Quantitative Trading Python Library
Project description
QTPyLib, Pythonic Algorithmic Trading
=====================================
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\
QTPyLib (**Q**\ uantitative **T**\ rading **Py**\ thon **Lib**\ rary)
is a simple, event-driven algorithmic trading system written in Python 3,
that supports backtesting and live trading using
`Interactive Brokers <https://www.interactivebrokers.com>`_
for market data and order execution.
I originally developed QTPyLib because I wanted for a simple
(but powerful) trading library that will let me to focus on the
trading logic itself and ignore everything else.
`Full Documentation » <http://www.qtpylib.io/>`_
`Changelog » <./CHANGELOG.rst>`_
-----
Features
========
- A continuously-running Blotter that lets you capture market data even when your algos aren't running.
- Tick, Bar and Trade data is stored in MySQL for later analisys and backtesting.
- Using pub/sub architecture using `ØMQ <http://zeromq.org>`_ (ZeroMQ) for communicating between the Algo and the Blotter allows for a single Blotter/multiple Algos running on the same machine.
- Includes many common indicators that you can seamlessly use in your algorithm.
- **Support for Order Book, Quote, Time, Tick or Volume based strategy resolutions**
- Have orders delivered to your mobile via SMS (requires a `Nexmo <https://www.nexmo.com/>`_ or `Twilio <https://www.twilio.com/>`_ account)
- Full integration with `TA-Lib <http://ta-lib.org>`_ via dedicated module (`see documentation <http://qtpylib.io/docs/latest/indicators.html#ta-lib-integration>`_)
- Ability to import any Python library (such as `scikit-learn <http://scikit-learn.org>`_ or `TensorFlow <https://www.tensorflow.org>`_) to use them in your algorithms.
-----
Quickstart
==========
There are 5 main components to QTPyLib:
1. ``Blotter`` - handles market data retreival and processing.
2. ``Broker`` - sends and proccess orders/positions (abstracted layer).
3. ``Algo`` - (sub-class of ``Broker``) communicates with the ``Blotter`` to pass market data to your strategies, and proccess/positions orders via ``Broker``.
4. ``Reports`` - provides real time monitoring of trades and open opsitions via Web App, as well as a simple REST API for trades, open positions and market data.
5. Lastly, **Your Strategies**, which are sub-classes of ``Algo``, handle the trading logic/rules. This is where you'll write most of your code.
1. Get Market Data
------------------
To get started, you need to first create a Blotter script:
.. code:: python
# blotter.py
from qtpylib.blotter import Blotter
class MainBlotter(Blotter):
pass # we just need the name
if __name__ == "__main__":
blotter = MainBlotter()
blotter.run()
Then, with IB TWS/GW running, run the Blotter from the command line:
.. code:: bash
$ python blotter.py
If your strategy needs order book / market depth data, add the ``--orderbook`` flag to the command:
.. code:: bash
$ python blotter.py --orderbook
2. Write your Algorithm
-----------------------
While the Blotter running in the background, write and execute your algorithm:
.. code:: python
# strategy.py
from qtpylib.algo import Algo
class CrossOver(Algo):
def on_start(self):
pass
def on_fill(self, instrument, order):
pass
def on_quote(self, instrument):
pass
def on_orderbook(self, instrument):
pass
def on_tick(self, instrument):
pass
def on_bar(self, instrument):
# get instrument history
bars = instrument.get_bars(window=100)
# or get all instruments history
# bars = self.bars[-20:]
# skip first 20 days to get full windows
if len(bars) < 20:
return
# compute averages using internal rolling_mean
bars['short_ma'] = bars['close'].rolling_mean(window=10)
bars['long_ma'] = bars['close'].rolling_mean(window=20)
# get current position data
positions = instrument.get_positions()
# trading logic - entry signal
if bars['short_ma'].crossed_above(bars['long_ma'])[-1]:
if not instrument.pending_orders and positions["position"] == 0:
# buy one contract
instrument.buy(1)
# record values for later analysis
self.record(ma_cross=1)
# trading logic - exit signal
elif bars['short_ma'].crossed_below(bars['long_ma'])[-1]:
if positions["position"] != 0:
# exit / flatten position
instrument.exit()
# record values for later analysis
self.record(ma_cross=-1)
if __name__ == "__main__":
strategy = CrossOver(
instruments = [ ("ES", "FUT", "GLOBEX", "USD", 201609, 0.0, "") ], # ib tuples
resolution = "1T", # Pandas resolution (use "K" for tick bars)
tick_window = 20, # no. of ticks to keep
bar_window = 5, # no. of bars to keep
preload = "1D", # preload 1 day history when starting
timezone = "US/Central" # convert all ticks/bars to this timezone
)
strategy.run()
To run your algo in a **live** enviroment, from the command line, type:
.. code:: bash
$ python strategy.py --logpath ~/qtpy/
The resulting trades be saved in ``~/qtpy/STRATEGY_YYYYMMDD.csv`` for later analysis.
