Reference

polars_ta.tdx.reference

通过import直接导入或更名的函数

from polars_ta.ta.overlap import SMA as MA
from polars_ta.ta.volatility import TRANGE as TR  # noqa
from polars_ta.wq.arithmetic import max_ as MAX  # noqa
from polars_ta.wq.arithmetic import min_ as MIN  # noqa
from polars_ta.wq.time_series import ts_arg_max as HHVBARS  # noqa
from polars_ta.wq.time_series import ts_arg_min as LLVBARS  # noqa
from polars_ta.wq.time_series import ts_count as COUNT  # noqa
from polars_ta.wq.time_series import ts_decay_linear as WMA  # noqa
from polars_ta.wq.time_series import ts_delay as REF  # noqa
from polars_ta.wq.time_series import ts_delta as DIFF  # noqa
from polars_ta.wq.time_series import ts_max as HHV  # noqa
from polars_ta.wq.time_series import ts_min as LLV  # noqa
from polars_ta.wq.time_series import ts_product as MULAR  # noqa
from polars_ta.wq.time_series import ts_sum as SUM

Functions:

Name	Description
BARSLAST	of Observations since last time condition was true
BARSLASTCOUNT	Cumulative count of continuous true observations
BARSSINCE	of observations since the first time condition was true
BARSSINCEN	of Observations since the first time condition was true (rolling within N observations)
CUMSUM	SUM(close, 0)
DMA	DMA(X,alpha), (Exponential moving average given alpha)
EMA	EMA(X,N): Exponential moving average given N
EXPMA
EXPMEMA	Slow version of EMA. Do not use it unless you have to
FILTER
HOD	rolling rank of each data in descending order
LOD	rolling rank of each data in ascending order
LOWRANGE	rolling rank of each data in ascending order
MEMA	Exponential moving average given N
RANGE	A在B和C范围之间,B<A<C.
REFX	属于未来函数,引用若干周期后的数据
SMA_CN	Exponential Moving average given alpha = M/N
SUMIF
TMA	TMA(X,A,B),A和B必须小于1,算法 Y=(AY'+BX),其中Y'表示上一周期Y值.初值为X

BARSLAST(condition: Expr) -> Expr

of Observations since last time condition was true

上一次X不为0到现在的天数

BARSLASTCOUNT(condition: Expr) -> Expr

Cumulative count of continuous true observations 统计连续满足条件的周期数

BARSSINCE(condition: Expr) -> Expr

of observations since the first time condition was true

第一次X不为0到现在的天数

BARSSINCEN(condition: Expr, N: int = 30) -> Expr

of Observations since the first time condition was true (rolling within N observations)

N周期内第一次X不为0到现在的天数

CUMSUM(close: Expr) -> Expr

SUM(close, 0)

DMA(close: Expr, alpha: float = 0.5) -> Expr

DMA(X,alpha), (Exponential moving average given alpha) Y = alpha * X + (1 - alpha) * last_Y requires 0 < alpha < 1

求X的动态移动平均. 算法:Y=AX+(1-A)Y',其中Y'表示上一周期Y值,A必须大于0且小于1.A支持变量

EMA(close: Expr, N: int = 30) -> Expr

EMA(X,N): Exponential moving average given N

Y = X * 2/(N+1) + last_Y * (N-1)/(N+1)

X的N日指数移动平均.算法:Y=(X2+Y'(N-1))/(N+1) EMA(X,N)相当于SMA(X,N+1,2),N支持变量

EXPMA(close: Expr, N: int = 30) -> Expr

EXPMEMA(close: Expr, N: int = 30) -> Expr

Slow version of EMA. Do not use it unless you have to EXPMEMA(X,M),X的M日指数平滑移动平均。EXPMEMA同EMA(即EXPMA)的差别在于他的起始值为一平滑值

Notes

等价于talib.EMA，由于比EMA慢，少用

FILTER(close: Expr, N: int = 30) -> Expr

HOD(close: Expr, N: int = 30) -> Expr

rolling rank of each data in descending order HOD(X,N):求当前X数据是N周期内的第几个高值,N=0则从第一个有效值开始

LOD(close: Expr, N: int = 30) -> Expr

rolling rank of each data in ascending order LOD(X,N):求当前X数据是N周期内的第几个低值

LOWRANGE(close: Expr) -> Expr

rolling rank of each data in ascending order LOD(X,N):求当前X数据是N周期内的第几个低值

MEMA(close: Expr, N: int = 30) -> Expr

Exponential moving average given N Y = X / N + last_Y * (N-1) / N

MEMA(X,N):X的N日平滑移动平均,如Y=(X+Y'*(N-1))/N MEMA(X,N)相当于SMA(X,N,1)

RANGE(a: Expr, b: Expr, c: Expr) -> Expr

A在B和C范围之间,B<A<C.

REFX(close: Expr, N: int = 30) -> Expr

属于未来函数,引用若干周期后的数据

SMA_CN(X: Expr, N: int, M: int) -> Expr

Exponential Moving average given alpha = M/N Y = X * M/N + last_Y * (N-M)/N

用法:SMA(X,N,M),X的N日移动平均,M为权重,若Y=SMA(X,N,M)则Y=(XM+Y'(N-M))/N

!!!为防止与talib版SMA误用，这里去了默认值1

SUMIF(condition: Expr, close: Expr, N: int = 30) -> Expr

TMA(close: Expr, N: int = 30) -> Expr

TMA(X,A,B),A和B必须小于1,算法 Y=(AY'+BX),其中Y'表示上一周期Y值.初值为X