From 614a688bf840c85dc3764818392648a9d931e80d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=A8=8B=E5=BE=81=E5=BE=81?= Date: Sat, 5 Apr 2025 09:38:45 +0800 Subject: [PATCH 1/2] =?UTF-8?q?docs(qlib=5Fweb=5Fplatform):=20=E6=B7=BB?= =?UTF-8?q?=E5=8A=A0=20UI/UX=20=E4=BC=98=E5=8C=96=E6=96=B9=E6=A1=88?= =?UTF-8?q?=E8=AE=BE=E8=AE=A1=E6=96=87=E6=A1=A3=E5=B9=B6=E5=AE=9E=E7=8E=B0?= =?UTF-8?q?=E6=A8=A1=E5=9E=8B=E5=88=9B=E5=BB=BA=E9=A1=B5=E9=9D=A2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 新增《Qlib Web 平台 UI/UX 优化方案设计文档》,详细阐述了平台的设计目标、核心哲学和模块设计 - 实现了模型创建页面(create_model.html),包括基本信息、训练配置、模型配置和数据配置等部分- 页面采用了模块化设计,支持动态显示不同模型类型的参数输入- 集成了高级配置切换功能,用户可以选择使用常用参数或原始配置 - 页面样式使用了 Tailwind CSS,符合现代 Web 设计标准 --- DESIGN_DOC_UI_UX.md | 33 ++ .../LSTM/workflow_config_lstm_Alpha158.yaml | 12 +- my_work/cursor_rules.md | 23 + my_work/demo.py | 321 ++++++++++++ qlib_web_platform/DESIGN_DOC_UI_UX.md | 133 +++++ qlib_web_platform/create_model.html | 249 +++++++++ qlib_web_platform/create_strategy.html | 276 ++++++++++ qlib_web_platform/dashboard.html | 115 +++++ qlib_web_platform/data_management.html | 203 ++++++++ qlib_web_platform/edit_model.html | 255 ++++++++++ qlib_web_platform/live_trading.html | 213 ++++++++ qlib_web_platform/models.html | 150 ++++++ qlib_web_platform/strategies.html | 477 ++++++++++++++++++ qlib_web_platform/strategy_detail.html | 377 ++++++++++++++ 14 files changed, 2829 insertions(+), 8 deletions(-) create mode 100644 DESIGN_DOC_UI_UX.md create mode 100644 my_work/cursor_rules.md create mode 100644 my_work/demo.py create mode 100644 qlib_web_platform/DESIGN_DOC_UI_UX.md create mode 100644 qlib_web_platform/create_model.html create mode 100644 qlib_web_platform/create_strategy.html create mode 100644 qlib_web_platform/dashboard.html create mode 100644 qlib_web_platform/data_management.html create mode 100644 qlib_web_platform/edit_model.html create mode 100644 qlib_web_platform/live_trading.html create mode 100644 qlib_web_platform/models.html create mode 100644 qlib_web_platform/strategies.html create mode 100644 qlib_web_platform/strategy_detail.html diff --git a/DESIGN_DOC_UI_UX.md b/DESIGN_DOC_UI_UX.md new file mode 100644 index 0000000000..099e4e2240 --- /dev/null +++ b/DESIGN_DOC_UI_UX.md @@ -0,0 +1,33 @@ +### 2.1. 模型管理 (Model Management) + +* **目的**: 创建、训练、管理和评估用于预测的机器学习模型。产出可供策略使用的"已训练模型"。 +* **主要页面**: + * **`models.html` (模型列表页)**: + * **功能**: 展示所有已创建的模型及其状态和关键信息。提供新建模型入口。 + * **表格列**: + * 名称 + * 类型 (LightGBM, LSTM, ...) + * **训练周期** (例如: "2018-01-01 - 2022-12-31") + * **训练股票池** (例如: "CSI 300") + * **特征集** (例如: "Alpha158", 使用 `truncate` 显示，`title` 显示完整内容) + * **标签** (例如: "Ref($close,-2)/Ref($close,-1)-1", 使用 `truncate` 显示，`title` 显示完整内容) + * 状态 (草稿, 训练中..., 已训练, 失败) + * 创建时间 + * 操作 (详情/编辑, 训练/重新训练, 删除, 取消训练等，根据状态显示不同按钮) + * **`create_model.html` (新建模型页)**: + * **功能**: 配置并启动一个新的模型训练任务。 + * **卡片/区域**: + * **基本信息**: 模型名称 (必填), 模型描述。 + * **训练配置 (Training Configuration - 新增)**: + * 训练开始日期 (必填, `date`) + * 训练结束日期 (必填, `date`) + * 训练股票池 (必填, `select`, 包含 CSI 300, CSI 500, 全市场A股等) + * 所用特征集 (必填, `text`, placeholder: "例如: Alpha158, VWAP") + * 标签/预测目标 (必填, `text`, placeholder: "例如: Ref($close,-2)/Ref($close,-1)-1") + * **模型配置 (Model Configuration)**: + * 模型类型 (必填, `select`, LightGBM, LSTM, ...) + * 模型参数 (根据类型动态显示常用参数输入，或提供高级 YAML/JSON 配置入口) + * **数据配置 (Data Configuration - 已精简)**: *此部分原始目的是映射 Qlib DataHandler 配置。为避免与"训练配置"重复，已移除标的范围(Universe)、特征、标签、训练期、验证期等字段。当前仅保留"数据源"和"数据频率"字段，用于定义基础数据访问信息。其最终必要性待后续审视。* + * **操作按钮**: 取消, 保存草稿, **保存并开始训练**。 + * **`edit_model.html` (编辑/详情页)**: + * **功能**: 查看模型详细配置、训练状态、训练日志（未来）、评估结果（未来），并可编辑配置（对于草稿状态）或触发重新训练。 \ No newline at end of file diff --git a/examples/benchmarks/LSTM/workflow_config_lstm_Alpha158.yaml b/examples/benchmarks/LSTM/workflow_config_lstm_Alpha158.yaml index 522f6443cb..c967da66b5 100755 --- a/examples/benchmarks/LSTM/workflow_config_lstm_Alpha158.yaml +++ b/examples/benchmarks/LSTM/workflow_config_lstm_Alpha158.yaml @@ -17,18 +17,14 @@ data_handler_config: &data_handler_config "ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5", "RSQR20", "CORD60", "CORD10", "CORR20", "KLOW" ] - - class: RobustZScoreNorm + - class: ZScoreNorm kwargs: fields_group: feature - clip_outlier: true - class: Fillna kwargs: fields_group: feature learn_processors: - class: DropnaLabel - - class: CSRankNorm - kwargs: - fields_group: label label: ["Ref($close, -2) / Ref($close, -1) - 1"] port_analysis_config: &port_analysis_config @@ -59,13 +55,13 @@ task: hidden_size: 64 num_layers: 2 dropout: 0.0 - n_epochs: 200 - lr: 1e-3 + n_epochs: 20 + lr: 1e-5 early_stop: 10 batch_size: 800 metric: loss loss: mse - n_jobs: 20 + n_jobs: 8 GPU: 0 dataset: class: TSDatasetH diff --git a/my_work/cursor_rules.md b/my_work/cursor_rules.md new file mode 100644 index 0000000000..4370d450bc --- /dev/null +++ b/my_work/cursor_rules.md @@ -0,0 +1,23 @@ +# Cursor AI Assistant Rules & Context + +## Project Goal +Learn the Qlib framework for quantitative trading by studying its source code. + +## Qlib Source Location +The main Qlib source code is located at the workspace root: `/Users/chengzheng/workspace/trade/qlib` + +## User Workspace +My personal learning scripts and experiments related to Qlib will be created and stored within the `/Users/chengzheng/workspace/trade/qlib/my_work/` directory. Please place any new scripts or generated files here unless otherwise specified. + +## Learning Objectives +- Master the application of all algorithms within Qlib for quantitative trading, including strategy design and backtesting. +- Select and implement specific AI algorithms (learned through Qlib) to build custom trading strategies. +- Gain knowledge in Deep Learning (DL) and Reinforcement Learning (RL) concepts, particularly their application in quantitative finance, potentially using Qlib examples. +- Understand the overall architecture and design principles of the Qlib framework, referencing official documentation (e.g., [Qlib Quick Start](https://www.wuzao.com/document/qlib/introduction/quick.html)) and source code, to enable effective modular usage and customization. + +## Interaction Style +- Assume I have access to the Qlib source code in the specified location. +- When discussing code, refer to file paths relative to the Qlib root (`/Users/chengzheng/workspace/trade/qlib`). +- When creating new code or examples for me, place them in the `my_work/` directory. +- Follow the RIPER-5 protocol for structured interaction. +- Prioritize Chinese for general conversation, while adhering to the protocol's format requirements for specific elements (e.g., mode declarations, checklists). \ No newline at end of file diff --git a/my_work/demo.py b/my_work/demo.py new file mode 100644 index 0000000000..925e41707e --- /dev/null +++ b/my_work/demo.py @@ -0,0 +1,321 @@ +import json +import uuid + +import mysql.connector +from typing import List, Dict +from urllib.parse import quote + + +def get_mysql_connection(): + """获取MySQL连接""" + try: + connection = mysql.connector.connect( + host='gaea.test.mysql03.b2c.srv', # 数据库主机地址 + port=13616, + user='micar_site_sv22_wn', # 数据库用户名 + password='e3vpM8dEtctXFMgKqt-eneLzK_iOoyY9', # 数据库密码 + database='micar_site_staging' # 数据库名称 + ) + return connection + except mysql.connector.Error as e: + print(f"连接MySQL失败: {e}") + return None + + +connection = get_mysql_connection() + + +def execute_query(query: str) -> List[Dict]: + """执行SQL查询并返回结果列表""" + results = [] + # connection = get_mysql_connection() + + if connection: + try: + cursor = connection.cursor(dictionary=True) + cursor.execute(query) + results = cursor.fetchall() + cursor.close() + except mysql.connector.Error as e: + print(f"执行查询失败: {e}") + # finally: + # connection.close() + + return results + + +def url_encode(url: str) -> str: + """对URL进行编码""" + return 'https://www.xiaomiev.com/communityWeb?_rt=ft&url=' + quote(url) + + +def get_module_list(page_id: str) -> List[str]: + """获取指定页面的模块列表""" + query = f"select module_id from site_page_module_tmp where page_id='{page_id}' order by priority" + result_list = execute_query(query) + return [row['module_id'] for row in result_list] + + +def get_module_items(page_id: str, module_id: str) -> List[Dict]: + """获取指定页面和模块的内容项列表""" + query = f"select * from site_page_module_item_tmp where page_id='{page_id}' and module_id='{module_id}' and parent_key='' order by priority" + result_list = execute_query(query) + return result_list + + +def get_module_items_by_parent(page_id: str, module_id: str, parent_key: str) -> List[Dict]: + """获取指定页面和模块的内容项列表""" + query = f"select * from site_page_module_item_tmp where page_id='{page_id}' and module_id='{module_id}' and parent_key='{parent_key}' order by priority" + result_list = execute_query(query) + return result_list + + +def parse_img(item: Dict) -> Dict: + """解析图片类型的内容项""" + img = { + "src": item['src'], + "height": item['height'], + "width": item['width'] + } + img_fold = { + "src": item['src_fold'], + "height": item['height'], + "width": item['width'] + } + action = None + if item['action_type'] == 'link': + link_url = item['link_url'] + if item['link_type'] == 0: + link_url = url_encode(link_url) + + action = { + "type": item['action_type'], + "linkUrl": link_url + } + if item['action_text'] != '': + action['text'] = item['action_text'] + + res = { + "type": 'image', + "image": img, + "imageFold": img_fold, + "action": action + } + + if item['title'] != '': + res['title'] = item['title'] + if item['subtitle'] != '': + res['subTitle'] = item['subtitle'] + + uuid1 = str(uuid.uuid4()).replace('-', '') + res['_track'] = { + 'itemId': uuid1, + 'itemName': item['title'] + } + + return res + + +def parse_video(item: Dict) -> Dict: + video = { + "src": item['src'], + 'cover': item['cover'], + "height": int(item['height']), + "width": int(item['width']), + 