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We support daily, weekly, and monthly timeframes, line, bar, and candlestick charts, 180+ technical indicators, 100+ market breadth metrics, 100+ toplists for stock screening, historical quarterly fundamental data, and key economic indicators from Federal Reserve.
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Comprehensive set of technical indicators and metrics.
In terms of charting and technical analysis, we provide:
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- Arms Index / TRIN and 28 metrics derived from technical indicators: 18 momentum-related metrics (based on Moving Averages, RSI, MACD, Stochastic, Bollinger Bands, Keltner Channel, Standard Error Bands, Ichimoku Cloud, recent price ranges, and Linear Regression) and metrics capturing bullish / bearish divergence, correlation with market indices, decorrelation with 100+ top ETFs and stocks, and decorrelation with 40+ key momentum oscillators.
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- Top gainers and losers, highest volume, most volatile, highest number of and largest gapups / gapdowns, strongest breakouts / breakdowns, widest price range, longest series of higher highs and lower lows, farthest from the recent highs and lows.
- Most over-extended on Moving Average distance, RSI, MACD, Stochastic, Bollinger %, Keltner %, Standard Error %, Linear Regression %, and Ichimoku.
- Steepest Linear Regression Channel, widest Bollinger Bands, Keltner Channel, and Standard Error Bands.
- Strongest cross of Moving Average, MACD signal line, Stochastic signal line, and Ichimoku.
- Lowest correlation with key momentum oscillators: RSI, MACD, Stochastic, Bollinger %, Keltner %, and Standard Error %.
- Highest price-momentum divergence: bullish, bearish, hidden bullish, and hidden bearish.
- Largest decorrelation with 100+ top ETFs and stocks and with 40+ key momentum oscillators.
- 180+ technical indicators on daily, weekly, and monthly timeframes, including historical data:
- Momentum oscillators including RSI, MACD, Stochastic, Bollinger %, Keltner %, Standard Error %, ATR, True Strength Index, Chaikin, Relative Vigor Index, Fisher, Commodity Channel Index, Pring, Parabolic SAR, Stochastic RSI, Ultimate Oscillator, Chande, ADX, Rate of Change, Bull / Bear Power, Aroon, Vortex, Balance of Power, Johnson, Bressert, TRIX, Random Walk, Accelerator, Derivative Oscillator, Wilder, Disparity, Wave Trend, Schaff, Ulcer, Center of Gravity, Kurtosis, Polarized Fractal Efficiency, Kase Peak, Didi, Relative Spread Strength, Repulse, Belkhayate, Cyber Cycle, Inertia, Quick Stick, Vervoort, Firefly, Roofing, Recursive Median, Decycler, Pass Band, Projection Bands, Fractal Dimension, Damiani, Body Momentum, Exponential Deviation, Forward Reverse, Kuskus, Universal Oscillator, Ehlers Correlation Trend, Elegant Oscillator, Brown Composite, Trend Score, Rapid RSI, Absolute Strength, Kaufman Efficiency, Trend Detection / Trigger / Intensity / Continuation / Strength, Laguerre RSI, Alligator, Supertrend, Connors RSI, Coppock Curve, and Mass Index.
- Pivot Points: Standard, Camarilla, Woodie, and Fibonacci.
- Channels: Linear Regression, Bollinger Bands, Keltner Channel, Price Channel, Standard Error Bands, Donchian Channel, Hurst Cycle Channel, Interquartile Range Bound Channel, etc.
- Moving Averages: simple, exponential, volume-weighted, and adaptive (9 variants).
- Volume-based indicators: Volume Shelves, Pivot-Anchored VWAP, On-Balance Volume, Money Flow Index, Volume Price Trend, Ease of Movement, Chaikin Money Flow, Twiggs, Klinger, Elder Force, Accumulation / Distribution, Volume Zone, Volume Flow, Demand Index, etc.
- Moving Average Envelopes, Ichimoku Cloud, Zigzag, Elder Safe Zone, Relative Volatility Index, Detrended Price, Volatility Stop, Choppiness Index, Chande-Kroll Stop, McGinley Dynamic, and Chandelier.
- DeMark Sequential (9 and 13), DeMarker, and Range Expansion Index.
- Trendlines for top 5, 7, 9, and 11 recent pivots.
- Divergence with 40+ key momentum indicators: bullish, bearish, hidden bullish, and hidden bearish.
- Correlations:
- Daily, weekly, and monthly ratio and comparison of any two symbols, supporting all 180+ technical indicators.
- Market overview: summary of latest price movements in top ETFs and stocks as well as key macro-economic data from Federal Reserve.
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Technical IndicatorsToplists: Stock ScreenersMarket Breadth MetricsOption Chain Data and TechnicalsCorrelation With 100+ ETFs / StocksCorrelation With 40+ Momentum OscillatorsPrice-Momentum Divergence For 40+ Momentum OscillatorsTechnical Indicators
We compute and plot a comprehensive set of overlays and oscillators that can be used to technically analyze trends, momentum, and reversal levels, as well as determine support, resistance, and trade entry and exit points.
- Volume. We provide a bar plot of daily/weekly/monthly volume and its 8-period moving average to smooth out volume fluctuations. Green bars correspond to price moving up and red ones to price moving down.
- Relative Strength Index (RSI). One of the most commonly-used momentum indicators calculated over the 14 period time window. Measures the speed and magnitude of recent price changes. Oscillates on a scale of 0 to 100. Values above 70 signify overbought conditions while values below 30 indicate strong selling pressure and an overextended downside move. RSI formula takes into account average gain in up-ticks and average loss in down-ticks. RSI tends to correlate strongly with price (their correlation coefficient is normally close to 1) and deviations often occur around trend turns or acceleration points. In addition, RSI being around its extremes can provide short-term traders with buy and sell signals, especially when used in conjunction with other indicators that confirm the same market sentiment.
- MACD (Moving Average Convergence Divergence). Important momentum oscillator calculated by subtracting the 26-period exponential moving average (EMA) from the 12-period EMA. The signal line is a nine-period EMA of the MACD line. There are several popular trading strategies driven by MACD. One of them is signal line cross-overs: going long when MACD crosses above the signal line and going short when the opposite scenario happens. Divergence with price is another commonly-employed trading setup: since MACD tends to move together with price, whenever the two diverge (e.g. when price makes higher highs while MACD is making lower highs), trend change often follows. Another set of strategies is based on mean reversion plays whenever MACD gets very far from the signal line (MACD histogram graphs the distance between MACD and its signal line). To avoid false positives / traps, which often get triggered by lagging indicators like MACD, it is best to seek confluence of other trend-following and momentum signals.
- Bollinger Bands. Envelopes plotted above and below the 20-period simple moving average at the distance that equals 2 standard deviations (it is assumed that prices follow the normal distribution which may not always be accurate). Helps gauge the volatility and how overextended the price currently is versus its recent swings / fluctuations. In other words, Bollinger Bands indicate when prices are statistically high or low. The bands widen whenever price becomes more volatile and contract when it is more stable. Typically periods of high volatility are followed by consolidation and vice versa. Since prices tend to remain within the bands' upper and lower limits (approximately 95% of the time), a commonly-used trading setup is betting on reversion once price is outside the bands. Bollinger Bands are also useful for identifying price targets and support/resistance levels in a ranging market. For instance, after bouncing off the lower band, the middle or upper band can be used as potential exit points (unless a very strong trend is in place like in Elliot Wave 3). In a strong uptrend, the price might repeatedly touch or stay above the upper band for an extended period of time. A popular setup, called Bollinger Squeeze, is when a price breakout, confirmed by increased volume, is accompanied by narrow Bollinger Bands that expand rapidly. In general, Bollinger Bands should be treated as a way to add clarity to charts, not as a sole price direction prediction mechanism.
- Stochastic. Time-tested (developed in 1950s) momentum indicator commonly employed to detect overbought and oversold conditions. Measures the location of price vs its recent range where price being at recent lows maps to 0 and price being at recent highs maps to 100. The stochastic oscillator may go outside this range when a strong trend is in place. We compute its value based on the last 14 price bars (using the “fast stochastic” formula or %K) and then smooth it using the 3-bar simple moving average (thus obtaining the “slow stochastic” line or %D, which we plot). In addition, we also calculate and plot the signal line which is the 3-bar simple moving average of the “slow stochastic” oscillator. Widely-used trading techniques based on the stochastic indicator include reversal plays following signal line cross-overs, the oscillator staying at extreme values for an extended period of time, and price-stochastic divergences.
- Keltner Channel. Volatility-based bands placed above and below the 20-period exponential moving average at the distance being double Average True Range computed over the last 10 periods. Conceptually similar to Bollinger Bands but using a different price volatility metric. Since price action is expected to stay within the channel most of the time, moves outside the bands are often interpreted as overextended. In a range-bound market they could be good reversal triggers. However, in strong trends, it is not uncommon for the price to touch and cross the bands repeatedly without major pullbacks. The bands expand and contract as the market alternates between low- and high-volatility (measured by ATR) regimes. Keltner bands are also used to compute price target points for corrections of major moves as the opposite side of the channel is often re-tested after significant band expansion. Another example is confirmation of breakdowns and breakouts by an expanding channel on high volume (in this case, a continuation is favored over a retracement).
- Ichimoku Cloud. A set of indicators and technical analysis methods that can be used to determine trend direction, price momentum, and important support and resistance levels. Originally invented in the 1960s, Ichimoku Cloud comprises four main components: two lines that constitute the cloud (also referred to as span A and span B) and two moving averages (also known as the base line and conversion line). The cloud is plotted 26 periods to the right (it provides support / resistance levels projected into the future) and its color represents the trend (green is bullish and red is bearish). The conversion line is calculated as the average of the highest high and the lowest low over the previous 9 periods. The base line is similar but it is computed over the past 26 periods. When the price is above the cloud, the trend is up, and vice versa for downtrends. If the price is inside Ichimoku Cloud, the trend is flat or undetermined / transitioning. Whenever the conversion line moves above the base line, especially when the price is above the cloud, a buy signal is generated (and for the opposite / mirror setup, we have a sell signal). Span A is computed as the average of the conversion and base line. Span B is the average of the highest high and the lowest low taken over the past 52 time periods. Whenever price moves very far away from the cloud or the distance between the base and conversion lines widens significantly relative to historical / typical ranges, a mean reversion signal is triggered. There are also numerous other trading strategies associated with Ichimoku Cloud. The overall best practice is combining multiple technical signals at once to reduce bias before taking any position. Ichimoku Cloud, just like any other indicator, has its own limitations and may lead to incorrect price action predictions.
- Standard Error Bands. Conceptually similar to Bollinger Bands, but use linear regression (21-period) instead of moving average as the middle line. The upper and lower bands are placed at the distance equal to two standard errors. The indicator shows the current trend and the volatility around it. An example trading signal provided by Standard Error Bands is when the bands tighten and price starts to move. This is interpreted as a steady trend. The bands will remain contracted as long as the trend continues to be strong. Once the bands start to widen again, mean reversion becomes more likely as momentum shifts and price consolidates (which commonly corresponds to distribution at tops and accumulation at bottoms). Since most of the price points will fall into the Standard Error Bands channel, another common trading approach is taking a counter-trend position when price is significantly outside the bands that are widening as this indicates a high probability of a trend correction. Normally, the bands will alternate between wide and narrow over time. Standard Error Bands can also be used as price targets, e.g. after a reversal following crossing of the upper band, the middle or lower line is often tested.
- Moving Average. We provide 5-, 13-, 20-, 50-, and 200-period simple, exponential, and volume-weighted moving averages (MAs). All of them plot the average price of an asset over a given period of time and are useful for smoothing out price movements as well as identifying trends and price exhaustion setups prone to mean reversion. The key difference between these 3 types of MAs is how they weigh various price points in the respective time windows. Simple MA treats all data points the same way, exponential MA gives more weight to more recent prices, and volume-weighted MA uses volume as the weighing factor. One of the trading strategies associated with MAs is cross-overs, where a lower-time-frame MA goes above or below a higher-time-frame MA (e.g. 5-week MA crossing 13-week MA while moving up could be interpreted as a buy signal). MAs also provide important price targets (e.g. support levels) as they are often re-rested in trend corrections. While MAs are widely used by technical traders to reduce noise, they have some limitations and, in choppy markets, they tend to generate conflicting signals.
- Bollinger %. Oscillator that maps the current price location within the Bollinger Bands to a percentage value. 0% corresponds to the lower band and 100% to the upper one. This indicator can be used to gauge how overextended price is. For example, values above 100% mean that price is above the upper Bollinger band and therefore a pullback is more likely. Similarly, negative percentages point to downtrend exhaustion which often is followed by a bounce (a countertrend rally or a trend reversal). Bolliger % tends to strongly correlate with price movements. Hence, it is commonly used to detect divergences. For instance, when price is making new highs but Bolliger % is making lower highs, momentum has slowed down and the current leg may be nearing its completion. Analogously, whenever the correlation coefficient between price and Bollinger % drops significantly below 1, a decisive price move is expected (either trend reversal or trend acceleration).
- Keltner %. Translates price location within the Keltner Channel to a percentage value. Semantically, similar to Bollinger %. The lower band maps to 0 and the upper band corresponds to 100. Values outside this range typically mean overbought or oversold conditions and can be used as a short and long signal, respectively. Another trading strategy based on Keltner % is breakout / breakdown confirmation: whenever a significant level has been broken right after price has shown divergences with this oscillator (e.g. lower high vs higher high), a solid move is likely in the making. Correlation between Keltner % and price should be close to 1 and whenever the two deviate, momentum is no longer confirming price, and either of them will soon adjust (e.g. momentum will move up in a bullish setup or price will drop in a bearish scenario).
