VWAP Reversal Probability Signals🟠 OVERVIEW
VWAP Reversal Probability Signals tracks price movements around an anchored VWAP and two volume-weighted standard deviation bands. It looks for price excursions outside these bands and waits for price to move back through the same band before marking a potential reversal.
Each reversal signal is paired with a fixed VWAP target. The script records whether price reaches that target within a user-defined number of bars and displays the historical success rate for each band independently. This allows traders to compare how different reversal distances have performed over time instead of treating every signal the same.
🟠 CONCEPTS
Anchored VWAP — A volume-weighted average price that resets at the selected session, week, month, quarter, or year and acts as the central reference level.
VWAP Deviation Bands — Upper and lower bands created from volume-weighted standard deviation multiples around the anchored VWAP to define progressively larger price extensions.
Reversal Signal — Generated when price first extends beyond a deviation band and then closes back through that same band, indicating that the extreme move has started to reverse.
VWAP Target — Every signal uses the current anchored VWAP as its fixed target, allowing completed signals to be measured using the same destination.
Reversal Probability — The historical percentage of completed signals from each individual band that reached the VWAP target before the expiry period.
🟠 FEATURES
Anchored VWAP and Reversal Bands — Displays the anchored VWAP together with two configurable upper and lower deviation bands.
Reversal Signal Markers — Shows bullish and bearish reversal signals after price returns back
through the selected deviation band.
Historical Probability Labels — Displays the historical VWAP target hit rate beside each new reversal signal for the corresponding band.
VWAP Target Lines — Draws a projected target from every signal to the current VWAP until the trade either succeeds or expires.
Target Confirmation Marks — Places a confirmation mark when a tracked signal reaches its VWAP target within the selected expiry window.
🟠 HOW TO USE
Choose the VWAP anchor period that matches your trading style, such as session, week, or month.
Watch for price to extend beyond a VWAP deviation band and then move back through that same band before considering a reversal signal.
Compare the probability label shown with the signal to understand how that band has performed historically.
Use the dashed VWAP target line as the expected mean reversion objective for the active signal.
Treat the displayed probability as historical context rather than a prediction of future performance.
🟠 CONCLUSION
VWAP Reversal Probability Signals combines an anchored VWAP, volume-weighted deviation bands, reversal signals, and historical outcome tracking. By measuring how often each type of reversal has returned to the VWAP, it provides both reversal locations and statistical context for those signals. Indicator
High Volume Breakout Targets [AlgoAlpha]🟠 OVERVIEW
High Volume Breakout Targets identifies price zones formed by related pivot highs or pivot lows. These zones represent areas where price previously reacted around overlapping wick and candle-body levels.
The indicator then checks whether price closes through a zone with enough of the breakout candle extending beyond its boundary. Qualified breakouts can display directional labels, an entry level, and three targets based on the height of the broken zone.
Normalized volume candles are also shown inside recent active zones. This helps traders compare current volume with its recent average while watching price interact with a potential support or resistance area.
🟠 CONCEPTS
Pivot High Zone — A resistance area formed when a confirmed pivot-high wick falls within the body of a previous pivot-high candle. The zone spans the associated wick highs and body-top levels.
Pivot Low Zone — A support area formed when a confirmed pivot-low wick falls within the body of a previous pivot-low candle. The zone spans the associated wick lows and body-bottom levels.
Pivot Confirmation — A pivot requires the selected number of bars on both sides of the turning point. A higher Pivot Length identifies broader structures but confirms them later and less often.
Zone Maximum Age — The maximum number of bars during which two pivots can be associated and an active zone can continue extending. An expired zone remains visible but no longer produces a breakout.
Qualified Breakout — A breakout requires a confirmed close above a bearish zone or below a bullish zone. It must also place the selected percentage of the candle’s full range beyond the broken boundary.
Normalized Volume — Current volume is divided by its 20-bar average. The resulting ratio controls the size and transparency of the volume candle displayed inside an active zone.
Breakout Targets — The breakout close becomes the entry level. The broken zone’s height is divided into three equal steps to calculate TP1, TP2, and TP3 in the breakout direction.
Target Expiry — Each target setup remains active for a selected number of bars. When TP1 or TP2 is reached, the remaining unhit targets receive a new expiry period from the hit candle.
🟠 FEATURES
Pivot Zones — Displays bullish support zones and bearish resistance zones created from associated pivot structures.
Breakout Labels — Marks bullish and bearish closes that satisfy the selected outside-range requirement.
Three-Level Targets — Displays the breakout entry, a target area, and TP1, TP2, and TP3 levels derived from the broken zone’s height.
Zone Volume Display — Shows normalized volume candles inside the four most recently active zones.
Target Completion Marker — Prints a checkmark on the first candle whose wick reaches TP3.
🟠 HOW TO USE
Adjust Pivot Length to match the structure you trade. Use lower values for smaller and more frequent zones, or higher values for broader and less frequent zones.
Treat bullish zones as potential support and bearish zones as potential resistance while they continue extending.
Watch how price behaves inside a zone. Use the normalized volume candles to compare participation with the recent volume average.
Wait for a breakout label rather than treating every wick through a zone as a breakout. A label appears only after the candle closes beyond the boundary and meets the Minimum Breakout Range setting.
Use a higher Minimum Breakout Range to require more of the breakout candle to trade beyond the zone. Use a lower value to accept less decisive moves.
After a qualified breakout, use the entry line as the breakout reference and TP1, TP2, and TP3 as zone-based projection levels.
Check whether targets are reached before their expiry. TP1 and TP2 extend the active period for the remaining targets when reached.
