Technical Indicators & Studies

ADX quantifies how strongly a market is trending on a scale of 0–100. It is derived from the Directional Movement Index (DMI) system developed by J. Welles Wilder and focuses purely on the intensity of a trend, regardless of whether price is moving up or down. A reading above 25 signals that a meaningful trend is in place and trend-following strategies are appropriate. A reading below 20 indicates a weak or non-existent trend where oscillators and range strategies work better. ADX rises during both uptrends and downtrends — it only tells you how strong the move is, never which direction it is heading. Readings above 40–50 suggest an exceptionally strong trend that may be approaching exhaustion.

Created by Bill Williams, the Awesome Oscillator calculates the difference between a 5-period and a 34-period simple moving average of the bar midpoint (high + low) / 2 — not the closing price. The result is displayed as a histogram: green bars indicate the histogram is rising (increasing bullish momentum), red bars indicate it is falling (increasing bearish momentum). When the histogram crosses from negative to positive, bulls have taken control of momentum; a cross from positive to negative signals bears are winning. Williams identified three specific setups: the Zero-Line Cross (most straightforward), the Twin Peaks (divergence-based reversal), and the Saucer (continuation within an established trend).

Developed by Donald Lambert in 1980, CCI measures how far the current typical price (average of high, low, close) deviates from its simple moving average, normalized by a mean absolute deviation factor. The resulting oscillator is unbounded but typically oscillates between +100 and −100 during normal conditions. Readings above +100 suggest price is well above its average — signalling either a strong uptrend breakout or overbought conditions depending on context. Readings below −100 suggest price is well below its average — either a strong downtrend or oversold. Originally designed for commodities, CCI is now applied across stocks, forex, and crypto. It is particularly useful for identifying cyclical turns when used on longer-period settings (e.g., 40 or 50 periods).

The Directional Movement Index, also developed by J. Welles Wilder, is the system from which ADX is derived. It plots two lines: +DI (Positive Directional Indicator), measuring upward price movement, and −DI (Negative Directional Indicator), measuring downward price movement. When +DI is above −DI, buyers have been more dominant than sellers over the lookback period — a bullish reading. When −DI is above +DI, sellers dominate — a bearish reading. The classic DMI crossover signal (where the two lines cross) is one of Wilder's original entry triggers, though it works far better when confirmed by a rising ADX above 20, filtering out crossovers in choppy sideways markets.

Keltner Channels plot an upper and lower band around an Exponential Moving Average (typically 20-period), with the band width set at a multiple of the Average True Range (typically 2× ATR). Unlike Bollinger Bands which use standard deviation, Keltner's ATR-based bands are smoother and less reactive to single-bar price spikes. When price moves outside the upper band, it indicates strong bullish momentum — price is 'walking up' the channel. The channel is widely used alongside Bollinger Bands in the TTM Squeeze strategy: when Bollinger Bands contract inside the Keltner Channel, a major volatility expansion is typically imminent.

MACD is one of the most widely used technical indicators in existence, developed by Gerald Appel in the late 1970s. It computes the difference between a 12-period and 26-period Exponential Moving Average of closing prices, producing the MACD line. A 9-period EMA of the MACD line (the 'signal line') is then plotted alongside it, and a histogram shows the gap between the two. When the MACD line is above zero, the short-term EMA is above the long-term EMA — indicating net upward momentum. When the MACD line crosses above the signal line, it is considered a bullish momentum signal; crossing below is bearish. The histogram is particularly valuable: when it is above zero and growing, momentum is accelerating upward; when it is shrinking toward zero even as price rises, momentum is fading — an early divergence warning.

A moving average calculates the mean closing price over a rolling window of bars, producing a smooth line that filters out short-term noise. The Simple Moving Average (SMA) weights all bars equally; the Exponential Moving Average (EMA) weights recent bars more heavily, making it more responsive to new price information. Common periods include the 20-day (short-term trend), 50-day (medium-term), and 200-day (long-term institutional trend). Moving averages serve as dynamic support and resistance — price often 'bounces' off key MAs during trends. They are also the building blocks of dozens of other indicators including MACD, Bollinger Bands, ADX, and VWAP. When a fast MA crosses above a slow MA, it is called a 'Golden Cross' (bullish); crossing below is a 'Death Cross' (bearish).

Pivot Points are calculated mathematically from the prior period's high, low, and close, producing a central pivot (PP) plus a series of support levels (S1, S2, S3) and resistance levels (R1, R2, R3). They are redrawn at the start of each new period — daily pivots reset every day, weekly pivots reset every Monday. Floor traders have used Pivot Points for decades as intraday road maps, making them self-fulfilling: because so many participants watch them, price often reacts at these levels. Standard, Fibonacci, Woodie, and Camarilla are the main calculation variants, each slightly adjusting the formula. The central pivot is the most important level — price above it sets a bullish intraday bias; below it sets a bearish bias.

RSI, developed by J. Welles Wilder in 1978, measures the speed and magnitude of price changes on a 0–100 scale. It compares the average gain on up days to the average loss on down days over a 14-period window (though 9 and 21 are also common). Traditional interpretation: above 70 = overbought (potential sell), below 30 = oversold (potential buy). However, in strong uptrends RSI can remain above 70 for extended periods — treating 70 as an automatic sell is a common beginner mistake. The most powerful RSI signal is divergence: when price makes a new high but RSI prints a lower high, it signals momentum is fading — one of the most reliable leading indicators of trend reversals. RSI can also identify support and resistance levels on the oscillator itself that forecast price behaviour.

VWAP calculates the cumulative average price of a security weighted by the volume traded at each price point throughout the session. It resets at the start of each trading day, making it a purely intraday tool. Institutional traders — mutual funds, pension funds, and market makers — use VWAP as a benchmark: buying below VWAP is considered a good execution (you paid less than average); selling above is favorable for sellers. Because large players actively trade around VWAP, it becomes a self-reinforcing support/resistance level throughout the day. Price repeatedly finding support at VWAP on pullbacks confirms intraday bullish control; price unable to reclaim VWAP after losing it signals institutional selling pressure dominating the session.

The Comparison study plots a second security (such as SPY, QQQ, or a sector ETF) on top of your primary chart, normalizing both series to a common starting value so relative performance is immediately visible. It answers the essential question every stock trader should ask: is this security outperforming or underperforming the broader market? A stock that rises 10% while the S&P 500 rises 15% is technically in an uptrend but showing relative weakness — potentially a signal that smarter money is rotating elsewhere. Conversely, a stock holding flat while the market sells off demonstrates exceptional relative strength, which often precedes an outperformance move when conditions improve.