- Parabolic SAR. Proven, time-tested indicator employed by technical analysts to confirm trend direction and its momentum and identify trailing stops / adaptive exit points (SAR stands for “stop and reverse”). Plotted as dots that are below price in uptrends and above it in downtrends. As the price rises, the dots will move up as well, first slowly and then accelerating with the trend (following the parabolic curve), and finally catching up with price. When the dots flip, a potential change in trend direction is underway. Parabolic SAR has a minimal lag, works well in a trending market but tends to produce many false signals when the price moves sideways or is choppy. Therefore, it is important to combine it with other methods. The SAR line is calculated independently for each trend in the price (it resets whenever the dots meet the price).
- Stochastic RSI. Momentum oscillator that is computed by applying the formula of Stochastic to the values of RSI, thus combining the strengths of both indicators into one. Stochastic RSI measures the level of the RSI relative to its recent highs and lows and, therefore, in terms of sensitivity to price movements, is more reactive than RSI. The oscillator readings are between 0 and 100. Values above 80 are considered overbought (and below 20 oversold). Stochastic RSI is a second-order derivative of price that attempts to strike a balance between the slow-moving RSI and the highly-dynamic Stochastic. It can help traders identify price extremes as well as reversal points. For example, divergence or de-correlation with price tends to produce reliable trading signals. In volatile markets, Stochastic RSI may generate false positives, hence it is recommended to use it in conjunction with and as a complement to other techniques. Given the quick response time of Stochastic RSI, the oscillator is typically smoothed using its moving average. The resulting signal line is often used by traders for cross-over setups (e.g. Stochastic RSI crossing its signal line upwards is interpreted as bullish).
- Linear Regression. We compute and plot 20-, 50-, and 100-period linear regression channels across daily, weekly, and monthly timeframes. Linear regression provides a linear approximation of price movements and can be helpful in identifying fluctuations around the primary trend. There are 5 lines in the plots: the center one is the actual linear regression line and the remaining lines are drawn at the distance being a multiple of standard deviation. Most price points should lie within 2 standard deviations, i.e. between the uppermost and lowermost lines. Besides confirming the trend slope, the linear regression channel can also be used for short-term swing trading: the price will often bounce off the outer lines while following the channel in one direction. For example, a buy signal is generated when, in an upward channel, price retests the lowest linear regression line. Linear regression is computed using the least-squares method where we fit the price points by minimizing the sum of the squares of the residuals, i.e. the differences between an observed value and the modeled value.
- Standard Error %. Oscillator that indicates the current price location within the Standard Error Bands channel. 100 corresponds to the upper band and 0 to the lower band. Values outside of this range mean that price is outside of the channel. Standard Error % normally correlates well with price and therefore any divergence or decorrelation there can be used as a potential reversal signal (for example, price making new highs while the oscillator is making lower highs or the correlation coefficient between the two dropping significantly). In addition, since price is mostly contained within the Standard Error Bands channel, readings well above 100 or below 0 tend to coincide with price exhaustion. A potential trading strategy is to expect a counter-trend move whenever this happens, especially when other indicators agree in terms of the predicted price direction.
- Donchian Channel. Relatively simple yet effective technical indicator that facilitates volatility assessment and breakout detection. The upper band marks the highest high while the lower band corresponds to the lowest low. Both are computed over the last 20 bars in a daily, weekly, or monthly timeframe. Donchian Channel depicts how price relates to its recent range. In range-bound markets, this indicator can be used to signal reversal points whenever price approaches the channel boundaries. In trending markets, on the other hand, Donchian Channel provides breakout / breakdown confirmations. Because of this duality, it is best to first determine the overall market phase (e.g. strong trend typical of Elliot Wave 3 vs a sideways movement commonly present in Elliot Wave 4), before reaching conclusions based on Donchian Channel signals. The width of the channel indicates price volatility. Wider channel signifies larger price swings. When price is consolidating in a narrow channel vs recent history, probability of a larger move is gradually increasing. This is because volatility tends to alternate between low and high over time. In addition, Donchian Channels are often used to identify stop loss levels for risk management.
- Ultimate. Range-bound momentum oscillator fluctuating between 0 and 100. Considered to be less reactive and thus generating fewer false positives (vs for example RSI or MACD) because it is based on a weighted average of three signals, each computed on a different time scale (7, 14, and 28 bars). Each signal is calculated by dividing average buying pressure by average true range. Oscillator levels below 30 are deemed as oversold, and correspondingly, values above 70 are interpreted as overbought. There are two commonly-used trading methods associated with Ultimate Oscillator. One is going counter-trend wherever we reach extremes. Another one is based on divergence with price, e.g. when price is making lower lows and the oscillator is making higher lows, or when correlation between price and the oscillator moves far away from its average. In these cases, mean reversal is the expected scenario (although there are setups that resolve in the direction of the current trend despite divergence / decorrelation being present – momentum simply returns and catches up with price instead of price reversing). Hence, Ultimate Oscillator should not be relied upon in isolation, and like all indicators, works best when combined with other technical analysis methods.
- Chandelier. Trend-following indicator based on volatility. Identifies stop loss exit points. Helps traders stay in the trend until a reversal takes place. Uses average true range (ATR) as volatility metric. Chandelier exits are computed as two lines, one for going long and one for going short. The underlying principle is that a trend reversal becomes likely once price moves against the trend more than three times ATR. One of the lines is calculated as the highest high minus 3 ATR. The other one is the lowest low plus 3 ATR. In both cases, we look back 22 bars. An exit alert is generated whenever price crosses the line that corresponds to the current position (long or short). Chandelier exits are designed mainly for trending markets and may lead to frequent false signals in periods of price consolidation. Elliot Wave 1 and 3 typically work well with this indicator. It is important to understand the bigger picture before relying on Chandelier Exits.
- Adaptive Moving Average. The most commonly used moving average lines are: simple, exponential, and volume-weighted. While easy to understand, reason about, and calculate, they have certain limitations, and often do not capture important characteristics of recent price action. There are a number of moving average lines that adapt and respond to price more dynamically and are often more accurate in terms of predicting mean reversion targets.
- Arnaud Legoux. Based on a Gaussian distribution curve with a variable width that adapts to market volatility. Designed to be more responsive by incorporating the difference between price and its average into the moving average formula. Tends to be smoother and follow price movements more closely than basic moving averages.
- Hamming. Derived from spectral analysis developed to dissect sound waves with arbitrary frequency. Responds to the cyclical tendencies of data and reduces the effect of erratic price changes. Applies weighting factors to price data based on the Hamming function, designed to compute the spectrum of a finite-sized block of sample waveforms.
- Hull. Strives to reduce lag, improve agility, and provide more accurate trend identification while retaining the smoothness characteristics of basic moving averages. Calculated using a series of weighted moving averages (WMA) to prioritize more recent prices over older ones. Uses two different WMAs of price: one short-term and one long-term and combines them using a weighted multiplier.
- Wilder Smoothed. Aims at creating a responsive yet smooth moving average. Computed by subtracting the prior average from the current price and adding this difference to the previous average. Places heightened significance on recent price action.
- Kaufman. Designed to account for market noise and volatility. Tends to closely follow prices when prices are stable. Adjusts when price swings widen. Adapts its sensitivity to price movements (by changing its smoothing factor and period dynamically) based on the market volatility. Effective at noise filtering, hence less prone to whipsaw signals and typically generates fewer false positives.
- Weighted. Attaches more weight to recent data and less to past data. Calculated by multiplying each price in the lookback window by a predetermined weighting factor.
- Ehlers. Derived from the Hilbert Transform Discriminator. Incorporates fractals and sine waves into price analysis. Decomposes the market into a cyclical regime and a trending one. Adapts to price based on the rate change of phase. Features a fast and a slow moving average and produces two outputs, MAMA (which is reacting faster) and FAMA (which has more lag). Their crossover typically happens only when a major trend change occurs.
- Least Squares. Moving average variation that attempts to minimize the effect of price outliers. Calculated using the least-squares regression analysis method which finds the best-fitting line given a set of price points (i.e. one that minimizes the overall deviation).
- Double EMA. Improvement over exponential moving average (EMA) calculated by applying the EMA formula twice. Reduces lag and noise. Provides a better instrument for trend reversal identification.
- Average True Range (ATR). Measures volatility (i.e. how much an asset's price swings over time) by computing the 14-period moving average of true range (TR). TR is defined as the greatest of three values: (1) the current high minus the current low, (2) the absolute value of the current high minus the previous close, and (3) the absolute value of the current low minus the previous close. Elevated ATR typically does not last very long and tends to accompany corrections or Elliot Wave 3 moves. Consolidation periods correspond to low ATR values. The market alternates between high and low volatility regimes over time. ATR can be used to determine stop-loss placement and position sizing (e.g. trading smaller while tolerating larger drawdowns during volatile markets).
- Chaikin Oscillator. Calculated by applying the MACD formula (i.e. subtracting 10-period EMA from 3-period EMA) to the accumulation-distribution (A/D) line (thus measuring its momentum). Used for spotting trends and reversals. Interpretation and trading signals are analogous to MACD (e.g. divergence with price hinting at a possible turning point, negative / positive territory indicating distribution / accumulation picking up speed i.e. net selling / buying pressure).
- True Strength Index (TSI). Used to determine overbought and oversold conditions and assess trend stage. The oscillator fluctuates between positive (bullish) and negative (bearish) territory and has a signal line (being its 12-period EMA). Divergence with price indicates a weakening trend and a potential price reversal. Signal line crossovers are interpreted as price action confirmations (e.g. TSI crossing above the signal line could be used as a trigger to open a long position, especially if other technical analysis methods support such a trade). TSI getting very far away from the signal line typically means price exhaustion and a higher probability of a countertrend move.
- Price Channel. Plots the highest and lowest prices over the last 20 bars. Used to identify trends and breakouts / breakdowns. The upper and lower bands indicate resistance and support areas. In range-bound markets, Price Channels can be used to identify oversold or overbought conditions. If price surges / plunges outside of the channel with momentum in a sustained way (ideally first re-testing the channel boundary and then resuming its original move), a new trend may be starting.
- Chande Momentum. Oscillates between -100 and +100. Obtained by calculating the difference between the sum of gains and the sum of losses and then dividing it by the sum of all price movements over the last 20 periods. Does not use smoothing hence may be relatively jittery. Overbought territory is above 50 and oversold one below -50. Crossing above / below 0 is often used as a bullish / bearish confirmation signal, especially when combined with trend strength analysis or breakouts / breakdowns above / below key levels. Similarly to other momentum indicators, divergence with price often occurs at turning points. In a ranging market, the oscillator usually stays in a narrow band surrounding 0 which is considered a neutral zone. Extreme readings are associated with price getting tired and being prone to a reversal.
- McGinley Dynamic (MD). Moving average designed to more accurately track prices by automatically adjusting to varying market speeds, slowing down during consolidation and accelerating during strong trends. MD’s smoothing factor is derived from volatility, enabling adaptive behavior and reducing lag. Analogously to other moving averages, trading strategies for MD are based on bullish / bearish crossovers (when price crosses above / below the moving average), move exhaustion / mean reversion (when price is very far from the moving average relative to its historical distance distribution), and trend confirmation.
- Average Directional Index (ADX). Technical momentum indicator measuring trend strength over time regardless of its direction. Fluctuates between 0 and 100. Often plotted in conjunction with +DI and -DI that determine trend direction. ADX reading above 25 typically indicates a solid and clear trend. Sideways tape tends to map to values below 20. Crossovers of the -DI and +DI lines can be used to generate trade entry / exit signals (+DI getting above -DI is bullish while the opposite setup is bearish), whenever ADX is above 25.
- Rate Of Change. Simple yet effective unbounded momentum / velocity indicator. Computed as the percentage change in price over the last 9 bars. Positive values indicate an upward movement while negative ones point to a downtrend. Hovers around zero during periods of consolidation. Prone to whipsaws in sideways markets. Can be used to detect oversold and overbought zones by comparing the current indicator levels with the ones present around major reversals in the past. Whenever diverging with price, especially in cases of getting significantly out of sync, trend change points become more likely as the momentum starts to shift into the other direction.
- Indicator-Price Correlation. RSI, MACD, Stochastic, Keltner %, Standard Error %, and Bollinger % are momentum indicators that correlate with price very strongly (i.e. the correlation coefficient tends to be close to 1.0 most of the time). Divergences often precede trend change or trend acceleration and therefore are of interest from the point of view of swing trading. We provide 5- and 10-bar correlation coefficient (Pearson) plots across daily, weekly, and monthly timeframes. Since each trading instrument behaves differently, it is important to research historical patterns, for example to what extent prior instances of decorrelation between price and these 6 critical momentum indicators have been predictive of the following price action. Such backtest analysis can help to construct a proper risk / reward profile and inform entry / exit points.
- Pring KST. Momentum oscillator aiming at making it easier to interpret rate of change (ROC) readings. Computed by combining four simple moving averages of ROC lines (using different periods). Accompanied by a signal line to trigger buying and selling based on crossovers. Traders may also look for convergence and divergence with price and overbought or oversold conditions.
- Bull / Bear Power. Measures the balance between buyers (bulls) and sellers (bears). Used to confirm trend strength, identify trend reversals, and detect divergences between price and momentum. Calculated by comparing the highest and lowest prices to an exponential moving average. Positive values mean more buyer pressure and negative ones indicate a bearish sentiment.
- Relative Vigor Index. Evaluates the strength of a trend by comparing a closing price to the recent trading range. Positively correlates with price (tends to be in phase with the cycle of price action). Has a signal line which is used for crossover detection. Divergence between the indicator and price is typically interpreted as a predictor of a near-term change in the trend (the slope of the oscillator often changes direction ahead of price).
- Interquartile Range Bands. Bands visualizing the interquartile range (IQR) of recent price points highlighting the "middle 50%" of price fluctuations, with the upper band representing the 75th percentile and the lower band representing the 25th percentile. Helps identify overbought or oversold conditions by evaluating price deviation from its typical range. Traders often look for squeeze / expansion periods.
