From f1811815e72e5329ef2b91f0d7658fbdb3510efe Mon Sep 17 00:00:00 2001 From: liaozhaorun <1300336796@qq.com> Date: Mon, 9 Mar 2026 23:37:20 +0800 Subject: [PATCH] =?UTF-8?q?fix(factors):=20=E4=BF=AE=E5=A4=8D=20ts=5Fcorr/?= =?UTF-8?q?ts=5Fcov=20=E5=AE=9E=E7=8E=B0=E5=B9=B6=E6=B7=BB=E5=8A=A0=20abs?= =?UTF-8?q?=20=E5=87=BD=E6=95=B0=E6=94=AF=E6=8C=81=20-=20=E4=BF=AE?= =?UTF-8?q?=E5=A4=8D=20ts=5Fcorr=20=E5=92=8C=20ts=5Fcov=20=E4=BD=BF?= =?UTF-8?q?=E7=94=A8=20pl.rolling=5Fcorr/pl.rolling=5Fcov=20=E6=A8=A1?= =?UTF-8?q?=E5=9D=97=E7=BA=A7=E5=87=BD=E6=95=B0=20-=20=E6=B7=BB=E5=8A=A0?= =?UTF-8?q?=20abs=20=E5=87=BD=E6=95=B0=E5=A4=84=E7=90=86=E5=99=A8=E5=88=B0?= =?UTF-8?q?=20translator=20-=20=E6=89=A9=E5=B1=95=20notebook=20=E4=B8=AD?= =?UTF-8?q?=E7=9A=84=E5=9B=A0=E5=AD=90=E5=AE=9A=E4=B9=89=EF=BC=8824=20->?= =?UTF-8?q?=2049=20=E4=B8=AA=EF=BC=89=20-=20=E6=9B=B4=E6=96=B0=20AGENTS.md?= =?UTF-8?q?=20=E6=96=87=E6=A1=A3=E7=BB=93=E6=9E=84=E5=92=8C=20Training=20?= =?UTF-8?q?=E6=A8=A1=E5=9D=97=E8=AF=B4=E6=98=8E?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- AGENTS.md | 457 +++++++++++---------- src/experiment/regression.ipynb | 683 +++++++++++++++++++------------- src/factors/translator.py | 17 +- 3 files changed, 675 insertions(+), 482 deletions(-) diff --git a/AGENTS.md b/AGENTS.md index 025e54f..788bdd6 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -6,9 +6,9 @@ A股量化投资框架 - Python 项目，用于量化股票投资分析。 **⚠️ 强制要求：所有沟通和思考过程必须使用中文。** - 所有与 AI Agent 的交流必须使用中文 - 代码中的注释和文档字符串使用中文 - 禁止使用英文进行思考或沟通 +- 所有与 AI Agent 的交流必须使用中文 +- 代码中的注释和文档字符串使用中文 +- 禁止使用英文进行思考或沟通 ## 构建/检查/测试命令 @@ -44,7 +44,7 @@ uv run pytest --cov=src --cov-report=term-missing ```bash # 禁止直接使用 python -python -c "..." # 禁止！ +python -c "…" # 禁止！ python script.py # 禁止！ python -m pytest # 禁止！ python -m pip install # 禁止！ @@ -59,7 +59,7 @@ pip list # 禁止！ ```bash # 运行 Python 代码 -uv run python -c "..." # ✅ 正确 +uv run python -c "…" # ✅ 正确 uv run python script.py # ✅ 正确 # 安装依赖 @@ -82,65 +82,79 @@ ProStock/ │ │ │ ├── data/ # 数据获取与存储 │ │ ├── api_wrappers/ # Tushare API 封装 -│ │ │ ├── base_sync.py # 同步基础抽象类(BaseDataSync/StockBasedSync/DateBasedSync) -│ │ │ ├── api_daily.py # 日线数据接口(DailySync) -│ │ │ ├── api_pro_bar.py # Pro Bar 数据接口(ProBarSync) +│ │ │ ├── base_sync.py # 同步基础抽象类 +│ │ │ ├── api_daily.py # 日线数据接口 +│ │ │ ├── api_pro_bar.py # Pro Bar 数据接口 │ │ │ ├── api_stock_basic.py # 股票基础信息接口 │ │ │ ├── api_trade_cal.py # 交易日历接口 -│ │ │ ├── api_bak_basic.py # 历史股票列表接口(BakBasicSync) +│ │ │ ├── api_bak_basic.py # 历史股票列表接口 │ │ │ ├── api_namechange.py # 股票名称变更接口 +│ │ │ ├── api_stock_st.py # ST股票信息接口 +│ │ │ ├── api_daily_basic.py # 每日指标接口 +│ │ │ ├── api_stk_limit.py # 涨跌停价格接口 │ │ │ ├── financial_data/ # 财务数据接口 │ │ │ │ ├── api_income.py # 利润表接口 -│ │ │ │ └── api_financial_sync.py # 财务数据同步 +│ │ │ │ ├── api_balance.py # 资产负债表接口 +│ │ │ │ ├── api_cashflow.py # 现金流量表接口 +│ │ │ │ ├── api_fina_indicator.py # 财务指标接口 +│ │ │ │ └── api_financial_sync.py # 财务数据同步调度中心 │ │ │ └── __init__.py │ │ ├── __init__.py │ │ ├── client.py # Tushare API 客户端(带速率限制) -│ │ ├── config.py # 数据模块配置 -│ │ ├── data_router.py # 数据路由器（factors/engine 专用） -│ │ ├── db_inspector.py # 数据库信息查看工具 -│ │ ├── db_manager.py # DuckDB 表管理和同步 -│ │ ├── rate_limiter.py # 令牌桶速率限制器 │ │ ├── storage.py # 数据存储核心 +│ │ ├── db_manager.py # DuckDB 表管理和同步 +│ │ ├── db_inspector.py # 数据库信息查看工具 │ │ ├── sync.py # 数据同步调度中心 -│ │ └── utils.py # 数据模块工具函数 +│ │ ├── sync_registry.py # 同步器注册表 +│ │ ├── rate_limiter.py # 令牌桶速率限制器 +│ │ ├── catalog.py # 数据目录管理 +│ │ ├── config.py # 数据模块配置 +│ │ ├── utils.py # 数据模块工具函数 +│ │ └── financial_loader.py # 财务数据加载器 │ │ │ ├── factors/ # 因子计算框架（DSL 表达式驱动） │ │ ├── engine/ # 执行引擎子模块 │ │ │ ├── __init__.py # 导出引擎组件 -│ │ │ ├── data_spec.py # 数据规格定义(DataSpec, ExecutionPlan) +│ │ │ ├── data_spec.py # 数据规格定义 │ │ │ ├── data_router.py # 数据路由器 -│ │ │ ├── planner.py # 执行计划生成器(ExecutionPlanner) -│ │ │ ├── compute_engine.py # 计算引擎(ComputeEngine) -│ │ │ └── factor_engine.py # 因子引擎统一入口(FactorEngine) +│ │ │ ├── planner.py # 执行计划生成器 +│ │ │ ├── compute_engine.py # 计算引擎 +│ │ │ ├── schema_cache.py # 表结构缓存 +│ │ │ └── factor_engine.py # 因子引擎统一入口 │ │ ├── __init__.py # 导出所有公开 API │ │ ├── dsl.py # DSL 表达式层 - 节点定义和运算符重载 -│ │ ├── api.py # API 层 - 常用符号(close/open等)和函数(ts_mean/cs_rank等) +│ │ ├── api.py # API 层 - 常用符号和函数 │ │ ├── compiler.py # AST 编译器 - 依赖提取 │ │ ├── translator.py # Polars 表达式翻译器 -│ │ ├── parser.py # 字符串公式解析器(FormulaParser) -│ │ ├── registry.py # 函数注册表(FunctionRegistry) -│ │ └── exceptions.py # 异常定义(FormulaParseError等) +│ │ ├── parser.py # 字符串公式解析器 +│ │ ├── registry.py # 函数注册表 +│ │ ├── decorators.py # 装饰器工具 +│ │ └── exceptions.py # 异常定义 │ │ -│ ├── pipeline/ # 模型训练管道 -│ │ ├── __init__.py -│ │ ├── pipeline.py # 处理流水线(ProcessingPipeline) -│ │ ├── registry.py # 插件注册中心(PluginRegistry) -│ │ ├── core/ # 核心抽象 +│ ├── training/ # 训练模块 +│ │ ├── core/ # 训练核心组件 │ │ │ ├── __init__.py -│ │ │ ├── base.py # 基类定义(BaseProcessor/BaseModel/BaseSplitter等) -│ │ │ └── splitter.py # 时间序列划分策略 -│ │ ├── models/ # 模型实现 +│ │ │ ├── trainer.py # 训练器主类 +│ │ │ └── stock_pool_manager.py # 股票池管理器 +│ │ ├── components/ # 组件 +│ │ │ ├── base.py # 基础抽象类 +│ │ │ ├── splitters.py # 数据划分器 +│ │ │ ├── selectors.py # 股票选择器 +│ │ │ ├── filters.py # 数据过滤器 +│ │ │ ├── models/ # 模型实现 +│ │ │ │ ├── __init__.py +│ │ │ │ └── lightgbm.py # LightGBM 模型 +│ │ │ └── processors/ # 数据处理器 +│ │ │ ├── __init__.py +│ │ │ └── transforms.py # 变换处理器 +│ │ ├── config/ # 配置 │ │ │ ├── __init__.py -│ │ │ └── models.py # LightGBM、CatBoost 等 -│ │ └── processors/ # 数据处理器 -│ │ ├── __init__.py -│ │ └── processors.py # 标准化、缩尾、中性化等 +│ │ │ └── config.py # 训练配置 +│ │ ├── registry.py # 组件注册中心 +│ │ └── __init__.py # 导出所有组件 │ │ -│ └── training/ # 训练入口 -│ ├── __init__.py -│ ├── pipeline.py # 训练流程配置 -│ └── output/ # 训练输出 -│ └── top_stocks.tsv # 推荐股票结果 +│ └── experiment/ # 实验代码 +│ └── regression.ipynb # 完整训练流程示例 │ ├── tests/ # 测试文件 │ ├── test_sync.py @@ -148,8 +162,6 @@ ProStock/ │ ├── test_factor_engine.py │ ├── test_factor_integration.py │ ├── test_pro_bar.py -│ ├── test_601117_factors.py -│ ├── test_two_stocks_string_factors.py │ ├── test_db_manager.py │ ├── test_daily_storage.py │ ├── test_tushare_api.py @@ -183,6 +195,7 @@ import threading # 第三方包 import pandas as pd import numpy as np +import polars as pl from tqdm import tqdm from pydantic_settings import BaseSettings @@ -250,7 +263,7 @@ except Exception as e: ### 配置 - 对所有配置使用 **pydantic-settings** - 从 `config/.env.local` 文件加载 -- 环境变量自动转换：`tushare_token` → `TUSHARE_TOKEN` +- 环境变量自动转换：`tushare_token` -> `TUSHARE_TOKEN` - 对配置单例使用 `@lru_cache()` ### 数据存储 @@ -291,7 +304,6 @@ except Exception as e: - `pydantic>=2.0.0`、`pydantic-settings>=2.0.0` - 配置 - `tqdm>=4.65.0` - 进度条 - `lightgbm>=4.0.0` - 机器学习模型 -- `catboost>=1.2.0` - 机器学习模型 - `pytest` - 测试（开发） ### 环境变量 @@ -315,124 +327,13 @@ uv run python -c "from src.data.sync import sync_all; sync_all(force_full=True)" # 自定义线程数 uv run python -c "from src.data.sync import sync_all; sync_all(max_workers=20)" +# 同步财务数据 +uv run python -c "from src.data.api_wrappers.financial_data import sync_financial; sync_financial()" + # 运行因子计算测试 uv run pytest tests/test_factor_engine.py -v ``` -## 架构变更历史 - -### v2.2 (2026-03-01) - 因子框架 DSL 化重构 - -#### 因子计算框架重构 - **变更**: 从基类继承方式迁移到 DSL 表达式方式 - **原因**: - - 提供更直观的数学公式表达方式 - - 支持因子表达式的组合和嵌套 - - 更好的类型安全和编译期检查 - **架构变化**: - - 新增 `dsl.py`: 表达式节点基类和运算符重载（Symbol、FunctionNode等） - - 新增 `api.py`: 常用符号（close/open/volume等）和函数（ts_mean/cs_rank等） - - 新增 `compiler.py`: AST 编译器，提取表达式依赖 - - 新增 `translator.py`: 将 DSL 表达式翻译为 Polars 表达式 - - 新增 `parser.py`: 字符串公式解析器（FormulaParser），支持从字符串解析 DSL 表达式 - - 新增 `registry.py`: 函数注册表（FunctionRegistry），管理字符串函数名到 Python 函数的映射 - - 新增 `exceptions.py`: 公式解析异常定义（FormulaParseError、UnknownFunctionError等） - - 重构 `engine/` 子模块: - - `factor_engine.py`: 因子引擎统一入口（FactorEngine） - - `data_spec.py`: 数据规格定义（DataSpec, ExecutionPlan） - - `data_router.py`: 数据路由器 - - `planner.py`: 执行计划生成器（ExecutionPlanner） - - `compute_engine.py`: 计算引擎（ComputeEngine） - - 移除: `base.py`、`composite.py`、`data_loader.py`、根目录的 `data_spec.py` - - 移除: `factors/momentum/` 和 `factors/financial/` 子目录 - **使用方式对比**: - ```python - # 旧方式（基类继承） - class MA20Factor(TimeSeriesFactor): - name = "ma20" - data_specs = [DataSpec("daily", ["close"], 20)] - def compute(self, data): - return data.get_column("close").rolling_mean(20) - - # 新方式（DSL 表达式） - from src.factors.api import close, ts_mean - ma20 = ts_mean(close, 20) # 直接编写数学表达式 - - engine = FactorEngine() - engine.register("ma20", ma20) - result = engine.compute(["ma20"], "20240101", "20240131") - - # 