diff --git a/README.md b/README.md
index c48f474..5ca2e5f 100644
--- a/README.md
+++ b/README.md
@@ -5,176 +5,199 @@
- 
---
+
# ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism
-**ContestTrade** is an innovative multi-agent (Multi-Agent) trading framework. With **ContestTrade** you can easily build a dedicated AI trading team: just set the analysis time, and it will autonomously scan the whole market without human intervention, mine potential event-driven investment opportunities from massive data, and, through an internal selection mechanism, construct the most trustworthy investment portfolio for you.
+**ContestTrade** 是一个创新的多智能体（Multi-Agent）交易框架，通过**ContestTrade** 您可以轻松的打造一支专属的AI交易团队。只需设定分析时刻，它能在无人工干预的情况下自主扫描全市场，从海量数据中挖掘潜在事件驱动型的投资机会，并通过内部优选机制，为您构建最值得信赖的投资组合。
+
+## Introduction (项目简介)
-## Introduction
+ContestTrade 是一个面向事件驱动选股的多智能体交易框架。系统目标是：在没有人工干预的条件下，自动发现、评估并跟踪具有投资价值的事件型机会，最终输出可执行的资产配置建议。
-ContestTrade is a multi-agent trading framework focused on event-driven stock selection. The system's goal is to automatically discover, evaluate, and track event-driven opportunities with investment value without human intervention, and finally output executable asset allocation recommendations.
+**核心功能**：
-**Core features:**
-- **Automatic stock selection**: whole-market scanning and signal generation that automatically outputs tradable candidate stock lists without manual screening.
-- **Event-driven**: triggered by catalysts such as news, announcements, capital flows, and policy; focuses on opportunities with significant information impact.
-- **Personalized configuration**: supports user-defined agent research preferences and strategies to flexibly adapt to different investment styles.
+- **自动选股**：全市场扫描与信号生成，自动输出可交易的候选股票列表，无需人工逐一筛选。
+- **事件驱动**：以新闻、公告、资金流、政策等催化事件为触发，关注具有显著信息冲击的机会。
+- **个性化配置**：支持用户自定义智能体的研究偏好和策略，灵活适应不同投资风格。
-## Framework Overview
+## Framework Overview (框架概览)
-The workflow of ContestTrade operates through a structured two-stage pipeline, simulating the dynamic decision-making process of an investment firm. This dual-contest framework ensures that final decisions are driven only by the most robust and effective insights, maintaining strong adaptability and resilience in complex markets.
+ContestTrade 的工作流程通过一个结构化的双阶段管道来运作，模拟了投资公司的动态决策过程。这个双重竞赛框架确保了最终的决策只被最稳健、最有效的洞察所驱动，从而在复杂的市场中保持了强大的适应性和抗干扰能力。
-1. **Data Processing Stage:** First, raw market data from multiple sources is fed into the **Data Team**. Multiple Data Analysis Agents work in parallel to refine this raw data into structured "textual factors." At this stage, the internal contest mechanism evaluates the potential value of the factors generated by each data agent and constructs an optimal "factor portfolio."
+1. **数据处理阶段:** 首先，来自多个来源的原始市场数据被输入到**数据团队**。团队中的多个数据分析智能体 (Data Analysis Agents) 并行工作，将这些原始数据提炼成结构化的“文本因子”。在这一阶段，内部竞赛机制会评估每个数据智能体生成的因子的潜在价值，并构建出一个最优的“因子投资组合”。
+2. **研究决策阶段:** 这个最优的因子组合随后被传递给**研究团队**。团队中的多个研究员智能体 (Research Agents) 会基于各自独特的“交易信念” (Trading Beliefs) 和强大的金融工具集，对这些因子进行并行的深度分析，并各自提交交易提案。随后，第二轮内部竞赛会评估这些交易提案，并最终合成一个统一、可靠的资产配置策略作为最终输出。
-2. **Research and Decision-Making Stage:** This optimal factor portfolio is then passed to the **Research Team**. Multiple Research Agents, each with their unique "Trading Beliefs" and access to a powerful set of financial tools, conduct parallel, in-depth analyses of these factors and submit their respective trading proposals. A second round of internal contests then evaluates these proposals, ultimately synthesizing a unified and reliable asset allocation strategy as the final output.
-
-## Installation
+## Installation (安装)
```bash
-# 1. Clone the project repository
+# 1. 克隆项目仓库
git clone https://github.com/FinStep-AI/ContestTrade.git
cd ContestTrade
-# 2. (Recommended) Create and activate a virtual environment
+# 2. (推荐) 创建并激活虚拟环境
conda create -n contesttrade python=3.10
conda activate contesttrade
-# 3. Install project dependencies
+# 3. 安装项目依赖
pip install -r requirements.txt
+
```
-Or deploy with [Docker](https://docs.n8n.io/hosting/installation/docker/):
+或克隆后修改配置，通过 [Docker](https://docs.n8n.io/hosting/installation/docker/)启动:
```
docker run -it --rm --name contest_trade -v $(pwd)/config.yaml:/ContestTrade/config.yaml finstep/contesttrade:v2.0
```
-## Configuration
+## Configuration (配置)
-Before running ContestTrade, you need to configure the necessary API keys and LLM parameters.
+在运行ContestTrade之前，您需要配置必要的API密钥和LLM参数。
-Edit the `config_us.yaml` file and enter your API keys. The table below lists all required and optional keys:
+编辑 `config.yaml` 文件，填入您的API密钥。下表列出了所有必需和可选的密钥：
-| Key | Description | Required |
-| :----------------------- | :----------------------------------------- | :------- |
-| `TUSHARE_KEY` | Tushare data interface key | No |
-| `BOCHA_KEY` | Bocha search engine key | No |
-| `SERP_KEY` | SerpAPI search engine key | No |
-| `FMP_KEY` | FMP API key | **Yes** |
-| `FINNHUB_KEY` | Finnhub API key | No |
-| `ALPHA_VANTAGE_KEY` | Alpha Vantage API key | **Yes** |
-| `POLYGON_KEY` | Polygon API key | **Yes** |
-| `LLM_API_KEY` | LLM API key for general tasks | **Yes** |
-| `LLM_BASE_URL` | LLM API base URL for general tasks | **Yes** |
-| `LLM_THINKING_API_KEY` | LLM API key for complex reasoning | No |
-| `LLM_THINKING_BASE_URL` | LLM API base URL for complex reasoning | No |
-| `VLM_API_KEY` | VLM API key for visual analysis | No |
-| `VLM_BASE_URL` | VLM API base URL for visual analysis | No |
+
+
+| 配置项 (Key) | 描述 (Description) | 必需 (Required) |
+| :--------------- | :-------------------- | :-------------: |
+| `TUSHARE_KEY` | Tushare 数据接口密钥 | ❌ |
+| `BOCHA_KEY` | Bocha 搜索引擎密钥 | ❌ |
+| `SERP_KEY` | SerpAPI 搜索引擎密钥 | ❌ |
+| `LLM` | 用于通用任务的LLM API | ✅ |
+| `LLM_THINKING` | 用于复杂推理的LLM API | ❌ |
+| `VLM` | 用于视觉分析的VLM API | ❌ |
+
+
-> Note: You need to apply for the LLM API and VLM API by yourself. Fill in the URL, API Key, and model name according to the platform and model you use.
+> 注意：LLM API 和 VLM API 需要您自行申请，具体根据您使用的平台和模型填写URL、API Key以及模型名称。当前配置文件默认支持AKShare数据接口，您也可以自行配置Tushare接口及配套配置来获得更好的表现。
-## Preference
+## Preference (选股偏好)
-Each Research Agent corresponds to a "trading belief". The system will generate investment signals based on these beliefs combined with data and tools (each belief outputs up to 5 signals). The configuration file is located at `contest_trade/config/belief_list.json`, and its format is a JSON array of strings.
+每个 Research Agent 对应一条“交易信念（belief）”，系统会根据这些信念并结合数据与工具生成投资信号（每条信念最多输出5个信号）。配置文件位于 `contest_trade/config/belief_list.json`，其格式为一个 JSON 字符串数组。
+
+示例 1 — 偏好短期事件驱动（更激进）:
-Example 1 — preference for short-term event-driven (more aggressive):
```json
[
- "Focus on short-term event-driven opportunities: prioritize company announcements, M&A and restructuring, sudden order increases, technological breakthroughs and other catalysts; prefer mid/small-cap, high-volatility thematic stocks, suitable for aggressive arbitrage strategies."
