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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | Handler._build_session | def _build_session(self, name, start_info, end_info):
"""Builds a session object."""
assert start_info is not None
result = api_pb2.Session(
name=name,
start_time_secs=start_info.start_time_secs,
model_uri=start_info.model_uri,
metric_values=self._build_session_metric_values... | python | def _build_session(self, name, start_info, end_info):
"""Builds a session object."""
assert start_info is not None
result = api_pb2.Session(
name=name,
start_time_secs=start_info.start_time_secs,
model_uri=start_info.model_uri,
metric_values=self._build_session_metric_values... | [
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | Handler._build_session_metric_values | def _build_session_metric_values(self, session_name):
"""Builds the session metric values."""
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result = []
metric_infos = self._experiment.metric_infos
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metric_name = metric_info.name
try:
metric... | python | def _build_session_metric_values(self, session_name):
"""Builds the session metric values."""
# result is a list of api_pb2.MetricValue instances.
result = []
metric_infos = self._experiment.metric_infos
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metric_name = metric_info.name
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | Handler._aggregate_metrics | def _aggregate_metrics(self, session_group):
"""Sets the metrics of the group based on aggregation_type."""
if (self._request.aggregation_type == api_pb2.AGGREGATION_AVG or
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elif self._request... | python | def _aggregate_metrics(self, session_group):
"""Sets the metrics of the group based on aggregation_type."""
if (self._request.aggregation_type == api_pb2.AGGREGATION_AVG or
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tensorflow/tensorboard | tensorboard/plugins/hparams/list_session_groups.py | Handler._sort | def _sort(self, session_groups):
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session_groups.sort(key=operator.attrgetter('name'))
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"""Sorts 'session_groups' in place according to _request.col_params."""
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioAnalysisRecordAlsa.py | recordAnalyzeAudio | def recordAnalyzeAudio(duration, outputWavFile, midTermBufferSizeSec, modelName, modelType):
'''
recordAnalyzeAudio(duration, outputWavFile, midTermBufferSizeSec, modelName, modelType)
This function is used to record and analyze audio segments, in a fix window basis.
ARGUMENTS:
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'''
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audacityAnnotation2WAVs.py | annotation2files | def annotation2files(wavFile, csvFile):
'''
Break an audio stream to segments of interest,
defined by a csv file
- wavFile: path to input wavfile
- csvFile: path to csvFile of segment limits
Input CSV file must be of the format <T1>\t<T2>\t<Label>
... | python | def annotation2files(wavFile, csvFile):
'''
Break an audio stream to segments of interest,
defined by a csv file
- wavFile: path to input wavfile
- csvFile: path to csvFile of segment limits
Input CSV file must be of the format <T1>\t<T2>\t<Label>
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioBasicIO.py | convertDirMP3ToWav | def convertDirMP3ToWav(dirName, Fs, nC, useMp3TagsAsName = False):
'''
This function converts the MP3 files stored in a folder to WAV. If required, the output names of the WAV files are based on MP3 tags, otherwise the same names are used.
ARGUMENTS:
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'''
This function converts the MP3 files stored in a folder to WAV. If required, the output names of the WAV files are based on MP3 tags, otherwise the same names are used.
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioBasicIO.py | convertFsDirWavToWav | def convertFsDirWavToWav(dirName, Fs, nC):
'''
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ARGUMENTS:
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioBasicIO.py | readAudioFile | def readAudioFile(path):
'''
This function returns a numpy array that stores the audio samples of a specified WAV of AIFF file
'''
extension = os.path.splitext(path)[1]
try:
#if extension.lower() == '.wav':
#[Fs, x] = wavfile.read(path)
if extension.lower() == '.aif' or ... | python | def readAudioFile(path):
'''
This function returns a numpy array that stores the audio samples of a specified WAV of AIFF file
'''
extension = os.path.splitext(path)[1]
try:
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioBasicIO.py | stereo2mono | def stereo2mono(x):
'''
This function converts the input signal
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'''
if isinstance(x, int):
return -1
if x.ndim==1:
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i... | python | def stereo2mono(x):
'''
This function converts the input signal
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'''
if isinstance(x, int):
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | selfSimilarityMatrix | def selfSimilarityMatrix(featureVectors):
'''
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ARGUMENTS:
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'''
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ARGUMENTS:
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | flags2segs | def flags2segs(flags, window):
'''
ARGUMENTS:
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RETURNS:
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'''
ARGUMENTS:
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | segs2flags | def segs2flags(seg_start, seg_end, seg_label, win_size):
'''
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ARGUMENTS:
- seg_start: segment start points (in seconds)
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- seg_label: segment ... | python | def segs2flags(seg_start, seg_end, seg_label, win_size):
'''
This function converts segment endpoints and respective segment
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ARGUMENTS:
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | computePreRec | def computePreRec(cm, class_names):
'''
This function computes the precision, recall and f1 measures,
given a confusion matrix
'''
n_classes = cm.shape[0]
if len(class_names) != n_classes:
print("Error in computePreRec! Confusion matrix and class_names "
"list must be of th... | python | def computePreRec(cm, class_names):
'''
This function computes the precision, recall and f1 measures,
given a confusion matrix
'''
n_classes = cm.shape[0]
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | plotSegmentationResults | def plotSegmentationResults(flags_ind, flags_ind_gt, class_names, mt_step, ONLY_EVALUATE=False):
'''
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | trainHMM_computeStatistics | def trainHMM_computeStatistics(features, labels):
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | trainHMM_fromFile | def trainHMM_fromFile(wav_file, gt_file, hmm_model_name, mt_win, mt_step):
'''
This function trains a HMM model for segmentation-classification using a single annotated audio file
ARGUMENTS:
- wav_file: the path of the audio filename
- gt_file: the path of the ground truth filename
... | python | def trainHMM_fromFile(wav_file, gt_file, hmm_model_name, mt_win, mt_step):
'''
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | trainHMM_fromDir | def trainHMM_fromDir(dirPath, hmm_model_name, mt_win, mt_step):
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | mtFileClassification | def mtFileClassification(input_file, model_name, model_type,
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'''
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | silenceRemoval | def silenceRemoval(x, fs, st_win, st_step, smoothWindow=0.5, weight=0.5, plot=False):
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ARGUMENTS:
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- fs: sampling freq
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | speakerDiarization | def speakerDiarization(filename, n_speakers, mt_size=2.0, mt_step=0.2,
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'''
ARGUMENTS:
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | speakerDiarizationEvaluateScript | def speakerDiarizationEvaluateScript(folder_name, ldas):
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioSegmentation.py | musicThumbnailing | def musicThumbnailing(x, fs, short_term_size=1.0, short_term_step=0.5,
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'''
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioVisualization.py | generateColorMap | def generateColorMap():
'''
This function generates a 256 jet colormap of HTML-like
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'''
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stringColors = []
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if (sys.vers... | python | def generateColorMap():
'''
This function generates a 256 jet colormap of HTML-like
hex string colors (e.g. FF88AA)
'''
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stringColors = []
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioVisualization.py | levenshtein | def levenshtein(str1, s2):
'''
Distance between two strings
'''
N1 = len(str1)
N2 = len(s2)
stringRange = [range(N1 + 1)] * (N2 + 1)
for i in range(N2 + 1):
stringRange[i] = range(i,i + N1 + 1)
for i in range(0,N2):
for j in range(0,N1):
if str1[j] == s2[i]:
... | python | def levenshtein(str1, s2):
'''
Distance between two strings
'''
N1 = len(str1)
N2 = len(s2)
stringRange = [range(N1 + 1)] * (N2 + 1)
for i in range(N2 + 1):
stringRange[i] = range(i,i + N1 + 1)
for i in range(0,N2):
for j in range(0,N1):
if str1[j] == s2[i]:
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioVisualization.py | text_list_to_colors | def text_list_to_colors(names):
'''
Generates a list of colors based on a list of names (strings). Similar strings correspond to similar colors.
'''
# STEP A: compute strings distance between all combnations of strings
Dnames = np.zeros( (len(names), len(names)) )
for i in range(len(names)):
... | python | def text_list_to_colors(names):
'''
Generates a list of colors based on a list of names (strings). Similar strings correspond to similar colors.
'''
# STEP A: compute strings distance between all combnations of strings
Dnames = np.zeros( (len(names), len(names)) )
for i in range(len(names)):
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioVisualization.py | text_list_to_colors_simple | def text_list_to_colors_simple(names):
'''
Generates a list of colors based on a list of names (strings). Similar strings correspond to similar colors.
'''
uNames = list(set(names))
uNames.sort()
textToColor = [ uNames.index(n) for n in names ]
textToColor = np.array(textToColor)
textTo... | python | def text_list_to_colors_simple(names):
'''
Generates a list of colors based on a list of names (strings). Similar strings correspond to similar colors.
'''
uNames = list(set(names))
uNames.sort()
textToColor = [ uNames.index(n) for n in names ]
textToColor = np.array(textToColor)
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioVisualization.py | chordialDiagram | def chordialDiagram(fileStr, SM, Threshold, names, namesCategories):
'''
Generates a d3js chordial diagram that illustrates similarites
'''
colors = text_list_to_colors_simple(namesCategories)
SM2 = SM.copy()
SM2 = (SM2 + SM2.T) / 2.0
for i in range(SM2.shape[0]):
M = Threshold
# ... | python | def chordialDiagram(fileStr, SM, Threshold, names, namesCategories):
'''
Generates a d3js chordial diagram that illustrates similarites
'''
colors = text_list_to_colors_simple(namesCategories)
SM2 = SM.copy()
SM2 = (SM2 + SM2.T) / 2.0
for i in range(SM2.shape[0]):
M = Threshold
# ... | [
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioVisualization.py | visualizeFeaturesFolder | def visualizeFeaturesFolder(folder, dimReductionMethod, priorKnowledge = "none"):
'''
This function generates a chordial visualization for the recordings of the provided path.
ARGUMENTS:
- folder: path of the folder that contains the WAV files to be processed
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'''
This function generates a chordial visualization for the recordings of the provided path.
