mirror of
https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
synced 2026-09-12 15:44:18 +08:00
Justin Gu
e374a0da28
对 v1.21→v1.32.5 的 249 文件 4.2 万行改动做六路专项审查，本轮落地全部发现： 回测正确性：组合收益 fillna(0) 虚增、轮动停牌日过期价成交、单标的 WF 逐窗指标 被预热区稀释（三件套均带先红后绿回归）；worst_drawdown 方向、grading 容错、 组合体检品种费率、寻优端点费率透传。 安全：LLM api_url 仅 http/https 且禁 userinfo（封死 file:// 读取与 Key 外送链）、 错误响应不回显原始 body、响应体 2MB 上限、配置原子写、坏配置字段级防御。 数据：涨跌停价整数分币舍入（67/318/90 个价位错 1 分漏判清零）、交易时段/采样/ provisional 统一沪时区、warehouse 增量缺口自动全量重拉、provisional 定点转正、 baostock 真故障抛错 + W/M 去 tradestatus（实测服务端报错，周月兜底此前从未工作） + 指数 vol 股→手（实测锚定）、ccpm 结构变更抛错。 Web API：缓存键补 count/vipdoc、NaN 清洗先于缓存、count>800 分页取全量、 submit 透传真实状态、pending 不再被淘汰成幽灵、watchlist/server 入参约束。 公式：FILTER 去副作用、0-1 值域误判收严、递归深度上限、REF 负移位显式禁止。 前端：4 处请求竞态序号守卫、Sparkline viewBox、北交所 market=2 映射、 空数据缓存死角、AI 弹窗卸载中止轮询、量能/资金日历口径修正。 CLI/CI：warehouse sync 失败 exit 1、参数校验干净报错、release 真实发布 SHA256、 CI 超时与缓存、spec 补 baostock 前提。 约 60 条回归测试先红后绿；pytest 1820 全过，ruff/mypy/vue-tsc/node --test 全绿。
785 lines
29 KiB
Python
785 lines
29 KiB
Python
# MyTT 麦语言-通达信-同花顺指标实现 https://github.com/mpquant/MyTT
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# MyTT高级函数验证版本： https://github.com/mpquant/MyTT/blob/main/MyTT_plus.py
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# Python2老版本pandas特别的MyTT： https://github.com/mpquant/MyTT/blob/main/MyTT_python2.py
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# V2.1 2021-6-6 新增 BARSLAST函数 SLOPE,FORCAST线性回归预测函数
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# V2.3 2021-6-13 新增 TRIX,DPO,BRAR,DMA,MTM,MASS,ROC,VR,ASI等指标
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# V2.4 2021-6-27 新增 EXPMA,OBV,MFI指标, 改进SMA核心函数(核心函数彻底无循环)
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# V2.7 2021-11-21 修正 SLOPE,BARSLAST,函数,新加FILTER,LONGCROSS,
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# 感谢qzhjiang对SLOPE,SMA等函数的指正
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# V2.8 2021-11-23 修正 FORCAST,WMA函数,欢迎qzhjiang,stanene,bcq加入社群，一起来完善myTT库
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# V2.9 2021-11-29 新增 HHVBARS,LLVBARS,CONST, VALUEWHEN功能函数
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# V2.92 2021-11-30 新增 BARSSINCEN函数,现在可以 pip install MyTT 完成安装
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# V3.0 2021-12-04 改进 DMA函数支持序列,新增XS2 薛斯通道II指标
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# V3.1 2021-12-19 新增 TOPRANGE,LOWRANGE一级函数
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# V3.2 2023-04-04 新增 CR指标
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# V3.3 2023-11-09 新增 SIN,COS,TAN序列处理的三角函数
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# V4.0 2026-06-02 handsomejustin 新增 ZHUOYAO,BIAS_SIGNAL两个自创函数
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# V4.1 2026-06-14 新增 SAR(抛物线转向), VWAP(成交量加权均价), AROON(阿隆指标); 注册 FK
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# V4.2 2026-07-09 新增 FSL(分水岭指标)
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# V4.3 2026-09-03 新增无未来函数指标16个：HMA,KAMA,SUPERTREND,CHANDELIER,ICHIMOKU,
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# UOS,CMO,TSI,FISHER,SQUEEZE,CHOP,AD,CMF,EFI,BBP,BBW（全部只引用当期及历史数据）
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# 以下所有函数如无特别说明，输入参数S均为numpy序列或者列表list，N为整型int
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# 应用层1级函数完美兼容通达信或同花顺，具体使用方法请参考通达信
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import numpy as np
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import pandas as pd
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# ------------------ 0级：核心工具函数 --------------------------------------------
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def RD(N, D=3):
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return np.round(N, D) # 四舍五入取3位小数
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def RET(S, N=1):
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return np.array(S)[-N] # 返回序列倒数第N个值,默认返回最后一个
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def ABS(S):
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return np.abs(S) # 返回N的绝对值
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def LN(S):
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return np.log(S) # 求底是e的自然对数,
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def POW(S, N):
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return np.power(S, N) # 求S的N次方
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def SQRT(S):
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return np.sqrt(S) # 求S的平方根
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def SIN(S):
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return np.sin(S) # 求S的正弦值（弧度)
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def COS(S):
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return np.cos(S) # 求S的余弦值（弧度)
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def TAN(S):
