The Research HUB: FX Primer, Pt 5

The primer for understanding and trading FX

Hey everyone,

This is Part 5 of the FX Primer!

5-Part FX Primer Breakdown:

• Part 1: FX - Resources, The Big Picture, Variables, Aggregating Knowledge, and Essential Tools.

• Part 2: FX - Synthesizing Information from Part 1: Theory, Practice, Causal Mechanics vs. Regression Analysis.

• Part 3: Delving into Historical Case Studies: The Importance of Studying History, Continuity vs. Discontinuity, and the Challenges of Backtesting in FX.

• Part 4: Examining the Current Environment.

• Part 5: Integrating Knowledge: Top-Down and Bottom-Up Analysis, Attribution Analysis, the Expectations vs. Actual Matrix, and Quantitative Models.

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Intro:

We have finally made it to the last part of the FX primer!

My goal has been to cover each part of the FX analysis process, from identifying the structural regime all the way to understanding how GIP impulses impact returns and risk premiums. The ultimate task is to connect all these dynamics with price action and execution. In my mind, I always aim for a seamless causal chain between all parts of my processes. That is the purpose of this article.

A quick side note on building models: A number of you have reached out asking for examples of FX models or how to build them. Let me provide several principles and thoughts on this:

• A model doesn’t necessarily need to be in Excel or code. A model can be a series of functions that you stack, leading you to a specific action. For example, I can create three “if/then” statements:

  • a: If headline and core CPI are accelerating YoY, then = a bearish bond regime.

  • b: If the forward curve is pricing an increasing rate of hikes, then = bearish bond regime.

  • c: If the breakeven curve is upward sloping, then = bearish bond regime.

• Then I create an additional function that says, if a, b, and c all = bearish bond regime, then short bonds on a moving average crossover with a 3 stdv stop loss. From here, you can begin to backtest your ideas and see where you can refine them. I basically have spreadsheets filled with very specific and detailed functions like this. I also have many things coded in Python.

• Much of this process of model building is about understanding the moving parts, extracting signals from them, and then stacking those signals with functions so that you can align as many things as possible with your execution.

• Having all these things in Excel or code is just an added bonus. In my opinion, it's more important to start with the functions and actually know WHAT they mean and WHY they are moving than starting by backtesting a bunch of potential rules.

Alright, let’s delve into some more specifics...