'duration': int(item['duration']) + } + video_fold = { + "src": item['src_fold'], + 'cover': item['cover_fold'], + "height": int(item['height']), + "width": int(item['width']), + 'duration': int(item['duration']) + } + action = None + if item['action_type'] == 'link': + + link_url = item['link_url'] + if item['link_type'] == 0: + link_url = url_encode(link_url) + '&transition_background_color=%23000000' + + action = { + "type": item['action_type'], + "linkUrl": link_url + } + + res = { + "type": 'video', + "video": video, + "videoFold": video_fold, + "image": video, + "imageFold": video_fold, + "action": action + } + + if item['title'] != '': + res['title'] = item['title'] + if item['subtitle'] != '': + res['subTitle'] = item['subtitle'] + + uuid1 = str(uuid.uuid4()).replace('-', '') + res['_track'] = { + 'itemId': uuid1, + 'itemName': item['title'] + } + + return res + + +def parse_tab_item(page_id, module_id, item: Dict) -> Dict: + tab_item = { + "tabId": item['key'], + "text": item['text'], + "selected": True if item['selected'] == 1 else False + } + if item['key'] == '': + return tab_item + items = get_module_items_by_parent(page_id, module_id, item['key']) + if len(items) > 0: + tab_item_content = [] + for sub_item in items: + item = {} + sub_type = sub_item['type'] + if sub_type == 'image': + item = parse_img(sub_item) + elif sub_type == 'video': + item = parse_video(sub_item) + tab_item_content.append(item) + + if len(tab_item_content) > 0: + tab_item['content'] = { + 'data': tab_item_content + } + + return tab_item + + +def parse_module_config(page_id, module: Dict) -> Dict: + component = module['component'] + module_id = module['module_id'] + items = get_module_items(page_id, module_id) + data_list = [] + # 遍历并打印每个模块项 + for item in items: + item_type = item['type'] + obj = {} + if item_type == 'image': + obj = parse_img(item) + data_list.append(obj) + elif item_type == 'video': + obj = parse_video(item) + data_list.append(obj) + elif item_type == 'tabItem': + obj = parse_tab_item(page_id, module_id, item) + data_list.append(obj) + + extra = None + if module['extra'] is not None: + extra = json.loads(module['extra']) + + if component == 'Tabs': + return { + "items": data_list + } + + if component == 'Banner': + return { + "data": data_list + } + + if component == 'Navigation': + return { + 'title': module['title'], + 'columns': int(module['columns']), + "data": data_list, + 'extra': extra + } + + if component == 'News': + return { + 'title': module['title'], + "data": data_list, + 'extra': extra + } + + if component == 'TabNews': + return { + 'title': module['title'], + "items": data_list + } + + if component == 'ActivityList': + return { + 'title': module['title'], + "more": extra + } + + if component == 'TabFeeds': + return { + 'title': module['title'], + "items": data_list + } + + return {} + + +def get_page_config(page_id, page_title): + modules = execute_query(f'select * from site_page_module_tmp where page_id = \'{page_id}\' order by priority ') + module_config_list = [] + module_ids = [] + for module in modules: + config = parse_module_config(page_id, module) + + track = { + 'sectionId': str(uuid.uuid4()).replace('-', ''), + 'sectionName': module['module_name'] + } + config['_track'] = track + + print('---------------------------') + print(module['module_id']) + print(json.dumps(config, ensure_ascii=False)) + print('---------------------------') + + module_ids.append(module['module_id']) + m = { + 'id': module['module_id'], + 'name': module['module_name'], + 'component': module['component'], + 'config': json.dumps(config, ensure_ascii=False) + } + + if module['data_provider'] != '': + m['dataProvider'] = module['data_provider'] + + module_config_list.append(m) + + result = { + 'title': page_title, + 'components': module_config_list, + "groupId": "1", + "groupConfig": [ + { + "list": module_ids, + "name": "全部用户", + "strategy": "all", + "default": True + } + ] + } + + return result + + +# 使用示例 +if __name__ == "__main__": + explore_page_config = get_page_config('explore', '探索页面') + activity_page_config = get_page_config('activity', '活动页面') + + print('##################') + print(json.dumps(explore_page_config, ensure_ascii=False)) + print(json.dumps(activity_page_config, ensure_ascii=False)) diff --git a/qlib_web_platform/DESIGN_DOC_UI_UX.md b/qlib_web_platform/DESIGN_DOC_UI_UX.md new file mode 100644 index 0000000000..e61e10c676 --- /dev/null +++ b/qlib_web_platform/DESIGN_DOC_UI_UX.md @@ -0,0 +1,133 @@ +# Qlib Web 平台 UI/UX 优化方案设计文档 + +**(基于方案二：模块化与上下文关联)** + +## 1. 目标与哲学 + +* **目标**: 构建一个直观、易用且功能强大的 Web UI，用于管理 Qlib 量化策略的整个生命周期，从模型训练到策略配置、回测和实盘部署。 +* **核心哲学 (方案二)**: + * **模块化**: 明确区分**模型管理** (负责训练和评估预测模型) 和**策略管理** (负责定义如何使用模型信号进行交易决策)。 + * **上下文关联**: 在 UI 的各个环节清晰地展示和利用关键上下文信息（如模型的训练周期、股票池、特征等），帮助用户做出明智决策。 + * **智能引导**: 提供合理的默认值，减少用户输入负担，并引导用户遵循最佳实践。 + * **风险提示**: 在可能导致结果不可靠或策略失效的操作前（如训练/执行宇宙不匹配），提供明确警告。 + +## 2. 模块设计详解 + +### 2.1. 模型管理 (Model Management) + +* **目的**: 创建、训练、管理和评估用于预测的机器学习模型。产出可供策略使用的“已训练模型”。 +* **主要页面**: + * **`models.html` (模型列表页)**: + * **功能**: 展示所有已创建的模型及其状态和关键信息。提供新建模型入口。 + * **表格列**: + * 名称 + * 类型 (LightGBM, LSTM, ...) + * **训练周期** (例如: "2018-01-01 - 2022-12-31") + * **训练股票池** (例如: "CSI 300") + * **特征集** (例如: "Alpha158", 使用 `truncate` 显示，`title` 显示完整内容) + * **标签** (例如: "Ref($close,-2)/Ref($close,-1)-1", 使用 `truncate` 显示，`title` 显示完整内容) + * 状态 (草稿, 训练中..., 已训练, 失败) + * 创建时间 + * 操作 (详情/编辑, 训练/重新训练, 删除, 取消训练等，根据状态显示不同按钮) + * **`create_model.html` (新建模型页)**: + * **功能**: 配置并启动一个新的模型训练任务。 + * **卡片/区域**: + * **基本信息**: 模型名称 (必填), 模型描述。 + * **训练配置 (Training Configuration - 新增)**: + * 训练开始日期 (必填, `date`) + * 训练结束日期 (必填, `date`) + * 训练股票池 (必填, `select`, 包含 CSI 300, CSI 500, 全市场A股等) + * 所用特征集 (必填, `text`, placeholder: "例如: Alpha158, VWAP") + * 标签/预测目标 (必填, `text`, placeholder: "例如: Ref($close,-2)/Ref($close,-1)-1") + * **模型配置 (Model Configuration)**: + * 模型类型 (必填, `select`, LightGBM, LSTM, ...)\ + * 模型参数 (根据类型动态显示常用参数输入，或提供高级 YAML/JSON 配置入口) + * **数据配置 (Data Configuration - *待审视*)**: *此部分与“训练配置”有重叠，可能需要合并或移除。目前 `create_model.html` 中存在这部分，包含了数据源、频率、标的(Universe)、特征(重复)、标签(重复)、训练/验证期(重复)。应精简，将核心的训练上下文放在“训练配置”中。* + * **操作按钮**: 取消, 保存草稿, **保存并开始训练**。 + * **`edit_model.html` (编辑/详情页)**: + * **功能**: 查看模型详细配置、训练状态、训练日志（未来）、评估结果（未来），并可编辑配置（对于草稿状态）或触发重新训练。 + * **内容**: 应包含 `create_model.html` 中的所有配置项（根据状态可能只读），并增加显示训练日志、状态详情、模型评估指标等区域。 +* **关键记录数据 (模型元数据)**: 对于每个模型，后端需记录：名称、描述、模型类型、模型参数、**训练开始/结束日期**、**训练股票池**、**特征集**、**标签配置**、训练状态、创建/更新时间、指向训练好的模型文件的指针、训练日志路径、评估结果等。 + +### 2.2. 策略管理 (Strategy Management) + +* **目的**: 定义如何利用一个“已训练模型”的信号来构建和执行交易策略。 +* **主要页面**: + * **`strategies.html` (策略列表页)**: + * **功能**: 展示所有已创建的策略配置及其运行状态（如果有）。提供新建策略入口和执行操作。 + * **表格列**: + * 名称 + * 状态 (草稿, 回测中, 实盘运行中, 已停止等) + * 关联模型 (例如: "LSTM Alpha158") + * 关键回测指标 (例如: 年化收益, 最大回撤 - *来自最近一次成功回测*) + * 创建/更新日期 + * 操作 (详情, **运行回测 (按钮)**, **提交实盘 (按钮)**, 编辑 (草稿状态), 停止等) + * **`create_strategy.html` (新建策略页)**: + * **功能**: 创建一个策略配置实例，核心是“选择一个已训练模型”并“定义如何使用其信号”。 + * **卡片/区域**: + * **基本信息**: 策略名称 (必填), 策略描述。 + * **选择模型**: + * 下拉框 (`select`, 必填) **只显示状态为“已训练”的模型** (需后端配合)。 + * 下方区域 (`#selected_model_info`) **显著展示选中模型的关键训练上下文**: 训练周期, 训练股票池, 特征集, 标签。 + * **组合策略配置 (Portfolio Strategy Configuration)**: + * 组合策略类型 (必填, `select`, TopkDropoutStrategy, WeightStrategy, ...) + * 策略参数 (根据类型动态显示常用参数输入，或提供高级 YAML/JSON 配置入口) + * 交易成本 (手续费率, 滑点)。 + * **操作按钮**: 取消, 保存草稿。 (*不再包含“运行回测”按钮*) + +### 2.3. 执行与结果 (Execution & Results) + +* **目的**: 基于一个已定义的策略配置，设置具体的执行参数（时间、范围、资金）来运行回测或提交实盘，并查看结果。 +* **主要组件/页面**: + * **运行回测模态框 (在 `strategies.html` 触发)**: + * **触发**: 点击策略列表行的“运行回测”按钮。 + * **参数**: + * 回测开始日期 (必填, `date`) - **默认值**: 根据策略关联模型的训练结束日期自动计算下一天。 + * 回测结束日期 (必填, `date`) - 默认值: 空。 + * 初始资金 (必填, `number`) - 默认值: 例如 1,000,000。 + * 基准 (可选, `text`)。 + * **股票池/Universe** (必填, `select`) - **默认值**: 根据策略关联模型的训练股票池自动选中。 + * **警告**: 如果用户修改了股票池，导致与模型训练股票池不一致，**点击“开始回测”时弹出 `confirm` 警告**。 + * **操作**: 开始回测 (触发后端任务), 取消。 + * **提交实盘模态框 (在 `strategies.html` 触发)**: + * **触发**: 点击策略列表行的“提交实盘”按钮。 + * **参数**: + * 选择账户 (必填, `select`)。\ + * 分配类型 (固定金额/账户百分比, `radio`)。\ + * 金额/百分比 (必填, `number`)。\ + * **股票池/Universe** (必填, `select`) - **默认值**: 根据策略关联模型的训练股票池自动选中。 + * **警告**: 如果用户修改了股票池，导致与模型训练股票池不一致，**点击“提交至实盘”时弹出 `confirm` 警告**。 + * **操作**: 提交至实盘 (触发后端任务), 取消。 + * **`strategy_detail.html` (策略详情页)**: + * **功能**: 集中展示一个策略配置的详细信息以及其下所有相关的执行（回测/实盘）记录和结果。 + * **内容区域**: + * **策略配置回顾**: + * 策略名称、描述。 + * **关联模型**: 显示模型名称，并提供链接到模型详情/编辑页 (`edit_model.html`)。 + * **组合策略**: 显示类型和参数。 + * **交易成本**: 显示费率和滑点。 + * **执行记录列表 (关键部分)**: + * 以表格或卡片列表形式展示**每一次**基于此策略配置运行的回测或实盘实例。 + * 每条记录包含： + * 执行类型 (回测/实盘)。 + * **执行参数**: 开始日期, 结束日期, 初始资金, 基准, **股票池/Universe**。 + * 执行状态 (运行中, 已完成, 失败)。 + * **关键结果指标** (年化收益, 最大回撤, Sharpe 等)。 + * 操作 (查看详细报告/日志, 停止实盘等)。 + * 提供触发“运行新回测”（类似列表页弹窗）或“提交新实盘”的入口。 + +## 3. 关键 UX 原则总结 + +* **上下文感知**: 用户在选择模型、配置策略、执行回测/实盘时，都能看到相关的训练背景信息。 +* **智能默认**: 通过预填股票池和回测开始日期，简化用户操作，并鼓励一致性。 +* **风险缓解**: 对潜在的配置不匹配（尤其是 Universe 不匹配）进行明确警告，让用户确认风险。 +* **职责分离**: UI 结构清晰反映模型训练、策略定义、执行配置这三个不同阶段。 +* **记录与追溯**: 策略详情页汇集所有执行记录，方便比较不同参数下的回测结果或追踪实盘表现。 + +## 4. 后续步骤 (概要) + +1. **后端 API 设计**: 为前端提供数据（模型/策略列表及详情）、接收配置、触发后台任务（训练/回测/实盘）、查询任务状态和结果。 +2. **模型元数据存储**: 设计数据库表或文件结构来存储模型的完整配置和训练上下文。 +3. **动态数据加载**: 将前端的硬编码数据（模型列表、上下文、下拉选项）替换为通过 API 从后端获取。 +4. **后台任务管理**: 实现可靠的后台任务队列来处理耗时的训练和回测。 +5. **回测结果展示**: 设计 `strategy_detail.html` 中展示详细回测报告的组件（例如，使用图表库绘制收益曲线）。 \ No newline at end of file diff --git a/qlib_web_platform/create_model.html b/qlib_web_platform/create_model.html new file mode 100644 index 0000000000..478a7ce634 --- /dev/null +++ b/qlib_web_platform/create_model.html @@ -0,0 +1,249 @@ + + + + + + Qlib Web 平台 - 新建模型 + + + + + + +
+ +
+ +
+ Qlib 平台 +
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+ + + 设置 + + + + 登出 + +
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新建模型

