Stock-Prediction-Models VS ent.hpp

Compare Stock-Prediction-Models vs ent.hpp and see what are their differences.

Stock-Prediction-Models

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations (by huseinzol05)
Lstm lstm-sequence evolution-strategies stock-prediction-models Seq2seq trading-bot stock-market stock-price-prediction stock-price-forecasting deep-learning-stock Deep Learning monte-carlo strategy-agent learning-agents monte-carlo-markov-chain

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ent.hpp

A header-only library that applies various tests to sequences of bytes stored in files and reports the results of those tests. The class is useful for evaluating pseudorandom number generators for encryption and statistical sampling applications, compression algorithms, and other applications where the information density of a file is of interest. (by chbtoys)
Entropy Optimum compression Chi square distribution Arithmetic mean monte-carlo Serial correlation coefficient

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Stock-Prediction-Models ent.hpp
215 1
6,620 3
- -
0.0 10.0
about 1 year ago 10 months ago
Jupyter Notebook c++20
Apache License 2.0 MIT
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Stock-Prediction-Models

Posts with mentions or reviews of Stock-Prediction-Models. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-06-06.

ent.hpp

Posts with mentions or reviews of ent.hpp. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-27.
  • entropy VS ent - a user suggested alternative
    2 projects | 27 Jun 2023
    A header-only library that does the same thing and more. ent.hpp - applies various tests to sequences of bytes stored in files and reports the results of those tests. The class is useful for evaluating pseudorandom number generators for encryption and statistical sampling applications, compression algorithms, and other applications where the information density of a file is of interest. Gives you the following result: Entropy = 7.990144 bits per byte. Optimum compression would reduce the size of this 469842 byte file by 0 percent. Chi square distribution for 469842 samples is 6481.037481, and randomly would exceed this value less than 0.01 percent of the times. Arithmetic mean value of data bytes is 127.740998 (127.500000 = random). Monte Carlo value for Pi is 3.104039 (error 1.195363 percent). Serial correlation coefficient is -0.016080 (totally uncorrelated = 0.0).

What are some alternatives?

When comparing Stock-Prediction-Models and ent.hpp you can also consider the following projects:

Behavior-Sequence-Transformer-Pytorch - This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf

ebisu - Public-domain Python library for flashcard quiz scheduling using Bayesian statistics. (JavaScript, Java, Dart, and other ports available!)

Alpaca-API - The Alpaca API is a developer interface for trading operations and market data reception through the Alpaca platform.

volesti - Practical volume computation and sampling in high dimensions

SectorTradingAlgorithm

Ashes - WebGL2.0 3D Engine - Global Illumination - RayTracing

CryptoGPU - Cryptocurrency prices analysis

paramonte - ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.

ubique - A mathematical and quantitative library for Javascript and Node.js

c-ray - c-ray is a small, simple path tracer written in C

Reinforcement-Learning - Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning

SectorTradingAlgorithm

Stock-Prediction-Models vs Behavior-Sequence-Transformer-Pytorch ent.hpp vs ebisu Stock-Prediction-Models vs Alpaca-API ent.hpp vs volesti Stock-Prediction-Models vs SectorTradingAlgorithm ent.hpp vs Ashes Stock-Prediction-Models vs CryptoGPU ent.hpp vs paramonte Stock-Prediction-Models vs ubique ent.hpp vs c-ray Stock-Prediction-Models vs Reinforcement-Learning Stock-Prediction-Models vs SectorTradingAlgorithm