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return np.tan(S) # 求S的正切值（弧度)
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def MAX(S1, S2):
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return np.maximum(S1, S2) # 序列max
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def MIN(S1, S2):
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return np.minimum(S1, S2) # 序列min
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def IF(S, A, B):
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return np.where(S, A, B) # 序列布尔判断 return=A if S==True else B
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def REF(S, N=1): # 对序列整体下移动N,返回序列(shift后会产生NAN)
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return pd.Series(S).shift(N).values
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def DIFF(S, N=1): # 前一个值减后一个值,前面会产生nan
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return pd.Series(S).diff(N).values # np.diff(S)直接删除nan，会少一行
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def STD(S, N): # 求序列的N日标准差，返回序列
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return pd.Series(S).rolling(N).std(ddof=0).values
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def SUM(S, N): # 对序列求N天累计和，返回序列 N=0对序列所有依次求和
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return pd.Series(S).rolling(N).sum().values if N > 0 else pd.Series(S).cumsum().values
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def CONST(S): # 返回序列S最后的值组成常量序列
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return np.full(len(S), S[-1])
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def HHV(S, N): # HHV(C, 5) 最近5天收盘最高价
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return pd.Series(S).rolling(N).max().values
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def LLV(S, N): # LLV(C, 5) 最近5天收盘最低价
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return pd.Series(S).rolling(N).min().values
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def HHVBARS(S, N): # 求N周期内S最高值到当前周期数, 返回序列
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return pd.Series(S).rolling(N).apply(lambda x: np.argmax(x[::-1]), raw=True).values
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def LLVBARS(S, N): # 求N周期内S最低值到当前周期数, 返回序列
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return pd.Series(S).rolling(N).apply(lambda x: np.argmin(x[::-1]), raw=True).values
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def MA(S, N): # 求序列的N日简单移动平均值，返回序列
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return pd.Series(S).rolling(N).mean().values
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def EMA(S, N): # 指数移动平均,为了精度 S>4*N EMA至少需要120周期 alpha=2/(span+1)
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return pd.Series(S).ewm(span=N, adjust=False).mean().values
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def SMA(S, N, M=1): # 中国式的SMA,至少需要120周期才精确 (雪球180周期) alpha=1/(1+com)
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return pd.Series(S).ewm(alpha=M / N, adjust=False).mean().values # com=N-M/M
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def WMA(S, N): # 通达信S序列的N日加权移动平均 Yn = (1*X1+2*X2+3*X3+...+n*Xn)/(1+2+3+...+Xn)
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return (
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pd.Series(S)
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.rolling(N)
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.apply(lambda x: x[::-1].cumsum().sum() * 2 / N / (N + 1), raw=True)
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.values
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)
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def DMA(S, A): # 求S的动态移动平均，A作平滑因子,必须 0<A<1 (此为核心函数，非指标）
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if isinstance(A, int | float):
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return pd.Series(S).ewm(alpha=A, adjust=False).mean().values
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A = np.array(A)
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A[np.isnan(A)] = 1.0
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Y = np.zeros(len(S))
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Y[0] = S[0]
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for i in range(1, len(S)):
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Y[i] = A[i] * S[i] + (1 - A[i]) * Y[i - 1] # A支持序列 by jqz1226
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return Y
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def AVEDEV(S, N): # 平均绝对偏差 (序列与其平均值的绝对差的平均值)
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return pd.Series(S).rolling(N).apply(lambda x: (np.abs(x - x.mean())).mean()).values
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def SLOPE(S, N): # 返S序列N周期回线性回归斜率
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return (
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pd.Series(S).rolling(N).apply(lambda x: np.polyfit(range(N), x, deg=1)[0], raw=True).values