This variant of the Comparison study normalizes both securities to percentage change from a defined start date, making the gap in returns explicit regardless of the two securities' absolute price levels. While the standard Comparison study can visually distort when one security is priced much higher than the other, the percentage version makes the gap immediately legible — for instance showing that stock A is up 42% while the benchmark is up 18% since a chosen reference date. This is the format favored for earnings-season analysis and year-to-date portfolio attribution.

Linear Regression fits a straight line through a set of price data using the least-squares method, representing the 'average price path' over the chosen lookback period. The slope of the line indicates trend direction and speed — a steeper positive slope = faster uptrend. Price trading above the regression line means it is running ahead of its average trend path (potentially extended); below the line means it is lagging (potentially due for a catch-up move or confirming weakness). Linear regression is the mathematical foundation for several more complex studies including the Standard Error Channel, Time Series Forecast, and the various Linear Reg Channel studies. Unlike a moving average, the entire line recalculates with each new bar — it does not lag in the same way.

Open Interest tracks the total number of outstanding options or futures contracts that have not been settled or closed. It differs from volume: volume counts every transaction; open interest counts only net new positions that remain open. Rising open interest alongside rising price signals new money entering the long side — confirming bullish conviction. Rising open interest alongside falling price signals new money entering the short side — confirming bearish conviction. Falling open interest during a price move means existing positions are being closed (profit-taking or stop-outs), which suggests the move may be losing commitment and could reverse. Open Interest is primarily relevant for options, futures, and leveraged products — it is not applicable to spot equity trading.

The Price Channel (also called Donchian Channel, named after Richard Donchian) plots the highest high and lowest low over a specified lookback period — typically 20 bars — as two parallel lines with a midline. The upper band represents recent resistance; the lower band represents recent support. Breakouts above the upper band signal potential bullish trending moves; breaks below the lower band signal bearish. The classic Turtle Trading system used a 20-day Donchian Channel for entries and a 10-day channel for exits, one of the most well-documented trend-following systems in history. Price staying in the upper half of the channel indicates bullish control; the lower half indicates bearish pressure.

The Probability of Explosion indicator monitors volatility compression and calculates a statistical probability score representing how likely a major directional breakout move is in the near term. The underlying logic is that markets cycle between volatility expansion and contraction — after sustained periods of compression (narrow ranges, contracting bands), explosive moves reliably follow. The indicator attempts to quantify how close to a 'coiled spring' state the current market is. High readings suggest conditions are aligned for an imminent large move; low readings indicate the market is not yet primed. Critically, the indicator identifies the potential for a large move but does not predict direction — additional analysis of trend, momentum, and context is required to determine which way the breakout will go.

The Standard Deviation Channel combines a linear regression line with parallel bands drawn at 1, 2, or 3 standard deviations above and below. It gives both a trend direction (the slope of the regression line) and a volatility context (the width of the bands). Price staying within 1 standard deviation is considered 'normal' for the current trend. Price reaching 2 standard deviations represents a statistical extreme — roughly 95% of price action stays within this range. 3 standard deviations is a near-extreme that often marks short-term exhaustion points. Unlike Bollinger Bands which use a horizontal moving average, the StdDev Channel's baseline is a sloping regression line, making it more appropriate for trending markets where price is not oscillating around a flat mean.

The Stochastic Oscillator, developed by George Lane in the 1950s, measures where the closing price sits within the high-low range over a given period (typically 14 bars), expressed as 0–100. A close near the top of the range = high %K reading; near the bottom = low reading. It plots two lines: %K (the fast raw stochastic) and %D (a 3-period SMA of %K used as a signal line). Readings above 80 are traditionally considered overbought; below 20 are oversold. The most commonly used signal is %K crossing above %D in the oversold zone (bullish) or %K crossing below %D in the overbought zone (bearish). Divergence between Stochastic and price is a powerful secondary signal similar to RSI divergence.

TRIX applies EMA smoothing three times sequentially to the closing price, then calculates the one-period percentage rate of change of the result. Because the triple smoothing heavily filters out minor fluctuations and false signals, TRIX produces very clean crossover signals. It functions as both a trend indicator (zero-line position) and a momentum oscillator (histogram bars or signal line). The primary signals are: TRIX crossing above zero (bullish trend confirmation), crossing below zero (bearish), and crossovers of TRIX with its own 9-period signal line (earlier but noisier). Its strong lag due to triple smoothing means it is better suited for swing and position trading than intraday scalping.

Volume Profile renders a horizontal bar chart alongside the price chart, with each bar representing the total volume traded at a specific price level over a defined session or period. The longest bar — the price level with the most total volume — is called the Point of Control (POC). The Value Area (VA) contains the top 70% of volume, bounded by the Value Area High (VAH) and Value Area Low (VAL). Price tends to gravitate toward the POC over time because that is where the most liquidity exists. Thin areas with very little volume ('volume voids') represent price levels where transactions happened quickly — price tends to move rapidly through these zones in both directions. Volume Profile is one of the most powerful tools for identifying meaningful support and resistance based on actual trading activity rather than visual chart patterns.

The ADX Crossover study generates a visual alert or plotted signal the moment the ADX line crosses above or below a user-defined threshold (most commonly 20 or 25). When ADX crosses above 20, it signals that a market previously lacking directional conviction is beginning to trend — this is the trigger point where trend-following systems become appropriate. When ADX crosses back below 20, it warns that the trend is decelerating and range strategies may be more suitable. The signal is most powerful early in a new trend, before the trend becomes widely obvious. It is typically used as a filter overlay: combine with +DI / −DI lines from DMI to determine direction, and use the ADX crossover as the 'permission' signal that a directional trade is justified.

This study generates a signal each time price closes outside the upper or lower Bollinger Band. Closing outside the upper band signals either a bullish breakout in a trending market or an overbought extreme in a ranging market — context determines the interpretation. John Bollinger himself notes that closes outside the bands are not automatic reversal signals: in a genuine trend, price can 'walk the band' for many bars, with each close above the upper band confirming momentum rather than signalling exhaustion. The crossover signal is best combined with volume — a band penetration on heavy volume in the direction of the trend is continuation; a penetration on declining volume suggests an unsustainable spike. Used in automated systems for scanning multiple securities for breakout events simultaneously.

