def setUpClass(self):
self.DEBUG = False
self.METRICS = False
self.data_api_impl = DataApi('../../../data/')
self.cross_validator_impl = CrossValidator()
self.preprocessor_impl = Preprocessor()
def setUpClass(self):
self.DEBUG = False
self.METRICS = False
# construct DataApi instance with path prefix to data directory (relative from here)
self.data_api_impl = DataApi('../../../data/')
self.distance_functions_impl = DistanceFunctions()
def __init__(self):
self.DEBUG = True
self.VERBOSE = False
self.data_api_impl = DataApi('../../../data/')
self.data_set = None
self.CLASSIFICATION = True
self.REGRESSION = False
self.algorithm_name = None
def __init__(self):
# logger instance - VERBOSE level is highest (most verbose) level for logging
self.logger = Logger('DEMO') # configure log level here
# datalayer instance - read csv data files and convert into raw data frames
self.datalayer = DataApi('../../data/')
# preprocessor instance - everything for prerocessing data frames
self.preprocessor = Preprocessor()
# cross_validator instance - setup cross validation partitions
self.cross_validator = CrossValidator()
# utils instance - random things
self.utils = Utils()
def __init__(self):
KNN.__init__(self)
self.DEBUG = True
self.VERBOSE = False
self.data_api_impl = DataApi('../../data/')
self.utilities_impl = Utilities()
self.distance_functions_impl = DistanceFunctions()
# threshold for clustering convergence
# stop iterating when differences between consecutive centroids is smaller than this
self.CONVERGENCE_THRESHOLD = 0.25
# maximum clustering iterations allowed before returning answer
self.MAX_ITERATIONS = 5
def __init__(self):
self.DEBUG = False
# get instances of all the classes needed to run an experiment
self.data_api_impl = DataApi('../../data/')
self.preprocessor_impl = Preprocessor()
self.cross_validator_impl = CrossValidator()
self.parameter_tuner_impl = ParameterTuner()
# algorithm implementations
self.knn_impl = KNN()
self.enn_impl = EditedKNN()
self.cnn_impl = CondensedKNN()
self.kmeans_knn_impl = KMeansClustering()
self.k_medoids_clustering_impl = KMedoidsClustering()
self.results_processor_impl = Results()
self.CLASSIFICATION = False
self.REGRESSION = False
def __init__(self):
KNN.__init__(self)
self.DEBUG = True
self.data_api_impl = DataApi('../../data/')
print("Number of Previous Edits: ")
print(number_of_edits_previous)
loopcounter += 1
print("Number of While Loops: ")
return edited_train_set.reset_index(drop=True)
# EXECUTE SCRIPT
if __name__ == '__main__':
print('running edited knn...')
edited_knn = EditedKNN()
data_api_impl = DataApi('../../data/')
cross_validator_impl = CrossValidator()
preprocessor_impl = Preprocessor()
wine_data = data_api_impl.get_raw_data_frame('segmentation')
prep_wine_data = preprocessor_impl.preprocess_raw_data_frame(
wine_data, 'segmentation')
wine_data_train_set = cross_validator_impl.get_training_set(
prep_wine_data, test_set_number=3)
print('wine_data_train_set.shape: ' + str(wine_data_train_set.shape))
wine_data_test_set = cross_validator_impl.get_test_set(
prep_wine_data, test_set_number, indexes_list)
edited_knn.enn(wine_data_train_set, wine_data_test_set, prep_wine_data, k)
def __init__(self):
self.DEBUG = False
self.data_api_impl = DataApi('../../data/')
info = {
"PATTERN": "looper",
"LOOPER": {
"initial_capital": 100000,
"margin_ratio": {
"rb2010.CTP": 0.00003,
},
"commission_ratio": {
"rb2010.CTP": {
"close": 0.00001
},
},
"size_map": {
"rb2010.CTP": 10
}
}
}
app.config.from_mapping(info)
strategy = DoubleMaStrategy("ma")
data_api = DataApi()
data = data_api.get_tick("rb2010",
start_date="2020-04-10",
end_date="2020-07-21",
today=False)
# data = data_support.get_future_min("rb2010.SHFE", frq="1min", start="2019-10-01", end="2020-07-15")
app.add_data(data)
app.add_extension(strategy)
app.start()
result = app.get_result(report=True, auto_open=True)
def __init__(self):
KNN.__init__(self)
self.DEBUG = False
self.data_api_impl = DataApi('../../data/')
self.enn_impl = EditedKNN()
self.cnn_impl = CondensedKNN()
def __init__(self):
self.DEBUG = False
# construct DataApi instance with path prefix to data directory (relative from here)
self.data_api_impl = DataApi('../../data/')
trading_record: 格式为 [(1,datetime1)]
1: 开多
2: 开空
-1: 平多
-2： 平空
"""
self.data.setdefault(local_symbol, {})["record"] = trading_record
self.data.setdefault(local_symbol, {})["kline"] = [[
str(kline.datetime), kline.open_price, kline.high_price,
kline.low_price, kline.close_price, kline.volume
] for kline in klines]
def render(self, path):
for local_symbol, obj in self.data.items():
with open(path, "w") as f:
print(obj)
kline_string = kline_template.render(draw_klines=obj["kline"],
bs=obj["record"])
f.write(kline_string)
if __name__ == '__main__':
plot = Plot("some")
from data_api import DataApi
code = "rb2105.SHFE"
data_api = DataApi(uri="http://192.168.1.239:8124")
kline = data_api.get_n_min_bar(code, 1, "2021-04-15", "2021-04-16")
plot.add_kline(code, klines=kline, trading_record=[])
plot.render("x.html")
def __init__(self):
self.DEBUG = False
self.data_api_impl = DataApi('../../data/')
self.utilities_impl = Utilities()