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.
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🔥 QUANT MOMENTUM SKORQUANT MOMENTUM SCORE – Description (EN)
Summary: This indicator fuses Price ROC, RSI, MACD, Trend Strength (ADX+EMA) and Volume into a single 0-100 “Momentum Score.” Guide bands (50/60/70/80) and ready-to-use alert conditions are included.
How it works
Price Momentum (ROC): Rate of change normalized to 0-100.
RSI Momentum: RSI treated as a momentum proxy and mapped to 0-100.
MACD Momentum: MACD histogram normalized to capture acceleration.
Trend Strength: ADX is direction-aware (DI+ vs DI–) and blended with EMA state (above/below) to form a combined trend score.
Volume Momentum: Volume relative to its moving average (ratio-based).
Weighting: All five components are weighted, auto-normalized, and summed into the final 0-100 score.
Visuals & Alerts: Score line with 50/60/70/80 guides; threshold-cross alerts for High/Strong/Ultra-Strong regimes.
Inputs, weights and thresholds are configurable; total weights are normalized automatically.
How to use
Timeframes: Works on any timeframe—lower TFs react faster; higher TFs reduce noise.
Reading the score:
<50: Weak momentum
50-60: Transition
60-70: Moderate-Strong (potential acceleration)
≥70: Strong, ≥80: Ultra Strong
Practical tip: Use it as a filter, not a stand-alone signal. Combine score breakouts with market structure/trend context (e.g., pullback-then-re-acceleration) to improve selectivity.
Disclaimer: This is not financial advice; past performance does not guarantee future results.
MLB Momentum IndicatorMLB Momentum Indicator is a no‐lookahead technical indicator designed to signal intraday trend shifts and potential reversal points. It combines several well‐known technical components—Moving Averages, MACD, RSI, and optional ADX & Volume filters—to deliver high‐probability buy/sell signals on your chart.
Below is an overview of how it works and what each part does:
1. Moving Average Trend Filter
The script uses two moving averages (fast and slow) to determine the primary trend:
isUpTrend if Fast MA > Slow MA
isDownTrend if Fast MA < Slow MA
You can select the MA method—SMA, EMA, or WMA—and customize lengths.
Why it matters: The indicator only gives bullish signals if the trend is up, and bearish signals if the trend is down, helping avoid trades that go against the bigger flow.
2. MACD Confirmation (Momentum)
Uses MACD (with user‐defined Fast, Slow, and Signal lengths) to check momentum:
macdBuySignal if the MACD line crosses above its signal line (bullish)
macdSellSignal if the MACD line crosses below its signal line (bearish)
Why it matters: MACD crossovers confirm an emerging momentum shift, aligning signals with actual price acceleration rather than random fluctuation.
3. RSI Overbought/Oversold Filter
RSI (Relative Strength Index) is calculated with a chosen length, plus Overbought & Oversold thresholds:
For long signals: the RSI must be below the Overbought threshold (e.g. 70).
For short signals: the RSI must be above the Oversold threshold (e.g. 30).
Why it matters: Prevents buying when price is already overbought or shorting when price is too oversold, filtering out possible poor‐risk trades.
4. Optional ADX Filter (Trend Strength)
If enabled, ADX must exceed a chosen threshold (e.g., 20) for a signal to be valid:
This ensures you’re only taking trades in markets that have sufficient directional momentum.
Why it matters: It weeds out choppy, sideways conditions where signals are unreliable.
5. Optional Volume Filter (High‐Participation Moves)
If enabled, the indicator checks whether current volume is above a certain multiple of its moving average (e.g., 1.5× average volume).
Why it matters: High volume often indicates stronger institutional interest, validating potential breakouts or reversals.
6. ATR & Chandelier (Visual Reference)
For reference only, the script can display ATR‐based stop levels or a Chandelier Exit line:
ATR (Average True Range) helps gauge volatility and can inform stop‐loss distances.
Chandelier Exit is a trailing stop technique that adjusts automatically as price moves.
Why it matters: Though this version of the script doesn’t execute trades, these lines help you see how far to place stops or how to ride a trend.
7. Final Bullish / Bearish Signal
When all conditions (trend, MACD, RSI, optional ADX, optional Volume) line up for a long, a green “Long” arrow appears.
When all conditions line up for a short, a red “Short” arrow appears.
Why it matters: You get a clear, on‐chart signal for each potential entry, rather than needing to check multiple indicators manually.
8. Session & Date Filtering
The script allows choosing a start/end date and an optional session window (e.g. 09:30–16:00).
Why it matters: Helps limit signals to a specific historical backtest range or trading hours, which can be crucial for day traders (e.g., stock market hours only).
Putting It All Together
Primary Trend → ensures you trade in line with the bigger direction.
MACD & RSI → confirm momentum and avoid overbought/oversold extremes.
ADX & Volume → optional filters for strong trend strength & genuine interest.
Arrows → each potential buy (Long) or sell (Short) signal is clearly shown on your chart.
Use Cases
5‐Minute Scalping: Shorter RSI/MACD lengths to catch small, frequent intraday moves.
Swing Trading: Larger MAs, bigger RSI thresholds, and using ADX to filter only major trends.
Cautious Approach: Enable volume & ADX filters to reduce false signals in choppy markets.
Benefits & Limitations
Benefits:
Consolidates multiple indicators into one overlay.
Clear buy/sell signals with optional dynamic volatility references.
Flexible user inputs adapt to different trading styles/timeframes.
Limitations:
Like all technical indicators, it can produce false signals in sideways or news‐driven markets.
Success depends heavily on user settings and the particular market’s behavior.
Summary
The MLB Momentum Indicator combines a trend filter (MAs), momentum check (MACD), overbought/oversold gating (RSI), and optional ADX/Volume filters to create clear buy/sell arrows on your chart. This approach encourages trading in sync with both trend and momentum, and helps avoid suboptimal entries when volume or trend strength is lacking. It can be tailored to scalp micro‐moves on lower timeframes or used for higher‐timeframe swing trading by adjusting the input settings.
ATR + Momentum Shifts w/Take ProfitThis script is a technical analysis indicator designed to assist in identifying potential entry points and setting take profit levels in trading. It combines the Average True Range (ATR) indicator, momentum shifts, and customizable take profit levels to provide insights into potential market movements.
Differences from Currently Published Ones:
This script is unique due to its use of a combination of elements:
ATR and Momentum: The script combines the ATR indicator to provide dynamic support and resistance levels with the momentum indicator to identify shifts in the underlying momentum.
Customizable Take Profit Levels: It offers the ability to set take profit levels based on customizable multipliers of the ATR, helping traders manage potential profits.
How to Use:
ATR Bands: The script plots upper and lower ATR bands as potential dynamic support and resistance levels.
Shift Arrows: Arrows are plotted below bars for potential long entry opportunities (green triangle) and above bars for potential short entry opportunities (yellow triangle).
Take Profit Levels: The script also plots take profit levels both above and below the source price based on the ATR multipliers set in the inputs.
Markets and Conditions:
This script can be used across various financial markets, including stocks, forex, commodities, and cryptocurrencies. It's most effective in trending markets where momentum shifts can signal potential reversals or continuation of trends. Traders should consider the following conditions:
Trend Confirmation: Look for momentum shifts in the direction of the prevailing trend for higher probability setups.
Volatility: Higher volatility can amplify ATR movements and subsequently affect the placement of ATR bands and take profit levels.
Risk Management: Always implement proper risk management strategies to protect your capital.
Additional Considerations:
Customization: Traders can adjust input parameters like ATR length, momentum length, and take profit multipliers to match their trading style and market conditions.
Combining with Other Indicators: Consider using this indicator in conjunction with other technical indicators or chart patterns for confirmation.
Adaptive Squeeze Momentum (Zeiierman)█ Overview
Adaptive Squeeze Momentum (Zeiierman) is a hybrid oscillator that models the market’s transition between trend-efficient and inefficient (choppy or reversal-prone) regimes. It fuses a price efficiency framework, volatility-adaptive responsiveness, and a bounded directional trend score, then intelligently blends them through a dynamic transition band to emphasize either smooth, directional structure or spiky inefficiency.
Unlike traditional momentum tools, it does not simply measure speed or volatility. It measures how efficiently the price is moving. This multidimensional approach allows it to identify when markets are compressed and ready to expand, or stretched and ready to revert.
In essence, Adaptive Squeeze Momentum (Zeiierman) gives traders a deep structural perspective of efficiency, momentum, and compression energy, helping to expose hidden squeezes, early reversals, and trend continuations with remarkable accuracy.
⚪ Why This One Is Unique
Adaptive Squeeze Momentum (Zeiierman) stands out because it doesn’t rely on static momentum or volatility measures alone. It merges price efficiency, volatility adaptivity, and trend structure into one cohesive system capable of detecting:
Early squeeze build-ups and directional releases
Reversals emerging from inefficiency spikes
Shifts between clean trends and noisy, unstable ranges
Adaptive behavior across changing volatility conditions
In essence, it acts as a self-adjusting market intelligence tool, revealing when the price is efficient, exhausted, or preparing for significant expansion.
█ Main Features
⚪ Adaptive Squeeze Momentum Oscillator
The Adaptive Squeeze Momentum (Zeiierman) captures how price structure transitions between compression, expansion, and inefficiency. It merges a volatility-adaptive momentum engine with an efficiency-aware structure filter, creating a responsive oscillator that reflects not just how fast the market moves, but how efficiently it moves.
Adaptive Momentum: Reacts dynamically to changing volatility regimes
Efficiency Awareness: Distinguishes stable, directional flow from unstable or noisy price action
Volatility Squeeze Detection: Identifies buildup (compression) and breakout (release) conditions
Directional Context: Shows momentum alignment, exhaustion, or imbalance through color-coded histograms
This hybrid model provides a multidimensional view of trend quality, squeeze pressure, and inefficiency, ideal for precision-based trend following and structural reversal recognition.
⚪ Understanding the Squeeze
A squeeze occurs when volatility contracts, prices compress, and market energy builds, much like a coiled spring preparing to release. Once volatility expands, that stored energy transforms into directional movement, often marking the beginning of a significant breakout or new trend phase.
Traditional squeeze indicators focus only on volatility contraction. Adaptive Squeeze Momentum (Zeiierman) goes further by incorporating efficiency analysis to evaluate how organized or chaotic the buildup is. This reveals not only when a squeeze may fire but also whether the ensuing release is likely to be sustainable or structurally weak.
Visual Interpretation:
Lighter Dots: Represent low-volatility compression, energy building within an efficient or balanced structure.
Yellow Dots: Mark volatility release, the expansion phase where momentum accelerates.
⚪ Inefficiency Columns
Efficient Price Action
Efficient moves are clean, directional, and purposeful. Price advances with minimal back-and-forth noise, revealing strong conviction and structural alignment. In the indicator, these appear as smooth, non-blue histogram columns. Such periods indicate organized, high-probability momentum where breakouts, trends, and continuations are supported by efficient order flow.
Inefficient Price Action
Inefficient moves are noisy, erratic, and spiky, showing that price is fighting its own structure. Liquidity is thin, reactions are emotional, and momentum lacks balance. In the oscillator, inefficiency is displayed through blue columns, path-break spikes, or OB/OS inefficiency crosses (green/red). These elements act as early warnings of structural instability, suggesting the market may be overextended, losing organization, or entering a mean-reversion or reversal phase.
█ How to Use
⚪ Trend Following
When oscillator bars are above or below zero and growing in color intensity, momentum is expanding, confirming active, efficient trends. When colors fade, momentum is decelerating, signaling potential exhaustion or consolidation.
Interpretation:
Sustained smooth growth above zero indicates strong, efficient bullish momentum.
Sustained smooth growth below zero indicates efficient bearish continuation.
Color fading or a zero-line cross suggests trend fatigue or rotation.
Tip: Confirm trend continuation when yellow squeeze-release dots appear in the same direction. They often precede sustained breakouts following compression.
⚪ Inefficiency Reversals or Breakouts
Adaptive Squeeze Momentum (Zeiierman) excels at detecting instability within active trends, highlighting when moves lose internal efficiency and become unsustainable. When bars change color, outbreak columns appear, or Overbought/Oversold Inefficiency Crosses (green/red) appear, the structure is shifting toward potential reversal or exhaustion.
Blue Inefficiency Columns:
Above Zero: Indicates inefficient buying pressure where markets push higher impulsively or emotionally. Often signals an overstretched or unstable rally prone to short-term corrections or FOMO-driven peaks.
Below Zero: Reflects inefficient selling pressure where panic-driven or disorderly sell-offs often precede exhaustion and recovery phases.
Main Oscillator Inefficiency:
Spiky Column Above Zero
When the main oscillator suddenly surges upward and prints a tall, spiky column above the zero line without turning blue, it signals an inefficient and overextended push to the upside. This type of move breaks the oscillator’s usual smooth rhythm, showing that buyers are becoming overaggressive or emotional, often driven by FOMO or reactive momentum chasing.
Even though the column remains green, the abrupt path break reveals that the move is losing internal structure and may be unsustainable. Such inefficient upward bursts often indicate upside exhaustion or an overshoot, where the market temporarily loses control.
From here, two scenarios can unfold:
A sustained breakout if structure confirms (for example, a valid squeeze release or strong volume support).
A snapback or reversal if follow-through fails on the next bars and the oscillator begins to fade (growth → fall).
Spiky Column Below Zero
When the oscillator sharply dives and prints a tall, spiky column below the zero line without turning blue, it reflects an inefficient and emotional push to the downside. This behavior typically emerges during panic-driven selling, where traders aggressively dump positions, breaking the oscillator’s normal rhythm.
Even though the color remains red, this kind of path-break spike shows that the market has become disordered or overstretched. These inefficient downward thrusts often suggest bear exhaustion, capitulation, or a temporary overshoot below fair structure.
From this point, two potential outcomes can occur:
A sustained breakdown if the move is supported by strong volume or new structural lows.
A rebound or mean reversion occurs when momentum fails to extend, indicating that selling pressure has burned out.
Setting Tip: To focus purely on the oscillator’s structural peaks, set Inefficiency Detection and Inefficiency Duration to identical values, then adjust Inefficiency Price Peaks to control sensitivity. Use lower values for crisp detection and higher values for smoother blending.
Overbought and Oversold Inefficiency Signals
Green Overbought Cross: Signals that buyers are driving the market too aggressively, often from emotional extension or FOMO. This imbalance typically leads to short-term pauses, pullbacks, or corrections.
Red Oversold Cross: Signals panic-driven or emotionally imbalanced selling, marking structural exhaustion. Often precedes rebounds or base formations as volatility normalizes.