+ "专注于短期事件驱动机会：优先关注公司公告、并购重组、订单暴增、技术突破等催化事件；偏好中小市值、高波动的题材股，适合激进套利策略。"
]
```
-Example 2 — preference for stable events (more conservative):
+示例 2 — 偏好稳健事件（更保守）:
+
```json
[
- "Focus on stable, high-certainty events: pay attention to dividends, buybacks, earnings forecast confirmations, major contract signings and policy tailwinds; prefer large-cap blue-chips with low volatility and high certainty, suitable for conservative allocation."
+ "专注于稳健的确定性事件：关注分红、回购、业绩预告确认、重大合同落地和政策利好等；偏好大盘蓝筹、低波动、确定性高的标的，适合稳健配置。"
]
```
-Default configuration:
+默认配置:
+
```json
[
- "Based on the provided information, comprehensively consider each company's business dynamics, industry trends and potential market impact. Recommend stock portfolios with short-term investment potential for two groups: Group 1 — risk-seekers (prefer high volatility, high returns, mid/low market cap stocks); Group 2 — conservative investors (prefer low volatility, stable returns, large-cap stocks).",
- "Based on recent sudden events, policy adjustments and company announcements as catalysts, combined with market sentiment transmission paths and capital game features. Screen event-driven opportunities for two different styles: Group 1 — aggressive arbitrage (prefer restructuring expectation, sudden order increases, technical breakthroughs in small-cap stocks); Group 2 — defensive arbitrage (prefer dividend increases, large buybacks, acquisition of franchise rights in blue-chip stocks). Pay attention to northbound capital movement and institutional seat trends on the trading leaderboard for resonance effects."
+ "基于提供的资讯内容综合考虑各公司的业务动态、行业趋势以及潜在的市场影响。为两个群体推荐接下来的交易日具有投资潜力的股票组合:群体1：风险偏好者（偏好波动率大、收益率高，中低市值的股票）;群体2：稳健投资者（偏好低波动率、稳定收益、高市值的股票）。",
+ "基于近期突发性事件、政策调整及企业公告等催化剂因素，结合市场情绪传导路径和资金博弈特征。为两类不同风格的投资者筛选事件驱动型机会：群体1：激进套利者（偏好重组预期、订单暴增、技术突破等强题材的小盘股）；群体2：防御套利者（偏好分红上调、大宗回购、特许经营权获取等确定性事件的蓝筹股）。需注意北向资金异动与龙虎榜机构席位动向形成的共振效应。"
]
```
-## Usage
+说明（可选）：将您偏好的文本描述逐条加入 `contest_trade/config/belief_list.json` 中，系统会为每条信念分别运行相应的 Research Agent 并输出信号。
+
+## Usage (使用方法)
-You can easily start ContestTrade using the Command Line Interface (CLI).
+您可以通过命令行界面（CLI）轻松启动ContestTrade。
```bash
python -m cli.main run
```
-After the program starts, you will enter the terminal interactive interface and can choose the market you want to analyze. The default analysis time is the current time.
+程序启动后，您将进入终端交互式界面，可以选择您要分析的市场。
+
-After all agents have finished running, you can view the signals they provide in the results summary.
+然后，您可以选择具体分析时间。
+
- 
+ 
-You can choose to view a more detailed research report.
+验证完成后，您将会看到以下显示并进入运行界面。
+
- 
+ 
+
+
+所有Agent运行完成后可在结果摘要中查看Agent给出的信号。
+
+
+ 
+
+
+同时可以进一步选择查看详细的研究报告。
+
+
+ 
- 
+ 
-You can also choose to view a more detailed data analysis report.
+或者您也可以选择查看详细的数据分析报告。
+
- 
+ 
-> All the above reports will be saved in the `contest_trade/agents_workspace/results` directory in Markdown format for your future reference and sharing.
+> 以上报告都会以Markdown格式保存在 `contest_trade/agents_workspace/results` 目录下，方便您后续查看和分享。
+
+## 🌟 我们的愿景与路线图 (Vision & Roadmap)
+
+我们坚信AGI时代即将到来，我们希望能够借助开源社区的力量，探索AGI时代下量化交易的新范式。
-## 🌟 Vision & Roadmap
+该项目致力于研发更完善的基础设施和更丰富的Agent，探索AI在金融交易的能力边界，打造一个稳定、可信、且能规模化扩展的Agent交易框架。
-We firmly believe that the era of AGI is approaching, and we hope to leverage the power of the open-source community to explore new paradigms of quantitative trading in the AGI era.
+### 项目路线图 (Roadmap)
-The project is dedicated to developing more robust infrastructure and a wider variety of agents, exploring the boundaries of AI in financial trading, and creating a stable, trustworthy, and scalable agent trading framework.
+**V1.1 (已完成): 框架稳定性增强 & 核心体验优化**
-### Roadmap
+- [✓] 核心数据源模块解耦，实现多数据源的adaptor (`data-provider` refactor)
+- [✓] 优化CLI日志与交互体验
-**V1.1 (Finished): Framework Stability Enhancement & Core Experience Optimization**
-- [✓] The core data source module is decoupled to achieve adaptors for multiple data sources. (`data-provider` refactor)
-- [✓] Optimized CLI logging and interaction experience.
+**V2.0 (已完成): 市场与功能拓展**
-**V2.0 (Finished): Market and Function Expansion**
-- [✓] Access to **US stock** market data.
-- [✓] Introduced richer factors and signal sources.
+- [✓] 接入 **美股** 市场数据
+- [✓] 引入更丰富的因子与信号来源
-**Future Plans:**
-- [ ] Support for Hong Kong stocks and other markets
-- [ ] Visual backtesting and analysis interface
-- [ ] Support for scaling up more agents
+**未来规划:**
-## Contributing
+- [ ] 支持港股及其他市场
+- [ ] 可视化回测与分析界面
+- [ ] 支持更多Agent的scale up
-ContestTrade is a community-driven open-source project, and we welcome contributions in any form!
+## Contributing (贡献指南)
-If you are a developer, you can refer to our **[Contributing Guide (CONTRIBUTING.md)](CONTRIBUTING.md)** to become a contributor to ContestTrade.
+ContestTrade 是一个由社区驱动的开源项目，我们欢迎任何形式的贡献！
-We also value non-code contributions, including:
-* **Proposing feature suggestions or reporting bugs:** [Go to Issues page](https://github.com/FinStep-AI/ContestTrade/issues)
-* **Providing feedback on your testing results:** Including test results, user experience, etc.
+如果您是开发者，可以参考我们的 **[贡献指南 (CONTRIBUTING.md)](CONTRIBUTING.md)** 成为ContestTrade的贡献者。
-## Star History
+我们同样珍视非代码形式的贡献，包括：
+
+* **提出功能建议或反馈Bug:** [前往 Issues 页面](https://github.com/FinStep-AI/ContestTrade/issues)
+* **反馈您的测试效果:** 包括测试结果、使用体验等。
+
+## Star History（社区关注）
-## Disclaimer
+## Disclaimer（风险声明）
+
+**重要声明:** 本项目 `ContestTrade` 是一个开源的量化交易Agent框架研究项目，仅供学术研究和教育目的使用。项目中包含的示例、数据和分析结果不构成任何形式的投资建议。
-**Important Disclaimer:** This project, `ContestTrade`, is an open-source quantitative trading research framework intended for academic and educational purposes only. The examples, data, and analysis results included in the project do not constitute any form of investment advice.
+**风险提示:**
-**Risk Warning:**
-* **Market Risk:** This project does not constitute any form of investment, financial, legal, or tax advice. All outputs, including trading signals and analyses, are the results of AI model deductions based on historical data and should not be considered a basis for any buy or sell operations.
-* **Data Accuracy:** The data sources used by the framework may be subject to delays, inaccuracies, or incompleteness. We do not guarantee the reliability of the data.
-* **Model Hallucination:** AI models (including Large Language Models) have inherent limitations and are subject to the risk of "hallucination." We do not guarantee the accuracy, completeness, or timeliness of the information generated by the framework.
-* **Liability:** The developers assume no liability for any direct or indirect losses resulting from the use of or inability to use this framework. Investing involves risks; enter the market with caution.
