def HCL(df, n):
"""均线通道"""
new_df = pd.DataFrame()
new_df["mah"] = ta_func.ma(df["high"], n)
new_df["mal"] = ta_func.ma(df["low"], n)
new_df["mac"] = ta_func.ma(df["close"], n)
return new_df
def DMA(df, short, long, m):
"""平均线差"""
new_df = pd.DataFrame()
new_df["ddd"] = ta_func.ma(df["close"], short) - ta_func.ma(
df["close"], long)
new_df["ama"] = ta_func.ma(new_df["ddd"], m)
return new_df
def PRICEOSC(df, long, short):
"""价格震荡指数 Price Oscillator"""
ma_s = ta_func.ma(df["close"], short)
ma_l = ta_func.ma(df["close"], long)
new_df = pd.DataFrame(data=list((ma_s - ma_l) / ma_s * 100),
columns=["priceosc"])
return new_df
def MICD(df, n, n1, n2):
"""异同离差动力指数"""
new_df = pd.DataFrame()
mi = df["close"] - df["close"].shift(1)
ami = ta_func.sma(mi, n, 1)
new_df["dif"] = ta_func.ma(ami.shift(1), n1) - ta_func.ma(ami.shift(1), n2)
new_df["micd"] = ta_func.sma(new_df["dif"], 10, 1)
return new_df
def RCCD(df, n, n1, n2):
"""异同离差变化率指数"""
new_df = pd.DataFrame()
rc = df["close"] / df["close"].shift(n)
arc = ta_func.sma(rc.shift(1), n, 1)
new_df["dif"] = ta_func.ma(arc.shift(1), n1) - ta_func.ma(arc.shift(1), n2)
new_df["rccd"] = ta_func.sma(new_df["dif"], n, 1)
return new_df
def BBIBOLL(df, n, m):
"""多空布林线"""
new_df = pd.DataFrame()
new_df["bbiboll"] = (
ta_func.ma(df["close"], 3) + ta_func.ma(df["close"], 6) +
ta_func.ma(df["close"], 12) + ta_func.ma(df["close"], 24)) / 4
new_df["upr"] = new_df["bbiboll"] + m * new_df["bbiboll"].rolling(n).std()
new_df["dwn"] = new_df["bbiboll"] - m * new_df["bbiboll"].rolling(n).std()
return new_df
def DBCD(df, n, m, t):
"""异同离差乖离率"""
new_df = pd.DataFrame()
bias = (df["close"] - ta_func.ma(df["close"], n)) / ta_func.ma(
df["close"], n)
dif = bias - bias.shift(m)
new_df["dbcd"] = ta_func.sma(dif, t, 1)
new_df["mm"] = ta_func.ma(new_df["dbcd"], 5)
return new_df
def ZDZB(df, n1, n2, n3):
"""筑底指标"""
new_df = pd.DataFrame()
a = pd.Series(np.where(df["close"] >= df["close"].shift(1), 1,
0)).rolling(n1).sum() / pd.Series(
np.where(df["close"] < df["close"].shift(1), 1,
0)).rolling(n1).sum()
new_df["b"] = ta_func.ma(a, n2)
new_df["d"] = ta_func.ma(a, n3)
return new_df
def CDP(df, n):
"""逆势操作"""
new_df = pd.DataFrame()
pt = df["high"].shift(1) - df["low"].shift(1)
cdp = (df["high"].shift(1) + df["low"].shift(1) + df["close"].shift(1)) / 3
new_df["ah"] = ta_func.ma(cdp + pt, n)
new_df["al"] = ta_func.ma(cdp - pt, n)
new_df["nh"] = ta_func.ma(2 * cdp - df["low"], n)
new_df["nl"] = ta_func.ma(2 * cdp - df["high"], n)
return new_df
def ROC(df, n, m):
"""变动速率"""
new_df = pd.DataFrame()
new_df["roc"] = (df["close"] -
df['close'].shift(n)) / df["close"].shift(n) * 100
new_df["rocma"] = ta_func.ma(new_df["roc"], m)
return new_df
def PSY(df, n, m):
"""心理线"""
new_df = pd.DataFrame()
new_df["psy"] = ta_func.count(df["close"] > df["close"].shift(1),
n) / n * 100
new_df["psyma"] = ta_func.ma(new_df["psy"], m)
return new_df
def BOLL(df, n, p):
"""布林线"""
new_df = pd.DataFrame()
mid = ta_func.ma(df["close"], n)
std = df["close"].rolling(n).std()
new_df["mid"] = mid
new_df["top"] = mid + p * std
new_df["bottom"] = mid - p * std
return new_df
def CR(df, n, m):
"""CR能量"""
new_df = pd.DataFrame()
mid = (df["high"] + df["low"] + df["close"]) / 3
new_df["cr"] = pd.Series(
np.where(0 > df["high"] - mid.shift(1), 0,
df["high"] - mid.shift(1))).rolling(n).sum() / pd.Series(
np.where(0 > mid.shift(1) - df["low"], 0,
mid.shift(1) - df["low"])).rolling(n).sum() * 100