Interpretation: Both crosses identify where momentum is inefficient rather than simply overbought or oversold, helping detect exhaustion before conventional oscillators do.
⚪ Squeeze Builds and Releases (Volatility Expansion)
The squeeze system highlights volatility cycles when the market compresses, builds energy, and releases it through expansion.
Lighter Dots: Compression, where volatility contracts, structure tightens, and energy builds.
Yellow Dots: Release, where volatility expands, triggering directional movement.
Interpretation: Light dots signal potential breakout setups. When followed by a yellow dot and an aligned oscillator direction, they confirm momentum ignition —the moment the market transitions from equilibrium to expansion.
█ How It Works
⚪ Inefficiency Logic
The indicator evaluates how efficiently the price progresses relative to internal volatility. When market flow loses directional clarity, it transitions into an inefficiency state, represented by blue columns or sudden outbreaks within the oscillator. These moments reveal when the underlying structure becomes unstable or overly reactive, often preceding reversals or false momentum bursts.
Calculation: Applies an adaptive efficiency model that compares directional movement to internal dispersion and classifies it within a controlled stability band. A dual-threshold hysteresis with smooth interpolation ensures stable transitions between efficient (trend) and inefficient (spike) phases.
⚪ Directional Tendency Core
The directional tendency component models the underlying slope of market intent, expressing the balance between structured progression and reactive drift. It transforms directional bias into a bounded, volatility-normalized range that remains consistent across instruments and timeframes.
Calculation: Constructs a normalized directional field derived from trend displacement over an adaptive window, filtered through a nonlinear bounding transform and dynamic smoothing to control lag and oscillation.
⚪ Squeeze Engine
The squeeze system isolates compression and release cycles, the rhythmic build-up and discharge of volatility. Compression phases represent equilibrium and contraction, while release phases signify volatility expansion and directional acceleration.
Calculation: Integrates a volatility-adjusted energy model with directional gating. Compression forms during sustained contraction, while release occurs as volatility and momentum expand.
⚪ OB/OS Inefficiency Extremes
The OB/OS inefficiency system detects imbalanced extremes in order flow, when price pushes too far in one direction without structural support. These regions often precede exhaustion or rapid mean reversion.
Calculation: Generates an efficiency-weighted propagation field that identifies directional overextension. Threshold analysis and adaptive state classification determine overbought (green) and oversold (red) inefficiency zones.
⚪ Final Oscillator Composition
The final oscillator is an adaptive blend of inefficiency, directional tendency, and volatility state. It continuously morphs between spike-dominant and trend-dominant behavior based on the evolving efficiency environment. This produces a responsive structural map that visualizes the interplay between market stability, momentum, and compression energy.
Calculation: Combines the signed inefficiency signal and the bounded trend vector through a soft-weighted blending function governed by the efficiency band.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Платный скрипт
Simple Volatility MomentumOverview:
The Simple Volatility Momentum indicator calculates the mean and standard deviation of the changes of price (returns) using various types of moving averages (Incremental, Rolling, and Exponential). With quantifying the dispersion of price data around the mean, statistical insights are provided on the volatility and the movements of price and returns. The indicator also ranks the mean absolute value of the changes of price over a specified time period which helps you assess the strength of the "trend" and "momentum" regardless of the direction of returns.
Simple Volatility Momentum
This indicator can be used for mean reversion strategies and "momentum" or trend based strategies.
The indicator calculates the average return as the momentum metric and then gets the moving average of the average return and standard deviations from average return average. On the options you can determine if you want to use 1 or 2 standard deviation bands or have both of them enabled.
Settings:
Source: By default it's at close.
M Length: This is the length of the "momentum".
Rank Length: This is the length of the rank calculation of absolute value of the average return
MA Type: This is the different type of calculations for the mean and standard deviation. By default its at incremental.
Smoothing factor: (Only used if you choose the exponential MA type.)
The absolute value of the average return helps you see the strength of the "momentum" and trend. If there is a low ranking of the absolute value of the average return then you can eventually expect it to increase which means that the average return is trending, leading to trending price moves. If the Mean ABS rank value is at or near the maximum value 100 and the average return is at -2 standard deviation from the mean, you can see it as the negative momentum or trend being "finished". Similarly, if the Mean ABS value is near or at the maximum value 100 and the average return is at +2 standard deviation from the mean, you can view the uptrend, as "finished" and the Mean ABS rank can't really go higher than 100.
Moving Average Calculations type:
Incremental: Incremental moving averages use an incremental approach to update the moving average by adding the newest data point and subtracting the oldest one.
Exponential: The exponential moving average gives more weight to recent data points while still considering older ones. This is achieved by applying a smooth factor to the previous EMA value and the current data point. EMA's react more quickly to recent changes in the data compared to simple moving averages, making them useful for short term trends and momentum in financial markets.
Rolling: The moving average is calculated by taking the average of a fixed number of data points within a defined window. As new data becomes available, the window moves forward and the average is recalculated. Rolling Moving Averages are useful for smoothing out short-fluctuations and identifying trends over time.
Important thing to note about indicators involving bands and "momentum" or "trend" or prices:
For the explanation we will assume that stock returns follow a normal distribution and price follows a log normal distribution. Please note that in the live market this assumption isn't always true. Many people incorrectly use standard deviations on prices and trade them as mean reversion strategies or overbought or oversold levels which is not what standard deviations are meant for. Assuming you have applied the log transformation on the standard deviation bands (if your input is raw price then you should use a log transformation to remove the skewness of price), and you have a range of 2 standard deviations from the mean, under the empirical rule with enough occurrences 95% of the values will be within the 2 standard deviation range. This doesn't mean that if price falls to the bottom of the 2 standard deviation bound, there is a 95% chance it will revert back to mean, this is incorrect and not how standard deviations or mean reversion works.
"MOMENTUM"
In finance "momentum" refers to the rate of change of a time series data point. It shows the persistence or tendency for a data series to continue moving in its current direction. In finance, "momentum" based strategies capitalize on the observed tendency of assets that have performed well (or poorly) in the recent past to continue performing well (or poorly) in the near future. This persistence is often observed in various financial instruments including stocks, currencies and commodities.
"Momentum" is commonly calculated with the average return, and relies on the assumption that assets with positive "momentum" or a positive average return will likely continue to perform well in the short to medium term, while assets with a negative average return are expected to continue underperforming. This average return or expected value is derived from historical observations and statistical analysis of previous price movements. However, real markets are subject to levels of efficiencies, market fluctuations, randomness, and may not always produce consistent returns over time involving momentum based strategies.
Mean Reversion:
In finance, the average return is an important parameter in mean reversion strategies. Using statistical methodologies, mean reversion strategies aim to exploit the deviations from the historical average return by identifying instances where current prices and their changes diverge from their expected levels based on past performance. This approach involves statistical analysis and predictive modelling techniques to check where and when the average rate of change is likely to revert towards the mean. It's important to know that mean reversion is a temporary state and will not always be present in a specific timeseries.
Using the average return over price offers several advantages in finance and trading since it is less sensitive to extreme price movements or outliers compared to raw price data. Price itself contains a distribution that is usually positively-skewed and has no upper bound. Mean reversion typically occurs in distributions where extreme values are followed by a tendency for the variable to return towards its mean over time, however the probability distribution of price has no tendency for values to revert towards any specific level. Instead, values may continue to increase without a bound. Returns themself contain more stationary behavior than price levels. Mean reversion strategies rely on the assumption that deviations from the mean will eventually revert back to the mean. Returns, being more likely to exhibit stationary, are better suited for mean reversion based strategies.
The distribution of returns are often more symmetrically distributed around their mean compared to price distributions. This symmetry makes it easier to identify deviations from the mean and assess the likelihood of mean reversion occurrence. Returns are also less sensitive to trends and long-term price movements compared to price levels. Mean reversion strategies aim to exploit deviations from mean, which can be obscured when analyzing raw price data since raw price is almost always trending. Returns can filter out the trend component of price movements, making it easier to identify opportunities.
Stationary Process: Implication that properties like mean and variance remain relatively constant over time.
TradeChartist Mean Momentum Drift Oscillator (MMDO)™TradeChartist Mean Momentum Drift Oscillator (MMDO) is the Oscillator version of the ™TradeChartist Mean Momentum Drift Bands (MMDB) indicator with some added visual features to spot Momentum, divergences and Price action using ™TradeChartist Zone Visualizer model.
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Features of ™TradeChartist MMDO
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Price zone detection using ™TradeChartist Zone Visualizer model.
No User input required.
3 Visual colour schemes - Chilli, Flame and Custom.
Clear Visualization of Overbought and Oversold zones.
Colour Bars based on Momentum strength.
MDDO highs and lows tracker helps detect divergences.
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Example Charts
1. MMDO used along with ™TradeChartist MMDB (Mean Momentum Drift Bands) on 4hr chart of BINANCE:BTCUSDT
2. MMDO on 1hr chart of OANDA:EURUSD to confirm Drift Bands breakout entries on MMDB
3. MMDO on 1hr chart of BINANCE:LUNAUSDT
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Best Practice: Test with different settings first using Paper Trades before trading with real money
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Volatility Based Momentum by QTX Algo SystemsVolatility Based Momentum by QTX Algo Systems
Overview
This indicator is designed to determine whether a market trend is genuinely supported by both momentum and volatility. It produces per-candle signals when a smoothed momentum oscillator is above its moving average, a Price – Moving Average Ratio confirms overall trend strength by remaining above a preset level with a positive slope, and when at least one of two distinct volatility metrics is rising. This integrated approach offers traders a consolidated and dynamic view of market energy, delivering more actionable insights than a simple merger of standard indicators.
How It Works
The indicator fuses two complementary volatility measures with dual momentum assessments to ensure robust signal generation. One volatility metric evaluates long-term market behavior by analyzing the dispersion of logarithmic price changes, while the other—derived from a Bollinger Band Width Percentile—captures recent price variability and confirms that market volatility remains above a minimum threshold. A trading signal is generated only when at least one of these volatility measures shows a sustained upward trend over several candles.
For momentum, a double‐smoothed Stochastic Momentum Index provides a refined, short-term view of price action, filtering out market noise. In addition, the PMARP serves as a confirmation tool by comparing the current price to its moving average, requiring that its value remains above a defined level with a positive slope to indicate a strong trend. Together, these elements ensure that a signal is only produced when both the market’s momentum and volatility are in alignment.
Although the components used are based on well-known technical analysis methods, the thoughtful integration of these elements creates a tool that is more than the sum of its parts. By combining long-term volatility assessment with a real-time measure of recent price variability—and by merging short-term momentum analysis with a confirmation of overall trend strength—the indicator delivers a more reliable and comprehensive view of market energy. This holistic approach distinguishes it from standard indicators.
How to Use
Traders can adjust the volatility threshold setting to tailor the indicator to their preferred market or timeframe. The indicator displays per-candle signals when both the refined momentum criteria and the dynamic volatility conditions are met. These signals are intended to be used as part of a broader trading strategy, in conjunction with other technical analysis tools for confirming entries and exits.
Disclaimer
This indicator is for educational purposes only and is intended to support your trading strategy. It does not guarantee performance, and past results are not indicative of future outcomes. Always use proper risk management and perform your own analysis before trading.
TEWY - Magic Momentum IndicatorMy goal is to equip every trader and investor with the essential tools necessary to confidently navigate the complexities of the financial markets, enabling them to consistently identify opportunities and maintain a position of strength on the winning side of their trades. This indicator stands as a potent tool, offering the capability to effectively assess longer-term momentum trends.
Allow me to provide some context regarding the genesis of this indicator. By keenly observing the pattern of momentum loss preceding each trend reversal, coupled with the notable decrease in the rate of price change, I've formulated this indicator. This design is rooted in the understanding that these dynamics hold key insights into the market's shifting trends.
So, I've developed this indicator with the purpose of granting you the ability to select and construct optional combinations of up to two comparable symbols. Through this, you gain a comprehensive and insightful perspective on the ever-evolving dynamics of the market.
This indicator acts like an oscillator and momentum line serves as a key determinant. When the line is positioned above 0, it signifies a positive momentum; conversely, if it rests below 0, it indicates a sideways to negative trend. This mechanism offers a clear and intuitive means of gauging prevailing market conditions.
Should you have any inquiries or require further clarification regarding this indicator, please do not hesitate to reach out to me via direct message. I am here to provide you with the necessary guidance and support to ensure your experience with this tool is both seamless and enriching. Your understanding and satisfaction remain my utmost priority.
By TEWY - Trade Easy With Yogesh
I am Yogesh
Ultimate Momentum OscillatorThe Ultimate Momentum Oscillator is a tool designed to help traders identify the current trend direction and the momentum of the prices.
This oscillator is composed of one histogram and one line, paired with the two overbought and the two oversold levels.
The histogram is a trend-based algorithm that allows the user to read the market bias with multiple trend lengths combined.
The line is a momentum-based formula that allows traders to identify potential reversal and the speed of the price.
This tool can be used to:
- Identify the current trend direction
- Identify the momentum of the price
- Identify oversold and overbought levels
Price & Momentum Reversal Indicator [TradeDots]Price & Momentum Divergence Indicator is a variant of the Chande Momentum Oscillator (CMO), designed to identify reversal patterns in overvalued and undervalued markets. This indicator aims to mitigate the common problem of all oscillating indicators: false buy/sell signals during prolonged market trends, by incorporating a volume-weighted approach and momentum analysis.
📝 HOW IT WORKS
Price Extremeness Calculation
The indicator evaluates the extremeness of the current price by analyzing price changes over a fixed window of candlesticks.
It separates the price changes into positive and negative changes, then multiplies them by the bar volume to obtain volume-weighted values, giving higher significance to bars with larger volumes.
Extremeness Ratio
The ratio is calculated by taking the difference between the total positive changes and total negative changes, and then dividing this result by the sum of the total positive and negative changes.
The result is then smoothed to reduce market noise and rescaled to a range between -100 to 100, where 100 indicates all price changes within the window are positive.
Momentum Analysis
Momentum is calculated by measuring the rate of change of the smoothed extremeness ratio, indicating whether market extremeness is slowing and showing signs of reversion.
Reversal Signal Confirmation
For an asset to be considered a reversal, it has to be in the overvalued or undervalued zone (exceeding the overvalued & undervalued threshold). It must then show a slowed momentum change and a price reversion.
Lastly, candlestick analysis is used to confirm the reversal signal, ensuring there is no room for further breakout price movement.
🛠️ HOW TO USE
Candlestick Visualization
Candlestick bodies are painted with gradient colors representing the smoothed price extremeness (OBOS Index), ranging from -100 (solid red) to 100 (solid green). The exact value is displayed in a table at the bottom right corner.