字符串公式解析（Phase 1 新增） - from src.factors import FormulaParser, FunctionRegistry - parser = FormulaParser(FunctionRegistry()) - expr = parser.parse("ts_mean(close, 20) / close") # 从字符串解析 - ``` - -#### data 模块补充完善 - **新增文件**: - - `api_wrappers/base_sync.py`: 数据同步基础抽象类（BaseDataSync、StockBasedSync、DateBasedSync） - - `data_router.py`: 数据路由器（已集成到 factors/engine.py 中的 DataRouter） - - `utils.py`: 日期工具函数（get_today_date、get_next_date、is_quarter_end等） - **影响**: 数据同步逻辑更加规范化，支持按股票和按日期两种同步模式 - -### v2.1 (2026-02-28) - 同步模块规范更新 - **变更**: 明确 `sync.py` 只包含每日更新的数据同步 - **原因**: 区分高频（每日）和低频（季度/年度）数据，避免不必要的 API 调用 - **规范**: - - `sync.py` / `sync_all_data()`: **仅包含每日更新的数据** - - 日线数据 (`api_daily`) - - Pro Bar 数据 (`api_pro_bar`) - - 交易日历 (`api_trade_cal`) - - 股票基本信息 (`api_stock_basic`) - - 历史股票列表 (`api_bak_basic`) - - - **不应放入 `sync.py` 的季度/低频数据**: - - 财务数据 (`financial_data/` 目录): 利润表、资产负债表、现金流量表等 - - 名称变更 (`api_namechange`): 已移除自动同步，建议手动定期同步 - - - **季度数据同步方式**: - ```python - # 财务数据单独同步（不在 sync_all_data 中） - from src.data.api_wrappers.financial_data.api_financial_sync import sync_financial - sync_financial() # 增量同步利润表 - - # 名称变更手动同步 - from src.data.api_wrappers import sync_namechange - sync_namechange(force=True) - ``` - -### v2.0 (2026-02-23) - 重要更新 - -#### 存储层重构 - **变更**: 从 HDF5 迁移到 DuckDB - **原因**: DuckDB 提供更好的查询性能、SQL 下推能力、并发支持 - **影响**: 所有数据表现在使用 DuckDB 存储，旧 HDF5 文件可手动迁移 - -#### Sync 类迁移 - **变更**: `DataSync` 类从 `sync.py` 迁移到 `api_daily.py` - **原因**: 实现代码职责分离，每个 API 文件包含自己的同步逻辑 - **影响**: - - `sync.py` 保留为调度中心 - - `api_daily.py` 包含 `DailySync` 类和 `sync_daily` 函数 - -#### 新增模块 - **pipeline 模块**: 机器学习流水线组件（处理器、模型、划分策略） - **training 模块**: 训练入口程序 - **factors/momentum**: 动量因子（MA、收益率排名） - **factors/financial**: 财务因子框架 - **data/utils.py**: 日期工具函数集中管理 - -#### 新增 API 接口 - `api_namechange.py`: 股票曾用名接口（手动同步） - `api_bak_basic.py`: 历史股票列表接口 - -#### 工具函数统一 - `get_today_date()`、`get_next_date()`、`DEFAULT_START_DATE` 等函数统一在 `src/data/utils.py` 中管理 - 其他模块应从 `utils.py` 导入这些函数，避免重复定义 - -其他模块应从 `utils.py` 导入这些函数，避免重复定义 - - ## Factors 框架设计说明 ### 架构层次 @@ -468,58 +369,28 @@ Engine (engine/) <- 执行引擎 数据层 (data_router.py + DuckDB) <- 数据获取和存储 ``` -### 使用方式 - -#### 1. 基础表达式（DSL 方式） - -```python -from src.factors.api import close, open, ts_mean, cs_rank - -# 定义因子表达式（惰性计算） -ma20 = ts_mean(close, 20) # 20日移动平均 -price_rank = cs_rank(close) # 收盘价截面排名 - -# 组合运算 -alpha = ma20 * 0.6 + price_rank * 0.4 -``` - -#### 2. 字符串公式解析（Phase 1 新增） - -```python -from src.factors import FormulaParser, FunctionRegistry - -# 创建解析器（自动加载 api.py 中的所有函数） -parser = FormulaParser(FunctionRegistry()) - -# 从字符串解析公式 -expr = parser.parse("ts_mean(close, 20) / close") -complex_expr = parser.parse("cs_rank(ts_mean(close, 5) - ts_mean(close, 20))") - -# 支持完整运算符和函数调用 -expr2 = parser.parse("(close - open) / open * 100") # 涨跌幅 -expr3 = parser.parse("ts_corr(close, volume, 20)") # 量价相关性 -``` - -#### 3. 注册和执行 +### FactorEngine 核心 API ```python from src.factors import FactorEngine +# 初始化引擎 engine = FactorEngine() -# 注册 DSL 表达式 -engine.register("ma20", ma20) -engine.register("price_rank", price_rank) +# 方式1: 使用 DSL 表达式 +from src.factors.api import close, ts_mean, cs_rank +engine.register("ma20", ts_mean(close, 20)) +engine.register("price_rank", cs_rank(close)) -# 或注册字符串解析的表达式 -engine.register("alpha", parser.parse("ma20 * 0.6 + price_rank * 0.4")) +# 方式2: 使用字符串表达式（推荐） +engine.add_factor("ma20", "ts_mean(close, 20)") +engine.add_factor("alpha", "cs_rank(ts_mean(close, 5) - ts_mean(close, 20))") -# 执行计算 -result = engine.compute( - factor_names=["ma20", "price_rank"], - start_date="20240101", - end_date="20240131", -) +# 计算因子 +result = engine.compute(["ma20", "price_rank"], "20240101", "20240131") + +# 查看已注册因子 +print(engine.list_registered()) ``` ### 支持的函数 @@ -584,7 +455,6 @@ expr4 = ts_mean(cs_rank(close), 20) # 排名后的20日平滑 - `EmptyExpressionError` - 空表达式错误 - `DuplicateFunctionError` - 函数重复注册错误 -示例： ```python from src.factors import FormulaParser, FunctionRegistry, UnknownFunctionError @@ -596,7 +466,183 @@ except UnknownFunctionError as e: ``` -## AI 行为准则 +## Training 模块设计说明 + +### 架构概述 + +Training 模块位于 `src/training/` 目录，负责从因子数据到模型训练、预测的完整流程。采用组件化设计，支持数据处理器、模型、过滤器、股票池管理器的灵活组合。 + +``` +src/training/ +├── core/ +│ ├── trainer.py # Trainer 主类 +│ └── stock_pool_manager.py # 股票池管理器 +├── components/ +│ ├── base.py # BaseModel、BaseProcessor 抽象基类 +│ ├── splitters.py # DateSplitter 日期划分器 +│ ├── filters.py # STFilter 等过滤器 +│ ├── models/ +│ │ └── lightgbm.py # LightGBMModel +│ └── processors/ +│ └── transforms.py # 数据处理器实现 +├── config/ +│ └── config.py # TrainingConfig +└── registry.py # 组件注册中心 +``` + +### Trainer 核心流程 + +```python +from src.training import Trainer, DateSplitter, StockPoolManager +from src.training.components.models import LightGBMModel +from src.training.components.processors import Winsorizer, StandardScaler +from src.training.components.filters import STFilter +import polars as pl + +# 1. 创建模型 +model = LightGBMModel(params={ + "objective": "regression", + "metric": "mae", + "num_leaves": 20, + "learning_rate": 0.01, + "n_estimators": 1000, +}) + +# 2. 创建数据划分器（正确的 train/val/test 三分法） +splitter = DateSplitter( + train_start="20200101", + train_end="20231231", + val_start="20240101", + val_end="20241231", + test_start="20250101", + test_end="20261231", +) + +# 3. 创建数据处理器 +processors = [ + NullFiller(strategy="mean"), + Winsorizer(lower=0.01, upper=0.99), + StandardScaler(exclude_cols=["ts_code", "trade_date", "target"]), +] + +# 4. 创建股票池筛选函数 +def stock_pool_filter(df: pl.DataFrame) -> pl.Series: + """筛选小市值股票，排除创业板/科创板/北交所""" + code_filter = ( + ~df["ts_code"].str.starts_with("300") & # 排除创业板 + ~df["ts_code"].str.starts_with("688") & # 排除科创板 + ~df["ts_code"].str.starts_with("8") & # 排除北交所 + ~df["ts_code"].str.starts_with("9") & + ~df["ts_code"].str.starts_with("4") + ) + valid_df = df.filter(code_filter) + n = min(1000, len(valid_df)) + small_cap_codes = valid_df.sort("total_mv").head(n)["ts_code"] + return df["ts_code"].is_in(small_cap_codes) + +pool_manager = StockPoolManager( + filter_func=stock_pool_filter, + required_columns=["total_mv"], + data_router=engine.router, +) + +# 5. 创建 ST 过滤器 +st_filter = STFilter(data_router=engine.router) + +# 6. 创建训练器 +trainer = Trainer( + model=model, + pool_manager=pool_manager, + processors=processors, + filters=[st_filter], + splitter=splitter, + target_col="future_return_5", + feature_cols=["ma_5", "ma_20", "volume_ratio", "roe"], +) + +# 7. 执行训练 +trainer.train(data) + +# 8. 获取结果 +results = trainer.get_results() +``` + +### 数据处理器 + +**NullFiller** - 缺失值填充： +```python +from src.training.components.processors import NullFiller + +# 使用 0 填充 +filler = NullFiller(strategy="zero") + +# 使用均值填充（每天独立计算截面均值） +filler = NullFiller(strategy="mean", by_date=True) + +# 使用指定值填充 +filler = NullFiller(strategy="value", fill_value=-999) +``` + +**Winsorizer** - 缩尾处理： +```python +from src.training.components.processors import Winsorizer + +# 全局缩尾（默认） +winsorizer = Winsorizer(lower=0.01, upper=0.99, by_date=False) + +# 每天独立缩尾 +winsorizer = Winsorizer(lower=0.01, upper=0.99, by_date=True) +``` + +**StandardScaler** - 标准化： +```python +from src.training.components.processors import StandardScaler + +# 全局标准化（学习训练集的均值和标准差） +scaler = StandardScaler(exclude_cols=["ts_code", "trade_date", "target"]) +``` + +**CrossSectionalStandardScaler** - 截面标准化： +```python +from src.training.components.processors import CrossSectionalStandardScaler + +# 每天独立标准化（不需要 fit） +cs_scaler = CrossSectionalStandardScaler( + exclude_cols=["ts_code", "trade_date", "target"], + date_col="trade_date", +) +``` + +### 组件注册机制 + +```python +from src.training.registry import register_model, register_processor +from src.training.components.base import BaseModel, BaseProcessor + +# 注册自定义模型 +@register_model("custom_model") +class CustomModel(BaseModel): + name = "custom_model" + + def fit(self, X, y): + # 训练逻辑 + return self + + def predict(self, X): + # 预测逻辑 + return predictions + +# 注册自定义处理器 +@register_processor("custom_processor") +class CustomProcessor(BaseProcessor): + name = "custom_processor" + + def transform(self, X): + # 转换逻辑 + return X +``` + + ## AI 行为准则 ### LSP 检测报错处理 @@ -649,3 +695,4 @@ LSP 报错：Syntax error on line 45 4. **检查方法** - 使用正则表达式搜索 emoji：`[\U0001F600-\U0001F64F\U0001F300-\U0001F5FF\U0001F680-\U0001F6FF\U0001F1E0-\U0001F1FF\u2600-\u26FF\u2700-\u27BF]` - 提交前自查，确保无 emoji 混入代码 + diff --git a/src/experiment/regression.ipynb b/src/experiment/regression.ipynb index 4a5641a..1b9a290 100644 --- a/src/experiment/regression.ipynb +++ b/src/experiment/regression.ipynb @@ -11,8 +11,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:04.748286Z", - "start_time": "2026-03-09T14:21:04.170825Z" + "end_time": "2026-03-09T15:26:35.493553Z", + "start_time": "2026-03-09T15:26:34.919597Z" } }, "source": [ @@ -49,8 +49,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:04.761117Z", - "start_time": "2026-03-09T14:21:04.756245Z" + "end_time": "2026-03-09T15:26:35.507113Z", + "start_time": "2026-03-09T15:26:35.502592Z" } }, "cell_type": "code", @@ -124,8 +124,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:04.770022Z", - "start_time": "2026-03-09T14:21:04.766036Z" + "end_time": "2026-03-09T15:26:35.516098Z", + "start_time": "2026-03-09T15:26:35.511343Z" } }, "cell_type": "code", @@ -133,41 +133,96 @@ "# 特征因子定义字典：新增因子只需在此处添加一行\n", "LABEL_NAME = 'future_return_5'\n", "\n", - "FACTOR_DEFINITIONS = {\n", - " # 1. 