Market Heat map

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Morningstar Market Barometer

Morningstar's free market maps are among the simplest. They provide interactive snapshots of the U.S. and global markets.

The U.S. Market Barometer is a grid that lets you see how nine asset classes have performed compared to each other in areas such as growth, core, value, large-cap, mid-cap, and small-cap.

You get detailed data on the risk of the asset class when you click on a square in the grid, and you can see how it performs. The data on the U.S. market is updated each day at market close, but you can also see a mini-map of the asset class over the past day, year, three years, or five years. The tool shows that "the market" is not one cohesive entity that always moves the same.

Use the Global Market Barometer on the same page for a view of the world equities markets. Green regions highlight countries with gains in Morningstar global equities market indexes. Red depicts countries with losses. You can view data in the same time frames as you can in the U.S. Market Barometer.

Why is Alpha Vantage free?

At Alpha Vantage, the majority of our API endpoints can be accessed for free. For use cases that exceed our standard API usage limit (5 API requests per minute; 500 API requests per day) or require certain premium API functions, we offer a premium plan to help you scale your application.

The Bottom Line

Active traders can gain from the real-time data in maps of the market, such as share price changes and daily trading volume, but market maps can assist the average investor, too.

Stock market maps allow you to get a quick snapshot of a stock's performance before you do deeper research. They can show someone with a long-term investment strategy why diversification matters by detailing how certain sectors and asset classes move up and down over time.

You can use the insights gained from a map of the market to make more informed picks about which securities to add to or remove from your portfolio. This can help you achieve your short- or long-term investment goals.

Heatmap Example

Heatmaps can be employed in a variety of situations and industries. For example, a heatmap of foreclosures data could show parts of the U.S. experiencing high foreclosure rates in a dark color and states with low foreclosure rates in lighter colors, which could be useful for real estate professionals looking to understand more about the market and identify market trends.

A color-gradient legend typically accompanies a heatmap to specify the data and help the map reader understand the data. Heatmaps are also widely used in webpage industries to show where users are clicking.

Heatmaps can be used to quickly view foreclosures across the United States.

How do you use a heat map in trading?

Heatmap is a visual representation of the limit orders put into the order book. On the right side of the vertical timeline is the current order book. On the left side of the vertical timeline is the position of the order book in the past. This information is recorded as a color-coded map.

What is Heatmap?

Heatmap is a visual representation of the limit orders put into the order book. On the right side of the vertical timeline is the current order book. On the left side of the vertical timeline is the position of the order book in the past. This information is recorded as a color-coded map.

In this image, a large number of limit buy orders

In this image, a large number of limit buy orders can be seen at 2741.50 (orange line). The red, dotted line is the “best ask” (lowest price limit sell order). The green, dotted line is the “best bid” (highest price limit buy order). When the best bid and ask first began to hit the area of high liquidity at 2741, they rose.

They are now testing the area again, and support can be seen at this level. There is also a large number of limit sell orders at 2745, as represented by the yellow line at that level. This implies that if the best bid and ask rise to 2745, resistance can be expected.

In Bookmap, there is no limitation on zoom. Traders can analyze the market on different time frames up to nanoseconds (although in practice it’s never necessary). Here is an example of a higher resolution view:

This provides a close-up shot of the best ask (red

This provides a close-up shot of the best ask (red line) and best bid (green line). The price of the most recent transaction is indicated with a rectangle on the right side of the screen. In this case, the most recent transaction was a market sell order for $2740.25. This transaction occurred at the best bid (the highest price limit buy order).

The bubbles shown indicate the volume of market orders. A green bubble means there were significantly more market buys than market sells. A red bubble means the opposite (significantly more market sells than market buys). A bubble that is partially red or green indicates that the ratio between market sells and buys was more balanced.

Market data visualization in the era of HFT and AI

High-frequency trading computers can execute round-trip trades within milliseconds. Artificial intelligence algorithms can detect patterns in vast amounts of market data. As human day traders, we do not want to compete against any of these powers.

So, what is the area in which a chart trader can still get a competitive advantage over machines? The answer is visualization. A quick proof of it is the existence of Captchas:

 The reason captchas are being used is because of

The reason captchas are being used is because of the human ability to spot complex patterns better than computer programs.

We can also observe a similar “proof” using Bookmap itself. Take a look at the image below. We can determine that it was generated by a single trader with a single glance, and we can see how it affects the price. Those who are familiar with computer science and machine learning understand how challenging it would be to detect this with a computer program in real time.

The typical “stairs” pattern

The typical “stairs” pattern

The pattern itself consists of just several order replacements, but this small snapshot of the chart contains many thousands of market data events. This is a noise that such a program must filter out. In addition, the program must deal with the noise generated by this trader himself in a form of different time intervals and order size during order modifications.

Large traders are usually more influential on the market than small traders. On one hand, they can use their weight to move the market in the desired direction. On the other hand, they have the challenge to execute or manage large orders due to liquidity constraints. Because of this, they try to keep their activity undetected – to mask the fact that this activity belongs to a single trader.

One of the techniques they use to obscure their activity is to split large orders into smaller orders and place them one by one. To make it even more difficult for other robots, they add “noise” in a form of non-equal sizes of smaller orders and non-equal time periods between them. This is an effective method against other robots, but the human eye can spot it with a glance, as shown below.

Notice the different time scale of the two charts

Notice the different time scale of the two charts

Here is another example which is even more complicated for computer vision but still easy for human sight:

This is unlikely to be a spontaneously created pat

This is unlikely to be a spontaneously created pattern by multiple traders

This is how Heatmap can be used in trading. By visualizing liquidity, Heatmap allows a trader to get the same information the robo-trading algorithms have access to, but with the added advantage of human sight and human understanding. Like any other tool in trading, this doesn’t guarantee success. But it does offer an excellent means of gaining an edge over other traders who do not have it.

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