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)
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def FORCAST(S, N): # 返回S序列N周期回线性回归后的预测值， jqz1226改进成序列出
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return (
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pd.Series(S)
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.rolling(N)
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.apply(lambda x: np.polyval(np.polyfit(range(N), x, deg=1), N - 1), raw=True)
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.values
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)
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def LAST(S, A, B): # 从前A日到前B日一直满足S_BOOL条件, 要求A>B & A>0 & B>=0
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return np.array(
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pd.Series(S).rolling(A + 1).apply(lambda x: np.all(x[::-1][B:]), raw=True), dtype=bool
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)
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# -- 1级：应用层函数(通过0级核心函数实现）使用方法请参考通达信 --------------------
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def COUNT(S, N): # COUNT(CLOSE>O, N): 最近N天满足S_BOO的天数 True的天数
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return SUM(S, N)
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def EVERY(S, N): # EVERY(CLOSE>O, 5) 最近N天是否都是True
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return IF(SUM(S, N) == N, True, False)
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def EXIST(S, N): # EXIST(CLOSE>3010, N=5) n日内是否存在一天大于3000点
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return IF(SUM(S, N) > 0, True, False)
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def FILTER(S, N): # FILTER函数，S满足条件后，将其后N周期内的数据置为0, FILTER(C==H,5)
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# 无副作用实现：在副本上置零。曾直接改写输入序列——公式通道里
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# FILTER(C, N) 会把同一公式后续语句引用的 C 一并污染（或对只读
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# 数组直接报错）。
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out = np.array(S, copy=True)
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for i in range(len(out)):
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out[i + 1 : i + 1 + N] = 0 if out[i] else out[i + 1 : i + 1 + N]
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return out # 例：FILTER(C==H,5) 涨停后，后5天不再发出信号
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def BARSLAST(S): # 上一次条件成立到当前的周期, BARSLAST(C/REF(C,1)>=1.1) 上一次涨停到今天的天数
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M = np.concatenate(([0], np.where(S, 1, 0)))
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for i in range(1, len(M)):
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M[i] = 0 if M[i] else M[i - 1] + 1
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return M[1:]
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def BARSLASTCOUNT(S): # 统计连续满足S条件的周期数 by jqz1226
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rt = np.zeros(len(S) + 1) # BARSLASTCOUNT(CLOSE>OPEN)表示统计连续收阳的周期数
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for i in range(len(S)):
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rt[i + 1] = rt[i] + 1 if S[i] else rt[i + 1]
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return rt[1:]
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def BARSSINCEN(S, N): # N周期内第一次S条件成立到现在的周期数,N为常量 by jqz1226
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return (
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pd.Series(S)
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.rolling(N)
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.apply(lambda x: N - 1 - np.argmax(x) if np.argmax(x) or x[0] else 0, raw=True)
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.fillna(0)
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.values.astype(int)
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)
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def CROSS(
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S1, S2
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): # 判断向上金叉穿越 CROSS(MA(C,5),MA(C,10)) 判断向下死叉穿越 CROSS(MA(C,10),MA(C,5))
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return np.concatenate(
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([False], np.logical_not((S1 > S2)[:-1]) & (S1 > S2)[1:])
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) # 不使用0级函数,移植方便 by jqz1226
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def LONGCROSS(
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S1, S2, N
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): # 两条线维持一定周期后交叉,S1在N周期内都小于S2,本周期从S1下方向上穿过S2时返回1,否则返回0
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return np.array(
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np.logical_and(LAST(S1 < S2, N, 1), (S1 > S2)), dtype=bool