The MACD Histogram Crossover fires precisely when the MACD histogram changes sign — crossing from negative to positive (the MACD line has crossed above its signal line) or from positive to negative (MACD below signal). Many traders consider this the most reliable of all MACD-based signals because the histogram zero-cross occurs slightly before the MACD line crosses the signal line visually, offering a fractional lead. A histogram turning positive while the MACD line is still below zero is an especially powerful early signal: momentum is turning upward before a full trend reversal is confirmed — a pre-signal for the main crossover. The zero-cross eliminates the need to visually judge when the two lines have crossed and is well-suited to automated alert systems.

The Momentum indicator measures how much the current price has changed relative to the closing price N periods ago, typically plotted as the raw difference or as a percentage. The Momentum Crossover study generates a signal precisely when this value crosses zero from either direction. A zero-line cross from below means price is now higher than it was N periods ago — the definition of positive short-to-medium-term momentum. A zero-line cross from above means price has turned below its N-period-ago level — a negative momentum shift. Because the Momentum indicator is very sensitive (it uses raw price difference), its zero-line crossovers can be noisy; applying a smoothing period (using the Momentum signal line variant) reduces false signals at the cost of some lag.

The Money Flow Index (MFI) is a volume-weighted version of RSI that incorporates trading volume into its momentum calculation, making it more sensitive to the intensity of buying and selling activity. The MFI Crossover study generates a signal when MFI crosses above 20 (exiting oversold — potential bullish reversal) or below 80 (exiting overbought — potential bearish reversal). Because MFI weighs each price change by its volume, an overbought reading that occurred on very high volume is more significant than one that occurred on weak volume — the indicator automatically adjusts for participation levels. MFI crossovers at divergence points (where price makes a new extreme but MFI does not) are considered particularly high-probability reversal signals.

The Moving Average Crossover generates a bullish signal when a faster moving average crosses above a slower one, and a bearish signal when it crosses below. The most famous variants are the 50-day SMA crossing above the 200-day SMA (Golden Cross — widely covered in financial media and considered a long-term bullish signal) and the reverse (Death Cross — long-term bearish). On shorter timeframes, combinations like 9/21 EMA or 20/50 EMA are popular for swing trading. The signal is inherently lagging — by the time the crossover occurs, a significant portion of the move has already happened. Despite this, the Golden Cross and Death Cross have historically had reasonable predictive value on daily and weekly charts, largely because they reflect the reality of trend changes rather than predicting them.

Parabolic SAR places a series of dots above the price candles (bearish — indicating a downtrend) or below them (bullish — indicating an uptrend). When price crosses through the SAR level, the dots 'flip' to the opposite side — the Parabolic SAR Crossover study generates an explicit signal at the moment of this flip. The flip from above to below represents a bullish reversal signal; below to above is bearish. As the trend develops, the SAR dots accelerate toward price using an acceleration factor, acting as a progressively tighter trailing stop that locks in profits as the trend matures. The crossover signal works best in trending markets; in choppy conditions, the SAR flips frequently, generating a series of false signals that produce net losses.

The simplest possible crossover signal: price crossing above a specified moving average generates a bullish signal; price crossing below generates a bearish signal. This is the foundation of almost all moving-average-based trading systems — it is the minimal viable trend signal that every more complex MA-based system builds upon. Common applications include price crossing the 200-day SMA (long-term trend filter), price crossing the 50-day EMA (medium-term swing signal), and price reclaiming VWAP intraday. While extremely simple, the Price Average Crossover remains useful as a systematic filter: many professional managers will only buy a stock that is above its 200-day SMA, automatically excluding downtrending stocks.

The RSI Crossover study generates an alert or plotted signal when RSI crosses above 30 (exiting the oversold zone — potential bullish reversal) or below 70 (exiting the overbought zone — potential bearish reversal). Some traders also use 50 as a threshold: RSI crossing above 50 signals that net momentum has turned bullish; below 50 is bearish. The exit from an extreme zone (crossing above 30 or below 70) is generally considered more reliable than the entry into one (crossing below 70 or above 30) because it confirms the reversal has begun rather than merely suggesting the market is extreme. RSI crossover combined with a price-action confirmation (such as a bullish engulfing candle at a support level) significantly improves the win rate of the setup.

The Rate of Change (ROC) indicator measures how much the current price has changed relative to the price N periods ago, expressed as a percentage. A positive reading means price is higher now than N periods ago; negative means lower. The Rate of Change Crossover study fires when this value crosses the zero line in either direction. A zero-line cross from below means price has turned higher than its N-period-ago reference — bullish momentum returning. A zero-line cross from above means price has fallen below its N-period-ago level — net negative momentum. Because ROC is a percentage, it normalizes the signal across assets of different price levels, making it useful for cross-asset comparison and ranking securities by momentum strength.

The Stochastic Crossover is the primary signal produced by the Stochastic Oscillator: %K crossing above %D in the oversold zone (below 20) generates a bullish signal; %K crossing below %D in the overbought zone (above 80) generates a bearish signal. George Lane, the developer of the Stochastic, considered this the indicator's primary use case. The study automates the visual identification of these events, making it practical for screening large watchlists. A 'hidden' Stochastic signal occurs when the crossover happens while the indicator is near 50 (midpoint) in the direction of the prevailing trend — a continuation signal rather than a reversal one. False signals are common in strong trends; filtering by requiring the crossover to occur within the extreme zones (below 20 or above 80) significantly improves accuracy.

The Detrended Price Oscillator eliminates the dominant long-term trend from price data by comparing the current closing price to a displaced moving average from a prior period (typically shifted back by half the lookback period plus one). The result is an oscillator that bounces above and below zero purely based on shorter-term price cycles, completely independent of the dominant trend direction. This is valuable because many short-term cycles are masked by the trend — DPO makes them visible. By measuring the distance between consecutive DPO peaks or troughs, traders can estimate the approximate length of the prevailing price cycle and project when the next high or low is likely to occur. DPO is not designed for timing entries and exits in real time; it is primarily an analytical tool for understanding cyclical structure.

Market Forecast is a composite indicator containing three distinct oscillators — Nearterm, Intermediate, and Momentum — each measuring a different time cycle simultaneously on a single chart. The three lines oscillate between 0 and 100, allowing traders to see whether short, medium, and long cycles are aligned. When all three components are above 50 and rising together, it represents the highest-confidence bullish confluence. When the short-term Nearterm line diverges from the longer Momentum line, it signals that the short cycle is turning while the longer trend hasn't yet — an early warning of potential change. Originally designed for U.S. stock market timing by Jerry Williams, it has been adapted for individual securities as well. Because it measures multiple cycle lengths simultaneously, it provides a more complete picture than any single oscillator.

