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fix: K线响应截断容错 + 一键寻优并发默认8进程
- security_bars: parse_response 遇末尾残缺记录时丢弃并返回前 N-1 条， 避免 TDX 服务端截断响应导致整页 500（如 000408 count=800 日线） - OptimizeView: 一键寻优并发默认 8 进程，用户选择持久化到 localStorage bump version to 1.18.3
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@@ -2,6 +2,18 @@
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本文件记录 easy-tdx 的版本变更。格式遵循 [Keep a Changelog](https://keepachangelog.com/zh-CN/)。
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## [1.18.3] — 2026-07-06
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**K 线响应截断容错 + 一键寻优并发默认值优化** —— 两个小修复合并发布。(1) 修复 `000408` 等标的请求 `count=800` 日线时，TDX 服务端返回截断响应（响应头声称有数据但 body 末尾若干条记录被切掉）导致整页 500 的问题：解析器现在丢弃残缺的末条记录，返回已成功解析的前 N-1 条，避免一条坏数据让整页请求失败。(2) 一键寻优并发默认值从「串行」改为「8 进程」，并把用户选择持久化到 `localStorage`，下次以其最后一次选择为默认。
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### 修复
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- **K 线响应截断容错**（`src/easy_tdx/commands/security_bars.py`）—— `GetSecurityBarsCmd` / `GetIndexBarsCmd` 的 `parse_response` 在逐条解析时，若某条记录的 datetime/price/volume 字段因数据不足抛 `TdxDecodeError`，改为丢弃残缺的末条并返回已成功解析的前若干条（记 warning 日志），而非整体抛 500。仅当连第一条都无法解析时才继续抛错（说明是真正的坏包而非尾部截断）。覆盖日线/分钟线/指数 K 线全部分支。
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### 变更
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- **一键寻优并发默认 8 进程 + 持久化**（`web-ui/src/views/OptimizeView.vue`）—— 「一键寻优并发」工作进程默认值从串行（0）改为 8 进程；用户修改后写入 `localStorage`（key `optimize.workers`），下次打开以其最后一次选择为默认。无历史记录或值非法时回退默认 8；`localStorage` 不可用时（隐私模式等）静默回退，不影响使用。
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## [1.18.2] — 2026-07-06
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**回退拼音声母搜索功能，回到稳定的 6 位代码输入** —— v1.19.0 引入的拼音声母搜索（输 `zjxc` 命中中际旭创）因底层依赖过重被移除。该功能首次使用时需从通达信服务器爬取沪深 A 股约 5000 条完整名单（几十次协议往返，慢机器耗时几十秒到超时），且与共享的 TDX 连接耦合——爬名单期间会阻塞行情请求。虽经多轮优化（按需加载 / 全站遮罩 / 单飞去重 / 后台预热），均无法兼顾"不阻塞核心行情"与"首次可用"。本次回到 v1.18.1 的干净基线，代码输入框恢复为纯 6 位代码输入（市场自动识别）。
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+1
-1
@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
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[project]
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name = "easy-tdx"
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version = "1.18.2"
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version = "1.18.3"
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description = "通达信 TCP 协议行情数据客户端，支持在线行情、离线数据读取与写入同步"
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readme = "README.md"
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requires-python = ">=3.10"
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@@ -1,15 +1,19 @@
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"""获取 K 线数据命令（支持全部周期）。"""
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import logging
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import struct
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from .._binary import unpack_from
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from ..codec.datetime_ import get_datetime
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from ..codec.price import get_price
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from ..codec.volume import get_volume
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from ..exceptions import TdxDecodeError
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from ..models.bar import SecurityBar
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from ..models.enums import KlineCategory, Market
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from .base import BaseCommand
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_log = logging.getLogger(__name__)
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class GetSecurityBarsCmd(BaseCommand[list[SecurityBar]]):
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"""获取指定股票的 K 线数据。
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@@ -63,17 +67,33 @@ class GetSecurityBarsCmd(BaseCommand[list[SecurityBar]]):
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pre_diff_base = 0
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cat = int(self.category)
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for _ in range(ret_count):
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for i in range(ret_count):
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record_start = pos
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year, month, day, hour, minute, pos = get_datetime(cat, body, pos)
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try:
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year, month, day, hour, minute, pos = get_datetime(cat, body, pos)
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open_diff, pos = get_price(body, pos)
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close_diff, pos = get_price(body, pos)
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high_diff, pos = get_price(body, pos)
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low_diff, pos = get_price(body, pos)
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open_diff, pos = get_price(body, pos)
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close_diff, pos = get_price(body, pos)
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high_diff, pos = get_price(body, pos)
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low_diff, pos = get_price(body, pos)
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vol, pos = get_volume(body, pos)
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amount, pos = get_volume(body, pos)
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vol, pos = get_volume(body, pos)
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amount, pos = get_volume(body, pos)
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except TdxDecodeError as e:
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# TDX 服务端偶发截断：响应头声称有 N 条，但 body 末尾若干条
