Average True Range Stop Loss Finder [MasterYodi]This indicator utilizes the Average True Range (ATR) to help traders identify optimal stop-loss levels that reduce the risk of premature exits caused by market volatility or tight stop placements. The default multiplier is set to 1.5, providing a balanced stop-loss buffer. For more conservative setups, a multiplier of 2 is recommended; for tighter risk management, use 1.
ATR values and corresponding stop-loss levels are displayed in a table at the bottom of the chart.
Use the high-based (red) level for short positions
Use the low-based (teal) level for long positions
波動率
Market Echo Screener [BigBeluga]
The Market Echo Screener is a structured multi-asset dashboard capable of tracking up to 15 symbols simultaneously .
Designed to condense complex market data into an actionable format. Each column represents a specialized calculation, giving traders insight into signals, phases, retests, and volatility — all updated in real time.
For each symbol, it displays a full set of analytics: trend signals, take profit progression, wave structure, equilibrium pulls, volatility-adjusted flows, smart band retests, volatility regimes, and live price context — all condensed into one unified table.
Instead of flipping through multiple charts, traders get an instant overview of market dynamics across an entire watchlist, making it easier to spot alignment and high-probability opportunities.
⬤ Trend Signals
This column is powered by a low-pass digital trend filter that smooths short-term fluctuations and isolates directional momentum.
It produces Buy and Sell signals when price crosses adaptive thresholds relative to the smoothed baseline. Stronger “＋” signals appear when slope acceleration or momentum divergence confirms additional conviction.
• Uses recursive filtering to eliminate noise.
• Signal strength is determined by the magnitude of deviation from the baseline.
• Tracks how many bars back the signal occurred, using a bar-counting algorithm.
• Combines both normal and power signals to reflect phases of market conviction.
⬤ TPs (Take Profits)
The take profit ladder is generated through an adaptive volatility-projection model .
When a signal fires, projected levels are based on volatility-weighted extensions. Each level (TP1–TP6) represents an incrementally wider confidence band, dynamically recalculated with every new bar.
• Uses volatility-normalized ranges for TP distances.
• Level activation is sequential, progressing as price reaches thresholds.
• Reset occurs when opposite signals are detected.
• Higher TPs imply extended momentum runs, while early TP triggers highlight conservative exits.
⬤ ActionWave
The ActionWave column applies a dual-smoothing algorithm combining custom MA stacks and polynomial regression to capture the underlying wave structure.
It identifies macro phases (Bullish ∆ / Bearish ∇) and flags retests when price folds back into the average after expansion.
• Wave slope is calculated using gradient differentials.
• Retests are confirmed within a bar-window threshold (e.g., 20–25 bars).
• Distinguishes continuation from exhaustion by analyzing whether slope remains positive/negative.
• Provides a clean map of trend rhythm without intrabar noise.
⬤ Magnet
The Magnet measure calculates a dynamic equilibrium band around price.
By averaging the midpoints of recent high–low ranges and weighting them by volatility, it defines a “fair zone” where price tends to trend and mean-revert.
• Bullish/Bearish status is derived from price position relative to the equilibrium mean.
• Retests occur when price leaves the zone and then re-enters within a tolerance band.
• Incorporates a mean-reversion index to highlight strength of pull.
• Acts as a gravitational anchor, showing when price is likely to snap back.
⬤ FlowTrend
FlowTrend is calculated using volatility and noise adjusted envelope bands .
It determines the active market flow by testing whether price consistently holds above or below the smoothed envelope. Retests are logged when price touches the envelope and respects trend direction.
• Bands expand/contract based on ATR and rolling variance.
• Flow state = Bullish if closing above upper envelope, Bearish if below.
• Retests validated only if trend slope and band alignment remain intact.
• Helps identify continuation setups by filtering false flips.
⬤ Smart Bands
Smart Bands employ an adaptive trailing stop framework that shifts with volatility and momentum.
Price interaction with these bands is tracked for bullish (∆) or bearish (∇) retests, highlighting whether the current move has revalidated at its volatility boundary.
• Bands derived from trailing volatility-adjusted stops.
• Upward retest fires when price tests support bands during uptrend.
• Downward retest occurs when resistance bands are tapped in downtrend.
• Provides structured “confirmation points” that validate signals.
⬤ Volatility
Volatility is measured via a hybrid standard deviation logic .
First, the standard deviation of closing prices over 10 bars is scaled by a factor, then normalized against its own 20-bar rolling standard deviation. The result is converted into a 0–100 index, producing three regimes:
❄️ Calm (<50): low dispersion, mean-reversion conditions dominate.
⚠️ Elevated (50–70): directional expansion likely, watch for breakout tension.
💥 Explosive (>70): strong dispersion, trend-following setups favored.
• Uses layered smoothing to dampen noise.
• Normalization ensures comparability across different assets.
• Acts as a meta-filter for selecting strategy type (range vs. momentum).
⬤ Price
The price column displays the latest close rounded to the nearest tick size.
It is color-coded by candle bias: green for bullish closes, red for bearish closes.
• Tick normalization ensures clean display across assets with different decimal precision.
• Color-coding gives instant sentiment context.
• Serves as the anchor reference for all other metrics in the row.
The Market Echo Screener is not a simple signal table — it’s a layered analytics framework.
Each column is driven by technical calculations: smoothing filters, volatility projections, equilibrium models, and adaptive band logic. Together, they create a unified lens on multiple assets, allowing traders to rapidly identify alignment, filter out noise, and focus on the clearest opportunities.
ATR (No Gap) - Advanced Volatility IndicatorA customizable Average True Range indicator that eliminates gap distortion between trading sessions, providing cleaner volatility measurements for intraday and swing traders.
Key Features:
Gap Filtering: Optional toggle to ignore overnight/weekend gaps that distort volatility readings
EMA Smoothing: Defaults to EMA for more responsive volatility tracking (also supports RMA and SMA)
Half ATR Display: Shows 50% ATR value for quick stop-loss and take-profit calculations
Clean Value Table: Real-time values displayed on chart with configurable decimal precision
Flexible Settings: Customize length, smoothing method, and display options
Ideal for:
Setting dynamic stop losses and take profits
Position sizing based on current volatility
Comparing gap vs. no-gap volatility measurements
Trading instruments with large overnight gaps (indices, forex, crypto)
Use this indicator to get a more accurate picture of intraday volatility without the noise from session gaps!
Volatility-Targeted Momentum Portfolio [BackQuant]Volatility-Targeted Momentum Portfolio
A complete momentum portfolio engine that ranks assets, targets a user-defined volatility, builds long, short, or delta-neutral books, and reports performance with metrics, attribution, Monte Carlo scenarios, allocation pie, and efficiency scatter plots. This description explains the theory and the mechanics so you can configure, validate, and deploy it with intent.
