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train | generateColorMap | This function generates a 256 jet colormap of HTML-like
hex string colors (e.g. FF88AA) | pyAudioAnalysis/audioVisualization.py | def generateColorMap():
'''
This function generates a 256 jet colormap of HTML-like
hex string colors (e.g. FF88AA)
'''
Map = cm.jet(np.arange(256))
stringColors = []
for i in range(Map.shape[0]):
rgb = (int(255*Map[i][0]), int(255*Map[i][1]), int(255*Map[i][2]))
if (sys.vers... | def generateColorMap():
'''
This function generates a 256 jet colormap of HTML-like
hex string colors (e.g. FF88AA)
'''
Map = cm.jet(np.arange(256))
stringColors = []
for i in range(Map.shape[0]):
rgb = (int(255*Map[i][0]), int(255*Map[i][1]), int(255*Map[i][2]))
if (sys.vers... | [
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train | levenshtein | Distance between two strings | pyAudioAnalysis/audioVisualization.py | 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]:
... | 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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train | text_list_to_colors | Generates a list of colors based on a list of names (strings). Similar strings correspond to similar colors. | pyAudioAnalysis/audioVisualization.py | 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)):
... | 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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train | text_list_to_colors_simple | Generates a list of colors based on a list of names (strings). Similar strings correspond to similar colors. | pyAudioAnalysis/audioVisualization.py | 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... | 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... | [
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train | chordialDiagram | Generates a d3js chordial diagram that illustrates similarites | pyAudioAnalysis/audioVisualization.py | 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
# ... | 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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train | visualizeFeaturesFolder | 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
- dimReductionMethod: method used to reduce the dimension of the initial feature space before computing the similari... | pyAudioAnalysis/audioVisualization.py | 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
- dimReductionMethod: ... | 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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train | stZCR | Computes zero crossing rate of frame | pyAudioAnalysis/audioFeatureExtraction.py | 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)) | 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)) | [
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train | stEnergyEntropy | Computes entropy of energy | pyAudioAnalysis/audioFeatureExtraction.py | 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]
# ... | 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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train | stSpectralCentroidAndSpread | Computes spectral centroid of frame (given abs(FFT)) | pyAudioAnalysis/audioFeatureExtraction.py | 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:
... | 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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train | stSpectralEntropy | Computes the spectral entropy | pyAudioAnalysis/audioFeatureExtraction.py | 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
if L != sub_win_len * n... | 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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train | stSpectralFlux | 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 | pyAudioAnalysis/audioFeatureExtraction.py | 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... | 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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train | stSpectralRollOff | Computes spectral roll-off | pyAudioAnalysis/audioFeatureExtraction.py | 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
CumSum = numpy.cumsum(X ** 2) + ... | 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
CumSum = numpy.cumsum(X ** 2) + ... | [
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train | stHarmonic | Computes harmonic ratio and pitch | pyAudioAnalysis/audioFeatureExtraction.py | 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... | 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... | [
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train | mfccInitFilterBanks | 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 | pyAudioAnalysis/audioFeatureExtraction.py | 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
"""
... | 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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train | stMFCC | 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)
Note: MFCC calculation is, in general, taken from the
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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
ceps: MFCCs (13 element vector)
Note: MFCC calculation is, in general, taken fr... | 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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train | stChromaFeaturesInit | This function initializes the chroma matrices used in the calculation of the chroma features | pyAudioAnalysis/audioFeatureExtraction.py | 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)
... | 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)])
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nChroma = numpy.round(12.0 * numpy.log2(freqs / Cp)).astype(int)
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train | stChromagram | Short-term FFT mag for spectogram estimation:
Returns:
a numpy array (nFFT x numOfShortTermWindows)
ARGUMENTS:
signal: the input signal samples
fs: the sampling freq (in Hz)
win: the short-term window size (in samples)
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"""
Short-term FFT mag for spectogram estimation:
Returns:
a numpy array (nFFT x numOfShortTermWindows)
ARGUMENTS:
signal: the input signal samples
fs: the sampling freq (in Hz)
win: the short-term... | def stChromagram(signal, fs, win, step, PLOT=False):
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train | beatExtraction | This function extracts an estimate of the beat rate for a musical signal.
ARGUMENTS:
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- win_len: window size in seconds
RETURNS:
- BPM: estimates of beats per minute
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ARGUMENTS:
- st_features: a numpy array (n_feats x numOfShortTermWindows)
- win_len: window size in seconds
RETURNS:
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"""
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train | stSpectogram | Short-term FFT mag for spectogram estimation:
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ARGUMENTS:
signal: the input signal samples
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win: the short-term window size (in samples)
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Returns:
a numpy array (nFFT x numOfShortTermWindows)
ARGUMENTS:
signal: the input signal samples
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Short-term FFT mag for spectogram estimation:
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train | stFeatureExtraction | 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
fs: the sampling freq (in Hz)
win: ... | pyAudioAnalysis/audioFeatureExtraction.py | 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
... | 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.
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ARGUMENTS
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train | mtFeatureExtraction | Mid-term feature extraction | pyAudioAnalysis/audioFeatureExtraction.py | 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)
... | 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))
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st_features, f_names = stFeatureExtraction(signal, fs, st_win, st_step)
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train | dirWavFeatureExtraction | 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.
Therefore ONE FEATURE VECTOR is extracted for each WAV file.
ARGUMENTS:
- dirName: the path of the WAVE directory
... | pyAudioAnalysis/audioFeatureExtraction.py | def dirWavFeatureExtraction(dirName, mt_win, mt_step, st_win, st_step,
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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.
