Quantitative Trading Python Library
QTPyLib, Pythonic Algorithmic Trading
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.
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:
Blotter - handles market data retrieval and processing.
Broker - sends and process orders/positions (abstracted layer).
Algo - (sub-class of Broker) communicates with the Blotter to pass market data to your strategies, and process/positions orders via Broker.
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.
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
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): 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_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 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:
$ 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.
Install using pip:
$ pip install qtpylib --upgrade --no-cache-dir
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
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.
I’m very interested in your experience with QTPyLib. Please drop me a note with any feedback you have.
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