3. Viewing Live Trades
----------------------
While the Blotter running in the background, write the dashboard:
.. code:: python
# dashboard.py
from qtpylib.reports import Reports
class Dahboard(Reports):
pass # we just need the name
if __name__ == "__main__":
dashboard = Dahboard(port = 5000)
dashboard.run()
To run your dashboard, run it from the command line:
.. code:: bash
$ python dashboard.py
>>> Dashboard password is: a0f36d95a9
>>> Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
Now, point your browser to http://localhost:5000 and use the password generated to access your dashboard.
-----
.. note::
Please refer to the `Full Documentation <http://www.qtpylib.io/>`_ to learn
how to enable SMS notifications, use the bundled Indicators, and more.
Installation
============
Install using ``pip``:
.. code:: bash
$ pip install qtpylib --upgrade --no-cache-dir
Requirements
------------
* `Python <https://www.python.org>`_ >=3.4
* `Pandas <https://github.com/pydata/pandas>`_ (tested to work with >=0.18.1)
* `Numpy <https://github.com/numpy/numpy>`_ (tested to work with >=1.11.1)
* `ØMQ <https://github.com/zeromq/pyzmq>`_ (tested to with with >=15.2.1)
* `PyMySQL <https://github.com/PyMySQL/PyMySQL>`_ (tested to with with >=0.7.6)
* `pytz <http://pytz.sourceforge.net>`_ (tested to with with >=2016.6.1)
* `dateutil <https://pypi-hypernode.com/pypi/python-dateutil>`_ (tested to with with >=2.5.1)
* `Nexmo <https://github.com/Nexmo/nexmo-python>`_ for SMS support (tested to with with >=1.2.0)
* `Twilio <https://github.com/twilio/twilio-python>`_ for SMS support (tested to with with >=5.4.0)
* `Flask <http://flask.pocoo.org>`_ for the Dashboard (tested to work with >=0.11)
* `Requests <https://github.com/kennethreitz/requests>`_ (tested to with with >=2.10.0)
* `Beautiful Soup <https://pypi-hypernode.com/pypi/beautifulsoup4>`_ (tested to work with >=4.3.2)
* `IbPy2 <https://github.com/blampe/IbPy>`_ (tested to work with >=0.8.0)
* `ezIBpy <https://github.com/ranaroussi/ezibpy>`_ (IbPy wrapper, tested to with with >=1.12.26)
* Latest Interactive Brokers’ `TWS <https://www.interactivebrokers.com/en/index.php?f=15875>`_ or `IB Gateway <https://www.interactivebrokers.com/en/index.php?f=16457>`_ installed and running on the machine
* `MySQL Server <https://www.mysql.com/>`_ installed and running with a database for QTPyLib
-----
Legal Stuff
===========
QTPyLib is distributed under the **GNU Lesser General Public License v3.0**. See the `LICENSE.txt <./LICENSE.txt>`_ file in the release for details.
QTPyLib is not a product of Interactive Brokers, nor is it affiliated with Interactive Brokers.
You can find other examples in the qtpylib/examples directory.
P.S.
----
I'm very interested in your experience with QTPyLib. Please drop me an note with any feedback you have.
