feat(strategies): 内置策略从 5 个扩充到 18 个

新增 13 个经典策略，覆盖趋势/通道/震荡/均线四大类：

趋势类：
- ema_cross  EMA 双线交叉（比 MA 反应更灵敏）
- triple_ma  三均线系统（多头/空头排列）
- dmi        DMI 趋向指标（+DI/-DI 交叉）
- trix       TRIX 三重平滑（过滤短期波动）

通道/突破类：
- donchian   唐安奇通道突破（海龟交易法）
- keltner    肯特纳通道（ATR-based）
- atr_breakout ATR 通道突破（均线±K×ATR）

震荡/反转类：
- cci        CCI 超卖反弹
- wr_reversal WR 威廉超卖
- bias_reversal BIAS 乖离反弹
- emv        EMV 简易波动（量价结合）
- dpo        DPO 区间震荡

均线类：
- bbi        BBI 多空指标（4 均线综合）

全部基于 MyTT 现成指标实现，每个策略声明参数 schema 供 Web 表单动态渲染。
18/18 策略冒烟测试通过（合成数据回测无运行时错误）。

src/easy_tdx/backtest/strategies/builtin.py

@@ -14,7 +14,26 @@ from easy_tdx.backtest.strategies.registry import (
     ParametrizedStrategy,
     register_strategy,
 )
-from easy_tdx.MyTT import BOLL, CROSS, KDJ, MA, MACD, RSI
+from easy_tdx.MyTT import (
+    ATR,
+    BBI,
+    BIAS,
+    BOLL,
+    CCI,
+    CROSS,
+    DMI,
+    DPO,
+    EMA,
+    EMV,
+    KDJ,
+    KTN,
+    MA,
+    MACD,
+    RSI,
+    TAQ,
+    TRIX,
+    WR,
+)
 
