Indicator
Statistics
cloudTheoryLibrary "cloudTheory"
// OBJECTTIVE: A Combination of EMA to Draw Cloud Pattern on Chart
//
// The "2-EMA Line System" is one of the most oldest and durable
// frameworks in technical analysis where we plot one "fast" (short)
// and one "slow" (long) exponential moving average line on the same
// chart and their relationships determine the state of the security.
drawCloud(fastLength, slowLength, src)
Parameters:
fastLength (simple int) : - Fast EMA Length, the shorter length which
typically determines the current rhythm of the security.
slowLength (simple int) : - Slow EMA Length, the longer length which
determines the underlying drift of the security.
src (float) : - Which value of the candle should be used to calculate
the moving average; defaults to candle close value.
Returns: - EMA
Values and the flags to determine the current sentiment.
Library
Sector Power & Market RegimeThe Sector Power & Market Regime Indicator is a multi-asset dashboard designed to quantify market dynamics through the lens of volatility-adjusted momentum. Unlike standard price-action indicators, this tool uses statistical normalization to compare the 11 major S&P 500 sectors, a benchmark (SPY), and custom equities on a leveled playing field.
1. The Rationale: Quantifying Market Behavior
The core philosophy of this indicator is that markets do not just move "up" or "down"—they move through different behavioral archetypes.
Statistical Normalization: By utilizing a Z-Score Window (defaulted to 252 days, or one trading year), the script calculates how an asset’s current performance deviates from its historical mean
. This allows a high-volatility sector like Technology (XLK) to be compared directly to a lower-volatility sector like Utilities (XLU)
.
Volatility Adjusted Momentum: By dividing price momentum (ROC) by the asset's current volatility (ATR), the script ensures that a high-volatility stock doesn't automatically look "stronger" than a stable one. It measures how much an asset is moving relative to its typical daily range.
Z-Scored X-Axis: This ROC/ATR value is then Z-scored over a 252-bar window (one trading year)
. This identifies how many standard deviations the current volatility-adjusted momentum is from its one-year mean.
Volatility Disorder (Z-Scored TR Dispersion):
The script evaluates the standard deviation of True Range relative to average True Range, then z-scores that value over the historical window. This quantifies how unusually dispersed or unstable volatility is versus the asset’s normal behavior, allowing the indicator to distinguish between smooth/trending price action and noisy/chaotic conditions.
. It essentially asks: Is this move backed by sustainable strength, or is it becoming dangerously erratic?
2. Understanding the Four Market States
The indicator categorizes every asset into one of four color-coded regimes
:
Efficient (Green): Characterized by strong, steady momentum with manageable volatility. This represents a healthy, sustainable trend where trend-following strategies typically thrive.
Explosive (Red): This state occurs during high-velocity breakouts or parabolic moves. While highly profitable, these are often "hot" zones that require active trade management.
Chaotic (Orange): Signals high volatility or trend exhaustion. Price action is often unpredictable here, suggesting a "hands-off" approach or a transition to a different regime.
Stable (Blue): Indicates consolidation or low-volatility environments. These are "quiet" periods where the asset is building energy for its next major move.
3. Key Metrics: Power & Age
Power (X-Value): This is the numerical strength of the asset
. A higher number indicates stronger relative momentum. Traders use this to rank sectors from strongest to weakest at a single glance.
Age (d): This tracks how many bars (days) the asset has remained in its current state
.
Fresh Trends: An asset in the Efficient state with an age of "2d" suggests a brand-new trend.
Overextended Trends: An asset in the Explosive state with an age of "20d" suggests the move may be nearing a climax.
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How to Use This in Your Trading Strategy
A. Alpha Generation (Relative Strength) Compare your Custom Stocks to the SPY benchmark
. If SPY is in a "Stable" (Blue) state but your stock is "Efficient" (Green) with higher "Power," your stock is showing significant relative strength and is a prime candidate for a long position.
B. Sector movement Tracking Monitor the 11 sectors to see where "Big Money" is moving
. If you see the "Defensive" sectors (XLU, XLP, XLV) moving into Efficient states while "Growth" sectors (XLK, XLY) move into Chaotic states, it is a strong signal of a defensive market shift.
C. Risk Management & Exit Timing The "Age" metric is your primary tool for timing exits
. If a sector you are holding moves from Efficient to Chaotic or stays in Explosive for an extended period, the "Age" will alert you that the trend is maturing, signaling it may be time to trail your stops tighter or harvest profits.
D. Customization Traders can input their own specific watchlist (e.g., MU, STX, BIIB) to see exactly how their personal portfolio is performing relative to the broader market sectors
Indicator
Institutional Order Flow Map
WHAT IT DOES
Institutional Order Flow Map (IOFM) is a fully original Smart Money
Concepts indicator built in Pine Script v6. It detects and visualizes
where institutional players are actively placing orders by analyzing
three simultaneous data streams: abnormal volume activity, price
rejection behavior, and structural pivot zones.
The result is a clean, layered map of the market showing exactly where
large participants have been buying and selling, how strong each zone
is relative to others, and which direction the institutional order
flow is currently pointing.
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WHAT MAKES IT ORIGINAL
Most SMC indicators detect Order Blocks using only price structure
(pivot highs and lows). IOFM adds two additional layers that are
not combined in any existing public indicator:
1. Institutional Footprint Detection
Each candle is evaluated for three conditions simultaneously:
volume exceeds the 20-bar SMA by a user-defined multiplier,
the rejection wick exceeds a minimum percentage of the total
candle range, and the candle closes in the direction of the
move. When all three align, the candle is marked as an
institutional footprint. A Volume Climax marker appears when
volume exceeds 1.5x the standard threshold, indicating extreme
institutional participation.
2. Order Density Score (0-100)
Every zone receives a weighted quality score calculated from
three independent components. Volume Score contributes up to
40 points based on how elevated volume was at the pivot.
Rejection Score contributes up to 30 points based on the
wick-to-range ratio at the pivot. Trend Alignment Score
contributes 30 points when the zone type agrees with the
EMA200 trend direction. Only zones above the minimum score
threshold are displayed, ensuring the chart shows only the
strongest institutional areas.
3. Flow Direction Engine
A signed cumulative delta is calculated for each bar as
(close - open) / range x volume. This measures whether buying
or selling pressure dominated each candle. The sum of this
delta over the calculation period is normalized to a -100
to +100 scale and displayed as a directional flow label.
This tells traders whether institutional money is currently
flowing into or out of the market, independent of price
direction alone.
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HOW TO USE
Step 1 — Read the Footprint markers
Green upward arrows (▲) mark candles where institutions were
actively buying: high volume, strong lower wick, and bullish
close. Red downward arrows (▼) mark institutional selling.
The 🏛 BUY and 🏛 SELL labels appear on Volume Climax candles
where institutional activity was extreme. These are the highest
conviction footprint signals.
Step 2 — Identify the strongest zones
Green boxes are Buy Zones (demand areas) where institutional
buyers previously accumulated positions. Red boxes are Sell
Zones (supply areas) where institutional sellers distributed.
Each zone displays a score (for example B 78 or S 65).
Higher scores indicate stronger institutional interest at
that level. Only zones above the minimum score threshold
are shown to keep the chart clean.
Step 3 — Watch for mitigation
When price closes through a Buy Zone from above, the zone
fades to indicate it has been mitigated (consumed). When
price closes through a Sell Zone from below, the same
happens. Mitigated zones are no longer considered active
institutional areas and should be treated with lower
confidence.
Step 4 — Check Flow Direction
The Flow Direction label in the top-right of the chart
shows the current institutional bias: Buying, Selling,
or Neutral. This is based on the cumulative signed delta
calculation, not just price direction. Use this to confirm
whether the footprint markers and zone interactions are
backed by actual order flow.
Step 5 — Read the Dashboard
The top-right panel shows: Institutional Bias (Bullish
or Bearish based on zone strength comparison), Flow
Direction and Strength (0-100), Top Buy Zone score,
Top Sell Zone score, current Footprint reading, Volume
status, and count of active zones on each side.
Step 6 — Combine signals
The strongest setups occur when multiple elements align:
price returning to a high-score zone, a 🏛 footprint
marker on that candle, Flow Direction agreeing with
the zone type, and EMA200 trend in the same direction.
When all four align, the institutional evidence is
at its strongest.