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# 被切掉（停牌/退市/分页边界常见）。丢弃残缺的末条，保留已
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# 成功解析的前若干条，避免一条坏数据让整页 500。
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if bars:
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_log.warning(
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"K线响应在第 %d/%d 条处被截断（%s），已丢弃末尾残缺记录，"
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"返回前 %d 条",
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i + 1,
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ret_count,
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e,
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len(bars),
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)
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return bars
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raise
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# 差分还原（与 pytdx 完全一致）
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open_abs = open_diff + pre_diff_base
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@@ -116,21 +136,35 @@ class GetIndexBarsCmd(GetSecurityBarsCmd):
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pre_diff_base = 0
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cat = int(self.category)
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for _ in range(ret_count):
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for i in range(ret_count):
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record_start = pos
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year, month, day, hour, minute, pos = get_datetime(cat, body, pos)
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try:
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year, month, day, hour, minute, pos = get_datetime(cat, body, pos)
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open_diff, pos = get_price(body, pos)
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close_diff, pos = get_price(body, pos)
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high_diff, pos = get_price(body, pos)
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low_diff, pos = get_price(body, pos)
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open_diff, pos = get_price(body, pos)
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close_diff, pos = get_price(body, pos)
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high_diff, pos = get_price(body, pos)
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low_diff, pos = get_price(body, pos)
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vol, pos = get_volume(body, pos)
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amount, pos = get_volume(body, pos)
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vol, pos = get_volume(body, pos)
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amount, pos = get_volume(body, pos)
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# 指数记录额外 4 字节：上涨家数 + 下跌家数（各 uint16 LE）
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pos += 4
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# 指数记录额外 4 字节：上涨家数 + 下跌家数（各 uint16 LE）
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pos += 4
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except TdxDecodeError as e:
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if bars:
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_log.warning(
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"指数K线响应在第 %d/%d 条处被截断（%s），已丢弃末尾残缺记录，"
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"返回前 %d 条",
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i + 1,
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ret_count,
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e,
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len(bars),
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)
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return bars
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raise
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# 差分还原（与 pytdx 完全一致）
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open_abs = open_diff + pre_diff_base
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close_abs = open_abs + close_diff
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high_abs = open_abs + high_diff
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@@ -10,6 +10,8 @@ from __future__ import annotations
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import pathlib
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import struct
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import pytest
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FIXTURES = pathlib.Path(__file__).parent.parent / "fixtures"
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@@ -125,6 +127,40 @@ def test_security_bars_parse():
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assert len(bar._raw) > 0
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def test_security_bars_truncated_drops_partial_last_record():
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"""TDX 服务端偶发截断：响应头声称有 N 条，但末尾记录被切。
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解析器应丢弃残缺的末条，返回已成功解析的前若干条，而非整体抛 500。
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"""
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from easy_tdx.commands.security_bars import GetSecurityBarsCmd
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from easy_tdx.models.enums import KlineCategory, Market
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body = load_hex("security_bars") # 完整 5 条
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# 把最后一条的 body 切掉 3 字节 → 末条 zipday 4 字节不够，触发截断
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truncated = body[:-3]
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cmd = GetSecurityBarsCmd(Market.SH, "600000", KlineCategory.DAY, 0, 5)
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bars = cmd.parse_response(truncated)