ARGUMENTS:
- folder: path of the folder that contains the WAV files to be processed
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stZCR | def stZCR(frame):
"""Computes zero crossing rate of frame"""
count = len(frame)
countZ = numpy.sum(numpy.abs(numpy.diff(numpy.sign(frame)))) / 2
return (numpy.float64(countZ) / numpy.float64(count-1.0)) | python | def stZCR(frame):
"""Computes zero crossing rate of frame"""
count = len(frame)
countZ = numpy.sum(numpy.abs(numpy.diff(numpy.sign(frame)))) / 2
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stEnergyEntropy | def stEnergyEntropy(frame, n_short_blocks=10):
"""Computes entropy of energy"""
Eol = numpy.sum(frame ** 2) # total frame energy
L = len(frame)
sub_win_len = int(numpy.floor(L / n_short_blocks))
if L != sub_win_len * n_short_blocks:
frame = frame[0:sub_win_len * n_short_blocks]
# ... | python | def stEnergyEntropy(frame, n_short_blocks=10):
"""Computes entropy of energy"""
Eol = numpy.sum(frame ** 2) # total frame energy
L = len(frame)
sub_win_len = int(numpy.floor(L / n_short_blocks))
if L != sub_win_len * n_short_blocks:
frame = frame[0:sub_win_len * n_short_blocks]
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stSpectralCentroidAndSpread | def stSpectralCentroidAndSpread(X, fs):
"""Computes spectral centroid of frame (given abs(FFT))"""
ind = (numpy.arange(1, len(X) + 1)) * (fs/(2.0 * len(X)))
Xt = X.copy()
Xt = Xt / Xt.max()
NUM = numpy.sum(ind * Xt)
DEN = numpy.sum(Xt) + eps
# Centroid:
C = (NUM / DEN)
# Spread:
... | python | def stSpectralCentroidAndSpread(X, fs):
"""Computes spectral centroid of frame (given abs(FFT))"""
ind = (numpy.arange(1, len(X) + 1)) * (fs/(2.0 * len(X)))
Xt = X.copy()
Xt = Xt / Xt.max()
NUM = numpy.sum(ind * Xt)
DEN = numpy.sum(Xt) + eps
# Centroid:
C = (NUM / DEN)
# Spread:
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stSpectralEntropy | def stSpectralEntropy(X, n_short_blocks=10):
"""Computes the spectral entropy"""
L = len(X) # number of frame samples
Eol = numpy.sum(X ** 2) # total spectral energy
sub_win_len = int(numpy.floor(L / n_short_blocks)) # length of sub-frame
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"""Computes the spectral entropy"""
L = len(X) # number of frame samples
Eol = numpy.sum(X ** 2) # total spectral energy
sub_win_len = int(numpy.floor(L / n_short_blocks)) # length of sub-frame
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stSpectralFlux | def stSpectralFlux(X, X_prev):
"""
Computes the spectral flux feature of the current frame
ARGUMENTS:
X: the abs(fft) of the current frame
X_prev: the abs(fft) of the previous frame
"""
# compute the spectral flux as the sum of square distances:
sumX = numpy.sum... | python | def stSpectralFlux(X, X_prev):
"""
Computes the spectral flux feature of the current frame
ARGUMENTS:
X: the abs(fft) of the current frame
X_prev: the abs(fft) of the previous frame
"""
# compute the spectral flux as the sum of square distances:
sumX = numpy.sum... | [
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stSpectralRollOff | def stSpectralRollOff(X, c, fs):
"""Computes spectral roll-off"""
totalEnergy = numpy.sum(X ** 2)
fftLength = len(X)
Thres = c*totalEnergy
# Ffind the spectral rolloff as the frequency position
# where the respective spectral energy is equal to c*totalEnergy
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"""Computes spectral roll-off"""
totalEnergy = numpy.sum(X ** 2)
fftLength = len(X)
Thres = c*totalEnergy
# Ffind the spectral rolloff as the frequency position
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stHarmonic | def stHarmonic(frame, fs):
"""
Computes harmonic ratio and pitch
"""
M = numpy.round(0.016 * fs) - 1
R = numpy.correlate(frame, frame, mode='full')
g = R[len(frame)-1]
R = R[len(frame):-1]
# estimate m0 (as the first zero crossing of R)
[a, ] = numpy.nonzero(numpy.diff(numpy.sign(R... | python | def stHarmonic(frame, fs):
"""
Computes harmonic ratio and pitch
"""
M = numpy.round(0.016 * fs) - 1
R = numpy.correlate(frame, frame, mode='full')
g = R[len(frame)-1]
R = R[len(frame):-1]
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | mfccInitFilterBanks | def mfccInitFilterBanks(fs, nfft):
"""
Computes the triangular filterbank for MFCC computation
(used in the stFeatureExtraction function before the stMFCC function call)
This function is taken from the scikits.talkbox library (MIT Licence):
https://pypi.python.org/pypi/scikits.talkbox
"""
... | python | def mfccInitFilterBanks(fs, nfft):
"""
Computes the triangular filterbank for MFCC computation
(used in the stFeatureExtraction function before the stMFCC function call)
This function is taken from the scikits.talkbox library (MIT Licence):
https://pypi.python.org/pypi/scikits.talkbox
"""
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stMFCC | def stMFCC(X, fbank, n_mfcc_feats):
"""
Computes the MFCCs of a frame, given the fft mag
ARGUMENTS:
X: fft magnitude abs(FFT)
fbank: filter bank (see mfccInitFilterBanks)
RETURN
ceps: MFCCs (13 element vector)
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"""
Computes the MFCCs of a frame, given the fft mag
ARGUMENTS:
X: fft magnitude abs(FFT)
fbank: filter bank (see mfccInitFilterBanks)
RETURN
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X: fft magnitude abs(FFT)
fbank: filter bank (see mfccInitFilterBanks)
RETURN
ceps: MFCCs (13 element vector)
Note: MFCC calculation is, in general, taken from the
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stChromaFeaturesInit | def stChromaFeaturesInit(nfft, fs):
"""
This function initializes the chroma matrices used in the calculation of the chroma features
"""
freqs = numpy.array([((f + 1) * fs) / (2 * nfft) for f in range(nfft)])
Cp = 27.50
nChroma = numpy.round(12.0 * numpy.log2(freqs / Cp)).astype(int)
... | python | def stChromaFeaturesInit(nfft, fs):
"""
This function initializes the chroma matrices used in the calculation of the chroma features
"""
freqs = numpy.array([((f + 1) * fs) / (2 * nfft) for f in range(nfft)])
Cp = 27.50
nChroma = numpy.round(12.0 * numpy.log2(freqs / Cp)).astype(int)
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stChromagram | def stChromagram(signal, fs, win, step, PLOT=False):
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Short-term FFT mag for spectogram estimation:
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a numpy array (nFFT x numOfShortTermWindows)
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"""
Short-term FFT mag for spectogram estimation:
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a numpy array (nFFT x numOfShortTermWindows)
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | beatExtraction | def beatExtraction(st_features, win_len, PLOT=False):
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This function extracts an estimate of the beat rate for a musical signal.
