Have you ever wondered how the Dow Jones performs in the second half of the year based on its performance in the first half?

In this exciting video, we reveal the results of a century-long backtest on a strategy that buys into the Dow Jones during the second half of the year—but only if the first six months ended with a positive performance.

The Dow Jones Industrial Average, one of America’s premier stock indices, tracks 30 of the most influential companies in the industrial and financial sectors.

Analyzing historical data from 1920 to today (yes, over 100 years!), we found that implementing this strategy by buying at the start of July when the market is up for the year so far could yield remarkable results.

In today’s video, you’ll discover:
-A fascinating pattern in the Dow Jones that could give you a trading edge
-A deeper dive into the strategy’s logic, complete with implementation details (and yes, the code is included!)

Sounds interesting, right?

Discover all the details now!

Happy viewing! 😉

Transcription

What does the market have in store for us?

Often, as traders and investors, we wonder what the market has in store for us in the coming months.

And it's certainly not a question that can be answered with certainty.

Stock markets: first half of the year vs. the second half

In this video, however, I want to dive into an interesting aspect of stock markets, explaining how the performance in the first half of the year might relate to the second half.

Testing information using a quantitative approach

Specifically, can observing the first six months' performance provide any useful insights for the rest of the year?

Well, we certainly won’t know until we test this hypothesis, as always, using a quantitative approach.

Visualizing the Dow Jones Industrial Average on a chart

So, let’s skip the chatter and go straight to the chart.

First, let’s take a look at what’s plotted on the chart.

Here, I’ve charted the Dow Jones Industrial Average—one of the most significant and historically rich indices.

The advantages of having a broad historical dataset

In this case, as you can see, I’ve plotted data from 1920 up to the present day.

What’s the advantage of having such a long historical record?

When taking a longer-term approach with fewer trades, a larger sample size helps us gather more relevant data.

Understanding the strategy

Now, let’s take a closer look at the strategy.

Here, as you can see, I’ve drawn a simple blue rectangle that indicates the performance for the first six months of the year, selected without any particular criterion.

As you can see, the rectangle starts on the first available trading day in January and ends on the last trading day in June.

In this example, you’ll notice that the performance for the first six months was positive.

The goal now is to evaluate whether there’s any relationship between this performance in the first half and the remaining months of the year.

Analyzing the code

Now, let’s dive into the code.

As you can see here, it’s just 11 lines, with comments and spaces, so nothing overly complex.

Let’s start with the first part.

In this part, I calculated the performance for the first six months—essentially, what’s shown in this blue box.

How did I pinpoint the opening value for these first six months?

With a simple condition: if the current year’s bar differs from the previous year, then it’s the first day of a new year.

So, on that first day, we assign that value to this variable.

Next, how did I identify the closing value on the last day of June?

It’s similar in logic—if the current month differs from the previous month and it’s July, we assign the previous bar's closing value to this variable.

Understanding the code or the conditions isn’t essential here.

What’s important is that the variable value2 represents this closing value, while value1 represents the opening value.

Now, let’s look at the market entry point.

The entry occurs if value2 is greater than value1 and it’s July.

In other words, there needs to be a positive performance, and it must be July.

As for the exit? Well, it’s straightforward.

When we move into the next year, we sell on the next bar with a market order.

We do this because our aim is to assess what happens in the following six months after a positive first half.

Strategy performance review

Now, let’s look at the strategy’s performance, which I’ve already added to the chart, but before seeing that, there’s an interesting aspect to highlight regarding the trade size.

In this case, instead of using a single contract per trade, I assigned a specific capital value to each entry.

Here, I chose a million dollars, and that’s for a very simple reason. It’s because the Dow Jones index is worth around 40,000 points today, making a million-dollar position size more efficient for our backtest.

Alright, let’s go and see the strategy’s performance.

In this case, since the first six months’ performance was positive, we bought at the first available July bar and closed at year-end.

Examining the equity line

Moving to the performance, as you can see, we have a fairly steady equity line.

There are noticeable drawdowns, such as during the 1929-1930 crash or the 1987 crisis.

When such events occur, since this simple strategy doesn’t have a stop loss or other exit techniques, we remain exposed in the market for six months each year.

Yet, overall, the equity line is strong.

Insights from the average trade of the strategy

Now, let’s examine the average trade.

Here, you’ll notice that the average trade is around $55,000.

It’s a substantial figure, but keep in mind we’re entering with a million dollars. This gives us an average trade profit of about 5.5%.

Which is definitely not bad, especially since we’re only in the market for six months.

Did the performance in the first six months impact the remaining months?

But let’s answer our initial question and check if the first six months’ performance really affects the rest of the year.

To do this, let’s reverse the entry condition.

Instead of looking for a positive performance, let’s see what happens if the first six months are negative.

After compiling the script, you’ll notice that here there’s no entry anymore. This is correct, since the first six months were positive.

Let’s have a look at the equity line, and you’ll see it’s less consistent than the previous one.

The average trade here is about $28,000—around 2.8%, so that’s about half of what we saw with a positive start.

A potential explanation: investor psychology

Alright, so, let’s now try to explain what we’ve observed.

The most logical explanation ties back to investor psychology.

When the first half of the year shows a positive performance, there might be a greater willingness to invest, boosting the second half’s performance.

On the other hand, if the first half is negative, investors might be more cautious, leading to a weaker second half.

This could be a plausible explanation, though there are certainly others. The key takeaway is to maximize insights gained from market data, which in this case, we did using a quantitative approach.

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