Historical Volatility: Barometer of Stock Market's Ups and Downs

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Historical volatility, sounds like a historical drama, recording the hustle and bustle of the stock market. This indicator, like a meticulous historian, records the ups and downs of securities or market indices during specific periods through accurate statistical methods. By calculating the average deviation between a financial instrument and its average price, it reveals the true pulse of the market.

You may ask, "Is historical volatility the same as that guy called the fear index?" Oh dear, that title is really misleading. It is not just about fear, but also about understanding the speed and magnitude of market price changes. It's like an experienced captain who can predict upcoming storms or calm seas based on the waves.

When it comes to calculation methods, the most commonly used one for historical volatility is standard deviation. However, there are many other calculation methods available here. It's like various seasonings that can be adjusted according to personal taste. Higher values of historical volatility indicate higher risk, just like a gamble where higher stakes make your heart race faster. But remember, high risk is not always a bad thing; it can also indicate high returns.

Historical volatility is not only a measure of price fluctuation, but also an important tool for analyzing risk tolerance. A stock with high historical volatility is like a wild horse that is difficult to tame, requiring more skill and patience to control. This has a significant impact on trading strategies, such as stop-loss settings and margin requirements.

For traders who prefer a quieter trading environment, a market with low volatility is like a calm pond. Although it may be peaceful, the opportunities for profit are relatively limited. On the other hand, a market with high volatility is full of rapids and currents, providing opportunities for those who are willing to take risks to achieve high returns.

As for finding the "golden mean" where there is neither excessive risk nor boredom, it requires the wisdom and experience of traders. By comparing the volatility of different securities and combining other technical analysis tools, we can find a relatively suitable level of volatility.

Now, let's take a look at the source code of historical volatility. This is a magical formula that reveals market sentiment, allowing traders to understand market dynamics and find their own trading rhythm. A highly volatile market is like an intense and thrilling gamble, while trading at a neutral volatility level is like sailing steadily in the market.

This code is a TradingView Pine Script script used to calculate the Historical Volatility indicator.

First, the script declares the version number as 5 using the //@version=5 comment on the first line.

Next, the indicator() function is used to define the indicator. The function has several parameters:

title: The title of the indicator

shorttitle: The abbreviated title of the indicator

format: Price formatting options

precision: Decimal point precision

timeframe: The timeframe (default is empty)

timeframe_gaps: Whether to include timeframe gaps

Then, an integer input variable named "length" is created using the input.int() function. It has a default value of 10 and a minimum value of 1.

After that, two constant variables are defined:

"annual" represents the number of trading days in a year, with a default value of 365.

"per" determines the number of periods within a year based on the current chart's timeframe. If it's a minute or daily chart with no multiplier factor, the number of trading days per week is set to 1. Otherwise, it's set to 7.

Finally, the Historical Volatility (HV) is calculated using the following formula:

In this case,

Use the built-in function ta.stdev() to calculate the sample standard deviation of the logarithm of the closing price relative to the previous day's closing price.

Multiply the result by 100 to convert it into percentage form.

Use the math.sqrt() function to calculate the square root of the annual period divided by the number of periods within a year.

Finally, use the plot() function to plot the chart of the historical volatility indicator.