Combine the zones and breakout signals with market structure, trend direction, liquidity, and risk controls. The indicator does not define a stop-loss or position size.
🟠 CONCLUSION
High Volume Breakout Targets combines pivot-based support and resistance zones, normalized volume context, qualified breakout signals, and zone-height target projections. It gives traders a structured way to assess price interaction with established zones and track the progression of confirmed breakouts. Indicator
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Machine Learning Adaptive DMI Signals [AlgoAlpha]🟠 OVERVIEW
The Directional Movement Index (DMI) is commonly calculated using a fixed lookback length. But market conditions change over time, and a length that works well during one period may become less effective during another.
This script builds multiple DMI models across a user-defined range of lengths and continuously evaluates their past performance. Each DMI length acts as an independent expert. As new directional flips occur, the script measures how well each expert performed and updates its internal scoring system.
The result is an adaptive DMI that automatically shifts toward lengths that have recently produced better directional signals while reducing the influence of weaker performers.
🟠 CONCEPTS
Expert DMI — A DMI calculation running at a specific lookback length within the tested range.
Directional Flip — A change in trend state when +DI crosses above -DI or when -DI crosses above +DI.
Reward Score — A performance score assigned to each completed flip based on return, move quality, pullback behavior, or win rate.
Maximum Favorable Excursion (MFE) — The largest move in the trade's favor before the next directional flip.
Maximum Adverse Excursion (MAE) — The largest move against the trade before the next directional flip.
Recency Decay — A weighting system that gradually reduces the influence of older observations so recent market behavior has greater impact.
Softmax Weighting — A probability-style weighting process that gives greater influence to higher-scoring DMI lengths when estimating the adaptive length.
🟠 FEATURES
Adaptive +DI and -DI Lines — Displays directional movement using a dynamically selected DMI length that adjusts over time.
Directional Clouds — Color-filled regions between the DI lines help visualize which side currently has directional control.
Bullish and Bearish Flip Signals — ▲ and ▼ markers appear when the Adaptive +DI and -DI lines cross.
ADX Strength Display — Strength squares at the bottom of the pane become more visible as trend strength increases and fade as strength decreases.
Information Table — Displays the active adaptive length, selected scoring mode, memory count, and current bullish or bearish trend state in a customizable table.
🟠 HOW TO USE
Watch for bullish flips when Adaptive +DI crosses above Adaptive -DI to identify potential shifts toward upward directional control.
Watch for bearish flips when Adaptive -DI crosses above Adaptive +DI to identify potential shifts toward downward directional control.
Use the ADX strength squares to gauge whether directional movement is strengthening or weakening.
Increase the tested length range when evaluating a wider variety of market conditions.
Increase Memory and Forget Old Trades values for more stable adaptation and slower length changes.
Decrease Memory or lower the decay factor when faster adaptation to recent behavior is preferred.
Experiment with the available scoring methods to determine whether return, trend quality, or consistency is more important for your analysis.
🟠 CONCLUSION
Machine Learning Adaptive DMI combines traditional DMI calculations with a performance-driven adaptive length selection process. Instead of relying on a fixed lookback period, it continuously evaluates how different DMI lengths have behaved and adjusts accordingly. This provides a dynamic view of directional strength, trend bias, and signal quality that reflects recent market behavior. Indicator
Regression Trend Reversal Signals & Forecasts [AlgoAlpha]🟠 OVERVIEW
Regression Trend Reversal Signals & Forecasts combines multiple regression methods into a single trend and reversal framework. It allows traders to choose between Linear Regression, Theil-Sen Regression, LOESS smoothing, Nadaraya-Watson smoothing, Polynomial Regression, and a Kalman Filter to estimate the underlying price path.
The selected regression line acts as the center of a dynamic channel. The channel width is based on the standard deviation of the distance between price and the regression line, allowing it to adapt to changing market conditions.
The script also identifies potential reversal conditions when price extends beyond the channel and then shows signs of rejection. In addition, it can project the current regression slope forward to provide a simple forecast of the current trend path.
🟠 CONCEPTS
Regression Line — A statistical estimate of the underlying price trend. Different methods can be selected, ranging from straight-line regressions to adaptive smoothing techniques.
Theil-Sen Regression — A robust regression method that uses median slopes from all point pairs, reducing the influence of outlier price spikes.
LOESS Regression — A locally weighted regression that fits nearby observations more heavily than distant observations to create a smooth trend curve.
Nadaraya-Watson Smoothing — A kernel-weighted averaging method that estimates trend by assigning larger weights to recent observations.
Kalman Filter — A recursive estimation method that continuously updates the trend estimate as new prices arrive.
Polynomial Regression — A curved regression model that can capture non-linear trend structures using higher-order polynomial functions.
Regression Deviation Bands — Channel boundaries calculated from the standard deviation of price relative to the regression line.
Regression Slope — The rate of change of the regression estimate used to determine trend direction and forecast projections.
🟠 FEATURES
Regression Channel — Dynamic bands expand and contract based on how far price deviates from the regression line.
Trend Flip Signals — Generates directional markers when the regression trend changes from rising to falling or from falling to rising.
Reversal Signals — Marks potential bullish and bearish reversals when price extends beyond the channel and begins rejecting those extremes.
Forecast Projection — Extends the current regression slope into future bars and optionally displays projected channel boundaries.
🟠 HOW TO USE
Select a regression method that matches the market behavior you want to analyze. Linear and Theil-Sen are suited to directional trends, while LOESS, Nadaraya-Watson, and Kalman provide smoother adaptive estimates.
Use the regression line as the primary trend reference. Rising regression values indicate strengthening conditions, while falling values indicate weakening conditions.
