复制 local 代码到 serve

2026-07-04 16:59:25 +08:00
parent cee04c1b46
commit 0474e5fb46
302 changed files with 78290 additions and 0 deletions
@@ -0,0 +1,422 @@
+from __future__ import annotations
+
+from datetime import date, timedelta
+
+import polars as pl
+
+from app.backtest.engine import BacktestEngine, MatcherConfig
+
+
+def _panel(symbols: list[str], days: int = 4, price: float = 10.0, overrides: dict[tuple[str, int], dict] | None = None) -> pl.DataFrame:
+    overrides = overrides or {}
+    start = date(2024, 1, 1)
+    rows = []
+    for sym in symbols:
+        for i in range(days):
+            patch = overrides.get((sym, i), {})
+            rows.append({
+                "symbol": sym,
+                "name": sym,
+                "date": start + timedelta(days=i),
+                "open": patch.get("open", price),
+                "high": patch.get("high", price),
+                "low": patch.get("low", price),
+                "close": patch.get("close", price),
+                "volume": patch.get("volume", 100_000),
+                "score": patch.get("score", {"A": 4, "B": 3, "C": 2, "D": 1}.get(sym, 0)),
+                "signal_limit_up": patch.get("signal_limit_up", False),
+                "signal_limit_down": patch.get("signal_limit_down", False),
+            })
+    return pl.DataFrame(rows).sort(["symbol", "date"])
+
+
+def _mask(panel: pl.DataFrame, marks: set[tuple[str, int]]) -> pl.Series:
+    values = []
+    base = date(2024, 1, 1)
+    for row in panel.select(["symbol", "date"]).iter_rows(named=True):
+        day = (row["date"] - base).days
+        values.append((row["symbol"], day) in marks)
+    return pl.Series(values, dtype=pl.Boolean)
+
+
+def _engine() -> BacktestEngine:
+    return BacktestEngine(repo=None)  # simulate_portfolio 不访问 repo
+
+
+def test_max_exposure_sets_target_position_and_caps_count():
+    panel = _panel(["A", "B", "C", "D"], days=3)
+    entries = _mask(panel, {("A", 0), ("B", 0), ("C", 0), ("D", 0)})
+    exits = _mask(panel, set())
+
+    result = _engine().simulate_portfolio(
+        panel,
+        entries,
+        exits,
+        MatcherConfig(
+            matching="open_t+1",
+            fees_pct=0,
+            slippage_bps=0,
+            max_positions=3,
+            max_exposure_pct=0.6,
+            initial_capital=100_000,
+        ),
+    )
+
+    assert len(result.trades) == 3
+    assert {t.symbol for t in result.trades} == {"A", "B", "C"}
+    assert all(abs(t.position_pct - 0.2) < 0.001 for t in result.trades)
+    assert result.stats["max_exposure"] <= 0.61
+
+
+def test_one_price_limit_up_blocks_buy():
+    panel = _panel(
+        ["A"],
+        days=3,
+        overrides={
+            ("A", 1): {"open": 11, "high": 11, "low": 11, "close": 11, "signal_limit_up": True},
+        },
+    )
+    entries = _mask(panel, {("A", 0)})
+    exits = _mask(panel, set())
+
+    result = _engine().simulate_portfolio(
+        panel,
+        entries,
+        exits,
+        MatcherConfig(matching="open_t+1", fees_pct=0, slippage_bps=0, max_positions=1, initial_capital=100_000),
+    )
+
+    assert result.trades == []
+    assert result.stats["execution"]["buy_limit_up"] == 1
+
+
+def test_failed_open_exit_keeps_slot_and_blocks_replacement_buy():
+    panel = _panel(
+        ["A", "B", "C", "D"],
+        days=4,
+        overrides={
+            ("A", 2): {"open": 9, "high": 9, "low": 9, "close": 9, "signal_limit_down": True},
+        },
+    )
+    entries = _mask(panel, {
+        ("A", 0), ("B", 0), ("C", 0),
+        ("D", 1),
