哈哈

run_bb_full_grid_search.py

@@ -18,6 +18,7 @@ from __future__ import annotations
 
 import argparse
 import hashlib
+import heapq
 import io
 import json
 import math
@@ -25,7 +26,6 @@ import os
 import sys
 import tempfile
 import time
-from collections import defaultdict
 from concurrent.futures import ProcessPoolExecutor, as_completed
 from pathlib import Path
 
@@ -145,8 +145,9 @@ def _init_worker(df_path: str, df_1m_path: str | None, use_1m: bool, step_min: i
     G_STEP_MIN = int(step_min)
 
 
-def _eval_period_task(args: tuple[int, list[float]]) -> list[dict]:
-    period, std_list = args
+def _eval_single_task(args: tuple[int, float]) -> dict:
+    """单个 (period, std) 组合的回测任务"""
+    period, std = args
     assert G_DF is not None
 
     arr_touch_dir = None
@@ -155,8 +156,6 @@ def _eval_period_task(args: tuple[int, list[float]]) -> list[dict]:
         bb_mid, _, _, _ = bollinger(close, period, 1.0)
         arr_touch_dir = get_1m_touch_direction(G_DF, G_DF_1M, bb_mid.values, kline_step_min=G_STEP_MIN)
 
-    rows: list[dict] = []
-    for std in std_list:
     cfg = BBMidlineConfig(
         bb_period=period,
         bb_std=float(std),
@@ -200,7 +199,7 @@ def _eval_period_task(args: tuple[int, list[float]]) -> list[dict]:
     sharpe = float(pnl.mean() / pnl.std()) * math.sqrt(365.0) if pnl.std() > 0 else 0.0
     stable_score = score_stable(ret_pct, sharpe, dd_pct, n_trades)
 
-        rows.append({
+    return {
         "period": period,
         "std": round(float(std), 2),
         "final_eq": final_eq,
@@ -211,8 +210,42 @@ def _eval_period_task(args: tuple[int, list[float]]) -> list[dict]:
         "max_dd_u": dd_u,
         "max_dd_pct": dd_pct,
         "stable_score": stable_score,
-        })
-    return rows
+    }
+
+
+def _eval_period_task(args: tuple[int, list[float]]) -> list[dict]:
+    """兼容旧接口：一个 period 组的批量回测"""
+    period, std_list = args
+    return [_eval_single_task((period, s)) for s in std_list]
+
+
+
+def _format_eta(seconds: float) -> str:
+    """格式化剩余时间"""
+    if seconds < 60:
+        return f"{seconds:.0f}s"
+    elif seconds < 3600:
+        return f"{seconds / 60:.1f}min"
+    else:
+        h = int(seconds // 3600)
+        m = int((seconds % 3600) // 60)
+        return f"{h}h{m:02d}m"
+
+
+def _print_top_n(rows: list[dict], n: int = 10, label: str = "当前 Top 10") -> None:
+    """打印当前 Top N 排行榜"""
+    if not rows:
+        return
+    sorted_rows = sorted(rows, key=lambda r: r["stable_score"], reverse=True)[:n]
+    print(f"\n{'─' * 90}", flush=True)
+    print(f"  📊 {label} (按稳定性评分排序)", flush=True)
+    print(f"  {'排名':>4s}  {'period':>6s}  {'std':>7s}  {'收益%':>9s}  {'回撤%':>7s}  {'夏普':>7s}  {'交易数':>6s}  {'评分':>8s}", flush=True)
+    print(f"  {'─' * 82}", flush=True)
+    for i, r in enumerate(sorted_rows, 1):
+        print(f"  {i:4d}  {int(r['period']):6d}  {r['std']:7.2f}  {r['ret_pct']:+9.2f}  "
+              f"{r['max_dd_pct']:7.2f}  {r['sharpe']:7.3f}  {int(r['n_trades']):6d}  "
+              f"{r['stable_score']:8.1f}", flush=True)
+    print(f"{'─' * 90}\n", flush=True)
 
 
 def run_grid_search(
@@ -229,100 +262,94 @@ def run_grid_search(
     meta: dict | None = None,
     checkpoint_interval: int = 5,
 ) -> pd.DataFrame:
-    by_period: dict[int, set[float]] = defaultdict(set)
-    for p, s in params:
-        by_period[int(p)].add(round(float(s), 2))
-
-    tasks = [(p, sorted(stds)) for p, stds in sorted(by_period.items())]
-    total_periods = len(tasks)
-    total_combos = sum(len(stds) for _, stds in tasks)
+    total_combos = len(params)
     rows: list[dict] = list(existing_rows) if existing_rows else []
 