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CALCULATION LOGIC
Institutional Footprint conditions:
inst_vol = volume > SMA(volume, 20) x vol_multiplier
lower_rej = lower_wick / candle_range x 100
upper_rej = upper_wick / candle_range x 100
inst_buy = inst_vol AND lower_rej >= wick_threshold AND close > open
inst_sell = inst_vol AND upper_rej >= wick_threshold AND close < open
vol_climax = volume > SMA(volume, 20) x (vol_multiplier x 1.5)
Order Density Score:
vol_score = min(round(volume_at_pivot / SMA_volume x 25), 40)
rej_score = min(round(wick_pct x 0.4), 30)
trend_score = 30 if zone aligns with EMA200, else 10
total_score = min(vol_score + rej_score + trend_score, 100)
Flow Direction Engine:
bar_delta = (close - open) / candle_range x volume
flow_sum = SUM(bar_delta, period)
flow_max = HIGHEST(ABS(flow_sum), 50)
flow_norm = flow_sum / flow_max x 100
flow_bull = flow_norm > 10
flow_bear = flow_norm < -10
Zone mitigation:
Buy Zone mitigated when low <= zone_bottom
Sell Zone mitigated when high >= zone_top
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SETTINGS OVERVIEW
Module A — Institutional Footprint:
Show Footprint candles — toggle markers on/off
Volume threshold (SMA x) — multiplier for institutional volume
Min rejection wick (%) — minimum wick size to qualify
Module B — Order Flow Zones:
Show Order Flow Zones — toggle zones on/off
Zone pivot lookback — bars used for pivot detection
Zone height (ATR x) — vertical size of each zone
Extend zones (bars) — how far right zones extend
Fade mitigated zones — toggle mitigation visual
Module C — Flow Direction:
Flow calculation period — bars for cumulative delta
Show Flow Direction label — toggle directional label
Module D — Order Density:
Show score on zones — toggle score labels
Min score to show — filter threshold (default 60)
Dashboard:
Show Dashboard — toggle info panel
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ALERTS INCLUDED
Institutional Buy footprint detected
Institutional Sell footprint detected
Volume Climax — extreme institutional activity
Strong Buy — flow direction and footprint aligned bullish
Strong Sell — flow direction and footprint aligned bearish
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COMPATIBILITY
Works on all assets — Crypto, Forex, Stocks, Futures, Indices.
Works on all timeframes.
Best results on 15m to 4H for active trading.
No repainting. All signals calculated on bar close.
No request.security() calls. No lookahead bias.
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DISCLAIMER
This indicator is provided for educational and informational
purposes only. It does not constitute financial advice or a
recommendation to buy or sell any asset. The Order Density
Score and Flow Direction calculations are mathematical models
based on price and volume data and do not represent actual
order book information. Past institutional footprint patterns
do not guarantee future results. Always apply your own
analysis and use proper risk management before placing
any trade. The author is not responsible for any trading
losses incurred from use of this indicator.
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Indicator
Zen RTH Trading ToolkitOVERVIEW
Three research modules combined into one overlay indicator, with every feature independently toggleable. Built to answer three practical questions on intraday index futures charts: where are the normal-day targets from yesterday's close, where are the breakout targets from the 18-bar opening range, and how stretched is today's move relative to the average.
WHAT MAKES THIS DIFFERENT
Most ABR/ADR and opening-range tools either use 24-hour ranges or assume a single session. This indicator does three things differently:
1. Session-aware RTH tracking. The ADR uses session-filtered highs and lows, not 24-hour daily bars. Snapshot and rollover both happen on the first bar of the session, which makes it work correctly on RTH-only charts where there is no session-end transition, and on charts that include extended hours.
2. Instrument presets with timezones. A single dropdown sets the correct RTH session and timezone for ES, FDAX, HSI, and Nikkei 225 futures. No typing session strings, no DST adjustments.
3. Data Window exposure. Every stats-table metric is also published as a Data Window plot, so the values remain available even when the on-chart table is hidden. Useful for spreadsheet export, historical bar inspection, and clean-chart workflows.
MODULE 1 - ABR MEASURED MOVES
Projects 0.5x, 1x, 1.5x, and 2x ABR targets from yesterday's RTH close. Also plots yesterday's high and low, and the original 1x measured move from yesterday's range extremes.
ABR here is the Average Bar Range - the rolling average of daily RTH ranges over a configurable lookback (default 8 days). 1x from yesterday's close represents a typical day's travel on index futures; 2x is rare, hit roughly 1 percent of days.
MODULE 2 - OPENING RANGE
Default 18 bars, which is 90 minutes on a 5-minute chart. Locks the range at the end of bar 18 and draws a box plus configurable breakout projections at 0.25x, 0.5x, 0.75x, 1x, 1.5x, and 2x of the range, in both directions.
MODULE 3 - VOLATILITY STATS TABLE
Compact on-chart table showing:
- ABR: chart-timeframe average bar range
- ADR: average daily range, RTH and ETH columns
- Today: current session's range
- % ADR: percentage of ADR already used (turns red at 80%)
- OR %: opening range as a percentage of RTH ADR (fills in once the OR locks)
- Swing: swing target in points (% of RTH ADR, configurable, default 40%)
- Scalp: scalp target in points (% of RTH ADR, configurable, default 10%)
INSTRUMENT PRESETS
- ES (CME E-mini S&P 500): 0830-1600 America/Chicago
- FDAX (EUREX DAX): 0900-1730 Europe/Berlin
- HSI (HKEX Hang Seng): 0915-1630 Asia/Hong_Kong
- Nikkei (OSE Nikkei 225): 0845-1550 Asia/Tokyo
- Custom: set your own session and timezone
HOW TO USE
1. Add to a 5-minute chart of one of the supported instruments.
2. In settings, pick your instrument from the dropdown.
3. Toggle the modules and features you want. If you only want the stats table, turn the MM lines and the OR box off. If you only want the OR, hide the table.
4. Adjust the Swing and Scalp percentages to match how you size your targets.
WORKS ON
RTH-only charts and charts including extended hours. Designed for 5-minute and other intraday timeframes on index futures.
NOTES
This is a research tool. It shows volatility and structure context; it does not generate buy or sell signals and makes no claim about future performance.
Open-source under the Mozilla Public License 2.0.
Indicator
Nova Flow Lite (Free)Clean and non‑intrusive trend tool for instant market clarity.
Nova Flow Lite is a clean and non‑intrusive trend tool designed to give traders instant clarity without cluttering the chart.
It highlights the dominant trend with a smooth visual ribbon and adds a soft equilibrium zone to show market balance.
Works on all assets, all timeframes, and both directional and spread trading.
What it shows
• Clear trend direction through a smooth visual ribbon
• A soft equilibrium zone showing market balance
• A minimal info panel for instant trend confirmation
• Zero signals, zero noise — pure visual context
Why traders use it
For a quick, elegant and intuitive read of market direction without overwhelming the chart.
Perfect for directional setups, spread structures, and multi‑timeframe analysis.
Notes
Nova Flow Lite is a simplified version of the Nova series.
A more advanced tool is available for traders who need deeper precision.
Feedback and questions are welcome — you can reach me here on PulseWire.
Indicator
Adaptive Anomaly Beast [MarketFragments]Adaptive Anomaly Mk II — Adaptive Cycle
A composite anomaly detector that combines an autoencoder reconstruction
error, true k-nearest-neighbor distance, and an Ehlers adaptive cycle
with self-tuning alpha. The indicator flags bars where the market's
structure differs from its recent self in a way that survives three
independent mathematical tests, and only inside a market regime that
supports it.
The main plot is a ratio. Values near zero mean normal market
structure. Values above 1.0 mean the reconstruction error has exceeded
its dynamic threshold. When that condition persists and the other
confirmations agree, the plot turns red and a composite anomaly
signal fires.
─────────────────────────────────────────────────────────────
HOW IT WORKS
─────────────────────────────────────────────────────────────
STEP 1 -- AUTOENCODER RECONSTRUCTION ERROR
Three features -- VWAP, activity density (volume per unit price
range), and true range -- are each independently z-score normalized
over a rolling window, smoothed through a Hull Moving Average,
compressed via a short-period average (encoder), and expanded back
through a mirror average (decoder). The absolute difference between
the normalized input and its reconstructed output is the per-feature
reconstruction error. The final error is the arithmetic mean of
the three, ensuring no single feature dominates by scale.
Large reconstruction error means the market's recent structure no
longer matches its own short-term pattern -- a compression failure.
STEP 2 -- TRUE K-NEAREST-NEIGHBOR DISTANCE
For each bar, absolute differences |close - close | are computed
across a lookback window. Five sequential passes extract the five
smallest distances without requiring sortable arrays -- each pass
finds the smallest distance strictly greater than the previous
pass's winner. The kNN score is the mean of those five.
High kNN score means the current bar is far from its nearest
neighbors in recent price history -- a local outlier.
STEP 3 -- EHLERS ADAPTIVE CYCLE WITH WIN-RATE HILL CLIMBING
A two-pole Ehlers cycle filter runs with a self-tuning alpha
coefficient. Each direction change of the cycle simulates a trade;
realized PnL is tracked across all simulated trades. When the
rolling win rate falls below 55% and the cycle's speed is
visibly mismatched to price -- defined as cycle delta greater
than 2x price delta (too fast) or less than 0.3x (too slow) --
alpha is nudged by one step in the corrective direction.
Alpha is bounded to and only adapts after
a minimum number of simulated trades (default 5) to avoid
chasing early noise.
Recent cycle direction changes within the lookback window count
as a confirmation signal.
STEP 4 -- REGIME FILTERS
Two independent regime gates must agree for an anomaly to fire:
Volume Break Hull Moving Average of Volume RSI > 49
Volatility Break RMA(TR, 5) > RMA(TR, 20) * 1.2
Without active regime expansion, anomaly signals are suppressed.
This prevents flagging during flat tape.
STEP 5 -- COMPOSITE ANOMALY SIGNAL
A signal fires when all of the following are true:
-- Autoencoder reconstruction error exceeds the dynamic threshold
-- EMA-smoothed error is above its rolling mean
-- Either kNN distance exceeds its threshold OR cycle has recently turned
-- Either volume break OR volatility break is active
-- All of the above persist for at least N consecutive bars (default 2)
When the signal becomes active, the main plot turns red, red
markers appear on the ratio line, and an alert fires on the
fresh signal bar.