Monitor the channel boundaries for extended price movement away from the regression estimate.
Watch for bullish reversal markers below the lower band after downside extensions and bearish reversal markers above the upper band after upside extensions.
Use trend flip signals as confirmation that the regression slope has changed direction.
Compare price location within the channel to gauge whether price is trading near trend equilibrium or at an extreme deviation.
Use the forecast projection as a continuation estimate of the current regression slope rather than a prediction of future market behavior.
🟠 CONCLUSION
Regression Reversal Signals combines multiple regression techniques, adaptive deviation channels, reversal detection, and forward projections into a single framework. By allowing traders to switch between several trend estimation methods, it provides different perspectives on trend structure and price deviation. The indicator helps identify trend direction, potential reversals, and areas where price has moved unusually far from its estimated path. Indicator
Nadaraya-Watson Regression Liquidity Sweeps [AlgoAlpha]🟠 OVERVIEW
This script combines Nadaraya-Watson regression, momentum analysis, and liquidity level tracking into a single workflow. It measures the slope of a smoothed price regression curve, converts that slope into a normalized oscillator, and uses momentum shifts to identify areas where liquidity may be resting.
The oscillator is built from the rate of change of the Nadaraya-Watson estimate rather than price itself. This allows momentum transitions to be measured relative to the underlying regression trend. When momentum weakens after an extended move, the script records swing-based liquidity levels that can later be swept by price.
A volatility-adjusted Nadaraya-Watson band is also displayed on the chart. This provides context for trend direction, momentum strength, and potential rebound conditions around the regression value.
🟠 CONCEPTS
Nadaraya-Watson Regression — A kernel-based smoothing method that estimates an underlying price curve by weighting nearby historical data more heavily than distant data.
Normalized Regression Slope — The change in the Nadaraya-Watson estimate divided by its recent standard deviation, allowing momentum strength to be compared across different market conditions.
Liquidity Sweep Level — A horizontal level created from a swing high or swing low when momentum begins to weaken, representing an area that may later attract price.
Oscillator Signal Line — An EMA of the normalized oscillator used to identify momentum crossovers and momentum phase changes.
Rebound Condition — A signal generated when price moves back through the Nadaraya-Watson value while oscillator direction remains aligned with the prevailing momentum bias.
🟠 FEATURES
Normalized Nadaraya-Watson Oscillator — Measures momentum using the slope of a smoothed regression curve.
Liquidity Sweep Detection — Creates liquidity levels when bullish or bearish momentum begins to weaken.
Volatility-Adjusted Regression Band — Displays a dynamic overlay around the Nadaraya-Watson estimate using smoothed ATR values.
Momentum Weakening Signals — Marks locations where oscillator momentum begins to lose strength against the current directional bias.
Rebound Signals — Highlights situations where price reclaims or loses the regression value while momentum remains aligned with trend direction.
🟠 HOW TO USE
Monitor the oscillator relative to its signal line to identify momentum shifts and changes in directional bias.
Watch for newly created liquidity levels after momentum weakening events, as these levels may become future sweep targets.
Use sweeps of upper or lower liquidity levels to identify areas where price has taken resting liquidity.
Look for bullish rebound signals when price reclaims the regression value while bullish momentum remains active.
Look for bearish rebound signals when price loses the regression value while bearish momentum remains active.
Combine oscillator direction, liquidity levels, and regression band structure to build context around trend continuation or reversal scenarios.
🟠 CONCLUSION
The Nadaraya-Watson Regression Liquidity Sweeps indicator combines regression-based momentum analysis, volatility-adjusted trend structure, and liquidity level tracking. By linking momentum transitions to swing-derived liquidity zones, it helps identify where liquidity may be forming and when it has been swept. This provides traders with additional context for trend analysis, pullbacks, and potential reversal areas. Indicator
Strategy Sensitivity MatrixThe Strategy Sensitivity Matrix is an institutional-grade backtesting tool designed to evaluate the robustness and parameter sensitivity of trend-following strategies. It enables users to compare the historical performance of a broad range of parameter combinations across multiple metrics to assess the overall stability of the selected strategy. The model displays the complete backtest landscape in a structured, color-coded matrix that allows investors to quickly identify robust parameter regions and evaluate historical performance stability across parameter combinations.
At its core, the matrix systematically evaluates a wide range of parameter combinations, where every individual cell represents the backtest result for one unique parameter configuration. Users can switch between volatility-based strategies and moving-average strategies. In volatility mode, the matrix rows represent volatility lengths and the matrix columns represent volatility factors. In crossover mode, the rows represent fast moving-average lengths and the columns represent slow moving-average lengths. Supported volatility types include the Average True Range (ATR), Standard Deviation (SD), and Mean Absolute Deviation (MAD). Supported moving-average types include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), and Weighted Moving Average (WMA). Supported display metrics include:
CAGR = Compounded Annual Growth Rate.
Sharpe = CAGR per unit of standard deviation.
Sortino = CAGR per unit of downside deviation.
Martin = CAGR relative to the Ulcer Index (UI).
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Alpha (α) = Excess annualized risk-adjusted returns.
Expectancy = Average expected return per trade.
Profit Factor = Total gross profit per unit of losses.
Win Rate = Ratio of profitable trades to total trades.
Trades/Year = Average number of trades per year.
The matrix follows an intuitive percentile-based coloring framework that dynamically compares the relative performance and stability of all parameter combinations. Stronger values above or equal to the matrix median are highlighted in green, with bright green representing the top 10% of all parameter combinations. Weaker values below the matrix median are highlighted in orange, while red represents objectively weak performance based on the selected metric. Broad clusters of consistently strong results generally suggest lower parameter sensitivity and potentially greater robustness, while isolated peaks generally suggest elevated parameter sensitivity.
