QTPyLib, Pythonic Algorithmic Trading

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QTPyLib (Quantitative Trading Python Library) 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 »


  • 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.


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:

# blotter.py
from qtpylib.blotter import Blotter

class MainBlotter(Blotter):
    pass # we just need the name

if __name__ == "__main__":
    blotter = MainBlotter()

Then, with IB TWS/GW running, run the Blotter from the command line:

$ python blotter.py

If your strategy needs order book / market depth data, add the --orderbook flag to the command:

$ python blotter.py --orderbook

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):

    def on_fill(self, instrument, order):

    def on_quote(self, instrument):

    def on_orderbook(self, instrument):

    def on_tick(self, instrument):

    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:

        # 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

                # record values for later analysis

        # trading logic - exit signal
        elif bars['short_ma'].crossed_below(bars['long_ma'])[-1]:
            if positions["position"] != 0:

                # exit / flatten position

                # record values for later analysis

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

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

if __name__ == "__main__":
    dashboard = Dashboard(port = 5000)

To run your dashboard, run it from the command line:

$ python dashboard.py

>>> Dashboard password is: a0f36d95a9
>>> Running on (Press CTRL+C to quit)

Now, point your browser to http://localhost:5000 and use the password generated to access your dashboard.


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.


Install using pip:

$ pip install qtpylib --upgrade --no-cache-dir


  • 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