ARGUMENTS:
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- win_len: window size in seconds
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"""
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stSpectogram | def stSpectogram(signal, fs, win, step, PLOT=False):
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a numpy array (nFFT x numOfShortTermWindows)
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a numpy array (nFFT x numOfShortTermWindows)
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | stFeatureExtraction | def stFeatureExtraction(signal, fs, win, step):
"""
This function implements the shor-term windowing process. For each short-term window a set of features is extracted.
This results to a sequence of feature vectors, stored in a numpy matrix.
ARGUMENTS
signal: the input signal samples
... | python | def stFeatureExtraction(signal, fs, win, step):
"""
This function implements the shor-term windowing process. For each short-term window a set of features is extracted.
This results to a sequence of feature vectors, stored in a numpy matrix.
ARGUMENTS
signal: the input signal samples
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | mtFeatureExtraction | def mtFeatureExtraction(signal, fs, mt_win, mt_step, st_win, st_step):
"""
Mid-term feature extraction
"""
mt_win_ratio = int(round(mt_win / st_step))
mt_step_ratio = int(round(mt_step / st_step))
mt_features = []
st_features, f_names = stFeatureExtraction(signal, fs, st_win, st_step)
... | python | def mtFeatureExtraction(signal, fs, mt_win, mt_step, st_win, st_step):
"""
Mid-term feature extraction
"""
mt_win_ratio = int(round(mt_win / st_step))
mt_step_ratio = int(round(mt_step / st_step))
mt_features = []
st_features, f_names = stFeatureExtraction(signal, fs, st_win, st_step)
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | dirWavFeatureExtraction | def dirWavFeatureExtraction(dirName, mt_win, mt_step, st_win, st_step,
compute_beat=False):
"""
This function extracts the mid-term features of the WAVE files of a particular folder.
The resulting feature vector is extracted by long-term averaging the mid-term features.
Ther... | python | def dirWavFeatureExtraction(dirName, mt_win, mt_step, st_win, st_step,
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | dirsWavFeatureExtraction | def dirsWavFeatureExtraction(dirNames, mt_win, mt_step, st_win, st_step, compute_beat=False):
'''
Same as dirWavFeatureExtraction, but instead of a single dir it
takes a list of paths as input and returns a list of feature matrices.
EXAMPLE:
[features, classNames] =
a.dirsWavFeatureExtrac... | python | def dirsWavFeatureExtraction(dirNames, mt_win, mt_step, st_win, st_step, compute_beat=False):
'''
Same as dirWavFeatureExtraction, but instead of a single dir it
takes a list of paths as input and returns a list of feature matrices.
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | dirWavFeatureExtractionNoAveraging | def dirWavFeatureExtractionNoAveraging(dirName, mt_win, mt_step, st_win, st_step):
"""
This function extracts the mid-term features of the WAVE
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ARGUMENTS:
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tyiannak/pyAudioAnalysis | pyAudioAnalysis/audioFeatureExtraction.py | mtFeatureExtractionToFile | def mtFeatureExtractionToFile(fileName, midTermSize, midTermStep, shortTermSize, shortTermStep, outPutFile,
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"""
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ricequant/rqalpha | rqalpha/model/base_account.py | BaseAccount.market_value | def market_value(self):
"""
[float] 市值
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return sum(position.market_value for position in six.itervalues(self._positions)) | python | def market_value(self):
"""
[float] 市值
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ricequant/rqalpha | rqalpha/model/base_account.py | BaseAccount.transaction_cost | def transaction_cost(self):
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[float] 总费用
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[float] 总费用
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_future.py | buy_open | def buy_open(id_or_ins, amount, price=None, style=None):
"""
买入开仓。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要用于简化 `style` 参数。
... | python | def buy_open(id_or_ins, amount, price=None, style=None):
"""
买入开仓。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要用于简化 `style` 参数。
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:param int amount: 下单手数
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_future.py | buy_close | def buy_close(id_or_ins, amount, price=None, style=None, close_today=False):
"""
平卖仓
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要... | python | def buy_close(id_or_ins, amount, price=None, style=None, close_today=False):
"""
平卖仓
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要... | [
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_future.py | sell_open | def sell_open(id_or_ins, amount, price=None, style=None):
"""
卖出开仓
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要用于简化 `style` 参数。
... | python | def sell_open(id_or_ins, amount, price=None, style=None):
"""
卖出开仓
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要用于简化 `style` 参数。