- Fisher. Converts prices into a Gaussian normal distribution. Used to spot market turning points based on divergence with price or extreme readings. The indicator also helps clarify trends and isolate price waves within each leg as the Fisher transform smoothes out price swings and makes major tops and bottoms more pronounced. Its key limitation is that prices generally do not follow the normal distribution and, as a result, false signals may be generated.
- Aroon. Designed based on the observation that strong uptrends see new highs on a regular basis (and vice versa for downtrends). Computed based on the time elapsed since recent highest highs / lowest lows. Readings above zero indicate that an uptrend is in place. Plotted as a line graph oscillating between -100 and +100. Can be used to measure the strength of a trend.
- Chaikin Volatility. Calculated by first obtaining exponential moving average of the difference between the daily high and low and then computing the percent that this value has changed over a period of time. Quantifies volatility (larger values indicate windening daily range). Useful for identifying tops and bottoms especially when price divergence is present.
- Commodity Channel Index. Assesses price trend direction and strength. Momentum oscillator used to determine overbought or oversold levels. Measures the difference between the current price and its historical average. Helpful for spotting weakness in trends when the indicator no longer correlates with price.
- Moving Average Envelopes. Parallel bands above and below a moving average that are set at a fixed percentage distance. Can help identify price breakouts or overextended moves. We provide envelopes for 5-, 13-, 20-, and 50-bar simple and exponential moving averages. The fixed percentage is computed dynamically so that 95% of price points fall within the bands.
- DSS Bressert. Stochastic with exponential smoothing to reduce choppiness while still maintaining fast response to price (also referred to as Double Smoothed Stochastic). Overall oscillator interpretation is similar to the regular stochastic. Values above 80 indicate buying pressure saturation and below 20 oversold conditions. Signal line crossovers can be used as bullish / bearish trend confirmation.
- Vortex. Composed of two lines, Plus and Minus, capturing positive and negative trends in a recent trading window. Calculated based on summing up upward / downward price movements and then normalizing the result using average true range. A buy signal is generated whenever Plus moves above Minus (and conversely for a sell signal). In a healthy uptrend / downtrend, Plus tends to be clearly above / below Minus.
- Johnson PGO. Momentum indicator, also known as “Pretty Good Oscillator“. Measures how far price is from its moving average relative to average true range. Positively correlated with price, hence divergences can be used to signal trend change. Extreme readings tend to precede mean reversion.
- Balance of Power. Oscillator designed to measure the relative strength of buying and selling pressure. Sentiment is bullish when readings are positive and bearish otherwise. Zero-line crossovers are often interpreted as trading entry / exit signals. Computed as a simple moving average of the difference between close and open relative to the trading range.
- Coppock Curve. Computed as 10-bar weighted moving average of the sum of rate of change for 11 and 14 bars. Most often used on monthly timeframes. Buy signal is generated whenever the indicator moves above zero from the negative territory.
- Mass Index. Helps predict trend reversals. Calculated based on the difference between high and low prices over a period of time. Trend change is likely to take place whenever the indicator range widens beyond a certain point (which needs to be determined empirically based on historical values) and then contracts.
- Random Walk. Determines whether a statistically-significant trend is in place by comparing price movements to a random process / noise. Plotted as two lines, High and Low, representing uptrend and downtrend strength, respectively. Traders often use their crossovers as entry / exit signals.
- Relative Momentum. Similar to RSI but uses 14 bars as distance when calculating gains and losses instead of 1 bar. Exhibits characteristics typical of a momentum oscillator. Used for trend identification, reversal confirmation, divergence detection, and mean reversion prediction.
- Based On Volume. A group of indicators that use volume as a primary component in their formulas.
- Demand Index. Estimates buying and selling pressure. Used to identify turning points. Values near the zero line indicate a weakening trend that will not last much longer. Divergence with price often precedes a top or bottom. Whenever the indicator reading is making new extremes, price action often follows and also trades at higher highs or lower lows. Calculated based on price change rate, volume, and volatility. Considered to be a leading indicator.
- On Balance Volume. Cumulative indicator that measures positive and negative volume flow: adds volume whenever price moves up and subtracts volume otherwise. Based on the observation that often when volume increases sharply without a significant change in price, a big movement follows as institutions have been either accumulating or distributing shares.
- Volume Price Trend. Cumulative volume line that accrues current volume multiplied by the percentage price change. Used to determine the balance between demand and supply and identify changes in money flow. Traders often look for divergence with price as potential entry / exit points.
- Intraday Intensity. Helps assess the strength of price movements. Used to estimate the flow of money into or out of a financial instrument. Computed based on recent 21 bars by summing up a portion of volume depending on the position of close within the range between high and low. The intuition here is that, in a bullish market, closes tend to be located near highs. Positive indicator values indicate the presence of buying pressure while negative ones point to bearish market dynamics.
- Volume Shelves. Also known as volume profile. Helps traders identify key levels of support and resistance and spot breakouts / breakdowns. Plotted as a histogram overlaid on the price chart, displaying the total volume traded at each of 66 price levels in the time window corresponding to the last 150 bars.
- Ease of Movement. Oscillator that calculates how easily volume can move price up or down by computing how much price change occurs per unit of trading volume. Divergence or decreasing correlation with price is often used as a signal of an upcoming reversal. For example, if a stock continues to rally while ease of movement is no longer moving up or is decreasing, the uptrend is likely losing steam and will soon end. Fluctuates around zero. The further from zero, the greater the ease with which prices are advancing or declining.
- Elder Force Index. Calculated by subtracting prior close from current close and multiplying the result by volume. Upward trends are associated with the indicator moving up (and conversely for downtrends). Elevated readings happen on large moves confirmed by high volume. Significant price changes that lack volume result in subdued / flattening indicator values that often diverge with price (a common setup in Elliot Wave 5 when a major move is about to end). As long as the indicator is making new extremes, price is likely to continue its trend.
- Klinger. Attempts to strike a balance between capturing the long-term trend of money flow and remaining sensitive enough to react to short-term price fluctuations. Computed as the difference between two exponential moving averages (34 and 55 bars) of the timeseries created by multiplying volume by 1 (on upticks) or -1 (on downticks). Can be used to spot potential price reversals based on divergence with price. Another trading strategy involves the oscillator crossing above the zero line which indicates positive money flow and may present a potential buying opportunity.
- Accumulation/Distribution (A/D). Cumulative indicator that helps traders determine whether an asset or security is being accumulated or distributed (gauging its demand and supply). Computed by summing up the values obtained by multiplying volume by where the price closed within the period’s range (determined by high and low). Therefore, a close near the high on high volume moves A/D significantly up. Divergence with price can help identify forthcoming reversals. For example, it is common for the A/D line to show distribution well before price turns at the end of a trend.
- Money Flow Index. Oscillator that moves between 0 and 100 and helps identify overextended trends. Its formula is based on a ratio of positive and negative money flow (calculated as volume multiplied by price), the former computed based on upticks and the latter on downticks (in the recent price history). Considered a leading indicator (like most oscillators that use volume as a primary component) and believed to provide timely reversal signals. Divergence with price often warns of an upcoming trend change.
- Chaikin Money Flow. Computed as a volume-weighted average of accumulation and distribution over the last 21 bars. Intuitively, will be increasing whenever price closes above its range midpoint, especially if this happens on significant volume. Traders may look for divergence with price (e.g. a buy signal being generated when the indicator makes higher lows while the price is still dropping). Tends to be above zero in bullish medium- and long-term trends.
- Twiggs Money Flow. Similar to Chaikin Money Flow but uses true range as a way to gauge the location of close (thus better accounting for gaps) and applies smoothing using exponential moving average. Indicator interpretation is analogous as well. The oscillator has a positive correlation with price, and therefore can be used to evaluate trend strength, confirm breakouts / breakdowns, and warn about impending market turns.
- Volume Accumulation %. A variation of On Balance Volume that, in its calculation, uses volume weights based on intraday volatility. The indicator is rising when price keeps closing in the upper half of its range (and its uptrend gets stronger if volume supports the move and if closes are near the highs). Common trading techniques include spotting decorrelation with price, indicator readings getting into extreme ranges, and zero-line crossovers.
- VP Confirmation (VPC). Designed to determine if current prices are supported by volume. When the indicator is positive, there is price trend confirmation, otherwise contradiction is in place. Conceptually, VPC reveals the imbalances between price trends and volume-adjusted price trends. Volume typically leads price action and divergences between price and volume often foreshadow changes in the trend. For example, a downtrend and falling VPC is a bullish setup, similarly to an uptrend and a rising VPC.
- Volume Zone (VZ). Intuitively, computed as a ratio of average signed volume to average volume in a recent time window. Signed volume is a timeseries where volume is multiplied by -1 on downticks and left unchanged otherwise. The indicator formula uses the exponential moving average for better sensitivity to price action. VZ points to an uptrend when it rises and stays above 5%. Oscillations between the 5% and 40% levels are considered to be within the bullish trend zone. At the same time, the -40% and 5% range corresponds to the bearish trend zone. Oversold and overbought conditions are below -40% and above 40%, respectively.
- Volume Flow (VF). Conceptually, similar to Volume Zone, but computes signed volume by comparing change in typical price with its standard deviation to determine if it is past a certain threshold. The indicator formula is calculated as a ratio of total sum of signed volume to average volume. Values above zero are considered bullish. Negative values indicate distribution. Uptrends making new cycle highs without VF confirmation tend to lose momentum and are likely to reverse (and an analogous rule applies to downtrends).
- Supertrend. Trend-following indicator, plotted as a line on the price chart representing a dynamically adapting level of support or resistance. When the line is below price, the sentiment is considered to be bullish. Otherwise, it may be time to sell or go short. Computed by adding or subtracting a multiple of average true range (a measure of market volatility) to price. Helps traders identify or confirm market trends and manage risk (the indicator reading is often used as a stop loss).
- Stochastic Momentum. Refinement of the Stochastic oscillator aimed at reducing false swings. Computed by first obtaining the difference between the current closing price and the median price range over a specified period, and then dividing the result by the price range over the same time window. Plotted alongside its signal line (which is calculated using simple moving average). Can be interpreted in ways analogous to other momentum indicators. For example, crossing and staying over the signal line is considered bullish, while the opposite scenario is regarded as bearish and pointing to a potential down move. In addition, overbought (40+) and oversold (40-) levels can provide useful reference points / confirmation for taking profits or opening a countertrend position. Divergence with asset’s price is another signal that can be used for gauging when to expect a rebound or correction.
- Accelerator. Designed to filter out noise and detect early signals of change in trend. Based on the observation that, before a reversal, momentum in the direction of the current trend will be decelerating. The indicator is derived from Awesome Oscillator (AO) and computed by subtracting a 5-period simple moving average of the AO line from the AO. Positive values are a sign of bullishness and negative ones of a bearish tendency in the market. Shows when an uptrend or downtrend momentum is slowing down.
- Volatility Stop. Provides a dynamic stop-loss level that adjusts according to the changing market conditions. Helps traders manage risk. Calculated by adding (in a downtrend) or subtracting (in an uptrend) a multiple of average true range to / from closing price. Also used for identifying prevailing trends. In addition, its price crossovers can generate entry / exit signals.
- Chande Kroll Stop (CKS). Plotted as two lines overlaid on the price chart. Helps determine stop-loss levels for long and short positions. Computed in two steps. First, average true range is subtracted from the highest high and added to the lowest low in a recent time window, forming two timeseries: H and L. Next, CKS is calculated within the same time period: the upper line as the highest value in H and the lower line as the lowest value in L. The indicator is also used to detect trend changes (based on line and price crossovers).
- Choppiness Index. Volatility indicator designed to help determine if the market is trending (in either direction) or moving sideways. Its formula takes into account average true range and the difference between highest high and lowest low, in a recent time window. Values above 61.8 are interpreted as price choppiness and below 38.2 as clear uptrend / downtrend. Can be used to predict phase changes in market movements since long periods of consolidation are often followed by extended trends and vice versa. Another potential trading approach is to anticipate a reversal when the indicator reading increases significantly while the price is still continuing to make new extremes.
- Intraday Momentum Index. Variation of RSI where gains and losses in the formula are computed based on open and close prices in the same bar (as opposed to using two subsequent bars’ closes). Designed to shift focus from cross-bar price changes to candlestick bodies (excluding shadows). Similarly to RSI, calculated as the sum of gains on upticks divided by the sum of losses on downticks plus the sum of gains on upticks. Used to generate overbought (values greater than 70) and oversold (readings below 30) signals. The indicator tends to correlate well with price. It is common for trend change points to be preceded by divergences between the two (hence they are often used as a relatively reliable warning sign of a potential reversal).
- Connors RSI. Momentum indicator oscillating between 0 and 100. Its primary use is identifying overbought (above 90) and oversold (below 10) conditions in shorter trading timeframes. Computed as an average of three values: percent rank of ROC and two RSI calculations - one over price and one over a timeseries derived by counting consecutive upticks or downticks (the latter represented by negative numbers). Attempts to overcome some of the limitations of RSI, e.g. slow reaction to price movements.
- Relative To Another. We support three ways of combining the analysis of the currently displayed ticker with another one and all of them can be enabled simultaneously.
- Price Comparison. It is a common practice to plot two charts next to each other when looking for trading opportunities or researching financial instruments. It is possible to select a symbol to display next to the current one, by using the “search by symbol” dropdown. Once that’s done, each plot (regardless of whether it shows price bars or indicator data) will have two instances and they will be rendered next to each other. In order to disable / re-enable this interleaving, without having to clear the selected (paired) symbol, one can click the “Price Comparison” button.
- Price Ratio. Another commonly-used approach is to compute a ratio of two symbols. The denominator symbol can be picked in the “search by symbol” dropdown. This will result in adding another full set of charts (including price bars and all currently selected indicators) for the generated ratio timeseries. They will be located on the webpage right after the graphs for the numerator symbol. Clicking on the “Price Ratio” button, will hide / re-display these additional graphs without the need to clear the denominator symbol (so that it is easy to come back to it later).