Market Sentiment indicators aggregate measures of crowd opinion and positioning — such as the put/call ratio, short interest as a percentage of float, surveys of bullish vs. bearish individual investors (AAII), or options-implied volatility skew — to identify when market participants have reached an emotional extreme. The core logic is contrarian: when virtually everyone is bullish, there are few buyers left and the market is vulnerable; when sentiment is at extreme bearish lows, there are few sellers left and a recovery becomes likely. Sentiment extremes are most useful at major market turning points and cycle lows/highs. However, sentiment can remain extended for longer than expected during powerful trends — it is a timing supplement, not a standalone signal. Platforms implement this differently; some use proprietary composite scores derived from multiple sentiment data sources.

The MESA Sine Wave, developed by John Ehlers using Maximum Entropy Spectral Analysis (MESA), is designed to determine whether the market is currently in a cyclic phase or a trending phase — the most important regime distinction a trader can make. It plots two sine wave lines: a sine line and a lead sine line. When the market is in a genuine cycle, the two lines follow smooth sinusoidal patterns, alternating above and below zero in regular waves. When the market is trending, the two lines run roughly parallel and do not oscillate cleanly. This regime detection allows traders to intelligently switch between mean-reversion strategies (cycle mode) and momentum/trend-following strategies (trend mode) rather than applying a single method in all conditions.

TTM Wave (developed by John Carter of Simpler Trading) displays three MACD histograms simultaneously, each computed on a different timeframe: Wave A covers the shortest cycle, Wave B the intermediate, and Wave C the longest. The core insight is that the highest-probability trading setups occur when all three waves are aligned in the same direction — short, medium, and long cycles all pointing the same way simultaneously. When all three histogram bars are positive and growing, it represents multi-timeframe bullish momentum confluence. Divergence between waves — for instance Wave A turning negative while B and C remain positive — signals a short-cycle pullback within a larger uptrend, suggesting a buying opportunity rather than a trend reversal. This multi-cycle view eliminates the need to manually switch between chart timeframes to check momentum alignment.

The Accumulation/Distribution line was developed by Marc Chaikin and measures the cumulative flow of money into and out of a security by multiplying the 'money flow multiplier' (where the close sits within the day's range) by the period's volume. A close near the top of the daily range receives a high positive multiplier; a close near the bottom receives a negative one. This produces a running cumulative total that reflects whether volume is weighted toward accumulation (institutions buying) or distribution (institutions selling). The key insight is that a stock can be rising in price while simultaneously showing a declining A/D line, suggesting that 'smart money' is quietly distributing shares into retail buying — a classic topping pattern. Conversely, a stock falling in price while the A/D line rises suggests stealth accumulation at lower prices.

This variant of the standard Accumulation/Distribution line modifies the weighting formula to give additional significance to large-volume moves that occur at price extremes — either near the top of a session's range on high volume (strong accumulation signal) or near the bottom on high volume (strong distribution signal). The effect is a more nuanced picture of institutional buying and selling that is less easily 'gamed' by normal price variance. It tends to diverge from the standard A/D line at important turning points, providing earlier warning when the weighting of extraordinary volume events matters more than average sessions. Best used as a confirmation tool alongside the standard A/D line — significant divergence between the two versions is itself a signal worth investigating.

Chaikin Money Flow, created by Marc Chaikin, sums the Money Flow Volume over a 21-period window and divides by total volume, producing a normalized oscillator that ranges from approximately −1 to +1. This normalization makes CMF directly comparable across different securities and time periods. Values above zero indicate buying pressure (accumulation); values below indicate selling pressure (distribution). A reading above +0.25 or below −0.25 is generally considered a strong signal of institutional activity. CMF's most powerful application is as a divergence tool: when price makes a new high but CMF is declining, it indicates that each successive rally is being bought by less and less volume-weighted commitment — a topping warning. It is particularly useful for filtering out weak-volume breakouts from high-conviction ones.

The Chaikin Oscillator applies the MACD formula — specifically, the difference between a 3-period EMA and a 10-period EMA — to the Accumulation/Distribution line rather than to price. This means it measures not whether money flow is positive, but whether money flow momentum is accelerating or decelerating. A zero-line cross from below indicates that accumulation momentum is picking up — a bullish signal even before price necessarily reflects it. A zero-line cross from above indicates distribution momentum is building. The Chaikin Oscillator tends to lead price turns because institutional money flow shifts before retail price action responds. Divergence between the oscillator and price — oscillator making lower highs while price is still rising — is a particularly reliable early warning of an approaching correction.

Ease of Movement (developed by Richard Arms) divides the midpoint move of the bar — (today's midpoint minus yesterday's midpoint) — by the 'box ratio' (volume divided by the high-low range). A high positive EOM reading means price rose significantly on relatively low volume — the market is moving upward 'easily' without requiring heavy buying pressure. This tends to happen in well-established uptrends where sellers are scarce. A high negative EOM reading means price fell easily on light volume — sellers have control. Values near zero indicate price is not moving easily in either direction — either heavy volume is required to produce small moves (a sign of stiff resistance or support), or the market is simply consolidating quietly.

The Klinger Oscillator was developed by Stephen Klinger to predict long-term money flow trends while remaining sensitive to short-term price fluctuations. It calculates 'Volume Force' by combining the direction of price movement, the daily high-low range, and volume — in essence, it asks: how much directional force did today's volume generate? This Volume Force is then smoothed with two EMAs (typically 34 and 55 periods), and their difference is the oscillator. A 13-period EMA of the oscillator serves as the signal line. The resulting indicator is particularly suited to identifying long-term accumulation and distribution cycles that precede major trend reversals, making it more useful on weekly or monthly charts than intraday settings.

The Money Flow Index incorporates trading volume into the RSI formula by weighting each period's typical price (average of high, low, close) move by the volume traded during that period. Periods where price rose on heavy volume contribute strongly to the positive money flow total; price declines on heavy volume contribute to negative money flow. The ratio of positive to negative money flow is then normalized to a 0–100 scale using the RSI formula. The result: a momentum indicator that gives greater weight to high-volume moves and discounts low-volume noise. Above 80 is overbought; below 20 is oversold — the same thresholds as RSI, but with volume incorporated. MFI divergences are considered particularly reliable because they require both price and volume to align for divergence to form.