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train | dirsWavFeatureExtraction | Same as dirWavFeatureExtraction, but instead of a single dir it
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EXAMPLE:
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Same as dirWavFeatureExtraction, but instead of a single dir it
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EXAMPLE:
[features, classNames] =
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train | dirWavFeatureExtractionNoAveraging | This function extracts the mid-term features of the WAVE
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ARGUMENTS:
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This function extracts the mid-term features of the WAVE
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ARGUMENTS:
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train | mtFeatureExtractionToFile | This function is used as a wrapper to:
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train | BaseAccount.market_value | [float] 市值 | rqalpha/model/base_account.py | def market_value(self):
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[float] 市值
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[float] 总费用
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[float] 总费用
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train | buy_open | 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。
:param style: 下单类型, 默认是市价单。目前支持的订单类型有 :class:`~LimitOrder` 和 :cla... | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_future.py | 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。
... | 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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] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/mod/rqalpha_mod_sys_accounts/api/api_future.py#L208-L231 | [
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train | buy_close | 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
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"""
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... | 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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train | sell_open | 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
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:param style: 下单类型, 默认是市价单。目前支持的订单类型有 :class:`~LimitOrder` 和 :clas... | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_future.py | 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。
... | 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。
... | [
"卖出开仓"
] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/mod/rqalpha_mod_sys_accounts/api/api_future.py#L265-L281 | [
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train | sell_close | 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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:param style: 下单类型, 默认是市价单。目前支持的订单类型有 :class:`~LimitOrder` 和 :class... | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_future.py | 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... | 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... | [
"平买仓"
] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/mod/rqalpha_mod_sys_accounts/api/api_future.py#L285-L304 | [
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train | get_future_contracts | 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]
logger.info(get_future_contracts('IF'))
... | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_future.py | 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... | 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... | [
"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。"
] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/mod/rqalpha_mod_sys_accounts/api/api_future.py#L329-L349 | [
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train | Order.quantity | [int] 订单数量 | rqalpha/model/order.py | 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 | 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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train | Order.filled_quantity | [int] 订单已成交数量 | rqalpha/model/order.py | 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 | 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 | [
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train | Order.frozen_price | [float] 冻结价格 | rqalpha/model/order.py | 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 | 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 | [
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train | TickObject.datetime | [datetime.datetime] 当前快照数据的时间戳 | rqalpha/model/tick.py | 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:
if not isinstance(dt, datetime.datetime):
if dt > 1000000... | 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
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train | StockPosition.value_percent | [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] | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/stock_position.py | 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
if DEFAULT_ACCOUNT_TYPE.STOCK.name not in accounts:
return 0
total_value = accounts[DEFAULT_ACCOUNT_TYPE.STOCK.name].total_v... | 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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train | StockPosition.is_de_listed | 判断合约是否过期 | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/stock_position.py | 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... | 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... | [
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] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/mod/rqalpha_mod_sys_accounts/position_model/stock_position.py#L166-L179 | [
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train | StockPosition.bought_value | [已弃用] | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/stock_position.py | def bought_value(self):
"""
[已弃用]
"""
user_system_log.warn(_(u"[abandon] {} is no longer valid.").format('stock_position.bought_value'))