+* **市场风险:** 本项目不构成任何形式的投资、财务、法律或税务建议。所有输出，包括交易信号和分析，均为基于历史数据的AI模型推演结果，不应被视为任何买卖操作的依据。
+* **数据准确性:** 框架使用的数据源可能存在延迟、不准确或不完整的情况。我们不对数据的可靠性做任何保证。
+* **模型幻觉:** AI模型（包括大型语言模型）存在固有的局限性和“幻觉”风险。我们不保证框架生成信息的准确性、完整性或及时性。
+* **责任自负:** 开发者不对任何因使用或无法使用本框架而导致的直接或间接损失承担任何责任。投资有风险，入市需谨慎。
-**Before using this framework for any actual trading decisions, be sure to fully understand the associated risks.**
+**在将本框架用于任何实际交易决策之前，请务必充分了解相关风险。**
-## Citation
+## Citation (引用)
-If you use ContestTrade in your research, please cite our paper:
+如果您在您的研究中使用了ContestTrade，请引用我们的论文：
```bibtex
@misc{zhao2025contesttrade,
@@ -209,6 +233,6 @@ If you use ContestTrade in your research, please cite our paper:
}
```
-## License
+## License (许可证)
-This project is licensed under the [Apache 2.0 License](LICENSE).
+本项目采用 [Apache 2.0 License](LICENSE) 许可证。
diff --git a/README_cn.md b/README_cn.md
deleted file mode 100644
index 8b87542..0000000
--- a/README_cn.md
+++ /dev/null
@@ -1,238 +0,0 @@
-
- 
-
-
-
-
----
-
-# ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism
-
-**ContestTrade** 是一个创新的多智能体（Multi-Agent）交易框架，通过**ContestTrade** 您可以轻松的打造一支专属的AI交易团队。只需设定分析时刻，它能在无人工干预的情况下自主扫描全市场，从海量数据中挖掘潜在事件驱动型的投资机会，并通过内部优选机制，为您构建最值得信赖的投资组合。
-
-## Introduction (项目简介)
-
-ContestTrade 是一个面向事件驱动选股的多智能体交易框架。系统目标是：在没有人工干预的条件下，自动发现、评估并跟踪具有投资价值的事件型机会，最终输出可执行的资产配置建议。
-
-**核心功能**：
-
-- **自动选股**：全市场扫描与信号生成，自动输出可交易的候选股票列表，无需人工逐一筛选。
-- **事件驱动**：以新闻、公告、资金流、政策等催化事件为触发，关注具有显著信息冲击的机会。
-- **个性化配置**：支持用户自定义智能体的研究偏好和策略，灵活适应不同投资风格。
-
-## Framework Overview (框架概览)
-
-
-
-
-
-ContestTrade 的工作流程通过一个结构化的双阶段管道来运作，模拟了投资公司的动态决策过程。这个双重竞赛框架确保了最终的决策只被最稳健、最有效的洞察所驱动，从而在复杂的市场中保持了强大的适应性和抗干扰能力。
-
-1. **数据处理阶段:** 首先，来自多个来源的原始市场数据被输入到**数据团队**。团队中的多个数据分析智能体 (Data Analysis Agents) 并行工作，将这些原始数据提炼成结构化的“文本因子”。在这一阶段，内部竞赛机制会评估每个数据智能体生成的因子的潜在价值，并构建出一个最优的“因子投资组合”。
-2. **研究决策阶段:** 这个最优的因子组合随后被传递给**研究团队**。团队中的多个研究员智能体 (Research Agents) 会基于各自独特的“交易信念” (Trading Beliefs) 和强大的金融工具集，对这些因子进行并行的深度分析，并各自提交交易提案。随后，第二轮内部竞赛会评估这些交易提案，并最终合成一个统一、可靠的资产配置策略作为最终输出。
-
-## Installation (安装)
-
-```bash
-# 1. 克隆项目仓库
-git clone https://github.com/FinStep-AI/ContestTrade.git
-cd ContestTrade
-
-# 2. (推荐) 创建并激活虚拟环境
-conda create -n contesttrade python=3.10
-conda activate contesttrade
-
-# 3. 安装项目依赖
-pip install -r requirements.txt
-
-```
-
-或克隆后修改配置，通过 [Docker](https://docs.n8n.io/hosting/installation/docker/)启动:
-
-```
-docker run -it --rm --name contest_trade -v $(pwd)/config.yaml:/ContestTrade/config.yaml finstep/contesttrade:v2.0
-```
-
-## Configuration (配置)
-
-在运行ContestTrade之前，您需要配置必要的API密钥和LLM参数。
-
-编辑 `config.yaml` 文件，填入您的API密钥。下表列出了所有必需和可选的密钥：
-
-
-
-| 配置项 (Key) | 描述 (Description) | 必需 (Required) |
-| :--------------- | :-------------------- | :-------------: |
-| `TUSHARE_KEY` | Tushare 数据接口密钥 | ❌ |
-| `BOCHA_KEY` | Bocha 搜索引擎密钥 | ❌ |
-| `SERP_KEY` | SerpAPI 搜索引擎密钥 | ❌ |
-| `LLM` | 用于通用任务的LLM API | ✅ |
-| `LLM_THINKING` | 用于复杂推理的LLM API | ❌ |
-| `VLM` | 用于视觉分析的VLM API | ❌ |
-
-
-
-> 注意：LLM API 和 VLM API 需要您自行申请，具体根据您使用的平台和模型填写URL、API Key以及模型名称。当前配置文件默认支持AKShare数据接口，您也可以自行配置Tushare接口及配套配置来获得更好的表现。
-
-## Preference (选股偏好)
-
-每个 Research Agent 对应一条“交易信念（belief）”，系统会根据这些信念并结合数据与工具生成投资信号（每条信念最多输出5个信号）。配置文件位于 `contest_trade/config/belief_list.json`，其格式为一个 JSON 字符串数组。
-
-示例 1 — 偏好短期事件驱动（更激进）:
-
-```json
-[
- "专注于短期事件驱动机会：优先关注公司公告、并购重组、订单暴增、技术突破等催化事件；偏好中小市值、高波动的题材股，适合激进套利策略。"
-]
-```
-
-示例 2 — 偏好稳健事件（更保守）:
-
-```json
-[
- "专注于稳健的确定性事件：关注分红、回购、业绩预告确认、重大合同落地和政策利好等；偏好大盘蓝筹、低波动、确定性高的标的，适合稳健配置。"
-]
-```
-
-默认配置:
-
-```json
-[
- "基于提供的资讯内容综合考虑各公司的业务动态、行业趋势以及潜在的市场影响。为两个群体推荐接下来的交易日具有投资潜力的股票组合:群体1：风险偏好者（偏好波动率大、收益率高，中低市值的股票）;群体2：稳健投资者（偏好低波动率、稳定收益、高市值的股票）。",
- "基于近期突发性事件、政策调整及企业公告等催化剂因素，结合市场情绪传导路径和资金博弈特征。为两类不同风格的投资者筛选事件驱动型机会：群体1：激进套利者（偏好重组预期、订单暴增、技术突破等强题材的小盘股）；群体2：防御套利者（偏好分红上调、大宗回购、特许经营权获取等确定性事件的蓝筹股）。需注意北向资金异动与龙虎榜机构席位动向形成的共振效应。"
-]
-```
-
-说明（可选）：将您偏好的文本描述逐条加入 `contest_trade/config/belief_list.json` 中，系统会为每条信念分别运行相应的 Research Agent 并输出信号。
-
-## Usage (使用方法)
-
-您可以通过命令行界面（CLI）轻松启动ContestTrade。
-
-```bash
-python -m cli.main run
-```
-
-程序启动后，您将进入终端交互式界面，可以选择您要分析的市场。
-
-
-
-
-
-然后，您可以选择具体分析时间。
-
-
-
-
-
-验证完成后，您将会看到以下显示并进入运行界面。
-
-
-
-
-
-所有Agent运行完成后可在结果摘要中查看Agent给出的信号。
-
-
-
-
-
-同时可以进一步选择查看详细的研究报告。
-
-
-
-
-
-
-
-
-
-或者您也可以选择查看详细的数据分析报告。
-
-
-
-
-
-> 以上报告都会以Markdown格式保存在 `contest_trade/agents_workspace/results` 目录下，方便您后续查看和分享。
-
-## 🌟 我们的愿景与路线图 (Vision & Roadmap)
-
-我们坚信AGI时代即将到来，我们希望能够借助开源社区的力量，探索AGI时代下量化交易的新范式。
-
-该项目致力于研发更完善的基础设施和更丰富的Agent，探索AI在金融交易的能力边界，打造一个稳定、可信、且能规模化扩展的Agent交易框架。
-
-### 项目路线图 (Roadmap)
-
-**V1.1 (已完成): 框架稳定性增强 & 核心体验优化**
-
-- [✓] 核心数据源模块解耦，实现多数据源的adaptor (`data-provider` refactor)
-- [✓] 优化CLI日志与交互体验
-
-**V2.0 (已完成): 市场与功能拓展**
-
-- [✓] 接入 **美股** 市场数据
-- [✓] 引入更丰富的因子与信号来源
-
-**未来规划:**
-
-- [ ] 支持港股及其他市场
-- [ ] 可视化回测与分析界面
-- [ ] 支持更多Agent的scale up
-
-## Contributing (贡献指南)
-
-ContestTrade 是一个由社区驱动的开源项目，我们欢迎任何形式的贡献！
-
-如果您是开发者，可以参考我们的 **[贡献指南 (CONTRIBUTING.md)](CONTRIBUTING.md)** 成为ContestTrade的贡献者。
-
-我们同样珍视非代码形式的贡献，包括：
-
-* **提出功能建议或反馈Bug:** [前往 Issues 页面](https://github.com/FinStep-AI/ContestTrade/issues)
-* **反馈您的测试效果:** 包括测试结果、使用体验等。
-
-## Star History（社区关注）
-
-
-
-## Disclaimer（风险声明）
-
-**重要声明:** 本项目 `ContestTrade` 是一个开源的量化交易Agent框架研究项目，仅供学术研究和教育目的使用。项目中包含的示例、数据和分析结果不构成任何形式的投资建议。
-
-**风险提示:**
-
-* **市场风险:** 本项目不构成任何形式的投资、财务、法律或税务建议。所有输出，包括交易信号和分析，均为基于历史数据的AI模型推演结果，不应被视为任何买卖操作的依据。
-* **数据准确性:** 框架使用的数据源可能存在延迟、不准确或不完整的情况。我们不对数据的可靠性做任何保证。
-* **模型幻觉:** AI模型（包括大型语言模型）存在固有的局限性和“幻觉”风险。我们不保证框架生成信息的准确性、完整性或及时性。
-* **责任自负:** 开发者不对任何因使用或无法使用本框架而导致的直接或间接损失承担任何责任。投资有风险，入市需谨慎。
-
-**在将本框架用于任何实际交易决策之前，请务必充分了解相关风险。**
-
-## Citation (引用)
-
-如果您在您的研究中使用了ContestTrade，请引用我们的论文：
-
-```bibtex
-@misc{zhao2025contesttrade,
- title={ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism},
- author={Li Zhao and Rui Sun and Zuoyou Jiang and Bo Yang and Yuxiao Bai and Mengting Chen and Xinyang Wang and Jing Li and Zuo Bai},
- year={2025},
- eprint={2508.00554},
- archivePrefix={arXiv},
- primaryClass={q-fin.TR}
-}
-```
-
-## License (许可证)
-
-本项目采用 [Apache 2.0 License](LICENSE) 许可证。
diff --git a/README_en.md b/README_en.md
new file mode 100644
index 0000000..c48f474
--- /dev/null
+++ b/README_en.md
@@ -0,0 +1,214 @@
+
+
+
+
+
+
+---
+# ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism
+
