Heiken Ashi zero lag EMA v1.1 by JustUncleLI originally wrote this script earlier this year for my own use. This released version is an updated version of my original idea based on more recent script ideas. As always with my Alert scripts please do not trade the CALL/PUT indicators blindly, always analyse each position carefully. Always test indicator in DEMO mode first to see if it profitable for your trading style.
DESCRIPTION:
This Alert indicator utilizes the Heiken Ashi with non lag EMA was a scalping and intraday trading system
that has been adapted also for trading with binary options high/low. There is also included
filtering on MACD direction and trend direction as indicated by two MA: smoothed MA(11) and EMA(89).
The the Heiken Ashi candles are great as price action trending indicator, they shows smooth strong
and clear price fluctuations.
Financial Markets: any.
Optimsed settings for 1 min, 5 min and 15 min Time Frame;
Expiry time for Binary options High/Low 3-6 candles.
Indicators used in calculations:
- Exponential moving average, period 89
- Smoothed moving average, period 11
- Non lag EMA, period 20
- MACD 2 colour (13,26,9)
Generate Alerts use the following Trading Rules
Heiken Ashi with non lag dot
Trade only in direction of the trend.
UP trend moving average 11 period is above Exponential moving average 89 period,
Doun trend moving average 11 period is below Exponential moving average 89 period,
CALL Arrow appears when:
Trend UP SMA11>EMA89 (optionally disabled),
Non lag MA blue dot and blue background.
Heike ashi green color.
MACD 2 Colour histogram green bars (optional disabled).
PUT Arrow appears when:
Trend UP SMA11
Cari dalam skrip untuk "binary"
Bollinger Bands NEW
var tradingview_embed_options = {};
tradingview_embed_options.width = 640;
tradingview_embed_options.height = 400;
tradingview_embed_options.chart = 's48QJlfi';
new TradingView.chart(tradingview_embed_options);
Vdub Binary Options SniperVX v1 by vdubus on TradingView.com
MLActivationFunctionsLibrary "MLActivationFunctions"
Activation functions for Neural networks.
binary_step(value) Basic threshold output classifier to activate/deactivate neuron.
Parameters:
value : float, value to process.
Returns: float
linear(value) Input is the same as output.
Parameters:
value : float, value to process.
Returns: float
sigmoid(value) Sigmoid or logistic function.
Parameters:
value : float, value to process.
Returns: float
sigmoid_derivative(value) Derivative of sigmoid function.
Parameters:
value : float, value to process.
Returns: float
tanh(value) Hyperbolic tangent function.
Parameters:
value : float, value to process.
Returns: float
tanh_derivative(value) Hyperbolic tangent function derivative.
Parameters:
value : float, value to process.
Returns: float
relu(value) Rectified linear unit (RELU) function.
Parameters:
value : float, value to process.
Returns: float
relu_derivative(value) RELU function derivative.
Parameters:
value : float, value to process.
Returns: float
leaky_relu(value) Leaky RELU function.
Parameters:
value : float, value to process.
Returns: float
leaky_relu_derivative(value) Leaky RELU function derivative.
Parameters:
value : float, value to process.
Returns: float
relu6(value) RELU-6 function.
Parameters:
value : float, value to process.
Returns: float
softmax(value) Softmax function.
Parameters:
value : float array, values to process.
Returns: float
softplus(value) Softplus function.
Parameters:
value : float, value to process.
Returns: float
softsign(value) Softsign function.
Parameters:
value : float, value to process.
Returns: float
elu(value, alpha) Exponential Linear Unit (ELU) function.
Parameters:
value : float, value to process.
alpha : float, default=1.0, predefined constant, controls the value to which an ELU saturates for negative net inputs. .
Returns: float
selu(value, alpha, scale) Scaled Exponential Linear Unit (SELU) function.
Parameters:
value : float, value to process.
alpha : float, default=1.67326324, predefined constant, controls the value to which an SELU saturates for negative net inputs. .
scale : float, default=1.05070098, predefined constant.
Returns: float
exponential(value) Pointer to math.exp() function.
Parameters:
value : float, value to process.
Returns: float
function(name, value, alpha, scale) Activation function.
Parameters:
name : string, name of activation function.
value : float, value to process.
alpha : float, default=na, if required.
scale : float, default=na, if required.
Returns: float
derivative(name, value, alpha, scale) Derivative Activation function.
Parameters:
name : string, name of activation function.
value : float, value to process.
alpha : float, default=na, if required.
scale : float, default=na, if required.
Returns: float
Whole NumbersThis is a simple indicator for the whole numbers.
It breaks down every pair for 10 pips.
Its also simple and nice to use
Stochastic with Outlier Labels/MTFTL;DR This indicator is an update to a simple stochastic ('Stoch_MTF' by binarytrader666) that provides a novel outlier highlighting feature
Improvements on stochastic:
1. Novel outlier highlighting that points out crosses that are the Nth consecutive cross or greater.
2. Allowing for multiple timeframes to be shown on the same chart
3. Highlighting/Labelling crosses and providing labels for alerts
A cross of the stochastics in the high or low zones establishes a trend. Successive crosses in the same region seem to indicate a continuation of that trend. The outlier functionality here provides a signal for when X number of crosses have been in the same trend, signaling further strength of that signal.
I also provided the necessary code for converting this to a strategy if you so wish at the bottom.
Linear Regression Trend Channel with Entries & AlertsPlease Use this Indicator If you understand the risk posed by linear regression trend channel
Features
Provides trend channel (best value for period is 40 on 5 minute timeframe
Provides BUY/SELL entries based on current channel
Provides custom color for channel
Best used with MattyPips strategy indicators
Risks : Please note, this script is the likes of Bollinger bands and poses a risk of falling in a trend range.
Entries may keep running on the same direction while the market is moving.
Price Volume Trend BBHey guys,
Ive been thinking about Price Volume Trend for a while and tried adding different moving averages to it, but seems its not as binary.
Therefore adding the bollinger bands as a no-trade-zone made it alot better. Indicator is pretty basic at the moment since I just implemented the idea but im planning to do some add-ons later on to make it easier to read.
Will keep you updated!
VEMA Band_v2 - 'Centre of GravityConcept taken from the MT4 indicator 'Centre of Gravity'except this one doesn't repaint.
Modified / BinaryPro 3 / Permanent Marker
Ema configuration instead of sma & centralised.
Vdub_Tetris_Stoch_V1Vdub_Tetris_Stoch_V1
A combination lower based indicators based on the period channel indicator Vdub_Tetris_V2
Blue line is more reactive fast moving, Red line in more accurate to highs / Lows with divergence.- Still testing
Code title error
Change % = Over Bought / Over Sold
Vdub Tetris_V2
Vdubus BinaryPro 2 /Tops&Bottoms
StochDM
Intrabar Volume Flow IntelligenceIntrabar Volume Flow Intelligence: A Comprehensive Analysis:
The Intrabar Volume Flow Intelligence indicator represents a sophisticated approach to understanding market dynamics through the lens of volume analysis at a granular, intrabar level. This Pine Script version 5 indicator transcends traditional volume analysis by dissecting price action within individual bars to reveal the true nature of buying and selling pressure that often remains hidden when examining only the external characteristics of completed candlesticks. At its core, this indicator operates on the principle that volume is the fuel that drives price movement, and by understanding where volume is being applied within each bar—whether at higher prices indicating buying pressure or at lower prices indicating selling pressure—traders can gain a significant edge in anticipating future price movements before they become obvious to the broader market.
The foundational innovation of this indicator lies in its use of lower timeframe data to analyze what happens inside each bar on your chart timeframe. While most traders see only the open, high, low, and close of a five-minute candle, for example, this indicator requests data from a one-minute timeframe by default to see all the individual one-minute candles that comprise that five-minute bar. This intrabar analysis allows the indicator to calculate a weighted intensity score based on where the price closed within each sub-bar's range. If the close is near the high, that volume is attributed more heavily to buying pressure; if near the low, to selling pressure. This methodology is far more nuanced than simple tick volume analysis or even traditional volume delta calculations because it accounts for the actual price behavior and distribution of volume throughout the formation of each bar, providing a three-dimensional view of market participation.
The intensity calculation itself demonstrates the coding sophistication embedded in this indicator. For each intrabar segment, the indicator calculates a base intensity using the formula of close minus low divided by the range between high and low. This gives a value between zero and one, where values approaching one indicate closes near the high and values approaching zero indicate closes near the low. However, the indicator doesn't stop there—it applies an open adjustment factor that considers the relationship between the close and open positions within the overall range, adding up to twenty percent additional weighting based on directional movement. This adjustment ensures that strongly directional intrabar movement receives appropriate emphasis in the final volume allocation. The adjusted intensity is then bounded between zero and one to prevent extreme outliers from distorting the analysis, demonstrating careful consideration of edge cases and data integrity.
The volume flow calculation multiplies this intensity by the actual volume transacted in each intrabar segment, creating buy volume and sell volume figures that represent not just quantity but quality of market participation. These figures are accumulated across all intrabar segments within the parent bar, and simultaneously, a volume-weighted average price is calculated for the entire bar using the typical price of each segment multiplied by its volume. This intrabar VWAP becomes a critical reference point for understanding whether the overall bar is trading above or below its fair value as determined by actual transaction levels. The deviation from this intrabar VWAP is then used as a weighting mechanism—when the close is significantly above the intrabar VWAP, buying volume receives additional weight; when below, selling volume is emphasized. This creates a feedback loop where volume that moves price away from equilibrium is recognized as more significant than volume that keeps price near balance.
