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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

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QTPyLib, Pythonic Algorithmic Trading

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README 

        
QTPyLib, Pythonic Algorithmic Trading

=====================================



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\



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**