tqsdk.ta - 技术指标计算函数¶

Functions

`AD`(df)	累积/派发指标 Accumulation/Distribution
`ADTM`(df, n, m)	动态买卖气指标
`ARBR`(df, n)	人气意愿指标
`ASI`(df)	振动升降指标
`ATR`(df, n)	平均真实波幅
`B3612`(df)	三减六日乖离率
`BBI`(df, n1, n2, n3, n4)	多空指数
`BBIBOLL`(df, n, m)	多空布林线
`BIAS`(df, n)	乖离率
`BOLL`(df, n, p)	布林线
`CCI`(df, n)	顺势指标
`CCL`(df)	持仓异动
`CDP`(df, n)	逆势操作
`CJL`(df)	成交量
`CR`(df, n, m)	CR能量
`DBCD`(df, n, m, t)	异同离差乖离率
`DDI`(df, n, n1, m, m1)	方向标准离差指数
`DKX`(df, m)	多空线
`DMA`(df, short, long, m)	平均线差
`DMI`(df, n, m)	动向指标
`DPO`(df)	区间震荡线
`EMA`(df, n)	指数加权移动平均线
`EMA2`(df, n)	线性加权移动平均 WMA
`ENV`(df, n, k)	包略线 (Envelopes)
`EXPMA`(df, p1, p2)	指数加权移动平均线组合
`HCL`(df, n)	均线通道
`KD`(df, n, m1, m2)	随机指标
`KDJ`(df, n, m1, m2)	随机指标
`LON`(df)	长线指标
`LWR`(df, n, m)	威廉指标
`MA`(df, n)	简单移动平均线
`MACD`(df, short, long, m)	异同移动平均线
`MASS`(df, n1, n2)	梅斯线
`MFI`(df, n)	资金流量指标
`MI`(df, n)	动量指标
`MICD`(df, n, n1, n2)	异同离差动力指数
`MIKE`(df, n)	麦克指标
`MTM`(df, n, n1)	MTM动力指标
`MV`(df, n, m)	均量线
`OBV`(df)	能量潮
`OPI`(df)	持仓量
`PRICEOSC`(df, long, short)	价格震荡指数 Price Oscillator
`PSY`(df, n, m)	心理线
`PUBU`(df, m)	瀑布线
`PVT`(df)	价量趋势指数
`QHLSR`(df)	阻力指标
`RC`(df, n)	变化率指数
`RCCD`(df, n, n1, n2)	异同离差变化率指数
`ROC`(df, n, m)	变动速率
`RSI`(df, n)	相对强弱指标
`SAR`(df, n, step, max)	抛物线指标
`SHORT`(df)	短线指标
`SLOWKD`(df, n, m1, m2, m3)	慢速KD
`SMA`(df, n, m)	扩展指数加权移动平均
`SRDM`(df, n)	动向速度比率
`SRMI`(df, n)	MI修正指标
`TRMA`(df, n)	三角移动平均线
`VOSC`(df, short, long)	移动平均成交量指标 Volume Oscillator
`VR`(df, n)	VR 容量比率
`VROC`(df, n)	量变动速率
`VRSI`(df, n)	量相对强弱
`WAD`(df, n, m)	威廉多空力度线
`WR`(df, n)	威廉指标
`WVAD`(df)	威廉变异离散量
`ZDZB`(df, n1, n2, n3)	筑底指标

tqsdk.ta.ATR(df, n)¶

平均真实波幅

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 平均真实波幅的周期

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 分别是"tr"和"atr", 分别代表真实波幅和平均真实波幅

Example:

# 获取 CFFEX.IF1903 合约的平均真实波幅
from tqsdk import TqApi, TqSim
from tqsdk.ta import ATR

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
atr = ATR(klines, 14)
print(atr.tr)  # 真实波幅
print(atr.atr)  # 平均真实波幅

# 预计的输出是这样的:
[..., 143.0, 48.0, 80.0, ...]
[..., 95.20000000000005, 92.0571428571429, 95.21428571428575, ...]

tqsdk.ta.BIAS(df, n)¶

乖离率

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 移动平均的计算周期

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"bias", 代表计算出来的乖离率值

Example:

# 获取 CFFEX.IF1903 合约的乖离率
from tqsdk import TqApi, TqSim
from tqsdk.ta import BIAS

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
bias = BIAS(klines, 6)
print(list(bias["bias"]))  # 乖离率

# 预计的输出是这样的:
[..., 2.286835533357118, 2.263301549041151, 0.7068445823271412, ...]

tqsdk.ta.BOLL(df, n, p)¶

布林线

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

p (int): 计算参数p

Returns:

pandas.DataFrame: 返回的dataframe包含3列, 分别是"mid", "top"和"bottom", 分别代表布林线的中、上、下轨

Example:

# 获取 CFFEX.IF1903 合约的布林线
from tqsdk import TqApi, TqSim
from tqsdk.ta import BOLL

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
boll=BOLL(klines, 26, 2)
print(list(boll["mid"]))
print(list(boll["top"]))
print(list(boll["bottom"]))

# 预计的输出是这样的:
[..., 3401.338461538462, 3425.600000000001, 3452.3230769230777, ...]
[..., 3835.083909752222, 3880.677579320277, 3921.885406954584, ...]
[..., 2967.593013324702, 2970.5224206797247, 2982.760746891571, ...]

tqsdk.ta.DMI(df, n, m)¶

动向指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

m (int): 周期m

Returns:

pandas.DataFrame: 返回的DataFrame包含5列, 是"atr", "pdi", "mdi", "adx"和"adxr", 分别代表平均真实波幅, 上升方向线, 下降方向线, 趋向平均值以及评估数值

Example:

