Quantitative Trading Python Library
Project description
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, yet powerful, trading library that will let me focus on the trading logic itself and ignore everything else.
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:
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.pyIf your strategy needs order book / market depth data, add the --orderbook flag to the command:
$ python blotter.py --orderbook2. 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.
Installation
Install using pip:
$ pip install qtpylib --upgrade --no-cache-dirRequirements
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 >=1.12.62)
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
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