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Showing 251 open source projects for "strategy"

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    MongoDB Atlas runs apps anywhere

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    All-in-one solution to control corporate spending

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  • 1
    QuantDinger

    QuantDinger

    AI-driven, local-first quantitative trading platform for research

    QuantDinger is a local-first, open-source quantitative trading platform designed to bring AI-assisted analysis, strategy development, backtesting, and live execution into a self-hosted workspace where data and API credentials remain under your control. Unlike cloud-locked quant services, it lets users run the entire trading workflow on their own infrastructure using Docker, with a PostgreSQL database backend, a Python backend API, and a web frontend UI that supports visualization and strategy management. ...
    Downloads: 6 This Week
    Last Update:
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  • 2
    Freqtrade

    Freqtrade

    Free, open source crypto trading bot

    ...Write your strategy in python, using pandas. Example strategies to inspire you are available in the strategy repository. Download historical data of the exchange and the markets you may want to trade with. Find the best parameters for your strategy using hyper optimization which employs machining learning methods.
    Downloads: 2 This Week
    Last Update:
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  • 3
    OctoBot

    OctoBot

    Cryptocurrency trading bot for TA, arbitrage and social trading

    ...Moreover, when a favorable trend is spotted, it can be difficult to maximize profit from it: trading takes a lot of time, and when it's done automatically how to be sure that the robot will do what it is supposed to? By using OctoBot, you will be able to automate your trades with the strategy you have chosen and the markets you want. Whether you are a beginner or an expert trader, each strategy is testable easily without any limit. You can use an existing setup or try and customize your own until you identify the perfect settings for your trading goals and proceed with real money trading and make real gains.
    Downloads: 7 This Week
    Last Update:
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  • 4
    ThetaGang

    ThetaGang

    ThetaGang is an IBKR bot for collecting money

    ThetaGang is an IBKR trading bot for collecting premiums by selling options using "The Wheel" strategy. The Wheel is a strategy that surfaced on Reddit but has been used by many in the past. This bot implements a slightly modified version of The Wheel, with my own personal tweaks. The strategy, as implemented here, does a few things differently from the one described in the post above. For one, it's intended to be used to augment a typical index-fund-based portfolio with specific asset allocations. ...
    Downloads: 3 This Week
    Last Update:
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  • ACI Learning: Internal Audit, Cybersecurity, and IT Training Icon
    ACI Learning: Internal Audit, Cybersecurity, and IT Training

    Proven skill building for every aspect of your support or IT team.

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  • 5
    Qbot

    Qbot

    AI-powered Quantitative Investment Research Platform

    ...For evaluation and analysis, Qbot integrates reporting and visualization (tearsheets, metrics) so you can compare performance across runs and inspect trade-level behavior. It supports multiple strategy runtimes and backtesting engines, is organized for extensibility (strategies live in a dedicated folder).
    Downloads: 11 This Week
    Last Update:
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  • 6
    Backtrack Sampler

    Backtrack Sampler

    An easy-to-understand framework for LLM samplers

    Backtrack Sampler is a framework designed for experimenting with custom sampling strategies for language models (LLMs), enabling the ability to rewind and revise generated tokens. It allows developers to create and test their own token generation strategies by providing a base structure for manipulating logits and probabilities, making it a flexible tool for those interested in fine-tuning the behavior of LLMs.
    Downloads: 0 This Week
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  • 7
    AIQuant

    AIQuant

    AI-powered platform for quantitative trading

    ai_quant_trade is an AI-powered, one-stop open-source platform for quantitative trading—ranging from learning and simulation to actual trading. It consolidates stock trading knowledge, strategy examples, factor discovery, traditional rules-based strategies, various machine learning and deep learning methods, reinforcement learning, graph neural networks, high-frequency trading, C++ deployment, and Jupyter Notebook examples for practical hands-on use. Stock trading strategies: large models, factor mining, traditional strategies, machine learning, deep learning, reinforcement learning, graph networks, high-frequency trading, etc. ...
    Downloads: 3 This Week
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  • 8
    DeepSeek-V3