return self._quantity * self._avg_price | def bought_value(self):
"""
[已弃用]
"""
user_system_log.warn(_(u"[abandon] {} is no longer valid.").format('stock_position.bought_value'))
return self._quantity * self._avg_price | [
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train | BookingPosition.trading_pnl | [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 | rqalpha/model/booking.py | 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) | 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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train | BookingPosition.position_pnl | [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 | rqalpha/model/booking.py | def position_pnl(self):
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train | Portfolio.register_event | 注册事件 | rqalpha/model/portfolio.py | def register_event(self):
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注册事件
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event_bus = Environment.get_instance().event_bus
event_bus.prepend_listener(EVENT.PRE_BEFORE_TRADING, self._pre_before_trading)
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注册事件
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train | Portfolio.unit_net_value | [float] 实时净值 | rqalpha/model/portfolio.py | def unit_net_value(self):
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[float] 实时净值
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if self._units == 0:
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train | Portfolio.daily_returns | [float] 当前最新一天的日收益 | rqalpha/model/portfolio.py | def daily_returns(self):
"""
[float] 当前最新一天的日收益
"""
if self._static_unit_net_value == 0:
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return 0 if self._static_unit_net_value == 0 else self.unit_net_value / self._static_unit_net_value - 1 | def daily_returns(self):
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[float] 当前最新一天的日收益
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train | Portfolio.total_value | [float]总权益 | rqalpha/model/portfolio.py | def total_value(self):
"""
[float]总权益
"""
return sum(account.total_value for account in six.itervalues(self._accounts)) | def total_value(self):
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[float]总权益
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train | Portfolio.positions | [dict] 持仓 | rqalpha/model/portfolio.py | def positions(self):
"""
[dict] 持仓
"""
if self._mixed_positions is None:
self._mixed_positions = MixedPositions(self._accounts)
return self._mixed_positions | def positions(self):
"""
[dict] 持仓
"""
if self._mixed_positions is None:
self._mixed_positions = MixedPositions(self._accounts)
return self._mixed_positions | [
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train | Portfolio.cash | [float] 可用资金 | rqalpha/model/portfolio.py | def cash(self):
"""
[float] 可用资金
"""
return sum(account.cash for account in six.itervalues(self._accounts)) | def cash(self):
"""
[float] 可用资金
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return sum(account.cash for account in six.itervalues(self._accounts)) | [
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train | Portfolio.market_value | [float] 市值 | rqalpha/model/portfolio.py | def market_value(self):
"""
[float] 市值
"""
return sum(account.market_value for account in six.itervalues(self._accounts)) | def market_value(self):
"""
[float] 市值
"""
return sum(account.market_value for account in six.itervalues(self._accounts)) | [
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train | FuturePosition.buy_holding_pnl | [float] 买方向当日持仓盈亏 | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | 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 | def buy_holding_pnl(self):
"""
[float] 买方向当日持仓盈亏
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train | FuturePosition.sell_holding_pnl | [float] 卖方向当日持仓盈亏 | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | def sell_holding_pnl(self):
"""
[float] 卖方向当日持仓盈亏
"""
return (self.sell_avg_holding_price - self.last_price) * self.sell_quantity * self.contract_multiplier | def sell_holding_pnl(self):
"""
[float] 卖方向当日持仓盈亏
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return (self.sell_avg_holding_price - self.last_price) * self.sell_quantity * self.contract_multiplier | [
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train | FuturePosition.buy_pnl | [float] 买方向累计盈亏 | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | 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 | def buy_pnl(self):
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[float] 买方向累计盈亏
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train | FuturePosition.sell_pnl | [float] 卖方向累计盈亏 | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | 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 | def sell_pnl(self):
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[float] 卖方向累计盈亏
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train | FuturePosition.buy_open_order_quantity | [int] 买方向挂单量 | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | def buy_open_order_quantity(self):
"""
[int] 买方向挂单量
"""
return sum(order.unfilled_quantity for order in self.open_orders if
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[int] 买方向挂单量
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train | FuturePosition.sell_open_order_quantity | [int] 卖方向挂单量 | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | def sell_open_order_quantity(self):
"""
[int] 卖方向挂单量
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[int] 卖方向挂单量
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train | FuturePosition.buy_close_order_quantity | [int] 买方向挂单量 | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | def buy_close_order_quantity(self):
"""
[int] 买方向挂单量
"""
return sum(order.unfilled_quantity for order in self.open_orders if order.side == SIDE.BUY and
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[int] 买方向挂单量
"""
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train | FuturePosition.sell_close_order_quantity | [int] 卖方向挂单量 | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | def sell_close_order_quantity(self):
