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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from ...data.dataset.handler import DataHandlerLP
from ...data.dataset.processor import Processor
from ...utils import get_callable_kwargs
from ...data.dataset import processor as processor_module
from inspect import getfullargspec
def check_transform_proc(proc_l, fit_start_time, fit_end_time):
new_l = []
for p in proc_l:
if not isinstance(p, Processor):
klass, pkwargs = get_callable_kwargs(p, processor_module)
args = getfullargspec(klass).args
if "fit_start_time" in args and "fit_end_time" in args:
assert (
fit_start_time is not None and fit_end_time is not None
), "Make sure `fit_start_time` and `fit_end_time` are not None."
pkwargs.update(
{
"fit_start_time": fit_start_time,
"fit_end_time": fit_end_time,
}
)
proc_config = {"class": klass.__name__, "kwargs": pkwargs}
if isinstance(p, dict) and "module_path" in p:
proc_config["module_path"] = p["module_path"]
new_l.append(proc_config)
else:
new_l.append(p)
return new_l
_DEFAULT_LEARN_PROCESSORS = [
{"class": "DropnaLabel"},
{"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
]
_DEFAULT_INFER_PROCESSORS = [
{"class": "ProcessInf", "kwargs": {}},
{"class": "ZScoreNorm", "kwargs": {}},
{"class": "Fillna", "kwargs": {}},
]
class Alpha360(DataHandlerLP):
def __init__(
self,
instruments="csi500",
start_time=None,
end_time=None,
freq="day",
infer_processors=_DEFAULT_INFER_PROCESSORS,
learn_processors=_DEFAULT_LEARN_PROCESSORS,
fit_start_time=None,
fit_end_time=None,
filter_pipe=None,
inst_processors=None,
**kwargs
):
infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
data_loader = {
"class": "QlibDataLoader",
"kwargs": {
"config": {
"feature": self.get_feature_config(),
"label": kwargs.pop("label", self.get_label_config()),
},
"filter_pipe": filter_pipe,
"freq": freq,
"inst_processors": inst_processors,
},
}
super().__init__(
instruments=instruments,
start_time=start_time,
end_time=end_time,
data_loader=data_loader,
learn_processors=learn_processors,
infer_processors=infer_processors,
**kwargs
)
def get_label_config(self):
return ["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"]
@staticmethod
def get_feature_config():
# NOTE:
# Alpha360 tries to provide a dataset with original price data
# the original price data includes the prices and volume in the last 60 days.
# To make it easier to learn models from this dataset, all the prices and volume
# are normalized by the latest price and volume data ( dividing by $close, $volume)
# So the latest normalized $close will be 1 (with name CLOSE0), the latest normalized $volume will be 1 (with name VOLUME0)
# If further normalization are executed (e.g. centralization), CLOSE0 and VOLUME0 will be 0.
fields = []
names = []
for i in range(59, 0, -1):
fields += ["Ref($close, %d)/$close" % i]
names += ["CLOSE%d" % i]
fields += ["$close/$close"]
names += ["CLOSE0"]
for i in range(59, 0, -1):
fields += ["Ref($open, %d)/$close" % i]
names += ["OPEN%d" % i]
fields += ["$open/$close"]
names += ["OPEN0"]
for i in range(59, 0, -1):
fields += ["Ref($high, %d)/$close" % i]
names += ["HIGH%d" % i]
fields += ["$high/$close"]
names += ["HIGH0"]
for i in range(59, 0, -1):
fields += ["Ref($low, %d)/$close" % i]
names += ["LOW%d" % i]
fields += ["$low/$close"]
names += ["LOW0"]
for i in range(59, 0, -1):
fields += ["Ref($vwap, %d)/$close" % i]
names += ["VWAP%d" % i]
fields += ["$vwap/$close"]
names += ["VWAP0"]
for i in range(59, 0, -1):
fields += ["Ref($volume, %d)/($volume+1e-12)" % i]
names += ["VOLUME%d" % i]
fields += ["$volume/($volume+1e-12)"]
names += ["VOLUME0"]
return fields, names
class Alpha360vwap(Alpha360):
def get_label_config(self):
return ["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["LABEL0"]
class Alpha158(DataHandlerLP):
def __init__(
self,
instruments="csi500",
start_time=None,
end_time=None,
freq="day",
infer_processors=[],
learn_processors=_DEFAULT_LEARN_PROCESSORS,