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) # N=1时等同于CROSS(S1, S2)
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def VALUEWHEN(S, X): # 当S条件成立时,取X的当前值,否则取VALUEWHEN的上个成立时的X值 by jqz1226
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return pd.Series(np.where(S, X, np.nan)).ffill().values
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def BETWEEN(S, A, B): # S处于A和B之间时为真。 包括 A<S<B 或 A>S>B
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return ((A < S) & (S < B)) | ((A > S) & (S > B))
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def TOPRANGE(S): # TOPRANGE(HIGH)表示当前最高价是近多少周期内最高价的最大值 by jqz1226
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rt = np.zeros(len(S))
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for i in range(1, len(S)):
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rt[i] = np.argmin(np.flipud(S[:i] < S[i]))
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return rt.astype("int")
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def LOWRANGE(S): # LOWRANGE(LOW)表示当前最低价是近多少周期内最低价的最小值 by jqz1226
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rt = np.zeros(len(S))
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for i in range(1, len(S)):
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rt[i] = np.argmin(np.flipud(S[:i] > S[i]))
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return rt.astype("int")
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# ------------------ 2级：技术指标函数(全部通过0级，1级函数实现） ------------------------------
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def MACD(CLOSE, SHORT=12, LONG=26, M=9): # EMA的关系，S取120日，和雪球小数点2位相同
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DIF = EMA(CLOSE, SHORT) - EMA(CLOSE, LONG)
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DEA = EMA(DIF, M)
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MACD = (DIF - DEA) * 2
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return RD(DIF), RD(DEA), RD(MACD)
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def KDJ(CLOSE, HIGH, LOW, N=9, M1=3, M2=3): # KDJ指标
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low_n = LLV(LOW, N)
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high_n = HHV(HIGH, N)
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high_low_diff = high_n - low_n
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# 避免除零：当最高价等于最低价时，RSV 应该为 50（中性）
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with np.errstate(divide="ignore", invalid="ignore"):
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rsv = (CLOSE - low_n) / high_low_diff * 100
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rsv = np.where(high_low_diff == 0, 50, rsv) # 除零时返回 50
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K = EMA(rsv, (M1 * 2 - 1))
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D = EMA(K, (M2 * 2 - 1))
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J = K * 3 - D * 2
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return K, D, J
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def RSI(CLOSE, N=24): # RSI指标,和通达信小数点2位相同
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DIF = CLOSE - REF(CLOSE, 1)
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abs_dif_sma = SMA(ABS(DIF), N)
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# 避免除零：当价格完全不变时，RSI 应该为 50（中性）
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with np.errstate(divide="ignore", invalid="ignore"):
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rsi_value = SMA(MAX(DIF, 0), N) / abs_dif_sma * 100
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rsi_value = np.where(abs_dif_sma == 0, 50, rsi_value) # 除零时返回 50
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return RD(rsi_value)
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def WR(CLOSE, HIGH, LOW, N=10, N1=6): # W&R 威廉指标
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high_n = HHV(HIGH, N)
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low_n = LLV(LOW, N)
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high_low_diff = high_n - low_n
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with np.errstate(divide="ignore", invalid="ignore"):
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wr = (high_n - CLOSE) / high_low_diff * 100
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wr = np.where(high_low_diff == 0, 50, wr) # 除零时返回 50
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high_n1 = HHV(HIGH, N1)
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low_n1 = LLV(LOW, N1)
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high_low_diff1 = high_n1 - low_n1
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with np.errstate(divide="ignore", invalid="ignore"):
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wr1 = (high_n1 - CLOSE) / high_low_diff1 * 100