- Price Correlation. Pair correlation is a useful tool for identifying regularities and anomalies in how two instruments co-trade which can help uncover profitable setups. We provide 5- and 10-bar correlation (based on the Pearson correlation coefficient) for any two symbols, across daily, weekly, and monthly timeframes. The “search by symbol” dropdown can be used to pick or change the correlation pair.
- TRIX. Momentum oscillator computed as the percent rate of change of a triple-exponentially-smoothed moving average of price. Usually, interpreted similarly to MACD. Designed to filter out insignificant price movements or market noise. Plotted together with its signal line. Used to spot diversions with price (useful for identifying turning points), momentum changes, and overextended buying or selling. Considered a leading indicator by many technicians. Oscillates around the zero line. Extreme readings are often correlated with tops and bottoms where the market has moved too far in one direction and is prone to a correction of the current trend. Signal-line and zero-level crossovers are used as bullish / bearish confirmations when entering or closing trades.
- Derivative Oscillator. Blends the concepts of MACD and RSI in an attempt to capture the advantages of both indicators while mitigating their individual shortcomings. Calculated by applying the MACD formula to double-smoothed RSI. Positive values are considered bullish and negative ones bearish. Zero-line crossovers are often treated as a trade signal. In addition, any significant loss of correlation with price (divergence) could be an early sign of an upcoming reversal in the dominant trend.
- Wilder ASI. Plots a cumulative value of the Swing Index (SI) over a certain period of time. For each bar, SI is calculated based on the open/close as well as high/low values for the current and previous candlestick. SI ranges between -100 and 100. In long-term uptrends, ASI is positive (and conversely for downtrends). In sideways, choppy markets, ASI fluctuates around zero. The indicator is commonly used to confirm breakouts / breakdowns and identify false ones (e.g. when prices penetrate a resistance line but ASI stays below it). In addition, ASI helps spot possible points of reversal by determining the strength of a trend and uncovering divergence between momentum and price.
- ZigZag. Price chart overlay that clarifies trends, lowers noise levels, and filters out less significant price fluctuations. Approximates price movements by straight lines highlighting trend segments where price changed by at least a pre-computed percentage threshold. Often used in conjunction with Elliot Wave analysis as well as Fibonacci retracements and extensions. Makes it easier to recognize patterns like double bottoms/tops or head and shoulders, as well as identify support / resistance zones based on plotted swing highs and lows. The percentage threshold is calculated dynamically based on the smallest distance from the 13-bar simple moving average that covers at least 95% of price points. This makes manual determination of ZigZag parameters via trial and error unnecessary.
- Detrended Price Oscillator. Indicator that strips out price trends in an effort to estimate the length of price cycles (including trough, recovery, expansion, and peak phases). Helps time opening and closing positions in line with the market cycle. For example, during an uptrend, cycle bottoms present buying opportunities and cycle peaks are favorable for profit taking. Calculated by first computing a simple moving average for n periods, and then subtracting it from the closing price n/2 periods ago (i.e. near the middle of the look-back window).
- Relative Volatility Index. Determines the direction (upward / downward) and magnitude of volatility. Its formula resembles one used for computing RSI but it replaces gains and losses with standard deviation calculated over the recent price history. Ranges between 0 and 100. Higher readings (above 50) indicate that the volatility is to the upside (and vice versa for lower values). Commonly used to confirm a buy / sell signal obtained from other technical analysis methods. The indicator can also help detect overbought and oversold conditions (when its readings become extreme relative to historical ranges). Plotted together with a signal line to facilitate spotting more fine-grain trading opportunities associated with its bullish and bearish crossovers.
- Trend Strength. Evaluates how steadily price rises or falls over a period of time. Computed by dividing total absolute price change by volatility. The latter is calculated by summing up absolute price changes, bar-to-bar. The result is smoothed using a simple moving average. Choppy markets are associated with lower indicator readings. Divergence with price points to a weakening movement and a potential countertrend trading opportunity targeting mean reversion. Also used for direction confirmation, particularly when combined and aligned with signals from other indicators.
- Elder Safe Zone. Primarily used to time stop-loss exits in a trending market. Based on Directional Movement (akin to +DI and -DI used in the calculation of ADX) rather than ATR as a metric of volatility. This shifts the focus of risk estimation entirely onto counter-trend corrections (thus removing the irrelevant components of volatility, i.e. ones associated with movements in the direction of the prevailing trend). This approach adapts well to scenarios of very strong trends without significant retracements (characteristic of Elliot Wave 3), where stops can closely track the accelerating runaway move all the way to a blow-off top or bottom. Relies on an exponential moving average to determine trend direction (hence has a certain amount of lag).
- Pivot Points. Levels of support / resistance derived from recent price history that help traders identify exit / entry points and assess risk / reward. Plotted on the main price chart. Each variant consists of one central pivot line plus two support and two resistance lines. These are computed using a linear combination of high, low, and close of the last monthly or yearly bar (depending on the chart timeframe). We provide four types of pivot points, each using a slightly different formula: Standard, Camarilla, Woodie, and Fibonacci.
- Disparity. Indicator measuring the distance between the latest closing price and its simple moving average (typically 14-period). Reported as a percentage. Positive values are associated with an uptrend and negative ones with a downward move. Extreme readings are often used by contrarian traders to identify price exhaustion which is vulnerable to a correction / mean reversion. The indicator can also help spot momentum changes (e.g. weakening of a trend when a rally continues upwards while the distance between price and its moving average is contracting).
- Wave Trend Oscillator. Momentum indicator primarily used for reversal detection. Its value fluctuates in the range from approximately -100 to 100. Calculated as a normalized and smoothed distance between typical price and its exponential moving average. Triggers a sell when the indicator is above the overbought band (50+) and it crosses down the signal line (and symmetrically for a buy). In addition, the oscillator shows trend direction and helps spot move exhaustion based on divergence with price. Considered to react fast without a significant lag.
- TTM Squeeze. Indicator with two main components (each plotted as a line): one showing whether a squeeze (price compression during a low-volatility period) is active and one pointing to the most likely direction of price breakout / breakdown that may soon follow. Helps identify when a market consolidation phase is going to end (which facilitates timing and capturing significant price moves). Based on the empirical observation that sideways price action and strong trends tend to alternate (i.e. market has a tendency to breakout / breakdown forcefully after being confined within a tight trading range). A squeeze is detected whenever the Bollinger Bands are completely enclosed within the Keltner Channels. The expected price direction is determined based on the calculation of the delta between the close and the average of the Donchian middle line and simple moving average (which is smoothed using linear regression). If the resulting timeseries is positive and rising, a buying opportunity may be present (and vice versa for bearish scenarios).
- Schaff Trend Cycle. Indicator that attempts to combine trend and cycle analysis to leverage the benefits of both while avoiding their drawbacks (such as lags or false signals). Calculated as smoothed Stochastic of MACD. Oscillates between 0 and 100. Moves up if the market is accelerating an uptrend (and vice versa for downtrends). Helps identify reversals by measuring the changes in velocity of price movements. Indicates oversold conditions when rising after falling below 25, and symmetrically, overbought market when falling after testing the 75+ area. Values above 50 are bullish (and bearish below that threshold).
- Price Momentum. Indicator used for trend confirmation. Derived from 1-bar ROC by applying double smoothing using exponential moving average. Plotted together with its signal line. Oscillates around zero line and its steepness corresponds to the power behind market moves. A key benefit of being based on ROC is normalization (i.e. independence of absolute price values). This makes it possible to use the indicator across various financial instruments, e.g. to rank stocks. Extreme readings (relative to historical ranges) point to overextended market moves that are prone to a correction. Once the indicator starts reversing from the overbought / oversold zones, a potential counter-trend trading opportunity may be present. Tends to align with price movements well hence any divergences between the two often signify upcoming trend changes. Signal line crossovers, especially when followed by a retest and hold (i.e. support / resistance flip) are considered reliable buy / sell setups.
- Williams Alligator. Indicator based on convergence and divergence of three smoothed moving averages (called alligator lips, teeth, and jaw). These are calculated on the 5-, 8-, and 13-bar time windows and time-shifted into the future by 3, 5, and 8 periods, respectively (all these numbers come from the Fibonacci sequence). Plotted on the main price chart. Helps traders identify clear trends - whenever the three lines are stretched apart and moving higher or lower in tandem, long or short positions should be held (with proper risk management, e.g. a trailing stop loss). This favorable setup is referred to as eating alligator. In all other cases, it is recommended to stay on the sidelines until another trend emerges and the corresponding swing trade opportunity presents itself again. Can also be used to spot trend weakness ahead of time (when the moving averages start to get close to each other or cross over, a state also known as sated alligator).
- Ulcer Index. Indicator measuring the depth and duration of price declines from a previous high. Elevated readings mean that price has dropped significantly relative to a recent top and it may need more time to return to it. Quantifies the risk of further correction / drop by estimating volatility to the downside (ignoring the upside moves unlike standard deviation or ATR). The indicator moves towards zero if prices keep closing higher for an extended period of time. Calculated as the square root of the average of squared percentage drawdowns across the last 14 bars. Has a signal line computed based on simple moving average. Its crossovers to the upside can be used as a warning sign that a significant drawdown may be underway.
- VH Filter. Vertical Horizontal Filter helps traders identify trending and range-bound (sideways) market phases so that they can apply the most effective entry / exit strategies (e.g. trend following or mean reversion). Calculated by dividing the price range over a specific time window by the cumulative price movement (i.e. sum of the absolute price changes) within the same period. Higher readings (especially above 0.3) indicate stronger trends and lower ones point to choppy conditions (price stabilization around a narrow zone). Extremely elevated values often precede the end of a trend so the indicator can be also used to confirm reversals.
- DeMark. A set of indicators that focus on market timing, providing insights into trend phases, strength of price movements, and the likelihood of mean reversion.
- Sequential 9 and 13. Two indicators that measure price exhaustion by assigning counts to price bars. The interpretation of their readings is straightforward. Traders looking for a reversal signal want to see at least one of the counts, 9 or 13, completed (ideally both since then there is a high likelihood of a correction). There are quite a few rules for calculating these indicators. Intuitively, Sequential 9 counts the number of consecutive closes moving in one direction (each one is compared to the close 4 bars earlier). Sequential 13 attempts to detect depletion of buyers or sellers in the move determined by the current trend. It starts only after the 9 count completes and it is calculated by comparing the close of each bar to the high or low (depending on the move direction) 2 periods earlier.
- DeMarker Oscillator. Leading indicator that helps traders determine when to enter the market by gauging trend exhaustion. Readings are bounded between 0 and 1 (values below 0.3 and above 0.7 indicate that a price turn may be imminent). Computed based on simple moving averages calculated over two timeseries derived from price lows and highs: L, the difference between current low and prior low (zero on a higher low), and H, the delta between current high and prior high (zero on a lower high). The indicator formula is essentially H / (H + L).
- Range Expansion Index. Momentum oscillator estimating the velocity and magnitude of directional price movements. Computed as a ratio of the sum of price changes that form a trend and the sum of all price changes in the lookback time window. Fluctuates on a scale of −100 to +100. Helps identify overbought (readings 60+) and oversold (values below -60) conditions. Whenever the indicator gets into an extreme zone (relative to its historical behavior), stays there for a while and then retracts, there is a good likelihood that the prevailing trend is weakening and may be reversing or is about to complete its last leg (e.g. Elliot Wave 5). Typically used to evaluate the stage of an established trend.
- Trend Trigger Factor. Indicator designed to help traders determine uptrends and downtrends and follow them. There are several simple rules and strategies suggested by the creators of the oscillator. First, whenever the readings are above 100, the sentiment is bullish and it is best to be on the long side (even if this means reversing the current position). Below -100, a strong downtrend is confirmed, and hence entering or continuing to be short is recommended. As long as the indicator is within the -100 to 100 range, no trades should be made (i.e. maintaining the existing portfolio may be the best approach). Calculated based on buying and selling power derived from highest high and lowest low in the recent time window.
- Center Of Gravity. Oscillator that computes where highest prices are concentrated in the recent time window. Calculated based on weighted moving average of bar positions (i.e. how far they are from the latest tick in time) where weights are derived from prices. The result is then subtracted from the midpoint of the lookback period. Positive values mean that more recent prices are higher hence an uptrend is present. Similarly, readings below 0 point to a downtrend. Extreme readings can be used as a signal of overextended moves in one direction. Considered to have a very low lag.
- Trend Detection Index. Indicator designed to identify the beginning and end of trends. Conceptually, based on momentum computed as the difference between the current close and the one 20 bars ago. Calculated as the difference between the absolute value of the sum of momenta and the sum of absolute momentum in the lookback window. Positive readings signal the presence of a trend (regardless of whether it is up or down) and negative ones point to price consolidation and a sideways tape. Tends to be used in conjunction with oscillators that identify trend direction.
- Trend Continuation Factor. Indicator whose primary goal is to identify the dominant trend and its direction based on recent trading history. Composed of two lines, Plus and Minus, which are positive in bullish and bearish markets, respectively. They can both be negative at the same time, if there is no trend. A common strategy is using their crossover as a trade entry signal. Calculated based on two timeseries that capture absolute close-to-close price changes but one of them is used for upticks and the other for downticks. Consecutive non-zero values are summed up and then subtracted from the opposite timeseries bar-to-bar. Finally, the two indicator lines are computed by adding up the resulting values across the last 35 periods. The intuition here is that, in strong trends, there will be multiple, long series of upticks which will be reflected in Plus going up and Minus decreasing significantly.
- Trend Intensity Index. Helps gauge the strength of the current trend. Computed by first obtaining the distances between price and its 13-bar simple moving average and then calculating the ratio of the sum of positive ones to the sum of all of them. Ranges between 0 and 100. Above 50, indicates a bullish trend (and a bearish one otherwise). Extreme values can be used as a confirmation of a potential reversal. Divergence with price points to a weakening move and an increased probability of a market turn.