fit_start_time=None,
fit_end_time=None,
process_type=DataHandlerLP.PTYPE_A,
filter_pipe=None,
inst_processors=None,
**kwargs
):
infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
data_loader = {
"class": "QlibDataLoader",
"kwargs": {
"config": {
"feature": self.get_feature_config(),
"label": kwargs.pop("label", self.get_label_config()),
},
"filter_pipe": filter_pipe,
"freq": freq,
"inst_processors": inst_processors,
},
}
super().__init__(
instruments=instruments,
start_time=start_time,
end_time=end_time,
data_loader=data_loader,
infer_processors=infer_processors,
learn_processors=learn_processors,
process_type=process_type,
**kwargs
)
def get_feature_config(self):
conf = {
"kbar": {},
"price": {
"windows": [0],
"feature": ["OPEN", "HIGH", "LOW", "VWAP"],
},
"rolling": {},
}
return self.parse_config_to_fields(conf)
def get_label_config(self):
return ["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"]
@staticmethod
def parse_config_to_fields(config):
"""create factors from config
config = {
'kbar': {}, # whether to use some hard-code kbar features
'price': { # whether to use raw price features
'windows': [0, 1, 2, 3, 4], # use price at n days ago
'feature': ['OPEN', 'HIGH', 'LOW'] # which price field to use
},
'volume': { # whether to use raw volume features
'windows': [0, 1, 2, 3, 4], # use volume at n days ago
},
'rolling': { # whether to use rolling operator based features
'windows': [5, 10, 20, 30, 60], # rolling windows size
'include': ['ROC', 'MA', 'STD'], # rolling operator to use
#if include is None we will use default operators
'exclude': ['RANK'], # rolling operator not to use
}
}
"""
fields = []
names = []
if "kbar" in config:
fields += [
"($close-$open)/$open",
"($high-$low)/$open",
"($close-$open)/($high-$low+1e-12)",
"($high-Greater($open, $close))/$open",
"($high-Greater($open, $close))/($high-$low+1e-12)",
"(Less($open, $close)-$low)/$open",
"(Less($open, $close)-$low)/($high-$low+1e-12)",
"(2*$close-$high-$low)/$open",
"(2*$close-$high-$low)/($high-$low+1e-12)",
]
names += [
"KMID",
"KLEN",
"KMID2",
"KUP",
"KUP2",
"KLOW",
"KLOW2",
"KSFT",
"KSFT2",
]
if "price" in config:
windows = config["price"].get("windows", range(5))
feature = config["price"].get("feature", ["OPEN", "HIGH", "LOW", "CLOSE", "VWAP"])
for field in feature:
field = field.lower()
fields += ["Ref($%s, %d)/$close" % (field, d) if d != 0 else "$%s/$close" % field for d in windows]
names += [field.upper() + str(d) for d in windows]
if "volume" in config:
windows = config["volume"].get("windows", range(5))
fields += ["Ref($volume, %d)/($volume+1e-12)" % d if d != 0 else "$volume/($volume+1e-12)" for d in windows]
names += ["VOLUME" + str(d) for d in windows]
if "rolling" in config:
windows = config["rolling"].get("windows", [5, 10, 20, 30, 60])
include = config["rolling"].get("include", None)
exclude = config["rolling"].get("exclude", [])
# `exclude` in dataset config unnecessary filed
# `include` in dataset config necessary field
def use(x):
return x not in exclude and (include is None or x in include)
# Some factor ref: https://guorn.com/static/upload/file/3/134065454575605.pdf
if use("ROC"):
# https://www.investopedia.com/terms/r/rateofchange.asp
# Rate of change, the price change in the past d days, divided by latest close price to remove unit
fields += ["Ref($close, %d)/$close" % d for d in windows]
names += ["ROC%d" % d for d in windows]
if use("MA"):
# https://www.investopedia.com/ask/answers/071414/whats-difference-between-moving-average-and-weighted-moving-average.asp
# Simple Moving Average, the simple moving average in the past d days, divided by latest close price to remove unit
fields += ["Mean($close, %d)/$close" % d for d in windows]
names += ["MA%d" % d for d in windows]
if use("STD"):
# The standard diviation of close price for the past d days, divided by latest close price to remove unit
fields += ["Std($close, %d)/$close" % d for d in windows]
names += ["STD%d" % d for d in windows]
if use("BETA"):