Table of contents
What the script does at a glance
Momentum, what it is, how to know if it is present
Volatility targeting, why and how it is done here
Portfolio construction modes: Long Only, Short Only, Delta Neutral
Regime filter and when the strategy goes to cash
Transaction cost modelling in this script
Backtest metrics and definitions
Performance attribution chart
Monte Carlo simulation
Scatter plot analysis modes
Asset allocation pie chart
Inputs, presets, and deployment checklist
Suggested workflow
1) What the script does at a glance
Pulls a list of up to 15 tickers, computes a simple momentum score on each over a configurable lookback, then volatility-scales their bar-to-bar return stream to a target annualized volatility.
Ranks assets by raw momentum, selects the top 3 and bottom 3, builds positions according to the chosen mode, and gates exposure with a fast regime filter.
Accumulates a portfolio equity curve with risk and performance metrics, optional benchmark buy-and-hold for comparison, and a full alert suite.
Adds visual diagnostics: performance attribution bars, Monte Carlo forward paths, an allocation pie, and scatter plots for risk-return and factor views.
2) Momentum: definition, detection, and validation
Momentum is the tendency of assets that have performed well to continue to perform well, and of underperformers to continue underperforming, over a specific horizon. You operationalize it by selecting a horizon, defining a signal, ranking assets, and trading the leaders versus laggards subject to risk constraints.
Signal choices . Common signals include cumulative return over a lookback window, regression slope on log-price, or normalized rate-of-change. This script uses cumulative return over lookback bars for ranking (variable cr = price/price - 1). It keeps the ranking simple and lets volatility targeting handle risk normalization.
How to know momentum is present .
Leaders and laggards persist across adjacent windows rather than flipping every bar.
Spread between average momentum of leaders and laggards is materially positive in sample.
Cross-sectional dispersion is non-trivial. If everything is flat or highly correlated with no separation, momentum selection will be weak.
Your validation should include a diagnostic that measures whether returns are explained by a momentum regression on the timeseries.
Recommended diagnostic tool . Before running any momentum portfolio, verify that a timeseries exhibits stable directional drift. Use this indicator as a pre-check: It fits a regression to price, exposes slope and goodness-of-fit style context, and helps confirm if there is usable momentum before you force a ranking into a flat regime.
3) Volatility targeting: purpose and implementation here
Purpose . Volatility targeting seeks a more stable risk footprint. High-vol assets get sized down, low-vol assets get sized up, so each contributes more evenly to total risk.
Computation in this script (per asset, rolling):
Return series ret = log(price/price ).
Annualized volatility estimate vol = stdev(ret, lookback) * sqrt(tradingdays).
Leverage multiplier volMult = clamp(targetVol / vol, 0.1, 5.0).
This caps sizing so extremely low-vol assets don’t explode weight and extremely high-vol assets don’t go to zero.
Scaled return stream sr = ret * volMult. This is the per-bar, risk-adjusted building block used in the portfolio combinations.
Interpretation . You are not levering your account on the exchange, you are rescaling the contribution each asset’s daily move has on the modeled equity. In live trading you would reflect this with position sizing or notional exposure.
4) Portfolio construction modes
Cross-sectional ranking . Assets are sorted by cr over the chosen lookback. Top and bottom indices are extracted without ties.
Long Only . Averages the volatility-scaled returns of the top 3 assets: avgRet = mean(sr_top1, sr_top2, sr_top3). Position table shows per-asset leverages and weights proportional to their current volMult.
Short Only . Averages the negative of the volatility-scaled returns of the bottom 3: avgRet = mean(-sr_bot1, -sr_bot2, -sr_bot3). Position table shows short legs.
Delta Neutral . Long the top 3 and short the bottom 3 in equal book sizes. Each side is sized to 50 percent notional internally, with weights within each side proportional to volMult. The return stream mixes the two sides: avgRet = mean(sr_top1,sr_top2,sr_top3, -sr_bot1,-sr_bot2,-sr_bot3).
Notes .
The selection metric is raw momentum, the execution stream is volatility-scaled returns. This separation is deliberate. It avoids letting volatility dominate ranking while still enforcing risk parity at the return contribution stage.
If everything rallies together and dispersion collapses, Long Only may behave like a single beta. Delta Neutral is designed to extract cross-sectional momentum with low net beta.
5) Regime filter
A fast EMA(12) vs EMA(21) filter gates exposure.
Long Only active when EMA12 > EMA21. Otherwise the book is set to cash.
Short Only active when EMA12 < EMA21. Otherwise cash.
Delta Neutral is always active.
This prevents taking long momentum entries during obvious local downtrends and vice versa for shorts. When the filter is false, equity is held flat for that bar.
6) Transaction cost modelling
There are two cost touchpoints in the script.
Per-bar drag . When the regime filter is active, the per-bar return is reduced by fee_rate * avgRet inside netRet = avgRet - (fee_rate * avgRet). This models proportional friction relative to traded impact on that bar.
Turnover-linked fee . The script tracks changes in membership of the top and bottom baskets (top1..top3, bot1..bot3). The intent is to charge fees when composition changes. The template counts changes and scales a fee by change count divided by 6 for the six slots.
Use case: increase fee_rate to reflect taker fees and slippage if you rebalance every bar or trade illiquid assets. Reduce it if you rebalance less often or use maker orders.
Practical advice .
If you rebalance daily, start with 5–20 bps round-trip per switch on liquid futures and adjust per venue.
For crypto perp microcaps, stress higher cost assumptions and add slippage buffers.
If you only rotate on lookback boundaries or at signals, use alert-driven rebalances and lower per-bar drag.
7) Backtest metrics and definitions
The script computes a standard set of portfolio statistics once the start date is reached.
Net Profit percent over the full test.
Max Drawdown percent, tracked from running peaks.
Annualized Mean and Stdev using the chosen trading day count.
Variance is the square of annualized stdev.
Sharpe uses daily mean adjusted by risk-free rate and annualized.
Sortino uses downside stdev only.
Omega ratio of sum of gains to sum of losses.
Gain-to-Pain total gains divided by total losses absolute.
CAGR compounded annual growth from start date to now.
Alpha, Beta versus a user-selected benchmark. Beta from covariance of daily returns, Alpha from CAPM.
Skewness of daily returns.
VaR 95 linear-interpolated 5th percentile of daily returns.
CVaR average of the worst 5 percent of daily returns.
Benchmark Buy-and-Hold equity path for comparison.
8) Performance attribution
Cumulative contribution per asset, adjusted for whether it was held long or short and for its volatility multiplier, aggregated across the backtest. You can filter to winners only or show both sides. The panel is sorted by contribution and includes percent labels.
9) Monte Carlo simulation
The panel draws forward equity paths from either a Normal model parameterized by recent mean and stdev, or non-parametric bootstrap of recent daily returns. You control the sample length, number of simulations, forecast horizon, visibility of individual paths, confidence bands, and a reproducible seed.
Normal uses Box-Muller with your seed. Good for quick, smooth envelopes.
Bootstrap resamples realized returns, preserving fat tails and volatility clustering better than a Gaussian assumption.
Bands show 10th, 25th, 75th, 90th percentiles and the path mean.
10) Scatter plot analysis
Four point-cloud modes, each plotting all assets and a star for the current portfolio position, with quadrant guides and labels.
