Historical Precedent Engine [HPE]WHAT IT DOES
HPE takes the last few candles on your chart, searches that chart's own history for
earlier sequences that resemble them, and shows you what price did after those earlier
sequences. It is an analog study. The output is a summary of precedent, not a forecast.
TUNING IS NOT OPTIONAL — READ THIS FIRST
This is a matcher, and a matcher only speaks when it finds something. Every enabled
filter is a hard gate applied to every candle in the fingerprint, and the gates compound:
a sequence qualifies only if candle 1 passes wick, body and volume, and candle 2 passes
all three, and so on, and the sequence momentum passes, and the direction rule passes.
Tighten two of those and the survivor count does not halve, it collapses.
So the normal failure mode is an empty dashboard. Median outcome, tolerance band, delta
and range all read "—", Bias reads Neutral, and Matches Used reads 0. That is not a bug
and it is not the tool being broken. It means nothing in this chart's history was close
enough to the present under the settings you have. The honest answer for that bar is
silence, and the tool gives it.
The tolerance units
Wick and body are measured as a percentage of the candle's own high-to-low range, not of
price. An upper wick occupying a fifth of its candle scores 20, whether that candle is a
one-minute Bitcoin bar or a daily equity bar. A tolerance of 12 therefore means "within
12 percentage points of range", and it means the same thing on every instrument and every
timeframe.
That is deliberate. Measured against price instead, the same tolerance would need to be
roughly a hundred times larger on a daily equity chart than on a one-minute crypto chart,
and no single default could serve both — one setting would accept everything on one chart
and nothing on the other.
On Auto-Tune, which ships OFF
Auto-Tune moves the wick and body tolerances based on how well recent projections
resolved. It ships disabled, for two measured reasons.
It cannot start from nothing. It does not act until at least five projections have been
scored, so if your tolerances are too tight to ever produce a match, there are no
projections, nothing is scored, and it never moves. It is a regulator, not a starter
motor.
And once it does start, it tends not to stop. It can only travel between a quarter and
four times your input, and when widening fails to improve fit — which is the usual case
if the matches were poor to begin with — it widens every bar until it pins at four times
your input and stays there. On the test chart it did exactly that, and the difference it
made was 50 resolved projections instead of 49. It bought one projection out of fifty
while making the number in the settings box a fiction.
So it is off, and what you type is what runs. Turn it on if you want it, knowing both of
the above.
The order to loosen in, most effective first:
1. Strict Direction off. With it on, every candle must match direction, which is a
1-in-2^N filter before any tolerance is applied. This is the single biggest lever.
2. Shorten Sequence Length. Fewer candles means fewer conjunctive conditions. Three is
the minimum and is the default for that reason.
3. Raise Wick and Body Tolerance, in the units described above.
4. Turn off Require Per-Candle Volume Match and Require Momentum Match. Volume ratios in
particular are noisy on short timeframes and reject a lot for little gain.
5. Lower Min Matches Required. It ships at 2 rather than 3 because on the instrument
these defaults were measured on, 3 never fires. Read the last paragraph of this
description before you take that as a recommendation.
Where the defaults came from
They were measured with a full 1,000-sequence library on three charts chosen to be as
unalike as possible, and they were picked to make the engine speak at all rather than to
make it look good:
COINBASE:BTCUSD 1-minute 73 projections over 25,837 bars
COINBASE:BTCUSD 1-hour 33 projections over 22,764 bars
AMEX:SPY daily 29 projections over 8,436 bars
That is between one bar in 290 and one bar in 690 — the same order of magnitude across a
crypto intraday chart and an equity daily chart, with no per-instrument tuning. It should
still be quiet, and you should still retune for your own instrument and horizon, but the
defaults are a measured starting point rather than a guess.
One note on reading the dashboard while you do that. The calibration row shows total
projections alongside how many sit in the calibration window, and that window is capped by
Calibration window (samples) — 50 by default. Watch the total, not the window. The window
fills early and then stops moving, which makes a well-tuned setup and a barely-working one
look identical.
ON LIBRARY SIZE
Max Stored Sequences is the pool the matcher searches, and a bigger pool is the one way
to get more matches without making each match mean less. It is capped at 1,000 by default
for a practical reason: raising it substantially can push the script past PulseWire's
calculation limit, at which point it stops reporting entirely. If you raise it and the
indicator goes blank rather than merely empty, that is what happened. Put it back. This
cap is also the real ceiling on how often the engine can fire at a tolerance tight enough
to be meaningful, and it is worth knowing that before you go hunting for settings.
HOW IT WORKS
1. Fingerprint. On every confirmed bar, the last N candles are reduced to a five-field
vector per candle: upper wick, lower wick, body, direction, and volume measured
against its own moving average.
2. Store. That fingerprint is written to a rolling library along with what price did over
the following bars.
3. Match. The current fingerprint is compared against every stored sequence. A stored
sequence qualifies only if each candle falls inside the wick, body and volume
tolerances, and only if the sequence momentum falls inside its tolerance. Direction
matching is separate: with Strict Direction on, every candle must match direction;
with it off, only the final candle must. An optional session filter restricts matches
to the same trading session.
4. Summarise. Qualifying matches are ranked by how well their own past projections
resolved, and the strongest are combined into a single percentile outcome — the median
by default. If fewer than Min Matches Required qualify, nothing is drawn.
5. Calibrate. Once the horizon elapses, each projection is scored against what actually
happened. That score weights how much a stored sequence counts in future matches, and
feeds Auto-Tune if you have enabled it.
READING THE CHART
Projection line and band — the percentile outcome of the current match set, extended to
the horizon.
Consensus paths — the individual paths of the top matches, drawn separately, so you can
see the spread the single summary line came from. A tight cluster and a wide scatter
produce the same median.
Rolling projection trail — past projections left on the chart beside what price actually
did. This is deliberate. A tool that hides its misses is not worth reading.
Dashboard — match count, median outcome, ±1σ range, session, library size, live
tolerances, and the calibration block. The projection values — median outcome, tolerance
band, delta, range, bias, match count and best-match error — are cleared at the start of
every confirmed bar, so those rows always show that bar's answer and never a leftover
from an earlier bar that happened to match. The library and calibration counters are
cumulative by design and do not clear.
The same state is also published to the Data Window as plain numbers, which is easier to
read than canvas text while you are tuning.
ON THE CALIBRATION NUMBERS
The dashboard reports mean projection error, not accuracy.
It is the average distance between projection and outcome, expressed as a share of the
size of the move that actually occurred, measured over the most recent resolved
projections on the chart you are looking at. It is computed in-sample, on bars the engine
had already stored, and it is not a forward result.
It is there so you can tell whether your tolerances are set sensibly. It is not evidence
that the tool works, and it should not be read as a hit rate. Because the actual move is
the denominator, the figure also moves with volatility regime rather than with skill
alone — quiet bars punish it, large moves flatter it.
ON REPAINTING
Two specific claims, both checkable in the source:
There are no request.security() calls anywhere in this script. Every value is computed
from the chart's own bars, so there is no higher-timeframe lookahead question to get
wrong in the first place.
Every drawing and every dashboard write sits inside a single barstate.isconfirmed gate.
Nothing is created, moved or deleted while the live bar is still forming.
A projection does extend to bars that have not happened yet. It does not move once drawn.
It is simply right or wrong, and the trail is there so you can see which.
SETTINGS WORTH KNOWING
Sequence Length — how many candles form the fingerprint. Longer is stricter and finds
fewer matches, and the effect is multiplicative rather than linear.
Min Matches Required — below this count nothing is drawn.
Delta Percentile — 50 is the median. Move it to read the pessimistic or optimistic tail
of the same match set rather than its centre.
Auto-Tune Tolerances — off by default; see above before enabling.
Strict Direction — the difference between "these candles had the same shape" and "these
candles had the same shape and went the same way."
WHAT THIS IS NOT
This is a visualization and analysis tool, not a trading system. It does not produce
advice. Nothing here is a signal to enter or exit a position, and no performance is
claimed or implied. Markets change regime, and any tool built on historical structure
will fail when they do. Use it as context alongside your own analysis.
One more thing worth saying plainly, and it is the honest counterweight to the tuning
advice above: a small sample of matches is a small sample. Two historical analogs tell
you very little, and the engine will draw a line from two just as readily as from thirty.
Min Matches ships at 2 because that is what it took to get the engine to speak on the
instrument it was measured on — which is a statement about how hard analogs are to find
in a 1,000-sequence library, not a claim that two is enough to believe. Loosening the
filters until something appears is easy, and it is exactly how you end up reading noise.
Watch the match count before you read the line, and treat a projection drawn from a
handful of precedents as the weak evidence it is.
Indicator
Volatility Cone & Analog Path ProjectionVolatility Cone & Analog Path Projection — Forward Price Envelope with Fractal Replay and Terminal Probability Distribution
Overview
Nearly every overlay on PulseWire describes the past: where price has been, where volume traded, where structure broke. This tool points in the other direction. It builds a forward projection zone from the current bar using three independent layers — a realized-volatility cone, a replay of the historically most similar price fractals, and a terminal probability profile that combines both into a distribution of possible outcomes at the projection horizon.
The result is not a forecast. It is a bounded expectation: a visual answer to "given how this instrument has actually been moving, what range is normal over the next N bars, and where has price historically ended up after conditions that looked like this?"
Conceptual Framework
Price uncertainty grows with the square root of time, not linearly. A 24-bar projection is not 24 times as wide as a 1-bar projection — it is roughly 4.9 times as wide. Traders who size targets and stops on a straight-line mental model consistently misjudge what is achievable in a given number of bars.
The cone makes that curvature visible. Its width at each future bar is sigma * sqrt(t), where sigma is the standard deviation of log returns over the volatility window. Three nested bands are drawn, so you can immediately see which targets sit inside the ordinary range, which sit at the statistical edge, and which would require an exceptional move.
The Gaussian model alone, however, is a poor description of real markets: returns have fat tails, and volatility clusters. The analog layer addresses this by ignoring models entirely and asking an empirical question instead — what actually happened, historically, after the market printed this exact shape?
How It Works
Volatility estimation. Log returns are computed bar to bar. Their standard deviation over the volatility window gives the per-bar sigma; their mean gives the drift. Drift can be included or excluded from the cone's centerline.
Cone construction. For each future bar t from 1 to the horizon, the upper and lower bounds are close * exp(drift*t ± k*sigma*sqrt(t)) for each of the three band multipliers. Each band is rendered as a closed polygon with layered transparency, producing depth from the centerline outward.
Fingerprint extraction. The most recent N bars of log returns are z-scored — mean removed, divided by their own standard deviation. This makes the pattern scale-invariant: the same shape is recognised whether it happened during a quiet range or a volatile expansion, and at any price level.
Historical scan. Every candidate window inside the scan depth is z-scored the same way and compared to the current fingerprint by summed squared difference. Lower distance means a closer shape match. Candidates that overlap an already-selected match without improving on it are rejected, so the top results are not five copies of the same event shifted by one bar.
Forward replay. For each of the top matches, the bars that followed it are converted into a relative path and re-anchored to the current close. The path each analog is drawing forward is exactly the move that occurred after that historical fingerprint — nothing is fitted or optimised. Paths ending above the current price are drawn bullish, below bearish, and a thick median line traces the bar-by-bar median across all analogs.
