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Chan/research/out/step46_baseline.json
T
jackyu66gitandCursor 0b4d7693b8 缠论引擎提速 2.6x，瓶颈是逐行 Series 查找而非指标计算 
原以为浪费在 add_indicators 算了太多用不到的指标，实测它只占全量构建的
1.3%——talib 是向量化 C 代码，便宜。真正的两处：

cal_kl_data 占 96%：每根 K 线 df.iloc[i] 新建一个 40 列 Series，再在其上做
几十次逐键查找。改为预取 ndarray 后 2 万根 1946ms → 824ms。

ChanKLC.cal_all_ema_status 占 25%：每次合并 KLU 都立即重算，而它产出的
ema_status / ema52_pos / ema52_status 全仓无任何读取方（含前端）。改为惰性
求值，保留属性形式以防将来有人读。顺带删掉 get_klc_list 里累加一整轮后直接
丢弃的 ema_up_list / ema_down_list。

另加 TF_DF(lean=True)：只构建到中枢，跳过线段/走势中枢/MACD 状态机——这些
只服务 bsp_list 与 web 展示，笔和中枢不依赖。研究与实盘走这条快 3.6x。

结果 2 万根 5m：full 1946 → 754ms，lean → 543ms。

step46_engine_parity.py 是配套的安全网，改引擎前先跑一次 --save。它对 KLC
端点与分型、笔起止价与 is_sure、中枢 zg/zd/available_ts/阶梯、信号全部输出列，
以及 26 个被下游消费的 dataframe 列取哈希。本次三处改动逐步验证，另用
git stash 切回改动前代码在 20 万根 × 5 用例上做了跨版本逐位对拍，全部一致；
增量路径与 web API 也各验一遍。

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-08-28 04:04:44 +08:00

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]
}
}
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