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stockstats.py
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stockstats.py
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# coding=utf-8
# Copyright (c) 2016, Cedric Zhuang
# All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of disclaimer nor the names of its contributors may
# be used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS "AS IS" AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE REGENTS AND CONTRIBUTORS BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from __future__ import unicode_literals
import itertools
import re
import numpy as np
import pandas as pd
__author__ = 'Cedric Zhuang'
from numpy.lib.stride_tricks import as_strided
def wrap(df, index_column=None):
""" wraps a pandas DataFrame to StockDataFrame
:param df: pandas DataFrame
:param index_column: the name of the index column, default to ``date``
:return: an object of StockDataFrame
"""
return StockDataFrame.retype(df, index_column)
def unwrap(sdf):
""" convert a StockDataFrame back to a pandas DataFrame """
return pd.DataFrame(sdf)
class StockDataFrame(pd.DataFrame):
# Start of options.
KDJ_PARAM = (2.0 / 3.0, 1.0 / 3.0)
KDJ_WINDOW = 9
BOLL_PERIOD = 20
BOLL_STD_TIMES = 2
MACD_EMA_SHORT = 12
MACD_EMA_LONG = 26
MACD_EMA_SIGNAL = 9
PPO_EMA_SHORT = 12
PPO_EMA_LONG = 26
PPO_EMA_SIGNAL = 9
PDI_SMMA = 14
MDI_SMMA = 14
DX_SMMA = 14
ADX_EMA = 6
ADXR_EMA = 6
CR_MA1 = 5
CR_MA2 = 10
CR_MA3 = 20
TRIX_EMA_WINDOW = 12
TEMA_EMA_WINDOW = 5
ATR_SMMA = 14
SUPERTREND_MUL = 3
SUPERTREND_WINDOW = 14
VWMA = 14
CHOP = 14
MFI = 14
CCI = 14
RSI = 14
VR = 26
WR = 14
CMO = 14
WAVE_TREND_1 = 10
WAVE_TREND_2 = 21
KAMA_SLOW = 34
KAMA_FAST = 5
AO_SLOW = 34
AO_FAST = 5
COPPOCK = (10, 11, 14)
ICHIMOKU = (9, 26, 52)
CTI = 12
MULTI_SPLIT_INDICATORS = ("kama",)
# End of options
@staticmethod
def _change(series, window):
return series.pct_change(periods=-window).fillna(0.0) * 100
def _get_change(self):
""" Get the percentage change column
:return: result series
"""
self['change'] = self._change(self['close'], -1)
def _get_p(self, column, shifts):
""" get the permutation of specified range
example:
index x x_-2,-1_p
0 1 NaN
1 -1 NaN
2 3 2 (0.x > 0, and assigned to weight 2)
3 5 1 (2.x > 0, and assigned to weight 1)
4 1 3
:param column: the column to calculate p from
:param shifts: the range to consider
:return:
"""
column_name = '{}_{}_p'.format(column, shifts)
# initialize the column if not
self.get(column)
shifts = self.to_ints(shifts)[::-1]
indices = None
count = 0
for shift in shifts:
shifted = self.shift(-shift)
index = (shifted[column] > 0) * (2 ** count)
if indices is None:
indices = index
else:
indices += index
count += 1
if indices is not None:
cp = indices.copy()
self.set_nan(cp, shifts)
self[column_name] = cp
@classmethod
def to_ints(cls, shifts):
items = map(cls._process_shifts_segment, shifts.split(','))
return sorted(list(set(itertools.chain(*items))))
@classmethod
def to_int(cls, shifts):
numbers = cls.to_ints(shifts)
if len(numbers) != 1:
raise IndexError("only accept 1 number.")
return numbers[0]
@staticmethod
def _process_shifts_segment(shift_segment):
if '~' in shift_segment:
start, end = shift_segment.split('~')
shifts = range(int(start), int(end) + 1)
else:
shifts = [int(shift_segment)]
return shifts
@staticmethod
def set_nan(pd_obj, shift):
try:
iter(shift)
max_shift = max(shift)
min_shift = min(shift)
StockDataFrame._set_nan_of_single_shift(pd_obj, max_shift)
StockDataFrame._set_nan_of_single_shift(pd_obj, min_shift)
except TypeError:
# shift is not iterable
StockDataFrame._set_nan_of_single_shift(pd_obj, shift)
@staticmethod
def _set_nan_of_single_shift(pd_obj, shift):
val = np.nan
if shift > 0:
pd_obj.iloc[-shift:] = val
elif shift < 0:
pd_obj.iloc[:-shift] = val
def _get_r(self, column, shifts):
""" Get rate of change of column
:param column: column name of the rate to calculate
:param shifts: periods to shift, accept one shift only
:return: None
"""
shift = self.to_int(shifts)
rate_key = '{}_{}_r'.format(column, shift)
self[rate_key] = self._change(self[column], shift)
@staticmethod
def _shift(series, window):
""" Shift the series
When window is negative, shift the past period to current.
