QTPyLib, Pythonic Algorithmic Trading

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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 »

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 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.
  • 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 uses 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 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()

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.


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 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-Python for SMS support (tested to with with >=1.2.0)
  • Twilio-Python 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)
  • IbPy2 (tested to work with >=0.8.0)
  • ezIBpy (IbPy wrapper, tested to with with >=1.12.56)
  • Latest Interactive Brokers’ TWS or IB Gateway installed and running on the machine
  • MySQL Server installed and running with a database for QTPyLib