QTPyLib Built-In Indicators and TA-Lib Integration
Article QTPyLib
Summary
This reference page catalogs technical indicators and data utilities available in QTPyLib for use with bar data. The built-in list covers volatility and range measures, moving averages, channels, momentum oscillators, returns, volume-related measures, price transforms, and rolling statistics. It also demonstrates crossing checks and session-based trimming, including the need to account for timezone when selecting a session.
The page explains that QTPyLib can integrate TA-Lib functions through a wrapper that prepares bar or tick inputs, while requiring TA-Lib to be installed. Examples show how the methods are called and where results can be stored, but they do not explain indicator formulas, trading rules, parameter selection, or performance. This is implementation documentation rather than evidence that any listed indicator provides an edge; researchers still need to understand each measure and evaluate it within their own strategy and data setup.
Key ideas
- QTPyLib provides built-in indicators for momentum, volatility, price, volume, and rolling statistics.
- Indicator methods can be applied to bar data and their outputs stored as columns.
- Crossing utilities can identify when one series moves above or below another.
- Session trimming supports time-bounded calculations and depends on correct timezone handling.
- TA-Lib integration requires the external library and supplies functions through a QTPyLib wrapper.
Tags
Full text
# indicators
Technical Indicators
====================
Although you can import technical indicator libraries and use them in your strategies,
QTPyLib does come bundled with some common indicators that work as Pandas Objects.
Built-In Indicators
~~~~~~~~~~~~~~~~~~~
ATR
---
.. code:: python
bars['atr'] = bars.atr(window=14 [, exp=False])
...
Awesome Oscillator
------------------
.. code:: python
bars['ao'] = bars.awesome_oscillator(weighted=False, fast=5, slow=34])
...
Bollinger Bands
---------------
.. code:: python
bb = bars.bollinger_bands(window=20, stds=2)
bars['bb_upper'] = bb['upper']
bars['bb_lower'] = bb['lower']
bars['bb_mid'] = bb['mid']
...
Weighted Bollinger Bands
------------------------
.. code:: python
wbb = bars.weighted_bollinger_bands(window=20, stds=2)
bars['wbb_upper'] = wbb['upper']
bars['wbb_lower'] = wbb['lower']
bars['wbb_mid'] = wbb['mid']
...
CCI
---------------------------
.. code:: python
bars['cci'] = bars.cci(window=14)
...
Crossed Above/Below
-------------------
.. code:: python
bars['sma'] = bars['close'].rolling_mean(10)
if bars['close'].crossed_above(bars['sma']):
# crossed above
...
if bars['rsi'].crossed_below(10):
# crossed below
...
if bars['close'].crossed(bars['open']):
# crossed either above or below
...
Heikin Ashi
-----------
.. code:: python
# return heiken ashi ohlc based on bar's ohlc
heikinashi = bars.heikinashi()
heikinashi[['open', 'high', 'low', 'close']]
...
Hull Moving Average
-------------------
.. code:: python
bars['hma'] = bars.hull_moving_average(window=200 [, min_periods=None])
# also available via shorthand
# bars['hma'] = bars.hma(...)
...
IBS
---------------------------
.. code:: python
bars['ibs'] = bars.ibs()
...
Implied Volatility
---------------------------
.. code:: python
bars['iv'] = bars.implied_volatility(window=252)
...
Keltner Channel
---------------------------
.. code:: python
kc = bars.keltner_channel(window=14, atrs=2)
bars['kc_upper'] = kc['upper']
bars['kc_lower'] = kc['lower']
bars['kc_mid'] = kc['mid']
...
MACD
---------------------------
.. code:: python
macd = bars.macd(fast=3, slow=10, smooth=16)
bars['macd'] = macd['macd']
bars['macd_signal'] = macd['signal']
bars['macd_hist'] = macd['histogram']
...
Moving Average: Simple
----------------------
Shorthand for ``bars.rolling_mean(...)``
.. code:: python
bars['sma'] = bars.sma(window=200 [, min_periods=None])
...
Moving Average: Weighted
-------------------------
Shorthand for ``bars.rolling_weighted_mean(...)``
.. code:: python
bars['wma'] = bars.wma(window=200 [, min_periods=None])
...