Risk-Return Efficiency . X is risk proxy from leverage, Y is expected return from annualized momentum. The star shows the current book’s composite.
Momentum vs Volatility . Visualizes whether leaders are also high vol, a cue for turnover and cost expectations.
Beta vs Alpha . X is a beta proxy, Y is risk-adjusted excess return proxy. Useful to see if leaders are just beta.
Leverage vs Momentum . X is volMult, Y is momentum. Shows how volatility targeting is redistributing risk.
11) Asset allocation pie chart
Builds a wheel of current allocations.
Long Only, weights are proportional to each long asset’s current volMult and sum to 100 percent.
Short Only, weights show the short book as positive slices that sum to 100 percent.
Delta Neutral, 50 percent long and 50 percent short books, each side leverage-proportional.
Labels can show asset, percent, and current leverage.
12) Inputs and quick presets
Core
Portfolio Strategy . Long Only, Short Only, Delta Neutral.
Initial Capital . For equity scaling in the panel.
Trading Days/Year . 252 for stocks, 365 for crypto.
Target Volatility . Annualized, drives volMult.
Transaction Fees . Per-bar drag and composition change penalty, see the modelling notes above.
Momentum Lookback . Ranking horizon. Shorter is more reactive, longer is steadier.
Start Date . Ensure every symbol has data back to this date to avoid bias.
Benchmark . Used for alpha, beta, and B&H line.
Diagnostics
Metrics, Equity, B&H, Curve labels, Daily return line, Rolling drawdown fill.
Attribution panel. Toggle winners only to focus on what matters.
Monte Carlo mode with Normal or Bootstrap and confidence bands.
Scatter plot type and styling, labels, and portfolio star.
Pie chart and labels for current allocation.
Presets
Crypto Daily, Long Only . Lookback 25, Target Vol 50 percent, Fees 10 bps, Regime filter on, Metrics and Drawdown on. Monte Carlo Bootstrap with Recent 200 bars for bands.
Crypto Daily, Delta Neutral . Lookback 25, Target Vol 50 percent, Fees 15–25 bps, Regime filter always active for this mode. Use Scatter Risk-Return to monitor efficiency and keep the star near upper left quadrants without drifting rightward.
Equities Daily, Long Only . Lookback 60–120, Target Vol 15–20 percent, Fees 5–10 bps, Regime filter on. Use Benchmark SPX and watch Alpha and Beta to keep the book from becoming index beta.
13) Suggested workflow
Universe sanity check . Pick liquid tickers with stable data. Thin assets distort vol estimates and fees.
Check momentum existence . Run on your timeframe. If slope and fit are weak, widen lookback or avoid that asset or timeframe.
Set risk budget . Choose a target volatility that matches your drawdown tolerance. Higher target increases turnover and cost sensitivity.
Pick mode . Long Only for bull regimes, Short Only for sustained downtrends, Delta Neutral for cross-sectional harvesting when index direction is unclear.
Tune lookback . If leaders rotate too often, lengthen it. If entries lag, shorten it.
Validate cost assumptions . Increase fee_rate and stress Monte Carlo. If the edge vanishes with modest friction, refine selection or lengthen rebalance cadence.
Run attribution . Confirm the strategy’s winners align with intuition and not one unstable outlier.
Use alerts . Enable position change, drawdown, volatility breach, regime, momentum shift, and crash alerts to supervise live runs.
Important implementation details mapped to code
Momentum measure . cr = price / price - 1 per symbol for ranking. Simplicity helps avoid overfitting.
Volatility targeting . vol = stdev(log returns, lookback) * sqrt(tradingdays), volMult = clamp(targetVol / vol, 0.1, 5), sr = ret * volMult.
Selection . Extract indices for top1..top3 and bot1..bot3. The arrays rets, scRets, lev_vals, and ticks_arr track momentum, scaled returns, leverage multipliers, and display tickers respectively.
Regime filter . EMA12 vs EMA21 switch determines if the strategy takes risk for Long or Short modes. Delta Neutral ignores the gate.
Equity update . Equity multiplies by 1 + netRet only when the regime was active in the prior bar. Buy-and-hold benchmark is computed separately for comparison.
Tables . Position tables show current top or bottom assets with leverage and weights. Metric table prints all risk and performance figures.
Visualization panels . Attribution, Monte Carlo, scatter, and pie use the last bars to draw overlays that update as the backtest proceeds.
Final notes
Momentum is a portfolio effect. The edge comes from cross-sectional dispersion, adequate risk normalization, and disciplined turnover control, not from a single best asset call.
Volatility targeting stabilizes path but does not fix selection. Use the momentum regression link above to confirm structure exists before you size into it.
Always test higher lag costs and slippage, then recheck metrics, attribution, and Monte Carlo envelopes. If the edge persists under stress, you have something robust.
Stochastic + Bollinger Bands Multi-Timeframe StrategyThis strategy fuses the Stochastic Oscillator from the 4-hour timeframe with Bollinger Bands from the 1-hour timeframe, operating on a 10-hour chart to capture a unique volatility rhythm and temporal alignment discovered through observational alpha.
By blending momentum confirmation from the higher timeframe with short-term volatility extremes, the strategy leverages what some traders refer to as “rotating volatility” — a phenomenon where multi-timeframe oscillations sync to reveal hidden trade opportunities.
🧠 Strategy Logic
✅ Long Entry Condition:
Stochastic on the 4H timeframe:
%K crosses above %D
Both %K and %D are below 20 (oversold zone)
Bollinger Bands on the 1H timeframe:
Price crosses above the lower Bollinger Band, indicating a potential reversal
→ A long trade is opened when both momentum recovery and volatility reversion align.
✅ Long Exit Condition:
Stochastic on the 4H:
%K crosses below %D
Both %K and %D are above 80 (overbought zone)
Bollinger Bands on the 1H:
Price reaches or exceeds the upper Bollinger Band, suggesting exhaustion
→ The long trade is closed when either signal suggests a potential reversal or overextension.
🧬 Temporal Structure & Alpha
This strategy is deployed on a 10-hour chart — a non-standard timeframe that may align more effectively with multi-timeframe mean reversion dynamics.
This subtle adjustment exploits what some traders identify as “temporal drift” — the desynchronization of volatility across timeframes that creates hidden rhythm in price action.
→ For example, Stochastic on 4H (lookback 17) and Bollinger Bands on 1H (lookback 20) may periodically sync around 10H intervals, offering unique alpha windows.
📊 Indicator Components
🔹 Stochastic Oscillator (4H, Length 17)
Detects momentum reversals using %K and %D crossovers
Helps define overbought/oversold zones from a mid-term view
🔹 Bollinger Bands (1H, Length 20, ±2 StdDev)
Measures price volatility using standard deviation around a moving average
Entry occurs near lower band (support), exits near upper band (resistance)
🔹 Multi-Timeframe Logic
Uses request.security() to safely reference 4H and 1H indicators from a 10H chart
Avoids repainting by using closed higher-timeframe candles only
📈 Visualization
A plot selector input allows toggling between:
Stochastic Plot (%K & %D, with overbought/oversold levels)
Bollinger Bands Plot (Upper, Basis, Lower from 1H data)
This helps users visually confirm entry/exit triggers in real time.
