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feat(strategy): filter_history 列依赖 AST 推导 — 消灭回测全量兜底 (P1a)
- feature_deps: 追踪 pl.col/cols 常量引用推导输入列; 动态列名/pl.all/cs.* 等不可证明形态一律返回 None, 保持全量兜底 (fail-closed, 宁慢不错列) - engine: REQUIRED_FEATURES 缺失且存在动态过滤时加载期自动推导 - bench_feature_resolution: legacy(空依赖) vs 推导 的解析宽度对比基准 - 实测: 看板策略等 4 个无声明策略 37→3~5 指标列 (宽度比 7~12x); short_term_top10 动态列名循环正确保持兜底 Signed-off-by: shy3130 <shy3130@qq.com>
This commit is contained in:
shy3130
@@ -233,6 +233,28 @@ class _RealtimeMatrixEntry:
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buffer: Any
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def _resolve_required_features(meta: dict, mod, filter_history_fn) -> frozenset[str]:
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"""显式声明 (META/module REQUIRED_FEATURES) 优先; 缺失且存在动态
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filter_history 时, AST 推导列依赖兜底 — 推导不可证明则保持为空,
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回测层继续走全量指标计算 (fail-closed)。"""
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declared = frozenset(meta.get("required_features", []) or []) | frozenset(
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getattr(mod, "REQUIRED_FEATURES", []) or []
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)
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if declared or filter_history_fn is None:
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return declared
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from app.strategy.feature_deps import infer_filter_history_features
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inferred = infer_filter_history_features(filter_history_fn)
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if inferred is None:
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return declared
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logger.info(
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"strategy %s: filter_history AST 推导出 %d 个依赖列, 免除回测全量兜底",
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meta.get("id", "<unknown>"),
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len(inferred),
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)
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return inferred
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class StrategyEngine:
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"""策略引擎 — 策略加载 + 执行 + 评分"""
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@@ -582,8 +604,7 @@ class StrategyEngine:
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max_hold_days=getattr(mod, "MAX_HOLD_DAYS", None),
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filter_fn=filter_fn,
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filter_history_fn=filter_history_fn,
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required_features=frozenset(meta.get("required_features", []) or [])
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| frozenset(getattr(mod, "REQUIRED_FEATURES", []) or []),
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required_features=_resolve_required_features(meta, mod, filter_history_fn),
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lookback_days=int(getattr(mod, "LOOKBACK_DAYS", meta.get("lookback_days", 1)) or 1),
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source=source,
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file_path=path,
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@@ -0,0 +1,100 @@
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"""filter_history 列依赖的 AST 推导 — REQUIRED_FEATURES 缺失时的自动补全。
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背景: 声明了 filter_history (动态 Python 过滤) 但没有声明 REQUIRED_FEATURES 的
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策略, 回测无法静态推断需要哪些列, 只能退回"计算全部指标"(backtest/strategy.py
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的 full_fallback, 结果正确但白白多算)。本模块对函数体做 AST 静态追踪, 收集
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列引用常量, 让绝大多数此类策略拿到精确依赖, 消灭全量兜底。
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fail-closed 契约: 任何无法证明的形态 — 动态列名、pl.all()/polars.selectors、
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完全收集不到列引用 — 一律返回 None, 调用方保持原有全量兜底。宁可慢,
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绝不漏列 (漏列会静默算错, 是金融数据的 P0 事故形态)。
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"""
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from __future__ import annotations
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import ast
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import inspect
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import logging
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import textwrap
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logger = logging.getLogger(__name__)
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# 列引用形态: <...>.col("x") / 裸 col("x") / pl.cols("a", "b")。
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_COL_FORMS = ("pl.col", "col")
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_COLS_FORMS = ("pl.cols", "cols")
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# 列名动态函数 (polars 表达式里按正则/前后缀选列): 出现即不可推导。
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_DYNAMIC_NAME_FUNCS = frozenset({"contains", "starts_with", "ends_with", "matches"})
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def _dotted(func: ast.expr) -> str | None:
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"""AST 调用目标 → 点链名 ("pl.col" / "col" / "cs.numeric" / None)。"""
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if isinstance(func, ast.Name):
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return func.id
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if isinstance(func, ast.Attribute):
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base = _dotted(func.value)
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return f"{base}.{func.attr}" if base else func.attr