**Ran Aroussi**
=====================================
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:target: https://github.com/ranaroussi/qtpylib
:alt: Star this repo
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:target: https://twitter.com/aroussi
:alt: Follow me on twitter
\
QTPyLib (**Q**\ uantitative **T**\ rading **Py**\ thon **Lib**\ rary)
is a simple, event-driven algorithmic trading system written in Python 3,
that supports backtesting and live trading using
`Interactive Brokers <https://www.interactivebrokers.com>`_
for market data and order execution.
I originally developed QTPyLib because I wanted for a simple
(but powerful) trading library that will let me to focus on the
trading logic itself and ignore everything else.
`Full Documentation » <http://www.qtpylib.io/>`_
`Changelog » <./CHANGELOG.rst>`_
-----
Features
========
- A continuously-running Blotter that lets you capture market data even when your algos aren't running.
- Tick, Bar and Trade data is stored in MySQL for later analisys and backtesting.
- Using pub/sub architecture using `ØMQ <http://zeromq.org>`_ (ZeroMQ) for communicating between the Algo and the Blotter allows for a single Blotter/multiple Algos running on the same machine.
- Includes many common indicators that you can seamlessly use in your algorithm.
- **Support for Order Book, Quote, Time, Tick or Volume based strategy resolutions**
- Have orders delivered to your mobile via SMS (requires a `Nexmo <https://www.nexmo.com/>`_ or `Twilio <https://www.twilio.com/>`_ account)
- Full integration with `TA-Lib <http://ta-lib.org>`_ via dedicated module (`see documentation <http://qtpylib.io/docs/latest/indicators.html#ta-lib-integration>`_)
- Ability to import any Python library (such as `scikit-learn <http://scikit-learn.org>`_ or `TensorFlow <https://www.tensorflow.org>`_) to use them in your algorithms.
-----
Quickstart
==========
There are 5 main components to QTPyLib:
1. ``Blotter`` - handles market data retreival and processing.
2. ``Broker`` - sends and proccess orders/positions (abstracted layer).
3. ``Algo`` - (sub-class of ``Broker``) communicates with the ``Blotter`` to pass market data to your strategies, and proccess/positions orders via ``Broker``.
4. ``Reports`` - provides real time monitoring of trades and open opsitions via Web App, as well as a simple REST API for trades, open positions and market data.
5. Lastly, **Your Strategies**, which are sub-classes of ``Algo``, handle the trading logic/rules. This is where you'll write most of your code.
1. Get Market Data
------------------
To get started, you need to first create a Blotter script:
.. code:: python
# blotter.py
from qtpylib.blotter import Blotter
class MainBlotter(Blotter):
pass # we just need the name
if __name__ == "__main__":
blotter = MainBlotter()
blotter.run()
Then, with IB TWS/GW running, run the Blotter from the command line:
.. code:: bash
$ python blotter.py
If your strategy needs order book / market depth data, add the ``--orderbook`` flag to the command:
.. code:: bash
$ python blotter.py --orderbook
2. Write your Algorithm
-----------------------
While the Blotter running in the background, write and execute your algorithm:
.. code:: python
# strategy.py
from qtpylib.algo import Algo
class CrossOver(Algo):
def on_start(self):
pass
def on_fill(self, instrument, order):
pass
def on_quote(self, instrument):
pass
def on_orderbook(self, instrument):
pass
def on_tick(self, instrument):
pass
def on_bar(self, instrument):
# get instrument history
bars = instrument.get_bars(window=100)
# or get all instruments history
# bars = self.bars[-20:]
# skip first 20 days to get full windows
if len(bars) < 20:
return
# compute averages using internal rolling_mean
bars['short_ma'] = bars['close'].rolling_mean(window=10)
bars['long_ma'] = bars['close'].rolling_mean(window=20)
# get current position data
positions = instrument.get_positions()
# trading logic - entry signal
if bars['short_ma'].crossed_above(bars['long_ma'])[-1]:
if not instrument.pending_orders and positions["position"] == 0:
# buy one contract
instrument.buy(1)
# record values for later analysis
self.record(ma_cross=1)
# trading logic - exit signal
elif bars['short_ma'].crossed_below(bars['long_ma'])[-1]:
if positions["position"] != 0:
# exit / flatten position
instrument.exit()
# record values for later analysis
self.record(ma_cross=-1)
if __name__ == "__main__":
strategy = CrossOver(
instruments = [ ("ES", "FUT", "GLOBEX", "USD", 201609, 0.0, "") ], # ib tuples
resolution = "1T", # Pandas resolution (use "K" for tick bars)
tick_window = 20, # no. of ticks to keep
bar_window = 5, # no. of bars to keep
preload = "1D", # preload 1 day history when starting
timezone = "US/Central" # convert all ticks/bars to this timezone
)
strategy.run()
To run your algo in a **live** enviroment, from the command line, type:
.. code:: bash
$ python strategy.py --logpath ~/qtpy/
The resulting trades be saved in ``~/qtpy/STRATEGY_YYYYMMDD.csv`` for later analysis.