- Laguerre RSI. Uses spectral analysis of maximum entropy based on the Laguerre polynomials in order to filter out random price movements without introducing a significant lag. Improves on the basic RSI in terms of noise reduction while also increasing its responsiveness. Computes a series of transformations of the prices with the gamma smoothing coefficient which makes it possible to obtain a non-choppy line using a relatively short lookback window. A buy signal is generated when the indicator crosses upwards above 0.2. Downtrend is expected to continue if the readings remain flat below this threshold. When the oscillator drops below 0.8, a bearish price move becomes more likely (the uptrend is considered strong as long as indicator values consistently hold above this level). Can be used for both mean reversion and trend following strategies.
- Polarized Fractal Efficiency. Uses fractal geometry to determine the efficiency of price movement. Helps traders assess the direction and strength of a trend. Moves between -100 and 100. When the indicator is close to zero, supply and demand balance each other out and price may be choppy and undergoing consolidation. A buy signal is generated when the indicator crosses above zero, moves higher, and then retests that level as support (and symmetrically for the bearish setup). Since the oscillator is bounded and correlates positively with price, it can be leveraged in a way akin to RSI (e.g. traders may look for divergences or extreme readings to gauge the stage of a trend and anticipate price turns).
- Kurtosis. In statistics, measures the tailedness of a distribution or how often outliers occur. Positive values mean a more peaked distribution compared to a normal distribution (and negative readings point to a flatter one). Provides insights into how recent price changes are concentrated around the mean (or how heavily the tails of price distribution differ from what would be expected in a normal distribution). When elevated, the likelihood of a medium-sized move is lowered and the probability of both very large and very small price changes is higher. Hence, can be used as a measure of risk. It is worth noting that Kurtosis, by focusing only on the tails of a distribution, differs from variance and skewness, which quantify dispersion and symmetry, respectively. Trading signals are generated when the indicator crosses its zero line (which corresponds to the market dynamics in line with a normal distribution).
- Anchored VWAP. Plots volume-weighted average price anchored at a pivot point (meaning its calculation starts at a bar corresponding to a recent low or high). For each of the lookback windows, i.e. 50, 100, and 150 bars, we compute 5 VWAP lines, anchored at the strongest pivot points (most significant tops and bottoms) identified in that particular time period. It is possible to add all 15 averages at once. Used primarily for detecting support and resistance levels (which tend to be more reliable in the zones where multiple VWAP lines converge). In addition, helps traders confirm trends and breakouts / breakdowns.
- Kaufman Efficiency Ratio. Indicator that gauges the efficiency of price movements (or how much volatility it takes for a swing to materialize). Helps traders assess the degree of price trendiness or choppiness. Moves between 0 and 1. Calculated as the ratio of net price change (ignoring direction) over a specific number of bars to the sum of absolute price changes in the same time period. Higher readings point to a strong trend and lower ones to a sideways market. A buy / sell signal is generated after the indicator crosses above 0.3. Reversal is expected when the indicator registers a significant drop from extreme readings.
- Chande Quick Stick. Designed to measure buying and selling pressure. Computed as a simple moving average of the difference between close and open. The principle underlying this approach is that, in a bullish market, prices tend to gap-down on open and close higher (and vice versa in downtrends). The indicator can be used to evaluate trend strength and predict reversals (e.g. when there are diversions with price movements or extreme readings outside of normal historical ranges).
- Chande Trend Score. Computed as a simple moving average of the number of upticks in the last 10 periods. Designed based on the observation that, in uptrending markets, price tends to close higher more frequently than lower (relative to the prior bar). Fluctuates between -10 and +10. Can be used to determine trend direction and strength. Traders may also assess indicator correlation with price (and follow the same rules and practices as with any other momentum oscillator when planning entry / exit points).
- Dorsey Inertia. Inspired by inertia in physics (i.e. tendency of an object to continue in its existing state of rest or uniform motion in a straight line until an external force is applied to it). Interprets market trends as price inertia where more energy is needed to reverse the direction of the ongoing move than to extend it. Calculated by smoothing Relative Volatility Index using linear regression. The intuition here is that volatility is an important measure of market inertia. Plotted on a scale of 0 to 100. Indicator readings below 50 point to a bearish sentiment. Above 50, a bullish trend is likely present. Crossing this key level is often used as a buy or sell signal (once confirmed by closing on the other side and ideally also retesting the 50 zone).
- Repulse. Designed to gauge buying and selling pressure. Computed as the delta between smoothed (by exponential moving average) bull and bear power (which are derived by using a linear combination of highs, lows, opens, and closes in the recent 5 bars and then dividing the result by the most recent close in order to normalize it). Strongly correlates with price movements, hence any significant or prolonged divergence there tends to be a reliable trend exhaustion signal. Another common trading approach is breakout / breakdown confirmation as well as reversals off unusually high or low readings.
- Belkhayate Timing. Helps traders spot favorable setups by calculating the center of market movement gravity. Conceptually, the oscillator is derived from recent price history (5 bars) by first computing the average middle price, then subtracting the result from the current price, and finally dividing the thus-obtained value by the average range. Indicator interpretation is based on three horizontal areas in its chart that are determined based on historical data. Whenever oscillator readings are in the middle (neutral, centroid) band, it is recommended not to open any positions. Buys and sells are most likely to result in profitable trades when entered while the indicator is in one of the two most extreme zones. The intermediate bands are considered warning areas - this is when closing any open positions (started in the opposite outlier zone) may be a good idea. The indicator tends to be more effective when combined with channel indicators like Bollinger Bands.
- Ehlers Cyber Cycle. Indicator designed to isolate the cycle component of the market from its trend counterpart. The oscillator swings with variable (unbounded) amplitude. Helps traders assess the current state of a trend. Readings in the zero band indicate that the price is in a sideways movement. Positive and rising values point to a bullish trend (and conversely for downtrends). When the indicator diverges with price or becomes overextended, a reversal becomes more likely to follow.
- Absolute Strength. Oscillator intended for trend-following trading strategies. Makes it easier to determine the direction and strength of price movements. Tends to reflect the dynamics of the current trend (e.g. moves up in bullish markets and down when the sentiment is bearish). Correlates very well with price - any diversions between the two are considered a strong warning sign of a possible near-term reversal.
- Kase Peak. Momentum oscillator aimed at evaluating trend power and reducing market noise. Calculated as a smoothed average of the differences between the current and past (22 bars ago) high / low (normalized by dividing each of these deltas by average true range). Useful for spotting weakening price movements based on divergence. Can also help confirm trend exhaustion when the indicator readings become extreme.
- Relative Spread Strength. Indicator designed to help identify cycle peaks and lows. Computes a smoothed RSI of the spread between two moving averages (10- and 40-bar). The intuition here is to capture the momentum of the expansion and contraction of the distance between a short-term (fast) and long-term (slow) average. Has positive values during uptrends (and negative ones in bearish markets). Can be interpreted similarly to other momentum oscillators in terms of reversal signals such as divergence with price and extreme indicator readings. Also helps assess and confirm the strength and stage of a trend.
- Rapid RSI. Momentum indicator that provides insights into trend health. A variation of RSI that uses a simple sum instead of exponential moving average when combining bar-to-bar gains and losses. Its overall meaning and interpretation is similar to RSI. However, the oscillator exhibits a slightly different behavior and often reacts to price movements in a different way. Thus, it may uncover market characteristics that are not visible based on RSI alone (for example, some of the more subtle divergences with price). Combining the two is believed to improve trading signal quality.
- Didi Index. Indicator measuring market momentum, detecting price turn points, and generating buy / sell signals. Computed based on three simple moving averages, 3-, 8-, and 20-bar (referred to as short-, medium-, and long-term). Plotted as two lines, obtained by dividing the short-term and long-term average by the medium-term one (in order to normalize the oscillator readings). These lines move at different speeds and their crossovers can help time profitable trade setups. A bullish signal is in place when the short-term line is above the long-term one (and vice versa for the bearish sentiment).
- Divergence. We provide price-momentum divergence (bullish, bearish, hidden bullish, and hidden bearish) based on 40+ momentum oscillators that tend to correlate with price the most.
- Brown Composite. Developed in order to mitigate some of the limitations of RSI, in particular to improve price reversal detection. The oscillator is unbounded (it does not use normalization). Its formula comprises two parts which are summed together. The first one is the 9-bar RSI delta. The second one is the 3-bar simple moving average of RSI. Thus the indicator combines momentum and trend components into a single reading. An effective trading signal generation technique is comparing RSI and Brown Composite – divergences between the two often catch market turns ahead of time. De-correlation with price itself is also considered a potential reversal sign. Similarly for any overextended oscillator moves.
- Vervoort. Momentum indicator created to facilitate trend strength confirmation. Derived from price in a few steps. First, 10 simple moving average lines (a rainbow) are calculated in a cascading fashion (i.e. using the output of the prior computation as input to the following one). Next, these lines get combined using a weighted average, and smoothing based on the exponential moving average is applied. Finally, the indicator value is derived from the result via additional transformations including standard deviation and linear combinations involving the distances between exponential moving averages. Typical use cases of this oscillator include correlating its movements with price (divergences being early warning signals about potential trend completion) and anticipating reversals whenever its readings reach unusually low or high levels.
- Firefly. Indicator designed to measure price momentum and confirm trend direction. Conceptually, computed as the distance between price and its simple moving average, which is normalized (divided by standard deviation) and smoothed. Aligns well with price swings. Hence, commonly used to validate trends and identify market conditions prone to a reversal (including divergences and readings outside of normal ranges).
- Roofing Filter. Momentum-tracking oscillator based on a smoothing technique inspired by aerospace analog bandpass filters used in radio receivers. Aims at reducing random price fluctuations by eliminating wave spectral components with both short (below 10 bars) and long (above 48 bars) periods. This approach reduces lag and isolates price turning points more effectively. Intuitively, the idea is to attenuate high-frequency noise and low-frequency distortion to create an oscillating signal with a nearly zero mean to simplify its analysis. The indicator is typically used to generate trade entry / exit points based on divergences and outlier readings.
- Recursive Median. Momentum indicator using a non-linear digital filtering technique which is typically applied to remove noise from an image. Eliminates data with longer wavelengths, thus ignoring price irregularities like large outliers. Computed as an exponential moving average of median close over the last 5 bars. The smoothing factor is calculated based on a trigonometrical formula that incorporates the critical period of the filter. Using median instead of average makes the oscillator more immune to noise caused by, for example, large price spikes. This, in turn, enables shorter lookback windows and renders the indicator more reactive and less laggy. Traders look for alignment with price movements to determine the presence of divergence or confirm trend direction.
- Ehlers Decycler. Trend-following oscillator filtering out high-frequency short-wavelength noise, which is often associated with short-term price fluctuations. Helps traders detect trend reversals with almost no lag. Removes non-trend components from the input timeseries using half-period high-pass filters (borrowing ideas from digital signal processing). Fundamentally, if we consider market movements to be a continuum of cycle periods with different amplitudes, trends will correspond to low-frequency segments. The plotted indicator value is essentially the ratio of price after filtering to its original value. The oscillator tends to be relatively reliable when it comes to uncovering early signs of divergence (due to its focus on extracting trend representation from input data as quickly as possible).
- Hurst Cycle Channel. Momentum indicator that tries to identify and highlight the shorter cycles in the context of higher-degree ones. Developed based on the theory that lows and highs tend to repeat at consistent intervals and price fluctuations can be decomposed into a sum of waves, across a spectrum of lengths and amplitudes. Major tops and bottoms occur when multiple sine waves align on their peaks and troughs. Created to help traders decipher market rhythms and identify turning points. Computes two ATR-based channels: short-term (10-bar) and long-term (30-bar). Then, calculates the oscillator reading as the position of the middle line of the former within the latter. Thus, the value of 0 corresponds to the setup where a smaller-degree cycle touches the bottom of a larger channel (and 1 indicates the crossover of the upper band). Matches price movements (directionally) most of the time. Whenever they diverge, a reversal signal is generated. In addition, readings above 1 (and below 0) tend to point to overextended conditions.
- Denoised RSI. Also known as Relative Strength Quality Index (RSX), is a variant of RSI that makes it less jittery without adding any lag (unlike smoothing-based approaches). Classic RSI works very well when a trend is strong and clean but generates false signals in other market conditions. RSX uses filtration instead of moving averages to avoid the smoothness-lag tradeoff. The oscillator is bounded between the range of 0 to 100. Measures momentum direction and quality (as opposed to speed). If a trend is moving without large retracements, the RSX signal is strong. If price starts consolidating more and more or volatility increases, this is immediately reflected in the oscillator reading. These characteristics make RSX well-suited for detecting reversals and weakening price movements (e.g via momentum divergence analysis).
- PassBand Filter. Indicator developed based on analog filter design. Removes unnecessary cycle data which provides smoothing without adding any lag. Calculated as the z-transformed difference of two exponential moving averages. Eliminates both low- and high-frequency components, thus filtering out distracting, noisy fluctuations from the price signal while keeping the indicator reactive and inducing minimal delays. Commonly used for planning counter-trend trades (mean reversion). Tends to detect diversions earlier than some of the other smoothed oscillators that are based on formulas requiring long lookback periods (in order to avoid outlier distortions in averages).
- Range Bound Channel. Indicator calculated by using a channel-bandwidth filter which removes both low- and high-frequency components of the signal spectrum. The oscillator peaks occur when price is close to the upper band of its trading channel (and conversely for troughs). Its main purpose is denoising the signal without adding a lag effect. Models a quasi-stationary process bound by the frequency range both from above and below. Essentially measures the degree to which price movement is confined within a channel. Helps time buys and sells so that they are near support and resistance zones.
