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simpson vwap

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bxdd
2021-01-26 14:32:08 +00:00
parent e4ecea55e4
commit 1b569d371d
3 changed files with 78 additions and 65 deletions
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54
examples/high_freq/highfreq_handler.py
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@@ -7,7 +7,7 @@ from qlib.log import TimeInspector
class HighFreqHandler(DataHandlerLP): class HighFreqHandler(DataHandlerLP):
def __init__( def __init__(
self, self,
instruments="csi500", instruments="csi300",
start_time=None, start_time=None,
end_time=None, end_time=None,
freq="1min", freq="1min",
@@ -55,8 +55,10 @@ class HighFreqHandler(DataHandlerLP):
names = [] names = []
template_if = "If(IsNull({1}), {0}, {1})" template_if = "If(IsNull({1}), {0}, {1})"
template_paused = "Select(Eq($paused, 0.0), {0})" #template_paused = "Select(Eq($paused, 0.0), {0})"
template_paused="{0}"
template_fillnan = "FFillNan({0})" template_fillnan = "FFillNan({0})"
simpson_vwap = "($open + 2*$high + 2*$low + $close)/6"
fields += [ fields += [
"{0}/Ref(DayLast({1}), 240)".format( "{0}/Ref(DayLast({1}), 240)".format(
template_if.format( template_if.format(
@@ -87,11 +89,9 @@ class HighFreqHandler(DataHandlerLP):
fields += ["{0}/Ref(DayLast({0}), 240)".format(template_fillnan.format(template_paused.format("$close")))] fields += ["{0}/Ref(DayLast({0}), 240)".format(template_fillnan.format(template_paused.format("$close")))]
fields += [ fields += [
"{0}/Ref(DayLast({1}), 240)".format( "{0}/Ref(DayLast({1}), 240)".format(
"If(IsNull({1}), {0}, If(Or(Or(Or(Eq({1}, np.inf), Eq({1}, -np.inf)), Eq({1}, 0)), Or(Gt({1}, Mul(1.001, {3})), Lt({1}, Mul(0.999, {2})))), {0}, {1}))".format( template_if.format(
template_fillnan.format(template_paused.format("$close")), template_fillnan.format(template_paused.format("$close")),
template_paused.format("$vwap"), template_paused.format(simpson_vwap),
template_paused.format("$low"),
template_paused.format("$high"),
), ),
template_fillnan.format(template_paused.format("$close")), template_fillnan.format(template_paused.format("$close")),
) )
@@ -128,13 +128,12 @@ class HighFreqHandler(DataHandlerLP):
fields += [ fields += [
"Ref({0}, 240)/Ref(DayLast({0}), 240)".format(template_fillnan.format(template_paused.format("$close"))) "Ref({0}, 240)/Ref(DayLast({0}), 240)".format(template_fillnan.format(template_paused.format("$close")))
] ]
fields += [ fields += [
"Ref({0}, 240)/Ref(DayLast({1}), 240)".format( "Ref({0}, 240)/Ref(DayLast({1}), 240)".format(
"If(IsNull({1}), {0}, If(Or(Or(Or(Eq({1}, np.inf), Eq({1}, -np.inf)), Eq({1}, 0)), Or(Gt({1}, Mul(1.001, {3})), Lt({1}, Mul(0.999, {2})))), {0}, {1}))".format( template_if.format(
template_fillnan.format(template_paused.format("$close")), template_fillnan.format(template_paused.format("$close")),
template_paused.format("$vwap"), template_paused.format(simpson_vwap),
template_paused.format("$low"),
template_paused.format("$high"),
