# OpenByteInc/QuantDinger

AI quantitative trading platform for crypto, stocks, and forex with backtesting, live trading, market data, and multi-agent research.vibe-trading ,trading-agents,ai-trader,ai-trading

Repository: https://github.com/OpenByteInc/QuantDinger
Canonical: https://ross.abutalabs.com/products/quantdinger
Homepage: https://ai.quantdinger.com
Language: Python
License: Apache-2.0
License Family: permissive
Topics: quantitative-finance, quant, trade, python, fintech, agent, forex, alpaca, binance, backtesting, stocks, finance, crypto, exchange, saas, coinbase, strategy, mcp-server, ai, trading-toolkit
Last push: 2026-08-24T07:38:06+00:00

## Health v2 (maintenance only)
Score: 82/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 98, longevity 17
- inputs: {"age_days": 248, "days_push": 9, "days_rel": 15, "gap_med": 0, "n_releases_24m": 46}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 11112, forks 2332 (observed 2026-08-28T04:10:46.123574+00:00)

## What it is
QuantDinger is an open-source, self-hosted AI quantitative trading platform covering crypto, stocks, and forex. It combines AI multi-agent research with Python strategy development, backtesting, paper trading, live execution, and monitoring in one stack.

## Use cases
- backtest trading strategies on crypto and stocks
- run AI agents to research trading ideas
- execute live trades via Binance, Alpaca, or Coinbase
- paper trade strategies before going live
- self-host a quantitative trading platform
- monitor live trading performance
- connect trading tools to LLMs via MCP server

## When to choose
- you want an all-in-one self-hosted quant trading stack with AI research, backtesting, and live execution
- you trade across crypto, stocks, and forex and need multi-exchange support
- you want to expose trading capabilities to LLM agents through MCP

## When to avoid
- you need a lightweight backtesting library to embed in your own code rather than a full platform
- you require guaranteed profitability or financial advice - trading involves risk
- you cannot self-host Docker-based services with PostgreSQL and Redis

## Facets
- artifact type: application
- maturity: active
- function: trading, agent-framework, mcp, monitoring, data-visualization, self-hosted
- domain: fintech, artificial-intelligence, analytics
- platform: python, self-hosted, windows
- tags: quantitative-finance, backtesting, live-trading, crypto, stocks, forex, trading-agents, mcp-server, alpaca, binance, coinbase, paper-trading, strategy-development, cryptocurrency, ai-agents, docker, web-server, linux, macos

## Member repositories
- OpenByteInc/QuantDinger (main) score 82

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:46.123574+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:16:47.213987+00:00, confidence not recorded.
  - readme: https://github.com/OpenByteInc/QuantDinger (fetched 2026-08-28T04:10:46.123574+00:00, sha 6a8ab0e4cda9)
  - homepage: https://ai.quantdinger.com (fetched 2026-08-29T08:15:23.639182+00:00, sha aa4e72897025)
- Data as of 2026-08-30T08:39:29.467469+00:00.