─────────────────────────────────────────────────────────────
DYNAMIC THRESHOLD
─────────────────────────────────────────────────────────────
The anomaly threshold is computed as:
thresholdMean + thresholdAdjust * thresholdStd
where thresholdMean and thresholdStd are computed from the
reconstruction error series over a rolling lookback window.
During the configured active session (default 0930-1600 New
York), thresholdAdjust is raised by a configurable amount
to reflect that active hours produce more signal noise and
require a higher bar. Outside the session, thresholdAdjust is
reduced by the same amount.
The main plot divides reconstruction error by the adjusted
threshold, so crossings of 1.0 always mean "above threshold"
regardless of current market volatility.
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WHAT YOU SEE
─────────────────────────────────────────────────────────────
Ratio line Green below threshold, orange above, red when signal fires
Yellow dashed Threshold at 1.0
Cyan line EMA of ratio for slower confirmation
Red dots Marker circles on ratio line at each signal bar
Info table Anomaly state, regime, persistence, ratio, cycle direction,
adapted alpha, cycle win rate, session status
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SETTINGS
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Encoding Size 5 Autoencoder compression window
Decoding Size 5 Autoencoder decompression window
Threshold Lookback 20 Window for dynamic threshold stats
Threshold Multiplier 1.5 Standard deviations above mean
EMA Length for Error 15 EMA smoothing on error
Persistence (bars) 2 Bars signal must persist to fire
kNN Lookback 20 Bars searched for nearest neighbors
Cycle Smoothing Length 4 EMA length feeding the cycle
Alpha Min 0.05 Lower bound on adapted alpha
Alpha Max 0.45 Upper bound on adapted alpha
Alpha Adaptation Step 0.01 Per-bar nudge size
Fallback Alpha 0.20 Initial alpha and pre-adaptation value
Min Trades Before Adapt 5 Trades required before adapter activates
Cycle Turn Lookback 3 Bars within which a turn counts
Active Session 0930-1600 Session for threshold boost
Session Timezone America/New_York Configurable for non-US markets
Active Hours Threshold Bump 0.2 Threshold multiplier delta during session
Intended for 5-minute charts. A warning label appears on other timeframes.
─────────────────────────────────────────────────────────────
IMPORTANT NOTES
─────────────────────────────────────────────────────────────
-- This indicator has not been backtested or forward tested
-- No performance claims are made
-- The cycle's alpha adaptation is hill-climbing, not full grid
search; it converges slowly and may not converge in whipsaw
markets
-- The cycle's win rate is measured on its own direction changes
and can be statistically noisy at low trade counts
-- The active session default is US equity regular hours; non-US
markets require the session and timezone inputs to be updated
-- k is fixed at 5 in the implementation regardless of input
settings related to k
-- Shared as a research tool for community review
-- Results will vary by instrument, timeframe, and market conditions
-- This is not financial advice
-- Trading involves substantial risk of loss
-- Past results do not guarantee future performance
-- Use for educational and research purposes only
─────────────────────────────────────────────────────────────
LINEAGE
─────────────────────────────────────────────────────────────
Derived from "Beast Autoencoder-RNN for Anomaly Detection" by
marketframents/mcdon030 (originally for ThinkOrSwim). This
version rewrites the autoencoder normalization to be scale-
consistent across features, replaces a mean-absolute-deviation
score that was labeled kNN with a real k-nearest-neighbor
implementation, removes a regularization penalty that was
subtracted from activations (regularization belongs in a loss
function, not the output), removes injected random noise from
the encoder (denoising autoencoders need noise during training,
not during streaming inference), and adds the Ehlers adaptive
cycle layer with win-rate-driven alpha adaptation.
─────────────────────────────────────────────────────────────
Free for public use
Indicator
Market Crystallization Gauge [forexobroker]Market Crystallization Gauge detects the exact moment a market transitions from chaotic noise into structured directional movement. It fuses three independent regime signals -- bar-to-bar return correlation, ADX trend strength and its slope, and standard-deviation contraction of returns -- into a single 0-100 Crystallization Score. When the score breaches the threshold with a clear directional bias (DI+ vs DI- plus price versus regime EMA), the market is classified as CRYSTALLIZED and a signal fires.
This indicator is designed for traders who want to avoid chop and only deploy capital once the market commits to a direction. The regime background gradient makes chaos visually distinct from forming and fully crystallized conditions, turning the chart into a live regime map.
🔶 CONCEPTS
Financial markets oscillate between two states: noise (random, mean-reverting chaos) and trend (structured, persistent movement). The transition between these states is usually sharp and identifiable by three concurrent changes: the autocorrelation of bar-to-bar returns becomes positive (each move tends to continue in the same direction), the ADX rises and accelerates (directional strength compounds), and the volatility of returns contracts relative to baseline (the market stabilizes around its new trajectory). When all three happen together, the market has crystallized.
🔶 HOW IT WORKS
- Computes return autocorrelation across the correlation window (structure indicator)
- Calculates ADX, DI+, DI-, and the 3-bar ADX slope (strength indicator)
- Measures short-term standard deviation of returns versus a longer baseline (contraction indicator)
- Combines the three into a 0-100 Crystallization Score and labels the regime (CHAOS / TRANSITIONING / FORMING / CRYSTALLIZED)
- Signal fires on the bar where the score first crosses above the threshold with matching directional bias
🔶 HOW TO USE
1. Add the indicator -- the regime background gradient tints bars by regime (bright gold for fully crystallized, cyan for forming, purple for transition)
2. Watch the dashboard (top-right) for live Crystallization Score, regime label, directional bias, and component breakdowns
3. Green BUY triangles with "CRYST ↑" label fire on bullish crystallization onsets; pink SELL triangles on bearish
4. The regime EMA plotted with glow defines price-vs-axis bias that gates signals
5. Raise the Crystallization Threshold for only fully crystallized entries; lower it for earlier but less confirmed entries
🔶 FEATURES
- Non-repainting signals (barstate.isconfirmed)
- Works on all timeframes and instruments
- 9 alert conditions with JSON webhook support
- Three-factor regime composite (correlation + ADX + std-dev contraction)
- Regime background gradient that visualizes market state across history
🔶 SETTINGS GUIDE
- Correlation Length -- Lookback for bar-to-bar return autocorrelation
- ADX/DMI Length -- Period for Average Directional Index and directional indicators
- StdDev Length -- Short-term return volatility window
- StdDev Baseline Length -- Longer baseline used as the contraction reference
- Volume SMA Length -- Baseline for volume confirmation
- Regime EMA Length -- EMA used for directional bias classification
- Crystallization Threshold -- Minimum 0-100 score to trigger signal
- Signal Cooldown Bars -- Minimum bars between consecutive signals
🔶 ALERTS
- MCG Buy Crystallize -- Bullish crystallization transition confirmed
- MCG Sell Crystallize -- Bearish crystallization transition confirmed
- MCG Any Signal -- Any directional crystallization
- MCG Fully Crystallized -- Crystallization score reaches 85+
- MCG Transition -- Score crosses threshold (either direction)
- MCG Strong ADX -- ADX exceeds 30
- MCG StdDev Contraction -- Return volatility contracts below 0.7 of baseline
- MCG Bullish Regime -- Bullish bias with forming or higher regime
- MCG Webhook JSON -- Generic webhook payload for external automation
🔶 LIMITATIONS & DISCLAIMER
- This is a technical analysis tool, not financial advice. Always use proper risk management.
- Past regime transitions do not guarantee future price movements.
- ADX and correlation measurements are lagging by nature; crystallization is confirmed only after the evidence accumulates.
- The Crystallization Score is a composite heuristic, not a statistically calibrated probability of trend persistence.
Indicator
RandomForestLibraryRandomForestLibrary is a self-contained Random Forest library for Pine Script v6 that other Pine developers can import and use to build their own machine learning indicators and strategies.
What Makes This Different
Random Forest is one of the most widely used ensemble methods in applied machine learning. Until now, Pine Script developers wanting to use it had only two choices: call out to an external Python / ONNX pipeline, or hand-roll a single decision tree inline. This library closes that gap by providing a complete Random Forest implementation — CART trees, bootstrap aggregation, Gini / MSE splitting, out-of-bag scoring, weighted feature importance — reachable with a few lines of import code.
The API intentionally mirrors scikit-learn's RandomForestClassifier and RandomForestRegressor (init → fit → predict → evaluate), so practitioners already familiar with scikit-learn can translate existing logic directly.
What This Library Provides
Binary classification: fit(X, y) , predict , predict_proba , predict_batch , oob_score
Multi-output regression: fit_regressor(X, Y) , predict_multi , predict_multi_per_tree , oob_r2 , oob_residual_std
Weighted Gini / MSE feature importance: feature_importance()
Deterministic Park-Miller RNG for reproducible forests
Exported Types
Forest — the ensemble model. Holds all trees, hyperparameters, training data references, OOB accumulators, and feature importances.
Tree — a single decision tree with its node array, max depth, leaf count, and split-failure count.
Node — a single node storing feature index, threshold, children indices, leaf label / probability, Gini impurity (or MSE in regressor mode), sample count, and a per-horizon output array for regression.
RNG — a Park-Miller linear congruential generator with a=48271, m=2^31-1. Deterministic given the same seed.