 __all__: list[str] = []  # 注册副作用即可，无需导出符号
 
@@ -175,3 +194,361 @@ class KdjCrossStrategy(ParametrizedStrategy):
             self.buy()
         elif self.dead[i] and self.position["size"] > 0:
             self.sell()
+
+
+# ── EMA 双线交叉 ──────────────────────────────────────────────────────────────
+
+
+@register_strategy(
+    name="ema_cross",
+    label="EMA 双线交叉",
+    description="指数均线金叉买入、死叉卖出。比简单均线反应更灵敏。",
+)
+class EmaCrossStrategy(ParametrizedStrategy):
+    params = [
+        Param("fast", int, default=12, min_value=2, max_value=60, label="快线周期"),
+        Param("slow", int, default=26, min_value=5, max_value=120, label="慢线周期"),
+    ]
+
+    def init(self) -> None:
+        self.ema_fast = self.I(EMA, self.data.close, self.p["fast"])
+        self.ema_slow = self.I(EMA, self.data.close, self.p["slow"])
+        self.gold = self.I(CROSS, self.ema_fast, self.ema_slow)
+        self.dead = self.I(CROSS, self.ema_slow, self.ema_fast)
+
+    def next(self) -> None:
+        i = self._bar_index
+        if self.gold[i]:
+            self.buy()
+        elif self.dead[i] and self.position["size"] > 0:
+            self.sell()
+
+
+# ── 三均线系统 ────────────────────────────────────────────────────────────────
+
+
+@register_strategy(
+    name="triple_ma",
+    label="三均线系统",
+    description="短中长期均线多头排列买入、空头排列卖出。",
+)
+class TripleMaStrategy(ParametrizedStrategy):
+    params = [
+        Param("short", int, default=5, min_value=1, max_value=30, label="短期"),
+        Param("mid", int, default=20, min_value=5, max_value=60, label="中期"),
+        Param("long", int, default=60, min_value=20, max_value=250, label="长期"),
+    ]
+
+    def init(self) -> None:
+        self.ma_s = self.I(MA, self.data.close, self.p["short"])
+        self.ma_m = self.I(MA, self.data.close, self.p["mid"])
+        self.ma_l = self.I(MA, self.data.close, self.p["long"])
+
+    def next(self) -> None:
+        i = self._bar_index
+        if self.ma_s[i] > self.ma_m[i] > self.ma_l[i] and self.position["size"] == 0:
+            self.buy()
+        elif self.ma_s[i] < self.ma_m[i] < self.ma_l[i] and self.position["size"] > 0:
+            self.sell()
+
+
+# ── 唐安奇通道（海龟）────────────────────────────────────────────────────────
+
+
+@register_strategy(
+    name="donchian",
+    label="唐安奇通道突破",
+    description="突破N日最高价买入，跌破N日最低价卖出。海龟交易法核心。",
+)
+class DonchianStrategy(ParametrizedStrategy):
+    params = [
+        Param("n", int, default=20, min_value=5, max_value=100, label="通道周期"),
+    ]
+
+    def init(self) -> None:
+        self.upper, self._mid, self.lower = self.I(TAQ, self.data.high, self.data.low, self.p["n"])
+
+    def next(self) -> None:
+        i = self._bar_index
+        close = self.data.close[0]
+        if close >= self.upper[i] and self.position["size"] == 0:
+            self.buy()
+        elif close <= self.lower[i] and self.position["size"] > 0:
+            self.sell()
+
+
+# ── 肯特纳通道 ────────────────────────────────────────────────────────────────
+
+
+@register_strategy(
+    name="keltner",
+    label="肯特纳通道",
+    description="收盘价突破上轨买入，跌破下轨卖出。ATR-based 通道。",
+)
+class KeltnerStrategy(ParametrizedStrategy):
+    params = [
+        Param("n", int, default=20, min_value=5, max_value=100, label="均线周期"),
+        Param("m", int, default=10, min_value=2, max_value=50, label="ATR周期"),
+    ]
+
+    def init(self) -> None:
+        self.upper, self._mid, self.lower = self.I(
+            KTN, self.data.close, self.data.high, self.data.low, self.p["n"], self.p["m"]
+        )
+
+    def next(self) -> None:
+        i = self._bar_index
+        close = self.data.close[0]
+        if close >= self.upper[i] and self.position["size"] == 0:
+            self.buy()
+        elif close <= self.lower[i] and self.position["size"] > 0:
+            self.sell()
+
+
+# ── BBI 多空指标 ──────────────────────────────────────────────────────────────
+
+
+@register_strategy(
+    name="bbi",
+    label="BBI 多空指标",
+    description="收盘价上穿BBI买入，下穿BBI卖出。多空综合指标。",
+)
+class BbiStrategy(ParametrizedStrategy):
+    params = [
+        Param("m1", int, default=3, min_value=1, max_value=20, label="均线1"),
+        Param("m2", int, default=6, min_value=2, max_value=30, label="均线2"),
