Add bilingual model card (Chinese default + English) with figures

Model card for Alpha-R1: overview, quick start (transformers/vLLM), output contract, full results (paper Table 1), citation.

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README.md

@@ -1,3 +1,154 @@
----
-license: mit
----
+---
+license: mit
+base_model: Qwen/Qwen3-8B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - finance
+  - quantitative-trading
+  - alpha-factor
+  - reinforcement-learning
+  - grpo
+  - qlib
+---
+
+# Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning
+
+<p align="center">
+  <a href="README_en.md">English</a> | <a href="README.md">中文</a>
+</p>
+
+**Alpha-R1** 是一个面向量化 Alpha 筛选的推理增强型 LLM：基于 Qwen3-8B，通过 GRPO 强化学习（[verl](https://github.com/volcengine/verl)）以市场反馈奖励训练。它阅读 Alpha101 因子的**语义化描述**——每个因子如何起作用、何时有效、何时失效——并针对当前市场环境筛选出最值得激活的因子组合。
+
+- 📄 Paper: [arXiv:2512.23515](https://arxiv.org/abs/2512.23515)
+- 💻 Code: [FinStep-AI/Alpha-R1](https://github.com/FinStep-AI/Alpha-R1)（推理管线 / qlib 回测 / 训练配置）
+- 📜 License: MIT
+
+## 模型概览 (Model Overview)
+
+<p align="center">
+  <img src="assets/framework.png" alt="Alpha-R1 framework overview" style="width: 100%;">
+</p>
+
+| 项目 | 内容 |
+|---|---|
+| Base model | [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) |
+| 训练方法 | GRPO（verl），市场反馈奖励 |
+| 输入 | 决策上下文 prompt：拼接的因子语义描述 `α_des` |
+| 输出 | `<alpha_list>` 中列出的选中因子 |
+| 候选因子池 | 82 个 Alpha101 因子（论文筛选后） |
+| 推荐解码 | temperature=0（greedy），top_p=0.7，max_new_tokens=4096 |
+
+## 快速开始 (Quick Start)
+
+### transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "FinStep/Alpha-R1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")
+
+prompt = "<decision context: concatenated factor descriptions>"  # see the GitHub repo for the prompt builder
+inputs = tokenizer.apply_chat_template(
+    [{"role": "user", "content": prompt}],
+    add_generation_prompt=True, return_tensors="pt",
+).to(model.device)
+
+# paper setting: temperature=0 (greedy), top_p=0.7
+out = model.generate(inputs, max_new_tokens=4096, do_sample=False)
+print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+### vLLM
+
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(model="FinStep/Alpha-R1")
+params = SamplingParams(temperature=0.0, top_p=0.7, max_tokens=4096)
+outputs = llm.chat([[{"role": "user", "content": prompt}]], params)
+```
+
+完整的端到端管线（因子描述生成 → Alpha-R1 推理 → 输出解析 → qlib 策略回测）见 [GitHub 仓库](https://github.com/FinStep-AI/Alpha-R1)。
+
+## 输出契约 (Output Contract)
+
+模型在 `<alpha_list>...</alpha_list>` 中输出选中的因子 id，例如：
+
+```
+<alpha_list>alpha001, alpha021, alpha053</alpha_list>
+```
+
+GitHub 仓库的 `src/alpha_r1/parsing/` 提供了配套的校验与解析脚本。
+
+## 表现 (Performance)
+
+12 个月样本外测试（2025-01-01 ~ 2025-12-31，论文 Table 1）：
+
+<p align="center">
+  <img src="assets/main_result_sp500.png" alt="Backtest NAV comparison on the S&P 500 asset pool" style="width: 49%;">
+  <img src="assets/main_result_csi300.png" alt="Backtest NAV comparison on the CSI 300 asset pool" style="width: 49%;">
+</p>
+
+<table>
+  <thead>
+    <tr>
+      <th rowspan="2">类型</th>
+      <th rowspan="2" width="160">方法</th>
+      <th colspan="3">S&amp;P 500</th>
+      <th colspan="3">CSI 300</th>
+    </tr>
+    <tr>
+      <th>AR (%)</th>
+      <th>SR</th>
+      <th>MDD (%)</th>
