def RSI(df, n):
"""相对强弱指标"""
lc = df["close"].shift(1)
rsi = ta_func.sma(pd.Series(np.where(df["close"] - lc > 0, df["close"] - lc, 0)), n, 1) / \
ta_func.sma(np.absolute(df["close"] - lc), n, 1) * 100
new_df = pd.DataFrame(data=rsi, columns=["rsi"])
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 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 SLOWKD(df, n, m1, m2, m3):
"""慢速KD"""
new_df = pd.DataFrame()
rsv = (df["close"] - df["low"].rolling(n).min()) / \
(df["high"].rolling(n).max() - df["low"].rolling(n).min()) * 100
fastk = ta_func.sma(rsv, m1, 1)
new_df["k"] = ta_func.sma(fastk, m2, 1)
new_df["d"] = ta_func.sma(new_df["k"], m3, 1)
return new_df
def VRSI(df, n):
"""量相对强弱"""
vrsi = ta_func.sma(
pd.Series(
np.where(df["volume"] - df["volume"].shift(1) > 0,
df["volume"] - df["volume"].shift(1), 0)), n,
1) / ta_func.sma(np.absolute(df["volume"] - df["volume"].shift(1)), n,
1) * 100
new_df = pd.DataFrame(data=list(vrsi), columns=["vrsi"])
return new_df
def KD(df, n, m1, m2):
"""随机指标"""
new_df = pd.DataFrame()
hv = df["high"].rolling(n).max()
lv = df["low"].rolling(n).min()
rsv = pd.Series(np.where(hv == lv, 0,
(df["close"] - lv) / (hv - lv) * 100))
new_df["k"] = ta_func.sma(rsv, m1, 1)
new_df["d"] = ta_func.sma(new_df["k"], m2, 1)
return new_df
def WAD(df, n, m):
"""威廉多空力度线"""
new_df = pd.DataFrame()
new_df["a"] = np.absolute(
np.where(
df["close"] > df["close"].shift(1),
df["close"] - np.where(df["close"].shift(1) < df["low"],
df["close"].shift(1), df["low"]),
np.where(
df["close"] < df["close"].shift(1),
df["close"] - np.where(df["close"].shift(1) > df["high"],
df["close"].shift(1), df["high"]),
0)).cumsum())
new_df["b"] = ta_func.sma(new_df["a"], n, 1)
new_df["e"] = ta_func.sma(new_df["a"], m, 1)
return new_df
def LWR(df, n, m):
"""威廉指标"""
hv = df["high"].rolling(n).max()
lv = df["low"].rolling(n).min()
rsv = pd.Series(np.where(hv == lv, 0,
(df["close"] - hv) / (hv - lv) * 100))
new_df = pd.DataFrame(data=list(ta_func.sma(rsv, m, 1)), columns=["lwr"])
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 SRMI(df, n):
"""MI修正指标"""
new_df = pd.DataFrame()
new_df["a"] = np.where(
df["close"] < df["close"].shift(n),
(df["close"] - df["close"].shift(n)) / df["close"].shift(n),
np.where(df["close"] == df["close"].shift(n), 0,
(df["close"] - df["close"].shift(n)) / df["close"]))
new_df["mi"] = ta_func.sma(new_df["a"], n, 1)
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 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 SMA(df, n, m):
"""扩展指数加权移动平均"""
new_df = pd.DataFrame(data=list(ta_func.sma(df["close"], n, m)),
columns=["sma"])
return new_df
def MV(df, n, m):
"""均量线"""
new_df = pd.DataFrame()
new_df["mv1"] = ta_func.sma(df["volume"], n, 1)
new_df["mv2"] = ta_func.sma(df["volume"], m, 1)
return new_df
def RC(df, n):
"""变化率指数"""
rc = df["close"] / df["close"].shift(n)
new_df = pd.DataFrame(data=list(ta_func.sma(rc.shift(1), n, 1)),
columns=["arc"])
return new_df
def MI(df, n):
"""动量指标"""
new_df = pd.DataFrame()
new_df["a"] = df["close"] - df["close"].shift(n)
new_df["mi"] = ta_func.sma(new_df["a"], n, 1)
return new_df
