add Bayesian and BOHB optimizers for global parameter search

app/backtest/optimizer.py

@@ -1,10 +1,13 @@
 """Optimization utilities for DecisionEnv-based parameter tuning."""
 from __future__ import annotations
 
+import math
 import random
 from dataclasses import dataclass, field
 from typing import Any, Dict, Iterable, List, Mapping, Sequence, Tuple
 
+import numpy as np
+
 from app.backtest.decision_env import DecisionEnv, EpisodeMetrics
 from app.backtest.decision_env import ParameterSpec
 from app.utils.logging import get_logger
@@ -16,13 +19,18 @@ LOG_EXTRA = {"stage": "decision_bandit"}
 
 @dataclass
 class BanditConfig:
-    """Configuration for epsilon-greedy bandit optimization."""
+    """Configuration shared by all global parameter search strategies."""
 
     experiment_id: str
     strategy: str = "epsilon_greedy"
     episodes: int = 20
     epsilon: float = 0.2
     seed: int | None = None
+    exploration_weight: float = 0.01
+    candidate_pool: int = 128
+    initial_candidates: int = 27
+    eta: int = 3
+    max_rounds: int = 3
 
 
 @dataclass
@@ -53,84 +61,69 @@ class BanditSummary:
         return sum(item.reward for item in self.episodes) / len(self.episodes)
 
 
-class EpsilonGreedyBandit:
-    """Simple epsilon-greedy tuner using DecisionEnv as the reward oracle."""
+class _BaseOptimizer:
+    """Shared helpers for global parameter search algorithms."""
 
     def __init__(self, env: DecisionEnv, config: BanditConfig) -> None:
         self.env = env
         self.config = config
-        self._random = random.Random(config.seed)
         self._specs: List[ParameterSpec] = list(getattr(env, "_specs", []))
         if not self._specs:
             raise ValueError("DecisionEnv does not expose parameter specs")
-        self._value_estimates: Dict[Tuple[float, ...], float] = {}
-        self._counts: Dict[Tuple[float, ...], int] = {}
         self._history = BanditSummary()
+        self._random = random.Random(config.seed)
 
-    def run(self) -> BanditSummary:
-        for episode in range(1, self.config.episodes + 1):
-            action = self._select_action()
-            self.env.reset()
-            done = False
-            cumulative_reward = 0.0
-            obs = {}
-            info: Dict[str, Any] = {}
-            while not done:
-                obs, reward, done, info = self.env.step(action)
-                cumulative_reward += reward
+    def _evaluate_action(self, action: Sequence[float]) -> Tuple[float, EpisodeMetrics, Dict[str, float], Dict[str, Any]]:
+        self.env.reset()
+        cumulative_reward = 0.0
+        obs: Dict[str, float] = {}
+        info: Dict[str, Any] = {}
+        done = False
+        while not done:
+            obs, reward, done, info = self.env.step(action)
+            cumulative_reward += reward
+        metrics = self.env.last_metrics
+        if metrics is None:
+            raise RuntimeError("DecisionEnv did not populate last_metrics")
+        return cumulative_reward, metrics, obs, info
 
