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What is the best time to buy and sell? Generate Trade Ideas using Seasonal Patterns

Trying to bring back the detailed DDs that were here before everything exploded last year with growth. This is from a blog post I recently wrote.

Seasonal patterns repeat themselves time and again in financial markets. From many corrections taking place in Feb-March, to the end of year Santa Clause Rally, retail traders can learn about them, and leverage historical data and seasonality to generate alpha in the markets. For instance, going into small caps IWM at the start of October and staying in till end December every year has yielded a profit of about 9% every year. Compare that with SPY where the average yearly profits are about 17%, despite the holding period being 4 times more i.e 3 months compared to full year. These types of statistics can help us come up with trade ideas, and that is what we are going to discuss in this blog post.

Seasonality Data

We will start with more macro analysis and move towards analysis that is very granular and can help us do a deep dive into seasonal data.

Month, Week, and Hour Seasonality

We are going to talk about the radar charts for months, weeks, and hours seasonality in Stocks Dashboard. The figure below shows a summary of the charts for SPY. Each corner is either a month, hour, or a week day, and the values are the number of times price has closed green for each variable. More specifically, at each point, we look at data from the last 20 years, and calculate how many times the price closed green. For instance, the value at August would be the percentage of how many times in the last 20 years SPY’s price closed higher than it opened in the month of August.


We can see that SPY seasonality starts to get better from July onwards and continues to increase till the year end. This can inform us that trading SPY for bullish plays is best done during the last six months of the year. On the other hand, we can see that the seasonality is quite even/bad at the start of the year, which can actually prevent traders from going heavy into bullish plays at the start of the year. Although this data is useful, it is still not granular enough to give us specific plays. Therefore, let’s talk about a few more tools we have.

Granular Seasonality Across Time

Knowing what’s the best half of the year to buy a stock is good information. But we can do better, and look at more granular information using our Seasonality & Price Movements widget in Stocks Dashboard. This tool shows the cumulative average daily, minute level, and hourly change for the stock under analysis. At each analysis point, we observe how much did the price change, and create a cumulative graph of that change, averaged across time. The purple line demonstrates that while the green and red lines are simply the upper and lower bounds of how much price is expected to move at a maximum.


The chart above illustrates the granular seasonality of SPY from 2010 to 2019. We skip 2020 because of the COVID crash, and want to do testing in 2021. From the chart, we can observe a number of things. First, the start of the year is a very choppy time period for the market in general and stocks do not have a clear direction. From March onwards, price starts to get in a bullish trend, but the trend is not very strong. In around June-August period, there is on average a quick mini-rally. Finally, after August, price starts to gradually continue going up historically with a strong uptrend, leading to an end-of-year Santa Rally. Those are quite a few observations, let us now see if they actually worked in 2021 and 2022. We advise readers to do this analysis on 2020 as well, because they will find very similar stats.


Having the knowledge from looking at the seasonality from 2010 to 2019, let us now see if that applies to future data as well. Analyzing the last two years, we can observe very similar behavior in SPY. Dips and choppy markets both happened in the start of 2021 and 2022, which is not surprising given we had already observed that fact over the historical data. Another great thing is the rally from June to August, which netted about 7% in just three months in 2021. Finally, we can see the big rally at the end of year, which we call the Santa Rally. Overall, the price of the entire year looks very similar to the average price of the last 10 years. That is a very strong edge for retail traders because it lets them stay in and out of positions at the right time, and somewhat time the market, which is an incredibly hard thing to do without looking at any seasonality data.

Intraday Seasonality

Most people look at seasonality based on days, weeks, or months of years. However, there also exists seasonality in how the price of a stock moves intraday, and retail traders can leverage that to great effect. Let us take a look at AMD and how it moves intraday. The first thing we need to do is pick the minute level seasonality in the tool. The figure below shows that the price of the stock, on average, goes down for the first half of the day and goes up on the second half of the day. It is important to note that this is averaged data across many months, and it will not work out exactly every single day. Despite that, if we see a dip on the first half of the day, we can somewhat assume that the stock can potentially move up on the second half of the day.


Let us actually test this theory on minute level data for AMD from Feb 1st to Feb 4th, 2022. The figure below demonstrates the effectiveness of the intraday seasonality. Looking at the four days of data, we can see that the stock generally tends to go down after the market opens. There was only one day where it did go up for a while, but by the mid day, the stock is either down or break even. Despite that, it starts to go up in the later part of the market hours. It was only on Feb 3rd, where it went down a bit towards the end. However, an accuracy of 3/4 is quite good especially since we are simply looking at seasonality without doing any additional analysis.


Analysis like this can help us find patterns that different stocks follow, and make plays based on them. For instance, if you want to go bullish on AMD for a day trade, the second part of the day is where you should enter into your position. For readers, we would urge you to take a look at SPY’s intraday seasonality chart. You might have heard that SPY makes most of its move pre-market and after hours – you will see a proof of that.

Macro Comparisons

Seasonality data also lets us analyze differences in how different sectors or indices move across the year. The chart below shows a comparison between QQQ and IWM.


Looking at the charts, there are some differences that catch the eye. First, QQQ can often rally at the start of the year, but IWM rarely ever goes up much at the start of the year. In contrast, IWM goes up significantly more at the year end compared to QQQ. Although the principle here is valid for all types of comparisons, if you look at the data for IWM in 2021, you will see that we actually did not get any rally at the end of year. This example is added deliberately to show you some limitations of seasonality analysis. It only looks at historical data, and if market regimes change, it can be invalid for future. Therefore, please always do some additional due diligence on top of your seasonality analysis.

Final Thoughts

As discussed in the last section, it is paramount that you do some additional due diligence on top of your seasonality analysis. As long as you’re doing your due diligence, seasonality data can provide you great insights and trade ideas, that can help you become a more profitable trader. We hope this guide will prove useful to you and help you become a better trader. Thanks for reading.

Submitted February 09, 2022 at 11:53PM by hydershykh
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