-    print(f"待运行: {total_combos} 组合 ({total_periods} period组), workers={workers}" + (
+    print(f"待运行: {total_combos} 组合, workers={workers}" + (
         f", 断点续跑 (已有 {len(rows)} 条)" if rows else ""
     ))
-    start = time.time()
-    last_save = 0
+    t_start = time.time()
+    done_combos = 0
+    last_save_count = 0
+    last_top_time = t_start
+    # checkpoint 按组合数保存，默认每 500 个组合保存一次
+    ckpt_combo_interval = max(100, checkpoint_interval * 50)
+    # Top N 排行榜刷新间隔（秒）
+    top_n_interval = 30.0
 
-    def maybe_save(n_done_periods: int):
-        nonlocal last_save
-        if ckpt_path and meta_path and meta and n_done_periods > 0:
-            if n_done_periods - last_save >= checkpoint_interval:
+    def maybe_save():
+        nonlocal last_save_count
+        if ckpt_path and meta_path and meta and done_combos > 0:
+            if done_combos - last_save_count >= ckpt_combo_interval:
                 save_checkpoint(ckpt_path, meta_path, meta, rows)
-                last_save = n_done_periods
+                last_save_count = done_combos
+
+    def maybe_print_top():
+        nonlocal last_top_time
+        now = time.time()
+        if now - last_top_time >= top_n_interval:
+            _print_top_n(rows)
+            last_top_time = now
+
+    def on_result(row: dict):
+        nonlocal done_combos
+        rows.append(row)
+        done_combos += 1
+        elapsed = time.time() - t_start
+        speed = done_combos / elapsed if elapsed > 0 else 0
+        remaining = total_combos - done_combos
+        eta = remaining / speed if speed > 0 else 0
+        pct = done_combos / total_combos * 100
+
+        # 每个结果都实时打印
+        print(f"✓ [{done_combos:>7d}/{total_combos} {pct:5.1f}% ETA {_format_eta(eta)}] "
+              f"p={int(row['period']):4d} s={row['std']:7.2f} | "
+              f"收益:{row['ret_pct']:+8.2f}% 回撤:{row['max_dd_pct']:6.2f}% "
+              f"夏普:{row['sharpe']:7.3f} 交易:{int(row['n_trades']):5d} "
+              f"评分:{row['stable_score']:8.1f}", flush=True)
+
+        maybe_save()
+        maybe_print_top()
 