Fill the gap with the first data available.
When window is positive, shift the future period to current.
Fill the gap with last data available.
:param series: the series to shift
:param window: number of periods to shift
:return: the shifted series with filled gap
"""
ret = series.shift(-window)
if window < 0:
ret.iloc[:-window] = series.iloc[0]
elif window > 0:
ret.iloc[-window:] = series.iloc[-1]
return ret
def _get_s(self, column, shifts):
""" Get the column shifted by periods
:param column: name of the column to shift
:param shifts: periods to shift, accept one shift only
:return: None
"""
shift = self.to_int(shifts)
shifted_key = "{}_{}_s".format(column, shift)
self[shifted_key] = self._shift(self[column], shift)
def _get_log_ret(self):
close = self['close']
self['log-ret'] = np.log(close / self._shift(close, -1))
def _get_c(self, column, window):
""" get the count of column in range (shifts)
example: change_20_c
:param column: column name
:param window: range to count, only to previous
:return: result series
"""
column_name = '{}_{}_c'.format(column, window)
window = self.get_int_positive(window)
self[column_name] = self._rolling(
self[column], window).apply(np.count_nonzero, raw=True)
return self[column_name]
def _get_fc(self, column, shifts):
""" get the count of column in range of future (shifts)
example: change_20_fc
:param column: column name
:param shifts: range to count, only to future
:return: result series
"""
column_name = '{}_{}_fc'.format(column, shifts)
shift = self.get_int_positive(shifts)
reversed_series = self[column][::-1]
reversed_counts = self._rolling(
reversed_series, shift).apply(np.count_nonzero, raw=True)
counts = reversed_counts[::-1]
self[column_name] = counts
return counts
def _init_shifted_columns(self, column, shifts):
# initialize the column if not
self.get(column)
shifts = self.to_ints(shifts)
shift_column_names = ['{}_{}_s'.format(column, shift) for shift in
shifts]
[self.get(name) for name in shift_column_names]
return shift_column_names
def _get_max(self, column, shifts):
column_name = '{}_{}_max'.format(column, shifts)
shift_column_names = self._init_shifted_columns(column, shifts)
self[column_name] = np.max(self[shift_column_names], axis=1)
def _get_min(self, column, shifts):
column_name = '{}_{}_min'.format(column, shifts)
shift_column_names = self._init_shifted_columns(column, shifts)
self[column_name] = np.min(self[shift_column_names], axis=1)
def _get_rsv(self, window):
""" Calculate the RSV (Raw Stochastic Value) within N periods
This value is essential for calculating KDJs
Current day is included in N
:param window: number of periods
:return: None
"""
window = self.get_int_positive(window)
column_name = 'rsv_{}'.format(window)
low_min = self.mov_min(self['low'], window)
high_max = self.mov_max(self['high'], window)
cv = (self['close'] - low_min) / (high_max - low_min)
self[column_name] = cv.fillna(0.0) * 100
def _get_rsi(self, window=None):
""" Calculate the RSI (Relative Strength Index) within N periods
calculated based on the formula at:
https://en.wikipedia.org/wiki/Relative_strength_index
:param window: number of periods
:return: None
"""
if window is None:
window = self.RSI
column_name = 'rsi'
else:
column_name = 'rsi_{}'.format(window)
window = self.get_int_positive(window)
change = self._delta(self['close'], -1)
close_pm = (change + change.abs()) / 2
close_nm = (-change + change.abs()) / 2
p_ema = self.smma(close_pm, window)
n_ema = self.smma(close_nm, window)
rs_column_name = 'rs_{}'.format(window)
self[rs_column_name] = rs = p_ema / n_ema
self[column_name] = 100 - 100 / (1.0 + rs)
def _get_stochrsi(self, window=None):
""" Calculate the Stochastic RSI
calculated based on the formula at:
https://www.investopedia.com/terms/s/stochrsi.asp
:param window: number of periods
:return: None
"""
if window is None:
window = self.RSI
column_name = 'stochrsi'
else:
column_name = 'stochrsi_{}'.format(window)
window = self.get_int_positive(window)
rsi = self['rsi_{}'.format(window)]
rsi_min = self.mov_min(rsi, window)
rsi_max = self.mov_max(rsi, window)
cv = (rsi - rsi_min) / (rsi_max - rsi_min)
self[column_name] = cv * 100
def _get_wave_trend(self):
""" Calculate LazyBear's Wavetrend
Check the algorithm described below:
https://medium.com/@samuel.mcculloch/lets-take-a-look-at-wavetrend-with-crosses-lazybear-s-indicator-2ece1737f72f
n1: period of EMA on typical price
n2: period of EMA
:return: None
"""
n1 = self.WAVE_TREND_1
n2 = self.WAVE_TREND_2
tp = self._tp()
esa = self.ema(tp, n1)
d = self.ema((tp - esa).abs(), n1)
ci = (tp - esa) / (0.015 * d)
tci = self.ema(ci, n2)
self["wt1"] = tci
self["wt2"] = self.sma(tci, 4)
@staticmethod
def smma(series, window):
return series.ewm(
ignore_na=False,
alpha=1.0 / window,
min_periods=0,
adjust=True).mean()
def _get_smma(self, column, windows):
""" get smoothed moving average.