new_df["crma"] = ta_func.ma(new_df["cr"], m).shift(int(m / 2.5 + 1))
return new_df
def CCI(df, n):
"""顺势指标"""
typ = (df["high"] + df["low"] + df["close"]) / 3
ma = ta_func.ma(typ, n)
def mad(x):
return np.fabs(x - x.mean()).mean()
md = typ.rolling(window=n).apply(mad, raw=True) # 平均绝对偏差
new_df = pd.DataFrame(data=list((typ - ma) / (md * 0.015)),
columns=["cci"])
return new_df
def DMI(df, n, m):
"""动向指标"""
new_df = pd.DataFrame()
new_df["atr"] = ATR(df, n)["atr"]
pre_high = df["high"].shift(1)
pre_low = df["low"].shift(1)
hd = df["high"] - pre_high
ld = pre_low - df["low"]
admp = ta_func.ma(pd.Series(np.where((hd > 0) & (hd > ld), hd, 0)), n)
admm = ta_func.ma(pd.Series(np.where((ld > 0) & (ld > hd), ld, 0)), n)
new_df["pdi"] = pd.Series(
np.where(new_df["atr"] > 0, admp / new_df["atr"] * 100,
np.NaN)).ffill()
new_df["mdi"] = pd.Series(
np.where(new_df["atr"] > 0, admm / new_df["atr"] * 100,
np.NaN)).ffill()
ad = pd.Series(
np.absolute(new_df["mdi"] - new_df["pdi"]) /
(new_df["mdi"] + new_df["pdi"]) * 100)
new_df["adx"] = ta_func.ma(ad, m)
new_df["adxr"] = (new_df["adx"] + new_df["adx"].shift(m)) / 2
return new_df
def DKX(df, m):
"""多空线"""
new_df = pd.DataFrame()
a = (3 * df["close"] + df["high"] + df["low"] + df["open"]) / 6
new_df["b"] = (20 * a + 19 * a.shift(1) + 18 * a.shift(2) +
17 * a.shift(3) + 16 * a.shift(4) + 15 * a.shift(5) +
14 * a.shift(6) + 13 * a.shift(7) + 12 * a.shift(8) +
11 * a.shift(9) + 10 * a.shift(10) + 9 * a.shift(11) +
8 * a.shift(12) + 7 * a.shift(13) + 6 * a.shift(14) +
5 * a.shift(15) + 4 * a.shift(16) + 3 * a.shift(17) +
2 * a.shift(18) + a.shift(20)) / 210
new_df["d"] = ta_func.ma(new_df["b"], m)
return new_df
def SRDM(df, n):
"""动向速度比率"""
new_df = pd.DataFrame()
dmz = np.where((df["high"] + df["low"]) <=
(df["high"].shift(1) + df["low"].shift(1)), 0,
np.where(
np.absolute(df["high"] - df["high"].shift(1)) >
np.absolute(df["low"] - df["low"].shift(1)),
np.absolute(df["high"] - df["high"].shift(1)),
np.absolute(df["low"] - df["low"].shift(1))))
dmf = np.where((df["high"] + df["low"]) >=
(df["high"].shift(1) + df["low"].shift(1)), 0,
np.where(
np.absolute(df["high"] - df["high"].shift(1)) >
np.absolute(df["low"] - df["low"].shift(1)),
np.absolute(df["high"] - df["high"].shift(1)),
np.absolute(df["low"] - df["low"].shift(1))))
admz = ta_func.ma(pd.Series(dmz), 10)
admf = ta_func.ma(pd.Series(dmf), 10)
new_df["srdm"] = np.where(admz > admf, (admz - admf) / admz,
np.where(admz == admf, 0, (admz - admf) / admf))
new_df["asrdm"] = ta_func.sma(new_df["srdm"], n, 1)
return new_df
def ATR(df, n):
"""平均真实波幅"""
new_df = pd.DataFrame()
pre_close = df["close"].shift(1)
new_df["tr"] = np.where(
df["high"] - df["low"] > np.absolute(pre_close - df["high"]),
np.where(df["high"] - df["low"] > np.absolute(pre_close - df["low"]),
df["high"] - df["low"], np.absolute(pre_close - df["low"])),
np.where(
np.absolute(pre_close - df["high"]) >
np.absolute(pre_close - df["low"]),
np.absolute(pre_close - df["high"]),
np.absolute(pre_close - df["low"])))
new_df["atr"] = ta_func.ma(new_df["tr"], n)
return new_df
def SHORT(df):
"""短线指标"""
new_df = pd.DataFrame()
tb = np.where(df["high"] > df["close"].shift(1),
df["high"] - df["close"].shift(1) + df["close"] - df["low"],
df["close"] - df["low"])
ts = np.where(df["close"].shift(1) > df["low"],
df["close"].shift(1) - df["low"] + df["high"] - df["close"],
df["high"] - df["close"])