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_future.py | sell_close | def sell_close(id_or_ins, amount, price=None, style=None, close_today=False):
"""
平买仓
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主... | python | def sell_close(id_or_ins, amount, price=None, style=None, close_today=False):
"""
平买仓
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param int amount: 下单手数
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_future.py | get_future_contracts | def get_future_contracts(underlying_symbol):
"""
获取某一期货品种在策略当前日期的可交易合约order_book_id列表。按照到期月份，下标从小到大排列，返回列表中第一个合约对应的就是该品种的近月合约。
:param str underlying_symbol: 期货合约品种，例如沪深300股指期货为'IF'
:return: list[`str`]
:example:
获取某一天的主力合约代码（策略当前日期是20161201）:
.. code-block:: python
[In... | python | def get_future_contracts(underlying_symbol):
"""
获取某一期货品种在策略当前日期的可交易合约order_book_id列表。按照到期月份，下标从小到大排列，返回列表中第一个合约对应的就是该品种的近月合约。
:param str underlying_symbol: 期货合约品种，例如沪深300股指期货为'IF'
:return: list[`str`]
:example:
获取某一天的主力合约代码（策略当前日期是20161201）:
.. code-block:: python
[In... | [
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:return: list[`str`]
:example:
获取某一天的主力合约代码（策略当前日期是20161201）:
.. code-block:: python
[In]
logger.info(get_future_contracts('IF'))
... | [
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ricequant/rqalpha | rqalpha/model/order.py | Order.quantity | def quantity(self):
"""
[int] 订单数量
"""
if np.isnan(self._quantity):
raise RuntimeError("Quantity of order {} is not supposed to be nan.".format(self.order_id))
return self._quantity | python | def quantity(self):
"""
[int] 订单数量
"""
if np.isnan(self._quantity):
raise RuntimeError("Quantity of order {} is not supposed to be nan.".format(self.order_id))
return self._quantity | [
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ricequant/rqalpha | rqalpha/model/order.py | Order.filled_quantity | def filled_quantity(self):
"""
[int] 订单已成交数量
"""
if np.isnan(self._filled_quantity):
raise RuntimeError("Filled quantity of order {} is not supposed to be nan.".format(self.order_id))
return self._filled_quantity | python | def filled_quantity(self):
"""
[int] 订单已成交数量
"""
if np.isnan(self._filled_quantity):
raise RuntimeError("Filled quantity of order {} is not supposed to be nan.".format(self.order_id))
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ricequant/rqalpha | rqalpha/model/order.py | Order.frozen_price | def frozen_price(self):
"""
[float] 冻结价格
"""
if np.isnan(self._frozen_price):
raise RuntimeError("Frozen price of order {} is not supposed to be nan.".format(self.order_id))
return self._frozen_price | python | def frozen_price(self):
"""
[float] 冻结价格
"""
if np.isnan(self._frozen_price):
raise RuntimeError("Frozen price of order {} is not supposed to be nan.".format(self.order_id))
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ricequant/rqalpha | rqalpha/model/tick.py | TickObject.datetime | def datetime(self):
"""
[datetime.datetime] 当前快照数据的时间戳
"""
try:
dt = self._tick_dict['datetime']
except (KeyError, ValueError):
return datetime.datetime.min
else:
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if dt > 1000000... | python | def datetime(self):
"""
[datetime.datetime] 当前快照数据的时间戳
"""
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/stock_position.py | StockPosition.value_percent | def value_percent(self):
"""
[float] 获得该持仓的实时市场价值在股票投资组合价值中所占比例，取值范围[0, 1]
"""
accounts = Environment.get_instance().portfolio.accounts
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return 0
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"""
[float] 获得该持仓的实时市场价值在股票投资组合价值中所占比例，取值范围[0, 1]
"""
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/stock_position.py | StockPosition.is_de_listed | def is_de_listed(self):
"""
判断合约是否过期
"""
env = Environment.get_instance()
instrument = env.get_instrument(self._order_book_id)
current_date = env.trading_dt
if instrument.de_listed_date is not None:
if instrument.de_listed_date.date() > env.config.bas... | python | def is_de_listed(self):
"""
判断合约是否过期
"""
env = Environment.get_instance()
instrument = env.get_instrument(self._order_book_id)
current_date = env.trading_dt
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/stock_position.py | StockPosition.bought_value | def bought_value(self):
"""
[已弃用]
"""
user_system_log.warn(_(u"[abandon] {} is no longer valid.").format('stock_position.bought_value'))
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"""
[已弃用]
"""
user_system_log.warn(_(u"[abandon] {} is no longer valid.").format('stock_position.bought_value'))
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ricequant/rqalpha | rqalpha/model/booking.py | BookingPosition.trading_pnl | def trading_pnl(self):
"""
[float] 交易盈亏，策略在当前交易日产生的盈亏中来源于当日成交的部分
"""
last_price = self._data_proxy.get_last_price(self._order_book_id)
return self._contract_multiplier * (self._trade_quantity * last_price - self._trade_cost) | python | def trading_pnl(self):
"""
[float] 交易盈亏，策略在当前交易日产生的盈亏中来源于当日成交的部分
"""
last_price = self._data_proxy.get_last_price(self._order_book_id)
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ricequant/rqalpha | rqalpha/model/booking.py | BookingPosition.position_pnl | def position_pnl(self):
"""
[float] 昨仓盈亏，策略在当前交易日产生的盈亏中来源于昨仓的部分
"""
last_price = self._data_proxy.get_last_price(self._order_book_id)
if self._direction == POSITION_DIRECTION.LONG:
price_spread = last_price - self._last_price
else:
price_spread = s... | python | def position_pnl(self):
"""
[float] 昨仓盈亏，策略在当前交易日产生的盈亏中来源于昨仓的部分
"""
last_price = self._data_proxy.get_last_price(self._order_book_id)
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price_spread = last_price - self._last_price
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price_spread = s... | [
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ricequant/rqalpha | rqalpha/model/portfolio.py | Portfolio.register_event | def register_event(self):
"""
注册事件
"""
event_bus = Environment.get_instance().event_bus
event_bus.prepend_listener(EVENT.PRE_BEFORE_TRADING, self._pre_before_trading)
event_bus.prepend_listener(EVENT.POST_SETTLEMENT, self._post_settlement) | python | def register_event(self):
"""
注册事件
"""
event_bus = Environment.get_instance().event_bus
event_bus.prepend_listener(EVENT.PRE_BEFORE_TRADING, self._pre_before_trading)
event_bus.prepend_listener(EVENT.POST_SETTLEMENT, self._post_settlement) | [
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"""
[float] 实时净值
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"""
[float] 当前最新一天的日收益
"""
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"""
[float] 当前最新一天的日收益
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ricequant/rqalpha | rqalpha/model/portfolio.py | Portfolio.total_value | def total_value(self):
"""
[float]总权益
"""
return sum(account.total_value for account in six.itervalues(self._accounts)) | python | def total_value(self):
"""
[float]总权益
"""
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ricequant/rqalpha | rqalpha/model/portfolio.py | Portfolio.positions | def positions(self):
"""
[dict] 持仓
"""
if self._mixed_positions is None:
self._mixed_positions = MixedPositions(self._accounts)
return self._mixed_positions | python | def positions(self):
"""
[dict] 持仓
"""
if self._mixed_positions is None:
self._mixed_positions = MixedPositions(self._accounts)
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ricequant/rqalpha | rqalpha/model/portfolio.py | Portfolio.cash | def cash(self):
"""
[float] 可用资金
"""
return sum(account.cash for account in six.itervalues(self._accounts)) | python | def cash(self):
"""
[float] 可用资金
"""
return sum(account.cash for account in six.itervalues(self._accounts)) | [
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ricequant/rqalpha | rqalpha/model/portfolio.py | Portfolio.market_value | def market_value(self):
"""
[float] 市值
"""
return sum(account.market_value for account in six.itervalues(self._accounts)) | python | def market_value(self):
"""
[float] 市值
"""
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | FuturePosition.buy_holding_pnl | def buy_holding_pnl(self):
"""
[float] 买方向当日持仓盈亏
"""
return (self.last_price - self.buy_avg_holding_price) * self.buy_quantity * self.contract_multiplier | python | def buy_holding_pnl(self):
"""
[float] 买方向当日持仓盈亏
"""
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | FuturePosition.sell_holding_pnl | def sell_holding_pnl(self):
"""
[float] 卖方向当日持仓盈亏
"""
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"""
[float] 卖方向当日持仓盈亏
"""
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | FuturePosition.buy_pnl | def buy_pnl(self):
"""
[float] 买方向累计盈亏
"""
return (self.last_price - self._buy_avg_open_price) * self.buy_quantity * self.contract_multiplier | python | def buy_pnl(self):
"""
[float] 买方向累计盈亏
"""
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | FuturePosition.sell_pnl | def sell_pnl(self):
"""
[float] 卖方向累计盈亏
"""
return (self._sell_avg_open_price - self.last_price) * self.sell_quantity * self.contract_multiplier | python | def sell_pnl(self):
"""
[float] 卖方向累计盈亏
"""
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | FuturePosition.buy_open_order_quantity | def buy_open_order_quantity(self):
"""
[int] 买方向挂单量
"""
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"""
[int] 买方向挂单量
"""
return sum(order.unfilled_quantity for order in self.open_orders if
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | FuturePosition.sell_open_order_quantity | def sell_open_order_quantity(self):
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[int] 卖方向挂单量
"""
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"""
[int] 卖方向挂单量
"""
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | FuturePosition.buy_close_order_quantity | def buy_close_order_quantity(self):
"""
[int] 买方向挂单量
"""
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"""
[int] 买方向挂单量
"""
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | FuturePosition.sell_close_order_quantity | def sell_close_order_quantity(self):
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[int] 卖方向挂单量
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"""
[int] 卖方向挂单量
"""
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | FuturePosition.buy_avg_holding_price | def buy_avg_holding_price(self):
"""
[float] 买方向持仓均价
"""
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"""
[float] 买方向持仓均价
"""
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | FuturePosition.sell_avg_holding_price | def sell_avg_holding_price(self):
"""
[float] 卖方向持仓均价
"""
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"""
[float] 卖方向持仓均价
"""
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | FuturePosition.is_de_listed | def is_de_listed(self):
