It’s All Alphas
The core driver of PnL across strategies
Introduction
Alphas make up the core of what drives a large majority of firms trading PnL. You can have complicated quoting rules, great models, amazing optimizers, advanced vol curves, or fast latency, but the gist of most trading firms edge can be compiled into some alpha representation. Even execution relies heavily on “execution alphas” in order to deliver highly effective execution costs to trading strategies. In this article, we will cover how to structure and trade alphas in the context of MFT, HFT, execution trading, arbitrage, and options (OMM + options MFT).
Out of all the ways to find an edge, one stands out the most and it is having very strong alphas signals. HFT strategies use this, MFT strategies use this, options strategies use this, execution algorithms use this - practically all advanced trading operations will have some set of proprietary alphas they use to inform their understanding of where price is going. The only exception will be arbitrage, but even that we will see becomes quite alpha like in some cases.
MFT Alphas
This is where everyone hears about alphas mostly, and when I say alphas I am specifically talking about features or “formulaic alphas”. You can generate logical alphas as well, but I find these to be very inefficient. The pipeline I will mention, is a very well known pipeline - one I have talked about in the past. You take in data, you engineer it into your features or “alphas” which is where the true profitability lives and then from here everything else serves as a performance enhancer which builds on your alphas. This is your forecasting, and then your optimizer. I like to view each block in the pipeline as a converter. We convert data into alphas (feature engineering), alphas into forecasts (forecasting / ML), forecasts into target portfolios (portfolio optimization) and target portfolios into individual trades (execution - which we will go over in the next section). The reason logical alphas are inefficient is because they bypass the portfolio optimization and forecasting stages and directly output a target portfolio in many cases. You can include them in the pipeline, but it is quite tricky to use binary outputs, and the best case is that you find a way to represent the idea as a formulaic alpha or run it separately from your formulaic pipeline (they don’t tend to like to work together and this is often what has to occur).
For MFT, it couldn’t be more clear that the alphas are the foundation. In logical form they are the whole strategy and in formulaic form they are the base of what makes all the money. If you do not have good alphas, you will find it impossible to make any money trading in any reasonably competitive market. Unless you find some horribly inefficient market (in which case finding & accessing said market is the alpha itself because such a thing is so rare!) you will rely on alphas to drive profits. Even in the case of a horribly inefficient market you will still need to have some alphas although they may be extremely basic such as quoting around Binance on a small exchange (in this case Binance’s price is the feature itself). This is a common mistake among beginner quants - they falsely believe that you can save bad features with advanced & complicated machine learning. Mentally you should view what happens after the alphas as a multiplier, ESPECIALLY machine learning as it is the most multiplier like of them all. 2 * 0 is still 0! Portfolio optimization is also important and can save you from trading yourself to death, but the boosts from here on are very incremental - and the alpha is driven by your alphas (aptly named!).
Execution
How does really high-performance execution work? Well, at some level it is very much an HFT problem, since you are trying to get limit orders filled with great markouts (hence having some HFT overlap), but at a higher timeframe it becomes an alpha problem. Say we want to do a 1h TWAP. We can choose to speed up or slow down our execution based on our view of liquidity and our view of price. Part of this is deciding the parameters of the execution algorithm itself, this is fairly simple for a market order TWAP as we can take the alpha decay of our signals we want to trade and the curve for how our execution improves as we wait longer and find the optimal intersection through interval iteration where we maximize total edge (inclusive of trading costs). From here, we can figure out the optimal number of chunks and how aggressive to be on limit orders through similar forms of analysis. This gets us to a fairly basic execution setup.