Slowing price momentum is indicated with blue (bearish) and purple (bullish) colors, showing market pressure from the opposite side.
Reversal Confirmation
A decrease in price momentum combined with a price reversal triggers a signal label on the candlestick, indicating a potential pullback or reversal. This can serve as a reference for better entry and exit points.
⭐️ Premium Features
Higher Timeframe (HTF) Analysis
The indicator includes a feature to apply the same algorithm to a selected higher timeframe, ensuring trend alignment across multiple timeframes.
Alert Functions
Real-time notifications for overvalued and undervalued conditions, allowing traders to monitor trades and reversal signals anywhere and anytime.
❗️LIMITATIONS
Accuracy decreases in volatile and noisy markets.
Extended bullish or bearish market conditions may affect performance.
See Author's instructions below to get instant access to this indicator.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Alpha Momentum Trade - AMT (QUAD Financial)The "Alpha Momentum Trend" indicator was conceived by Tiago Friedrich and programmed by Conrado Villaça.
The indicator description applies to the daily chart. When used on other timeframes, the indicator also changes its signals based on the timeframe used.
It has five fields, from top to bottom:
1. "ATR Multiple MA" greater than multiple: shows how many candles the asset stayed 7 times the ATR (average true range) above the 50-period simple moving average (SMA) in the last 126 candles. The purpose is to identify the strength of the asset because the more times it stayed at this distance from the SMA 50, the greater the acceleration of its prices tends to be, indicating a high momentum asset. You can change the period of the SMA in the indicator settings.
2. ATR% Multiple from MA: shows the multiple of ATR that the asset is from the same SMA as in the upper field. The default is the SMA 50, and the indicator helps identify interesting regions to take profits from long positions. When the asset is more than 7 ATRs above the SMA 50, the asset is considered "stretched," and a correction or price consolidation becomes likely. For high beta assets with a very strong trend, you can use a multiple of 10 ATRs for this purpose.
3. ATR% Multiple from 52w Low: shows the multiple of ATR that the asset is in relation to the 52-week low price. The higher the number, the more the asset has risen relative to its volatility standards, indicating a stronger trend. For momentum traders, it's ideal for the asset to be at least 15 ATRs above the minimum for this period to ensure that it's in a strong uptrend and far from the lows.
4. Longest streak above SMA: within the last 126 candles, it shows the longest streak of days when the asset didn't close below a specific simple moving average. The default definition is with the 10-day SMA, but you can change it in the indicator settings. The more consecutive days the asset can stay above the SMA10, the sign that its trend is consistent and not very volatile, which is desirable. Ideally, an asset should have previously formed an uptrend by staying at least 20 consecutive days above the SMA10.
5. Longest streak above EMA: within the last 126 candles, it shows the longest streak of days when the asset didn't close below a specific exponential moving average. The default definition is with the 21-day EMA, but you can change it in the indicator settings. The more consecutive days the asset can stay above the EMA21, the sign that its trend is consistent and not very volatile, which is desirable. Ideally, an asset should have previously formed an uptrend by staying at least 35 consecutive days above the EMA21.
It's also possible to visualize on the chart the moving averages used for the calculation of the "ATR Multiple MA," "Longest streak above SMA," and "Longest streak above EMA". In the default configuration, this results in a simple 50-day moving average, a simple 10-day moving average, and an exponential 21-day moving average being displayed on the chart, respectively.
Stochastic Momentum Channel with Volume Filter [IkkeOmar]A stochastic version of my momentum channel volume filter
The "Stochastic Momentum" indicator combines the concepts of Stochastic and Bollinger Bands to provide insights into price momentum and potential trend reversals. It can be used to identify overbought and oversold conditions, as well as potential bullish and bearish signals.
The indicator calculates a Stochastic RSI using the RSI (Relative Strength Index) of a given price source. It applies smoothing to the Stochastic RSI values using moving averages to generate two lines: the %K line and the %D line. The %K line represents the current momentum, while the %D line represents a filtered version of the momentum.
Additionally, the indicator plots Bollinger Bands around the moving average of the Stochastic RSI. The upper and lower bands represent levels where the price is considered relatively high or low compared to its recent volatility. The distance between the bands reflects the current market volatility.
Here's how the indicator can be interpreted:
Stochastic Momentum (%K and %D lines):
When the %K line crosses above the %D line, it suggests a potential upward move or bullish momentum.
When the %K line crosses below the %D line, it indicates a potential downward move or bearish momentum.
The color of the plot changes based on the relationship between the %K and %D lines. Green indicates %K > %D, while red indicates %K < %D.
Bollinger Bands (Upper and Lower Bands):
When the price crosses above the upper band, it suggests an overbought condition, indicating a potential reversal or pullback.
When the price crosses below the lower band, it suggests an oversold condition, indicating a potential reversal or bounce.
To identify potential upward moves, consider the following conditions:
If the price is not in a contraction phase (the bands are not narrowing), and the price crosses above the lower band, it may signal a potential upward move or bounce.
If the %K line crosses above the %D line while the %K line is below the upper band, it may indicate a potential upward move.
To identify potential downward moves, consider the following conditions:
If the price is not in a contraction phase (the bands are not narrowing), and the price crosses below the upper band, it may signal a potential downward move or pullback.
If the %K line crosses below the %D line while the %K line is above the lower band, it may indicate a potential downward move.
Code explanation
Input Variables:
The input function is used to create customizable input variables that can be adjusted by the user.
smoothK and smoothD are inputs for the smoothing periods of the %K and %D lines, respectively.
lengthRSI represents the length of the RSI calculation.
lengthStoch is the length parameter for the stochastic calculation.
volumeFilterLength determines the length of the volume filter used to filter the RSI.
Source Definition:
The src variable is an input that defines the price source used for the calculations.
By default, the close price is used, but the user can choose a different price source.
RSI Calculation:
The rsi1 variable calculates the RSI using the ta.rsi function.
The RSI is a popular oscillator that measures the strength and speed of price movements.
It is calculated based on the average gain and average loss over a specified period.
In this case, the RSI is calculated using the src price source and the lengthRSI parameter.
Volume Filter:
The code calculates a volume filter to filter the RSI values based on the average volume.
The volumeAvg variable calculates the simple moving average of the volume over a specified period (volumeFilterLength).
The filteredRsi variable stores the RSI values that meet the condition of having a volume greater than or equal to the average volume (volume >= volumeAvg).
Stochastic Calculation:
The k variable calculates the %K line of the Stochastic RSI using the ta.stoch function.
The ta.stoch function takes the filtered RSI values (filteredRsi) as inputs and calculates the %K line based on the length parameter (lengthStoch).
The smoothK parameter is used to smooth the %K line by applying a moving average.
The d variable represents the %D line, which is a smoothed version of the %K line obtained by applying another moving average with a period defined by smoothD.
Momentum Calculation:
The kd variable calculates the average of the %K and %D lines, representing the momentum of the Stochastic RSI.
Bollinger Bands Calculation:
The ma variable calculates the moving average of the momentum values (kd) using the ta.sma function with a period defined by bandLength.
The offs variable calculates the offset by multiplying the standard deviation of the momentum values with a factor of 1.6185.
The up and dn variables represent the upper and lower bands, respectively, by adding and subtracting the offset from the moving average.
The Bollinger Bands provide a measure of volatility and can indicate potential overbought and oversold conditions.
Color Assignments:
The colors for the plot and Bollinger Bands are assigned based on certain conditions.
If the %K line is greater than the %D line, the plotCol variable is set to green. Otherwise, it is set to red.
The upCol and dnCol variables are set to different colors based on whether the fast moving average (fastMA) is above or below the upper and lower bands, respectively.
Plotting:
The Stochastic Momentum (%K) is plotted using the plot function with the assigned color (plotCol).
The upper and lower Bollinger Bands are plotted using the plot function with the respective colors (upCol and dnCol).
The fast moving average (fastMA) is plotted in black color to distinguish it from the bands.
The hline function is used to plot horizontal lines representing the upper and lower bands of the Stochastic Momentum.
The code combines the Stochastic RSI, Bollinger Bands, and color logic to provide visual representations of momentum and potential trend reversals. It allows traders to observe the interaction between the Stochastic Momentum lines, the Bollinger Bands, and price movements, enabling them to make informed trading decisions.
Multi Time Frame Trend, Volume and Momentum ProfileWHAT DOES THIS INDICATOR DO?
I created this indicator to address some of the significant inconveniences when analyzing a security, such as continually switching between different time frames to determine the trend and potential pullbacks, adding volume or volume-derived indicators, and finally, something that would help me determine the strength of the trend (maybe two additional indicators here). So I decided to code this all-in-one indicator that you can add multiple times to your chart depending on the settings you want to use, or just optimize the parameters for the particular asset and then switch between the options.
As the name suggests, it consists of three main sections - Trend , Volume , and Momentum . You have complete control over the parameters, including the Time Frames you want to use for each one (they can be different). So, let me explain each section in more detail.
HOW DOES THE INDICATOR WORK?
1. Trend Settings
In order to determine the trend, you need to set up two Moving Averages. You have a wide choice here - SMA, EMA, WMA, RMA, HMA, DEMA, TEMA, VWMA, and ALMA. Since the indicator does not plot the moving averages on the chart, I strongly suggest using this indicator along with the free "Trend Indicator for Directional Trading(main)" , which you can find in the Public Library. Once you set up the Trend Resolution, the Types of MAs, and their lengths, the indicator will generate a histogram of their convergences and divergences.
The change in colors should help you more easily determine the trend:
a) Bright Green - bull trend and price trending up (a good place to open long)
b) Dark Green - bull trend and price trending down (stay flat or open a long position with great caution)
c) Bright Red - bear trend and price trending down (a good place to open short)
d) Dark Red - bear trend and price trending up (stay flat or open a short position with great caution)
e) In addition, you can change the color palette to reflect the bull/bear trend momentum by scrolling to the bottom and selecting "Color Based on Bull/Bear Momentum", but I will discuss this in more detail below.
This part of the indicator is useful for opening a trade in the direction of the trend or for spotting a potential divergence. Both cases are illustrated below.
2. Volume Settings
The calculations for this part of the indicator are partially taken from "Multi Time Frame Effective Volume Profile" . I will quickly outline the specifics here, but if you want a more thorough understanding of how it works, please check the description of the MTF Effective Volume Profile indicator .
You have three elements with the following default settings - Resolution (5-min), Lookback (100), and Average (1). This means that the indicator will analyze the last one hundred 5-min bars and will plot a sum of only those that are at least 1 times bigger than the average. Those that are smaller than the average will be left out from the calculation. What you get is a trend line showing you accumulation/distribution based on modified volume parameters.
This part of the indicator is useful for spotting exhaustions and increased buying/selling volume that is opposite to the price trend. As you will see in the picture below, in frame 1 the selling pressure is decreasing, while buying volume is increasing. At one point supply dries out and the bulls take control, thus reverting the price. In frame 2, however, you can see that the higher high is not met with nearly as much buying volume as in the previous peak, showing that the bulls are exhausted and maybe a trend change will follow or at the very least that the bull trend will take a break.
3. Momentum Settings
The final part is an RSI smoothed through a Moving Average with the addition of some minor optimizations. Thus, the parameters you have to configure here aside from the resolution are the RSI length, the moving average that will be used, and its length. Out of the three, this is the most lagging component, but it's also the most accurate one. I must mention that due to the modified nature of this RSI, overbought and oversold levels carry less weight to the trading signals. Rather, pay attention to the change of colors, as they do so when the RSI changes direction based on preset parameters. The picture below shows such instances.
4. Additional Settings
This section consists of 4 elements:
a) Length of Trend - filters out the noise and gives a signal only when the trend becomes more established
b) ADX Threshold - filters out trading ranges and indecision zones when it's not recommended to open a trade
c) Select Analysis - choose what part of the indicator you want to see from a drop-down menu
d) Color Based on Bull/Bear Momentum - a global setting that will override the preset coloring of each indicator and will replace it with colors based on bull/bear strength and momentum - green for bulls, red for bears, and gray for non-trading zones.
The last part of this indicator is a combination of all of the above and is called a Points-Based System . It generates 3 rows of dots that go light green when bull criteria are met, orange when bear criteria are met, or gray when it's neither of the two. When you get a column of 3 green dots you get a buy signal. Similarly, a column of 3 orange dots gives you a sell signal. Grey zones are non-tradeable. It goes without saying that the frequency and quality of the signals you get will almost entirely depend on your settings, so feel free to experiment and adjust the indicator to catch the best moves for the given security.
In terms of indicator adjustments, I have left almost every part open to configuration. That is 15 parameters and 35 adjustable colors.
HOW MUCH DOES THE INDICATOR COST ?
As much as I would like to offer it for free (as some of my other ones), a great deal of work, trading logic, and testing have gone into creating this indicator. More than a few hundred iterations and a few dozen branches were required to reach the end result which is a precise combination of usefulness, simplicity, and practicality. Furthermore, this indicator will continue to be updated and user-requested features that improve its performance will be added.
Disclaimer: The purpose of all indicators is to indicate potential setups, which may lead to profitable results. No indicator is perfect and certainly, no indicator has a 100% success rate. They are subject to flaws, wrongful interpretation, bugs, etc. This indicator makes no exception. It must be used with a sound money management plan that puts the main emphasis on protecting your capital. Please, do not rely solely on any single indicator to make trading decisions instead of you. Indicators are storytellers, not fortune tellers. They help you see the bigger picture, not the future.
To find out more about how to gain access to this indicator, please use the provided information below or just message me. Thank you for your time.
Volatility Based Momentum Oscillator (VBMO)There is a frequent and definitive pattern in price movement, whereby price will steadily drift lower, then accelerate before bottoming out. Similarly, price will often steadily rise, then accelerate into a climax top.
The Volatility Based Momentum Oscillator (VBMO) is designed to delineate between steady versus more accelerated and climactic price movements.
VBMO is calculated using a short-term moving average, the distance of price from this moving average, and the trading instrument’s historical volatility. Even though VBMO’s calculation is relatively simple, the resulting values can help traders identify, analyze and act upon many scenarios, such as climax tops, reversals, and capitulation. Moreover, since the units and scale for VBMO are always the same, the indicator can be used in a consistent manner across multiple timeframes and instruments.
For more details, there is an article further describing VBMO and its applicability.
VMDM - Volume, Momentum & Divergence Master [BullByte]VMDM - Volume, Momentum and Divergence Master
Educational Multi-Layer Market Structure Analysis System
Multi-factor divergence engine that scores RSI momentum, volume pressure, and institutional footprints into one non-repainting confluence rating (0-100).