+    })
+    exits = _mask(panel, {("A", 1)})
+
+    result = _engine().simulate_portfolio(
+        panel,
+        entries,
+        exits,
+        MatcherConfig(
+            matching="open_t+1",
+            fees_pct=0,
+            slippage_bps=0,
+            max_positions=3,
+            max_exposure_pct=0.6,
+            initial_capital=100_000,
+        ),
+    )
+
+    assert "D" not in {t.symbol for t in result.trades}
+    assert result.stats["execution"]["sell_limit_down"] == 1
+    assert result.stats["execution"]["pending_exit"] == 1
+    assert result.stats["execution"]["buy_no_slot"] >= 1
+    a_trade = next(t for t in result.trades if t.symbol == "A")
+    assert a_trade.blocked_exit_days == 1
+    assert a_trade.exit_reason == "signal"
+
+
+def test_trailing_stop_uses_high_water_mark():
+    panel = _panel(
+        ["A"],
+        days=5,
+        overrides={
+            ("A", 2): {"open": 10, "high": 12, "low": 11.8, "close": 12},
+            ("A", 3): {"open": 12, "high": 12, "low": 11.3, "close": 11.3},
+        },
+    )
+    entries = _mask(panel, {("A", 0)})
+    exits = _mask(panel, set())
+
+    result = _engine().simulate_portfolio(
+        panel,
+        entries,
+        exits,
+        MatcherConfig(
+            matching="open_t+1",
+            fees_pct=0,
+            slippage_bps=0,
+            max_positions=1,
+            initial_capital=100_000,
+            trailing_stop_pct=0.05,
+        ),
+    )
+
+    assert len(result.trades) == 1
+    trade = result.trades[0]
+    assert trade.exit_reason == "trailing_stop"
+    assert trade.exit_price == 11.4
+
+
+def test_trailing_take_profit_requires_activation():
+    panel = _panel(
+        ["A"],
+        days=5,
+        overrides={
+            ("A", 2): {"open": 10, "high": 10.8, "low": 10.4, "close": 10.8},
+            ("A", 3): {"open": 10.8, "high": 10.8, "low": 10.4, "close": 10.4},
+        },
+    )
+    entries = _mask(panel, {("A", 0)})
+    exits = _mask(panel, set())
+
+    result = _engine().simulate_portfolio(
+        panel,
+        entries,
+        exits,
+        MatcherConfig(
+            matching="open_t+1",
+            fees_pct=0,
+            slippage_bps=0,
+            max_positions=1,
+            initial_capital=100_000,
+            trailing_take_profit_activate_pct=0.10,
+            trailing_take_profit_drawdown_pct=0.03,
+        ),
+    )
+
+    assert result.trades[0].exit_reason == "end"
+
+
+def test_trailing_take_profit_exits_after_activation():
+    panel = _panel(
+        ["A"],
+        days=5,
+        overrides={
+            ("A", 2): {"open": 10, "high": 12, "low": 11.8, "close": 12},
+            ("A", 3): {"open": 12, "high": 12, "low": 11.5, "close": 11.5},
+        },
+    )
+    entries = _mask(panel, {("A", 0)})
+    exits = _mask(panel, set())
+
+    result = _engine().simulate_portfolio(
+        panel,
+        entries,
+        exits,
+        MatcherConfig(
+            matching="open_t+1",
+            fees_pct=0,
+            slippage_bps=0,
+            max_positions=1,
+            initial_capital=100_000,
+            trailing_take_profit_activate_pct=0.10,
+            trailing_take_profit_drawdown_pct=0.03,
+        ),
+    )
+
+    assert len(result.trades) == 1
+    trade = result.trades[0]
+    assert trade.exit_reason == "trailing_take_profit"
+    assert trade.exit_price == 11.7
+
+
+def test_score_filter_uses_signal_day_score_range():
+    panel = _panel(
+        ["A", "B", "C"],
+        days=3,
+        overrides={