-            metrics = self.env.last_metrics
-            if metrics is None:
-                raise RuntimeError("DecisionEnv did not populate last_metrics")
-            key = tuple(action)
-            old_estimate = self._value_estimates.get(key, 0.0)
-            count = self._counts.get(key, 0) + 1
-            self._counts[key] = count
-            self._value_estimates[key] = old_estimate + (cumulative_reward - old_estimate) / count
-
-            action_payload = self._raw_action_mapping(action)
-            resolved_action = self._resolved_action_mapping(action)
-            metrics_payload = _metrics_to_dict(metrics)
-            department_controls = info.get("department_controls")
-            if department_controls:
-                metrics_payload["department_controls"] = department_controls
-            metrics_payload["resolved_action"] = resolved_action
-            try:
-                log_tuning_result(
-                    experiment_id=self.config.experiment_id,
-                    strategy=self.config.strategy,
-                    action=action_payload,
-                    reward=cumulative_reward,
-                    metrics=metrics_payload,
-                    weights=info.get("weights"),
-                )
-            except Exception:  # noqa: BLE001
-                LOGGER.exception("failed to log tuning result", extra=LOG_EXTRA)
-
-            episode_record = BanditEpisode(
+    def _record_episode(
+        self,
+        action: Sequence[float],
+        reward: float,
+        metrics: EpisodeMetrics,
+        obs: Dict[str, float],
+        info: Dict[str, Any],
+    ) -> None:
+        action_payload = self._raw_action_mapping(action)
+        resolved_action = self._resolved_action_mapping(action)
+        metrics_payload = _metrics_to_dict(metrics)
+        department_controls = info.get("department_controls")
+        if department_controls:
+            metrics_payload["department_controls"] = department_controls
+        metrics_payload["resolved_action"] = resolved_action
+        try:
+            log_tuning_result(
+                experiment_id=self.config.experiment_id,
+                strategy=self.config.strategy,
                 action=action_payload,
-                resolved_action=resolved_action,
-                reward=cumulative_reward,
-                metrics=metrics,
-                observation=obs,
+                reward=reward,
+                metrics=metrics_payload,
                 weights=info.get("weights"),
-                department_controls=department_controls,
             )
-            self._history.episodes.append(episode_record)
-            LOGGER.info(
-                "Bandit episode=%s reward=%.4f action=%s",
-                episode,
-                cumulative_reward,
-                action_payload,
-                extra=LOG_EXTRA,
-            )
-        return self._history
+        except Exception:  # noqa: BLE001
+            LOGGER.exception("failed to log tuning result", extra=LOG_EXTRA)
 
-    def _select_action(self) -> List[float]:
-        if self._value_estimates and self._random.random() > self.config.epsilon:
-            best = max(self._value_estimates.items(), key=lambda item: item[1])[0]
-            return list(best)
-        return [self._sample_value(spec) for spec in self._specs]
+        episode_record = BanditEpisode(
+            action=action_payload,
+            resolved_action=resolved_action,
+            reward=reward,
+            metrics=metrics,
+            observation=obs,
+            weights=info.get("weights"),
+            department_controls=department_controls,
+        )
+        self._history.episodes.append(episode_record)
 
     def _raw_action_mapping(self, action: Sequence[float]) -> Dict[str, float]:
         return {
@@ -144,13 +137,142 @@ class EpsilonGreedyBandit:
             for spec, value in zip(self._specs, action, strict=True)
         }
 
