update
This commit is contained in:
sam
parent
e234d66687
commit
dc2d82f685
@ -370,7 +370,9 @@ def render_backtest_review() -> None:
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action_values.append(action_val)
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action_values.append(action_val)
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controls_valid = True
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controls_valid = True
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with st.expander("部门 LLM 参数", expanded=False):
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st.divider()
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st.subheader("部门参数调整（可选）")
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dept_codes = sorted(app_cfg.departments.keys())
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dept_codes = sorted(app_cfg.departments.keys())
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if not dept_codes:
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if not dept_codes:
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st.caption("当前未配置部门。")
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st.caption("当前未配置部门。")
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@ -509,213 +511,6 @@ def render_backtest_review() -> None:
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help="关闭部门调用后不依赖外部 LLM 网络，仅根据规则代理权重模拟。",
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help="关闭部门调用后不依赖外部 LLM 网络，仅根据规则代理权重模拟。",
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)
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)
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st.divider()
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st.subheader("单次调参")
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run_decision_env = st.button("执行单次调参", key="run_decision_env_button")
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just_finished_single = False
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if run_decision_env:
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if not specs:
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st.warning("请至少配置一个动作维度（代理或部门参数）。")
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elif selected_agents and not range_valid:
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st.error("请确保所有代理的最大权重大于最小权重。")
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elif not controls_valid:
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st.error("请修正部门参数的取值范围。")
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else:
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LOGGER.info(
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"离线调参（单次）按钮点击，已选择代理=%s 动作=%s disable_departments=%s",
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selected_agents,
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action_values,
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disable_departments,
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extra=LOG_EXTRA,
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)
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baseline_weights = app_cfg.agent_weights.as_dict()
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for agent in agent_objects:
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baseline_weights.setdefault(agent.name, 1.0)
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universe_env = [code.strip() for code in universe_text.split(',') if code.strip()]
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if not universe_env:
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st.error("请先指定至少一个股票代码。")
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else:
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bt_cfg_env = BtConfig(
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id="decision_env_streamlit",
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name="DecisionEnv Streamlit",
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start_date=start_date,
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end_date=end_date,
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universe=universe_env,
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params={
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"target": target,
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"stop": stop,
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"hold_days": int(hold_days),
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},
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method=app_cfg.decision_method,
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)
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env = DecisionEnv(
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bt_config=bt_cfg_env,
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parameter_specs=specs,
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baseline_weights=baseline_weights,
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disable_departments=disable_departments,
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)
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env.reset()
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LOGGER.debug(
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"离线调参（单次）启动 DecisionEnv：cfg=%s 参数维度=%s",
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bt_cfg_env,
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len(specs),
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extra=LOG_EXTRA,
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)
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with st.spinner("正在执行离线调参……"):
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try:
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observation, reward, done, info = env.step(action_values)
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LOGGER.info(
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"离线调参（单次）完成，obs=%s reward=%.4f done=%s",
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observation,
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reward,
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done,
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extra=LOG_EXTRA,