+            ("A", 0): {"score": 70},
+            ("B", 0): {"score": 80},
+            ("C", 0): {"score": 90},
+            ("A", 1): {"score": 100},
+            ("B", 1): {"score": 1},
+            ("C", 1): {"score": 1},
+        },
+    )
+    entries = _mask(panel, {("A", 0), ("B", 0), ("C", 0)})
+    exits = _mask(panel, set())
+
+    result = _engine().simulate_portfolio(
+        panel,
+        entries,
+        exits,
+        MatcherConfig(
+            matching="open_t+1",
+            fees_pct=0,
+            slippage_bps=0,
+            max_positions=3,
+            initial_capital=100_000,
+            score_min=71,
+            score_max=85,
+        ),
+    )
+
+    assert {t.symbol for t in result.trades} == {"B"}
+    assert result.trades[0].entry_score == 80
+    assert result.stats["execution"]["buy_score_filter"] == 2
+
+
+def test_independent_candidates_allow_overlapping_same_symbol_trades():
+    panel = _panel(
+        ["A"],
+        days=5,
+        overrides={
+            ("A", 0): {"close": 10},
+            ("A", 1): {"close": 11},
+            ("A", 2): {"close": 12},
+            ("A", 3): {"close": 13},
+            ("A", 4): {"close": 14},
+        },
+    )
+    entries = _mask(panel, {("A", 0), ("A", 1)})
+    exits = _mask(panel, set())
+
+    result = _engine().simulate_independent_candidates(
+        panel,
+        entries,
+        exits,
+        MatcherConfig(matching="close_t", fees_pct=0, slippage_bps=0, max_hold_days=2),
+    )
+
+    assert result.stats["full_kind"] == "candidate_execution"
+    assert result.stats["n_candidates"] == 2
+    assert len(result.trades) == 2
+    assert [t.entry_date for t in result.trades] == ["2024-01-01", "2024-01-02"]
+    assert [t.exit_date for t in result.trades] == ["2024-01-03", "2024-01-04"]
+    assert all(t.exit_reason == "max_hold" for t in result.trades)
+
+
+def test_independent_candidates_apply_stop_loss():
+    panel = _panel(
+        ["A"],
+        days=4,
+        overrides={
+            ("A", 0): {"close": 10, "low": 10},
+            ("A", 1): {"open": 10, "high": 10, "low": 8.9, "close": 9},
+        },
+    )
+    entries = _mask(panel, {("A", 0)})
+    exits = _mask(panel, set())
+
+    result = _engine().simulate_independent_candidates(
+        panel,
+        entries,
+        exits,
+        MatcherConfig(matching="close_t", fees_pct=0, slippage_bps=0, stop_loss_pct=0.1),
+    )
+
+    assert len(result.trades) == 1
+    assert result.trades[0].exit_reason == "stop_loss"
+    assert result.trades[0].exit_price == 9.0
+
+
+def test_signal_exit_takes_priority_over_max_hold():
+    """同一日既有卖点信号又到期 → 应按 signal 平仓 (卖点优先于 max_hold 兜底)。"""
+    panel = _panel(
+        ["A"],
+        days=4,
+        overrides={
+            # day1 次日开盘买入 (open_t+1), 价 10
+            ("A", 1): {"open": 10, "high": 10, "low": 10, "close": 10},
+            # day2 持有 (hold_days 计到 1)
+            ("A", 2): {"open": 11, "high": 11, "low": 11, "close": 11},
+            # day3: 既到期 (hold_days=2 >= max_hold_days=2) 又有卖点信号 → signal 优先
+            ("A", 3): {"open": 12, "high": 12, "low": 12, "close": 12},
+        },
+    )
+    entries = _mask(panel, {("A", 0)})  # day0 收盘确认 → day1 开盘买
+    exits = _mask(panel, {("A", 2)})    # day2 收盘确认卖点 → day3 开盘卖
+
+    result = _engine().simulate_portfolio(
+        panel,
+        entries,
+        exits,
+        MatcherConfig(
+            matching="open_t+1",
+            fees_pct=0,
+            slippage_bps=0,
+            max_positions=1,
+            max_hold_days=2,