"""
判断合约是否过期
"""
instrument = Environment.get_instance().get_instrument(self._order_book_id)
current_date = Environment.get_instance().trading_dt
if instrument.de_listed_date is not None and current_date >= instrument.de_listed_date:
re... | python | def is_de_listed(self):
"""
判断合约是否过期
"""
instrument = Environment.get_instance().get_instrument(self._order_book_id)
current_date = Environment.get_instance().trading_dt
if instrument.de_listed_date is not None and current_date >= instrument.de_listed_date:
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | FuturePosition.apply_trade | def apply_trade(self, trade):
"""
应用成交，并计算交易产生的现金变动。
开仓：
delta_cash
= -1 * margin
= -1 * quantity * contract_multiplier * price * margin_rate
平仓：
delta_cash
= old_margin - margin + delta_realized_pnl
= (sum of (cost_price * quantity) of c... | python | def apply_trade(self, trade):
"""
应用成交，并计算交易产生的现金变动。
开仓：
delta_cash
= -1 * margin
= -1 * quantity * contract_multiplier * price * margin_rate
平仓：
delta_cash
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ricequant/rqalpha | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | FuturePosition._close_holding | def _close_holding(self, trade):
"""
应用平仓，并计算平仓盈亏
买平：
delta_realized_pnl = sum of ((trade_price - cost_price)* quantity) of closed trades * contract_multiplier
卖平：
delta_realized_pnl = sum of ((cost_price - trade_price)* quantity) of closed trades * contract_multiplier
... | python | def _close_holding(self, trade):
"""
应用平仓，并计算平仓盈亏
买平：
delta_realized_pnl = sum of ((trade_price - cost_price)* quantity) of closed trades * contract_multiplier
卖平：
delta_realized_pnl = sum of ((cost_price - trade_price)* quantity) of closed trades * contract_multiplier
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ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.sector_code | def sector_code(self):
"""
[str] 板块缩写代码，全球通用标准定义（股票专用）
"""
try:
return self.__dict__["sector_code"]
except (KeyError, ValueError):
raise AttributeError(
"Instrument(order_book_id={}) has no attribute 'sector_code' ".format(self.order_book_i... | python | def sector_code(self):
"""
[str] 板块缩写代码，全球通用标准定义（股票专用）
"""
try:
return self.__dict__["sector_code"]
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ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.sector_code_name | def sector_code_name(self):
"""
[str] 以当地语言为标准的板块代码名（股票专用）
"""
try:
return self.__dict__["sector_code_name"]
except (KeyError, ValueError):
raise AttributeError(
"Instrument(order_book_id={}) has no attribute 'sector_code_name' ".format(sel... | python | def sector_code_name(self):
"""
[str] 以当地语言为标准的板块代码名（股票专用）
"""
try:
return self.__dict__["sector_code_name"]
except (KeyError, ValueError):
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ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.industry_code | def industry_code(self):
"""
[str] 国民经济行业分类代码，具体可参考“Industry列表” （股票专用）
"""
try:
return self.__dict__["industry_code"]
except (KeyError, ValueError):
raise AttributeError(
"Instrument(order_book_id={}) has no attribute 'industry_code' ".form... | python | def industry_code(self):
"""
[str] 国民经济行业分类代码，具体可参考“Industry列表” （股票专用）
"""
try:
return self.__dict__["industry_code"]
except (KeyError, ValueError):
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ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.industry_name | def industry_name(self):
"""
[str] 国民经济行业分类名称（股票专用）
"""
try:
return self.__dict__["industry_name"]
except (KeyError, ValueError):
raise AttributeError(
"Instrument(order_book_id={}) has no attribute 'industry_name' ".format(self.order_book_... | python | def industry_name(self):
"""
[str] 国民经济行业分类名称（股票专用）
"""
try:
return self.__dict__["industry_name"]
except (KeyError, ValueError):
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ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.concept_names | def concept_names(self):
"""
[str] 概念股分类，例如：’铁路基建’，’基金重仓’等（股票专用）
"""
try:
return self.__dict__["concept_names"]
except (KeyError, ValueError):
raise AttributeError(
"Instrument(order_book_id={}) has no attribute 'concept_names' ".format(sel... | python | def concept_names(self):
"""
[str] 概念股分类，例如：’铁路基建’，’基金重仓’等（股票专用）
"""
try:
return self.__dict__["concept_names"]
except (KeyError, ValueError):
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ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.board_type | def board_type(self):
"""
[str] 板块类别，’MainBoard’ - 主板,’GEM’ - 创业板（股票专用）
"""
try:
return self.__dict__["board_type"]
except (KeyError, ValueError):
raise AttributeError(
"Instrument(order_book_id={}) has no attribute 'board_type' ".format(se... | python | def board_type(self):
"""
[str] 板块类别，’MainBoard’ - 主板,’GEM’ - 创业板（股票专用）
"""
try:
return self.__dict__["board_type"]
except (KeyError, ValueError):
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ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.status | def status(self):
"""
[str] 合约状态。’Active’ - 正常上市, ‘Delisted’ - 终止上市, ‘TemporarySuspended’ - 暂停上市,
‘PreIPO’ - 发行配售期间, ‘FailIPO’ - 发行失败（股票专用）
"""
try:
return self.__dict__["status"]
except (KeyError, ValueError):
raise AttributeError(
... | python | def status(self):
"""
[str] 合约状态。’Active’ - 正常上市, ‘Delisted’ - 终止上市, ‘TemporarySuspended’ - 暂停上市,
‘PreIPO’ - 发行配售期间, ‘FailIPO’ - 发行失败（股票专用）
