Functions

apply_for_axis	Apply a function expecting 2D input to a tensor along a specified dimension.
bfill	Backward fill missing values along the specified dimension.
bincount	Count number of occurrences of each value in a tensor.
bollinger_band	Compute Bollinger Bands for a tensor.
correl	Compute Pearson correlation coefficient between two tensors.
covar	Compute covariance between two tensors along a specified dimension.
cumcount	Number each occurrence of unique values along a dimension.
eigh	Compute eigenvalues and eigenvectors of a symmetric/Hermitian matrix.
einsum	Sums the product of tensor elements over specified indices using Einstein notation.
ema	Compute exponential moving average along a specified dimension.
ffill	Forward fill missing values along the specified dimension.
fillna	Replace NaN and infinity values with specified numbers.
gaussian_blur	Apply Gaussian blur to a tensor along a specified dimension.
group_shift	Shift tensor elements along a dimension, skipping masked positions.
groupby	Group tensor elements by group identifiers along a dimension.
ma	Compute the moving (sliding window) average of a tensor.
mmax	Compute the moving (sliding window) maximum of a tensor.
mmin	Compute the moving (sliding window) minimum of a tensor.
mstd	Compute the moving (sliding window) standard deviation of a tensor.
msum	Compute the moving (sliding window) sum of a tensor.
mulmean	Compute the mean of element-wise product in a memory-efficient way.
mulsum	Compute sum or mean of element-wise product in a memory-efficient way.
mvar	Compute the moving (sliding window) variance of a tensor.
nanamax	Compute the maximum of tensor elements along specified dimensions, ignoring NaN values.
nanamin	Compute the minimum of tensor elements along specified dimensions, ignoring NaN values.
nancorrel	Compute Pearson correlation coefficient between two tensors, ignoring NaN values.
nancovar	Compute covariance between two tensors, ignoring NaN values.
nancumprod	Compute the cumulative product along a dimension, treating NaN as 1.
nancumsum	Compute the cumulative sum along a dimension, treating NaN as 0.
nankurtosis	Compute the kurtosis along specified dimensions, ignoring NaN values.
nanmax	Return the maximum values and indices along a dimension, ignoring NaN values.
nanmean	Compute the arithmetic mean along specified dimensions, ignoring NaN values.
nanmin	Return the minimum values and indices along a dimension, ignoring NaN values.
nanmulmean	Compute the mean of element-wise product, ignoring NaN values, in a memory-efficient way.
nanmulsum	Compute the sum of element-wise product, ignoring NaN values, in a memory-efficient way.
nanones	Create a tensor filled with ones, preserving NaN positions from the input tensor.
nanpca	Compute Principal Component Analysis (PCA) on data, ignoring NaN values.
nanshift	Shift tensor elements along a dimension while preserving NaN positions.
nanskew	Compute the skewness along specified dimensions, ignoring NaN values.
nanslope	Compute the slope of simple linear regression between two tensors, ignoring NaN values.
nansum	Compute the sum of tensor elements along specified dimensions, ignoring NaN values.
nanvar	Compute the variance of a tensor, ignoring NaN values.
orthogonalize	Orthogonalizes x with respect to y along the specified dimension.
orthonormalize	Orthonormalizes the given vectors and returns the corresponding orthonormal vectors.
pca	Computes principal components on the given input x. The returned value represents principal components. Specifically, result[*, i, :] represents the \((i+1)\)-th largest principal component of the batch specified by *. Shape: - x: \((*, S, D)\) where * means any number of additional dimensions, S means the number of sections, and D means the number of dimensions. In qfeval, dimensions and sections often represent symbols and timestamps respectively. :returns:.
project	Projects the given tensor x using the given projection matrix a.
rand	Returns a tensor filled with random numbers from a uniform distribution on the interval [0, 1).
rand_like	Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval [0, 1).
randint	Returns a tensor filled with random integers generated uniformly between low (inclusive) and high (exclusive).
randn	Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution).
randn_like	Returns a tensor with the same size as input that is filled with random numbers from a normal distribution with mean 0 and variance 1.
randperm	Returns a random permutation of integers from 0 to n - 1.
rci
rcummax	Returns the reversely cumulative max of elements of input in the dimension dim.
rcumsum	Returns the reversely cumulative sum of elements of input in the dimension dim.
rms	Returns the root mean square of each row of the input tensor in the given dimension dim.
rsi	Definition(use_sma=False): - https://www.investopedia.com/terms/r/rsi.asp - Compatible with TA-lib Definition(use_sma=True): - https://info.monex.co.jp/technical-analysis/indicators/005.html
shift
signif	Rounds the numbers of the given tensor to the specified number of significant digits.
skipna	Applies the given data to the given function after removing NaNs.
slope	Returns the slope of correlation bewteen x and y.
soft_topk_bottomk	Apply SoftTopKBottomK module along with given dimension.
stable_sort	Sorts the given tensor preserving the order of equivalent elements.