Terminal probability profile. At the projection horizon a horizontal distribution is built across the cone's full range. Each row's density blends the Gaussian probability implied by the volatility model with an empirical kernel centred on each analog's endpoint. The Model Weight input controls that mix: 1.0 is purely theoretical, 0.0 is purely historical, and the default sits between them. The widest row — the mode of the blended distribution — is marked as the most probable zone.
Interpretation
Cone bands define what is statistically ordinary. A target beyond the outer band within the horizon is not impossible, it is simply rare — treat it accordingly when planning holding time.
Cone width itself is information. A narrow cone means compressed volatility, which historically resolves into expansion. A wide cone means the market is already moving; chasing inside it carries a worse risk profile.
Analog dispersion matters more than analog direction. Five paths that fan out in all directions means the current shape carried no historical edge. Five paths clustering in one direction is the meaningful configuration.
Best Match Quality in the panel scores how closely the nearest historical fingerprint resembles the present one. Below roughly 60%, treat the analog layer as noise and rely on the cone alone.
The most probable zone is where the blended distribution peaks. It is a magnet-style reference, not a target — the distribution is wide by construction.
Volatility Regime compares short-window volatility to the full window. Expanding means the cone is likely to understate near-term movement; contracting means the opposite.
Settings
Setting Effect
Projection Horizon Bars projected forward. Also the endpoint of the profile
Volatility Window Sample size for sigma and drift. Longer = smoother, slower to adapt
Include Drift Tilts the cone with the window's mean return
Inner / Mid / Outer Band Sigma multipliers for the three layers
Fingerprint Length Bars compared for similarity. Shorter = more matches, less specific
Scan Depth How far back to search for analogs
Number of Analogs How many historical paths to replay
Profile Rows / Width Resolution and horizontal size of the terminal distribution
Model Weight Gaussian versus empirical blend in the distribution
Redraw on Bar Close Only Recommended on. The scan is heavy; this runs it once per bar
Limitations — read this
This is not a prediction and must not be traded as one. The cone describes a statistical range under an assumption of stable volatility. Real volatility is not stable, and returns have fatter tails than the Gaussian model implies, so moves outside the outer band occur more often than the model suggests.
Analog matching is weak evidence. A few dozen bars of shape similarity is a small sample; markets are non-stationary and a pattern that resolved one way in the past carries no obligation to repeat. The paths are historical context, not a probability statement about the future.
Nothing repaints, but the whole projection is recomputed each bar. Yesterday's cone is not preserved — the drawing always reflects current data only. It is anchored to the last bar by design.
On low-volume, illiquid, or heavily gapped instruments the return distribution is distorted and both layers degrade.
No entries, no stops, no targets, no signals. This is a context tool for sizing expectations and holding time. Indicator
Flag Pattern Breakout [Dots3Red]█ FLAG PATTERN BREAKOUT
This script detects bull and bear flag patterns using two structural components: the staff (the impulsive pole) and the edge (the consolidation channel). Rather than relying on generic pivot-to-pivot zigzag lines, both components are built from regression-fitted geometry so the drawn shapes reflect the actual price structure rather than an approximation.
█ THE STAFF
The staff is the sharp, near-straight-line impulsive move that starts a flag. Two checks work together to identify it:
Structural continuity — instead of checking candle color (green vs red), the script checks whether each bar's wick still overlaps the bar before it. For a rising staff, a bar whose high fails to reach the previous bar's low counts as a break in the move. This catches genuine gaps in the advance while tolerating a normal red pullback candle that still overlaps the prior bar.
Straightness (R²) — a linear regression is fit through the closing prices of the candidate window, and its R² (coefficient of determination) is required to clear a minimum threshold (default 0.85). An R² of 1.0 would mean the closes sit exactly on a straight line; lower values reflect real curvature. Since real price data rarely produces a perfectly straight move, the threshold is adjustable rather than fixed at 1.0.
The search checks the longest possible window first and works down to shorter ones. A long window is only accepted if it clears both the wick-overlap test and the R² threshold — so the result favors the longest staff that still qualifies as straight, rather than the first short segment that happens to pass.
The staff line itself is drawn using the actual price at the two boundary bars (not the highest/lowest price found anywhere inside the scanning window), so it sits flush against the real candle wicks at both ends.
█ THE EDGE
The edge is the flag itself — a channel that runs opposite to the staff's direction. It is built as follows:
Slope — a running linear regression is fit through the highs (for a bull flag's falling edge) or the lows (for a bear flag's rising edge) of every bar since the staff ended. This produces one slope value that updates each bar as more data arrives.
Direction and steepness constraints — the edge's slope must run opposite to the staff (negative for a bull flag, positive for a bear flag) and must be shallower than the staff's own slope by a configurable ratio (default 60%). A flag that slopes as steeply as its pole is not behaving like a consolidation.
Width cap — the vertical distance between the tracked high and low extremes of the edge cannot exceed a percentage of the staff's height (default 60%). This is measured in absolute price distance, not bar count, so a slow-forming edge and a fast-forming edge are held to the same physical size constraint.
Breakout confirmation — the pattern is not finalized as soon as it meets minimum criteria. It keeps extending, bar by bar, for as long as price stays inside the channel. Confirmation only happens when price closes beyond the channel boundary by a configurable ATR buffer, in the direction that continues the original staff move (upward for a bull flag, downward for a bear flag). This means the edge is drawn at its full, longest actual duration rather than being cut short at an arbitrary minimum.
Envelope construction — once a pattern confirms, the two boundary lines are built by taking the regression-fitted line and shifting it by the maximum deviation observed on each side across every bar in the edge. The upper boundary is shifted up by the largest high-to-line distance seen; the lower boundary is shifted down by the largest line-to-low distance seen. Both lines share the identical slope, so they are parallel by construction, and together they contain the full price range of the consolidation rather than only touching two points.
█ VISUALS
The staff is drawn with a glow layer behind a sharp core line, color-coded green for bull and red for bear. The edge channel is filled with a soft translucent tint between its two boundary lines. A label at the breakout point shows the staff's height in ATR units, the edge's duration in bars, and the channel's width in ATR units.
█ SETTINGS
Staff
• Min Staff Height (×ATR) — minimum impulsive move size relative to ATR
• Min/Max Staff Duration (bars) — bounds on how many bars the staff can span
• Max Opposite-Direction Bars — structural wick-overlap tolerance
• Min Straightness (R²) — how closely the staff must fit a straight line
Edge
• Min Bars Before Breakout Eligible — minimum edge duration before a breakout can confirm
• Max Edge Duration (bars) — safety cap; abandons the pattern if no breakout occurs in time
• Max Channel Width (% of Staff Height) — absolute-distance cap on the edge's vertical size
• Max Edge Slope (× staff slope) — how much shallower the edge must be than the staff
• Min Edge Slope (×ATR per bar) — minimum slope magnitude so the edge counts as genuinely sloped
• Breakout Buffer (×ATR) — margin required beyond the boundary to confirm a breakout
█EXAMPLE ( DAILY APPLE STOCK )
█ NOTES
Because confirmation only happens at breakout, the pattern appears on the chart once the move has already resumed — this script identifies completed flag-and-breakout structures for review and study, not an early-warning signal before the breakout occurs. Works on any timeframe; behavior depends on how the ATR-based thresholds interact with the instrument's typical volatility.
However (!), with the ATR Breakout setting, it is possible to set a lower value, and by doing so we might "anticipate" Flag Development and consequent Breakout.
█ DISCLAIMER
This is a pattern visualization tool. It does not generate trade signals and does not constitute financial advice. Historical pattern detection does not guarantee that similar structures will behave the same way in the future. Indicator
Chart-Pattern Analogs SMC-Graded Chart-Pattern Analogs — Empirical Matching, SMC-Graded & Calibrated
## What it is
A chart-pattern engine that does not trust textbook shapes — it predicts from **precedent**. Instead of asking "does this look like a Head & Shoulders?", it asks "**how did the closest historical shapes on this symbol actually resolve?**" and answers from data.
A PIP (Perceptually Important Points) skeleton of the recent window is matched by **Dynamic Time Warping** against a live memory of past shapes, each labelled by what happened next. The direction, probability and target come from the *k* nearest analogs. Each completed pattern is then graded with Smart-Money context (liquidity sweeps, premium/discount location, breakout quality), and its forward outcome is tracked **per family, per regime, with Wilson confidence intervals**. Templates and geometry survive only to *name* the family.
## Why these components belong in ONE script (not a stack of indicators)
This matters because the components form **one closed loop**, where each answers a question the others cannot:
- **PIP skeleton + Ehlers-adaptive window** reduce price to its structurally important turning points, over a window sized by the live dominant cycle so the skeleton tracks the market's actual rhythm rather than a fixed bar count.
- **DTW analog matching** replaces fixed templates: the current skeleton is compared to a memory of *resolved* shapes, and the forward outcome of its nearest neighbours **is** the prediction. Empirical, not assumed.
- **SMC context** (liquidity sweeps, EQH/EQL pools, premium/discount, breakout quality) answers *"is this the right place?"* — a pattern that forms after a real liquidity grab, in the correct half of the dealing range, is graded higher; one firing into opposing liquidity is withheld. Shape alone cannot tell you location.
- **Regime** (efficiency ratio) conditions trust: a family's edge is measured separately in trend vs chop, because the same shape behaves differently in each.
- **Calibration** closes the loop: per-family, per-regime hit-rate versus an unconditional directional base rate, recency-weighted, with Wilson 95% intervals — so reliability is *earned on this instrument*, not claimed.
The skeleton says **what**, the analogs say **how it resolved before**, SMC says **whether the location is right**, the grade **fuses** them, and calibration keeps the whole thing **honest**. Remove any layer and the read loses a dimension it cannot recover.
## How it works (mechanics)
On bar close, the PIP skeleton is extracted and matched by DTW k-NN to the analog memory. The consensus direction and probability, plus the single nearest analog's forward path (the **ghost overlay**), are drawn. The pattern is graded **A/B/C** from the calibrated probability + quality + sweep + location, and gated by a minimum grade and a location veto. Every directional emission locks entry and ATR, tracks MFE/MAE in R, and at the horizon records a win if the close moved the chosen ATR multiple in its direction — feeding both the analog memory and the per-family/per-regime calibration tables.
Detection, drawing and resolution happen **on bar close over closed bars only** — no `request.security`, no dynamic-length `ta()`, no intrabar revision. Patterns are drawn once and never moved.
## How to use
1. Read the dashboard: the active pattern's **analog probability**, its **A/B/C setup grade**, **sweep validation** and **premium/discount location**.
2. Favour higher grades — a calibrated-positive family, sweep-validated, in the correct half of the range, with the ghost pointing your way.
3. Check **"This family"** — the regime-conditional and recent hit-rate is the number that matters *now*; the Pro table lists every family with Wilson confidence intervals.
4. The **ghost** shows the single closest historical precedent; the **target** is the analog mean.
5. Everything here is descriptive, probabilistic context — never an instruction.
## Use on any market
Works on any symbol and timeframe. The **Data Source** inputs (High / Low / Close) drive the entire engine — PIP skeleton, pivots, liquidity, premium/discount — from any series, so you can run it on standard candles, Heikin-Ashi, or another instrument. All thresholds are **ATR-relative**, the detection window is **cycle-adaptive**, and the analog memory and calibration rebuild **per instrument** — nothing is hard-coded to a price scale or a single market. Defaults are set for NIFTY index-futures intraday; change the sources or lengths for other assets.
