this week's lesson.
I’ve received many requests to dive into statistics more, especially for those who don’t know where to start.
To simplify things, I’ve made a template that allows you to manually put in your data and help you visualize the charts being developed. Statistics bring an edge to your trading that enables consistent profits in the long-term due to the market reaction to a specific sector, spiking percentage, and understanding level two’s.
I created this template with the generalized idea that the people using it aren’t familiar with spreadsheets, let alone tracking statistics.
But because it may come across as more advanced, I’m going to explain the template structure and how each section benefits the overall data. These periods are the group of months when trading is at its prime or in a lull: January-March, March-August, August-October, and October-December. When you split your data into seasons, it’s easier to see what setups are more likely to occur in a specific time period. With October, we see overstaying gap-down more frequently, so I know that’s a pattern to focus on that month, and that applies for the whole year.
Once you build your data, looking back on previous years helps narrow down entry and exit points in addition to which pattern fits the best. After the date, we move on to the ticker’s name, followed by the sector that this ticker resides. Acknowledging sectors seem to be a missing key element for most traders; however, their unique characteristics are useful in choosing the pattern that compliments that specific sector. Not only that, but when you know how sectors like biotech are going to react, having data to compare will show you which sectors to avoid as well.
Not only that, but when you know how sectors like biotech are going to react, having data to compare will show you which sectors to avoid as well.
Next is the index, which can either be NASDAQ, NYSE, OTC, or pink sheets.
When you’re tracking a stock index, it’s important to know the difference between these markets. The New York Stock Exchange, NYSE, typically has much higher market caps than NASDAQ, which deals with most small caps. Highlighting the index amplifies your spreadsheet because, with OTC, the liquidity of these stocks is low, which makes for a difficult read on entry and exit points. Noting the index brings you one step closer to understanding where you are most comfortable as a trader.
Which pattern you use will be logged in under setups next, and this is useful data that you can look back on to find a better edge in those detailed patterns. After that, we have outstanding shares and then float, which are one and the same, but outstanding shares may have more institution owners, so it’s a good thing to track. I find that float is one of the more important factors, and to narrow it down, I separate float into zones. Based on my data, the highest percentage runner typically happens in low-float, and with that understanding, I can also figure where I went wrong in a specific float.
From there, we have daily volume, which is the overall volume traded per day, followed by where the stock opened, how high it spiked, and then the category of the top tick.
The top tick category will be the pattern you’re using while also noting the top tick for that specific pattern. Let’s say that the top tick is $5, which can be compared to the recurrent entry, and if those two numbers are far off, you need improvements on your execution.