Exported Methods
Initialization and training
init(n_estimators, max_depth, max_features, min_samples_leaf, n_threshold_candidates, seed) — configure hyperparameters. max_features=0 auto-selects ceil(sqrt(n_features)) for classification and ceil(n_features/3) for regression.
fit(X, y) — train classifier on a feature matrix X (rows = samples, columns = features) and integer label array y (values 0 or 1).
fit_regressor(X, Y) — train multi-output regressor. Y is a matrix whose columns are separate regression horizons / targets.
Inference
predict(sample) — classify a single sample via soft voting (threshold 0.5).
predict_proba(sample) — average class-1 probability across all trees.
predict_batch(X) — classify every row of a matrix.
predict_multi(sample) — regressor output: averaged per-horizon predictions.
predict_multi_per_tree(sample) — per-tree, per-horizon predictions for custom uncertainty analysis.
tree_predict , tree_predict_proba , tree_predict_multi — single-tree inference for advanced use.
Evaluation
oob_score() — classification out-of-bag accuracy (0.0 to 1.0), computed by soft voting on samples not selected in each tree's bootstrap.
oob_r2() — regression out-of-bag R^2, averaged across horizons.
oob_residual_std() — per-horizon standard deviation of OOB residuals. Useful for prediction interval construction (Wager, Hastie, and Efron 2014).
feature_importance() — normalized weighted Gini (or MSE) decrease per feature, averaged across trees. Sums to approximately 1.0.
How It Works
Tree construction (CART, iterative, level-by-level)
Each tree is built top-down, one depth level at a time, using complete binary tree indexing ( left = 2i+1 , right = 2i+2 ). At every internal node:
A random subset of features of size max_features is drawn without replacement.
For each feature, n_threshold_candidates thresholds are sampled uniformly between the feature's min and max on the samples at that node.
For classification, the split minimizing weighted Gini impurity is chosen. For regression, the split minimizing weighted MSE (summed over all horizons) is chosen.
A node becomes a leaf when it is pure (classification), too small ( n < 2 * min_samples_leaf ), at max depth, or when no valid split exists.
Bootstrap aggregation and OOB
Each tree is trained on a bootstrap sample (same size as the training set, sampled with replacement). Samples that were not drawn for a given tree become its out-of-bag set and are used to compute unbiased performance estimates ( oob_score / oob_r2 ) and residual variance ( oob_residual_std ), avoiding the need for a separate holdout.
Feature importance
Each split records its weighted impurity decrease ( n_node * impurity_node - n_left * impurity_left - n_right * impurity_right ). Per-tree importances are normalized to sum to 1, then averaged across trees — matching scikit-learn's definition.
Quick Start
//@version=6
indicator("My RF Indicator")
import ShigemiQuant/RandomForestLibrary/2 as RF
// 1. Build feature matrix X and label array y over recent bars
// (not shown: accumulate features into a matrix)
// 2. Initialize and train
var RF.Forest model = RF.Forest.new().init(
n_estimators = 10,
max_depth = 4,
seed = 42)
if barstate.islast
model.fit(X, y)
// 3. Predict on current bar
array sample = array.from(rsi_val, atr_pct, cci_val, adx_val)
float prob = model.predict_proba(sample)
// 4. Evaluate
float oob = model.oob_score()
label.new(bar_index, close, "prob=" + str.tostring(prob, "#.##") + " oob=" + str.tostring(oob, "#.##"))
Compatibility Notes
scikit-learn parity : same init → fit → predict / predict_proba workflow, same default for max_features , OOB uses soft voting, importances use weighted Gini decrease.
Determinism : given identical seed , training set, and hyperparameters, the resulting forest and all predictions are bit-identical across reruns.
Binary classification only in fit() : labels must be 0 or 1. Multi-class is not yet supported.
Numeric features only : all columns of X must be float .
Limitations
This is a machine-learning library , not a trading signal. Indicators built with it make no guarantee of profit, do not predict the future, and depend entirely on the quality of the features, labels, and hyperparameters that the caller supplies.
Binary classification only in fit() (labels must be 0 or 1); multi-class is not supported. Regression via fit_regressor() supports multi-output targets but assumes they are numeric float values.
PulseWire runtime budget limits tree size. A reasonable starting point is n_estimators between 5 and 20 with max_depth between 3 and 6. Total node budget per tree is 2^(max_depth+1) - 1 — depth 6 allows up to 127 nodes per tree, and 15 trees means up to roughly 1,905 nodes total.
Large training sets combined with deep trees (thousands of bars × depth 6) can hit Pine Script's loop iteration caps. Start small and scale up while watching compile / runtime warnings.
OOB metrics ( oob_score , oob_r2 , oob_residual_std ) are valid only when each sample is out-of-bag in at least one tree. For very small training sets or very few estimators, some samples may never be OOB and those metrics will be biased or undefined.
Overfitting is the caller's responsibility. The library exposes standard controls ( max_depth , min_samples_leaf , max_features , n_estimators ) but applies no automatic regularization. Trees that are too deep on a noisy training window will memorize noise.
Features must be stationary enough to generalize. Raw price levels or unnormalized indicators that drift with the market will cause training-test distribution shift. Prefer bounded or ratio-based features (RSI, ATR%, percentile ranks).
Training happens on the chart's own bar history. There is no external data upload; the library cannot import pre-trained models, and the forest must be rebuilt whenever the script recomputes. Designs that rely on very large historical context may conflict with Pine Script's bar-history window.
References
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.
Wager, S., Hastie, T., and Efron, B. (2014). Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife. Journal of Machine Learning Research, 15, 1625–1651.
Disclaimer
This library is an educational and research tool. It does not constitute financial advice. All trading decisions based on code built with this library are the sole responsibility of the user. Past model performance does not guarantee future results.
Library
Simple Buy/Sell Indicator
Create you own custom strategies using this 'Simple Buy/Sell Indicator'. It allows other indicators to be combined together into a BUY/SELL strategy, using up to four separate rules.
The output is:
1 = BUY
0 = None
-1 = SELL
This can then be used as an input to my 'Simple Pyramid Strategy' or my (coming soon - watch this space) 'Complex Single Trade Strategy'.
Each rule compares two values with can be zero centred with an offset of -50 to make it
So instead of testing for overbought and oversold with > 70 and < 30, you would offset by -50. Then you would test for > 20 (overbought), which becomes < -20 (oversold) when inverted.
Indicator
Deviation Lens [JOAT]Deviation Lens
Introduction
Deviation Lens is an open-source multi-dimensional statistical displacement tool that applies Z-Score analysis simultaneously to three market dimensions: price level, close-to-close price change, and volume. Rather than using arbitrary overbought/oversold thresholds derived from historical maxima and minima, Deviation Lens computes exactly how many standard deviations each dimension is from its recent rolling mean. This provides a precise, adaptive, distribution-aware measure of how statistically extreme current market conditions are.
The core insight is that markets are mean-reverting systems over short time horizons. Statistical extremes — conditions where price, momentum, or volume are far from their recent averages — represent transient states. The further from the mean, the greater the statistical probability that conditions will normalize. Deviation Lens quantifies this probability directly, from 0% (at the mean) to 99.7% (at three standard deviations), and displays it as a live reversal probability for every bar.
Core Concepts
1. Three-Dimensional Z-Score Calculation
Three independent Z-Scores are computed on every bar:
The Price Z-Score measures how far the current close is from the rolling mean close in standard deviation units. This captures whether the current price level is statistically cheap or expensive relative to recent history.
The Change Z-Score measures how far the current bar's close-to-close price change is from the rolling mean change — quantifying momentum extremity rather than price level extremity.
The Volume Z-Score measures how far the current volume is from the rolling mean volume. High-volume Z-Score values identify bars where unusual institutional participation is statistically evident:
priceZ = priceStd > 0 ? (close - priceMean) / priceStd : 0.0
changeZ = changeStd > 0 ? (chg - changeMean) / changeStd : 0.0
volumeZ = volStd > 0 ? (volume - volMean) / volStd : 0.0
2. Reversal Probability Mapping
The absolute Z-Score is mapped to a reversal probability percentage based on the properties of the normal distribution. A Z-Score of 1.0 corresponds to 68.3% of values lying within one standard deviation — meaning only 31.7% of readings exceed this level, implying a 68.3% probability of mean reversion. A Z-Score of 2.0 corresponds to 95.4%, and 3.0 to 99.7%:
calcRevProb(float z) =>
float absZ = math.abs(z)
absZ >= 3.0 ? 99.7 : absZ >= 2.5 ? 98.8 : absZ >= 2.0 ? 95.4 : absZ >= 1.5 ? 86.6 : absZ >= 1.0 ? 68.3 : absZ >= 0.5 ? 38.3 : 0.0
This probability is displayed in the dashboard alongside the live Z-Score value, giving the trader both the raw statistical reading and its corresponding reversal likelihood.
3. Composite Z-Score and Zone Classification
The three individual Z-Scores are combined into a composite score using configurable weights for each dimension. The composite is then classified into a zone: EXTREME (above the configurable extreme threshold), ELEVATED, NEUTRAL, or the opposing directional equivalents. Zone classification determines the dashboard color coding and alert triggers:
composite = (priceZ * wPrice + changeZ * wChange + volumeZ * wVolume) / totalWeight
4. Divergence and Hidden Divergence Detection
Deviation Lens monitors for two divergence conditions. Standard divergence occurs when the Z-Score direction disagrees with the price direction — price makes a higher high but the Z-Score makes a lower high (bearish divergence), or price makes a lower low but the Z-Score makes a higher low (bullish divergence). Hidden divergence occurs when the Z-Score makes an extreme move while price action is relatively contained — a potential continuation pattern. Divergence events are labeled directly on the chart with bold, clearly sized labels:
bullDiv = close > close and priceZ < priceZ // Price up, Z down = bull div
bearDiv = close < close and priceZ > priceZ // Price down, Z up = bear div
Labels: BULL DIV, BEAR DIV (size.small), H.BULL, H.BEAR (size.tiny for hidden divergence).