🛠 Customization
Fully configurable Stochastic and BB settings
Timeframes are independently adjustable
Strategy settings like position sizing, slippage, and commission are editable
⚠️ Disclaimer
This strategy is intended for educational and informational purposes only.
It does not constitute financial advice or a recommendation to buy or sell any asset.
Market conditions vary, and past performance does not guarantee future results.
Always test any trading strategy in a simulated environment and consult a licensed financial advisor before making real-world investment decisions.
VWAP CATS background flipped 4.0VWAP CATS Background Flipped 4.0 is a sophisticated Pine Script v5 indicator for TradingView that combines a configurable moving average (MA) with dynamic Gann Square of 9 levels to create a multi-layered background shading system for price action analysis. It visualizes support/resistance zones around a central MA (often VWAP or RVWAP) using incremental offsets (either % or absolute points), generating symmetrical bands that resemble a "CATS" (Concentric Adaptive Tiered System) — hence the name.The background is "flipped" in the sense that shading intensity and structure emphasize higher-tier zones, and labels are placed to the right of the chart for future projection.Key FeaturesFeature
Description
Multi-MA Engine
Supports 20+ MA types: EMA, DEMA, TEMA, SMA, VWAP, RVWAP, HMA, ALMA, custom volume blends (CVB1–4)
RVWAP Mode
Rolling VWAP with adaptive or fixed time window (days/hours/minutes)
Gann Square of 9 Logic
Generates 80+ symmetric levels (0.25x to 17x increment) above/below the MA
Dual Increment Mode
Choose Percent or Points for spacing
Background Fills
Tiered transparency fills between Gann levels (darker = stronger zones)
Visual MA Offset
Shift MA line left/right without breaking fill alignment
Smart Labels
Projected labels on last bar: "FV", "normal", "high", "3/4" at key levels
Performance Optimized
Hidden plots + label cleanup to prevent lag
Primary Use Cases
1. Institutional VWAP Anchoring
Use RVWAP (1-day fixed) as maRaw
Set Increment = 0.5 points or 0.05%
Watch price interaction with "normal" (2x), "high" (4x), "3/4" (6x) zones
Ideal for intraday scalping on indices (ES, NQ) or forex
2. Swing Trading with Gann Projections
Use 400-period SMA/EMA on daily chart
Increment in Percent mode (~1.22%)
Identify confluence when price rejects at 2x, 4x, or 6x bands
Labels project future targets to the right
3. Volume-Weighted Mean Reversion
Select CVB1–CVB4 for heavy volume smoothing
Use Points mode for stocks with stable tick sizes (e.g. $0.50 increments)
Trade mean reversion between ±1x and ±2x bands
4. Risk Management & Stop Placement
Place stops beyond 2x or 4x bands
Take profits at next major tier (e.g. 4x → 6x)
Pro Tips
Enable "Use Fixed Time Period" for RVWAP to avoid session reset issues
Increase i_label_offset on lower timeframes to avoid overlap
Combine with volume profile or order flow for confluence
The "FV" label marks the Fair Value MA — core anchor
Summary"VWAP CATS Background Flipped 4.0" turns any moving average into a dynamic Gann-based pricing grid with intelligent background shading and forward-projected labels — perfect for institutional-style mean reversion, swing targeting, and risk-defined trading."
Smarter Money Volume Rejection Blocks [PhenLabs]📊 Smarter Money Volume Rejection Blocks – Institutional Rejection Zone Detection
The Smarter Money Volume Rejection Blocks indicator combines high-volume analysis with statistical confidence intervals to identify where institutional traders are actively defending price levels through volume spikes and rejection patterns.
🔥 Core Methodology
Volume Spike Detection analyzes when current volume exceeds moving average by configurable multipliers (1.0-5.0x) to identify institutional activity
Rejection Candle Analysis uses dual-ratio system measuring wick percentage (30-90%) and maximum body ratio (10-60%) to confirm genuine rejections
Statistical Confidence Channels create three-level zones (upper, center, lower) based on ATR or Standard Deviation calculations
Smart Invalidation Logic automatically clears zones when price significantly breaches confidence levels to maintain relevance
Dynamic Channel Projection extends confidence intervals forward up to 200 bars with customizable length
Support Zone Identification detects bullish rejections where smart money absorbs selling pressure with high volume and strong lower wicks
Resistance Zone Mapping identifies bearish rejections where institutions defend price levels with volume spikes and pronounced upper wicks
Visual Information Dashboard displays real-time status table showing volume spike conditions and active support/resistance zones
⚙️ Technical Configuration
Dual Confidence Interval Methods: Choose between ATR-Based for trend-following environments or StdDev-Based for range-bound statistical precision
Volume Moving Average: Configurable period (default 20) for baseline volume comparison calculations
Volume Spike Multiplier: Adjustable threshold from 1.0 to 5.0 times average volume to filter institutional activity
Rejection Wick Percentage: Set minimum wick size from 30% to 90% of candle range for valid rejection detection
Maximum Body Ratio: Configure body-to-range ratio from 10% to 60% to ensure genuine rejection structures
Confidence Multiplier: Statistical multiplier (default 1.96) for 95% confidence interval calculations
Channel Projection Length: Extend confidence zones forward from 10 to 200 bars for anticipatory analysis
ATR Period: Customize Average True Range lookback from 5 to 50 bars for volatility-based calculations
StdDev Period: Adjust Standard Deviation period from 10 to 100 bars for statistical precision
🎯 Real-World Trading Applications
Identify high-probability support zones where institutional buyers have historically defended price with significant volume
Map resistance levels where smart money sellers consistently reject higher prices with volume confirmation
Combine with price action analysis to confirm breakout validity when price approaches confidence channel boundaries
Use invalidation signals to exit positions when smart money zones are definitively breached
Monitor the real-time dashboard to quickly assess current market structure and active rejection zones
Adapt strategy based on calculation method: ATR for trending markets, StdDev for ranging conditions
Set alerts on confidence level breaches to catch potential trend reversals or continuation patterns
📈 Visual Interpretation Guide
Green Zones indicate bullish rejection blocks where buyers defended with high volume and lower wicks
Red Zones indicate bearish rejection blocks where sellers defended with high volume and upper wicks
Solid Center Lines represent the core rejection price level where maximum volume activity occurred
Dashed Confidence Boundaries show upper and lower statistical limits based on volatility calculations
Zone Opacity decreases as channels extend forward to indicate decreasing confidence over time
Dashboard Color Coding provides instant visual feedback on active volume spike and zone conditions
⚠️ Important Considerations
Volume-based indicators identify historical rejection zones but cannot predict future price action with certainty
Market conditions change rapidly and institutional activity patterns evolve continuously
High volume does not guarantee level defense as market structure can shift without warning
Confidence intervals represent statistical probabilities, not guaranteed price boundaries
ORB | Feng FuturesThe ORB | Feng Futures indicator automatically detects the Opening Range Breakout (ORB) for each trading session, plotting the High, Low, and Midline in real time. This tool is built for futures traders who rely on ORB structure to confirm trends, identify breakout zones, and recognize reversal areas early in the session.
