Join the Quantcademy private membership portal that caters to the rapidly-growing retail quant trader community. You'll find a knowledgeable, like-minded group of quant traders ready to answer your most pressing quant trading questions.
Check out my ebook on quant trading where I teach you how to build profitable systematic trading strategies with Python tools, from scratch. Take a look at my new ebook on advanced trading strategies using time series analysis, machine learning and Bayesian statistics, with Python and R.
In this article Frank Smietana, one of QuantStart's expert guest contributors describes the Python open-source backtesting software landscape, and provides advice on which backtesting framework is suitable for your own project needs. Backtesting is arguably the most critical part of the Systematic Trading Strategy STS production process, sitting between strategy development and deployment live trading. If a strategy is flawed, rigorous backtesting will hopefully expose this, preventing a loss-making strategy from being deployed.
A number of related capabilities overlap with backtesting, including trade simulation and live trading. Backtesting uses historic data to quantify STS performance. Trading simulators take backtesting a step further by visualizing the triggering of trades and price performance on a bar-by-bar basis. Most frameworks go beyond backtesting to include some live trading capabilities. This is convenient if you want to deploy from your backtesting framework, which also works with your preferred broker and data sources.
The Python community is well served, with at least six open source backtesting frameworks available. They are however, in various stages of development and documentation. If you enjoy working on a team building an open source backtesting framework, check out their Github repos.
What asset class es are you trading? While most of the frameworks support US Equities data via YahooFinance, if a strategy incorporates derivatives, ETFs, or EM securities, the data needs to be importable or provided by the framework.
Asset class coverages goes beyond data. What about illiquid markets, how realistic an assumption must be made when executing large orders? What data frequency and detail is your STS built on? What order type s does your STS require? At a minimum, limit, stops and OCO should be supported by the framework. The early stage frameworks have scant documentation, few have support other than community boards. Data and STS acquisition: If the framework requires any STS to be recoded before backtesting, then the framework should support canned functions for the most popular technical indicators to speed STS testing.
Users determine how long of a historical period to backtest based on what the framework provides, or what they are capable of importing. Most all of the frameworks support a decent number of visualization capabilities, including equity curves and deciled-statistics. In the context of strategies developed using technical indicators , system developers attempt to find an optimal set of parameters for each indicator. Most simply, optimization might find that a 6 and 10 day moving average crossover STS accumulated more profit over the historic test data than any other combination of time periods between 1 and In a portfolio context , optimization seeks to find the optimal weighting of every asset in the portfolio, including shorted and leveraged instruments.
On a periodic basis, the portfolio is rebalanced, resulting in the purchase and sale of portfolio holdings as required to align with the optimized weights. Position sizing is an additional use of optimization, helping system developers simulate and analyze the impact of leverage and dynamic position sizing on STS and portfolio performance. PyAlgoTrade is a muture, fully documented backtesting framework along with paper- and live-trading capabilities.
Data support includes Yahoo! The framework is particularly suited to testing portfolio-based STS, with algos for asset weighting and portfolio rebalancing. Modifying a strategy to run over different time frequencies or alternate asset weights involves a minimal code tweak.
This platform is exceptionally well documented, with an accompanying blog and an active on-line community for posting questions and feature requests. Backtrader supports a number of data formats, including CSV files, Pandas DataFrames, blaze iterators and real time data feeds from three brokers. These data feeds can be accessed simultaneously, and can even represent different timeframes.
Open source contributors are welcome. Zipline is an algorithmic trading simulator with paper and live trading capabilities. Accessible via the browser-based IPython Notebook interface, Zipline provides an easy to use alternative to command line tools. Zipline provides 10 years of minute-resolution historical US stock data and a number of data import options. QSTrader is a backtesting framework with live trading capabilities. QuantStart Founder Michael Halls-Moore launched QSTrader with the intent of building a platform robust and scalable enough to service the needs of institutional quant hedge funds as well as retail quant traders.
Both backtesting and live trading are completely event-driven, streamlining the transition of strategies from research to testing and finally live trading. The main benefit of QSTrader is in its modularity, allowing extensive customisation of code for those who have specific risk or portfolio management requirements. It is human nature to focus on the reward of developing a hopefully profitable STS, then rush to deploy a funded account because we are hopeful , without spending sufficient time and resources thoroughly backtesting the strategy.
But backtesting is not just a gatekeeper to prevent us from deploying flawed strategies and losing trading capital, it also provides a number of diagnostics that can inform the STS development process. In future posts, we'll cover backtesting frameworks for non-Python environments, and the use of various sampling techniques like bootstrapping and jackknife for backtesting predictive trading models.
You'll get instant access to a free part email course packed with hints and tips to help you get started in quantitative trading! Backtesting Systematic Trading Strategies in Python: Considerations and Open Source Frameworks. By Frank Smietana on July 18th, In this article Frank Smietana, one of QuantStart's expert guest contributors describes the Python open-source backtesting software landscape, and provides advice on which backtesting framework is suitable for your own project needs.
Six Backtesting Frameworks for Python Standard capabilities of open source Python backtesting platforms seem to include: MIT Backtrader This platform is exceptionally well documented, with an accompanying blog and an active on-line community for posting questions and feature requests.
MIT Embracing the Backtest It is human nature to focus on the reward of developing a hopefully profitable STS, then rush to deploy a funded account because we are hopeful , without spending sufficient time and resources thoroughly backtesting the strategy. Just Getting Started with Quantitative Trading? Quant Trading Lessons You'll get instant access to a free part email course packed with hints and tips to help you get started in quantitative trading!
No Spam Real, actionable quant trading tips with no nonsense.More...