WHAT THIS INDICATOR IS
VMDM is an educational indicator designed to teach traders how to recognize high-probability reversal and continuation patterns by analyzing four independent market dimensions simultaneously. Instead of relying on a single indicator that may produce frequent false signals, VMDM creates a confluence-based scoring system that weights multiple confirmation factors, helping you understand which setups have stronger technical backing and which are lower quality.
This is NOT a trading system or signal generator. It is a learning tool that visualizes complex market structure concepts in an accessible format for both coders and non-coders.
THE PROBLEM IT SOLVES
Most traders face these common challenges:
Challenge 1 - Indicator Overload: Running RSI, volume analysis, and divergence detection separately creates chart clutter and conflicting signals. You waste time cross-referencing multiple windows trying to determine if all factors align.
Challenge 2 - False Divergences: Standard divergence indicators trigger on every minor pivot, creating noise. Many divergences fail because they lack supporting evidence from volume or market structure.
Challenge 3 - Missed Context: A bullish RSI divergence means nothing if it occurs during weak volume or in the middle of strong distribution. Context determines quality.
Challenge 4 - Repainting Confusion: Many divergence scripts repaint, showing perfect historical signals that never actually triggered in real-time, leading to false confidence.
Challenge 5 - Institutional Pattern Recognition: Absorption zones, stop hunts, and exhaustion patterns are taught in trading education but difficult to identify systematically without manual analysis.
VMDM addresses all five challenges by combining complementary analytical layers into one transparent, non-repainting, confluence-weighted system with visual clarity.
WHY THIS SPECIFIC COMBINATION - MASHUP JUSTIFICATION
This indicator is NOT a random mashup of popular indicators. Each of the four layers serves a specific analytical purpose and together they create a complete market structure assessment framework.
THE FOUR ANALYTICAL LAYERS
LAYER 1 - RSI MOMENTUM DIVERGENCE (Trend Exhaustion Detection)
Purpose: Identifies when price momentum is weakening before price itself reverses.
Why RSI: The Relative Strength Index measures momentum on a bounded 0-100 scale, making divergence detection mathematically consistent across all assets and timeframes. Unlike raw price oscillators, RSI normalizes momentum regardless of volatility regime.
How It Contributes: Divergence between price pivots and RSI pivots reveals early momentum exhaustion. A lower price low with a higher RSI low (bullish regular divergence) signals sellers are losing strength even as price makes new lows. This is the PRIMARY signal generator in VMDM.
Limitation If Used Alone: RSI divergence by itself produces many false signals because momentum can remain weak during continued trends. It needs confirmation from volume and structural evidence.
LAYER 2 - VOLUME PRESSURE ANALYSIS (Buying vs Selling Intensity)
Purpose: Quantifies whether the current bar's volume reflects buying pressure or selling pressure based on where price closed within the bar's range.
Methodology: Instead of just measuring volume size, VMDM calculates WHERE in the bar range the close occurred. A close near the high on high volume indicates strong buying absorption. A close near the low indicates selling pressure. The calculation accounts for wick size (wicks reduce pressure quality) and uses percentile ranking over a lookback period to normalize pressure strength on a 0-100 scale.
Formula Concept:
Buy Pressure = Volume × (Close - Low) / (High - Low) × Wick Quality Factor
Sell Pressure = Volume × (High - Close) / (High - Low) × Wick Quality Factor
Net Pressure = Buy Pressure - Sell Pressure
Pressure Strength = Percentile Rank of Net Pressure over lookback period
Why Percentile Ranking: Absolute volume varies by asset and session. Percentile ranking makes 85th percentile pressure on low-volume crypto comparable to 85th percentile pressure on high-volume forex.
How It Contributes: When a bullish divergence occurs at a pivot low AND pressure strength is above 60 (strong buying), this adds 25 confluence points. It confirms that the divergence is occurring during actual accumulation, not just weak selling.
Limitation If Used Alone: Pressure analysis shows current bar intensity but cannot identify trend exhaustion or reversal timing. High buying pressure can exist during a strong uptrend with no reversal imminent.
LAYER 3 - BEHAVIORAL FOOTPRINT PATTERNS (Volume Anomaly Detection)
CRITICAL DISCLAIMER: The terms "institutional footprint," "absorption," "stop hunt," and "exhaustion" used in this indicator are EDUCATIONAL LABELS for specific price and volume behavioral patterns. These patterns are detected through technical analysis of publicly available price, volume, and bar structure data. This indicator does NOT have access to actual institutional order flow, market maker data, broker stop-loss locations, or any non-public data source. These pattern names are used because they are common terminology in trading education to describe these technical behaviors. The analysis is interpretive and based on observable price action, not privileged information.
Purpose: Detect volume anomalies and price patterns that historically correlate with potential reversal zones or trend continuation failure.
Pattern Type 1 - Absorption (Labeled as "ACCUMULATION" or "DISTRIBUTION")
Detection Criteria: Volume is more than 2x the moving average AND bar range is less than 50 percent of the average bar range.
Interpretation: High volume compressed into a tight range suggests large participants are absorbing supply (accumulation) or distribution (distribution) without allowing price to move significantly. This often precedes directional moves once absorption completes.
Visual: Colored box zone highlighting the absorption area.
Pattern Type 2 - Stop Hunt (Labeled as "BULL HUNT" or "BEAR HUNT")
Detection Criteria: Price penetrates a recent 10-bar high or low by a small margin (0.2 percent), then closes back inside the range on above-average volume (1.5x+).
Interpretation: Price briefly spikes beyond recent structure (likely triggering stop losses placed just beyond obvious levels) then reverses. This is a classic false breakout pattern often seen before reversals.
Visual: Label at the wick extreme showing hunt direction.
Pattern Type 3 - Exhaustion (Labeled as "SELL EXHAUST" or "BUY EXHAUST")
Detection Criteria: Lower wick is more than 2.5x the body size with volume above 1.8x average and RSI below 35 (sell exhaustion), OR upper wick more than 2.5x body size with volume above 1.8x average and RSI above 65 (buy exhaustion).
Interpretation: Large wicks with high volume and extreme RSI suggest aggressive buying or selling was met with equally aggressive rejection. This exhaustion often marks short-term extremes.
Visual: Label showing exhaustion type.
How These Contribute: When a divergence forms at a pivot AND one of these behavioral patterns is active, the confluence score increases by 20 points. This confirms the divergence is occurring during structural anomaly activity, not just normal price flow.
Limitation If Used Alone: These patterns can occur mid-trend and do not indicate direction without momentum context. Absorption in a strong uptrend may just be continuation accumulation.
LAYER 4 - CONFLUENCE SCORING MATRIX (Quality Weighting System)
Purpose: Translate all detected conditions into a single 0-100 quality score so you can objectively compare setups.
Scoring Breakdown:
Divergence Present: +30 points (primary signal)
Pressure Confirmation: +25 points (volume supports direction)
Behavioral Footprint Active: +20 points (structural anomaly present)
RSI Extreme: +15 points (RSI below 30 or above 70 at pivot)
Volume Spike: +10 points (current volume above 1.5x average)
Maximum Possible Score: 100 points
Why These Weights: The weights reflect reliability hierarchy based on backtesting observation. Divergence is the core signal (30 points), but without volume confirmation (25 points) many fail. Behavioral patterns add meaningful context (20 points). RSI extremes and volume spikes are secondary confirmations (15 and 10 points).
Quality Tiers:
90-100: TEXTBOOK (all factors aligned)
75-89: HIGH QUALITY (strong confluence)
60-74: VALID (meets minimum threshold)
Below 60: DEVELOPING (not displayed unless threshold lowered)
How It Contributes: The confluence score allows you to filter noise. You can set your minimum quality threshold in settings. Higher thresholds (75+) show fewer but higher-quality patterns. Lower thresholds (50-60) show more patterns but include lower-confidence setups. This teaches you to distinguish strong setups from weak ones.
Limitation: Confluence scoring is historical observation-based, not predictive guarantee. A 95-point setup can still fail. The score represents technical alignment, not future certainty.
WHY THIS COMBINATION WORKS TOGETHER
Each layer addresses a limitation in the others:
RSI Divergence identifies WHEN momentum is exhausting (timing)
Volume Pressure confirms WHETHER the exhaustion is accompanied by opposite-side accumulation (confirmation)
Behavioral Footprint shows IF structural anomalies support the reversal hypothesis (context)
Confluence Scoring weights ALL factors into an objective quality metric (filtering)
Using only RSI divergence gives you timing without confirmation. Using only volume pressure gives you intensity without directional context. Using only pattern detection gives you anomalies without trend exhaustion context. Using all four together creates a complete analytical framework where each layer compensates for the others' weaknesses.
This is not a mashup for the sake of combining indicators. It is a structured analytical system where each component has a defined role in a multi-dimensional market assessment process.
HOW TO READ THE INDICATOR - VISUAL ELEMENTS GUIDE
VMDM displays up to five visual layer types. You can enable or disable each layer independently in settings under "Visual Layers."
VISUAL LAYER 1 - MARKET STRUCTURE (Pivot Points and Lines)
What You See:
Small labels at swing highs and lows marked "PH" (Pivot High) and "PL" (Pivot Low) with horizontal dashed lines extending right from each pivot.
What It Means:
These are CONFIRMED pivots, not real-time. A pivot low appears AFTER the required right-side confirmation bars pass (default 3 bars). This creates a delay but prevents repainting. The pivot only appears once it is mathematically confirmed.
The horizontal lines represent support (from pivot lows) and resistance (from pivot highs) levels where price previously found significant rejection.
Color Coding:
Green label and line: Pivot Low (potential support)
Red label and line: Pivot High (potential resistance)
How To Use:
These pivots are the foundation for divergence detection. Divergence is only calculated between confirmed pivots, ensuring all signals are non-repainting. The lines help you see historical structure levels.
VISUAL LAYER 2 - PRESSURE ZONES (Background Color)
What You See:
Subtle background color shading on bars - light green or light red tint.
What It Means:
This visualizes volume pressure strength in real-time.
Color Coding:
Light Green Background: Pressure Strength above 70 (strong buying pressure - price closing near highs on volume)
Light Red Background: Pressure Strength below 30 (strong selling pressure - price closing near lows on volume)
No Color: Neutral pressure (pressure between 30-70)
How To Use:
When a bullish divergence pattern appears during green pressure zones, it suggests the divergence is forming during accumulation. When a bearish divergence appears during red zones, distribution is occurring. Pressure zones help you filter divergences - those forming in supportive pressure environments have higher probability.
VISUAL LAYER 3 - DIVERGENCE LINES (Dotted Connectors)
What You See:
Dotted lines connecting two pivot points (either two pivot lows or two pivot highs).
What It Means:
A divergence has been detected between those two pivots. The line connects the price pivots where RSI showed opposite behavior.
Color Coding:
Bright Green Line: Bullish divergence (regular or hidden)
Bright Red Line: Bearish divergence (regular or hidden)
How To Use:
The divergence line appears ONLY after the second pivot is confirmed (delayed by right-side confirmation bars). This is intentional to prevent repainting. When you see the line appear, it means:
For Bullish Regular Divergence:
Price made a lower low (second pivot lower than first)
RSI made a higher low (RSI at second pivot higher than first)
Interpretation: Downtrend losing momentum
For Bullish Hidden Divergence:
Price made a higher low (second pivot higher than first)
RSI made a lower low (RSI at second pivot lower than first)
Interpretation: Uptrend continuation likely (pullback within uptrend)
For Bearish Regular Divergence:
Price made a higher high (second pivot higher than first)
RSI made a lower high (RSI at second pivot lower than first)
Interpretation: Uptrend losing momentum
For Bearish Hidden Divergence:
Price made a lower high (second pivot lower than first)
RSI made a higher high (RSI at second pivot higher than first)
Interpretation: Downtrend continuation likely (bounce within downtrend)
If "Show Consolidated Analysis Label" is disabled, a small label will appear on the divergence line showing the divergence type abbreviation.
VISUAL LAYER 4 - BEHAVIORAL FOOTPRINT MARKERS
What You See:
Boxes, labels, and markers at specific bars showing pattern detection.
ABSORPTION ZONES (Boxes):
Colored rectangular boxes spanning one or more bars.
Purple Box: Accumulation absorption zone (high volume, tight range, bullish close)
Red Box: Distribution absorption zone (high volume, tight range, bearish close)
If absorption continues for multiple consecutive bars, the box extends and a counter appears in the label showing how many bars the absorption lasted.
What It Means: Large volume is being absorbed without significant price movement. This often precedes directional breakouts once the absorption phase completes.
STOP HUNT MARKERS (Labels):
Small labels below or above wicks labeled "BULL HUNT" or "BEAR HUNT" (may show bar count if consecutive).
What It Means:
BULL HUNT : Price spiked below recent lows then reversed back up on volume - likely triggered sell stops before reversing
BEAR HUNT : Price spiked above recent highs then reversed back down on volume - likely triggered buy stops before reversing
EXHAUSTION MARKERS (Labels):
Labels showing "SELL EXHAUST" or "BUY EXHAUST."
What It Means:
SELL EXHAUST : Large lower wick with high volume and low RSI - aggressive selling met with strong rejection
BUY EXHAUST : Large upper wick with high volume and high RSI - aggressive buying met with strong rejection
How To Use:
These markers help you identify WHERE structural anomalies occurred. When a divergence signal appears AT THE SAME TIME as one of these patterns, the confluence score increases. You are looking for alignment - divergence + behavioral pattern + pressure confirmation = high-quality setup.
VISUAL LAYER 5 - CONSOLIDATED ANALYSIS LABEL (Main Pattern Signal)
What You See:
A large label appearing at pivot points (or in real-time mode, at current bar) containing full pattern analysis.