# The rate of close price change in the past d days, divided by latest close price to remove unit
# For example, price increase 10 dollar per day in the past d days, then Slope will be 10.
fields += ["Slope($close, %d)/$close" % d for d in windows]
names += ["BETA%d" % d for d in windows]
if use("RSQR"):
# The R-sqaure value of linear regression for the past d days, represent the trend linear
fields += ["Rsquare($close, %d)" % d for d in windows]
names += ["RSQR%d" % d for d in windows]
if use("RESI"):
# The redisdual for linear regression for the past d days, represent the trend linearity for past d days.
fields += ["Resi($close, %d)/$close" % d for d in windows]
names += ["RESI%d" % d for d in windows]
if use("MAX"):
# The max price for past d days, divided by latest close price to remove unit
fields += ["Max($high, %d)/$close" % d for d in windows]
names += ["MAX%d" % d for d in windows]
if use("LOW"):
# The low price for past d days, divided by latest close price to remove unit
fields += ["Min($low, %d)/$close" % d for d in windows]
names += ["MIN%d" % d for d in windows]
if use("QTLU"):
# The 80% quantile of past d day's close price, divided by latest close price to remove unit
# Used with MIN and MAX
fields += ["Quantile($close, %d, 0.8)/$close" % d for d in windows]
names += ["QTLU%d" % d for d in windows]
if use("QTLD"):
# The 20% quantile of past d day's close price, divided by latest close price to remove unit
fields += ["Quantile($close, %d, 0.2)/$close" % d for d in windows]
names += ["QTLD%d" % d for d in windows]
if use("RANK"):
# Get the percentile of current close price in past d day's close price.
# Represent the current price level comparing to past N days, add additional information to moving average.
fields += ["Rank($close, %d)" % d for d in windows]
names += ["RANK%d" % d for d in windows]
if use("RSV"):
# Represent the price position between upper and lower resistent price for past d days.
fields += ["($close-Min($low, %d))/(Max($high, %d)-Min($low, %d)+1e-12)" % (d, d, d) for d in windows]
names += ["RSV%d" % d for d in windows]
if use("IMAX"):
# The number of days between current date and previous highest price date.
# Part of Aroon Indicator https://www.investopedia.com/terms/a/aroon.asp
# The indicator measures the time between highs and the time between lows over a time period.
# The idea is that strong uptrends will regularly see new highs, and strong downtrends will regularly see new lows.
fields += ["IdxMax($high, %d)/%d" % (d, d) for d in windows]
names += ["IMAX%d" % d for d in windows]
if use("IMIN"):
# The number of days between current date and previous lowest price date.
# Part of Aroon Indicator https://www.investopedia.com/terms/a/aroon.asp
# The indicator measures the time between highs and the time between lows over a time period.
# The idea is that strong uptrends will regularly see new highs, and strong downtrends will regularly see new lows.
fields += ["IdxMin($low, %d)/%d" % (d, d) for d in windows]
names += ["IMIN%d" % d for d in windows]
if use("IMXD"):
# The time period between previous lowest-price date occur after highest price date.
# Large value suggest downward momemtum.
fields += ["(IdxMax($high, %d)-IdxMin($low, %d))/%d" % (d, d, d) for d in windows]
names += ["IMXD%d" % d for d in windows]
if use("CORR"):
# The correlation between absolute close price and log scaled trading volume
fields += ["Corr($close, Log($volume+1), %d)" % d for d in windows]
names += ["CORR%d" % d for d in windows]
if use("CORD"):
# The correlation between price change ratio and volume change ratio
fields += ["Corr($close/Ref($close,1), Log($volume/Ref($volume, 1)+1), %d)" % d for d in windows]
names += ["CORD%d" % d for d in windows]
if use("CNTP"):
