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"""
Technical Analysis Factors
--------------------------
"""
from __future__ import division
from numpy import (
abs,
average,
clip,
diff,
dstack,
inf,
)
from numexpr import evaluate
from zipline.pipeline.data import EquityPricing
from zipline.pipeline.factors import CustomFactor
from zipline.pipeline.mixins import SingleInputMixin
from zipline.utils.input_validation import expect_bounded
from zipline.utils.math_utils import (
nanargmax,
nanargmin,
nanmax,
nanmean,
nanstd,
nanmin,
)
from zipline.utils.numpy_utils import rolling_window
from .basic import exponential_weights
from .basic import ( # noqa reexport
# These are re-exported here for backwards compatibility with the old
# definition site.
LinearWeightedMovingAverage,
MaxDrawdown,
SimpleMovingAverage,
VWAP,
WeightedAverageValue
)
class RSI(SingleInputMixin, CustomFactor):
"""
Relative Strength Index
**Default Inputs**: [EquityPricing.close]
**Default Window Length**: 15
"""
window_length = 15
inputs = (EquityPricing.close,)
window_safe = True
def compute(self, today, assets, out, closes):
diffs = diff(closes, axis=0)
ups = nanmean(clip(diffs, 0, inf), axis=0)
downs = abs(nanmean(clip(diffs, -inf, 0), axis=0))
return evaluate(
"100 - (100 / (1 + (ups / downs)))",
local_dict={'ups': ups, 'downs': downs},
global_dict={},
out=out,
)
class BollingerBands(CustomFactor):
"""
Bollinger Bands technical indicator.
https://en.wikipedia.org/wiki/Bollinger_Bands
**Default Inputs:** :data:`zipline.pipeline.data.EquityPricing.close`
Parameters
----------
inputs : length-1 iterable[BoundColumn]
The expression over which to compute bollinger bands.
window_length : int > 0
Length of the lookback window over which to compute the bollinger
bands.
k : float
The number of standard deviations to add or subtract to create the
upper and lower bands.
"""
params = ('k',)
inputs = (EquityPricing.close,)
outputs = 'lower', 'middle', 'upper'
def compute(self, today, assets, out, close, k):
difference = k * nanstd(close, axis=0)
out.middle = middle = nanmean(close, axis=0)
out.upper = middle + difference
out.lower = middle - difference
class Aroon(CustomFactor):
"""
Aroon technical indicator.
https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/aroon-indicator # noqa
**Defaults Inputs:** EquityPricing.low, EquityPricing.high
Parameters
----------
window_length : int > 0
Length of the lookback window over which to compute the Aroon
indicator.
"""
inputs = (EquityPricing.low, EquityPricing.high)
outputs = ('down', 'up')
def compute(self, today, assets, out, lows, highs):
wl = self.window_length
high_date_index = nanargmax(highs, axis=0)
low_date_index = nanargmin(lows, axis=0)
evaluate(
'(100 * high_date_index) / (wl - 1)',
local_dict={
'high_date_index': high_date_index,
'wl': wl,
},
out=out.up,
)
evaluate(
'(100 * low_date_index) / (wl - 1)',
local_dict={
'low_date_index': low_date_index,
'wl': wl,
},
out=out.down,
)
class FastStochasticOscillator(CustomFactor):
"""
Fast Stochastic Oscillator Indicator [%K, Momentum Indicator]
https://wiki.timetotrade.eu/Stochastic
This stochastic is considered volatile, and varies a lot when used in
market analysis. It is recommended to use the slow stochastic oscillator
or a moving average of the %K [%D].
**Default Inputs:** :data: `zipline.pipeline.data.EquityPricing.close`
:data: `zipline.pipeline.data.EquityPricing.low`
:data: `zipline.pipeline.data.EquityPricing.high`
**Default Window Length:** 14
Returns
-------
out: %K oscillator
"""
inputs = (EquityPricing.close, EquityPricing.low, EquityPricing.high)
window_safe = True
window_length = 14
def compute(self, today, assets, out, closes, lows, highs):
highest_highs = nanmax(highs, axis=0)
lowest_lows = nanmin(lows, axis=0)
today_closes = closes[-1]
evaluate(
'((tc - ll) / (hh - ll)) * 100',
local_dict={
'tc': today_closes,
'll': lowest_lows,
'hh': highest_highs,
},
global_dict={},
out=out,
)
class IchimokuKinkoHyo(CustomFactor):
"""Compute the various metrics for the Ichimoku Kinko Hyo (Ichimoku Cloud).
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud # noqa
**Default Inputs:** :data:`zipline.pipeline.data.EquityPricing.high`
:data:`zipline.pipeline.data.EquityPricing.low`
:data:`zipline.pipeline.data.EquityPricing.close`
**Default Window Length:** 52
Parameters
----------
window_length : int > 0
The length the the window for the senkou span b.
tenkan_sen_length : int >= 0, <= window_length
The length of the window for the tenkan-sen.
kijun_sen_length : int >= 0, <= window_length
The length of the window for the kijou-sen.
chikou_span_length : int >= 0, <= window_length
The lag for the chikou span.