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基本信息

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训练配置

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模型配置

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根据所选模型类型，将在此处显示常用参数输入框。

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数据配置

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+ + + + \ No newline at end of file diff --git a/qlib_web_platform/create_strategy.html b/qlib_web_platform/create_strategy.html new file mode 100644 index 0000000000..5df90ae7a6 --- /dev/null +++ b/qlib_web_platform/create_strategy.html @@ -0,0 +1,276 @@ + + + + + + Qlib Web 平台 - 新建策略 + + + + + + +
+ +
+ +
+ Qlib 平台 +
+ + + +
+ + + 设置 + + + + 登出 + +
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新建策略

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基本信息

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选择模型

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选择模型后将在此处显示其训练上下文信息。

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组合策略配置

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定义如何根据模型预测信号来构建投资组合、管理仓位和执行交易。

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根据所选策略类型，将在此处显示常用参数输入框。

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+ + + + \ No newline at end of file diff --git a/qlib_web_platform/dashboard.html b/qlib_web_platform/dashboard.html new file mode 100644 index 0000000000..de8bd35043 --- /dev/null +++ b/qlib_web_platform/dashboard.html @@ -0,0 +1,115 @@ + + + + + + Qlib Web 平台 - 仪表盘 + + + + + + + + +
+ +
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+ Qlib 平台 +
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仪表盘

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+ 欢迎, 用户! + +
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我的策略

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您有 5 个运行中策略。

+ 查看策略 +
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+

近期回测

+

今日完成 2 次回测。

+ 查看回测记录 +
+
+

实盘交易状态

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IB 连接: 已连接

+ 查看实盘状态 +
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整体表现 (占位符)

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+ 图表区域 +
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+ + \ No newline at end of file diff --git a/qlib_web_platform/data_management.html b/qlib_web_platform/data_management.html new file mode 100644 index 0000000000..5360d76b06 --- /dev/null +++ b/qlib_web_platform/data_management.html @@ -0,0 +1,203 @@ + + + + + + Qlib Web 平台 - 数据管理 + + + + + + +
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数据管理

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内置数据包

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中国 A 股日线数据 (qlib_data)

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来源: 爬虫/公开数据

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覆盖范围: 2007-至今

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状态: 可用

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Alpha158 因子

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来源: 基于日线数据计算

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依赖: 中国 A 股日线数据

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状态: 可用

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自定义数据包

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点击此处选择文件或拖放文件到此区域

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仅支持 CSV 文件

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已导入的数据包

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包名称源文件名导入日期大小操作
港股通日线hk_daily_data_2024.csv2024-07-20150 MB + 查看 + 删除 +
美股科技股us_tech_stocks.csv2024-07-1585 MB + 查看 + 删除 +
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编辑模型

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数据配置

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使用 Qlib 识别的名称或 Python 列表格式。

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每行输入一个特征表达式。

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输入标签表达式。

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+ + + + \ No newline at end of file diff --git a/qlib_web_platform/live_trading.html b/qlib_web_platform/live_trading.html new file mode 100644 index 0000000000..7760a4e981 --- /dev/null +++ b/qlib_web_platform/live_trading.html @@ -0,0 +1,213 @@ + + + + + + Qlib Web 平台 - 实盘交易 (IB) + + + + + + +
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实盘交易 (IB)

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IB 连接状态

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状态: 已连接

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账户ID: U1234567

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服务器时间: 2024-07-21 10:35:00 CST

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账户概要

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净清算价值

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$105,234.50

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总权益含贷款

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$105,234.50

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可用资金

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$80,112.00

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当日盈亏

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+$350.75

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运行中的策略

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策略名称状态开始时间分配资金/比例当日盈亏累计盈亏操作
LightGBM Alpha158 TopK运行中2024-07-18 09:30:00$10,000+$85.50+$210.20 + 停止 + 日志 +
LSTM Momentum已停止2024-07-15 10:00:005%$0.00-$55.00 + 重启 + 日志 +
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近期订单

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时间戳标的方向数量类型价格状态
2024-07-21 10:30:05SH600036买入100市价35.50已成交
2024-07-21 09:45:10SZ000001卖出200限价15.20已提交
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模型管理

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模型列表

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名称类型训练周期训练股票池特征集标签状态创建时间操作
A股日线预测模型v1LightGBM2018-01-01 - 2022-12-31CSI 300Alpha158Ref($close, -2) / Ref($close, -1) - 1已训练2024-07-20 10:30:00 + 详情/编辑 + 重新训练 + 删除 +
分钟择时模型-LSTMLSTM2020-01-01 - 2023-06-30CSI 500Vwap, Amount, High-LowRef($close, -60)/Ref($close, -1)-1训练中...2024-07-19 15:00:00 + 查看配置 + 训练 + 取消训练 +
新因子测试模型XGBoost---A-ShareMyNewFactor, VolumeReturn(lag=5)草稿2024-07-18 09:00:00 + 编辑 + 训练 + 删除 +
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名称状态模型年化收益最大回撤开始日期操作
LSTM Alpha158 TopK回测中LSTM-1.83%-15.76%2024-07-19 + 详情 + + +
LightGBM Alpha158 TopK实盘运行中LightGBM8.07%-8.61%2024-07-18 + 详情 + + +
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+ ← 返回策略列表 +

策略: LSTM Alpha158 TopK

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策略配置详情

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策略名称:

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LSTM Alpha158 TopK

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策略描述:

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此策略基于 LSTM 模型预测的 Alpha158 因子信号，采用 TopK 排序选股。

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关联模型:

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A股日线预测模型v1 (LightGBM)

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训练于: CSI 300 (2018-01-01 - 2022-12-31)

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组合策略类型:

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TopkDropoutStrategy

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组合策略参数:

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topk: 50, n_drop: 5

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交易成本:

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手续费: 0.03%, 滑点: 0.001

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运行时间回测周期股票池初始资金基准状态年化最大回撤Sharpe操作
2024-07-22 10:15:302023-01-01 - 2023-12-31CSI 3001,000,000SH000300已完成15.2%-12.5%1.20报告
2024-07-21 16:05:002021-01-01 - 2022-12-31CSI 3001,000,000SH000300已完成9.8%-9.1%0.95报告
2024-07-23 09:30:102023-01-01 - 2024-06-30CSI 500 500,000SH000905运行中...---------报告
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+ + + + \ No newline at end of file From fb5d5ed4992281af3c0b9e7baf108c5c88c1cb47 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=A8=8B=E5=BE=81=E5=BE=81?= Date: Tue, 8 Apr 2025 16:46:21 +0800 Subject: [PATCH 2/2] =?UTF-8?q?refactor(qlib=5Fweb=5Fplatform):=20?= =?UTF-8?q?=E9=87=8D=E6=9E=84=E6=A8=A1=E5=9E=8B=E5=88=9B=E5=BB=BA=E5=92=8C?= =?UTF-8?q?=E7=AD=96=E7=95=A5=E5=88=9B=E5=BB=BA=E9=A1=B5=E9=9D=A2=E5=B8=83?= =?UTF-8?q?=E5=B1=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 合并训练配置、模型配置和数据配置，简化为模型定义与训练上下文- 新增回测配置区域，支持在策略创建页面设置回测参数 - 优化模型和策略创建页面的布局结构，提高用户体验 - 删除冗余的训练配置，避免与数据配置重复 - 增加模型关键训练上下文的展示，便于用户选择和理解模型 --- my_work/demo.py | 321 ------ ...04\347\220\206\350\257\246\350\247\243.md" | 932 ++++++++++++++++++ ...4\347\220\206\345\216\237\347\220\206.png" | Bin 0 -> 219012 bytes ...4\345\216\237\347\220\206\345\233\276.png" | Bin 0 -> 200145 bytes qlib/contrib/data/handler.py | 36 +- qlib/contrib/data/loader.py | 24 +- qlib_web_platform/DESIGN_DOC_UI_UX.md | 42 +- qlib_web_platform/create_model.html | 193 ++-- qlib_web_platform/create_strategy.html | 55 +- qlib_web_platform/edit_model.html | 211 ++-- 10 files changed, 1257 insertions(+), 557 deletions(-) delete mode 100644 my_work/demo.py create mode 100644 "my_work/doc/QLib\346\225\260\346\215\256\345\212\240\350\275\275\344\270\216\347\211\271\345\276\201\345\244\204\347\220\206\350\257\246\350\247\243.md" create mode 100644 "my_work/doc/\346\225\260\346\215\256\345\244\204\347\220\206\345\216\237\347\220\206.png" create mode 100644 "my_work/doc/\350\277\220\350\241\214\345\216\237\347\220\206\345\233\276.png" diff --git a/my_work/demo.py b/my_work/demo.py deleted file mode 100644 index 925e41707e..0000000000 --- a/my_work/demo.py +++ /dev/null @@ -1,321 +0,0 @@ -import json -import uuid - -import mysql.connector -from typing import List, Dict -from urllib.parse import quote - - -def get_mysql_connection(): - """获取MySQL连接""" - try: - connection = mysql.connector.connect( - host='gaea.test.mysql03.b2c.srv', # 数据库主机地址 - port=13616, - user='micar_site_sv22_wn', # 数据库用户名 - password='e3vpM8dEtctXFMgKqt-eneLzK_iOoyY9', # 数据库密码 - database='micar_site_staging' # 数据库名称 - ) - return connection - except mysql.connector.Error as e: - print(f"连接MySQL失败: {e}") - return None - - -connection = get_mysql_connection() - - -def execute_query(query: str) -> List[Dict]: - """执行SQL查询并返回结果列表""" - results = [] - # connection = get_mysql_connection() - - if connection: - try: - cursor = connection.cursor(dictionary=True) - cursor.execute(query) - results = cursor.fetchall() - cursor.close() - except mysql.connector.Error as e: - print(f"执行查询失败: {e}") - # finally: - # connection.close() - - return results - - -def url_encode(url: str) -> str: - """对URL进行编码""" - return 'https://www.xiaomiev.com/communityWeb?_rt=ft&url=' + quote(url) - - -def get_module_list(page_id: str) -> List[str]: - """获取指定页面的模块列表""" - query = f"select module_id from site_page_module_tmp where page_id='{page_id}' order by priority" - result_list = execute_query(query) - return [row['module_id'] for row in result_list] - - -def get_module_items(page_id: str, module_id: str) -> List[Dict]: - """获取指定页面和模块的内容项列表""" - query = f"select * from site_page_module_item_tmp where page_id='{page_id}' and module_id='{module_id}' and parent_key='' order by priority" - result_list = execute_query(query) - return result_list - - -def get_module_items_by_parent(page_id: str, module_id: str, parent_key: str) -> List[Dict]: - """获取指定页面和模块的内容项列表""" - query = f"select * from site_page_module_item_tmp where page_id='{page_id}' and module_id='{module_id}' and parent_key='{parent_key}' order by priority" - result_list = execute_query(query) - return result_list - - -def parse_img(item: Dict) -> Dict: - """解析图片类型的内容项""" - img = { - "src": item['src'], - "height": item['height'], - "width": item['width'] - } - img_fold = { - "src": item['src_fold'], - "height": item['height'], - "width": item['width'] - } - action = None - if item['action_type'] == 'link': - link_url = item['link_url'] - if item['link_type'] == 0: - link_url = url_encode(link_url) - - action = { - "type": item['action_type'], - "linkUrl": link_url - } - if item['action_text'] != '': - action['text'] = item['action_text'] - - res = { - "type": 'image', - "image": img, - "imageFold": img_fold, - "action": action - } - - if item['title'] != '': - res['title'] = item['title'] - if item['subtitle'] != '': - res['subTitle'] = item['subtitle'] - - uuid1 = str(uuid.uuid4()).replace('-', '') - res['_track'] = { - 'itemId': uuid1, - 'itemName': item['title'] - } - - return res - - -def parse_video(item: Dict) -> Dict: - video = { - "src": item['src'], - 'cover': item['cover'], - "height": int(item['height']), - "width": int(item['width']), - 'duration': int(item['duration']) - } - video_fold = { - "src": item['src_fold'], - 'cover': item['cover_fold'], - "height": int(item['height']), - "width": int(item['width']), - 'duration': int(item['duration']) - } - action = None - if item['action_type'] == 'link': - - link_url = item['link_url'] - if item['link_type'] == 0: - link_url = url_encode(link_url) + '&transition_background_color=%23000000' - - action = { - "type": item['action_type'], - "linkUrl": link_url - } - - res = { - "type": 'video', - "video": video, - "videoFold": video_fold, - "image": video, - "imageFold": video_fold, - "action": action - } - - if item['title'] != '': - res['title'] = item['title'] - if item['subtitle'] != '': - res['subTitle'] = item['subtitle'] - - uuid1 = str(uuid.uuid4()).replace('-', '') - res['_track'] = { - 'itemId': uuid1, - 'itemName': item['title'] - } - - return res - - -def parse_tab_item(page_id, module_id, item: Dict) -> Dict: - tab_item = { - "tabId": item['key'], - "text": item['text'], - "selected": True if item['selected'] == 1 else False - } - if item['key'] == '': - return tab_item - items = get_module_items_by_parent(page_id, module_id, item['key']) - if len(items) > 0: - tab_item_content = [] - for sub_item in items: - item = {} - sub_type = sub_item['type'] - if sub_type == 'image': - item = parse_img(sub_item) - elif sub_type == 'video': - item = parse_video(sub_item) - tab_item_content.append(item) - - if len(tab_item_content) > 0: - tab_item['content'] = { - 'data': tab_item_content - } - - return tab_item - - -def parse_module_config(page_id, module: Dict) -> Dict: - component = module['component'] - module_id = module['module_id'] - items = get_module_items(page_id, module_id) - data_list = [] - # 遍历并打印每个模块项 - for item in items: - item_type = item['type'] - obj = {} - if item_type == 'image': - obj = parse_img(item) - data_list.append(obj) - elif item_type == 'video': - obj = parse_video(item) - data_list.append(obj) - elif item_type == 'tabItem': - obj = parse_tab_item(page_id, module_id, item) - data_list.append(obj) - - extra = None - if module['extra'] is not None: - extra = json.loads(module['extra']) - - if component == 'Tabs': - return { - "items": data_list - } - - if component == 'Banner': - return { - "data": data_list - } - - if component == 'Navigation': - return { - 'title': module['title'], - 'columns': int(module['columns']), - "data": data_list, - 'extra': extra - } - - if component == 'News': - return { - 'title': module['title'], - "data": data_list, - 'extra': extra - } - - if component == 'TabNews': - return { - 'title': module['title'], - "items": data_list - } - - if component == 'ActivityList': - return { - 'title': module['title'], - "more": extra - } - - if component == 'TabFeeds': - return { - 'title': module['title'], - "items": data_list - } - - return {} - - -def get_page_config(page_id, page_title): - modules = execute_query(f'select * from site_page_module_tmp where page_id = \'{page_id}\' order by priority ') - module_config_list = [] - module_ids = [] - for module in modules: - config = parse_module_config(page_id, module) - - track = { - 'sectionId': str(uuid.uuid4()).replace('-', ''), - 'sectionName': module['module_name'] - } - config['_track'] = track - - print('---------------------------') - print(module['module_id']) - print(json.dumps(config, ensure_ascii=False)) - print('---------------------------') - - module_ids.append(module['module_id']) - m = { - 'id': module['module_id'], - 'name': module['module_name'], - 'component': module['component'], - 'config': json.dumps(config, ensure_ascii=False) - } - - if module['data_provider'] != '': - m['dataProvider'] = module['data_provider'] - - module_config_list.append(m) - - result = { - 'title': page_title, - 'components': module_config_list, - "groupId": "1", - "groupConfig": [ - { - "list": module_ids, - "name": "全部用户", - "strategy": "all", - "default": True - } - ] - } - - return result - - -# 使用示例 -if __name__ == "__main__": - explore_page_config = get_page_config('explore', '探索页面') - activity_page_config = get_page_config('activity', '活动页面') - - print('##################') - print(json.dumps(explore_page_config, ensure_ascii=False)) - print(json.dumps(activity_page_config, ensure_ascii=False)) diff --git "a/my_work/doc/QLib\346\225\260\346\215\256\345\212\240\350\275\275\344\270\216\347\211\271\345\276\201\345\244\204\347\220\206\350\257\246\350\247\243.md" "b/my_work/doc/QLib\346\225\260\346\215\256\345\212\240\350\275\275\344\270\216\347\211\271\345\276\201\345\244\204\347\220\206\350\257\246\350\247\243.md" new file mode 100644 index 0000000000..371c7b4352 --- /dev/null +++ "b/my_work/doc/QLib\346\225\260\346\215\256\345\212\240\350\275\275\344\270\216\347\211\271\345\276\201\345\244\204\347\220\206\350\257\246\350\247\243.md" @@ -0,0 +1,932 @@ +# QLib数据加载与特征处理详解 + +## 目录 + +- [1. 