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wr1 = np.where(high_low_diff1 == 0, 50, wr1) # 除零时返回 50
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return RD(wr), RD(wr1)
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def BIAS(CLOSE, L1=6, L2=12, L3=24): # BIAS乖离率
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BIAS1 = (CLOSE - MA(CLOSE, L1)) / MA(CLOSE, L1) * 100
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BIAS2 = (CLOSE - MA(CLOSE, L2)) / MA(CLOSE, L2) * 100
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BIAS3 = (CLOSE - MA(CLOSE, L3)) / MA(CLOSE, L3) * 100
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return RD(BIAS1), RD(BIAS2), RD(BIAS3)
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def BOLL(CLOSE, N=20, P=2): # BOLL指标，布林带
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MID = MA(CLOSE, N)
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UPPER = MID + STD(CLOSE, N) * P
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LOWER = MID - STD(CLOSE, N) * P
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return RD(UPPER), RD(MID), RD(LOWER)
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def PSY(CLOSE, N=12, M=6):
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PSY = COUNT(CLOSE > REF(CLOSE, 1), N) / N * 100
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PSYMA = MA(PSY, M)
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return RD(PSY), RD(PSYMA)
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def CCI(CLOSE, HIGH, LOW, N=14):
|
||
TP = (HIGH + LOW + CLOSE) / 3
|
||
return (TP - MA(TP, N)) / (0.015 * AVEDEV(TP, N))
|
||
|
||
|
||
def ATR(CLOSE, HIGH, LOW, N=20): # 真实波动N日平均值
|
||
TR = MAX(MAX((HIGH - LOW), ABS(REF(CLOSE, 1) - HIGH)), ABS(REF(CLOSE, 1) - LOW))
|
||
return MA(TR, N)
|
||
|
||
|
||
def BBI(CLOSE, M1=3, M2=6, M3=12, M4=20): # BBI多空指标
|
||
return (MA(CLOSE, M1) + MA(CLOSE, M2) + MA(CLOSE, M3) + MA(CLOSE, M4)) / 4
|
||
|
||
|
||
def DMI(CLOSE, HIGH, LOW, M1=14, M2=6): # 动向指标：结果和同花顺，通达信完全一致
|
||
TR = SUM(MAX(MAX(HIGH - LOW, ABS(HIGH - REF(CLOSE, 1))), ABS(LOW - REF(CLOSE, 1))), M1)
|
||
HD = HIGH - REF(HIGH, 1)
|
||
LD = REF(LOW, 1) - LOW
|
||
DMP = SUM(IF((HD > 0) & (HD > LD), HD, 0), M1)
|
||
DMM = SUM(IF((LD > 0) & (LD > HD), LD, 0), M1)
|
||
PDI = DMP * 100 / TR
|
||
MDI = DMM * 100 / TR
|
||
ADX = MA(ABS(MDI - PDI) / (PDI + MDI) * 100, M2)
|
||
ADXR = (ADX + REF(ADX, M2)) / 2
|
||
return PDI, MDI, ADX, ADXR
|
||
|
||
|
||
def TAQ(HIGH, LOW, N): # 唐安奇通道(海龟)交易指标，大道至简，能穿越牛熊
|
||
UP = HHV(HIGH, N)
|
||
DOWN = LLV(LOW, N)
|
||
MID = (UP + DOWN) / 2
|
||
return UP, MID, DOWN
|
||
|
||
|
||
def KTN(CLOSE, HIGH, LOW, N=20, M=10): # 肯特纳交易通道, N选20日，ATR选10日
|
||
MID = EMA((HIGH + LOW + CLOSE) / 3, N)
|
||
ATRN = ATR(CLOSE, HIGH, LOW, M)
|
||
UPPER = MID + 2 * ATRN
|
||
LOWER = MID - 2 * ATRN
|
||
return UPPER, MID, LOWER
|
||
|
||
|
||
def TRIX(CLOSE, M1=12, M2=20): # 三重指数平滑平均线
|
||
TR = EMA(EMA(EMA(CLOSE, M1), M1), M1)
|
||
TRIX = (TR - REF(TR, 1)) / REF(TR, 1) * 100
|
||
TRMA = MA(TRIX, M2)
|
||
return TRIX, TRMA
|
||
|
||
|
||
def VR(CLOSE, VOL, M1=26): # VR容量比率
|
||
LC = REF(CLOSE, 1)
|
||
return SUM(IF(CLOSE > LC, VOL, 0), M1) / SUM(IF(CLOSE <= LC, VOL, 0), M1) * 100
|
||
|
||
|
||
def CR(CLOSE, HIGH, LOW, N=20): # CR价格动量指标
|
||
MID = REF(HIGH + LOW + CLOSE, 1) / 3
|
||
num = SUM(MAX(0, HIGH - MID), N)
|
||
den = SUM(MAX(0, MID - LOW), N)
|
||
with np.errstate(divide="ignore", invalid="ignore"):
|
||
return np.where(den > 0, num / den * 100, 100)
|
||
|
||
|
||
def EMV(HIGH, LOW, VOL, N=14, M=9): # 简易波动指标
|
||
VOLUME = MA(VOL, N) / VOL
|
||
MID = 100 * (HIGH + LOW - REF(HIGH + LOW, 1)) / (HIGH + LOW)
|
||
EMV = MA(MID * VOLUME * (HIGH - LOW) / MA(HIGH - LOW, N), N)
|
||
MAEMV = MA(EMV, M)
|
||
return EMV, MAEMV
|
||
|
||
|
||
def DPO(CLOSE, M1=20, M2=10, M3=6): # 区间震荡线
|
||
DPO = CLOSE - REF(MA(CLOSE, M1), M2)
|
||
MADPO = MA(DPO, M3)
|
||
return DPO, MADPO
|
||
|
||
|
||
def BRAR(OPEN, CLOSE, HIGH, LOW, M1=26): # BRAR-ARBR 情绪指标
|
||
AR = SUM(HIGH - OPEN, M1) / SUM(OPEN - LOW, M1) * 100
|
||
BR = SUM(MAX(0, HIGH - REF(CLOSE, 1)), M1) / SUM(MAX(0, REF(CLOSE, 1) - LOW), M1) * 100
|
||
return AR, BR
|
||
|
||
|
||
def DFMA(CLOSE, N1=10, N2=50, M=10): # 平行线差指标
|
||
DIF = MA(CLOSE, N1) - MA(CLOSE, N2)
|
||
DIFMA = MA(DIF, M) # 通达信指标叫DMA 同花顺叫新DMA
|
||
return DIF, DIFMA
|
||
|
||
|
||
def MTM(CLOSE, N=12, M=6): # 动量指标
|
||
MTM = CLOSE - REF(CLOSE, N)
|
||
MTMMA = MA(MTM, M)
|
||
return MTM, MTMMA
|
||
|
||
|
||
def MASS(HIGH, LOW, N1=9, N2=25, M=6): # 梅斯线
|
||
MASS = SUM(MA(HIGH - LOW, N1) / MA(MA(HIGH - LOW, N1), N1), N2)
|
||
MA_MASS = MA(MASS, M)
|
||
return MASS, MA_MASS
|
||
|
||
|
||
def ROC(CLOSE, N=12, M=6): # 变动率指标
|
||
ROC = 100 * (CLOSE - REF(CLOSE, N)) / REF(CLOSE, N)
|
||
MAROC = MA(ROC, M)
|
||
return ROC, MAROC
|
||
|
||
|
||
def EXPMA(CLOSE, N1=12, N2=50): # EMA指数平均数指标
|
||
return EMA(CLOSE, N1), EMA(CLOSE, N2)
|
||
|
||
|
||