Label Appearance:
Depending on your "Use Compact Label Format" setting:
COMPACT MODE (Single Line):
Example: "BULLISH REGULAR | Q:HIGH QUALITY C:82"
Breakdown:
BULLISH REGULAR: Divergence type detected
Q:HIGH QUALITY: Pattern quality tier
C:82: Confluence score (82 out of 100)
FULL MODE (Multi-Line Detailed):
Example:
PATTERN DETECTED
-------------------
BULLISH REGULAR
Quality: HIGH QUALITY
Price: Lower Low
Momentum: Higher Low
Signal: Weakening Downtrend
CONFLUENCE: 82/100
-------------------
Divergence: 30
Pressure: 25
Institutional: 20
RSI Extreme: 0
Volume: 10
Breakdown:
Top section: Pattern type and quality
Middle section: Divergence explanation (what price did vs what RSI did)
Bottom section: Confluence score with itemized breakdown showing which factors contributed
Label Position:
In Confirmed modes: Label appears AT the pivot point (delayed by confirmation bars)
In Real-time mode: Label appears at current bar as conditions develop
Label Color:
Gold: Textbook quality (90+ confluence)
Green: High quality (75-89 confluence)
Blue: Valid quality (60-74 confluence)
How To Use:
This is your primary decision-making label. When it appears:
Check the divergence type (regular divergences are reversal signals, hidden divergences are continuation signals)
Review the quality tier (textbook and high quality have better historical win rates)
Examine the confluence breakdown to see which factors are present and which are missing
Look at the chart context (trend, support/resistance, timeframe)
Use this information to assess whether the setup aligns with your strategy
The label does NOT tell you to buy or sell. It tells you a technical pattern has formed and provides the quality assessment. Your trading decision must incorporate risk management, market context, and your strategy rules.
UNDERSTANDING THE THREE DETECTION MODES
VMDM offers three signal detection modes in settings to accommodate different trading styles and learning objectives.
MODE 1: "Confluence Only (Real-Time)"
How It Works: Displays signals AS THEY DEVELOP on the current bar without waiting for pivot confirmation. The system calculates confluence score from pressure, volume, RSI extremes, and behavioral patterns. Divergence signals are NOT required in this mode.
Delay: ZERO - signals appear immediately.
Use Case: Real-time scanning for high-confluence zones without divergence requirement. Useful for intraday traders who want immediate alerts when multiple factors align.
Tradeoff: More frequent signals but includes setups without confirmed divergence. Higher false signal rate. Signals can change as the bar develops (not repainting in historical bars, but current bar updates).
Visual Behavior: Labels appear at the current bar. No divergence lines unless divergence happens to be present.
MODE 2: "Divergence + Confluence (Confirmed)" - DEFAULT RECOMMENDED
How It Works: Full system engagement. Signals appear ONLY when:
A pivot is confirmed (requires right-side confirmation bars to pass)
Divergence is detected between current pivot and previous pivot
Total confluence score meets or exceeds your minimum threshold
Delay: Equal to your "Pivot Right Bars" setting (default 3 bars). This means signals appear 3 bars AFTER the actual pivot formed.
Use Case: Highest-quality, non-repainting signals for swing traders and learners who want to study confirmed pattern completion.
Tradeoff: Delayed signals. You will not receive the signal until confirmation occurs. In fast-moving markets, price may have already moved significantly by the time the signal appears.
Visual Behavior: Labels appear at the historical pivot location (in the past). Divergence lines connect the two pivots. This is the most educational mode because it shows completed, confirmed patterns.
Non-Repainting Guarantee: Yes. Once a signal appears, it never disappears or changes.
MODE 3: "Divergence + Confluence (Relaxed)"
How It Works: Same as Confirmed mode but with adaptive thresholds. If confluence is very high (10 points above threshold), the signal may appear even if some factors are weak. If divergence is present but confluence is slightly below threshold (within 10 points), it may still appear.
Delay: Same as Confirmed mode (right-side confirmation bars).
Use Case: Slightly more signals than Confirmed mode for traders willing to accept near-threshold setups.
Tradeoff: More signals but lower average quality than Confirmed mode.
Visual Behavior: Same as Confirmed mode.
DASHBOARD GUIDE - READING THE METRICS
The dashboard appears in the corner of your chart (position selectable in settings) and provides real-time market state analysis.
You can choose between four dashboard detail levels in settings: Off, Compact, Optimized (default), Full.
DASHBOARD ROW EXPLANATIONS
ROW 1 - Header Information
Left: Current symbol and timeframe
Center: "VMDM "
Right: Version number
ROW 2 - Mode and Delay
Shows which detection mode you are using and the signal delay.
Example: "CONFIRMED | Delay: 3 bars"
This reminds you that signals in confirmed mode appear 3 bars after the pivot forms.
ROW 3 - Market Regime
Format: "TREND UP HV" or "RANGING NV"
First Part - Trend State:
TREND UP: 20 EMA above 50 EMA with strong separation
TREND DOWN: 20 EMA below 50 EMA with strong separation
RANGING: EMAs close together, low trend strength
TRANSITION: Between trending and ranging states
Second Part - Volatility State:
HV: High Volatility (current ATR more than 1.3x the 50-bar average ATR)
NV: Normal Volatility (current ATR between 0.7x and 1.3x average)
LV: Low Volatility (current ATR less than 0.7x average)
Third Column: Volatility ratio (example: "1.45x" means current ATR is 1.45 times normal)
How To Use: Regime context helps you interpret signals. Reversal divergences are more reliable in ranging or transitional regimes. Continuation divergences (hidden) are more reliable in trending regimes. High volatility means wider stops may be needed.
ROW 4 - Pressure
Shows current volume pressure state.
Format: "BUYING | ██████████░░░░░░░░░"
States:
BUYING : Pressure strength above 60 (closes near highs)
SELLING : Pressure strength below 40 (closes near lows)
NEUTRAL : Pressure strength between 40-60
Bar Visualization: Each block represents 10 percentile points. A full bar (10 filled blocks) = 100th percentile pressure.
Color: Green for buying, red for selling, gray for neutral.
How To Use: When pressure aligns with divergence direction (bullish divergence during buying pressure), confluence is stronger.
ROW 5 - Volume and RSI
Format: "1.8x | RSI 68 | OB"
First Value: Current volume ratio (1.8x = volume is 1.8 times the moving average)
Second Value: Current RSI reading
Third Value: RSI state
OB: Overbought (RSI above 70)
OS: Oversold (RSI below 30)
Blank: Neutral RSI
How To Use: Volume spikes (above 1.5x) during divergence formation add confluence. RSI extremes at pivots add confluence.
ROW 6 - Behavioral Footprint
Format: "BULL HUNT | 2 bars"
Shows the most recent behavioral pattern detected and how long ago.
States:
ACCUMULATION / DISTRIBUTION: Absorption detected
BULL HUNT / BEAR HUNT: Stop hunt detected
SELL EXHAUST / BUY EXHAUST: Exhaustion detected
SCANNING: No recent pattern
NOW: Pattern is active on current bar
How To Use: When footprint activity is recent (within 50 bars) or active now, it adds context to divergence signals forming in that area.
ROW 7 - Current Pattern
Shows the divergence type currently detected (if any).
Examples: "BULLISH REGULAR", "BEARISH HIDDEN", "Scanning..."
Quality: Shows pattern quality (TEXTBOOK, HIGH QUALITY, VALID)
How To Use: This tells you what type of signal is active. Regular divergences are reversal setups. Hidden divergences are continuation setups.
ROW 8 - Session Summary
Format: "14 events | A3 H8 E3"
First Value: Total institutional events this session
Breakdown:
A: Absorption events
H: Stop hunt events
E: Exhaustion events
How To Use: High event counts suggest an active, volatile session with frequent structural anomalies. Low counts suggest quiet, orderly price action.
ROW 9 - Confluence Score (Optimized/Full mode only)
Format: "78/100 | ████████░░"
Shows current real-time confluence score even if no pattern is confirmed yet.
How To Use: Watch this in real-time to see how close you are to pattern formation. When it exceeds your threshold and divergence forms, a signal will appear (after confirmation delay).
ROW 10 - Patterns Studied (Optimized/Full mode only)
Format: "47 patterns | 12 bars ago"
First Value: Total confirmed patterns detected since chart loaded
Second Value: How many bars since the last confirmed pattern appeared
How To Use: Helps you understand pattern frequency on your selected symbol and timeframe. If many bars have passed since last pattern, market may be trending without reversal opportunities.
ROW 11 - Bull/Bear Ratio (Optimized/Full mode only)
Format: "28:19 | BULL"
Shows count of bullish vs bearish patterns detected.
Balance:
BULL: More bullish patterns detected (suggests market has had more bullish reversals/continuations)
BEAR: More bearish patterns detected
BAL: Equal counts
How To Use: Extreme imbalances can indicate directional bias in the studied period. A heavily bullish ratio in a downtrend might suggest frequent failed rallies (bearish continuation). Context matters.
ROW 12 - Volume Ratio Detail (Optimized/Full mode only)
Shows current volume vs average volume in absolute terms.
Example: "1.4x | 45230 / 32300"
How To Use: Confirms whether current activity is above or below normal.
ROW 13 - Last Institutional Event (Full mode only)
Shows the most recent institutional pattern type and how many bars ago it occurred.
Example: "DISTRIBUTION | 23 bars"
How To Use: Tracks recency of last anomaly for context.
SETTINGS GUIDE - EVERY PARAMETER EXPLAINED
PERFORMANCE SECTION
Enable All Visuals (Master Toggle)
Default: ON
What It Does: Master kill switch for ALL visual elements (labels, lines, boxes, background colors, dashboard). When OFF, only plot outputs remain (invisible unless you open data window).
When To Change: Turn OFF on mobile devices, 1-second charts, or slow computers to improve performance. You can still receive alerts even with visuals disabled.
Impact: Dramatic performance improvement when OFF, but you lose all visual feedback.
Maximum Object History
Default: 50 | Range: 10-100
What It Does: Limits how many of each object type (labels, lines, boxes) are kept in memory. Older objects beyond this limit are deleted.
When To Change: Lower to 20-30 on fast timeframes (1-minute charts) to prevent slowdown. Increase to 100 on daily charts if you want more historical pattern visibility.
Impact: Lower values = better performance but less historical visibility. Higher values = more history visible but potential slowdown on fast timeframes.
Alert Cooldown (Bars)
Default: 5 | Range: 1-50
What It Does: Minimum number of bars that must pass before another alert of the same type can fire. Prevents alert spam when multiple patterns form in quick succession.
When To Change: Increase to 20+ on 1-minute charts to reduce noise. Decrease to 1-2 on daily charts if you want every pattern alerted.
Impact: Higher cooldown = fewer alerts. Lower cooldown = more alerts.
USER EXPERIENCE SECTION
Show Enhanced Tooltips
Default: ON
What It Does: Enables detailed hover-over tooltips on labels and visual elements.
When To Change: Turn OFF if you encounter Pine Script compilation errors related to tooltip arguments (rare, platform-specific issue).
Impact: Minimal. Just adds helpful hover text.
MARKET STRUCTURE DETECTION SECTION
Pivot Left Bars
Default: 3 | Range: 2-10
What It Does: Number of bars to the LEFT of the center bar that must be higher (for pivot low) or lower (for pivot high) than the center bar for a pivot to be valid.
Example: With value 3, a pivot low requires the center bar's low to be lower than the 3 bars to its left.
When To Change:
Increase to 5-7 on noisy timeframes (1-minute charts) to filter insignificant pivots
Decrease to 2 on slow timeframes (daily charts) to catch more pivots
Impact: Higher values = fewer, more significant pivots = fewer signals. Lower values = more frequent pivots = more signals but more noise.
Pivot Right Bars
Default: 3 | Range: 2-10
What It Does: Number of bars to the RIGHT of the center bar that must pass for confirmation. This creates the non-repainting delay.
Example: With value 3, a pivot is confirmed 3 bars AFTER it forms.
When To Change:
Increase to 5-7 for slower, more confirmed signals (better for swing trading)
Decrease to 2 for faster signals (better for intraday, but still non-repainting)
Impact: Higher values = longer delay but more reliable confirmation. Lower values = faster signals but less confirmation. This setting directly controls your signal delay in Confirmed and Relaxed modes.
Minimum Confluence Score
Default: 60 | Range: 40-95
What It Does: The threshold score required for a pattern to be displayed. Patterns with confluence scores below this threshold are not shown.
When To Change:
Increase to 75+ if you only want high-quality textbook setups (fewer signals)
Decrease to 50-55 if you want to see more developing patterns (more signals, lower average quality)
Impact: This is your primary signal filter. Higher threshold = fewer, higher-quality signals. Lower threshold = more signals but includes weaker setups. Recommended starting point is 60-65.
TECHNICAL PERIODS SECTION
RSI Period
Default: 14 | Range: 5-50
What It Does: Lookback period for RSI calculation.
When To Change:
Decrease to 9-10 for faster, more sensitive RSI that detects shorter-term momentum changes
Increase to 21-28 for slower, smoother RSI that filters noise
Impact: Lower values make RSI more volatile (more frequent extremes and divergences). Higher values make RSI smoother (fewer but more significant divergences). 14 is industry standard.
Volume Moving Average Period
Default: 20 | Range: 10-200
What It Does: Lookback period for calculating average volume. Current volume is compared to this average to determine volume ratio.
When To Change:
Decrease to 10-14 for shorter-term volume comparison (more sensitive to recent volume changes)
Increase to 50-100 for longer-term volume comparison (smoother, less sensitive)
Impact: Lower values make volume ratio more volatile. Higher values make it more stable. 20 is standard.
ATR Period
Default: 14 | Range: 5-100
What It Does: Lookback period for Average True Range calculation used for volatility measurement and label positioning.
When To Change: Rarely needs adjustment. Use 7-10 for faster volatility response, 21-28 for slower.
Impact: Affects volatility ratio calculation and visual label spacing. Minimal impact on signals.
Pressure Percentile Lookback
Default: 50 | Range: 10-300
What It Does: Lookback period for calculating volume pressure percentile ranking. Your current pressure is ranked against the pressure of the last X bars.
When To Change:
Decrease to 20-30 for shorter-term pressure context (more responsive to recent changes)
Increase to 100-200 for longer-term pressure context (smoother rankings)
Impact: Lower values make pressure strength more sensitive to recent bars. Higher values provide more stable, long-term pressure assessment. Capped at 300 for performance reasons.
SIGNAL DETECTION SECTION
Signal Detection Mode
Default: "Divergence + Confluence (Confirmed)"
Options:
Confluence Only (Real-time)
Divergence + Confluence (Confirmed)
Divergence + Confluence (Relaxed)
What It Does: Selects which detection logic mode to use (see "Understanding The Three Detection Modes" section above).
When To Change: Use Confirmed for learning and non-repainting signals. Use Real-time for live scanning without divergence requirement. Use Relaxed for slightly more signals than Confirmed.
Impact: Fundamentally changes when and how signals appear.
VISUAL LAYERS SECTION
All toggles default to ON. Each controls visibility of one visual layer:
Show Market Structure: Pivot markers and support/resistance lines
Show Pressure Zones: Background color shading
Show Divergence Lines: Dotted lines connecting pivots
Show Institutional Footprint Markers: Absorption boxes, hunt labels, exhaustion labels
Show Consolidated Analysis Label: Main pattern detection label
Use Compact Label Format
Default: OFF
What It Does: Switches consolidated label between single-line compact format and multi-line detailed format.