"""
[int] 卖方向挂单量
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[int] 卖方向挂单量
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train | FuturePosition.buy_avg_holding_price | [float] 买方向持仓均价 | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | def buy_avg_holding_price(self):
"""
[float] 买方向持仓均价
"""
return 0 if self.buy_quantity == 0 else self._buy_holding_cost / self.buy_quantity / self.contract_multiplier | def buy_avg_holding_price(self):
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[float] 买方向持仓均价
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train | FuturePosition.sell_avg_holding_price | [float] 卖方向持仓均价 | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | def sell_avg_holding_price(self):
"""
[float] 卖方向持仓均价
"""
return 0 if self.sell_quantity == 0 else self._sell_holding_cost / self.sell_quantity / self.contract_multiplier | def sell_avg_holding_price(self):
"""
[float] 卖方向持仓均价
"""
return 0 if self.sell_quantity == 0 else self._sell_holding_cost / self.sell_quantity / self.contract_multiplier | [
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train | FuturePosition.is_de_listed | 判断合约是否过期 | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | 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... | 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... | [
"判断合约是否过期"
] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py#L372-L380 | [
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train | FuturePosition.apply_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 closed trade) * contract_multiplier * margin_rate +... | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | 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... | 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... | [
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train | FuturePosition._close_holding | 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
:param trade: rqalpha.model.trade.Trade
... | rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py | 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
... | 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
... | [
"应用平仓，并计算平仓盈亏"
] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/mod/rqalpha_mod_sys_accounts/position_model/future_position.py#L457-L520 | [
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train | Instrument.sector_code | [str] 板块缩写代码，全球通用标准定义（股票专用） | rqalpha/model/instrument.py | 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... | 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(
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train | Instrument.sector_code_name | [str] 以当地语言为标准的板块代码名（股票专用） | rqalpha/model/instrument.py | 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... | 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(
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train | Instrument.industry_code | [str] 国民经济行业分类代码，具体可参考“Industry列表” （股票专用） | rqalpha/model/instrument.py | 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... | 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"]
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raise AttributeError(
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train | Instrument.industry_name | [str] 国民经济行业分类名称（股票专用） | rqalpha/model/instrument.py | 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_... | 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(
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train | Instrument.concept_names | [str] 概念股分类，例如：’铁路基建’，’基金重仓’等（股票专用） | rqalpha/model/instrument.py | 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... | 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(
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train | Instrument.board_type | [str] 板块类别，’MainBoard’ - 主板,’GEM’ - 创业板（股票专用） | rqalpha/model/instrument.py | 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... | 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(
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train | Instrument.status | [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） | rqalpha/model/instrument.py | 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(
... | 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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train | Instrument.special_type | [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） | rqalpha/model/instrument.py | 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(
... | 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(
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train | Instrument.contract_multiplier | [float] 合约乘数，例如沪深300股指期货的乘数为300.0（期货专用） | rqalpha/model/instrument.py | 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... | 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(
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train | Instrument.margin_rate | [float] 合约最低保证金率（期货专用） | rqalpha/model/instrument.py | 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)
... | 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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train | Instrument.underlying_order_book_id | [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） | rqalpha/model/instrument.py | 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）
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try:
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except (KeyError, ValueError):
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[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）
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train | Instrument.underlying_symbol | [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） | rqalpha/model/instrument.py | 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）
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try:
return self.__dict__["underlying_symbol"]
except (KeyError, ValueError):