## Credits
This engine builds on established, published concepts:
- **Perceptually Important Points** — Chung, Fu, Luk & Ng
- **Dynamic Time Warping** — Sakoe & Chiba
- **Homodyne-discriminator dominant cycle / adaptive windows** — John F. Ehlers
- **Efficiency Ratio** (trend vs noise) — Perry Kaufman
- **Wilson score confidence interval** — Edwin B. Wilson
- **Liquidity, premium/discount and order-block context** — smart-money / auction-market concepts (Wyckoff lineage; popularised in the ICT body of work)
The empirical analog loop (label-by-outcome DTW matching), the SMC location-grading, and the per-family/per-regime Wilson-bounded calibration framework are the author's original implementation.
## Originality
The contribution is the **closed loop**, not the objects: empirical DTW analog matching (label by outcome, not by template), SMC location-grading, and per-family/per-regime Wilson-bounded calibration, fused into one self-prioritising read with a nearest-precedent ghost overlay. The shape is only the index into history.
## Limitations (honest)
The analog memory and calibration are **in-sample, close-to-close at a fixed horizon, with no costs, slippage or stops** — a study aid, not a backtest, and not a probability of future results. The engine needs to build memory before it leaves bootstrap (watch the "Analog mem" counter). Volume-derived quality degrades on symbols without real volume. Past behaviour of a pattern does not assure future behaviour.
## Disclaimer
Educational / informational study for chart analysis only. **NOT financial advice, NOT a strategy, NOT a recommendation.** It places no orders and guarantees no outcome. Markets carry risk; do your own research and manage your own risk. Paper-trade before risking real money.
Indicator
Historical Pattern Projection [MarkitTick]💡 An advanced analytical framework engineered to identify, isolate, and project current price action based on historically correlated market structures. Rather than relying on traditional lagging oscillators or subjective chart patterns, this tool continuously evaluates the most recent sequence of price movements—termed the "fingerprint"—and algorithmically scans historical data to find statistically similar precedents. By projecting the historical outcomes of these matching patterns onto the current chart, it provides an empirical, data-driven perspective on potential near-term price trajectories, seamlessly bridging the gap between quantitative correlation analysis and practical trade management.
✨ Originality and Utility
Traditional technical analysis often relies on rigid, subjective patterns (such as head-and-shoulders or flags) which can be open to interpretation and cognitive bias.
This script completely bypasses subjective pattern drawing by employing a strictly mathematical approach to shape-matching.
It normalizes price action into a pure structural format, allowing it to compare the geometry of the current market with historical markets, regardless of the absolute price levels.
The utility lies in its ability to automatically synthesize the "what happened next" data from historical matches.
Instead of merely signaling overbought or oversold conditions, it provides a probabilistic projection path—a "ghost line"—complete with expected volatility bands and automated risk management levels based on the anticipated outcome.
🔬 Methodology and Concepts
● Core Recognition Engine
• Data Normalization: The current price sequence (the fingerprint) is converted using a statistical Z-Score. This transformation removes the absolute price values and leaves behind the raw volatility-adjusted shape of the trend.
• Deep Historical Scanning: The algorithm iterates backwards through user-defined historical bars (Search Depth) to extract rolling arrays of previous price action.
• Statistical Correlation: Each historical array is compared to the current fingerprint using the Pearson Correlation Coefficient. The resulting value (-1.0 to +1.0) is mathematically scaled into a percentage (0% to 100%) to represent a "Similarity Score."
● Outcome Synthesis and Extrapolation
• Match Aggregation: The script filters out matches that fall below the minimum Similarity Score threshold, keeping only the top configured matches.
• Trajectory Calculation: For each valid match, the script records the price movement that occurred immediately after the historical pattern completed.
• Price Scaling: The historical outcomes are structurally scaled and tethered to the current closing price, allowing the indicator to plot a composite average of these historical outcomes directly into the future empty space of the chart.
🎨 Visual Guide
● On-Chart Projections and Highlights
• ECHO Match Zones: The historical periods that closely match the current price action are highlighted with thick, colored vertical bands (defaulting to deep orange). These zones allow for immediate visual verification of the structural similarity.
• Ghost Line (Projection): Plotted into the future, this solid, fading purple line illustrates the average expected trajectory based on the historical matches. It features an arrow and percentage label at the terminus to indicate the total projected directional move.
• Range Bands: Dashed, semi-transparent purple lines expanding outward from the Ghost Line. These bands represent the expected volatility expansion over the projection period, calculated using a dynamically scaling Average True Range (ATR).
● Trade Management Ecosystem
• Entry Box: A highlighted zone (default yellow) projecting forward from the current bar, representing an optimal entry width based on a fraction of the current ATR.
• Stop Loss (SL) Line: A solid red horizontal line indicating the suggested invalidation level, dynamically placed away from the entry using an ATR multiplier.
• Take Profit (TP) Lines: Three dashed green horizontal lines representing tiered profit targets (TP1, TP2, and TP3), scaled mathematically via ATR multipliers in the direction of the historical bias.
● The ECHO Dashboard Table
• Top Match Score: Displays the similarity percentage of the most highly correlated historical pattern. Color-coded for rapid assessment (Green for Strong >80%, Yellow for Moderate, Orange for Weak).
• Fingerprint & Depth: Confirms the lookback length and the total bars scanned.
• Outcome Metrics: Displays the historical "Votes" (percentage of matches that went Bullish, Bearish, or Neutral) and the overall Average Move.
• Historical Roster: The bottom half of the dashboard ranks the individual top historical matches, detailing their exact score, how many bars ago they occurred, and their specific post-pattern return.
📖 How to Use
• Pattern Validation: Monitor the ECHO Dashboard for patterns that achieve a Similarity Score of 80% or higher. Lower correlation scores should be treated with high skepticism as the historical geometries are not closely aligned.
• Bias Confirmation: Check the "Proj Bias" and "Votes" metrics on the dashboard. A strong projection should ideally have unanimous or near-unanimous historical consensus (e.g., 100% Bullish votes).
• Trade Execution: If a high-probability setup is identified, utilize the projected Trade Management levels. The highlighted Entry Box provides a buffer for execution, while the SL and TP lines offer an objective, volatility-adjusted framework for placing orders.
• Alert Integration: The indicator can be tied to dynamic webhooks. Set an alert on the indicator, and when a "Strong" match is found, it will automatically transmit a JSON payload containing the Entry, Stop Loss, and Take Profit levels for automated systems or notifications.
⚙️ Inputs and Settings
● Core Settings
• Freeze Data: A toggle that stops the algorithm from updating on every tick, locking the current projection in place for stable analysis.
• Fingerprint Length: The number of current bars used to form the recognizable pattern. Shorter lengths are highly responsive but prone to noise; longer lengths find deep structural macro-patterns.
• Search Depth: The maximum number of historical bars the algorithm will scan. Increasing this expands the database but requires more computational resources.
• Min Score (%): The correlation threshold required for a historical pattern to be considered valid.
● Projection & Risk Settings
• Ghost Length: Defines how many bars into the future the algorithm should project the historical outcome.
• Entry Width (xATR): Defines the vertical height of the entry box based on current volatility.
• SL & TP Multipliers: Adjustable factors that determine the distance of Stop Loss and Take Profit levels based on the current 14-period ATR.
🔍 Deconstruction of the Underlying Scientific and Academic Framework
• Z-Score Standardization: The algorithm employs standard score normalization to analyze price action. By subtracting the moving average (mean) from the price and dividing by the standard deviation, the data is transformed into a dimensionless unit. This ensures that a pattern occurring in a low-volatility environment can be mathematically matched to the exact same geometric structure occurring in a high-volatility environment.
• Pearson Correlation Coefficient: The core matching engine relies on Pearson's *r*, a measure of linear correlation between two sets of data. The formula computes the covariance of the current fingerprint and the historical candidate window, divided by the product of their standard deviations. This rigorously quantifies how closely the two price paths mirror each other over the specified timeframe.
• Volatility-Adjusted Target Extrapolation: Rather than using fixed percentages or subjective support/resistance, the script utilizes the Average True Range (ATR) to govern its forward-looking risk management bands. Because market regimes shift, the ATR ensures that the projected bands and trade levels expand during turbulent market phases and contract during periods of consolidation, maintaining mathematical proportionality to current market conditions.
• Algorithmic Caveats: Because the script continuously scans and matches the most recent data, the projected path will shift dynamically as new bars form, unless the "Freeze" function is engaged. Furthermore, the indicator evaluates the close of bars; running this framework on non-standard synthetic charts (such as Heikin Ashi or Renko) is fundamentally flawed due to the artificial smoothing of synthetic price data, which alters the underlying statistical distribution.
⚠️ Disclaimer
All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. We expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion. Indicator
Indicator
AI Predictive Flow (Zeiierman)█ Overview
AI Predictive Flow (Zeiierman) is a pattern-based oscillator that estimates future price direction by comparing the current market state to similar historical conditions.
Instead of relying on traditional indicators like momentum or moving averages alone, the script builds a multi-feature representation of price behavior and uses a k-Nearest Neighbors (kNN) model to identify past patterns that closely resemble the present.
From those matches, it derives an expected forward return, which is then transformed into a smooth oscillator and a predicted trend regime.
The result is a forward-looking signal that reflects a data-driven expectation based on similar past patterns, not just current price movement.
█ How It Works
⚪ Feature Extraction (Market State Model)
The script converts price into a compact feature set that describes the current market state.
It uses four core features:
Short-term return
Momentum
RSI bias
EMA spread
These are created inside the feature function:
feat(shift, mode) =>
c = close
c1 = close
cm = close
ef = ta.ema(close, fLen)
es = ta.ema(close, sLen)
r = ta.rsi(close, rsiLn)
float v = 0.0
if mode == 1
v := c1 != 0 ? math.log(c / c1) : 0.0
else if mode == 2
v := cm != 0 ? (c - cm) / cm : 0.0
else if mode == 3
v := (r - 50.0) / 50.0
else
v := c != 0 ? (ef - es) / c : 0.0
v
Each feature captures a different dimension of price behavior:
return measures immediate movement
momentum measures directional displacement
RSI bias measures internal pressure
EMA spread measures trend structure
These values are then stacked across multiple bars to form the pattern used for comparison.
⚪ Pattern Memory (Historical Pattern Library)
The script stores rolling sequences of each feature into separate matrices so the current market state can be compared against past states.
That process is built here:
pushFeat(mat, mode) =>
vals = array.new(tot, 0.0)
for i = 0 to tot - 1
array.set(vals, tot - 1 - i, feat(i, mode))
cur = array.slice(vals, tot - len, tot)
old = array.slice(vals, 0, len)
matrix out = matrix.new(1, len, 0.0)
for i = 0 to len - 1
matrix.set(out, 0, i, array.get(cur, i))
hist = array.new(len, 0.0)
for i = 0 to len - 1
array.set(hist, i, array.get(old, i))
if mat.rows() >= mem
mat.remove_row(0)
mat.add_row(mat.rows(), hist)
out
This creates:
a current feature row
a rolling history of prior feature patterns
So rather than comparing single-bar values, the model compares multi-bar pattern structure.
