{"id":42472400,"url":"https://github.com/delta-f/deltafstation","last_synced_at":"2026-04-01T17:12:34.591Z","repository":{"id":327387006,"uuid":"1062267764","full_name":"Delta-F/deltafstation","owner":"Delta-F","description":"🚀 Professional Web-based Quant Platform: High-performance Strategy Backtesting, Real-time Paper Trading, and Live Log Monitoring. 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height:200px; object-fit:contain;\" /\u003e\n\u003cimg src=\"assets/backtest.png\" style=\"width:32%; height:200px; object-fit:contain;\" /\u003e\n\u003cimg src=\"assets/monitor.png\" style=\"width:32%; height:200px; object-fit:contain;\" /\u003e\n\n\u003c/div\u003e\n\n## 🎓 官方教程\n\n#### [慕课网 - 程序员 AI 量化理财体系课](https://class.imooc.com/sale/aiqwm)\n\n\u003e 项目官方配套课程：深度解析本框架从 0 到 1 的架构设计，涵盖实盘闭环逻辑与工业级量化开发实战，是掌握本项目精髓的进阶必修课。\n\n## 🚀 安装与启动\n\n```bash\npip install -r requirements.txt\npython run.py\n```\n\n## ✨ 核心功能\n\n- 📉 回测中心 - 策略创建、历史数据回测、绩效分析与可视化报告\n- 🧾 手动交易 - 管理账户（选择/新建）、本地模拟基于 deltafq 按 tick 撮合、买卖执行与持仓盈亏跟踪\n- ⚡ 策略运行 - 自动交易、实时监控、信号执行与日志追踪\n- 🤖 AI Agent - 支持 LLM 配置、对话与工具调用（趣味签文、`run_backtest` 模糊匹配与结构化摘要、`run_backtest_auto` 自动拉数回测；命中关键词时注入回测 Skill）\n\n## 🗂️ 项目结构\n\n```\ndeltafstation/\n├── assets/           # 文档与展示图片\n├── backend/\n│   ├── api/          # REST API\n│   │   ├── data_api.py\n│   │   ├── strategy_api.py\n│   │   ├── backtest_api.py\n│   │   ├── ai_api.py          # AI Agent：LLM 对话（SSE 流式）；可选注入回测 SKILL\n│   │   ├── simulation_api.py   # 手动交易：账户、下单\n│   │   └── gostrategy_api.py   # 策略运行：启动/停止、K 线\n│   ├── core/         # 核心引擎\n│   │   ├── data_manager.py\n│   │   ├── live_data_manager.py\n│   │   ├── backtest_engine.py\n│   │   ├── simulation_engine.py      # 手动交易 tick 撮合\n│   │   ├── strategy_engine.py     # 策略自动化 LiveEngine\n│   │   ├── agent/                   # AI Agent 编排层（OpenAI 兼容：DeepSeek / OpenAI / 通义等）\n│   │   │   ├── llm_client.py\n│   │   │   ├── skill_prompt.py      # 关键词命中时加载 skills/*/SKILL.md 注入 system prompt\n│   │   │   ├── skills/              # Markdown Skill（如 backtest/SKILL.md）\n│   │   │   ├── tool_registry.py     # 工具 schema / handler 注册（TOOL_DEFINITIONS）\n│   │   │   ├── tool_runner.py       # 多轮 tool_calls 编排执行\n│   │   │   └── tools/              # 工具实现（handler）\n│   │   │       ├── fun_tools.py\n│   │   │       ├── backtest_tools.py\n│   │   │       └── backtest_auto_tools.py\n│   │   ├── utils/\n│   │   │   ├── engine_snapshot.py\n│   │   │   ├── sim_persistence.py\n│   │   │   └── strategy_loader.py\n│   └── app.py        # Flask 入口\n├── config/\n├── data/\n│   ├── raw/          # 原始行情 CSV\n│   ├── results/      # 回测结果 JSON\n│   ├── simulations/  # 仿真账户配置 JSON\n│   └── strategies/   # 策略 Python 文件\n├── frontend/\n│   ├── templates/    # index / backtest / trader / gostrategy\n│   └── static/           # 静态资源（css/js）\n├── requirements.txt\n└── run.py\n```\n\n## 🏗️ 技术架构\n\nDeltaFStation 基于 Flask 构建 Web 端，后端集成 deltafq 量化框架，实现从策略研发到交易接入的云端工作流：\nhttps://github.com/Delta-F/deltafq\n\n\u003ctable\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003cimg src=\"assets/arch1.png\" style=\"width:100%; height:220px; object-fit:contain;\" /\u003e\u003c/td\u003e\n    \u003ctd\u003e\u003cimg src=\"assets/arch2.png\" style=\"width:100%; height:220px; object-fit:contain;\" /\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n## 🤝 社区与贡献\n\n- 欢迎通过 [Issue](https://github.com/delta-f/deltafstation/issues) 或 [PR](https://github.com/delta-f/deltafstation/pulls) 反馈问题、提交改进。\n- 微信公众号：关注 `DeltaFQ开源量化`，获取版本更新与量化资料。\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"assets/wechat_qr.png\" width=\"150\" alt=\"微信公众号\" /\u003e\n\u003c/p\u003e\n\n## ⚖️ 许可证\n\nMIT License，详见 [LICENSE](LICENSE)。\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdelta-f%2Fdeltafstation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdelta-f%2Fdeltafstation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdelta-f%2Fdeltafstation/lists"}