TuneMap on Young Stocks — A New TradingView Indicator for Realized Volatility

A non-negligible number of people have contacted us recently about stocks that don’t appear in TuneMap. In almost every case, the reason we can’t find these stocks is simply that they only recently went public. A common example we’ve been asked about is Reddit (RDDT). While Reddit has been around as a social media platform for years, it IPO’d only about a year and a half ago (March 21, 2024). Some of the other stocks mentioned have even less price history.

Running our script on such short datasets would technically produce a map and related statistics, but the limited history would make those results highly unreliable. To build a relevant and durable map — one that has a good chance of staying valid in the future — we need a sufficiently long period of data. Not so long that the stock’s behavior has evolved beyond recognition, but long enough to capture a variety of market conditions. The period we selected for TuneMap (May 2020 to December 31, 2024) meets that balance: it’s rich in diverse market environments yet still recent enough to remain relevant.

If you’re new to WealthUmbrella, I encourage you to read this article on TuneMap and backtesting periods. It’s probably one of the least exciting texts you’ll find here, but it’s an important one — it helps explain how the chosen historical period affects the reliability of TuneMap results.

Stock too young

Now, if the available history isn’t sufficient to build a meaningful map, how can we still develop strategies for these younger stocks to protect our capital? The key lies in realized volatility.

Each stock has its own unique path shaped by specific events — bullish news, earnings surprises, misses, etc. But the way a stock reacts to those events is strongly tied to its realized volatility. For instance, Apple, which has low realized volatility, will typically move far less on good or bad news than a stock like OKLO, given similar circumstances.

All the technical strategies available in TuneMap are based on EMAs. Although EMA stands for Exponential Moving Average, it’s fundamentally a digital filter — the same concept engineers use in microcontrollers to smooth noisy sensor signals. In essence, EMA-based strategies (2EMA, MACD, RSI, etc.) are different methods of filtering out noise to reveal the underlying trend, with pre-defined rules for when to enter or exit a position. The relationship between noise and trend is highly correlated with a stock’s realized volatility. This means that a filter setup that works well for one stock should also work well for another stock with a similar realized volatility profile.

To illustrate, imagine applying the optimal 2EMA configuration for Apple to a very volatile stock like FUBO was in 2021. The strategy would constantly cross over and under because the EMAs wouldn’t filter enough of OKLO’s volatility. The fix is to use less responsive (longer-period) EMAs for such stocks.

So where I’m going with this is: if you can find a stock from the past with a similar realized volatility to a newly listed one, that older stock’s historical performance becomes an excellent proxy for how the younger one might behave.

If you doubt that, take Upstart (UPST) and NIO (NIO) during 2020–2022. Both had very similar realized volatility profiles. Using a 2EMA crossover strategy with EMA45 and EMA48 would have captured most of their COVID-era uptrends and exited in time to avoid the major 2022 declines (NIO declined much slowly but still).

Today, stocks like OKLO, APLD, and WULF show realized volatility levels close to those two 2020–2022 high-flyers. Be mindful that my choice of EMA 45–48 reflects my personal investor profile. I dislike constant trading, so I prefer strategies that stay long as much as possible, even if it means exiting after a deeper decline. Others might prefer different setups on NIO and Upstart during 2020–2022. The key point, however, is that a strategy that worked well on one performed similarly on the other, since both shared a comparable realized volatility during that period.

The same logic applies to Monday.com (MNDY) and Intellia Therapeutics (NTLA) in 2020–2022. Both exhibited similar realized volatility, and a 2EMA setup with EMA40 and EMA46 worked beautifully — capturing the uptrend and cutting exposure before the correction. Currently, IREN, GLXY, and CRVW display comparable realized volatility to those stocks from that era.

Likewise, TLN and GEV have volatility profiles similar to SNOW, HUBS, and CRWD in 2020–2022, where EMA26 and EMA35 combinations performed well.

So, to sum up: one effective way to find strategies for young stocks is to identify historical analogs — stocks with similar realized volatility during periods that include both strong uptrends and downtrends — and study which technical configurations performed best to avoid what you want to avoid. (Well, we all want to avoid losses, but everyone has a different tolerance level. As I mentioned above, I prefer strategies that stay long, even if they tend to give back more near the top. I know many people would rather have more frequent exits but smaller losses each time.)

To do this, we first need a reliable way to estimate realized volatility, then compare it across stocks, and finally see what strategies worked well under similar conditions. For the last part, I use TuneMap — but rather than looking at the full five-year map (where realized volatility may have evolved), I focus on specific historical periods, testing EMA combinations that hold during uptrends and disconnect properly during drawdowns. For estimating volatility, I use either our GARCH modeling (a bit more complex) or my Realized Volatility script.

To help you do the same, I’ve added my Realized Volatility script to WU Advanced on TradingView.

New WU ADV Realized Volatility Indicator

Realized volatility measures the actual variability of an asset’s returns over time and is used to gauge how turbulent a stock has really been. While there are plenty of realized-volatility scripts available on TradingView, most use incorrect or overly simplistic formulas. The most basic approach — taking the standard deviation of daily close-to-close returns — is easy to compute but flawed because it ignores overnight gaps and intraday volatility, both of which contribute significantly to total price movement.

Over the years, several improved estimators have been proposed. The Garman–Klass method was long considered one of the best, but the Yang–Zhang estimator is now recognized as the most powerful, offering the lowest estimation error. It combines three components — the overnight return, the open-to-close volatility, and a bias correction (introduced by Rogers and Satchell) — into one equation. The result is an unbiased, drift-independent, and highly accurate measure of true market volatility, especially for assets with frequent trading gaps.

For those interested in the math behind it, you can find the Yang–Zhang equation on page 10 of the paper linked here. But you don’t need to dig into the equations — we’ve got you covered. The WU script already implements the correct version.

In the indicator, you’ll see in green the 30-day realized volatility, and in blue its 200-day SMA (RV30day). The SMA is useful for seeing where volatility sits relative to its longer-term average. When the 30-day RV dip under it's SMA 200, it's line become red.

In the parameters, you can also adjust the 30-day lookback period to a longer one if you prefer smoother averaging, without extending as far as the 200SMA.

Besides helping you identify analog stocks, monitoring realized volatility can offer other insights. For instance, when a stock’s realized volatility drops well below its 200-day average, it often signals a mean reversion ahead. It’s also valuable for assessing how much a stock’s behavior has evolved over time. If, for example, you found that a given stock realized volatility had changed drastically since the TuneMap historical window, that would be a reason to exclude it from comparison.

In any case, I hope you’ll enjoy this new indicator — and that it helps you better protect your gains when trading younger, less-established stocks.