概述](#1-概述) +- [2. 数据加载流程](#2-数据加载流程) + - [2.1 初始化阶段](#21-初始化阶段) + - [2.2 数据加载器配置](#22-数据加载器配置) + - [2.3 实际数据加载过程](#23-实际数据加载过程) + - [2.4 时间范围的使用](#24-时间范围的使用) +- [3. 特征计算流程](#3-特征计算流程) + - [3.1 特征配置生成](#31-特征配置生成) + - [3.2 表达式计算](#32-表达式计算) + - [3.3 特征标准化和归一化](#33-特征标准化和归一化) + - [3.4 Alpha158特征详解](#34-alpha158特征详解) +- [4. 数据处理流程](#4-数据处理流程) + - [4.1 处理器初始化](#41-处理器初始化) + - [4.2 数据处理执行](#42-数据处理执行) + - [4.3 处理器拟合](#43-处理器拟合) + - [4.4 infer_processors和learn_processors详解](#44-infer_processors和learn_processors详解) +- [5. 常用处理器详解](#5-常用处理器详解) + - [5.1 RobustZScoreNorm（稳健Z分数标准化）](#51-robustzscorenorm稳健z分数标准化) + - [5.2 CSZScoreNorm（截面Z分数标准化）](#52-cszscorenorm截面z分数标准化) + - [5.3 Fillna（填充缺失值）](#53-fillna填充缺失值) + - [5.4 DropnaLabel（删除缺失标签）](#54-dropnalabel删除缺失标签) +- [6. 数据处理的特殊化处理](#6-数据处理的特殊化处理) + - [6.1 处理类型（process_type）](#61-处理类型process_type) + - [6.2 拟合时间范围](#62-拟合时间范围) + - [6.3 特征分组处理](#63-特征分组处理) + - [6.4 缓存机制](#64-缓存机制) + - [6.5 并行处理](#65-并行处理) +- [7. 完整流程示例](#7-完整流程示例) +- [8. 常见问题解答](#8-常见问题解答) + - [8.1 特征计算的时间范围](#81-特征计算的时间范围) + - [8.2 处理器的作用和区别](#82-处理器的作用和区别) + - [8.3 如何自定义处理器](#83-如何自定义处理器) +- [9. 总结](#9-总结) + +## 1. 概述 + +QLib是一个用于量化投资的开源Python库，提供了强大的数据加载和特征处理功能。本文档详细介绍QLib的数据加载和特征处理流程，包括各个阶段的具体操作和特殊处理方法。 + +Alpha158是QLib中的一个重要数据处理组件，它是一个特征工程工具，用于从原始金融市场数据中提取158个金融因子（特征）。这些特征可以用于量化投资模型的训练和预测。 + +## 2. 数据加载流程 + +### 2.1 初始化阶段 + +当用户创建一个数据处理器（如Alpha158）时，会经历以下初始化步骤： + +```python +h = Alpha158( + instruments="csi300", # 股票池 + start_time="2008-01-01", # 开始时间 + end_time="2020-08-01", # 结束时间 + fit_start_time="2008-01-01", # 拟合开始时间 + fit_end_time="2014-12-31", # 拟合结束时间 + infer_processors=[...], # 推理处理器 + learn_processors=[...], # 学习处理器 +) +``` + +这些参数会被传递给`DataHandlerLP`的初始化方法，然后： + +1. 创建数据加载器（DataLoader）配置 +2. 设置处理器（Processors） +3. 调用`setup_data`方法加载数据 + +### 2.2 数据加载器配置 + +Alpha158会创建一个数据加载器配置： + +```python +data_loader = { + "class": "QlibDataLoader", + "kwargs": { + "config": { + "feature": self.get_feature_config(), # 特征配置 + "label": self.get_label_config(), # 标签配置 + }, + "filter_pipe": filter_pipe, # 过滤管道 + "freq": freq, # 数据频率 + "inst_processors": inst_processors, # 实例处理器 + }, +} +``` + +### 2.3 实际数据加载过程 + +在`DataHandler`的`setup_data`方法中，会调用数据加载器的`load`方法： + +```python +self._data = lazy_sort_index(self.data_loader.load(self.instruments, self.start_time, self.end_time)) +``` + +这个过程包括： + +1. **QlibDataLoader初始化**：解析特征和标签配置 +2. **数据加载**：调用`load`方法，传入股票池和时间范围 +3. **表达式计算**：对于每个特征表达式，QLib会使用内部的表达式引擎计算结果 +4. **数据过滤**：根据股票池和时间范围过滤数据 +5. **数据组装**：将所有特征和标签组装成一个DataFrame + +### 2.4 时间范围的使用 + +在QLib中，有几个不同的时间范围参数： + +- **数据时间范围**：由`start_time`和`end_time`定义，指定要加载的所有数据的时间范围 +- **训练时间范围**：由`fit_start_time`和`fit_end_time`定义，指定用于训练模型和拟合处理器的数据时间范围 + +特征计算会在**整个数据时间范围**（`start_time`到`end_time`）内进行，而不仅仅是训练时间范围。这是因为： + +1. 模型训练需要训练数据（通常是历史数据） +2. 模型评估需要验证数据（通常是训练数据之后的一段时间） +3. 模型应用需要测试数据（通常是最新的数据） + +而`fit_start_time`和`fit_end_time`主要用于： + +1. **处理器的拟合**：处理器需要学习数据的统计特性，为了避免未来信息泄露，这些统计特性只应该从训练数据中学习 +2. **数据集划分**：将数据划分为训练集、验证集和测试集 + +## 3. 特征计算流程 + +### 3.1 特征配置生成 + +Alpha158通过`get_feature_config`方法生成特征配置： + +```python +def get_feature_config(self): + conf = { + "kbar": {}, # K线特征 + "price": { + "windows": [0], # 时间窗口 + "feature": ["OPEN", "HIGH", "LOW", "VWAP"], # 价格特征 + }, + "rolling": {}, # 滚动特征 + } + return Alpha158DL.get_feature_config(conf) +``` + +然后`Alpha158DL.get_feature_config`方法会根据配置生成具体的特征表达式和名称。 + +### 3.2 表达式计算 + +QLib使用一个表达式引擎来计算特征。表达式引擎支持各种操作符，如： + +- **Ref**：引用过去或未来的数据 +- **Mean**：计算移动平均 +- **Std**：计算标准差 +- **Max/Min**：计算最大/最小值 +- **Rank**：计算排名 +- **Corr**：计算相关性 +- 等等... + +这些操作符在`qlib/data/ops.py`中定义，例如： + +```python +class Mean(Rolling): + """Rolling Mean (MA)""" + def __init__(self, feature, N): + super(Mean, self).__init__(feature, N, "mean") +``` + +### 3.3 特征标准化和归一化 + +Alpha158中的特征通常会进行标准化处理，主要有两种方式： + +1. **内置标准化**：在特征表达式中直接除以收盘价，如`"$open/$close"` +2. **处理器标准化**：通过`infer_processors`中的处理器进行，如`RobustZScoreNorm` + +### 3.4 Alpha158特征详解 + +Alpha158中的特征可以分为以下几类： + +1. **K线特征（9个）**：基于开盘价、收盘价、最高价、最低价计算的特征 +2. **价格特征**：原始价格数据 +3. **成交量特征**：成交量数据 +4. **滚动特征（最多可达140个）**：基于滚动窗口计算的各种技术指标 + +滚动特征是Alpha158的核心，包含了大量基于滚动窗口计算的技术指标。默认的滚动窗口大小为[5, 10, 20, 30, 60]天。主要包括： + +- **ROC（价格变化率）**：过去d天的价格变化率 +- **MA（移动平均线）**：过去d天的简单移动平均线 +- **STD（标准差）**：过去d天收盘价的标准差 +- **BETA（斜率）**：过去d天价格变化的斜率 +- **RSQR（R方值）**：过去d天线性回归的R方值，表示趋势的线性程度 +- **RESI（残差）**：过去d天线性回归的残差，表示过去d天趋势的线性程度 +- **MAX（最高价）**：过去d天的最高价 +- **LOW（最低价）**：过去d天的最低价 +- **QTLU（上分位数）**：过去d天收盘价的80%分位数 +- **QTLD（下分位数）**：过去d天收盘价的20%分位数 +- **RANK（排名）**：当前收盘价在过去d天收盘价中的百分位排名 +- **RSV（相对强弱值）**：当前价格在过去d天价格区间中的位置 +- **IMAX（最高价天数）**：当前日期与过去d天最高价日期之间的天数 +- **IMIN（最低价天数）**：当前日期与过去d天最低价日期之间的天数 +- **IMXD（高低价时间差）**：过去d天最高价日期与最低价日期之间的天数差 +- **CORR（相关性）**：收盘价与成交量的相关性 +- **CORD（变化率相关性）**：价格变化率与成交量变化率的相关性 +- **CNTP（上涨天数比例）**：过去d天中价格上涨的天数比例 +- **CNTN（下跌天数比例）**：过去d天中价格下跌的天数比例 +- **CNTD（上涨下跌天数差）**：过去d天中上涨天数与下跌天数的差值 +- **SUMP（总涨幅/总变化）**：过去d天中总涨幅与总价格变化的比值（类似RSI指标） +- **SUMN（总跌幅/总变化）**：过去d天中总跌幅与总价格变化的比值 +- **SUMD（涨跌幅差值比）**：过去d天中总涨幅与总跌幅的差值比 +- **VMA（成交量移动平均）**：过去d天的成交量移动平均 +- **VSTD（成交量标准差）**：过去d天成交量的标准差 +- **WVMA（加权成交量波动率）**：成交量加权的价格变化波动率 +- **VSUMP（成交量增加比例）**：过去d天中成交量增加的比例 +- **VSUMN（成交量减少比例）**：过去d天中成交量减少的比例 +- **VSUMD（成交量增减差值比）**：过去d天中成交量增加与减少的差值比 + +## 4. 数据处理流程 + +### 4.1 处理器初始化 + +在Alpha158初始化时，会设置`infer_processors`和`learn_processors`： + +```python +infer_processors = [ + {"class": "RobustZScoreNorm", "kwargs": {"fields_group": "feature", "clip_outlier": true}}, + {"class": "Fillna", "kwargs": {"fields_group": "feature"}} +] + +learn_processors = [ + {"class": "DropnaLabel"}, + {"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}} +] +``` + +这些处理器配置会被转换为实际的处理器对象。 + +### 4.2 数据处理执行 + +在`DataHandlerLP`的`process_data`方法中，会按照以下流程处理数据： + +``` +原始数据(self._data) -> 共享处理器(shared_processors) -> 推理处理器(infer_processors) -> 学习处理器(learn_processors) +``` + +具体代码流程： + +```python +def process_data(self, with_fit: bool = False): + # 处理共享数据 + _shared_df = self._data + _shared_df = self._run_proc_l(_shared_df, self.shared_processors, with_fit=with_fit, check_for_infer=True) + + # 处理推理数据 + _infer_df = _shared_df + _infer_df = self._run_proc_l(_infer_df, self.infer_processors, with_fit=with_fit, check_for_infer=True) + self._infer = _infer_df + + # 处理学习数据 + if self.process_type == DataHandlerLP.PTYPE_I: + _learn_df = _shared_df # 独立模式 + elif self.process_type == DataHandlerLP.PTYPE_A: + _learn_df = _infer_df # 附加模式 + _learn_df = self._run_proc_l(_learn_df, self.learn_processors, with_fit=with_fit, check_for_infer=False) + self._learn = _learn_df +``` + +### 4.3 处理器拟合 + +如果`with_fit=True`，处理器会先调用`fit`方法学习数据的统计特性，然后再调用`__call__`方法处理数据： + +```python +@staticmethod +def _run_proc_l(df, proc_l, with_fit, check_for_infer): + for proc in proc_l: + if check_for_infer and not proc.is_for_infer(): + raise TypeError("Only processors usable for inference can be used in `infer_processors`") + if with_fit: + proc.fit(df) # 拟合处理器 + df = proc(df) # 应用处理器 + return df +``` + +### 4.4 infer_processors和learn_processors详解 + +`infer_processors`和`learn_processors`是应用于已经计算好的特征的处理器，它们在特征计算完成后对数据进行进一步的处理。 + +#### infer_processors（推理处理器） + +`infer_processors`主要用于处理**用于模型推理（预测）的数据**。它们的主要作用包括： + +- **数据标准化**：使特征分布更适合模型处理，如`RobustZScoreNorm` +- **缺失值处理**：填充或处理特征中的缺失值，如`Fillna` +- **异常值处理**：处理或裁剪异常值，如`RobustZScoreNorm`中的`clip_outlier`参数 +- **特征转换**：对特征进行变换，如对数变换、幂变换等 + +#### learn_processors（学习处理器） + +`learn_processors`主要用于处理**用于模型训练的数据**，特别是标签数据。它们的主要作用包括： + +- **标签标准化**：对标签进行标准化，如`CSZScoreNorm`（截面Z分数标准化） +- **样本过滤**：过滤不适合训练的样本，如`DropnaLabel`（删除标签为空的样本） +- **标签变换**：对标签进行变换，使其更适合模型学习 +- **样本权重调整**：调整不同样本的权重 + +#### infer_processors 和 learn_processors 的区别 + +1. **处理对象不同** + - **infer_processors**：主要处理特征（feature）数据 + - **learn_processors**：主要处理标签（label）数据，有时也会处理特征数据 + +2. **应用场景不同** + - **infer_processors**：用于模型推理阶段，也用于训练阶段的特征预处理 + - **learn_processors**：仅用于模型训练阶段 + +3. **处理顺序不同** + 在`process_type=PTYPE_A`（附加模式）下： + - 先应用`infer_processors` + - 再应用`learn_processors` + +4. **信息使用限制不同** + - **infer_processors**：不能使用标签信息或未来信息 + - **learn_processors**：可以使用标签信息，但仍不应使用未来信息 + +## 5. 常用处理器详解 + +### 5.1 RobustZScoreNorm（稳健Z分数标准化） + +这是一个特殊的标准化处理器，用于处理异常值： + +```python +class RobustZScoreNorm(Processor): + """Robust ZScore Normalization""" + + def __init__(self, fields_group=None, clip_outlier=True): + self.fields_group = fields_group + self.clip_outlier = clip_outlier + + def fit(self, df): + # 计算每个特征的中位数和中位绝对偏差 + self.median = df[self.fields].median() + self.mad = (df[self.fields] - self.median).abs().median() + + def __call__(self, df): + # 应用稳健Z分数标准化 + df_copy = df.copy() + df_copy[self.fields] = (df_copy[self.fields] - self.median) / (self.mad * 1.4826) + + # 可选：裁剪异常值 + if self.clip_outlier: + df_copy[self.fields] = df_copy[self.fields].clip(-3, 3) + + return df_copy +``` + +与普通的Z分数标准化不同，稳健Z分数使用中位数和中位绝对偏差（MAD）而不是均值和标准差，这使得它对异常值不敏感。 + +### 5.2 CSZScoreNorm（截面Z分数标准化） + +这个处理器在每个时间点对所有股票进行标准化，常用于标签处理： + +```python +class CSZScoreNorm(Processor): + """Cross Sectional ZScore Normalization""" + + def __init__(self, fields_group=None): + self.fields_group = fields_group + + def __call__(self, df): + # 按时间点分组，对每组应用Z分数标准化 + cols = get_group_columns(df, self.fields_group) + df[cols] = df[cols].groupby("datetime").apply(lambda x: (x - x.mean()) / x.std()) + return df +``` + +### 5.3 Fillna（填充缺失值） + +用于填充特征中的缺失值： + +```python +class Fillna(Processor): + """Fill NA values in the features""" + + def __init__(self, fields_group=None, fill_value=0): + self.fields_group = fields_group + self.fill_value = fill_value + + def __call__(self, df): + cols = get_group_columns(df, self.fields_group) + df[cols] = df[cols].fillna(self.fill_value) + return df +``` + +### 5.4 DropnaLabel（删除缺失标签） + +删除标签为缺失值的样本： + +```python +class DropnaLabel(Processor): + """Drop samples with NA in label""" + + def __call__(self, df): + label_cols = get_group_columns(df, "label") + return df.dropna(subset=label_cols) +``` + +## 6. 