- Projection Bands. Channel plotted on the main price chart. Computed based on linear regression (in contrast to indicators like Bollinger Bands that use standard deviation) in a few steps. First, the best straight line fitting all the bar highs is found (and similarly, for bar lows). These two lines are then moved (without changing their slopes), so that all the bars used to derive them are fully contained within the resulting channel. Finally, the upper / lower bands are obtained by projecting the prices (based on the fitted and adjusted lines) to create the corresponding linear regression curves. Considered to be a leading indicator since it avoids introducing any lag. In terms of trading strategies, touching channel boundaries is expected to produce a reversal or at least a minor trend correction (which may be followed by a continuation of the prior movement).
- Smoothed Rate Of Change. Essentially ROC computed on top of the exponential moving average (instead of price). Due to its smoother input signal, the oscillator is less erratic and gives fewer false signals. Can be used to confirm trends, assess their health, and identify reversals ahead of time based on divergence with price. An example trading approach could be to buy whenever, in an uptrend, the oscillator falls below its center line, and then starts to move up again (and symmetrically for downtrends). In ranging markets, overbought and oversold conditions can be identified based on heightened indicator readings (relative to recent history that should include the current sideways price channel).
- Trendlines. Automatically-generated lines connecting the top 5, 7, 9, or 11 price pivots pair-wise (all-to-all). Help identify zones of high confluence (where multiple lines intersect or converge into a cluster) that could be important support and resistance levels. Pivots are selected as the most significant highs and lows across the last 500 bars. It is much more time-efficient to simply visually identify and inspect potential areas of interest as opposed to manually stack rank all turning points and then draw the lines across them.
- Projection Bands %. Momentum oscillator derived from the Projection Bands indicator. Its reading maps to the location of price within the channel boundaries. 100% corresponds to the upper band and 0% to the lower one. Can be used to anticipate reversals, e.g. when price moves outside of the channel (which is considered to be an unsustainable level in the longer run) or when a degradation or complete loss of correlation with the current trend is observed. Helps traders formulate mean-reversion or trend-continuation strategies, depending on the market phase and sentiment.
- Exponential Deviation Bands. Channel plotted next to the price chart. Conceptually, similar to Bollinger Bands, but based on exponential deviation (which makes the indicator more sensitive to the market movements as it gives more weight to recent data). The upper and lower bands are distanced at two exponential deviations above and below the middle line (being an exponential moving average of close). Interpretation guidelines are similar to other price channel indicators. Tends to work better in sideways markets (in strong uptrends, price often penetrates the bands and continues its movement). Can be used to identify trends and overextended moves. Breakout detection is another potential use case here.
- Fractal Dimension Index. Indicator that measures the strength of a trend by assessing the fractal dimension of price fluctuations. Developed based on the observation that prices move in fractal patterns. Based on volatility, helps traders determine whether the market is moving sideways within a range or is trending (either in a sustainable fractal fashion or moving too fast in a straight line or even going parabolic). Oscillates between 1 (which means a simple one-dimensional type of movement) and 2 (which corresponds to a more complex, two-dimensional fractal behavior). Readings above the midpoint (1.5) mean range-bound price movements (and a trending market below that level). 1.3 is the threshold pointing to an unsustainable velocity, prone to a reversal. The indicator does not show the direction of a trend, only its quality.
- Kaufman Body Momentum. Momentum oscillator that calculates the percentage of bars where close was above open during a specific time period. Oscillator reading is additionally smoothed via simple moving average. The intuition behind this design is that, during uptrends, price closes tend to be higher than opens (in fact, in bull markets, prices are inclined to gap down slightly on open and then close strong). A reverse dynamic takes place in downtrends. Can help assess trend direction and momentum behind it. Divergence with price can be used to anticipate market turn points.
- Damiani Volatmeter. Indicator measuring price volatility (independently from price direction). Computationally, derived from the ratio of two exponential moving averages calculated over the short- and long-term average true range. Can help identify transitions between choppy and strongly-trending markets (which normally take turns over time). Filters out market noise pretty well. Can be used to detect nascent trends.
- Ehlers Correlation Trend. Indicator evaluating the quality of the trend in the lookback time window of 20 bars. Computed as the Spearman correlation coefficient between close prices and a perfect trend, i.e. a straight line. Can be used to identify reversals as well as the emergence of new trends. Oscillates between -1 and 1. Negative values point to a downtrend (which is considered strong below -0.5) and positive ones to bullish price movement (the higher the reading, the more healthy it is). The neutral zone around the zero line essentially signifies a sidaways tape. Typically combined with channel indicators to confirm entry / exit points.
- Ehlers Elegant Oscillator. Indicator derived by applying the inverse Fisher transform to a waveform with a normal (Gaussian) probability distribution using root mean square scaling (akin to a N-dimensional distance metric) and smoothing (by a low-lag lowpass filter). Converts a normalized derivative of a price series to a range of -1 to 1. Founded on the observation that prices tend to return to their mean over time. Designed to detect momentum shifts and reduce market noise. Generates reversal signals whenever reaching extreme peaks and troughs (relative to historical patterns exhibited by a given trading instrument).
- Ehlers Forward Reverse. Momentum indicator derived from the delta between two exponential moving averages of price: forward (classic) and reverse one. Uses a filtering technique based on Z-transform. Applies double smoothing at the high-end of the spectrum to attenuate aliased components. Reduces the impact of spectral dilation on the low-frequency end. Has a low lag and extracts a combination of trend and cycle information. Improved responsiveness is achieved by reversing the smoothing process. Overcomes one of the key limitations of EMA, i.e. inconsistent lag across the spectrum which causes a non-linear relationship between frequency and phase, leading to waveform distortions. A leading indicator, i.e. intended to provide a forward-looking perspective. Relatively reliable detector of trend changes. In terms of price direction, the oscillator generates a bullish signal when it is moving upwards (and a bearish one otherwise). Divergences point to trend weakness setting in.
- Decorrelation. We provide 5- and 10-bar decorrelation of any ticker with its momentum indicators (aggregating 40+ readings into a single value) and a number of key stocks / ETFs (to highlight any abnormalities, if present, relative to the main segments of the broader market).
- Ehlers MADH (Moving Average Difference – Hann). Momentum and trend-following oscillator. A variant of the MAD indicator (which is essentially computed as the percentage difference between two SMAs) but uses the Hann window function instead of simple moving average for smoothing. This mitigates some of the limitations of MAD, namely a rectangular window which leads to sidelobe leakage in its filter response because the Fourier transform is applied to sharp edges. The solution in MADH is to soften them by Hann windows which have a cosine squared shape coefficient amplitude distribution across the length of the filter. The indicator is computed as the difference of two finite impulse response filters (they have different lengths - one of them is longer by half-period of the dominant cycle). Tops and bottoms of the oscillator generate relatively reliable trading signals. Direction-wise, correlates well with price hence divergence-based trading strategies can be applied here as well.
- Kuskus Starlight. Momentum oscillator developed to help traders analyze market trends. Calculated by first determining the highest and lowest price within a given period and then using the such-obtained range to normalize the latest middle price. After smoothing the result (based on exponential moving average) and bounding it to be within the [-1, 1] interval, Fisher transform is applied. An example trading strategy could be to go short when the indicator value turns negative while a bearish pattern is present in the chart near key resistance levels. (An analogous, mirror setup applies when opening long positions.) Another approach is to look for diversions and anticipate trend turning points when they happen, especially when confirmed by other technical analysis signals.
- Traders Dynamic Index. Indicator combining RSI and volatility. Can be used to identify shifts in market sentiment, overbought / oversold conditions, and potential reversal points. Computes RSI as well as its moving average and standard deviation. Plotted similarly to Bollinger Bands. Addresses one of the key challenges inherent to standalone RSI - fixed overextended levels of 30 and 70. The bands surrounding the smoothed RSI provide a dynamic channel that adapts to market characteristics thus generating more trading signals (for example, in sideways consolidation periods, RSI may never reach 70, while crossing the upper standard deviation band multiple times because of subdued volatility). Overall, indicator interpretation is analogous to Bollinger Bands (in terms of price divergence detection, trend confirmation, and reversals from extreme readings).
- Universal Oscillator. Indicator inspired by digital signal processing techniques, in particular by filtering of pink noise (i.e. one where the power spectral density is stronger at lower frequencies). Isolates the white spectrum (which is the same at all frequencies) via a momentum-based equation and eliminates undesirable wave components using denoising transformations borrowed from the aerospace analog filters domain. The oscillator line is obtained from the filtered data based on the automatic gain control algorithm to maintain a consistent level of responsiveness. Has almost no lag (follows the swings in price without introducing any delays). Zero-line crossovers generate trade signals (bullish when going positive and bearish otherwise). In addition, the overbought / oversold threshold of ±0.85 can be used as a trigger to buy / sell.
Toplists: Stock Screeners
Toplists provide out-of-the-box stock screeners based on commonly-used technical signals such as indicator and price extremes, line crossovers, higher highs, lower lows, breakdowns, breakouts, etc. They can be used to identify stocks that are outliers or exhibit certain technical patterns or setups in the most pronounced way. A lot of great trading opportunities can be uncovered this way across daily, weekly, and monthly timeframes.
- Top gainers. Largest percentage gainers in the last 1, 2, or 3 days/weeks/months. Useful to identify stocks that are the strongest winners (potential buy candidates).
- Top losers. Largest percentage losers in the last 1, 2, or 3 days/weeks/months. Can help find stocks that have dropped the most (potential short candidates or bounce plays).
- Top volume. Stocks whose trading volume has increased the most in the last 1, 2, or 3 days/weeks/months. Points to the most liquid and actively-traded stocks. High volume increases tend to correlate with strong price moves in momentum plays. Increased volume also provides price action confirmation.
- RSI. Stocks that have the highest and lowest RSI on daily, weekly, and monthly timeframes. Tend to be good candidates for mean reversion, especially when other indicators confirm the anticipated price direction.
- MACD. Stocks whose MACD line is the farthest away from the MACD signal line. The distance is measured in percentage. Can help identify setups that are overextended and should be watched for reversal or consolidation.
- Stochastic. Two metrics useful for finding overbought or oversold stocks. One based on the Stochastic oscillator extremes and the other derived from the distance between Stochastic and its signal line (measured in percentage).
- Bollinger Bands %. Stocks that are most overextended in terms of the upper or lower Bolliger band. Bollinger % refers to where the price is within the current Bollinger channel. 0 represents the lower band and 100 maps to the upper band. Values outside this range mean that the price is outside the Bollinger channel and such setups are prone to mean reversion.
- Widest Bollinger Bands. Stocks whose Bollinger Bands channel is the widest (as measured in percentage). Since Bollinger Bands channels alternate between narrow and wide over time (as volatility fluctuates between low and high regimes), this toplist can help find stocks that have been relatively volatile and may be entering a period of consolidation or range-bound trading next.
- Keltner %. Stocks that have the highest or lowest Keltner % (i.e. are at the most extreme locations relative to the Keltner Channel bands). Useful for identifying the most overbought and oversold setups, similarly to Bollinger Bands %.
- Widest Keltner Channel. Can help find stocks that have been trading in a wide range and are likely to enter a lower volatility regime next. Similar in spirit to the widest Bollinger Bands toplist.
- Standard Error %. Useful for discovering stocks that are at extremes in terms of Standard Error Bands (which correspond to lines located at 2 standard deviations from the linear regression line based on 21 most recent price bars). Values in the 0-100 range map to the prices between lower and upper Standard Error Bands. In very overextended stocks, Standard Error % can be negative or exceed 100%. Standard Error % values for 20 and 50 day/week/month linear regression channels are provided in the "Linear regression" toplist (e.g. "Highest 50-day channel %").
- Widest Standard Error Bands. Similarly to Bollinger and Keltner channels, this toplist helps find stocks that have recently had the widest trading range and are more likely to trade sideways next.
- Linear regression. Two metrics related to 20 and 50 day/week/month linear regression channels. One essentially equivalent to "Standard Error %" mentioned above but for 20 and 50 bars. The other one helps find the most upward and downward trends based on the slope of the linear regression line.
- Moving average. Stocks that are the most distant from their 13, 20, and 50 moving average on daily/weekly/monthly charts. Since, eventually, stocks tend to retest these important moving averages, these toplists are helpful for creating reversal watchlists.
- Ichimoku. Stocks that have the most extreme readings in terms of Ichimoku Cloud metrics: farthest away from the cloud, conversion line, and whose base line is the most above or below the conversion line. In all these cases, we would be looking for mean reversion and retesting of Ichimoku lines.
- Price correlation. Stocks being the largest outliers in terms of their correlation with key momentum oscillators, specifically RSI, MACD, Stochastic, Bollinger %, Keltner %, and Standard Error %. These oscillators normally strongly correlate with price (meaning the correlation coefficient is close to 1). Low correlation with the above momentum oscillators often occurs before trend changes and it typically normalizes relatively quickly. Therefore, these toplists can be useful in identifying setups where price action is not confirmed by momentum and is prone to either reverse or to gain brand new momentum. 5 and 10 bar correlations are provided out of the box.
- Most volatile. Stocks whose Average True Range in the last 5 and 10 days/weeks/months is the largest. Useful for finding trading opportunities for strategies that depend on large percentage changes in price.
- Gapdowns and gapups. These toplists help identify stocks that have the largest gapdown or gapup or the highest number of gapdowns or gapups of at least 2% in the last 5 days/weeks/months. Since most gaps get filled over time, these scanners can be useful for finding price targets that are likely to be hit once reversal starts.
- Price-momentum divergence. As explained in more detail in the section below, we count the number of price-momentum divergences (bullish, bearish, hidden-bullish, and hidden-bearish) for each stock using 40+ momentum indicators that tend to correlate with price the most. These toplists can be used to identify stocks that diverge with momentum the most (for each of the 4 divergence types). Highly divergent setups tend to occur around major price turning points. These signals can help discover interesting anomalies to watch and trade as well as inform risk management profiles.