# 获取 CFFEX.IF1903 合约的动向指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import DMI

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
dmi=DMI(klines, 14, 6)
print(list(dmi["atr"]))
print(list(dmi["pdi"]))
print(list(dmi["mdi"]))
print(list(dmi["adx"]))
print(list(dmi["adxr"]))

# 预计的输出是这样的:
[..., 95.20000000000005, 92.0571428571429, 95.21428571428575, ...]
[..., 51.24549819927972, 46.55493482309126, 47.14178544636161, ...]
[..., 6.497599039615802, 6.719428926132791, 6.4966241560389655, ...]
[..., 78.80507786697127, 76.8773544355082, 75.11662664555287, ...]
[..., 70.52493837227118, 73.28531799111778, 74.59341569051983, ...]

tqsdk.ta.KDJ(df, n, m1, m2)¶

随机指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

m1 (int): 参数m1

m2 (int): 参数m2

Returns:

pandas.DataFrame: 返回的DataFrame包含3列, 是"k", "d"和"j", 分别代表计算出来的K值, D值和J值

Example:

# 获取 CFFEX.IF1903 合约的随机指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import KDJ

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
kdj = KDJ(klines, 9, 3, 3)
print(list(kdj["k"]))
print(list(kdj["d"]))
print(list(kdj["j"]))

# 预计的输出是这样的:
[..., 80.193148635668, 81.83149521546302, 84.60665654726242, ...]
[..., 82.33669997171852, 82.16829838630002, 82.98108443995415, ...]
[..., 77.8451747299365, 75.90604596356695, 81.15788887378903, ...]

tqsdk.ta.MACD(df, short, long, m)¶

异同移动平均线

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

short (int): 短周期

long (int): 长周期

m (int): 移动平均线的周期

Returns:

pandas.DataFrame: 返回的DataFrame包含3列, 是"diff", "dea"和"bar", 分别代表离差值, DIFF的指数加权移动平均线, MACD的柱状线

(注: 因 DataFrame 有diff()函数，因此获取到此指标后："diff"字段使用 macd["diff"] 方式来取值，而非 macd.diff )

Example:

# 获取 CFFEX.IF1903 合约的异同移动平均线
from tqsdk import TqApi, TqSim
from tqsdk.ta import MACD

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
macd = MACD(klines, 12, 26, 9)
print(list(macd["diff"]))
print(list(macd["dea"]))
print(list(macd["bar"]))

# 预计的输出是这样的:
[..., 149.58313904045826, 155.50790712365142, 160.27622505636737, ...]
[..., 121.46944573796466, 128.27713801510203, 134.6769554233551, ...]
[..., 56.2273866049872, 54.46153821709879, 51.19853926602451, ...]

tqsdk.ta.SAR(df, n, step, max)¶

抛物线指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): SAR的周期n

step (float): 步长

max (float): 极值

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"sar", 代表计算出来的SAR值

Example:

# 获取 CFFEX.IF1903 合约的抛物线指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import SAR

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
sar=SAR(klines, 4, 0.02, 0.2)
print(list(sar["sar"]))


# 预计的输出是这样的:
[..., 3742.313604622293, 3764.5708836978342, 3864.4, ...]

tqsdk.ta.WR(df, n)¶

威廉指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"wr", 代表计算出来的威廉指标

Example:

# 获取 CFFEX.IF1903 合约的威廉指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import WR

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
wr = WR(klines, 14)
print(list(wr["wr"]))

# 预计的输出是这样的:
[..., -12.843029637760672, -8.488840102451537, -16.381322957198407, ...]

tqsdk.ta.RSI(df, n)¶

相对强弱指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"rsi", 代表计算出来的相对强弱指标

Example:

# 获取 CFFEX.IF1903 合约的相对强弱指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import RSI

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
rsi = RSI(klines, 7)
print(list(rsi["rsi"]))

# 预计的输出是这样的:
[..., 80.21169825630794, 81.57315806032297, 72.34968324924667, ...]

tqsdk.ta.ASI(df)¶

振动升降指标

Args:: df (pandas.DataFrame): Dataframe格式的K线序列
Returns:: pandas.DataFrame: 返回的DataFrame包含1列, 是"asi", 代表计算出来的振动升降指标

Example:

# 获取 CFFEX.IF1903 合约的振动升降指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import ASI

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
asi = ASI(klines)
print(list(asi["asi"]))


# 预计的输出是这样的:
[..., -4690.587005986468, -4209.182816350308, -4699.742010304962, ...]

tqsdk.ta.VR(df, n)¶

VR 容量比率

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"vr", 代表计算出来的VR

Example:

# 获取 CFFEX.IF1903 合约的VR
from tqsdk import TqApi, TqSim
from tqsdk.ta import VR

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
vr = VR(klines, 26)
print(list(vr["vr"]))


# 预计的输出是这样的:
[..., 150.1535316212112, 172.2897559521652, 147.04236342791924, ...]

tqsdk.ta.ARBR(df, n)¶

人气意愿指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"ar"和"br" , 分别代表人气指标和意愿指标

Example:

# 获取 CFFEX.IF1903 合约的人气意愿指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import ARBR

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
arbr = ARBR(klines, 26)
print(list(arbr["ar"]))
print(list(arbr["br"]))


# 预计的输出是这样的:
[..., 183.5698517817721, 189.98732572877034, 175.08802816901382, ...]
[..., 267.78549382716034, 281.567546278062, 251.08041091037902, ...]

tqsdk.ta.DMA(df, short, long, m)¶

平均线差

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

short (int): 短周期

long (int): 长周期

m (int): 计算周期m

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"ddd"和"ama", 分别代表长短周期均值的差和ddd的简单移动平均值