数据处理的特殊化处理 + +### 6.1 处理类型（process_type） + +QLib支持两种处理类型： + +1. **PTYPE_I（独立模式）**： + ``` + 原始数据 -> 共享处理器 -> 推理处理器 + \ + -> 学习处理器 + ``` + +2. **PTYPE_A（附加模式）**： + ``` + 原始数据 -> 共享处理器 -> 推理处理器 -> 学习处理器 + ``` + +独立模式适用于推理和学习需要不同处理流程的情况，而附加模式适用于学习处理是在推理处理基础上进行的情况。 + +### 6.2 拟合时间范围 + +处理器的拟合通常只在特定的时间范围内进行，以避免未来信息泄露： + +```python +fit_start_time="2008-01-01" # 拟合开始时间 +fit_end_time="2014-12-31" # 拟合结束时间 +``` + +这确保了模型只使用历史数据来学习数据的统计特性。 + +### 6.3 特征分组处理 + +QLib支持按特征组进行处理，常见的分组有： + +- **feature**：所有特征列 +- **label**：所有标签列 +- 自定义分组：可以指定特定的列名模式 + +这使得可以对不同类型的数据应用不同的处理方法。 + +### 6.4 缓存机制 + +为了提高性能，QLib实现了数据缓存机制： + +```python +def setup_data(self, enable_cache: bool = False): + # 如果启用缓存，会尝试从缓存加载数据 + if enable_cache: + # 缓存处理逻辑... +``` + +### 6.5 并行处理 + +对于大规模数据，QLib支持并行处理： + +```python +def parallel_process(self, n_jobs=-1): + """并行处理数据""" + # 并行处理逻辑... +``` + +## 7. 完整流程示例 + +以下是使用Alpha158的完整流程示例： + +```python +import qlib +from qlib.contrib.data.handler import Alpha158 + +# 1. 初始化QLib +qlib.init(provider_uri="~/.qlib/qlib_data/cn_data", region="cn") + +# 2. 创建数据处理器 +handler = Alpha158( + instruments="csi300", # 股票池：沪深300成分股 + start_time="2008-01-01", # 数据开始时间 + end_time="2020-08-01", # 数据结束时间 + fit_start_time="2008-01-01", # 拟合开始时间 + fit_end_time="2014-12-31", # 拟合结束时间 + infer_processors=[ + {"class": "RobustZScoreNorm", "kwargs": {"fields_group": "feature", "clip_outlier": True}}, + {"class": "Fillna", "kwargs": {"fields_group": "feature"}} + ], + learn_processors=[ + {"class": "DropnaLabel"}, + {"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}} + ], + process_type="A", # 附加模式 +) + +# 3. 获取处理后的数据 +feature_df = handler.fetch(col_set="feature") # 获取特征数据 +label_df = handler.fetch(col_set="label") # 获取标签数据 +``` + +## 8. 常见问题解答 + +### 8.1 特征计算的时间范围 + +**问题**：特征计算是只会在训练时间范围上进行计算吗？ + +**回答**：特征计算会在整个指定的数据时间范围（`start_time`到`end_time`）内进行，而不仅限于训练时间范围（`fit_start_time`到`fit_end_time`）。训练时间范围主要用于处理器的拟合和数据集划分，以避免未来信息泄露。 + +在实际应用中，通常会将数据划分为训练集、验证集和测试集： + +``` +全部数据时间范围（start_time到end_time） +|-------------------------------------------| +| | +|训练集 |验证集 |测试集 | +|-----------------|------------|------------| +fit_start_time fit_end_time end_time +``` + +### 8.2 处理器的作用和区别 + +**问题**：infer_processors和learn_processors是干什么的？ + +**回答**：`infer_processors`和`learn_processors`是应用于已经计算好的特征的处理器，它们在特征计算完成后对数据进行进一步的处理： + +- **infer_processors**：处理用于模型推理的特征数据，主要进行特征标准化、缺失值处理等 +- **learn_processors**：处理用于模型训练的数据，特别是标签数据，主要进行标签标准化、样本过滤等 + +### 8.3 如何自定义处理器 + +**问题**：如何创建自定义处理器？ + +**回答**：您可以创建自定义处理器来满足特定需求： + +```python +class MyCustomProcessor(Processor): + def __init__(self, param1, param2): + self.param1 = param1 + self.param2 = param2 + + def fit(self, df): + # 学习数据统计特性 + pass + + def __call__(self, df): + # 处理数据 + return processed_df +``` + +然后在`infer_processors`或`learn_processors`中使用它： + +```python +infer_processors = [ + {"class": "MyCustomProcessor", "kwargs": {"param1": value1, "param2": value2}} +] +``` + +## 9. 总结 + +QLib的数据加载和特征处理流程是一个复杂而强大的系统，它包括： + +1. **数据加载**：通过DataLoader加载原始数据，支持多种数据源和过滤条件 +2. **特征计算**：使用表达式引擎计算各种技术指标和特征 +3. **数据处理**：通过处理器链对数据进行标准化、缺失值处理等 +4. **特殊化处理**：支持不同的处理模式、拟合时间范围、特征分组等 + +Alpha158是QLib中的一个重要组件，它提供了158个金融因子，涵盖了价格趋势、波动性、动量、相关性等多个方面，为量化投资模型提供了丰富的输入信息。 + +通过合理配置数据加载和处理流程，可以使数据更适合模型训练和推理，提高模型性能。 + +## 10. QLib数据存储结构与时间范围过滤机制 + +### 10.1 QLib数据存储结构 + +QLib的数据存储结构是经过精心设计的，以支持高效的数据访问和过滤。主要包括以下几个部分： + +#### 10.1.1 股票池文件（instruments目录） + +`~/.qlib/qlib_data/cn_data/instruments`目录下的文件（如`csi300.txt`）定义了各个股票池包含的股票代码。例如，`csi300.txt`包含沪深300指数的成分股列表。 + +这些文件的内容通常是股票代码的列表，例如： +``` +SH600000 +SH600004 +SH600009 +... +``` + +#### 10.1.2 特征数据（features目录） + +`~/.qlib/qlib_data/cn_data/features`目录存储了所有股票的特征数据。这些数据通常以二进制格式（.bin文件）存储，以提高读取效率。 + +虽然这些.bin文件本身没有明显的时间范围标记，但QLib内部维护了一个索引结构，使其能够高效地按时间范围访问数据。 + +#### 10.1.3 特征元数据（calendars目录） + +`~/.qlib/qlib_data/cn_data/calendars`目录存储了交易日历信息，定义了哪些日期是交易日。 + +#### 10.1.4 股票元数据（instruments目录下的其他文件） + +除了股票池文件外，instruments目录还可能包含股票的元数据，如上市日期、退市日期等信息。 + +### 10.2 QLib如何根据时间范围过滤数据 + +当您指定`start_time`和`end_time`参数时，QLib通过以下机制确定要获取哪部分时间的特征数据： + +#### 10.2.1 内部索引结构 + +QLib在加载数据时会构建内部索引结构，这个结构将股票代码、日期和特征值关联起来。这个索引结构使QLib能够高效地按时间范围和股票代码过滤数据。 + +#### 10.2.2 数据加载过程 + +当您调用`handler.fetch`或在初始化DataHandler时，QLib会执行以下步骤： + +1. **解析时间范围**：将`start_time`和`end_time`转换为标准格式 +2. **获取交易日列表**：从calendars目录获取指定时间范围内的交易日 +3. **过滤股票列表**：根据instruments文件获取股票列表 +4. **加载特征数据**：从features目录加载特定股票在特定时间范围内的特征数据 + +#### 10.2.3 二进制数据文件的内部结构 + +虽然.bin文件看起来没有明显的时间标记，但它们内部是有结构的： + +- 数据通常按股票代码和日期组织 +- 每个股票的数据包含时间索引，使QLib能够快速定位特定日期的数据 +- 这种结构使QLib能够只读取需要的数据部分，而不是加载整个文件 + +### 10.3 具体实现细节 + +#### 10.3.1 数据提供器（Provider） + +QLib使用Provider类来管理底层数据访问。主要的Provider是`LocalProvider`，它负责从本地文件系统读取数据。 + +```python +class LocalProvider(Provider): + def __init__(self, data_dir): + self.data_dir = data_dir + # 初始化各种路径 + self.instruments_dir = os.path.join(data_dir, "instruments") + self.features_dir = os.path.join(data_dir, "features") + self.calendars_dir = os.path.join(data_dir, "calendars") +``` + +#### 10.3.2 特征存储（FeatureStorage） + +QLib使用FeatureStorage类来管理特征数据的存储和访问。它提供了按时间范围和股票代码过滤数据的功能。 + +```python +class FeatureStorage: + def __init__(self, data_dir): + self.data_dir = data_dir + # 加载元数据和索引 + self._load_metadata() + + def get_feature(self, instrument, field, start_time=None, end_time=None): + # 根据时间范围过滤数据 + start_idx, end_idx = self._locate_time_range(instrument, start_time, end_time) + # 从文件中读取特定范围的数据 + return self._read_data(instrument, field, start_idx, end_idx) +``` + +#### 10.3.3 时间索引定位 + +QLib使用二分查找等算法快速定位时间范围在索引中的位置： + +```python +def _locate_time_range(self, instrument, start_time, end_time): + # 获取股票的时间索引 + time_index = self._get_instrument_time_index(instrument) + + # 如果未指定时间范围，返回全部数据的索引 + if start_time is None and end_time is None: + return 0, len(time_index) + + # 使用二分查找定位开始时间 + if start_time is not None: + start_idx = bisect.bisect_left(time_index, start_time) + else: + start_idx = 0 + + # 使用二分查找定位结束时间 + if end_time is not None: + end_idx = bisect.bisect_right(time_index, end_time) + else: + end_idx = len(time_index) + + return start_idx, end_idx +``` + +#### 10.3.4 数据读取优化 + +为了提高性能，QLib使用了多种优化技术： + +- **内存映射**：使用mmap技术直接映射文件到内存，避免全部加载 +- **缓存机制**：缓存常用数据，减少重复读取 +- **并行处理**：在可能的情况下并行加载多个股票的数据 + +### 10.4 实际例子 + +假设您有以下代码： + +```python +handler = Alpha158( + instruments="csi300", + start_time="2018-01-01", + end_time="2019-12-31", + # 其他参数... +) +``` + +QLib会执行以下步骤： + +1. 从`~/.qlib/qlib_data/cn_data/instruments/csi300.txt`读取股票列表 +2. 从`~/.qlib/qlib_data/cn_data/calendars`获取2018-01-01到2019-12-31之间的交易日 +3. 对于每个股票，从`~/.qlib/qlib_data/cn_data/features`加载该时间范围内的特征数据 +4. 使用内部索引结构快速定位每个股票在特定时间范围内的数据位置 +5. 只读取需要的数据部分，而不是整个文件 + +### 10.5 总结 + +QLib通过精心设计的数据存储结构和索引机制，能够高效地根据指定的时间范围和股票池过滤数据： + +1. **股票池文件**（如csi300.txt）定义了要处理的股票列表 +2. **特征数据文件**（.bin文件）存储了所有股票的特征数据 +3. **内部索引结构**使QLib能够快速定位特定时间范围的数据 +4. **优化技术**（如内存映射、缓存）提高了数据访问效率 + +虽然.bin文件本身没有明显的时间范围标记，但QLib通过内部索引结构和元数据，能够高效地按时间范围访问数据，只加载需要的部分，而不是整个文件。 + +这种设计使QLib能够处理大规模的金融数据，同时保持高效的数据访问性能。 + +## 11. 数据存储与模型训练/预测的关系 + +### 11.1 数据的存储位置 + +在QLib中，数据加载和处理完成后，数据主要存储在`DataHandlerLP`类的实例属性中。具体来说，有三个关键的数据存储位置： + +#### 11.1.1 原始数据：`self._data` + +这是从数据加载器加载的原始数据，包含所有计算出的特征和标签，没有经过任何处理器的处理。 + +```python +# 在DataHandler的setup_data方法中 +self._data = lazy_sort_index(self.data_loader.load(self.instruments, self.start_time, self.end_time)) +``` + +#### 11.1.2 推理数据：`self._infer` + +这是经过推理处理器处理后的数据，适用于模型预测阶段。 + +```python +# 在DataHandlerLP的process_data方法中 +_infer_df = self._run_proc_l(_shared_df, self.infer_processors, with_fit=with_fit, check_for_infer=True) +self._infer = _infer_df +``` + +#### 11.1.3 学习数据：`self._learn` + +这是经过学习处理器处理后的数据，适用于模型训练阶段。 + +```python +# 在DataHandlerLP的process_data方法中 +_learn_df = self._run_proc_l(_learn_df, self.learn_processors, with_fit=with_fit, check_for_infer=False) +self._learn = _learn_df +``` + +### 11.2 数据的访问方式 + +QLib提供了统一的接口来访问这些数据，主要通过`DataHandlerLP`类的`fetch`方法： + +```python +def fetch(self, selector: Union[pd.Timestamp, slice, str] = None, level: Union[str, int] = None, + col_set=DataHandlerLP.DK_I, data_key=DK_I, **kwargs) -> pd.DataFrame: + """ + Fetch data from underlying data source + + Parameters + ---------- + selector : Union[pd.Timestamp, slice, str] + Data selector. + level : Union[str, int] + The level of the index to be used. + col_set : str + The col_set will be passed to self.get_cols. + data_key : str + The data to fetch: + - DK_I: infer data + - DK_L: learn data + - DK_R: raw data + """ +``` + +其中，`col_set`参数指定要获取的列集合（如特征、标签等），`data_key`参数指定要获取的数据类型（推理数据、学习数据或原始数据）。 + +### 11.3 数据提供给模型的流程 + +#### 11.3.1 模型训练阶段 + +在模型训练阶段，通常使用学习数据（`self._learn`）： + +```python +# 获取训练数据 +train_data = handler.fetch( + selector=slice("2008-01-01", "2014-12-31"), # 时间范围 + col_set=["feature", "label"], # 获取特征和标签列 + data_key=DataHandlerLP.DK_L # 使用学习数据 +) + +# 分离特征和标签 +X_train = train_data["feature"] +y_train = train_data["label"] + +# 训练模型 +model.fit(X_train, y_train) +``` + +#### 11.3.2 模型预测阶段 + +在模型预测阶段，通常使用推理数据（`self._infer`）： + +```python +# 获取预测数据 +pred_data = handler.fetch( + selector=slice("2015-01-01", "2020-08-01"), # 时间范围 + col_set="feature", # 只获取特征列 + data_key=DataHandlerLP.DK_I # 使用推理数据 +) + +# 使用模型预测 +X_pred = pred_data +y_pred = model.predict(X_pred) +``` + +### 11.4 在QLib工作流中的应用 + +在QLib的工作流（Workflow）中，数据处理器的数据会被自动提供给模型： + +```python +# 创建工作流 +workflow = Workflow( + # 数据集配置 + dataset=DatasetConfig( + handler=Alpha158( + instruments="csi300", + start_time="2008-01-01", + end_time="2020-08-01", + fit_start_time="2008-01-01", + fit_end_time="2014-12-31", + infer_processors=[...], + learn_processors=[...], + ), + segments={ + "train": ("2008-01-01", "2014-12-31"), + "valid": ("2015-01-01", "2016-12-31"), + "test": ("2017-01-01", "2020-08-01"), + }, + ), + # 模型配置 + model=ModelConfig( + model_class=LGBModel, + model_kwargs={...