The imbalance filter represents another layer of analytical sophistication that separates meaningful volume flows from normal market noise. The indicator calculates the absolute difference between buy and sell volume as a percentage of total volume, and this imbalance must exceed a user-defined threshold—defaulted to twenty-five percent but adjustable from five to eighty percent—before the volume flow is considered significant enough to register on the indicator. This filtering mechanism ensures that only bars with clear directional conviction contribute to the cumulative flow measurements, while bars with balanced buying and selling are essentially ignored. This is crucial because markets spend considerable time in equilibrium states where volume is simply facilitating position exchanges without directional intent. By filtering out these neutral periods, the indicator focuses trader attention exclusively on moments when one side of the market is demonstrating clear dominance.
The decay factor implementation showcases advanced state management in Pine Script coding. Rather than allowing imbalanced volume to simply disappear after one bar, the indicator maintains decayed values using variable state that persists across bars. When a new significant imbalance occurs, it replaces the decayed value; when no significant imbalance is present, the previous value is multiplied by the decay factor, which defaults to zero point eight-five. This means that a large volume imbalance continues to influence the indicator for several bars afterward, gradually diminishing in impact unless reinforced by new imbalances. This decay mechanism creates persistence in the flow measurements, acknowledging that large institutional volume accumulation or distribution campaigns don't execute in single bars but rather unfold across multiple bars. The cumulative flow calculation then sums these decayed values over a lookback period, creating a running total that represents sustained directional pressure rather than momentary spikes.
The dual moving average crossover system applied to these volume flows creates actionable trading signals from the underlying data. The indicator calculates both a fast exponential moving average and a slower simple moving average of the buy flow, sell flow, and net flow values. The use of EMA for the fast line provides responsiveness to recent changes while the SMA for the slow line provides a more stable baseline, and the divergence or convergence between these averages signals shifts in volume flow momentum. When the buy flow EMA crosses above its SMA while volume is elevated, this indicates that buying pressure is not only present but accelerating, which is the foundation for the strong buy signal generation. The same logic applies inversely for selling pressure, creating a symmetrical approach to detecting both upside and downside momentum shifts based on volume characteristics rather than price characteristics.
The volume threshold filtering ensures that signals only generate during periods of statistically significant market participation. The indicator calculates a simple moving average of total volume over a user-defined period, defaulted to twenty bars, and then requires that current volume exceed this average by a multiplier, defaulted to one point two times. This ensures that signals occur during periods when the market is actively engaged rather than during quiet periods when a few large orders can create misleading volume patterns. The indicator even distinguishes between high volume—exceeding the threshold—and very high volume—exceeding one point five times the threshold—with the latter triggering background color changes to alert traders to exceptional participation levels. This tiered volume classification allows traders to calibrate their position sizing and conviction levels based on the strength of market participation supporting the signal.
The flow momentum calculation adds a velocity dimension to the volume analysis. By calculating the rate of change of the net flow EMA over a user-defined momentum length—defaulted to five bars—the indicator measures not just the direction of volume flow but the acceleration or deceleration of that flow. A positive and increasing flow momentum indicates that buying pressure is not only dominant but intensifying, which typically precedes significant upward price movements. Conversely, negative and decreasing flow momentum suggests selling pressure is building upon itself, often preceding breakdowns. The indicator even calculates a second derivative—the momentum of momentum, termed flow acceleration—which can identify very early turning points when the rate of change itself begins to shift, providing the most forward-looking signal available from this methodology.
The divergence detection system represents one of the most powerful features for identifying potential trend reversals and continuations. The indicator maintains separate tracking of price extremes and flow extremes over a lookback period defaulted to fourteen bars. A bearish divergence is identified when price makes a new high or equals the recent high, but the net flow EMA is significantly below its recent high—specifically less than eighty percent of that high—and is declining compared to its value at the divergence lookback distance. This pattern indicates that while price is pushing higher, the volume support for that movement is deteriorating, which frequently precedes reversals. Bullish divergences work inversely, identifying situations where price makes new lows without corresponding weakness in volume flow, suggesting that selling pressure is exhausted and a reversal higher is probable. These divergence signals are plotted as distinct diamond shapes on the indicator, making them visually prominent for trader attention.
The accumulation and distribution zone detection provides a longer-term context for understanding institutional positioning. The indicator uses the bars-since function to track consecutive periods where the net flow EMA has remained positive or negative. When buying pressure has persisted for at least five consecutive bars, average intensity exceeds zero point six indicating strong closes within bar ranges, and volume is elevated above the threshold, the indicator identifies an accumulation zone. These zones suggest that smart money is systematically building long positions across multiple bars despite potentially choppy or sideways price action. Distribution zones are identified through the inverse criteria, revealing periods when institutions are systematically exiting or building short positions. These zones are visualized through colored fills on the indicator pane, creating a backdrop that helps traders understand the broader volume flow context beyond individual bar signals.
The signal strength scoring system provides a quantitative measure of conviction for each buy or sell signal. Rather than treating all signals as equal, the indicator assigns point values to different signal components: twenty-five points for the buy flow EMA-SMA crossover, twenty-five points for the net flow EMA-SMA crossover, twenty points for high volume presence, fifteen points for positive flow momentum, and fifteen points for bullish divergence presence. These points are summed to create a buy score that can range from zero to one hundred percent, with higher scores indicating that multiple independent confirmation factors are aligned. The same methodology creates a sell score, and these scores are displayed in the information table, allowing traders to quickly assess whether a signal represents a tentative suggestion or a high-conviction setup. This scoring approach transforms the indicator from a binary signal generator into a nuanced probability assessment tool.
The visual presentation of the indicator demonstrates exceptional attention to user experience and information density. The primary display shows the net flow EMA as a thick colored line that transitions between green when above zero and above its SMA, indicating strong buying, to a lighter green when above zero but below the SMA, indicating weakening buying, to red when below zero and below the SMA, indicating strong selling, to a lighter red when below zero but above the SMA, indicating weakening selling. This color gradient provides immediate visual feedback about both direction and momentum of volume flows. The net flow SMA is overlaid in orange as a reference line, and a zero line is drawn to clearly delineate positive from negative territory. Behind these lines, a histogram representation of the raw net flow—scaled down by thirty percent for visibility—shows bar-by-bar flow with color intensity reflecting whether flow is strengthening or weakening compared to the previous bar. This layered visualization allows traders to simultaneously see the raw data, the smoothed trend, and the trend of the trend, accommodating both short-term and longer-term trading perspectives.
The cumulative delta line adds a macro perspective by maintaining a running sum of all volume deltas divided by one million for scale, plotted in purple as a separate series. This cumulative measure acts similar to an on-balance volume calculation but with the sophisticated volume attribution methodology of this indicator, creating a long-term sentiment gauge that can reveal whether an asset is under sustained accumulation or distribution across days, weeks, or months. Divergences between this cumulative delta and price can identify major trend exhaustion or reversal points that might not be visible in the shorter-term flow measurements.
The signal plotting uses shape-based markers rather than background colors or arrows to maximize clarity while preserving chart space. Strong buy signals—meeting multiple criteria including EMA-SMA crossover, high volume, and positive momentum—appear as full-size green triangle-up shapes at the bottom of the indicator pane. Strong sell signals appear as full-size red triangle-down shapes at the top. Regular buy and sell signals that meet fewer criteria appear as smaller, semi-transparent circles, indicating they warrant attention but lack the full confirmation of strong signals. Divergence-based signals appear as distinct diamond shapes in cyan for bullish divergences and orange for bearish divergences, ensuring these critical reversal indicators are immediately recognizable and don't get confused with momentum-based signals. This multi-tiered signal hierarchy helps traders prioritize their analysis and avoid signal overload.
The information table in the top-right corner of the indicator pane provides real-time quantitative feedback on all major calculation components. It displays the current bar's buy volume and sell volume in millions with appropriate color coding, the imbalance percentage with color indicating whether it exceeds the threshold, the average intensity score showing whether closes are generally near highs or lows, the flow momentum value, and the current buy and sell scores. This table transforms the indicator from a purely graphical tool into a quantitative dashboard, allowing discretionary traders to incorporate specific numerical thresholds into their decision frameworks. For example, a trader might require that buy score exceed seventy percent and intensity exceed zero point six-five before taking a long position, creating objective entry criteria from subjective chart reading.
The background shading that occurs during very high volume periods provides an ambient alert system that doesn't require focused attention on the indicator pane. When volume spikes to one point five times the threshold and net flow EMA is positive, a very light green background appears across the entire indicator pane; when volume spikes with negative net flow, a light red background appears. These backgrounds create a subliminal awareness of exceptional market participation moments, ensuring traders notice when the market is making important decisions even if they're focused on price action or other indicators at that moment.
The alert system built into the indicator allows traders to receive notifications for strong buy signals, strong sell signals, bullish divergences, bearish divergences, and very high volume events. These alerts can be configured in TradingView to send push notifications to mobile devices, emails, or webhook calls to automated trading systems. This functionality transforms the indicator from a passive analysis tool into an active monitoring system that can watch markets continuously and notify the trader only when significant volume flow developments occur. For traders monitoring multiple instruments, this alert capability is invaluable for efficient time allocation, allowing them to analyze other opportunities while being instantly notified when this indicator identifies high-probability setups on their watch list.
The coding implementation demonstrates advanced Pine Script techniques including the use of request.security_lower_tf to access intrabar data, array manipulation to process variable-length intrabar arrays, proper variable scoping with var keyword for persistent state management across bars, and efficient conditional logic that prevents unnecessary calculations. The code structure with clearly delineated sections for inputs, calculations, signal generation, plotting, and alerts makes it maintainable and educational for those studying Pine Script development. The use of input groups with custom headers creates an organized settings panel that doesn't overwhelm users with dozens of ungrouped parameters, while still providing substantial customization capability for advanced users who want to optimize the indicator for specific instruments or timeframes.