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[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）
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train | Instrument.maturity_date | [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） | rqalpha/model/instrument.py | 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(
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"""
[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）
"""
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train | Instrument.settlement_method | [str] 交割方式，’CashSettlementRequired’ - 现金交割, ‘PhysicalSettlementRequired’ - 实物交割（期货专用） | rqalpha/model/instrument.py | def settlement_method(self):
"""
[str] 交割方式，’CashSettlementRequired’ - 现金交割, ‘PhysicalSettlementRequired’ - 实物交割（期货专用）
"""
try:
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except (KeyError, ValueError):
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"""
[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"]
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train | Instrument.listing | [bool] 该合约当前日期是否在交易 | rqalpha/model/instrument.py | 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 | 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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train | StockTransactionCostDecider.get_trade_commission | 计算手续费这个逻辑比较复杂，按照如下算法来计算：
1. 定义一个剩余手续费的概念，根据order_id存储在commission_map中，默认为min_commission
2. 当trade来时计算该trade产生的手续费cost_money
3. 如果cost_money > commission
3.1 如果commission 等于 min_commission，说明这是第一笔trade，此时，直接commission置0，返回cost_money即可
3.2 如果commission 不等于 min_commission... | rqalpha/mod/rqalpha_mod_sys_transaction_cost/deciders.py | def get_trade_commission(self, trade):
"""
计算手续费这个逻辑比较复杂，按照如下算法来计算：
1. 定义一个剩余手续费的概念，根据order_id存储在commission_map中，默认为min_commission
2. 当trade来时计算该trade产生的手续费cost_money
3. 如果cost_money > commission
3.1 如果commission 等于 min_commission，说明这是第一笔trade，此时，直接commission置0，返回c... | def get_trade_commission(self, trade):
"""
计算手续费这个逻辑比较复杂，按照如下算法来计算：
1. 定义一个剩余手续费的概念，根据order_id存储在commission_map中，默认为min_commission
2. 当trade来时计算该trade产生的手续费cost_money
3. 如果cost_money > commission
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train | HKStockTransactionCostDecider._get_tax | 港交所收费项目繁多，按照如下逻辑计算税费：
1. 税费比例为 0.11%，不足 1 元按 1 元记，四舍五入保留两位小数（包括印花税、交易征费、交易系统使用费）。
2，五元固定费用（包括卖方收取的转手纸印花税、买方收取的过户费用）。 | rqalpha/mod/rqalpha_mod_sys_transaction_cost/deciders.py | def _get_tax(self, order_book_id, _, cost_money):
"""
港交所收费项目繁多，按照如下逻辑计算税费：
1. 税费比例为 0.11%，不足 1 元按 1 元记，四舍五入保留两位小数（包括印花税、交易征费、交易系统使用费）。
2，五元固定费用（包括卖方收取的转手纸印花税、买方收取的过户费用）。
"""
instrument = Environment.get_instance().get_instrument(order_book_id)
if instrument.type ... | def _get_tax(self, order_book_id, _, cost_money):
"""
港交所收费项目繁多，按照如下逻辑计算税费：
1. 税费比例为 0.11%，不足 1 元按 1 元记，四舍五入保留两位小数（包括印花税、交易征费、交易系统使用费）。
2，五元固定费用（包括卖方收取的转手纸印花税、买方收取的过户费用）。
"""
instrument = Environment.get_instance().get_instrument(order_book_id)
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train | BarObject.prev_close | [float] 昨日收盘价 | rqalpha/model/bar.py | def prev_close(self):
"""
[float] 昨日收盘价
"""
try:
return self._data['prev_close']
except (ValueError, KeyError):
pass
if self._prev_close is None:
trading_dt = Environment.get_instance().trading_dt
data_proxy = Environment.g... | def prev_close(self):
"""
[float] 昨日收盘价
"""
try:
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except (ValueError, KeyError):
pass
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data_proxy = Environment.g... | [
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train | BarObject._bar_status | WARNING: 获取 bar_status 比较耗费性能，而且是lazy_compute，因此不要多次调用！！！！ | rqalpha/model/bar.py | def _bar_status(self):
"""
WARNING: 获取 bar_status 比较耗费性能，而且是lazy_compute，因此不要多次调用！！！！
"""
if self.isnan or np.isnan(self.limit_up):
return BAR_STATUS.ERROR
if self.close >= self.limit_up:
return BAR_STATUS.LIMIT_UP
if self.close <= self.limit_down:... | def _bar_status(self):
"""
WARNING: 获取 bar_status 比较耗费性能，而且是lazy_compute，因此不要多次调用！！！！
"""
if self.isnan or np.isnan(self.limit_up):
return BAR_STATUS.ERROR
if self.close >= self.limit_up:
return BAR_STATUS.LIMIT_UP
if self.close <= self.limit_down:... | [
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train | BarObject.prev_settlement | [float] 昨日结算价（期货专用） | rqalpha/model/bar.py | def prev_settlement(self):
"""
[float] 昨日结算价（期货专用）
"""
try:
return self._data['prev_settlement']
except (ValueError, KeyError):
pass
if self._prev_settlement is None:
trading_dt = Environment.get_instance().trading_dt
data_... | def prev_settlement(self):
"""
[float] 昨日结算价（期货专用）
"""
try:
return self._data['prev_settlement']
except (ValueError, KeyError):
pass
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train | submit_order | 通用下单函数，策略可以通过该函数自由选择参数下单。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str`
:param float amount: 下单量，需为正数
:param side: 多空方向，多（SIDE.BUY）或空（SIDE.SELL）
:type side: :class:`~SIDE` enum
:param float price: 下单价格，默认为None，表示市价单
:param position_effect: 开平方向，开仓（POSITION... | rqalpha/api/api_base.py | def submit_order(id_or_ins, amount, side, price=None, position_effect=None):
"""
通用下单函数，策略可以通过该函数自由选择参数下单。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str`
:param float amount: 下单量，需为正数
:param side: 多空方向，多（SIDE.BUY）或空（SIDE.SELL）
:type side: :class:`~SIDE` enum
... | def submit_order(id_or_ins, amount, side, price=None, position_effect=None):
"""
通用下单函数，策略可以通过该函数自由选择参数下单。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str`
:param float amount: 下单量，需为正数
:param side: 多空方向，多（SIDE.BUY）或空（SIDE.SELL）
:type side: :class:`~SIDE` enum
... | [
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... | ac40a62d4e7eca9494b4d0a14f46facf5616820c |
train | cancel_order | 撤单
:param order: 需要撤销的order对象
:type order: :class:`~Order` object | rqalpha/api/api_base.py | def cancel_order(order):
"""
撤单
:param order: 需要撤销的order对象
:type order: :class:`~Order` object
"""
env = Environment.get_instance()
if env.can_cancel_order(order):
env.broker.cancel_order(order)
return order | def cancel_order(order):
"""
撤单
