search

ranaroussi / qtpylib

QTPyLib, Pythonic Algorithmic Trading
http://qtpylib.io
Apache License 2.0
2.15k stars 511 forks source link
algo-trading algorithmic-trading algotrading backtester interactive-brokers interactivebrokers quantitative-finance

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 <https://www.interactivebrokers.com>_.

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 » <http://www.qtpylib.io/>_

Changelog » <./CHANGELOG.rst>_

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

Features

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.

  6. 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

  1. 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.

  1. 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 <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

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