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)
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except Exception as exc: # noqa: BLE001
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LOGGER.exception("DecisionEnv 调用失败", extra=LOG_EXTRA)
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st.error(f"离线调参失败：{exc}")
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st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None)
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else:
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if observation.get("failure"):
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st.error("调参失败：回测执行未完成，可能是 LLM 网络不可用或参数异常。")
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st.json(observation)
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st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None)
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else:
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resolved_experiment_id = experiment_id or str(uuid.uuid4())
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resolved_strategy = strategy_label or "DecisionEnv"
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action_payload = {
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label: value for label, value in zip(spec_labels, action_values)
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}
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metrics_payload = dict(observation)
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metrics_payload["reward"] = reward
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metrics_payload["department_controls"] = info.get("department_controls")
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log_success = False
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try:
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log_tuning_result(
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experiment_id=resolved_experiment_id,
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strategy=resolved_strategy,
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action=action_payload,
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reward=reward,
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metrics=metrics_payload,
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weights=info.get("weights", {}),
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)
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except Exception: # noqa: BLE001
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LOGGER.exception("记录调参结果失败", extra=LOG_EXTRA)
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else:
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log_success = True
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LOGGER.info(
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"离线调参（单次）日志写入成功：experiment=%s strategy=%s",
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resolved_experiment_id,
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resolved_strategy,
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extra=LOG_EXTRA,
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)
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st.session_state[_DECISION_ENV_SINGLE_RESULT_KEY] = {
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"observation": dict(observation),
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"reward": float(reward),
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"weights": info.get("weights", {}),
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"department_controls": info.get("department_controls"),
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"actions": action_payload,
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"nav_series": info.get("nav_series"),
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"trades": info.get("trades"),
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"portfolio_snapshots": info.get("portfolio_snapshots"),
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"portfolio_trades": info.get("portfolio_trades"),
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"risk_breakdown": info.get("risk_breakdown"),
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"spec_labels": list(spec_labels),
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"action_values": list(action_values),
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"experiment_id": resolved_experiment_id,
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"strategy_label": resolved_strategy,
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"logged": log_success,
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}
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just_finished_single = True
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single_result = st.session_state.get(_DECISION_ENV_SINGLE_RESULT_KEY)
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if single_result:
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if just_finished_single:
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st.success("离线调参完成")
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else:
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st.success("离线调参结果（最近一次运行）")
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st.caption(
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f"实验 ID：{single_result.get('experiment_id', '-') } | 策略：{single_result.get('strategy_label', 'DecisionEnv')}"
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)
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observation = single_result.get("observation", {})
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reward = float(single_result.get("reward", 0.0))
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col_metrics = st.columns(4)
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col_metrics[0].metric("总收益", f"{observation.get('total_return', 0.0):+.2%}")
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col_metrics[1].metric("最大回撤", f"{observation.get('max_drawdown', 0.0):+.2%}")
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col_metrics[2].metric("波动率", f"{observation.get('volatility', 0.0):+.2%}")