+            initial_capital=100_000,
+        ),
+    )
+
+    assert len(result.trades) == 1
+    trade = result.trades[0]
+    assert trade.exit_reason == "signal"
+    assert trade.exit_price == 12.0  # 卖点用 day3 开盘 (exit_fill 跟随 matching=open_t+1)
+
+
+def test_stop_loss_triggers_even_when_expired_in_open_mode():
+    """open_t+1 模式下仓位到期且当日破止损 → 应按 stop_loss 平仓 (风控优先于 max_hold)。"""
+    panel = _panel(
+        ["A"],
+        days=4,
+        overrides={
+            ("A", 1): {"open": 10, "high": 10, "low": 10, "close": 10},
+            # day3 开盘跳空跌破止损 (-10%): open=8.9 < 9.0 止损线, low=8.5
+            ("A", 3): {"open": 8.9, "high": 8.9, "low": 8.5, "close": 8.7},
+        },
+    )
+    entries = _mask(panel, {("A", 0)})
+    exits = _mask(panel, set())
+
+    result = _engine().simulate_portfolio(
+        panel,
+        entries,
+        exits,
+        MatcherConfig(
+            matching="open_t+1",
+            fees_pct=0,
+            slippage_bps=0,
+            max_positions=1,
+            max_hold_days=2,
+            stop_loss_pct=0.1,
+            initial_capital=100_000,
+        ),
+    )
+
+    assert len(result.trades) == 1
+    trade = result.trades[0]
+    assert trade.exit_reason == "stop_loss"
+    # 风控盘中触发: 开盘价 8.9 <= 止损线 9.0 → 按开盘价 8.9 成交
+    assert trade.exit_price == 8.9
+
+
+def test_default_fill_is_buy_open_sell_close():
+    """拆分口径: 建仓=次日开盘, 清仓=收盘。entry_price 用次日 open, exit_price 用收盘价。"""
+    panel = _panel(
+        ["A"],
+        days=4,
+        overrides={
+            # day1: 次日开盘买入, 开盘 10
+            ("A", 1): {"open": 10, "high": 10.5, "low": 9.5, "close": 10.2},
+            # day2: 到期 (max_hold_days=1), 收盘卖
+            ("A", 2): {"open": 11, "high": 11, "low": 10, "close": 10.8},
+        },
+    )
+    entries = _mask(panel, {("A", 0)})  # day0 收盘确认
+    exits = _mask(panel, set())
+
+    result = _engine().simulate_portfolio(
+        panel,
+        entries,
+        exits,
+        MatcherConfig(
+            entry_fill="open_t+1",
+            exit_fill="close_t",
+            fees_pct=0,
+            slippage_bps=0,
+            max_positions=1,
+            max_hold_days=1,
+            initial_capital=100_000,
+        ),
+    )
+
+    assert len(result.trades) == 1
+    trade = result.trades[0]
+    assert trade.entry_price == 10.0   # 次日开盘
+    assert trade.exit_price == 10.8    # 到期日收盘
+    assert trade.exit_reason == "max_hold"
@@ -0,0 +1,59 @@
+"""全量模拟 (full mode) 尾部执行回归测试。"""
+from __future__ import annotations
+
+from datetime import date, timedelta
+
+import polars as pl
+
+from app.backtest.engine import BacktestEngine, MatcherConfig
+
+
+def _panel_with_tail(symbols: list[str], n_data_days: int) -> pl.DataFrame:
+    start = date(2024, 1, 1)
+    rows = []
+    for sym in symbols:
+        for i in range(n_data_days):
+            px = 10.0 + i
+            rows.append({
+                "symbol": sym,
+                "date": start + timedelta(days=i),
+                "open": px,
+                "high": px,
+                "low": px,
+                "close": px,
+                "volume": 100_000,
+                "signal_limit_up": False,
+                "signal_limit_down": False,
+            })
+    return pl.DataFrame(rows).sort(["symbol", "date"])
+
+
+def test_full_simulation_executes_signal_at_tail():
+    """信号集中在正式区间最后一天时, tail 数据应允许次日开盘买入并按策略退出。"""
+    n_days = 6
+    panel = _panel_with_tail(["A"], n_days + 3)
+
+    start = date(2024, 1, 1)
+    end = start + timedelta(days=n_days - 1)
+    entry_vals = []
