Algo-Trading 101 Part 1: Getting Started
So you want to get started in algo-trading huh? While the start of an algo-trading operation may seem like a daunting task, it can be possible to beat the institutional traders at their own game (or at least share in the profits), have fun and learn an immense about trading the markets autonomously in the process. That is what this post will be about, searching for quantitative strategies and a systematic approach to starting an algorithmic trading operation.
What this post will aim to do is explore the journey of the trading business startup, from finding a viable trading strategy, backtesting it for verification on historical performance, setting up the tech-infrastructure (low-cost!) and building and deploying an automated trading system (ATS) to execute your strategy.
| If, like me, you prefer using Linux (personally I’m a fan of the Kubuntu Hardy Heron flavor), you will need to install a virtual machine such as vmware to run a copy of Windows. While OpenOffice is a great solution, from personal experience I have found that Excel really is the best solution when it comes to complex spreadsheets involving macros. If you’re a die-hard OpenOffice Calc fan, go ahead and try it’s macro language but be warned, its not pretty (and not compatible with Visual Basic). |
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Other than that we’ll be looking at using Matlab or using its cheaper alternative, R for the backtesting of some quantitative trading strategies.
Being proficient at basic stats and basic programming will definitely help, but you won’t need a PhD to put the theories into practice. Strategies will mostly be of the quantitative nature e.g. statistical arbitrage, and the aim is to keep things simple for now. StatArb deals with the simplest financial instruments: stocks, futures and sometimes currencies.
Let’s look at the process of developing a successful trading strategies and putting it into action!
Finding A Quantitative Strategy That Works (And Suits You!)
Unlike popular opinion would suggest, many strategies are publicly available and while they may not be super-profitable can be tweaked to become profitable.
The key is to backtest strategies for historical performance, thereby verifying that they represent a sound hypothesis. While this alone does not guarantee a profitable strategy, it is certainly a very good indication on the future success of a strategy.
Key things to consider when deciding on a suitable strategy are:
- Available time: If you’re working full time and only able to trade part time, you probably don’t want to trade intraday strategies but rather stick to ones that trade overnight. Quantitative trading lends itself well to automation in this regard, since the computer can handle most things automatically, if set up correctly.
- How programmatically inclined you are: Knowing some languages like Java, C# or C++ will definitely be a huge plus when it comes to algo-trading (after all its not called ‘algorithmic trading’ for nothing – trading algorithms are just computer programs firing off buy and sell orders to the markets.
- Trading capital: Depending on your level of available capital, you may not be in a position to trading algos in the first place. Your capital will determine alot of things such as what type of accounts you’ll be setting up, brokerage discounts (based on volume traded), etc, etc. Generally, the more you have to begin trading with, the better.
- Does it have a high Sharpe ratio?
- Does it outperform a benchmark? (Say the ASX/S&P200 Index)
- Does it have small enough drawdown and short enough drawdown duration?
- Does the strategy’s historical performance start declining towards the more recent years compared to that of the earlier years?
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