- Decorrelation. For each stock, we compute its aggregate decorrelation with 100+ ETFs / stocks and with 40+ price-momentum oscillators for 5 and 10 day/week/month timeframes. These toplists can help uncover stocks that have changed their normal trading patterns relative to other ETFs/stocks and momentum indicators the most. Such abnormal de-correlations may indicate a temporary anomaly or a trend change, depending on technical and fundamental factors at play. Highly decorrelated stocks are of interest to swing traders as they often are prone to corrections of the preceding move.
- Higher highs and lower lows. Stocks that have the largest number of higher highs and lower lows as well as the longest series of consecutive higher highs and lower lows in the last 10 days/weeks/months. These screeners can be useful for discovering stocks lending themselves to trend-following trading strategies.
- Breakdowns and breakouts. These toplists can help find the most convincing breakouts and breakdowns across 3 short-term timeframes (1, 2, and 3 months) and 3 long-term ones (6, 9, and 12 months). The former apply to daily while the latter to weekly and monthly charts. There are many trading strategies that are driven by breakouts and breakdowns, including retest of the broken support/resistance levels, continuation, and fakeouts. Breakouts and breakdowns, when aligned with signals derived from other technical indicators, can increase the odds of picking the right position size, entry, and exit levels.
- Off the highs / lows. Help find stocks that are the farthest away from their recent highs and lows. The time window depends on the chart timeframe: there are 3 short-term ones (1, 2, and 3 months), for daily charts, and 3 long-term ones (6, 9, and 12 months), for weekly and monthly charts. A common trading setup applicable here is when a stock is at strong support but significantly off its recent highs (and, symmetrically, for being at resistance and off recent lows).
- Largest range. Stocks that have traded in the widest range when taking into account all the lows and highs from the last 1, 2, and 3 days/weeks/months. Useful for identifying short-term swing trading opportunities and finding the biggest movers, regardless of the price direction.
- Moving average cross. These toplists facilitate finding stocks that have recently had the most convincing 13, 20, and 50 moving average cross up or down. Price-moving-average crosses are commonly-used trading signals, usually as confirmation of short/medium/long-term trend change. There are several trading strategies associated with moving-average crosses, for example retest of the moving average line and continuation.
- MACD cross. Lists stocks whose MACD has crossed the MACD signal line in the strongest way. Can be useful for identifying stocks that have just changed their momentum from bullish to bearish or vice versa. When confluent with the outcomes of other technical analysis methods, MACD cross tends to be a reliable indicator and it is used by many traders.
- Stochastic cross. Stocks with the most convincing cross of the signal line in the Stochastic oscillator. Can be used to find stocks that are about to flip their momentum, especially when the signal is agreeing with other indicators. Cross up typically happens at bottoms and cross down at tops.
- Ichimoku cross. Toplists that help discover stocks based on the strongest line crosses in the Ichimoku Cloud indicator. These include price crossing the cloud, price crossing the conversion line, and conversion line crossing the base line. Crossups are bullish and crossdowns are bearish.
- Fundamentals. Toplists based on key business financials and performance metrics, specifically: EPS, revenue, gross profit, operating expenses, net income, free cash flow, net profit margin, assets, liabilities, EBITDA, price/earnings, price/sales, price/book, debt/equity, and working capital. For each of them, we list stocks with the largest quarterly change (increase and decrease).
Market Breadth Metrics
One of the key characteristics that help determine the strength or weakness of moves in a major index such as S&P 500 or Nasdaq 100 is the level of participation by individual stocks, referred to as market breadth. Trends confirmed by breadth metrics are considered healthy and likely to continue. On the other hand, market tops and bottoms are often formed when market moves diverge from breadth metrics. For example, Nasdaq 100 making new highs while the number of stocks comprising the index being above their 20-day moving average is declining signals that the current move may be approaching exhaustion and a correction is likely to follow.
We provide a number of breadth metrics for 4 main market indices: S&P 500 (largest companies across multiple sectors), Dow 30 (large-cap blue chip stocks), Nasdaq 100 (technology companies), and Russell 2000 (small cap segment). These metrics are available on daily, weekly, and monthly timeframes and cover current and historical data. To make plots more readable, we smooth each breadth metric by computing its 8-period moving average.
- Price Advancing vs Price Declining. We count the number of stocks whose price is advancing (PA) and declining (PD) in each index, for each timeframe, and then derive the following metrics (which can be plotted over time):
- Percent advancing: PA divided by the total number of stocks in the index.
- Percent declining: PD divided by the total number of stocks in the index.
- Advancing to declining ratio: PA divided by PD.
- Advancing to changing ratio: PA divided by (PA + PD).
- Advancing minus declining: PA minus PD. We refer to this metric as (1).
- Advancing minus declining to changing ratio: (PA - PD) divided by (PA + PD). We refer to this metric as (2).
- Cumulative values for (1) and (2), meaning the summation of all the values over time.
- McClellan oscillator for (1) and (2), which is computed as 19-period EMA (exponential moving average) minus 39-period EMA. Positive values indicate the dominance of advancing securities over declining ones. Can be used for identifying overbought and oversold conditions, trend confirmation, or divergence analysis.
- McClellan summation index for (1) and (2), which is obtained as the sum of all the values of the McClellan Oscillator (over daily, weekly, or monthly chart).
- Absolute value for (1) and (2), for example | PA - PD | for (1). Useful for determining how skewed the market breadth is.
- Volume Advancing vs Volume Declining. The former corresponds to price going up (advancing) and the latter to price going down (declining). In order to calculate these breadth metrics, we sum up the volume of stocks whose price is advancing (VA) and declining (VD) in each index, for each timeframe (i.e. day, week, and month). Similarly to the above-mentioned Price Advancing vs Price Declining (but this time based on VA and VD which represent aggregate volume as opposed to PA and PD which correspond to stock counts), we compute and provide plots (over time) for the following:
- Percent advancing: VA divided by the total volume traded in the index.
- Percent declining: VD divided by the total volume traded in the index.
- Advancing to declining ratio: VA divided by VD.
- Advancing to changing ratio: VA divided by (VA + VD).
- Advancing minus declining: VA minus VD. We refer to this metric as (1).
- Advancing minus declining to changing ratio: (VA - VD) divided by (VA + VD). We refer to this metric as (2).
- Cumulative values for (1) and (2), i.e. the summation of all the values over time. Cumulative plots make longer-term plots easier to interpret.
- McClellan oscillator for (1) and (2), which is calculated as 19-period EMA (exponential moving average) minus 39-period EMA. Negative values signify that most volume has been traded in declining securities.
- McClellan summation index for (1) and (2), which is computed as the sum of all the values of the McClellan Oscillator.
- Absolute value for (1) and (2), for example | (VA - VD) / (VA + VD) | for (2).
- New Highs vs New Lows. We count the number of stocks whose price is making new highs (NH) and new lows (NL) within 3 distinct time windows that are determined by the chart timeframe (i.e. 1, 2, and 3 months for daily charts and 6, 9, and 12 months for weekly and monthly charts). We derive the following (plottable over time) breadth metrics for each time window:
- Percent new highs: NH divided by the total number of stocks in the index.
- Percent new lows: NL divided by the total number of stocks in the index.
- New highs to new lows ratio: NH divided by NL.
- New highs to changing ratio: NH divided by (NH + NL).
- New highs minus new lows: NH minus NL. We refer to this metric as (1).
- New highs minus new lows to changing ratio: (NH - NL) divided by (NH + NL). We refer to this metric as (2).
- Cumulative values for (1) and (2), calculated as the summation of all the values over time.
- McClellan oscillator for (1) and (2), which is computed as 19-period EMA (exponential moving average) minus 39-period EMA.
- McClellan summation index for (1) and (2), obtained as the sum of all the values of the McClellan Oscillator (e.g. daily or weekly).
- Absolute value for (1) and (2), for example | NH - NL | for (1).
- Technical Indicators. We provide a number of breadth metrics that are derived from other technical signals, for example from momentum oscillators, divergences, correlations, channels, etc.
- Arms Index / TRIN. Based on the number of advancing and declining stocks (PA / PD ratio) and advancing and declining volume (VA / VD ratio). Its value is computed as PA / PD ratio divided by VA / VD ratio. A TRIN reading below 1 typically accompanies a healthy bull trend as volume of advancing stocks is supporting the rally. Conversely, TRIN above 1 suggests a negative market sentiment. TRIN normally tends to move inversely to the index (they have a negative correlation). This relationship often breaks around major market turns. The farther away from 1, the more imbalanced the market is. For example, values above 3 suggest oversold conditions and a good likelihood of an upward reversal. At the same time, a TRIN reading that dips below 0.5 may indicate an overextended bullish move and overheated market. Many technical analysts believe that the long-term equilibrium is slightly below 1.
- Open-10 TRIN. Essentially a smoothed version of the Arms Index and it has a similar interpretation. In its formula, instead of the raw values of PA, PD and VA, VD, 10-period moving averages are being used. Open-10 TRIN tends to provide fewer but more reliable signals vs TRIN because smoothing eliminates some of the noise.
- Moving average. For each timeframe (i.e. daily, weekly, and monthly), we provide 3 breadth metrics here: percentage of stocks in a given index whose price is above their 50-, 20-, and 13-period simple moving average. In a bullish move, as long as price momentum is sustained, stock participation should be increasing which translates into more stocks being above their moving averages. Therefore, these metrics are a good gauge of trend strength: they usually weaken significantly well before any index reversal materializes.
- Momentum. We compute the percentage of stocks that exhibit positive momentum readings in terms of their RSI, MACD, and Stochastic, and report that for each index. These momentum-based breadth metrics can be used to evaluate to what extent index moves are supported by bullish momentum setups in individual stocks that constitute each index. While not all divergence signals are 100% reliable, they tend to work pretty well in practice and most traders include them in their technical analysis. For example, a strong index price move on weakening momentum across its stocks, could signal price exhaustion with a high degree of confidence. For RSI and Stochastic, we use the midpoint as the positive/negative momentum threshold. We also provide 2 metrics based on the relative position of MACD and Stochastic vs their signal lines (whenever these oscillators are above their respective signal lines, price momentum is positive).
- Channels. Bollinger Bands, Standard Error Bands, and Keltner Channel offer statistically-significant guidance regarding how far price has moved off its recent extremes. We provide 3 breadth metrics that can help gauge the stage of the current index trend: percentage of stocks in the upper half of each of the above-mentioned channels. One way to interpret these readings could be to exercise caution once we start seeing more and more stocks dropping below the middle line of Bolligner, Keltner, and Standard Error channels while the index continues moving higher. And vice versa for the downside moves. Such behavior normally signals that not much extra room may be left in the current trend and risk/reward favors no position.
- Ichimoku. Time-tested indicator popular among traders because of its versatility. We provide 3 breadth metrics derived from Ichimoku, namely: percentage of stocks whose price is above the cloud, above the conversion line, and whose conversion line is above the base line. These are all bullish setups, hence we expect all of these metrics to positively correlate with the index moves. In an uptrend that is still developing, the readings will be low and increasing rapidly. Once the uptrend stabilizes, the pace of increases will slow down and for the majority of stocks Ichimoku signals will be bullish. Finally, as we get closer towards trend reversal, we will be seeing decreasing Ichimoku breadth metrics as the weakest stocks will already have started their corrections while the leaders continue to sustain the last stages of the index move.
- Price range. An important indicator of where we are in a trend is the relative position of price vs its recent ranges. For example, in early phases of a downtrend, most stocks will be near their highs and their prices will be gradually shifting towards their lows as the overall move progresses. For each stock, we identify 3 recent price ranges. They are based on the last 1, 2, and 3 months for daily timeframes and on the last 6, 9, and 12 months for weekly and monthly timeframes. For each index, we compute the percentage of stocks that are in the upper half of each recent price range. Similarly to several other breadth metrics, an entry / exit trading signal here is generated whenever the index trend and the price-range breadth metrics no longer align with each other.
- Linear regression. Adds structure to range-bound price movements within a larger trend. Price position within the linear regression channel can help gauge whether the current move is nearing completion. For each index, we compute the percentage of stocks that are located in the upper half of the 20- and 50-period linear regression channel. This statistic, when overlaid on top of the index trend, can serve as an early warning signal regarding price direction change.
- Divergence. We aggregate price-momentum divergence counts across all stocks in each index. This is done separately for each type of divergence (bullish, bearish, hidden bullish, and hidden bearish) and is based on 40+ momentum indicators for each stock. Once the total number of divergences approaches significant levels (vs recent history), either price or momentum catchup follows. In other words, price may reverse or momentum will pick up to close the gap.
- Decorrelation. We sum up 2 decorrelation metrics across stocks within each index for 5 and 10 day/week/month timeframes. They are based on aggregate decorrelation with 100+ ETFs / stocks and with 40+ price-momentum oscillators. The resulting breadth metrics indicate to what extent the stocks belonging to a given index behave in an anomalous way in respect to their own momentum as well as other important tickers across market segments. Sharp increases in these breadth metrics, especially when they reach historical highs, typically point to sentiment / direction changes being around a corner.
- Index correlation. We calculate 5- and 10-period correlation of each stock and the index and then we sum up the correlation coefficients obtained this way within each index. The resulting values can be interpreted as a gauge of how well recent index moves agree with the moves of individual stocks constituting the index. This alignment tends to be strong in the middle of solid trends and often weakens significantly near reversal points.