+    for row in panel.select(["symbol", "date"]).iter_rows(named=True):
+        entry_vals.append(row["date"] == end)
+    entry_mask = pl.Series(entry_vals, dtype=pl.Boolean)
+    exit_mask = pl.Series([False] * len(panel), dtype=pl.Boolean)
+
+    result = BacktestEngine(repo=None).simulate_independent_candidates(  # type: ignore[arg-type]
+        panel,
+        entry_mask,
+        exit_mask,
+        MatcherConfig(matching="open_t+1", fees_pct=0, slippage_bps=0, max_hold_days=2),
+    )
+
+    assert not result.stats.get("error"), f"unexpected error: {result.stats.get('error')}"
+    assert result.stats.get("full_kind") == "candidate_execution"
+    assert result.stats.get("n_candidates") == 1
+    assert result.stats.get("n_trades") == 1
+    assert len(result.trades) == 1
+    trade = result.trades[0]
+    assert trade.entry_signal_date == str(end)
+    assert trade.entry_date == str(end + timedelta(days=1))
+    assert trade.exit_reason == "max_hold"
@@ -0,0 +1,163 @@
+from __future__ import annotations
+
+from datetime import date, timedelta
+from types import SimpleNamespace
+
+import polars as pl
+
+from app.backtest.engine import BacktestEngine, SimResult
+from app.backtest.strategy import StrategyBacktestConfig, StrategyBacktestService
+from app.strategy.engine import StrategyDef
+
+
+def _strategy(**kwargs) -> StrategyDef:
+    defaults = dict(
+        meta={"id": "test", "name": "test", "scoring": {}, "params": [], "limit": 100},
+        basic_filter={"enabled": True, "amount_min": 100.0},
+        entry_signals=[],
+        exit_signals=[],
+        stop_loss=None,
+        trailing_stop=None,
+        trailing_take_profit_activate=None,
+        trailing_take_profit_drawdown=None,
+        max_hold_days=None,
+        alerts=[],
+        filter_fn=lambda df, params: pl.lit(True),
+        filter_history_fn=None,
+        lookback_days=1,
+        source="custom",
+        file_path=None,
+    )
+    defaults.update(kwargs)
+    return StrategyDef(**defaults)
+
+
+class _StrategyEngineStub:
+    def __init__(self, strategy: StrategyDef) -> None:
+        self.strategy = strategy
+
+    def get(self, strategy_id: str) -> StrategyDef:
+        return self.strategy
+
+
+class _RepoStub:
+    def get_index_daily(self, *args, **kwargs) -> pl.DataFrame:
+        return pl.DataFrame()
+
+
+class _EngineStub:
+    def __init__(self, panel: pl.DataFrame) -> None:
+        self.panel = panel
+        self.repo = _RepoStub()
+        self.load_args = None
+        self.sim_panel: pl.DataFrame | None = None
+        self.sim_entries: pl.Series | None = None
+
+    def load_panel(self, symbols, start: date, end: date) -> pl.DataFrame:
+        self.load_args = (symbols, start, end)
+        return self.panel
+
+    def simulate_portfolio(self, panel, entries, exits, config, progress_cb=None, cancel_event=None) -> SimResult:
+        self.sim_panel = panel
+        self.sim_entries = entries
+        return SimResult(
+            equity_curve=[{"date": "2024-01-01", "value": config.initial_capital}],
+            drawdown_curve=[{"date": "2024-01-01", "value": 0.0}],
+            trades=[],
+            per_symbol_stats=[],
+            stats={"total_return": 0.0, "n_trades": 0},
+        )
+
+
+def test_basic_filter_only_limits_entries_not_panel_rows():
+    start = date(2024, 1, 1)
+    rows = []
+    for i, amount in enumerate([1000.0, 0.0, 1000.0]):
+        rows.append({
+            "symbol": "A",
+            "name": "A",
+            "date": start + timedelta(days=i),
+            "open": 10.0 + i,
