Here at QiT, we use an algorithm to identify all our trading signals.
This forces us to make decisions about our trades before we put them on. This is something all traders should do yet few accomplish.
When I started QiT, and before I started this long distance marathon that got me to where QiT is today, I was truly amazed at how many decisions had to be made before I was able to dissect a very simple trade.
Let’s take, for example, a simple moving average crossover type of trade. Here is a short list of decisions you have to make before you even consider entering a trade:
- What timeframe are you using? Daily? Weekly? Minute? Hourly?
- Which Market are you going to use? All of them? US Only?
- Which subset of #2 are you going to trade? Large Cap? Small Cap? ETFs?
- Which moving average type are you going to use? Simple? Weighted? Exponential?
- Which averages are you going to use? Of course, #1 has to be answered before you can answer this one.
- How are you going to enter a position? Market? Limit?
- What criteria will you use to exit a position?
- How are you going to exit a position? Market? Limit?
- Will you use a Stop Loss or a Dynamic Stop?
“Most traders, especially those who are new to the game, seem to be on a lookout for the ideal trading system. And while there are many trading strategies and systems that work well, such systems are the direct result of relentless testing, tweaking, optimizing, and require disciplined traders to follow through with the rules of the trading system in its entirety. Most, if not all, of these trading systems, are the result of months, maybe even years, of trading system development.” QuantShare.com
Objective Functions
Howard Bandy calls parameters “objective functions”. Defining the following objective functions will help narrow down the number of trading systems considered:
* Timeframe – What timeframe will the system use and how long will the strategy hold positions? Are the portfolios going to hold positions for minutes to hours? Or days to weeks. Or for the old proverbial, buy and hold? The longer a position is held the greater the chance of encountering the “black swan” events. Therefore, it’s most useful to utilize a timeframe that gives the shortest holding period logistically possible. One option is day trading (holding for minutes to hours) but this increases complexity and discards the ability to use end of day data (EOD). The best option is swing trading which keeps a position for the shortest period of time, while still using EOD and no need for the complexity of intraday data.
* Trading Vehicle or Market – Will the algorithm trade options? How about Forex, commodities or the futures markets? Those markets do not fit a model of keeping it simple while appealing to a wide audience. Therefore here at QiT, we chose the boring but liquid US equities and ETFs.
Therefore here at QiT, we chose the boring but liquid US equities and ETFs.
* Long or Short – In order to achieve balance, the algorithm must have both long and short strategies – at least in a margin account, where short positions are allowed. Develop long-only position so they step aside in bear markets.
* Mean Reversion or Momentum – Since most traders trade momentum strategies, it’s surprising to learn that mounting evidence and research support the superiority of Mean Reversion strategies. With that said, QiT understands that momentum strategies have their place in a portfolio.
* Annual Return and Maximum Drawdown – What is the minimum Compounded Annual Return (CAR) will you accept? What is the Maximum Drawdown you will accept?
Final analysis
So the parameters for our strategies are:
- Swing trading,
- US equities both long and short
- Hold for days
- Both mean reversion and momentum.
The universe of possible strategies opens up an algorithm developer makes these decisions. Now the job of development becomes a great deal easier.
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