For practical trading application, this indicator excels in several specific use cases. Scalpers and day traders can use the intrabar analysis to identify accumulation or distribution happening within the bars of their entry timeframe, providing early entry signals before momentum indicators or price patterns complete. Swing traders can use the cumulative delta and accumulation-distribution zones to understand whether short-term pullbacks in an uptrend are being bought or sold, helping distinguish between healthy retracements and trend reversals. Position traders can use the divergence detection to identify major turning points where price extremes are not supported by volume, providing low-risk entry points for counter-trend positions or warnings to exit with-trend positions before significant reversals.
The indicator is particularly valuable in ranging markets where price-based indicators produce numerous false breakout signals. By requiring that breakouts be accompanied by volume flow imbalances, the indicator filters out failed breakouts driven by low participation. When price breaks a range boundary accompanied by a strong buy or sell signal with high buy or sell score and very high volume, the probability of successful breakout follow-through increases dramatically. Conversely, when price breaks a range but the indicator shows low imbalance, opposing flow direction, or low volume, traders can fade the breakout or at minimum avoid chasing it.
During trending markets, the indicator helps traders identify the healthiest entry points by revealing where pullbacks are being accumulated by smart money. A trending market will show the cumulative delta continuing in the trend direction even as price pulls back, and accumulation zones will form during these pullbacks. When price resumes the trend, the indicator will generate strong buy or sell signals with high scores, providing objective entry points with clear invalidation levels. The flow momentum component helps traders stay with trends longer by distinguishing between healthy momentum pauses—where momentum goes to zero but doesn't reverse—and actual momentum reversals where opposing pressure is building.
The VWAP deviation weighting adds particular value for traders of liquid instruments like major forex pairs, stock indices, and high-volume stocks where VWAP is widely watched by institutional participants. When price deviates significantly from the intrabar VWAP and volume flows in the direction of that deviation with elevated weighting, it indicates that the move away from fair value is being driven by conviction rather than mechanical order flow. This suggests the deviation will likely extend further, creating continuation trading opportunities. Conversely, when price deviates from intrabar VWAP but volume flow shows reduced intensity or opposing direction despite the weighting, it suggests the deviation will revert to VWAP, creating mean reversion opportunities.
The ATR normalization option makes the indicator values comparable across different volatility regimes and different instruments. Without normalization, a one-million share buy-sell imbalance might be significant for a low-volatility stock but trivial for a high-volatility cryptocurrency. By normalizing the delta by ATR, the indicator accounts for the typical price movement capacity of the instrument, making signal thresholds and comparison values meaningful across different trading contexts. This is particularly valuable for traders running the indicator on multiple instruments who want consistent signal quality regardless of the underlying instrument characteristics.
The configurable decay factor allows traders to adjust how persistent they want volume flows to remain influential. For very short-term scalping, a lower decay factor like zero point five will cause volume imbalances to dissipate quickly, keeping the indicator focused only on very recent flows. For longer-term position trading, a higher decay factor like zero point nine-five will allow significant volume events to influence the indicator for many bars, revealing longer-term accumulation and distribution patterns. This flexibility makes the single indicator adaptable to trading styles ranging from one-minute scalping to daily chart position trading simply by adjusting the decay parameter and the lookback bars.
The minimum imbalance percentage setting provides crucial noise filtering that can be optimized per instrument. Highly liquid instruments with tight spreads might show numerous small imbalances that are meaningless, requiring a higher threshold like thirty-five or forty percent to filter noise effectively. Thinly traded instruments might rarely show extreme imbalances, requiring a lower threshold like fifteen or twenty percent to generate adequate signals. By making this threshold user-configurable with a wide range, the indicator accommodates the full spectrum of market microstructure characteristics across different instruments and timeframes.
In conclusion, the Intrabar Volume Flow Intelligence indicator represents a comprehensive volume analysis system that combines intrabar data access, sophisticated volume attribution algorithms, multi-timeframe smoothing, statistical filtering, divergence detection, zone identification, and intelligent signal scoring into a cohesive analytical framework. It provides traders with visibility into market dynamics that are invisible to price-only analysis and even to conventional volume analysis, revealing the true intentions of market participants through their actual transaction behavior within each bar. The indicator's strength lies not in any single feature but in the integration of multiple analytical layers that confirm and validate each other, creating high-probability signal generation that can form the foundation of complete trading systems or provide powerful confirmation for discretionary analysis. For traders willing to invest time in understanding its components and optimizing its parameters for their specific instruments and timeframes, this indicator offers a significant informational advantage in increasingly competitive markets where edge is derived from seeing what others miss and acting on that information before it becomes consensus.
[CT] D&W PPO + RBF + DivergenceThis indicator combines two separate ideas into one tool so you can read trend context from your price chart while timing momentum shifts from a clean oscillator panel. The first component is the Daily and Weekly Percentage Price Oscillator (D&W PPO), which measures the relationship between two EMA spreads that are intentionally built to reflect two “speeds” of market structure. The “weekly” leg is calculated as the percentage distance between a slower and faster EMA pair (L1 and L2), and the “daily” leg is calculated as the percentage distance between a shorter EMA pair (L3 and L4), but both are normalized by the same long EMA (e2) so the values behave like a percent-based oscillator rather than raw points. The script then combines those two legs by creating R = W + D, and it plots the histogram as R − W, which simplifies to D. That is not a mistake, it is the point of the design. By setting the baseline at “R equals W,” the zero line becomes a very intuitive threshold that tells you whether the shorter-term push is adding to the longer-term bias or subtracting from it. When the histogram is above zero, the daily component is supportive of the larger trend pressure, and when it is below zero, the daily component is opposing it. The histogram color is intentionally binary and stable, green when the histogram is at or above zero and red when it is below, so the panel reads like a momentum confirmation tool rather than a noisy oscillator that constantly shifts shades.
The second component is the RBF Price Trail, which is drawn on the upper price chart even though the indicator itself lives in a lower panel. This line is not a moving average in the traditional sense. It is a Radial Basis Function kernel smoother that weights recent prices based on their similarity rather than only their recency. In plain terms, the kernel attempts to build a smoother “baseline” that adapts to the shape of price action, and then the script optionally wraps that baseline inside an ATR band and applies a Supertrend-like trailing clamp. When the ATR band is enabled, the line will not simply track the kernel value, it will trail price and hold its position until price forces it to ratchet. This behavior is what makes it useful as a structure-aligned trend line rather than just another smoothing curve. When the adaptive band boost is enabled, the band width is multiplied by a factor that grows when recent price change is large relative to a lookback normalization window. That means the trailing mechanism can adapt to fast markets by changing the effective band behavior, which helps reduce whipsaws in choppy conditions while still allowing the line to respond when volatility expands. The line color is determined by where price closes relative to the trail, bullish when price is above the trail and bearish when price is below it, and you can optionally color your actual chart candles from either the PPO state or the RBF state depending on what you want your eyes to follow.
The settings are organized so you can control each module without changing how the core PPO trend logic behaves. The PPO settings L1, L2, L3, and L4 define the EMA lengths used to compute the weekly leg W and the daily leg D. Increasing these values makes the oscillator slower and smoother, while decreasing them makes it react faster to recent movement. “Show W line” is simply a visual aid, it plots the W line in the oscillator panel so you can see the longer-term component, but it does not change the histogram logic. “Histogram thickness” is purely visual and controls how thick the column bars are. The PPO colors are the two base colors used for the histogram state, green when the daily component is supportive and red when it is opposing.
The RBF settings control what you see on the upper chart. “Show RBF on Price Chart” turns the trail line on or off. “Source” chooses which price series feeds the kernel, and close is usually the cleanest choice. “Kernel Length” determines how many bars the kernel uses; a larger value makes the baseline smoother and slower, and a smaller value makes it more reactive. “Gamma Adj” controls how quickly the kernel’s weights decay as price becomes dissimilar, so higher gamma tends to make the kernel react more sharply to changes while lower gamma produces a broader smoothing effect. “Use ATR Trail Band” is the switch that turns the kernel baseline into a trailing band line, and it is the reason the line can “hold” and then ratchet instead of moving continuously like a normal moving average. “ATR Length” and “ATR Factor” control the width of that band, and widening the band will generally reduce flips and noise at the cost of later signals. “Use Adaptive Band Boost” turns on the volatility normalization idea, “Boost Normalization Lookback” defines how far back the script looks to determine what counts as a large price change, and “Boost Multiplier” controls how strongly the band behavior is adjusted during those periods. The line width and bull/bear colors are visual controls only.
Price bar coloring is intentionally handled with a single selector so you do not end up with two modules fighting to color candles differently. If you choose “Off,” nothing on the main chart is recolored. If you choose “PPO,” your price candles reflect whether the PPO histogram is above or below zero. If you choose “RBF,” your price candles reflect whether price is above or below the RBF trail. Most traders will pick one and stick with it so the chart communicates a single bias at a glance.
The divergence module is optional and is designed to be a confirmation layer rather than a primary trigger. When enabled, it can mark regular divergence and hidden divergence, and it lets you decide what the pivots should be based on. The divergence source can be the PPO histogram or the R line, depending on whether you want divergence measured on the cleaner momentum component or on the combined series. “Key off pivots” determines whether pivot detection is driven by oscillator pivots or by price pivots. If you choose oscillator pivots, divergence anchors are found where the oscillator makes pivot highs or lows and those are compared against price at the same points. If you choose price pivots, the pivots are taken from price first and the oscillator value at those pivot bars is used for the comparison, which can feel more intuitive when you want divergence to respect obvious swing structure on the chart. Pivot Left and Pivot Right control how strict the swing definition is, larger values create fewer but more meaningful pivots and smaller values create more frequent signals. “Mark on Price Chart” adds tiny markers on the candles at the pivot location so you can see where the divergence event was confirmed, while the oscillator panel uses lines and labels to make the divergence relationship obvious.
