Search Results for "algorithmic trading software"
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Showing 14 open source projects for "algorithmic trading software"
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PyBroker
Algorithmic Trading in Python with Machine Learning
Are you looking to enhance your trading strategies with the power of Python and machine learning? Then you need to check out PyBroker! This Python framework is designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance. -
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Quantitative Trading System
A comprehensive quantitative trading system with AI-powered analysis
Quantitative Trading System is a comprehensive quantitative trading platform that integrates artificial intelligence, financial data analysis, and automated strategy execution within a unified software system. The project is designed to provide an end-to-end infrastructure for building and operating algorithmic trading strategies in financial markets. -
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zvt
Modular quant framework
For practical trading, a complex algorithm is fragile, a complex algorithm building on a complex facility is more fragile, complex algorithm building on a complex facility by a complex team is more and more fragile. zvt wants to provide a simple facility for building a straightforward algorithm. Technologies come and technologies go, but market insight is forever. Your world is built by core concepts inside you, so it’s you. zvt world is built by core concepts inside the market, so it’s zvt.... -
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CUTLASS
CUDA Templates for Linear Algebra Subroutines
...It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS and cuDNN. CUTLASS decomposes these "moving parts" into reusable, modular software components abstracted by C++ template classes. These thread-wide, warp-wide, block-wide, and device-wide primitives can be specialized and tuned via custom tiling sizes, data types, and other algorithmic policy. The resulting flexibility simplifies their use as building blocks within custom kernels and applications. To support a wide variety of applications, CUTLASS provides extensive support for mixed-precision computations, providing specialized data-movement and multiply-accumulate abstractions for half-precision floating point (FP16), BFloat16 (BF16), Tensor Float 32 (TF32), etc. -
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5
fastquant
Backtest and optimize your ML trading strategies with only 3 lines
fastquant is a Python library designed to simplify quantitative financial analysis and algorithmic trading strategy development. The project focuses on making backtesting accessible by providing a high-level interface that allows users to test investment strategies with only a few lines of code. It integrates historical market data sources and trading frameworks so that users can quickly build experiments without constructing complex data pipelines. -
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QuantResearch
Quantitative analysis, strategies and backtests
QuantResearch is a large educational repository dedicated to quantitative finance, algorithmic trading, and financial machine learning research. The project contains numerous notebooks and research materials demonstrating quantitative analysis techniques used in financial markets. These include implementations of factor models, statistical arbitrage strategies, portfolio optimization methods, and reinforcement learning approaches to trading. -
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Machine Learning Financial Laboratory
MlFinLab helps portfolio managers and traders
MlFinLab is a comprehensive Python library designed to support the development of machine learning strategies in quantitative finance and algorithmic trading. The project provides a large collection of tools that implement techniques from academic research on financial machine learning. It covers the full lifecycle of developing data-driven trading strategies, including data preprocessing, feature engineering, labeling techniques, model training, and performance evaluation. ... -
8
ML for Trading
Code for machine learning for algorithmic trading, 2nd edition
On over 800 pages, this revised and expanded 2nd edition demonstrates how ML can add value to algorithmic trading through a broad range of applications. Organized in four parts and 24 chapters, it covers the end-to-end workflow from data sourcing and model development to strategy backtesting and evaluation. Covers key aspects of data sourcing, financial feature engineering, and portfolio management. The design and evaluation of long-short strategies based on a broad range of ML algorithms, how to extract tradeable signals from financial text data like SEC filings, earnings call transcripts or financial news. ... -
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surpriver
Find big moving stocks before they move using machine learning
...The project is intended as a research tool for quantitative finance experiments and algorithmic trading strategy development. -
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gradslam
gradslam is an open source differentiable dense SLAM library
gradslam is an open-source framework providing differentiable building blocks for simultaneous localization and mapping (SLAM) systems. We enable the usage of dense SLAM subsystems from the comfort of PyTorch. The question of “representation” is central in the context of dense simultaneous localization and mapping (SLAM). Newer learning-based approaches have the potential to leverage data or task performance to directly inform the choice of representation. However, learning representations... -
11
AIAlpha
Use unsupervised and supervised learning to predict stocks
AIAlpha is a machine learning project focused on building predictive models for financial markets and algorithmic trading strategies. The repository explores how artificial intelligence techniques can analyze historical financial data and generate predictions about asset price movements. It provides a research-oriented environment where users can experiment with data processing pipelines, model training workflows, and quantitative trading strategies. -
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Coach
Enables easy experimentation with state of the art algorithms
Coach is a python framework that models the interaction between an agent and an environment in a modular way. With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments... -
13
Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual... -
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AlgoWiki
Repository which contains links and resources on different topics
AlgoWiki is an open educational repository that aggregates a large collection of curated resources covering many areas of computer science, programming, and algorithmic problem solving. The project functions as a structured knowledge index that links to tutorials, articles, courses, and research materials across numerous technical domains. Topics include algorithms, machine learning, artificial intelligence, programming languages, web development, and software engineering practices. The repository is organized into directories by subject so that users can easily locate relevant learning materials within a specific discipline. ...
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