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return None
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def _is_dynamic_name_expr(expr: ast.expr) -> bool:
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"""col(...) 参数是否为动态列名 (非常量字符串)。"""
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return not (isinstance(expr, ast.Constant) and isinstance(expr.value, str))
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def _collect_from_source(source: str) -> tuple[set[str], bool]:
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"""返回 (收集到的列名, 是否存在不可证明形态)。"""
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tree = ast.parse(source)
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names: set[str] = set()
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uninferable = False
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for node in ast.walk(tree):
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if not isinstance(node, ast.Call):
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continue
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dotted = _dotted(node.func)
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if dotted is None:
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continue
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# pl.all() 是列选择器 (裸 all(...) 是 python 内建, 与列无关, 忽略);
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# polars.selectors (import polars.selectors as cs; cs.numeric()) 全族同样不可推导。
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if dotted == "pl.all" or dotted.startswith("cs."):
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uninferable = True
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elif dotted in _COL_FORMS:
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if not node.args:
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continue
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if _is_dynamic_name_expr(node.args[0]):
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# 动态列名: col(f"{p}_col") / col(name_var) — 漏掉就是错列。
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uninferable = True
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else:
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assert isinstance(node.args[0], ast.Constant)
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names.add(node.args[0].value)
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elif dotted in _COLS_FORMS:
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for arg in node.args:
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if _is_dynamic_name_expr(arg):
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uninferable = True
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else:
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assert isinstance(arg, ast.Constant)
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names.add(arg.value)
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return names, uninferable
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def infer_filter_history_features(fn) -> frozenset[str] | None:
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"""推导 filter_history 引用的输入列; None = 不可推导 (调用方保持全量兜底)。
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只保留已知输入列 (enriched 存储/指标/证券维表), `_` 前缀的中间列
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(函数内 with_columns 先建后用的临时列) 不是输入依赖, 剔除。
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"""
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from app.indicators.pipeline import ENRICHED_STORAGE_COLS, INDICATOR_COLUMNS
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try:
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# 嵌套定义的函数 getsource 带原始缩进, 直接 parse 会 IndentationError。
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source = textwrap.dedent(inspect.getsource(fn))
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except (OSError, TypeError) as e:
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logger.debug("feature inference: no source for %r: %s", getattr(fn, "__name__", fn), e)
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return None
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try:
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names, uninferable = _collect_from_source(source)
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except SyntaxError:
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return None # 已加载模块不应有语法错误; 防御性 fail-closed
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if uninferable or not names:
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# 收集不到列引用 (如 df["x"] 下标风格) 与存在动态形态同样不可证明。
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return None
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known = set(ENRICHED_STORAGE_COLS) | set(INDICATOR_COLUMNS) | {"name", "total_shares", "float_shares"}
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features = frozenset(n for n in names if n in known and not n.startswith("_"))
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return features or None
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@@ -0,0 +1,120 @@
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"""P0/P1a 基准: 动态 filter_history 策略的依赖解析宽度对比。
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对同一策略比较两种依赖声明下的 ResolvedFeaturePlan:
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legacy — required_features 为空 (P1a 之前的行为, 触发全量指标兜底)
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inferred — 引擎 AST 推导结果 (P1a 之后, 不可推导的策略无此行)
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记录解析耗时与 indicator/base 列宽。数据加载 IO 大致随列宽线性增长,
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宽度比即回测数据准备阶段的近似提速上界 (非端到端回测耗时)。
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用法:
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cd backend && uv run python scripts/bench_feature_resolution.py [strategy_id ...]