For trading, the cleanest way to use this tool is to separate “bias” from “timing.” The RBF Price Trail is your bias filter because it is structure-like and tends to hold and ratchet rather than constantly drifting. When price is closing above the trail and the trail is colored bullish, you treat the market as long-biased and you focus on long setups, pullbacks, and continuation entries. When price is closing below the trail and the trail is bearish, you treat the market as short-biased and you focus on short setups, rallies, and continuation shorts. The PPO histogram is then your timing and pressure confirmation. In an up-bias, the highest quality continuation conditions are when the histogram is above zero and stays above zero through pullbacks, because that means the shorter-term pressure is still supporting the longer-term drift. When the histogram dips below zero during an up-bias, it is a warning that the daily component is now opposing, which often corresponds to a deeper pullback, a rotation, or a period of consolidation, so you either wait for the histogram to recover above zero or you tighten expectations and manage risk more aggressively. In a down-bias, the mirror logic applies: the best continuation conditions are when the histogram is below zero, and pushes above zero tend to represent countertrend rotations or pauses inside the bearish condition.
Divergence is best used as an early warning and a location filter, not as a standalone entry button. Regular bullish divergence, where price makes a lower low but the oscillator makes a higher low, can signal bearish pressure is weakening and is most useful when it appears while price is below the RBF trail but failing to continue downward, because it often precedes a reclaim of the trail or at least a meaningful rotation. Regular bearish divergence, where price makes a higher high but the oscillator makes a lower high, can signal bullish pressure is weakening and is most useful when it appears while price is above the trail but extension is failing, because it often precedes a drop back to the trail or a full flip. Hidden divergence is a continuation concept. Hidden bullish divergence, where price makes a higher low while the oscillator makes a lower low, often shows up during pullbacks in an uptrend and can help you confirm continuation as long as the RBF bias remains bullish. Hidden bearish divergence, where price makes a lower high while the oscillator makes a higher high, often shows up during rallies in a downtrend and can help you confirm continuation as long as the RBF bias remains bearish. In practice, you’ll get the best results when you only act on divergence that aligns with the RBF bias for hidden divergence continuation, and you treat regular divergence as a caution or reversal setup only when it occurs near a meaningful swing and is followed by a bias change or a strong momentum shift on the PPO.
The most practical workflow is to keep the RBF trail visible on the price chart as your regime guide, keep the PPO histogram as your momentum confirmation, and decide in advance whether you want candle coloring to represent the PPO state or the RBF state so your eyes are not reading two different meanings at once. if you want the cleanest “trend-following” behavior, color candles by the RBF trail and use the PPO histogram as the timing trigger. If you want the cleanest “momentum-first” behavior, color candles by PPO and treat the RBF trail as the higher-level filter for whether you should press a move or fade it.
Digital MACD Divergences MTF [LUPEN]Digital MACD Divergences MTF V1.0
Overview:
Digital MACD Divergences MTF is an advanced momentum oscillator based on digital signal processing techniques.
Instead of relying on traditional moving-average smoothing, it applies Finite Impulse Response (FIR) digital filters to extract momentum more cleanly, reducing lag and short-term market noise.
The indicator is designed to provide a clear visualization of momentum structure, divergence behavior, and multi-timeframe context, rather than discrete trading signals.
Conceptual Architecture
At its core, the indicator reinterprets the classic MACD framework through digital convolution logic:
FIR filters are used to compute momentum in a more responsive and stable manner than standard EMA-based MACD.
The resulting histogram represents momentum intensity and direction as a continuous state rather than binary conditions.
A digitally smoothed signal line provides structural reference without introducing excessive delay.
This approach emphasizes momentum quality and structure, not signal frequency.
Divergence Detection Logic:
The script includes automatic divergence detection based on pivot analysis:
Regular bullish and bearish divergences are identified using confirmed pivot points.
Divergences are visualized with explicit line structures and optional filled areas, highlighting the zone of disagreement between price behavior and momentum.
The visualization is designed to remain readable without obscuring price action.
Divergences are presented as contextual information, not as mandatory actions.
Multi-Timeframe (MTF) Context
Digital MACD Divergences MTF supports native multi-timeframe analysis through a dual-pane workflow:
A lower-timeframe instance visualizes local momentum dynamics.
A higher-timeframe instance visualizes the broader momentum regime within which lower-timeframe fluctuations occur.
The higher-timeframe view is not intended as confirmation or filtering logic, but as a contextual background layer that helps interpret short-term momentum behavior inside a larger structural environment.
This separation avoids decision compression and keeps each timeframe’s role conceptually distinct.
Visual Design
Gradient-based histogram fills represent momentum intensity in a continuous manner.
Positive and negative momentum regions are clearly differentiated while remaining adaptable to both dark and light chart themes.
All visual elements are designed to emphasize state and regime, not discrete events.
Reliability
No repainting: all divergences and momentum states are confirmed on candle close and remain fixed.
Designed for consistency across instruments and timeframes.
Customization Options
Timeframe selection for MTF mode (leave empty to use the chart’s timeframe).
Adjustable signal smoothing parameters.
Divergence visibility controls, pivot sensitivity, and optional divergence fill.
Fully customizable color palette.
Usage Notes
This indicator is a visual market analysis tool intended to support momentum interpretation and structural context.
It does not provide investment advice, trading signals, or automated decision logic, and should be used as part of a broader analytical framework.
Final quotes:
"Trading is not about prediction, but about understanding momentum structure.
Digital MACD removes noise to make that structure visible."
BTC - CII: Drawdown DNA | RMBTC - CII: Drawdown DNA | Rob_Maths
The "Broken Cycle" Series: Pt 1
Welcome to the debut of the Cycle Integrity Index (CII) . This quantitative diagnostic suite was engineered for a singular mission: to determine if Bitcoin’s historical 4-year cycle is still the primary track rhythm, or if the market has shifted into a high-downforce Institutional Regime.
As of January 2026 , the Bitcoin market is at a historical crossroads. According to the classical 4-year model, we have passed the "Theoretical Peak" and are now on the long descent toward a projected cycle low in late 2026 . However, a massive debate is raging: Is the cycle broken?
While legacy models expect a total engine failure (an -80% wipeout) by the end of this year, the ETF-era market structure suggests we may have "re-engineered" the asset's DNA. Pt 1: Drawdown DNA acts as our first telemetry check, auditing the "Structural Fatigue" of every correction to see if we are taking a tactical pit stop or heading for a catastrophic crash.
How to Read the Telemetry
Think of the Bitcoin market as a Formula 1 engine. This indicator audits the "Wear and Tear" (drawdowns) to see if the chassis can sustain its pace or if the structural integrity is failing as we approach the legacy "finish line."
• Vibrant Green (Institutional Sync): Optimal Performance. The engine is healthy. Pullbacks are shallow (-20% to -35% range), representing professional re-fueling stops by smart money. This suggests the "Supercycle" narrative is overriding the 4-year clock.
• Red/Dark Blue (Regime Decay): Loss of Traction. The "Institutional" heartbeat is weakening. Volatility is rising as the engine stalls, drifting back toward the chaotic, un-buffered "Drift" patterns of the retail era.
• Blue Shaded Zones (Legacy DNA): SYSTEMIC CRASH. The price has breached the -50% "G-Force Threshold." At this depth, the correction carries the genetic makeup of a Legacy Bear Market (historically bottoming near -80%). The 4-year cycle is still very much alive—and it's painful.
Behind the Math: ECU Tuning
This script is an original quantitative work utilizing Gaussian Probability Density logic to categorize market drawdowns into distinct historical regimes.
Instead of simple binary "on/off" logic, the code acts like an ECU (Electronic Control Unit) , calculating the mathematical "fit" of the current drawdown against a specific Institutional Mean (-25%) . Why 25%? I chose -25% as the Institutional DNA anchor based on the structural shift observed between 2023 and 2025. While legacy retail cycles were defined by violent 30-40% "shakeouts" during bull phases, the introduction of spot ETFs and corporate treasury adoption has significantly compressed volatility. A -25% correction now represents the maximum "healthy" absorption of sell-side liquidity by institutional "bids." Staying near this level maintains high aerodynamic sync; dropping further suggests the chassis is failing.
How it Audits the Regime
The closer the price stays to this -25% target, the higher the Integrity Score (10/10). By providing unique "DNA Match" calculations and background shading based on specific threshold crossings, this indicator provides utility beyond standard price-change indicators. It allows you to mathematically distinguish between an "Institutional Rebalancing" and the start of a "Legacy Cycle-Ending Termination."
User Inputs & Navigation
• Rolling High Lookback: Default 52 Weeks . Defines our diagnostic lap. It ensures the audit focuses on the current race, not the entire history of the track.
• Inst. Drawdown Target: Default -25% . The "Perfect Pit Stop." Corrections near this level maintain the highest aerodynamic sync.
• Legacy Threshold: Default -50% . The "Point of No Return" where the engine enters total failure and the Blue Legacy Shading triggers.
• Legacy Crash Target: Default -80% . The historical baseline for previous 4-year cycle bear market floors (Expected mid-to-late 2026 in legacy models).
Instructions & Performance
• Preferred Timeframe: This is a macro-telemetry tool. It performs best on Weekly (1W) or Daily (1D) charts.
• The Dashboard: Monitor the INST. DNA MATCH in the table. A score of 8.0+ / 10 provides the "Green Light" that the Supercycle is still the primary driver, effectively breaking the 4-year "Crash" script.