Features:
• Auto-calculated ORB High, Low, and Midline
• Multi-timezone session support (NY, Chicago, London, Tokyo, etc.)
• Customize ORB time range and time window for display
• Real-time updating lines that freeze at session close
• Optional labels with customizable size, color, and offset
• Save and view multiple previous ORB sessions
• Full color customization for all levels
• Automatically hides on higher timeframes (Daily+) to reduce clutter
• Works on ES, NQ, and all intraday futures charts
• Works on stocks, crypto, forex, and other tradeable assets where ORB is applicable
Disclaimer: This indicator is for educational purposes only and does not constitute financial advice. Trading futures involves significant risk and may not be suitable for all investors. Always do your own research and use proper risk management.
Buy/Sell Volume Tracker [wjdtks255]Indicator Description
Function: Separates buy and sell volume based on candle direction (close ≥ open) and displays the buy−sell difference (hist_val) as a histogram.
Visuals: Buy/sell bars are distinguished by user-selectable colors and opacity; two moving averages (MA1 and MA2) are shown to smooth the flow.
Meaning: A positive histogram indicates buy dominance; a negative histogram indicates sell dominance.
Limitation: The current separation is estimated from candle direction and may differ from execution-side (tick/trade-side) based data.
Trading Rules (Summary)
Conservative trend-following long
Entry: Enter long when hist_val turns above 0 and MA1 crosses MA2 from below.
Stop-loss: Exit if hist_val falls back below 0 or MA1 drops below MA2.
Take-profit: Use a risk:reward of 1:1.5 or set targets based on ATR.
Short-term rebound long
Entry: Enter a short-term long when a large negative histogram region begins to narrow and shows a recovery sign.
Stop-loss: Exit if hist_val drops below the previous low or bearish candles continue.
Take-profit: Prefer quick partial profit-taking.
Short (sell) strategy
Entry: Enter short when hist_val falls below 0 and MA1 crosses MA2 from above.
Stop-loss / Take-profit: Apply the inverse rules of the long strategy.
Filters and risk management
Volume filter: Only accept signals when volume exceeds a fraction of average volume to reduce noise.
Entry strength: Require |hist_val| to exceed a historical average threshold (e.g., avg(|hist_val|, N) × factor) to strengthen signals.
Position sizing: Size positions so that account risk per trade is within limits (e.g., 1–2% of account equity).
Timeframe: Use short timeframes for scalping and 1h+ for swing trading.
Customized Double Bollinger Bands [wjdtks255]This indicator combines two Bollinger Bands to visualize both short-term and extreme volatility zones on the same chart.
While a standard Bollinger Band shows how far price deviates from its mean,
this customized version displays two standard deviation ranges, allowing traders to distinguish between mild and extreme volatility conditions.
Band 1 (StdDev 0.5) captures short-term fluctuations near the price average,
while Band 2 (StdDev 3.0) highlights overbought or oversold conditions at market extremes.
When the distance between the two bands widens, volatility is increasing;
when it narrows, the market is stabilizing or preparing for a breakout.
ㆍPrice breaking above Band 2 → Potential overbought or strong bullish trend
ㆍPrice falling below Band 2 → Possible oversold or bearish continuation
ㆍBands tightening → Volatility compression, potential reversal zone
This indicator is designed primarily for volatility visualization rather than directional prediction.
For higher accuracy, use it alongside RSI, MACD, or trend-based indicators.
Developed by wjdtks255
VWAP – Pivot Pairs (SECONDS‑BASED RESET)VWAP – Pivot Pairs (SECONDS-BASED RESET) is a Pine Script v6 indicator for TradingView that combines pivot-based breakout detection with resettable VWAP (Volume Weighted Average Price) calculations over user-defined rolling time periods in seconds.It identifies high and low swing pivots via breakout logic, then calculates two VWAP lines per anchor:One using high/low as the price source,
One using close as the price source.
These form "pivot pairs" that reset automatically at the start of each custom-duration period (e.g., every 300 seconds), starting from a user-defined UTC time of day (default: 09:30 UTC).Visuals include:Colored VWAP lines (high pair: red, low pair: green),
Semi-transparent fill zones between each pair,
Optional toggles to show/hide high or low pairs.
Use CasesUse Case
Description
Intraday Scalping (1–15 min charts)
Use 60–300 second resets to capture micro-trends within larger sessions. VWAP pairs act as dynamic support/resistance after breakouts.
High-Frequency / Algo Validation
Backtest strategies on tick/second charts where traditional session resets fail. Align resets with exchange micro-sessions or volatility windows.
Opening Range Breakout (ORB) Enhancement
Set period_seconds = 1800 (30 min) and start time = 09:30 UTC → VWAP builds only on first 30 mins post-open, then floats. Pairs show deviation from ORB mean.
Range-Bound Market Analysis
In choppy markets, VWAP pairs converge near fair value. Divergence signals potential breakout. Fill color intensity shows conviction.
Multi-Timeframe Confluence
Overlay on 1-second chart with 300s reset → matches 5-minute structure. Use close-based VWAP for entries, high/low-based for stops.
Key Features SummaryFeature
Function
period_seconds
Rolling window length in seconds (e.g., 300 = 5 min)
period_start_time
UTC time-of-day anchor (default: 09:30)
new_period logic
Triggers full reset of pivots + VWAP on exact second boundary
breakingHigher / breakingLower
Detects confirmed breakouts (not just close above high)
Dual VWAP per anchor
ta.vwap(high) and ta.vwap(close) for range-aware mean
Fill zones
Visual value area between high/close VWAPs
Toggle visibility
Independently show/hide high or low pivot pairs
How It Works – Step-by-StepTime Engine Converts user inputs → milliseconds
Calculates current period start time using integer division from epoch
Detects exact bar when new period begins (new_period = true)
On New Period Resets both high/low anchors to current bar’s h and l
Forces VWAP recalculation from this bar forward
Breakout Detection Only triggers on strong candles (rising/falling, non-doji)
Requires open/close beyond prior pivot → avoids wicks-only breaks
VWAP Accumulation ta.vwap(source, reset_condition) restarts when anchor resets
Two sources per side → shows where volume clustered (at highs vs closes)
Plotting Four lines + two fills
Clean, customizable, overlay-friendly
Pro TipsUse on Heikin Ashi for smoother breakout signals.
Combine with volume profile to validate VWAP clusters.
For crypto, set period_start_time = 0 (00:00 UTC) for clean 4-hour resets.
Add alerts on new_period or breakingHigher for automation.
In short: This is a precision VWAP tool for time-boxed, pivot-driven mean reversion and breakout trading, ideal for scalpers, day traders, and algo developers needing sub-session granularity.