+      <th>AR (%)</th>
+      <th>SR</th>
+      <th>MDD (%)</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr><td rowspan="9">Non-LLM</td><td>Buy&nbsp;&amp;&nbsp;Hold</td><td>19.34</td><td>0.80</td><td>18.75</td><td>22.16</td><td>1.31</td><td>10.49</td></tr>
+    <tr><td>PCA</td><td>7.98</td><td>0.27</td><td>17.30</td><td>2.93</td><td>0.17</td><td>14.46</td></tr>
+    <tr><td>XGBoost</td><td>3.49</td><td>0.03</td><td>18.45</td><td>8.99</td><td>0.50</td><td>16.26</td></tr>
+    <tr><td>LightGBM</td><td>-5.42</td><td>-0.43</td><td>20.93</td><td>18.44</td><td>1.05</td><td>14.92</td></tr>
+    <tr><td>A2C</td><td>10.82</td><td>0.40</td><td>17.70</td><td>22.96</td><td>1.20</td><td>14.86</td></tr>
+    <tr><td>PPO</td><td>7.68</td><td>0.25</td><td>14.97</td><td>14.96</td><td>0.81</td><td>12.95</td></tr>
+    <tr><td>DDPG</td><td>2.53</td><td>-0.02</td><td>15.04</td><td>1.97</td><td>0.12</td><td>16.54</td></tr>
+    <tr><td>TD3</td><td>5.54</td><td>0.14</td><td>16.58</td><td>8.66</td><td>0.52</td><td>10.26</td></tr>
+    <tr><td>SAC</td><td>37.60</td><td>1.44</td><td>15.18</td><td>9.77</td><td>0.56</td><td>11.68</td></tr>
+    <tr><td rowspan="5">LLM</td><td>Gemini&nbsp;2.5&nbsp;Pro</td><td>14.23</td><td>0.55</td><td>17.01</td><td>16.29</td><td>0.90</td><td>14.01</td></tr>
+    <tr><td>Claude&nbsp;3.7&nbsp;Sonnet</td><td>10.92</td><td>0.40</td><td>18.88</td><td>10.13</td><td>0.57</td><td>14.49</td></tr>
+    <tr><td>DeepSeek&#8209;R1</td><td>21.94</td><td>0.93</td><td><b>14.36</b></td><td>14.66</td><td>0.81</td><td>14.60</td></tr>
+    <tr><td>Qwen3&#8209;8B</td><td>12.85</td><td>0.47</td><td>19.52</td><td>15.44</td><td>0.79</td><td>14.38</td></tr>
+    <tr><td><b>Alpha&#8209;R1&nbsp;(Ours)</b></td><td><b>47.87</b></td><td><b>1.62</b></td><td>16.91</td><td><b>40.57</b></td><td><b>2.23</b></td><td><b>6.58</b></td></tr>
+  </tbody>
+</table>
+
+域外泛化（无需重训，论文 Table 2）：Russell 2000 上 80.54% AR（SR 2.46），CSI 1000 上 73.52% AR（SR 2.80）。AR = 年化收益，SR = 超额夏普比率，MDD = 最大回撤。
+
+## 训练 (Training)
+
+基于 Qwen3-8B，使用 verl 进行 GRPO 训练，奖励为市场反馈奖励（`R_final = R_adjusted - P_structural`，论文 §3.4）。训练配置与参考奖励实现见 GitHub 仓库的 `training/` 目录。
+
+## 局限性 (Limitations)
+
+- 本模型面向学术研究场景，输出不构成任何投资建议。
+- 因子筛选依赖上游的描述生成与回测管线（见 GitHub 仓库），模型本身不直接产出可交易信号。
+
+## 引用 (Citation)
+
+```bibtex
+@article{jiang2025alphar1,
+  title={Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning},
+  author={Jiang, Zuoyou and Zhao, Li and Sun, Rui and Sun, Ruohan and Li, Zhongjian and Li, Jing and Jiang, Daxin and Bai, Zuo and Hua, Cheng},
+  journal={arXiv preprint arXiv:2512.23515},
+  year={2025}
+}
+```
+
+## License
+
+本项目基于 [MIT License](https://opensource.org/licenses/MIT) 发布。

README_en.md

@@ -0,0 +1,140 @@
+# Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning
+
+<p align="center">
+  <a href="README_en.md">English</a> | <a href="README.md">中文</a>
+</p>
+
+**Alpha-R1** is a reasoning-enhanced LLM for quantitative alpha selection: built on Qwen3-8B and trained with GRPO reinforcement learning ([verl](https://github.com/volcengine/verl)) using a market-feedback reward. It reasons over **semantic factor descriptions** — how each factor works, when it works, and when it fails — and selects the Alpha101 factors that best fit current market conditions.