Disclaimer
Trading and investing in digital assets involve significant risk. The Cycle Integrity Index (CII) is a quantitative tool for informational and educational purposes only. Past performance does not guarantee future results. This is not financial advice. Your capital is at risk.
Tags
robmaths, Rob Maths, Bitcoin, CycleTheory, Institutional, Drawdown, Quant, RegimeShift, CII
Check out my published scripts here: de.tradingview.com
Quantum Reversal Detector [JOAT]
Quantum Reversal Detector - Multi-Factor Reversal Probability Analysis
Introduction and Purpose
Quantum Reversal Detector is an open-source overlay indicator that combines multiple reversal detection methods into a unified probability-based framework. The core problem this indicator addresses is the unreliability of single-factor reversal signals. A price touching support means nothing without momentum confirmation; an RSI oversold reading means nothing without price structure context.
This indicator solves that by requiring multiple independent factors to align before generating reversal signals, then expressing the result as a probability score rather than a binary signal.
Why These Components Work Together
The indicator combines five analytical approaches, each addressing a different aspect of reversal detection:
1. RSI Extremes - Identifies momentum exhaustion (overbought/oversold)
2. MACD Crossovers - Confirms momentum direction change
3. Support/Resistance Proximity - Ensures price is at a significant level
4. Multi-Depth Momentum - Analyzes momentum across multiple timeframes
5. Statistical Probability - Quantifies reversal likelihood using Bayesian updating
These components are not randomly combined. Each filter catches reversals that others miss:
RSI catches momentum exhaustion but misses structural reversals
MACD catches momentum shifts but lags price action
S/R proximity catches structural levels but ignores momentum
Multi-depth momentum catches divergences across timeframes
Probability scoring combines all factors into actionable confidence levels
How the Detection System Works
Step 1: Pattern Detection
The indicator first identifies potential reversal conditions:
// Check if price is at support/resistance
float lowestLow = ta.lowest(low, period)
float highestHigh = ta.highest(high, period)
bool atSupport = low <= lowestLow * 1.002
bool atResistance = high >= highestHigh * 0.998
// Check RSI conditions
float rsi = ta.rsi(close, 14)
bool oversold = rsi < 30
bool overbought = rsi > 70
// Check MACD crossover
float macd = ta.ema(close, 12) - ta.ema(close, 26)
float signal = ta.ema(macd, 9)
bool macdBullish = ta.crossover(macd, signal)
bool macdBearish = ta.crossunder(macd, signal)
// Combine for reversal detection
if atSupport and oversold and macdBullish
bullishReversal := true
Step 2: Multi-Depth Momentum Analysis
The indicator calculates momentum across multiple periods to detect divergences:
calculateQuantumMomentum(series float price, simple int period, simple int depth) =>
float totalMomentum = 0.0
for i = 0 to depth - 1
int currentPeriod = period * (i + 1)
float momentum = ta.roc(price, currentPeriod)
totalMomentum += momentum
totalMomentum / depth
This creates a composite momentum reading that smooths out noise while preserving genuine momentum shifts.
Step 3: Bayesian Probability Calculation
The indicator uses Bayesian updating to calculate reversal probability:
bayesianProbability(series float priorProb, series float likelihood, series float evidence) =>
float posterior = evidence > 0 ? (likelihood * priorProb) / evidence : priorProb
math.min(math.max(posterior, 0.0), 1.0)
The prior probability starts at 50% and updates based on:
RSI extreme readings increase likelihood
MACD crossovers increase likelihood
S/R proximity increases likelihood
Momentum divergence increases likelihood
Step 4: Confidence Intervals
Using Monte Carlo simulation concepts, the indicator estimates price distribution:
monteCarloSimulation(series float price, series float volatility, simple int iterations) =>
float sumPrice = 0.0
float sumSqDiff = 0.0
for i = 0 to iterations - 1
float randomFactor = (i % 10 - 5) / 10.0
float simulatedPrice = price + volatility * randomFactor
sumPrice += simulatedPrice
float avgPrice = sumPrice / iterations
// Calculate standard deviation for confidence intervals
This provides 95% and 99% confidence bands around the current price.
Signal Classification
Signals are classified by confirmation level:
Confirmed Reversal : Pattern detected for N consecutive bars (default 3)
High Probability : Confirmed + Bayesian probability > 70%
Ultra High Probability : High probability + PDF above average
Dashboard Information
The dashboard displays:
Bayesian Probability - Updated reversal probability (0-100%)
Quantum Momentum - Multi-depth momentum average
RSI - Current RSI value with overbought/oversold status
Volatility - Current ATR as percentage of price
Reversal Signal - BULLISH, BEARISH, or NONE
Divergence - Momentum divergence detection
MACD - Current MACD histogram value
S/R Zone - AT SUPPORT, AT RESISTANCE, or NEUTRAL
95% Confidence - Price range with 95% probability
Bull/Bear Targets - ATR-based reversal targets
Visual Elements
Quantum Bands - ATR-based upper and lower channels
Probability Field - Circle layers showing probability distribution
Confidence Bands - 95% and 99% confidence interval circles
Reversal Labels - REV markers at confirmed reversals
High Probability Markers - Star diamonds at high probability setups
Reversal Zones - Boxes around confirmed reversal areas
Divergence Markers - Triangles at momentum divergences
How to Use This Indicator
For Reversal Trading:
1. Wait for Bayesian Probability to exceed 70%
2. Confirm price is at S/R zone (dashboard shows AT SUPPORT or AT RESISTANCE)
3. Check that RSI is in extreme territory (oversold for longs, overbought for shorts)
4. Enter when REV label appears with high probability marker
For Risk Management:
1. Use the 95% confidence band as a stop-loss reference
2. Use Bull/Bear Targets for take-profit levels
3. Higher probability readings warrant larger position sizes
For Filtering False Signals:
1. Increase Confirmation Bars to require more consecutive signals
2. Only trade when probability exceeds 70%
3. Require divergence confirmation for highest conviction
Input Parameters
Reversal Period (21) - Lookback for S/R and momentum calculations
Quantum Depth (5) - Number of momentum layers for multi-depth analysis
Confirmation Bars (3) - Consecutive bars required for confirmation
Detection Sensitivity (1.2) - Band width and target multiplier
Bayesian Probability (true) - Enable probability calculation
Monte Carlo Simulation (true) - Enable confidence interval calculation
Normal Distribution (true) - Enable PDF calculation
Confidence Intervals (true) - Enable confidence bands
Timeframe Recommendations
1H-4H: Best for swing trading reversals
Daily: Fewer but more significant reversal signals
15m-30m: More signals, requires higher probability threshold
Limitations
Statistical concepts are simplified implementations for Pine Script
Monte Carlo uses deterministic pseudo-random factors, not true randomness
Bayesian probability uses simplified prior/likelihood model
Reversal detection does not guarantee actual reversals will occur
Confirmation bars add lag to signal generation
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. The source code is fully visible and can be studied to understand how each component works.
This indicator does not constitute financial advice. Reversal detection is probabilistic, not predictive. The probability scores represent statistical likelihood based on historical patterns, not guaranteed outcomes. Past performance does not guarantee future results. Always use proper risk management, position sizing, and stop-losses.
- Made with passion by officialjackofalltrades
Adaptive Trend Envelope [BackQuant]Adaptive Trend Envelope
Overview
Adaptive Trend Envelope is a volatility-aware trend-following overlay designed to stay responsive in fast markets while remaining stable during slower conditions. It builds a dynamic trend spine from two exponential moving averages and surrounds it with an adaptive envelope whose width expands and contracts based on realized return volatility. The result is a clean, self-adjusting trend structure that reacts to market conditions instead of relying on fixed parameters.
This indicator is built to answer three core questions directly on the chart:
Is the market trending or neutral?
If trending, in which direction is the dominant pressure?
Where is the dynamic trend boundary that price should respect?
Core trend spine
At the heart of the indicator is a blended trend spine:
A fast EMA captures short-term responsiveness.
A slow EMA captures structural direction.
A volatility-based blend weight dynamically shifts influence between the two.
When short-term volatility is low relative to long-term volatility, the fast EMA has more influence, keeping the trend responsive. When volatility rises, the blend shifts toward the slow EMA, reducing noise and preventing overreaction. This blended output is then smoothed again to form the final trend spine, which acts as the structural backbone of the system.
Volatility-adaptive envelope
The envelope surrounding the trend spine is not based on ATR or fixed percentages. Instead, it is derived from:
Log returns of price.
An exponentially weighted variance estimate.
A configurable multiplier that scales envelope width.
This creates bands that automatically widen during volatile expansions and tighten during compression. The envelope therefore reflects the true statistical behavior of price rather than an arbitrary distance.
Inner hysteresis band
Inside the main envelope, an inner band is constructed using a hysteresis fraction. This inner zone is used to stabilize regime transitions:
It prevents rapid flipping between bullish and bearish states.
It allows trends to persist unless price meaningfully invalidates them.
It reduces whipsaws in sideways conditions.
Trend regime logic
The indicator operates with three regime states:
Bullish
Bearish
Neutral
Regime changes are confirmed using a configurable number of bars outside the adaptive envelope:
A bullish regime is confirmed when price closes above the upper envelope for the required number of bars.
A bearish regime is confirmed when price closes below the lower envelope for the required number of bars.
A trend exits back to neutral when price reverts through the trend spine.
This structure ensures that trends are confirmed by sustained pressure rather than single-bar spikes.
Active trend line
Once a regime is active, the indicator plots a single dominant trend line:
In a bullish regime, the lower envelope becomes the active trend support.
In a bearish regime, the upper envelope becomes the active trend resistance.
