tqsdk.ta - 技术指标计算函数¶
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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
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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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', ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]
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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, ...]