The summary table displayed above the matrix provides a broader distribution-level statistical overview of results across all parameter combinations. This structure allows investors to evaluate whether strong historical performance appears statistically widespread or narrowly concentrated across the parameter landscape. Stable parameter landscapes generally exhibit lower standard deviation, similar median and average values, and smaller performance gaps between the best and top 10% parameter combinations. The summary table includes the following sections:
Start = Start month and year of the selected backtest period.
End = End month and year of the selected backtest period.
Metric = Performance metric currently displayed in the matrix.
B&H = Buy-and-hold performance for the selected metric.
Best = Best-performing parameter combination in the matrix.
Top 10% = Average value of the top 10% parameter combinations.
Median = Median value across all parameter combinations.
Average = Average value across all parameter combinations.
Std Dev = Standard deviation of all parameter combinations.
≥ B&H = Percentage of combinations equal or better than B&H.
In summary, the Strategy Sensitivity Matrix is a powerful robustness analysis tool designed to help investors make data-driven decisions when evaluating parameter combinations across trend-following strategies. By evaluating the full parameter landscape, investors can quickly determine whether strong historical performance appears broadly distributed across stable parameter regions or narrowly concentrated within isolated parameter combinations. While historical robustness can provide valuable insight into past market behavior over the selected backtest period, users should remain mindful that market structures evolve over time and that historically stable parameter regions may not necessarily persist in future market conditions. Indicator
Indicator
SuperTrend Take-Profit Dimensions [AlgoAlpha]🟠 OVERVIEW
A multi-dimensional take-profit aid that scores how typical the current bar looks compared to past SuperTrend pivots, so you can tell when a trend has reached favorable exit conditions.
The indicator runs a standard SuperTrend and records every confirmed zigzag pivot that occurs during a matching-direction run. Tops go into a bull pool , bottoms into a bear pool . Each pivot is stored as a set of readings across several independent axes, such as relative volume , time of day , and price position inside the recent range .
On every bar, the current reading on each axis is compared to that historical pool. A blended score from 0 to 100 tells you how closely the current conditions resemble where past pivots in the same direction have clustered. The idea is to give trend followers a data-backed sense of when to start tightening up, rather than guessing an exit or using a fixed R-multiple.
The three built-in axes were chosen deliberately to be as uncorrelated as possible , each describing a different dimension of market context: volume (relative volume percentile), time (time of day), and price (position in recent range). Correlated inputs would double-count the same information and distort the blended score; picking axes that describe genuinely different aspects of the market means each one contributes independent evidence, and the score reflects how many distinct dimensions are currently in agreement.
🟠 CONCEPTS
SuperTrend — An ATR-based trailing stop that flips between bullish and bearish states. Controls which pool of historical pivots the script reads from.
Pivot pool — A rolling store of confirmed zigzag pivots, split by direction. Bull pool holds pivot highs that printed during bullish SuperTrend runs; bear pool holds pivot lows from bearish runs. Capped at 2000 entries per side .
Context axis — A 0–100 value measured at the pivot bar. The script ships with three built-ins ( relative volume percentile , time of day , position in recent range ) and one optional user-plugged signal.
Axis independence — The three built-in axes cover volume , time , and price respectively, chosen so each describes a structurally different part of the market. Low correlation between axes keeps the blended score from being dominated by any single factor.
Conditional histogram — For each active axis, the script walks its pool and keeps only pivots whose bins on every other active axis match the current bar. The survivors are binned to form a histogram.
Axis score — For one axis, the count of pivots in the current bar's bin divided by the count in the histogram's tallest bin, scaled to 0–100 . 100 means the current context sits in the densest part of past pivots.
Blended favourability score — Arithmetic mean of the active per-axis scores. This is what the gauge and table display.
Density-match scoring — The score measures how common the current context is among past pivots. It is not a forward probability and makes no claim about what happens next.
🟠 FEATURES
Right-side context profiles — Stacked mini histograms render to the right of price, one per active axis.
• Bar heights show how pivots in each axis's conditional pool distribute across bins.
• A dashed vertical line marks the current bar's bin on that axis, so you can see at a glance where today sits against history.
• Bar hue tracks the active SuperTrend direction.
Favourability gauge — A vertical gradient table in the bottom-right showing the blended score, with a chevron marking the current level. Green at the top, red at the bottom.
Favourability breakdown table — A two-column readout of each active axis's individual score out of 100, plus a final row that classifies the blended score as Good , Neutral , or Bad . Position and text size are configurable.
Bar coloring — Bars fade from neutral grey toward the opposing trend colour as the blended score rises toward 100, so the chart itself signals when the context is stretched.
Take-profit markers — Small orange markers print above or below the bar when the blended score hits 100 for the active SuperTrend direction.
Timeframe guard — The time-of-day axis disables automatically on daily and higher timeframes, where the reading has no meaning, and a banner explains this so the blended score stays honest.
Multi-dimensional scoring engine — Four independent axes feed into a single score, each conditioned on all the others.
• Three built-in axes can be toggled on or off individually.
• A fourth axis accepts any plot via source input , provided the series stays within 0–100 on all loaded bars.
• An on-chart warning prints if the custom signal leaves that range, and the axis is ignored until it is corrected.
Deliberately uncorrelated built-in axes — Volume ( relative volume percentile ), time ( time of day ), and price ( position in recent range ) cover three structurally different facets of market context. Keeping the axes independent means each one adds new information to the blend rather than reinforcing the others.