    DeepSeek-V3

    Powerful AI language model (MoE) optimized for efficiency/performance

    ...It employs Multi-head Latent Attention (MLA) and the DeepSeekMoE architecture to enhance computational efficiency. The model introduces an auxiliary-loss-free load balancing strategy and a multi-token prediction training objective to boost performance. Trained on 14.8 trillion diverse, high-quality tokens, DeepSeek-V3 underwent supervised fine-tuning and reinforcement learning to fully realize its capabilities. Evaluations indicate that it outperforms other open-source models and rivals leading closed-source models, achieving this with a training duration of 55 days on 2,048 Nvidia H800 GPUs, costing approximately $5.58 million.
    Downloads: 66 This Week
    Last Update:
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  • 9
    openage

    openage

    Open source clone of the Age of Empires II engine

    openage is a free cross-platform RTS game engine that provides the mechanics of Age of Empires. Using modern technologies as C++17, OpenGL/GLSL, Python, Qt5 and CMake allows people using GNU/Linux, BSD, macOS or Windows to play the game natively. Our aim is to make openage a platform for the original Age of Empires games providing the same look and feel, but with more features for modding and multiplayer. openage uses an open API powered by our human-readable configuration language nyan. We...
    Downloads: 8 This Week
    Last Update:
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  • Build experiences that drive engagement and increase transactions Icon
    Build experiences that drive engagement and increase transactions

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  • 10
    AutoTrader

    AutoTrader

    A Python-based development platform for automated trading systems

    AutoTrader is a Python-based platform—now archived—designed to facilitate the full lifecycle of automated trading systems. It provides tools for backtesting, strategy optimization, visualization, and live trading integration. A feature-rich trading simulator, supporting backtesting and paper trading. The 'virtual broker' allows you to test your strategies in a risk-free, simulated environment before going live. Capable of simulating multiple order types, stop-losse,s and take-profits, cross-exchange arbitrage and portfolio strategies, AutoTrader has more than enough to build a profitable trading system.
    Downloads: 5 This Week
    Last Update:
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  • 11
    ValueCell

    ValueCell

    Community-driven, multi-agent platform for financial applications

    ValueCell is a community-driven multi-agent AI platform focused on financial research, analysis, and decision-making that lets users leverage multiple specialized AI agents for tasks like data retrieval, investment research, strategy execution, and market tracking. The system brings together a suite of collaborative agents—such as research agents that gather and interpret fundamentals, strategy agents that implement trading logic, and news agents that deliver personalized updates—to help users make more informed financial decisions across stocks, crypto, and other markets. ...
    Downloads: 2 This Week
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  • 12
    talos

    talos

    Hyperparameter Optimization for TensorFlow, Keras and PyTorch

    Talos radically changes the ordinary Keras, TensorFlow (tf.keras), and PyTorch workflow by fully automating hyperparameter tuning and model evaluation. Talos exposes Keras and TensorFlow (tf.keras) and PyTorch functionality entirely and there is no new syntax or templates to learn. Talos is made for data scientists and data engineers that want to remain in complete control of their TensorFlow (tf.keras) and PyTorch models, but are tired of mindless parameter hopping and confusing...
    Downloads: 0 This Week
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  • 13
    Nevergrad

    Nevergrad

    A Python toolbox for performing gradient-free optimization

    ...Nevergrad supports parallelization, budget scheduling, and multiple cost/resource constraints, allowing it to scale to nontrivial optimization problems. It includes visualization tools and diagnostic metrics to compare strategy performance, track parameter evolution, and detect stagnation.
    Downloads: 0 This Week
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  • 14
    Ricks-Lab GPU Utilities

    Ricks-Lab GPU Utilities

    A set of utilities for monitoring and customizing GPU performance

    A set of utilities for monitoring GPU performance and modifying control settings. In order to get the maximum capability of these utilities, you should be running with a kernel that provides support for the GPUs you have installed. If using AMD GPUs, installing the latest AMD GPU driver or ROCm package may provide additional capabilities. If you have Nvidia GPUs installed, you should have Nvidia-smi installed in order for the utility reading of the cards to be possible. Writing to GPUs is...
    Downloads: 12 This Week
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  • 15
    Hummingbot

    Hummingbot

    Build trading bots that run on any exchange or blockchain

    Hummingbot is an open-source project that integrates cryptocurrency trading on both centralized exchanges and decentralized protocols. It allows users to run a client that executes customized, automated trading strategies for cryptocurrencies. Hummingbot is software that helps you build and run automated trading strategies or bots. Its codebase is free and publicly available on Github under the Apache 2.0 open source license. Hummingbot Foundation is a not-for-profit foundation that...
    Downloads: 8 This Week
    Last Update:
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  • 16
    DreamO

    DreamO

    A Unified Framework for Image Customization

    ...DreamO’s design introduces a feature routing constraint that helps disentangle different control conditions (like identity, style, clothing) when more than one is specified, which significantly reduces conflicts and artifacts when combining controls. It also uses a “placeholder strategy” to precisely align conditional inputs (e.g. where to place clothing or objects) in generated images, giving users fine-grained control over composition.
    Downloads: 0 This Week
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  • 17
    Diplomacy Cicero