def OBV(CLOSE, VOL): # 能量潮指标
|
||
return SUM(IF(CLOSE > REF(CLOSE, 1), VOL, IF(CLOSE < REF(CLOSE, 1), -VOL, 0)), 0) / 10000
|
||
|
||
|
||
def MFI(CLOSE, HIGH, LOW, VOL, N=14): # MFI指标是成交量的RSI指标
|
||
TYP = (HIGH + LOW + CLOSE) / 3
|
||
pos_mf = SUM(IF(TYP > REF(TYP, 1), TYP * VOL, 0), N)
|
||
neg_mf = SUM(IF(TYP < REF(TYP, 1), TYP * VOL, 0), N)
|
||
with np.errstate(divide="ignore", invalid="ignore"):
|
||
V1 = np.where(neg_mf > 0, pos_mf / neg_mf, np.where(pos_mf > 0, np.inf, 0))
|
||
return 100 - (100 / (1 + V1))
|
||
|
||
|
||
def ASI(OPEN, CLOSE, HIGH, LOW, M1=26, M2=10): # 振动升降指标
|
||
LC = REF(CLOSE, 1)
|
||
AA = ABS(HIGH - LC)
|
||
BB = ABS(LOW - LC)
|
||
CC = ABS(HIGH - REF(LOW, 1))
|
||
DD = ABS(LC - REF(OPEN, 1))
|
||
R = IF(
|
||
(AA > BB) & (AA > CC),
|
||
AA + BB / 2 + DD / 4,
|
||
IF((BB > CC) & (BB > AA), BB + AA / 2 + DD / 4, CC + DD / 4),
|
||
)
|
||
X = CLOSE - LC + (CLOSE - OPEN) / 2 + LC - REF(OPEN, 1)
|
||
SI = 16 * X / R * MAX(AA, BB)
|
||
ASI = SUM(SI, M1)
|
||
ASIT = MA(ASI, M2)
|
||
return ASI, ASIT
|
||
|
||
|
||
def XSII(CLOSE, HIGH, LOW, N=102, M=7): # 薛斯通道II
|
||
AA = MA((2 * CLOSE + HIGH + LOW) / 4, 5) # 最新版DMA才支持 2021-12-4
|
||
TD1 = AA * N / 100
|
||
TD2 = AA * (200 - N) / 100
|
||
CC = ABS((2 * CLOSE + HIGH + LOW) / 4 - MA(CLOSE, 20)) / MA(CLOSE, 20)
|
||
DD = DMA(CLOSE, CC)
|
||
TD3 = (1 + M / 100) * DD
|
||
TD4 = (1 - M / 100) * DD
|
||
return TD1, TD2, TD3, TD4
|
||
|
||
|
||
def ZHUOYAO(CLOSE, N1=120, N2=60, N3=20, M=10): # 捉妖大师指标：中长短线趋势共振
|
||
LONG1 = (CLOSE / REF(CLOSE, N1) - 1) * 100 # 120日涨跌幅
|
||
LONG = EMA(LONG1, M) # 长线 EXPMA(长线1,10)
|
||
MID = (CLOSE / REF(CLOSE, N2) - 1) * 100 # 中线 60日涨跌幅
|
||
SHORT = (CLOSE / REF(CLOSE, N3) - 1) * 100 # 短线 20日涨跌幅
|
||
TREND = EMA(MID, M) # 趋势 EXPMA(中线,10)
|
||
return RD(LONG), RD(MID), RD(SHORT), RD(TREND)
|
||
|
||
|
||
def BIAS_SIGNAL(CLOSE, P=10, M=30): # 乖离率信号指标：M日乖离 + 短/长信号线趋势判断
|
||
X = (CLOSE - MA(CLOSE, M)) / MA(CLOSE, M) * 100 # M日乖离率
|
||
S_SMA = MA(X, P) # 短周期信号线 MA(X,P)
|
||
X_LMA = MA(X, M) # 长周期信号线 MA(X,M)
|
||
return RD(X), RD(S_SMA), RD(X_LMA)
|
||
|
||
|
||
def FK(CLOSE): # FK趋势指标：快线EMA(2)与斜率外推慢线EMA(42)比较
|
||
fast = EMA(CLOSE, 2)
|
||
slow = EMA(SLOPE(CLOSE, 21) * 20 + CLOSE, 42)
|
||
return fast > slow
|
||
|
||
|
||
def OUTPERFORM_20D(CLOSE, INDEX_CLOSE): # 20日相对强度：个股涨幅跑赢大盘返回1，否则返回0
|
||
stock_ret = (CLOSE - REF(CLOSE, 20)) / REF(CLOSE, 20)
|
||
index_ret = (INDEX_CLOSE - REF(INDEX_CLOSE, 20)) / REF(INDEX_CLOSE, 20)
|
||
return IF(stock_ret > index_ret, 1, 0)
|
||
|
||
|
||
def SAR(HIGH, LOW, AF_STEP=0.02, AF_MAX=0.2): # 抛物线转向指标：基于 ATR 思想的动态止损位
|
||
HIGH = np.asarray(HIGH, dtype=float)
|
||
LOW = np.asarray(LOW, dtype=float)
|
||
n = len(HIGH)
|
||
sar = np.full(n, np.nan)
|
||
if n == 0:
|
||
return sar
|
||
# 初始假设上涨趋势：SAR 起点取首根低点，极值点取首根高点
|
||
bull = True
|
||
af = AF_STEP
|
||
ep = HIGH[0]
|
||
sar[0] = LOW[0]
|
||
for i in range(1, n):
|
||
# 下一根 SAR = 前一根 SAR + AF * (EP - 前一根 SAR)
|
||
new_sar = sar[i - 1] + af * (ep - sar[i - 1])
|
||
# SAR 不能进入前两根 K 线极值范围（Wilder 标准限制，避免 SAR 被价格穿越）
|
||
prev2 = max(i - 2, 0)
|
||
if bull:
|
||
new_sar = min(new_sar, LOW[i - 1], LOW[prev2])
|
||
else:
|
||
new_sar = max(new_sar, HIGH[i - 1], HIGH[prev2])
|
||
sar[i] = new_sar
|
||
# 反转判断：上涨时 LOW 穿越止损位 / 下跌时 HIGH 穿越止损位
|
||
if bull and LOW[i] <= new_sar:
|
||
bull = False
|
||
sar[i] = ep # 反转点 SAR = 前极值点
|
||
ep = LOW[i]
|
||
af = AF_STEP
|
||
elif not bull and HIGH[i] >= new_sar:
|
||
bull = True
|
||
sar[i] = ep
|
||
ep = HIGH[i]
|
||
af = AF_STEP
|
||
else:
|
||
# 无反转，更新极值点和加速因子
|
||
if bull and HIGH[i] > ep:
|
||
ep = HIGH[i]
|
||
af = min(af + AF_STEP, AF_MAX)
|
||
elif not bull and LOW[i] < ep:
|
||
ep = LOW[i]
|
||
af = min(af + AF_STEP, AF_MAX)
|
||
return sar
|
||
|
||
|
||
def VWAP(CLOSE, HIGH, LOW, VOL, N=20): # 成交量加权均价：N日滚动机构基准成本价
|
||
TP = (HIGH + LOW + CLOSE) / 3.0 # 典型价格
|
||
num = pd.Series(TP * VOL).rolling(N).sum().values
|
||
den = pd.Series(VOL).rolling(N).sum().values
|
||
with np.errstate(divide="ignore", invalid="ignore"):
|
||
return np.where(den > 0, num / den, np.nan)
|
||
|
||
|
||
def AROON(HIGH, LOW, N=25): # 阿隆指标：趋势启动时机识别（N周期内新高/新低距今多少根）
|
||
# HHVBARS/LLVBARS 返回极值距今的周期数
|
||
up_bars = HHVBARS(HIGH, N) # N周期最高价距今周期数
|
||
down_bars = LLVBARS(LOW, N) # N周期最低价距今周期数
|
||
AROON_UP = (N - up_bars) / N * 100 # 越接近100=近期创新高=上涨动能强
|
||
AROON_DOWN = (N - down_bars) / N * 100 # 越接近100=近期创新低=下跌动能强
|
||
OSC = AROON_UP - AROON_DOWN # 震荡指标：正值多头，负值空头
|
||
return RD(AROON_UP), RD(AROON_DOWN), RD(OSC)
|
||
|
||
|
||
def FSL(CLOSE, VOL, CAPITAL): # 分水岭指标：多空趋势强弱分界（SWS含换手率动态平滑）
|
||
# SWL = (EMA(C,5)*7 + EMA(C,10)*3) / 10 : 5日/10日指数均值的加权合成
|
||
SWL = (EMA(CLOSE, 5) * 7 + EMA(CLOSE, 10) * 3) / 10
|
||
# SWS = DMA(EMA(C,12), MAX(1, 100*SUM(VOL,5)/(3*CAPITAL)))
|
||
# 平滑因子 = 5日成交量换手率放大值，CAPITAL 为流通股本
|
||
A = MAX(1, 100 * (SUM(VOL, 5) / (3 * CAPITAL)))
|
||
A = MIN(A, 1.0) # 模拟通达信 DMA(X,A) 内部钳制 A<=1，避免序列因子越界发散
|
||
SWS = DMA(EMA(CLOSE, 12), A)