), ),
template_fillnan.format(template_paused.format("$close")), template_fillnan.format(template_paused.format("$close")),
) )
@@ -143,10 +142,9 @@ class HighFreqHandler(DataHandlerLP):
fields += [ fields += [
"{0}/Ref(DayLast(Mean({0}, 7200)), 240)".format( "{0}/Ref(DayLast(Mean({0}, 7200)), 240)".format(
"If(IsNull({1}), 0, If(Or(Gt({2}, Mul(1.001, {4})), Lt({2}, Mul(0.999, {3}))), 0, {1}))".format( "If(IsNull({0}), 0, If(Or(Gt({1}, Mul(1.001, {3})), Lt({1}, Mul(0.999, {2}))), 0, {0}))".format(
template_fillnan.format(template_paused.format("$close")),
template_paused.format("$volume"), template_paused.format("$volume"),
template_paused.format("$vwap"), template_paused.format(simpson_vwap),
template_paused.format("$low"), template_paused.format("$low"),
template_paused.format("$high"), template_paused.format("$high"),
) )
@@ -155,10 +153,9 @@ class HighFreqHandler(DataHandlerLP):
names += ["$volume"] names += ["$volume"]
fields += [ fields += [
"Ref({0}, 240)/Ref(DayLast(Mean({0}, 7200)), 240)".format( "Ref({0}, 240)/Ref(DayLast(Mean({0}, 7200)), 240)".format(
"If(IsNull({1}), 0, If(Or(Gt({2}, Mul(1.001, {4})), Lt({2}, Mul(0.999, {3}))), 0, {1}))".format( "If(IsNull({0}), 0, If(Or(Gt({1}, Mul(1.001, {3})), Lt({1}, Mul(0.999, {2}))), 0, {0}))".format(
template_fillnan.format(template_paused.format("$close")),
template_paused.format("$volume"), template_paused.format("$volume"),
template_paused.format("$vwap"), template_paused.format(simpson_vwap),
template_paused.format("$low"), template_paused.format("$low"),
template_paused.format("$high"), template_paused.format("$high"),
) )
@@ -199,21 +196,26 @@ class HighFreqBacktestHandler(DataHandler):
names = [] names = []
template_if = "If(Eq({1}, np.nan), {0}, {1})" template_if = "If(Eq({1}, np.nan), {0}, {1})"
template_paused = "Select(Eq($paused, 0.0), {0})" #template_paused = "Select(Eq($paused, 0.0), {0})"
template_paused="{0}"
template_fillnan = "FFillNan({0})" template_fillnan = "FFillNan({0})"
simpson_vwap = "($open + 2*$high + 2*$low + $close)/6"
#fields += [
# template_fillnan.format(template_paused.format("$close")),
#]
fields += [template_if.format(
template_fillnan.format(template_paused.format("$close")),
template_paused.format(simpson_vwap),
)]
names += ["$vwap_0"]
fields += [ fields += [
template_fillnan.format(template_paused.format("$close")), "If(IsNull({0}), 0, If(Or(Gt({1}, Mul(1.001, {3})), Lt({1}, Mul(0.999, {2}))), 0, {0}))".format(
]
names += ["$vwap0"]
fields += [
"If(Eq({1}, np.nan), 0, If(Or(Gt({2}, Mul(1.001, {4})), Lt({2}, Mul(0.999, {3}))), 0, {1}))".format(
template_fillnan.format(template_paused.format("$close")),
template_paused.format("$volume"), template_paused.format("$volume"),
template_paused.format("$vwap"), template_paused.format(simpson_vwap),
template_paused.format("$low"), template_paused.format("$low"),
template_paused.format("$high"), template_paused.format("$high"),
) )
] ]
names += ["$volume0"] names += ["$volume_0"]
return fields, names return fields, names