The Negative Volume Index, developed by Paul Dysart and later popularized by Norman Fosback, is built on the hypothesis that sophisticated institutional investors ('smart money') are most active on quiet, low-volume days while retail investors dominate high-volume sessions. NVI updates only on days when trading volume is lower than the previous day, accumulating the price change from those sessions into a running line. When NVI is trending upward, it suggests smart money is quietly accumulating in the background on low-volume days. Fosback's research showed that when NVI is above its 255-day EMA, the probability of the market being in a bull market is very high; when below, the probability shifts significantly toward a bear market.

On-Balance Volume, developed by Joseph Granville in 1963, operates on the principle that volume is the 'fuel' behind price moves. It accumulates volume by adding the entire session's volume on up days (close above prior close) and subtracting it on down days (close below prior close). The resulting cumulative line indicates whether volume is flowing into or out of a security over time. Granville's key insight: when OBV makes a new all-time high before price does, it signals institutional accumulation — smart money is buying aggressively while price has not yet responded. Conversely, OBV making new lows while price holds near highs signals distribution — someone with large positions is selling into strength.

The Positive Volume Index is the complement to NVI — it updates only on days when trading volume increases from the prior day, capturing the behavior of the crowd and retail participants who concentrate their activity on high-volume sessions. The theory: PVI tracks what retail money is doing (active on busy, news-driven high-volume days), while NVI tracks what smart money is doing (active on quiet days). Used in isolation, PVI tends to be a less reliable trend indicator than NVI because crowd behavior is noisier. Its most powerful application is in comparison with NVI: when PVI is declining while NVI is rising, it indicates the crowd is bearish while smart money is accumulating — a classic setup for a contrarian long position.

Price and Volume Trend improves on On-Balance Volume by scaling each day's volume contribution to the size of the price change. While OBV adds or subtracts the full session volume regardless of whether price moved 0.1% or 5%, PVT multiplies volume by the percentage price change — so a +5% day contributes 50× more than a +0.1% day. This makes PVT more sensitive to significant price moves backed by volume and less influenced by days with trivial price changes. The resulting cumulative line is generally smoother and more informative than OBV, especially in markets where many sessions have very small price changes that OBV treats equally to large moves.

The Trade Volume Index classifies each intraday tick as either a buy tick (price above a midpoint threshold) or a sell tick (price below the threshold), and accumulates separate running totals for buying volume and selling volume. The indicator is designed for very short timeframe analysis — tick charts, 1-minute charts — where the distinction between aggressive buying and selling is visible at a granular level. When buying ticks are dominating, TVI rises; when selling ticks dominate, it falls. TVI is particularly useful for scalpers and market microstructure analysis, where understanding whether buyers or sellers are the more aggressive party in the immediate term drives very short-term price direction.

Volume Average plots a simple or exponential moving average of trading volume (typically 20 or 50 periods) as a reference line overlaid on the volume histogram bars. Every serious technical analyst uses this as their first reference check when evaluating any price move: is this bar's volume above or below average? A price breakout accompanied by 2× average volume carries fundamentally more conviction than an identical breakout on half-average volume. Volume spikes to 3–5× average typically signal climactic events — either capitulation selling at bottoms or euphoric buying blowoffs at tops — which are frequently followed by reversals or consolidation. No price action analysis is complete without checking the volume context.

Aroon (Sanskrit for 'dawn's early light') was developed by Tushar Chande to answer a specific question: how recently, within the last N bars, did price make its highest high and lowest low? It plots two lines: Aroon Up (100 × (N − bars since highest high) / N) and Aroon Down (100 × (N − bars since lowest low) / N). Both oscillate between 0 and 100. An Aroon Up reading of 100 means price just made a new N-period high; a reading of 0 means the new high was N bars ago and hasn't been surpassed since — a trend losing freshness. The relationship between the two lines reveals whether an uptrend or downtrend is in force, how strong it is, and whether a consolidation phase is setting up.

The Aroon Oscillator simplifies the dual-line Aroon Indicator into a single line by subtracting Aroon Down from Aroon Up. The result oscillates between −100 and +100. A reading above zero means Aroon Up is dominant — recent price highs are fresher than recent lows — bullish. A reading below zero means Aroon Down is dominant — recent lows are fresher — bearish. Readings above +50 indicate a strong, well-established uptrend; below −50 indicate a strong downtrend. The oscillator crossing zero is the primary signal for a potential trend change. It compresses the same information as the full Aroon Indicator into one line, making it easier to screen across many securities.

DEMA was developed by Patrick Mulloy to address the lag inherent in standard exponential moving averages. It uses the formula: DEMA = (2 × EMA) − EMA(EMA), which subtracts the doubly-smoothed EMA from twice the standard EMA. The result tracks price more closely than a standard EMA of the same period, giving traders earlier signals in both trend identification and crossover-based systems. At the same 20-period setting, DEMA will react meaningfully faster than a regular EMA while producing smoother output than simply shortening the EMA period (which would increase noise). DEMA is a natural upgrade for traders already comfortable with EMA-based strategies who want reduced lag without switching to a shorter period.

A Displaced EMA shifts the entire EMA line forward (positive displacement, creating a leading indicator) or backward (negative displacement, creating a lagging indicator) by a defined number of bars. The most common application is displacing a medium-period EMA (e.g., 21-period EMA shifted forward by 3–8 bars) to create a dynamic support/resistance zone rather than a single line. Price trading within this shifted zone suggests it is at a meaningful mean-reversion level. Some trading systems — notably those taught by Daryl Guppy and others — use displaced MAs as the backbone of entry and exit decisions because the displacement filters out minor violations of the moving average line.

DMA calculates the difference between a fast and a slow moving average and plots the result as an oscillator below the price chart. When the fast MA is above the slow MA, DMA is positive; when below, it is negative. The zero line represents the point where both averages are equal — a classic crossover point. Unlike the MACD (which specifically uses 12- and 26-period EMAs), DMA allows fully customizable MA periods and types, making it a flexible tool for traders who want the conceptual clarity of MACD but with different underlying averages. The distance of DMA from zero measures how far apart the two moving averages are — an expanding gap signals an accelerating trend; a narrowing gap signals deceleration.

