180 skills found · Page 4 of 6
AI4S2S / S2spyA high-level python package integrating expert knowledge and artificial intelligence to boost (sub) seasonal forecasting
paarthmadan / Bass BoostA python script that boosts the bass line in given mp3 files.
ideas4u / Trading PlatformThis project is the most awaited project in open source community where every user who belongs to Stock Trading always wanted to develop its own software. This project has been developed specifically for Indian Market Stock Trading. It encompasses end to end trading cycle for intraday trading but the design would be such that it can be easily extended for delivery trading. During the lifecycle of this project we will be using most advance technologies but the base code will always be C/C++. Development Methodology: ======================== We use "Incremental Life Cycle Model" along with Cross-Platform Development (Portable). Project Priorities and Assumptions: =================================== 1) Low Latency, High Performance all the time. 2) Wherever choice has to be made between memory and execution speed, we give preference to speed. 3) Every module devloped will be exhaustively tested. How the work Proceed: ===================== Before the beginning of any new project, we should know the "PROBLEM STATEMENT", so here it is "Problem Statement" ------------------- To Build a high performance, low latency, end to end Trading Platform for Indian Stock Market but not limited to which home users should be able use for trading which guarantees (99% of the times) the profit but does not guarantees maximized profit for intraday trading. First Step: ----------- To provide the optimal solution to any problem is "UNDERSTAING THE PROBLEM". To understand the above problem statement you need to really extract the explicit and implcit requirements from the statement. Here is the List of requirements: Explicit: --------- 1) High Performance 2) Low-Latency 3) End-to-End Trading Platform 4) Focus on Indian Stock Market but not limited to it. 5) Guarantees (99% of the times) the profit but does not guarantees maximized profit. 6) Only for Intraday Trading. Implicit: --------- 1) Book Keeping of the order and trade (Order Management System). 2) Availability of Market Data to End-Users on Demand for identifying the stock and placing the order. 3) User Account Management. Might be I missed something please suggest and after reveiw we add it here. Second Step: ------------ To understand the above Explicit/Implicit requirements, you should have the "KNOWLEDGE OF VARIOUS TECHNOLOGIES" and indepth undertstanding of the "PROBLEM DOMAIN" i.e. Stock Market. Once this is achieved we need to architect the solution in terms of Software and Hardware nodes and their integration. Third Step: ----------- To solve the problem statement, the above requirements should be "DECOMPOSED IN MODULES" and map to them with technolgoies/software/hardware used. Below is the list of modules we are able to identify: Modules Included: ================= Core Modules: -------------- 1) Core Libraries 2) Manual Order Entry System 3) Auto Order Entry System 4) Artificial Exchange 5) Algorithmic Trading Platform 6) Smart Order Router 7) Direct Trading Platform (Ooptional) Utility Modules: ---------------- 8) Logger Server 9) HeartBeat Server Technologies Used: ================= Software: --------- We always use freeware, Open Source Softwares or APIs which are the part of GPL, LGPL.xx licence. Any special requirement for building/using the modules will be detailed in specific module. For development, we generally use: ---------------------------------- Windows-7 for Operating System but any other OS ca be used. Our Code is Platform Indepandant. Visual Studio 2013 in built compiler for build or Intel@ Compilers which can be easily integrated with Visual Studio IDE. For real time, we generally use: -------------------------------- Linux-susse 10 or above with real time extensions. gcc 4.4.1 for build. vi editor Hardware: --------- No special requirement for development purpose. For real time use, it depands how much Stock you are interested in and the various configuration of modules. We prefer generally the below configuration for any number of Stock Trading: 256 GB RAM 16 core processor 1 TB of HDD/SDD Programming Languages and other Technologies: --------------------------------------------- C, C++99/c++11, Lua, ZeroMq, nanodbc, Lock-Free Data Structures, Intel TBB, Boost, Google Protobuf, MySql, Python. Fourth Step: ------------ Dcompose each module till it becomes entity to provide the useful functionality. We are going to explain this in each module detailed section. Fifth Step: ------------ We do design/develop/benchmark/unit test/integration testing of the above modules. Sixth Step: ------------ We deploy the delivered software on various hardware nodes as per the deployment architecture and integrate them. Seventh Step: ------------ Observe the behaviour of deployed software on live traffic and cut two branches at this level : 1st branch continue to do incremental development and 2nd branch fix the issues reported which can be later merged with 1st branch for another release. Any suggestions for improvement are most welcome.
