INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
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This function generates a 256 jet colormap of HTML - like hex string colors ( e. g. FF88AA ) | 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... |
Distance between two strings | 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]:
... |
Generates a list of colors based on a list of names ( strings ). Similar strings correspond to similar 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)):
... |
Generates a list of colors based on a list of names ( strings ). Similar strings correspond to similar colors. | 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... |
Generates a d3js chordial diagram that illustrates similarites | 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
# ... |
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 similarity. - priorKnowledge: if this ... | 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: ... |
Computes zero crossing rate of frame | 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)) |
Computes entropy of energy | 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]
# ... |
Computes spectral centroid of frame ( given abs ( FFT )) | 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:
... |
Computes the spectral entropy | 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... |
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 | 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... |
Computes spectral roll - off | 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) + ... |
Computes harmonic ratio and pitch | 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... |
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
"""
... |
Computes the MFCCs of a frame given the fft mag | 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)
Note: MFCC calculation is, in general, taken fr... |
This function initializes the chroma matrices used in the calculation of the chroma features | 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)
... |
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 ) step: the short - term window step ( in samples ) PLOT: flag 1 if results are to be ploted ... | def stChromagram(signal, fs, win, step, PLOT=False):
"""
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... |
This function extracts an estimate of the beat rate for a musical signal. ARGUMENTS: - st_features: a numpy array ( n_feats x numOfShortTermWindows ) - win_len: window size in seconds RETURNS: - BPM: estimates of beats per minute - Ratio: a confidence measure | def beatExtraction(st_features, win_len, PLOT=False):
"""
This function extracts an estimate of the beat rate for a musical signal.
ARGUMENTS:
- st_features: a numpy array (n_feats x numOfShortTermWindows)
- win_len: window size in seconds
RETURNS:
- BPM: estimates o... |
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 ) step: the short - term window step ( in samples ) PLOT: flag 1 if results are to be ploted ... | def stSpectogram(signal, fs, win, step, PLOT=False):
"""
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... |
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. | 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
... |
Mid - term feature extraction | 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)
... |
This function extracts the mid - term features of the WAVE files of a particular folder. | 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... |
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. dirsWavFeatureExtraction ( [ audioData/ classSegmentsRec/ noise audioData/ classSegmentsRec/ speech audioData/ classSegmentsRec/ brush - teeth audio... | 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... |
This function extracts the mid - term features of the WAVE files of a particular folder without averaging each file. | def dirWavFeatureExtractionNoAveraging(dirName, mt_win, mt_step, st_win, st_step):
"""
This function extracts the mid-term features of the WAVE
files of a particular folder without averaging each file.
ARGUMENTS:
- dirName: the path of the WAVE directory
- mt_win, mt_step: m... |
This function is used as a wrapper to: a ) read the content of a WAV file b ) perform mid - term feature extraction on that signal c ) write the mid - term feature sequences to a numpy file | def mtFeatureExtractionToFile(fileName, midTermSize, midTermStep, shortTermSize, shortTermStep, outPutFile,
storeStFeatures=False, storeToCSV=False, PLOT=False):
"""
This function is used as a wrapper to:
a) read the content of a WAV file
b) perform mid-term feature extract... |
[ float ] 市值 | def market_value(self):
"""
[float] 市值
"""
return sum(position.market_value for position in six.itervalues(self._positions)) |
[ float ] 总费用 | def transaction_cost(self):
"""
[float] 总费用
"""
return sum(position.transaction_cost for position in six.itervalues(self._positions)) |
买入开仓。 | 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。
... |
平卖仓 | 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... |
卖出开仓 | 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。
... |
平买仓 | 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... |
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。 | 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... |
[ int ] 订单数量 | 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 |
[ int ] 订单已成交数量 | 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 |
[ float ] 冻结价格 | 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 |
[ datetime. datetime ] 当前快照数据的时间戳 | 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... |
[ 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 ] | 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... |
判断合约是否过期 | 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... |
[ 已弃用 ] | 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 |
[ 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 | 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) |
[ 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 | 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... |
注册事件 | 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) |
[ float ] 实时净值 | def unit_net_value(self):
"""
[float] 实时净值
"""
if self._units == 0:
return np.nan
return self.total_value / self._units |
[ float ] 当前最新一天的日收益 | def daily_returns(self):
"""
[float] 当前最新一天的日收益
"""
if self._static_unit_net_value == 0:
return np.nan
return 0 if self._static_unit_net_value == 0 else self.unit_net_value / self._static_unit_net_value - 1 |
[ float ] 总权益 | def total_value(self):
"""
[float]总权益
"""
return sum(account.total_value for account in six.itervalues(self._accounts)) |
[ dict ] 持仓 | def positions(self):
"""
[dict] 持仓
"""
if self._mixed_positions is None:
self._mixed_positions = MixedPositions(self._accounts)
return self._mixed_positions |
[ float ] 可用资金 | def cash(self):
"""
[float] 可用资金
"""
return sum(account.cash for account in six.itervalues(self._accounts)) |
[ float ] 市值 | def market_value(self):
"""
[float] 市值
"""
return sum(account.market_value for account in six.itervalues(self._accounts)) |
[ float ] 买方向当日持仓盈亏 | 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 |
[ float ] 卖方向当日持仓盈亏 | 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 |
[ float ] 买方向累计盈亏 | 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 |
[ float ] 卖方向累计盈亏 | 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 |
[ int ] 买方向挂单量 | 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
order.side == SIDE.BUY and order.position_effect == POSITION_EFFECT.OPEN) |
[ int ] 卖方向挂单量 | def sell_open_order_quantity(self):
"""
[int] 卖方向挂单量
"""
return sum(order.unfilled_quantity for order in self.open_orders if
order.side == SIDE.SELL and order.position_effect == POSITION_EFFECT.OPEN) |
[ int ] 买方向挂单量 | 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
order.position_effect in [POSITION_EFFECT.CLOSE, POSITION_EFFECT.CLOSE_TODAY]) |
[ int ] 卖方向挂单量 | def sell_close_order_quantity(self):
"""
[int] 卖方向挂单量
"""
return sum(order.unfilled_quantity for order in self.open_orders if order.side == SIDE.SELL and
order.position_effect in [POSITION_EFFECT.CLOSE, POSITION_EFFECT.CLOSE_TODAY]) |
[ float ] 买方向持仓均价 | 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 |
[ float ] 卖方向持仓均价 | 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 |
判断合约是否过期 | 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... |
应用成交，并计算交易产生的现金变动。 | 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... |
应用平仓，并计算平仓盈亏 | 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
... |
[ str ] 板块缩写代码，全球通用标准定义（股票专用） | 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... |
[ str ] 以当地语言为标准的板块代码名（股票专用） | 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... |
[ str ] 国民经济行业分类代码，具体可参考“Industry列表” （股票专用） | 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... |
[ str ] 国民经济行业分类名称（股票专用） | 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_... |
[ str ] 概念股分类，例如：’铁路基建’，’基金重仓’等（股票专用） | 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... |
[ str ] 板块类别，’MainBoard’ - 主板 ’GEM’ - 创业板（股票专用） | 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... |
[ 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） | 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(
... |
[ 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） | 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(
... |
[ float ] 合约乘数，例如沪深300股指期货的乘数为300. 0（期货专用） | 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... |
[ float ] 合约最低保证金率（期货专用） | 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)
... |
[ 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） | 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={})... |
[ 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） | 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... |
[ 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） | 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... |
[ str ] 交割方式，’CashSettlementRequired’ - 现金交割 ‘PhysicalSettlementRequired’ - 实物交割（期货专用） | 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... |
[ bool ] 该合约当前日期是否在交易 | 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 |
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 则说明这不是第一笔trade 此时，直接cost_money - commission即可 4.... | 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... |
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）。 | 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 ... |
[ float ] 昨日收盘价 | 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... |
WARNING: 获取 bar_status 比较耗费性能，而且是lazy_compute，因此不要多次调用！！！！ | 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:... |
[ float ] 昨日结算价（期货专用） | 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_... |
通用下单函数，策略可以通过该函数自由选择参数下单。 | 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
... |
撤单 | 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 |
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。 | 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... |
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。 | 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... |
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。 | 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(... |
获取某个国家市场指定日期的收益率曲线水平。 | 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... |
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。 | 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
========================= ========================... |
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。 | 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：
=======... |
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。 | 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... |
[ 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 | 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)) |
获取历史数据 | 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
... |
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）。 | 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。
... |
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）。 | 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... |
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。 | 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... |
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。 | 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... |
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。 | 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... |
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。 | 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... |
判断某只股票是否全天停牌。 | 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... |
Sync Data Bundle | 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) |
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