Search Results for "quantitative"
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71 projects for "quantitative" with 1 filter applied:
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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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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Finance
150+ quantitative finance Python programs
Finance is a repository that compiles structured notes and educational material related to financial analysis, markets, and quantitative finance concepts. The project focuses on explaining key principles used in finance and investment analysis, including topics such as financial statements, valuation models, portfolio theory, and financial markets. The repository is designed as a study reference for students and professionals who want to understand financial systems and the analytical frameworks used in financial decision-making. ... -
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Kronos
A Foundation Model for the Language of Financial Markets
...The system introduces a novel tokenization approach that converts continuous financial data into discrete tokens, enabling the model to process market behavior similarly to language. This allows Kronos to perform a variety of quantitative tasks such as forecasting, pattern recognition, and anomaly detection within financial datasets. It is optimized for the noisy and complex nature of market data, distinguishing it from general-purpose time-series models. The project includes multiple pre-trained model sizes and tools for fine-tuning, making it adaptable to different computational constraints and use cases. -
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AI Hedge Fund
An AI Hedge Fund Team
...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. Its structure is educational: intended more as a proof-of-concept than a ready-to-use financial product, giving learners insight into the mechanics of quantitative finance automation. The project underlines AI’s potential in investment strategies but also carries disclaimers that it is for research and not financial advice. The implementation is designed so developers can study the pipeline end-to-end: from data ingestion through modeling to simulated portfolio management. -
Application Monitoring That Won't Slow Your App DownFull APM with errors, performance, logs, and uptime monitoring. 99.999% uptime SLA on the platform itself.
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Kalshi Trading Bot CLI
AI-native CLI for trading Kalshi prediction markets
Kalshi Trading Bot CLI is an AI-driven command-line tool designed to automate trading strategies on Kalshi prediction markets by combining quantitative modeling with real-time market data. It operates by conducting deep research on events, generating independent probability estimates, and comparing those estimates against current market prices to identify trading opportunities. The system incorporates advanced decision-making logic, including Kelly criterion-based position sizing and a structured multi-step risk evaluation process before executing trades. ... -
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darwin-skill
Autoresearch-inspired autonomous skill optimization for Claude Code
...Instead of treating prompts or skill definitions as static assets, the system applies a continuous improvement cycle that evaluates performance, proposes changes, tests outcomes, and either retains or reverts modifications. The framework introduces a scoring system across multiple dimensions, enabling quantitative assessment of skill quality and ensuring that only improvements are preserved over time. It incorporates a “ratchet mechanism” similar to version control workflows, guaranteeing that performance never degrades as iterations progress. The system also separates the agents responsible for editing and evaluating skills to avoid bias, which improves the reliability of optimization results. -
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BTC Trading Since 2020
Public BTC trading context since 2020
...It is structured in a way that allows users to modify or extend strategies, making it suitable for both learning and experimentation. The system is particularly useful for individuals interested in quantitative trading and algorithmic finance. -
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Book3_Elements-of-Mathematics
From Addition, Subtraction, Multiplication, and Division to ML
...Its goal is to reduce the intimidation barrier often associated with formal mathematics by combining diagrams, structured explanations, and applied examples. The content is organized progressively so learners can build confidence before moving into more advanced quantitative subjects. It is particularly useful for self-taught developers and students transitioning into technical fields that require mathematical literacy. Overall, the project functions as a bridge between basic math education and more specialized machine learning study. -
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Smart Money Concepts
Discover our Python package designed for algorithmic trading
Smart Money Concepts is a Python library that implements advanced trading indicators based on the “Smart Money Concepts” methodology, which focuses on institutional market behavior and price action analysis. It is designed for algorithmic traders and quantitative analysts who want to incorporate professional trading strategies into automated systems. The library processes structured OHLC or OHLCV market data and computes indicators such as fair value gaps, order blocks, liquidity zones, and market structure changes. These indicators are inspired by ICT trading principles and are used to identify trends, reversals, and potential entry or exit points in financial markets. ... -
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skfolio
Python library for portfolio optimization built on top of scikit-learn
...The project provides a unified machine learning-style framework for building, validating, and comparing portfolio allocation strategies using financial data. By following the familiar scikit-learn API design, the library allows quantitative researchers and developers to apply techniques such as model selection, cross-validation, and hyperparameter tuning to portfolio construction workflows. It supports a wide range of allocation methods, from classical mean-variance optimization to modern techniques that rely on clustering, factor models, and risk-based allocations. ... -