Example:

# 获取 CFFEX.IF1903 合约的平均线差
from tqsdk import TqApi, TqSim
from tqsdk.ta import DMA

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
dma = DMA(klines, 10, 50, 10)
print(list(dma["ddd"]))
print(list(dma["ama"]))


# 预计的输出是这样的:
[..., 409.2520000000022, 435.68000000000166, 458.3360000000025, ...]
[..., 300.64360000000147, 325.0860000000015, 349.75200000000166, ...]

tqsdk.ta.EXPMA(df, p1, p2)¶

指数加权移动平均线组合

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

p1 (int): 周期1

p2 (int): 周期2

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"ma1"和"ma2", 分别代表指数加权移动平均线1和指数加权移动平均线2

Example:

# 获取 CFFEX.IF1903 合约的指数加权移动平均线组合
from tqsdk import TqApi, TqSim
from tqsdk.ta import EXPMA

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
expma = EXPMA(klines, 5, 10)
print(list(expma["ma1"]))
print(list(expma["ma2"]))


# 预计的输出是这样的:
[..., 3753.679549224137, 3784.6530328160916, 3792.7020218773946, ...]
[..., 3672.4492964832566, 3704.113060759028, 3723.1470497119317, ...]

tqsdk.ta.CR(df, n, m)¶

CR能量

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

m (int): 周期m

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"cr"和"crma", 分别代表CR值和CR值的简单移动平均值

Example:

# 获取 CFFEX.IF1903 合约的CR能量
from tqsdk import TqApi, TqSim
from tqsdk.ta import CR

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
cr = CR(klines, 26, 5)
print(list(cr["cr"]))
print(list(cr["crma"]))


# 预计的输出是这样的:
[..., 291.5751884671343, 316.71058105671943, 299.50578748862046, ...]
[..., 316.01257308163747, 319.3545725665982, 311.8275184876805, ...]

tqsdk.ta.CCI(df, n)¶

顺势指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"cci", 代表计算出来的CCI值

Example:

# 获取 CFFEX.IF1903 合约的顺势指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import CCI

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
cci = CCI(klines, 14)
print(list(cci["cci"]))


# 预计的输出是这样的:
[..., 98.13054698810375, 93.57661788413617, 77.8671380173813, ...]

tqsdk.ta.OBV(df)¶

能量潮

Args:: df (pandas.DataFrame): Dataframe格式的K线序列
Returns:: pandas.DataFrame: 返回的DataFrame包含1列, 是"obv", 代表计算出来的OBV值

Example:

# 获取 CFFEX.IF1903 合约的能量潮
from tqsdk import TqApi, TqSim
from tqsdk.ta import OBV

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
obv = OBV(klines)
print(list(obv["obv"]))


# 预计的输出是这样的:
[..., 267209, 360351, 264476, ...]

tqsdk.ta.CDP(df, n)¶

逆势操作

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

Returns:

pandas.DataFrame: 返回的DataFrame包含4列, 是"ah", "al", "nh", "nl", 分别代表最高值, 最低值, 近高值, 近低值

Example:

# 获取 CFFEX.IF1903 合约的逆势操作指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import CDP

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
cdp = CDP(klines, 3)
print(list(cdp["ah"]))
print(list(cdp["al"]))
print(list(cdp["nh"]))
print(list(cdp["nl"]))


# 预计的输出是这样的:
[..., 3828.244444444447, 3871.733333333336, 3904.37777777778, ...]
[..., 3656.64444444444, 3698.3999999999955, 3734.9111111111065, ...]
[..., 3743.8888888888837, 3792.3999999999946, 3858.822222222217, ...]
[..., 3657.2222222222213, 3707.6666666666656, 3789.955555555554, ...]

tqsdk.ta.HCL(df, n)¶

均线通道

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

Returns:

pandas.DataFrame: 返回的DataFrame包含3列, 是"mah", "mal", "mac", 分别代表最高价的移动平均线, 最低价的移动平均线以及收盘价的移动平均线

Example:

# 获取 CFFEX.IF1903 合约的均线通道指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import HCL

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
hcl = HCL(klines, 10)
print(list(hcl["mah"]))
print(list(hcl["mal"]))
print(list(hcl["mac"]))


# 预计的输出是这样的:
[..., 3703.5400000000022, 3743.2800000000025, 3778.300000000002, ...]
[..., 3607.339999999999, 3643.079999999999, 3677.579999999999, ...]
[..., 3666.1600000000008, 3705.8600000000006, 3741.940000000001, ...]

tqsdk.ta.ENV(df, n, k)¶

包略线 (Envelopes)

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

k (float): 参数k

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"upper", "lower", 分别代表上线和下线

Example:

# 获取 CFFEX.IF1903 合约的包略线
from tqsdk import TqApi, TqSim
from tqsdk.ta import ENV

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
env = ENV(klines, 14, 6)
print(list(env["upper"]))
print(list(env["lower"]))


# 预计的输出是这样的:
[..., 3842.2122857142863, 3876.7531428571433, 3893.849428571429, ...]
[..., 3407.244857142857, 3437.875428571429, 3453.036285714286, ...]

tqsdk.ta.MIKE(df, n)¶

麦克指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

Returns:

pandas.DataFrame: 返回的DataFrame包含6列, 是"wr", "mr", "sr", "ws", "ms", "ss", 分别代表初级压力价,中级压力,强力压力,初级支撑,中级支撑和强力支撑

Example:

# 获取 CFFEX.IF1903 合约的麦克指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import MIKE

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
mike = MIKE(klines, 12)
print(list(mike["wr"]))
print(list(mike["mr"]))
print(list(mike["sr"]))
print(list(mike["ws"]))
print(list(mike["ms"]))
print(list(mike["ss"]))