Developed by John Ehlers using digital signal processing principles, the Super Smoother is a two-pole Butterworth filter that removes aliasing and high-frequency noise from price data with minimal lag. Traditional moving averages introduce significant lag because they weight all periods equally (SMA) or use exponential decay (EMA). The Super Smoother uses filter coefficients derived from the target cutoff frequency, producing a curve that responds to genuine trend changes quickly while eliminating random bar-to-bar noise completely. The result is a moving average line that looks similar to an EMA on a chart but behaves differently at turns: it accelerates toward price during genuine trend changes and remains stable during choppy non-directional periods. It is the foundation of many of Ehlers' more complex indicators.

Parabolic SAR, developed by J. Welles Wilder, places dots above price candles during a downtrend and below price candles during an uptrend. The dots start far from price when a new trend begins and accelerate toward price as the trend matures — the 'parabolic' shape that gives the indicator its name. When price crosses through the SAR dots, the indicator reverses — dots flip to the other side — signaling a potential trend change. The accelerating factor is key: it tightens the trailing stop progressively, locking in more profit as the trend ages while still leaving room for normal price fluctuations early in the trend. In practice, Parabolic SAR is most useful in clearly trending markets and generates frequent false signals in sideways conditions.

TEMA extends DEMA's lag-reduction principle by applying a third EMA layer. The formula is: TEMA = 3×EMA − 3×EMA(EMA) + EMA(EMA(EMA)). The result is a moving average that tracks price so closely that on fast-moving price action, it can actually lead price momentarily — appearing to anticipate the direction. This responsiveness makes TEMA excellent for trend confirmation and crossover signals in trending markets. However, the very same responsiveness amplifies noise in choppy, non-directional markets, producing frequent and unhelpful whipsaws. TEMA should be used in conjunction with ADX or a trend filter to ensure it's only applied when a genuine trend is in place.

The Vortex Indicator, created by Etienne Botes and Douglas Siepman and published in 2010, plots two lines: VI+ (positive vortex movement) and VI− (negative vortex movement). VI+ measures the sum of the distance between today's high and yesterday's low; VI− measures the sum of the distance between today's low and yesterday's high — both normalized by the True Range sum. When VI+ crosses above VI−, bullish directional movement is dominating; when VI− crosses above VI+, bearish directional movement dominates. Conceptually similar to DMI, but with a different calculation methodology that some traders find generates earlier crossover signals.

ZigZag draws lines connecting only significant price swings that exceed a defined threshold — specified as a percentage, ATR multiple, or absolute point move. The different ZigZag variants apply slightly different methods for identifying swing significance: Percent uses a percentage-based threshold, High Low uses actual high-low extremes, Trend Sign and Trend Percent focus on trend direction confirmation, and Step Pattern uses fixed bar count rules. The primary use case is identifying the structure of price — alternating swing highs and swing lows — without the noise of minor fluctuations. Elliott Wave analysts use ZigZag to count wave structures; swing traders use it to measure swing size and identify support/resistance pivots. Important: ZigZag repaints — the final line redraws as new price data arrives, making it unsuitable for real-time trading signals.

The MACD Histogram is the bar chart displayed below the MACD and signal lines, representing the arithmetic difference between the MACD line and its signal line at each bar. When the bars are above zero and growing, the MACD line is accelerating away from the signal line — momentum is increasing in the bullish direction. When bars are above zero but shrinking back toward zero, momentum is slowing even though price may still be rising — this is often the earliest warning of an impending correction. The histogram's most powerful signal is divergence: if price makes a higher high but the histogram makes a lower high, the momentum behind the move is fading — a high-probability setup for a reversal. Alexei Elder described this divergence as one of the strongest signals in all of technical analysis.

MACD Two Lines presents the indicator in its original form as envisioned by Gerald Appel: just the MACD line and the signal line, without the histogram overlay. Some traders find the cleaner line view easier to read, particularly when monitoring multiple indicators simultaneously and need to reduce visual clutter. The signals are identical to the standard MACD setup — crossovers of the MACD line above the signal line are bullish; crossovers below are bearish; zero-line crossovers confirm momentum direction. The absence of the histogram does sacrifice the early divergence warning that the histogram provides, so this version is best for traders who rely on price action divergence rather than histogram divergence.

These studies draw a linear regression line with parallel channel bands (set at ±2 standard errors of the regression) over a 50-period lookback (Ch50), 100-period lookback (Ch100), or user-defined variable lookback (ChVar). The regression line represents the 'fair value' path of price; the upper band is where price is statistically extended to the upside; the lower band is where it is extended to the downside. The 50-period version captures short-to-medium-term trends; the 100-period captures longer structural trends. Price returning to the channel after moving outside it confirms mean reversion is in play. The slope of the regression line provides an objective measure of trend angle and direction.

Unlike the Linear Regression line (which forces a straight line fit), the Linear Regression Curve fits a polynomial (curved) regression through price data, allowing it to better capture accelerating or decelerating trends that don't follow a straight-line path. A stock in a parabolic uptrend, for example, will not fit well to a straight regression line — the curve adapts its shape to the actual trajectory of the move. The distance of price from the curve serves the same purpose as with linear regression: large deviations above the curve suggest overextension; below the curve suggests a lagging-but-intact trend. The curve's direction (rising or declining curvature) can indicate acceleration or deceleration even before the direction changes.

R-Squared (R²) measures the proportion of price movement that can be explained by a linear trend, expressed as a value between 0 and 1 (or 0–100%). An R² of 0.90 means 90% of price movement is directional and linear — the market is in a clean, predictable trend. An R² near 0 means price movement is essentially random with no discernible linear component — the market is in noise mode. This is perhaps the most mathematically rigorous answer to the question every trader should ask first: 'Is this market trending right now?' High R² validates the use of trend-following indicators (MACD, MAs, ADX); low R² is the objective signal to switch to oscillators and mean-reversion strategies.

Rainbow Average plots multiple moving averages simultaneously — typically 10–20 EMAs with periods from 2 to 40 — each colored with a gradient from warm (fast, short-period) to cool (slow, long-period), creating a visual 'rainbow' effect on the chart. When all averages are aligned in the same direction and spread apart ('fanning out'), it confirms a strong trend. When the averages are tangled together in a tight band, the market is in consolidation with no clear directional bias. The distance between the outer fast and slow averages serves as a visual measure of trend strength — wide separation = strong momentum; compression = weakening trend or transition period. Many traders use the rainbow to identify when the trend is at its cleanest for momentum entries.