5. Multi-Dimensional Dashboard
The institutional dashboard presents all three Z-Scores, the composite Z-Score, current zone classification, reversal probability, and divergence status simultaneously. The layout is designed so the most actionable information — Zone and Rev. Probability — is displayed at the largest text size, with supporting metrics at smaller sizes.
Features
Three independent Z-Scores: Price level, price change (momentum), and volume — each computed on its own rolling mean and standard deviation
Configurable Z-Score weights: The composite score uses adjustable per-dimension weights allowing emphasis on price, momentum, or volume depending on trading context
Live reversal probability: Probability percentage mapped directly from the Z-Score using normal distribution properties (68.3% at 1σ through 99.7% at 3σ)
Zone classification: Composite Z-Score classified as Extreme, Elevated, or Neutral in both directions with color-coded dashboard display
Divergence labels (BULL DIV / BEAR DIV): Z-Score vs price direction disagreement labeled on-chart at size.small
Hidden divergence labels (H.BULL / H.BEAR): Z-Score extreme with contained price action labeled at size.tiny
Configurable extreme and elevated thresholds: Both Z-Score thresholds independently adjustable
Institutional dashboard (top right): 14-row table with Price Z, Change Z, Volume Z, Composite Z, Zone, Reversal Probability, and divergence status
Adaptive thresholds: All calculations normalize to the rolling lookback period, adapting to current instrument and timeframe volatility
Alerts: Separate alertconditions for extreme bull and extreme bear composite Z-Score readings
Input Parameters
Z-Score Settings:
Z-Score Length: Rolling window for all three Z-Score calculations (default: 20)
Extreme Threshold: Z-Score magnitude classified as Extreme zone (default: 2.0)
Elevated Threshold: Z-Score magnitude classified as Elevated zone (default: 1.0)
Dimension Weights:
Price Weight: Relative weight of the price Z-Score in composite (default: 1.0)
Change Weight: Relative weight of the momentum Z-Score in composite (default: 1.0)
Volume Weight: Relative weight of the volume Z-Score in composite (default: 0.5)
Divergence:
Divergence Lookback: Bars back for divergence comparison (default: 5)
Show Divergence Labels toggle
Display:
Show Dashboard toggle
Bull and Bear color inputs
How to Use This Indicator
Step 1: Read the Composite Zone
The Zone row in the dashboard shows the current composite Z-Score classification. EXTREME readings at the top of the scale indicate the highest statistical probability of mean reversion. NEUTRAL readings indicate current conditions are close to the mean and have low statistical directional edge from this tool alone.
Step 2: Check Reversal Probability
The Rev. Probability row translates the Z-Score magnitude directly into a percentage. A reading above 95% means the current composite Z-Score is in the outer 5% of its historical distribution — a statistical extreme that has preceded mean reversion 95% of the time in the measured period.
Step 3: Assess Each Dimension Independently
The three individual Z-Score rows reveal which dimension is driving the composite. A high composite driven entirely by volume Z-Score is a different setup than one driven by price Z-Score. Understanding which dimension is extreme helps filter entries: a price Z-Score extreme without supporting momentum or volume Z-Score extremes may be a lower-conviction reading.
Step 4: React to Divergence Labels
BULL DIV and BEAR DIV labels appear when Z-Score momentum diverges from price direction. These signal that the statistical driver of a move is weakening even as price continues. H.BULL and H.BEAR hidden divergence labels flag potential continuation setups where Z-Score is extreme but price is not.
Step 5: Combine with Structural Context
Deviation Lens produces the highest value when its extreme readings coincide with a structural confluence point — an order block, session low, or structure level. A 99.7% reversal probability at a tested support zone is a higher-conviction setup than the same reading in open air.
Indicator Limitations
All Z-Scores are computed relative to the rolling lookback window. The lookback defines what "normal" means. A very short lookback will produce extreme readings frequently; a very long lookback will rarely reach the extreme threshold. Calibration to the instrument and timeframe is required
The reversal probability percentages are derived from the normal distribution assumption. Price change and volume distributions are not perfectly normal — they exhibit fat tails and skew. The probabilities are approximations, not precise statistical guarantees
The composite Z-Score uses equal weights by default. Changing dimension weights significantly alters which market conditions produce extreme readings. Weight adjustments should be based on the specific instrument's characteristics
Divergence detection uses a simple lookback comparison, not a peak-detection algorithm. In choppy markets, divergence labels may appear frequently without providing actionable signals
Originality Statement
Deviation Lens is original in its simultaneous, weighted multi-dimensional Z-Score framework that maps composite statistical extremity directly to a reversal probability percentage. This indicator is published because:
Applying Z-Score analysis to three independent market dimensions simultaneously — price level, momentum (close-to-close change), and volume — rather than a single oscillator provides a richer statistical picture of current market extremity than any single-dimension Z-Score tool
The direct mapping of Z-Score magnitude to reversal probability percentages using normal distribution properties gives traders an immediately interpretable statistic rather than a raw number requiring subjective interpretation
The composite weighted Z-Score system, where each dimension's contribution to the overall reading is configurable, allows the indicator to be tuned toward price-mean-reversion strategies, momentum exhaustion strategies, or volume anomaly detection depending on the trader's methodology
The combined detection of standard divergence and hidden divergence between the Z-Score and price direction provides trend continuation and reversal signals from the same framework
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Trading involves substantial risk of loss. Z-Score readings and reversal probability percentages are statistical tools based on historical distributions and do not guarantee any future price behavior. The normal distribution assumption applied to price and volume data is an approximation. Always use proper risk management. The author is not responsible for any trading losses resulting from the use of this indicator.
-Made with passion by jackofalltrades
Indicator
ETH VWAP ATR Scalper Pro Stats BoxBest fix
Use labels for BUY/SELL instead of only plotshape triangles.
Create one label per signal and delete the old one if needed.
Keep the stats table separate so it doesn’t compete with the signal tag.
What I can do
I can make the script show:
a green BUY box below the candle,
a red SELL box above the candle,
plus the same entry, stop, TP1, TP2, and stats.
Indicator
The OpenRange Strategy For The Boys. Limit it. Its making me type a description before publishing ts
Indicator
ORDER FLOW DASHBOARD [DOM, Tape, Big Contracts] Percentage Based
A compact, non-intrusive percentile order flow dashboard for futures scalping and intraday trading on /NQ, /MNQ, /ES, /MES and other CME instruments. Three panels, one glance.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
WHAT IS IT
Most order flow tools require expensive data feeds or dedicated platforms like Bookmap or Sierra Chart. This dashboard brings the core concepts of DOM pressure, tape reading and big contract detection directly onto your PulseWire chart using bar structure and volume as proxies. It is designed to sit quietly in the corner of your chart, update in real time, and give you directional context without cluttering your price action.
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THE THREE PANELS
DOM Panel
Estimates bid vs ask pressure by splitting each bar's volume based on where price closed within the bar's range. A bar closing near its high suggests buying pressure — near the low suggests selling. Displays Buyers %, Sellers % and a Net Delta over your chosen lookback period. The dominant side lights up automatically.
Tape Panel
Estimates aggressive vs passive order flow. Bars closing in the upper half of their range are classified as aggressive buyers (market orders lifting the offer). Lower half = aggressive sellers (hitting the bid). Shows aggressive buy %, aggressive sell % and passive %. The leading side highlights.
Big Contracts Panel
Flags bars where total volume exceeds your threshold — a proxy for institutional or block trade activity. Each entry shows price, volume, direction (BID or ASK) and a New York exchange-time timestamp for chart cross-reference. Supports a lower detection timeframe — use 1 min detection on a 2 min chart for more granular results.
Confluence Signal
Combines DOM and Tape into a single directional read at the bottom of the Tape panel.
▲ BUYERS LEAD — both DOM and Tape show buyers in control. Strongest bullish signal.
▼ SELLERS LEAD — both DOM and Tape show sellers in control. Strongest bearish signal.
? DOM BULL / TAPE BEAR — conflicting signals. Potential absorption or reversal brewing.
? DOM BEAR / TAPE BULL — conflicting signals. Same as above but reversed.
BALANCED — neither side clearly dominant on either panel.
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DISPLAY MODES
Minimalistic Mode — strips all row background colors from DOM and Tape panels. Only text colors remain. Blends cleanly into any chart style or theme. Big Contracts panel keeps its colors for easy scanning.
Color Blind Friendly Mode — replaces red/green color scheme with blue/orange throughout the entire dashboard. One toggle in settings. Works alongside minimalistic mode.
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⚠ IMPORTANT — NOT REAL ORDER BOOK DATA
Pine Script does not have access to real Level 2 DOM data, true bid/ask volume splits or individual order sizes. Everything this dashboard shows is a bar structure approximation, not real order book data.
