Found 45 packages
An arbitrary precision decimal (base 10) floating point number type using a mantissa and exponent. Supports nth roots, trigonometric functions, logarithms, natural e, pi, etc.
SQLite-Net Extensions is a very simple ORM that provides cascade operations, one-to-one, one-to-many, many-to-one, many-to-many, inverse and text-blobbed relationships on top of the sqlite-net library.
Inverse Fisher Transform on STOCHASTIC About John EHLERS: From California, USA, John is a veteran trader. With 35 years trading experience he has seen it all. John has an engineering background that led to his technical approach to trading ignoring fundamental analysis (with one important exception). John strongly believes in cycles. He’d rather exit a trade when the cycle ends or a new one starts. He uses the MESA principle to make predictions about cycles in the market and trades one hundred percent automatically. In the show John reveals: • What is more appropriate than trading individual stocks • The one thing he relies upon in his approach to the market • The detail surrounding his unique trading style • What important thing underpins the market and gives every trader an edge About INVERSE FISHER TRANSFORM: The purpose of technical indicators is to help with your timing decisions to buy or sell. Hopefully, the signals are clear and unequivocal. However, more often than not your decision to pull the trigger is accompanied by crossing your fingers. Even if you have placed only a few trades you know the drill. In this article I will show you a way to make your oscillator-type indicators make clear black-or-white indication of the time to buy or sell. I will do this by using the Inverse Fisher Transform to alter the Probability Distribution Function ( PDF ) of your indicators. In the past12 I have noted that the PDF of price and indicators do not have a Gaussian, or Normal, probability distribution. A Gaussian PDF is the familiar bell-shaped curve where the long “tails” mean that wide deviations from the mean occur with relatively low probability. The Fisher Transform can be applied to almost any normalized data set to make the resulting PDF nearly Gaussian, with the result that the turning points are sharply peaked and easy to identify. The Fisher Transform is defined by the equation Whereas the Fisher Transform is expansive, the Inverse Fisher Transform is compressive. The Inverse Fisher Transform is found by solving equation 1 for x in terms of y. The Inverse Fisher Transform is: The transfer response of the Inverse Fisher Transform is shown in Figure 1. If the input falls between –0.5 and +0.5, the output is nearly the same as the input. For larger absolute values (say, larger than 2), the output is compressed to be no larger than unity . The result of using the Inverse Fisher Transform is that the output has a very high probability of being either +1 or –1. This bipolar probability distribution makes the Inverse Fisher Transform ideal for generating an indicator that provides clear buy and sell signals.
Data types: - arbitrary precision rational numbers Matrix algebra (integer, rational): - Row echelon form - Smith normal form - Kernel, cokernel and (pseudo-)inverse Matrix decomposition (floating point): - Principal component analysis (PCA) - ZCA whitening Misc: - Bezout's identity - Loading of NumPy's .npy and .npz files.
Simple Dependency Container
Simulation based inverse kinematics solver.
**Bybit.Net.Api** offers an official, powerful, and efficient .NET connector to the [Bybit public Trading API](https://bybit-exchange.github.io/docs/v5/intro) Dive into a plethora of functionalities: - Public Websocket Streaming - Private Websocket Streaming - Market Data Retrieval - Trade Execution - Position Management - Account and Asset Info Retrieval - User Management - Upgrade History - Spot Margin UTA & Classical Service - Broker Earning Data
General purpose matrix library.
Implements communication with Universal Robots industrial robots (RTDE, Primary interfaces, Dashboard Server, REST API, SSH, SFTP, XML-RPC, Sockets, Interpreter Mode) - Fully-managed .NET commercial DLL without dependencies. Offline tools like forward and invert kinematics, or pose conversion
Package Description
Classes to support a business logic layer or an API. Keep child-parent relationships in sync. Wrap a result with data along with a Successful flag and a list of messages. Keep business logic units separate, implementing ISideEffect. Check whether a list is dirty.
FreshEssentials for Xamarin.Forms has ONLY the most common elements you need for Xamarin.Forms. Designed so that it bring in just enough for the majority of projects. Controls: -BindablePicker -AdvancedFrame (flexible rounded corners) -SegmentedButtonGroup -AutoGrid Converters: -InverseBooleanConverter -HasDataConverter Attached Properties -ListViewItemTappedAttached -TappedGestureAttached
The VTS (Virtual Tissue Simulator) is an open source library for .NET that was designed as a modular and scalable platform to provide an integrated suite of computational tools to define, solve, visualize, and analyze relevant forward and inverse radiative transport problems in Biomedical Optics.
C# library for fast multi-dimensional inverse distance weighting (IDW) interpolation.
Tensor (n-dimensional array) library for F# Core features: - n-dimensional arrays (tensors) in host memory or on CUDA GPUs - element-wise operations (addition, multiplication, absolute value, etc.) - basic linear algebra operations (dot product, SVD decomposition, matrix inverse, etc.) - reduction operations (sum, product, average, maximum, arg max, etc.) - logic operations (comparision, and, or, etc.) - views, slicing, reshaping, broadcasting (similar to NumPy) - scatter and gather by indices - standard functional operations (map, fold, etc.) Data exchange: - read/write support for HDF5 (.h5) - interop with standard F# types (Seq, List, Array, Array2D, Array3D, etc.) Performance: - host: SIMD and BLAS accelerated operations - by default Intel MKL is used (shipped with NuGet package) - other BLASes (OpenBLAS, vendor-specific) can be selected by configuration option - CUDA GPU: all operations performed locally on GPU and cuBLAS used for matrix operations Requirements: - Linux, MacOS or Windows on x64 - Linux requires libgomp.so.1 installed. Additional algorithms are provided in the Tensor.Algorithm package.