3. Viewing Live Trades
----------------------
While the Blotter running in the background, write the dashboard:
.. code:: python
# dashboard.py
from qtpylib.reports import Reports
class Dahboard(Reports):
pass # we just need the name
if __name__ == "__main__":
dashboard = Dahboard(port = 5000)
dashboard.run()
To run your dashboard, run it from the command line:
.. code:: bash
$ python dashboard.py
>>> Dashboard password is: a0f36d95a9
>>> Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
Now, point your browser to http://localhost:5000 and use the password generated to access your dashboard.
-----
.. note::
Please refer to the `Full Documentation <http://www.qtpylib.io/>`_ to learn
how to enable SMS notifications, use the bundled Indicators, and more.
Installation
============
Install using ``pip``:
.. code:: bash
$ pip install qtpylib --upgrade --no-cache-dir
Requirements
------------
* `Python <https://www.python.org>`_ >=3.4
* `Pandas <https://github.com/pydata/pandas>`_ (tested to work with >=0.18.1)
* `Numpy <https://github.com/numpy/numpy>`_ (tested to work with >=1.11.1)
* `ØMQ <https://github.com/zeromq/pyzmq>`_ (tested to with with >=15.2.1)
* `PyMySQL <https://github.com/PyMySQL/PyMySQL>`_ (tested to with with >=0.7.6)
* `pytz <http://pytz.sourceforge.net>`_ (tested to with with >=2016.6.1)
* `dateutil <https://pypi-hypernode.com/pypi/python-dateutil>`_ (tested to with with >=2.5.1)
* `Nexmo <https://github.com/Nexmo/nexmo-python>`_ for SMS support (tested to with with >=1.2.0)
* `Twilio <https://github.com/twilio/twilio-python>`_ for SMS support (tested to with with >=5.4.0)
* `Flask <http://flask.pocoo.org>`_ for the Dashboard (tested to work with >=0.11)
* `Requests <https://github.com/kennethreitz/requests>`_ (tested to with with >=2.10.0)
* `Beautiful Soup <https://pypi-hypernode.com/pypi/beautifulsoup4>`_ (tested to work with >=4.3.2)
* `IbPy2 <https://github.com/blampe/IbPy>`_ (tested to work with >=0.8.0)
* `ezIBpy <https://github.com/ranaroussi/ezibpy>`_ (IbPy wrapper, tested to with with >=1.12.26)
* Latest Interactive Brokers’ `TWS <https://www.interactivebrokers.com/en/index.php?f=15875>`_ or `IB Gateway <https://www.interactivebrokers.com/en/index.php?f=16457>`_ installed and running on the machine
* `MySQL Server <https://www.mysql.com/>`_ installed and running with a database for QTPyLib
-----
Legal Stuff
===========
QTPyLib is distributed under the **GNU Lesser General Public License v3.0**. See the `LICENSE.txt <./LICENSE.txt>`_ file in the release for details.
QTPyLib is not a product of Interactive Brokers, nor is it affiliated with Interactive Brokers.
You can find other examples in the qtpylib/examples directory.
P.S.
----
I'm very interested in your experience with QTPyLib. Please drop me an note with any feedback you have.
**Ran Aroussi**
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