+**ContestTrade** is an innovative multi-agent (Multi-Agent) trading framework. With **ContestTrade** you can easily build a dedicated AI trading team: just set the analysis time, and it will autonomously scan the whole market without human intervention, mine potential event-driven investment opportunities from massive data, and, through an internal selection mechanism, construct the most trustworthy investment portfolio for you.
+
+## Introduction
+
+ContestTrade is a multi-agent trading framework focused on event-driven stock selection. The system's goal is to automatically discover, evaluate, and track event-driven opportunities with investment value without human intervention, and finally output executable asset allocation recommendations.
+
+**Core features:**
+- **Automatic stock selection**: whole-market scanning and signal generation that automatically outputs tradable candidate stock lists without manual screening.
+- **Event-driven**: triggered by catalysts such as news, announcements, capital flows, and policy; focuses on opportunities with significant information impact.
+- **Personalized configuration**: supports user-defined agent research preferences and strategies to flexibly adapt to different investment styles.
+
+## Framework Overview
+
+
+
+
+
+The workflow of ContestTrade operates through a structured two-stage pipeline, simulating the dynamic decision-making process of an investment firm. This dual-contest framework ensures that final decisions are driven only by the most robust and effective insights, maintaining strong adaptability and resilience in complex markets.
+
+1. **Data Processing Stage:** First, raw market data from multiple sources is fed into the **Data Team**. Multiple Data Analysis Agents work in parallel to refine this raw data into structured "textual factors." At this stage, the internal contest mechanism evaluates the potential value of the factors generated by each data agent and constructs an optimal "factor portfolio."
+
+2. **Research and Decision-Making Stage:** This optimal factor portfolio is then passed to the **Research Team**. Multiple Research Agents, each with their unique "Trading Beliefs" and access to a powerful set of financial tools, conduct parallel, in-depth analyses of these factors and submit their respective trading proposals. A second round of internal contests then evaluates these proposals, ultimately synthesizing a unified and reliable asset allocation strategy as the final output.
+
+## Installation
+
+```bash
+# 1. Clone the project repository
+git clone https://github.com/FinStep-AI/ContestTrade.git
+cd ContestTrade
+
+# 2. (Recommended) Create and activate a virtual environment
+conda create -n contesttrade python=3.10
+conda activate contesttrade
+
+# 3. Install project dependencies
+pip install -r requirements.txt
+```
+
+Or deploy with [Docker](https://docs.n8n.io/hosting/installation/docker/):
+
+```
+docker run -it --rm --name contest_trade -v $(pwd)/config.yaml:/ContestTrade/config.yaml finstep/contesttrade:v2.0
+```
+
+## Configuration
+
+Before running ContestTrade, you need to configure the necessary API keys and LLM parameters.
+
+Edit the `config_us.yaml` file and enter your API keys. The table below lists all required and optional keys:
+
+| Key | Description | Required |
+| :----------------------- | :----------------------------------------- | :------- |
+| `TUSHARE_KEY` | Tushare data interface key | No |
+| `BOCHA_KEY` | Bocha search engine key | No |
+| `SERP_KEY` | SerpAPI search engine key | No |
+| `FMP_KEY` | FMP API key | **Yes** |
+| `FINNHUB_KEY` | Finnhub API key | No |
+| `ALPHA_VANTAGE_KEY` | Alpha Vantage API key | **Yes** |
+| `POLYGON_KEY` | Polygon API key | **Yes** |
+| `LLM_API_KEY` | LLM API key for general tasks | **Yes** |
+| `LLM_BASE_URL` | LLM API base URL for general tasks | **Yes** |
+| `LLM_THINKING_API_KEY` | LLM API key for complex reasoning | No |
+| `LLM_THINKING_BASE_URL` | LLM API base URL for complex reasoning | No |
+| `VLM_API_KEY` | VLM API key for visual analysis | No |
+| `VLM_BASE_URL` | VLM API base URL for visual analysis | No |
+
+> Note: You need to apply for the LLM API and VLM API by yourself. Fill in the URL, API Key, and model name according to the platform and model you use.
+
+## Preference
+
+Each Research Agent corresponds to a "trading belief". The system will generate investment signals based on these beliefs combined with data and tools (each belief outputs up to 5 signals). The configuration file is located at `contest_trade/config/belief_list.json`, and its format is a JSON array of strings.
+
+Example 1 — preference for short-term event-driven (more aggressive):
+```json
+[
+ "Focus on short-term event-driven opportunities: prioritize company announcements, M&A and restructuring, sudden order increases, technological breakthroughs and other catalysts; prefer mid/small-cap, high-volatility thematic stocks, suitable for aggressive arbitrage strategies."
+]
+```
+
+Example 2 — preference for stable events (more conservative):
+```json
+[
+ "Focus on stable, high-certainty events: pay attention to dividends, buybacks, earnings forecast confirmations, major contract signings and policy tailwinds; prefer large-cap blue-chips with low volatility and high certainty, suitable for conservative allocation."
+]
+```
+
+Default configuration:
+```json
+[
+ "Based on the provided information, comprehensively consider each company's business dynamics, industry trends and potential market impact. Recommend stock portfolios with short-term investment potential for two groups: Group 1 — risk-seekers (prefer high volatility, high returns, mid/low market cap stocks); Group 2 — conservative investors (prefer low volatility, stable returns, large-cap stocks).",
+ "Based on recent sudden events, policy adjustments and company announcements as catalysts, combined with market sentiment transmission paths and capital game features. Screen event-driven opportunities for two different styles: Group 1 — aggressive arbitrage (prefer restructuring expectation, sudden order increases, technical breakthroughs in small-cap stocks); Group 2 — defensive arbitrage (prefer dividend increases, large buybacks, acquisition of franchise rights in blue-chip stocks). Pay attention to northbound capital movement and institutional seat trends on the trading leaderboard for resonance effects."