}, + ), +) + +# 运行工作流 +workflow.run() +``` + +在这个例子中，工作流会： + +1. 使用`handler.fetch(data_key=DK_L)`获取训练数据 +2. 使用`handler.fetch(data_key=DK_I)`获取验证和测试数据 +3. 自动将这些数据提供给模型进行训练和预测 + +### 11.5 数据的内部结构 + +在QLib中，数据通常以pandas DataFrame的形式存储，具有多级索引结构： + +``` + feature label + $close $volume Ref($close, 1) Mean($close, 3) $high-$low LABEL0 +datetime instrument +2010-01-04 SH600000 81.807068 17145150.0 83.737389 83.016739 2.741058 0.0032 + SH600004 13.313329 11800983.0 13.313329 13.317701 0.183632 0.0042 + SH600005 37.796539 12231662.0 38.258602 37.919757 0.970325 0.0289 +``` + +- 第一级索引是日期时间（datetime） +- 第二级索引是股票代码（instrument） +- 列分为特征（feature）和标签（label）两组 + +### 11.6 数据缓存和内存管理 + +为了提高性能，QLib实现了数据缓存机制： + +```python +def setup_data(self, enable_cache: bool = False): + # 如果启用缓存，会尝试从缓存加载数据 + if enable_cache: + # 缓存处理逻辑... +``` + +此外，QLib还提供了选项来释放原始数据，以节省内存： + +```python +handler = Alpha158( + # 其他参数... + drop_raw=True # 处理完数据后释放原始数据 +) +``` + +当`drop_raw=True`时，`self._data`会在处理完成后被删除，只保留`self._infer`和`self._learn`。 + +### 11.7 总结 + +在QLib中，数据加载和处理完成后： + +1. **存储位置**： + - 原始数据存储在`DataHandlerLP`实例的`self._data`属性中 + - 推理数据存储在`self._infer`属性中 + - 学习数据存储在`self._learn`属性中 + +2. **数据访问**： + - 通过`handler.fetch`方法访问数据 + - 使用`data_key`参数指定要获取的数据类型 + - 使用`col_set`参数指定要获取的列集合 + +3. **提供给模型**： + - 训练阶段：使用`data_key=DK_L`获取学习数据 + - 预测阶段：使用`data_key=DK_I`获取推理数据 + +4. **工作流集成**： + - QLib的工作流会自动将适当的数据提供给模型 + - 训练段使用学习数据，验证和测试段使用推理数据 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zjyc0J3B=r4%D~9>)9nG?u@13zK{GI6^Ce|LyVbDR5CJ9%?KKOQ!L=J`0G%NOcLvnO zAA!+xjSUt~@JSZ9D=rnlQ> zUpgpe3hgAo<))~7IAeT!Y3(=5+&h}Sw}4Cj+a{^y=)tXi%B>UsZ|*1c%r>|XgmFNO z(`RkGle6ZNSRN`RB^q>7_ W0T;`c&?(IfK;Y@>=d#Wzp$PzoTa%0c literal 0 HcmV?d00001 diff --git a/qlib/contrib/data/handler.py b/qlib/contrib/data/handler.py index 2fe5258daa..facd9eb85e 100644 --- a/qlib/contrib/data/handler.py +++ b/qlib/contrib/data/handler.py @@ -98,17 +98,17 @@ def get_label_config(self): class Alpha158(DataHandlerLP): def __init__( self, - instruments="csi500", - start_time=None, - end_time=None, - freq="day", - infer_processors=[], - learn_processors=_DEFAULT_LEARN_PROCESSORS, - fit_start_time=None, - fit_end_time=None, - process_type=DataHandlerLP.PTYPE_A, - filter_pipe=None, - inst_processors=None, + instruments="csi500",# 默认使用中证500成分股 + start_time=None,# 开始时间 + end_time=None,# 结束时间 + freq="day",# 数据频率，默认为日频 + infer_processors=[],# 推理阶段的数据处理器 + learn_processors=_DEFAULT_LEARN_PROCESSORS,# 学习阶段的数据处理器 + fit_start_time=None,# 拟合开始时间 + fit_end_time=None,# 拟合结束时间 + process_type=DataHandlerLP.PTYPE_A,# 处理类型 + filter_pipe=None,# 过滤管道 + inst_processors=None,# 实例处理器 **kwargs, ): infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time) @@ -139,16 +139,22 @@ def __init__( def get_feature_config(self): conf = { - "kbar": {}, + "kbar": {},# K线特征 "price": { - "windows": [0], - "feature": ["OPEN", "HIGH", "LOW", "VWAP"], + "windows": [0],# 时间窗口 + "feature": ["OPEN", "HIGH", "LOW", "VWAP"],# 价格特征 }, - "rolling": {}, + "rolling": {},# 滚动特征 } + # Alpha158DL中的特征可以分为以下几类： + # K线特征（kbar）：基于开盘价、收盘价、最高价、最低价计算的特征 + # 价格特征（price）：原始价格数据 + # 成交量特征（volume）：成交量数据 + # 滚动特征（rolling）：基于滚动窗口计算的各种技术指标 return Alpha158DL.get_feature_config(conf) def get_label_config(self): + # 这个标签表示从T+1到T+2的收盘价变化率，而不是T到T+1的变化率。这是因为在中国股市，T日获取收盘价后，可以在T+1日买入，T+2日卖出。 return ["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"] diff --git a/qlib/contrib/data/loader.py b/qlib/contrib/data/loader.py index 4d11f3a34c..11e3b277d3 100644 --- a/qlib/contrib/data/loader.py +++ b/qlib/contrib/data/loader.py @@ -103,15 +103,15 @@ def get_feature_config( names = [] if "kbar" in config: fields += [ - "($close-$open)/$open", - "($high-$low)/$open", - "($close-$open)/($high-$low+1e-12)", - "($high-Greater($open, $close))/$open", - "($high-Greater($open, $close))/($high-$low+1e-12)", - "(Less($open, $close)-$low)/$open", - "(Less($open, $close)-$low)/($high-$low+1e-12)", - "(2*$close-$high-$low)/$open", - "(2*$close-$high-$low)/($high-$low+1e-12)", + "($close-$open)/$open",# KMID：收盘价与开盘价的差值比率 + "($high-$low)/$open",# KLEN：最高价与最低价的差值比率 + "($close-$open)/($high-$low+1e-12)",# KMID2：收盘价与开盘价的差值在价格区间中的位置 + "($high-Greater($open, $close))/$open",# KUP：上影线长度 + "($high-Greater($open, $close))/($high-$low+1e-12)",# KUP2：上影线占总区间的比例 + "(Less($open, $close)-$low)/$open",# KLOW：下影线长度 + "(Less($open, $close)-$low)/($high-$low+1e-12)",# KLOW2：下影线占总区间的比例 + "(2*$close-$high-$low)/$open",# KSFT：价格偏移程度 + "(2*$close-$high-$low)/($high-$low+1e-12)", # KSFT2：价格偏移在区间中的位置 ] names += [ "KMID", @@ -124,8 +124,10 @@ def get_feature_config( "KSFT", "KSFT2", ] - if "price" in config: - windows = config["price"].get("windows", range(5)) + # 价格特征是基于原始价格数据（开盘价、收盘价、最高价、最低价、成交量加权平均价）计算的特征： + if "price" in config: + # 默认使用0-4天的窗口 + windows = config["price"].get("windows", range(5)) feature = config["price"].get("feature", ["OPEN", "HIGH", "LOW", "CLOSE", "VWAP"]) for field in feature: field = field.lower() diff --git a/qlib_web_platform/DESIGN_DOC_UI_UX.md b/qlib_web_platform/DESIGN_DOC_UI_UX.md index e61e10c676..558a219809 100644 --- a/qlib_web_platform/DESIGN_DOC_UI_UX.md +++ b/qlib_web_platform/DESIGN_DOC_UI_UX.md @@ -15,7 +15,7 @@ ### 2.1. 模型管理 (Model Management) -* **目的**: 创建、训练、管理和评估用于预测的机器学习模型。产出可供策略使用的“已训练模型”。 +* **目的**: 创建、训练、管理和评估用于预测的机器学习模型。产出可供策略使用的"已训练模型"。 * **主要页面**: * **`models.html` (模型列表页)**: * **功能**: 展示所有已创建的模型及其状态和关键信息。提供新建模型入口。 @@ -33,25 +33,18 @@ * **功能**: 配置并启动一个新的模型训练任务。 * **卡片/区域**: * **基本信息**: 模型名称 (必填), 模型描述。 - * **训练配置 (Training Configuration - 新增)**: - * 训练开始日期 (必填, `date`) - * 训练结束日期 (必填, `date`) - * 训练股票池 (必填, `select`, 包含 CSI 300, CSI 500, 全市场A股等) - * 所用特征集 (必填, `text`, placeholder: "例如: Alpha158, VWAP") - * 标签/预测目标 (必填, `text`, placeholder: "例如: Ref($close,-2)/Ref($close,-1)-1") - * **模型配置 (Model Configuration)**: - * 模型类型 (必填, `select`, LightGBM, LSTM, ...)\ - * 模型参数 (根据类型动态显示常用参数输入，或提供高级 YAML/JSON 配置入口) - * **数据配置 (Data Configuration - *待审视*)**: *此部分与“训练配置”有重叠，可能需要合并或移除。目前 `create_model.html` 中存在这部分，包含了数据源、频率、标的(Universe)、特征(重复)、标签(重复)、训练/验证期(重复)。应精简，将核心的训练上下文放在“训练配置”中。* + * **模型定义与训练上下文 (Model Definition & Training Context - 整合自原"训练配置"、"模型配置"和"数据配置")**: + * **模型类型与参数**: 模型类型 (必填, `select`), 模型参数 (常用输入或高级 YAML/JSON)。 + * **数据与训练设定**: 数据源 (必填, `select`), 数据频率 (必填, `select`), 训练股票池 (必填, `select`/`text`), 所用特征集 (必填, `textarea`), 标签/预测目标 (必填, `text`), 训练/验证/测试时间段 (必填, `date` * 6)。 * **操作按钮**: 取消, 保存草稿, **保存并开始训练**。 * **`edit_model.html` (编辑/详情页)**: * **功能**: 查看模型详细配置、训练状态、训练日志（未来）、评估结果（未来），并可编辑配置（对于草稿状态）或触发重新训练。 - * **内容**: 应包含 `create_model.html` 中的所有配置项（根据状态可能只读），并增加显示训练日志、状态详情、模型评估指标等区域。 -* **关键记录数据 (模型元数据)**: 对于每个模型，后端需记录：名称、描述、模型类型、模型参数、**训练开始/结束日期**、**训练股票池**、**特征集**、**标签配置**、训练状态、创建/更新时间、指向训练好的模型文件的指针、训练日志路径、评估结果等。 + * **内容**: 包含与 `create_model.html` 类似的"**基本信息**"和"**模型定义与训练上下文**"区域（根据状态可能只读），并增加显示训练日志、状态详情、模型评估指标等区域。 +* **关键记录数据 (模型元数据)**: 对于每个模型，后端需记录：名称、描述、模型类型、模型参数、**数据源**、**数据频率**、**训练开始/结束日期**、**训练股票池**、**特征集**、**标签配置**、训练状态、创建/更新时间、指向训练好的模型文件的指针、训练日志路径、评估结果等。 ### 2.2. 策略管理 (Strategy Management) -* **目的**: 定义如何利用一个“已训练模型”的信号来构建和执行交易策略。 +* **目的**: 定义如何利用一个"已训练模型"的信号来构建和执行交易策略。 * **主要页面**: * **`strategies.html` (策略列表页)**: * **功能**: 展示所有已创建的策略配置及其运行状态（如果有）。提供新建策略入口和执行操作。 @@ -63,40 +56,45 @@ * 创建/更新日期 * 操作 (详情, **运行回测 (按钮)**, **提交实盘 (按钮)**, 编辑 (草稿状态), 停止等) * **`create_strategy.html` (新建策略页)**: - * **功能**: 创建一个策略配置实例，核心是“选择一个已训练模型”并“定义如何使用其信号”。 + * **功能**: 创建一个策略配置实例，核心是"选择一个已训练模型"并"定义如何使用其信号"。 * **卡片/区域**: * **基本信息**: 策略名称 (必填), 策略描述。 * **选择模型**: - * 下拉框 (`select`, 必填) **只显示状态为“已训练”的模型** (需后端配合)。 + * 下拉框 (`select`, 必填) **只显示状态为"已训练"的模型** (需后端配合)。 * 下方区域 (`#selected_model_info`) **显著展示选中模型的关键训练上下文**: 训练周期, 训练股票池, 特征集, 标签。 + * **回测配置 (Backtest Configuration - 新增于此页面)**: + * 回测时段 (必填, `date` * 2, 开始/结束日期)。 + * 回测基准 (可选, `select`)。 + * 回测股票池 (可选, `text`, 默认为模型训练池)。 * **组合策略配置 (Portfolio Strategy Configuration)**: * 组合策略类型 (必填, `select`, TopkDropoutStrategy, WeightStrategy, ...) * 策略参数 (根据类型动态显示常用参数输入，或提供高级 YAML/JSON 配置入口) * 交易成本 (手续费率, 滑点)。 - * **操作按钮**: 取消, 保存草稿。 (*不再包含“运行回测”按钮*) + * **操作按钮**: 取消, 保存草稿, **开始回测**。 ### 2.3. 执行与结果 (Execution & Results) * **目的**: 基于一个已定义的策略配置，设置具体的执行参数（时间、范围、资金）来运行回测或提交实盘，并查看结果。 * **主要组件/页面**: * **运行回测模态框 (在 `strategies.html` 触发)**: - * **触发**: 点击策略列表行的“运行回测”按钮。 + * **(注意：由于回测参数（时段、基准、股票池）已移至 `create_strategy.html` 页面配置，此"运行回测模态框"的设计及其触发逻辑需要重新审视。可能不再需要在此处收集参数，或者 `create_strategy.html` 保存的是一个包含完整回测设置的策略实例。)** + * **触发**: 点击策略列表行的"运行回测"按钮。 * **参数**: * 回测开始日期 (必填, `date`) - **默认值**: 根据策略关联模型的训练结束日期自动计算下一天。 * 回测结束日期 (必填, `date`) - 默认值: 空。 * 初始资金 (必填, `number`) - 默认值: 例如 1,000,000。 * 基准 (可选, `text`)。 * **股票池/Universe** (必填, `select`) - **默认值**: 根据策略关联模型的训练股票池自动选中。 - * **警告**: 如果用户修改了股票池，导致与模型训练股票池不一致，**点击“开始回测”时弹出 `confirm` 警告**。 + * **警告**: 如果用户修改了股票池，导致与模型训练股票池不一致，**点击"开始回测"时弹出 `confirm` 警告**。 * **操作**: 开始回测 (触发后端任务), 取消。 * **提交实盘模态框 (在 `strategies.html` 触发)**: - * **触发**: 点击策略列表行的“提交实盘”按钮。 + * **触发**: 点击策略列表行的"提交实盘"按钮。 * **参数**: * 选择账户 (必填, `select`)。\ * 分配类型 (固定金额/账户百分比, `radio`)。\ * 金额/百分比 (必填, `number`)。\ * **股票池/Universe** (必填, `select`) - **默认值**: 根据策略关联模型的训练股票池自动选中。 - * **警告**: 如果用户修改了股票池，导致与模型训练股票池不一致，**点击“提交至实盘”时弹出 `confirm` 警告**。 + * **警告**: 如果用户修改了股票池，导致与模型训练股票池不一致，**点击"提交至实盘"时弹出 `confirm` 警告**。 * **操作**: 提交至实盘 (触发后端任务), 取消。 * **`strategy_detail.html` (策略详情页)**: * **功能**: 集中展示一个策略配置的详细信息以及其下所有相关的执行（回测/实盘）记录和结果。 @@ -114,7 +112,7 @@ * 执行状态 (运行中, 已完成, 失败)。 * **关键结果指标** (年化收益, 最大回撤, Sharpe 等)。 * 操作 (查看详细报告/日志, 停止实盘等)。 - * 提供触发“运行新回测”（类似列表页弹窗）或“提交新实盘”的入口。 + * 提供触发"运行新回测"（类似列表页弹窗）或"提交新实盘"的入口。 ## 3. 关键 UX 原则总结 diff --git a/qlib_web_platform/create_model.html b/qlib_web_platform/create_model.html index 478a7ce634..f554b386e6 100644 --- a/qlib_web_platform/create_model.html +++ b/qlib_web_platform/create_model.html @@ -94,104 +94,121 @@

