def bbands(close,
length=None,
std=None,
mamode=None,
ddof=0,
offset=None,
**kwargs):
"""Indicator: Bollinger Bands (BBANDS)"""
# Validate arguments
length = int(length) if length and length > 0 else 5
std = float(std) if std and std > 0 else 2.0
mamode = mamode if isinstance(mamode, str) else "sma"
ddof = int(ddof) if ddof >= 0 and ddof < length else 1
close = verify_series(close, length)
offset = get_offset(offset)
if close is None: return
# Calculate Result
if Imports["talib"]:
from talib import BBANDS
upper, mid, lower = BBANDS(close, length)
else:
standard_deviation = stdev(close=close, length=length, ddof=ddof)
deviations = std * standard_deviation
# deviations = std * standard_deviation.loc[standard_deviation.first_valid_index():,]
mid = ma(mamode, close, length=length, **kwargs)
lower = mid - deviations
upper = mid + deviations
ulr = non_zero_range(upper, lower)
bandwidth = 100 * ulr / mid
percent = non_zero_range(close, lower) / ulr
# Offset
if offset != 0:
lower = lower.shift(offset)
mid = mid.shift(offset)
upper = upper.shift(offset)
bandwidth = bandwidth.shift(offset)
percent = bandwidth.shift(offset)
# Handle fills
if "fillna" in kwargs:
lower.fillna(kwargs["fillna"], inplace=True)
mid.fillna(kwargs["fillna"], inplace=True)
upper.fillna(kwargs["fillna"], inplace=True)
bandwidth.fillna(kwargs["fillna"], inplace=True)
percent.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
lower.fillna(method=kwargs["fill_method"], inplace=True)
mid.fillna(method=kwargs["fill_method"], inplace=True)
upper.fillna(method=kwargs["fill_method"], inplace=True)
bandwidth.fillna(method=kwargs["fill_method"], inplace=True)
percent.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
lower.name = f"BBL_{length}_{std}"
mid.name = f"BBM_{length}_{std}"
upper.name = f"BBU_{length}_{std}"
bandwidth.name = f"BBB_{length}_{std}"
percent.name = f"BBP_{length}_{std}"
upper.category = lower.category = "volatility"
mid.category = bandwidth.category = upper.category
# Prepare DataFrame to return
data = {
lower.name: lower,
mid.name: mid,
upper.name: upper,
bandwidth.name: bandwidth,
percent.name: percent
}
bbandsdf = DataFrame(data)
bbandsdf.name = f"BBANDS_{length}_{std}"
bbandsdf.category = mid.category
return bbandsdf
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