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col_metrics[3].metric("奖励", f"{reward:+.4f}")
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turnover_ratio = float(observation.get("turnover", 0.0) or 0.0)
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turnover_value = float(observation.get("turnover_value", 0.0) or 0.0)
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risk_count = float(observation.get("risk_count", 0.0) or 0.0)
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col_metrics_extra = st.columns(3)
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col_metrics_extra[0].metric("平均换手率", f"{turnover_ratio:.2%}")
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col_metrics_extra[1].metric("成交额", f"{turnover_value:,.0f}")
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col_metrics_extra[2].metric("风险事件数", f"{int(risk_count)}")
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weights_dict = single_result.get("weights") or {}
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if weights_dict:
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st.write("调参后权重：")
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st.json(weights_dict)
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if st.button("保存这些权重为默认配置", key="save_decision_env_weights_single"):
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try:
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app_cfg.agent_weights.update_from_dict(weights_dict)
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save_config(app_cfg)
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except Exception as exc: # noqa: BLE001
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LOGGER.exception("保存权重失败", extra={**LOG_EXTRA, "error": str(exc)})
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st.error(f"写入配置失败：{exc}")
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else:
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st.success("代理权重已写入 config.json")
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if single_result.get("logged"):
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st.caption("调参结果已写入 tuning_results 表。")
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nav_series = single_result.get("nav_series") or []
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if nav_series:
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try:
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nav_df = pd.DataFrame(nav_series)
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if {"trade_date", "nav"}.issubset(nav_df.columns):
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nav_df = nav_df.sort_values("trade_date")
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nav_df["trade_date"] = pd.to_datetime(nav_df["trade_date"])
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st.line_chart(nav_df.set_index("trade_date")["nav"], height=220)
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except Exception: # noqa: BLE001
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LOGGER.debug("导航曲线绘制失败", extra=LOG_EXTRA)
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trades = single_result.get("trades") or []
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if trades:
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st.write("成交记录：")
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st.dataframe(pd.DataFrame(trades), hide_index=True, width='stretch')
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snapshots = single_result.get("portfolio_snapshots") or []
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if snapshots:
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with st.expander("投资组合快照", expanded=False):
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st.dataframe(pd.DataFrame(snapshots), hide_index=True, width='stretch')
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portfolio_trades = single_result.get("portfolio_trades") or []
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if portfolio_trades:
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with st.expander("组合成交明细", expanded=False):
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st.dataframe(pd.DataFrame(portfolio_trades), hide_index=True, width='stretch')
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risk_breakdown = single_result.get("risk_breakdown") or {}
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if risk_breakdown:
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with st.expander("风险事件统计", expanded=False):
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st.json(risk_breakdown)
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department_info = single_result.get("department_controls") or {}
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if department_info:
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with st.expander("部门控制参数", expanded=False):
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st.json(department_info)
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action_snapshot = single_result.get("actions") or {}
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if action_snapshot:
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with st.expander("动作明细", expanded=False):
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st.json(action_snapshot)
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if st.button("清除单次调参结果", key="clear_decision_env_single"):
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st.session_state.pop(_DECISION_ENV_SINGLE_RESULT_KEY, None)
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st.success("已清除单次调参结果缓存。")
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st.divider()
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st.divider()
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st.subheader("自动探索（epsilon-greedy）")
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st.subheader("自动探索（epsilon-greedy）")
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col_ep, col_eps, col_seed = st.columns([1, 1, 1])
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col_ep, col_eps, col_seed = st.columns([1, 1, 1])
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@ -880,232 +675,4 @@ def render_backtest_review() -> None:
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st.session_state.pop(_DECISION_ENV_BANDIT_RESULTS_KEY, None)
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st.session_state.pop(_DECISION_ENV_BANDIT_RESULTS_KEY, None)
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st.success("已清除自动探索结果。")
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st.success("已清除自动探索结果。")
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st.divider()
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st.subheader("批量调参")
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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。")
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default_grid = "\n".join(
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[
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",".join(["0.2" for _ in specs]),
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",".join(["0.5" for _ in specs]),
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",".join(["0.8" for _ in specs]),
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]
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) if specs else ""
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action_grid_raw = st.text_area(
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"动作列表",
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value=default_grid,
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height=120,
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key="decision_env_batch_actions",
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)
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run_batch = st.button("批量执行调参", key="run_decision_env_batch")
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batch_just_ran = False
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if run_batch:
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if not specs:
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st.warning("请至少配置一个动作维度。")
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elif selected_agents and not range_valid:
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st.error("请确保所有代理的最大权重大于最小权重。")
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elif not controls_valid:
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st.error("请修正部门参数的取值范围。")
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else:
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LOGGER.info(
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"离线调参（批量）按钮点击，已选择代理=%s disable_departments=%s",
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selected_agents,
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disable_departments,
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extra=LOG_EXTRA,
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)
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lines = [line.strip() for line in action_grid_raw.splitlines() if line.strip()]
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if not lines:
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st.warning("请在文本框中输入至少一组动作。")
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else:
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LOGGER.debug(
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"离线调参（批量）原始输入=%s",
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lines,
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extra=LOG_EXTRA,
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)
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parsed_actions: List[List[float]] = []
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for line in lines:
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try:
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values = [float(val.strip()) for val in line.split(',') if val.strip()]
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except ValueError:
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st.error(f"无法解析动作行：{line}")
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parsed_actions = []
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break
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if len(values) != len(specs):
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st.error(f"动作维度不匹配（期望 {len(specs)} 个值）：{line}")
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parsed_actions = []
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break
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parsed_actions.append(values)
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if parsed_actions:
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LOGGER.info(
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"离线调参（批量）解析动作成功，数量=%s",
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len(parsed_actions),
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extra=LOG_EXTRA,
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)
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baseline_weights = app_cfg.agent_weights.as_dict()
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for agent in agent_objects:
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baseline_weights.setdefault(agent.name, 1.0)
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universe_env = [code.strip() for code in universe_text.split(',') if code.strip()]
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if not universe_env:
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st.error("请先指定至少一个股票代码。")
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else:
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bt_cfg_env = BtConfig(
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id="decision_env_streamlit_batch",
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name="DecisionEnv Batch",
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start_date=start_date,
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end_date=end_date,
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universe=universe_env,
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params={
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"target": target,
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"stop": stop,