DOM proxy: buyVol = volume × (close − low) / (high − low)
Tape proxy: close position in range = aggressor classification
Big Contracts proxy: total bar volume spike — NOT individual order size
Use this dashboard as a confirming tool alongside price action. For true order flow data use Bookmap, Sierra Chart, Quantower or NinjaTrader with a proper CME data feed.
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HOW TO USE IT
Watch for confluence — when DOM and Tape both agree on a direction that is your strongest signal. A single panel reading alone is less reliable.
Use the Big Contracts panel to identify when institutional-sized volume hits. A cluster of big BID bars at a support level, combined with DOM buyers leading and tape aggressive buy dominant, is a classic accumulation pattern. The timestamp on each entry makes it easy to find the bar on your chart.
Watch the Net Delta — a rising price with falling net delta can signal a weakening move or absorption. A falling price with rising net delta may indicate buyers stepping in.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
RECOMMENDED SETTINGS BY TIMEFRAME
1 min — DOM: 8-10 bars | Tape: 5-8 bars | NQ: 1500-2000 | ES: 300-500
2 min — DOM: 5-8 bars | Tape: 4-6 bars | NQ: 2000-3000 | ES: 500-800
3 min — DOM: 5-6 bars | Tape: 4-5 bars | NQ: 2500-3500 | ES: 600-1000
5 min — DOM: 4-6 bars | Tape: 3-5 bars | NQ: 3000-5000 | ES: 800-1500
15 min — DOM: 3-5 bars | Tape: 3-4 bars | NQ: 5000-8000 | ES: 1500-3000
MNQ and MES: divide NQ/ES thresholds by 10.
Tip: set Big Contracts detection timeframe to 1 min when charting on 2 min.
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CUSTOMISATION
Every color, background, transparency and threshold is adjustable from settings.
General — show/hide dashboard, DOM + Tape panels, Big Contracts panel, confluence row, minimalistic mode, color blind mode, dashboard position, text size, background transparency, chart background tint, session filter.
DOM — lookback period, alert threshold, full color and background customization per row.
Tape — lookback period, alert threshold, full color and background customization per row.
Cumulative Delta — optional full-width row at bottom of dashboard, display as number only, bar chart only or both, daily reset time.
Big Contracts — volume threshold, extra large threshold, detection timeframe, max entries to display, daily reset time, full color customization.
Alerts — 8 individually toggleable alert conditions covering big contracts, DOM dominance, tape dominance and confluence signals.
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DESIGNED FOR
Futures scalpers and intraday traders who want order flow context without cluttering their chart or paying for a separate platform. Works on any instrument with volume data — optimized for CME futures.
Indicator
Premium & Discount Zones with Bias═══════════════════════════════════════════════
PREMIUM & DISCOUNT ZONES WITH BIAS
═══════════════════════════════════════════════
A statistical mean-reversion framework that identifies premium (sell) and discount (buy) zones on your entry timeframe, derived from higher-timeframe structure. Built for intraday traders who want meaningful, stable reference levels without the noise of lower-timeframe volatility.
WHAT IT DOES
─────────────────────────────────────────────
This indicator projects five statistical zones from higher-timeframe candle distributions:
• Upper Sell Zone (95th percentile) — extreme premium, mean reversion likely
• Sell Zone (75th percentile) — standard premium zone
• EQ / Equilibrium (50th percentile) — the statistical midpoint, natural target
• Buy Zone (25th percentile) — standard discount zone
• Lower Buy Zone (5th percentile) — extreme discount, mean reversion likely
The zones are calculated from the distribution of recent higher-timeframe candles (default: 18 × 4H candles = 3 days of structure) using Monte Carlo projection. A Naive Bayes classifier runs on the current chart timeframe to produce a directional bias based on relative volume and momentum.
The key advantage: zones update only when a new HTF candle closes, giving you stable reference levels that stay fixed for hours at a time. No more chasing shifting lines on a 1m chart.
INSPIRATION & CREDIT
─────────────────────────────────────────────
This indicator is inspired by and builds on "Monte Carlo CT " by © Steversteves, published on PulseWire under Mozilla Public License 2.0 at mozilla.org
The original script provided the mathematical foundation of Monte Carlo price projection combined with a Naive Bayes directional classifier. This version reimagines that concept as a premium/discount zone framework: instead of projecting bands forward across the chart, zones are derived from higher-timeframe structure and displayed as stable horizontal reference levels on the entry timeframe. Session awareness, bias table, and full visual customisation have been added for intraday traders.
Full credit and thanks to Steversteves for the original work.
HOW TO USE IT
─────────────────────────────────────────────
RECOMMENDED SETUP
Apply to your entry timeframe (1m, 3m, or 5m recommended). The zones will reflect your chosen higher timeframe (default 4H), giving you structural context without cluttering the entry chart.
CORE CONCEPT
The indicator identifies where price is statistically extended relative to recent structure. When price enters a buy or sell zone, it has a statistical tendency to revert toward the EQ (equilibrium). This is NOT a signal to blindly buy or sell — it is a confluence tool that tells you whether your chosen entry is at a statistically favourable location.
EXAMPLE SETUPS
─────────────────────────────────────────────
BULLISH SETUP (long from discount)
Wait for London or NY kill zone — the Session cell in the table will turn active
Price trades down into the Buy Zone (25th percentile) or Lower Buy Zone (5th percentile)
Check the Bias table — is it showing LONG with elevated probability?
Confirm with your own entry trigger (sweep + reclaim, break of structure, CISD, order block, etc.)
Enter long, targeting EQ as first target or the opposing Sell Zone as runner target
Why this works: you are entering long at a statistically discounted price with directional bias confirmation, targeting the statistical mean or opposing extreme.
BEARISH SETUP (short from premium)
Wait for an active kill zone
Price trades up into the Sell Zone (75th percentile) or Upper Sell Zone (95th percentile)
Check the Bias table — is it showing SHORT with elevated probability?
Confirm with your own entry trigger (failure to break, rejection candle, bearish CISD, etc.)
Enter short, targeting EQ as first target or the opposing Buy Zone as runner target
Why this works: you are selling at a statistically premium price against the likely mean reversion move.
WHAT NOT TO DO
─────────────────────────────────────────────
• Do not trade against the Bias signal. If price is in the Buy Zone but Bias shows SHORT, the setup lacks confluence — skip it
• Do not trade outside active kill zones unless you have another strong edge — the zones are most reliable during high-volume sessions
• Do not treat this indicator as a standalone entry signal. It is a confluence filter that works best combined with your existing framework
• Do not expect zones to hold every time. These are statistical probabilities, not guarantees
SETTINGS EXPLAINED
─────────────────────────────────────────────
ZONE CALCULATION
• Higher Timeframe — Controls which timeframe the zones are derived from. Default 240 (4H) balances structure and responsiveness. For longer-term reference, try Daily. For faster updates, try 60 or 120.
• HTF Candles Lookback — How many HTF candles feed the distribution. Default 18 = 3 days of 4H data. Minimum 10-12 for statistical significance. Higher values give smoother zones; lower values adapt faster to regime changes.
• Use Monte Carlo Projection — ON uses Monte Carlo simulation for distribution projection (more robust, slightly slower). OFF uses direct percentile calculation of historical returns (faster, tighter zones).
• Monte Carlo Simulations — Number of simulation runs when MC is enabled. 200 is the sweet spot. More sims give smoother bands but slower calculation.
• MC Forecast Horizon — How many HTF candles ahead to project. Default 6 × 4H = 24 hours. Increase for longer-term projection, decrease for closer zones.
• Line Offset — How far right the horizontal zone lines extend on your chart. Adjust for visual preference.
BIAS CLASSIFIER SETTINGS
• NB Train Lookback — Training window in current chart bars for the Naive Bayes classifier. Default 240.
• NB Momentum Period — ROC period for the momentum feature. Higher = smoother and less noisy on low timeframes. Default 30.
KILL ZONES
• London and NY kill zone windows in NY time. Adjust if you trade different sessions or different time zones.
COLOURS
• Fully customisable for both the zone lines and the bias table. Separate controls for background, borders, header text, label text, value text, and all highlight colours. Works on both light and dark chart themes.
TABLE DISPLAY
• Toggle table on/off
• Six position options (top/middle/bottom × left/right)
• Three size options: Normal, Small, Tiny
ALERTS
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Two alert conditions are available:
• Price entered BUY ZONE — when close drops below the 25th percentile line
• Price entered SELL ZONE — when close rises above the 75th percentile line
Set these as audio alerts if you want to focus on other charts and be notified only when price reaches a zone.
INSTRUMENTS & TIMEFRAMES
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This indicator is designed for:
• Futures (Gold, Silver, Indices, Oil, etc.)
• Forex majors
• Major crypto pairs
Best performance on liquid instruments with consistent volume. Recommended entry timeframes: 1m, 3m, 5m. Recommended higher timeframe for zones: 4H (default), 2H for faster updates, Daily for swing trading.
FINAL NOTES
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This is a confluence indicator, not a signal generator. Use it in combination with your own entry methodology — order flow reading, structure analysis, liquidity concepts, or any systematic entry framework.
The zones tell you WHERE. Your framework tells you WHEN.
Feedback and suggestions welcome. Trade safe.
Indicator
Volatility Managed Kelly LeverageThe Volatility Managed Kelly Leverage (VMKL) indicator is a tool that dynamically adjusts position sizing based on forecasted market volatility. It helps you to optimize leverage exposure by systematically reducing risk during high volatility periods and increasing exposure when markets are calm.