+]
+```
+
+## Usage
+
+You can easily start ContestTrade using the Command Line Interface (CLI).
+
+```bash
+python -m cli.main run
+```
+
+After the program starts, you will enter the terminal interactive interface and can choose the market you want to analyze. The default analysis time is the current time.
+
+
+
+
+After all agents have finished running, you can view the signals they provide in the results summary.
+
+
+
+
+You can choose to view a more detailed research report.
+
+
+
+
+
+
+
+
+You can also choose to view a more detailed data analysis report.
+
+
+
+
+> All the above reports will be saved in the `contest_trade/agents_workspace/results` directory in Markdown format for your future reference and sharing.
+
+## 🌟 Vision & Roadmap
+
+We firmly believe that the era of AGI is approaching, and we hope to leverage the power of the open-source community to explore new paradigms of quantitative trading in the AGI era.
+
+The project is dedicated to developing more robust infrastructure and a wider variety of agents, exploring the boundaries of AI in financial trading, and creating a stable, trustworthy, and scalable agent trading framework.
+
+### Roadmap
+
+**V1.1 (Finished): Framework Stability Enhancement & Core Experience Optimization**
+- [✓] The core data source module is decoupled to achieve adaptors for multiple data sources. (`data-provider` refactor)
+- [✓] Optimized CLI logging and interaction experience.
+
+**V2.0 (Finished): Market and Function Expansion**
+- [✓] Access to **US stock** market data.
+- [✓] Introduced richer factors and signal sources.
+
+**Future Plans:**
+- [ ] Support for Hong Kong stocks and other markets
+- [ ] Visual backtesting and analysis interface
+- [ ] Support for scaling up more agents
+
+## Contributing
+
+ContestTrade is a community-driven open-source project, and we welcome contributions in any form!
+
+If you are a developer, you can refer to our **[Contributing Guide (CONTRIBUTING.md)](CONTRIBUTING.md)** to become a contributor to ContestTrade.
+
+We also value non-code contributions, including:
+* **Proposing feature suggestions or reporting bugs:** [Go to Issues page](https://github.com/FinStep-AI/ContestTrade/issues)
+* **Providing feedback on your testing results:** Including test results, user experience, etc.
+
+## Star History
+
+
+
+## Disclaimer
+
+**Important Disclaimer:** This project, `ContestTrade`, is an open-source quantitative trading research framework intended for academic and educational purposes only. The examples, data, and analysis results included in the project do not constitute any form of investment advice.
+
+**Risk Warning:**
+* **Market Risk:** This project does not constitute any form of investment, financial, legal, or tax advice. All outputs, including trading signals and analyses, are the results of AI model deductions based on historical data and should not be considered a basis for any buy or sell operations.
+* **Data Accuracy:** The data sources used by the framework may be subject to delays, inaccuracies, or incompleteness. We do not guarantee the reliability of the data.
+* **Model Hallucination:** AI models (including Large Language Models) have inherent limitations and are subject to the risk of "hallucination." We do not guarantee the accuracy, completeness, or timeliness of the information generated by the framework.
+* **Liability:** The developers assume no liability for any direct or indirect losses resulting from the use of or inability to use this framework. Investing involves risks; enter the market with caution.
+
+**Before using this framework for any actual trading decisions, be sure to fully understand the associated risks.**
+
+## Citation
+
+If you use ContestTrade in your research, please cite our paper:
+
+```bibtex
+@misc{zhao2025contesttrade,
+ title={ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism},
+ author={Li Zhao and Rui Sun and Zuoyou Jiang and Bo Yang and Yuxiao Bai and Mengting Chen and Xinyang Wang and Jing Li and Zuo Bai},
+ year={2025},
+ eprint={2508.00554},
+ archivePrefix={arXiv},
+ primaryClass={q-fin.TR}
+}
+```
+
+## License
+
+This project is licensed under the [Apache 2.0 License](LICENSE).
diff --git a/assets/contest_trade_cli_data_report_us.jpg b/assets/contest_trade_cli_data_report_us.jpg
index 6e0dddf..d324c08 100644
Binary files a/assets/contest_trade_cli_data_report_us.jpg and b/assets/contest_trade_cli_data_report_us.jpg differ
diff --git a/assets/contest_trade_cli_main_us.jpg b/assets/contest_trade_cli_main_us.jpg
index 3db33a7..03a1fbd 100644
Binary files a/assets/contest_trade_cli_main_us.jpg and b/assets/contest_trade_cli_main_us.jpg differ
diff --git a/assets/contest_trade_cli_research_report_us.jpg b/assets/contest_trade_cli_research_report_us.jpg
index 6e0dddf..4c363da 100644
Binary files a/assets/contest_trade_cli_research_report_us.jpg and b/assets/contest_trade_cli_research_report_us.jpg differ
diff --git a/cli/utils.py b/cli/utils.py
index b2f4794..b45a804 100644
--- a/cli/utils.py
+++ b/cli/utils.py
@@ -64,8 +64,8 @@ def validate_llm_connection():
test_messages = [
{"role": "user", "content": "请回复'连接测试成功'，不要添加任何其他内容。"}
]
- result = GLOBAL_LLM.run(test_messages, max_tokens=1, temperature=0.1, max_retries=0)
- if result and hasattr(result, 'content') and result.content:
+ result = GLOBAL_LLM.run(test_messages, max_tokens=5, temperature=1.0, max_retries=0)
+ if result and ((hasattr(result, 'content') and result.content) or (hasattr(result, 'reasoning_content') and result.reasoning_content)):
console.print(f"✅ [green]LLM连接成功[/green] - 模型: {GLOBAL_LLM.model_name}")
return True
else:
diff --git a/config.yaml b/config.yaml
index dfc5e21..b23e59a 100644
--- a/config.yaml
+++ b/config.yaml
@@ -13,27 +13,69 @@ fmp_key: ""
#YOUR_FINNHUB_KEY
finnhub_key: ""
+# ==================================================
+# LLM 配置说明 / LLM Configuration
+# ==================================================
+# 支持的 provider 选项 / Supported provider options:
+# - "openai" : OpenAI官方API或兼容接口
+# - "gemini" : Google Gemini API
+# - "ollama" : 本地Ollama服务
+#
+# 配置示例 / Configuration examples:
+#
+# OpenAI:
+# provider: "openai"
+# base_url: "https://api.openai.com/v1"
+# api_key: "your_openai_api_key"
+# model_name: "gpt-4"
+#
+# DeepSeek (OpenAI compatible):
+# provider: "openai"
+# base_url: "https://api.deepseek.com"
+# api_key: "your_deepseek_api_key"
+# model_name: "deepseek-chat"
+#
+# 阶跃星辰 (OpenAI compatible):
+# provider: "openai"
+# base_url: "https://models-proxy.stepfun-inc.com/v1"
+# api_key: "your_stepfun_api_key"
+# model_name: "step-1o-turbo-vision"
+#
+# Google Gemini:
+# provider: "gemini"
+# base_url: "https://generativelanguage.googleapis.com/v1beta"
+# api_key: "your_gemini_api_key"
+# model_name: "gemini-2.0-flash"
+#
+# Local Ollama:
+# provider: "ollama"
+# base_url: "http://localhost:11434"
+# api_key: "" # Ollama usually doesn't require an API key
+# model_name: "llama3.2:1b"
+# ==================================================
llm:
+ provider: "openai"
base_url: "https://api.deepseek.com"
#YOUR_DEEPSEEK_API_KEY
api_key: null
model_name: "deepseek-chat"
llm_thinking:
+ provider: "openai"
base_url: "https://api.deepseek.com"
#YOUR_DEEPSEEK_API_KEY
api_key: null
model_name: "deepseek-reasoner"
vlm:
+ provider: "openai"
base_url: "https://api.stepfun.com/v1"
#YOUR_STEPFUN_API_KEY
api_key: null
model_name: "step-1o-turbo-vision"
-
data_agents_config:
- agent_name: "sina_summary_agent"
data_source_list:
diff --git a/config_us.yaml b/config_us.yaml
index 705aa61..4393f46 100644
--- a/config_us.yaml
+++ b/config_us.yaml
@@ -20,16 +20,19 @@ alpha_vantage_key: ""
polygon_key: ""
llm:
+ provider: "openai"
base_url: "https://api.deepseek.com"
api_key: "" #YOUR_DEEPSEEK_API_KEY
model_name: "deepseek-chat"
llm_thinking:
+ provider: "openai"
base_url: "https://api.deepseek.com"
api_key: "" #YOUR_DEEPSEEK_API_KEY
model_name: "deepseek-reasoner"
vlm:
+ provider: "openai"
base_url: "https://api.stepfun.com/v1"
api_key: "" #YOUR_STEPFUN_API_KEY
model_name: "step-1o-turbo-vision"
diff --git a/contest_trade/models/llm_model.py b/contest_trade/models/llm_model.py
index 61c36c3..f48254c 100644
--- a/contest_trade/models/llm_model.py
+++ b/contest_trade/models/llm_model.py
@@ -1,19 +1,19 @@
"""
-OpenAI Model Implementation
+Multi-Provider LLM Model Implementation
-This module provides an implementation of the BaseAgentModel for OpenAI models.