# 预计的输出是这样的:
[..., 4242.4, 4203.333333333334, 3986.266666666666, ...]
[..., 4303.6, 4283.866666666667, 4175.333333333333, ...]
[..., 4364.8, 4364.4, 4364.4, ...]
[..., 3770.5999999999995, 3731.9333333333343, 3514.866666666666, ...]
[..., 3359.9999999999995, 3341.066666666667, 3232.533333333333, ...]
[..., 2949.3999999999996, 2950.2, 2950.2, ...]

tqsdk.ta.PUBU(df, m)¶

瀑布线

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

m (int): 周期m

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"pb", 代表计算出的瀑布线

Example:

# 获取 CFFEX.IF1903 合约的瀑布线
from tqsdk import TqApi, TqSim
from tqsdk.ta import PUBU

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
pubu = PUBU(klines, 4)
print(list(pubu["pb"]))


# 预计的输出是这样的:
[..., 3719.087702972829, 3728.9326217836974, 3715.7537397368856, ...]

tqsdk.ta.BBI(df, n1, n2, n3, n4)¶

多空指数

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n1 (int): 周期n1

n2 (int): 周期n2

n3 (int): 周期n3

n4 (int): 周期n4

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"bbi", 代表计算出的多空指标

Example:

# 获取 CFFEX.IF1903 合约的多空指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import BBI

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
bbi = BBI(klines, 3, 6, 12, 24)
print(list(bbi["bbi"]))


# 预计的输出是这样的:
[..., 3679.841666666668, 3700.9645833333348, 3698.025000000002, ...]

tqsdk.ta.DKX(df, m)¶

多空线

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

m (int): 周期m

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"b", "d", 分别代表计算出来的DKX指标及DKX的m日简单移动平均值

Example:

# 获取 CFFEX.IF1903 合约的多空线
from tqsdk import TqApi, TqSim
from tqsdk.ta import DKX

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
dkx = DKX(klines, 10)
print(list(dkx["b"]))
print(list(dkx["d"]))


# 预计的输出是这样的:
[..., 3632.081746031746, 3659.4501587301593, 3672.744761904762, ...]
[..., 3484.1045714285706, 3516.1797301587294, 3547.44857142857, ...]

tqsdk.ta.BBIBOLL(df, n, m)¶

多空布林线

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 参数n

m (int): 参数m

Returns:

pandas.DataFrame: 返回的DataFrame包含3列, 是"bbiboll", "upr", "dwn", 分别代表多空布林线, 压力线和支撑线

Example:

# 获取 CFFEX.IF1903 合约的多空布林线
from tqsdk import TqApi, TqSim
from tqsdk.ta import BBIBOLL

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
bbiboll=BBIBOLL(klines,10,3)
print(list(bbiboll["bbiboll"]))
print(list(bbiboll["upr"]))
print(list(bbiboll["dwn"]))


# 预计的输出是这样的:
[..., 3679.841666666668, 3700.9645833333348, 3698.025000000002, ...]
[..., 3991.722633271389, 3991.796233444868, 3944.7721466057383, ...]
[..., 3367.960700061947, 3410.1329332218015, 3451.2778533942655, ...]

tqsdk.ta.ADTM(df, n, m)¶

动态买卖气指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

m (int): 周期m

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"adtm", "adtmma", 分别代表计算出来的ADTM指标及其M日的简单移动平均

Example:

# 获取 CFFEX.IF1903 合约的动态买卖气指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import ADTM

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
adtm = ADTM(klines, 23, 8)
print(list(adtm["adtm"]))
print(list(adtm["adtmma"]))


# 预计的输出是这样的:
[..., 0.8404011965511171, 0.837919942816297, 0.8102215868477481, ...]
[..., 0.83855483869397, 0.8354743499113684, 0.8257261282040207, ...]

tqsdk.ta.B3612(df)¶

三减六日乖离率

Args:: df (pandas.DataFrame): Dataframe格式的K线序列
Returns:: pandas.DataFrame: 返回的DataFrame包含2列, 是"b36", "b612", 分别代表收盘价的3日移动平均线与6日移动平均线的乖离值及收盘价的6日移动平均线与12日移动平均线的乖离值

Example:

# 获取 CFFEX.IF1903 合约的三减六日乖离率
from tqsdk import TqApi, TqSim
from tqsdk.ta import B3612

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
b3612=B3612(klines)
print(list(b3612["b36"]))
print(list(b3612["b612"]))


# 预计的输出是这样的:
[..., 57.26666666667188, 44.00000000000546, -5.166666666660603, ...]
[..., 99.28333333333285, 88.98333333333221, 69.64999999999918, ...]

tqsdk.ta.DBCD(df, n, m, t)¶

异同离差乖离率

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

m (int): 参数m

t (int): 参数t

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"dbcd", "mm", 分别代表离差值及其简单移动平均值

Example:

# 获取 CFFEX.IF1903 合约的异同离差乖离率
from tqsdk import TqApi, TqSim
from tqsdk.ta import DBCD

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
dbcd=DBCD(klines, 5, 16, 76)
print(list(dbcd["dbcd"]))
print(list(dbcd["mm"]))


# 预计的输出是这样的:
[..., 0.0038539724453411045, 0.0034209659500908517, 0.0027130669520015094, ...]
[..., 0.003998499673401192, 0.003864353204606074, 0.0035925052896395872, ...]

tqsdk.ta.DDI(df, n, n1, m, m1)¶

方向标准离差指数

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

n1 (int): 参数n1

m (int): 参数m

m1 (int): 周期m1

Returns:

pandas.DataFrame: 返回的DataFrame包含3列, 是"ddi", "addi", "ad", 分别代表DIZ与DIF的差值, DDI的加权平均, ADDI的简单移动平均