VMKL adapts in real-time to changing market conditions, potentially generating alpha while smoothing volatility and reducing maximum drawdown.
This indicator implements the Optimal Volatility Plus Mean Strategy (OVPMS) from one of my favorite leverage papers:
" Alpha Generation and Risk Smoothing using Managed Volatility " by Tony Cooper (2010)
These are the key findings from the paper, which this indicator translates to real life:
Volatility is predictable while returns are not
Dynamic leverage based on volatility forecasts can generate significant excess returns
The strategy reduces volatility of volatility (vovo), kurtosis, and maximum drawdown
Tested on 125+ years of market data across multiple global indices
The OVPMS strategy (translated into this indicator) returned 12.6% annual return vs 7.0% for buy-and-hold, with the same volatility as the underlying index. Outstanding.
The indicator calculates optimal leverage using a three-step process
1. Volatility Forecasting
Uses Exponential Weighted Moving Average (EWMA):
σ²(t) = λ·σ²(t-1) + (1-λ)·r²(t-1)
This predicts next-day volatility from recent price movements
2. Return Prediction
Expected Return = a × σ^(b+1)
Where:
a = Power coefficient (baseline return, default: 0.10)
b = Power exponent (return-volatility relationship, default: -1.76 for SPY)
σ = Forecasted volatility
The negative exponent means returns decrease as volatility increases - a well-documented market behaviour.
3. Optimal Leverage Calculation
Full Kelly Leverage = μ / σ²
Actual Leverage = Full Kelly × Kelly Fraction × Caps × Smoothing
The Kelly Criterion provides the theoretically optimal leverage, which is then reduced via:
Kelly Fraction: Safety margin (default 75% = three-quarter Kelly)
Leverage Caps: Hard maximum and minimum limits
Smoothing: SMA to reduce rebalancing frequency
The Core Insight: Volatility varies over time (volatility of volatility), and this variation is costly. By targeting consistent volatility through dynamic leverage:
Reduces volatility drag - Compounding works better with stable volatility
Reduces drawdowns - Automatically deleverages before crashes
Reduces kurtosis - Fewer extreme return events
Generates alpha - Exploits the return-volatility relationship
The indicator calculates optimal leverage in real-time using EWMA volatility forecasting and Kelly Criterion mathematics, automatically detecting market regimes from CASH to VERY AGGRESSIVE and respective leverages. The statistics table shows Full Kelly leverage, Kelly Fraction leverage, forecasted volatility, predicted returns, and current regime.
Settings Guide
Please check the informational "i" in setting to get a lot more info.
You can also use preset configurations:
Conservative (Safe)
Kelly Fraction: 0.50
Max Leverage: 2.0x
Lambda: 0.97
Sensitivity: Enhanced
Moderate (Balanced) ⭐ DEFAULT
Kelly Fraction: 0.75
Max Leverage: 3.0x
Lambda: 0.94
Sensitivity: Enhanced
Aggressive (Maximum)
Kelly Fraction: 1.0
Max Leverage: 5.0x
Lambda: 0.90
Sensitivity: Standard
Paper Replication (Academic)
Kelly Fraction: 1.0
Max Leverage: 3.0x
Lambda: 0.94
Sensitivity: Standard
Adaptive: ON
Smoothing: 1
Remember: LEVERAGE MAGNIFIES BOTH GAINS AND LOSSES
Let me know if you have questions!
By Henrique Centieiro
Indicator
Volume 24h + Price + changeVolume (24H/1H)Показатели монет в углу графика помогают не тратить время на мертвые монетки
Indicator
BoaBias: MTF ALMA SuperTrend & Stats█ WHAT IT DOES
Multi-timeframe ALMA with SuperTrend-style regime logic on 15m, 1H, 4H, 1D, 1W on one chart. Per-TF ALMA lines ; separate bar colors for regime; optional filled bands between adjacent TF ALMAs. Optional statistics table : average and current run lengths, State ( BULL / BEAR / NEUT ), 1-bar and short-run (≤ N bars) counts, max runs, and Avg OH stretch columns. OH vs avg (per TF) controls how Cur Short / Cur Long cells highlight vs their rolling averages. MTF overheat labels on history (toggle in settings; on in default preset): ·S (bear run stretch) below the bar, ·L (bull run stretch) above the bar; one timeframe per text line; minimum overheated TF count is separate for bear vs bull; vertical stack spacing limits overlap on consecutive bars. No alert() in Pine — use PulseWire’s alert UI if needed.
█ HOW IT WORKS
ALMA: Arnaud Legoux moving average via ta.alma() (length, sigma, offset).
SuperTrend-style bands: upper = ALMA + factor × stdev; lower = ALMA − factor × stdev; regime flips on band-cross style logic.
MTF: request.security with barmerge.gaps_off and barmerge.lookahead_off (no lookahead). Parameters are per timeframe.
Statistics: Rolling window per TF for completed and current regime runs; chop metrics for very short runs; max run and Avg OH scans use min(window, Max bars for Max/OH scan) internally.
Labels: Toggle in inputs (on in defaults). Same heat thresholds as table per TF ; label fill colors follow bar regime colors (bear / bull) with white text.
█ ORIGINALITY
MTF overlay of ALMA-based SD SuperTrend combined with a per-TF run statistics layer, per-TF overheat multipliers , and MTF overheat labels (separate bear/bull gating + stack spacing; toggleable). Pine v6, self-contained. Inspired by public ALMA / SuperTrend discussion; wiring, table, heat logic, and labels are specific to this script.
█ HOW TO USE
First use: Wrong scale? Right-click → Pin to scale → Pin to right scale.
Enable/disable each timeframe; tune Factor, SD length, ALMA length, sigma, offset per TF.
Statistics: Set window length and Max bars for Max/OH scan (performance vs accuracy).
OH vs avg (per TF): Adjust each TF’s multiplier — drives red highlight on Cur Short/Long vs average and optional label conditions.
MTF overheat labels: On in the default preset (you can disable or change which TFs are included). Tune Min TFs per side (·S vs ·L), stack step (× ATR), and max label count (platform cap 500). Optional: only if chart ALMA flips — MTF overheat (min TFs + list) is read from the previous bar; ·S prints on the current bar if that prior bar was MTF·S overheated and the chart closes BEAR→BULL; ·L if prior was MTF·L and chart BULL→BEAR. With the option off, overheat and label stay on the same bar.
Chop rows: “Runs ≤ N bars” counts short regime lengths for context — not broker “fake” fills.
Interpretation: Stretched runs vs average are context , not predictions.
Performance: First attach or long history: full recalculation may take several seconds — wait until processing finishes. To speed up: less chart history, turn off the table or MTF labels, or reduce statistics windows / Max bars for Max/OH scan.
█ PERFORMANCE / LOAD TIME
The script runs multiple MTF security calls and statistics on each bar. On first load or a long visible history, PulseWire may need several seconds to finish — wait before assuming the table or labels failed. If the chart feels heavy, reduce loaded history, disable the table or overheat labels, or lower window sizes and the Max/OH scan cap.
█ LIMITATIONS
Trend logic lags; ranges whipsaw; MTF adds context, not certainty. Combine with other analysis.
Indicator
Global Liquidity Index (Z-Score)Global Liquidity Index (Quant Normalized Edition)
Overview
The Global Liquidity Index (Quant Normalized Edition) is a professional-grade macro indicator designed to track the "lifeblood" of financial markets: Fiat Liquidity. Unlike retail-focused momentum oscillators, this tool dives deep into the Market Plumbing of the world’s major central banks to provide a lead-lag perspective on asset prices.
The Core Logic
Liquidity is not a single number; it is a flow. This indicator aggregates data from the Federal Reserve (FED), ECB, PBoC, BoJ, BoE, and SNB.
It offers two distinct calculation modes:
-Net Central Banks: Focuses on the "true" spendable liquidity by calculating: (Balance Sheet - RRP - TGA) for the US, plus major global central bank assets.
-Global M2: Tracks the broad money supply growth across the world's largest economies.
Key Features
-Z-Score Normalization Layer: The breakthrough feature of this version. By applying a Z-score normalization to the liquidity Rate of Change (ROC), the script standardizes the different magnitudes of central bank balance sheets. This ensures that a massive move in the PBoC is weighted correctly against the FED, providing a balanced signal.
-4-Phase Liquidity Engine: The indicator categorizes the market regime into four distinct stages:
Phase 1: IGNITION (Blue) – Liquidity is beginning to accelerate, often preceding a major breakout.
Phase 2: FULL THRUST (Green) – Maximum liquidity momentum; the "Golden Window" for trend-following.
Phase 3: ROTATION (Orange) – Liquidity is decelerating. Momentum is fading, suggesting risk-off or distribution.
Phase 4: DROUGHT (Red) – Net liquidity is contracting.Historically associated with increased volatility and "rug pulls".
-X-Ray Projection (Offset): Includes a time-shifting engine that allows users to project the liquidity signal forward (or backward) to identify lead-lag correlations with price action.
-Dynamic Correlation: An integrated dashboard shows the real-time correlation between the liquidity flow and the current chart's price action.
How to Use
-Bullish Environments: Look for Phase 1 (Ignition) transitions into Phase 2 (Full Thrust).
-Risk Mitigation: Be cautious when the signal enters Phase 3 (Rotation), as this often marks the "topping out" of the liquidity cycle.
