Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/ranaroussi/qtpylib

QTPyLib, Pythonic Algorithmic Trading
https://github.com/ranaroussi/qtpylib

algo-trading algorithmic-trading algotrading backtester interactive-brokers interactivebrokers quantitative-finance

Last synced: about 2 months ago
JSON representation

QTPyLib, Pythonic Algorithmic Trading

Awesome Lists containing this project

README

        

QTPyLib, Pythonic Algorithmic Trading
=====================================

.. image:: https://img.shields.io/badge/python-3.4+-blue.svg?style=flat
:target: https://pypi.python.org/pypi/qtpylib
:alt: Python version

.. image:: https://img.shields.io/pypi/v/qtpylib.svg?maxAge=60
:target: https://pypi.python.org/pypi/qtpylib
:alt: PyPi version

.. image:: https://img.shields.io/pypi/status/qtpylib.svg?maxAge=60
:target: https://pypi.python.org/pypi/qtpylib
:alt: PyPi status

.. image:: https://img.shields.io/travis/ranaroussi/qtpylib/main.svg?maxAge=1
:target: https://travis-ci.org/ranaroussi/qtpylib
:alt: Travis-CI build status

.. image:: https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat
:target: http://qtpylib.io/docs/latest/?badge=latest
:alt: Documentation Status

.. image:: https://img.shields.io/badge/Chat-Discord-%237289d6.svg?style=flat&logo=discord&maxAge=60
:target: https://discord.gg/7wEzsuV
:alt: Chat on Discord

.. image:: https://img.shields.io/github/stars/ranaroussi/qtpylib.svg?style=social&label=Star&maxAge=60
:target: https://github.com/ranaroussi/qtpylib
:alt: Star this repo

.. image:: https://img.shields.io/twitter/follow/aroussi.svg?style=social&label=Follow&maxAge=60
: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 library** written in Python,
that supports backtesting, as well as paper and live trading via
`Interactive Brokers `_.

I developed QTPyLib because I wanted for a simple,
yet powerful, trading library that will let me focus on the
trading logic itself and ignore everything else.

`Full Documentation » `_

`Changelog » <./CHANGELOG.rst>`_

-----

**Read about the future of QTPyLib here:**
https://aroussi.com/post/the-future-of-qtpylib

-----

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 analysis and backtesting.
- Using pub/sub architecture using `ØMQ `_ (ZeroMQ) for communicating between the Algo and the Blotter allows for a single Blotter/multiple Algos running on the same machine.
- **Support for Order Book, Quote, Time, Tick or Volume based strategy resolutions**.
- Includes many common indicators that you can seamlessly use in your algorithm.
- **Market data events use asynchronous, non-blocking architecture**.
- Have orders delivered to your mobile via SMS (requires a `Nexmo `_ or `Twilio `_ account).
- Full integration with `TA-Lib `_ via dedicated module (`see documentation `_).
- Ability to import any Python library (such as `scikit-learn `_ or `TensorFlow `_) to use them in your algorithms.

-----

Quickstart
==========

There are 5 main components to QTPyLib:

1. ``Blotter`` - handles market data retrieval and processing.
2. ``Broker`` - sends and process orders/positions (abstracted layer).
3. ``Algo`` - (sub-class of ``Broker``) communicates with the ``Blotter`` to pass market data to your strategies, and process/positions orders via ``Broker``.
4. ``Reports`` - provides real-time monitoring of trades and open positions 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(window=10).mean()
bars['long_ma'] = bars['close'].rolling(window=20).mean()

# 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 Dashboard(Reports):
pass # we just need the name

if __name__ == "__main__":
dashboard = Dashboard(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::

You can find other examples in the ``qtpylib/examples directory``.
Please refer to the `Full Documentation `_ 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 `_ >=3.4
* `Pandas `_ (tested to work with >=0.18.1)
* `Numpy `_ (tested to work with >=1.11.1)
* `PyZMQ `_ (tested to work with >=15.2.1)
* `PyMySQL `_ (tested to work with >=0.7.6)
* `pytz `_ (tested to work with >=2016.6.1)
* `dateutil `_ (tested to work with >=2.5.1)
* `Nexmo-Python `_ for SMS support (tested to work with >=1.2.0)
* `Twilio-Python `_ for SMS support (tested to work with >=5.4.0)
* `Flask `_ for the Dashboard (tested to work with >=0.11)
* `Requests `_ (tested to work with >=2.10.0)
* `IbPy2 `_ (tested to work with >=0.8.0)
* `ezIBpy `_ (IbPy wrapper, tested to work with >=1.12.66)
* Latest Interactive Brokers’ `TWS `_ or `IB Gateway `_ installed and running on the machine
* `MySQL Server `_ installed and running with a database for QTPyLib

-----

Legal Stuff
===========

QTPyLib is licensed under the **Apache License, Version 2.0**. A copy of which is included in LICENSE.txt.

QTPyLib is not a product of Interactive Brokers, nor is it affiliated with Interactive Brokers.

P.S.
----

I'm very interested in your experience with QTPyLib. Please drop me a note with any feedback you have.

**Ran**