+This module provides an implementation of the BaseAgentModel that supports
+multiple LLM providers: OpenAI, Gemini, and Ollama.
It follows an async-first approach, where the primary implementation is the
asynchronous streaming method (a_stream_run).
"""
import os
import sys
import httpx
-import openai
import asyncio
+from abc import ABC, abstractmethod
from pathlib import Path
-from openai import OpenAI, AsyncOpenAI
-from openai.types.chat import ChatCompletionChunk
-from typing import Dict, List, Optional, AsyncIterator, Callable
+from typing import Dict, List, Optional, AsyncIterator, Callable, Any
+from enum import Enum
PROJECT_ROOT = Path(__file__).parent.parent.resolve()
if str(PROJECT_ROOT) not in sys.path:
sys.path.append(str(PROJECT_ROOT))
@@ -26,9 +26,18 @@
ModelResponse
)
+
+class ProviderType(Enum):
+ """Enumeration of supported LLM providers."""
+ OPENAI = "openai"
+ GEMINI = "gemini"
+ OLLAMA = "ollama"
+
class LLMModelConfig:
- def __init__(self, model_name: str, api_key: str, base_url: str,
- max_retries: int = 3, retry_delay: float = 20.0, timeout: float = 60.0, extra_headers: dict = None, proxys: dict = None):
+ def __init__(self, provider: str, model_name: str, api_key: str = None, base_url: str = None,
+ max_retries: int = 3, retry_delay: float = 20.0, timeout: float = 60.0,
+ extra_headers: dict = None, proxys: dict = None, **kwargs):
+ self.provider = provider
self.model_name = model_name
self.api_key = api_key
self.base_url = base_url
@@ -37,74 +46,138 @@ def __init__(self, model_name: str, api_key: str, base_url: str,
self.timeout = timeout
self.extra_headers = extra_headers
self.proxys = proxys
+ # Store additional provider-specific configuration
+ self.extra_config = kwargs
-class LLMModel(BaseAgentModel):
- """
- OpenAI model implementation.
-
- This class provides a concrete implementation of the BaseAgentModel.
- Following the async-first approach, it only implements the a_stream_run method,
- while all other methods (run, a_run, stream_run) are handled by the base class.
- """
+class BaseProvider(ABC):
+ """Abstract base class for LLM providers."""
- def __init__(
- self,
- config: LLMModelConfig,
- **kwargs
- ):
- """
- Initialize the OpenAI model.
-
- Args:
- model_name: The name of the OpenAI model to use
- api_key: OpenAI API key (optional)
- base_url: Base URL for API requests (optional)
- **kwargs: Additional configuration parameters
- """
- super().__init__(config, **kwargs)
-
+ def __init__(self, config: LLMModelConfig):
self.config = config
self.model_name = config.model_name
self.api_key = config.api_key
self.base_url = config.base_url
self.extra_headers = config.extra_headers
self.proxys = config.proxys
+
+ @abstractmethod
+ async def create_stream(self, messages: List[Dict[str, str]], temperature: float,
+ max_tokens: Optional[int], **kwargs) -> Any:
+ """Create a streaming response from the provider."""
+ pass
+
+ @abstractmethod
+ def process_chunk(self, chunk: Any) -> StreamingChunk[str]:
+ """Process a streaming chunk from the provider."""
+ pass
+
+ def preprocess_messages(self, messages: List[Dict[str, str]]) -> List[Dict[str, str]]:
+ """Preprocess messages for the provider. Can be overridden by subclasses."""
+ return messages
+
+
+class OpenAIProvider(BaseProvider):
+ """OpenAI provider implementation."""
+
+ def __init__(self, config: LLMModelConfig):
+ super().__init__(config)
+
+ # Import OpenAI modules
+ try:
+ import openai
+ from openai import OpenAI, AsyncOpenAI
+ from openai.types.chat import ChatCompletionChunk
+ self._openai = openai
+ self._ChatCompletionChunk = ChatCompletionChunk
+ except ImportError:
+ raise ImportError("openai package is required for OpenAI provider. Install with: pip install openai")
+
+ # Set default values from environment if not provided
if self.api_key is None:
self.api_key = os.environ.get("OPENAI_API_KEY")
if self.base_url is None:
self.base_url = os.environ.get("OPENAI_BASE_URL")
-
- # Initialize synchronous client
- self.client = OpenAI(
- api_key=self.api_key,
- base_url=self.base_url,
- http_client=httpx.Client(proxy=self.proxys) if self.proxys else None
- )
- # Initialize asynchronous client
- self.async_client = AsyncOpenAI(
- api_key=self.api_key,
- base_url=self.base_url,
- http_client=httpx.AsyncClient(proxy=self.proxys) if self.proxys else None
- )
-
- if self.extra_headers is not None:
- self.client = self.client.with_options(
- default_headers=self.extra_headers)
- self.async_client = self.async_client.with_options(
- default_headers=self.extra_headers)
+ # Initialize clients (lazy initialization if no API key)
+ if self.api_key:
+ self.client = OpenAI(
+ api_key=self.api_key,
+ base_url=self.base_url,
+ http_client=httpx.Client(proxy=self.proxys) if self.proxys else None
+ )
+
+ self.async_client = AsyncOpenAI(
+ api_key=self.api_key,
+ base_url=self.base_url,
+ http_client=httpx.AsyncClient(proxy=self.proxys) if self.proxys else None
+ )
+
+ if self.extra_headers is not None:
+ self.client = self.client.with_options(default_headers=self.extra_headers)
+ self.async_client = self.async_client.with_options(default_headers=self.extra_headers)
+ else:
+ # Defer client initialization until first use
+ self.client = None
+ self.async_client = None
+
+ def _ensure_clients(self):
+ """Ensure OpenAI clients are initialized."""
+ if self.async_client is None:
+ if not self.api_key:
+ raise ValueError("OpenAI API key is required but not provided. Set it in config or OPENAI_API_KEY environment variable.")
+
+ self.client = OpenAI(
+ api_key=self.api_key,
+ base_url=self.base_url,
+ http_client=httpx.Client(proxy=self.proxys) if self.proxys else None
+ )
+
+ self.async_client = AsyncOpenAI(
+ api_key=self.api_key,
+ base_url=self.base_url,
+ http_client=httpx.AsyncClient(proxy=self.proxys) if self.proxys else None
+ )
+
+ if self.extra_headers is not None:
+ self.client = self.client.with_options(default_headers=self.extra_headers)
+ self.async_client = self.async_client.with_options(default_headers=self.extra_headers)
+
+ async def create_stream(self, messages: List[Dict[str, str]], temperature: float,
+ max_tokens: Optional[int], **kwargs) -> Any:
+ self._ensure_clients() # Ensure clients are initialized
+ processed_messages = self.preprocess_messages(messages)
+
+ params = {
+ "model": self.model_name,
+ "messages": processed_messages,
+ "temperature": temperature,
+ "stream": True,
+ **kwargs
+ }
+
+ if max_tokens is not None:
+ params["max_tokens"] = max_tokens
+
+ # Handle thinking mode for compatible models
+ if 'thinking' in params:
+ thinking_flag = params.pop('thinking')
+ if thinking_flag:
+ params['extra_body'] = {"thinking": {"type": "enabled"}}
+ else:
+ params['extra_body'] = {"thinking": {"type": "disabled"}}
+
+ return await self.async_client.chat.completions.create(**params)
- def _process_chunk(self, chunk: ChatCompletionChunk) -> StreamingChunk[str]:
+ def process_chunk(self, chunk) -> StreamingChunk[str]:
"""Process a streaming chunk from OpenAI."""
