liaozhaorun
c3d1b157e9
- 添加 19 个筹码分布和胜率相关因子(包括chip_dispersion、winner_rate等系列) - LightGBM模型添加早停和训练指标记录功能 - 统一Label配置到common.py模块 - 新增list_factors.py因子列表脚本
323 lines
12 KiB
Python
323 lines
12 KiB
Python
"""因子批量注册脚本。
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使用 FactorManager 批量注册因子，用户只需提供 name、desc 和表达式，
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自动生成 factor_id 并保存到 factors.jsonl。
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使用方法:
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1. 在 FACTORS 列表中添加因子定义
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2. 运行: uv run python src/scripts/register_factors.py
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示例:
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FACTORS = [
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{
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"name": "mom_5d",
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"desc": "5日价格动量",
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"dsl": "cs_rank(close / ts_delay(close, 5) - 1)",
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"category": "momentum", # 可选扩展字段
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},
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...
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]
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"""
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import json
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import re
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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from src.factors.metadata import FactorManager
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from src.factors.metadata.exceptions import DuplicateFactorError, ValidationError
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from src.config.settings import get_settings
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# ============================================================================
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# 用户配置区域 - 在这里添加要注册的因子
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# ============================================================================
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FACTORS: List[Dict[str, Any]] =[
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# ==================== 第一类：筹码集中度与离散度因子 ====================
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{
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"name": "chip_dispersion_90",
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"desc": "90%筹码离散度：衡量市场90%持仓筹码的宽度，值越小表示筹码越高度集中（单峰密集），往往是洗盘结束的前兆",
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"dsl": "(cost_95pct - cost_5pct) / (cost_95pct + cost_5pct)",
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},
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{
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"name": "chip_dispersion_70",
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"desc": "70%核心筹码离散度：剔除极端的底部死筹和高位套牢盘，反映中间70%主流资金的成本集中度",
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"dsl": "(cost_85pct - cost_15pct) / (cost_85pct + cost_15pct)",
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},
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{
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"name": "cost_skewness",
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"desc": "筹码偏度：反映筹码分布的不对称性。大于1说明上方套牢盘拖尾严重，小于1说明下方获利盘雄厚",
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"dsl": "(cost_95pct - cost_50pct) / (cost_50pct - cost_5pct)",
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},
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{
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"name": "dispersion_change_20",
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"desc": "筹码集中度近期变化率：过去20天筹码宽度的变化比例，持续下降说明主力正在暗中吸筹",
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"dsl": "ts_pct_change((cost_95pct - cost_5pct) / cost_50pct, 20)",
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},
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# ==================== 第二类：筹码相对位置与压力/支撑因子 ====================
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{
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"name": "price_to_avg_cost",
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"desc": "整体浮盈比例：当前价格相对加权平均成本的溢价率。高溢价有均值回归压力，负溢价代表超跌",
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"dsl": "(close - weight_avg) / weight_avg",
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},
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{
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"name": "price_to_median_cost",
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"desc": "中位数成本偏离度：价格相对于50%分位点（绝对半数人持仓价）的偏离，向上突破通常是右侧买点",
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"dsl": "(close - cost_50pct) / cost_50pct",
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},
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{
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"name": "mean_median_dev",
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"desc": "均值中位数背离：均值显著大于中位数说明高位筹码堆积，上涨阻力大",
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"dsl": "(weight_avg - cost_50pct) / cost_50pct",
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},
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{
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"name": "trap_pressure",
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"desc": "高位套牢盘压力指数：当前价格距离上方95%高位套牢成本的距离。距离越大，反弹的真空期阻力越小",
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"dsl": "(cost_95pct - close) / close",
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},
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{
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"name": "bottom_profit",
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"desc": "底部支撑底仓利润率：当前价格距离底部5%筹码的利润空间。暴跌时大于0说明底仓极度稳定",
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"dsl": "(close - cost_5pct) / cost_5pct",
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},
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{
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"name": "history_position",
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"desc": "历史区间分位点：当前价格在个股上市以来历史最高点和最低点之间的相对位置",
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"dsl": "(close - his_low) / (his_high - his_low)",
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},
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# ==================== 第三类：胜率相关的动量与反转因子 ====================
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{
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"name": "winner_rate_surge_5",
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"desc": "获利盘短期爆发力：胜率在过去5天内的变化值，急剧上升是极强的动量做多信号",
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"dsl": "ts_delta(winner_rate, 5)",
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},
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{
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"name": "winner_rate_cs_rank",
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"desc": "获利盘高位反转信号：全市场胜率截面排名，极端高胜率往往面临多头踩踏的获利了结压力（反转因子）",
