def macd(close, fast=None, slow=None, signal=None, offset=None, **kwargs):
"""Indicator: Moving Average, Convergence/Divergence (MACD)"""
# Validate arguments
fast = int(fast) if fast and fast > 0 else 12
slow = int(slow) if slow and slow > 0 else 26
signal = int(signal) if signal and signal > 0 else 9
if slow < fast:
fast, slow = slow, fast
close = verify_series(close, max(fast, slow, signal))
offset = get_offset(offset)
if close is None: return
# Calculate Result
if Imports["talib"]:
from talib import MACD
macd, signalma, histogram = MACD(close, fast, slow)
else:
fastma = ema(close, length=fast)
slowma = ema(close, length=slow)
macd = fastma - slowma
signalma = ema(close=macd.loc[macd.first_valid_index():, ],
length=signal)
histogram = macd - signalma
# Offset
if offset != 0:
macd = macd.shift(offset)
histogram = histogram.shift(offset)
signalma = signalma.shift(offset)
# Handle fills
if "fillna" in kwargs:
macd.fillna(kwargs["fillna"], inplace=True)
histogram.fillna(kwargs["fillna"], inplace=True)
signalma.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
macd.fillna(method=kwargs["fill_method"], inplace=True)
histogram.fillna(method=kwargs["fill_method"], inplace=True)
signalma.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"_{fast}_{slow}_{signal}"
macd.name = f"MACD{_props}"
histogram.name = f"MACDh{_props}"
signalma.name = f"MACDs{_props}"
macd.category = histogram.category = signalma.category = "momentum"
# Prepare DataFrame to return
data = {
macd.name: macd,
histogram.name: histogram,
signalma.name: signalma
}
df = DataFrame(data)
df.name = f"MACD{_props}"
df.category = macd.category
signal_indicators = kwargs.pop("signal_indicators", False)
if signal_indicators:
signalsdf = concat(
[
df,
signals(
indicator=histogram,
xa=kwargs.pop("xa", 0),
xb=kwargs.pop("xb", None),
xserie=kwargs.pop("xserie", None),
xserie_a=kwargs.pop("xserie_a", None),
xserie_b=kwargs.pop("xserie_b", None),
cross_values=kwargs.pop("cross_values", True),
cross_series=kwargs.pop("cross_series", True),
offset=offset,
),
signals(
indicator=macd,
xa=kwargs.pop("xa", 0),
xb=kwargs.pop("xb", None),
xserie=kwargs.pop("xserie", None),
xserie_a=kwargs.pop("xserie_a", None),
xserie_b=kwargs.pop("xserie_b", None),
cross_values=kwargs.pop("cross_values", False),
cross_series=kwargs.pop("cross_series", True),
offset=offset,
),
],
axis=1,
)
return signalsdf
else:
return df
def priceTechnicalIndicator(value, tot_lags, prefix):
lags = np.array(tot_lags)
lags = lags[lags >= 2]
data_result = pd.DataFrame([])
# MA series
for lag in lags:
# SMA
sma = pd.Series(SMA(value, timeperiod=lag), name='%sMA_%dD' % (prefix, lag))
data_result = pd.concat([data_result, sma], axis=1)
# SMA derivatives
tmp = ma_derivative_indicator(value, sma, 'MA', lag, prefix)
data_result = data_result.join(tmp)
# EMA
ema = pd.Series(EMA(value, lag), name='%sEMA_%dD' % (prefix, lag))
data_result = data_result.join(ema)
# EMA derivatives
tmp = ma_derivative_indicator(value, ema, 'EMA', lag, prefix)
data_result = data_result.join(tmp)
# WMA
wma = pd.Series(WMA(value, lag), name='%sWMA_%dD' % (prefix, lag))
data_result = data_result.join(wma)
# WMA derivatives
tmp = ma_derivative_indicator(value, wma, 'WMA', lag, prefix)
data_result = data_result.join(tmp)
# change percent
lags = tot_lags
for lag in lags:
tmp = pd.Series(value.diff(lag) / value.shift(lag), name='%sRET_%dD' % (prefix, lag))
data_result = data_result.join(tmp)
# volatility
lags = np.array(tot_lags)
lags = lags[lags >= 5]
for lag in lags:
tmp = pd.Series(value.rolling(window=lag).std(), name='%sSTD_%dD' % (prefix, lag))
data_result = data_result.join(tmp)
# technical indicators
lags = [7, 14, 21, 28] # ****** 待修改，技术指标的参数只用最常用的一套
for lag in lags:
# bollinger brands
tmp_upper, tmp_middle, tmp_lower = BBANDS(value, lag, 2, 2)
tmp_upper.name = '%sBBANDS_UPPER_%dD' % (prefix, lag)
tmp_lower.name = '%sBBANDS_LOWER_%dD' % (prefix, lag)
data_result = data_result.join(tmp_upper)
data_result = data_result.join(tmp_lower)
# MACD
tmp, tmp_signal, tmp_hist = MACD(value, 12, 26, lag)
tmp.name = '%sMACD_DIF_12_26D' % prefix # macd 对应 macd_dif 线
tmp_signal.name = '%sMACD_DEA_12_26_%dD' % (prefix, lag) # macd_signal 对应 macd_dea 线
tmp_hist = 2 * tmp_hist
tmp_hist.name = '%sMACD_12_26_%dD' % (prefix, lag) # 2 * macd_hist 对应 macd 线
if tmp.name not in data_result.columns: # macd_dif is the same for all lags
data_result = data_result.join(tmp)
data_result = data_result.join(tmp_signal)
data_result = data_result.join(tmp_hist)
return data_result