In neutral conditions, price itself is used as a placeholder.
This creates a simple, actionable visual reference for trend-following decisions.
Directional energy visualization
The indicator uses layered fills to visualize directional pressure:
Bullish energy fills appear when price holds above the active trend line.
Bearish energy fills appear when price holds below the active trend line.
Opacity gradients communicate strength and persistence rather than binary states.
A subtle “rim” effect is added using ATR-based offsets to give depth and reinforce the active side of the trend without cluttering the chart.
Signals and trend starts
Discrete signals are generated only when a new trend regime begins:
Buy signals appear at the first confirmed transition into a bullish regime.
Sell signals appear at the first confirmed transition into a bearish regime.
Signals are intentionally sparse. They are designed to mark regime shifts, not every pullback or continuation, making them suitable for higher-quality trend entries rather than frequent trading.
Candle coloring
Optional candle coloring reinforces regime context:
Bullish regimes tint candles toward the bullish color.
Bearish regimes tint candles toward the bearish color.
Neutral states remain visually muted.
This allows the chart to communicate trend state even when the envelope itself is partially hidden or de-emphasized.
Alerts
Built-in alerts are provided for key trend events:
Bull trend start.
Bear trend start.
Transition from trend to neutral.
Price crossing the trend spine.
These alerts support hands-off trend monitoring across multiple instruments and timeframes.
How to use it for trend following
Trend identification
Only trade in the direction of the active regime.
Ignore counter-trend signals during confirmed trends.
Entry alignment
Use the first regime signal as a structural entry.
Use pullbacks toward the active trend line as continuation opportunities.
Trend management
As long as price respects the active envelope boundary, the trend remains valid.
A move back through the spine signals loss of trend structure.
Market filtering
Periods where the indicator remains neutral highlight non-trending environments.
This helps avoid forcing trades during chop or compression.
Adaptive Trend Envelope is designed to behave like a living trend structure. Instead of forcing price into static rules, it adapts to volatility, confirms direction through sustained pressure, and presents trend information in a clean, readable form that supports disciplined trend-following workflows.
DeeptestDeeptest: Quantitative Backtesting Library for Pine Script
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█ OVERVIEW
Deeptest is a Pine Script library that provides quantitative analysis tools for strategy backtesting. It calculates over 100 statistical metrics including risk-adjusted return ratios (Sharpe, Sortino, Calmar), drawdown analysis, Value at Risk (VaR), Conditional VaR, and performs Monte Carlo simulation and Walk-Forward Analysis.
█ WHY THIS LIBRARY MATTERS
Pine Script is a simple yet effective coding language for algorithmic and quantitative trading. Its accessibility enables traders to quickly prototype and test ideas directly within TradingView. However, the built-in strategy tester provides only basic metrics (net profit, win rate, drawdown), which is often insufficient for serious strategy evaluation.
Due to this limitation, many traders migrate to alternative backtesting platforms that offer comprehensive analytics. These platforms require other language programming knowledge, environment setup, and significant time investment—often just to test a simple trading idea.
Deeptest bridges this gap by bringing institutional-level quantitative analytics directly to Pine Script. Traders can now perform sophisticated analysis without leaving TradingView or learning complex external platforms. All calculations are derived from strategy.closedtrades.* , ensuring compatibility with any existing Pine Script strategy.
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█ ORIGINALITY AND USEFULNESS
This library is original work that adds value to the TradingView community in the following ways:
1. Comprehensive Metric Suite: Implements 112+ statistical calculations in a single library, including advanced metrics not available in TradingView's built-in tester (p-value, Z-score, Skewness, Kurtosis, Risk of Ruin).
2. Monte Carlo Simulation: Implements trade-sequence randomization to stress-test strategy robustness by simulating 1000+ alternative equity curves.
3. Walk-Forward Analysis: Divides historical data into rolling in-sample and out-of-sample windows to detect overfitting by comparing training vs. testing performance.
4. Rolling Window Statistics: Calculates time-varying Sharpe, Sortino, and Expectancy to analyze metric consistency throughout the backtest period.
5. Interactive Table Display: Renders professional-grade tables with color-coded thresholds, tooltips explaining each metric, and period analysis cards for drawdowns/trades.
6. Benchmark Comparison: Automatically fetches S&P 500 data to calculate Alpha, Beta, and R-squared, enabling objective assessment of strategy skill vs. passive investing.
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█ KEY FEATURES
Performance Metrics
Net Profit, CAGR, Monthly Return, Expectancy
Profit Factor, Payoff Ratio, Sample Size
Compounding Effect Analysis
Risk Metrics
Sharpe Ratio, Sortino Ratio, Calmar Ratio (MAR)
Martin Ratio, Ulcer Index
Max Drawdown, Average Drawdown, Drawdown Duration
Risk of Ruin, R-squared (equity curve linearity)
Statistical Distribution
Value at Risk (VaR 95%), Conditional VaR
Skewness (return asymmetry)
Kurtosis (tail fatness)
Z-Score, p-value (statistical significance testing)
Trade Analysis
Win Rate, Breakeven Rate, Loss Rate
Average Trade Duration, Time in Market
Consecutive Win/Loss Streaks with Expected values
Top/Worst Trades with R-multiple tracking
Advanced Analytics
Monte Carlo Simulation (1000+ iterations)
Walk-Forward Analysis (rolling windows)
Rolling Statistics (time-varying metrics)
Out-of-Sample Testing
Benchmark Comparison
Alpha (excess return vs. benchmark)
Beta (systematic risk correlation)
Buy & Hold comparison
R-squared vs. benchmark
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█ QUICK START
Basic Usage
//@version=6
strategy("My Strategy", overlay=true)
// Import the library
import Fractalyst/Deeptest/1 as *
// Your strategy logic
fastMA = ta.sma(close, 10)
slowMA = ta.sma(close, 30)
if ta.crossover(fastMA, slowMA)
strategy.entry("Long", strategy.long)
if ta.crossunder(fastMA, slowMA)
strategy.close("Long")
// Run the analysis
DT.runDeeptest()
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█ METRIC EXPLANATIONS
The Deeptest table displays 23 metrics across the main row, with 23 additional metrics in the complementary row. Each metric includes detailed tooltips accessible by hovering over the value.
Main Row — Performance Metrics (Columns 0-6)
Net Profit — (Final Equity - Initial Capital) / Initial Capital × 100
— >20%: Excellent, >0%: Profitable, <0%: Loss
— Total return percentage over entire backtest period
Payoff Ratio — Average Win / Average Loss
— >1.5: Excellent, >1.0: Good, <1.0: Losses exceed wins
— Average winning trade size relative to average losing trade. Breakeven win rate = 100% / (1 + Payoff)
Sample Size — Count of closed trades
— >=30: Statistically valid, <30: Insufficient data
— Number of completed trades. Includes 95% confidence interval for win rate in tooltip
Profit Factor — Gross Profit / Gross Loss
— >=1.5: Excellent, >1.0: Profitable, <1.0: Losing
— Ratio of total winnings to total losses. Uses absolute values unlike payoff ratio
CAGR — (Final / Initial)^(365.25 / Days) - 1
— >=10%: Excellent, >0%: Positive growth
— Compound Annual Growth Rate - annualized return accounting for compounding
Expectancy — Sum of all returns / Trade count
— >0.20%: Excellent, >0%: Positive edge
— Average return per trade as percentage. Positive expectancy indicates profitable edge
Monthly Return — Net Profit / (Months in test)
— >0%: Profitable month average
— Average monthly return. Geometric monthly also shown in tooltip
Main Row — Trade Statistics (Columns 7-14)
Avg Duration — Average time in position per trade
— Mean holding period from entry to exit. Influenced by timeframe and trading style
Max CW — Longest consecutive winning streak
— Maximum consecutive wins. Expected value = ln(trades) / ln(1/winRate)
Max CL — Longest consecutive losing streak
— Maximum consecutive losses. Important for psychological risk tolerance
Win Rate — Wins / Total Trades
— Higher is better
— Percentage of profitable trades. Breakeven win rate shown in tooltip
BE Rate — Breakeven Trades / Total Trades
— Lower is better
— Percentage of trades that broke even (neither profit nor loss)
Loss Rate — Losses / Total Trades
— Lower is better
— Percentage of unprofitable trades. Together with win rate and BE rate, sums to 100%
Frequency — Trades per month
— Trading activity level. Displays intelligently (e.g., "12/mo", "1.5/wk", "3/day")
Exposure — Time in market / Total time × 100
— Lower = less risk
— Percentage of time the strategy had open positions
Main Row — Risk Metrics (Columns 15-22)
Sharpe Ratio — (Return - Rf) / StdDev × sqrt(Periods)
— >=3: Excellent, >=2: Good, >=1: Fair, <1: Poor
— Measures risk-adjusted return using total volatility. Annualized using sqrt(252) for daily
Sortino Ratio — (Return - Rf) / DownsideDev × sqrt(Periods)
— >=2: Excellent, >=1: Good, <1: Needs improvement
— Similar to Sharpe but only penalizes downside volatility. Can be higher than Sharpe
Max DD — (Peak - Trough) / Peak × 100
— <5%: Excellent, 5-15%: Moderate, 15-30%: High, >30%: Severe
— Largest peak-to-trough decline in equity. Critical for risk tolerance and position sizing
RoR — Risk of Ruin probability
— <1%: Excellent, 1-5%: Acceptable, 5-10%: Elevated, >10%: Dangerous
— Probability of losing entire trading account based on win rate and payoff ratio
R² — R-squared of equity curve vs. time
— >=0.95: Excellent, 0.90-0.95: Good, 0.80-0.90: Moderate, <0.80: Erratic
— Coefficient of determination measuring linearity of equity growth
MAR — CAGR / |Max Drawdown|
— Higher is better, negative = bad
— Calmar Ratio. Reward relative to worst-case loss. Negative if max DD exceeds CAGR