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不传 id 时扫描全部 filter_history 策略。--json PATH 另存结果。
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"""
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from __future__ import annotations
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import argparse
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import dataclasses
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import json
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import logging
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import sys
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import time
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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# legacy 臂会故意触发全量兜底 WARNING, 基准输出只保留表格。
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logging.getLogger("app.backtest.strategy").setLevel(logging.ERROR)
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from app.backtest.strategy import StrategyDependencyResolver # noqa: E402
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from app.config import settings # noqa: E402
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from app.strategy.engine import StrategyEngine # noqa: E402
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def _build_engine() -> StrategyEngine:
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dirs = [
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Path(__file__).resolve().parents[1] / "app" / "strategy" / "research",
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settings.data_dir / "strategies" / "custom",
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settings.data_dir / "strategies" / "ai",
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settings.data_dir / "strategies" / "composite",
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]
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return StrategyEngine(strategy_dirs=[d for d in dirs if d.exists()])
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def _resolve_quiet(resolver: StrategyDependencyResolver, strategy, required: frozenset):
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"""给定 required_features 解析 FeaturePlan, 返回 (耗时ms, plan)。"""
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patched = dataclasses.replace(strategy, required_features=required)
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t0 = time.perf_counter()
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plan = resolver.resolve(
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patched,
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params={p.get("id"): p.get("default") for p in strategy.meta.get("params", [])},
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basic_filter=strategy.basic_filter or {"enabled": False},
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entry_signals=strategy.entry_signals,
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exit_signals=strategy.exit_signals,
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)
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elapsed_ms = (time.perf_counter() - t0) * 1000
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return elapsed_ms, plan
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("ids", nargs="*", help="策略 id; 缺省扫描全部动态策略")
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parser.add_argument("--json", dest="json_path", default=None)
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args = parser.parse_args()
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engine = _build_engine()
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resolver = StrategyDependencyResolver()
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strategies = [
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s for s in engine.list_strategies_full() if s.filter_history_fn is not None
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] if hasattr(engine, "list_strategies_full") else []
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if not strategies:
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# list 接口名差异兜底: 直接遍历注册表
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strategies = [
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s for s in engine._strategies.values() if s.filter_history_fn is not None
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]
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if args.ids:
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strategies = [s for s in strategies if s.meta.get("id") in set(args.ids)]
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rows: list[dict] = []
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for s in strategies:
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sid = s.meta.get("id", "<unknown>")
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declared = bool(s.required_features)
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legacy_ms, legacy_plan = _resolve_quiet(resolver, s, frozenset())
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inferred_ms, inferred_plan = _resolve_quiet(resolver, s, s.required_features)
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legacy_w = len(legacy_plan.indicator_columns)
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inferred_w = len(inferred_plan.indicator_columns)
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rows.append({
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"strategy": sid,
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"declared_required_features": declared,
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"legacy": {
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"resolve_ms": round(legacy_ms, 2),
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"indicator_cols": legacy_w,
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"base_cols": len(legacy_plan.base_columns),
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},
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"inferred": {
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"resolve_ms": round(inferred_ms, 2),
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"indicator_cols": inferred_w,
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"base_cols": len(inferred_plan.base_columns),
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},
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"indicator_width_ratio": round(legacy_w / inferred_w, 2) if inferred_w else None,
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})
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print(f"{'strategy':36} {'依赖':4} {'legacy列宽':>9} {'当前列宽':>8} {'宽度比':>6}")
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for r in rows:
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# 依赖列非空 = 显式声明或 P1a 推导; 空的唯一情形是不可证明 (保持全量兜底)。
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has_deps = "有" if r["declared_required_features"] else "无"
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ratio = r["indicator_width_ratio"]
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print(
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f"{r['strategy']:36} {has_deps:4} "
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f"{r['legacy']['indicator_cols']:>9} {r['inferred']['indicator_cols']:>8} "
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f"{(f'{ratio:.1f}x') if ratio else '—':>6}"
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)
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if args.json_path:
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Path(args.json_path).write_text(
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json.dumps({"rows": rows}, ensure_ascii=False, indent=2), encoding="utf-8"
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)
