advanced-algorithmic-trading

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While vectorised backtests are simple to code they are often severely lacking in respect of
proper implementation details. Transaction costs are usually ignored and "nitty gritty" implementation
details are idealised in order to minimise code complexity.
This motivated the philosophy and subsequent development of QSTrader. QSTrader has
been designed to provide a realistic simulation environment that attempts to mirror the live
deployment of a trading strategy as much as possible. For instance, realistic brokerage fees are
turned on by default–an unusual design choice compared to many other backtesting systems.
It is the author’s wish that the presented backtests of trading strategies within this book
are as realistic as possible. This provides readers with a detailed insight into their real-world
performance before deciding to trade them live.
Another motivating goal for QSTrader was to ensure that it was freely available by releasing
it under a permissive open source license (the MIT license). This was to encourage contributions
from the wider community. It has been developed and tested in Python 2.7.x, 3.4.x and 3.5.x to
allow straightforward cross-platform compatibility.
In the twelve months since development began QSTrader has attracted more than a dozen
volunteer developers, of which five have made significant contributions, with three in particular
having provided exceptional contributions to the project. The author would like to personally
thank Ryan Kennedy, Femto Trader and Nick Willemse for their extensive generosity and
fantastic contributions to the QSTrader project.
QSTrader is strongly influenced by another QuantStart project–QSForex–also available under
a permissive open-source license. QSForex only supports one brokerage–OANDA–via their
RESTful API. QSTrader, however, is being developed to connect to the newly released Interactive
Brokers Python API, which was released very close to the publication date of this book.
The eventual goal is to allow equities, ETFs and forex to all be traded under the same portfolio
framework.
The project page of QSTrader will always be available on Github at https://github.com/
mhallsmoore/qstrader. Please head there in order to study the most recent installation instructions
and documentation.
Ultimately, QSTrader will be used for all of the trading strategies within this book. At this
stage however it is under active development both by the author and the community. As new
modules are released the book, strategies and their performance will be updated.
24.2 Design Considerations
The design of QSTrader is equivalent to the type of bespoke algorithmic trading infrastructure
stack that might be found in a small quantitative hedge fund manager. Thus, the author considers
the end goal of this project to be a fully open-source institutional grade production-ready portfolio
and order management system, with risk management layers across positions, portfolios and the
infrastructure as a whole.
QSTrader will eventually allow end-to-end automation, meaning that minimal human intervention
is required for the system to trade once it is set "live". It is of course impossible to
completely eliminate human involvement, especially where input data quality is concerned as
with erroneous ticks from data vendors. However it is certainly possible to have the system
running in an automated fashion for the majority of the time.