"""
params = {
'tenkan_sen_length': 9,
'kijun_sen_length': 26,
'chikou_span_length': 26,
}
inputs = (EquityPricing.high, EquityPricing.low, EquityPricing.close)
outputs = (
'tenkan_sen',
'kijun_sen',
'senkou_span_a',
'senkou_span_b',
'chikou_span',
)
window_length = 52
def _validate(self):
super(IchimokuKinkoHyo, self)._validate()
for k, v in self.params.items():
if v > self.window_length:
raise ValueError(
'%s must be <= the window_length: %s > %s' % (
k, v, self.window_length,
),
)
def compute(self,
today,
assets,
out,
high,
low,
close,
tenkan_sen_length,
kijun_sen_length,
chikou_span_length):
out.tenkan_sen = tenkan_sen = (
high[-tenkan_sen_length:].max(axis=0) +
low[-tenkan_sen_length:].min(axis=0)
) / 2
out.kijun_sen = kijun_sen = (
high[-kijun_sen_length:].max(axis=0) +
low[-kijun_sen_length:].min(axis=0)
) / 2
out.senkou_span_a = (tenkan_sen + kijun_sen) / 2
out.senkou_span_b = (high.max(axis=0) + low.min(axis=0)) / 2
out.chikou_span = close[chikou_span_length]
class RateOfChangePercentage(CustomFactor):
"""
Rate of change Percentage
ROC measures the percentage change in price from one period to the next.
The ROC calculation compares the current price with the price `n`
periods ago.
Formula for calculation: ((price - prevPrice) / prevPrice) * 100
price - the current price
prevPrice - the price n days ago, equals window length
"""
def compute(self, today, assets, out, close):
today_close = close[-1]
prev_close = close[0]
evaluate('((tc - pc) / pc) * 100',
local_dict={
'tc': today_close,
'pc': prev_close
},
global_dict={},
out=out,
)
class TrueRange(CustomFactor):
"""
True Range
A technical indicator originally developed by J. Welles Wilder, Jr.
Indicates the true degree of daily price change in an underlying.
**Default Inputs:** :data:`zipline.pipeline.data.EquityPricing.high`
:data:`zipline.pipeline.data.EquityPricing.low`
:data:`zipline.pipeline.data.EquityPricing.close`
**Default Window Length:** 2
"""
inputs = (
EquityPricing.high,
EquityPricing.low,
EquityPricing.close,
)
window_length = 2
def compute(self, today, assets, out, highs, lows, closes):
high_to_low = highs[1:] - lows[1:]
high_to_prev_close = abs(highs[1:] - closes[:-1])
low_to_prev_close = abs(lows[1:] - closes[:-1])
out[:] = nanmax(
dstack((
high_to_low,
high_to_prev_close,
low_to_prev_close,
)),
2
)
class MovingAverageConvergenceDivergenceSignal(CustomFactor):
"""
Moving Average Convergence/Divergence (MACD) Signal line
https://en.wikipedia.org/wiki/MACD
A technical indicator originally developed by Gerald Appel in the late
1970's. MACD shows the relationship between two moving averages and
reveals changes in the strength, direction, momentum, and duration of a
trend in a stock's price.
**Default Inputs:** :data:`zipline.pipeline.data.EquityPricing.close`
Parameters
----------
fast_period : int > 0, optional
The window length for the "fast" EWMA. Default is 12.
slow_period : int > 0, > fast_period, optional
The window length for the "slow" EWMA. Default is 26.
signal_period : int > 0, < fast_period, optional
The window length for the signal line. Default is 9.
Notes
-----
Unlike most pipeline expressions, this factor does not accept a
``window_length`` parameter. ``window_length`` is inferred from
``slow_period`` and ``signal_period``.
"""
inputs = (EquityPricing.close,)
# We don't use the default form of `params` here because we want to
# dynamically calculate `window_length` from the period lengths in our
# __new__.
params = ('fast_period', 'slow_period', 'signal_period')
@expect_bounded(
__funcname='MACDSignal',
fast_period=(1, None), # These must all be >= 1.
slow_period=(1, None),
signal_period=(1, None),
)
def __new__(cls,
fast_period=12,
slow_period=26,
signal_period=9,
*args,
**kwargs):
if slow_period <= fast_period:
raise ValueError(
"'slow_period' must be greater than 'fast_period', but got\n"
"slow_period={slow}, fast_period={fast}".format(
slow=slow_period,
fast=fast_period,
)
)
return super(MovingAverageConvergenceDivergenceSignal, cls).__new__(
cls,
fast_period=fast_period,
slow_period=slow_period,
signal_period=signal_period,
window_length=slow_period + signal_period - 1,
*args, **kwargs
)
def _ewma(self, data, length):
decay_rate = 1.0 - (2.0 / (1.0 + length))
return average(
data,
axis=1,
weights=exponential_weights(length, decay_rate)
)
def compute(self, today, assets, out, close, fast_period, slow_period,
signal_period):
slow_EWMA = self._ewma(
rolling_window(close, slow_period),
slow_period
)
fast_EWMA = self._ewma(
rolling_window(close, fast_period)[-signal_period:],
fast_period
)
macd = fast_EWMA - slow_EWMA
out[:] = self._ewma(macd.T, signal_period)
# Convenience aliases.
MACDSignal = MovingAverageConvergenceDivergenceSignal
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