⚪ Pattern Matching Engine (kNN Distance Model)
Once the current feature pattern is built, it is compared to all stored historical patterns.
Distance is measured feature-by-feature across the full pattern length:
getDist(matrix a1, matrix a2, matrix a3, matrix a4, matrix b1, matrix b2, matrix b3, matrix b4) =>
out = array.new(b1.rows(), 0.0)
for i = 0 to b1.rows() - 1
s = 0.0
d1 = a1.diff(b1.submatrix(i, i + 1)).row(0)
d2 = a2.diff(b2.submatrix(i, i + 1)).row(0)
d3 = a3.diff(b3.submatrix(i, i + 1)).row(0)
d4 = a4.diff(b4.submatrix(i, i + 1)).row(0)
for j = 0 to len - 1
s += math.pow(d1.get(j), 2) * 0.25 +
math.pow(d2.get(j), 2) * 0.25 +
math.pow(d3.get(j), 2) * 0.25 +
math.pow(d4.get(j), 2) * 0.25
out.set(i, math.sqrt(s))
out
This produces a similarity score for every stored pattern. A smaller distance means the past setup looked more like the present one.
⚪ Prediction Model (kNN Forward Expectation)
After the distances are ranked, the script selects the nearest neighbors and averages their future outcomes.
The kNN model is implemented here:
knn(dist, n) =>
ix = dist.sort_indices()
useN = math.min(n, ix.size())
sumD = 0.0
avg = 0.0
for i = 0 to useN - 1
sumD += dist.get(ix.get(i))
if useN > 0
for i = 0 to useN - 1
d = dist.get(ix.get(i))
w = useN > 1 ? (sumD != 0 ? (1 - d / sumD) : 1.0) : 1.0
avg += Y.get(ix.get(i)) * w
avg
The forward return used for comparison is defined here:
y := math.log(base) - math.log(base )
This represents the forward return following each historical pattern. The result is a weighted expectation of future movement, not just a reading of current trend.
⚪ Predictive Oscillator
The raw kNN prediction is smoothed and transformed into the main oscillator and signal line.
pred_ = ta.ema(pred, smth)
if not na(pred)
predSm := smth > 1 ? pred_ : pred
osc = ta.ema(predSm, oscLn)
sig = ta.ema(osc, sigLn)
hist = osc - sig
This creates:
Oscillator = smoothed expected return
Signal line = secondary smoothing for crossover confirmation
Histogram = distance between oscillator and signal
⚪ Predicted Trend Regime
Beyond the oscillator, the script also builds a broader trend regime using the predicted price path.
First, the raw prediction is converted into a projected price line:
predLine := base + base * (math.exp(pred) - 1)
Then a regime band is created using ATR:
hiRef = predLine + bandM * atr
loRef = predLine - bandM * atr
if ta.highest(hiRef, regLn) == hiRef
trendUp := true
if ta.lowest(loRef, regLn) == loRef
trendUp := false
This background state represents:
bullish predicted regime when the projected path is pressing into new highs
bearish predicted regime when the projected path is pressing into new lows
So the background is not showing the raw price trend. It is showing the model’s predicted regime bias.
█ How to Use
⚪ Read the Oscillator
Above 0 → bullish expectation
Below 0 → bearish expectation
Near 0 → neutral/low conviction
Far from 0 → strong directional push
Use crossovers for entry timing:
Bullish crossover → potential upward continuation
Bearish crossover → potential downward continuation
⚪ Use the Predicted Trend Regime
The background highlights the model’s broader directional bias:
Green → predicted bullish regime
Red → predicted bearish regime
Regime shifts often indicate:
early trend transitions
continuation confirmation
structural changes in expectation
⚪ Combine Signals
Best use comes from alignment:
Oscillator above zero + bullish regime + signal → strong continuation bias
Oscillator below zero + bearish regime + signal → strong downside bias
Divergence between the two → caution / mixed signals
█ Settings
Pattern Length – Controls how many bars define the current pattern. Higher values capture more structure, lower values increase responsiveness.
Memory Size – Number of historical patterns stored for comparison. Larger values improve context but increase computation.
Neighbors (k) – Number of closest matches used in prediction. Lower values are more reactive, higher values are smoother.
Prediction Smoothing – EMA smoothing applied to the raw prediction. Reduces noise at the cost of lag.
Signal Length – Smoothing of the signal line used for crossover signals.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Indicator
Triangle Pattern Detection [Dots3Red]Triangle Pattern Detection Indicator detects and draws triangle chart patterns in real time. The indicator identifies pivot highs and lows, validates converging trendlines, calculates the mathematical apex, and classifies each pattern by type.
How it works
The indicator scans for pivot highs and pivot lows using a configurable lookback length. Once two valid pivot highs and two valid pivot lows are found, it verifies that:
The trendlines are genuinely converging (spread shrinks from left to right)
No bar between the pivots violates the trendline boundary
The upper and lower patterns are aligned in time
The pattern meets minimum and maximum width requirements
If all conditions pass, the apex is calculated mathematically as the exact intersection point of the two trendlines, and the full triangle is drawn from the shared start bar to the apex.
Pattern Types Detected
Symmetrical Triangle — both lines converging toward each other
Ascending Triangle — flat upper resistance, rising lower support
Descending Triangle — declining upper resistance, flat lower support
Rectangle — both lines near horizontal
Visuals
🟡 Electric glow triangle
▼ / ▲ Pivot markers at pattern boundaries ( commented out in the code )
🔵 Bullish breakout signal / 🔴 Bearish breakout signal with glow effect and stem line
Number label at each triangle's start for cross-referencing with the info table
Info Table (top right)
Keeps a record of the last 5 detected triangles showing:
Column - Description
ID - Triangle number
Type- Pattern classification
Width - Pattern width in bars
BO date - The day the price broke out from the triangle pattern
Status - Live breakout status — Bull / Bear / Inside
The Status column updates on every bar so you can see in real time whether price has broken out of any recent pattern.
Settings
Pivot Length - Sensitivity of pivot detection (1–10). Lower = more patterns
Max Pattern Width - Maximum bars between first and second pivots
Start Bar Alignment Tolerance - Max distance between the start of the upper and lower pivots of the triangle's sides
Max Apex Distance - How far ahead the apex can project
Min Pattern Width - Filters out patterns that are too narrow to be meaningful
Breakout Threshold % - How far the price must close beyond the trendline to confirm a breakout
Notes
All triangles are drawn on the last bar only, avoiding repainting of historical patterns
Breakout detection scans from the leftmost bar of the pattern to the apex, firing on the first confirmed close beyond the boundary
Example on BTC 🪙 chart
Indicator
CVD Flow Labels for Sessions Ranges [AMT Edition]CVD Flow Labels for Session Ranges
Description:
This script provides a session-aware Cumulative Volume Delta (CVD) analysis designed to enhance the “Session Ranges ” framework by combining price extremes with detailed volume flow dynamics. Unlike generic trend or scalping indicators, this tool focuses on identifying aggressive buying and selling pressure, distinguishing between absorption (failed auctions where aggressive flows are rejected) and acceptance (confirmed continuation of flows).
How it works:
CVD Calculation: The script calculates delta for each bar using a choice of Total, Periodic, or EMA-based cumulative methods. Delta represents the net difference between estimated buying and selling volume per bar.
Normalization: By normalizing delta relative to recent volatility, it highlights extreme flows that are statistically significant, making large shifts in market sentiment easier to spot.
Session-Specific Analysis: The indicator separates Asia, London, and New York sessions to allow context-sensitive interpretation of price and volume interactions. Each session’s extremes are monitored, and flow labels are plotted relative to these extremes.
Flow Labels: Bullish and bearish absorption (“ABS”) and acceptance (“ACC WEAK/STRONG”) labels provide immediate visual cues about whether aggressive flows are being absorbed or accepted at key price levels.
Alerts: Configurable alerts trigger when absorption or acceptance occurs, supporting active trading or strategy automation.
Originality & Usefulness:
This script is original because it integrates volume-based auction theory with session-specific market structure, rather than simply showing trend or scalping signals. By combining CVD dynamics with session extreme levels from the “Session Ranges ” script, traders can:
Identify where price is likely to be accepted or rejected.
Confirm aggressive buying or selling flows before entering trades.
Time entries near session extremes with higher probability setups.
How to use:
Apply the “Session Ranges ” to see session highs, lows, and interaction lines.
Use this CVD Flow Labels script to visualize absorption and acceptance at these session levels.
Enter trades based on alignment of session extremes and flow signals:
Absorption at a session extreme may indicate a potential reversal.
Acceptance suggests continuation in the direction of the flow.
Alerts can help manage trades without constant screen monitoring.
This tool is designed to give traders a structured, session-based view of market auctions, providing actionable insights that go beyond typical trend-following or scalping methods. It emphasizes flow analysis and statistical extremes, enabling traders to make more informed decisions grounded in market microstructure. Indicator
PatternTransitionTablesPatternTransitionTables Library
🌸 Part of GoemonYae Trading System (GYTS) 🌸
🌸 --------- 1. INTRODUCTION --------- 🌸
💮 Overview
This library provides precomputed state transition tables to enable ultra-efficient, O(1) computation of Ordinal Patterns. It is designed specifically to support high-performance indicators calculating Permutation Entropy and related complexity measures.
💮 The Problem & Solution
Calculating Permutation Entropy, as introduced by Bandt and Pompe (2002), typically requires computing ordinal patterns within a sliding window at every time step. The standard successive-pattern method (Equations 2+3 in the paper) requires ≤ 4d-1 operations per update.
Unakafova and Keller (2013) demonstrated that successive ordinal patterns "overlap" significantly. By knowing the current pattern index and the relative rank (position l) of just the single new data point, the next pattern index can be determined via a precomputed look-up table. Computing l still requires d comparisons, but the table lookup itself is O(1), eliminating the need for d multiplications and d additions. This reduces total operations from ≤ 4d-1 to ≤ 2d per update (Table 4). This library contains these precomputed tables for orders d = 2 through d = 5.
🌸 --------- 2. THEORETICAL BACKGROUND --------- 🌸
💮 Permutation Entropy
Bandt, C., & Pompe, B. (2002). Permutation entropy: A natural complexity measure for time series.
doi.org
This concept quantifies the complexity of a system by comparing the order of neighbouring values rather than their magnitudes. It is robust against noise and non-linear distortions, making it ideal for financial time series analysis.
💮 Efficient Computation
Unakafova, V. A., & Keller, K. (2013). Efficiently Measuring Complexity on the Basis of Real-World Data.
doi.org
This library implements the transition function φ_d(n, l) described in Equation 5 of the paper. It maps a current pattern index (n) and the position of the new value (l) to the successor pattern, reducing the complexity of updates to constant time O(1).
🌸 --------- 3. LIBRARY FUNCTIONALITY --------- 🌸
💮 Data Structure
The library stores transition matrices as flattened 1D integer arrays. These tables are mathematically rigorous representations of the factorial number system used to enumerate permutations.
💮 Core Function: get_successor()
This is the primary interface for the library for direct pattern updates.
