QUANTAXIS指标系统

​QUANTAXIS的指标系统​
​指标计算入口​
​基础类指标 [基本和同花顺/通达信一致]​
​应用级指标  add_func(func)​
​得到指标​
​QUANTAXIS的指标类 QA_DataStruct_Indicators()​
​指标类可以直接加载计算出来的指标​
​获取一段时段时间的某个股票的指标序列​
​获取一个股票的指标序列​
​获取某一个时刻的某个股票的某个指标值​
​获取某个时刻某个股票的所有指标值​
指标计算入口
QUANTAXIS的核心数据结构有一个方法叫add_func(func,args,*kwargs),作为一个指标入口,会返回一个和DataStruct中股票数量一致长度的Dataframe
QUANTAXIS有两种类型的指标:
基础指标(输入为Series的指标)
应用级指标(可应用于DataStruct的指标)
其中,基础指标是为了应用级指标做准备的,及对应于Series的分析和dataframe的分析的关系
基础类指标 [基本和同花顺/通达信一致]
import QUANTAXIS as QA
QA.MA(Series, N)
QA.EMA(Series, N)
QA.SMA(Series, N, M=1)
QA.DIFF(Series, N=1)
QA.HHV(Series, N)
QA.LLV(Series, N)
QA.SUM(Series, N)
QA.ABS(Series)
QA.MAX(A, B)
QA.MIN(A, B)
QA.CROSS(A, B)
QA.COUNT(COND, N)
QA.IF(COND, V1, V2)
QA.REF(Series, N)
QA.STD(Series, N)
QA.AVEDEV(Series, N)
QA.BBIBOLL(Series, N1, N2, N3, N4, N, M)
应用级指标  add_func(func)
import QUANTAXIS as QA
QA.QA_indicator_OSC(DataFrame, N, M)
QA.QA_indicator_BBI(DataFrame, N1, N2, N3, N4)
QA.QA_indicator_PBX(DataFrame, N1, N2, N3, N4, N5, N6)
QA.QA_indicator_BOLL(DataFrame, N)
QA.QA_indicator_ROC(DataFrame, N, M)
QA.QA_indicator_MTM(DataFrame, N, M)
QA.QA_indicator_KDJ(DataFrame, N=9, M1=3, M2=3)
QA.QA_indicator_MFI(DataFrame, N)
QA.QA_indicator_ATR(DataFrame, N)
QA.QA_indicator_SKDJ(DataFrame, N, M)
QA.QA_indicator_WR(DataFrame, N, N1)
QA.QA_indicator_BIAS(DataFrame, N1, N2, N3)
QA.QA_indicator_RSI(DataFrame, N1, N2, N3)
QA.QA_indicator_ADTM(DataFrame, N, M)
QA.QA_indicator_DDI(DataFrame, N, N1, M, M1)
QA.QA_indicator_CCI(DataFrame, N=14)
自己写一个指标:
比如 绝路航标
import QUANTAXIS as QA
def JLHB(data, m=7, n=5):
    """
    通达信定义
    VAR1:=(CLOSE-LLV(LOW,60))/(HHV(HIGH,60)-LLV(LOW,60))*80; 
    B:SMA(VAR1,N,1); 
    VAR2:SMA(B,M,1); 
    绝路航标:IF(CROSS(B,VAR2) AND B<40,50,0);
    """
    var1 = (data['close'] - QA.LLV(data['low'], 60)) / \
        (QA.HHV(data['high'], 60) - QA.LLV(data['low'], 60)) * 80
    B = QA.SMA(var1, m)
    var2 = QA.SMA(B, n)
    return pd.DataFrame({'JLHB':QA.CROSS(B,var2)*(B<40)})
金叉死叉
def MACD_JCSC(dataframe, SHORT=12, LONG=26, M=9):
    """
    1.DIF向上突破DEA，买入信号参考。
    2.DIF向下跌破DEA，卖出信号参考。
    """
    CLOSE = dataframe.close
    DIFF = QA.EMA(CLOSE, SHORT) - QA.EMA(CLOSE, LONG)
    DEA = QA.EMA(DIFF, M)
    MACD = 2*(DIFF-DEA)
​
    CROSS_JC = QA.CROSS(DIFF, DEA)
    CROSS_SC = QA.CROSS(DEA, DIFF)
    ZERO = 0
    return pd.DataFrame({'DIFF': DIFF, 'DEA': DEA, 'MACD': MACD, 'CROSS_JC': CROSS_JC, 'CROSS_SC': CROSS_SC, 'ZERO': ZERO})
得到指标
QA.QA_fetch_stock_day_adv('000001','2017-01-01','2017-05-31').to_qfq().add_func(JLHB)
QUANTAXIS的指标类 QA_DataStruct_Indicators()
(新增于1.0.42)
指标类可以直接加载计算出来的指标
ind=data.add_func(QA.QA_indicator_WR,1,2)
inc=QA.QA_DataStruct_Indicators(ind)
inc
< QA_DATASTRUCT_INDICATOR FROM 2018-01-02 00:00:00 TO 2018-01-31 00:00:00 WITH 2 CODES >
获取一段时段时间的某个股票的指标序列
inc.get_timerange('2018-01-07','2018-01-12','000001')
                        WR1            WR2
date    code        
2018-01-08    000001    76.744186    79.591837
2018-01-09    000001    42.857143    48.837209
2018-01-10    000001    3.508772    3.508772
2018-01-11    000001    59.375000    28.358209
2018-01-12    000001    48.148148    31.707317
获取一个股票的指标序列
inc.get_code('000002')
​
                    WR1            WR2
date    code        
2018-01-02    000002    27.922078    NaN
2018-01-03    000002    93.548387    62.231760
2018-01-04    000002    28.671329    39.285714
2018-01-05    000002    36.363636    29.629630
2018-01-08    000002    52.432432    23.317308
2018-01-09    000002    23.275862    55.721393
2018-01-10    000002    85.833333    58.857143
2018-01-11    000002    50.806452    71.759259
2018-01-12    000002    69.934641    51.442308
2018-01-15    000002    38.075314    31.271478
2018-01-16    000002    11.627907    9.823183
2018-01-17    000002    36.086957    21.627907
2018-01-18    000002    50.574713    37.606838
2018-01-19    000002    90.760870    76.255708
2018-01-22    000002    23.308271    60.465116
2018-01-23    000002    34.868421    17.666667
2018-01-24    000002    62.359551    49.333333
2018-01-25    000002    39.655172    59.302326
2018-01-26    000002    40.259740    46.551724
2018-01-29    000002    80.566802    81.322957
2018-01-30    000002    94.797688    97.902098
2018-01-31    000002    9.345794    18.487395
获取某一个时刻的某个股票的某个指标值
inc.get_indicator('2018-01-12','000001','WR1')
​
WR1    48.148148
Name: (2018-01-12 00:00:00, 000001), dtype: float64
获取某个时刻某个股票的所有指标值
inc.get_indicator('2018-01-12','000001')
​
WR1    48.148148
WR2    31.707317
Name: (2018-01-12 00:00:00, 000001), dtype: float64
QUANTAXIS的数据结构
QUANTAXIS的数据获取指南
Last updated 9 months ago