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"hold_days": int(hold_days),
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},
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method=app_cfg.decision_method,
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)
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env = DecisionEnv(
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bt_config=bt_cfg_env,
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parameter_specs=specs,
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baseline_weights=baseline_weights,
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disable_departments=disable_departments,
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)
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results: List[Dict[str, object]] = []
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resolved_experiment_id = experiment_id or str(uuid.uuid4())
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resolved_strategy = strategy_label or "DecisionEnv"
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LOGGER.debug(
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"离线调参（批量）启动 DecisionEnv：cfg=%s 动作组=%s",
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bt_cfg_env,
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len(parsed_actions),
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extra=LOG_EXTRA,
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)
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with st.spinner("正在批量执行调参……"):
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|
||||||
for idx, action_vals in enumerate(parsed_actions, start=1):
|
|
||||||
env.reset()
|
|
||||||
try:
|
|
||||||
observation, reward, done, info = env.step(action_vals)
|
|
||||||
except Exception as exc: # noqa: BLE001
|
|
||||||
LOGGER.exception("批量调参失败", extra=LOG_EXTRA)
|
|
||||||
results.append(
|
|
||||||
{
|
|
||||||
"序号": idx,
|
|
||||||
"动作": action_vals,
|
|
||||||
"状态": "error",
|
|
||||||
"错误": str(exc),
|
|
||||||
}
|
|
||||||
)
|
|
||||||
continue
|
|
||||||
if observation.get("failure"):
|
|
||||||
results.append(
|
|
||||||
{
|
|
||||||
"序号": idx,
|
|
||||||
"动作": action_vals,
|
|
||||||
"状态": "failure",
|
|
||||||
"奖励": -1.0,
|
|
||||||
}
|
|
||||||
)
|
|
||||||
else:
|
|
||||||
LOGGER.info(
|
|
||||||
"离线调参（批量）第 %s 组完成，reward=%.4f obs=%s",
|
|
||||||
idx,
|
|
||||||
reward,
|
|
||||||
observation,
|
|
||||||
extra=LOG_EXTRA,
|
|
||||||
)
|
|
||||||
action_payload = {
|
|
||||||
label: value
|
|
||||||
for label, value in zip(spec_labels, action_vals)
|
|
||||||
}
|
|
||||||
metrics_payload = dict(observation)
|
|
||||||
metrics_payload["reward"] = reward
|
|
||||||
metrics_payload["department_controls"] = info.get("department_controls")
|
|
||||||
weights_payload = info.get("weights", {})
|
|
||||||
try:
|
|
||||||
log_tuning_result(
|
|
||||||
experiment_id=resolved_experiment_id,
|
|
||||||
strategy=resolved_strategy,
|
|
||||||
action=action_payload,
|
|
||||||
reward=reward,
|
|
||||||
metrics=metrics_payload,
|
|
||||||
weights=weights_payload,
|
|
||||||
)
|
|
||||||
except Exception: # noqa: BLE001
|
|
||||||
LOGGER.exception("记录调参结果失败", extra=LOG_EXTRA)
|
|
||||||
results.append(
|
|
||||||
{
|
|
||||||
"序号": idx,
|
|
||||||
"动作": action_payload,
|
|
||||||
"状态": "ok",
|
|
||||||
"总收益": observation.get("total_return", 0.0),
|
|
||||||
"最大回撤": observation.get("max_drawdown", 0.0),
|
|
||||||
"波动率": observation.get("volatility", 0.0),
|
|
||||||
"奖励": reward,
|
|
||||||
"权重": weights_payload,
|
|
||||||
"部门控制": info.get("department_controls"),
|
|
||||||
}
|
|
||||||
)
|
|
||||||
st.session_state[_DECISION_ENV_BATCH_RESULTS_KEY] = {
|
|
||||||
"results": results,
|
|
||||||
"selectable": [
|
|
||||||
row
|
|
||||||
for row in results
|
|
||||||
if row.get("状态") == "ok" and row.get("权重")
|
|
||||||
],
|
|
||||||
"experiment_id": resolved_experiment_id,
|
|
||||||
"strategy_label": resolved_strategy,
|
|
||||||
}
|
|
||||||
batch_just_ran = True
|
|
||||||
LOGGER.info(
|
|
||||||
"离线调参（批量）执行结束，总结果条数=%s",
|
|
||||||
len(results),
|
|
||||||
extra=LOG_EXTRA,
|
|
||||||
)
|
|
||||||
batch_state = st.session_state.get(_DECISION_ENV_BATCH_RESULTS_KEY)
|
|
||||||
if batch_state:
|
|
||||||
results = batch_state.get("results") or []
|
|
||||||
if results:
|
|
||||||
if batch_just_ran:
|
|
||||||
st.success("批量调参完成")
|
|
||||||
else:
|
|
||||||
st.success("批量调参结果（最近一次运行）")
|
|
||||||
st.caption(
|
|
||||||
f"实验 ID：{batch_state.get('experiment_id', '-') } | 策略：{batch_state.get('strategy_label', 'DecisionEnv')}"
|
|
||||||
)
|
|
||||||
results_df = pd.DataFrame(results)
|
|
||||||
st.write("批量调参结果：")
|
|
||||||
st.dataframe(results_df, hide_index=True, width='stretch')
|
|
||||||
selectable = batch_state.get("selectable") or []
|
|
||||||
if selectable:
|
|
||||||
option_labels = [
|
|
||||||
f"序号 {row['序号']} | 奖励 {row.get('奖励', 0.0):+.4f}"
|
|
||||||
for row in selectable
|
|
||||||
]
|
|
||||||
selected_label = st.selectbox(
|
|
||||||
"选择要保存的记录",
|
|
||||||
option_labels,
|
|
||||||
key="decision_env_batch_select",
|
|
||||||
)
|
|
||||||
selected_row = None
|
|
||||||
for label, row in zip(option_labels, selectable):
|
|
||||||
if label == selected_label:
|
|
||||||
selected_row = row
|
|
||||||
break
|
|
||||||
if selected_row and st.button(
|
|
||||||
"保存所选权重为默认配置",
|
|
||||||
key="save_decision_env_weights_batch",
|
|
||||||
):
|
|
||||||
try:
|
|
||||||
app_cfg.agent_weights.update_from_dict(selected_row.get("权重", {}))
|
|
||||||
save_config(app_cfg)
|
|
||||||
except Exception as exc: # noqa: BLE001
|
|
||||||
LOGGER.exception("批量保存权重失败", extra={**LOG_EXTRA, "error": str(exc)})
|
|
||||||
st.error(f"写入配置失败：{exc}")
|
|
||||||
else:
|
|
||||||
st.success(
|
|
||||||
f"已将序号 {selected_row['序号']} 的权重写入 config.json"
|
|
||||||
)
|
|
||||||
else:
|
|
||||||
st.caption("暂无成功的结果可供保存。")
|
|
||||||
else:
|
|
||||||
st.caption("批量调参在最近一次执行中未产生结果。")
|
|
||||||
if st.button("清除批量调参结果", key="clear_decision_env_batch"):
|
|
||||||
st.session_state.pop(_DECISION_ENV_BATCH_RESULTS_KEY, None)
|
|
||||||
st.session_state.pop("decision_env_batch_select", None)
|
|
||||||
st.success("已清除批量调参结果缓存。")
|
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user