• Input: The current pattern index and the rank position of the incoming price data.
• Process: Routes the request to the specific transition table for the chosen order (d=2 to d=5).
• Output: The integer index of the next ordinal pattern.
💮 Table Access: get_table()
This function returns the entire flattened transition table for a specified dimension. This enables local caching of the table (e.g. in an indicator's init() method), avoiding the overhead of repeated library calls during the calculation loop.
💮 Supported Orders & Terminology
The parameter d is the order of ordinal patterns (following Bandt & Pompe 2002). Each pattern of order d contains (d+1) data points, yielding (d+1)! unique patterns:
• d=2: 3 points → 6 unique patterns, 3 successor positions
• d=3: 4 points → 24 unique patterns, 4 successor positions
• d=4: 5 points → 120 unique patterns, 5 successor positions
• d=5: 6 points → 720 unique patterns, 6 successor positions
Note: d=6 is not implemented. The resulting code size (approx. 191k tokens) exceeds the Pine Script limit of 100k tokens (as of 2025-12). Library
SMC N-Gram Probability Matrix [PhenLabs]📊 SMC N-Gram Probability Matrix
Version: PineScript™ v6
📌 Description
The SMC N-Gram Probability Matrix applies computational linguistics methodology to Smart Money Concepts trading. By treating SMC patterns as a discrete “alphabet” and analyzing their sequential relationships through N-gram modeling, this indicator calculates the statistical probability of which pattern will appear next based on historical transitions.
Traditional SMC analysis is reactive—traders identify patterns after they form and then anticipate the next move. This indicator inverts that approach by building a transition probability matrix from up to 5,000 bars of pattern history, enabling traders to see which SMC formations most frequently follow their current market sequence.
The indicator detects and classifies 11 distinct SMC patterns including Fair Value Gaps, Order Blocks, Liquidity Sweeps, Break of Structure, and Change of Character in both bullish and bearish variants, then tracks how these patterns transition from one to another over time.
🚀 Points of Innovation
First indicator to apply N-gram sequence modeling from computational linguistics to SMC pattern analysis
Dynamic transition matrix rebuilds every 50 bars for adaptive probability calculations
Supports bigram (2), trigram (3), and quadgram (4) sequence lengths for varying analysis depth
Priority-based pattern classification ensures higher-significance patterns (CHoCH, BOS) take precedence
Configurable minimum occurrence threshold filters out statistically insignificant predictions
Real-time probability visualization with graphical confidence bars
🔧 Core Components
Pattern Alphabet System: 11 discrete SMC patterns encoded as integers for efficient matrix indexing and transition tracking
Swing Point Detection: Uses ta.pivothigh/pivotlow with configurable sensitivity for non-repainting structure identification
Transition Count Matrix: Flattened array storing occurrence counts for all possible pattern sequence transitions
Context Encoder: Converts N-gram pattern sequences into unique integer IDs for matrix lookup
Probability Calculator: Transforms raw transition counts into percentage probabilities for each possible next pattern
🔥 Key Features
Multi-Pattern SMC Detection: Simultaneously identifies FVGs, Order Blocks, Liquidity Sweeps, BOS, and CHoCH formations
Adjustable N-Gram Length: Choose between 2-4 pattern sequences to balance specificity against sample size
Flexible Lookback Range: Analyze anywhere from 100 to 5,000 historical bars for matrix construction
Pattern Toggle Controls: Enable or disable individual SMC pattern types to customize analysis focus
Probability Threshold Filtering: Set minimum occurrence requirements to ensure prediction reliability
Alert Integration: Built-in alert conditions trigger when high-probability predictions emerge
🎨 Visualization
Probability Table: Displays current pattern, recent sequence, sample count, and top N predicted patterns with percentage probabilities
Graphical Probability Bars: Visual bar representation (█░) showing relative probability strength at a glance
Chart Pattern Markers: Color-coded labels placed directly on price bars identifying detected SMC formations
Pattern Short Codes: Compact notation (F+, F-, O+, O-, L↑, L↓, B+, B-, C+, C-) for quick pattern identification
Customizable Table Position: Place probability display in any corner of your chart
📖 Usage Guidelines
N-Gram Configuration
N-Gram Length: Default 2, Range 2-4. Lower values provide more samples but less specificity. Higher values capture complex sequences but require more historical data.
Matrix Lookback Bars: Default 500, Range 100-5000. More bars increase statistical significance but may include outdated market behavior.
Min Occurrences for Prediction: Default 2, Range 1-10. Higher values filter noise but may reduce prediction availability.
SMC Detection Settings
Swing Detection Length: Default 5, Range 2-20. Controls pivot sensitivity for structure analysis.
FVG Minimum Size: Default 0.1%, Range 0.01-2.0%. Filters insignificant gaps.
Order Block Lookback: Default 10, Range 3-30. Bars to search for OB formations.
Liquidity Sweep Threshold: Default 0.3%, Range 0.05-1.0%. Minimum wick extension beyond swing points.
Display Settings
Show Probability Table: Toggle the probability matrix display on/off.
Show Top N Probabilities: Default 5, Range 3-10. Number of predicted patterns to display.
Show SMC Markers: Toggle on-chart pattern labels.
✅ Best Use Cases
Anticipating continuation or reversal patterns after liquidity sweeps
Identifying high-probability BOS/CHoCH sequences for trend trading
Filtering FVG and Order Block signals based on historical follow-through rates
Building confluence by comparing predicted patterns with other technical analysis
Studying how SMC patterns typically sequence on specific instruments or timeframes
⚠️ Limitations
Predictions are based solely on historical pattern frequency and do not account for fundamental factors
Low sample counts produce unreliable probabilities—always check the Samples display
Market regime changes can invalidate historical transition patterns
The indicator requires sufficient historical data to build meaningful probability matrices
Pattern detection uses standardized parameters that may not capture all institutional activity
💡 What Makes This Unique
Linguistic Modeling Applied to Markets: Treats SMC patterns like words in a language, analyzing how they “flow” together
Quantified Pattern Relationships: Transforms subjective SMC analysis into objective probability percentages
Adaptive Learning: Matrix rebuilds periodically to incorporate recent pattern behavior
Comprehensive SMC Coverage: Tracks all major Smart Money Concepts in a unified probability framework
🔬 How It Works
1. Pattern Detection Phase
Each bar is analyzed for SMC formations using configurable detection parameters
A priority hierarchy assigns the most significant pattern when multiple detections occur
2. Sequence Encoding Phase
Detected patterns are stored in a rolling history buffer of recent classifications
The current N-gram context is encoded into a unique integer identifier
3. Matrix Construction Phase
Historical pattern sequences are iterated to count transition occurrences
Each context-to-next-pattern transition increments the appropriate matrix cell
4. Probability Calculation Phase
Current context ID retrieves corresponding transition counts from the matrix
Raw counts are converted to percentages based on total context occurrences
5. Visualization Phase
Probabilities are sorted and the top N predictions are displayed in the table
Chart markers identify the current detected pattern for visual reference
💡 Note:
This indicator performs best when used as a confluence tool alongside traditional SMC analysis. The probability predictions highlight statistically common pattern sequences but should not be used as standalone trading signals. Always verify predictions against price action context, higher timeframe structure, and your overall trading plan. Monitor the sample count to ensure predictions are based on adequate historical data. Indicator
Historical Matrix Analyzer [PhenLabs]📊Historical Matrix Analyzer
Version: PineScriptv6
📌Description
The Historical Matrix Analyzer is an advanced probabilistic trading tool that transforms technical analysis into a data-driven decision support system. By creating a comprehensive 56-cell matrix that tracks every combination of RSI states and multi-indicator conditions, this indicator reveals which market patterns have historically led to profitable outcomes and which have not.
At its core, the indicator continuously monitors seven distinct RSI states (ranging from Extreme Oversold to Extreme Overbought) and eight unique indicator combinations (MACD direction, volume levels, and price momentum). For each of these 56 possible market states, the system calculates average forward returns, win rates, and occurrence counts based on your configurable lookback period. The result is a color-coded probability matrix that shows you exactly where you stand in the historical performance landscape.
The standout feature is the Current State Panel, which provides instant clarity on your active market conditions. This panel displays signal strength classifications (from Strong Bullish to Strong Bearish), the average return percentage for similar past occurrences, an estimated win rate using Bayesian smoothing to prevent small-sample distortions, and a confidence level indicator that warns you when insufficient data exists for reliable conclusions.
🚀Points of Innovation
Multi-dimensional state classification combining 7 RSI levels with 8 indicator combinations for 56 unique trackable market conditions
Bayesian win rate estimation with adjustable smoothing strength to provide stable probability estimates even with limited historical samples
Real-time active cell highlighting with “NOW” marker that visually connects current market conditions to their historical performance data
Configurable color intensity sensitivity allowing traders to adjust heat-map responsiveness from conservative to aggressive visual feedback
Dual-panel display system separating the comprehensive statistics matrix from an easy-to-read current state summary panel
Intelligent confidence scoring that automatically warns traders when occurrence counts fall below reliable thresholds
🔧Core Components
RSI State Classification: Segments RSI readings into 7 distinct zones (Extreme Oversold <20, Oversold 20-30, Weak 30-40, Neutral 40-60, Strong 60-70, Overbought 70-80, Extreme Overbought >80) to capture momentum extremes and transitions
Multi-Indicator Condition Tracking: Simultaneously monitors MACD crossover status (bullish/bearish), volume relative to moving average (high/low), and price direction (rising/falling) creating 8 binary-encoded combinations
Historical Data Storage Arrays: Maintains rolling lookback windows storing RSI states, indicator states, prices, and bar indices for precise forward-return calculations
Forward Performance Calculator: Measures price changes over configurable forward bar periods (1-20 bars) from each historical state, accumulating total returns and win counts per matrix cell
Bayesian Smoothing Engine: Applies statistical prior assumptions (default 50% win rate) weighted by user-defined strength parameter to stabilize estimated win rates when sample sizes are small
Dynamic Color Mapping System: Converts average returns into color-coded heat map with intensity adjusted by sensitivity parameter and transparency modified by confidence levels
🔥Key Features
56-Cell Probability Matrix: Comprehensive grid displaying every possible combination of RSI state and indicator condition, with each cell showing average return percentage, estimated win rate, and occurrence count for complete statistical visibility
Current State Info Panel: Dedicated display showing your exact position in the matrix with signal strength emoji indicators, numerical statistics, and color-coded confidence warnings for immediate situational awareness
Customizable Lookback Period: Adjustable historical window from 50 to 500 bars allowing traders to focus on recent market behavior or capture longer-term pattern stability across different market cycles
Configurable Forward Performance Window: Select target holding periods from 1 to 20 bars ahead to align probability calculations with your trading timeframe, whether day trading or swing trading
Visual Heat Mapping: Color-coded cells transition from red (bearish historical performance) through gray (neutral) to green (bullish performance) with intensity reflecting statistical significance and occurrence frequency
Intelligent Data Filtering: Minimum occurrence threshold (1-10) removes unreliable patterns with insufficient historical samples, displaying gray warning colors for low-confidence cells
Flexible Layout Options: Independent positioning of statistics matrix and info panel to any screen corner, accommodating different chart layouts and personal preferences
Tooltip Details: Hover over any matrix cell to see full RSI label, complete indicator status description, precise average return, estimated win rate, and total occurrence count
🎨Visualization
Statistics Matrix Table: A 9-column by 8-row grid with RSI states labeling vertical axis and indicator combinations on horizontal axis, using compact abbreviations (XOverS, OverB, MACD↑, Vol↓, P↑) for space efficiency
Active Cell Indicator: The current market state cell displays “⦿ NOW ⦿” in yellow text with enhanced color saturation to immediately draw attention to relevant historical performance
Signal Strength Visualization: Info panel uses emoji indicators (🔥 Strong Bullish, ✅ Bullish, ↗️ Weak Bullish, ➖ Neutral, ↘️ Weak Bearish, ⛔ Bearish, ❄️ Strong Bearish, ⚠️ Insufficient Data) for rapid interpretation
Histogram Plot: Below the price chart, a green/red histogram displays the current cell’s average return percentage, providing a time-series view of how historical performance changes as market conditions evolve
Color Intensity Scaling: Cell background transparency and saturation dynamically adjust based on both the magnitude of average returns and the occurrence count, ensuring visual emphasis on reliable patterns
Confidence Level Display: Info panel bottom row shows “High Confidence” (green), “Medium Confidence” (orange), or “Low Confidence” (red) based on occurrence counts relative to minimum threshold multipliers
📖Usage Guidelines
RSI Period
Default: 14
Range: 1 to unlimited
Description: Controls the lookback period for RSI momentum calculation. Standard 14-period provides widely-recognized overbought/oversold levels. Decrease for faster, more sensitive RSI reactions suitable for scalping. Increase (21, 28) for smoother, longer-term momentum assessment in swing trading. Changes affect how quickly the indicator moves between the 7 RSI state classifications.
