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shy3130
5d06af60b2
- POST /api/definitions/smoke 支持 id 直查已存定义 (smoke_stored), 统一选择器逐条冒烟无需回传原始定义; 不存在 fail-closed 404 - DefinitionPicker 可复用组件: 跨 kind 目录 (21 内置信号实测) + kind 筛选/搜索/依赖 chips/预热/坏定义隔离展示 + 逐条冒烟 - 设置页新增「定义目录」页签; Backup 导入结果新增「导入冒烟」小节 (apply 响应 smoke 逐条渲染, 失败条目带 issue 编码) - 验收: pnpm build 过; 浏览器 E2E 导出→覆盖导入 275 项→ 冒烟 21/21 通过零 issue; 后端 36 契约测试过 Signed-off-by: shy3130 <shy3130@qq.com>
229 lines
9.5 KiB
Python
229 lines
9.5 KiB
Python
"""P4 统一目录与导入冒烟 — 验收: 新增信号导入到可回测 ≤2 分钟。
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链路 (单测试自动化证明): 构建含新信号+新因子的备份 ZIP → apply 导入
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(响应内含逐条冒烟结果) → 立即用引用该信号的策略跑回测 → 成功产出信号。
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全程秒级, 远低于 2 分钟验收线。
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"""
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from __future__ import annotations
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import time
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from datetime import date, timedelta
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from pathlib import Path
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from types import SimpleNamespace
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import polars as pl
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import pytest
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from app.config import settings
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from app.definitions import catalog, smoke_definition, smoke_stored
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from app.services import config_backup
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@pytest.fixture(autouse=True)
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def _isolated_dirs(tmp_path: Path, monkeypatch):
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monkeypatch.setattr(settings, "data_dir", tmp_path)
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# 注入迁移自内置的信号定义 (signal_limit_up 等) — 回测解析硬依赖
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from tests.signal_seeds import install_pipeline_caches
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install_pipeline_caches(monkeypatch)
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yield tmp_path
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# ── 冒烟 (静态层) ────────────────────────────────────────
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_GOOD_SIGNAL = {
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"id": "smoke_ok", "name": "好信号", "kind": "entry", "timeframe": "daily",
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"conditions": [{"left": "close", "op": ">", "right": "field:ma20"}],
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}
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_TYPO_SIGNAL = {
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"id": "smoke_typo", "name": "拼错信号", "kind": "entry", "timeframe": "daily",
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"conditions": [{"left": "cloes", "op": ">", "right": 0}], # 拼错列名
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}
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_GOOD_FACTOR = {
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"id": "uf_smoke", "label": "好因子", "kind": "custom", "lang": "python",
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"formula": 'ctx.col("close").rolling_mean(window_size=5).over("symbol")',
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"status": "active",
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}
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_TYPO_FACTOR = {
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"id": "uf_typo", "label": "拼错因子", "kind": "custom", "lang": "python",
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"formula": 'ctx.col("cloes") + 1', # ctx.col 任意名 — 冒烟要拦住
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"status": "active",
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}
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def test_smoke_ok_for_valid_signal_and_factor() -> None:
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result = smoke_definition("signal", _GOOD_SIGNAL)
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assert result["ok"], result["issues"]
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assert result["dependencies"] == ["close", "ma20"]
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factor = smoke_definition("factor", _GOOD_FACTOR)
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assert factor["ok"], factor["issues"]
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assert factor["dependencies"] == ["close"]
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assert factor["warmup_bars"] == 6
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def test_smoke_stored_by_id_roundtrip(tmp_path: Path):
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"""统一选择器逐条冒烟: 按 id 从存储复检, 不存在时 fail-closed 404。"""
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from app.strategy import custom_signals
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custom_signals.save_one(tmp_path, {**_GOOD_SIGNAL})
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result = smoke_stored("signal", "smoke_ok", tmp_path)
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assert result["ok"] is True
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import fastapi
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with pytest.raises(fastapi.HTTPException) as ei:
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smoke_stored("signal", "missing_id", tmp_path)
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assert ei.value.status_code == 404
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def test_smoke_flags_typos_and_invalid_definitions() -> None:
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typo_signal = smoke_definition("signal", _TYPO_SIGNAL)
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assert not typo_signal["ok"]
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assert any(i["code"] in ("V001", "W001") for i in typo_signal["issues"])
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typo_factor = smoke_definition("factor", _TYPO_FACTOR)
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assert not typo_factor["ok"]
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assert typo_factor["issues"][0]["code"] == "W001"
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invalid = smoke_definition("signal", {"id": "BAD", "name": "x", "kind": "both", "conditions": []})
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assert not invalid["ok"] and invalid["issues"][0]["code"] == "V001"
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def test_catalog_lists_both_kinds_with_dependencies() -> None:
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from app.strategy import custom_signals
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custom_signals.save_one(settings.data_dir, _GOOD_SIGNAL)
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from app.factors import store as factor_store
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factor_store.save_one(settings.data_dir, _GOOD_FACTOR)
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items = catalog(settings.data_dir)
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by_key = {(item["kind"], item["id"]): item for item in items}
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signal = by_key[("signal", "smoke_ok")]
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assert signal["label"] == "好信号" and signal["lang"] == "conditions"
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assert "ma20" in signal["dependencies"]
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factor = by_key[("factor", "uf_smoke")]
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assert factor["lang"] == "python" and factor["warmup_bars"] == 6
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# ── 验收: 导入 → 冒烟 → 可回测 (≤2 分钟) ─────────────────
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_E2E_SIGNAL = {
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"id": "e2e_new", "name": "端到端新信号", "kind": "entry", "timeframe": "daily",
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"conditions": [{"left": "close", "op": ">", "right": 0}], # 恒真, 保证有候选
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}
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def test_import_to_backtest_within_two_minutes(tmp_path: Path, monkeypatch) -> None:
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"""备份导入新信号 → 导入响应带冒烟 → 引用该信号的回测立即可跑。"""
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started = time.perf_counter()
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# 1) 源目录造定义, 打备份 ZIP
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source_dir = tmp_path / "source"
