technical – Technical indicators¶

class pyalgotrade.technical.EventWindow(windowSize, dtype=<type 'float'>, skipNone=True)¶

Bases: object

An EventWindow class is responsible for making calculation over a moving window of values.

Parameters:	windowSize (int.) – The size of the window. Must be greater than 0. dtype (data-type.) – The desired data-type for the array. skipNone (boolean.) – True if None values should not be included in the window.

Note

This is a base class and should not be used directly.

getValue()¶: Override to calculate a value using the values in the window.

getValues()¶: Returns a numpy.array with the values in the window.

getWindowSize()¶: Returns the window size.

class pyalgotrade.technical.EventBasedFilter(dataSeries, eventWindow, maxLen=None)¶

Bases: pyalgotrade.dataseries.SequenceDataSeries

An EventBasedFilter class is responsible for capturing new values in a pyalgotrade.dataseries.DataSeries and using an EventWindow to calculate new values.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
eventWindow (EventWindow.) – The EventWindow instance to use to calculate new values.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

Example¶

The following example shows how to combine an EventWindow and an EventBasedFilter to build a custom filter:

from __future__ import print_function

from pyalgotrade import dataseries
from pyalgotrade import technical


# An EventWindow is responsible for making calculations using a window of values.
class Accumulator(technical.EventWindow):
    def getValue(self):
        ret = None
        if self.windowFull():
            ret = self.getValues().sum()
        return ret

# Build a sequence based DataSeries.
seqDS = dataseries.SequenceDataSeries()
# Wrap it with a filter that will get fed as new values get added to the underlying DataSeries.
accum = technical.EventBasedFilter(seqDS, Accumulator(3))

# Put in some values.
for i in range(0, 50):
    seqDS.append(i)

# Get some values.
print(accum[0])  # Not enough values yet.
print(accum[1])  # Not enough values yet.
print(accum[2])  # Ok, now we should have at least 3 values.
print(accum[3])

# Get the last value, which should be equal to 49 + 48 + 47.
print(accum[-1])

The output should be:

None
None
3.0
6.0
144.0

Moving Averages¶

class pyalgotrade.technical.ma.SMA(dataSeries, period, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

Simple Moving Average filter.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
period (int.) – The number of values to use to calculate the SMA.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

class pyalgotrade.technical.ma.EMA(dataSeries, period, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

Exponential Moving Average filter.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
period (int.) – The number of values to use to calculate the EMA. Must be an integer greater than 1.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

class pyalgotrade.technical.ma.WMA(dataSeries, weights, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

Weighted Moving Average filter.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
weights (list.) – A list of int/float with the weights.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

class pyalgotrade.technical.vwap.VWAP(dataSeries, period, useTypicalPrice=False, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

Volume Weighted Average Price filter.

Parameters:

dataSeries (pyalgotrade.dataseries.bards.BarDataSeries.) – The DataSeries instance being filtered.
period (int.) – The number of values to use to calculate the VWAP.
useTypicalPrice (boolean.) – True if the typical price should be used instead of the closing price.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

Momentum Indicators¶

class pyalgotrade.technical.macd.MACD(dataSeries, fastEMA, slowEMA, signalEMA, maxLen=None)¶

Bases: pyalgotrade.dataseries.SequenceDataSeries

Moving Average Convergence-Divergence indicator as described in http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_average_convergence_divergence_macd.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
fastEMA (int.) – The number of values to use to calculate the fast EMA.
slowEMA (int.) – The number of values to use to calculate the slow EMA.
signalEMA (int.) – The number of values to use to calculate the signal EMA.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

getHistogram()¶: Returns a pyalgotrade.dataseries.DataSeries with the histogram (the difference between the MACD and the Signal).

getSignal()¶: Returns a pyalgotrade.dataseries.DataSeries with the EMA over the MACD.

class pyalgotrade.technical.rsi.RSI(dataSeries, period, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

Relative Strength Index filter as described in http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:relative_strength_index_rsi.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
period (int.) – The period. Note that if period is n, then n+1 values are used. Must be > 1.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

class pyalgotrade.technical.stoch.StochasticOscillator(barDataSeries, period, dSMAPeriod=3, useAdjustedValues=False, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

Fast Stochastic Oscillator filter as described in http://stockcharts.com/school/doku.php?st=stochastic+oscillator&id=chart_school:technical_indicators:stochastic_oscillator_fast_slow_and_full. Note that the value returned by this filter is %K. To access %D use getD().

Parameters:

barDataSeries (pyalgotrade.dataseries.bards.BarDataSeries.) – The BarDataSeries instance being filtered.
period (int.) – The period. Must be > 1.
dSMAPeriod (int.) – The %D SMA period. Must be > 1.
useAdjustedValues (boolean.) – True to use adjusted Low/High/Close values.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

getD()¶: Returns a pyalgotrade.dataseries.DataSeries with the %D values.

class pyalgotrade.technical.roc.RateOfChange(dataSeries, valuesAgo, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

Rate of change filter as described in http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:rate_of_change_roc_and_momentum.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
valuesAgo (int.) – The number of values back that a given value will compare to. Must be > 0.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

Other Indicators¶

class pyalgotrade.technical.atr.ATR(barDataSeries, period, useAdjustedValues=False, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

Average True Range filter as described in http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_true_range_atr

Parameters:

barDataSeries (pyalgotrade.dataseries.bards.BarDataSeries.) – The BarDataSeries instance being filtered.
period (int.) – The average period. Must be > 1.
useAdjustedValues (boolean.) – True to use adjusted Low/High/Close values.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

class pyalgotrade.technical.bollinger.BollingerBands(dataSeries, period, numStdDev, maxLen=None)¶

Bases: object

Bollinger Bands filter as described in http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:bollinger_bands.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
period (int.) – The number of values to use in the calculation. Must be > 1.
numStdDev (int.) – The number of standard deviations to use for the upper and lower bands.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

getLowerBand()¶: Returns the lower band as a pyalgotrade.dataseries.DataSeries.

getMiddleBand()¶: Returns the middle band as a pyalgotrade.dataseries.DataSeries.

getUpperBand()¶: Returns the upper band as a pyalgotrade.dataseries.DataSeries.

pyalgotrade.technical.cross.cross_above(values1, values2, start=-2, end=None)¶

Checks for a cross above conditions over the specified period between two DataSeries objects.