When To Change: Turn ON if you find full labels too large or distracting.
Impact: Visual clarity vs. information density tradeoff.
DASHBOARD SECTION
Dashboard Mode
Default: "Optimized"
Options: Off, Compact, Optimized, Full
What It Does: Controls how much information the dashboard displays.
Off: No dashboard
Compact: 8 rows (essential metrics only)
Optimized: 12 rows (recommended balance)
Full: 13 rows (every available metric)
Dashboard Position
Default: "Top Right"
Options: Top Right, Top Left, Bottom Right, Bottom Left
What It Does: Screen corner where dashboard appears.
HOW TO USE VMDM - PRACTICAL WORKFLOW
STEP 1 - INITIAL SETUP
Add VMDM to your chart
Select your detection mode (Confirmed recommended for learning)
Set your minimum confluence score (start with 60-65)
Adjust pivot parameters if needed (default 3/3 is good for most timeframes)
Enable the visual layers you want to see
STEP 2 - CHART ANALYSIS
Let the indicator load and analyze historical data
Review the patterns that appear historically
Examine the confluence scores - notice which patterns had higher scores
Observe which patterns occurred during supportive pressure zones
Notice the divergence line connections - understand what price vs RSI did
STEP 3 - PATTERN RECOGNITION LEARNING
When a consolidated analysis label appears:
Read the divergence type (regular or hidden, bullish or bearish)
Check the quality tier (textbook, high quality, or valid)
Review the confluence breakdown - which factors contributed
Look at the chart context - where is price relative to structure, trend, etc.
Observe the behavioral footprint markers nearby - do they support the pattern
STEP 4 - REAL-TIME MONITORING
Watch the dashboard for real-time regime and pressure state
Monitor the current confluence score in the dashboard
When it approaches your threshold, be alert for potential pattern formation
When a new pattern appears (after confirmation delay), evaluate it using the workflow above
Use your trading strategy rules to decide if the setup aligns with your criteria
STEP 5 - POST-PATTERN OBSERVATION
After a pattern appears:
Mark the level on your chart
Observe what price does after the pattern completes
Did price respect the reversal/continuation signal
What was the confluence score of patterns that worked vs. those that failed
Learn which quality tiers and confluence levels produce better results on your specific symbol and timeframe
RECOMMENDED TIMEFRAMES AND ASSET CLASSES
VMDM is timeframe-agnostic and works on any asset with volume data. However, optimal performance varies:
BEST TIMEFRAMES
15-Minute to 1-Hour: Ideal balance of signal frequency and reliability. Pivot confirmation delay is acceptable. Sufficient volume data for pressure analysis.
4-Hour to Daily: Excellent for swing trading. Very high-quality signals. Lower frequency but higher significance. Recommended for learning because patterns are clearer.
1-Minute to 5-Minute: Works but requires adjustment. Increase pivot bars to 5-7 for filtering. Decrease max object history to 30 for performance. Expect more noise.
Weekly/Monthly: Works but very infrequent signals. Increase confluence threshold to 70+ to ensure only major patterns appear.
BEST ASSET CLASSES
Forex Majors: Excellent volume data and clear trends. Pressure analysis works well.
Crypto (Major Pairs): Good volume data. High volatility makes divergences more pronounced. Works very well.
Stock Indices (SPY, QQQ, etc.): Excellent. Clean price action and reliable volume.
Individual Stocks: Works well on high-volume stocks. Low-volume stocks may produce unreliable pressure readings.
Commodities (Gold, Oil, etc.): Works well. Clear trends and reactions.
WHAT THIS INDICATOR CANNOT DO - LIMITATIONS
LIMITATION 1 - It Does Not Predict The Future
VMDM identifies when technical conditions align historically associated with potential reversals or continuations. It does not predict what will happen next. A textbook 95-confluence pattern can still fail if fundamental events, news, or larger timeframe structure override the setup.
LIMITATION 2 - Confirmation Delay Means You Miss Early Entry
In Confirmed and Relaxed modes, the non-repainting design means you receive signals AFTER the pivot is confirmed. Price may have already moved significantly by the time you receive the signal. This is the tradeoff for non-repainting reliability. You can use Real-time mode for faster signals but sacrifice divergence confirmation.
LIMITATION 3 - It Does Not Tell You Position Sizing or Risk Management
VMDM provides technical pattern analysis. It does not calculate stop loss levels, take profit targets, or position sizing. You must apply your own risk management rules. Never risk more than you can afford to lose based on a technical signal.
LIMITATION 4 - Volume Pressure Analysis Requires Reliable Volume Data
On assets with thin volume or unreliable volume reporting, pressure analysis may be inaccurate. Stick to major liquid assets with consistent volume data.
LIMITATION 5 - It Cannot Detect Fundamental Events
VMDM is purely technical. It cannot predict earnings reports, central bank decisions, geopolitical events, or other fundamental catalysts that can override technical patterns.
LIMITATION 6 - Divergence Requires Two Pivots
The indicator cannot detect divergence until at least two pivots of the same type have formed. In strong trends without pullbacks, you may go long periods without signals.
LIMITATION 7 - Institutional Pattern Names Are Interpretive
The behavioral footprint patterns are named using common trading education terminology, but they are detected through technical analysis, not actual institutional data access. The patterns are interpretations based on price and volume behavior.
CONCEPT FOUNDATION - WHY THIS APPROACH WORKS
MARKET PRINCIPLE 1 - Momentum Divergence Precedes Price Reversal
Price is the final output of market forces, but momentum (the rate of change in those forces) shifts first. When price makes a new low but the momentum behind that move is weaker (higher RSI low), it signals that sellers are losing strength even though they temporarily pushed price lower. This precedes reversal. This is a fundamental principle in technical analysis taught by Charles Dow, widely observed in market behavior.
MARKET PRINCIPLE 2 - Volume Reveals Conviction
Price can move on low volume (low conviction) or high volume (high conviction). When price makes a new low on declining volume while RSI shows improving momentum, it suggests the new low is not confirmed by participant conviction. Adding volume pressure analysis to momentum divergence adds a confirmation layer that filters false divergences.
MARKET PRINCIPLE 3 - Anomalies Mark Structural Extremes
When volume spikes significantly but range contracts (absorption), or when price spikes beyond structure then reverses (stop hunt), or when aggressive moves are met with large-wick rejection (exhaustion), these anomalies often mark short-term extremes. Combining these structural observations with momentum analysis creates context.
MARKET PRINCIPLE 4 - Confluence Improves Probability
No single technical factor is reliable in isolation. RSI divergence alone fails frequently. Volume analysis alone cannot time entries. Combining multiple independent factors into a weighted system increases the probability that observed patterns have structural significance rather than random noise.
THE EDUCATIONAL VALUE
By visualizing all four layers simultaneously and breaking down the confluence scoring transparently, VMDM teaches you to think in terms of multi-dimensional analysis rather than single-indicator reliance. Over time, you will learn to recognize these patterns manually and understand which combinations produce better results on your traded assets.
INSTITUTIONAL TERMINOLOGY - IMPORTANT CLARIFICATION
This indicator uses the following terms that are common in trading education:
Institutional Footprint
Absorption (Accumulation / Distribution)
Stop Hunt
Exhaustion
CRITICAL DISCLAIMER:
These terms are EDUCATIONAL LABELS for specific price action and volume behavior patterns detected through technical analysis of publicly available chart data (open, high, low, close, volume). This indicator does NOT have access to:
Actual institutional order flow or order book data
Market maker positions or intentions
Broker stop-loss databases
Non-public trading data
Proprietary institutional information
The patterns labeled as "institutional footprint" are interpretations based on observable price and volume behavior that educational trading literature often associates with potential large-participant activity. The detection is algorithmic pattern recognition, not privileged data access.
When this indicator identifies "absorption," it means it detected high volume within a small range - a condition that MAY indicate large orders being filled but is not confirmation of actual institutional participation.
When it identifies a "stop hunt," it means price briefly penetrated a structural level then reversed - a pattern that MAY have triggered stop losses but is not confirmation that stops were specifically targeted.
When it identifies "exhaustion," it means high volume with large rejection wicks - a pattern that MAY indicate aggressive participation meeting strong opposition but is not confirmation of institutional involvement.
These are technical analysis interpretations, not factual statements about market participant identity or intent.
DISCLAIMER AND RISK WARNING
EDUCATIONAL PURPOSE ONLY
This indicator is designed as an educational tool to help traders learn to recognize technical patterns, understand multi-factor analysis, and practice systematic market observation. It is NOT a trading system, signal service, or financial advice.
NO PERFORMANCE GUARANTEE
Past pattern behavior does not guarantee future results. A pattern that historically preceded price movement in one direction may fail in the future due to changing market conditions, fundamental events, or random variance. Confluence scores reflect historical technical alignment, not future certainty.
TRADING INVOLVES SUBSTANTIAL RISK
Trading financial instruments involves substantial risk of loss. You can lose more than your initial investment. Never trade with money you cannot afford to lose. Always use proper risk management including stop losses, position sizing, and portfolio diversification.
NO PREDICTIVE CLAIMS
This indicator does NOT predict future price movement. It identifies when technical conditions align in patterns that historically have been associated with potential reversals or continuations. Market behavior is probabilistic, not deterministic.
BACKTESTING LIMITATIONS
If you backtest trading strategies using this indicator, ensure you account for:
Realistic commission costs
Realistic slippage (difference between signal price and actual fill price)
Sufficient sample size (minimum 100 trades for statistical relevance)
Reasonable position sizing (risking no more than 1-2 percent of account per trade)
The confirmation delay inherent in the indicator (you cannot enter at the exact pivot in Confirmed mode)
Backtests that do not account for these factors will produce unrealistic results.
AUTHOR LIABILITY
The author (BullByte) is not responsible for any trading losses incurred using this indicator. By using this indicator, you acknowledge that all trading decisions are your sole responsibility and that you understand the risks involved.
NOT FINANCIAL ADVICE
Nothing in this indicator, its code, its description, or its visual outputs constitutes financial, investment, or trading advice. Consult a licensed financial advisor before making investment decisions.
FREQUENTLY ASKED QUESTIONS
Q: Why do signals appear in the past, not at the current bar
A: In Confirmed and Relaxed modes, signals appear at confirmed pivots, which requires waiting for right-side confirmation bars (default 3). This creates a delay but prevents repainting. Use Real-time mode if you want current-bar signals without pivot confirmation.
Q: Can I use this for automated trading
A: You can create alert-based automation, but understand that Confirmed mode signals appear AFTER the pivot with delay, so your entry will not be at the pivot price. Real-time mode signals can change as the current bar develops. Automation requires careful consideration of these factors.
Q: How do I know which confluence score to use
A: Start with 60. Observe which patterns work on your symbol/timeframe. If too many false signals, increase to 70-75. If too few signals, decrease to 55. Quality vs. quantity tradeoff.
Q: Do regular divergences mean I should enter a reversal trade immediately
A: No. Regular divergences indicate momentum exhaustion, which is a WARNING sign that trend may reverse, not a confirmation that it will. Use confluence score, market context, support/resistance, and your strategy rules to make entry decisions. Many divergences fail.
Q: What's the difference between regular and hidden divergence
A: Regular divergence = price and momentum move in opposite directions at extremes = potential reversal signal. Hidden divergence = price and momentum move in opposite directions during pullbacks = potential continuation signal. Hidden divergence suggests the pullback is just a correction within the larger trend.
Q: Why does the pressure zone color sometimes conflict with the divergence direction
A: Pressure is real-time current bar analysis. Divergence is confirmed pivot analysis from the past. They measure different things at different times. A bullish divergence confirmed 3 bars ago might appear during current selling pressure. This is normal.
Q: Can I use this on stocks without volume data
A: No. Volume is required for pressure analysis and behavioral pattern detection. Use only on assets with reliable volume reporting.
Q: How often should I expect signals
A: Depends on timeframe and settings. Daily charts might produce 5-10 signals per month. 1-hour charts might produce 20-30. 15-minute charts might produce 50-100. Adjust confluence threshold to control frequency.
Q: Can I modify the code
A: Yes, this is open source. You can modify for personal use. If you publish a modified version, please credit the original and ensure your publication meets TradingView guidelines.
Q: What if I disagree with a pattern's confluence score
A: The scoring weights are based on general observations and may not suit your specific strategy or asset. You can modify the code to adjust weights if you have data-driven reasons to do so.
Final Notes
VMDM - Volume, Momentum and Divergence Master is an educational multi-layer market analysis system designed to teach systematic pattern recognition through transparent, confluence-weighted signal detection. By combining RSI momentum divergence, volume pressure quantification, behavioral footprint pattern recognition, and quality scoring into a unified framework, it provides a comprehensive learning environment for understanding market structure.
Use this tool to develop your analytical skills, understand how multiple technical factors interact, and learn to distinguish high-quality setups from noise. Remember that technical analysis is probabilistic, not predictive. No indicator replaces proper education, risk management, and trading discipline.
Trade responsibly. Learn continuously. Risk only what you can afford to lose.
-BullByte
Reduced-Lag Chande Momentum Oscillator [BOSWaves]Reduced-Lag Chande Momentum Oscillator – Adaptive Momentum Geometry with Reduced-Latency Reversion Logic
Overview
The Reduced-Lag Chande Momentum Oscillator represents a sophisticated extension of the classical Chande Momentum Oscillator, preserving the foundational measurement of net directional pressure while addressing inherent limitations in lag, noise, and signal clarity. The traditional CMO provides reliable snapshots of upward versus downward force but reacts slowly to rapid market accelerations and can obscure meaningful momentum inflections with delayed readings. This iteration integrates a dual-stage reduced-lag filter, optional advanced smoothing, and acceleration-based analytics, producing a real-time, multi-dimensional representation of market momentum.
The design reframes classical momentum using a layered curvature and gradient structure - main, midline, and shadow - to show trajectory, velocity, and intensity in one view. Instead of the usual ±70/30 extremes, it uses ±50 as a statistically grounded threshold where one side of the market begins exerting true dominance. This captures structural imbalance more reliably, exposing exhaustion and actionable inflection without amplifying noise.
This visualization gives traders a continuous, responsive read on market structure, revealing not just direction but rate of change, acceleration alignment, and curvature behavior. The oscillator becomes a momentum map, expressing both probability and intensity behind directional shifts.
Where conventional oscillators mislabel short-lived swings as signals, the Reduced-Lag CMO separates baseline shifts from high-conviction transitions, enabling cleaner, more decisive signal interpretation.
