We provide interactive charts and technical analysis tools for traders and investors. We cover stocks, ETFs, indices, futures, options, forex, and crypto.

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We support daily, weekly, and monthly timeframes, line, bar, and candlestick charts, 180+ technical indicators, 100+ market breadth metrics, and 100+ toplists for stock screening.

All charts are adjustable and editable – you can move and resize them or add annotations and drawings such as lines, text labels, or Fibonacci retracements and extensions.

In terms of charting and technical analysis, we provide:

• 100+ market breadth metrics covering S&P 500, Nasdaq 100, Dow 30, and Russell 2000, on daily, weekly, and monthly timeframes, including historical data:
  • Price Advancing / Declining, Volume Advancing / Declining, New Highs / Lows: total of 70 metrics including smoothed percentage, ratio, difference, absolute and cumulative values, and McClellan Oscillator and Summation Index.
  • 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.
• 100+ toplists for stock screening on daily, weekly, and monthly timeframes with filtering by price and volume:
  • 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 correlation between any two symbols.
  • Strongest positive and negative correlations with 100+ top ETFs / stocks for any symbol.
  • Largest deviations from average correlation with 100+ top ETFs / stocks for any symbol.
  • Price-indicator correlation for key momentum oscillators: RSI, MACD, Stochastic, Keltner %, Standard Error %, and Bollinger %.
  • Decorrelation with 100+ top ETFs / stocks and with 40+ key momentum indicators.
• 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, indices, stocks, futures, forex, and crypto.
• Option chain data (current and historical) for indices, bonds, commodities, and top stocks in the technology, financials, healthcare, consumer, and energy sectors. Includes open interest and volume for puts and calls, as well as put / call ratios.

You can connect with us, provide feedback, or ask questions via email at support@charted.market or anonymously using our contact form.




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.
• Moving Averages. We provide 5-, 13-, 20-, and 50-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.
• 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.
• 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.
• 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).
• 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.

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. 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.

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 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

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.



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.



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.