:param column: the column to calculate
:param windows: range
:return: result series
"""
window = self.get_int_positive(windows)
column_name = '{}_{}_smma'.format(column, window)
self[column_name] = self.smma(self[column], window)
def _get_trix(self, column=None, windows=None):
""" Triple Exponential Average
https://www.investopedia.com/articles/technical/02/092402.asp
:param column: the column to calculate
:param windows: range
:return: result series
"""
column_name = ""
if column is None and windows is None:
column_name = 'trix'
if column is None:
column = 'close'
if windows is None:
windows = self.TRIX_EMA_WINDOW
if column_name == "":
column_name = '{}_{}_trix'.format(column, windows)
window = self.get_int_positive(windows)
single = self.ema(self[column], window)
double = self.ema(single, window)
triple = self.ema(double, window)
prev_triple = self._shift(triple, -1)
triple_change = self._delta(triple, -1)
self[column_name] = triple_change * 100 / prev_triple
def _get_tema(self, column=None, windows=None):
""" Another implementation for triple ema
Check the algorithm described below:
https://www.forextraders.com/forex-education/forex-technical-analysis/triple-exponential-moving-average-the-tema-indicator/
:param column: column to calculate ema
:param windows: window of the calculation
:return: result series
"""
column_name = ""
if column is None and windows is None:
column_name = 'tema'
if column is None:
column = 'close'
if windows is None:
windows = self.TEMA_EMA_WINDOW
if column_name == "":
column_name = '{}_{}_tema'.format(column, windows)
window = self.get_int_positive(windows)
single = self.ema(self[column], window)
double = self.ema(single, window)
triple = self.ema(double, window)
self[column_name] = 3 * single - 3 * double + triple
def _get_wr(self, window=None):
""" Williams Overbought/Oversold Index
Definition: https://www.investopedia.com/terms/w/williamsr.asp
WMS=[(Hn—Ct)/(Hn—Ln)] × -100
Ct - the close price
Hn - N periods high
Ln - N periods low
:param window: number of periods
:return: None
"""
if window is None:
window = self.WR
column_name = 'wr'
else:
column_name = 'wr_{}'.format(window)
window = self.get_int_positive(window)
ln = self.mov_min(self['low'], window)
hn = self.mov_max(self['high'], window)
self[column_name] = (hn - self['close']) / (hn - ln) * -100
def _get_cci(self, window=None):
""" Commodity Channel Index
CCI = (Typical Price - 20-period SMA of TP) / (.015 x Mean Deviation)
* when amount is not available:
Typical Price (TP) = (High + Low + Close)/3
* when amount is available:
Typical Price (TP) = Amount / Volume
TP is also implemented as 'middle'.