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print(f"\n已写入 {args.json_path}")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,137 @@
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"""filter_history 列依赖 AST 推导 — 消灭回测全量兜底的 P1 契约。
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回归对象: REQUIRED_FEATURES 缺失 + 存在动态 filter_history 的策略。
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- 可证明形态 (col/cols 常量引用) → 引擎加载时自动推导精确依赖;
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- 不可证明形态 (动态列名/pl.all/cs.*) → 保持空依赖, 回测继续全量兜底 (fail-closed)。
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"""
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from __future__ import annotations
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from pathlib import Path
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import polars as pl
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import polars.selectors as cs
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from app.strategy.engine import StrategyEngine
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from app.strategy.feature_deps import infer_filter_history_features
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# ── 单元: 推导函数 ───────────────────────────────────────
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def test_infer_basic_column_refs():
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def filter_history(df: pl.DataFrame, params: dict) -> pl.DataFrame:
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return df.filter(
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(pl.col("close") > pl.col("ma20"))
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& (pl.col("kdj_k") < 25)
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)
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assert infer_filter_history_features(filter_history) == frozenset({"close", "ma20", "kdj_k"})
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def test_infer_cols_and_drop_intermediates():
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def filter_history(df: pl.DataFrame, params: dict) -> pl.DataFrame:
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d = df.with_columns(pl.col("_rsv")) # 中间列, 不是输入依赖
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return d.select(pl.cols("ma5", "ma10"))
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assert infer_filter_history_features(filter_history) == frozenset({"ma5", "ma10"})
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def test_infer_dynamic_column_name_is_uninferable():
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def filter_history(df: pl.DataFrame, params: dict) -> pl.DataFrame:
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col_name = str(params.get("col", "close"))
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return df.filter(pl.col(col_name) > 1)
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assert infer_filter_history_features(filter_history) is None
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def test_infer_no_column_refs_is_uninferable():
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"""df["x"] 下标风格收集不到 → 不可证明 → None (绝不声称零依赖)。"""
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def filter_history(df: pl.DataFrame, params: dict) -> pl.DataFrame:
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if df.is_empty():
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return df
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return df.filter(df["close"] > 1)
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assert infer_filter_history_features(filter_history) is None
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def test_infer_pl_all_selector_is_uninferable():
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def filter_history(df: pl.DataFrame, params: dict) -> pl.DataFrame:
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return df.select(pl.all())
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assert infer_filter_history_features(filter_history) is None
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def test_infer_polars_selectors_is_uninferable():
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def filter_history(df: pl.DataFrame, params: dict) -> pl.DataFrame:
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return df.with_columns((cs.numeric() * 2).name.suffix("_x"))
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assert infer_filter_history_features(filter_history) is None
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def test_infer_unknown_names_filtered_out():
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def filter_history(df: pl.DataFrame, params: dict) -> pl.DataFrame:
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return df.filter(pl.col("not_a_real_column") > pl.col("close"))
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assert infer_filter_history_features(filter_history) == frozenset({"close"})
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# ── 集成: 引擎加载路径 ──────────────────────────────────
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_INFERABLE = '''\
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META = {"id": "custom_inf_ok", "name": "ok", "description": "", "tags": []}
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import polars as pl
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def filter_history(df: pl.DataFrame, params: dict) -> pl.DataFrame:
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return df.filter((pl.col("close") > pl.col("ma20")) & (pl.col("kdj_k") < 25))
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'''
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_DYNAMIC = '''\
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META = {"id": "custom_inf_dyn", "name": "dyn", "description": "", "tags": []}
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import polars as pl
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def filter_history(df: pl.DataFrame, params: dict) -> pl.DataFrame:
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col_name = str(params.get("col", "close"))
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return df.filter(pl.col(col_name) > 1)
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'''
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def _engine_with(tmp_path: Path, source: str):
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# 引擎只 glob 各 strategy_dir 顶层的 *.py, 必须传叶子目录。
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d = tmp_path / "custom"
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d.mkdir(parents=True)
|
||||
(d / f"{_meta_id(source)}.py").write_text(source, encoding="utf-8")
|
||||
return StrategyEngine(strategy_dirs=[d])
|
||||
|
||||
|
||||
def _meta_id(source: str) -> str:
|
||||
return source.split('"id": "')[1].split('"')[0]
|
||||
|
||||
|
||||
def test_engine_infers_features_when_undeclared(tmp_path):
|
||||
engine = _engine_with(tmp_path, _INFERABLE)
|
||||
assert not engine.load_errors()
|
||||
s = engine.get("custom_inf_ok")
|
||||
assert s is not None
|
||||
assert s.required_features == frozenset({"close", "ma20", "kdj_k"})
|
||||
|
||||
|
||||
def test_engine_keeps_fallback_for_dynamic(tmp_path):
|
||||
engine = _engine_with(tmp_path, _DYNAMIC)
|
||||
assert not engine.load_errors()
|
||||
s = engine.get("custom_inf_dyn")
|
||||
assert s is not None
|
||||
# 不可证明 → 不推导, required_features 保持空 (回测层继续全量兜底)
|
||||
assert s.required_features == frozenset()
|
||||
|
||||
|
||||
def test_engine_explicit_declaration_wins(tmp_path):
|
||||
source = _INFERABLE.replace(
|
||||
'META = {"id": "custom_inf_ok"',
|
||||
'REQUIRED_FEATURES = ["close"]\nMETA = {"id": "custom_inf_ok"',
|
||||
)
|
||||
engine = _engine_with(tmp_path, source)
|
||||
assert not engine.load_errors()
|
||||
assert engine.get("custom_inf_ok").required_features == frozenset({"close"})
|
||||
Reference in New Issue
Block a user