Option Chain Data and Technicals
We provide put and call data (current and historical) across key stocks and ETFs that cover all major market segments. It is possible to plot open interest, volume, and put / call ratios. In addition, we compute a number of commonly-used technicals for the current option chain, including:
- Gamma Exposure (GEX). Metric used to determine the characteristics of the dealer / broker option flows. Computed across the entire option chain, for each strike, by summing up: gamma × open interest × contract size × -1 for puts and 1 for calls. Interpreted as the number of shares of the underlying that need to be traded by market makers to push price in the opposite direction (in order to hedge their market exposure) for each 1 dollar change in the price of the underlying. GEX is sometimes expressed in dollars, which can be calculated by multiplying the result obtained from the above formula by spot price. When GEX is positive, dealers generally are hedging their positions by buying into lows, and selling into highs (thus adding liquidity and stabilizing the market by countering price swings and supporting mean reversion). In this scenario, the activities of dealers and market makers act as a buffer against large price swings, reducing the overall market volatility. Conversely, when GEX is negative, dealers are selling into lows, and buying into highs (i.e. stripping liquidity from the market by being forced to chase the moves). A primary objective for market markers is to create a bid / ask spread, without exposing themselves towards unwarranted risk (i.e. by being delta-neutral and seeking to avoid any directional bets). In a negative GEX regime, there is more volatility and flows become more powerful. Bull market legs are often associated with positive gamma where any excessive moves are quickly neutralized and a steady uptrend emerges. In bear markets, both selloffs and rallies tend to be sharp due to market moves being amplified by dealers operating in the negative gamma mode. GEX is basically the option dealer’s delta sensitivity and dictates the nature of their book hedging as deltas fluctuate through changes in price of the underlying, implied volatility, or the passage of time. In addition, extreme GEX levels relative to historical ranges may indicate important price turning points. GEX is more likely to significantly impact the market when there is a lot of open interest at a particular strike with a near-term expiration date. This is because gamma is highest when an option is at the money and closer to expiration, and lowest when an option is further away from the money or has more time to expiration. Around key options expiration dates, large concentrations of options can create significant gamma pinning effects. When a large number of options are clustered around a certain strike price, dealer’s hedging flows dampen any movement away from that strike price. At the same time, a significant break away from a big strike level, combined with negative GEX, can lead to rapid and outsized breakout / breakdown moves.
- Zero Gamma Level. Computed as the nearest strike (relative to spot price) where Gamma Exposure (GEX) changes its sign (from positive to negative or vice versa). Useful for determining important support or resistance levels, price inflection points, and detecting big shifts in market dynamics. E.g. crossing into positive GEX typically occurs at bottoms. Corrections are often associated with negative GEX. Generally, two reasons make GEX near zero: either dealers have a balanced book of options (i.e. delta hedged, which tends to happen at well-developed tops) or implied volatilities are particularly high (which normally coincides with bottoms). Implied volatility will generally rise when liquidity in the market is insufficient. In a positive GEX environment, we often witness volatility compression and subdued intraday price swings (which favors bulls). Negative GEX tends to produce structural weakness and vulnerability to corrective moves (either up or down) since liquidity can dry up as dealers become more aggressive in their hedging and the likelihood of extreme price movements increases. Zero Gamma levels mark an equilibrium where dealers transition their option flows between long and short. In most cases, until GEX becomes negative, bull trends tend to remain intact as dealers will generally continue to add liquidity to markets by buying dips and weakening any substantial price run-ups. Zero Gamma is calculated based on the Black-Scholes option pricing model by first computing gamma and then GEX for each option across all spot prices within the 20% distance from the last close and then aggregating all these GEX values per strike. When GEX is positive, the market tends to have a smaller price distribution (mean reversion results in a smaller trading range, choppiness, and scalping opportunities), with a slightly positive average daily return. Under the negative GEX regime, markets are prone to negative returns and wide price changes with more directional bias (strong intraday momentum is commonplace, e.g. price can get flushed hard without reverting back up). It is worth noting that institutional investors typically buy index puts for portfolio insurance and write calls to offset some of the hedging costs. Therefore, option dealers are normally net short on put options and net long on call options. When the market drops significantly, making dealers short gamma, they are forced to delta-hedge in the direction of the trend by selling futures in a falling market and buying futures in a rising market. This is what causes the characteristic volatility and sharp moves of a bear market.
- Put and Call IV Skew. Volatility skew is the difference in implied volatility (IV) between out of the money (OTM), at the money (ATM), and in the money (ITM) options. For puts and calls, we compute the percentage difference between total IV of ATM options and OTM / ITM options whose strikes are at the 5% distance from the current spot price. Options are matched by expiration date. Thus, the obtained IV skew should be interpreted as the % IV change when moving 5% away from the spot price while keeping expiration date unchanged. A common pattern is that IV rises as the distance from the spot price increases (a phenomenon known as a volatility smile or an U-shaped graph indicating that OTM and ITM options are more in demand than ATM options). In this particular setup, the IV skew is positive for both ATM vs ITM options and ATM vs OTM options. Other patterns, including asymmetric ones also occur in practice (e.g. a smirk where, for calls, ITM IVs are higher than both ATM and OTM IVs). A number of insights can be inferred from IV skew, e.g. where hedging is more concentrated which can be used to gauge the overall market sentiment. This is especially true when the skew gets outside of its typical historical ranges. A quickly changing IV skew over time can also signal a shifting risk profile and a potential trend reversal. The CBOE SKEW index is another useful metric when it comes to IV skew. It measures tail risk, i.e. returns two or more standard deviations from the mean in S&P 500 over the next 30 days. The main difference between VIX and SKEW is that the former is based upon implied volatility around the at-the-money (ATM) strike prices while the latter focuses on implied volatility of out-of-the-money (OTM) strikes. High SKEW readings indicate higher perceived tail risk and elevated likelihood of a black swan event (since traders are hedging against big unexpected market moves). Bottoms are often associated with high VIX and low SKEW (and vice versa for tops).
- Risk Reversal. A special case of IV skew focused on comparing IV of ITM puts and ITM calls whose strikes are located the same distance from the spot price (we use 3%, 5%, and 8%) and which have the same expiration dates. Since such options have identical moneyness, the IV skew can lend insight into how downside protection differs from upside one in terms of premiums. This may help identify opportunities to buy undervalued options or sell overpriced ones (i.e. potential for arbitrage). The reasons for the skew vary. Sometimes it is purely supply / demand, e.g. when the market is worried about a correction, people might buy more puts than calls (creating the imbalance of premiums and IVs). Generally, positive values of risk reversal are bearish and negative ones indicate a bullish sentiment.
- IV Median Skewness. Estimates how skewed the distribution of IVs is. Uses the Pearson's median skewness formula (which involves subtracting the median from the mean and then dividing the result by the standard deviation). Positive values indicate a right-skewed distribution of IVs across strike prices (which is bullish if it happens for calls). Similarly, strong left-skewness of puts suggests a bearish tendency in the market. The most reliable signals are generated when major anomalies occur, hence it is important to track typical skewness ranges across a longer period of time and ideally also correlate them with past market cycles.
- Maximum Pain Levels. Computed for the nearest expiration date (ignoring 0DTE) and also across the entire option chain. The maximum pain level for a given set of contracts is the spot price for which the total value of puts and calls is lowest (i.e. where the largest number of options expire worthless). Large option sellers / writers (typically market makers and institutions) are incentivized to move prices towards these levels (to maximize their profits from premiums) hence knowing them upfront can help traders get a better understanding of where important price magnets may be located. Calculated by extracting each option’s intrinsic and time value. The former is based on the difference between the strike and spot price. The latter is derived from the underlying asset's expected volatility and time until the option's expiration. The maximum pain level is selected across all spot prices within the 10% distance from the last close.
Correlation With 100+ ETFs / Stocks
We provide 5- and 10-day / -week / -month correlation and decorrelation between each symbol and 100+ key ETFs / stocks to enable analysis and insights related to unusual trading patterns such as pair decoupling.
Decorrelation is computed as total distance from average correlation coefficient (Pearson). Decorrelation signals are available per-pair (e.g. in the "Strongest 10-bar correlation deviations" table) and in aggregate (as an indicator, breadth metric, and in toplists) to help gauge how a given ticker (or market index) has been decorrelating with important ETFs / stocks covering key market sectors. For example, one could use the "Stock / ETF 5-week" indicator available in the "Decorrelation" section to research how a given symbol has been trading over time vs its aggregate decorrelation with key ETFs / stocks. Or, one could view the "Highest 10-day stock / ETF decorrelation" toplist to identify stocks that have recently been decorrelated with key ETFs / stocks the most. Another example could be using the "Total 5-day stock / ETF decorrelation" breadth metric located under "technicals" to investigate how stocks comprising a given market index have been decorrelating with key ETFs / stocks over time.
The overall intuition here is that we can use the deviation from typical correlation coefficients as a signal that a given trading instrument has changed its normal price patterns in relation to key market components (or "lighthouse" tickers) which may suggest an anomaly that presents an investment or trading opportunity. Since total decorrelation is computed automatically across 100+ ETFs / stocks, it can help assess trading setups with minimal extra complexity and time overhead.
In addition to decorrelation, we also compute pair correlations – for each symbol we list the most positively and negatively correlated symbols (among the 100+ ETFs and stocks mentioned above) as well as the ones that deviate from the usual coefficient the most. These pair correlation data (listed in the tables under the indicators buttons) can help identify market leader rotation and find a number of trading setups, e.g. pairs trade where we go short one symbol and long another one targeting correlation normalization. It is also possible to compute correlation (over various timeframes) between any two selected symbols: this functionality is available in the "Relative to Another" section (specifically, via the "Price Correlation" button and the associated search box).
As one of the breadth metrics, we also compute aggregate correlation of stocks comprising a given index with the index itself. Plotting this relationship against the index enables discovery of time windows with low market breadth (or low participation of stocks in the index moves). When combined with other technical analysis methods, these can generate high-precision position entry / exit signals around market tops / bottoms and trend reversals.
Correlation With 40+ Momentum Oscillators
We provide 5- and 10-day / -week / -month correlation and decorrelation between each symbol and 40+ key momentum oscillators to shed more light on how price action is supported by momentum metrics. Decorrelation is computed as total distance from average correlation coefficient (Pearson).
We use 40+ momentum oscillators that we have empirically found to best correlate with price across a number of market sectors. These include: RSI, MACD, Stochastic, Bollinger %, Standard Error %, Keltner %, Donchian %, Chaikin, Chande, ADX DI+, Accumulation / Distribution, Klinger, Ultimate, Volume Zone, Twiggs Money Flow, Vortex+, Connors RSI, Disparity, Projection Bands %, Wave Trend, Volume Price Trend, True Strength, Belkhayate Timing, ROC, Absolute Strength, Wilder ASI, Bull / Bear Power, Johnson PGO, Brown Composite, Commodity Channel Index, Demand Index, Detrended Price, Elder Force, On Balance Volume, Ease Of Movement, Rapid RSI, Relative Momentum, Repulse, Relative Volatility, Kuskus, PassBand Filter, Trend Continuation, Stochastic Momentum, Random Walk, DeMarker, etc.
Ability to combine 40+ momentum indicators into a single reading greatly simplifies analysis and removes the need to manually look at each indicator in isolation. Correlation and decorrelation signals are available as indicators, breadth metrics, and in toplists. For example, one could use the "Momentum 10-week" indicator available in the "Decorrelation" section to investigate how a given symbol has been trading over time compared to its aggregate 10-week decorrelation with 40+ momentum oscillators. Or, one could view the "Highest 5-day price-momentum decorrelation" toplist to identify stocks that have recently been decorrelated with momentum the most. Another example could be using the "Total 10-week price-momentum decorrelation" breadth metric located under "technicals" to investigate how stocks comprising a given market index have been decorrelating with momentum over time. In this case, the metric is computed by summing up the distances from average correlation across momentum oscillators and across stocks comprising the index.
Whenever a trading instrument decouples from its momentum, typically mean reversion follows. Thus, elevated decorrelation metrics can point to profitable swing trading setups, especially when there is a high degree of confirmation across a number of momentum oscillators. Automatically computed aggregate decorrelation can greatly improve risk / reward profiles of trading setups while saving a lot of repetitive work.
For momentum indicators that correlate with price very strongly (i.e. RSI, MACD, Stochastic, Keltner %, Standard Error %, and Bollinger %), we compute individual unaggregated correlation data as well. For example, there is a dedicated indicator for each of the above in the "Indicator-Price Correlation" section. Similarly, the toplists in the "Price correlation" row provide stock screeners for the lowest correlation coefficient for these 6 critical momentum oscillators, enabling the discovery of stocks that are the biggest outliers and therefore most prone to trend change.
Price-Momentum Divergence For 40+ Momentum Oscillators
We provide price-momentum divergence metrics for all symbols based on 40+ momentum oscillators that tend to correlate with price the most (they are listed in the prior section).
Bullish divergence is when price is making lower lows but momentum is making higher lows (i.e. price moves lower with less momentum;this often accompanies the last phases of a bear market). Bearish divergence is when price is making higher highs but momentum is making lower highs (i.e. price moves higher with less momentum;this type of setup tends to coincide with bull market tops). Hidden bullish divergence is when momentum is making lower lows and price is making higher lows (i.e. a trading instrument becomes oversold at a higher price;this commonly happens in bull market corrections). Hidden bearish divergence is when momentum is making higher highs and price is making lower highs (i.e. a trading instrument becomes overbought at a lower price;such a setup is common in bear market rallies).
All of these 4 types of divergence tend to signal a reversal, especially when there is a high degree of confluence across multiple momentum indicators. Therefore, it is critical to consider a number of indicators at once to confirm any setup. We provide such aggregate reading across 40+ momentum oscillators which were carefully selected based on how well they move together with price. For each momentum indicator, we compute lows and highs, and map them to price changes, and vice versa. Next, we count the number of divergences of each type in recent trading history on daily, weekly, and monthly timeframes.
There are 4 indicators in the "Divergence" section that can be used with any symbol. In addition, one can use "Price-momentum divergence" toplists to identify stocks that diverge with momentum the most. Divergence signals are also aggregated in breadth metrics across stocks comprising each index. For example, one could view the "Total hidden-bullish price-momentum divergences" metric for Nasdaq 100 (located under "technicals") to investigate to what extent Nasdaq components have been diverging with momentum (the metric is the total number of instances of divergence of each type). Comparing this metric against historical Nasdaq price action can provide insights on how predictive divergences are in terms of signaling major market turns.