The Time Series Forecast is a linear regression line shifted one period forward, representing where the regression model 'forecasts' price should be at the next bar. Unlike a moving average, which calculates the average of past prices, TSF calculates where price would be on the regression line one bar ahead, making it a true predictive indicator. When actual price is above the TSF, it is trading above the model's expectation — potentially extended. When below, it is lagging the model — potentially a catch-up opportunity in the direction of the trend. TSF is more responsive than a moving average and changes direction earlier at trend turns.

The Vertical Horizontal Filter divides the price range (highest high minus lowest low) over a period by the sum of the absolute price changes over that same period. In a trending market, price moves directionally — the numerator (range) is large relative to the denominator (total path traveled). In a choppy market, price oscillates back and forth — the total path is large relative to the net range — producing a low VHF reading. VHF essentially measures how 'efficient' price movement has been: high efficiency (large directional move on a direct path) = trending; low efficiency (lots of up-down movement) = ranging. This makes VHF an excellent strategy-selector: it tells you which type of indicator is appropriate for current conditions.

VWMA calculates a moving average where each bar's closing price is weighted by the volume traded during that bar. A session with 10× average volume contributes 10× more to the VWMA than a session with 1× volume. The result is a moving average that naturally emphasizes the prices where actual market participants transacted most, reflecting where the largest volume of real capital was committed. VWMA will differ most from a standard SMA after high-volume sessions at price extremes — the VWMA gravitates toward those high-volume prices while the SMA treats every bar equally. It is similar in spirit to VWAP but calculated as a rolling moving average rather than a daily reset benchmark.

The Triangular Moving Average applies simple moving average smoothing twice in sequence, creating a weighted average where data points near the middle of the lookback period are weighted more heavily than those at the edges. The result is an extremely smooth line with greater lag than a standard SMA of the same period. Because of this double-smoothing, TMA is rarely useful for short-term signals — it will always be late to turns. Its primary application is as a long-term trend direction indicator and as a reference line for identifying significant mean reversion setups — when price has moved far from a TMA, it's often statistically extreme.

Developed by Perry Kaufman, KAMA uses an Efficiency Ratio (ER) to dynamically adjust its smoothing constant. The Efficiency Ratio measures how directional price movement has been: a high ER means price moved far in a straight line (efficient, trending); a low ER means price moved a lot but went nowhere (choppy). When ER is high, KAMA uses a fast smoothing constant and tracks price closely — like a short-period EMA. When ER is low, KAMA uses a very slow smoothing constant and barely moves — filtering out the noise completely. The result is a moving average that automatically adapts to market conditions without the trader manually switching periods. KAMA is considered one of the most sophisticated adaptive moving averages and is used as the basis for many mechanical trend-following systems.

The Volume Flow Indicator improves on On-Balance Volume by adding two important refinements. First, a cutoff threshold filters out sessions with very small price changes — treating them as noise rather than directional signals. Second, each session's volume is weighted by the logarithm of the ratio of current volume to average volume, which compresses the influence of extreme volume spikes while amplifying the relative importance of above-average sessions. The result is a cumulative money flow measure that is both more stable (less affected by noise) and more nuanced (more sensitive to meaningful volume events) than raw OBV. VFI crossing above zero indicates net positive money flow over the period; below zero indicates net outflow.

TTM Scalper Alert, developed by John Carter, generates real-time buy (green triangle below bar) and sell (red triangle above bar) alerts based on short-term price bar calculations. It evaluates each bar using a proprietary momentum calculation comparing the current bar's structure against the prior few bars to identify small directional biases. It is designed for very active traders on 1-minute to 5-minute charts who are making multiple decisions per session. The alerts are frequent by design — at short timeframes there are many small momentum shifts to capture. Its effectiveness depends heavily on the broader market context: scalper alerts in the direction of the intraday trend and above the VWAP have a significantly higher probability than against-trend signals.

TTMLRC (TTM Linear Regression Channel) draws a linear regression line through price with upper and lower channel bands whose width is dynamically adjusted based on current market volatility. In volatile markets, the bands widen to accommodate larger price swings; in quiet markets, they tighten. This makes the channel more adaptive than fixed standard-error versions — price is less likely to break the channel purely due to a volatility expansion that was not itself a trend reversal. The channel provides a visual framework for the current trend: the slope shows direction, the width shows volatility context, and channel touches provide potential support/resistance zones.

Polarized Fractal Efficiency measures the ratio of the straight-line distance price traveled to the actual fractal (zigzag) path it took to get there over a defined period. A reading near +100 means price moved in an almost perfectly straight upward line — maximum efficiency, extremely strong trend. Near −100 means a perfectly straight downtrend. Near zero means price traveled a long, winding path and ended up close to where it started — maximum inefficiency, choppy market. PFE is related to the concept of fractal dimension: high efficiency corresponds to a low fractal dimension (closer to a straight line); low efficiency corresponds to higher fractal dimension (more complex, space-filling movement). It is used to identify the cleanest trending conditions for momentum entries.

Trend Quality is a composite indicator that evaluates multiple dimensions of trend health simultaneously — direction, momentum strength, consistency of the move, and duration — and compresses them into a single score. High scores indicate a clean, persistent, directional trend where trend-following strategies have historically performed well. Low scores indicate choppy or inconsistent price behavior where trend strategies will underperform and oscillator approaches are more appropriate. By monitoring Trend Quality alongside a trend indicator, traders can objectively quantify when conditions are optimal versus when it is better to stand aside and wait for cleaner setups to develop.

Reverse Engineering RSI inverts the standard RSI calculation: instead of computing RSI from price, it computes what closing price is required to move RSI to a specified target level (e.g., 70 for overbought). This is a planning tool, not a signal generator. Before entering a trade, a trader can ask: 'At what price would RSI reach 70, and how far is that from here?' If the answer is 8% higher, that defines both a potential target and an upper boundary for the trade. It is also used to set conditional orders: 'Alert me when price reaches the level that would put RSI at 70.' The indicator updates dynamically as each bar closes, recalculating the required price for the next bar.

Standard Error Bands draw a linear regression line with upper and lower bands set at ±2 standard errors of the regression estimate. This is subtly different from Bollinger Bands: standard error measures how accurately the regression model predicts price, while standard deviation measures how much price varies around its mean. The result: in a perfectly trending market, Standard Error Bands become narrow (the regression model is very accurate) while Bollinger Bands might widen (prices are still varying). Conversely, in a choppy market, Standard Error Bands widen (the regression model is inaccurate) while Bollinger Bands might be moderate. This makes Standard Error Bands a measure of trend quality as much as a volatility measure.