+            "high": 10.0 + i,
+            "low": 10.0 + i,
+            "close": 10.0 + i,
+            "volume": 100_000,
+            "amount": amount,
+            "signal_limit_up": False,
+            "signal_limit_down": False,
+        })
+    panel = pl.DataFrame(rows).sort(["symbol", "date"])
+    engine = _EngineStub(panel)
+    service = StrategyBacktestService(engine=engine, strategy_engine=_StrategyEngineStub(_strategy()))
+
+    result = service.run(StrategyBacktestConfig(
+        strategy_id="test",
+        symbols=None,
+        start=start,
+        end=start + timedelta(days=2),
+        matching="close_t",
+        mode="position",
+    ))
+
+    assert result.error is None
+    assert engine.sim_panel is not None
+    assert engine.sim_panel.height == 3
+    assert engine.sim_panel.filter(pl.col("amount") == 0.0).height == 1
+    assert engine.sim_entries is not None
+    assert engine.sim_entries.to_list() == [True, False, True]
+    assert engine.load_args is not None
+    assert engine.load_args[1] < start  # warmup 只用于计算, 不参与正式交易
+
+
+def test_score_normalizes_inside_strategy_candidate_universe():
+    panel = pl.DataFrame({
+        "symbol": ["A", "B", "C"],
+        "date": [date(2024, 1, 1)] * 3,
+        "factor": [10.0, 20.0, 1000.0],
+    })
+    universe = pl.Series([True, True, False], dtype=pl.Boolean)
+    strategy = SimpleNamespace(meta={"scoring": {"factor": 1.0}, "order_by": "score", "descending": True})
+
+    scored = StrategyBacktestService._apply_score(panel, strategy, None, universe_mask=universe)
+    scores = dict(zip(scored["symbol"].to_list(), scored["score"].to_list()))
+
+    assert scores["A"] == 0.0
+    assert scores["B"] == 100.0
+    assert scores["C"] == 0.0
+
+
+def test_full_mode_executes_every_candidate_with_strategy_rules():
+    start = date(2024, 1, 1)
+    panel = pl.DataFrame([
+        {"symbol": "A", "name": "A", "date": start, "open": 10.0, "high": 10.0, "low": 10.0, "close": 10.0, "volume": 1, "amount": 1000.0, "signal_limit_up": False, "signal_limit_down": False},
+        {"symbol": "A", "name": "A", "date": start + timedelta(days=1), "open": 11.0, "high": 11.0, "low": 11.0, "close": 11.0, "volume": 1, "amount": 0.0, "signal_limit_up": False, "signal_limit_down": False},
+        {"symbol": "A", "name": "A", "date": start + timedelta(days=2), "open": 20.0, "high": 20.0, "low": 20.0, "close": 20.0, "volume": 1, "amount": 1000.0, "signal_limit_up": False, "signal_limit_down": False},
+    ]).sort(["symbol", "date"])
+
+    engine = BacktestEngine(repo=None)  # type: ignore[arg-type]
+    engine.load_panel = lambda symbols, s, e: panel  # type: ignore[method-assign]
+    strategy = _strategy(
+        filter_fn=lambda df, params: pl.col("date") == start,
+        max_hold_days=1,
+    )
+    service = StrategyBacktestService(engine=engine, strategy_engine=_StrategyEngineStub(strategy))
+
+    result = service.run(StrategyBacktestConfig(
+        strategy_id="test",
+        symbols=None,
+        start=start,
+        end=start,
+        mode="full",
+        matching="open_t+1",
+        fees_pct=0,
+        slippage_bps=0,
+        holding_days=1,
+    ))
+
+    assert result.error is None
+    assert result.stats["full_kind"] == "candidate_execution"
+    assert result.stats["n_candidates"] == 1
+    assert result.stats["n_trades"] == 1
+    assert result.trades[0]["entry_date"] == str(start + timedelta(days=1))
+    assert result.trades[0]["exit_reason"] == "max_hold"
+    assert result.stats["avg_return"] == round(20 / 11 - 1, 4)