+
+- 📄 Paper: [arXiv:2512.23515](https://arxiv.org/abs/2512.23515)
+- 💻 Code: [FinStep-AI/Alpha-R1](https://github.com/FinStep-AI/Alpha-R1) (inference pipeline / qlib backtesting / training config)
+- 📜 License: MIT
+
+## Model Overview
+
+<p align="center">
+  <img src="assets/framework.png" alt="Alpha-R1 framework overview" style="width: 100%;">
+</p>
+
+| Item | Content |
+|---|---|
+| Base model | [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) |
+| Training | GRPO (verl) with a market-feedback reward |
+| Input | Decision-context prompt: concatenated semantic factor descriptions `α_des` |
+| Output | Selected factors listed in `<alpha_list>` |
+| Candidate pool | 82 Alpha101 factors (as screened in the paper) |
+| Recommended decoding | temperature=0 (greedy), top_p=0.7, max_new_tokens=4096 |
+
+## Quick Start
+
+### transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "FinStep/Alpha-R1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")
+
+prompt = "<decision context: concatenated factor descriptions>"  # see the GitHub repo for the prompt builder
+inputs = tokenizer.apply_chat_template(
+    [{"role": "user", "content": prompt}],
+    add_generation_prompt=True, return_tensors="pt",
+).to(model.device)
+
+# paper setting: temperature=0 (greedy), top_p=0.7
+out = model.generate(inputs, max_new_tokens=4096, do_sample=False)
+print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+### vLLM
+
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(model="FinStep/Alpha-R1")
+params = SamplingParams(temperature=0.0, top_p=0.7, max_tokens=4096)
+outputs = llm.chat([[{"role": "user", "content": prompt}]], params)
+```
+
+For the full end-to-end pipeline (factor description generation → Alpha-R1 inference → output parsing → qlib strategy backtest), see the [GitHub repository](https://github.com/FinStep-AI/Alpha-R1).
+
+## Output Contract
+
+The model lists the selected factor ids inside `<alpha_list>...</alpha_list>`, e.g.:
+
+```
+<alpha_list>alpha001, alpha021, alpha053</alpha_list>
+```
+
+Validation and parsing scripts are provided under `src/alpha_r1/parsing/` in the GitHub repository.
