# charliedream1/ai_quant_trade

股票AI操盘手：从学习、模拟到实盘，一站式平台。包含股票知识、策略实例、大模型、因子挖掘、传统策略、机器学习、深度学习、强化学习、图网络、高频交易、C++部署和聚宽实例代码等，可以方便学习、模拟及实盘交易

Repository: https://github.com/charliedream1/ai_quant_trade
Canonical: https://ross.abutalabs.com/products/ai_quant_trade
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: cpp, jupyter-notebook, keras, python, sklearn, tensorflow, trading-bot, trading-platform, trading-strategies, mlflow, pytorch
Last push: 2026-08-16T07:00:09+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 8, longevity 100
- inputs: {"age_days": 1546, "days_push": 17, "days_rel": 564, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6375, forks 1200 (observed 2026-08-28T04:09:42.887338+00:00)

## What it is
A one-stop AI quantitative trading platform covering learning, simulation, and live trading of stocks, funds, and crypto. It bundles example code for LLMs, factor mining, traditional strategies, machine learning, deep learning, reinforcement learning, graph networks, and high-frequency trading, plus market-watching tools and C++ deployment examples.

## Use cases
- learn quantitative trading with python
- build a stock trading bot with machine learning
- train an LLM for stock price prediction
- mine alpha factors automatically
- backtest reinforcement learning trading strategies
- monitor stock prices with alerting tools
- deploy a trading strategy in C++ for live trading

## When to choose
- you want a broad collection of quant trading examples spanning ML, RL, and LLMs
- you are learning quant finance and want runnable code from simulation to live trading
- you need factor mining, backtesting, and market-watching tools in one repo

## When to avoid
- you need a production-grade, battle-tested trading engine with guaranteed execution
- you only want a lightweight backtesting library without educational material
- you require guaranteed profitability or regulated brokerage integration

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, reinforcement-learning, llm-training, trading, data-science, nlp, developer-tools
- domain: fintech, machine-learning, large-language-models, reinforcement-learning, tutorials, data-science
- platform: python, cpp, cross-platform
- tags: quantitative-trading, stock-trading, factor-mining, backtesting, trading-bot, algorithmic-trading, stock-prediction, joinquant, mlflow, high-frequency-trading, gpu

## Member repositories
- charliedream1/ai_quant_trade (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:42.887338+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 2026-08-29T17:45:10.997639+00:00, confidence not recorded.
  - readme: https://github.com/charliedream1/ai_quant_trade (fetched 2026-08-28T04:09:42.887338+00:00, sha ef27ba4fed05)
- Data as of 2026-08-30T08:39:29.467469+00:00.