The article “Backtest Trading Python: Frameworks & Guide” was originally published on IBridgePy blog.
Implementing backtest trading Python is essential for validating systematic trading strategies before deployment. Using backtest trading Python, traders can identify flaws early. This guide covers the frameworks for implementing effective backtest trading Python with Interactive Brokers and Python.
Live trading and trade simulation are related capabilities that overlap with backtesting. STS performance can be quantified using historical data. By visualizing price behavior and trade triggering on a bar-by-bar basis, trading simulators take backtesting to the next level. In simulated/live trading, orders are generated, routed to brokers, and positions are maintained as the orders are executed. STS is tested in real-time: signaling trades, generating orders, routing orders to brokers, and maintaining positions.
STS for backtest trading in Python
Several open-source Python backtesting frameworks are available to the Python community. Documentation and development are, however, still in their infancy. The Github repositories for this open-source backtesting framework are a great resource if you like team-building.
It’s essential to define your STS requirements before evaluating backtesting frameworks.
Backtest Trading Python Frameworks for Multiple Asset Classes
Is your trading focused on one or more asset classes?
Backtest trading in python has the frameworks enjoy the ability to use YahooFinance’s US Equities data, but the framework must provide data for derivatives, ETFs, and emerging markets securities. We go beyond data to cover asset classes. Does the framework automatically generate roll-over trades for futures and options with finite lengths? Is illiquidity a concern? When executing large orders, what assumptions should be made?
How often and in what detail does your STS collect data? Trading systems that require every tick or bid/ask to have a very different set of data management issues compared to those that operate on a 5-minute or hourly basis. As a result, hedge funds and HFT shops have invested significantly in building robust, scalable backtesting frameworks that can handle those volumes and frequencies of data. For example, some platforms offer minute-level data for various asset classes, including S&P stocks.
Components– Data and STS acquisition are the components that consume the STS definition and script files and provides the requisite data to the testing component. To speed up STS testing, the framework should support canned functions for the most popular technical indicators if any STS must be recorded before backtesting. According to the framework or what the user is capable of importing, users determine how long of a historical period to backtest.
Components of Backtest trading in python
In performance testing, a broad range of risk and performance metrics are calculated, including max drawdown, Sharpe, and Sortino ratios, for the requested historical data window. Equity curves and decided statistics are supported by most frameworks.
The STS process is dominated by optimization, which consumes the majority of the computing resources. Consider a distributed/parallel processing framework if your STS requires optimization.
There are three more components technical indicators, portfolio context, and position sizing.
This blog will help you to understand the backtesting systematic trading strategies and its framework components and requirements for backtesting trading in python. For more information, visit our tutorials or check out our download page to get started with stock trading Python.
The author of this article is not affiliated with Interactive Brokers.
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Disclosure: API Examples Discussed
Throughout the lesson, please keep in mind that the examples discussed are purely for technical demonstration purposes, and do not constitute investment advice, an investment recommendation or investment research. Also, it is important to remember that placing trades in a paper account is recommended before any live trading.
Disclosure: Order Types / TWS
The order types available through Interactive Brokers Trader Workstation are designed to help you limit your loss and/or lock in a profit. Market conditions and other factors may affect execution. In general, orders guarantee a fill or guarantee a price, but not both. In extreme market conditions, an order may either be executed at a different price than anticipated or may not be filled in the marketplace.
Disclosure: Futures Trading
Futures are not suitable for all investors. The amount you may lose may be greater than your initial investment. Before trading futures, please read the CFTC Risk Disclosure. A copy and additional information are available at the Warnings and Disclosures section of your local Interactive Brokers website.
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