Alert conditions — Six alerts are included: SuperTrend bullish flip, SuperTrend bearish flip, score peak match, and crossovers into the Good , Neutral , and Bad bands.
🟠 HOW TO USE
Add the script to an intraday chart on a liquid instrument and let it run long enough to populate the pools. More history means more stable conditional histograms.
Let SuperTrend define the active regime. The script only scores in the direction of the current trend; bar coloring and take-profit markers respect that regime.
Read the gauge and breakdown table together. The gauge shows the blended level; the table shows which individual axes are pulling it up or down.
Use the right-side profiles as a sanity check. If the dashed current-bin marker is sitting on or near the tallest bar across most axes, the current context closely resembles past pivot contexts in that direction.
Treat high scores as a cue to tighten management, not as reversal signals. A reading of 100 means conditions match where pivots have historically clustered, not that the trend is guaranteed to end.
Adjust the zigzag pivot length to control how strict the pool is. Lower values admit more pivots ( bigger, noisier sample ); higher values keep only firmer pivots ( smaller, cleaner sample ).
Plug your own signal into the custom axis to test whether an existing 0–100 oscillator adds useful conditioning, such as an RSI or a normalised momentum reading. For best results, pick a signal that is not strongly correlated with the three built-ins, so the custom axis adds a new dimension rather than re-stating an existing one.
Enable only the alerts that fit your workflow. The band-crossover alerts fire once per transition , not on every bar inside a band.
🟠 LIMITATIONS
The pool holds every confirmed pivot during a matching-direction run, not only pivots that ended the trend. Intermediate pullbacks sit alongside genuine terminal pivots. Raising the zigzag pivot length filters the pool further if you want a cleaner sample.
On strongly trending symbols the pool is dominated by pullback pivots rather than true terminal exits, because strong trends have many small pullbacks and only one final top or bottom. On choppy symbols the ratio is more balanced. Read the score with this in mind.
The blended score is a density-match measure, not a forward probability . A high reading means today's context is common among past pivots of this direction. It does not predict that the trend is about to end.
The time-of-day axis has no meaning on daily and higher timeframes and is disabled automatically on those timeframes. A warning banner confirms when this is active.
The custom axis requires a source already scaled to 0–100 on every loaded bar. Values outside that range disable the axis and surface a warning. Toggling the custom axis on a live chart starts the range check from the current bar; reload the chart to validate against full loaded history .
Pools are capped at 2000 entries per direction , with the oldest entries dropped first. On very long intraday histories the effective lookback is symbol- and timeframe-dependent.
All scoring uses data up to and including the confirmation bar of each pivot; pivots themselves are detected with the standard zigzag confirmation lag, meaning the scoring population on any given bar reflects pivots confirmed at least zzLen bars earlier.
🟠 CONCLUSION
SuperTrend Take-Profit Dimensions combines a standard SuperTrend with a rolling pool of historical pivot contexts and scores the current bar against that pool across up to four independent axes spanning volume, time, and price. The output is a blended 0–100 favourability reading , a per-axis breakdown, and a set of context profiles that show where past pivots have clustered. It gives trend followers a structured, data-backed way to judge when the current context matches where trends have historically given back profit, without pretending to predict the next bar. Indicator
Dynamic Median Momentum Oscillator [AlgoAlpha]🟠 OVERVIEW
This script provides a momentum oscillator that uses a median-based approach rather than traditional averages to find the center of price action. By calculating the distance between the current price and a rolling median (HLC3), it identifies how far the market has stretched from its historical equilibrium. The indicator is designed to filter out the noise typical of standard momentum tools, using a standardized range calculation to provide fixed overbought and oversold zones. It helps traders identify trend strength, potential exhaustion, and mean reversion opportunities across different market conditions.
🟠 CONCEPTS
The core of this tool is the Dynamic Median basis, which uses a rolling median of the HLC3 price to establish a "fair value" line. Unlike a simple moving average, the median is less sensitive to extreme price spikes, making the resulting oscillator more robust against outliers. To ensure the oscillator remains readable across different assets, the raw difference between price and median is standardized by the average candle range (EMA of High-Low). This normalization allows for the use of fixed thresholds (e.g., +/- 200, 250, 300) regardless of the asset's price. The median sets the context for the baseline, while the smoothed MCD and its signal line provide the timing for entries and exits.
🟠 FEATURES
Standardization feature to enable fixed overbought/oversold levels across any asset
Multi-component display: Fast (histogram), Slow (lines), and Super Slow (filled zones)
Reversion markers (triangles) indicating price returning from extreme levels
🟠 USAGE
Setup : Add the script to your chart and choose your preferred Display Mode. Use "All" to see the full picture or "Slow" for a cleaner view of trend direction. Ensure "Standardize" is checked if you want to use the built-in overbought/oversold bands effectively.
Read the chart : Look for the Smooth MCD (white line) crossing the Signal (orange line) for momentum shifts. Values above 0 indicate bullish momentum, while values below 0 indicate bearish momentum. Triangles appear at the top or bottom of the oscillator when price reaches extreme levels (300/-300) and begins to revert to the mean.
Settings that matter : The Basis Length determines how much historical data defines the "center" of the market; longer lengths are better for higher timeframes. Smoothing Length controls the reactivity of the main white line—increase this if you find the oscillator is giving too many false signals in choppy markets.