Example:

# 获取 CFFEX.IF1903 合约的方向标准离差指数
from tqsdk import TqApi, TqSim
from tqsdk.ta import DDI

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
ddi = DDI(klines, 13, 30, 10, 5)
print(list(ddi["ddi"]))
print(list(ddi["addi"]))
print(list(ddi["ad"]))


# 预计的输出是这样的:
[..., 0.6513560804899388, 0.6129178985672046, 0.40480202190395936, ...]
[..., 0.6559570156346113, 0.6416106432788091, 0.5626744361538593, ...]
[..., 0.6960565490556135, 0.6765004585407994, 0.6455063893920429, ...]

tqsdk.ta.KD(df, n, m1, m2)¶

随机指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

m1 (int): 参数m1

m2 (int): 参数m2

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"k", "d", 分别代表计算出来的K值与D值

Example:

# 获取 CFFEX.IF1903 合约的随机指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import KD

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
kd = KD(klines, 9, 3, 3)
print(list(kd["k"]))
print(list(kd["d"]))


# 预计的输出是这样的:
[..., 84.60665654726242, 80.96145249909222, 57.54863147922147, ...]
[..., 82.98108443995415, 82.30787379300017, 74.05479302174061, ...]

tqsdk.ta.LWR(df, n, m)¶

威廉指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

m (int): 参数m

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"lwr", 代表计算出来的威廉指标

Example:

# 获取 CFFEX.IF1903 合约的威廉指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import LWR

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
lwr = LWR(klines, 9, 3)
print(list(lwr["lwr"]))


# 预计的输出是这样的:
[..., -15.393343452737565, -19.03854750090778, -42.45136852077853, ...]

tqsdk.ta.MASS(df, n1, n2)¶

梅斯线

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n1 (int): 周期n1

n2 (int): 周期n2

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"mass", 代表计算出来的梅斯线指标

Example:

# 获取 CFFEX.IF1903 合约的梅斯线
from tqsdk import TqApi, TqSim
from tqsdk.ta import MASS

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
mass = MASS(klines, 9, 25)
print(list(mass["mass"]))


# 预计的输出是这样的:
[..., 27.478822053291733, 27.485710830466964, 27.561223922342652, ...]

tqsdk.ta.MFI(df, n)¶

资金流量指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"mfi", 代表计算出来的MFI指标

Example:

# 获取 CFFEX.IF1903 合约的资金流量指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import MFI

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
mfi = MFI(klines, 14)
print(list(mfi["mfi"]))


# 预计的输出是这样的:
[..., 73.47968487105688, 70.2250476611595, 62.950450871062266, ...]

tqsdk.ta.MI(df, n)¶

动量指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 参数n

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"a", "mi", 分别代表当日收盘价与N日前收盘价的差值以及MI值

Example:

# 获取 CFFEX.IF1903 合约的动量指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import MI

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
mi = MI(klines, 12)
print(list(mi["a"]))
print(list(mi["mi"]))


# 预计的输出是这样的:
[..., 399.1999999999998, 370.8000000000002, 223.5999999999999, ...]
[..., 293.2089214076506, 299.67484462367975, 293.3352742383731, ...]

tqsdk.ta.MICD(df, n, n1, n2)¶

异同离差动力指数

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 参数n

n1 (int): 周期n1

n2 (int): 周期n2

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"dif", "micd", 代表离差值和MICD指标

Example:

# 获取 CFFEX.IF1903 合约的异同离差动力指数
from tqsdk import TqApi, TqSim
from tqsdk.ta import MICD

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
micd = MICD(klines, 3, 10, 20)
print(list(micd["dif"]))
print(list(micd["micd"]))


# 预计的输出是这样的:
[..., 6.801483500680234, 6.700989000453493, 6.527326000302342, ...]
[..., 6.2736377238314684, 6.3163728514936714, 6.3374681663745385, ...]

tqsdk.ta.MTM(df, n, n1)¶

MTM动力指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

n1 (int): 周期n1

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"mtm", "mtmma", 分别代表MTM值和MTM的简单移动平均值

Example:

# 获取 CFFEX.IF1903 合约的动力指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import MTM

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
mtm = MTM(klines, 6, 6)
print(list(mtm["mtm"]))
print(list(mtm["mtmma"]))


# 预计的输出是这样的:
[..., 144.79999999999973, 123.60000000000036, -4.200000000000273, ...]
[..., 198.5666666666667, 177.96666666666678, 139.30000000000004, ...]

tqsdk.ta.PRICEOSC(df, long, short)¶

价格震荡指数 Price Oscillator

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

long (int): 长周期

short (int): 短周期

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"priceosc", 代表计算出来的价格震荡指数

Example:

# 获取 CFFEX.IF1903 合约的价格震荡指数
from tqsdk import TqApi, TqSim
from tqsdk.ta import PRICEOSC

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
priceosc = PRICEOSC(klines, 26, 12)
print(list(priceosc["priceosc"]))


# 预计的输出是这样的:
[..., 5.730468338384374, 5.826866231225718, 5.776959240989803, ...]

tqsdk.ta.PSY(df, n, m)¶

心理线

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

m (int): 周期m

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"psy", "psyma", 分别代表心理线和心理线的简单移动平均

Example:

# 获取 CFFEX.IF1903 合约的心理线
from tqsdk import TqApi, TqSim
from tqsdk.ta import PSY

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
psy = PSY(klines, 12, 6)
print(list(psy["psy"]))
print(list(psy["psyma"]))


