Open-source alternatives to Algorithmic Trading With Python

aiquanttrade is an AI-driven quantitative trading platform that enables the development, backtesting, and deployment of trading strategies powered by machine learning and artificial intelligence. It provides a complete local environment for quantitative research, simulation, and automated live trading through brokerage APIs, supporting both historical backtesting and real-time paper trading without capital risk. The platform distinguishes itself through a modular, event-driven architecture that separates strategy logic from execution, allowing rule-based and machine learning models to be co

FinRL-Library is a reinforcement learning trading framework and algorithmic trading library used to develop and backtest automated financial trading strategies. It functions as a quantitative trading pipeline and financial market simulator, allowing users to build decision policies that optimize asset trading across various financial markets. The framework features a modular integration system for swapping reinforcement learning algorithms through a consistent API. It utilizes a standardized environment wrapper to encapsulate market dynamics into a state-action-reward interface, facilitating

A TD Ameritrade API client for Python. Includes historical data for equities and ETFs, options chains, streaming order book data, complex order construction, and more.

This is a machine learning educational repository consisting of a collection of notebooks and code examples. It provides practical implementations of diverse machine learning algorithms and workflows, ranging from traditional scientific computing to deep learning. The project features specific implementations of Scikit-Learn models, such as decision trees, random forests, and support vector machines, as well as TensorFlow examples for building neural networks, convolutional layers, and recurrent architectures. It also includes tutorials on reinforcement learning development and the creation o

Open Source Algorithmic Trading Engine

Async Python connector for Binance SPOT FIX testing, latency research, and feed/session comparison

Podcast about Android Development with Hannes Dorfmann, Artem Zinnatullin, Artur Dryomov and wonderful guests!

Pipeline Extension for Live Trading

Python live trade execution library with zipline interface.

A collection of links that teach python

Fuzzing101 is an educational resource providing a structured curriculum and containerized security labs for learning software fuzzing and vulnerability research. It functions as a training course that guides users through the process of identifying security flaws using systematic input manipulation and memory corruption analysis. The project distinguishes itself by providing isolated environments that ensure consistent build dependencies for practicing software instrumentation and crash triaging. It includes a practical tutorial on using evolutionary fuzzing engines and instrumentation tools

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This project is a collaborative knowledge base and technical learning resource that provides a detailed breakdown of the internal processes occurring within modern computing environments. It serves as a comprehensive educational reference, tracing the step-by-step operations triggered by common user interactions and network requests to explain how hardware and software components interact across the entire stack. The guide distinguishes itself by offering deep technical insights into the journey from physical input to visual output. It covers the low-level mechanics of hardware interrupt hand

This project provides educational materials and courseware focused on the theoretical and practical foundations of distributed systems design. It serves as a comprehensive curriculum covering the disciplines of consensus, data consistency, reliability engineering, and scalability. The instructional content focuses on achieving cluster agreement through consensus algorithms and managing system-wide state via coordination frameworks. It includes a dedicated guide to data theory, exploring replication strategies, consistency models, and data convergence. The courseware covers a broad capability

Curated list: Resources for machine learning in Ruby

Online resource of a practical machine learning course in the Department of Materials at Imperial College London.

State of the art resources for NLP sequence modeling tasks such as machine translation, image captioning, and dialog.

Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering

This project is a collection of structured study notes and notebooks serving as an educational resource for deep learning and neural network fundamentals. It provides a technical reference for implementing machine learning theory, covering everything from basic network design to the construction of advanced architectures. The material specifically focuses on the implementation of convolutional neural networks for computer vision and sequence models for natural language processing. It includes detailed guidance on building object detection systems, face recognition, and speech transcription mo

A curated list of applied machine learning and data science notebooks and libraries across different industries.

AI-powered trading research platform. Test any idea on stocks, futures, and crypto with event studies, backtesting, and statistical validation. MCP server with 8 tools. pip install varrd.

This project is a structured educational curriculum designed to guide developers through the fundamentals of machine learning. It functions as a technical skill builder, offering a curated roadmap of progressive coding challenges that cover core algorithms, statistical concepts, and essential data science libraries. The repository distinguishes itself through an iterative sequencing of content, organizing complex technical topics into a daily progression that facilitates incremental mastery. It integrates third-party academic lectures and educational resources to provide necessary theoretical

This project is a community-maintained, open-access directory of high-quality public datasets. It serves as a centralized reference point for researchers, developers, and data scientists to locate reliable information sources across a wide spectrum of industries and scientific fields. By providing a structured index, the repository facilitates the discovery of data necessary for exploratory analysis, machine learning model training, and the development of data-intensive applications. The directory distinguishes itself through a lightweight, platform-agnostic approach to resource indexing that

This is an Asciidoc book of Simon Wardley's "Wardley Maps". It simply takes all his medium posts and joins them together for ease of reading. The intention is to be entirely faithful to the original posts - I've not even fixed the few spelling mistakes - while allowing various output versions to…