-    def _sample_value(self, spec: ParameterSpec) -> float:
-        if spec.values:
-            if len(spec.values) <= 1:
-                return 0.0
-            index = self._random.randrange(len(spec.values))
-            return index / (len(spec.values) - 1)
-        return self._random.random()
+    def _sample_random_action(self) -> List[float]:
+        values: List[float] = []
+        for spec in self._specs:
+            if spec.values:
+                if len(spec.values) <= 1:
+                    values.append(0.0)
+                else:
+                    index = self._random.randrange(len(spec.values))
+                    values.append(index / (len(spec.values) - 1))
+            else:
+                values.append(self._random.random())
+        return values
+
+    def _mutate_action(self, action: Sequence[float], scale: float = 0.1) -> List[float]:
+        mutated = []
+        for value in action:
+            jitter = self._random.gauss(0.0, scale)
+            mutated.append(min(1.0, max(0.0, float(value + jitter))))
+        return mutated
+
+
+class EpsilonGreedyBandit(_BaseOptimizer):
+    """Epsilon-greedy tuner using DecisionEnv as the reward oracle."""
+
+    def __init__(self, env: DecisionEnv, config: BanditConfig) -> None:
+        super().__init__(env, config)
+        self._value_estimates: Dict[Tuple[float, ...], float] = {}
+        self._counts: Dict[Tuple[float, ...], int] = {}
+
+    def run(self) -> BanditSummary:
+        for episode in range(1, self.config.episodes + 1):
+            action = self._select_action()
+            reward, metrics, obs, info = self._evaluate_action(action)
+            key = tuple(action)
+            old_estimate = self._value_estimates.get(key, 0.0)
+            count = self._counts.get(key, 0) + 1
+            self._counts[key] = count
+            self._value_estimates[key] = old_estimate + (reward - old_estimate) / count
+
+            self._record_episode(action, reward, metrics, obs, info)
+            LOGGER.info(
+                "Bandit episode=%s reward=%.4f action=%s",
+                episode,
+                reward,
+                self._raw_action_mapping(action),
+                extra=LOG_EXTRA,
+            )
+        return self._history
+
+    def _select_action(self) -> List[float]:
+        if self._value_estimates and self._random.random() > self.config.epsilon:
+            best = max(self._value_estimates.items(), key=lambda item: item[1])[0]
+            return list(best)
+        return self._sample_random_action()
+
+
+class BayesianBandit(_BaseOptimizer):
+    """Gaussian-process based Bayesian optimization."""
+
+    def __init__(self, env: DecisionEnv, config: BanditConfig) -> None:
+        super().__init__(env, config)
+        self._X: List[np.ndarray] = []
+        self._y: List[float] = []
+        self._noise = 1e-6
+        self._length_scale = 0.3
+
+    def run(self) -> BanditSummary:
+        for _ in range(self.config.episodes):
+            action = self._propose_action()
+            reward, metrics, obs, info = self._evaluate_action(action)
+            self._record_episode(action, reward, metrics, obs, info)
+            self._X.append(np.array(action, dtype=float))
+            self._y.append(reward)
+        return self._history
+
+    def _propose_action(self) -> List[float]:
+        if not self._X:
+            return self._sample_random_action()
+
+        X = np.vstack(self._X)
+        y = np.asarray(self._y, dtype=float)
+        K = self._kernel(X, X) + self._noise * np.eye(len(X))
+        try:
+            K_inv = np.linalg.inv(K)
+        except np.linalg.LinAlgError:
+            K_inv = np.linalg.pinv(K)
+
+        best_y = max(y)
+        candidates = [self._sample_random_action() for _ in range(self.config.candidate_pool)]
+        ei_values: List[Tuple[float, List[float]]] = []
+        for candidate in candidates:
+            x = np.asarray(candidate, dtype=float)
+            k_star = self._kernel(X, x[None, :])[:, 0]
+            mean = float(k_star @ K_inv @ y)
+            k_ss = float(self._kernel(x[None, :], x[None, :])[0, 0])
+            variance = max(k_ss - k_star @ K_inv @ k_star, 1e-9)
+            std = math.sqrt(variance)
+            improvement = mean - best_y - self.config.exploration_weight
+            z = improvement / std if std > 0 else 0.0
+            cdf = 0.5 * (1.0 + math.erf(z / math.sqrt(2.0)))
+            pdf = (1.0 / math.sqrt(2.0 * math.pi)) * math.exp(-0.5 * z * z)
+            ei = improvement * cdf + std * pdf if std > 0 else max(improvement, 0.0)
+            ei_values.append((ei, candidate))
+
+        ei_values.sort(key=lambda item: item[0], reverse=True)
+        best = ei_values[0][1] if ei_values else self._sample_random_action()
+        return best
+
+    def _kernel(self, x1: np.ndarray, x2: np.ndarray) -> np.ndarray:
+        sq_dist = np.sum((x1[:, None, :] - x2[None, :, :]) ** 2, axis=2)
+        return np.exp(-0.5 * sq_dist / (self._length_scale ** 2))
+
+
+class SuccessiveHalvingOptimizer(_BaseOptimizer):
+    """Simplified BOHB-style successive halving optimizer."""
+
+    def run(self) -> BanditSummary:
+        num_candidates = max(1, self.config.initial_candidates)
+        eta = max(2, self.config.eta)
+        actions = [self._sample_random_action() for _ in range(num_candidates)]
+
+        for round_idx in range(self.config.max_rounds):
+            if not actions:
+                break
+            evaluations: List[Tuple[float, List[float]]] = []
+            for action in actions:
+                reward, metrics, obs, info = self._evaluate_action(action)
+                self._record_episode(action, reward, metrics, obs, info)
+                evaluations.append((reward, action))
+            evaluations.sort(key=lambda item: item[0], reverse=True)
+            survivors = max(1, len(evaluations) // eta)
+            actions = [action for _, action in evaluations[:survivors]]
+            if len(actions) == 1:
+                break
+            actions = [self._mutate_action(action, scale=0.05 * (round_idx + 1)) for action in actions]
+        return self._history
 