:param order: 需要撤销的order对象
:type order: :class:`~Order` object
"""
env = Environment.get_instance()
if env.can_cancel_order(order):
env.broker.cancel_order(order)
return order | [
"撤单"
] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/api/api_base.py#L304-L314 | [
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train | update_universe | 该方法用于更新现在关注的证券的集合（e.g.：股票池）。PS：会在下一个bar事件触发时候产生（新的关注的股票池更新）效果。并且update_universe会是覆盖（overwrite）的操作而不是在已有的股票池的基础上进行增量添加。比如已有的股票池为['000001.XSHE', '000024.XSHE']然后调用了update_universe(['000030.XSHE'])之后，股票池就会变成000030.XSHE一个股票了，随后的数据更新也只会跟踪000030.XSHE这一个股票了。
:param id_or_symbols: 标的物
:type id_or_symbols: :class:`~Ins... | rqalpha/api/api_base.py | def update_universe(id_or_symbols):
"""
该方法用于更新现在关注的证券的集合（e.g.：股票池）。PS：会在下一个bar事件触发时候产生（新的关注的股票池更新）效果。并且update_universe会是覆盖（overwrite）的操作而不是在已有的股票池的基础上进行增量添加。比如已有的股票池为['000001.XSHE', '000024.XSHE']然后调用了update_universe(['000030.XSHE'])之后，股票池就会变成000030.XSHE一个股票了，随后的数据更新也只会跟踪000030.XSHE这一个股票了。
:param id_or_sy... | def update_universe(id_or_symbols):
"""
该方法用于更新现在关注的证券的集合（e.g.：股票池）。PS：会在下一个bar事件触发时候产生（新的关注的股票池更新）效果。并且update_universe会是覆盖（overwrite）的操作而不是在已有的股票池的基础上进行增量添加。比如已有的股票池为['000001.XSHE', '000024.XSHE']然后调用了update_universe(['000030.XSHE'])之后，股票池就会变成000030.XSHE一个股票了，随后的数据更新也只会跟踪000030.XSHE这一个股票了。
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train | subscribe | 订阅合约行情。该操作会导致合约池内合约的增加，从而影响handle_bar中处理bar数据的数量。
需要注意，用户在初次编写策略时候需要首先订阅合约行情，否则handle_bar不会被触发。
:param id_or_symbols: 标的物
:type id_or_symbols: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`] | rqalpha/api/api_base.py | def subscribe(id_or_symbols):
"""
订阅合约行情。该操作会导致合约池内合约的增加，从而影响handle_bar中处理bar数据的数量。
需要注意，用户在初次编写策略时候需要首先订阅合约行情，否则handle_bar不会被触发。
:param id_or_symbols: 标的物
:type id_or_symbols: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
"""
current_universe = Environment... | def subscribe(id_or_symbols):
"""
订阅合约行情。该操作会导致合约池内合约的增加，从而影响handle_bar中处理bar数据的数量。
需要注意，用户在初次编写策略时候需要首先订阅合约行情，否则handle_bar不会被触发。
:param id_or_symbols: 标的物
:type id_or_symbols: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
"""
current_universe = Environment... | [
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] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/api/api_base.py#L353-L374 | [
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train | unsubscribe | 取消订阅合约行情。取消订阅会导致合约池内合约的减少，如果当前合约池中没有任何合约，则策略直接退出。
:param id_or_symbols: 标的物
:type id_or_symbols: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`] | rqalpha/api/api_base.py | def unsubscribe(id_or_symbols):
"""
取消订阅合约行情。取消订阅会导致合约池内合约的减少，如果当前合约池中没有任何合约，则策略直接退出。
:param id_or_symbols: 标的物
:type id_or_symbols: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
"""
current_universe = Environment.get_instance().get_universe()
if isinstance(... | def unsubscribe(id_or_symbols):
"""
取消订阅合约行情。取消订阅会导致合约池内合约的减少，如果当前合约池中没有任何合约，则策略直接退出。
:param id_or_symbols: 标的物
:type id_or_symbols: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
"""
current_universe = Environment.get_instance().get_universe()
if isinstance(... | [
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] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/api/api_base.py#L387-L407 | [
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train | get_yield_curve | 获取某个国家市场指定日期的收益率曲线水平。
数据为2002年至今的中债国债收益率曲线，来源于中央国债登记结算有限责任公司。
:param date: 查询日期，默认为策略当前日期前一天
:type date: `str` | `date` | `datetime` | `pandas.Timestamp`
:param str tenor: 标准期限，'0S' - 隔夜，'1M' - 1个月，'1Y' - 1年，默认为全部期限
:return: `pandas.DataFrame` - 查询时间段内无风险收益率曲线
:example:
.. code-block:... | rqalpha/api/api_base.py | def get_yield_curve(date=None, tenor=None):
"""
获取某个国家市场指定日期的收益率曲线水平。
数据为2002年至今的中债国债收益率曲线，来源于中央国债登记结算有限责任公司。
:param date: 查询日期，默认为策略当前日期前一天
:type date: `str` | `date` | `datetime` | `pandas.Timestamp`
:param str tenor: 标准期限，'0S' - 隔夜，'1M' - 1个月，'1Y' - 1年，默认为全部期限
:return: `pandas.DataFra... | def get_yield_curve(date=None, tenor=None):
"""
获取某个国家市场指定日期的收益率曲线水平。
数据为2002年至今的中债国债收益率曲线，来源于中央国债登记结算有限责任公司。
:param date: 查询日期，默认为策略当前日期前一天
:type date: `str` | `date` | `datetime` | `pandas.Timestamp`
:param str tenor: 标准期限，'0S' - 隔夜，'1M' - 1个月，'1Y' - 1年，默认为全部期限
:return: `pandas.DataFra... | [
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] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/api/api_base.py#L423-L465 | [
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train | history_bars | 获取指定合约的历史行情，同时支持日以及分钟历史数据。不能在init中调用。 注意，该API会自动跳过停牌数据。
日回测获取分钟历史数据：不支持
日回测获取日历史数据
========================= ===================================================
调用时间 返回数据
========================= ===================================================
T日before_trading ... | rqalpha/api/api_base.py | def history_bars(
order_book_id,
bar_count,
frequency,
fields=None,
skip_suspended=True,
include_now=False,
adjust_type="pre",
):
"""
获取指定合约的历史行情，同时支持日以及分钟历史数据。不能在init中调用。 注意，该API会自动跳过停牌数据。
日回测获取分钟历史数据：不支持
日回测获取日历史数据
========================= ========================... | def history_bars(
order_book_id,
bar_count,
frequency,
fields=None,
skip_suspended=True,
include_now=False,
adjust_type="pre",
):
"""
获取指定合约的历史行情，同时支持日以及分钟历史数据。不能在init中调用。 注意，该API会自动跳过停牌数据。
日回测获取分钟历史数据：不支持
日回测获取日历史数据
========================= ========================... | [
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] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/api/api_base.py#L487-L612 | [
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train | all_instruments | 获取某个国家市场的所有合约信息。使用者可以通过这一方法很快地对合约信息有一个快速了解，目前仅支持中国市场。
:param str type: 需要查询合约类型，例如：type='CS'代表股票。默认是所有类型
:param date: 查询时间点
:type date: `str` | `datetime` | `date`
:return: `pandas DataFrame` 所有合约的基本信息。
其中type参数传入的合约类型和对应的解释如下：
========================= ==================================... | rqalpha/api/api_base.py | def all_instruments(type=None, date=None):
"""
获取某个国家市场的所有合约信息。使用者可以通过这一方法很快地对合约信息有一个快速了解，目前仅支持中国市场。
:param str type: 需要查询合约类型，例如：type='CS'代表股票。默认是所有类型
:param date: 查询时间点
:type date: `str` | `datetime` | `date`
:return: `pandas DataFrame` 所有合约的基本信息。
其中type参数传入的合约类型和对应的解释如下：