趋势因子\n", + "FACTOR_DEFINITIONS = FACTOR_DICT = {\n", + " # ================= 1. 价格、趋势与路径依赖 (Trend, Momentum & Path Dependency) =================\n", " \"ma_5\": \"ts_mean(close, 5)\",\n", " \"ma_20\": \"ts_mean(close, 20)\",\n", - " \"ma_ratio_5_20\": \"ts_mean(close, 5) / (ts_mean(close, 20) + 1e-8) - 1\",\n", - " \"bias_10\": \"close / (ts_mean(close, 10) + 1e-8) - 1\",\n", - " \"bbi_ratio\": \"(ts_mean(close, 3) + ts_mean(close, 6) + ts_mean(close, 12) + ts_mean(close, 24)) / (4 * close + 1e-8)\",\n", + " \"ma_ratio_5_20\": \"ts_mean(close, 5) / (ts_mean(close, 20) + 1e-8) - 1\", # 均线发散度\n", + " \"bias_10\": \"close / (ts_mean(close, 10) + 1e-8) - 1\", # 10日乖离率\n", + " \"high_low_ratio\": \"(close - ts_min(low, 20)) / (ts_max(high, 20) - ts_min(low, 20) + 1e-8)\", # 威廉指标变形\n", + " \"bbi_ratio\": \"(ts_mean(close, 3) + ts_mean(close, 6) + ts_mean(close, 12) + ts_mean(close, 24)) / (4 * close + 1e-8)\", # 多空指标比率\n", + " \"return_5\": \"(close / (ts_delay(close, 5) + 1e-8)) - 1\", # 5日动量\n", + " \"return_20\": \"(close / (ts_delay(close, 20) + 1e-8)) - 1\", # 20日动量\n", "\n", - " # 2. 动量与反转\n", - " \"return_5\": \"(close / ts_delay(close, 5)) - 1\",\n", - " \"return_20\": \"(close / ts_delay(close, 20)) - 1\",\n", - " \"momentum_accel\": \"(close / ts_delay(close, 5)) - (close / ts_delay(close, 20))\",\n", - " \"volatility_ratio\": \"ts_std(close, 5) / (ts_std(close, 20) + 1e-8)\",\n", - " \"reversal_1\": \"close / ts_delay(close, 1) - 1\",\n", + " # [高阶] Kaufman 趋势效率 (极高价值) - 衡量趋势流畅度，剔除无序震荡\n", + " \"kaufman_ER_20\": \"abs(close - ts_delay(close, 20)) / (ts_sum(abs(close - ts_delay(close, 1)), 20) + 1e-8)\",\n", + " # [高阶] 动量加速度 - 寻找二阶导数大于0，正在加速爆发的股票\n", + " \"mom_acceleration_10_20\": \"(close / (ts_delay(close, 10) + 1e-8) - 1) - (ts_delay(close, 10) / (ts_delay(close, 20) + 1e-8) - 1)\",\n", + " #[高阶] 高点距离衰减 - 衡量套牢盘压力\n", + " \"drawdown_from_high_60\": \"close / (ts_max(high, 60) + 1e-8) - 1\",\n", + " # [高阶] 趋势一致性 - 过去20天内收红的天数比例\n", + " \"up_days_ratio_20\": \"ts_sum(close > ts_delay(close, 1), 20) / 20\",\n", "\n", - " # 3. 量能与微观结构\n", - " \"volume_ratio\": \"ts_mean(vol, 5) / (ts_mean(vol, 20) + 1e-8)\",\n", - " \"turnover_rate_mean_5\": \"ts_mean(turnover_rate, 5)\",\n", - " # \"vol_price_corr\": \"ts_corr(close, vol, 10)\",\n", - " \"turnover_deviation\": \"(turnover_rate - ts_mean(turnover_rate, 10)) / (ts_std(turnover_rate, 10) + 1e-8)\",\n", + " # ================= 2. 波动率、风险调整与高阶矩 (Volatility & Risk-Adjusted Returns) =================\n", + " \"volatility_5\": \"ts_std(close, 5)\",\n", + " \"volatility_20\": \"ts_std(close, 20)\",\n", + " \"volatility_ratio\": \"ts_std(close, 5) / (ts_std(close, 20) + 1e-8)\", # 波动率期限结构\n", + " \"std_return_20\": \"ts_std((close / (ts_delay(close, 1) + 1e-8)) - 1, 20)\", # 真实收益率波动率\n", "\n", - " # 4. 财务动量与质量\n", - " \"net_profit_growth\": \"(n_income / ts_delay(n_income, 4)) - 1\",\n", - " \"revenue_growth\": \"(revenue / ts_delay(revenue, 4)) - 1\",\n", - " \"roe\": \"n_income / (total_hldr_eqy_exc_min_int + 1e-8)\",\n", - " \"roe_delta\": \"roe - ts_delay(roe, 4)\",\n", - " \"debt_to_equity\": \"total_liab / (total_hldr_eqy_exc_min_int + 1e-8)\",\n", - " \"current_ratio\": \"total_cur_assets / (total_cur_liab + 1e-8)\",\n", + " # [高阶] 夏普趋势比率 - 惩罚暴涨暴跌，奖励稳健爬坡\n", + " \"sharpe_ratio_20\": \"ts_mean(close / (ts_delay(close, 1) + 1e-8) - 1, 20) / (ts_std(close / (ts_delay(close, 1) + 1e-8) - 1, 20) + 1e-8)\",\n", + " #[高阶] 尾部崩盘风险 - 过去一个月最大单日跌幅\n", + " \"min_ret_20\": \"ts_min(close / (ts_delay(close, 1) + 1e-8) - 1, 20)\",\n", + " # [高阶] 波动率挤压比 - 寻找盘整到极致面临变盘的股票 (布林带收口)\n", + " \"volatility_squeeze_5_60\": \"ts_std(close, 5) / (ts_std(close, 60) + 1e-8)\",\n", "\n", - " # 5. 财务估值与排名\n", - " \"EP_rank\": \"cs_rank(n_income / (total_mv * 10000 + 1e-8))\",\n", - " \"BP_rank\": \"cs_rank(total_hldr_eqy_exc_min_int / (total_mv * 10000 + 1e-8))\",\n", - " \"market_cap_rank\": \"cs_rank(total_mv)\",\n", - " \"cashflow_act_rank\": \"cs_rank(n_cashflow_act)\",\n", - " \"ebitda_rank\": \"cs_rank(ebitda)\"\n", + " # ================= 3. 日内微观结构与异象 (Intraday Microstructure & Anomalies) =================\n", + " # [高阶] 隔夜与日内背离 - 差值越小说明主力越喜欢在盘中吸筹\n", + " \"overnight_intraday_diff\": \"(open / (ts_delay(close, 1) + 1e-8) - 1) - (close / (open + 1e-8) - 1)\",\n", + " #[高阶] 上影线抛压极值 - 冲高回落被套牢的概率\n", + " \"upper_shadow_ratio\": \"(high - ((open + close + abs(open - close)) / 2)) / (high - low + 1e-8)\",\n", + " # [高阶] 资金沉淀率 - 衡量主力日内高抛低吸洗盘的剧烈程度\n", + " \"capital_retention_20\": \"ts_sum(abs(close - open), 20) / (ts_sum(high - low, 20) + 1e-8)\",\n", + " # [高阶] MAX 彩票效应 - 反转因子，剔除近期有过妖股连板特征的标的\n", + " \"max_ret_20\": \"ts_max(close / (ts_delay(close, 1) + 1e-8) - 1, 20)\",\n", + "\n", + " # ================= 4. 量能、流动性与量价背离 (Volume, Liquidity & Divergence) =================\n", + " \"volume_ratio_5_20\": \"ts_mean(vol, 5) / (ts_mean(vol, 20) + 1e-8)\", # 相对放量比\n", + " \"turnover_rate_mean_5\": \"ts_mean(turnover_rate, 5)\", # 活跃度\n", + " \"turnover_deviation\": \"(turnover_rate - ts_mean(turnover_rate, 10)) / (ts_std(turnover_rate, 10) + 1e-8)\", # 换手率偏离度\n", + "\n", + " # [高阶] Amihud 非流动性异象 (绝对核心) - 衡量砸盘/拉升的摩擦成本\n", + " \"amihud_illiq_20\": \"ts_mean(abs(close / (ts_delay(close, 1) + 1e-8) - 1) / (amount + 1e-8), 20)\",\n", + " # [高阶] 换手率惩罚因子 - 换手率忽高忽低说明游资接力，行情极不稳定\n", + " \"turnover_cv_20\": \"ts_std(turnover_rate, 20) / (ts_mean(turnover_rate, 20) + 1e-8)\",\n", + " # [高阶] 纯粹量价相关性 - 检验是否是\"放量上涨，缩量下跌\"的良性多头\n", + " \"pv_corr_20\": \"ts_corr(close / (ts_delay(close, 1) + 1e-8) - 1, vol, 20)\",\n", + " # [高阶] 收盘价与均价背离 - 专门抓尾盘突袭拉升骗线的股票\n", + " \"close_vwap_deviation\": \"close / (amount / (vol * 100 + 1e-8) + 1e-8) - 1\",\n", + "\n", + " # ================= 5. 基本面财务特征 (Fundamental Quality & Structure) =================\n", + " \"roe\": \"n_income / (total_hldr_eqy_exc_min_int + 1e-8)\", # 净资产收益率\n", + " \"roa\": \"n_income / (total_assets + 1e-8)\", # 总资产收益率\n", + " \"profit_margin\": \"n_income / (revenue + 1e-8)\", # 销售净利率\n", + " \"debt_to_equity\": \"total_liab / (total_hldr_eqy_exc_min_int + 1e-8)\", # 杠杆率\n", + " \"current_ratio\": \"total_cur_assets / (total_cur_liab + 1e-8)\", # 短期偿债安全垫\n", + "\n", + " #[高阶] 利润同比增速 (日频延后252天等于去年同期)\n", + " \"net_profit_yoy\": \"(n_income / (ts_delay(n_income, 252) + 1e-8)) - 1\",\n", + " # [高阶] 营收同比增速\n", + " \"revenue_yoy\": \"(revenue / (ts_delay(revenue, 252) + 1e-8)) - 1\",\n", + " # [高阶] 资产负债表扩张斜率 - 剔除单纯靠举债扩张的公司\n", + " \"healthy_expansion_velocity\": \"(total_assets / (ts_delay(total_assets, 252) + 1e-8) - 1) - (total_liab / (ts_delay(total_liab, 252) + 1e-8) - 1)\",\n", + "\n", + " # ================= 6. 