From fa46be501bb1f9b664133cec1a8d82f76de28432 Mon Sep 17 00:00:00 2001 From: sam Date: Mon, 6 Oct 2025 13:28:49 +0800 Subject: [PATCH] update --- app/backtest/optimizer.py | 36 +++++-- app/ui/views/backtest.py | 166 +++++++++++++++++++++++++++++++++ tests/test_bandit_optimizer.py | 6 ++ 3 files changed, 201 insertions(+), 7 deletions(-) diff --git a/app/backtest/optimizer.py b/app/backtest/optimizer.py index a7a5f1b..3e7bde9 100644 --- a/app/backtest/optimizer.py +++ b/app/backtest/optimizer.py @@ -3,7 +3,7 @@ from __future__ import annotations import random from dataclasses import dataclass, field -from typing import Dict, Iterable, List, Sequence, Tuple +from typing import Any, Dict, Iterable, List, Mapping, Sequence, Tuple from app.backtest.decision_env import DecisionEnv, EpisodeMetrics from app.backtest.decision_env import ParameterSpec @@ -28,9 +28,12 @@ class BanditConfig: @dataclass class BanditEpisode: action: Dict[str, float] + resolved_action: Dict[str, Any] reward: float metrics: EpisodeMetrics observation: Dict[str, float] + weights: Mapping[str, float] | None = None + department_controls: Mapping[str, Mapping[str, Any]] | None = None @dataclass @@ -78,8 +81,13 @@ class EpsilonGreedyBandit: self._counts[key] = count self._value_estimates[key] = old_estimate + (reward - old_estimate) / count - action_payload = self._action_to_mapping(action) + 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, @@ -94,9 +102,12 @@ class EpsilonGreedyBandit: 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) LOGGER.info( @@ -112,17 +123,28 @@ class EpsilonGreedyBandit: 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._random.uniform(spec.minimum, spec.maximum) - for spec in self._specs - ] + return [self._sample_value(spec) for spec in self._specs] - def _action_to_mapping(self, action: Sequence[float]) -> Dict[str, float]: + def _raw_action_mapping(self, action: Sequence[float]) -> Dict[str, float]: return { spec.name: float(value) for spec, value in zip(self._specs, action, strict=True) } + def _resolved_action_mapping(self, action: Sequence[float]) -> Dict[str, Any]: + return { + spec.name: spec.resolve(value) + 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 _metrics_to_dict(metrics: EpisodeMetrics) -> Dict[str, float | Dict[str, int]]: payload: Dict[str, float | Dict[str, int]] = { diff --git a/app/ui/views/backtest.py b/app/ui/views/backtest.py index 2cf9359..66273b4 100644 --- a/app/ui/views/backtest.py +++ b/app/ui/views/backtest.py @@ -18,6 +18,7 @@ from app.agents.base import AgentContext from app.agents.game import Decision from app.agents.registry import default_agents from app.backtest.decision_env import DecisionEnv, ParameterSpec +from app.backtest.optimizer import BanditConfig, EpsilonGreedyBandit from app.backtest.engine import BacktestEngine, PortfolioState, BtConfig, run_backtest from app.ingest.checker import run_boot_check from app.ingest.tushare import run_ingestion @@ -35,6 +36,7 @@ from app.ui.views.dashboard import update_dashboard_sidebar _DECISION_ENV_SINGLE_RESULT_KEY = "decision_env_single_result" _DECISION_ENV_BATCH_RESULTS_KEY = "decision_env_batch_results" +_DECISION_ENV_BANDIT_RESULTS_KEY = "decision_env_bandit_results" def render_backtest_review() -> None: """渲染回测执行、调参与结果复盘页面。""" @@ -675,6 +677,170 @@ def render_backtest_review() -> None: st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None) st.success("已清除单次调参结果缓存。") + 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( + "随机种子（可选）", + value="", + key="decision_env_bandit_seed", + help="填写整数可复现实验，不填写则随机。", + ).strip() + bandit_seed = None + if seed_text: + try: + bandit_seed = int(seed_text) + except ValueError: + 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 run_bandit: + if not specs: + st.warning("请至少配置一个动作维度再执行探索。") + elif selected_agents and not range_valid: + st.error("请确保所有代理的最大权重大于最小权重。") + elif not controls_valid: + st.error("请修正部门参数的取值范围。") + else: + baseline_weights = cfg.agent_weights.as_dict() + for agent in agent_objects: + baseline_weights.setdefault(agent.name, 1.0) + + universe_env = [code.strip() for code in universe_text.split(',') if code.strip()] + if not universe_env: + st.error("请先指定至少一个股票代码。") + else: + bt_cfg_env = BtConfig( + id="decision_env_bandit", + name="DecisionEnv Bandit", + start_date=start_date, + end_date=end_date, + universe=universe_env, + params={ + "target": target, + "stop": stop, + "hold_days": int(hold_days), + }, + method=cfg.decision_method, + ) + env = DecisionEnv( + bt_config=bt_cfg_env, + parameter_specs=specs, + baseline_weights=baseline_weights, + disable_departments=disable_departments, + ) + config = BanditConfig( + experiment_id=experiment_id or f"bandit_{datetime.now().strftime('%Y%m%d_%H%M%S')}", + strategy=strategy_label or "DecisionEnv", + episodes=bandit_episodes, + epsilon=bandit_epsilon, + seed=bandit_seed, + ) + bandit = EpsilonGreedyBandit(env, config) + with st.spinner("自动探索进行中，请稍候..."): + summary = bandit.run() + + episodes_dump: List[Dict[str, object]] = [] + for idx, episode in enumerate(summary.episodes, start=1): + episodes_dump.append( + { + "序号": idx, + "奖励": episode.reward, + "动作(raw)": json.dumps(episode.action, ensure_ascii=False), + "参数值": json.dumps(episode.resolved_action, ensure_ascii=False), + "总收益": episode.metrics.total_return, + "最大回撤": episode.metrics.max_drawdown, + "波动率": episode.metrics.volatility, + "权重": json.dumps(episode.weights or {}, ensure_ascii=False), + "部门控制": json.dumps(episode.department_controls or {}, ensure_ascii=False), + } + ) + + best_episode = summary.best_episode + best_index = summary.episodes.index(best_episode) + 1 if best_episode else None + st.session_state[_DECISION_ENV_BANDIT_RESULTS_KEY] = { + "episodes": episodes_dump, + "best_index": best_index, + "best": { + "reward": best_episode.reward if best_episode else None, + "action": best_episode.action if best_episode else None, + "resolved_action": best_episode.resolved_action if best_episode else None, + "weights": best_episode.weights if best_episode else None, + "department_controls": best_episode.department_controls if best_episode else None, + }, + "experiment_id": config.experiment_id, + "strategy": config.strategy, + } + st.success(f"自动探索完成，共执行 {len(episodes_dump)} 轮。") + + bandit_state = st.session_state.get(_DECISION_ENV_BANDIT_RESULTS_KEY) + if bandit_state: + st.caption( + f"实验 ID：{bandit_state.get('experiment_id')} | 策略：{bandit_state.get('strategy')}" + ) + episodes_dump = bandit_state.get("episodes") or [] + if episodes_dump: + st.dataframe(pd.DataFrame(episodes_dump), hide_index=True, width='stretch') + best_payload = bandit_state.get("best") or {} + if best_payload.get("reward") is not None: + st.success( + f"最佳结果：第 {bandit_state.get('best_index')} 轮，奖励 {best_payload['reward']:+.4f}" + ) + col_best1, col_best2 = st.columns(2) + col_best1.write("动作(raw)：") + col_best1.json(best_payload.get("action") or {}) + col_best2.write("参数值：") + col_best2.json(best_payload.get("resolved_action") or {}) + weights_payload = best_payload.get("weights") or {} + if weights_payload: + st.write("对应代理权重：") + st.json(weights_payload) + if st.button( + "将最佳权重写入默认配置", + key="save_decision_env_bandit_weights", + ): + try: + cfg.agent_weights.update_from_dict(weights_payload) + save_config(cfg) + except Exception as exc: # noqa: BLE001 + LOGGER.exception( + "保存 bandit 权重失败", + extra={**LOG_EXTRA, "error": str(exc)}, + ) + st.error(f"写入配置失败：{exc}") + else: + st.success("最佳权重已写入 config.json") + dept_ctrl = best_payload.get("department_controls") or {} + if dept_ctrl: + with st.expander("最佳部门控制参数", expanded=False): + st.json(dept_ctrl) + if st.button("清除自动探索结果", key="clear_decision_env_bandit"): + st.session_state.pop(_DECISION_ENV_BANDIT_RESULTS_KEY, None) + st.success("已清除自动探索结果。") + st.divider() st.caption("批量调参：在下方输入多组动作，每行表示一组 0-1 之间的值，用逗号分隔。") default_grid = "\n".join( diff --git a/tests/test_bandit_optimizer.py b/tests/test_bandit_optimizer.py index f82d5c8..0c06000 100644 --- a/tests/test_bandit_optimizer.py +++ b/tests/test_bandit_optimizer.py @@ -60,6 +60,7 @@ class DummyEnv: "weights": {"A_mom": value}, "risk_breakdown": metrics.risk_breakdown, "risk_events": [], + "department_controls": {"momentum": {"prompt": "baseline"}}, } return obs, reward, True, info @@ -92,3 +93,8 @@ 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 + + first_episode = summary.episodes[0] + assert first_episode.resolved_action + assert first_episode.department_controls == {"momentum": {"prompt": "baseline"}}