- # Extract content from the chunk
- #print(chunk.choices)
if hasattr(chunk.choices[0].delta, 'reasoning_content') and chunk.choices[0].delta.reasoning_content:
content = chunk.choices[0].delta.reasoning_content
is_reasoning = True
else:
content = chunk.choices[0].delta.content or ""
is_reasoning = False
+
is_finished = len(chunk.choices) > 0 and chunk.choices[0].finish_reason is not None
return StreamingChunk(
@@ -114,6 +187,224 @@ def _process_chunk(self, chunk: ChatCompletionChunk) -> StreamingChunk[str]:
is_reasoning=is_reasoning
)
+
+class GeminiProvider(BaseProvider):
+ """Google Gemini provider implementation."""
+
+ def __init__(self, config: LLMModelConfig):
+ super().__init__(config)
+
+ try:
+ import google.generativeai as genai
+ self.genai = genai
+ except ImportError:
+ raise ImportError("google-generativeai package is required for Gemini provider. Install with: pip install google-generativeai")
+
+ # Set API key
+ if self.api_key is None:
+ self.api_key = os.environ.get("GEMINI_API_KEY") or os.environ.get("GOOGLE_API_KEY")
+
+ if self.api_key:
+ genai.configure(api_key=self.api_key)
+
+ # Configure model
+ generation_config = {
+ "temperature": 0.7,
+ "top_p": 0.95,
+ "top_k": 64,
+ "max_output_tokens": 8192,
+ }
+
+ self.model = genai.GenerativeModel(
+ model_name=self.model_name,
+ generation_config=generation_config
+ )
+
+ def preprocess_messages(self, messages: List[Dict[str, str]]) -> List[Dict[str, str]]:
+ """Convert OpenAI format messages to Gemini format."""
+ gemini_messages = []
+
+ for msg in messages:
+ role = msg["role"]
+ content = msg["content"]
+
+ # Map OpenAI roles to Gemini roles
+ if role == "system":
+ # Gemini doesn't have system role, prepend to first user message
+ if gemini_messages and gemini_messages[-1].get("role") == "user":
+ gemini_messages[-1]["parts"] = [f"{content}\n\n{gemini_messages[-1]['parts'][0]}"]
+ else:
+ gemini_messages.append({"role": "user", "parts": [content]})
+ elif role == "user":
+ gemini_messages.append({"role": "user", "parts": [content]})
+ elif role == "assistant":
+ gemini_messages.append({"role": "model", "parts": [content]})
+
+ return gemini_messages
+
+ async def create_stream(self, messages: List[Dict[str, str]], temperature: float,
+ max_tokens: Optional[int], **kwargs) -> Any:
+ processed_messages = self.preprocess_messages(messages)
+
+ # Update generation config
+ generation_config = self.model._generation_config.copy()
+ generation_config["temperature"] = temperature
+ if max_tokens:
+ generation_config["max_output_tokens"] = max_tokens
+
+ # Create chat session
+ chat = self.model.start_chat(history=processed_messages[:-1] if len(processed_messages) > 1 else [])
+
+ # Get the last message content
+ last_message = processed_messages[-1]["parts"][0] if processed_messages else ""
+
+ # Generate streaming response
+ response = await chat.send_message_async(last_message, stream=True)
+ return response
+
+ def process_chunk(self, chunk) -> StreamingChunk[str]:
+ """Process a streaming chunk from Gemini."""
+ content = ""
+ is_finished = False
+
+ if hasattr(chunk, 'text'):
+ content = chunk.text
+
+ # Gemini doesn't provide explicit finish signals in chunks
+ # We'll rely on the async iterator to determine when finished
+
+ return StreamingChunk(
+ content=content,
+ is_finished=is_finished,
+ raw_chunk=chunk,
+ is_reasoning=False
+ )
+
+
+class OllamaProvider(BaseProvider):
+ """Ollama provider implementation."""
+
+ def __init__(self, config: LLMModelConfig):
+ super().__init__(config)
+
+ # Set default base URL for Ollama
+ if self.base_url is None:
+ self.base_url = os.environ.get("OLLAMA_BASE_URL", "http://localhost:11434")
+
+ # Ollama doesn't typically require API keys, but support it if provided
+ if self.api_key is None:
+ self.api_key = os.environ.get("OLLAMA_API_KEY")
+
+ async def create_stream(self, messages: List[Dict[str, str]], temperature: float,
+ max_tokens: Optional[int], **kwargs) -> Any:
+ processed_messages = self.preprocess_messages(messages)
+
+ headers = {"Content-Type": "application/json"}
+ if self.api_key:
+ headers["Authorization"] = f"Bearer {self.api_key}"
+ if self.extra_headers:
+ headers.update(self.extra_headers)
+
+ payload = {
+ "model": self.model_name,
+ "messages": processed_messages,
+ "stream": True,
+ "options": {
+ "temperature": temperature,
+ }
+ }
+
+ if max_tokens:
+ payload["options"]["num_predict"] = max_tokens
+
+ # Add any extra options from config
+ if self.config.extra_config:
+ payload["options"].update(self.config.extra_config)
+
+ # Make HTTP request to Ollama API
+ async with httpx.AsyncClient(proxy=self.proxys) as client:
+ response = await client.post(
+ f"{self.base_url}/api/chat",
+ json=payload,
+ headers=headers,
+ timeout=self.config.timeout
+ )
+ response.raise_for_status()
+ return response.aiter_lines()
+
+ def process_chunk(self, chunk_line: str) -> StreamingChunk[str]:
+ """Process a streaming chunk from Ollama."""
+ import json
+
+ try:
+ chunk_data = json.loads(chunk_line)
+ content = chunk_data.get("message", {}).get("content", "")
+ is_finished = chunk_data.get("done", False)
+
+ return StreamingChunk(
+ content=content,
+ is_finished=is_finished,
+ raw_chunk=chunk_data,
+ is_reasoning=False
+ )
+ except json.JSONDecodeError:
+ # Skip malformed JSON lines
+ return StreamingChunk(
+ content="",
+ is_finished=False,
+ raw_chunk=chunk_line,
+ is_reasoning=False
+ )
+
+
+def create_provider(config: LLMModelConfig) -> BaseProvider:
+ """Factory function to create the appropriate provider based on config."""
+ provider_type = config.provider.lower()
+
+ if provider_type == ProviderType.OPENAI.value:
+ return OpenAIProvider(config)
+ elif provider_type == ProviderType.GEMINI.value:
+ return GeminiProvider(config)
+ elif provider_type == ProviderType.OLLAMA.value:
+ return OllamaProvider(config)
+ else:
+ raise ValueError(f"Unsupported provider: {provider_type}. Supported providers: {[p.value for p in ProviderType]}")
+
+
+class LLMModel(BaseAgentModel):
+ """
+ Multi-provider LLM model implementation.
+
+ This class provides a concrete implementation of the BaseAgentModel that supports
+ multiple LLM providers (OpenAI, Gemini, Ollama).
+ Following the async-first approach, it delegates to the appropriate provider
+ for the actual implementation.
+ """
+
+ def __init__(
+ self,
+ config: LLMModelConfig,
+ **kwargs
+ ):
+ """
+ Initialize the LLM model with the specified provider.
+
+ Args:
+ config: LLMModelConfig instance specifying provider and configuration
+ **kwargs: Additional configuration parameters
+ """
+ super().__init__(config.model_name, **kwargs)
+
+ self.config = config
+ self.model_name = config.model_name
+
+ # Create the appropriate provider
+ self.provider = create_provider(config)
+
+ def preprocess_messages(self, messages: List[Dict[str, str]]) -> List[Dict[str, str]]:
+ """Preprocess messages using the provider's preprocessing."""