Indicator
Money Flow Divergence Zones [AlgoAlpha]🟠 OVERVIEW
This script identifies key price levels where volume and momentum show significant disagreement, visualizing these areas as Money Flow Liquidity Zones. By tracking the Money Flow Index (MFI) and its relationship to price action, the tool detects regular divergences that often precede market reversals. When a divergence is confirmed, the script projects a horizontal zone onto the chart based on the recent price extreme. These zones act as "liquidity pockets" that remain active until price successfully mitigates them through a wick or body cross, providing a clear map of potential support and resistance derived from volume-weighted momentum.
🟠 CONCEPTS
The core logic relies on the interaction between a smoothed MFI and pivot-based price extremes. While price might make a lower low, the MFI—calculated from $hlc3$ and volume—might make a higher low, signaling that selling pressure is exhausting despite the price drop. This script uses these divergences to set the initial context for a zone. The zones are then refined using a "Body vs Wick" logic, where the zone's depth is defined by the distance between the absolute high/low and the candle body. The MFI smoothing via a Hull Moving Average (HMA) ensures that the momentum signals are responsive yet filtered for high-frequency noise.
🟠 FEATURES
Dynamic Liquidity Zones : Automatically draws and extends support/resistance zones based on MFI divergence.
Divergence Engine : Detects regular bullish and bearish divergences with adjustable pivot lookbacks.
Visual Momentum Oscillator : Features a color-coded, smoothed MFI with a gradient midline to show volume strength.
Touch Signals : Small triangles appear when price interacts with an active zone while momentum aligns.
🟠 USAGE
Setup : Add the script to your chart. It is effective on most timeframes, but for intraday trading, the default 14-period MFI works well on 5m to 15m charts. Adjust the "Pivot Lookback" to fine-tune how sensitive the divergence detection is to local peaks.
Read the chart : Green zones represent bullish liquidity (potential support), while red zones represent bearish liquidity (potential resistance). Look for "▲" or "▼" symbols on the bars; these indicate price is currently touching a zone and might be ready for a reversal. The oscillator at the bottom confirms the trend: green for rising money flow and red for falling.
Settings that matter : The Sweep Type is critical—switching to "wick" will make zones disappear more easily (conservative), while "body" keeps zones active until a candle closes through them (aggressive). The Max Zone Age prevents old, irrelevant levels from cluttering your chart by removing them after a set number of bars.
Indicator
HTF Volume Liquidity Profile [AlgoAlpha]🟠 OVERVIEW
This tool projects a volume profile from a higher timeframe directly onto your current chart. By breaking down historical price action into vertical intervals, it creates a heatmap of where volume was concentrated during that larger period. It maps out areas of high liquidity, showing exactly where buyers and sellers were most active, and displays a detailed breakdown of bullish versus bearish volume at specific price levels within that higher timeframe block.
🟠 CONCEPTS
This script takes that concept and applies it to a higher timeframe, meaning you can look at a 5-minute chart and see the volume distribution for the entire day overlaid as a single block. It calculates the highest and lowest prices of the chosen timeframe and divides that range into smaller segments based on your resolution setting. It then tallies the volume of every lower timeframe candle that falls into each segment. It also splits this volume into positive and negative flows based on whether the individual candles closed green or red. This gives you a clear picture of net directional pressure at each price level, acting like a lightweight footprint chart.
🟠 FEATURES
Higher timeframe volume heatmap overlaid directly on the current lower timeframe chart.
Inner mini-boxes showing the exact proportion of bullish (green) vs bearish (red) volume at each specific price level.
Summary label displaying total up volume, down volume, volume delta, and overall sentiment for the entire interval.
🟠 USAGE
Setup : Add the script to your chart. Choose a higher timeframe that makes sense for your trading style (for example, use 1D if you are trading on a 5-minute or 15-minute chart). Adjust the resolution depending on how granular you want the price levels to be.
Read the chart : Darker, more opaque background boxes mean a high amount of total volume was traded at that price level (High Volume Nodes). The inner mini-boxes show the tug-of-war at that level; a longer green inner box means buyers dominated that specific price, while a longer red one means sellers dominated. The label at the bottom of each block summarizes the total volume delta so you instantly know who won the overall period.
Settings that matter : The Higher TimeFrame input dictates the width and duration of each volume profile block. The Profile Resolution input controls how many vertical slices the price range is divided into; increasing this adds finer detail but can visually clutter the chart if set too high.
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Breakout Targets [AlgoAlpha]🟠 OVERVIEW
This script identifies consolidation zones and provides automated breakout targets with risk management levels. It focuses on finding periods where price action compresses and then tracks the subsequent breakout from these ranges. When a price breakout is confirmed, the script automatically projects three take-profit (TP) levels and a stop-loss (SL) based on current market volatility. This helps traders move from identifying a range to executing a trade with predefined exit points without manual calculation.
🟠 CONCEPTS
The script uses a relationship between Weighted Moving Averages (WMA) and Exponential Moving Averages (EMA) of price ranges to detect consolidation. When these moving averages cross, it triggers the detection of recent pivot highs and lows to draw a visual "box" or channel. This channel represents the current trading range. Once price closes outside this box, the script uses the Average True Range (ATR) to determine the volatility-adjusted distance for the stop loss. The take-profit levels are then calculated as multiples of this risk distance, ensuring a consistent reward-to-risk approach.
🟠 FEATURES
Dynamic box drawing that highlights potential supply and demand zones within the range.
Real-time breakout signals with bullish (green) and bearish (red) markers.
Automated trade projection including Entry, SL, and three TP levels.
Integrated alert system for breakouts and hits on any profit or loss target.
🟠 USAGE
Setup : Add the script to your chart and adjust the "Range Detection Period." A higher period will find larger, more significant ranges, while a lower period will find smaller, short-term consolidation zones.