基本面估值与截面动量共振 (Valuation & Cross-Sectional Ranking) =================\n", + " # 估值水平绝对值 (Tushare 市值单位需要 * 10000 转换为元)\n", + " \"EP\": \"n_income / (total_mv * 10000 + 1e-8)\", # 盈利收益率 (1/PE)\n", + " \"BP\": \"total_hldr_eqy_exc_min_int / (total_mv * 10000 + 1e-8)\", # 账面市值比 (1/PB)\n", + " \"CP\": \"n_cashflow_act / (total_mv * 10000 + 1e-8)\", # 经营现金流收益率 (1/PCF)\n", + "\n", + " # 全市场截面排名因子\n", + " \"market_cap_rank\": \"cs_rank(total_mv)\", # 规模因子 (Size)\n", + " \"turnover_rank\": \"cs_rank(turnover_rate)\",\n", + " \"return_5_rank\": \"cs_rank((close / (ts_delay(close, 5) + 1e-8)) - 1)\",\n", + " \"EP_rank\": \"cs_rank(n_income / (total_mv + 1e-8))\", # 谁最便宜\n", + "\n", + " # [高阶] 戴维斯双击动量 - 估值相对上一年是否在扩张\n", + " \"pe_expansion_trend\": \"(total_mv / (n_income + 1e-8)) / (ts_delay(total_mv, 60) / (ts_delay(n_income, 60) + 1e-8) + 1e-8) - 1\",\n", + " # [高阶] 业绩与价格背离度 - 截面做差：利润排名全市场第一，但近20日价格排名倒数第一，捕捉被错杀的潜伏股\n", + " \"value_price_divergence\": \"cs_rank((n_income - ts_delay(n_income, 252)) / (abs(ts_delay(n_income, 252)) + 1e-8)) - cs_rank(close / (ts_delay(close, 20) + 1e-8))\",\n", + " # [高阶] 流动性溢价调整后市值 - 识别僵尸大盘股和极度活跃的小微盘\n", + " \"active_market_cap\": \"total_mv * ts_mean(turnover_rate, 20)\",\n", + " \"ebit_rank\": \"cs_rank(ebit)\",\n", "}\n", "\n", "# Label 因子定义（不参与训练，用于计算目标）\n", @@ -189,8 +244,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:04.778317Z", - "start_time": "2026-03-09T14:21:04.773776Z" + "end_time": "2026-03-09T15:26:35.526730Z", + "start_time": "2026-03-09T15:26:35.522343Z" } }, "source": [ @@ -303,8 +358,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:11.178131Z", - "start_time": "2026-03-09T14:21:04.783890Z" + "end_time": "2026-03-09T15:26:58.293090Z", + "start_time": "2026-03-09T15:26:35.532653Z" } }, "cell_type": "code", @@ -412,33 +467,58 @@ " - ma_20: ts_mean(close, 20)\n", " - ma_ratio_5_20: ts_mean(close, 5) / (ts_mean(close, 20) + 1e-8) - 1\n", " - bias_10: close / (ts_mean(close, 10) + 1e-8) - 1\n", + " - high_low_ratio: (close - ts_min(low, 20)) / (ts_max(high, 20) - ts_min(low, 20) + 1e-8)\n", " - bbi_ratio: (ts_mean(close, 3) + ts_mean(close, 6) + ts_mean(close, 12) + ts_mean(close, 24)) / (4 * close + 1e-8)\n", - " - return_5: (close / ts_delay(close, 5)) - 1\n", - " - return_20: (close / ts_delay(close, 20)) - 1\n", - " - momentum_accel: (close / ts_delay(close, 5)) - (close / ts_delay(close, 20))\n", + " - return_5: (close / (ts_delay(close, 5) + 1e-8)) - 1\n", + " - return_20: (close / (ts_delay(close, 20) + 1e-8)) - 1\n", + " - kaufman_ER_20: abs(close - ts_delay(close, 20)) / (ts_sum(abs(close - ts_delay(close, 1)), 20) + 1e-8)\n", + " - mom_acceleration_10_20: (close / (ts_delay(close, 10) + 1e-8) - 1) - (ts_delay(close, 10) / (ts_delay(close, 20) + 1e-8) - 1)\n", + " - drawdown_from_high_60: close / (ts_max(high, 60) + 1e-8) - 1\n", + " - up_days_ratio_20: ts_sum(close > ts_delay(close, 1), 20) / 20\n", + " - volatility_5: ts_std(close, 5)\n", + " - volatility_20: ts_std(close, 20)\n", " - volatility_ratio: ts_std(close, 5) / (ts_std(close, 20) + 1e-8)\n", - " - reversal_1: close / ts_delay(close, 1) - 1\n", - " - volume_ratio: ts_mean(vol, 5) / (ts_mean(vol, 20) + 1e-8)\n", + " - std_return_20: ts_std((close / (ts_delay(close, 1) + 1e-8)) - 1, 20)\n", + " - sharpe_ratio_20: ts_mean(close / (ts_delay(close, 1) + 1e-8) - 1, 20) / (ts_std(close / (ts_delay(close, 1) + 1e-8) - 1, 20) + 1e-8)\n", + " - min_ret_20: ts_min(close / (ts_delay(close, 1) + 1e-8) - 1, 20)\n", + " - volatility_squeeze_5_60: ts_std(close, 5) / (ts_std(close, 60) + 1e-8)\n", + " - overnight_intraday_diff: (open / (ts_delay(close, 1) + 1e-8) - 1) - (close / (open + 1e-8) - 1)\n", + " - upper_shadow_ratio: (high - ((open + close + abs(open - close)) / 2)) / (high - low + 1e-8)\n", + " - capital_retention_20: ts_sum(abs(close - open), 20) / (ts_sum(high - low, 20) + 1e-8)\n", + " - max_ret_20: ts_max(close / (ts_delay(close, 1) + 1e-8) - 1, 20)\n", + " - volume_ratio_5_20: ts_mean(vol, 5) / (ts_mean(vol, 20) + 1e-8)\n", " - turnover_rate_mean_5: ts_mean(turnover_rate, 5)\n", " - turnover_deviation: (turnover_rate - ts_mean(turnover_rate, 10)) / (ts_std(turnover_rate, 10) + 1e-8)\n", - " - net_profit_growth: (n_income / ts_delay(n_income, 4)) - 1\n", - " - revenue_growth: (revenue / ts_delay(revenue, 4)) - 1\n", + " - amihud_illiq_20: ts_mean(abs(close / (ts_delay(close, 1) + 1e-8) - 1) / (amount + 1e-8), 20)\n", + " - turnover_cv_20: ts_std(turnover_rate, 20) / (ts_mean(turnover_rate, 20) + 1e-8)\n", + " - pv_corr_20: ts_corr(close / (ts_delay(close, 1) + 1e-8) - 1, vol, 20)\n", + " - close_vwap_deviation: close / (amount / (vol * 100 + 1e-8) + 1e-8) - 1\n", " - roe: n_income / (total_hldr_eqy_exc_min_int + 1e-8)\n", - " - roe_delta: roe - ts_delay(roe, 4)\n", + " - roa: n_income / (total_assets + 1e-8)\n", + " - profit_margin: n_income / (revenue + 1e-8)\n", " - debt_to_equity: total_liab / (total_hldr_eqy_exc_min_int + 1e-8)\n", " - current_ratio: total_cur_assets / (total_cur_liab + 1e-8)\n", - " - EP_rank: cs_rank(n_income / (total_mv * 10000 + 1e-8))\n", - " - BP_rank: cs_rank(total_hldr_eqy_exc_min_int / (total_mv * 10000 + 1e-8))\n", + " - net_profit_yoy: (n_income / (ts_delay(n_income, 252) + 1e-8)) - 1\n", + " - revenue_yoy: (revenue / (ts_delay(revenue, 252) + 1e-8)) - 1\n", + " - healthy_expansion_velocity: (total_assets / (ts_delay(total_assets, 252) + 1e-8) - 1) - (total_liab / (ts_delay(total_liab, 252) + 1e-8) - 1)\n", + " - EP: n_income / (total_mv * 10000 + 1e-8)\n", + " - BP: total_hldr_eqy_exc_min_int / (total_mv * 10000 + 1e-8)\n", + " - CP: n_cashflow_act / (total_mv * 10000 + 1e-8)\n", " - market_cap_rank: cs_rank(total_mv)\n", - " - cashflow_act_rank: cs_rank(n_cashflow_act)\n", - " - ebitda_rank: cs_rank(ebitda)\n", + " - turnover_rank: cs_rank(turnover_rate)\n", + " - return_5_rank: cs_rank((close / (ts_delay(close, 5) + 1e-8)) - 1)\n", + " - EP_rank: cs_rank(n_income / (total_mv + 1e-8))\n", + " - pe_expansion_trend: (total_mv / (n_income + 1e-8)) / (ts_delay(total_mv, 60) / (ts_delay(n_income, 60) + 1e-8) + 1e-8) - 1\n", + " - value_price_divergence: cs_rank((n_income - ts_delay(n_income, 252)) / (abs(ts_delay(n_income, 252)) + 1e-8)) - cs_rank(close / (ts_delay(close, 20) + 1e-8))\n", + " - active_market_cap: total_mv * ts_mean(turnover_rate, 20)\n", + " - ebit_rank: cs_rank(ebit)\n", "\n", "注册 Label 因子:\n", " - future_return_5: (ts_delay(close, -5) / ts_delay(open, -1)) - 1\n", "\n", - "特征因子数: 24\n", + "特征因子数: 49\n", "Label: future_return_5\n", - "已注册因子总数: 25\n", + "已注册因子总数: 50\n", "\n", "[3] 准备数据\n", "\n", @@ -463,28 +543,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "数据形状: (7255513, 41)\n", - "数据列: ['ts_code', 'trade_date', 'turnover_rate', 'open', 'vol', 'close', 'total_mv', 'f_ann_date', 'revenue', 'n_income', 'total_hldr_eqy_exc_min_int', 'total_cur_assets', 'total_liab', 'total_cur_liab', 'roe', 'ebitda', 'n_cashflow_act', 'ma_5', 'ma_20', 'ma_ratio_5_20', 'bias_10', 'bbi_ratio', 'return_5', 'return_20', 'momentum_accel', 'volatility_ratio', 'reversal_1', 'volume_ratio', 'turnover_rate_mean_5', 'turnover_deviation', 'net_profit_growth', 'revenue_growth', 'roe_delta', 'debt_to_equity', 'current_ratio', 'EP_rank', 'BP_rank', 'market_cap_rank', 'cashflow_act_rank', 'ebitda_rank', 'future_return_5']\n", + "数据形状: (7255513, 70)\n", + "数据列: ['ts_code', 'trade_date', 'high', 'low', 'amount', 'vol', 'close', 'turnover_rate', 'open', 'total_assets', 'total_mv', 'f_ann_date', 'total_cur_liab', 'total_cur_assets', 'total_hldr_eqy_exc_min_int', 'total_liab', 'revenue', 'n_income', 'n_cashflow_act', 'ebit', 'ma_5', 'ma_20', 'ma_ratio_5_20', 'bias_10', 'high_low_ratio', 'bbi_ratio', 'return_5', 'return_20', 'kaufman_ER_20', 'mom_acceleration_10_20', 'drawdown_from_high_60', 'up_days_ratio_20', 'volatility_5', 'volatility_20', 'volatility_ratio', 'std_return_20', 'sharpe_ratio_20', 'min_ret_20', 'volatility_squeeze_5_60', 'overnight_intraday_diff', 'upper_shadow_ratio', 'capital_retention_20', 'max_ret_20', 'volume_ratio_5_20', 'turnover_rate_mean_5', 'turnover_deviation', 'amihud_illiq_20', 'turnover_cv_20', 'pv_corr_20', 'close_vwap_deviation', 'roe', 'roa', 'profit_margin', 'debt_to_equity', 'current_ratio', 'net_profit_yoy', 'revenue_yoy', 'healthy_expansion_velocity', 'EP', 'BP', 'CP', 'market_cap_rank', 'turnover_rank', 'return_5_rank', 'EP_rank', 'pe_expansion_trend', 'value_price_divergence', 'active_market_cap', 'ebit_rank', 'future_return_5']\n", "\n", "前5行预览:\n", - "shape: (5, 41)\n", - "┌───────────┬────────────┬───────────┬─────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", - "│ ts_code ┆ trade_date ┆ turnover_ ┆ open ┆ … ┆ market_ca ┆ cashflow_ ┆ ebitda_ra ┆ future_re │\n", - "│ --- ┆ --- ┆ rate ┆ --- ┆ ┆ p_rank ┆ act_rank ┆ nk ┆ turn_5 │\n", - "│ str ┆ str ┆ --- ┆ f64 ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ ┆ f64 ┆ ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", - "╞═══════════╪════════════╪═══════════╪═════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", - "│ 000001.SZ ┆ 20200102 ┆ 0.7885 ┆ 1817.67 ┆ … ┆ 0.993583 ┆ 0.997594 ┆ null ┆ -0.008857 │\n", - "│ 000001.SZ ┆ 20200103 ┆ 0.5752 ┆ 1849.33 ┆ … ┆ 0.993585 ┆ 0.997594 ┆ null ┆ -0.01881 │\n", - "│ 000001.SZ ┆ 20200106 ┆ 0.4442 ┆ 1856.97 ┆ … ┆ 0.993588 ┆ 0.997596 ┆ null ┆ -0.008171 │\n", - "│ 000001.SZ ┆ 20200107 ┆ 0.3755 ┆ 1870.07 ┆ … ┆ 0.993588 ┆ 0.997596 ┆ null ┆ -0.014117 │\n", - "│ 000001.SZ ┆ 20200108 ┆ 0.4369 ┆ 1855.88 ┆ … ┆ 0.993586 ┆ 0.997595 ┆ null ┆ -0.017252 │\n", - "└───────────┴────────────┴───────────┴─────────┴───┴───────────┴───────────┴───────────┴───────────┘\n", + "shape: (5, 70)\n", + "┌───────────┬────────────┬─────────┬─────────┬───┬────────────┬────────────┬───────────┬───────────┐\n", + "│ ts_code ┆ trade_date ┆ high ┆ low ┆ … ┆ value_pric ┆ active_mar ┆ ebit_rank ┆ future_re │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ e_divergen ┆ ket_cap ┆ --- ┆ turn_5 │\n", + "│ str ┆ str ┆ f64 ┆ f64 ┆ ┆ ce ┆ --- ┆ f64 ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ --- ┆ f64 ┆ ┆ f64 │\n", + "│ ┆ ┆ ┆ ┆ ┆ f64 ┆ ┆ ┆ │\n", + "╞═══════════╪════════════╪═════════╪═════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡\n", + "│ 000001.SZ ┆ 20200102 ┆ 1850.42 ┆ 1806.75 ┆ … ┆ null ┆ null ┆ null ┆ -0.008857 │\n", + "│ 000001.SZ ┆ 20200103 ┆ 1889.72 ┆ 1847.15 ┆ … ┆ null ┆ null ┆ null ┆ -0.01881 │\n", + "│ 000001.SZ ┆ 20200106 ┆ 1893.0 ┆ 1846.05 ┆ … ┆ null ┆ null ┆ null ┆ -0.008171 │\n", + "│ 000001.SZ ┆ 20200107 ┆ 1886.45 ┆ 1850.42 ┆ … ┆ null ┆ null ┆ null ┆ -0.014117 │\n", + "│ 000001.SZ ┆ 20200108 ┆ 1861.34 ┆ 1815.49 ┆ … ┆ null ┆ null ┆ null ┆ -0.017252 │\n", + "└───────────┴────────────┴─────────┴─────────┴───┴────────────┴────────────┴───────────┴───────────┘\n", "\n", "[配置] 训练期: 20200101 - 20231231\n", "[配置] 验证期: 20240101 - 20241231\n", "[配置] 测试期: 20250101 - 20261231\n", - "[配置] 特征数: 24\n", + "[配置] 特征数: 49\n", "[配置] 目标变量: future_return_5\n", "[股票池筛选] 使用自定义函数进行股票池筛选\n", "[股票池筛选] 所需基础列: ['total_mv']\n", @@ -502,8 +583,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:13.936037Z", - "start_time": "2026-03-09T14:21:11.188410Z" + "end_time": "2026-03-09T15:27:01.856502Z", + "start_time": "2026-03-09T15:26:58.307450Z" } }, "cell_type": "code", @@ -546,7 +627,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_29904\\547547317.py:75: DeprecationWarning: `is_in` with a collection of the same datatype is ambiguous and deprecated.\n", + "C:\\Users\\liaozhaorun\\AppData\\Local\\Temp\\ipykernel_37796\\547547317.py:75: DeprecationWarning: `is_in` with a collection of the same datatype is ambiguous and deprecated.\n", "Please use `implode` to return to previous behavior.\n", "\n", "See https://github.com/pola-rs/polars/issues/22149 for more information.\n", @@ -557,8 +638,8 @@ "name": "stdout", "output_type": "stream", "text": [ - " 筛选前数据规模: (7255513, 41)\n", - " 筛选后数据规模: (1494000, 41)\n", + " 筛选前数据规模: (7255513, 70)\n", + " 筛选后数据规模: (1494000, 70)\n", " 筛选前股票数: 5694\n", " 筛选后股票数: 2252\n", " 删除记录数: 5761513\n" @@ -570,8 +651,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:13.998861Z", - "start_time": "2026-03-09T14:21:13.946130Z" + "end_time": "2026-03-09T15:27:01.935567Z", + "start_time": "2026-03-09T15:27:01.869943Z" } }, "cell_type": "code", @@ -617,9 +698,9 @@ "\n", "[步骤 2/6] 划分训练集、验证集和测试集\n", "------------------------------------------------------------\n", - " 训练集数据规模: (970000, 41)\n", - " 验证集数据规模: (242000, 41)\n", - " 测试集数据规模: (282000, 41)\n", + " 训练集数据规模: (970000, 70)\n", + " 验证集数据规模: (242000, 70)\n", + " 测试集数据规模: (282000, 70)\n", " 训练集股票数: 1888\n", " 验证集股票数: 1377\n", " 测试集股票数: 1682\n", @@ -628,49 +709,54 @@ " 测试集日期范围: 20250102 - 20260306\n", "\n", " 训练集前5行预览:\n", - "shape: (5, 41)\n", - "┌───────────┬────────────┬────────────┬───────┬───┬────────────┬───────────┬───────────┬───────────┐\n", - "│ ts_code ┆ trade_date ┆ turnover_r ┆ open ┆ … ┆ market_cap ┆ cashflow_ ┆ ebitda_ra ┆ future_re │\n", - "│ --- ┆ --- ┆ ate ┆ --- ┆ ┆ _rank ┆ act_rank ┆ nk ┆ turn_5 │\n", - "│ str ┆ str ┆ --- ┆ f64 ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ ┆ f64 ┆ ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", - "╞═══════════╪════════════╪════════════╪═══════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n", - "│ 000004.SZ ┆ 20200102 ┆ 2.1613 ┆ 92.05 ┆ … ┆ 0.057219 ┆ 0.284759 ┆ null ┆ 0.000441 │\n", - "│ 000004.SZ ┆ 20200103 ┆ 1.6198 ┆ 90.67 ┆ … ┆ 0.0556 ┆ 0.284416 ┆ null ┆ 0.005875 │\n", - "│ 000004.SZ ┆ 20200106 ┆ 2.4595 ┆ 90.22 ┆ … ┆ 0.049158 ┆ 0.284264 ┆ null ┆ 0.05644 │\n", - "│ 000004.SZ ┆ 20200107 ┆ 2.1104 ┆ 88.59 ┆ … ┆ 0.049158 ┆ 0.283997 ┆ null ┆ 0.049753 │\n", - "│ 000004.SZ ┆ 20200108 ┆ 1.8769 ┆ 89.04 ┆ … ┆ 0.048904 ┆ 0.284073 ┆ null ┆ 0.019922 │\n", - "└───────────┴────────────┴────────────┴───────┴───┴────────────┴───────────┴───────────┴───────────┘\n", + "shape: (5, 70)\n", + "┌───────────┬────────────┬───────┬───────┬───┬─────────────┬─────────────┬───────────┬─────────────┐\n", + "│ ts_code ┆ trade_date ┆ high ┆ low ┆ … ┆ value_price ┆ active_mark ┆ ebit_rank ┆ future_retu │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ _divergence ┆ et_cap ┆ --- ┆ rn_5 │\n", + "│ str ┆ str ┆ f64 ┆ f64 ┆ ┆ --- ┆ --- ┆ f64 ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ f64 ┆ f64 ┆ ┆ f64 │\n", + "╞═══════════╪════════════╪═══════╪═══════╪═══╪═════════════╪═════════════╪═══════════╪═════════════╡\n", + "│ 000004.SZ ┆ 20200102 ┆ 93.06 ┆ 90.1 ┆ … ┆ null ┆ null ┆ null ┆ 0.000441 │\n", + "│ 000004.SZ ┆ 20200103 ┆ 91.36 ┆ 89.53 ┆ … ┆ null ┆ null ┆ null ┆ 0.005875 │\n", + "│ 000004.SZ ┆ 20200106 ┆ 90.22 ┆ 87.58 ┆ … ┆ null ┆ null ┆ null ┆ 0.05644 │\n", + "│ 000004.SZ ┆ 20200107 ┆ 90.22 ┆ 88.06 ┆ … ┆ null ┆ null ┆ null ┆ 0.049753 │\n", + "│ 000004.SZ ┆ 20200108 ┆ 92.54 ┆ 88.51 ┆ … ┆ null ┆ null ┆ null ┆ 0.019922 │\n", + "└───────────┴────────────┴───────┴───────┴───┴─────────────┴─────────────┴───────────┴─────────────┘\n", "\n", " 验证集前5行预览:\n", - "shape: (5, 41)\n", - "┌───────────┬────────────┬────────────┬───────┬───┬────────────┬───────────┬───────────┬───────────┐\n", - "│ ts_code ┆ trade_date ┆ turnover_r ┆ open ┆ … ┆ market_cap ┆ cashflow_ ┆ ebitda_ra ┆ future_re │\n", - "│ --- ┆ --- ┆ ate ┆ --- ┆ ┆ _rank ┆ act_rank ┆ nk ┆ turn_5 │\n", - "│ str ┆ str ┆ --- ┆ f64 ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ ┆ f64 ┆ ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", - "╞═══════════╪════════════╪════════════╪═══════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n", - "│ 000004.SZ ┆ 20240102 ┆ 2.2858 ┆ 65.43 ┆ … ┆ 0.07789 ┆ 0.247079 ┆ null ┆ -0.014188 │\n", - "│ 000004.SZ ┆ 20240103 ┆ 2.4017 ┆ 65.55 ┆ … ┆ 0.081629 ┆ 0.247079 ┆ null ┆ 0.002432 │\n", - "│ 000004.SZ ┆ 20240104 ┆ 12.6841 ┆ 65.8 ┆ … ┆ 0.0927 ┆ 0.247079 ┆ null ┆ 0.016919 │\n", - "│ 000004.SZ ┆ 20240105 ┆ 10.2752 ┆ 67.38 ┆ … ┆ 0.087038 ┆ 0.246986 ┆ null ┆ -0.013477 │\n", - "│ 000004.SZ ┆ 20240108 ┆ 6.5832 ┆ 66.04 ┆ … ┆ 0.091165 ┆ 0.246986 ┆ null ┆ -0.024684 │\n", - "└───────────┴────────────┴────────────┴───────┴───┴────────────┴───────────┴───────────┴───────────┘\n", + "shape: (5, 70)\n", + "┌───────────┬────────────┬───────┬───────┬───┬─────────────┬─────────────┬───────────┬─────────────┐\n", + "│ ts_code ┆ trade_date ┆ high ┆ low ┆ … ┆ value_price ┆ active_mark ┆ ebit_rank ┆ future_retu │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ _divergence ┆ et_cap ┆ --- ┆ rn_5 │\n", + "│ str ┆ str ┆ f64 ┆ f64 ┆ ┆ --- ┆ --- ┆ f64 ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ f64 ┆ f64 ┆ ┆ f64 │\n", + "╞═══════════╪════════════╪═══════╪═══════╪═══╪═════════════╪═════════════╪═══════════╪═════════════╡\n", + "│ 000004.SZ ┆ 20240102 ┆ 66.81 ┆ 65.23 ┆ … ┆ null ┆ 770442.9948 ┆ null ┆ -0.014188 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ 33 ┆ ┆ │\n", + "│ 000004.SZ ┆ 20240103 ┆ 66.24 ┆ 64.62 ┆ … ┆ null ┆ 751492.2017 ┆ null ┆ 0.002432 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ 8 ┆ ┆ │\n", + "│ 000004.SZ ┆ 20240104 ┆ 71.24 ┆ 64.7 ┆ … ┆ null ┆ 866443.5445 ┆ null ┆ 0.016919 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ 25 ┆ ┆ │\n", + "│ 000004.SZ ┆ 20240105 ┆ 71.08 ┆ 65.19 ┆ … ┆ null ┆ 907980.5905 ┆ null ┆ -0.013477 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ 95 ┆ ┆ │\n", + "│ 000004.SZ ┆ 20240108 ┆ 67.87 ┆ 65.02 ┆ … ┆ null ┆ 931205.3950 ┆ null ┆ -0.024684 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ 63 ┆ ┆ │\n", + "└───────────┴────────────┴───────┴───────┴───┴─────────────┴─────────────┴───────────┴─────────────┘\n", "\n", " 测试集前5行预览:\n", - "shape: (5, 41)\n", - "┌───────────┬────────────┬────────────┬───────┬───┬────────────┬───────────┬───────────┬───────────┐\n", - "│ ts_code ┆ trade_date ┆ turnover_r ┆ open ┆ … ┆ market_cap ┆ cashflow_ ┆ ebitda_ra ┆ future_re │\n", - "│ --- ┆ --- ┆ ate ┆ --- ┆ ┆ _rank ┆ act_rank ┆ nk ┆ turn_5 │\n", - "│ str ┆ str ┆ --- ┆ f64 ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ ┆ f64 ┆ ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", - "╞═══════════╪════════════╪════════════╪═══════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n", - "│ 000004.SZ ┆ 20250102 ┆ 9.4831 ┆ 55.8 ┆ … ┆ 0.099106 ┆ 0.310371 ┆ null ┆ -0.066193 │\n", - "│ 000004.SZ ┆ 20250103 ┆ 9.8133 ┆ 57.71 ┆ … ┆ 0.083783 ┆ 0.310313 ┆ null ┆ 0.00893 │\n", - "│ 000004.SZ ┆ 20250106 ┆ 6.7156 ┆ 50.39 ┆ … ┆ 0.079717 ┆ 0.310429 ┆ null ┆ -0.0142 │\n", - "│ 000004.SZ ┆ 20250107 ┆ 6.8175 ┆ 51.41 ┆ … ┆ 0.082837 ┆ 0.310254 ┆ null ┆ 0.013031 │\n", - "│ 000004.SZ ┆ 20250108 ┆ 7.9011 ┆ 52.95 ┆ … ┆ 0.088421 ┆ 0.310254 ┆ null ┆ 0.00442 │\n", - "└───────────┴────────────┴────────────┴───────┴───┴────────────┴───────────┴───────────┴───────────┘\n" + "shape: (5, 70)\n", + "┌───────────┬────────────┬───────┬───────┬───┬─────────────┬─────────────┬───────────┬─────────────┐\n", + "│ ts_code ┆ trade_date ┆ high ┆ low ┆ … ┆ value_price ┆ active_mark ┆ ebit_rank ┆ future_retu │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ _divergence ┆ et_cap ┆ --- ┆ rn_5 │\n", + "│ str ┆ str ┆ f64 ┆ f64 ┆ ┆ --- ┆ --- ┆ f64 ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ f64 ┆ f64 ┆ ┆ f64 │\n", + "╞═══════════╪════════════╪═══════╪═══════╪═══╪═════════════╪═════════════╪═══════════╪═════════════╡\n", + "│ 000004.SZ ┆ 20250102 ┆ 58.48 ┆ 54.17 ┆ … ┆ null ┆ 2.3754e6 ┆ null ┆ -0.066193 │\n", + "│ 000004.SZ ┆ 20250103 ┆ 58.52 ┆ 51.86 ┆ … ┆ null ┆ 2.1884e6 ┆ null ┆ 0.00893 │\n", + "│ 000004.SZ ┆ 20250106 ┆ 52.55 ┆ 49.17 ┆ … ┆ null ┆ 2.1549e6 ┆ null ┆ -0.0142 │\n", + "│ 000004.SZ ┆ 20250107 ┆ 53.32 ┆ 51.41 ┆ … ┆ null ┆ 2.2770e6 ┆ null ┆ 0.013031 │\n", + "│ 000004.SZ ┆ 20250108 ┆ 54.78 ┆ 52.38 ┆ … ┆ null ┆ 2.3533e6 ┆ null ┆ 0.00442 │\n", + "└───────────┴────────────┴───────┴───────┴───┴─────────────┴─────────────┴───────────┴─────────────┘\n" ] } ], @@ -679,8 +765,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:14.315924Z", - "start_time": "2026-03-09T14:21:14.008258Z" + "end_time": "2026-03-09T15:27:02.522387Z", + "start_time": "2026-03-09T15:27:01.942140Z" } }, "cell_type": "code", @@ -735,29 +821,30 @@ " 处理后记录数: 970000\n", "\n", " 训练集处理后前5行预览:\n", - "shape: (5, 41)\n", - "┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", - "│ ts_code ┆ trade_dat ┆ turnover_ ┆ open ┆ … ┆ market_ca ┆ cashflow_ ┆ ebitda_ra ┆ future_re │\n", - "│ --- ┆ e ┆ rate ┆ --- ┆ ┆ p_rank ┆ act_rank ┆ nk ┆ turn_5 │\n", - "│ str ┆ --- ┆ --- ┆ f64 ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ str ┆ f64 ┆ ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", - "╞═══════════╪═══════════╪═══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", - "│ 000004.SZ ┆ 20200102 ┆ -0.158161 ┆ 4.205157 ┆ … ┆ -1.231095 ┆ -0.740158 ┆ null ┆ 0.000441 │\n", - "│ 000004.SZ ┆ 20200103 ┆ -0.308973 ┆ 4.205157 ┆ … ┆ -1.248088 ┆ -0.741819 ┆ null ┆ 0.005875 │\n", - "│ 000004.SZ ┆ 20200106 ┆ -0.07511 ┆ 4.205157 ┆ … ┆ -1.315695 ┆ -0.742554 ┆ null ┆ 0.05644 │\n", - "│ 000004.SZ ┆ 20200107 ┆ -0.172337 ┆ 4.205157 ┆ … ┆ -1.315695 ┆ -0.743846 ┆ null ┆ 0.049753 │\n", - "│ 000004.SZ ┆ 20200108 ┆ -0.237369 ┆ 4.205157 ┆ … ┆ -1.318362 ┆ -0.743479 ┆ null ┆ 0.019922 │\n", - "└───────────┴───────────┴───────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘\n", + "shape: (5, 70)\n", + "┌───────────┬────────────┬──────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", + "│ ts_code ┆ trade_date ┆ high ┆ low ┆ … ┆ value_pri ┆ active_ma ┆ ebit_rank ┆ future_re │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ ce_diverg ┆ rket_cap ┆ --- ┆ turn_5 │\n", + "│ str ┆ str ┆ f64 ┆ f64 ┆ ┆ ence ┆ --- ┆ f64 ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ --- ┆ f64 ┆ ┆ f64 │\n", + "│ ┆ ┆ ┆ ┆ ┆ f64 ┆ ┆ ┆ │\n", + "╞═══════════╪════════════╪══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", + "│ 000004.SZ ┆ 20200102 ┆ 4.202576 ┆ 4.206515 ┆ … ┆ null ┆ null ┆ null ┆ 0.000441 │\n", + "│ 000004.SZ ┆ 20200103 ┆ 4.202576 ┆ 4.206515 ┆ … ┆ null ┆ null ┆ null ┆ 0.005875 │\n", + "│ 000004.SZ ┆ 20200106 ┆ 4.202576 ┆ 4.206515 ┆ … ┆ null ┆ null ┆ null ┆ 0.05644 │\n", + "│ 000004.SZ ┆ 20200107 ┆ 4.202576 ┆ 4.206515 ┆ … ┆ null ┆ null ┆ null ┆ 0.049753 │\n", + "│ 000004.SZ ┆ 20200108 ┆ 4.202576 ┆ 4.206515 ┆ … ┆ null ┆ null ┆ null ┆ 0.019922 │\n", + "└───────────┴────────────┴──────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘\n", "\n", " 训练集特征统计:\n", - " 特征数: 24\n", + " 特征数: 49\n", " 样本数: 970000\n", " 缺失值统计:\n", " ma_5: 4000 (0.41%)\n", " ma_20: 19000 (1.96%)\n", " ma_ratio_5_20: 19000 (1.96%)\n", " bias_10: 9000 (0.93%)\n", - " bbi_ratio: 23000 (2.37%)\n" + " high_low_ratio: 19000 (1.96%)\n" ] } ], @@ -766,8 +853,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:20.028734Z", - "start_time": "2026-03-09T14:21:14.320887Z" + "end_time": "2026-03-09T15:27:10.946482Z", + "start_time": "2026-03-09T15:27:02.528042Z" } }, "cell_type": "code", @@ -804,7 +891,7 @@ "------------------------------------------------------------\n", " 模型类型: LightGBM\n", " 训练样本数: 970000\n", - " 特征数: 24\n", + " 特征数: 49\n", " 目标变量: future_return_5\n", "\n", " 目标变量统计:\n", @@ -824,8 +911,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:20.079300Z", - "start_time": "2026-03-09T14:21:20.035212Z" + "end_time": "2026-03-09T15:27:10.997637Z", + "start_time": "2026-03-09T15:27:10.951980Z" } }, "cell_type": "code", @@ -871,8 +958,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:20.557802Z", - "start_time": "2026-03-09T14:21:20.083857Z" + "end_time": "2026-03-09T15:27:11.481411Z", + "start_time": "2026-03-09T15:27:11.004438Z" } }, "cell_type": "code", @@ -908,10 +995,10 @@ " 预测完成！\n", "\n", " 预测结果统计:\n", - " 均值: 0.003423\n", - " 标准差: 0.007422\n", - " 最小值: -0.117124\n", - " 最大值: 0.068818\n" + " 均值: 0.002266\n", + " 标准差: 0.009319\n", + " 最小值: -0.117614\n", + " 最大值: 0.080849\n" ] } ], @@ -925,8 +1012,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:21.403754Z", - "start_time": "2026-03-09T14:21:20.562703Z" + "end_time": "2026-03-09T15:27:15.029980Z", + "start_time": "2026-03-09T15:27:11.487153Z" } }, "cell_type": "code", @@ -1006,19 +1093,19 @@ "重新训练模型以收集训练指标...\n", "Training until validation scores don't improve for 100 rounds\n", "Early stopping, best iteration is:\n", - "[16]\ttrain's l1: 0.042162\tval's l1: 0.0636982\n", + "[285]\ttrain's l1: 0.0410031\tval's l1: 0.0634246\n", "训练完成，指标已收集\n", "\n", "评估指标: l1\n", "\n", "[早停信息]\n", " 配置的最大轮数: 1000\n", - " 实际训练轮数: 116\n", + " 实际训练轮数: 385\n", " 早停状态: 已触发（连续100轮验证指标未改善）\n", "\n", "最终指标:\n", - " 训练 l1: 0.041670\n", - " 验证 l1: 0.063778\n" + " 训练 l1: 0.040855\n", + " 验证 l1: 0.063455\n" ] } ], @@ -1028,8 +1115,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:21.662005Z", - "start_time": "2026-03-09T14:21:21.419111Z" + "end_time": "2026-03-09T15:27:15.271749Z", + "start_time": "2026-03-09T15:27:15.035816Z" } }, "source": [ @@ -1077,7 +1164,7 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data", @@ -1091,9 +1178,9 @@ "text": [ "\n", "[指标分析]\n", - " 最佳验证 l1: 0.063698\n", - " 最佳迭代轮数: 16\n", - " 早停建议: 如果验证指标连续10轮不下降，建议在第 16 轮停止训练\n", + " 最佳验证 l1: 0.063425\n", + " 最佳迭代轮数: 285\n", + " 早停建议: 如果验证指标连续10轮不下降，建议在第 285 轮停止训练\n", "\n", "[重要提醒] 验证集仅用于早停/调参，测试集完全独立于训练过程！