 
 def _metrics_to_dict(metrics: EpisodeMetrics) -> Dict[str, float | Dict[str, int]]:
@@ -159,6 +281,7 @@ def _metrics_to_dict(metrics: EpisodeMetrics) -> Dict[str, float | Dict[str, int
         "max_drawdown": metrics.max_drawdown,
         "volatility": metrics.volatility,
         "sharpe_like": metrics.sharpe_like,
+        "calmar_like": metrics.calmar_like,
         "turnover": metrics.turnover,
         "turnover_value": metrics.turnover_value,
         "trade_count": float(metrics.trade_count),

app/ui/views/backtest.py

@@ -638,6 +638,17 @@ def render_backtest_review() -> None:
             help="可选：为本次调参记录一个策略名称或备注。",
         )
 
+        strategy_choice = st.selectbox(
+            "搜索策略",
+            ["epsilon_greedy", "bayesian", "bohb"],
+            format_func=lambda x: {
+                "epsilon_greedy": "Epsilon-Greedy",
+                "bayesian": "贝叶斯优化",
+                "bohb": "BOHB/Successive Halving",
+            }.get(x, x),
+            key="decision_env_search_strategy",
+        )
+
         agent_objects = default_agents()
         agent_names = [agent.name for agent in agent_objects]
         if not agent_names:
@@ -841,34 +852,11 @@ def render_backtest_review() -> None:
             )
             
             st.divider()
-            st.subheader("自动探索（epsilon-greedy）")
-            col_ep, col_eps, col_seed = st.columns([1, 1, 1])
-            bandit_episodes = int(
-                col_ep.number_input(
-                    "迭代次数",
-                    min_value=1,
-                    max_value=200,
-                    value=10,
-                    step=1,
-                    key="decision_env_bandit_episodes",
-                    help="探索的回合数，越大越充分但耗时越久。",
-                )
-            )
-            bandit_epsilon = float(
-                col_eps.slider(
-                    "探索比例 ε",
-                    min_value=0.0,
-                    max_value=1.0,
-                    value=0.2,
-                    step=0.05,
-                    key="decision_env_bandit_epsilon",
-                    help="ε 越大，随机探索概率越高。",
-                )
-            )
-            seed_text = col_seed.text_input(
+            st.subheader("全局参数搜索")
+            seed_text = st.text_input(
                 "随机种子（可选）",
                 value="",
-                key="decision_env_bandit_seed",
+                key="decision_env_search_seed",
                 help="填写整数可复现实验，不填写则随机。",
             ).strip()
             bandit_seed = None
@@ -879,7 +867,113 @@ def render_backtest_review() -> None:
                     st.warning("随机种子需为整数，已忽略该值。")
                     bandit_seed = None
 