CVaR — Average of returns below VaR threshold
— Lower absolute is better
— Conditional Value at Risk (Expected Shortfall). Average loss in worst 5% of outcomes
p-value — Binomial test probability
— <0.05: Significant, 0.05-0.10: Marginal, >0.10: Likely random
— Probability that observed results are due to chance. Low p-value means statistically significant edge
Complementary Row — Extended Metrics
Compounding — (Compounded Return / Total Return) × 100
— Percentage of total profit attributable to compounding (position sizing)
Avg Win — Sum of wins / Win count
— Average profitable trade return in percentage
Avg Trade — Sum of all returns / Total trades
— Same as Expectancy (Column 5). Displayed here for convenience
Avg Loss — Sum of losses / Loss count
— Average unprofitable trade return in percentage (negative value)
Martin Ratio — CAGR / Ulcer Index
— Similar to Calmar but uses Ulcer Index instead of Max DD
Rolling Expectancy — Mean of rolling window expectancies
— Average expectancy calculated across rolling windows. Shows consistency of edge
Avg W Dur — Avg duration of winning trades
— Average time from entry to exit for winning trades only
Max Eq — Highest equity value reached
— Peak equity achieved during backtest
Min Eq — Lowest equity value reached
— Trough equity point. Important for understanding worst-case absolute loss
Buy & Hold — (Close_last / Close_first - 1) × 100
— >0%: Passive profit
— Return of simply buying and holding the asset from backtest start to end
Alpha — Strategy CAGR - Benchmark CAGR
— >0: Has skill (beats benchmark)
— Excess return above passive benchmark. Positive alpha indicates genuine value-added skill
Beta — Covariance(Strategy, Benchmark) / Variance(Benchmark)
— <1: Less volatile than market, >1: More volatile
— Systematic risk correlation with benchmark
Avg L Dur — Avg duration of losing trades
— Average time from entry to exit for losing trades only
Rolling Sharpe/Sortino — Dynamic based on win rate
— >2: Good consistency
— Rolling metric across sliding windows. Shows Sharpe if win rate >50%, Sortino if <=50%
Curr DD — Current drawdown from peak
— Lower is better
— Present drawdown percentage. Zero means at new equity high
DAR — CAGR adjusted for target DD
— Higher is better
— Drawdown-Adjusted Return. DAR^5 = CAGR if max DD = 5%
Kurtosis — Fourth moment / StdDev^4 - 3
— ~0: Normal, >0: Fat tails, <0: Thin tails
— Measures "tailedness" of return distribution (excess kurtosis)
Skewness — Third moment / StdDev^3
— >0: Positive skew (big wins), <0: Negative skew (big losses)
— Return distribution asymmetry
VaR — 5th percentile of returns
— Lower absolute is better
— Value at Risk at 95% confidence. Maximum expected loss in worst 5% of outcomes
Ulcer — sqrt(mean(drawdown^2))
— Lower is better
— Ulcer Index - root mean square of drawdowns. Penalizes both depth AND duration
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█ MONTE CARLO SIMULATION
Purpose
Monte Carlo simulation tests strategy robustness by randomizing the order of trades while keeping trade returns unchanged. This simulates alternative equity curves to assess outcome variability.
Method
Extract all historical trade returns
Randomly shuffle the sequence (1000+ iterations)
Calculate cumulative equity for each shuffle
Build distribution of final outcomes
Output
The stress test table shows:
Median Outcome: 50th percentile result
5th Percentile: Worst 5% of outcomes
95th Percentile: Best 95% of outcomes
Success Rate: Percentage of simulations that were profitable
Interpretation
If 95% of simulations are profitable: Strategy is robust
If median is far from actual result: High variance/unreliability
If 5th percentile shows large loss: High tail risk
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█ WALK-FORWARD ANALYSIS
Purpose
Walk-Forward Analysis (WFA) is the gold standard for detecting strategy overfitting. It simulates real-world trading by dividing historical data into rolling "training" (in-sample) and "validation" (out-of-sample) periods. A strategy that performs well on unseen data is more likely to succeed in live trading.
Method
The implementation uses a non-overlapping window approach following AmiBroker's gold standard methodology:
Segment Calculation: Total trades divided into N windows (default: 12), IS = ~75%, OOS = ~25%, Step = OOS length
Window Structure: Each window has IS (training) followed by OOS (validation). Each OOS becomes the next window's IS (rolling forward)
Metrics Calculated: CAGR, Sharpe, Sortino, MaxDD, Win Rate, Expectancy, Profit Factor, Payoff
Aggregation: IS metrics averaged across all IS periods, OOS metrics averaged across all OOS periods
Output
IS CAGR: In-sample annualized return
OOS CAGR: Out-of-sample annualized return ( THE key metric )
IS/OOS Sharpe: In/out-of-sample risk-adjusted return
Success Rate: % of OOS windows that were profitable
Interpretation
Robust: IS/OOS CAGR gap <20%, OOS Success Rate >80%
Some Overfitting: CAGR gap 20-50%, Success Rate 50-80%
Severe Overfitting: CAGR gap >50%, Success Rate <50%
Key Principles:
OOS is what matters — Only OOS predicts live performance
Consistency > Magnitude — 10% IS / 9% OOS beats 30% IS / 5% OOS
Window count — More windows = more reliable validation
Non-overlapping OOS — Prevents data leakage
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█ TABLE DISPLAY
Main Table — Organized into three sections:
Performance Metrics (Cols 0-6): Net Profit, Payoff, Sample Size, Profit Factor, CAGR, Expectancy, Monthly
Trade Statistics (Cols 7-14): Avg Duration, Max CW, Max CL, Win, BE, Loss, Frequency, Exposure
Risk Metrics (Cols 15-22): Sharpe, Sortino, Max DD, RoR, R², MAR, CVaR, p-value
Color Coding
🟢 Green: Excellent performance
🟠 Orange: Acceptable performance
⚪ Gray: Neutral / Fair
🔴 Red: Poor performance
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█ IMPLEMENTATION NOTES
Data Source: All metrics calculated from strategy.closedtrades , ensuring compatibility with any Pine Script strategy
Calculation Timing: All calculations occur on barstate.islastconfirmedhistory to optimize performance
Limitations: Requires at least 1 closed trade for basic metrics, 30+ trades for reliable statistical analysis
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█ QUICK NOTES
➙ This library has been developed and refined over two years of real-world strategy testing. Every calculation has been validated against industry-standard quantitative finance references.
➙ The entire codebase is thoroughly documented inline. If you are curious about how a metric is calculated or want to understand the implementation details, dive into the source code -- it is written to be read and learned from.
➙ This description focuses on usage and concepts rather than exhaustively listing every exported type and function. The library source code is thoroughly documented inline -- explore it to understand implementation details and internal logic.
➙ All calculations execute on barstate.islastconfirmedhistory to minimize runtime overhead. The library is designed for efficiency without sacrificing accuracy.
➙ Beyond analysis, this library serves as a learning resource. Study the source code to understand quantitative finance concepts, Pine Script advanced techniques, and proper statistical methodology.
➙ Metrics are their own not binary good/bad indicators. A high Sharpe ratio with low sample size is misleading. A deep drawdown during a market crash may be acceptable. Study each function and metric individually -- evaluate your strategy contextually, not by threshold alone.
➙ All strategies face alpha decay over time. Instead of over-optimizing a single strategy on one timeframe and market, build a diversified portfolio across multiple markets and timeframes. Deeptest helps you validate each component so you can combine robust strategies into a trading portfolio.
➙ Screenshots shown in the documentation are solely for visual representation to demonstrate how the tables and metrics will be displayed. Please do not compare your strategy's performance with the metrics shown in these screenshots -- they are illustrative examples only, not performance targets or benchmarks.
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█ HOW-TO
Using Deeptest is intentionally straightforward. Just import the library and call DT.runDeeptest() at the end of your strategy code in main scope. .
//@version=6
strategy("My Strategy", overlay=true)
// Import the library
import Fractalyst/Deeptest/1 as DT
// Your strategy logic
fastMA = ta.sma(close, 10)
slowMA = ta.sma(close, 30)
if ta.crossover(fastMA, slowMA)
strategy.entry("Long", strategy.long)
if ta.crossunder(fastMA, slowMA)
strategy.close("Long")
// Run the analysis
DT.runDeeptest()
And yes... it's compatible with any TradingView Strategy! 🪄
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█ CREDITS
Author: @Fractalyst
Font Library: by @fikira - @kaigouthro - @Duyck
Community: Inspired by the @PineCoders community initiative, encouraging developers to contribute open-source libraries and continuously enhance the Pine Script ecosystem for all traders.
if you find Deeptest valuable in your trading journey, feel free to use it in your strategies and give a shoutout to @Fractalyst -- Your recognition directly supports ongoing development and open-source contributions to Pine Script.
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█ DISCLAIMER
This library is provided for educational and research purposes. Past performance does not guarantee future results. Always test thoroughly and use proper risk management. The author is not responsible for any trading losses incurred through the use of this code.
Institutional Intermarket Score PRO V3.3 (Presets)This indicator is built on an unusual, non-traditional intermarket concept and is designed to provide market context rather than trading signals.
Institutional Intermarket Score – Indicator Description
Overview
The Institutional Intermarket Score is a contextual market indicator designed to provide a macro and intermarket perspective on the current market environment.
It aggregates information from multiple user-selected correlated and inversely correlated assets to determine whether the broader market context favors risk-on, risk-off, or neutral conditions.
This indicator is not a buy or sell signal.