     if workers <= 1:
         _init_worker(df_path, df_1m_path, use_1m, step_min)
-        done_periods = 0
-        done_combos = 0
-        for task in tasks:
-            res = _eval_period_task(task)
-            period = task[0]
-            # 打印该period的所有结果
-            for row in res:
-                print(f"✓ period={int(row['period']):4d}, std={float(row['std']):7.2f} | "
-                      f"收益: {row['ret_pct']:+7.2f}% | 回撤: {row['max_dd_pct']:6.2f}% | "
-                      f"夏普: {row['sharpe']:7.3f} | 交易: {int(row['n_trades']):6d} | "
-                      f"评分: {row['stable_score']:7.1f}", flush=True)
-            rows.extend(res)
-            done_periods += 1
-            done_combos += len(task[1])
-            maybe_save(done_periods)
-            if done_periods % max(1, total_periods // 20) == 0 or done_periods == total_periods:
-                elapsed = time.time() - start
-                print(f"进度 {done_combos}/{total_combos} ({done_periods}/{total_periods} periods), 用时 {elapsed:.0f}s", flush=True)
+        for p, s in params:
+            row = _eval_single_task((p, s))
+            on_result(row)
     else:
-        done_periods = 0
-        done_combos = 0
+        # 多进程：逐个 (period, std) 提交，实时返回
+        # 为避免提交 200 万个 future 占用过多内存，分批提交
+        batch_size = workers * 20  # 每批提交的任务数
         try:
             with ProcessPoolExecutor(
                 max_workers=workers,
                 initializer=_init_worker,
                 initargs=(df_path, df_1m_path, use_1m, step_min),
             ) as ex:
-                future_map = {ex.submit(_eval_period_task, task): task for task in tasks}
+                idx = 0
+                while idx < total_combos:
+                    batch_end = min(idx + batch_size, total_combos)
+                    batch = params[idx:batch_end]
+                    future_map = {ex.submit(_eval_single_task, (p, s)): (p, s) for p, s in batch}
                     for fut in as_completed(future_map):
-                    period, stds = future_map[fut]
-                    res = fut.result()
-                    # 打印该period的所有结果
-                    for row in res:
-                        print(f"✓ period={int(row['period']):4d}, std={float(row['std']):7.2f} | "
-                              f"收益: {row['ret_pct']:+7.2f}% | 回撤: {row['max_dd_pct']:6.2f}% | "
-                              f"夏普: {row['sharpe']:7.3f} | 交易: {int(row['n_trades']):6d} | "
-                              f"评分: {row['stable_score']:7.1f}", flush=True)
-                    rows.extend(res)
-                    done_periods += 1
-                    done_combos += len(stds)
-                    maybe_save(done_periods)
-                    if done_periods % max(1, total_periods // 20) == 0 or done_periods == total_periods:
-                        elapsed = time.time() - start
-                        print(f"进度 {done_combos}/{total_combos} ({done_periods}/{total_periods} periods), 用时 {elapsed:.0f}s", flush=True)
+                        row = fut.result()
+                        on_result(row)
+                    idx = batch_end
         except (PermissionError, OSError) as e:
             print(f"多进程不可用 ({e})，改用单进程...")
             _init_worker(df_path, df_1m_path, use_1m, step_min)
-            done_periods = 0
-            done_combos = 0
-            for task in tasks:
-                res = _eval_period_task(task)
-                # 打印该period的所有结果
-                for row in res:
-                    print(f"✓ period={int(row['period']):4d}, std={float(row['std']):7.2f} | "
-                          f"收益: {row['ret_pct']:+7.2f}% | 回撤: {row['max_dd_pct']:6.2f}% | "
-                          f"夏普: {row['sharpe']:7.3f} | 交易: {int(row['n_trades']):6d} | "
-                          f"评分: {row['stable_score']:7.1f}", flush=True)
-                rows.extend(res)
-                done_periods += 1
-                done_combos += len(task[1])
-                maybe_save(done_periods)
-                if done_periods % max(1, total_periods // 20) == 0 or done_periods == total_periods:
-                    elapsed = time.time() - start
-                    print(f"进度 {done_combos}/{total_combos} ({done_periods}/{total_periods} periods), 用时 {elapsed:.0f}s", flush=True)
+            for p, s in params[done_combos:]:
+                row = _eval_single_task((p, s))
+                on_result(row)
 
     if ckpt_path and meta_path and meta and rows:
         save_checkpoint(ckpt_path, meta_path, meta, rows)
 
+    # 最终排行榜
+    _print_top_n(rows, n=20, label="最终 Top 20")
+
     df = pd.DataFrame(rows)
-    print(f"完成, 总用时 {time.time() - start:.1f}s")
+    print(f"完成, 总用时 {time.time() - t_start:.1f}s, 平均 {total_combos / (time.time() - t_start):.1f} 组合/秒")
     return df
 
 

test.py

@@ -1,5 +1,38 @@
+import subprocess
+import time
 from wxautox4 import WeChat
 
-wx = WeChat()
-wx.ChatWith('Rainbow')  # 切换到目标聊天
-# 再通过 UIAutomation 定位并点击语音/视频通话按钮
+def make_wechat_call(contact_name, retry_times=3):
+    """
+    可靠的微信拨电话方法
+    
+    Args:
+        contact_name: 联系人名称
+        retry_times: 重试次数
+    """
+    for attempt in range(retry_times):
+        try:
+            # 打开微信应用
+            subprocess.Popen(['/Applications/WeChat.app/Contents/MacOS/WeChat'])
+            time.sleep(2)  # 等待微信启动
+            
+            # 初始化
+            wx = WeChat()
+            time.sleep(1)
+            
+            # 切换聊天
+            wx.ChatWith(contact_name)
+            time.sleep(1)
+            
+            print(f"✓ 已切换到 {contact_name} 聊天")
+            return True
+            
+        except Exception as e:
+            print(f"⚠ 第 {attempt+1} 次尝试失败: {e}")
+            if attempt < retry_times - 1:
+                time.sleep(1)
+    
+    return False
+
+# 使用
+make_wechat_call('Rainbow')