=======... | def all_instruments(type=None, date=None):
"""
获取某个国家市场的所有合约信息。使用者可以通过这一方法很快地对合约信息有一个快速了解，目前仅支持中国市场。
:param str type: 需要查询合约类型，例如：type='CS'代表股票。默认是所有类型
:param date: 查询时间点
:type date: `str` | `datetime` | `date`
:return: `pandas DataFrame` 所有合约的基本信息。
其中type参数传入的合约类型和对应的解释如下：
=======... | [
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] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/api/api_base.py#L630-L707 | [
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".... | ac40a62d4e7eca9494b4d0a14f46facf5616820c |
train | current_snapshot | 获得当前市场快照数据。只能在日内交易阶段调用，获取当日调用时点的市场快照数据。
市场快照数据记录了每日从开盘到当前的数据信息，可以理解为一个动态的day bar数据。
在目前分钟回测中，快照数据为当日所有分钟线累积而成，一般情况下，最后一个分钟线获取到的快照数据应当与当日的日线行情保持一致。
需要注意，在实盘模拟中，该函数返回的是调用当时的市场快照情况，所以在同一个handle_bar中不同时点调用可能返回的数据不同。
如果当日截止到调用时候对应股票没有任何成交，那么snapshot中的close, high, low, last几个价格水平都将以0表示。
:param str id_or_... | rqalpha/api/api_base.py | def current_snapshot(id_or_symbol):
"""
获得当前市场快照数据。只能在日内交易阶段调用，获取当日调用时点的市场快照数据。
市场快照数据记录了每日从开盘到当前的数据信息，可以理解为一个动态的day bar数据。
在目前分钟回测中，快照数据为当日所有分钟线累积而成，一般情况下，最后一个分钟线获取到的快照数据应当与当日的日线行情保持一致。
需要注意，在实盘模拟中，该函数返回的是调用当时的市场快照情况，所以在同一个handle_bar中不同时点调用可能返回的数据不同。
如果当日截止到调用时候对应股票没有任何成交，那么snapshot中的close, hig... | def current_snapshot(id_or_symbol):
"""
获得当前市场快照数据。只能在日内交易阶段调用，获取当日调用时点的市场快照数据。
市场快照数据记录了每日从开盘到当前的数据信息，可以理解为一个动态的day bar数据。
在目前分钟回测中，快照数据为当日所有分钟线累积而成，一般情况下，最后一个分钟线获取到的快照数据应当与当日的日线行情保持一致。
需要注意，在实盘模拟中，该函数返回的是调用当时的市场快照情况，所以在同一个handle_bar中不同时点调用可能返回的数据不同。
如果当日截止到调用时候对应股票没有任何成交，那么snapshot中的close, hig... | [
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train | StockAccount.dividend_receivable | [float] 投资组合在分红现金收到账面之前的应收分红部分。具体细节在分红部分 | rqalpha/mod/rqalpha_mod_sys_accounts/account_model/stock_account.py | def dividend_receivable(self):
"""
[float] 投资组合在分红现金收到账面之前的应收分红部分。具体细节在分红部分
"""
return sum(d['quantity'] * d['dividend_per_share'] for d in six.itervalues(self._dividend_receivable)) | def dividend_receivable(self):
"""
[float] 投资组合在分红现金收到账面之前的应收分红部分。具体细节在分红部分
"""
return sum(d['quantity'] * d['dividend_per_share'] for d in six.itervalues(self._dividend_receivable)) | [
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] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/mod/rqalpha_mod_sys_accounts/account_model/stock_account.py#L247-L251 | [
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train | AbstractDataSource.history_bars | 获取历史数据
:param instrument: 合约对象
:type instrument: :class:`~Instrument`
:param int bar_count: 获取的历史数据数量
:param str frequency: 周期频率，`1d` 表示日周期, `1m` 表示分钟周期
:param str fields: 返回数据字段
========================= ===================================================
fi... | rqalpha/interface.py | def history_bars(self, instrument, bar_count, frequency, fields, dt, skip_suspended=True,
include_now=False, adjust_type='pre', adjust_orig=None):
"""
获取历史数据
:param instrument: 合约对象
:type instrument: :class:`~Instrument`
:param int bar_count: 获取的历史数据数量
... | def history_bars(self, instrument, bar_count, frequency, fields, dt, skip_suspended=True,
include_now=False, adjust_type='pre', adjust_orig=None):
"""
获取历史数据
:param instrument: 合约对象
:type instrument: :class:`~Instrument`
:param int bar_count: 获取的历史数据数量
... | [
"获取历史数据"
] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/interface.py#L360-L398 | [
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train | order_shares | 落指定股数的买/卖单，最常见的落单方式之一。如有需要落单类型当做一个参量传入，如果忽略掉落单类型，那么默认是市价单（market order）。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str`
:param int amount: 下单量, 正数代表买入，负数代表卖出。将会根据一手xx股来向下调整到一手的倍数，比如中国A股就是调整成100股的倍数。
:param float price: 下单价格，默认为None，表示 :class:`~MarketOrder`, 此参数主要用于简化 `st... | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_stock.py | def order_shares(id_or_ins, amount, price=None, style=None):
"""
落指定股数的买/卖单，最常见的落单方式之一。如有需要落单类型当做一个参量传入，如果忽略掉落单类型，那么默认是市价单（market order）。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str`
:param int amount: 下单量, 正数代表买入，负数代表卖出。将会根据一手xx股来向下调整到一手的倍数，比如中国A股就是调整成100股的倍数。
... | def order_shares(id_or_ins, amount, price=None, style=None):
"""
落指定股数的买/卖单，最常见的落单方式之一。如有需要落单类型当做一个参量传入，如果忽略掉落单类型，那么默认是市价单（market order）。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str`
:param int amount: 下单量, 正数代表买入，负数代表卖出。将会根据一手xx股来向下调整到一手的倍数，比如中国A股就是调整成100股的倍数。
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"order）。"
] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/mod/rqalpha_mod_sys_accounts/api/api_stock.py#L68-L139 | [
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train | order_lots | 指定手数发送买/卖单。如有需要落单类型当做一个参量传入，如果忽略掉落单类型，那么默认是市价单（market order）。
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: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。
... | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_stock.py | def order_lots(id_or_ins, amount, price=None, style=None):
"""
指定手数发送买/卖单。如有需要落单类型当做一个参量传入，如果忽略掉落单类型，那么默认是市价单（market order）。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str`
:param int amount: 下单量, 正数代表买入，负数代表卖出。将会根据一手xx股来向下调整到一手的倍数，比如中国A股就是调整成100股的倍数。
:param float... | def order_lots(id_or_ins, amount, price=None, style=None):
"""
指定手数发送买/卖单。如有需要落单类型当做一个参量传入，如果忽略掉落单类型，那么默认是市价单（market order）。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str`
:param int amount: 下单量, 正数代表买入，负数代表卖出。将会根据一手xx股来向下调整到一手的倍数，比如中国A股就是调整成100股的倍数。
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] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/mod/rqalpha_mod_sys_accounts/api/api_stock.py#L162-L194 | [
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... | ac40a62d4e7eca9494b4d0a14f46facf5616820c |
train | order_value | 使用想要花费的金钱买入/卖出股票，而不是买入/卖出想要的股数，正数代表买入，负数代表卖出。股票的股数总是会被调整成对应的100的倍数（在A中国A股市场1手是100股）。如果资金不足，该API将不会创建发送订单。
需要注意：
当您提交一个买单时，cash_amount 代表的含义是您希望买入股票消耗的金额（包含税费），最终买入的股数不仅和发单的价格有关，还和税费相关的参数设置有关。
当您提交一个卖单时，cash_amount 代表的意义是您希望卖出股票的总价值。如果金额超出了您所持有股票的价值，那么您将卖出所有股票。
:param id_or_ins: 下单标的物
:type id_or_i... | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_stock.py | def order_value(id_or_ins, cash_amount, price=None, style=None):
"""