vol1 = (tb - ts) * df["volume"] / (tb + ts) / 10000
vol10 = vol1.ewm(alpha=0.1, adjust=False).mean() # DMA 动态均值
vol11 = vol1.ewm(alpha=0.05, adjust=False).mean() # DMA
new_df["short"] = vol10 - vol11
new_df["ma1"] = ta_func.ma(new_df["short"], 10)
return new_df
def ADTM(df, n, m):
"""动态买卖气指标"""
new_df = pd.DataFrame()
dtm = np.where(
df["open"] < df["open"].shift(1), 0,
np.where(df["high"] - df["open"] > df["open"] - df["open"].shift(1),
df["high"] - df["open"], df["open"] - df["open"].shift(1)))
dbm = np.where(
df["open"] >= df["open"].shift(1), 0,
np.where(df["open"] - df["low"] > df["open"] - df["open"].shift(1),
df["open"] - df["low"], df["open"] - df["open"].shift(1)))
stm = pd.Series(dtm).rolling(n).sum()
sbm = pd.Series(dbm).rolling(n).sum()
new_df["adtm"] = np.where(stm > sbm, (stm - sbm) / stm,
np.where(stm == sbm, 0, (stm - sbm) / sbm))
new_df["adtmma"] = ta_func.ma(new_df["adtm"], m)
return new_df
def DDI(df, n, n1, m, m1):
"""方向标准离差指数"""
new_df = pd.DataFrame()
tr = np.where(
np.absolute(df["high"] - df["high"].shift(1)) >
np.absolute(df["low"] - df["low"].shift(1)),
np.absolute(df["high"] - df["high"].shift(1)),
np.absolute(df["low"] - df["low"].shift(1)))
dmz = np.where((df["high"] + df["low"]) <=
(df["high"].shift(1) + df["low"].shift(1)), 0, tr)
dmf = np.where((df["high"] + df["low"]) >=
(df["high"].shift(1) + df["low"].shift(1)), 0, tr)
diz = pd.Series(dmz).rolling(n).sum() / (pd.Series(dmz).rolling(n).sum() +
pd.Series(dmf).rolling(n).sum())
dif = pd.Series(dmf).rolling(n).sum() / (pd.Series(dmf).rolling(n).sum() +
pd.Series(dmz).rolling(n).sum())
new_df["ddi"] = diz - dif
new_df["addi"] = ta_func.sma(new_df["ddi"], n1, m)
new_df["ad"] = ta_func.ma(new_df["addi"], m1)
return new_df
def MA(df, n):
"""简单移动平均线"""
new_df = pd.DataFrame(data=list(ta_func.ma(df["close"], n)),
columns=["ma"])
return new_df
def VOSC(df, short, long):
"""移动平均成交量指标 Volume Oscillator"""
vosc = (ta_func.ma(df["volume"], short) - ta_func.ma(
df["volume"], long)) / ta_func.ma(df["volume"], short) * 100
new_df = pd.DataFrame(data=list(vosc), columns=["vosc"])
return new_df
def DPO(df, n, m):
"""区间震荡线"""
dpo = df["close"] - (ta_func.ma(df["close"], 20)).shift(11)
new_df = pd.DataFrame(data=list(dpo), columns=["dpo"])
return new_df
def ENV(df, n, k):
"""包略线 (Envelopes)"""
new_df = pd.DataFrame()
new_df["upper"] = ta_func.ma(df["close"], n) * (1 + k / 100)
new_df["lower"] = ta_func.ma(df["close"], n) * (1 - k / 100)
return new_df
def BIAS(df, n):
"""乖离率"""
ma1 = ta_func.ma(df["close"], n)
new_df = pd.DataFrame(data=list((df["close"] - ma1) / ma1 * 100),
columns=["bias"])
return new_df
def PUBU(df, m):
"""瀑布线"""
pb = (ta_func.ema(df["close"], m) + ta_func.ma(df["close"], m * 2) +
ta_func.ma(df["close"], m * 4)) / 3
new_df = pd.DataFrame(data=list(pb), columns=["pb"])
return new_df
def BBI(df, n1, n2, n3, n4):
"""多空指数"""
bbi = (ta_func.ma(df["close"], n1) + ta_func.ma(df["close"], n2) +
ta_func.ma(df["close"], n3) + ta_func.ma(df["close"], n4)) / 4
new_df = pd.DataFrame(data=list(bbi), columns=["bbi"])
return new_df
def B3612(df):
"""三减六日乖离率"""
new_df = pd.DataFrame()
new_df["b36"] = ta_func.ma(df["close"], 3) - ta_func.ma(df["close"], 6)
new_df["b612"] = ta_func.ma(df["close"], 6) - ta_func.ma(df["close"], 12)
return new_df
def MTM(df, n, n1):
"""MTM动力指标"""
new_df = pd.DataFrame()
new_df["mtm"] = df["close"] - df["close"].shift(n)
new_df["mtmma"] = ta_func.ma(new_df["mtm"], n1)
return new_df