1
examples/high_freq/highfreq_processor.py
View File

@@ -58,6 +58,7 @@ class HighFreqNorm(Processor):
# print("start_call_feature_reshape") # print("start_call_feature_reshape")
idx = df_features.index.droplevel("datetime").drop_duplicates() idx = df_features.index.droplevel("datetime").drop_duplicates()
idx.set_names(["instrument", "datetime"], inplace=True) idx.set_names(["instrument", "datetime"], inplace=True)
print(df_values.shape)
feat = df_values[:, [0, 1, 2, 3, 4, 10]].reshape(-1, 6 * 240) feat = df_values[:, [0, 1, 2, 3, 4, 10]].reshape(-1, 6 * 240)
feat_1 = df_values[:, [5, 6, 7, 8, 9, 11]].reshape(-1, 6 * 240) feat_1 = df_values[:, [5, 6, 7, 8, 9, 11]].reshape(-1, 6 * 240)
df_new_features = pd.DataFrame( df_new_features = pd.DataFrame(

88
examples/high_freq/workflow.py
View File

@@ -27,7 +27,7 @@ from highfreq_ops import DayFirst, DayLast, FFillNan, Date, Select, IsNull
if __name__ == "__main__": if __name__ == "__main__":
# use default data # use default data
provider_uri = "/mnt/v-xiabi/data/qlib/high_freq" # target_dir provider_uri = "/nfs_data/qlib_data/yahoo_high_qlib" # target_dir
qlib.init( qlib.init(
provider_uri=provider_uri, provider_uri=provider_uri,
custom_ops=[DayFirst, DayLast, FFillNan, Date, Select, IsNull], custom_ops=[DayFirst, DayLast, FFillNan, Date, Select, IsNull],
@@ -38,12 +38,16 @@ if __name__ == "__main__":
MARKET = "all" MARKET = "all"
BENCHMARK = "SH000300" BENCHMARK = "SH000300"
DROP_LOAD_DATASET = False # flag wether to test [drop and load dataset]
start_time = "2019-01-01 00:00:00" #start_time = "2019-01-01 00:00:00"
end_time = "2019-12-31 15:00:00" #end_time = "2019-12-31 15:00:00"
train_end_time = "2019-05-31 15:00:00" #train_end_time = "2019-05-31 15:00:00"
test_start_time = "2019-06-01 00:00:00" #test_start_time = "2019-06-01 00:00:00"
start_time = "2020-09-14 00:00:00"
end_time = "2021-01-18 16:00:00"
train_end_time = "2020-11-30 16:00:00"
test_start_time = "2020-12-01 00:00:00"
################################### ###################################
# train model # train model
################################### ###################################
@@ -108,51 +112,57 @@ if __name__ == "__main__":
Cal.get_calendar_day(freq="1min") Cal.get_calendar_day(freq="1min")
##=============get data============= ##=============get data=============
dataset = init_instance_by_config(task["dataset"]) dataset = init_instance_by_config(task["dataset"])
xtrain, xtest = dataset.prepare(["train", "test"])
print(xtrain, xtest)
dataset_backtest = init_instance_by_config(task["dataset_backtest"]) dataset_backtest = init_instance_by_config(task["dataset_backtest"])
xtrain, xtest = dataset.prepare(["train", "test"])
backtest_train, backtest_test = dataset_backtest.prepare(["train", "test"]) backtest_train, backtest_test = dataset_backtest.prepare(["train", "test"])
print(xtrain, xtest)
print(backtest_train, backtest_test) print(backtest_train, backtest_test)
del xtrain, xtest del xtrain, xtest
del backtest_train, backtest_test del backtest_train, backtest_test
##=============dump dataset=============
dataset.to_pickle(path="dataset.pkl")
dataset_backtest.to_pickle(path="dataset_backtest.pkl")
del dataset, dataset_backtest if DROP_LOAD_DATASET:
##=============reload dataset=============
file_dataset = open("dataset.pkl", "rb")
dataset = pickle.load(file_dataset)
file_dataset.close()
file_dataset_backtest = open("dataset_backtest.pkl", "rb") ##=============dump dataset=============
dataset_backtest = pickle.load(file_dataset_backtest) dataset.to_pickle(path="dataset.pkl")
dataset_backtest.to_pickle(path="dataset_backtest.pkl")
file_dataset_backtest.close() del dataset, dataset_backtest
##=============reload dataset=============
file_dataset = open("dataset.pkl", "rb")
dataset = pickle.load(file_dataset)
file_dataset.close()
##=============reload_dataset============= file_dataset_backtest = open("dataset_backtest.pkl", "rb")
dataset.init(init_type=DataHandlerLP.IT_LS) dataset_backtest = pickle.load(file_dataset_backtest)
dataset_backtest.init(init_type=DataHandlerLP.IT_LS)
##=============reinit qlib============= file_dataset_backtest.close()
qlib.init(
provider_uri=provider_uri,
custom_ops=[DayFirst, DayLast, FFillNan, Date, Select, IsNull],
redis_port=-1,
region=REG_CN,
auto_mount=False,
)
Cal.calendar(freq="1min") # load the calendar for cache ##=============reload_dataset=============
Cal.get_calendar_day(freq="1min") # load the calendar for cache dataset.init(init_type=DataHandlerLP.IT_LS)
dataset_backtest.init(init_type=DataHandlerLP.IT_LS)
##=============test dataset ##=============reinit qlib=============
xtrain, xtest = dataset.prepare(["train", "test"]) qlib.init(
backtest_train, backtest_test = dataset_backtest.prepare(["train", "test"]) provider_uri=provider_uri,
custom_ops=[DayFirst, DayLast, FFillNan, Date, Select, IsNull],
redis_port=-1,
region=REG_CN,
auto_mount=False,
)
print(xtrain, xtest) Cal.calendar(freq="1min") # load the calendar for cache
print(backtest_train, backtest_test) Cal.get_calendar_day(freq="1min") # load the calendar for cache
del xtrain, xtest
del backtest_train, backtest_test ##=============test dataset
xtrain, xtest = dataset.prepare(["train", "test"])
backtest_train, backtest_test = dataset_backtest.prepare(["train", "test"])
print(xtrain, xtest)
print(backtest_train, backtest_test)
del xtrain, xtest
del backtest_train, backtest_test