使用想要花费的金钱买入/卖出股票，而不是买入/卖出想要的股数，正数代表买入，负数代表卖出。股票的股数总是会被调整成对应的100的倍数（在A中国A股市场1手是100股）。如果资金不足，该API将不会创建发送订单。
需要注意：
当您提交一个买单时，cash_amount 代表的含义是您希望买入股票消耗的金额（包含税费），最终买入的股数不仅和发单的价格有关，还和税费相关的参数设置有关。
当您提交一个卖单时，cash_amount 代表的意义是您希望卖出股票的总价... | def order_value(id_or_ins, cash_amount, price=None, style=None):
"""
使用想要花费的金钱买入/卖出股票，而不是买入/卖出想要的股数，正数代表买入，负数代表卖出。股票的股数总是会被调整成对应的100的倍数（在A中国A股市场1手是100股）。如果资金不足，该API将不会创建发送订单。
需要注意：
当您提交一个买单时，cash_amount 代表的含义是您希望买入股票消耗的金额（包含税费），最终买入的股数不仅和发单的价格有关，还和税费相关的参数设置有关。
当您提交一个卖单时，cash_amount 代表的意义是您希望卖出股票的总价... | [
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] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/mod/rqalpha_mod_sys_accounts/api/api_stock.py#L206-L282 | [
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train | order_percent | 发送一个花费价值等于目前投资组合（市场价值和目前现金的总和）一定百分比现金的买/卖单，正数代表买，负数代表卖。股票的股数总是会被调整成对应的一手的股票数的倍数（1手是100股）。百分比是一个小数，并且小于或等于1（<=100%），0.5表示的是50%.需要注意，如果资金不足，该API将不会创建发送订单。
需要注意：
发送买单时，percent 代表的是期望买入股票消耗的金额（包含税费）占投资组合总权益的比例。
发送卖单时，percent 代表的是期望卖出的股票总价值占投资组合总权益的比例。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~... | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_stock.py | def order_percent(id_or_ins, percent, price=None, style=None):
"""
发送一个花费价值等于目前投资组合（市场价值和目前现金的总和）一定百分比现金的买/卖单，正数代表买，负数代表卖。股票的股数总是会被调整成对应的一手的股票数的倍数（1手是100股）。百分比是一个小数，并且小于或等于1（<=100%），0.5表示的是50%.需要注意，如果资金不足，该API将不会创建发送订单。
需要注意：
发送买单时，percent 代表的是期望买入股票消耗的金额（包含税费）占投资组合总权益的比例。
发送卖单时，percent 代表的是期望卖出的股票... | def order_percent(id_or_ins, percent, price=None, style=None):
"""
发送一个花费价值等于目前投资组合（市场价值和目前现金的总和）一定百分比现金的买/卖单，正数代表买，负数代表卖。股票的股数总是会被调整成对应的一手的股票数的倍数（1手是100股）。百分比是一个小数，并且小于或等于1（<=100%），0.5表示的是50%.需要注意，如果资金不足，该API将不会创建发送订单。
需要注意：
发送买单时，percent 代表的是期望买入股票消耗的金额（包含税费）占投资组合总权益的比例。
发送卖单时，percent 代表的是期望卖出的股票... | [
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train | order_target_value | 买入/卖出并且自动调整该证券的仓位到一个目标价值。
加仓时，cash_amount 代表现有持仓的价值加上即将花费（包含税费）的现金的总价值。
减仓时，cash_amount 代表调整仓位的目标价至。
需要注意，如果资金不足，该API将不会创建发送订单。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:class:`~Instrument`] | List[`str`]
:param float cash_amount: 最终的该证券的仓位目标价值。
... | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_stock.py | def order_target_value(id_or_ins, cash_amount, price=None, style=None):
"""
买入/卖出并且自动调整该证券的仓位到一个目标价值。
加仓时，cash_amount 代表现有持仓的价值加上即将花费（包含税费）的现金的总价值。
减仓时，cash_amount 代表调整仓位的目标价至。
需要注意，如果资金不足，该API将不会创建发送订单。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:c... | def order_target_value(id_or_ins, cash_amount, price=None, style=None):
"""
买入/卖出并且自动调整该证券的仓位到一个目标价值。
加仓时，cash_amount 代表现有持仓的价值加上即将花费（包含税费）的现金的总价值。
减仓时，cash_amount 代表调整仓位的目标价至。
需要注意，如果资金不足，该API将不会创建发送订单。
:param id_or_ins: 下单标的物
:type id_or_ins: :class:`~Instrument` object | `str` | List[:c... | [
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] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/mod/rqalpha_mod_sys_accounts/api/api_stock.py#L338-L380 | [
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train | order_target_percent | 买入/卖出证券以自动调整该证券的仓位到占有一个目标价值。
加仓时，percent 代表证券已有持仓的价值加上即将花费的现金（包含税费）的总值占当前投资组合总价值的比例。
减仓时，percent 代表证券将被调整到的目标价至占当前投资组合总价值的比例。
其实我们需要计算一个position_to_adjust (即应该调整的仓位)
`position_to_adjust = target_position - current_position`
投资组合价值等于所有已有仓位的价值和剩余现金的总和。买/卖单会被下舍入一手股数（A股是100的倍数）的倍数。目标百分比应该是一个小数，并且最大值... | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_stock.py | def order_target_percent(id_or_ins, percent, price=None, style=None):
"""
买入/卖出证券以自动调整该证券的仓位到占有一个目标价值。
加仓时，percent 代表证券已有持仓的价值加上即将花费的现金（包含税费）的总值占当前投资组合总价值的比例。
减仓时，percent 代表证券将被调整到的目标价至占当前投资组合总价值的比例。
其实我们需要计算一个position_to_adjust (即应该调整的仓位)
`position_to_adjust = target_position - current_posit... | def order_target_percent(id_or_ins, percent, price=None, style=None):
"""
买入/卖出证券以自动调整该证券的仓位到占有一个目标价值。
加仓时，percent 代表证券已有持仓的价值加上即将花费的现金（包含税费）的总值占当前投资组合总价值的比例。
减仓时，percent 代表证券将被调整到的目标价至占当前投资组合总价值的比例。
其实我们需要计算一个position_to_adjust (即应该调整的仓位)
`position_to_adjust = target_position - current_posit... | [
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"/",
"卖出证券以自动调整该证券的仓位到占有一个目标价值。"
] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/mod/rqalpha_mod_sys_accounts/api/api_stock.py#L392-L445 | [
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"u\"percent should betw... | ac40a62d4e7eca9494b4d0a14f46facf5616820c |
train | is_suspended | 判断某只股票是否全天停牌。
:param str order_book_id: 某只股票的代码或股票代码，可传入单只股票的order_book_id, symbol
:param int count: 回溯获取的数据个数。默认为当前能够获取到的最近的数据
:return: count为1时 `bool`; count>1时 `pandas.DataFrame` | rqalpha/mod/rqalpha_mod_sys_accounts/api/api_stock.py | def is_suspended(order_book_id, count=1):
"""
判断某只股票是否全天停牌。
:param str order_book_id: 某只股票的代码或股票代码，可传入单只股票的order_book_id, symbol
:param int count: 回溯获取的数据个数。默认为当前能够获取到的最近的数据
:return: count为1时 `bool`; count>1时 `pandas.DataFrame`
"""
dt = Environment.get_instance().calendar_dt.date()
or... | def is_suspended(order_book_id, count=1):
"""
判断某只股票是否全天停牌。
:param str order_book_id: 某只股票的代码或股票代码，可传入单只股票的order_book_id, symbol
:param int count: 回溯获取的数据个数。默认为当前能够获取到的最近的数据
:return: count为1时 `bool`; count>1时 `pandas.DataFrame`
"""
dt = Environment.get_instance().calendar_dt.date()
or... | [
"判断某只股票是否全天停牌。"
] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/mod/rqalpha_mod_sys_accounts/api/api_stock.py#L457-L469 | [
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... | ac40a62d4e7eca9494b4d0a14f46facf5616820c |
train | update_bundle | Sync Data Bundle | rqalpha/__main__.py | def update_bundle(data_bundle_path, locale):
"""
Sync Data Bundle
"""
import rqalpha.utils.bundle_helper
rqalpha.utils.bundle_helper.update_bundle(data_bundle_path, locale) | def update_bundle(data_bundle_path, locale):
"""
Sync Data Bundle
"""
import rqalpha.utils.bundle_helper
rqalpha.utils.bundle_helper.update_bundle(data_bundle_path, locale) | [
"Sync",
"Data",
"Bundle"
] | ricequant/rqalpha | python | https://github.com/ricequant/rqalpha/blob/ac40a62d4e7eca9494b4d0a14f46facf5616820c/rqalpha/__main__.py#L77-L82 | [
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] | ac40a62d4e7eca9494b4d0a14f46facf5616820c |
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