advanced-algorithmic-trading

Chapter 24
Introduction to QSTrader
In this chapter the freely-available open-source backtesting engine–QSTrader–will be introduced.
This software is used for nearly all of the trading strategy simulations within this book.
QSTrader is a full portfolio and order management system (OMS) containing modules for data
ingestion, event-driven backtesting, portfolio construction, position tracking, position sizing, risk
management, execution simulation and simulated brokerage connection.
The project homepage is currently hosted on GitHub at https://github.com/mhallsmoore/
qstrader. Please visit it for the latest project news.
24.1 Motivation for QSTrader
One of the most important moments in the development of a quantitative trading strategy occurs
when a backtested strategy is finally set to trade live. This usually involves a complex transition
from "research style" code (commonly written without proper software engineering methodology)
to deployment in a production environment, often on a remote server.
Unfortunately the actual performance of the deployed strategy can be significantly worse than
that of the same backtested system. There are many reasons for this:
• Overfit Models - The trading strategy was fit too heavily to historical data and was not
sufficiently validated.
• Transaction costs - These include spread, fees, slippage and market impact.
• Latency to liquidity provider - This is the time taken between issuing an order to a
brokerage and the brokerage executing it.
• Market regime change - A strategy or portfolio might have behaved well in previous
market conditions but fundamental changes to the market, such as a new regulatory environment
reduce the performance of the strategy.
• Strategy decay - The strategy eventually ends up being replicated by too many traders
and thus becomes "arbitraged away".
The most common reason for significant underperformance compared to a backtest, apart
from overfit models, is incomplete transaction cost handling in the simulation. Spread, slippage,
fees and market impact all contribute to reduced profitability when a strategy is traded live.
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