bjodah / PyodeintPython wrapper around odeint (from the boost C++ library)
JaneliaSciComp / OsgpyplusplusPython bindings for OpenSceneGraph 3D graphics API, created using Boost.Python and Py++
surakshapai / RecommendationSystemRecommendation System implementation which includes user based collaborative filtering, item based recommender and content boosted collaborative filtering using Python.
younader / DnnrThe Python package of differential nearest neighbors regression (DNNR): Raising KNN-regression to levels of gradient boosting method. Build on-top of Numpy, Scikit-Learn, and Annoy.
erwinvaneijk / Bgl PythonBoost Graph Library - Python interface. This is the repository with the imported repository from Douglas Gregor
lisaalaz / SatbotAn empathetic counselling chatbot. Retrieval-based, uses finetuned LMs for emotion identification and to boost empathy, novelty and fluency of the retrieved responses. Backend: Python, frontend: Javascript.
JuliaAI / CatBoost.jlJulia wrapper of the python library CatBoost for boosted decision trees
personalrobotics / Chimera:snake: A CLI tool for generating Boost.Python/pybind11 bindings from C/C++
rahulraghatate / Housing Sale Price Prediction"Buying a house is a stressful thing." We built a model to predict the prices of residential homes in Ames, Iowa, using advanced regression techniques. This model will provide buyers with a rough estimate of what the houses are actually worth. We first analyzed the data to find trends. Then dimensionality reduction was performed on the dataset using PCA algorithm and feature selection module in sklearn package for python 3.5. The final house prices are predicted using linear regression models like Ridge and Lasso. We also utilised advanced regression techniques like gradient boosting using XGBoost library in python 3.5.
luator / Boost Python Catkin ExampleMinimal example on how to use Boost::Python in a ROS package build with catkin.
GurpreetKukkar / Weapon Detection Using Artificial IntelligenceAI-driven weapon detection system for real-time surveillance. Developed on TensorFlow, achieved precision of 0.8524 and 0.7006 at IoU 0.50 and 0.75. Utilizes key frame extraction and SSD-MobileNet, enhancing efficiency. Developed on Windows 10, Python 3.7.3, and TensorFlow 1.14.0. Boosts security with low-cost, automated threat recognition.
jcfr / Scipy2014 Boost Python Workshop Student MaterialNo description available
CodsXBlastin / YoutubeViewBotA Bot to Boost your Views on YouTube made in Python
qinhanmin2014 / Kaggle Bike Sharing Demandsimple solution based on Gradient Boost and Random Forest, rank 24/3251 (top 1%) within 60 lines of python code
Dcurig / Somnia Auto🔮 Retrodrop automation bot for the Somnia testnet, built with Python. Automates wallet activity, contract interactions, and scheduled farming to boost airdrop eligibility.
itsKayWat / Smart Text ExpanderSmart Text Expander: A smart text expansion tool that automates the insertion of frequently used phrases and snippets. Boost your productivity by managing and expanding trigger words seamlessly across applications. Built with Python and PySide6.
OriAshkenazi / NeuralRepoReaderNeuralRepoReader (NRR) uses OpenAI's GPT-4 to transform Python repositories into digestible overviews. Perfect for new developers, code reviewers, or AI systems needing structured code context. Analyze code, enhance comprehension, boost productivity. Future updates to include more languages.