    基本信息< - +
    -

    训练配置

    -
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    模型定义与训练上下文

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    模型配置

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    根据所选模型类型，将在此处显示常用参数输入框。

    - - -
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    数据配置

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    数据与训练设定

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    diff --git a/qlib_web_platform/create_strategy.html b/qlib_web_platform/create_strategy.html index 5df90ae7a6..c1e170e91e 100644 --- a/qlib_web_platform/create_strategy.html +++ b/qlib_web_platform/create_strategy.html @@ -107,10 +107,52 @@

    选择模型<

    - +
    - -

    选择模型后将在此处显示其训练上下文信息。

    + +

    请先选择一个模型

    + +

    选择模型后，将在此处显示其关键的训练上下文信息。

    +
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    回测配置

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    请指定回测的开始和结束日期。

    +
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    选择一个用于比较策略表现的基准指数。

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    留空则使用所选模型训练时的股票池。

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    @@ -169,6 +211,13 @@

    组合策略 +

    diff --git a/qlib_web_platform/edit_model.html b/qlib_web_platform/edit_model.html index b355e29aa6..87b4fa0410 100644 --- a/qlib_web_platform/edit_model.html +++ b/qlib_web_platform/edit_model.html @@ -85,7 +85,10 @@

    编辑模型

    -

    基本信息

    +

    + 基本信息 + 当前状态: 已训练 +

    @@ -98,113 +101,127 @@

    基本信息<

    - +
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    模型配置

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    根据所选模型类型，将在此处显示常用参数输入框。

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    模型定义与训练上下文

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    模型类型与参数

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    根据所选模型类型，将在此处显示常用参数输入框。

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    数据配置

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    使用 Qlib 识别的名称或 Python 列表格式。

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    每行输入一个特征表达式。

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    输入标签表达式。

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    数据与训练设定

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    使用 Qlib 识别的名称或 Python 列表格式。

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    每行输入一个特征表达式。

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