Theoretical Foundation
The classical Chande Momentum Oscillator, created by Tushar Chande, calculates the normalized net difference between consecutive upward and downward price changes over a defined window, generating readings from –100 to +100. While effective for capturing basic directional pressure, the unmodified CMO suffers from signal latency and sensitivity to abrupt market swings, which can obscure actionable inflection points.
The Reduced-Lag CMO augments this foundation with three key mechanisms:
Reduced-Lag Filtering : A dual-EMA structure eliminates inertial lag, aligning the oscillator curve closely with real-time market momentum without producing overshoot artifacts.
Smoothing Architecture : Optional SMA, EMA, or WMA smoothing is applied post-filter, balancing noise reduction with trajectory fidelity. A multi-layer line system (shadow → midline → main) communicates depth, curvature, and gradient dynamics.
Acceleration Integration : First and second derivatives of the smoothed curve quantify velocity and acceleration, allowing the indicator to identify not only momentum flips but the force behind each shift, forming the basis for the strong-signal overlay.
The combination of these mechanisms produces an oscillator that respects the original CMO framework while delivering real-time, context-sensitive intelligence. The ±50 boundaries are selected as the statistically validated pressure zones where directional dominance exceeds neutral oscillation. Crosses and rejections at these boundaries are not arbitrary overbought/oversold events, but measurable imbalances with actionable significance.
How It Works
The Reduced-Lag CMO is constructed through a multi-stage process:
Momentum Estimation Core : Raw CMO values are calculated and then passed through a reduced-lag filter to remove delay, creating a curve that closely tracks instantaneous directional pressure.
Smoothing & Layered Representation : The filtered curve can be smoothed and split into three layers - shadow, midline, and main - giving visual depth, trajectory clarity, and curvature instead of a single-line oscillator.
Gradient-Based Pressure Mapping : Color gradients encode momentum strength and polarity. Green-yellow transitions highlight increasing upward dominance, while red-yellow transitions indicate weakening downward force.
Pressure-Zone Anchoring (±50) : The system defines statistically significant pressure zones at ±50. Moves beyond these levels reflect dominant directional control, and rejections inside the zone signal potential exhaustion.
Signal Generation : Momentum events are evaluated through velocity and acceleration. Standard signals appear as triangle markers indicating validated momentum flips. Strong signals appear as triangles with diamonds when acceleration confirms a high-conviction transition.
A cooldown rule spaces signals apart to reduce clutter and emphasize structurally meaningful events.
Interpretation
The Reduced-Lag CMO reframes momentum as a dynamic equilibrium between directional force and structural pressure:
Positive Momentum Phases : Curves above zero with green-yellow gradients indicate sustained upward pressure. Shallow retracements or midline tests denote controlled pullbacks.
Negative Momentum Phases : Curves below zero with red-yellow gradients show downward dominance. Rejections from –50 highlight potential exhaustion and reversal readiness.
Pressure-Zone Dynamics (±50) : Crosses beyond ±50 confirm dominant directional force. Meanwhile, rejections and rotations inside the zone signal structural fatigue.
Velocity & Acceleration Analysis : Rising momentum with decelerating velocity suggests fading force; acceleration alignment amplifies signal strength and forms the basis of strong signals.
Signal Architecture
The Reduced-Lag CMO produces a single event type with two intensities: a validated momentum inflection.
Standard Signals - Triangles:
Triggered by momentum flips confirmed by velocity.
Represent moderate-intensity directional changes.
Appear at zero-line crosses or ±50 rejections with aligned velocity.
Strong Signals Triangles + Diamonds:
Triggered when acceleration confirms the directional change.
Represent high-intensity, high-conviction shifts.
Rare by design; indicate robust momentum inflections.
Cooldown mechanics prevent repeated signals in short succession, emphasizing structural reliability over noise.
Strategy Integration
Trend Confirmation : Align zero-line flips with higher-timeframe directional bias.
Reversal Detection : Strong signals from ±50 zones highlight potential inflection points.
Volatility Assessment : Gradient transitions reveal strengthening or weakening momentum.
Pullback Timing : Multi-layer curvature identifies controlled retracements vs trend exhaustion.
Confluence Mapping : Pair with structure-based indicators to filter signals in context.
Technical Implementation Details
Core Engine : Classical CMO with Ehlers reduced-lag extension
Lag Reduction : Dual EMA filtering
Smoothing : Optional SMA/EMA/WMA post-filter
Multi-Layer Curve : Shadow, midline, main
Signal System : Two-tier momentum-acceleration framework
Pressure Zones : ±50 statistically validated thresholds
Cooldown Logic : Bar-indexed suppression
Gradient Mapping : Encodes magnitude and direction
Alerts : Standard and strong signals
Optimal Application Parameters
Timeframes:
1 - 5 min : Intraday momentum tracking
15 - 60 min : Trend rotations & volatility transitions
4H - Daily : Macro momentum exhaustion & re-accumulation mapping
Suggested Ranges:
CMO Length : 7 - 12
Reduced-Lag Length : 5 - 15
Smoothing : 10 - 20
Cooldown Bars : 5 - 15
Performance Characteristics
High Effectiveness:
Markets with directional pulses & clean pressure transitions
Trending phases with measurable pullbacks
Instruments with stable volatility cycles
Reduced Edge:
Choppy consolidations
Ultra-low volatility environments
Disclaimer
The Reduced-Lag Chande Momentum Oscillator is a professional-grade analytical tool. It is not predictive and carries no guaranteed profitability. Effectiveness depends on asset class, volatility regime, parameter selection, and disciplined execution. Any suggested application timeframes or recommended ranges are guidance only - they are not universally optimal and will not deliver consistent accuracy on every asset or market condition. BOSWaves recommends using it in conjunction with structure, liquidity, and momentum context.
Gaussian Kernel Smoothing MomentumOverview:
The Gaussian Kernel Smoothing Momentum indicator analyzes and quantifies market momentum by applying statistical techniques to price and returns data. This indicator uses Gaussian kernel smoothing to filter noise and provide a more accurate representation of momentum. Additionally, it includes a option to evaluate the absolute score of the momentum to determine if the beginning of a "trend" is likely or if you can expect a "trend" to come to an end.
Kernels and Their Role In Time Series Analysis:
In statistical analysis, a kernel is a weighting function used to estimate the properties of a dataset. Kernels are particularly useful in non-parametric methods, where they serve to smooth data or estimate probability density functions without assuming a specific underlying distribution. The Gaussian kernel, one of the most commonly used, is characterized by its smooth, bell-shaped curve which provides a natural way to give more weight to data points closer to the target value and less weight to those further away.
Uses of Kernels in Time Series Analysis
Kernels play a significant role in time series analysis, especially in the context of smoothing and filtering. With kernel functions, you can reduce noise and extract the underlying systematic component or signal from the data. This process is essential for identifying long-term patterns in the data, which is often obscured by short-term fluctuations and random noise.
Kernel Smoothing
Kernel smoothing is a technique that applies a kernel function to a set of data points to create a smooth curve, effectively reducing the impact of random variations. In time series analysis, kernel smoothing helps to filter out short-term noise while retaining significant trends and "patterns". The Gaussian kernel, with its emphasis on nearby points, is particularly effective for this purpose, as it smooths the data in a way that highlights the underlying structure without overfitting to random fluctuations.
Additionally, kernels are used in non-parametric volatility estimation, option pricing models, and for detecting anomalies in financial data. Their flexibility and ability to handle complex, non-linear relationships make them well-suited for the often noisy data encountered in financial markets.
Momentum Component
The momentum component of the indicator is designed to quantify the directional movement of asset prices by applying the Gaussian kernel smoothing to the expected return of the price data. The data then has the variance stabilized and normalizes the distribution of price changes to be able to more efficiently analyze the momentum.
The Gaussian kernel smoothing function serves to filter out high-frequency noise, isolating the underlying systematic component of the momentum. This is achieved by weighting the data points based on their proximity to the current observation, with closer data points exerting a stronger influence. The resulting smoothed momentum provides a clearer of the directional bias in the market, devoid of short-term volatility.
Absolute Move Component
The absolute move component is a extension of the momentum analysis, focusing on the magnitude rather than the direction of the price movements. This component captures the absolute score of the smoothed momentum series, providing a measure of strength or intensity of the price movement, independent from its direction. The absolute move component also incorporates a Kalman filter to further smooth and refine the signal. The Kalman filter dynamically adjusts based on the observed variance in the data, to reduce the impact of outliers.
What to make of this indicator
The smoothed momentum line helps determine whether the market is experiencing upward and downward momentum. If the momentum line is above zero and rising, this suggest a positive expected returns. Conversely, if the momentum line is below zero and falling, it indicates negative expected returns.
You should also pay attention to changes in the slope of the momentum line and the moving average of the smoothed momentum(weighted with an optimal sampling size algorithm). A flattening or reversal of the slope may signal a potential shift in market direction. For example, if the momentum line and moving average transitions from rising to falling, it means that the expected return is going from positive to negative so you can see the "trend" as weakening or forming a trend of negative expected returns.
The absolute move component is designed to measure the intensity or strength of the current market movement. A low absolute move value, especially when they are negative or at the lower end of their band, indicates that the momentum and expected return is close to zero, which suggest that the market is experiencing minimal directional movement, which can be a sign of consolidation. High absolute values signal that the market is undergoing a significant price movement. When the absolute move is high and/or rising, it indicates that the movement of the momentum is strong, regardless of whether it is bullish or bearish.
If the absolute move reaches unusually high levels, it could indicate that the market is experiencing an exceptional price move, which might be unsustainable. Traders can anticipate potential reversals or profit taking targets. However, you should avoid trying to trade reversals as exceptionally high values in a time series do not guarantee an immediate reversal. This high values often occur during periods of strong trends or significant events, which can continue longer than expected, and you cant time when it will return to its mean. The mean-reverting nature of some statistical models can suggest a return to the mean, but this assumption can be misleading in financial markets, where trends can persist despite overextending conditions.
Squeeze Momentum TD - A Revisited Version of the TTM SqueezeDescription:
The "Squeeze Momentum TD" is our unique take on the highly acclaimed TTM Squeeze indicator, renowned in the trading community for its efficiency in pinpointing market momentum. This script is a tribute and an extension to the foundational work laid by several pivotal figures in the trading industry:
• John Carter, for his creation of the TTM Squeeze and TTM Squeeze Pro, which revolutionized the way traders interpret volatility and momentum.
• Lazybear, whose original interpretation of the TTM Squeeze, known as the "Squeeze Momentum Indicator", provided an invaluable foundation for further development.
• Makit0, who evolved Lazybear's script to incorporate enhancements from the TTM Squeeze Pro, resulting in the "Squeeze PRO Arrows".
Our script, "Squeeze Momentum TD", represents a custom version developed after reviewing all variations of the TTM Squeeze indicator. This iteration focuses on a distinct visualization approach, featuring an overlay band on the chart for an user-friendly experience. We've distilled the essence of the TTM Squeeze and its advanced version, the TTM Squeeze Pro, into a form that emphasizes intuitive usability while retaining comprehensive analytical depth.
Features:
-Customizable Bollinger Bands and Keltner Channels: These core components of the TTM Squeeze.
-Dynamic Squeeze Conditions: Ranging from No Squeeze to High Compression.
-Momentum Oscillator: A linear regression-based momentum calculation, offering clear insights into market trends.
-User-Defined Color Schemes: Personalize your experience with adjustable colors for bands and plot shapes.
-Advanced Alert System: Alerts for key market shifts like Bull Watch Out, Bear Watch Out, and Momentum shifts.
-Adaptive Band Widths: Modify the band widths to suit your preference.
How to use it?
• Transition from Light Green to Dark Green: Indicates a potential end to the bullish momentum. This 'Bull Watch Out' signal suggests that traders should be cautious about continuing bullish trends.
• Transition from Light Red to Dark Red: Signals that the bearish momentum might be fading, triggering a 'Bear Watch Out' alert. It's a hint for traders to be wary of ongoing bearish trends.
• Shift from Dark Green to Light Green: This change suggests an increase in bullish momentum. It's an indicator for traders to consider bullish positions.
• Change from Dark Red to Light Red: Implies that bearish momentum is picking up. Traders might want to explore bearish strategies under this condition.
• Rapid Change from Light Red to Light Green: This swift shift indicates a quick transition from bearish to bullish sentiment. It's a strong signal for traders to consider switching to bullish positions.
• Quick Shift from Light Green to Light Red: Demonstrates a speedy change from bullish to bearish momentum. It suggests that traders might want to adjust their strategies to align with the emerging bearish trend.
Acknowledgements:
Special thanks to Beardy_Fred for the significant contributions to the development of this script. This work stands as a testament to the collaborative spirit of the trading community, continuously evolving to meet the demands of diverse trading strategies.
Disclaimer:
This script is provided for educational and informational purposes only. Users should conduct their own due diligence before making any trading decisions.
Matrix Momentum Expansion [IkkeOmar]The indicator consists of several features:
Candlestick chart: The indicator plots a candlestick chart based on the input parameters of the user. The candlesticks are colored blue or orange depending on whether the closing price is above or below the upper and lower bands.
Support and Resistance levels: The indicator also plots support and resistance levels based on the CCI (Commodity Channel Index) of the asset's price. These levels are dynamic and change based on the user's input parameters.
Momentum: The indicator calculates the momentum of the market based on the smoothed and standard deviation of the asset's price. It uses this momentum to calculate upper and lower bands that are plotted on the chart.
Warning signals: The indicator can also be used to identify potential warning signals. When the closing price of the asset moves above the upper band, it could indicate that the market is overbought and a potential reversal could occur. Conversely, when the closing price moves below the lower band, it could indicate that the market is oversold and a potential reversal could occur.
Contractions and expansions in the bands can provide important information to traders about potential price movements.
When the bands contract, it indicates that the market is experiencing low volatility and the price is likely to move sideways. During these periods, traders may look for other signals, such as support and resistance levels or price patterns, to determine potential entry and exit points.
On the other hand, when the bands expand, it indicates that the market is experiencing high volatility and the price is likely to move in a particular direction. Traders can use this information to identify potential trend reversals or continuation patterns. When the upper and lower bands move further apart, it indicates that the trend is becoming stronger, while when they move closer together, it indicates that the trend may be weakening.
When the price moves outside of the bands, it can also provide important information to traders. If the price moves above the upper band, it could indicate that the market is overbought and a potential reversal could occur. Conversely, if the price moves below the lower band, it could indicate that the market is oversold and a potential reversal could occur.
Very important note!
When you see contractions, please understand that it's a wonderful opportunity to pivot into position to catch a good trade because we will see an expansion after!