Read the chart : Look for the grey boxes on your chart; these represent areas where the market is "coiling." A green arrow label indicates a bullish breakout from the top of the box, while a red arrow indicates a bearish breakout from the bottom. Once a breakout occurs, follow the projected horizontal levels for your trade management.
Settings that matter : The Stop Loss ATR Multiplier is the most critical setting for risk; increasing it will give the trade more room to breathe but will also push your TP levels further away. The Prevent Overlap toggle is useful for keeping the chart clean by ensuring the script doesn't draw new boxes until the current range has been resolved.
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Swing Failure Signals [AlgoAlpha]🟠 OVERVIEW
This script detects swing failure patterns by tracking how price interacts with recent swing highs and lows, then confirming those sweeps with a change in candle behavior. The goal is to highlight areas where price briefly breaks a key level, fails to continue, and then shifts direction. These events often occur around liquidity runs, where stops are triggered before price reverses. The script draws levels, colors bars, and prints clear markers to help visualize where these failures occur and when they are confirmed.
🟠 CONCEPTS
The logic starts with pivot-based swing detection. Recent swing highs and lows are stored and monitored. When price trades beyond one of these levels within a defined historical window, it is treated as a sweep. A sweep alone is not enough. The script then waits for a Change in State of Delivery (CISD), which is defined by a shift in candle structure that shows follow-through in the opposite direction. A tolerance filter measures how far price traveled beyond the level relative to the reaction that followed. If the reaction is strong enough and happens within a limited number of bars, the sweep is validated as a swing failure. In short: the swing defines the reference, the sweep shows intent, and the CISD confirms acceptance or rejection.
🟠 FEATURES
Sweep detection with a maximum lookback to avoid outdated levels
CISD confirmation using candle structure and price expansion
Alert conditions for bullish and bearish swing failures
🟠 USAGE
Setup : Add the script to your chart. It works on any market and timeframe. Lower timeframes highlight intraday liquidity runs, while higher timeframes show structural failures. Start with the default inputs before adjusting.
Read the chart : A bullish swing failure occurs when price sweeps a prior low, then reverses and confirms with a bullish CISD. A bearish swing failure is the opposite, sweeping a prior high and confirming with a bearish CISD. Dashed lines mark the swept swing. Solid lines mark the CISD level. Bars are colored while the SFP state is active.
Settings that matter : Increasing Pivot Detection Length finds more significant swings but fewer signals. Reducing Max Pivot Point Edge limits how far back sweeps are allowed, keeping signals more current. The Patience setting controls how many bars are allowed for confirmation after a sweep. The Trend Noise Filter raises or lowers how strong the reaction must be to qualify as a valid failure.
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Orderblock Footprints [AlgoAlpha]🟠 OVERVIEW
This script highlights orderblocks and then drills into what actually trades inside them. Zones are created only after an abnormal directional impulse, measured with a z-score on consecutive candle bodies, so the orderblocks are tied to real expansion rather than simple pivots. Once a zone exists, the script overlays lower-timeframe volume footprints inside the candle when price trades back into that zone. The goal is to show not just where an orderblock sits, but whether price is being accepted or absorbed when it is revisited.
🟠 CONCEPTS
Orderblocks are detected after extreme bullish or bearish impulses. The script tracks consecutive body movement up or down, normalizes that distance with a rolling z-score, and only triggers when the move is statistically large. The last opposite candle before that impulse defines the orderblock range. These zones then extend forward until they are either mitigated by price closing through them or they expire by age.
Inside an active zone, the script switches to a lower timeframe and builds a footprint-style profile for each bar. Each candle is split into price rows, counting time-at-price and volume delta. Positive and negative delta are colored separately. Absorption is flagged when opposing delta prints appear in the wick that rejects the zone. In practice: the impulse defines context ; the footprint shows interaction .
🟠 FEATURES
Separate bullish and bearish zones with automatic extension
Volume split inside each zone candle (up vs down volume)
Lower-timeframe footprint with TPO-style rows and delta gradient
Absorption detection using opposing delta in rejection wicks
Alerts for zone creation and absorption events
🟠 USAGE
Setup : Add the script to your chart. It works on any market and timeframe. The lower timeframe for footprints is fixed at 5 minutes, so higher chart timeframes show clearer structure. Use the Z-Score Window to control how strict impulse detection is and Max Box Age to limit how long old zones stay on the chart.
Read the chart : Bullish orderblocks are created after strong upward impulses and are invalidated when price closes below them. Bearish orderblocks are created after strong downward impulses and are invalidated when price closes above them. When price trades inside a zone, footprint rows appear. Green-tinted rows show positive delta; red-tinted rows show negative delta. Absorption labels appear when opposing delta prints into a rejecting wick.
Settings that matter : Increasing the Z-Score Window makes orderblocks rarer but more significant. Disabling Prevent Overlap allows stacked zones if you want to study clustering. Adjusting Rows per bar changes footprint resolution—lower values are cleaner, higher values show more detail but use more objects.
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Change in State of Delivery CISD [AlgoAlpha]🟠 OVERVIEW
This script tracks how price “changes delivery” after failed attempts to push in one direction. It builds swing levels from pivots, watches for those levels to be wicked, and then checks if price delivers cleanly in the opposite direction. When the pattern meets the script’s tolerance rules, it marks a Change in State of Delivery (CISD). These CISD levels are drawn as origin lines and are used to spot shifts in intent, failed pushes, and continuation attempts. A CISD becomes stronger when it forms after opposing liquidity is swept within a defined lookback.