EMA Breakout Algo StrategyA volatility‑based breakout strategy using EMA alignment and ATR filters for risk‑managed entries and exits.
Market Emotion Cycle DetectorThis indicator estimates emotional phases in price behavior by measuring how far price deviates from its dynamic mean.
It uses an adaptive Z-Score normalization with volatility-aware scaling and optional higher-timeframe blending.
Each candle is color-coded according to its deviation level, creating a clear visual map of market sentiment, from extreme panic (MAX FEAR) to euphoric exhaustion (MAX EUPHORIA).
The tool helps identify accumulation and distribution phases inside cyclical or mean-reverting markets.
🧩 Core Logic
Z-Score of EMA-smoothed price: measures standardized distance from the mean.
ATR regime scaling: adjusts sensitivity across volatility environments.
Optional higher-TF fusion: smooths sentiment transitions without lookahead.
Phase classification: seven discrete emotion zones (MAX FEAR → MAX EUPHORIA).
Non-repainting signals: phase changes confirmed on bar close only.
⚙️ Setup Instructions
To allow full color rendering by the Emotion Candles:
Open Chart Settings → Symbol → Candles
• Uncheck “Color bars based on previous close”
• Clear all Body, Wick, and Border colors
On the chart, right-click any overlay element (coin label, MTX, indicator tag …)
• Choose Hide from the ⋮ menu to keep the view clean
Ensure background contrast makes emotion colors visible.
🎯 Usage Notes
Designed for contextual sentiment analysis, not automated entries.
Works best when combined with independent trend or structure confirmation.
Webhook-ready alerts are available for LONG / SHORT / FLAT transitions.
Default parameters are calibrated for daily and 4-hour charts; shorter TFs may require reduced lookback.
📘 Classification Reference
MAX FEAR:
Capitulation & panic; potential deep-value accumulation zones
FEAR:
Negative bias but stabilizing volatility
CONCERN:
Early recovery interest; risk-reward starts improving
NEUTRAL:
Balanced sentiment, transition zone
MILD GREED:
Optimism emerges, trend continuation possible
GREED:
Late-stage rally; profit-taking often begins
MAX EUPHORIA:
Emotional climax, exhaustion and distribution signals
This publication is an original implementation of an adaptive sentiment model - not a mash-up or derivative of existing indicators.
Created by geokat
Squeeze + Short/Long (Futures) - WS🧠 Overview
The Squeeze + Short/Long (Futures) indicator combines Bollinger Bands, Keltner Channels, and momentum breakout logic to identify market compression phases (squeezes) followed by strong volatility expansion.
Ideal for crypto, futures, and FX traders who seek early breakout confirmation.
📊 Momentum Visualization
🟩 Green bars: positive momentum (bullish)
🟥 Red bars: negative momentum (bearish)
⚙️ Signals
LONG signal (green triangle) → squeeze just released + bullish momentum.
SHORT signal (red triangle) → squeeze just released + bearish momentum.
Gray background → Squeeze ON (low volatility / compression).
Includes a cooldown mechanism to prevent multiple false triggers.
💡 Trading Idea
1️⃣ Wait for a gray background (market compression).
2️⃣ When white dots and a triangle appear → volatility is expanding.
3️⃣ Trade in the direction of momentum (green for longs, red for shorts).
4️⃣ Use ATR or price structure for stops and targets.
⚙️ Recommended Settings
Market BB Len KC Len BB Mult KC Mult Momentum Len
Crypto (15m–1h) 20 20 2.0 1.5 12
Futures / FX (1h–4h) 20 20 2.0 1.5 20
🔔 Alerts
LONG Squeeze → breakout upward confirmed
SHORT Squeeze → breakout downward confirmed
Enable alerts in TradingView’s Alert Manager once added to the chart.
🧾 Credits
Created with ❤️ by WS Trading Tools
Built in Pine Script v6
Based on the classic TTM Squeeze logic with custom momentum and configurable cooldown.
© 2025 GuidoT | WS Trading Tools
ATR / Price RatioDescription:
This indicator plots the ratio of the Average True Range (ATR) to the current price, showing volatility as a percentage of price rather than in absolute terms. It helps compare volatility across assets and timeframes by normalizing for price level.
A higher ATR/Price ratio means the market is moving a larger percentage of its value each bar (high relative volatility). A lower ratio indicates tighter, quieter price action (low relative volatility).
Traders can use this ratio to:
• Compare volatility between instruments
• Identify shifts into high or low volatility regimes
• Adjust position sizing and stop distances relative to risk
Contango/Backwardation Monitor
This is an indicator to display the spread difference between two products. I designed it around VX1! and VX2! but any other two products can be chosen. It is a simple subtraction of VX2-VX1. I will go through the options first and what they do followed by what contango/backwardation is in my own words. You will need the data package for VX futures for the default version to work.
INPUTS
-Apply Smoothing: choose to apply smoothing or not.
-Smoothing Method: choose between SMA,EMA,WMA, etc.
-Line Width: Width of line if line is chosen style(can be changed in style section)
-Threshold 1-5: This is the level at which the line will change colors(defaults are for VX)
-Color 1-5: The color the line will change to when crossing threshold.
Towards Backwardation: Background color change when line is slanted down
Towards Contango: Background color change when line is slanted up
Bars to Confirm Trend: This is my method to cut down on background color changes. It is how many bars consecutive going back needed to change color.
STYLE
-All colors and whatnot can be changed here(threshold colors can be changed here or on the input page).
T1 Line-T5 line: These are simple horizontal lines that can be used to denote threshold areas or whatever you want.
Contango/Backwardation-These terms are used mostly with futures to define the calendar spread between two contracts. Contango is when that spread is is getting longer and backwardation is when that spread is closing. In terms of VIX futures, Contango would imply that volatility is stabilizing and the S and P will likely gain. Backwardation, woudl eb the opposite.
The most simple way to read this indicator with default settings- If the line is up, red, and the background is red, then you can assume S and P prices are going down. And if the opposite is true, then prices are likely going up.
Please feel free to ask any questions and I will do my best to answer them.
Order-Flow Proxy (VWAP Deviation Zones)Order-Flow Proxy (VWAP Deviation Zones) helps traders visualize when market price moves unusually far away from its Volume-Weighted Average Price (VWAP) — a key fair-value level used by institutional participants.
When price stretches too far above or below VWAP, it often signals temporary imbalance between buying and selling pressure.
This tool highlights those moments using simple color zones and an optional statistical Z-Score filter for deeper precision.
In short: it’s a clean, minimal mean-reversion indicator showing when price is statistically “too far” from fair value.
Red zone → Price extended above VWAP → possible buyer exhaustion or short setup.
Green zone → Price extended below VWAP → possible seller exhaustion or long setup.
VWAP line → Acts as a dynamic fair-value anchor.
Concept:
VWAP combines both price and traded volume to define where most transactions occurred.
Deviations from it — measured either by a fixed distance (1%) or by Z-Score — can reveal overvaluation or undervaluation zones used by professional traders for contrarian setups.