MACD Fast Length
Default: 12
Range: 1 to unlimited
Description: Sets the faster exponential moving average for MACD calculation. Standard 12-period setting works well for daily charts and captures short-term momentum shifts. Decreasing creates more responsive MACD crossovers but increases false signals. Increasing smooths out noise but delays signal generation, affecting the bullish/bearish indicator state classification.
MACD Slow Length
Default: 26
Range: 1 to unlimited
Description: Defines the slower exponential moving average for MACD calculation. Traditional 26-period setting balances trend identification with responsiveness. Must be greater than Fast Length. Wider spread between fast and slow increases MACD sensitivity to trend changes, impacting the frequency of indicator state transitions in the matrix.
MACD Signal Length
Default: 9
Range: 1 to unlimited
Description: Smoothing period for the MACD signal line that triggers bullish/bearish state changes. Standard 9-period provides reliable crossover signals. Shorter values create more frequent state changes and earlier signals but with more whipsaws. Longer values produce more confirmed, stable signals but with increased lag in detecting momentum shifts.
Volume MA Period
Default: 20
Range: 1 to unlimited
Description: Lookback period for volume moving average used to classify volume as “high” or “low” in indicator state combinations. 20-period default captures typical monthly trading patterns. Shorter periods (10-15) make volume classification more reactive to recent spikes. Longer periods (30-50) require more sustained volume changes to trigger state classification shifts.
Statistics Lookback Period
Default: 200
Range: 50 to 500
Description: Number of historical bars used to calculate matrix statistics. 200 bars provides substantial data for reliable patterns while remaining responsive to regime changes. Lower values (50-100) emphasize recent market behavior and adapt quickly but may produce volatile statistics. Higher values (300-500) capture long-term patterns with stable statistics but slower adaptation to changing market dynamics.
Forward Performance Bars
Default: 5
Range: 1 to 20
Description: Number of bars ahead used to calculate forward returns from each historical state occurrence. 5-bar default suits intraday to short-term swing trading (5 hours on hourly charts, 1 week on daily charts). Lower values (1-3) target short-term momentum trades. Higher values (10-20) align with position trading and longer-term pattern exploitation.
Color Intensity Sensitivity
Default: 2.0
Range: 0.5 to 5.0, step 0.5
Description: Amplifies or dampens the color intensity response to average return magnitudes in the matrix heat map. 2.0 default provides balanced visual emphasis. Lower values (0.5-1.0) create subtle coloring requiring larger returns for full saturation, useful for volatile instruments. Higher values (3.0-5.0) produce vivid colors from smaller returns, highlighting subtle edges in range-bound markets.
Minimum Occurrences for Coloring
Default: 3
Range: 1 to 10
Description: Required minimum sample size before applying color-coded performance to matrix cells. Cells with fewer occurrences display gray “insufficient data” warning. 3-occurrence default filters out rare patterns. Lower threshold (1-2) shows more data but includes unreliable single-event statistics. Higher thresholds (5-10) ensure only well-established patterns receive visual emphasis.
Table Position
Default: top_right
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the 56-cell statistics matrix table. Position to avoid overlapping critical price action or other indicators on your chart. Consider chart orientation and candlestick density when selecting optimal placement.
Show Current State Panel
Default: true
Options: true, false
Description: Toggle visibility of the dedicated current state information panel. When enabled, displays signal strength, RSI value, indicator status, average return, estimated win rate, and confidence level for active market conditions. Disable to declutter charts when only the matrix table is needed.
Info Panel Position
Default: bottom_left
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the current state information panel (when enabled). Position independently from statistics matrix to optimize chart real estate. Typically placed opposite the matrix table for balanced visual layout.
Win Rate Smoothing Strength
Default: 5
Range: 1 to 20
Description: Controls Bayesian prior weighting for estimated win rate calculations. Acts as virtual sample size assuming 50% win rate baseline. Default 5 provides moderate smoothing preventing extreme win rate estimates from small samples. Lower values (1-3) reduce smoothing effect, allowing win rates to reflect raw data more directly. Higher values (10-20) increase conservatism, pulling win rate estimates toward 50% until substantial evidence accumulates.
✅Best Use Cases
Pattern-based discretionary trading where you want historical confirmation before entering setups that “look good” based on current technical alignment
Swing trading with holding periods matching your forward performance bar setting, using high-confidence bullish cells as entry filters
Risk assessment and position sizing, allocating larger size to trades originating from cells with strong positive average returns and high estimated win rates
Market regime identification by observing which RSI states and indicator combinations are currently producing the most reliable historical patterns
Backtesting validation by comparing your manual strategy signals against the historical performance of the corresponding matrix cells
Educational tool for developing intuition about which technical condition combinations have actually worked versus those that feel right but lack historical evidence
⚠️Limitations
Historical patterns do not guarantee future performance, especially during unprecedented market events or regime changes not represented in the lookback period
Small sample sizes (low occurrence counts) produce unreliable statistics despite Bayesian smoothing, requiring caution when acting on low-confidence cells
Matrix statistics lag behind rapidly changing market conditions, as the lookback period must accumulate new state occurrences before updating performance data
Forward return calculations use fixed bar periods that may not align with actual trade exit timing, support/resistance levels, or volatility-adjusted profit targets
💡What Makes This Unique
Multi-Dimensional State Space: Unlike single-indicator tools, simultaneously tracks 56 distinct market condition combinations providing granular pattern resolution unavailable in traditional technical analysis
Bayesian Statistical Rigor: Implements proper probabilistic smoothing to prevent overconfidence from limited data, a critical feature missing from most pattern recognition tools
Real-Time Contextual Feedback: The “NOW” marker and dedicated info panel instantly connect current market conditions to their historical performance profile, eliminating guesswork
Transparent Occurrence Counts: Displays sample sizes directly in each cell, allowing traders to judge statistical reliability themselves rather than hiding data quality issues
Fully Customizable Analysis Window: Complete control over lookback depth and forward return horizons lets traders align the tool precisely with their trading timeframe and strategy requirements
🔬How It Works
1. State Classification and Encoding
Each bar’s RSI value is evaluated and assigned to one of 7 discrete states based on threshold levels (0: <20, 1: 20-30, 2: 30-40, 3: 40-60, 4: 60-70, 5: 70-80, 6: >80)
Simultaneously, three binary conditions are evaluated: MACD line position relative to signal line, current volume relative to its moving average, and current close relative to previous close
These three binary conditions are combined into a single indicator state integer (0-7) using binary encoding, creating 8 possible indicator combinations
The RSI state and indicator state are stored together, defining one of 56 possible market condition cells in the matrix
2. Historical Data Accumulation
As each bar completes, the current state classification, closing price, and bar index are stored in rolling arrays maintained at the size specified by the lookback period
When the arrays reach capacity, the oldest data point is removed and the newest added, creating a sliding historical window
This continuous process builds a comprehensive database of past market conditions and their subsequent price movements
3. Forward Return Calculation and Statistics Update
On each bar, the indicator looks back through the stored historical data to find bars where sufficient forward bars exist to measure outcomes
For each historical occurrence, the price change from that bar to the bar N periods ahead (where N is the forward performance bars setting) is calculated as a percentage return
This percentage return is added to the cumulative return total for the specific matrix cell corresponding to that historical bar’s state classification
Occurrence counts are incremented, and wins are tallied for positive returns, building comprehensive statistics for each of the 56 cells
The Bayesian smoothing formula combines these raw statistics with prior assumptions (neutral 50% win rate) weighted by the smoothing strength parameter to produce estimated win rates that remain stable even with small samples
💡Note:
The Historical Matrix Analyzer is designed as a decision support tool, not a standalone trading system. Best results come from using it to validate discretionary trade ideas or filter systematic strategy signals. Always combine matrix insights with proper risk management, position sizing rules, and awareness of broader market context. The estimated win rate feature uses Bayesian statistics specifically to prevent false confidence from limited data, but no amount of smoothing can create reliable predictions from fundamentally insufficient sample sizes. Focus on high-confidence cells (green-colored confidence indicators) with occurrence counts well above your minimum threshold for the most actionable insights. Indicator
CNN Statistical Trading System [PhenLabs]📌 DESCRIPTION
An advanced pattern recognition system utilizing Convolutional Neural Network (CNN) principles to identify statistically significant market patterns and generate high-probability trading signals.
CNN Statistical Trading System transforms traditional technical analysis by applying machine learning concepts directly to price action. Through six specialized convolution kernels, it detects momentum shifts, reversal patterns, consolidation phases, and breakout setups simultaneously. The system combines these pattern detections using adaptive weighting based on market volatility and trend strength, creating a sophisticated composite score that provides both directional bias and signal confidence on a normalized -1 to +1 scale.
🚀 CONCEPTS
• Built on Convolutional Neural Network pattern recognition methodology adapted for financial markets
• Six specialized kernels detect distinct price patterns: upward/downward momentum, peak/trough formations, consolidation, and breakout setups
• Activation functions create non-linear responses with tanh-like behavior, mimicking neural network layers
• Adaptive weighting system adjusts pattern importance based on current market regime (volatility < 2% and trend strength)
• Multi-confirmation signals require CNN threshold breach (±0.65), RSI boundaries, and volume confirmation above 120% of 20-period average
🔧 FEATURES
Six-Kernel Pattern Detection:
Simultaneous analysis of upward momentum, downward momentum, peak/resistance, trough/support, consolidation, and breakout patterns using mathematically optimized convolution kernels.
Adaptive Neural Architecture:
Dynamic weight adjustment based on market volatility (ATR/Price) and trend strength (EMA differential), ensuring optimal performance across different market conditions.