    Diplomacy Cicero

    Code for Cicero, an AI agent that plays the game of Diplomacy

    ...The repository includes training code, model checkpoints, and infrastructure for both language modelling (via the ParlAI framework) and reinforcement learning for strategy agents. It supports two variants: Cicero (which handles full “press” negotiation) and Diplodocus (a variant focused on no-press diplomacy) as described in the README. The codebase is implemented primarily in Python with performance-critical components in C++ (via pybind11 bindings) and is configured to run in a high‐GPU cluster environment. ...
    Downloads: 0 This Week
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  • 18
    F1 Race Replay

    F1 Race Replay

    An interactive Formula 1 race visualisation and data analysis tool

    ...It ingests official timing and positional data, then renders vehicle movements through track maps and 3D visualizations so fans, analysts, and engineers can review strategy, overtakes, tire degradation effects, and pit stop impacts in detail. Users can scrub through time, jump between cars, and overlay performance graphs such as speed, sector times, and gap differentials to evaluate performance trends across laps. This deep dive capability turns passive viewing into active exploration, empowering enthusiasts and professionals to discover insights usually hidden in raw data. ...
    Downloads: 5 This Week
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  • 19
    AI Hedge Fund

    AI Hedge Fund

    An AI Hedge Fund Team

    This repository demonstrates how to build a simplified, automated hedge fund strategy powered by AI/ML. It integrates financial data collection, preprocessing, feature engineering, and predictive modeling to simulate decision-making in trading. The code shows workflows for pulling stock or market data, applying machine learning algorithms to forecast trends, and generating buy/sell/hold signals based on the predictions.
    Downloads: 3 This Week
    Last Update:
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  • 20
    PyBroker

    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.
    Downloads: 4 This Week
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  • 21
    PaddleNLP

    PaddleNLP

    Easy-to-use and powerful NLP library with Awesome model zoo

    PaddleNLP It is a natural language processing development library for flying paddles, with Easy-to-use text area API, Examples of applications for multiple scenarios, and High-performance distributed training Three major features, aimed at improving the modeling efficiency of the flying oar developer's text field, aiming to improve the developer's development efficiency in the text field, and provide rich examples of NLP applications. Provide rich industry-level pre-task capabilities...
    Downloads: 7 This Week
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  • 22
    HY-Motion 1.0

    HY-Motion 1.0

    HY-Motion model for 3D character animation generation

    ...Built on advanced deep learning architectures that combine Diffusion Transformer (DiT) and flow matching techniques, HY-Motion scales these approaches to the billion-parameter level, resulting in strong instruction-following capabilities and richer motion outputs compared to existing open-source models. The training strategy for the HY-Motion series includes extensive pre-training on thousands of hours of varied motion data, fine-tuning on curated high-quality datasets, and reinforcement learning with human feedback, which improves both the plausibility and adaptability of generated motion sequences.
    Downloads: 5 This Week
    Last Update:
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  • 23
    FinRobot

    FinRobot

    An Open-Source AI Agent Platform for Financial Analysis using LLMs

    ...It provides developers and quants with structured modules to fetch market data, process time series, generate technical indicators, and construct features appropriate for machine learning models, while also supporting backtesting and evaluation metrics to measure strategy performance. Built with modularity in mind, FinRobot allows users to plug in custom models — from classical algorithms to deep learning architectures — and orchestrate components in pipelines that can run reproducibly across experiments. The framework also tends to include automation layers for deployment, enabling trained models to operate in live or simulated environments with scheduled re-training and risk controls in place.
    Downloads: 3 This Week
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  • 24
    NautilusTrader

    NautilusTrader

    A high-performance algorithmic trading platform

    NautilusTrader is an open-source, high-performance, production-grade algorithmic trading platform, provides quantitative traders with the ability to backtest portfolios of automated trading strategies on historical data with an event-driven engine, and also deploy those same strategies live, with no code changes. The platform is 'AI-first', designed to develop and deploy algorithmic trading strategies within a highly performant and robust Python native environment. This helps to address the...
    Downloads: 5 This Week
    Last Update:
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  • 25
    Optopsy

    Optopsy

    A nimble options backtesting library for Python

    ...Optopsy is a small simple library that offloads the heavy work of backtesting option strategies, the API is designed to be simple and easy to implement into your regular Panda's data analysis workflow. As such, we just need to call the long_calls() function to have Optopsy generate all combinations of a simple long call strategy for the specified time period and return a DataFrame. Here we also use Panda's round() function afterwards to return statistics within two decimal places.
    Downloads: 3 This Week
    Last Update:
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