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FinRobot
An Open-Source AI Agent Platform for Financial Analysis using LLMs
FinRobot is an open-source AI framework focused on automating financial data workflows by combining data ingestion, feature engineering, model training, and automated decision-making pipelines tailored for quantitative finance applications. 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. ... -
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MLE-bench
AI multi-agent framework for automating data-driven R&D workflows
...RD-Agent focuses heavily on automating complex tasks such as feature engineering, model design, and experimentation, which are traditionally time-consuming in machine learning and quantitative research workflows. RD-Agent can analyze data, generate experimental code, run evaluations, and learn from outcomes to improve future iterations. -
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DeepSeek Math
Pushing the Limits of Mathematical Reasoning in Open Language Models
DeepSeek-Math is DeepSeek’s specialized model (or dataset + evaluation) focusing on mathematical reasoning, symbolic manipulation, proof steps, and advanced quantitative problem solving. The repository is likely to include fine-tuning routines or task datasets (e.g. MATH, GSM8K, ARB), demonstration notebooks, prompt templates, and evaluation results on math benchmarks. The goal is to push DeepSeek’s performance in domains that require rigorous symbolic steps, calculus, linear algebra, number theory, or multi-step derivations. ... -
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Nemo
Individual-based forward-time genetics simulation software
Nemo is an individual-based, forward-time, genetically explicit, and stochastic simulation software designed for the study of the evolution of life history and quantitative traits, and genetic markers under various types of selection, in a spatially explicit, metapopulation framework. -
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Croizat
A software package for quantitative analysis in Panbiogeography
Croizat is a free, user-friendly, cross-platform desktop software package which biologists can use to integrate and analyze spatial data on species or other taxa and to explore geographical patterns in diversity under a panbiogeographic and graph-theoretic approach. -
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Criterial
The package for statistical data analysis and applied mathematics
Putting truth before show-off. Criterial is an add-in for desktop versions of LibreOffice Calc (and forks) for statistical data analysis. The project is built on the refined expertise and core concepts of the AtteStat and StatAnt projects. Completely free. No donations required. Comes with absolutely no warranty. Rating: Everyone (All ages). MEDICAL DISCLAIMER: This software is not certified for use in healthcare and should not be used for diagnostic or treatment purposes. NOTICE FOR EU... -
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StatAnt
The package for statistical data analysis
Aspire to perfection. StatAnt is a standalone cross-platform application for statistical data analysis. Completely free. No donations required. Comes with absolutely no warranty. Rating: Everyone (All ages). MEDICAL DISCLAIMER: This software is not certified for use in healthcare and should not be used for diagnostic or treatment purposes. NOTICE FOR EU RESIDENTS: This software is NOT compliant with EU regulations, including GDPR and the AI Act. It is not intended for use within the... -
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Libro
An interactive program for statistical analysis of texts
A cross-platform text analysis program written in Python and Free Pascal/Lazarus which scans a whole text file (in plain text, HTML, EPUB, or ODT formats) and ranks all used words according to frequency, performing a quantitative analysis of the text using Shannon-Weaver information statistic and Zipf power law function. It counts words, sentences, chars, spaces, and syllables. Also computes readability indexes (Gunning-Fog, Coleman-Liau, Automated Readability Index (ARI), SMOG grade, Flesch–Kincaid grade level and Flesch Reading Ease). -
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Gemi
PCR primers / probes design from multiple & degenerate sequences
...https://sites.google.com/view/hsa23/ Gemi, an automated, fast, and easy-to-use bioinformatics tool with a user-friendly interface to design primers and probes for polymerase chain reaction (PCR). Gemi accepts multiple aligned and long DNA and RNA sequences with degenerate nucleotide (non-A/C/G/T bases). Gemi can be used for quantitative, real-time and conventional PCR (qPCR, rt-PCR, etc.), and Sanger sequencing. Gemi can parse large dataset of sequences efficiently. Python codes can be supported upon request. Article: Gemi: PCR primers prediction from multiple alignments Comparative and functional genomics 2012;2012:783138 DOI: https://doi.org/10.1155/2012/783138 PMID: https://www.ncbi.nlm.nih.gov/pubmed/23316117 PMCID: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3535827/ -
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WoPeD is a Java-based graphical workflow process editor, simulator and analysor using Petri Nets and supporting the PNML format.
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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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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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quantitative
Quantized transactions python3
The “quantitative” repository by Jack-Cherish is a tutorial-style codebase for quantitative trading written in Python — essentially a learning resource that guides users through building algorithmic trading strategies step by step. It’s organized as a sequence of lessons (lesson1, lesson2, etc.), making it approachable for learners who want to understand both theory and practice in quantitative finance. -
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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. -
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Machine Learning in Asset Management
Machine Learning in Asset Management
...Many of the experiments focus on applying supervised learning, reinforcement learning, and statistical modeling techniques to financial data. By combining theory, research papers, and practical implementations, the repository functions as both a learning platform and a research resource for quantitative finance.