How to use:
Apply the indicator to any intraday chart (1m–1h recommended).
Watch for background color shifts — red or green.
Optionally enable the Z-Score filter to focus only on statistically extreme deviations.
Combine with volume spikes, liquidity sweeps, or your own order-flow tools for confirmation.
Tip:
Best used as a visual overlay for detecting stretched markets and potential reversals.
RED-E Gamma Range DetectorRED-E Gamma Range Detector
Overview
The RED-E Gamma Range Detector identifies key support and resistance zones based on recent price action and volume distribution, combined with a simple momentum ribbon to help traders visualize trend direction. It's designed to highlight potential areas where price may react, inspired by the concept of gamma exposure levels in options trading.
How It Works
1. Support & Resistance Zones (Green & Red Boxes)
RED-E analyzes the recent price range over a customizable lookback period
It identifies high-probability support levels (green boxes) below current price
It identifies high-probability resistance levels (red boxes) above current price
These zones represent areas where price has historically shown increased activity
2. Gamma Flip Level (Yellow Dashed Line)
The yellow line represents the approximate "gamma flip" - the midpoint of the recent range
Above this line: Price tends to be more stable with range-bound behavior
Below this line: Price tends to be more volatile with trending behavior
This level acts as a key pivot point for market structure
3. Momentum Ribbon (Green/Red Fill)
A simple visual indicator using 9 and 21 period EMAs
Green ribbon: 9 EMA is above 21 EMA (bullish momentum)
Red ribbon: 9 EMA is below 21 EMA (bearish momentum)
Ribbon width shows strength of trend (wider = stronger trend)
How to Use
For Range Trading:
Look for buy signals near green support zones when above gamma flip
Look for sell signals near red resistance zones when above gamma flip
Price tends to bounce between zones in stable conditions
For Trend Trading:
Watch for breakouts above resistance or below support zones
Use the momentum ribbon to confirm trend direction
Wider ribbon gaps indicate stronger directional moves
For Risk Management:
Use support/resistance zones for stop-loss placement
Recognize increased volatility potential below the gamma flip
Adjust position sizing based on your proximity to key zones
Settings
Lookback Period: Number of bars to analyze (default: 20)
Lower values = more responsive to recent price action
Higher values = more stable, longer-term levels
Best Practices
Works best on liquid instruments (major stocks, indices, forex pairs)
Combine with other technical analysis tools for confirmation
Most effective on 1H, 4H, and daily timeframes
Always use proper risk management and stop losses
Why "RED-E"?
RED-E stands for being Ready to identify critical gamma levels, support/resistance zones, and momentum shifts - keeping you prepared for market moves before they happen.
Educational Note
This indicator approximates gamma exposure concepts using price and volume analysis. It does not use actual options data. The term "gamma" refers to the rate of change in options delta and how market makers hedge their positions, which can create support/resistance at certain price levels.
Disclaimer
This indicator is for educational and informational purposes only. It does not guarantee profitable trades. Past performance is not indicative of future results. Always conduct your own analysis and manage risk appropriately. Trading involves substantial risk of loss.
Recommended Categories
Primary Category:
✅ Support and Resistance
Secondary Categories:
✅ Momentum
✅ Trend Analysis
✅ Volatility
FXGringo1.2FXGringo - Decision Points
This indicator identifies support and resistance zones based on reference points provided in the levels field, interpreting them as potential areas of price reaction. From these points, the script plots strength levels, allowing the trader to visualize regions where the price may encounter natural barriers to equilibrium between supply and demand.
Although the internal calculations do not directly reveal the complete methodology, its logic can be compared to concepts similar to gamma levels (GEX), insofar as it seeks to map zones where price movement tends to be more sensitive due to the concentration of positions or relevant market flows.
How the Indicator Works:
Input of External Points:
The user manually provides price points that represent potential support or resistance levels.
Strength Classification:
The indicator processes these points and plots each level based on criteria such as distance from the current price, frequency of occurrence in the history, and pre-calculated volatility variation. This generates a visual and quantitative hierarchy among the provided levels.
Context Analysis:
Based on the interaction between price and these levels, the script identifies and plots zones of greater relevance—where the price tends to react, consolidate, or reverse.
Confluence Analysis:
Observe how the external levels align with peaks, troughs, and volume zones. The overlap of strong levels often indicates areas of great institutional interest.
Risk Management:
Use the identified levels to plan entry and exit points and stop-loss or take-profit placement, based on the relative strength of the levels.
Modern Conceptual Basis: The methodology, although proprietary, can be compared to how gamma levels reflect zones of greater price sensitivity relative to the market's aggregate exposure.
Conclusion:
This indicator acts as an advanced tool for interpreting support and resistance levels, using external data to build a dynamic map of market interest zones. Its operation can be seen as an analogy to gamma levels (GEX), identifying regions where the price tends to react more significantly due to liquidity concentration or position imbalance. This approach provides the trader with a refined view of the areas of influence of large players, assisting in making decisions with greater precision and confidence.
ATR Daily (Classic vs Robust, NY-Fix, Spike Control)📘 What this indicator does
This tool provides an advanced view of daily market volatility by comparing two versions of the Average True Range (ATR):
• Classic ATR — standard Wilder smoothing
• Robust ATR — uses median-based filtering and spike-control logic to reduce distortion from abnormal candles
Both values are calculated using daily data aligned to the New York trading session, so volatility resets at the same moment each institutional trading day begins. This keeps readings consistent across crypto, forex and stocks, even on intraday charts.
⚙️ How it works (in simple terms)
The script evaluates each True Range (TR) value relative to a median-based threshold:
• Abnormally large ranges are either clamped to a limit or excluded from updating ATR
• A hard cap prevents single spikes from inflating the entire indicator
• The result is a smoother and more realistic representation of daily volatility
This allows ATR to reflect typical market behaviour instead of rare one-off events.
📊 What appears on the chart
• Two daily ATR lines (Classic and Robust)
• Histogram showing the percentage of daily range already completed
• Red bars when price exceeds 100% of daily ATR
• A data table with volatility metrics
• Background highlights on days with extreme values
💡 How traders can use it
• Identify when a market has already completed most of its typical daily move
• Compare Classic vs Robust ATR to spot news-driven distortion
• Use Robust ATR for more stable stop-loss and take-profit logic
• Track volatility expansion or contraction across sessions
⚙️ Key settings
Setting Purpose
ATR period Standard smoothing length (default 14)
Robust mode Clamp, Freeze or Off
MAD multiplier Sensitivity to outliers
Cap × median(TR) Maximum allowed spike size
Base for passed ATR Which ATR is used to measure daily %
Freeze weekends Keeps ATR unchanged on Sat/Sun
🧩 Unique concept
Unlike typical ATR indicators, this one combines robust statistics (median + MAD) with session-based fixation. ATR values update only once per New York session, creating stable volatility measurements that match institutional timing.
🔒 Source code
The script is published with protected source code to preserve its statistical structure and prevent unauthorized modification.