:param window: number of periods
:return: None
"""
if window is None:
window = self.CCI
column_name = 'cci'
else:
column_name = 'cci_{}'.format(window)
window = self.get_int_positive(window)
tp = self._tp()
tp_sma = self.sma(tp, window)
mad = self._mad(tp, window)
self[column_name] = (tp - tp_sma) / (.015 * mad)
def _tr(self):
prev_close = self._shift(self['close'], -1)
high = self['high']
low = self['low']
c1 = high - low
c2 = (high - prev_close).abs()
c3 = (low - prev_close).abs()
return pd.concat((c1, c2, c3), axis=1).max(axis=1)
def _get_tr(self):
""" True Range of the trading
TR is a measure of volatility of a High-Low-Close series
tr = max[(high - low), abs(high - close_prev), abs(low - close_prev)]
:return: None
"""
self['tr'] = self._tr()
def _get_supertrend(self, window=None):
""" Supertrend
Supertrend indicator shows trend direction.
It provides buy or sell indicators.
https://medium.com/codex/step-by-step-implementation-of-the-supertrend-indicator-in-python-656aa678c111
:param window: number of periods
:return: None
"""
if window is None:
window = self.SUPERTREND_WINDOW
window = self.get_int_positive(window)
high = self['high']
low = self['low']
close = self['close']
m_atr = self.SUPERTREND_MUL * self._atr(window)
hl_avg = (high + low) / 2.0
# basic upper band
b_ub = list(hl_avg + m_atr)
# basic lower band
b_lb = list(hl_avg - m_atr)
size = len(close)
ub = np.empty(size, dtype=np.float64)
lb = np.empty(size, dtype=np.float64)
st = np.empty(size, dtype=np.float64)
close = list(close)
for i in range(size):
if i == 0:
ub[i] = b_ub[i]
lb[i] = b_lb[i]
if close[i] <= ub[i]:
st[i] = ub[i]
else:
st[i] = lb[i]
continue
last_close = close[i - 1]
curr_close = close[i]
last_ub = ub[i - 1]
last_lb = lb[i - 1]
last_st = st[i - 1]
curr_b_ub = b_ub[i]
curr_b_lb = b_lb[i]
# calculate current upper band
if curr_b_ub < last_ub or last_close > last_ub:
ub[i] = curr_b_ub
else:
ub[i] = last_ub
# calculate current lower band
if curr_b_lb > last_lb or last_close < last_lb:
lb[i] = curr_b_lb
else:
lb[i] = last_lb
# calculate supertrend
if last_st == last_ub:
if curr_close <= ub[i]:
st[i] = ub[i]
else:
st[i] = lb[i]
elif last_st == last_lb:
if curr_close > lb[i]:
st[i] = lb[i]
else:
st[i] = ub[i]
self['supertrend_ub'] = ub
self['supertrend_lb'] = lb
self['supertrend'] = st
def _get_aroon(self, window=None):
""" Aroon Oscillator
The Aroon Oscillator measures the strength of a trend and
the likelihood that it will continue.
The default window is 25.
* Aroon Oscillator = Aroon Up - Aroon Down
* Aroon Up = 100 * (n - periods since n-period high) / n
* Aroon Down = 100 * (n - periods since n-period low) / n
* n = window size
"""
if window is None:
window = 25
column_name = 'aroon'
else:
window = self.get_int_positive(window)
column_name = 'aroon_{}'.format(window)
def _window_pct(s):
n = float(window)
return (n - (n - (s + 1))) / n * 100
high_since = self._rolling(
self['high'], window).apply(np.argmax, raw=True)
low_since = self._rolling(
self['low'], window).apply(np.argmin, raw=True)
aroon_up = _window_pct(high_since)
aroon_down = _window_pct(low_since)
self[column_name] = aroon_up - aroon_down
def _get_z(self, column, window):
""" Z score
Z-score is a statistical measurement that describes a value's
relationship to the mean of a group of values.
The statistical formula for a value's z-score is calculated using
the following formula:
z = ( x - μ ) / σ
Where:
* z = Z-score
* x = the value being evaluated
* μ = the mean
* σ = the standard deviation
"""
window = self.get_int_positive(window)
column_name = '{}_{}_z'.format(column, window)
col = self[column]
mean = self.sma(col, window)
std = self.mov_std(col, window)
self[column_name] = ((col - mean) / std).fillna(0.0)
def _atr(self, window):
tr = self._tr()
return self.smma(tr, window)
def _get_atr(self, window=None):
""" Average True Range
The average true range is an N-day smoothed moving average (SMMA) of
the true range values. Default to 14 periods.
https://en.wikipedia.org/wiki/Average_true_range
:param window: number of periods
:return: None
"""
if window is None:
window = self.ATR_SMMA
column_name = 'atr'
else:
column_name = 'atr_{}'.format(window)
window = self.get_int_positive(window)
self[column_name] = self._atr(window)
def _get_dma(self):
""" Difference of Moving Average
default to 10 and 50.