It returns the number of times values1 crossed above values2 during the given period.

Parameters:	values1 (`pyalgotrade.dataseries.DataSeries`.) – The DataSeries that crosses. values2 (`pyalgotrade.dataseries.DataSeries`.) – The DataSeries being crossed. start (int.) – The start of the range. end (int.) – The end of the range.

Note

The default start and end values check for cross above conditions over the last 2 values.

pyalgotrade.technical.cross.cross_below(values1, values2, start=-2, end=None)¶

Checks for a cross below conditions over the specified period between two DataSeries objects.

It returns the number of times values1 crossed below values2 during the given period.

Parameters:	values1 (`pyalgotrade.dataseries.DataSeries`.) – The DataSeries that crosses. values2 (`pyalgotrade.dataseries.DataSeries`.) – The DataSeries being crossed. start (int.) – The start of the range. end (int.) – The end of the range.

Note

The default start and end values check for cross below conditions over the last 2 values.

class pyalgotrade.technical.cumret.CumulativeReturn(dataSeries, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

This filter calculates cumulative returns over another dataseries.

Parameters:	dataSeries (`pyalgotrade.dataseries.DataSeries`.) – The DataSeries instance being filtered. maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

class pyalgotrade.technical.highlow.High(dataSeries, period, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

This filter calculates the highest value.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
period (int.) – The number of values to use to calculate the highest value.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

class pyalgotrade.technical.highlow.Low(dataSeries, period, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

This filter calculates the lowest value.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
period (int.) – The number of values to use to calculate the lowest value.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

class pyalgotrade.technical.hurst.HurstExponent(dataSeries, period, minLags=2, maxLags=20, logValues=True, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

Hurst exponent filter.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
period (int.) – The number of values to use to calculate the hurst exponent.
minLags (int.) – The minimum number of lags to use. Must be >= 2.
maxLags (int.) – The maximum number of lags to use. Must be > minLags.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

class pyalgotrade.technical.linebreak.Line(low, high, dateTime, white)¶

Bases: object

A line in a line break chart.

getDateTime()¶: The datetime.

getHigh()¶: The high value.

getLow()¶: The low value.

isBlack()¶: True if the line is black (falling prices).

isWhite()¶: True if the line is white (rising prices).

class pyalgotrade.technical.linebreak.LineBreak(barDataSeries, reversalLines, useAdjustedValues=False, maxLen=None)¶

Bases: pyalgotrade.dataseries.SequenceDataSeries

Line Break filter as described in http://stockcharts.com/school/doku.php?id=chart_school:chart_analysis:three_line_break. . This is a DataSeries of Line instances.

Parameters:

barDataSeries (pyalgotrade.dataseries.bards.BarDataSeries.) – The DataSeries instance being filtered.
reversalLines (int.) – The number of lines back to check to calculate a reversal. Must be greater than 1.
useAdjustedValues (boolean.) – True to use adjusted high/low/close values.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used. This value can’t be smaller than reversalLines.

class pyalgotrade.technical.linreg.LeastSquaresRegression(dataSeries, windowSize, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

Calculates values based on a least-squares regression.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
windowSize (int.) – The number of values to use to calculate the regression.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

getValueAt(dateTime)¶

Calculates the value at a given time based on the regression line.

Parameters:	dateTime (`datetime.datetime`.) – The datetime to calculate the value at. Will return None if there are not enough values in the underlying DataSeries.

class pyalgotrade.technical.linreg.Slope(dataSeries, period, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

The Slope filter calculates the slope of a least-squares regression line.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
period (int.) – The number of values to use to calculate the slope.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

Note

This filter ignores the time elapsed between the different values.

class pyalgotrade.technical.stats.StdDev(dataSeries, period, ddof=0, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

Standard deviation filter.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
period (int.) – The number of values to use to calculate the Standard deviation.
ddof (int.) – Delta degrees of freedom.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

class pyalgotrade.technical.stats.ZScore(dataSeries, period, ddof=0, maxLen=None)¶

Bases: pyalgotrade.technical.EventBasedFilter

Z-Score filter.

Parameters:

dataSeries (pyalgotrade.dataseries.DataSeries.) – The DataSeries instance being filtered.
period (int.) – The number of values to use to calculate the Z-Score.
ddof (int.) – Delta degrees of freedom to use for the standard deviation.
maxLen (int.) – The maximum number of values to hold. Once a bounded length is full, when new items are added, a corresponding number of items are discarded from the opposite end. If None then dataseries.DEFAULT_MAX_LEN is used.

technical – Technical indicators¶

Example¶

Moving Averages¶

Momentum Indicators¶

Other Indicators¶

Table Of Contents

Previous topic

Next topic

This Page

Navigation

technical – Technical indicators¶

Example¶

Moving Averages¶

Momentum Indicators¶

Other Indicators¶

Table Of Contents

Previous topic

Next topic

This Page

Quick search

Navigation