|
||
return RD(SWL), RD(SWS)
|
||
|
||
|
||
# ── V4.3 新增：无未来函数指标（只引用当期及历史数据，信号不漂移） ────────────
|
||
def HMA(S, N=16): # Hull 赫尔均线：WMA(2*WMA(N/2)-WMA(N), sqrt(N))，低滞后且不重绘
|
||
return WMA(2 * WMA(S, N // 2) - WMA(S, N), max(int(round(np.sqrt(N))), 1))
|
||
|
||
|
||
def KAMA(S, N=10, FAST=2, SLOW=30): # 考夫曼自适应均线：效率比驱动平滑系数（贴价/走平自适应）
|
||
S = np.asarray(S, dtype=float)
|
||
change = np.abs(S - REF(S, N)) # N 周期净变化
|
||
volatility = SUM(np.abs(DIFF(S)), N) # N 周期路径总波动
|
||
with np.errstate(divide="ignore", invalid="ignore"):
|
||
er = np.where(volatility > 0, change / volatility, 0.0) # 效率比 0~1
|
||
er = np.where(np.isnan(change) | np.isnan(volatility), np.nan, er) # 预热段保持 NaN
|
||
fast_sc = 2.0 / (FAST + 1)
|
||
slow_sc = 2.0 / (SLOW + 1)
|
||
sc = (er * (fast_sc - slow_sc) + slow_sc) ** 2 # 平滑系数（ER 高→贴价，ER 低→走平）
|
||
out = np.full(len(S), np.nan)
|
||
valid = np.flatnonzero(~np.isnan(sc))
|
||
if len(valid):
|
||
start = valid[0]
|
||
out[start] = S[start]
|
||
for i in range(start + 1, len(S)):
|
||
out[i] = out[i - 1] + sc[i] * (S[i] - out[i - 1])
|
||
return out
|
||
|
||
|
||
def SUPERTREND(CLOSE, HIGH, LOW, N=10, M=3.0): # 超级趋势：HL2±M*ATR 递推锁带，穿带翻多空
|
||
CLOSE = np.asarray(CLOSE, dtype=float)
|
||
HIGH = np.asarray(HIGH, dtype=float)
|
||
LOW = np.asarray(LOW, dtype=float)
|
||
n = len(CLOSE)
|
||
sar_like = np.full(n, np.nan)
|
||
if n == 0:
|
||
return sar_like, np.zeros(0, dtype=int)
|
||
# Wilder 平滑 ATR（与 TradingView ta.rma 同口径）；首根 TR 用自身 H-L
|
||
prev_c = np.concatenate(([CLOSE[0]], CLOSE[:-1]))
|
||
tr = MAX(MAX(HIGH - LOW, ABS(HIGH - prev_c)), ABS(LOW - prev_c))
|
||
atr = np.empty(n)
|
||
atr[0] = tr[0]
|
||
for i in range(1, n):
|
||
atr[i] = atr[i - 1] + (tr[i] - atr[i - 1]) / N
|
||
hl2 = (HIGH + LOW) / 2
|
||
basic_up = hl2 + M * atr
|
||
basic_dn = hl2 - M * atr
|
||
# 递推锁带：带只朝价格方向收紧，反向放宽被前收盘穿越时重置
|
||
final_up = basic_up.copy()
|
||
final_dn = basic_dn.copy()
|
||
for i in range(1, n):
|
||
final_up[i] = (
|
||
basic_up[i]
|
||
if (basic_up[i] < final_up[i - 1] or CLOSE[i - 1] > final_up[i - 1])
|
||
else final_up[i - 1]
|
||
)
|
||
final_dn[i] = (
|
||
basic_dn[i]
|
||
if (basic_dn[i] > final_dn[i - 1] or CLOSE[i - 1] < final_dn[i - 1])
|
||
else final_dn[i - 1]
|
||
)
|
||
direction = np.ones(n, dtype=int) # 1=多头（ST=下轨），-1=空头（ST=上轨）
|
||
for i in range(1, n):
|
||
if direction[i - 1] == 1:
|
||
direction[i] = -1 if CLOSE[i] < final_dn[i] else 1
|
||
else:
|
||
direction[i] = 1 if CLOSE[i] > final_up[i] else -1
|
||
st = np.where(direction == 1, final_dn, final_up)
|
||
return st, direction
|
||
|
||
|
||
def CHANDELIER(CLOSE, HIGH, LOW, N=22, M=22, K=3.0): # 吊灯止损：多头 HHV-K*ATR / 空头 LLV+K*ATR
|
||
atr = ATR(CLOSE, HIGH, LOW, M)
|
||
long_stop = HHV(HIGH, N) - atr * K
|
||
short_stop = LLV(LOW, N) + atr * K
|
||
return long_stop, short_stop
|
||
|
||
|
||
def ICHIMOKU(HIGH, LOW, CLOSE, P1=9, P2=26, P3=52, SHIFT=26): # 一目均衡表：五线一云
|
||
tenkan = (HHV(HIGH, P1) + LLV(LOW, P1)) / 2 # 转换线
|
||
kijun = (HHV(HIGH, P2) + LLV(LOW, P2)) / 2 # 基准线
|
||
span_a = REF((tenkan + kijun) / 2, SHIFT) # 先行带A：SHIFT期前的值画到当前（仅过去数据）
|
||
span_b = REF((HHV(HIGH, P3) + LLV(LOW, P3)) / 2, SHIFT) # 先行带B
|
||
chikou = REF(CLOSE, -SHIFT) # 迟行带：当前收盘画回SHIFT期前，仅作图示（当期信号禁用）
|
||
return tenkan, kijun, span_a, span_b, chikou
|
||
|
||
|
||
def UOS(CLOSE, HIGH, LOW, P1=7, P2=14, P3=28, M=6): # 终极指标：7/14/28 三周期 BP/TR 加权动量
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th = MAX(HIGH, REF(CLOSE, 1)) # 真高
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tl = MIN(LOW, REF(CLOSE, 1)) # 真低
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bp = CLOSE - tl # 买压
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tr = th - tl # 真波幅
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def _avg(p): # SMA(BP,p)/SMA(TR,p)，分母为零（一字段）时取中性 0.5
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num, den = MA(bp, p), MA(tr, p)
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with np.errstate(divide="ignore", invalid="ignore"):
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ratio = np.where(den > 0, num / den, 0.5)
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return np.where(np.isnan(num) | np.isnan(den), np.nan, ratio)
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uos = 100 * (4 * _avg(P1) + 2 * _avg(P2) + _avg(P3)) / 7
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uos_ma = MA(uos, M)
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return RD(uos), RD(uos_ma)
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||
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||
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def CMO(CLOSE, N=14): # 钱德动量振荡器：(涨幅和-跌幅和)/总波幅*100，比RSI更锐利对称
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dif = DIFF(CLOSE)
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up = SUM(MAX(dif, 0), N)
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||
dn = SUM(MAX(-dif, 0), N)
