QTPyLib, Pythonic Algorithmic Trading

QTPyLib (Quantitative Trading Python Library) is a simple, event-driven algorithmic trading system written in Python 3, that supports backtesting and live trading using Interactive Brokers 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 »

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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 (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 quote, time, tick or volume based strategy resolutions
• 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.

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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:

# 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:

$ python blotter.py

2. Write your Algorithm

While the Blotter running in the background, write and execute your algorithm:

# strategy.py
from qtpylib.algo import Algo

class CrossOver(Algo):

    def on_start(self):
        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:

$ 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:

# 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:

$ 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.

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Note

Please refer to the Full Documentation to learn how to enable SMS notifications, use the bundled Indicators, and more.

Installation

First, install IbPy (for some reason I can't get this installed automatically):

$ pip install git+git://github.com/blampe/IbPy --user --upgrade

Then, install QTPyLib using pip:

$ 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)
• ØMQ (tested to with with >=15.2.1)
• PyMySQL (tested to with with >=0.7.6)
• pytz (tested to with with >=2016.6.1)
• dateutil (tested to with with >=2.5.1)
• Nexmo for SMS support (tested to with with >=1.2.0)
• Twilio for SMS support (tested to with with >=5.4.0)
• Flask for the Dashboard (tested to work with >=0.11)
• Requests (tested to with with >=2.10.0)
• Beautiful Soup (tested to work with >=4.3.2)
• IbPy (tested to work with >=0.7.2-9.00)
• ezIBpy (IbPy wrapper, tested to with with >=1.12.19)
• Latest Interactive Brokers’ TWS or IB Gateway installed and running on the machine

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Legal Stuff

QTPyLib is distributed under the GNU Lesser General Public License v3.0. See the 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