:return: None
"""
self['dma'] = self['close_10_sma'] - self['close_50_sma']
def _get_dmi(self):
""" get the default setting for DMI
including:
+DI: 14 periods SMMA of +DM,
-DI: 14 periods SMMA of -DM,
DX: based on +DI and -DI
ADX: 6 periods SMMA of DX
:return:
"""
self['pdi'] = self._get_pdi(self.PDI_SMMA)
self['mdi'] = self._get_mdi(self.MDI_SMMA)
self['dx'] = self._get_dx(self.DX_SMMA)
self['adx'] = self.ema(self['dx'], self.ADX_EMA)
self['adxr'] = self.ema(self['adx'], self.ADXR_EMA)
def _get_um_dm(self):
""" Up move and down move
initialize up move and down move
"""
hd = self._col_delta('high')
self['um'] = (hd > 0) * hd
ld = -self._col_delta('low')
self['dm'] = (ld > 0) * ld
def _get_pdm_ndm(self, window):
hd = self._col_delta('high')
ld = -self._col_delta('low')
p = ((hd > 0) & (hd > ld)) * hd
n = ((ld > 0) & (ld > hd)) * ld
if window > 1:
p = self.smma(p, window)
n = self.smma(n, window)
return p, n
def _pdm(self, window):
ret, _ = self._get_pdm_ndm(window)
return ret
def _ndm(self, window):
_, ret = self._get_pdm_ndm(window)
return ret
def _get_pdm(self, windows):
""" +DM, positive directional moving
If window is not 1, calculate the SMMA of +DM
:param windows: range
:return:
"""
window = self.get_int_positive(windows)
if window > 1:
column_name = 'pdm_{}'.format(window)
else:
column_name = 'pdm'
self[column_name] = self._pdm(window)
def _get_ndm(self, windows):
""" -DM, negative directional moving accumulation
If window is not 1, return the SMA of -DM.
:param windows: range
:return:
"""
window = self.get_int_positive(windows)
if window > 1:
column_name = 'ndm_{}'.format(window)
else:
column_name = 'ndm'
self[column_name] = self._ndm(window)
def _get_vr(self, windows=None):
if windows is None:
window = self.VR
column_name = 'vr'
else:
window = self.get_int_positive(windows)
column_name = 'vr_{}'.format(window)
idx = self.index
gt_zero = np.where(self['change'] > 0, self['volume'], 0)
av = pd.Series(gt_zero, index=idx)
avs = self.mov_sum(av, window)
lt_zero = np.where(self['change'] < 0, self['volume'], 0)
bv = pd.Series(lt_zero, index=idx)
bvs = self.mov_sum(bv, window)
eq_zero = np.where(self['change'] == 0, self['volume'], 0)
cv = pd.Series(eq_zero, index=idx)
cvs = self.mov_sum(cv, window)
self[column_name] = (avs + cvs / 2) / (bvs + cvs / 2) * 100
def _get_pdi_ndi(self, window):
pdm, ndm = self._get_pdm_ndm(window)
atr = self._atr(window)
pdi = pdm / atr * 100
ndi = ndm / atr * 100
return pdi, ndi
def _get_pdi(self, windows):
""" +DI, positive directional moving index
:param windows: range
:return:
"""
window = self.get_int_positive(windows)
pdi, _ = self._get_pdi_ndi(window)
pdi_column = 'pdi_{}'.format(window)
self[pdi_column] = pdi
return self[pdi_column]
def _get_mdi(self, windows):
window = self.get_int_positive(windows)
_, ndi = self._get_pdi_ndi(window)
mdi_column = 'mdi_{}'.format(window)
self[mdi_column] = ndi
return self[mdi_column]
def _get_dx(self, windows):
window = self.get_int_positive(windows)
dx_column = 'dx_{}'.format(window)
pdi, mdi = self._get_pdi_ndi(window)
self[dx_column] = abs(pdi - mdi) / (pdi + mdi) * 100
return self[dx_column]
def _get_kdj_default(self):
""" default KDJ, 9 periods
:return: None
"""
self['kdjk'] = self['kdjk_{}'.format(self.KDJ_WINDOW)]
self['kdjd'] = self['kdjd_{}'.format(self.KDJ_WINDOW)]
self['kdjj'] = self['kdjj_{}'.format(self.KDJ_WINDOW)]
def _get_cr(self, windows=None):
""" Energy Index (Intermediate Willingness Index)
https://support.futunn.com/en/topic167/?lang=en-us
Use the relationship between the highest price, the lowest price and
yesterday's middle price to reflect the market's willingness to buy
and sell.