# 预计的输出是这样的:
[..., 58.333333333333336, 58.333333333333336, 50.0, ...]
[..., 54.16666666666671, 54.16666666666671, 54.16666666666671, ...]

tqsdk.ta.QHLSR(df)¶

阻力指标

Args:: df (pandas.DataFrame): Dataframe格式的K线序列
Returns:: pandas.DataFrame: 返回的DataFrame包含2列, 是"qhl5", "qhl10", 分别代表计算出来的QHL5值和QHL10值

Example:

# 获取 CFFEX.IF1903 合约的阻力指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import QHLSR

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
ndf = QHLSR(klines)
print(list(ndf["qhl5"]))
print(list(ndf["qhl10"]))


# 预计的输出是这样的:
[..., 0.9512796890171819, 1.0, 0.8061319699743583, 0.36506038490240567, ...]
[..., 0.8192641975527878, 0.7851545532504415, 0.5895613967067044, ...]

tqsdk.ta.RC(df, n)¶

变化率指数

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 参数n

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"arc", 代表计算出来的变化率指数

Example:

# 获取 CFFEX.IF1903 合约的变化率指数
from tqsdk import TqApi, TqSim
from tqsdk.ta import RC

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
rc = RC(klines, 50)
print(list(rc["arc"]))


# 预计的输出是这样的:
[..., 1.011782057069131, 1.0157160672001329, 1.019680175228899, ...]

tqsdk.ta.RCCD(df, n, n1, n2)¶

异同离差变化率指数

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 参数n

n1 (int): 周期n1

n2 (int): 周期n2

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"dif", "rccd", 分别代表离差值和异同离差变化率指数

Example:

# 获取 CFFEX.IF1903 合约的异同离差变化率指数
from tqsdk import TqApi, TqSim
from tqsdk.ta import RCCD

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
rccd = RCCD(klines, 10, 21, 28)
print(list(rccd["dif"]))
print(list(rccd["rccd"]))


# 预计的输出是这样的:
[..., 0.007700543190044096, 0.007914865667604465, 0.008297381119103608, ...]
[..., 0.007454465277084111, 0.007500505316136147, 0.0075801928964328935, ...]

tqsdk.ta.ROC(df, n, m)¶

变动速率

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 参数n

m (int): 周期m

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"roc", "rocma", 分别代表ROC值和ROC的简单移动平均值

Example:

# 获取 CFFEX.IF1903 合约的变动速率
from tqsdk import TqApi, TqSim
from tqsdk.ta import ROC

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
roc = ROC(klines, 24, 20)
print(list(roc["roc"]))
print(list(roc["rocma"]))


# 预计的输出是这样的:
[..., 21.389800555415288, 19.285937989351712, 15.183443085606768, ...]
[..., 14.597071588550435, 15.223202630466648, 15.537530180238516, ...]

tqsdk.ta.SLOWKD(df, n, m1, m2, m3)¶

慢速KD

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 周期n

m1 (int): 参数m1

m2 (int): 参数m2

m3 (int): 参数m3

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"k", "d", 分别代表K值和D值

Example:

# 获取 CFFEX.IF1903 合约的慢速KD
from tqsdk import TqApi, TqSim
from tqsdk.ta import SLOWKD

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
slowkd = SLOWKD(klines, 9, 3, 3, 3)
print(list(slowkd["k"]))
print(list(slowkd["d"]))


# 预计的输出是这样的:
[..., 82.98108443995415, 82.30787379300017, 74.05479302174061, ...]
[..., 83.416060393041, 83.04666485969405, 80.0493742470429, ...]

tqsdk.ta.SRDM(df, n)¶

动向速度比率

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 参数n

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"srdm", "asrdm", 分别代表计算出来的SRDM值和SRDM值的加权移动平均值

Example:

# 获取 CFFEX.IF1903 合约的动向速度比率
from tqsdk import TqApi, TqSim
from tqsdk.ta import SRDM

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
srdm = SRDM(klines, 30)
print(list(srdm["srdm"]))
print(list(srdm["asrdm"]))


# 预计的输出是这样的:
[..., 0.7865067466266866, 0.7570567713288928, 0.5528619528619526, ...]
[..., 0.45441550541510667, 0.4645035476122329, 0.4674488277872236, ...]

tqsdk.ta.SRMI(df, n)¶

MI修正指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 参数n

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"a", "mi", 分别代表A值和MI值

Example:

# 获取 CFFEX.IF1903 合约的MI修正指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import SRMI

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
srmi = SRMI(klines, 9)
print(list(srmi["a"]))
print(list(srmi["mi"]))


# 预计的输出是这样的:
[..., 0.10362397961836425, 0.07062591892459567, -0.03341929372138309, ...]
[..., 0.07583104758041452, 0.0752526999519902, 0.06317803398828206, ...]

tqsdk.ta.ZDZB(df, n1, n2, n3)¶

筑底指标

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n1 (int): 周期n1

n2 (int): 周期n2

n3 (int): 周期n3

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"b", "d", 分别代表A值的n2周期简单移动平均和A值的n3周期简单移动平均

Example:

# 获取 CFFEX.IF1903 合约的筑底指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import ZDZB

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
zdzb = ZDZB(klines, 50, 5, 20)
print(list(zdzb["b"]))
print(list(zdzb["d"]))


# 预计的输出是这样的:
[..., 1.119565217391305, 1.1376811594202905, 1.155797101449276, ...]
[..., 1.0722350515828771, 1.091644989471076, 1.1077480490523965, ...]

tqsdk.ta.DPO(df)¶

区间震荡线

Args:: df (pandas.DataFrame): Dataframe格式的K线序列
Returns:: pandas.DataFrame: 返回的DataFrame包含1列, 是"dpo", 代表计算出来的DPO指标