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"dsl": "cs_rank(winner_rate)",
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},
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{
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"name": "winner_rate_dev_20",
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"desc": "获利盘均线偏离：当前胜率相对过去20天平均胜率的偏离程度，捕捉筹码情绪的边际超买/超卖",
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"dsl": "winner_rate - ts_mean(winner_rate, 20)",
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},
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{
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"name": "winner_rate_volatility",
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"desc": "获利盘波动率：过去20天胜率的波动率。波动率低且胜率高说明单边上涨极度稳健",
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"dsl": "ts_std(winner_rate, 20)",
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},
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{
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"name": "smart_money_accumulation",
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"desc": "潜在主力吸筹隐蔽指标：胜率的60日时序分位数减去价格的时序分位数。值越大说明‘价平而获利盘增’，底部吸筹明显",
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"dsl": "ts_rank(winner_rate, 60) - ts_rank(close, 60)",
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},
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# ==================== 第四类：量价与筹码交乘因子 ====================
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{
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"name": "winner_vol_corr_20",
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"desc": "放量突破筹码密集区：胜率与成交量的20日时序相关性，正相关说明增量资金在主动解套上方筹码",
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"dsl": "ts_corr(winner_rate, vol, 20)",
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},
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{
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"name": "cost_base_momentum",
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"desc": "成本重心上移换手率：过去20天加权平均成本的变化幅度，快速上移说明高位换手极其充分",
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"dsl": "ts_pct_change(weight_avg, 20)",
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},
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{
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"name": "bottom_cost_stability",
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"desc": "底部坚如磐石因子：底部5%成本的60天波动率相对于中位数的比值，波动越小说明死筹越稳固",
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"dsl": "ts_std(cost_5pct, 60) / cost_50pct",
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},
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{
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"name": "pivot_reversion",
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"desc": "盈亏分界线乖离修复：价格偏离50%分位点除以近20日价格标准差，用于寻找超跌后的均值回归买点",
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"dsl": "(close - cost_50pct) / ts_std(close, 20)",
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},
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{
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"name": "chip_transition",
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"desc": "强弱筹码切换度：上方厚度与下方厚度差值的20日变化量。由正变负说明筹码彻底完成了自上而下的转移（洗盘结束）",
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"dsl": "ts_delta((cost_85pct - cost_50pct) - (cost_50pct - cost_15pct), 20)",
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},
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]
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# 因子存储路径（使用项目根路径下的 data 目录）
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settings = get_settings()
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OUTPUT_PATH = settings.data_path_resolved / "factors.jsonl"
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# ============================================================================
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# 核心实现
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# ============================================================================
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def get_next_factor_id(filepath: Path) -> str:
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"""生成下一个 factor_id。
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从现有文件中提取最大序号，生成新的 F_XXX 格式 ID。
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Args:
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filepath: JSONL 文件路径
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Returns:
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新的 factor_id，如 "F_001"
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"""
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if not filepath.exists():
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return "F_001"
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try:
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with open(filepath, "r", encoding="utf-8") as f:
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lines = f.readlines()
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except Exception:
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return "F_001"
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max_num = 0
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pattern = re.compile(r"^F_(\d+)$")
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for line in lines:
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line = line.strip()
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if not line:
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continue
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try:
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data = json.loads(line)
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factor_id = data.get("factor_id", "")
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match = pattern.match(factor_id)
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if match:
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num = int(match.group(1))
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max_num = max(max_num, num)
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except (json.JSONDecodeError, ValueError):
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continue
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return f"F_{max_num + 1:03d}"
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def validate_factor(factor: Dict[str, Any]) -> None:
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"""验证因子定义是否有效。
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Args:
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factor: 因子定义字典
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Raises:
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ValueError: 验证失败时抛出
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"""
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required_fields = ["name", "desc", "dsl"]
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for field in required_fields:
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if field not in factor or not factor[field]:
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raise ValueError(f"因子缺少必填字段 '{field}'")
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# 验证 name 格式（只允许字母、数字、下划线）
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if not re.match(r"^[a-zA-Z_][a-zA-Z0-9_]*$", factor["name"]):
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raise ValueError(
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f"因子名称 '{factor['name']}' 格式无效，只允许字母、数字、下划线"
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)
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def register_factors(
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factors: List[Dict[str, Any]],
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output_path: Optional[Path] = None,
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skip_duplicates: bool = True,
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) -> Dict[str, List[str]]:
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"""批量注册因子。
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Args:
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factors: 因子定义列表
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output_path: 输出文件路径，默认使用 OUTPUT_PATH
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skip_duplicates: 遇到重复因子是否跳过而不是报错
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Returns:
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注册结果统计，包含成功列表和失败列表
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"""
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output_path = output_path or OUTPUT_PATH
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manager = FactorManager(str(output_path))
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results = {
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"success": [],
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"failed": [],
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"skipped": [],
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}
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for factor in factors:
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try:
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# 验证因子定义
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validate_factor(factor)
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# 检查 name 是否已存在
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existing = manager.get_factors_by_name(factor["name"])
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if len(existing) > 0:
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if skip_duplicates:
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results["skipped"].append(factor["name"])
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print(f"[跳过] 因子 '{factor['name']}' 已存在")
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continue
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else:
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raise DuplicateFactorError(factor["name"])
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# 生成 factor_id
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factor_id = get_next_factor_id(output_path)
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# 构建完整的因子记录
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factor_record = {
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"factor_id": factor_id,
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"name": factor["name"],
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"desc": factor["desc"],
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"dsl": factor["dsl"],
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}
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# 添加可选扩展字段
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for key in ["category", "author", "tags", "notes"]:
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if key in factor:
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factor_record[key] = factor[key]
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# 注册因子
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manager.add_factor(factor_record)
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results["success"].append(factor["name"])
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print(f"[成功] {factor_id}: {factor['name']}")
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except DuplicateFactorError as e:
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results["failed"].append(factor.get("name", "unknown"))
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print(f"[失败] 因子 '{factor.get('name', 'unknown')}': {e}")
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except (ValidationError, ValueError) as e:
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results["failed"].append(factor.get("name", "unknown"))
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print(f"[失败] 因子 '{factor.get('name', 'unknown')}': {e}")
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except Exception as e:
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results["failed"].append(factor.get("name", "unknown"))
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print(f"[错误] 因子 '{factor.get('name', 'unknown')}': {e}")
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return results
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def main():
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"""主函数。"""
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print("=" * 60)
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print("因子批量注册工具")
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print("=" * 60)
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print(f"目标文件: {OUTPUT_PATH}")
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print(f"待注册因子数: {len(FACTORS)}")
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print("-" * 60)
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if not FACTORS:
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print("[警告] FACTORS 列表为空，请在脚本中配置要注册的因子")
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return
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results = register_factors(FACTORS)
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print("-" * 60)
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print("注册完成:")
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print(f" 成功: {len(results['success'])} 个")
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print(f" 跳过: {len(results['skipped'])} 个")
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print(f" 失败: {len(results['failed'])} 个")
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if results["success"]:
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print(f"\n已注册因子: {', '.join(results['success'])}")
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if results["skipped"]:
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print(f"已跳过因子: {', '.join(results['skipped'])}")
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if results["failed"]:
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print(f"失败因子: {', '.join(results['failed'])}")
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if __name__ == "__main__":
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main()
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