It does not attempt to predict short-term price movements, entries, or exits.
Its sole purpose is to help the trader understand the broader market context before making any trading decisions.
Core Concept
Markets do not move in isolation.
Institutional participants continuously monitor multiple related markets to assess risk, liquidity, and conviction before deploying capital.
This indicator replicates that process by:
Monitoring several correlated assets (assets that tend to move in the same direction)
Monitoring several inversely correlated assets (assets that typically move in the opposite direction)
Combining their behavior into a single, normalized intermarket score
The result is a context filter, not a trading system.
Asset Groups
The indicator supports up to:
5 correlated assets
5 inversely correlated assets
All assets are fully configurable by the user and can be enabled or disabled individually.
Only active assets are included in all calculations.
Market State Evaluation
Each asset is evaluated using a Price vs VWAP relationship:
Price above VWAP → bullish state
Price below VWAP → bearish state
This binary state is used consistently across all assets to maintain clarity and robustness.
Intermarket Score
----------------------
The Intermarket Score represents the average directional alignment of all active assets and is normalized between -1 and +1.
Positive values indicate a risk-on environment
Negative values indicate a risk-off environment
Values near zero indicate balance, rotation, or uncertainty
The score is smoothed to reduce noise and highlight regime persistence rather than short-term fluctuations.
Confirmation Metric (X / Y)
----------------------------------
In addition to the score, the indicator calculates a confirmation ratio:
Y = total number of active assets
X = number of assets aligned with the current regime
Alignment is evaluated relative to the current regime:
In bullish regimes, assets above VWAP confirm
In bearish regimes, assets below VWAP confirm
This metric reflects the quality and conviction of the intermarket consensus.
High confirmation indicates broad agreement across markets.
Low confirmation indicates divergence, uncertainty, or fragile conditions.
Heatmap
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A compact heatmap visually displays the state of each individual asset:
Green indicates alignment with the regime
Red indicates opposition
Neutral indicates inactive assets
This allows immediate identification of:
Which markets are confirming
Which markets are diverging
Whether consensus is broad or fragmented
Intended Use
----------------
This indicator is designed to be used:
Before evaluating trade setups
As a filter, not a trigger
In combination with price action, structure, and risk management
Typical applications include:
Avoiding trades against the broader market context
Distinguishing strong trends from fragile moves
Identifying periods of institutional alignment or hesitation
What This Indicator Is Not
It is not a buy or sell indicator
It does not provide entry or exit signals
It does not predict price direction on its own
It does not guarantee profitable trades
Any trading decisions remain entirely the responsibility of the user.
Summary
The Institutional Intermarket Score provides a high-level market image based on assets selected by the user.
It reflects context, alignment, and conviction, not timing.
Used correctly, it helps traders avoid low-quality trades, understand when markets are aligned or fragmented, and make decisions with greater awareness of the broader environment.
It is a decision support tool, not a trading system.
This indicator, is still evolving and its structure will continue to develop as new insights are tested...
BTC - RVPM: Run Velocity & Probability MapBTC – RVPM: Run Velocity & Probability Map | RM
Strategic Context: Understanding Price Runs
A "Price Run" (also known as a streak or consecutive sessions) is a foundational concept in time-series analysis that measures the duration of a price movement without a significant counter-signal. While common indicators like RSI or MACD measure magnitude or momentum, they often ignore the Persistence of the trend. Historically, markets move through cycles of expansion and mean-reversion. A Price Run represents a period of "Unidirectional Flow" — a fingerprint of institutional accumulation or systematic distribution. However, standard "run-counting" is often too simplistic for the volatile crypto markets.
What Makes RVPM Special?
Most community run-counters are binary; they simply tell you if X days were green or red. The RVPM distinguishes itself through three proprietary layers:
• The Intensity Filter: It doesnt just count days; it counts effort . By ignoring "flat" days through a percentage-return threshold, it filters out noise that would otherwise skew the statistical probability.
• Dynamic Benchmarking: Instead of using an arbitrary number (like "7 days"), the RVPM looks back at 200 bars of history to find the local "Persistence Ceiling." It adapts to the current volatility regime of Bitcoin.
• The Velocity Score: It transform simple counts into a -100 to +100 histogram, allowing traders to see momentum "decaying" (e.g., dropping from 90 to 70) even if the price continues to rise.
The 3 Pillars of the Engine
1. Velocity Mapping (Persistence Histogram)
The histogram calculates the density of directional effort within a defined window. It functions as the "Pulse" of the trend, mapping market behavior into three distinct zones:
• High Velocity Zone (> 80 or < -80): Institutional Expansion. This identifies a "clean" move where one side of the market possesses total structural control. In this zone, the trend is efficient, and counter-signals are immediately absorbed.
• The Neutral Zone (Near Zero): Momentum Equilibrium. When the histogram fluctuates near the zero line, the market is in a "Recharge Phase." Neither bulls nor bears are achieving persistent dominance. Tactically, this is the "Waiting Room" where range-bound chop is likely, and traders should wait for a new "Expansion" spike before committing.
• Velocity Decay: The Exhaustion Warning. Velocity Decay occurs when the indicator moves from an extreme (e.g., +95) back toward the zero line (e.g., +50) while the price is still rising. This is a "Persistence Divergence." It tells you that while the trend is still moving, the consistency of the bars is fragmenting. The "fuel" is being depleted, and the trend is transitioning from an "Institutional Expansion" into a "Speculative Exhaustion."
2. n-of-m Consistency (The Pips)
The "Pips" (Circles) mark when a specific consistency threshold is met (e.g., 5 out of 7 bars in one direction). This identifies "Leaky Trends" that are still statistically dominated by one side of the ledger.
3. Statistical Exhaustion (The Arrows)
The Dark Red (Top) and Dark Green (Bottom) triangles represent the engine's "Mean-Reversion Signal." The calculation is based on a Relative Maximum Streak (RMS) logic: the script tracks the current linear, consecutive bar count (ignoring bars that fail the Intensity Filter) and continuously benchmarks this against the highest streak recorded over the last 200 bars ( ta.highest(streak, 200) ). The triangles are triggered specifically when the current run reaches 80% of this historical record (the "Anomaly Threshold"). Mathematically, this identifies a move that is statistically pushing against its half-year limit. By using this dynamic threshold rather than a fixed number, the "Extreme" signal automatically tightens during low-volatility regimes and expands during high-volatility expansions, ensuring the signal only appears when the "statistical rubber band" is at a true breaking point.
Operational Interface: The RVPM Dashboard
The Status Dashboard (Top Right) serves as a real-time monitor for momentum health, providing a clean summary of the underlying persistence data:
• Current STREAK: The active, consecutive count of bars meeting the Intensity Filter. It is dynamically color-coded (Cyan/Bullish or Red/Bearish) to provide an instant read on trend seniority.
• WINDOW Consistency: Measures the Momentum Density (the n-of-m value). A value of "6" in a "7-bar" window indicates a high-conviction regime that is successfully absorbing pullbacks without losing its primary trajectory.
Tactical Playbook: The Mean-Reversion Rule
Price action typically follows a "Rubber Band" effect. The further it is stretched without a break, the more "unstable" the trend becomes as the pool of available buyers or sellers is depleted.
• The Setup: Wait for the Triangle Arrows to appear.
• The Logic: The move has reached a 200-day anomaly. A "Liquidity Vacuum" is forming on the opposite side.
• The Action: This is a high-probability Mean-Reversion signal. It is a tactical time to take profits or look for a sharp snap-back move toward the 20-period moving average or the "Institutional Mean."
Settings & Parameters
• Window Length (m): The lookback window used to calculate the Velocity Score.
• Required Days (n): The minimum number of directional bars needed within the window to trigger a "Consistency Pip."
• Intensity Filter (%): The minimum % change required for a bar to be counted toward a run.
• Lookback Period: The historical window (Default: 200 bars) used to calculate the "Maximum Streak" records for exhaustion alerts.
Timeframe Recommendation
The RVPM is best viewed on the Daily (1D) timeframe. This filters out intraday noise and provides the most reliable statistical mapping for macro exhaustion points.
Credits & Verification
The RVPM logic aligns with institutional "Persistence" models and Glassnode's Price Stretch benchmarks. By benchmarking against a rolling 200-day window, the indicator automatically adapts to changing market volatility.
Risk Disclaimer & No Financial Advice
The information, data, and analytical models provided in this publication are for educational and informational purposes only. This script does not constitute financial, investment, or trading advice. Trading cryptocurrencies and other financial instruments carries a high degree of risk, and statistical anomalies or "Extreme Runs" do not guarantee future price action. Past performance is never indicative of future results. Every trader is responsible for their own due diligence and risk management. Rob Maths and the associated entities are not liable for any financial losses incurred through the use of this tool. Always consult with a certified financial professional before making significant investment decisions.
Tags:
bitcoin, btc, persistence, streaks, price-runs, momentum, mean-reversion, exhaustion, Rob Maths
strongResistanceActually it is education purpose. This indicator is designed to help traders clearly identify strong Support & Resistance (SNR) levels along with high-probability Buy & Sell..
The indicator works smoothly on lower timeframes for binary trading.
Moving Average ExponentialThe EMA 50 Trend Filter At the heart of the Sniper system lies the 50-period Exponential Moving Average. Unlike simple moving averages, the EMA applies a weighting factor to recent price data, significantly reducing lag. Role in Strategy:
Trend Identification: Serves as the binary divider between Long and Short bias.
Dynamic Structure: Acts as dynamic support in uptrends and resistance in downtrends.
Signal Filtering: The algorithm automatically suppresses any 'Buy' signals below the line and 'Sell' signals above it, ensuring you never trade against the institutional momentum.