Acceleration Bands, developed by Price Headley of BigTrends, plot upper and lower bands around a simple moving average using a formula that incorporates the ratio of the high-low range to the midpoint price. Unlike Bollinger Bands (which use statistical standard deviation), Acceleration Bands expand geometrically as price accelerates, providing a channel specifically tuned to identify parabolic acceleration moves. When price closes above the upper Acceleration Band, it signals a powerful acceleration breakout — the beginning of a fast trending move. When price 'walks' along the upper band for consecutive sessions, the trend is exceptionally strong. A reversal close back inside the band signals that the acceleration phase has ended.

ATR, developed by J. Welles Wilder, measures the average of the 'true range' over N periods. True range is the greatest of three values: current high minus current low; current high minus prior close (captures upward gaps); and prior close minus current low (captures downward gaps). By incorporating gaps, ATR gives a complete picture of total price movement including overnight moves. ATR is one of the most practically useful indicators in trading: it is used for stop-loss placement (a common rule is placing a stop 1.5–2× ATR from entry), position sizing (risk a fixed dollar amount per trade, divided by ATR to determine share count), and comparing volatility across assets. ATR does not indicate direction — it is purely a measure of magnitude.

Bollinger Bands, developed by John Bollinger in the 1980s, plot a 20-period simple moving average in the center with upper and lower bands set 2 standard deviations from the mean. The key insight is that the bands are adaptive — they expand during volatile periods and contract during quiet ones. This means price touching the upper band in a high-volatility environment is less significant than touching it in a low-volatility environment. John Bollinger's 'Squeeze' concept — when bands are at their narrowest in years — is one of the most reliable setups in technical analysis for identifying imminent large moves. Contrary to popular belief, price touching the upper band is not automatically a sell signal; in a genuine uptrend, price can 'walk the band' for extended periods, with each touch confirming momentum.

Bollinger Bandwidth quantifies the percentage width of the Bollinger Bands relative to the middle band: (Upper Band − Lower Band) / Middle Band × 100. This single value precisely measures how compressed or expanded the bands are at any point in time. When Bollinger Bandwidth reaches a multi-month or multi-year low, it signals an extreme compression phase — the 'Bollinger Squeeze.' John Bollinger identified this Squeeze as one of the most powerful setups in technical analysis: extreme compression is always followed by expansion (a large directional move), though the direction is not predetermined. Bandwidth also reveals when a trend is healthy (consistently elevated bandwidth) versus when it is losing energy (bandwidth declining during a rally).

Bollinger %B measures price's position within the Bollinger Band envelope, expressed as a percentage: (Price − Lower Band) / (Upper Band − Lower Band). A reading of 1.0 means price is exactly at the upper band; 0.5 = at the middle band; 0 = at the lower band. Readings above 1.0 or below 0 mean price has moved outside the bands — a statistical extreme. %B can be used to identify overbought/oversold conditions relative to current volatility levels, and critically, it adjusts its interpretation based on band width — a %B of 1.0 during a high-volatility period is less extreme than during a compressed, low-volatility Squeeze phase. John Bollinger uses %B as a key component in his 'Method I' Squeeze breakout system.

Historical Volatility (also called realized volatility) calculates the annualized standard deviation of logarithmic daily price returns over a trailing lookback period. It measures what volatility actually was over that period — not what the market expects going forward. HV is expressed as an annualized percentage: an HV of 30% means the stock has been moving as if it could move roughly 30% up or down over a year at the current volatility pace. The most critical application is comparing HV against Implied Volatility (IV): when IV is significantly above HV (IV premium), options are expensive relative to actual realized moves — a condition that often favors premium-selling strategies. When IV is below HV (IV discount), options are cheap relative to what price has been doing.

Implied Volatility is calculated by working backward from current options prices using an options pricing model (typically Black-Scholes). Instead of computing option price from known inputs, IV solves for the volatility assumption embedded in the market price. High IV means the market is pricing large expected future moves — often before earnings announcements, FDA decisions, Fed meetings, or during market crises. Low IV means the market expects calm conditions ahead. The VIX is the best-known IV measure, calculating the implied volatility of S&P 500 index options. For options traders, IV is arguably the most important input: buying options when IV is low and selling when IV is high gives an edge independent of directional prediction. IV rank (current IV relative to its 52-week range) is generally more useful than raw IV.

The TTM Squeeze, developed by John Carter, is built on a specific observation: when Bollinger Bands (measuring statistical price variance) contract inside Keltner Channels (measuring ATR-based volatility), it signals an extreme state of price coiling — a 'spring loaded' condition. This state is marked by red dots on a midline; when Bollinger Bands expand back outside Keltner Channels, the Squeeze 'fires' and green dots appear — signaling the beginning of a directional expansion move. The accompanying momentum histogram (a modified MACD) provides the directional bias: if momentum is positive when the squeeze fires, the move is expected to be upward; negative = downward. Carter's research showed that Squeeze setups on daily charts produced some of the highest-probability directional moves he studied.

The Ulcer Index, developed by Peter Martin and Byron McCann, measures the psychological 'pain' of holding an investment through its drawdowns by calculating the root mean square of the percentage decline from recent highs over a lookback period. Unlike standard deviation (which treats upward and downward volatility equally), the Ulcer Index is entirely one-directional — it only captures downside drawdowns. A high Ulcer Index means the position has been experiencing sustained, deep pullbacks from its highs — causing investor stress. A low Ulcer Index means price has stayed close to its recent highs with minimal drawdown duration. The Ulcer Performance Index (UPI) divides returns by the Ulcer Index to produce a risk-adjusted return metric that is arguably more relevant to real investors than the Sharpe Ratio, which penalizes upside volatility equally.

The Volatility Switch monitors the current volatility environment and outputs a binary or graded signal indicating whether a trend-following regime or a mean-reversion regime is currently in force. The underlying logic is that markets exhibit two fundamentally different volatility states: persistent directional volatility (trending) — where momentum strategies work — and oscillating, mean-reverting volatility (ranging) — where oscillators and fades work. When the switch indicates trending mode, use MACD, moving averages, and breakout strategies. When it indicates mean-reversion mode, switch to RSI, Stochastic, and range-bound approaches. It is a systematic regime detection tool that automates what experienced traders do intuitively.