:param windows: window of the moving sum
:return: None
"""
if windows is None:
window = 26
else:
window = self.get_int_positive(windows)
middle = self._tp()
last_middle = self._shift(middle, -1)
ym = self._shift(middle, -1)
high = self['high']
low = self['low']
p1_m = pd.concat((last_middle, high), axis=1).min(axis=1)
p2_m = pd.concat((last_middle, low), axis=1).min(axis=1)
p1 = self.mov_sum(high - p1_m, window)
p2 = self.mov_sum(ym - p2_m, window)
if windows is None:
cr = 'cr'
cr_ma1 = 'cr-ma1'
cr_ma2 = 'cr-ma2'
cr_ma3 = 'cr-ma3'
else:
cr = 'cr_{}'.format(window)
cr_ma1 = 'cr_{}-ma1'.format(window)
cr_ma2 = 'cr_{}-ma2'.format(window)
cr_ma3 = 'cr_{}-ma3'.format(window)
self[cr] = cr = p1 / p2 * 100
self[cr_ma1] = self._shifted_cr_sma(cr, self.CR_MA1)
self[cr_ma2] = self._shifted_cr_sma(cr, self.CR_MA2)
self[cr_ma3] = self._shifted_cr_sma(cr, self.CR_MA3)
def _shifted_cr_sma(self, cr, window):
cr_sma = self.sma(cr, window)
return self._shift(cr_sma, -int(window / 2.5 + 1))
def _tp(self):
if 'amount' in self:
return self['amount'] / self['volume']
return (self['close'] + self['high'] + self['low']).divide(3.0)
def _get_tp(self):
self['tp'] = self._tp()
def _get_middle(self):
self['middle'] = self._tp()
def _calc_kd(self, column):
param0, param1 = self.KDJ_PARAM
k = 50.0
# noinspection PyTypeChecker
for i in param1 * column:
k = param0 * k + i
yield k
def _get_kdjk(self, window):
""" Get the K of KDJ
K ＝ 2/3 × (prev. K) +1/3 × (curr. RSV)
2/3 and 1/3 are the smooth parameters.
:param window: number of periods
:return: None
"""
rsv_column = 'rsv_{}'.format(window)
k_column = 'kdjk_{}'.format(window)
self[k_column] = list(self._calc_kd(self.get(rsv_column)))
def _get_kdjd(self, window):
""" Get the D of KDJ
D = 2/3 × (prev. D) +1/3 × (curr. K)
2/3 and 1/3 are the smooth parameters.
:param window: number of periods
:return: None
"""
k_column = 'kdjk_{}'.format(window)
d_column = 'kdjd_{}'.format(window)
self[d_column] = list(self._calc_kd(self.get(k_column)))
def _get_kdjj(self, window):
""" Get the J of KDJ
J = 3K-2D
:param self: data
:param window: number of periods
:return: None
"""
k_column = 'kdjk_{}'.format(window)
d_column = 'kdjd_{}'.format(window)
j_column = 'kdjj_{}'.format(window)
self[j_column] = 3 * self[k_column] - 2 * self[d_column]
@staticmethod
def _delta(series, window):
return series.diff(-window).fillna(0.0)
def _get_d(self, column, shifts):
shift = self.to_int(shifts)
column_name = '{}_{}_d'.format(column, shift)
self[column_name] = self._delta(self[column], shift)
@classmethod
def mov_min(cls, series, size):
return cls._rolling(series, size).min()
@classmethod
def mov_max(cls, series, size):
return cls._rolling(series, size).max()
@classmethod
def mov_sum(cls, series, size):
return cls._rolling(series, size).sum()
@classmethod
def sma(cls, series, size):
return cls._rolling(series, size).mean()
@staticmethod
def roc(series, size):
ret = series.diff(size) / series.shift(size)
ret.iloc[:size] = 0
return ret * 100
@classmethod
def _mad(cls, series, window):
""" Mean Absolute Deviation
:param series: Series
:param window: number of periods
:return: Series
"""
def f(x):
return np.fabs(x - x.mean()).mean()
return cls._rolling(series, window).apply(f, raw=True)
def _get_mad(self, column, window):
""" get mean absolute deviation
:param column: column to calculate
:param window: number of periods
:return: None
"""
window = self.get_int_positive(window)
column_name = '{}_{}_mad'.format(column, window)