🟠 CONCEPTS
The script first defines structure using swing highs/lows. These levels act as potential liquidity points. When price wicks through a swing, the script registers a mitigation event. After this, it looks for a reversal-style candle sequence: a failed push, followed by a counter-move strong enough to pass a tolerance ratio. This ratio compares how far price expanded away from the failed attempt versus the counter-move that followed. If the ratio is high enough, this becomes a CISD. The idea is simple: liquidity interaction sets context , and the tolerance logic identifies actual intent . CISD levels and sweep markers combine these two ideas into a clean map of where delivery flipped.
🟠 FEATURES
Liquidity tracking: marks swing highs/lows and updates them until expiry
Liquidity sweep confirmation when CISD aligns with recent mitigations
Alert conditions for all key events: mitigations, CISDs, and strong CISDs
🟠 USAGE
Setup : Add the script to your chart. Use it on any timeframe where swing behavior matters. Set the Swing Period for how wide a pivot must be. Set Noise Filter to control how strict the CISD detection is. Liquidity Lookback defines how recent a wick must be to confirm a sweep.
Read the chart : Origin lines mark where the CISD began. A green line signals bullish intent; a red line signals bearish intent. ▲ and ▼ shapes show CISDs that form after liquidity is swept, these mark strong signals for potential entry. Swing dots show recent swing highs/lows. Candle colors follow the latest CISD trend.
Settings that matter : Increasing Swing Period produces fewer but stronger swings. Raising Noise Filter requires cleaner counter-moves and reduces false CISDs. Liquidity Lookback controls how strict the sweep confirmation is. Expiry Bars decides how long swing levels remain active.
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Volume Sentiment Breakout Channels [AlgoAlpha]🟠 OVERVIEW
This tool visualizes breakout zones based on volume sentiment within dynamic price channels . It identifies high-impact consolidation areas, quantifies buy/sell dominance inside those zones, and then displays real-time shifts in sentiment strength. When the market breaks above or below these sentiment-weighted channels, traders can interpret the event as a change in conviction, not just a technical breakout.
🟠 CONCEPTS
The script builds on two layers of logic:
Channel Detection : A volatility-based algorithm locates price compression areas using normalized highs and lows over a defined lookback. These “boxes” mark accumulation or distribution ranges.
Volume Sentiment Profiling : Each channel is internally divided into small bins, where volume is aggregated and signed by candle direction. This produces a granular sentiment map showing which levels are dominated by buyers or sellers.
When a breakout occurs, the script clears the previous box and forms a new one, letting traders visually track transitions between phases of control. The colored gradients and text updates continuously reflect the internal bias—green for net-buying, red for net-selling—so you can see conviction strength at a glance.
🟠 FEATURES
Volume-weighted sentiment map inside each box, with gradient color intensity proportional to participation.
Dynamic text display of current and overall sentiment within each channel.
Real-time trail lines to show active bullish/bearish trend extensions after breakout.
🟠 USAGE
Setup : Add the script to your chart and enable Strong Closes Only if you prefer cleaner breakouts. Use shorter normalization length (e.g., 50–80) for fast markets; longer (100–200) for smoother transitions.
Read Signals : Transparent boxes mark active sentiment channels. Green gradients show buy-side dominance, red shows sell-side. The middle dashed line is the equilibrium of the channel. “▲” appears when price breaks upward, “▼” when it breaks downward.
Understanding Sentiment : The sentiment profile can be used to show the probability of the price moving up or down at respective price levels.
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Reverse RSI Signals [AlgoAlpha]🟠 OVERVIEW
This script introduces the Reverse RSI Signals system, an original approach that inverts traditional RSI values back into price levels and then overlays them directly on the chart as dynamic bands. Instead of showing RSI in a subwindow, the script calculates the exact price thresholds that correspond to common RSI levels (30/70/50) and displays them as upper, lower, and midline bands. These are further enhanced with an adaptive Supertrend filter and divergence detection, allowing traders to see overbought/oversold zones translated into actionable price ranges and trend signals. The script combines concepts of RSI inversion, volatility envelopes, and divergence tracking to provide a context-driven tool for spotting reversals and regime shifts.
🟠 CONCEPTS
The script relies on inverting RSI math: by solving for the price that would yield a given RSI level, it generates real chart levels tied to oscillator conditions. These RSI-derived price bands act like support/resistance, adapting each bar as RSI changes. On top of this, a Supertrend built around the RSI midline introduces directional bias, switching regimes when the midline is breached. Regular bullish and bearish divergences are detected by comparing RSI pivots against price pivots, highlighting early reversal conditions. This layered approach means the indicator is not just RSI on price but a hybrid of oscillator translation, volatility-tracking midline envelopes, and divergence analysis.
🟠 FEATURES
Inverted RSI bands: upper (70), lower (30), and midline (50), smoothed with EMA for noise reduction.
Supertrend overlay on the RSI midline to confirm regime direction (bullish or bearish).
Gradient-filled zones between outer and inner RSI bands to visualize proximity and exhaustion.
Non-repainting bullish and bearish divergence markers plotted directly on chart highs/lows.
🟠 USAGE
Apply the indicator to any chart and use the plotted RSI price bands as adaptive support/resistance. The midline defines equilibrium, while upper and lower bands represent classic RSI thresholds translated into real price action. In bullish regimes (green candles), long trades are stronger when price approaches or bounces from the lower band; in bearish regimes (red candles), shorts are favored near the upper band. Divergence markers (▲ for bullish, ▼ for bearish) flag potential reversal points early. Traders can combine the band proximity, divergence alerts, and Supertrend context to time entries, exits, or to refine ongoing trend trades. Adjust smoothing and Supertrend ATR settings to match the volatility of the instrument being analyzed.
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