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Quant focused on algorithmic trading systems, research tooling, broker and exchange integration, and performance-oriented engineering across Python, C++, Rust, and TypeScript.

• Quant credentials: CFA, FRM, CIIA, CFP
• Main interests:
  • Algorithmic trading
  • Trading strategies and strategy research
  • Backtesting and strategy engineering
  • Portfolio and factor analytics
  • High-performance systems
  • AI-assisted research and developer tooling

Over the last two years, my GitHub work has concentrated on three project ecosystems: the backtrader trading framework and its AI-native tooling, the fincore analytics family, and the bt_api multi-exchange SDK ecosystem.

Recent active contribution areas include:

• backtrader — framework maintenance, CI/CD, compatibility, documentation, and the AI-native workflow (MCP server, agent skills, web platform)
• bt_api_py — multi-exchange standardized API SDK, with 64 exchange adapters and 14 broker plugins
• backtrader_web — web-based strategy management and execution workflow
• fincore — unified quantitative performance & risk analytics toolkit

• Building production-friendly Python SDKs for quant workflows
• Researching and refining trading strategies across multiple market styles
• Exploring C++ / Rust / pybind / swig acceleration paths for quant infrastructure
• Connecting research, backtesting, live trading, and data tooling into a unified workflow
• Making quant systems easier to use with MCP / AI-native interfaces

• Trend following
• Mean reversion
• Momentum
• Breakout strategies
• Multi-factor stock selection
• Statistical arbitrage
• Market making
• Arbitrage and spread trading
• Event-driven strategies
• High-frequency trading research

Core framework and platform

• backtrader — high-performance Python backtesting & live-trading framework: 45%+ faster than upstream, 50+ indicators, tick-to-daily strategies
• backtrader_web — "AI for Investor" web platform (Vue 3 + FastAPI): research, AI strategy generation, backtesting, paper trading, live execution, and market-data management

AI-native workflow

• backtrader-mcp — local-first MCP server for AI-assisted strategy development: immutable datasets, private drafts, and bounded subprocess backtests via 30 typed tools
• backtrader-skills — offline author/review/test skills for AI coding agents (Claude Code, Codex, OpenCode)
• backtrader-agent — offline-first strategy-authoring agent runtime: StrategySpec validation, static review, hash-bound approvals

Performance rewrites and variants

• back_trader — the C++ version of backtrader (private)

• fincore — quantitative performance & risk analytics: 150+ financial metrics, portfolio optimization, Monte Carlo simulation, and attribution — the actively maintained successor to empyrical, pyfolio, and alphalens
• fincore_cpp — the C++ version of fincore

Core packages

• bt_api_py — Python SDK for multi-exchange integration with a standardized API
• bt_api_base — base package shared by all adapters
• bt_api_cpp — the C++ version of bt_api
• btapi — C++ API library: sync/async requests, WebSocket, and FIX
• bt_api_monitoring — unified monitoring and metrics module
• bt_api_risk — unified risk controls module
• bt_api_security — unified security controls and compliance module

Exchange adapters (64)

Broker plugins (14)

bt_api_5paisa · bt_api_aliceblue · bt_api_angelone · bt_api_dhan · bt_api_fyers · bt_api_groww · bt_api_iifl · bt_api_kotak · bt_api_motilal · bt_api_saxo · bt_api_shoonya · bt_api_tradier · bt_api_upstox · bt_api_zerodha

• Python quant libraries
• C++ strategy engines and bindings
• Rust for performance-critical systems
• Exchange and broker API integration
• Factor research and portfolio analytics
• Cross-platform automation and release engineering

• Email: yunjinqi@gmail.com

If you're building something around quant research, trading systems, analytics infrastructure, broker/exchange connectivity, or trading strategies, feel free to reach out.

我是 cloudQuant，一名专注于量化交易系统、研究工具、券商与交易所接口集成，以及 Python、C++、Rust、TypeScript 高性能工程实践的 quant。

• 量化相关资质：CFA、FRM、CIIA、CFP
• 主要兴趣方向：
  • 算法交易
  • 交易策略与策略研究
  • 回测系统与策略工程
  • 组合分析与因子研究
  • 高性能系统
  • AI 辅助研究与开发工具

近两年我在 GitHub 上的工作主要集中在三大项目生态：backtrader 交易框架及其 AI 原生工具链、fincore 分析库系列，以及 bt_api 多交易所 SDK 生态。

近期比较活跃的仓库方向包括：

• backtrader — 框架维护、CI/CD、兼容性、文档，以及 AI 原生工作流（MCP Server、Agent 技能、Web 平台）
• bt_api_py — 多交易所统一接口 SDK，含 64 个交易所适配器与 14 个券商插件
• backtrader_web — 基于 Web 的策略管理与执行平台
• fincore — 统一的量化绩效与风险分析工具包

• 构建更适合生产环境的 Python 量化 SDK
• 提升交易类库的跨平台可靠性与 CI/CD 质量
• 研究和优化不同风格的交易策略
• 探索 C++ / Rust / Cython 在量化基础设施中的加速路径
• 打通研究、回测、实盘执行和数据工具之间的完整工作流
• 让量化系统更容易与 MCP / AI 原生接口结合

• 趋势跟踪
• 均值回归
• 动量策略
• 突破策略
• 多因子选股
• 统计套利
• 做市策略
• 套利与价差交易
• 事件驱动策略
• 高频交易研究

核心框架与平台：

• backtrader — 高性能 Python 回测与实盘框架：比上游快 45%+，50+ 指标，支持从 tick 到日线策略
• backtrader_web — "AI for Investor" Web 平台（Vue 3 + FastAPI）：研究、AI 策略生成、回测、模拟盘、实盘执行与行情数据管理

AI 原生工作流：

• backtrader-mcp — 本地优先的 MCP Server：不可变数据集、私有草稿、受限子进程回测，30 个类型化工具
• backtrader-skills — 面向 AI 编程代理（Claude Code、Codex、OpenCode）的离线策略编写 / 审查 / 测试技能
• backtrader-agent — 离线优先的策略编写代理运行时：StrategySpec 校验、静态审查、哈希绑定审批

性能重写与变体：

• back_trader — C++ 版 （私有）

• fincore — 量化绩效与风险分析：150+ 金融指标、组合优化、蒙特卡洛模拟与归因分析，是 empyrical / pyfolio / alphalens 的活跃维护后继
• fincore_cpp — C++ 版

核心包：

• bt_api_py — 多交易所统一接口 Python SDK
• bt_api_base — 适配器共享基础包
• bt_api_cpp — C++ 版
• btapi — C++ API 库：同步 / 异步请求、WebSocket、FIX
• bt_api_monitoring — 统一监控与指标模块
• bt_api_risk — 统一风控模块
• bt_api_security — 统一安全与合规模块

64 个交易所适配器与 14 个券商插件的完整清单，见上方英文部分的 bt_api Ecosystem。

• Python 量化库
• C++ 策略引擎与绑定
• Rust 高性能系统
• 券商与交易所接口集成
• 因子研究与组合分析
• 跨平台自动化与发布工程

• Email: yunjinqi@gmail.com

如果你正在做量化研究、交易系统、分析基础设施、券商 / 交易所接口，或者交易策略相关项目，欢迎交流。