-Divergence: Watch for "Price vs. Liquidity" divergences, where price moves higher while liquidity (GLI) is making lower highs.
Credits & Disclaimer
Credits: The foundational macro-data fetching logic was inspired by the original work of QuantitativeAlpha. This version has been deeply modified and independently developed to include a proprietary Z-Score normalization layer, a time-shifting engine, and a 4-phase algorithmic regime filter.
Disclaimer: This indicator is for educational and informational purposes only. It does not constitute financial or investment advice.
Indicator
Market Regime Detector [forexobroker]Market Regime Detector is an overlay indicator that automatically classifies current market conditions into one of three regimes — Trending, Ranging, or Volatile — and adapts its signal logic accordingly. In trending regimes, it generates signals based on EMA crossovers in the direction of the larger trend. In ranging regimes, it switches to Bollinger Band bounce signals with RSI confirmation. In volatile regimes, it suppresses signals entirely to protect traders from whipsaws.
The core problem this solves is strategy mismatch. Most indicators use a single logic for all conditions, generating trend-following signals in sideways markets (resulting in losses) or mean-reversion signals in strong trends (resulting in missed moves). By first identifying the regime and then applying the appropriate strategy, this indicator dramatically reduces false signals caused by using the wrong tool for the current environment.
This tool is designed for traders who want their analysis to automatically adapt to changing market conditions rather than manually switching between strategies.
🔶 CONCEPTS
Market regime theory recognizes that financial markets alternate between distinct behavioral states. In trending regimes, price moves directionally with momentum — trend-following strategies work well, and mean-reversion strategies fail. In ranging regimes, price oscillates between support and resistance — mean-reversion strategies work well, and trend-following signals produce whipsaws. In volatile regimes, price moves erratically with wide ranges and frequent reversals — most strategies struggle.
The detector uses three independent measurements to classify regimes: ADX (Average Directional Index) measures trend strength, Bollinger Band width percentile measures volatility expansion relative to history, and ATR percentile rank measures absolute volatility. By combining these metrics with specific thresholds, the indicator achieves reliable regime classification that updates in real time.
🔶 HOW IT WORKS
• ADX is calculated over a configurable period — values above the trend threshold (default 25) indicate trending conditions, values below the range threshold (default 20) indicate ranging conditions
• Bollinger Band width is ranked as a percentile over 100 bars, and ATR is similarly ranked — high ATR percentile (above 80) flags volatile conditions regardless of ADX
• The regime priority is Volatile > Trending > Ranging, ensuring that extreme volatility always takes precedence
• In trending regimes, buy signals fire on EMA 9 crossing above EMA 21 with price above EMA 50 and volume confirmation; sell signals use the inverse
• In ranging regimes, buy signals fire when price touches the lower Bollinger Band with RSI below the oversold level and volume confirmation; sell signals use the inverse conditions at the upper band
• In volatile regimes, no signals are generated — the dashboard shows "NO TRADE" to protect capital
🔶 HOW TO USE
1. Add the indicator to any chart — the regime classification appears immediately in the dashboard and as a background color (green for trending, blue for ranging, red for volatile)
2. Pay attention to regime change vertical lines — dashed lines with labels mark exactly where the market shifted from one regime to another
3. When a signal fires, the label shows whether it came from a trend strategy (EMA Cross) or range strategy (BB Bounce), so you understand the logic behind it
4. In volatile regimes, respect the "NO TRADE" indication — sitting out during extreme volatility is a valid and often profitable strategy in itself
🔶 FEATURES
• Non-repainting signals (uses barstate.isconfirmed)
• Works on all timeframes and instruments
• 9 alert conditions with webhook JSON support (including individual regime change alerts)
• Professional dashboard display with ADX, BB Width, and ATR rank readings
• Regime change vertical lines for clear visual transition markers
• Dual strategy engine that automatically selects the appropriate signal logic
🔶 SETTINGS GUIDE
• ADX Length — Period for ADX calculation (default: 14)
• ADX Trend Threshold — ADX above this classifies as trending (default: 25)
• ADX Range Threshold — ADX below this classifies as ranging (default: 20)
• Bollinger Length / Multiplier — Parameters for BB width and bounce signals (default: 20 / 2.0)
• ATR Length — Period for ATR volatility measurement (default: 14)
• ATR Volatility Percentile — ATR rank above this classifies as volatile (default: 80)
• EMA Fast / Slow — EMA periods for trending signal crossovers (default: 9 / 21)
• RSI Length — RSI period for ranging signal confirmation (default: 14)
• RSI Overbought / Oversold — RSI levels for ranging signal triggers (default: 65 / 35)
• Signal Cooldown — Minimum bars between consecutive signals (default: 15)
• Volume Multiplier — Volume must exceed SMA by this factor (default: 1.0)
• Show Regime Background / Change Lines / Dashboard — Visual toggles
🔶 ALERTS
• MRD Buy Signal — Fires on any confirmed buy signal (trend or range strategy)
• MRD Sell Signal — Fires on any confirmed sell signal
• MRD Any Signal — Fires on any confirmed signal
• Regime Change: Trending — Fires when market shifts to trending regime
• Regime Change: Ranging — Fires when market shifts to ranging regime
• Regime Change: Volatile — Fires when market shifts to volatile regime
• Strong Trend ADX>35 — Fires when ADX exceeds 35, indicating a powerful trend
• Extreme Volatility — Fires when ATR rank exceeds the 90th percentile
• Low Volatility Squeeze — Fires when BB width rank is below 10% and ADX below 15, signaling a potential breakout setup
• Webhook JSON — JSON-formatted alert for automated bot trading
🔶 LIMITATIONS & DISCLAIMER
• This is a technical analysis tool, not financial advice
• Past patterns do not guarantee future results
• Best used alongside price action context and proper risk management
• Regime transitions can be noisy near threshold boundaries, causing brief flickers between states
• The ranging strategy performs best in clearly defined ranges — during slow, drifting markets that are technically "ranging" by ADX but lack clear boundaries, BB bounce signals may be less reliable
Indicator
Universal Futures Real Data Auto-ScaleThis indicator displays the volume of each candle clearly and directly. It is a simple tool for analyzing market flow and buying/selling pressure without complications.
Indicator
Solar Eclipse, Lunar Eclipse (down to 1 minute)these are the solar eclipse and lunar eclipse down to 1 minute level
this is highly useful to use it together other gann trading tools to predict market top and bottom.
once you see it with a differnt gann eye glasses. the same chart will be seen differently
and profit can be easily taken as in shown in www.gannexplained.com supported by both khit wong and his student's beokerage statement
the source time is from NASA, and they are all hard coded into the script. this will facilitate you to aware the upcoming and past solar eclipse time. we believe this is the best solar and lunar eclipse indicator you can have in the community
Indicator
Liquidation Tracker Pro v7 - Ultimate Portfolio & Risk DashboardLiquidation Tracker Pro v7 - Ultimate Portfolio & Risk Dashboard
Description:
Managing multiple leveraged positions across different assets can be chaotic. The Liquidation Tracker Pro v7 is designed to be your all-in-one on-chart command center. Whether you are scalping or managing long-term swing trades, this tool brings your entire portfolio's risk profile directly to your active chart.
Instead of constantly checking your exchange app, this indicator visualizes your entries, breakeven points, PnL, and exact liquidation levels (both Isolated and Cross margin) in real-time.
🌟 Key Features
Comprehensive Risk Dashboard: A highly optimized, customizable "Card-Format" UI that displays your account balance, margin utilization (%), and total PnL. It neatly organizes up to 6 manual positions.
Advanced Liquidation & Breakeven Math: Calculates precise Breakeven prices and Liquidation levels by factoring in your specific exchange's Maker/Taker fees and Maintenance Margin Rates (MMR). Includes built-in presets for Binance, Bybit, MEXC, OKX, and LBank.
Cross & Isolated Margin Tracking: Automatically calculates your liquidation points depending on whether your risk is isolated to the trade or shared across your account balance.
Unique Auto-Ladder Simulation: Planning to DCA (Dollar Cost Average) into a losing position? The built-in laddering feature lets you input a percentage to automatically calculate your newly diluted entry price and updated PnL, allowing you to visualize "saving" a trade before actually placing the orders.
On-Chart Visuals: Plots clean, dynamic lines and labels directly on your chart for Breakeven, Isolated Liquidation, and Cross Liquidation levels so you know exactly where your risk zones are.
Proximity Alerts: Set a custom safety threshold (e.g., 5%). If price action gets too close to your liquidation level, the indicator will fire an alert to warn you before the exchange does.
⚙️ How to Use
Configure General Settings: Enter your total account balance and select your exchange from the dropdown to ensure accurate fee calculations.
Input Your Positions: Open the indicator settings and input your current trades (up to 6). Toggle them "On" and input the Ticker, Long/Short direction, Leverage, Entry Price, and Margin used.
Customize the UI: Adjust the table position, text size, and background opacity to fit your chart layout. If you are tracking many positions, enable Landscape Mode to split the dashboard into dual tables and save vertical screen space.
💡 Who is this for?
This tool is perfect for multi-asset day traders, swing traders, and risk managers who need a crystal-clear, centralized view of their exposure and liquidation risks without leaving their PulseWire charts.
Disclaimer: This indicator is for informational and educational purposes only. Always verify your actual liquidation prices and margin requirements directly with your exchange, as API data and fast-moving markets can result in slight discrepancies.
Indicator