\n" ] @@ -1112,8 +1199,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:21.684871Z", - "start_time": "2026-03-09T14:21:21.668727Z" + "end_time": "2026-03-09T15:27:15.311741Z", + "start_time": "2026-03-09T15:27:15.288652Z" } }, "source": [ @@ -1152,43 +1239,43 @@ "训练结果\n", "================================================================================\n", "\n", - "结果数据形状: (282000, 42)\n", - "结果列: ['ts_code', 'trade_date', 'turnover_rate', 'open', 'vol', 'close', 'total_mv', 'f_ann_date', 'revenue', 'n_income', 'total_hldr_eqy_exc_min_int', 'total_cur_assets', 'total_liab', 'total_cur_liab', 'roe', 'ebitda', 'n_cashflow_act', 'ma_5', 'ma_20', 'ma_ratio_5_20', 'bias_10', 'bbi_ratio', 'return_5', 'return_20', 'momentum_accel', 'volatility_ratio', 'reversal_1', 'volume_ratio', 'turnover_rate_mean_5', 'turnover_deviation', 'net_profit_growth', 'revenue_growth', 'roe_delta', 'debt_to_equity', 'current_ratio', 'EP_rank', 'BP_rank', 'market_cap_rank', 'cashflow_act_rank', 'ebitda_rank', 'future_return_5', 'prediction']\n", + "结果数据形状: (282000, 71)\n", + "结果列: ['ts_code', 'trade_date', 'high', 'low', 'amount', 'vol', 'close', 'turnover_rate', 'open', 'total_assets', 'total_mv', 'f_ann_date', 'total_cur_liab', 'total_cur_assets', 'total_hldr_eqy_exc_min_int', 'total_liab', 'revenue', 'n_income', 'n_cashflow_act', 'ebit', 'ma_5', 'ma_20', 'ma_ratio_5_20', 'bias_10', 'high_low_ratio', 'bbi_ratio', 'return_5', 'return_20', 'kaufman_ER_20', 'mom_acceleration_10_20', 'drawdown_from_high_60', 'up_days_ratio_20', 'volatility_5', 'volatility_20', 'volatility_ratio', 'std_return_20', 'sharpe_ratio_20', 'min_ret_20', 'volatility_squeeze_5_60', 'overnight_intraday_diff', 'upper_shadow_ratio', 'capital_retention_20', 'max_ret_20', 'volume_ratio_5_20', 'turnover_rate_mean_5', 'turnover_deviation', 'amihud_illiq_20', 'turnover_cv_20', 'pv_corr_20', 'close_vwap_deviation', 'roe', 'roa', 'profit_margin', 'debt_to_equity', 'current_ratio', 'net_profit_yoy', 'revenue_yoy', 'healthy_expansion_velocity', 'EP', 'BP', 'CP', 'market_cap_rank', 'turnover_rank', 'return_5_rank', 'EP_rank', 'pe_expansion_trend', 'value_price_divergence', 'active_market_cap', 'ebit_rank', 'future_return_5', 'prediction']\n", "\n", "结果前10行预览:\n", - "shape: (10, 42)\n", - "┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", - "│ ts_code ┆ trade_dat ┆ turnover_ ┆ open ┆ … ┆ cashflow_ ┆ ebitda_ra ┆ future_re ┆ predictio │\n", - "│ --- ┆ e ┆ rate ┆ --- ┆ ┆ act_rank ┆ nk ┆ turn_5 ┆ n │\n", - "│ str ┆ --- ┆ --- ┆ f64 ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ str ┆ f64 ┆ ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", - "╞═══════════╪═══════════╪═══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", - "│ 000004.SZ ┆ 20250102 ┆ 1.881022 ┆ 2.213796 ┆ … ┆ -0.616292 ┆ null ┆ -0.066193 ┆ 0.008414 │\n", - "│ 000004.SZ ┆ 20250103 ┆ 1.972985 ┆ 2.34744 ┆ … ┆ -0.616573 ┆ null ┆ 0.00893 ┆ 0.004788 │\n", - "│ 000004.SZ ┆ 20250106 ┆ 1.11025 ┆ 1.835256 ┆ … ┆ -0.616011 ┆ null ┆ -0.0142 ┆ 0.011516 │\n", - "│ 000004.SZ ┆ 20250107 ┆ 1.13863 ┆ 1.906626 ┆ … ┆ -0.616854 ┆ null ┆ 0.013031 ┆ 0.01138 │\n", - "│ 000004.SZ ┆ 20250108 ┆ 1.440421 ┆ 2.01438 ┆ … ┆ -0.616854 ┆ null ┆ 0.00442 ┆ -0.000694 │\n", - "│ 000004.SZ ┆ 20250109 ┆ 1.067777 ┆ 2.10884 ┆ … ┆ -0.617135 ┆ null ┆ 0.024865 ┆ -0.004267 │\n", - "│ 000004.SZ ┆ 20250110 ┆ 1.048783 ┆ 2.080153 ┆ … ┆ -0.616854 ┆ null ┆ 0.073486 ┆ -0.005361 │\n", - "│ 000004.SZ ┆ 20250113 ┆ 0.783364 ┆ 1.832457 ┆ … ┆ -0.615668 ┆ null ┆ -0.04458 ┆ 0.001935 │\n", - "│ 000004.SZ ┆ 20250114 ┆ 0.971525 ┆ 1.87234 ┆ … ┆ -0.613797 ┆ null ┆ -0.152621 ┆ 0.003452 │\n", - "│ 000004.SZ ┆ 20250115 ┆ 1.829191 ┆ 2.168315 ┆ … ┆ -0.613797 ┆ null ┆ -0.152621 ┆ 0.007143 │\n", - "└───────────┴───────────┴───────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘\n", + "shape: (10, 71)\n", + "┌───────────┬────────────┬──────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", + "│ ts_code ┆ trade_date ┆ high ┆ low ┆ … ┆ active_ma ┆ ebit_rank ┆ future_re ┆ predictio │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ rket_cap ┆ --- ┆ turn_5 ┆ n │\n", + "│ str ┆ str ┆ f64 ┆ f64 ┆ ┆ --- ┆ f64 ┆ --- ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ f64 ┆ ┆ f64 ┆ f64 │\n", + "╞═══════════╪════════════╪══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", + "│ 000004.SZ ┆ 20250102 ┆ 2.325435 ┆ 2.168285 ┆ … ┆ 1.772242 ┆ null ┆ -0.066193 ┆ 0.023749 │\n", + "│ 000004.SZ ┆ 20250103 ┆ 2.328181 ┆ 2.003659 ┆ … ┆ 1.567863 ┆ null ┆ 0.00893 ┆ 0.041868 │\n", + "│ 000004.SZ ┆ 20250106 ┆ 1.918286 ┆ 1.811951 ┆ … ┆ 1.531265 ┆ null ┆ -0.0142 ┆ 0.054817 │\n", + "│ 000004.SZ ┆ 20250107 ┆ 1.971154 ┆ 1.971589 ┆ … ┆ 1.664744 ┆ null ┆ 0.013031 ┆ 0.025283 │\n", + "│ 000004.SZ ┆ 20250108 ┆ 2.071396 ┆ 2.040718 ┆ … ┆ 1.748053 ┆ null ┆ 0.00442 ┆ 0.013557 │\n", + "│ 000004.SZ ┆ 20250109 ┆ 2.085814 ┆ 2.134077 ┆ … ┆ 1.752624 ┆ null ┆ 0.024865 ┆ 0.005967 │\n", + "│ 000004.SZ ┆ 20250110 ┆ 2.03844 ┆ 1.928116 ┆ … ┆ 1.563176 ┆ null ┆ 0.073486 ┆ 0.012488 │\n", + "│ 000004.SZ ┆ 20250113 ┆ 1.800879 ┆ 1.745673 ┆ … ┆ 1.354823 ┆ null ┆ -0.04458 ┆ 0.010749 │\n", + "│ 000004.SZ ┆ 20250114 ┆ 1.993125 ┆ 1.936668 ┆ … ┆ 1.362504 ┆ null ┆ -0.152621 ┆ 0.004506 │\n", + "│ 000004.SZ ┆ 20250115 ┆ 2.188803 ┆ 2.154032 ┆ … ┆ 1.361079 ┆ null ┆ -0.152621 ┆ 0.005335 │\n", + "└───────────┴────────────┴──────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘\n", "\n", "结果后5行预览:\n", - "shape: (5, 42)\n", - "┌───────────┬───────────┬───────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", - "│ ts_code ┆ trade_dat ┆ turnover_ ┆ open ┆ … ┆ cashflow_ ┆ ebitda_ra ┆ future_re ┆ predictio │\n", - "│ --- ┆ e ┆ rate ┆ --- ┆ ┆ act_rank ┆ nk ┆ turn_5 ┆ n │\n", - "│ str ┆ --- ┆ --- ┆ f64 ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ ┆ str ┆ f64 ┆ ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", - "╞═══════════╪═══════════╪═══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", - "│ 605588.SH ┆ 20260302 ┆ 0.009947 ┆ 2.541258 ┆ … ┆ 1.00409 ┆ null ┆ null ┆ 0.00702 │\n", - "│ 605588.SH ┆ 20260303 ┆ 0.133214 ┆ 2.553853 ┆ … ┆ 1.372931 ┆ null ┆ null ┆ 0.021044 │\n", - "│ 605588.SH ┆ 20260304 ┆ 0.021087 ┆ 2.167616 ┆ … ┆ 1.004146 ┆ null ┆ null ┆ 0.01389 │\n", - "│ 605588.SH ┆ 20260305 ┆ -0.069317 ┆ 2.15852 ┆ … ┆ 1.003572 ┆ null ┆ null ┆ 0.010792 │\n", - "│ 605588.SH ┆ 20260306 ┆ -0.288642 ┆ 2.173913 ┆ … ┆ 1.002681 ┆ null ┆ null ┆ 0.009205 │\n", - "└───────────┴───────────┴───────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘\n", + "shape: (5, 71)\n", + "┌───────────┬────────────┬──────────┬──────────┬───┬───────────┬───────────┬───────────┬───────────┐\n", + "│ ts_code ┆ trade_date ┆ high ┆ low ┆ … ┆ active_ma ┆ ebit_rank ┆ future_re ┆ predictio │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ rket_cap ┆ --- ┆ turn_5 ┆ n │\n", + "│ str ┆ str ┆ f64 ┆ f64 ┆ ┆ --- ┆ f64 ┆ --- ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ f64 ┆ ┆ f64 ┆ f64 │\n", + "╞═══════════╪════════════╪══════════╪══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n", + "│ 605588.SH ┆ 20260302 ┆ 2.566428 ┆ 2.53816 ┆ … ┆ 0.112914 ┆ null ┆ null ┆ -0.002814 │\n", + "│ 605588.SH ┆ 20260303 ┆ 2.475112 ┆ 2.280174 ┆ … ┆ 0.061385 ┆ null ┆ null ┆ 0.021067 │\n", + "│ 605588.SH ┆ 20260304 ┆ 2.173698 ┆ 2.114835 ┆ … ┆ 0.028725 ┆ null ┆ null ┆ 0.007579 │\n", + "│ 605588.SH ┆ 20260305 ┆ 2.18537 ┆ 2.219597 ┆ … ┆ 0.044869 ┆ null ┆ null ┆ 0.006004 │\n", + "│ 605588.SH ┆ 20260306 ┆ 2.171638 ┆ 2.201068 ┆ … ┆ 0.021626 ┆ null ┆ null ┆ 0.004578 │\n", + "└───────────┴────────────┴──────────┴──────────┴───┴───────────┴───────────┴───────────┴───────────┘\n", "\n", "每日预测样本数统计:\n", " 最小: 1000\n", @@ -1202,16 +1289,16 @@ "│ --- ┆ --- ┆ --- ┆ --- │\n", "│ str ┆ str ┆ f64 ┆ f64 │\n", "╞═══════════╪════════════╪═════════════════╪════════════╡\n", - "│ 000004.SZ ┆ 20250102 ┆ -0.066193 ┆ 0.008414 │\n", - "│ 000007.SZ ┆ 20250102 ┆ 0.019858 ┆ -0.000127 │\n", - "│ 000010.SZ ┆ 20250102 ┆ 0.076274 ┆ -0.002403 │\n", - "│ 000014.SZ ┆ 20250102 ┆ -0.064651 ┆ -0.002853 │\n", - "│ 000040.SZ ┆ 20250102 ┆ -0.093583 ┆ -0.077776 │\n", - "│ 000042.SZ ┆ 20250102 ┆ -0.035958 ┆ 0.003656 │\n", - "│ 000056.SZ ┆ 20250102 ┆ -0.033205 ┆ 0.018378 │\n", - "│ 000068.SZ ┆ 20250102 ┆ -0.021277 ┆ 0.010857 │\n", - "│ 000153.SZ ┆ 20250102 ┆ -0.018193 ┆ 0.001929 │\n", - "│ 000159.SZ ┆ 20250102 ┆ -0.067833 ┆ 0.003916 │\n", + "│ 000004.SZ ┆ 20250102 ┆ -0.066193 ┆ 0.023749 │\n", + "│ 000007.SZ ┆ 20250102 ┆ 0.019858 ┆ 0.000582 │\n", + "│ 000010.SZ ┆ 20250102 ┆ 0.076274 ┆ 0.004461 │\n", + "│ 000014.SZ ┆ 20250102 ┆ -0.064651 ┆ 0.006334 │\n", + "│ 000040.SZ ┆ 20250102 ┆ -0.093583 ┆ -0.102073 │\n", + "│ 000042.SZ ┆ 20250102 ┆ -0.035958 ┆ 0.017509 │\n", + "│ 000056.SZ ┆ 20250102 ┆ -0.033205 ┆ 0.020625 │\n", + "│ 000068.SZ ┆ 20250102 ┆ -0.021277 ┆ 0.00916 │\n", + "│ 000153.SZ ┆ 20250102 ┆ -0.018193 ┆ 0.003244 │\n", + "│ 000159.SZ ┆ 20250102 ┆ -0.067833 ┆ 0.020453 │\n", "└───────────┴────────────┴─────────────────┴────────────┘\n" ] } @@ -1228,8 +1315,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:21.992341Z", - "start_time": "2026-03-09T14:21:21.689211Z" + "end_time": "2026-03-09T15:27:15.611776Z", + "start_time": "2026-03-09T15:27:15.317283Z" } }, "cell_type": "code", @@ -1301,17 +1388,17 @@ "│ --- ┆ --- ┆ --- │\n", "│ str ┆ f64 ┆ str │\n", "╞════════════╪══════════╪═══════════╡\n", - "│ 2025-01-02 ┆ 0.030035 ┆ 002427.SZ │\n", - "│ 2025-01-02 ┆ 0.02861 ┆ 002076.SZ │\n", - "│ 2025-01-02 ┆ 0.026407 ┆ 000518.SZ │\n", - "│ 2025-01-02 ┆ 0.026051 ┆ 603838.SH │\n", - "│ 2025-01-02 ┆ 0.025572 ┆ 002199.SZ │\n", + "│ 2025-01-02 ┆ 0.056168 ┆ 600421.SH │\n", + "│ 2025-01-02 ┆ 0.048049 ┆ 000668.SZ │\n", + "│ 2025-01-02 ┆ 0.042422 ┆ 000586.SZ │\n", + "│ 2025-01-02 ┆ 0.036414 ┆ 