GKD-C Smoother Momentum MACD w/ dual DSL [Loxx]Giga Kaleidoscope GKD-C Smoother Momentum MACD w/ dual DSL is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-C Smoother Momentum MACD w/ dual DSL
What is Smoother Momentum?
Smoother Momentum is a technical indicator used to evaluate the momentum of financial assets over a specific period. It is a popular tool among traders and analysts as it helps filter out noise from the price data and provides a clearer understanding of the underlying trend. The code snippet provided is a function, smmom(), that calculates the Smoother Momentum using a combination of Exponential Moving Averages (EMAs). In the following, we will delve into the concept of Smoother Momentum, its formulation, and the rationale behind the calculations.
Smoother Momentum Formula:
The Smoother Momentum calculation involves three EMAs with different smoothing factors. The function smmom() takes two input parameters: src, which represents the source data (such as price), and per, which represents the period for smoothing.
smmom(float src, float per)=>
float alphareg = 2.0 / (1.0 + per)
float alphadbl = 2.0 / (1.0 + math.sqrt(per))
float ema = src
float ema21 = src
float ema22 = src
if bar_index > 0
ema := nz(ema ) + alphareg * (src - nz(ema ))
ema21 := nz(ema21 ) + alphadbl * (src - nz(ema21 ))
ema22 := nz(ema22 ) + alphadbl * (ema21 - nz(ema22 ))
float out = (ema22 - ema)
out
The smoothing factors for the three EMAs are as follows:
alphareg = 2.0 / (1.0 + per)
alphadbl = 2.0 / (1.0 + sqrt(per))
These factors determine the degree of smoothing applied to the input data. The alphareg factor provides regular smoothing, while the alphadbl factor introduces a double smoothing effect.
The three EMAs are calculated as follows:
ema = src
ema21 = src
ema22 = src
For each bar index greater than zero, the EMAs are updated using the following formulas:
ema := nz(ema ) + alphareg * (src - nz(ema ))
ema21 := nz(ema21 ) + alphadbl * (src - nz(ema21 ))
ema22 := nz(ema22 ) + alphadbl * (ema21 - nz(ema22 ))
The Smoother Momentum (out) is then calculated as the difference between ema22 and ema:
out = (ema22 - ema)
Rationale Behind Smoother Momentum:
The Smoother Momentum indicator is designed to provide a refined view of an asset's momentum by employing multiple levels of smoothing. By incorporating the regular EMA (ema) and the double smoothed EMAs (ema21 and ema22), the indicator minimizes the impact of price fluctuations, resulting in a smoother momentum line.
The use of different smoothing factors allows the indicator to capture both short-term and long-term price movements, making it a valuable tool for various trading strategies. The Smoother Momentum provides traders with a better understanding of the underlying trend and helps them identify potential entry and exit points.
Smoother Momentum is a powerful technical indicator that offers valuable insights into an asset's momentum by leveraging a combination of Exponential Moving Averages with different smoothing factors. The smmom() function is an efficient implementation of the Smoother Momentum indicator, providing traders and analysts with a clear and concise view of the asset's underlying trend. By incorporating this indicator into their trading strategies, market participants can make more informed decisions and improve their overall performance.
What is the Moving Average Convergence Divergence (MACD)?
The Moving Average Convergence Divergence (MACD) is a widely-used technical indicator that measures the relationship between two Exponential Moving Averages (EMAs) of an asset's price. Developed by Gerald Appel in the 1970s, the MACD is employed by traders and analysts to identify trend reversals, bullish or bearish momentum, and potential entry or exit points in the market. This following will provide an in-depth understanding of the MACD, its formulation, and the rationale behind its calculations.
MACD Formula:
The MACD is derived from two Exponential Moving Averages of different periods, usually 12 and 26. The MACD line is calculated as the difference between the short-term (12-period) EMA and the long-term (26-period) EMA. Alongside the MACD line, a signal line, typically a 9-period EMA of the MACD line, is calculated. The interaction between the MACD line and the signal line forms the basis for generating trading signals.
Here are the formulas for calculating the MACD components:
1. Short-term EMA (12-period): EMA_short = EMA(price, 12)
2. Long-term EMA (26-period): EMA_long = EMA(price, 26)
3. MACD Line: MACD = EMA_short - EMA_long
4. Signal Line (9-period EMA of MACD): Signal = EMA(MACD, 9)
5. Additionally, the MACD Histogram represents the difference between the MACD line and the signal line, visualizing the degree of separation between the two lines.
MACD Histogram: Histogram = MACD - Signal
Rationale Behind MACD:
The MACD indicator is based on the principle that moving averages can provide insights into an asset's trend and momentum. By calculating the difference between two EMAs of different periods, the MACD line oscillates around the zero line, capturing the underlying trend and momentum of the asset. When the short-term EMA is above the long-term EMA, the MACD line is positive, indicating bullish momentum. Conversely, when the short-term EMA is below the long-term EMA, the MACD line is negative, signifying bearish momentum.
The signal line, a 9-period EMA of the MACD line, serves as a smoothing factor and a trigger for trading signals. When the MACD line crosses above the signal line, it generates a bullish signal, suggesting a potential buying opportunity. On the other hand, when the MACD line crosses below the signal line, it produces a bearish signal, indicating a possible selling opportunity.
The MACD Histogram visualizes the divergence between the MACD line and the signal line, helping traders assess the strength of the trend and the momentum. A widening histogram signifies an increasing divergence between the two lines, indicating stronger momentum, while a narrowing histogram denotes decreasing divergence, suggesting weakening momentum.
The Moving Average Convergence Divergence (MACD) is a powerful and versatile technical indicator that offers valuable insights into an asset's trend and momentum. By examining the interactions between the MACD line, the signal line, and the MACD Histogram, traders can identify potential trend reversals, bullish or bearish momentum, and entry or exit points in the market. The MACD's effectiveness in various market conditions and its compatibility with different trading strategies make it an indispensable tool for market participants seeking to make well-informed decisions and enhance their overall performance.
What is a Discontinued Signal Line (DSL)?
Many indicators employ signal lines to more easily identify trends or desired states of the indicator. The concept of a signal line is straightforward: by comparing a value to its smoothed, slightly lagging state, one can determine the current momentum or state.
The Discontinued Signal Line builds on this fundamental idea by extending it: rather than having a single signal line, multiple lines are used based on the indicator's current value.
The "signal" line is calculated as follows:
When a specific level is crossed in the desired direction, the EMA of that value is calculated for the intended signal line.
When that level is crossed in the opposite direction, the previous "signal" line value is "inherited," becoming a sort of level.
This approach combines signal lines and levels, aiming to integrate the advantages of both methods.
In essence, DSL enhances the signal line concept by inheriting the previous signal line's value and converting it into a level.
You can select between anchored and unanchored DSL, as well as utilize zero-line crosses without DSL.
What is the Smoother Momentum MACD w/ dual DSL?
This indicator uses the Smoother Momentum algorithm to calculate a MACD. Signals are created by middle crosses, signal crosses, or DSL crosses.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Smoother Momentum MACD w/ dual DSL as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
]█ Setting up the GKD
The GKD system involves chaining indicators together. These are the steps to set this up.
Use a GKD-C indicator alone on a chart
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
Use a GKD-V indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Use a GKD-B indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Baseline (Baseline, Backtest)
1. Import the GKD-B Baseline into the GKD-BT Backtest: "Input into Volatility/Volume or Backtest (Baseline testing)"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline"
Volatility/Volume (Volatility/Volume, Backte st)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Solo"
2. Inside the GKD-V indicator, change the "Signal Type" setting to "Crossing" (neither traditional nor both can be backtested)
3. Import the GKD-V indicator into the GKD-BT Backtest: "Input into C1 or Backtest"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Volatility/Volume"
5. Inside the GKD-BT Backtest, a) change the setting "Backtest Type" to "Trading" if using a directional GKD-V indicator; or, b) change the setting "Backtest Type" to "Full" if using a directional or non-directional GKD-V indicator (non-directional GKD-V can only test Longs and Shorts separately)
6. If "Backtest Type" is set to "Full": Inside the GKD-BT Backtest, change the setting "Backtest Side" to "Long" or "Short
7. If "Backtest Type" is set to "Full": To allow the system to open multiple orders at one time so you test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Solo Confirmation Simple (Confirmation, Backtest)
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
1. Import the GKD-C indicator into the GKD-BT Backtest: "Input into Backtest"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Solo Confirmation Simple"
Solo Confirmation Complex without Exits (Baseline, Volatility/Volume, Confirmation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
6. Import the GKD-C into the GKD-BT Backtest: "Input into Exit or Backtest"
Solo Confirmation Complex with Exits (Baseline, Volatility/Volume, Confirmation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Import the GKD-C indicator into the GKD-E indicator: "Input into Exit"
6. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
7. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Full GKD without Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
9. Import the GKD-E into the GKD-BT Backtest: "Input into Exit or Backtest"
Full GKD with Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Import the GKD-C Continuation indicator into the GKD-E indicator: "Input into Exit"
9. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
10. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Baseline + Volatility/Volume (Baseline, Volatility/Volume, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Baseline + Volatility/Volume"
2. Inside the GKD-V indicator, make sure the "Signal Type" setting is set to "Traditional"
3. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline + Volatility/Volume"
5. Import the GKD-V into the GKD-BT Backtest: "Input into C1 or Backtest"
6. Inside the GKD-BT Backtest, change the setting "Backtest Type" to "Full". For this backtest, you must test Longs and Shorts separately
7. To allow the system to open multiple orders at one time so you can test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Requirements
Inputs
Confirmation 1: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Continuation: GKD-C Confirmation indicator
Solo Confirmation Simple: GKD-B Baseline
Solo Confirmation Complex: GKD-V Volatility / Volume indicator
Solo Confirmation Super Complex: GKD-V Volatility / Volume indicator
Stacked 1: None
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 1
Outputs
Confirmation 1: GKD-C Confirmation 2 indicator
Confirmation 2: GKD-C Continuation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest
Solo Confirmation Complex: GKD-BT Backtest or GKD-E Exit indicator
Solo Confirmation Super Complex: GKD-C Continuation indicator
Stacked 1: GKD-C, GKD-V, or GKD-B Stacked 2+
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 2+ or GKD-BT Backtest
Additional features will be added in future releases.
Trend and Momentum DashboardI created this indicator to tell me when it's time to trade (going long) and when it's time to wait (or going short).
You can enter up to 13 ticker (default is S&P500 and key market segments).
For each ticker, fibonacci levels are calculated and represented either in 5 color or 3 color mode as single lines.
(Thanks to eykpunter for the fibonacci level implementation. I'm using his code and modified it slightly).
Color coding (5 color mode) explanation:
blue = in uptrend area
light blue = in prudent buyers area
gray = in center area
light red = in prudent sellers area
red = in downtrend area
The topline is a combination of all ticker and shows if the market is either bullish or bearish (threshold adjustable in settings)
The bullish/bearish trend can also be used as background color. Alternatively the last bar in the selected time period is been highlighted.
How to use it:
The indicator works on all timeframes. Use the color coding explanation above to see the status of each asset.
a) You can evaluate "long" term trend using day or week timeframe. e.g. I'm usually trading only long and stay out of the market when it is not bullish (top line & background = blue). I'm also using it to know which segments/assets are currently "hot".
b) You can evaluate short term momentum (using 1h or lower timeframe) and see in which direction the market/assets are moving. e.g. I use this when the exchanges open to see how the day is going to move.
I've attached 3 examples in the screenshot - first is the default, in the second one I'm using different asset classes and the third one is for crypto.
Limitations:
There are security request limits as well as string limitations for the security calls in pine script, so I went to the maximum what is currently possible.
(No financial advise, for testing purposes only)
WaveRider Momentum OscillatorWaveRider Momentum Oscillator
The WaveRider Momentum Oscillator applies principles inspired by fluid dynamics to model price momentum as a flowing system, rather than relying on traditional static calculations. By interpreting market movement through the lens of velocity, viscosity, and turbulence—core concepts in fluid mechanics—this indicator offers a more adaptive and nuanced view of momentum that adjusts dynamically to changing market conditions.
Conceptual Foundation
Velocity: Just as fluid velocity measures the speed of flow at a point, WaveRider calculates momentum velocity by measuring the rate of price change over a specified period, smoothed to reduce noise.
Viscosity: In fluid dynamics, viscosity represents internal friction that resists flow. Here, viscosity is modeled based on volatility, modulating momentum signals to account for the “thickness” or noise level of the market. High volatility increases viscosity’s damping effect, reducing false signals during turbulent price action.
Turbulence: Turbulence characterizes sudden, chaotic changes in fluid flow. WaveRider detects rapid acceleration bursts in momentum analogous to turbulence, highlighting moments when momentum is shifting sharply and potentially signaling strong upcoming price moves.
Technical Features and Interpretation
Adaptive Momentum Calculation: Momentum is scaled by volatility-adjusted viscosity, making the oscillator less prone to whipsaws and more responsive during stable trends.
Turbulence Burst Detection: The oscillator incorporates a turbulence factor, identifying abrupt momentum accelerations that traditional oscillators often miss. This feature provides early warning signals of potential breakout or reversal points.
HSV Gradient Color Mapping: The oscillator visualizes acceleration using a continuous hue gradient—ranging from red (deceleration) through yellow (neutral) to green (acceleration). This continuous color transition provides intuitive, real-time insight into momentum dynamics beyond mere numeric values.
Pivot Point Identification: WaveRider automatically marks momentum pivots, signaling local maxima and minima in momentum flow. These points serve as critical confirmation markers for potential entry and exit decisions.
How to Interpret WaveRider
Colors:
Green hues indicate positive acceleration — momentum is increasing, favoring bullish positions.
Yellow hues represent neutral momentum — the market is consolidating or pausing.
Red hues signal negative acceleration — momentum is weakening, suggesting caution or bearish bias.
Oscillator Direction:
An upward sloping oscillator line reflects strengthening momentum.
A downward slope indicates weakening momentum or a potential reversal.
Pivot Labels:
▲ (Pivot Low): Denotes local momentum troughs; potential points to consider initiating long positions.
▼ (Pivot High): Marks local momentum peaks; useful for identifying possible short entries or profit-taking zones.
Summary
By grounding momentum analysis in fluid dynamics, WaveRider transcends the limitations of traditional oscillators. It accounts for the market’s inherent volatility and captures real-time acceleration changes, enabling traders to detect meaningful momentum shifts with greater accuracy and clarity.
WaveRider is designed for traders seeking a scientifically informed tool that adapts fluidly with market conditions—offering deeper insight into momentum flow and better timing for entries and exits.