Example:

# 获取 CFFEX.IF1903 合约的区间震荡线
from tqsdk import TqApi, TqSim
from tqsdk.ta import DPO

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
dpo = DPO(klines)
print(list(dpo["dpo"]))


# 预计的输出是这样的:
[..., 595.4100000000021, 541.8300000000017, 389.7200000000016, ...]

tqsdk.ta.LON(df)¶

长线指标

Args:: df (pandas.DataFrame): Dataframe格式的K线序列
Returns:: pandas.DataFrame: 返回的DataFrame包含2列, 是"lon", "ma1", 分别代表长线指标和长线指标的10周期简单移动平均值

Example:

# 获取 CFFEX.IF1903 合约的长线指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import LON

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
lon = LON(klines)
print(list(lon["lon"]))
print(list(lon["ma1"]))


# 预计的输出是这样的:
[..., 6.419941948913239, 6.725451135494827, 6.483546043406369, ...]
[..., 4.366625464410439, 4.791685949556344, 5.149808865745246, ...]

tqsdk.ta.SHORT(df)¶

短线指标

Args:: df (pandas.DataFrame): Dataframe格式的K线序列
Returns:: pandas.DataFrame: 返回的DataFrame包含2列, 是"short", "ma1", 分别代表短线指标和短线指标的10周期简单移动平均值

Example:

# 获取 CFFEX.IF1903 合约的短线指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import SHORT

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
short = SHORT(klines)
print(list(short["short"]))
print(list(short["ma1"]))


# 预计的输出是这样的:
[..., 0.6650139934614072, 0.3055091865815881, -0.24190509208845834, ...]
[..., 0.41123378999608917, 0.42506048514590444, 0.35812291618890224, ...]

tqsdk.ta.MV(df, n, m)¶

均量线

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 参数n

m (int): 参数m

Returns:

pandas.DataFrame: 返回的DataFrame包含2列, 是"mv1", "mv2", 分别代表均量线1和均量线2

Example:

# 获取 CFFEX.IF1903 合约的均量线
from tqsdk import TqApi, TqSim
from tqsdk.ta import MV

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
mv = MV(klines, 10, 20)
print(list(mv["mv1"]))
print(list(mv["mv2"]))


# 预计的输出是这样的:
[..., 69851.39419881169, 72453.75477893051, 75423.57930103746, ...]
[..., 49044.75870654942, 51386.27077122195, 53924.557232660845, ...]

tqsdk.ta.WAD(df, n, m)¶

威廉多空力度线

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 参数n

m (int): 参数m

Returns:

pandas.DataFrame: 返回的DataFrame包含3列, 是"a", "b", "e", 分别代表A/D值,A/D值n周期的以1为权重的移动平均, A/D值m周期的以1为权重的移动平均

Example:

# 获取 CFFEX.IF1903 合约的威廉多空力度线
from tqsdk import TqApi, TqSim
from tqsdk.ta import WAD

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
wad = WAD(klines, 10, 30)
print(list(wad["a"]))
print(list(wad["b"]))
print(list(wad["e"]))


# 预计的输出是这样的:
[..., 90.0, 134.79999999999973, 270.4000000000001, ...]
[..., 344.4265821851701, 323.46392396665306, 318.1575315699878, ...]
[..., 498.75825781872277, 486.626315891432, 479.41877202838424, ...]

tqsdk.ta.AD(df)¶

累积/派发指标 Accumulation/Distribution

Args:: df (pandas.DataFrame): Dataframe格式的K线序列
Returns:: pandas.DataFrame: 返回的DataFrame包含1列, 是"ad", 代表计算出来的累积/派发指标

Example:

# 获取 CFFEX.IF1903 合约的累积/派发指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import AD

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
ad = AD(klines)
print(list(ad["ad"]))


# 预计的输出是这样的:
[..., 146240.57181105542, 132822.950945916, 49768.15024044845, ...]

tqsdk.ta.CCL(df)¶

持仓异动

Args:: df (pandas.DataFrame): Dataframe格式的K线序列
Returns:: pandas.DataFrame: 返回的DataFrame包含1列, 是"ccl", 代表计算出来的持仓异动指标

Example:

# 获取 CFFEX.IF1903 合约的持仓异动指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import CCL

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
ccl = CCL(klines)
print(list(ccl["ccl"]))


# 预计的输出是这样的:
[..., '多头增仓', '多头减仓', '空头增仓', ...]

tqsdk.ta.CJL(df)¶

成交量

Args:: df (pandas.DataFrame): Dataframe格式的K线序列
Returns:: pandas.DataFrame: 返回的DataFrame包含2列, 是"vol", "opid", 分别代表成交量和持仓量

Example:

# 获取 CFFEX.IF1903 合约的成交量
from tqsdk import TqApi, TqSim
from tqsdk.ta import CJL

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
ndf = CJL(klines)
print(list(ndf["vol"]))
print(list(ndf["opid"]))


# 预计的输出是这样的:
[..., 93142, 95875, 102152, ...]
[..., 69213, 66414, 68379, ...]

tqsdk.ta.OPI(df)¶

持仓量

Args:: df (pandas.DataFrame): Dataframe格式的K线序列
Returns:: pandas.DataFrame: 返回的DataFrame包含1列, 是"opi", 代表持仓量

Example:

# 获取 CFFEX.IF1903 合约的持仓量
from tqsdk import TqApi, TqSim
from tqsdk.ta import OPI

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
opi = OPI(klines)
print(list(opi["opi"]))


# 预计的输出是这样的:
[..., 69213, 66414, 68379, ...]

tqsdk.ta.PVT(df)¶

价量趋势指数

Args:: df (pandas.DataFrame): Dataframe格式的K线序列
Returns:: pandas.DataFrame: 返回的DataFrame包含1列, 是"pvt", 代表计算出来的价量趋势指数