Professional Visual Themes:
Four sophisticated color palettes (Professional, Ocean, Sunset, Monochrome) with cohesive design language. Default Monochrome theme provides clean, distraction-free analysis.
Confidence Band System:
Upper and lower confidence zones at 150% of threshold values (±0.975) help identify high-probability signal areas and potential exhaustion zones.
Real-Time Information Panel:
Live display of CNN score, market state with emoji indicators, net momentum, confidence percentage, and RSI confirmation with dynamic color coding based on signal strength.
Individual Feature Analysis:
Optional display of all six kernel outputs with distinct visual styles (step lines, circles, crosses, area fills) for advanced pattern component analysis.
User Guide
• Monitor CNN Score crossing above +0.65 for long signals or below -0.65 for short signals with volume confirmation
• Use confidence bands to identify optimal entry zones - signals within confidence bands carry higher probability
• Background intensity reflects signal strength - darker backgrounds indicate stronger conviction
• Enter long positions when blue circles appear above oscillator with RSI < 75 and volume > 120% average
• Enter short positions when dark circles appear below oscillator with RSI > 25 and volume confirmation
• Information panel provides real-time confidence percentage and momentum direction for position sizing decisions
• Individual feature plots allow granular analysis of specific pattern components for strategy refinement
💡Conclusion
CNN Statistical Trading System represents the evolution of technical analysis, combining institutional-grade pattern recognition with retail accessibility. The six-kernel architecture provides comprehensive market pattern coverage while adaptive weighting ensures relevance across all market conditions. Whether you’re seeking systematic entry signals or advanced pattern confirmation, this indicator delivers mathematically rigorous analysis with intuitive visual presentation. Indicator
Volume Predictor [PhenLabs]📊 Volume Predictor
Version: PineScript™ v6
📌 Description
The Volume Predictor is an advanced technical indicator that leverages machine learning and statistical modeling techniques to forecast future trading volume. This innovative tool analyzes historical volume patterns to predict volume levels for upcoming bars, providing traders with valuable insights into potential market activity. By combining multiple prediction algorithms with pattern recognition techniques, the indicator delivers forward-looking volume projections that can enhance trading strategies and market analysis.
🚀 Points of Innovation:
Machine learning pattern recognition using Lorentzian distance metrics
Multi-algorithm prediction framework with algorithm selection
Ensemble learning approach combining multiple prediction methods
Real-time accuracy metrics with visual performance dashboard
Dynamic volume normalization for consistent scale representation
Forward-looking visualization with configurable prediction horizon
🔧 Core Components
Pattern Recognition Engine : Identifies similar historical volume patterns using Lorentzian distance metrics
Multi-Algorithm Framework : Offers five distinct prediction methods with configurable parameters
Volume Normalization : Converts raw volume to percentage scale for consistent analysis
Accuracy Tracking : Continuously evaluates prediction performance against actual outcomes
Advanced Visualization : Displays actual vs. predicted volume with configurable future bar projections
Interactive Dashboard : Shows real-time performance metrics and prediction accuracy
🔥 Key Features
The indicator provides comprehensive volume analysis through:
Multiple Prediction Methods : Choose from Lorentzian, KNN Pattern, Ensemble, EMA, or Linear Regression algorithms
Pattern Matching : Identifies similar historical volume patterns to project future volume
Adaptive Predictions : Generates volume forecasts for multiple bars into the future
Performance Tracking : Calculates and displays real-time prediction accuracy metrics
Normalized Scale : Presents volume as a percentage of historical maximums for consistent analysis
Customizable Visualization : Configure how predictions and actual volumes are displayed
Interactive Dashboard : View algorithm performance metrics in a customizable information panel
🎨 Visualization
Actual Volume Columns : Color-coded green/red bars showing current normalized volume
Prediction Columns : Semi-transparent blue columns representing predicted volume levels
Future Bar Projections : Forward-looking volume predictions with configurable transparency
Prediction Dots : Optional white dots highlighting future prediction points
Reference Lines : Visual guides showing the normalized volume scale
Performance Dashboard : Customizable panel displaying prediction method and accuracy metrics
📖 Usage Guidelines
History Lookback Period
Default: 20
Range: 5-100
This setting determines how many historical bars are analyzed for pattern matching. A longer period provides more historical data for pattern recognition but may reduce responsiveness to recent changes. A shorter period emphasizes recent market behavior but might miss longer-term patterns.
🧠 Prediction Method
Algorithm
Default: Lorentzian
Options: Lorentzian, KNN Pattern, Ensemble, EMA, Linear Regression
Selects the algorithm used for volume prediction:
Lorentzian: Uses Lorentzian distance metrics for pattern recognition, offering excellent noise resistance
KNN Pattern: Traditional K-Nearest Neighbors approach for historical pattern matching
Ensemble: Combines multiple methods with weighted averaging for robust predictions
EMA: Simple exponential moving average projection for trend-following predictions
Linear Regression: Projects future values based on linear trend analysis
Pattern Length
Default: 5
Range: 3-10
Defines the number of bars in each pattern for machine learning methods. Shorter patterns increase sensitivity to recent changes, while longer patterns may identify more complex structures but require more historical data.
Neighbors Count
Default: 3
Range: 1-5
Sets the K value (number of nearest neighbors) used in KNN and Lorentzian methods. Higher values produce smoother predictions by averaging more historical patterns, while lower values may capture more specific patterns but could be more susceptible to noise.
Prediction Horizon
Default: 5
Range: 1-10
Determines how many future bars to predict. Longer horizons provide more forward-looking information but typically decrease accuracy as the prediction window extends.
📊 Display Settings
Display Mode
Default: Overlay
Options: Overlay, Prediction Only
Controls how volume information is displayed:
Overlay: Shows both actual volume and predictions on the same chart
Prediction Only: Displays only the predictions without actual volume
Show Prediction Dots
Default: false
When enabled, adds white dots to future predictions for improved visibility and clarity.
Future Bar Transparency (%)
Default: 70
Range: 0-90
Controls the transparency of future prediction bars. Higher values make future bars more transparent, while lower values make them more visible.
📱 Dashboard Settings
Show Dashboard
Default: true
Toggles display of the prediction accuracy dashboard. When enabled, shows real-time accuracy metrics.
Dashboard Location
Default: Bottom Right
Options: Top Left, Top Right, Bottom Left, Bottom Right
Determines where the dashboard appears on the chart.
Dashboard Text Size
Default: Normal
Options: Small, Normal, Large
Controls the size of text in the dashboard for various display sizes.
Dashboard Style
Default: Solid
Options: Solid, Transparent
Sets the visual style of the dashboard background.
Understanding Accuracy Metrics
The dashboard provides key performance metrics to evaluate prediction quality:
Average Error
Shows the average difference between predicted and actual values
Positive values indicate the prediction tends to be higher than actual volume
Negative values indicate the prediction tends to be lower than actual volume
Values closer to zero indicate better prediction accuracy
Accuracy Percentage
A measure of how close predictions are to actual outcomes
Higher percentages (>70%) indicate excellent prediction quality
Moderate percentages (50-70%) indicate acceptable predictions
Lower percentages (<50%) suggest weaker prediction reliability
The accuracy metrics are color-coded for quick assessment:
Green: Strong prediction performance
Orange: Moderate prediction performance
Red: Weaker prediction performance
✅ Best Use Cases
Anticipate upcoming volume spikes or drops
Identify potential volume divergences from price action
Plan entries and exits around expected volume changes
Filter trading signals based on predicted volume support
Optimize position sizing by forecasting market participation
Prepare for potential volatility changes signaled by volume predictions
Enhance technical pattern analysis with volume projection context
⚠️ Limitations
Volume predictions become less accurate over longer time horizons
Performance varies based on market conditions and asset characteristics
Works best on liquid assets with consistent volume patterns
Requires sufficient historical data for pattern recognition
Sudden market events can disrupt prediction accuracy
Volume spikes may be muted in predictions due to normalization
💡 What Makes This Unique
Machine Learning Approach : Applies Lorentzian distance metrics for robust pattern matching
Algorithm Selection : Offers multiple prediction methods to suit different market conditions
Real-time Accuracy Tracking : Provides continuous feedback on prediction performance
Forward Projection : Visualizes multiple future bars with configurable display options
Normalized Scale : Presents volume as a percentage of maximum volume for consistent analysis
Interactive Dashboard : Displays key metrics with customizable appearance and placement
🔬 How It Works
The Volume Predictor processes market data through five main steps:
1. Volume Normalization:
Converts raw volume to percentage of maximum volume in lookback period
Creates consistent scale representation across different timeframes and assets
Stores historical normalized volumes for pattern analysis
2. Pattern Detection:
Identifies similar volume patterns in historical data
Uses Lorentzian distance metrics for robust similarity measurement
Determines strength of pattern match for prediction weighting
3. Algorithm Processing:
Applies selected prediction algorithm to historical patterns
For KNN/Lorentzian: Finds K nearest neighbors and calculates weighted prediction
For Ensemble: Combines multiple methods with optimized weighting
For EMA/Linear Regression: Projects trends based on statistical models
4. Accuracy Calculation:
Compares previous predictions to actual outcomes
Calculates average error and prediction accuracy
Updates performance metrics in real-time
5. Visualization:
Displays normalized actual volume with color-coding
Shows current and future volume predictions
Presents performance metrics through interactive dashboard
💡 Note:
The Volume Predictor performs optimally on liquid assets with established volume patterns. It’s most effective when used in conjunction with price action analysis and other technical indicators. The multi-algorithm approach allows adaptation to different market conditions by switching prediction methods. Pay special attention to the accuracy metrics when evaluating prediction reliability, as sudden market changes can temporarily reduce prediction quality. The normalized percentage scale makes the indicator consistent across different assets and timeframes, providing a standardized approach to volume analysis. Indicator
AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend)The AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend) is a cutting-edge indicator that combines advanced mathematical modeling, AI-driven analytics, and segment-based pattern recognition to forecast price movements with precision. This tool is designed to provide traders with deep insights into market dynamics by leveraging multivariate pattern detection and sophisticated predictive algorithms.
👽 Core Features
Segment-Based Pattern Recognition
At its heart, the indicator divides price data into discrete segments, capturing key elements like candle bodies, high-low ranges, and wicks. These segments are normalized using ATR-based volatility adjustments to ensure robustness across varying market conditions.
AI-Powered k-Nearest Neighbors (kNN) Prediction
The predictive engine uses the kNN algorithm to identify the closest historical patterns in a multivariate dictionary. By calculating the distance between current and historical segments, the algorithm determines the most likely outcomes, weighting predictions based on either proximity (distance) or averages.
Dynamic Dictionary of Historical Patterns
The indicator maintains a rolling dictionary of historical patterns, storing multivariate data for:
Candle body ranges, High-low ranges, Wick highs and lows.
This dynamic approach ensures the model adapts continuously to evolving market conditions.
Volatility-Normalized Forecasting
Using ATR bands, the indicator normalizes patterns, reducing noise and enhancing the reliability of predictions in high-volatility environments.
AI-Driven Trend Detection
The indicator not only predicts price levels but also identifies market regimes by comparing current conditions to historically significant highs, lows, and midpoints. This allows for clear visualizations of trend shifts and momentum changes.