+ return self.provider.preprocess_messages(messages)
+
async def a_run_with_semaphore(
self,
messages: List[Dict[str, str]],
@@ -280,12 +571,24 @@ async def a_stream_run(
)
except (
asyncio.TimeoutError,
- openai.APITimeoutError,
- openai.APIConnectionError,
ConnectionError,
TimeoutError
) as e:
- if attempt < max_retries:
+ # Handle provider-specific exceptions
+ should_retry = False
+ if self.config.provider == ProviderType.OPENAI.value:
+ try:
+ import openai
+ if isinstance(e, (openai.APITimeoutError, openai.APIConnectionError)):
+ should_retry = True
+ except ImportError:
+ pass
+
+ # Always retry on these common exceptions
+ if isinstance(e, (asyncio.TimeoutError, ConnectionError, TimeoutError)):
+ should_retry = True
+
+ if should_retry and attempt < max_retries:
print(f"🔄 LLM API调用失败 (尝试 {attempt + 1}/{max_retries + 1}): {type(e).__name__}: {e}")
print(f"⏳ 等待 {retry_delay} 秒后重试...")
await asyncio.sleep(retry_delay)
@@ -314,66 +617,91 @@ async def _internal_a_stream_run(
Returns:
An AsyncResponseStream that yields chunks of the generated content
"""
- processed_messages = self.preprocess_messages(messages)
-
- # Prepare parameters
- params = {
- "model": self.model_name,
- "messages": processed_messages,
- "temperature": temperature,
- "stream": True,
- **kwargs
- }
+ # Get the stream from the provider
+ stream = await self.provider.create_stream(messages, temperature, max_tokens, **kwargs)
- if max_tokens is not None:
- params["max_tokens"] = max_tokens
-
- # Make API call
- if 'thinking' in params:
- thinking_flag = params.pop('thinking')
- if thinking_flag:
- params['extra_body'] = {"thinking": {"type": "enabled"}}
- else:
- params['extra_body'] = {"thinking": {"type": "disabled"}}
-
- stream = await self.async_client.chat.completions.create(**params)
-
- # Create async iterator that processes chunks
+ # Create async iterator that processes chunks using provider's chunk processor
async def chunk_iterator() -> AsyncIterator[StreamingChunk[str]]:
- async for chunk in stream:
- if not chunk.choices:
- continue
- yield self._process_chunk(chunk)
+ if self.config.provider == ProviderType.OLLAMA.value:
+ # Handle Ollama's line-by-line streaming
+ async for line in stream:
+ if line.strip():
+ chunk = self.provider.process_chunk(line)
+ if chunk.content or chunk.is_finished:
+ yield chunk
+ elif self.config.provider == ProviderType.GEMINI.value:
+ # Handle Gemini streaming
+ async for chunk in stream:
+ processed_chunk = self.provider.process_chunk(chunk)
+ if processed_chunk.content:
+ yield processed_chunk
+ else:
+ # Handle OpenAI and OpenAI-compatible streaming
+ async for chunk in stream:
+ if hasattr(chunk, 'choices') and not chunk.choices:
+ continue
+ yield self.provider.process_chunk(chunk)
return AsyncResponseStream(
iterator=chunk_iterator(),
model_name=self.model_name
)
+# Helper function to determine provider from model name or base URL
+def detect_provider(model_name: str, base_url: str = None) -> str:
+ """Auto-detect provider based on model name or base URL."""
+ model_name = model_name.lower()
+
+ # Check for Gemini models
+ if "gemini" in model_name or "google" in model_name:
+ return ProviderType.GEMINI.value
+
+ # Check for Ollama (usually local models or specific patterns)
+ if base_url and ("localhost" in base_url or "127.0.0.1" in base_url or ":11434" in base_url):
+ return ProviderType.OLLAMA.value
+
+ # Check for common Ollama model names
+ ollama_models = ["llama", "mistral", "codellama", "vicuna", "alpaca", "orca", "phi", "neural-chat"]
+ if any(ollama_model in model_name for ollama_model in ollama_models):
+ return ProviderType.OLLAMA.value
+
+ # Default to OpenAI (includes OpenAI-compatible APIs)
+ return ProviderType.OPENAI.value
+
+
+# Create global configurations with auto-detected providers
+llm_provider = cfg.llm.get("provider", detect_provider(cfg.llm["model_name"], cfg.llm.get("base_url")))
GLOBAL_LLM_CONFIG = LLMModelConfig(
+ provider=llm_provider,
model_name=cfg.llm["model_name"],
- api_key=cfg.llm["api_key"],
- base_url=cfg.llm["base_url"]
+ api_key=cfg.llm.get("api_key"),
+ base_url=cfg.llm.get("base_url")
)
GLOBAL_LLM = LLMModel(GLOBAL_LLM_CONFIG)
try:
+ thinking_provider = cfg.llm_thinking.get("provider", detect_provider(cfg.llm_thinking["model_name"], cfg.llm_thinking.get("base_url")))
GLOBAL_THINKING_LLM_CONFIG = LLMModelConfig(
+ provider=thinking_provider,
model_name=cfg.llm_thinking["model_name"],
- api_key=cfg.llm_thinking["api_key"],
- base_url=cfg.llm_thinking["base_url"]
+ api_key=cfg.llm_thinking.get("api_key"),
+ base_url=cfg.llm_thinking.get("base_url")
)
+ assert GLOBAL_THINKING_LLM_CONFIG.api_key is not None
GLOBAL_THINKING_LLM = LLMModel(GLOBAL_THINKING_LLM_CONFIG)
except Exception as e:
print(f"加载thinking模型失败，使用llm模型替代: {e}")
GLOBAL_THINKING_LLM = GLOBAL_LLM
try:
+ vlm_provider = cfg.vlm.get("provider", detect_provider(cfg.vlm["model_name"], cfg.vlm.get("base_url")))
GLOBAL_VLM_CONFIG = LLMModelConfig(
+ provider=vlm_provider,
model_name=cfg.vlm["model_name"],
- api_key=cfg.vlm["api_key"],
- base_url=cfg.vlm["base_url"]
+ api_key=cfg.vlm.get("api_key"),
+ base_url=cfg.vlm.get("base_url")
)
+ assert GLOBAL_VLM_CONFIG.api_key is not None
GLOBAL_VISION_LLM = LLMModel(GLOBAL_VLM_CONFIG)
except Exception as e:
print(f"加载vlm模型失败，vision能力不可用: {e}")
diff --git a/pyproject.toml b/pyproject.toml
index 19e828d..b2f380f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,5 +19,7 @@ dependencies = [
"pandas",
"langgraph",
"rich",
- "tabulate"
-]
\ No newline at end of file
+ "tabulate",
+ "akshare>=1.17.49",
+ "crawl4ai>=0.7.4",
+]
diff --git a/uv.lock b/uv.lock
index a260d6d..caff90d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -7,6 +7,194 @@ resolution-markers = [
"python_full_version < '3.11'",
]
+[[package]]
+name = "aiofiles"
+version = "24.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896 },
+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.12.15"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "aiohappyeyeballs" },
+ { name = "aiosignal" },
+ { name = "async-timeout", marker = "python_full_version < '3.11'" },
+ { name = "attrs" },
+ { name = "frozenlist" },
+ { name = "multidict" },
+ { name = "propcache" },
+ { name = "yarl" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9b/e7/d92a237d8802ca88483906c388f7c201bbe96cd80a165ffd0ac2f6a8d59f/aiohttp-3.12.15.tar.gz", hash = "sha256:4fc61385e9c98d72fcdf47e6dd81833f47b2f77c114c29cd64a361be57a763a2", size = 7823716 }
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---
+
# ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism
-**ContestTrade** is an innovative multi-agent (Multi-Agent) trading framework. With **ContestTrade** you can easily build a dedicated AI trading team: just set the analysis time, and it will autonomously scan the whole market without human intervention, mine potential event-driven investment opportunities from massive data, and, through an internal selection mechanism, construct the most trustworthy investment portfolio for you.
+**ContestTrade** 是一个创新的多智能体（Multi-Agent）交易框架，通过**ContestTrade** 您可以轻松的打造一支专属的AI交易团队。只需设定分析时刻，它能在无人工干预的情况下自主扫描全市场，从海量数据中挖掘潜在事件驱动型的投资机会，并通过内部优选机制，为您构建最值得信赖的投资组合。
+
+## Introduction (项目简介)
-## Introduction
+ContestTrade 是一个面向事件驱动选股的多智能体交易框架。系统目标是：在没有人工干预的条件下，自动发现、评估并跟踪具有投资价值的事件型机会，最终输出可执行的资产配置建议。
-ContestTrade is a multi-agent trading framework focused on event-driven stock selection. The system's goal is to automatically discover, evaluate, and track event-driven opportunities with investment value without human intervention, and finally output executable asset allocation recommendations.
+**核心功能**：
-**Core features:**
-- **Automatic stock selection**: whole-market scanning and signal generation that automatically outputs tradable candidate stock lists without manual screening.
-- **Event-driven**: triggered by catalysts such as news, announcements, capital flows, and policy; focuses on opportunities with significant information impact.
-- **Personalized configuration**: supports user-defined agent research preferences and strategies to flexibly adapt to different investment styles.
+- **自动选股**：全市场扫描与信号生成，自动输出可交易的候选股票列表，无需人工逐一筛选。
+- **事件驱动**：以新闻、公告、资金流、政策等催化事件为触发，关注具有显著信息冲击的机会。
+- **个性化配置**：支持用户自定义智能体的研究偏好和策略，灵活适应不同投资风格。
-## Framework Overview
+## Framework Overview (框架概览)