else:
return
def _calc_stop_price(self) -> typing.Union[None, float]:
if self.status > 0:
if self.stop_type == SignalType.LONG:
return self.init_price + \
(1.0 - self.stop_thresh[self.status - 1]) * \
(self.best_price - self.init_price)
elif self.stop_type == SignalType.SHORT:
return self.init_price - \
(1.0 - self.stop_thresh[self.status - 1]) * \
(self.init_price - self.best_price)
else:
raise Exception('unknown type')
else:
return self.init_stop_price
def _addOne(self, _data_struct: DataStruct):
time = _data_struct.index()[0]
price = _data_struct[self.use_key][0]
self._set_best_price(price)
profit_rate = self._profit_rate(price)
# if self.stop_type == SignalType.SHORT:
# input((profit_rate, self.status))
self._update_status(profit_rate)
self.data.addDict({
self.idx_key: time,
self.status_key: self.status,
self.ret_key: self._calc_stop_price(),
})
def _isStop(self, _data_struct: DataStruct):
price = _data_struct.toDict()[self.use_key]
stop_price = self.data[self.ret_key][-1]
if self.stop_type == SignalType.LONG:
if price < stop_price:
self.is_stop = True
elif self.stop_type == SignalType.SHORT:
if price > stop_price:
self.is_stop = True
else:
raise Exception('unknown type')