👽 Deep Dive into the Core Mathematics
👾 Segment-Based Multivariate Pattern Analysis
The indicator analyzes price data by dividing each bar into distinct segments, isolating key components such as:
Body Ranges: Differences between the open and close prices.
High-Low Ranges: Capturing the full volatility of a bar.
Wick Extremes: Quantifying deviations beyond the body, both above and below.
Each segment contributes uniquely to the predictive model, ensuring a rich, multidimensional understanding of price action. These segments are stored in a rolling dictionary of patterns, enabling the indicator to reference historical behavior dynamically.
👾 Volatility Normalization Using ATR
To ensure robustness across varying market conditions, the indicator normalizes patterns using Average True Range (ATR). This process scales each component to account for the prevailing market volatility, allowing the algorithm to compare patterns on a level playing field regardless of differing price scales or fluctuations.
👾 k-Nearest Neighbors (kNN) Algorithm
The AI core employs the kNN algorithm, a machine-learning technique that evaluates the similarity between the current pattern and a library of historical patterns.
Euclidean Distance Calculation:
The indicator computes the multivariate distance across four distinct dimensions: body range, high-low range, wick low, and wick high. This ensures a comprehensive and precise comparison between patterns.
Weighting Schemes: The contribution of each pattern to the forecast is either weighted by its proximity (distance) or averaged, based on user settings.
👾 Prediction Horizon and Refinement
The indicator forecasts future price movements (Y_hat) by predicting logarithmic changes in the price and projecting them forward using exponential scaling. This forecast is smoothed using a user-defined EMA filter to reduce noise and enhance actionable clarity.
👽 AI-Driven Pattern Recognition
Dynamic Dictionary of Patterns: The indicator maintains a rolling dictionary of N multivariate patterns, continuously updated to reflect the latest market data. This ensures it adapts seamlessly to changing market conditions.
Nearest Neighbor Matching: At each bar, the algorithm identifies the most similar historical pattern. The prediction is based on the aggregated outcomes of the closest neighbors, providing confidence levels and directional bias.
Multivariate Synthesis: By combining multiple dimensions of price action into a unified prediction, the indicator achieves a level of depth and accuracy unattainable by single-variable models.
Visual Outputs
Forecast Line (Y_hat_line):
A smoothed projection of the expected price trend, based on the weighted contribution of similar historical patterns.
Trend Regime Bands:
Dynamic high, low, and midlines highlight the current market regime, providing actionable insights into momentum and range.
Historical Pattern Matching:
The nearest historical pattern is displayed, allowing traders to visualize similarities
👽 Applications
Trend Identification:
Detect and follow emerging trends early using dynamic trend regime analysis.
Reversal Signals:
Anticipate market reversals with high-confidence predictions based on historically similar scenarios.
Range and Momentum Trading:
Leverage multivariate analysis to understand price ranges and momentum, making it suitable for both breakout and mean-reversion strategies.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions. Indicator
Indicator
Sniffer
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Overview
A vast majority of modern data analysis & modelling techniques rely upon the idea of hidden patterns, wether it is some type of visualisation tool or some form of a complex machine learning algorithm, the one thing that they have in common is the belief, that patterns tell us what’s hidden behind plain numbers. The same philosophy has been adopted by many traders & investors worldwide, there’s an entire school of thought that operates purely based on chart patterns. This is where Sniffer comes in, it is a tool designed to simplify & quantify the job of pattern recognition on any given price chart, by combining various factors & techniques that generate high-quality results.
This tool analyses bars selected by the user, and highlights bar clusters on the chart that exhibit similar behaviour across multiple dimensions. It can detect a single candle pattern like hammers or dojis, or it can handle multiple candles like morning/evening stars or double tops/bottoms, and many more. In fact, the tool is completely independent of such specific candle formations, instead, it works on the idea of vector similarity and generates a degree of similarity for every single combination of candles. Only the top-n matches are highlighted, users get to choose which patterns they want to analyse and to what degree, by customising the feature-space.
Background
In the world of trading, a common use-case is to scan a price chart for some specific candlestick formations & price structures, and then the chart is further analysed in reference to these events. Traders are often trying to answer questions like, when was the last time price showed similar behaviour, what are the instances similar to what price is doing right now, what happens when price forms a pattern like this, what were some of other indicators doing when this happened last(RSI, CCI, ADX etc), and many other abstract ideas to have a stronger confluence or to confirm a bias.Having such a context can be vital in making better informed decisions, but doing this manually on a chart that has thousands of candles can have many disadvantages. It’s tedious, human errors are rather likely, and even if it’s done with pin-point accuracy, chances are that we’ll miss out on many pieces of information. This is the thought that gave birth to Sniffer .
Sniffer tries to provide a general solution for pattern-based analysis by deploying vector-similarity computation techniques, that cover the full-breadth of a price chart and generate a list of top-n matches based on the criteria selected by the user. Most of these techniques come from the data science space, where vector similarity is often implemented to solve classification & clustering problems. Sniffer uses same principles of vector comparison, and computes a degree of similarity for every single candle formation within the selected range, and as a result generates a similarity matrix that captures how similar or dissimilar a set of candles is to the input set selected by the user.
How It Works
A brief overview of how the tool is implemented:
- Every bar is processed, and a set of features are mapped to it.
- Bars selected by the user are captured, and saved for later use.
- Once the all the bars have been processed, candles are back-tracked and degree of similarity is computed for every single bar(max-limit is 5000 bars).
- Degree of similarity is computed by comparing attributes like price range, candle breadth & volume etc.
- Similarity matrix is sorted and top-n results are highlighted on the chart through boxes of different colors.
A brief overview of the features space for bars:
- Range: Difference between high & low
- Body: Difference between close & open
- Volume: Traded volume for that candle
- Head: Upper wick for green candles & lower wick for red candles
- Tail: Lower wick for green candles & upper wick for red candles
- BTR: Body to Range ratio
- HTR: Head to Range ratio
- TTR: Tail to Range ratio
- HTB: Head to Body ratio
- TTB: Tail to Body ratio
- ROC: Rate of change for HL2 for four different periods
- RSI: Relative Strength Index
- CCI: Commodity Channel Index
- Stochastic: Stochastic Index
- ADX: DMI+, DMI- & ADX
A brief overview of how degree of similarity is calculated:
- Each bar set is compared to the inout bar set within the selected feature space
- Features are represented as vectors, and distance between the vectors is calculated
- Shorter the distance, greater the similarity
- Different distance calculation methods are available to choose from, such as Cosine, Euclidean, Lorentzian, Manhattan, & Pearson
- Each method is likely to generate slightly different results, users are expected to select the method & the feature space that best fits their use-case
How To Use It
- Usage of this tool is relatively straightforward, users can add this indicator to their chart and similar clusters will be highlighted automatically
- Users need to select a time range that will be treated as input, and bars within that range become the input formation for similarity calculations
- Boxes will be draw around the clusters that fit the matching criteria
- Boxes are color-coded, green color boxes represent the top one-third of the top-n matches, yellow boxes represent the middle third, red boxes are for bottom third, and white box represents user-input
- Boxes colors will be adjusted as you adjust input parameters, such as number of matches or look-back period
User Settings
Users can configure the following options:
- Select the time-range to set input bars
- Select the look-back period, number of candles to backtrack for similarity search
- Select the number of top-n matches to show on the chart
- Select the method for similarity calculation
- Adjust the feature space, this enables addition of custom features, such as pattern recognition, technical indicators, rate of change etc
- Toggle verbosity, shows degree of similarity as a percentage value inside the box
Top Features
- Pattern Agnostic: Designed to work with variable number of candles & complex patterns
- Customisable Feature Space: Users get to add custom features to each bar
- Comprehensive Comparison: Generates a degree of similarity for all possible combinations
Final Note
- Similarity matches will be shown only within last 4500 bars.
- In theory, it is possible to compute similarity for any size candle formations, indicator has been tested with formations of 50+ candles, but it is recommended to select smaller range for faster & cleaner results.
- As you move to smaller time frames, selected time range will provide a larger number of candles as input, which can produce undesired results, it is advised to adjust your selection when you change time frames. Seeking suggestions on how to directly receive bars as user input, instead of time range.
- At times, users may see array index out of bound error when setting up this indicator, this generally happens when the input range is not properly configured. So, it should disappear after you select the input range, still trying to figure out where it is coming from, suggestions are welcome.
Credits
- @HeWhoMustNotBeNamed for publishing such a handy PineScript Logger, it certainly made the job a lot easier. Indicator
FunctionPatternDecompositionLibrary "FunctionPatternDecomposition"
Methods for decomposing price into common grid/matrix patterns.
series_to_array(source, length) Helper for converting series to array.
Parameters:
source : float, data series.
length : int, size.
Returns: float array.
smooth_data_2d(data, rate) Smooth data sample into 2d points.
Parameters:
data : float array, source data.
rate : float, default=0.25, the rate of smoothness to apply.
Returns: tuple with 2 float arrays.
thin_points(data_x, data_y, rate) Thin the number of points.
Parameters:
data_x : float array, points x value.
data_y : float array, points y value.
rate : float, default=2.0, minimum threshold rate of sample stdev to accept points.
Returns: tuple with 2 float arrays.
extract_point_direction(data_x, data_y) Extract the direction each point faces.
Parameters:
data_x : float array, points x value.
data_y : float array, points y value.
Returns: float array.
find_corners(data_x, data_y, rate) ...
Parameters:
data_x : float array, points x value.
data_y : float array, points y value.
rate : float, minimum threshold rate of data y stdev.
Returns: tuple with 2 float arrays.
grid_coordinates(data_x, data_y, m_size) transforms points data to a constrained sized matrix format.
Parameters:
data_x : float array, points x value.
data_y : float array, points y value.
m_size : int, default=10, size of the matrix.
Returns: flat 2d pseudo matrix. Library
Pattern Recognition Probabilities [racer8]Brief 🌟
Pattern Recognition Probabilities (PRP) is a REALLY smart indicator. It uses the correlation coefficient formula to determine if the current set of bars resembles that of past patterns. It counts the number of times the current pattern has occurred in the past and looks at how it performed historically to determine the probability of an up move, down move, or neutral move.
I'd like to say, I'm proud of this indicator 😆🤙 This is the SMARTEST indicator I have ever made 🧠🧠🧠
Note: PRP doesn't give you actual probabilities, but gives you instead the historical occurrences of up, down, and neutral moves that resulted after the pattern. So you can calculate probabilities based on these valuable statistics. So for example, PRP can tell you this pattern has historically resulted in 55 up moves, 20 down moves, and 60 neutral moves.
Parameters 🌟
You can adjust the Pattern length, Minimum correlation, Statistics lookback, Exit after time, and Atr multiplier parameters.
Pattern length - determines how long the pattern is
Minimum correlation - determines the minimum correlation coefficient needed to pass as a similiar enough pattern.
Statistics lookback - lookback period for gathering all the patterns in the past.
Exit after time - determines when exit occurred (number of periods after pattern) ; is the point that represents the pattern's result.
Atr multiplier - determines minimum atr move needed to qualify whether result was an up/down move or a neutral move. If a particular historical pattern resulted in a move that was less than the min atr, then it is recorded as a neutral move in the statistics.
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