Moving Average: Hull
---------------------
Shorthand for ``bars.hull_moving_average(...)``
.. code:: python
bars['hma'] = bars.hma(window=200 [, min_periods=None])
...
Median Price
----------------------
.. code:: python
# (High + Low) / 2
bars['mid'] = bars.mid_price()
...
Typical Price
---------------------------------
.. code:: python
# (High + Low + Close) / 3
bars['typical'] = bars.typical_price()
...
Traders Dynamic Index (TDI)
---------------------------------
.. code:: python
bars['typical'] = bars['close'].tdi([rsi_len=13, bollinger_len=34,
rsi_smoothing=2, rsi_signal_len=7, bollinger_std=1.6185])
...
Price Volume Trend
------------------
.. code:: python
bars['pvt'] = bars.pvt()
...
Rolling Minimum
---------------
.. code:: python
bars['min'] = bars.rolling_min(window=14 [, min_periods=None])
...
Rolling Maximum
---------------
.. code:: python
bars['max'] = bars.rolling_max(window=14 [, min_periods=None])
...
Rolling Mean
------------
.. code:: python
bars['sma'] = bars.rolling_mean(window=200 [, min_periods=None])
# also available via shorthand
# bars['sma'] = bars.sma(...)
...
Rolling Standard Deviation
--------------------------
.. code:: python
bars['std'] = bars.rolling_std(window=200 [, min_periods=None])
...
Rolling VWAP
------------
.. code:: python
bars['rvwap'] = bars.rolling_vwap(window=200 [, min_periods=None])
...
Rolling Weighted Mean
---------------------
.. code:: python
bars['wma'] = bars.rolling_weighted_mean(window=200 [, min_periods=None])
# also available via shorthand
# bars['wma'] = bars.wma(...)
...
Rolling Returns
---------------
.. code:: python
bars['returns'] = bars.returns()
...
Rolling Log Returns
-------------------
.. code:: python
bars['log_returns'] = bars.log_returns()
...
ROC
---------------------------
.. code:: python
bars['roc'] = bars.roc(window=14)
...
RSI
---------------------------
.. code:: python
bars['rsi'] = bars.rsi(window=14)
...
Session
---------------------------
This isn't an indicator, but rather a utility that trims
the bars to a specified "Session" (useful when wanting to
work, for example, with the most recent PIT or GLOBEX
session to calculate VWAP, etc.).
.. code:: python
# make sure to specity timezone="US/Central" for your algo
# otherwise, the default timezone is UTC
# pit session
bars = bars.session(start='08:30', end='15:15')
# globex session
bars = bars.session(start='17:00', end='16:00')
...
Stochastics
---------------------------
.. code:: python
bars['stoch'] = bars.stoch([window=14, d=3, k=3, fast=True])
...
True Range
---------------------------
.. code:: python
bars['tr'] = bars.true_range()
...
VWAP
----
.. code:: python
bars['vwap'] = bars.vwap(bars)
...
Z-Score
-------
.. code:: python
bars['zscore'] = bars.zscore(window=20, stds=1, col='close')
...
-----
TA-Lib Integration
~~~~~~~~~~~~~~~~~~
QTPyLib also offers full integration with `TA-Lib <http://ta-lib.org>`_.
All the TA-Lib methods are available via the ``talib_indicators`` modules and
automatically extracts and prepare the relevant data your strategy's ``bars`` or ``ticks``.
To use the TA-Lib integration, you'll need to have TA-Lib installed on your system,
and import the ``talib_indicators`` module into your strategies:
.. code:: python
# strategy.py
from qtpylib import talib_indicators as ta
...
def on_bar(self, instrument):
# get OHLCV bars
bars = instrument.get_bars()
# add 14-period ATR column
bars['atr'] = ta.ATR(bars, timeperiod=14)
# same result using Vanilla TA-Lib:
# bars['atr'] = talib.ATR(bars['high'].values, bars['low'].values, bars['close'].values, timeperiod=14)
...
For more information on all available TA-Lib methods/indicators, please visit
`TA-Lib's website <http://mrjbq7.github.io/ta-lib/funcs.html>`_.Shown in full with attribution under the source's licence. Licence: Apache-2.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.