"""
try:
return self.__dict__["status"]
except (KeyError, ValueError):
raise AttributeError(
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ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.special_type | def special_type(self):
"""
[str] 特别处理状态。’Normal’ - 正常上市, ‘ST’ - ST处理, ‘StarST’ - *ST代表该股票正在接受退市警告,
‘PT’ - 代表该股票连续3年收入为负，将被暂停交易, ‘Other’ - 其他（股票专用）
"""
try:
return self.__dict__["special_type"]
except (KeyError, ValueError):
raise AttributeError(
... | python | def special_type(self):
"""
[str] 特别处理状态。’Normal’ - 正常上市, ‘ST’ - ST处理, ‘StarST’ - *ST代表该股票正在接受退市警告,
‘PT’ - 代表该股票连续3年收入为负，将被暂停交易, ‘Other’ - 其他（股票专用）
"""
try:
return self.__dict__["special_type"]
except (KeyError, ValueError):
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ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.contract_multiplier | def contract_multiplier(self):
"""
[float] 合约乘数，例如沪深300股指期货的乘数为300.0（期货专用）
"""
try:
return self.__dict__["contract_multiplier"]
except (KeyError, ValueError):
raise AttributeError(
"Instrument(order_book_id={}) has no attribute 'contract_mu... | python | def contract_multiplier(self):
"""
[float] 合约乘数，例如沪深300股指期货的乘数为300.0（期货专用）
"""
try:
return self.__dict__["contract_multiplier"]
except (KeyError, ValueError):
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ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.margin_rate | def margin_rate(self):
"""
[float] 合约最低保证金率（期货专用）
"""
try:
return self.__dict__["margin_rate"]
except (KeyError, ValueError):
raise AttributeError(
"Instrument(order_book_id={}) has no attribute 'margin_rate' ".format(self.order_book_id)
... | python | def margin_rate(self):
"""
[float] 合约最低保证金率（期货专用）
"""
try:
return self.__dict__["margin_rate"]
except (KeyError, ValueError):
raise AttributeError(
"Instrument(order_book_id={}) has no attribute 'margin_rate' ".format(self.order_book_id)
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ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.underlying_order_book_id | def underlying_order_book_id(self):
"""
[str] 合约标的代码，目前除股指期货(IH, IF, IC)之外的期货合约，这一字段全部为’null’（期货专用）
"""
try:
return self.__dict__["underlying_order_book_id"]
except (KeyError, ValueError):
raise AttributeError(
"Instrument(order_book_id={})... | python | def underlying_order_book_id(self):
"""
[str] 合约标的代码，目前除股指期货(IH, IF, IC)之外的期货合约，这一字段全部为’null’（期货专用）
"""
try:
return self.__dict__["underlying_order_book_id"]
except (KeyError, ValueError):
raise AttributeError(
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] | ac40a62d4e7eca9494b4d0a14f46facf5616820c | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/model/instrument.py#L238-L247 | train |
ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.underlying_symbol | def underlying_symbol(self):
"""
[str] 合约标的代码，目前除股指期货(IH, IF, IC)之外的期货合约，这一字段全部为’null’（期货专用）
"""
try:
return self.__dict__["underlying_symbol"]
except (KeyError, ValueError):
raise AttributeError(
"Instrument(order_book_id={}) has no attrib... | python | def underlying_symbol(self):
"""
[str] 合约标的代码，目前除股指期货(IH, IF, IC)之外的期货合约，这一字段全部为’null’（期货专用）
"""
try:
return self.__dict__["underlying_symbol"]
except (KeyError, ValueError):
raise AttributeError(
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] | ac40a62d4e7eca9494b4d0a14f46facf5616820c | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/model/instrument.py#L250-L259 | train |
ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.maturity_date | def maturity_date(self):
"""
[datetime] 期货到期日。主力连续合约与指数连续合约都为 datetime(2999, 12, 31)（期货专用）
"""
try:
return self.__dict__["maturity_date"]
except (KeyError, ValueError):
raise AttributeError(
"Instrument(order_book_id={}) has no attribute 'm... | python | def maturity_date(self):
"""
[datetime] 期货到期日。主力连续合约与指数连续合约都为 datetime(2999, 12, 31)（期货专用）
"""
try:
return self.__dict__["maturity_date"]
except (KeyError, ValueError):
raise AttributeError(
"Instrument(order_book_id={}) has no attribute 'm... | [
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] | ac40a62d4e7eca9494b4d0a14f46facf5616820c | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/model/instrument.py#L262-L271 | train |
ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.settlement_method | def settlement_method(self):
"""
[str] 交割方式，’CashSettlementRequired’ - 现金交割, ‘PhysicalSettlementRequired’ - 实物交割（期货专用）
"""
try:
return self.__dict__["settlement_method"]
except (KeyError, ValueError):
raise AttributeError(
"Instrument(order... | python | def settlement_method(self):
"""
[str] 交割方式，’CashSettlementRequired’ - 现金交割, ‘PhysicalSettlementRequired’ - 实物交割（期货专用）
"""
try:
return self.__dict__["settlement_method"]
except (KeyError, ValueError):
raise AttributeError(
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] | ac40a62d4e7eca9494b4d0a14f46facf5616820c | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/model/instrument.py#L274-L283 | train |
ricequant/rqalpha | rqalpha/model/instrument.py | Instrument.listing | def listing(self):
"""
[bool] 该合约当前日期是否在交易
"""
now = Environment.get_instance().calendar_dt
return self.listed_date <= now <= self.de_listed_date | python | def listing(self):
"""
[bool] 该合约当前日期是否在交易
"""
now = Environment.get_instance().calendar_dt
return self.listed_date <= now <= self.de_listed_date | [
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