+        Param("m3", int, default=12, min_value=5, max_value=60, label="均线3"),
+        Param("m4", int, default=20, min_value=10, max_value=120, label="均线4"),
+    ]
+
+    def init(self) -> None:
+        self.bbi = self.I(
+            BBI, self.data.close, self.p["m1"], self.p["m2"], self.p["m3"], self.p["m4"]
+        )
+
+    def next(self) -> None:
+        i = self._bar_index
+        close = self.data.close[0]
+        if close > self.bbi[i] and self.position["size"] == 0:
+            self.buy()
+        elif close < self.bbi[i] and self.position["size"] > 0:
+            self.sell()
+
+
+# ── CCI 顺势指标 ──────────────────────────────────────────────────────────────
+
+
+@register_strategy(
+    name="cci",
+    label="CCI 超卖反弹",
+    description="CCI 跌破-100后回升买入，涨破+100卖出。",
+)
+class CciStrategy(ParametrizedStrategy):
+    params = [
+        Param("n", int, default=14, min_value=2, max_value=50, label="CCI周期"),
+        Param("oversold", int, default=-100, min_value=-200, max_value=0, label="超卖线"),
+        Param("overbought", int, default=100, min_value=0, max_value=200, label="超买线"),
+    ]
+
+    def init(self) -> None:
+        self.cci = self.I(CCI, self.data.close, self.data.high, self.data.low, self.p["n"])
+
+    def next(self) -> None:
+        i = self._bar_index
+        cci = self.cci[i]
+        if cci <= self.p["oversold"] and self.position["size"] == 0:
+            self.buy()
+        elif cci >= self.p["overbought"] and self.position["size"] > 0:
+            self.sell()
+
+
+# ── WR 威廉指标 ───────────────────────────────────────────────────────────────
+
+
+@register_strategy(
+    name="wr_reversal",
+    label="WR 威廉超卖",
+    description="WR 进入超卖区（<-80）买入，进入超买区（>-20）卖出。",
+)
+class WrReversalStrategy(ParametrizedStrategy):
+    params = [
+        Param("n", int, default=14, min_value=2, max_value=50, label="WR周期"),
+        Param("oversold", int, default=-80, min_value=-100, max_value=-40, label="超卖线"),
+        Param("overbought", int, default=-20, min_value=-60, max_value=0, label="超买线"),
+    ]
+
+    def init(self) -> None:
+        self.wr, self._wr1 = self.I(WR, self.data.close, self.data.high, self.data.low, self.p["n"])
+
+    def next(self) -> None:
+        i = self._bar_index
+        wr = self.wr[i]
+        if wr <= self.p["oversold"] and self.position["size"] == 0:
+            self.buy()
+        elif wr >= self.p["overbought"] and self.position["size"] > 0:
+            self.sell()
+
+
+# ── BIAS 乖离率 ───────────────────────────────────────────────────────────────
+
+
+@register_strategy(
+    name="bias_reversal",
+    label="BIAS 乖离反弹",
+    description="乖离率低于负阈值（超跌）买入，高于正阈值（超涨）卖出。",
+)
+class BiasReversalStrategy(ParametrizedStrategy):
+    params = [
+        Param("n", int, default=6, min_value=2, max_value=30, label="均线周期"),
+        Param("threshold", float, default=5.0, min_value=1.0, max_value=20.0, label="乖离阈值%"),
+    ]
+
+    def init(self) -> None:
+        self.bias, self._b2, self._b3 = self.I(BIAS, self.data.close, self.p["n"], 12, 24)
+
+    def next(self) -> None:
+        i = self._bar_index
+        bias_pct = self.bias[i] * 100
+        threshold = self.p["threshold"]
+        if bias_pct <= -threshold and self.position["size"] == 0:
+            self.buy()
+        elif bias_pct >= threshold and self.position["size"] > 0:
+            self.sell()
+
+
+# ── DMI 趋向指标 ──────────────────────────────────────────────────────────────
+
+
+@register_strategy(
+    name="dmi",
+    label="DMI 趋向指标",
+    description="+DI 上穿-DI 买入（多头趋强），+DI 下穿-DI 卖出。",
+)
+class DmiStrategy(ParametrizedStrategy):
+    params = [
+        Param("m1", int, default=14, min_value=2, max_value=30, label="DI周期"),
+        Param("m2", int, default=6, min_value=2, max_value=20, label="ADX周期"),
+    ]
+
+    def init(self) -> None:
+        self.pdi, self.mdi, self._adx, self._adxr = self.I(
+            DMI, self.data.close, self.data.high, self.data.low, self.p["m1"], self.p["m2"]
+        )
+        self.gold = self.I(CROSS, self.pdi, self.mdi)
+        self.dead = self.I(CROSS, self.mdi, self.pdi)
+
+    def next(self) -> None:
+        i = self._bar_index
+        if self.gold[i]:
+            self.buy()
+        elif self.dead[i] and self.position["size"] > 0:
+            self.sell()
+
+
+# ── TRIX 三重平滑 ─────────────────────────────────────────────────────────────
+
+
+@register_strategy(
+    name="trix",
+    label="TRIX 三重平滑",
+    description="TRIX 上穿信号线买入，下穿卖出。过滤短期波动的趋势指标。",
+)
+class TrixStrategy(ParametrizedStrategy):
+    params = [
+        Param("m1", int, default=12, min_value=2, max_value=30, label="TRIX周期"),
+        Param("m2", int, default=20, min_value=5, max_value=60, label="信号周期"),
+    ]
+
+    def init(self) -> None:
+        self.trix, self.trma = self.I(TRIX, self.data.close, self.p["m1"], self.p["m2"])
+        self.gold = self.I(CROSS, self.trix, self.trma)
+        self.dead = self.I(CROSS, self.trma, self.trix)
+
+    def next(self) -> None:
+        i = self._bar_index
+        if self.gold[i]:
+            self.buy()
+        elif self.dead[i] and self.position["size"] > 0:
+            self.sell()
+
+
+# ── EMV 简易波动 ──────────────────────────────────────────────────────────────
+
+
+@register_strategy(
+    name="emv",
+    label="EMV 简易波动",
+    description="EMV 上穿0轴买入，下穿0轴卖出。量价结合指标。",
+)
+class EmvStrategy(ParametrizedStrategy):
+    params = [
+        Param("n", int, default=14, min_value=2, max_value=30, label="EMV周期"),
+    ]
+
+    def init(self) -> None:
+        self.emv, self._maemv = self.I(
+            EMV, self.data.high, self.data.low, self.data.vol, self.p["n"]
+        )
+
+    def next(self) -> None:
+        i = self._bar_index
+        if self.emv[i] > 0 and self.position["size"] == 0:
+            self.buy()
+        elif self.emv[i] < 0 and self.position["size"] > 0:
+            self.sell()
+
+
+# ── DPO 区间震荡 ──────────────────────────────────────────────────────────────
+
+
+@register_strategy(
+    name="dpo",
+    label="DPO 区间震荡",
+    description="DPO 上穿信号线买入，下穿卖出。去除趋势的震荡指标。",
+)
+class DpoStrategy(ParametrizedStrategy):
+    params = [
+        Param("m1", int, default=20, min_value=5, max_value=60, label="DPO周期"),
+    ]
+
+    def init(self) -> None:
+        self.dpo, self.madpo = self.I(DPO, self.data.close, self.p["m1"])
+        self.gold = self.I(CROSS, self.dpo, self.madpo)
+        self.dead = self.I(CROSS, self.madpo, self.dpo)
+
+    def next(self) -> None:
+        i = self._bar_index
+        if self.gold[i]:
+            self.buy()
+        elif self.dead[i] and self.position["size"] > 0:
+            self.sell()
+
+
+# ── ATR 通道突破 ──────────────────────────────────────────────────────────────
+
+
+@register_strategy(
+    name="atr_breakout",
+    label="ATR 通道突破",
+    description="收盘价突破 均线+K×ATR 买入，跌破 均线-K×ATR 卖出。",
+)
+class AtrBreakoutStrategy(ParametrizedStrategy):
+    params = [
+        Param("n_ma", int, default=20, min_value=5, max_value=100, label="均线周期"),
+        Param("n_atr", int, default=20, min_value=5, max_value=50, label="ATR周期"),
+        Param("k", float, default=2.0, min_value=0.5, max_value=5.0, label="ATR倍数"),
+    ]
+
+    def init(self) -> None:
+        self.ma = self.I(MA, self.data.close, self.p["n_ma"])
+        self.atr = self.I(ATR, self.data.close, self.data.high, self.data.low, self.p["n_atr"])
+
+    def next(self) -> None:
+        i = self._bar_index
+        close = self.data.close[0]
+        upper = self.ma[i] + self.p["k"] * self.atr[i]
+        lower = self.ma[i] - self.p["k"] * self.atr[i]
+        if close >= upper and self.position["size"] == 0:
+            self.buy()
+        elif close <= lower and self.position["size"] > 0:
+            self.sell()

tests/unit/test_web_backtest.py

@@ -59,7 +59,7 @@ def test_registry_has_builtin_strategies():
     assert "boll_breakout" in names
     assert "rsi_reversal" in names
     assert "kdj_cross" in names
-    assert len(names) >= 5
+    assert len(names) >= 18
 
 
 def test_strategy_schema_serialization():
@@ -522,7 +522,7 @@ def test_list_strategies_endpoint(client):
     resp = client.get("/api/v1/backtest/strategies")
     assert resp.status_code == 200
     body = resp.json()
-    assert body["count"] >= 5
+    assert body["count"] >= 18
     names = [s["name"] for s in body["strategies"]]
     assert "ma_cross" in names
     # 每个策略的 schema 结构完整