Example:

# 获取 CFFEX.IF1903 合约的价量趋势指数
from tqsdk import TqApi, TqSim
from tqsdk.ta import PVT

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
pvt = PVT(klines)
print(list(pvt["pvt"]))


# 预计的输出是这样的:
[..., 13834.536889431965, 12892.3866788564, 9255.595248484618, ...]

tqsdk.ta.VOSC(df, short, long)¶

移动平均成交量指标 Volume Oscillator

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

short (int): 短周期

long (int): 长周期

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"vosc", 代表计算出来的移动平均成交量指标

Example:

# 获取 CFFEX.IF1903 合约的移动平均成交量指标
from tqsdk import TqApi, TqSim
from tqsdk.ta import VOSC

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
vosc = VOSC(klines, 12, 26)
print(list(vosc["vosc"]))


# 预计的输出是这样的:
[..., 38.72537848731668, 36.61748077024136, 35.4059127302802, ...]

tqsdk.ta.VROC(df, n)¶

量变动速率

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 参数n

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"vroc", 代表计算出来的量变动速率

Example:

# 获取 CFFEX.IF1903 合约的量变动速率
from tqsdk import TqApi, TqSim
from tqsdk.ta import VROC

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
vroc = VROC(klines, 12)
print(list(vroc["vroc"]))


# 预计的输出是这样的:
[..., 41.69905854184833, 74.03274443327598, 3.549394666873177, ...]

tqsdk.ta.VRSI(df, n)¶

量相对强弱

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 参数n

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"vrsi", 代表计算出来的量相对强弱指标

Example:

# 获取 CFFEX.IF1903 合约的量相对强弱
from tqsdk import TqApi, TqSim
from tqsdk.ta import VRSI

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
vrsi = VRSI(klines, 6)
print(list(vrsi["vrsi"]))


# 预计的输出是这样的:
[..., 59.46573277427041, 63.3447660581749, 45.21081537920358, ...]

tqsdk.ta.WVAD(df)¶

威廉变异离散量

Args:: df (pandas.DataFrame): Dataframe格式的K线序列
Returns:: pandas.DataFrame: 返回的DataFrame包含1列, 是"wvad", 代表计算出来的威廉变异离散量

Example:

# 获取 CFFEX.IF1903 合约的威廉变异离散量
from tqsdk import TqApi, TqSim
from tqsdk.ta import WVAD

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
wvad = WVAD(klines)
print(list(wvad["wvad"]))


# 预计的输出是这样的:
[..., -32690.203562340674, -42157.968253968385, 32048.182305630264, ...]

tqsdk.ta.MA(df, n)¶

简单移动平均线

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 简单移动平均线的周期

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"ma", 代表计算出来的简单移动平均线

Example:

# 获取 CFFEX.IF1903 合约的简单移动平均线
from tqsdk import TqApi, TqSim
from tqsdk.ta import MA

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
ma = MA(klines, 30)
print(list(ma["ma"]))


# 预计的输出是这样的:
[..., 3436.300000000001, 3452.8733333333344, 3470.5066666666676, ...]

tqsdk.ta.SMA(df, n, m)¶

扩展指数加权移动平均

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 扩展指数加权移动平均的周期

m (int): 扩展指数加权移动平均的权重

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"sma", 代表计算出来的扩展指数加权移动平均线

Example:

# 获取 CFFEX.IF1903 合约的扩展指数加权移动平均线
from tqsdk import TqApi, TqSim
from tqsdk.ta import SMA

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
sma = SMA(klines, 5, 2)
print(list(sma["sma"]))


# 预计的输出是这样的:
[..., 3803.9478653510914, 3751.648719210655, 3739.389231526393, ...]

tqsdk.ta.EMA(df, n)¶

指数加权移动平均线

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 指数加权移动平均线的周期

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"ema", 代表计算出来的指数加权移动平均线

Example:

# 获取 CFFEX.IF1903 合约的指数加权移动平均线
from tqsdk import TqApi, TqSim
from tqsdk.ta import EMA

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
ema = EMA(klines, 10)
print(list(ema["ema"]))


# 预计的输出是这样的:
[..., 3723.1470497119317, 3714.065767946126, 3715.3265374104667, ...]

tqsdk.ta.EMA2(df, n)¶

线性加权移动平均 WMA

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 线性加权移动平均的周期

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"ema2", 代表计算出来的线性加权移动平均线

Example:

# 获取 CFFEX.IF1903 合约的线性加权移动平均线
from tqsdk import TqApi, TqSim
from tqsdk.ta import EMA2

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
ema2 = EMA2(klines, 10)
print(list(ema2["ema2"]))


# 预计的输出是这样的:
[..., 3775.832727272727, 3763.334545454546, 3757.101818181818, ...]

tqsdk.ta.TRMA(df, n)¶

三角移动平均线

Args:

df (pandas.DataFrame): Dataframe格式的K线序列

n (int): 三角移动平均线的周期

Returns:

pandas.DataFrame: 返回的DataFrame包含1列, 是"trma", 代表计算出来的三角移动平均线

Example:

# 获取 CFFEX.IF1903 合约的三角移动平均线
from tqsdk import TqApi, TqSim
from tqsdk.ta import TRMA

api = TqApi(TqSim())
klines = api.get_kline_serial("CFFEX.IF1903", 24 * 60 * 60)
trma = TRMA(klines, 10)
print(list(trma["trma"]))

# 预计的输出是这样的:
[..., 341.366666666669, 3759.160000000002, 3767.7533333333354, ...]