-            run_bandit = st.button("执行自动探索", key="run_decision_env_bandit")
+            if strategy_choice == "epsilon_greedy":
+                col_ep, col_eps = st.columns([1, 1])
+                bandit_episodes = int(
+                    col_ep.number_input(
+                        "迭代次数",
+                        min_value=1,
+                        max_value=200,
+                        value=10,
+                        step=1,
+                        key="decision_env_bandit_episodes",
+                        help="探索的回合数，越大越充分但耗时越久。",
+                    )
+                )
+                bandit_epsilon = float(
+                    col_eps.slider(
+                        "探索比例 ε",
+                        min_value=0.0,
+                        max_value=1.0,
+                        value=0.2,
+                        step=0.05,
+                        key="decision_env_bandit_epsilon",
+                        help="ε 越大，随机探索概率越高。",
+                    )
+                )
+                bayes_iterations = bandit_episodes
+                bayes_pool = 128
+                bayes_explore = 0.01
+                bohb_initial = 27
+                bohb_eta = 3
+                bohb_rounds = 3
+            elif strategy_choice == "bayesian":
+                col_ep, col_pool, col_xi = st.columns(3)
+                bayes_iterations = int(
+                    col_ep.number_input(
+                        "迭代次数",
+                        min_value=3,
+                        max_value=200,
+                        value=15,
+                        step=1,
+                        key="decision_env_bayes_iterations",
+                    )
+                )
+                bayes_pool = int(
+                    col_pool.number_input(
+                        "候选采样数",
+                        min_value=16,
+                        max_value=1024,
+                        value=128,
+                        step=16,
+                        key="decision_env_bayes_pool",
+                    )
+                )
+                bayes_explore = float(
+                    col_xi.number_input(
+                        "探索权重 ξ",
+                        min_value=0.0,
+                        max_value=0.5,
+                        value=0.01,
+                        step=0.01,
+                        format="%.3f",
+                        key="decision_env_bayes_xi",
+                    )
+                )
+                bandit_episodes = bayes_iterations
+                bandit_epsilon = 0.0
+                bohb_initial = 27
+                bohb_eta = 3
+                bohb_rounds = 3
+            else:  # bohb
+                col_init, col_eta, col_rounds = st.columns(3)
+                bohb_initial = int(
+                    col_init.number_input(
+                        "初始候选数",
+                        min_value=3,
+                        max_value=243,
+                        value=27,
+                        step=3,
+                        key="decision_env_bohb_initial",
+                    )
+                )
+                bohb_eta = int(
+                    col_eta.number_input(
+                        "压缩因子 η",
+                        min_value=2,
+                        max_value=6,
+                        value=3,
+                        step=1,
+                        key="decision_env_bohb_eta",
+                    )
+                )
+                bohb_rounds = int(
+                    col_rounds.number_input(
+                        "最大轮次",
+                        min_value=1,
+                        max_value=6,
+                        value=3,
+                        step=1,
+                        key="decision_env_bohb_rounds",
+                    )
+                )
+                bandit_episodes = bohb_initial
+                bandit_epsilon = 0.0
+                bayes_iterations = bandit_episodes
+                bayes_pool = 128
+                bayes_explore = 0.01
+
+            run_bandit = st.button("执行参数搜索", key="run_decision_env_bandit")
             if run_bandit:
                 if not specs:
                     st.warning("请至少配置一个动作维度再执行探索。")
@@ -912,14 +1006,25 @@ def render_backtest_review() -> None:
                             baseline_weights=baseline_weights,
                             disable_departments=disable_departments,
                         )
+                        search_strategy = strategy_choice
                         config = BanditConfig(
                             experiment_id=experiment_id or f"bandit_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
-                            strategy=strategy_label or "DecisionEnv",
+                            strategy=strategy_label or search_strategy,
                             episodes=bandit_episodes,
                             epsilon=bandit_epsilon,
                             seed=bandit_seed,
+                            exploration_weight=bayes_explore,
+                            candidate_pool=bayes_pool,
+                            initial_candidates=bohb_initial,
+                            eta=bohb_eta,
+                            max_rounds=bohb_rounds,
                         )
-                        bandit = EpsilonGreedyBandit(env, config)
+                        if search_strategy == "bayesian":
+                            bandit = BayesianBandit(env, config)
+                        elif search_strategy == "bohb":
+                            bandit = SuccessiveHalvingOptimizer(env, config)
+                        else:
+                            bandit = EpsilonGreedyBandit(env, config)
                         with st.spinner("自动探索进行中，请稍候..."):
                             summary = bandit.run()
 