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from app.factors import store as factor_store
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from app.strategy import custom_signals
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monkeypatch.setattr(settings, "data_dir", source_dir)
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custom_signals.save_one(source_dir, _E2E_SIGNAL)
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factor_store.save_one(source_dir, _GOOD_FACTOR)
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data, _filename = config_backup.build_backup(categories=["custom_signals", "custom_factors"])
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# 2) 导入到全新目录 — 响应内逐条冒烟
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target_dir = tmp_path / "target"
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monkeypatch.setattr(settings, "data_dir", target_dir)
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applied = config_backup.apply_archive(data, categories=["custom_signals", "custom_factors"])
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smoke_by_file = {
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s["file"].rsplit("/", 1)[-1]: s
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for category in applied["categories"]
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for s in category.get("smoke", [])
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}
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assert smoke_by_file["e2e_new.json"]["ok"], smoke_by_file["e2e_new.json"]
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assert smoke_by_file["uf_smoke.json"]["ok"], smoke_by_file["uf_smoke.json"]
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# 3) 立即用引用导入信号的策略跑回测 (面板 stub, 与 prepare_panel 测试同模式)
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from app.indicators.pipeline import invalidate_custom_signals
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invalidate_custom_signals() # 信号缓存指向新 data_dir 重载
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# 种子信号 (signal_limit_* 等迁移定义) 的缓存被上一行清掉, 重新注入 —
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# 并把刚导入的 e2e 信号并入同一次编译, 模拟真实进程的合并加载。
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from app.indicators import pipeline
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from app.strategy import custom_signals
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from tests.signal_seeds import load_seed_definitions
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seeds = load_seed_definitions()
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all_signals = [*seeds, _E2E_SIGNAL]
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monkeypatch.setattr(
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pipeline, "_custom_signal_exprs",
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custom_signals.build_expressions(all_signals),
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)
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prev_fields = custom_signals.prev_day_fields(all_signals)
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monkeypatch.setattr(
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pipeline, "_custom_signal_exprs_today",
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custom_signals.build_expressions_prev(all_signals, prev_fields),
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)
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monkeypatch.setattr(pipeline, "_custom_prev_fields_cache", prev_fields)
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from app.backtest.engine import SimResult
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from app.backtest.strategy import StrategyBacktestConfig, StrategyBacktestService
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from app.strategy.engine import StrategyDef
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class _EngineStub:
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def __init__(self, panel: pl.DataFrame) -> None:
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self.panel = panel
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self.repo = SimpleNamespace(store=SimpleNamespace(data_dir=target_dir))
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def load_panel(self, symbols, start, end, columns=None, asset_type="stock"):
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return self.panel
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def load_panel_for_backtest(self, symbols, start, end, feature_plan, asset_type="stock"):
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return self.load_panel(symbols, start, end)
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def simulate_market_matrix(self, matrix, config, progress_cb=None, cancel_event=None, options=None):
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return SimResult(
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equity_curve=[{"date": "2024-01-01", "value": config.initial_capital}],
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drawdown_curve=[{"date": "2024-01-01", "value": 0.0}],
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trades=[], per_symbol_stats=[],
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stats={"total_return": 0.0, "n_trades": 0},
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)
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class _StrategyEngineStub:
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def __init__(self) -> None:
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self.strategy = StrategyDef(
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meta={"id": "e2e", "name": "e2e", "scoring": {}, "params": [], "limit": 100},
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basic_filter=None,
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entry_signals=["csg_e2e_new"], # ← 引用刚导入的信号
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exit_signals=[],
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stop_loss=None, trailing_stop=None,
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trailing_take_profit_activate=None, trailing_take_profit_drawdown=None,
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max_hold_days=None,
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filter_fn=lambda df, params: pl.lit(True),
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filter_history_fn=None, lookback_days=1, source="custom", file_path=None,
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)
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def get(self, strategy_id):
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return self.strategy
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rows = []
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base = date(2024, 1, 1)
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for i in range(30):
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for sym in ("A", "B"):
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close = 10.0 + i * 0.1
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rows.append({
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"symbol": sym, "name": sym, "date": base + timedelta(days=i),
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"open": close - 0.05, "high": close + 0.1, "low": close - 0.1, "close": close,
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"volume": 100_000.0, "amount": close * 100_000.0,
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"ma20": close - 0.2,
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"signal_limit_up": False, "signal_limit_down": False,
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# 面板已带信号列时注入路径直接可用; 不带时由 compute_signals 补
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"csg_e2e_new": close > 0,
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})
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panel = pl.DataFrame(rows).sort(["symbol", "date"])
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service = StrategyBacktestService(_EngineStub(panel), _StrategyEngineStub())
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result = service.run(StrategyBacktestConfig(
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strategy_id="e2e", symbols=["A", "B"],
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start=date(2024, 1, 20), end=date(2024, 1, 30), mode="fast",
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))
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assert result.error is None, result.error
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elapsed = time.perf_counter() - started
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assert elapsed < 120.0, f"导入到可回测耗时 {elapsed:.1f}s 超过验收线 2 分钟"
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# 链路本身在秒级完成
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assert elapsed < 30.0
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