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total = up + dn
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||
with np.errstate(divide="ignore", invalid="ignore"):
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cmo = np.where(total > 0, (up - dn) / total * 100, 0.0)
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return RD(np.where(np.isnan(total), np.nan, cmo))
|
||
|
||
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||
def TSI(CLOSE, R=25, S=13, M=13): # 真实强度指数：动量双重EMA平滑归一（William Blau）
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m = DIFF(CLOSE)
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num = EMA(EMA(m, R), S)
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||
den = EMA(EMA(ABS(m), R), S)
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||
with np.errstate(divide="ignore", invalid="ignore"):
|
||
tsi = np.where(den != 0, 100 * num / den, 0.0)
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||
tsi = np.where(np.isnan(num) | np.isnan(den), np.nan, tsi)
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||
return RD(tsi), RD(EMA(tsi, M))
|
||
|
||
|
||
def FISHER(HIGH, LOW, N=9): # 费雪变换（John Ehlers）：区间位置正态化，拐点尖锐
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price = (HIGH + LOW) / 2.0
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||
highest = HHV(price, N)
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||
lowest = LLV(price, N)
|
||
rng = highest - lowest
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||
with np.errstate(divide="ignore", invalid="ignore"):
|
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raw = np.where(rng > 0, 2.0 * (price - lowest) / rng - 1.0, 0.0) # 归一化到 -1~1
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||
norm = np.where(np.isnan(rng), np.nan, raw)
|
||
n = len(price)
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||
value = np.full(n, np.nan)
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||
fisher = np.full(n, np.nan)
|
||
for i in range(n):
|
||
if np.isnan(norm[i]):
|
||
continue
|
||
prev_v = 0.0 if np.isnan(value[i - 1]) else value[i - 1]
|
||
prev_f = 0.0 if np.isnan(fisher[i - 1]) else fisher[i - 1]
|
||
v = 0.33 * norm[i] + 0.67 * prev_v
|
||
v = max(-0.999, min(0.999, v)) # 钳制避免 ln 发散
|
||
value[i] = v
|
||
fisher[i] = 0.5 * np.log((1 + v) / (1 - v)) + 0.5 * prev_f
|
||
return fisher, REF(fisher, 1) # 第二返回值为触发线（前一期值）
|
||
|
||
|
||
def SQUEEZE(CLOSE, HIGH, LOW, N=20, BB=2.0, KC=1.5): # TTM 挤压动量：布林收进肯特纳，回归动量定方向
|
||
mid = MA(CLOSE, N)
|
||
dev = STD(CLOSE, N) * BB
|
||
tr = MAX(MAX(HIGH - LOW, ABS(HIGH - REF(CLOSE, 1))), ABS(LOW - REF(CLOSE, 1)))
|
||
range_ma = MA(tr, N)
|
||
# 挤压判定：布林带（±dev）整体落在肯特纳通道（±KC×range_ma）内部（NaN 预热期比较为 False）
|
||
sqz_on = dev < KC * range_ma
|
||
# 动量：中价偏离 (HH+LL)/2 与 SMA(C,N) 的均值，取线性回归当前拟合值
|
||
delta = CLOSE - ((HHV(HIGH, N) + LLV(LOW, N)) / 2 + mid) / 2
|
||
mom = FORCAST(delta, N)
|
||
return sqz_on, RD(mom)
|
||
|
||
|
||
def CHOP(HIGH, LOW, CLOSE, N=14): # 盘整指数：>61.8 震荡 / <38.2 趋势（ΣTR/区间 对数缩放到 0~100）
|
||
tr = MAX(MAX(HIGH - LOW, ABS(HIGH - REF(CLOSE, 1))), ABS(LOW - REF(CLOSE, 1)))
|
||
tr_sum = SUM(tr, N)
|
||
rng = HHV(HIGH, N) - LLV(LOW, N)
|
||
with np.errstate(divide="ignore", invalid="ignore"):
|
||
ratio = np.where((tr_sum > 0) & (rng > 0), tr_sum / rng, np.nan)
|
||
return 100 * np.log10(ratio) / np.log10(N)
|
||
|
||
|
||
def AD(CLOSE, HIGH, LOW, VOL): # 累积/派发线（Marc Chaikin）：CLV*VOL 累计，OBV 的精细化版
|
||
rng = HIGH - LOW
|
||
with np.errstate(divide="ignore", invalid="ignore"):
|
||
clv = np.where(rng > 0, ((CLOSE - LOW) - (HIGH - CLOSE)) / rng, 0.0) # 收盘位置 -1~1
|
||
return SUM(clv * VOL, 0)
|
||
|
||
|
||
def CMF(CLOSE, HIGH, LOW, VOL, N=20): # 佳庆资金流量：N日 CLV*VOL 之和 / N日成交量之和
|
||
rng = HIGH - LOW
|
||
with np.errstate(divide="ignore", invalid="ignore"):
|
||
clv = np.where(rng > 0, ((CLOSE - LOW) - (HIGH - CLOSE)) / rng, 0.0)
|
||
mfv_sum = SUM(clv * VOL, N)
|
||
vol_sum = SUM(VOL, N)
|
||
with np.errstate(divide="ignore", invalid="ignore"):
|
||
cmf = np.where(vol_sum > 0, mfv_sum / vol_sum, 0.0)
|
||
return np.where(np.isnan(vol_sum) | np.isnan(mfv_sum), np.nan, cmf)
|
||
|
||
|
||
def EFI(CLOSE, VOL, N=13): # 艾尔德强力指数：EMA(ΔC×V)，同时融合方向/幅度/成交量三维
|
||
return EMA(DIFF(CLOSE) * VOL, N)
|
||
|
||
|
||
def BBP(CLOSE, N=20, P=2): # 布林位置 %B：收盘在带内位置百分比（0=下轨，50=中轨，100=上轨）
|
||
mid = MA(CLOSE, N)
|
||
dev = STD(CLOSE, N) * P
|
||
width = 2 * dev
|
||
with np.errstate(divide="ignore", invalid="ignore"):
|
||
pos = np.where(width > 0, (CLOSE - (mid - dev)) / width * 100, 50.0)
|
||
return RD(np.where(np.isnan(width), np.nan, pos))
|
||
|
||
|
||
def BBW(CLOSE, N=20, P=2): # 布林带宽：(上轨-下轨)/中轨*100，收窄=波动挤压/变盘预警
|
||
mid = MA(CLOSE, N)
|
||
dev = STD(CLOSE, N) * P
|
||
with np.errstate(divide="ignore", invalid="ignore"):
|
||
width = np.where(mid > 0, 2 * dev / mid * 100, np.nan)
|
||
return RD(width)
|
||
|
||
|
||
# 望大家能提交更多指标和函数 https://github.com/mpquant/MyTT
|