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assert len(bars) == 4 # 前 4 条完整，末条残缺被丢弃
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def test_security_bars_truncated_first_record_still_raises():
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"""若连第一条都无法解析（body 完全没有记录数据），仍抛 TdxDecodeError。"""
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from easy_tdx.commands.security_bars import GetSecurityBarsCmd
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from easy_tdx.exceptions import TdxDecodeError
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from easy_tdx.models.enums import KlineCategory, Market
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body = load_hex("security_bars")
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# 构造 header 声称 5 条但 body 只有 header(2 字节)+1 字节 → 第一条就截断
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truncated = body[:3]
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# 强行把 ret_count 写成 5
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truncated = struct.pack("<H", 5) + truncated[2:]
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cmd = GetSecurityBarsCmd(Market.SH, "600000", KlineCategory.DAY, 0, 5)
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with pytest.raises(TdxDecodeError):
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cmd.parse_response(truncated)
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# ---------------------------------------------------------------------------
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# security_quotes
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# ---------------------------------------------------------------------------
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@@ -2,7 +2,7 @@
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// 参数寻优主页面：左配置（选标的 + 策略 + 寻优参数）/ 右报告（排名表 + 热力图）。
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// 取行情已整合进「开始寻优」。另有「一键寻优所有策略」：用各策略预设网格逐策略寻优再全局排名。
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import { computed, onMounted, ref } from 'vue'
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import { computed, onMounted, ref, watch } from 'vue'
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import { useRouter } from 'vue-router'
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import GradeBadge from '../components/GradeBadge.vue'
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@@ -44,16 +44,38 @@ const cpuCount = (() => {
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const n = typeof navigator !== 'undefined' ? navigator.hardwareConcurrency : undefined
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return n && n > 0 ? n : 4
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})()
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// 推荐档：min(cpu, 8)。默认串行（workers=0），让用户实测后再开并发——
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// 小机器上 Windows spawn 子进程开销可能反而拖慢单个寻优任务。
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// 推荐档：min(cpu, 8)。默认 8 进程，若用户改过则记到 localStorage，
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// 下次以其最后一次选择为默认。
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const recommendedWorkers = Math.min(cpuCount, 8)
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const workers = ref(0)
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const DEFAULT_WORKERS = 8
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const WORKERS_STORAGE_KEY = 'optimize.workers'
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const WORKER_OPTIONS: { value: number; label: string }[] = [
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{ value: 0, label: '串行（不并发）' },
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{ value: 4, label: '4 进程' },
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{ value: 8, label: '8 进程' },
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{ value: 16, label: '16 进程' },
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]
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function loadWorkersFromStorage(): number {
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try {
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const raw = localStorage.getItem(WORKERS_STORAGE_KEY)
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if (raw == null) return DEFAULT_WORKERS
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const n = Number(raw)
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return Number.isFinite(n) && WORKER_OPTIONS.some((o) => o.value === n)
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? n
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: DEFAULT_WORKERS
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} catch {
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return DEFAULT_WORKERS
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}
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}
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const workers = ref(loadWorkersFromStorage())
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// 用户修改后持久化,下次以最后选择为默认
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watch(workers, (v) => {
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try {
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localStorage.setItem(WORKERS_STORAGE_KEY, String(v))
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} catch {
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/* localStorage 不可用时静默忽略 */
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}
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})
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// 成交价模式（精简为 开盘价/收盘价）
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const EXECUTIONS: { value: ExecutionMode; label: string }[] = [
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{ value: 'next_open', label: '开盘价' },
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