002076.SZ │\n", + "│ 2025-01-02 ┆ 0.035977 ┆ 301176.SZ │\n", "│ … ┆ … ┆ … │\n", - "│ 2025-01-06 ┆ 0.035263 ┆ 002427.SZ │\n", - "│ 2025-01-06 ┆ 0.032408 ┆ 600962.SH │\n", - "│ 2025-01-06 ┆ 0.03104 ┆ 605298.SH │\n", - "│ 2025-01-06 ┆ 0.030765 ┆ 301006.SZ │\n", - "│ 2025-01-06 ┆ 0.030541 ┆ 002522.SZ │\n", + "│ 2025-01-06 ┆ 0.069458 ┆ 600421.SH │\n", + "│ 2025-01-06 ┆ 0.067984 ┆ 603316.SH │\n", + "│ 2025-01-06 ┆ 0.062674 ┆ 301024.SZ │\n", + "│ 2025-01-06 ┆ 0.062283 ┆ 002691.SZ │\n", + "│ 2025-01-06 ┆ 0.060667 ┆ 000668.SZ │\n", "└────────────┴──────────┴───────────┘\n" ] } @@ -1329,8 +1416,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:22.000983Z", - "start_time": "2026-03-09T14:21:21.996749Z" + "end_time": "2026-03-09T15:27:15.622544Z", + "start_time": "2026-03-09T15:27:15.617560Z" } }, "source": [ @@ -1350,30 +1437,55 @@ "text": [ "\n", "特征重要性:\n", - "bias_10 883.242468\n", - "bbi_ratio 719.403482\n", - "reversal_1 616.343038\n", - "turnover_deviation 563.883571\n", - "turnover_rate_mean_5 553.710151\n", - "ma_ratio_5_20 493.930772\n", - "return_20 477.078960\n", - "return_5 465.129010\n", - "volume_ratio 438.578614\n", - "roe 386.244247\n", - "EP_rank 324.897361\n", - "ma_20 313.170879\n", - "momentum_accel 273.272078\n", - "net_profit_growth 227.346561\n", - "BP_rank 210.965235\n", - "volatility_ratio 200.314833\n", - "roe_delta 153.102754\n", - "cashflow_act_rank 136.542178\n", - "ma_5 116.355596\n", - "market_cap_rank 89.142452\n", - "debt_to_equity 68.378507\n", - "current_ratio 63.483876\n", - "revenue_growth 0.000000\n", - "ebitda_rank 0.000000\n", + "bias_10 966.502486\n", + "return_5_rank 960.507971\n", + "pe_expansion_trend 947.437883\n", + "return_5 860.686048\n", + "high_low_ratio 727.172214\n", + "revenue_yoy 562.656819\n", + "turnover_rank 558.328422\n", + "amihud_illiq_20 388.591107\n", + "overnight_intraday_diff 380.723414\n", + "bbi_ratio 373.573519\n", + "min_ret_20 366.693362\n", + "drawdown_from_high_60 356.182625\n", + "roa 334.564408\n", + "active_market_cap 275.635468\n", + "ma_ratio_5_20 261.307464\n", + "turnover_deviation 194.988921\n", + "return_20 192.620460\n", + "net_profit_yoy 175.035165\n", + "mom_acceleration_10_20 171.881004\n", + "turnover_rate_mean_5 168.826981\n", + "EP_rank 163.394623\n", + "volume_ratio_5_20 158.494889\n", + "turnover_cv_20 153.915966\n", + "healthy_expansion_velocity 146.708935\n", + "EP 142.683564\n", + "max_ret_20 135.638350\n", + "std_return_20 133.794949\n", + "sharpe_ratio_20 108.012163\n", + "close_vwap_deviation 103.748435\n", + "ma_20 80.322421\n", + "volatility_squeeze_5_60 77.509971\n", + "BP 64.954998\n", + "ma_5 60.884566\n", + "roe 59.556815\n", + "volatility_ratio 53.867742\n", + "capital_retention_20 45.922123\n", + "volatility_5 41.385944\n", + "volatility_20 34.698196\n", + "profit_margin 29.828428\n", + "debt_to_equity 28.911864\n", + "pv_corr_20 27.893618\n", + "current_ratio 22.564712\n", + "upper_shadow_ratio 16.570660\n", + "kaufman_ER_20 16.416594\n", + "market_cap_rank 15.822645\n", + "CP 13.325475\n", + "up_days_ratio_20 10.937868\n", + "value_price_divergence 0.000000\n", + "ebit_rank 0.000000\n", "dtype: float64\n", "\n", "================================================================================\n", @@ -1399,8 +1511,8 @@ "cell_type": "code", "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:22.011078Z", - "start_time": "2026-03-09T14:21:22.007446Z" + "end_time": "2026-03-09T15:27:15.634579Z", + "start_time": "2026-03-09T15:27:15.631711Z" } }, "source": [ @@ -1420,7 +1532,7 @@ "output_type": "stream", "text": [ "模型类型: \n", - "特征数量: 24\n" + "特征数量: 49\n" ] } ], @@ -1441,8 +1553,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2026-03-09T14:21:22.149719Z", - "start_time": "2026-03-09T14:21:22.016953Z" + "end_time": "2026-03-09T15:27:15.796755Z", + "start_time": "2026-03-09T15:27:15.641538Z" } }, "cell_type": "code", @@ -1492,7 +1604,7 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data", @@ -1506,35 +1618,60 @@ "text": [ "\n", "[特征重要性排名 - Gain]\n", - "bias_10 883.242468\n", - "bbi_ratio 719.403482\n", - "reversal_1 616.343038\n", - "turnover_deviation 563.883571\n", - "turnover_rate_mean_5 553.710151\n", - "ma_ratio_5_20 493.930772\n", - "return_20 477.078960\n", - "return_5 465.129010\n", - "volume_ratio 438.578614\n", - "roe 386.244247\n", - "EP_rank 324.897361\n", - "ma_20 313.170879\n", - "momentum_accel 273.272078\n", - "net_profit_growth 227.346561\n", - "BP_rank 210.965235\n", - "volatility_ratio 200.314833\n", - "roe_delta 153.102754\n", - "cashflow_act_rank 136.542178\n", - "ma_5 116.355596\n", - "market_cap_rank 89.142452\n", - "debt_to_equity 68.378507\n", - "current_ratio 63.483876\n", - "revenue_growth 0.000000\n", - "ebitda_rank 0.000000\n", + "bias_10 966.502486\n", + "return_5_rank 960.507971\n", + "pe_expansion_trend 947.437883\n", + "return_5 860.686048\n", + "high_low_ratio 727.172214\n", + "revenue_yoy 562.656819\n", + "turnover_rank 558.328422\n", + "amihud_illiq_20 388.591107\n", + "overnight_intraday_diff 380.723414\n", + "bbi_ratio 373.573519\n", + "min_ret_20 366.693362\n", + "drawdown_from_high_60 356.182625\n", + "roa 334.564408\n", + "active_market_cap 275.635468\n", + "ma_ratio_5_20 261.307464\n", + "turnover_deviation 194.988921\n", + "return_20 192.620460\n", + "net_profit_yoy 175.035165\n", + "mom_acceleration_10_20 171.881004\n", + "turnover_rate_mean_5 168.826981\n", + "EP_rank 163.394623\n", + "volume_ratio_5_20 158.494889\n", + "turnover_cv_20 153.915966\n", + "healthy_expansion_velocity 146.708935\n", + "EP 142.683564\n", + "max_ret_20 135.638350\n", + "std_return_20 133.794949\n", + "sharpe_ratio_20 108.012163\n", + "close_vwap_deviation 103.748435\n", + "ma_20 80.322421\n", + "volatility_squeeze_5_60 77.509971\n", + "BP 64.954998\n", + "ma_5 60.884566\n", + "roe 59.556815\n", + "volatility_ratio 53.867742\n", + "capital_retention_20 45.922123\n", + "volatility_5 41.385944\n", + "volatility_20 34.698196\n", + "profit_margin 29.828428\n", + "debt_to_equity 28.911864\n", + "pv_corr_20 27.893618\n", + "current_ratio 22.564712\n", + "upper_shadow_ratio 16.570660\n", + "kaufman_ER_20 16.416594\n", + "market_cap_rank 15.822645\n", + "CP 13.325475\n", + "up_days_ratio_20 10.937868\n", + "value_price_divergence 0.000000\n", + "ebit_rank 0.000000\n", "dtype: float64\n", "\n", "[低重要性特征] 以下2个特征重要性为0，可考虑删除:\n", - " - revenue_growth\n", - " - ebitda_rank\n" + " - value_price_divergence\n", + " - ebit_rank\n" ] } ], diff --git a/src/factors/translator.py b/src/factors/translator.py index 2c79443..43258bf 100644 --- a/src/factors/translator.py +++ b/src/factors/translator.py @@ -78,6 +78,7 @@ class PolarsTranslator: self.register_handler("cs_neutral", self._handle_cs_neutral) # 元素级数学函数 (element_wise) + self.register_handler("abs", self._handle_abs) self.register_handler("log", self._handle_log) self.register_handler("exp", self._handle_exp) self.register_handler("sqrt", self._handle_sqrt) @@ -297,23 +298,23 @@ class PolarsTranslator: @time_series def _handle_ts_corr(self, node: FunctionNode) -> pl.Expr: - """处理 ts_corr(x, y, window) -> rolling_corr(y, window)。""" + """处理 ts_corr(x, y, window) -> rolling_corr(x, y, window_size)。""" if len(node.args) != 3: raise ValueError("ts_corr 需要 3 个参数: (x, y, window)") x = self.translate(node.args[0]) y = self.translate(node.args[1]) window = self._extract_window(node.args[2]) - return x.rolling_corr(y, window_size=window) + return pl.rolling_corr(x, y, window_size=window) @time_series def _handle_ts_cov(self, node: FunctionNode) -> pl.Expr: - """处理 ts_cov(x, y, window) -> rolling_cov(y, window)。""" + """处理 ts_cov(x, y, window) -> rolling_cov(x, y, window_size)。""" if len(node.args) != 3: raise ValueError("ts_cov 需要 3 个参数: (x, y, window)") x = self.translate(node.args[0]) y = self.translate(node.args[1]) window = self._extract_window(node.args[2]) - return x.rolling_cov(y, window_size=window) + return pl.rolling_cov(x, y, window_size=window) @time_series def _handle_ts_var(self, node: FunctionNode) -> pl.Expr: @@ -494,6 +495,14 @@ class PolarsTranslator: expr = self.translate(node.args[0]) return expr.log() + @element_wise + def _handle_abs(self, node: FunctionNode) -> pl.Expr: + """处理 abs(expr) -> 绝对值。""" + if len(node.args) != 1: + raise ValueError("abs 需要 1 个参数: (expr)") + expr = self.translate(node.args[0]) + return expr.abs() + @element_wise def _handle_exp(self, node: FunctionNode) -> pl.Expr: """处理 exp(expr) -> 指数函数。"""