Issue #2 — Sharpening your edge with data, getting started

📣 Issues will now have podcasts attached, so I’m re-releasing this post with the added podcast.

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You can have an edge in the way you approach the markets, a method you use, a setup you trade, or even a mindset. Whatever floats your boat, as long as it’s measurable. It also doesn’t have to be unique to you, it can be a common setup and maybe you just exploit it better or differently than the next trader.

The only certain thing is that if you’re trading without an edge, you’re the sucker at the poker table. Trading without knowing your edge is arguably just as bad.

It’s worthwhile investing some time and effort into refining your trading by collecting and exploring your own data.

It can also be a powerful way to build conviction early in your trading journey. Okay, dead horse beat. Let’s explore methods for collecting data.

The example above is a regression analysis I ran on a specific setup of mine—intended to be traded in a specific way, with specific parameters, under specific market conditions. Dots on the chart that stray too far from the trend-line are setups that are outliers, performing out of the norm.

This type of analysis is a great way to introduce rigor to the process of trade selection. The outcomes from taking those setups, win or lose, are then by nature more easily measured; improving your data collection overall. Sounds great! But it’s no small task like anything worthwhile in trading.

At the end of the day, we’re looking for a pattern in your trading or setups that we can reproduce to fine-tune your performance and better define your edge.

a. Define your setup

For the example below, we’ll use a simple bull flag breakout. I would boil down the 1-3 setups that you’re willing to spend a significant amount of time studying and pick one to start.

You need a defined setup to continue.

b. Make the setup reproducible

Your goal now is to convert everything that may be relevant to the outcome of your setup into a data point. Begin by assigning letters to each of the key points in your pattern. 🥳 You now have reference points for your spreadsheet.

X-A references a the start of your pattern, and F-G references the breakout. Now you can begin collecting data on the setups you trade across different stocks and referencing them in your spreadsheet.

Here’s an example from one of my sheets on what this data may look like.

You can see that I was able to calculate the MFRR, Maximum Favorable Risk to Reward, from this data set. Just one of the many data explorations you can do that can help inform your trading decisions.

Be sure to include external factors as well, like fundamental parameters, or anything that weighs on how you would take the setup during live trading. You want to model the data to be as wide as possible, then work towards narrowing it down.

c. Trade the setup 1,000 times

To extract statistically significant insights from your data, you need a large data set.

It’s only after 1,000 trades, traded in the same way, under the same conditions, that you are able to confidently assign a statistical probability.

Don’t worry, there are shortcuts. You can backtest setups manually—or with an algorithm— and walk trades forward to glean more data from your backtest results. This can be as simple or complicated as you want. Just stick to the goal of collecting good consistent data.

In the past I’ve used the ThinkorSwim on-demand feature, I’ve also manually walked the chart back