+
+## Performance
+
+12-month out-of-sample testing (2025-01-01 to 2025-12-31, paper Table 1):
+
+<p align="center">
+  <img src="assets/main_result_sp500.png" alt="Backtest NAV comparison on the S&P 500 asset pool" style="width: 49%;">
+  <img src="assets/main_result_csi300.png" alt="Backtest NAV comparison on the CSI 300 asset pool" style="width: 49%;">
+</p>
+
+<table>
+  <thead>
+    <tr>
+      <th rowspan="2">Type</th>
+      <th rowspan="2" width="160">Method</th>
+      <th colspan="3">S&amp;P 500</th>
+      <th colspan="3">CSI 300</th>
+    </tr>
+    <tr>
+      <th>AR (%)</th>
+      <th>SR</th>
+      <th>MDD (%)</th>
+      <th>AR (%)</th>
+      <th>SR</th>
+      <th>MDD (%)</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr><td rowspan="9">Non-LLM</td><td>Buy&nbsp;&amp;&nbsp;Hold</td><td>19.34</td><td>0.80</td><td>18.75</td><td>22.16</td><td>1.31</td><td>10.49</td></tr>
+    <tr><td>PCA</td><td>7.98</td><td>0.27</td><td>17.30</td><td>2.93</td><td>0.17</td><td>14.46</td></tr>
+    <tr><td>XGBoost</td><td>3.49</td><td>0.03</td><td>18.45</td><td>8.99</td><td>0.50</td><td>16.26</td></tr>
+    <tr><td>LightGBM</td><td>-5.42</td><td>-0.43</td><td>20.93</td><td>18.44</td><td>1.05</td><td>14.92</td></tr>
+    <tr><td>A2C</td><td>10.82</td><td>0.40</td><td>17.70</td><td>22.96</td><td>1.20</td><td>14.86</td></tr>
+    <tr><td>PPO</td><td>7.68</td><td>0.25</td><td>14.97</td><td>14.96</td><td>0.81</td><td>12.95</td></tr>
+    <tr><td>DDPG</td><td>2.53</td><td>-0.02</td><td>15.04</td><td>1.97</td><td>0.12</td><td>16.54</td></tr>
+    <tr><td>TD3</td><td>5.54</td><td>0.14</td><td>16.58</td><td>8.66</td><td>0.52</td><td>10.26</td></tr>
+    <tr><td>SAC</td><td>37.60</td><td>1.44</td><td>15.18</td><td>9.77</td><td>0.56</td><td>11.68</td></tr>
+    <tr><td rowspan="5">LLM</td><td>Gemini&nbsp;2.5&nbsp;Pro</td><td>14.23</td><td>0.55</td><td>17.01</td><td>16.29</td><td>0.90</td><td>14.01</td></tr>
+    <tr><td>Claude&nbsp;3.7&nbsp;Sonnet</td><td>10.92</td><td>0.40</td><td>18.88</td><td>10.13</td><td>0.57</td><td>14.49</td></tr>
+    <tr><td>DeepSeek&#8209;R1</td><td>21.94</td><td>0.93</td><td><b>14.36</b></td><td>14.66</td><td>0.81</td><td>14.60</td></tr>
+    <tr><td>Qwen3&#8209;8B</td><td>12.85</td><td>0.47</td><td>19.52</td><td>15.44</td><td>0.79</td><td>14.38</td></tr>
+    <tr><td><b>Alpha&#8209;R1&nbsp;(Ours)</b></td><td><b>47.87</b></td><td><b>1.62</b></td><td>16.91</td><td><b>40.57</b></td><td><b>2.23</b></td><td><b>6.58</b></td></tr>
+  </tbody>
+</table>
+
+Out-of-domain generalization without retraining (paper Table 2): 80.54% AR (SR 2.46) on Russell 2000 and 73.52% AR (SR 2.80) on CSI 1000. AR = annualized return, SR = excess Sharpe ratio, MDD = max drawdown.
+
+## Training
+
+Alpha-R1 is trained on Qwen3-8B with GRPO using verl and a market-feedback reward (`R_final = R_adjusted - P_structural`, paper Section 3.4). The training configuration and a reference reward implementation live in the `training/` directory of the GitHub repository.
+
+## Limitations
+
+- This model is intended for academic research; its outputs do not constitute investment advice.
+- Factor selection depends on the upstream description-generation and backtesting pipeline (see the GitHub repository); the model alone does not produce tradable signals.
+
+## Citation
+
+```bibtex
+@article{jiang2025alphar1,
+  title={Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning},
+  author={Jiang, Zuoyou and Zhao, Li and Sun, Rui and Sun, Ruohan and Li, Zhongjian and Li, Jing and Jiang, Daxin and Bai, Zuo and Hua, Cheng},
+  journal={arXiv preprint arXiv:2512.23515},
+  year={2025}
+}
+```
+
+## License
+
+This project is released under the [MIT License](https://opensource.org/licenses/MIT).