# The percentage of days in past d days that price go up.
fields += ["Mean($close>Ref($close, 1), %d)" % d for d in windows]
names += ["CNTP%d" % d for d in windows]
if use("CNTN"):
# The percentage of days in past d days that price go down.
fields += ["Mean($close<Ref($close, 1), %d)" % d for d in windows]
names += ["CNTN%d" % d for d in windows]
if use("CNTD"):
# The diff between past up day and past down day
fields += ["Mean($close>Ref($close, 1), %d)-Mean($close<Ref($close, 1), %d)" % (d, d) for d in windows]
names += ["CNTD%d" % d for d in windows]
if use("SUMP"):
# The total gain / the absolute total price changed
# Similar to RSI indicator. https://www.investopedia.com/terms/r/rsi.asp
fields += [
"Sum(Greater($close-Ref($close, 1), 0), %d)/(Sum(Abs($close-Ref($close, 1)), %d)+1e-12)" % (d, d)
for d in windows
]
names += ["SUMP%d" % d for d in windows]
if use("SUMN"):
# The total lose / the absolute total price changed
# Can be derived from SUMP by SUMN = 1 - SUMP
# Similar to RSI indicator. https://www.investopedia.com/terms/r/rsi.asp
fields += [
"Sum(Greater(Ref($close, 1)-$close, 0), %d)/(Sum(Abs($close-Ref($close, 1)), %d)+1e-12)" % (d, d)
for d in windows
]
names += ["SUMN%d" % d for d in windows]
if use("SUMD"):
# The diff ratio between total gain and total lose
# Similar to RSI indicator. https://www.investopedia.com/terms/r/rsi.asp
fields += [
"(Sum(Greater($close-Ref($close, 1), 0), %d)-Sum(Greater(Ref($close, 1)-$close, 0), %d))"
"/(Sum(Abs($close-Ref($close, 1)), %d)+1e-12)" % (d, d, d)
for d in windows
]
names += ["SUMD%d" % d for d in windows]
if use("VMA"):
# Simple Volume Moving average: https://www.barchart.com/education/technical-indicators/volume_moving_average
fields += ["Mean($volume, %d)/($volume+1e-12)" % d for d in windows]
names += ["VMA%d" % d for d in windows]
if use("VSTD"):
# The standard deviation for volume in past d days.
fields += ["Std($volume, %d)/($volume+1e-12)" % d for d in windows]
names += ["VSTD%d" % d for d in windows]
if use("WVMA"):
# The volume weighted price change volatility
fields += [
"Std(Abs($close/Ref($close, 1)-1)*$volume, %d)/(Mean(Abs($close/Ref($close, 1)-1)*$volume, %d)+1e-12)"
% (d, d)
for d in windows
]
names += ["WVMA%d" % d for d in windows]
if use("VSUMP"):
# The total volume increase / the absolute total volume changed
fields += [
"Sum(Greater($volume-Ref($volume, 1), 0), %d)/(Sum(Abs($volume-Ref($volume, 1)), %d)+1e-12)"
% (d, d)
for d in windows
]
names += ["VSUMP%d" % d for d in windows]
if use("VSUMN"):
# The total volume increase / the absolute total volume changed
# Can be derived from VSUMP by VSUMN = 1 - VSUMP
fields += [
"Sum(Greater(Ref($volume, 1)-$volume, 0), %d)/(Sum(Abs($volume-Ref($volume, 1)), %d)+1e-12)"
% (d, d)
for d in windows
]
names += ["VSUMN%d" % d for d in windows]
if use("VSUMD"):
# The diff ratio between total volume increase and total volume decrease
# RSI indicator for volume
fields += [
"(Sum(Greater($volume-Ref($volume, 1), 0), %d)-Sum(Greater(Ref($volume, 1)-$volume, 0), %d))"
"/(Sum(Abs($volume-Ref($volume, 1)), %d)+1e-12)" % (d, d, d)
for d in windows
]
names += ["VSUMD%d" % d for d in windows]
return fields, names
class Alpha158vwap(Alpha158):
def get_label_config(self):
return ["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["LABEL0"]
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