docs/TODO.md

@@ -22,7 +22,7 @@
 | 强化学习基线 | ✅ | PPO/SAC 等连续动作算法已接入并形成实验基线。 |
 | 奖励与评估体系 | 🔄 | 决策环境奖励已纳入风险/Turnover/Sharpe-Calmar，待接入成交与资金曲线指标。 |
 | 实时持仓链路 | ⏳ | 建立线上持仓/成交写入与离线调参与监控共享的数据源。 |
-| 全局参数搜索 | ⏳ | 引入 Bandit、贝叶斯优化或 BOHB 提供权重/参数候选。 |
+| 全局参数搜索 | 🔄 | 已上线 epsilon-greedy 调参与指标输出，后续补充贝叶斯优化 / BOHB。 |
 
 ## 多智能体协同与 LLM
 

tests/test_bandit_optimizer.py

@@ -1,10 +1,15 @@
-"""Tests for epsilon-greedy bandit optimizer."""
+"""Tests for global parameter search optimizers."""
 from __future__ import annotations
 
 import pytest
 
 from app.backtest.decision_env import EpisodeMetrics, ParameterSpec
-from app.backtest.optimizer import BanditConfig, EpsilonGreedyBandit
+from app.backtest.optimizer import (
+    BanditConfig,
+    EpsilonGreedyBandit,
+    BayesianBandit,
+    SuccessiveHalvingOptimizer,
+)
 from app.utils import tuning
 
 
@@ -84,11 +89,11 @@ def patch_logging(monkeypatch):
     return records
 
 
-def test_bandit_optimizer_runs_and_logs(patch_logging):
+def test_epsilon_greedy_optimizer(patch_logging):
     env = DummyEnv()
     optimizer = EpsilonGreedyBandit(
         env,
-        BanditConfig(experiment_id="exp", episodes=5, epsilon=0.5, seed=42),
+        BanditConfig(experiment_id="exp_eps", episodes=5, epsilon=0.5, seed=42),
     )
     summary = optimizer.run()
 
@@ -98,8 +103,42 @@ def test_bandit_optimizer_runs_and_logs(patch_logging):
     payload = patch_logging[0]["metrics"]
     assert isinstance(payload, dict)
     assert "risk_breakdown" in payload
-    assert "department_controls" in payload
+    assert summary.best_episode.department_controls == {"momentum": {"prompt": "baseline"}}
 
-    first_episode = summary.episodes[0]
-    assert first_episode.resolved_action
-    assert first_episode.department_controls == {"momentum": {"prompt": "baseline"}}
+
+def test_bayesian_optimizer(patch_logging):
+    env = DummyEnv()
+    optimizer = BayesianBandit(
+        env,
+        BanditConfig(
+            experiment_id="exp_bayes",
+            strategy="bayesian",
+            episodes=6,
+            candidate_pool=32,
+            exploration_weight=0.01,
+            seed=123,
+        ),
+    )
+    summary = optimizer.run()
+    assert summary.best_episode is not None
+    assert summary.best_episode.reward > 0.3
+    assert len(patch_logging) >= 6
+
+
+def test_successive_halving_optimizer(patch_logging):
+    env = DummyEnv()
+    optimizer = SuccessiveHalvingOptimizer(
+        env,
+        BanditConfig(
+            experiment_id="exp_bohb",
+            strategy="bohb",
+            initial_candidates=9,
+            eta=3,
+            max_rounds=2,
+            seed=7,
+        ),
+    )
+    summary = optimizer.run()
+    assert summary.best_episode is not None
+    assert summary.best_episode.reward > 0.3
+    assert len(patch_logging) >= 9