🧭 Summary
ATR Daily (Classic vs Robust, NY-Fix) provides a clearer and more reliable view of daily volatility.
It helps determine whether the market is still in the early phase of its daily range or already exhausted.
Average Candle Body (24h Rolling)This indicator calculates the average size of candle bodies (|Close – Open|) over the last 24 hours, regardless of your current chart timeframe.
Unlike ATR or ADR, which measure total range (High – Low) or day-to-day volatility, this tool focuses purely on the real body size of candles — a more accurate representation of in-session price momentum and liquidity activity.
🔍 How it works
The script automatically determines how many candles represent the last 24 hours based on your current timeframe (e.g. 288 candles on a 5-minute chart).
It then computes a Simple Moving Average (SMA) of the absolute candle body size across that rolling 24-hour window.
Optionally, the script also plots the current candle body size as a grey histogram for quick comparison.
⚙️ Use cases
Gauge intraday volatility based on average body movement rather than wicks.
Build dynamic stop-loss models (e.g., Stop = 1.2 × AverageBodySize).
Detect periods of compression or expansion in price action.
Filter or confirm setups (e.g., only trade when candle bodies exceed their 24 h average).
📈 Displayed elements
Orange line: average candle body size (rolling 24 hours)
Grey histogram: current candle body size for each bar
Works automatically across all timeframes and assets (crypto, forex, indices, etc.)
💡 Pro tip
This indicator pairs exceptionally well with:
EMA-based momentum systems (e.g. EMA 8/21 crosses)
Session-based reversal or sweep strategies (Asia-London transitions)
VWAP or liquidity-based frameworks where candle compression matters
📘 How to Interpret
When the orange line (24h average candle body) is rising, it indicates that average body sizes are expanding — signaling increasing intraday momentum and participation. This often aligns with periods of higher volatility, stronger trends, or major session opens (London/New York).
When the orange line is falling, it shows contracting body sizes, meaning the market is entering consolidation, reduced volatility, or indecision. Such periods often precede major breakouts or reversals.
Use this reading to:
Avoid false breakouts during low-body periods.
Tighten or widen stops based on real-time market compression or expansion.
Confirm reversals: a shrinking average body after a strong impulse can signal momentum exhaustion.
Elastic Trend OscillatorThe Elastic Trend Oscillator (ETO) is a volatility-adaptive momentum indicator that measures price displacement from a trend baseline while accounting for market volatility conditions. Unlike traditional oscillators that use fixed scaling, ETO dynamically adjusts its sensitivity based on current volatility levels relative to recent market conditions, providing context-aware momentum readings across different market regimes.
What Makes This Indicator Different
Volatility-Adaptive Scaling:
The core innovation of ETO is its dynamic volatility adjustment mechanism. The indicator calculates an ATR percentile rank over a lookback period and uses this to scale the momentum readings. When volatility is elevated, the indicator becomes less sensitive to price moves, recognizing that larger displacements are normal in volatile conditions. Conversely, in low volatility environments, smaller price moves are given more weight. This prevents false signals during volatility expansions and maintains sensitivity during quiet periods.
Low Volatility Compression:
During periods of extremely low volatility, the oscillator naturally compresses toward the midline and exhibits minimal movement. This midline-hugging behavior serves as a visual indicator that the market lacks directional energy and momentum readings are unreliable. Unlike indicators that continue oscillating during quiet periods and potentially generate false signals, ETO's compression around the midline is supposed to identify low-conviction environments where trend-following strategies underperform. When you see the oscillator stuck near 50 with little movement, recognize this as a consolidation phase where ranges dominate and breakout setups may be developing.
Trend Slope Analysis with Dynamic Thresholds:
The indicator monitors both the trend direction (EMA slope) and the rate of slope change. Dynamic thresholds based on ATR identify when trend acceleration is slowing. The oscillator becomes semi-transparent when slope deceleration exceeds the threshold, warning of potential trend exhaustion before actual reversals occur.
Relatively Linear Transformation:
Unlike many oscillators that use non-linear transformations, ETO applies a more linear scaling of the ATR-normalized displacement. This preserves the proportional relationship between price moves and oscillator readings, making divergences and momentum shifts more intuitive to interpret.
How to Use the Indicator
Trend Direction:
Green oscillator = Bullish trend (price above EMA with positive slope)
Red oscillator = Bearish trend (price below EMA with negative slope)
Oscillator compressed near 50 with minimal movement = Low volatility, consolidation phase. These phases often precede volatility expansions and significant directional moves, making them more ideal for monitoring breakout setups rather than taking positions.
Momentum Quality:
Solid color = Strong, accelerating trend
Semi-transparent = Decelerating trend, potential exhaustion, potential consolidation ahead
The transparency change acts as an early warning before actual trend reversals or consolidations.
Trading Signals:
Crossovers: When the oscillator crosses the signal line to the other side of momentum while oversold/overbought, it suggests potential reversals (better in combination with transparency loss).
Overbought/Oversold: Levels above 70 indicate overbought conditions; below 30 indicate oversold. These are not reversal signals themselves but identify extended moves where momentum may be extreme.
Midline: Oscillator above 50 indicates price is above the trend baseline with positive displacement. Below 50 indicates negative displacement.
Divergences: Like with other momentum indicators compare oscillator highs/lows with price highs/lows.
Settings
EMA Length: Controls the trend baseline period. Lower values make the indicator more responsive to short-term price changes; higher values focus on longer-term trends. This directly affects how quickly the oscillator responds to trend changes.
ATR Length: Determines the period for volatility measurement. This affects both the normalization of price displacement and the momentum confirmation filter. Lower values make volatility measurements more reactive; higher values provide smoother volatility assessment.
Oscillator Smoothing: Applies EMA smoothing to the raw oscillator values. A value of 1 shows unsmoothed, more volatile readings. Higher values produce smoother oscillations with less noise but more lag.
Signal Line Length: The EMA period for the signal line. Lower values create more frequent crossovers; higher values generate fewer but potentially more significant crossovers. This acts as a moving average of the oscillator itself.
Slope Change Sensitivity: Multiplier that sets how much slope deceleration triggers the transparency effect. Lower values make the indicator more sensitive to trend exhaustion, showing transparency earlier. Higher values require more pronounced deceleration before visual warning.
Overbought Level: Defines the upper extreme threshold.
Oversold Level: Defines the lower extreme threshold.
Best Practices
Use on any timeframe, but adjust EMA and ATR lengths according to your trading style (shorter for shorter term trades, longer for longer term trading like swing trading)
Combine with price action — the indicator identifies momentum conditions, not specific entry/exit points.
In strongly trending markets, the oscillator may remain in overbought/oversold territory for extended periods—this is normal and indicates persistent momentum rather than imminent reversal.
This indicator does not provide investment or trading advice. All trading decisions should be made based on your own analysis and risk management.
Expected Move for Futures (Daily Fixed ATR Levels)I noticed there are plenty of indicators for Expected Move in Options. I am creating something similar, tracking the Average True Range of the past 7 days (day by day) to get a fixed amount of where price might be expected to move within ATR.
