[Sharpe projection SGM]Dynamic Support and Resistance: Traces adjustable support and resistance lines based on historical prices, signaling new market barriers.
Price Projections and Volatility: Calculates future price projections using moving averages and plots annualized standard deviation-based volatility bands to anticipate price dispersion.
Intuitive Coloring: Colors between support and resistance lines show up or down trends, making it easy to analyze quickly.
Analytics Dashboard: Displays key metrics such as the Sharpe Ratio, which measures average ROI adjusted for asset volatility
Volatility Management for Options Trading: The script helps evaluate strike prices and strategies for options, based on support and resistance levels and projected volatility.
Importance of Diversification: It is necessary to diversify investments to reduce risks and stabilize returns.
Disclaimer on Past Performance: Past performance does not guarantee future results, projections should be supplemented with other analyses.
The script settings can be adjusted according to the specific needs of each user.
The mean and standard deviation are two fundamental statistical concepts often represented in a Gaussian curve, or normal distribution. Here's a quick little lesson on these concepts:
Average
The mean (or arithmetic mean) is the result of the sum of all values in a data set divided by the total number of values. In a data distribution, it represents the center of gravity of the data points.
Standard Deviation
The standard deviation measures the dispersion of the data relative to its mean. A low standard deviation indicates that the data is clustered near the mean, while a high standard deviation shows that it is more spread out.
Gaussian curve
The Gaussian curve or normal distribution is a graphical representation showing the probability of distribution of data. It has the shape of a symmetrical bell centered on the middle. The width of the curve is determined by the standard deviation.
68-95-99.7 rule (rule of thumb): Approximately 68% of the data is within one standard deviation of the mean, 95% is within two standard deviations, and 99.7% is within three standard deviations.
In statistics, understanding the mean and standard deviation allows you to infer a lot about the nature of the data and its trends, and the Gaussian curve provides an intuitive visualization of this information.
In finance, it is crucial to remember that data dispersion can be more random and unpredictable than traditional statistical models like the normal distribution suggest. Financial markets are often affected by unforeseen events or changes in investor behavior, which can result in return distributions with wider standard deviations or non-symmetrical distributions.
Cerca negli script per "curve"
The Investment ClockThe Investment Clock was most likely introduced to the general public in a research paper distributed by Merrill Lynch. It’s a simple yet useful framework for understanding the various stages of the US economic cycle and which asset classes perform best in each stage.
The Investment Clock splits the business cycle into four phases, where each phase is comprised of the orientation of growth and inflation relative to their sustainable levels:
Reflation phase (6:01 to 8:59): Growth is sluggish and inflation is low. This phase occurs during the heart of a bear market. The economy is plagued by excess capacity and falling demand. This keeps commodity prices low and pulls down inflation. The yield curve steepens as the central bank lowers short-term rates in an attempt to stimulate growth and inflation. Bonds are the best asset class in this phase.
Recovery phase (9:01 to 11:59): The central bank’s easing takes effect and begins driving growth to above the trend rate. Though growth picks up, inflation remains low because there’s still excess capacity. Rising growth and low inflation are the Goldilocks phase of every cycle. Stocks are the best asset class in this phase.
Overheat phase(12:01 to 2:59): Productivity growth slows and the GDP gap closes causing the economy to bump up against supply constraints. This causes inflation to rise. Rising inflation spurs the central banks to hike rates. As a result, the yield curve begins flattening. With high growth and high inflation, stocks still perform but not as well as in recovery. Volatility returns as bond yields rise and stocks compete with higher yields for capital flows. In this phase, commodities are the best asset class.
Stagflation phase (3:01 to 5:59): GDP growth slows but inflation remains high (sidenote: most bear markets are preceded by a 100%+ increase in the price of oil which drives inflation up and causes central banks to tighten). Productivity dives and a wage-price spiral develops as companies raise prices to protect compressing margins. This goes on until there’s a steep rise in unemployment which breaks the cycle. Central banks keep rates high until they reign in inflation. This causes the yield curve to invert. During this phase, cash is the best asset.
Additional notes from Merrill Lynch:
Cyclicality: When growth is accelerating (12 o'clock), Stocks and Commodities do well. Cyclical sectors like Tech or Steel outperform. When growth is slowing (6 o'clock), Bonds, Cash, and defensives outperform.
Duration: When inflation is falling (9 o'clock), discount rates drop and financial assets do well. Investors pay up for long duration Growth stocks. When inflation is rising (3 o'clock), real assets like Commodities and Cash do best. Pricing power is plentiful and short-duration Value stocks outperform.
Interest Rate-Sensitives: Banks and Consumer Discretionary stocks are interest-rate sensitive “early cycle” performers, doing best in Reflation and Recovery when central banks are easing and growth is starting to recover.
Asset Plays: Some sectors are linked to the performance of an underlying asset. Insurance stocks and Investment Banks are often bond or equity price sensitive, doing well in the Reflation or Recovery phases. Mining stocks are metal price-sensitive, doing well during an Overheat.
About the indicator:
This indicator suggests iShares ETFs for sector rotation analysis. There are likely other ETFs to consider which have lower fees and are outperforming their sector peers.
You may get errors if your chart is set to a different timeframe & ticker other than 1d for symbol/tickers GDPC1 or CPILFESL.
Investment Clock settings are based on a "sustainable level" of growth and inflation, which are each slightly subjective depending on the economist and probably have changed since the last time this indicator was updated. Hence, the sustainable levels are customizable in the settings. When I was formally educated I was trained to use average CPI of 3.1% for financial planning purposes, the default for the indicator is 2.5%, and the Medium article backtested and optimized a 2% sustainable inflation rate. Again, user-defined sustainable growth and rates are slightly subjective and will affect results.
I have not been trained or even had much experience with MetaTrader code, which is how this indicator was originally coded. See the original Medium article that inspired this indicator if you want to audit & compare code.
Hover over info panel for detailed information.
Features: Advanced info panel that performs Investment Clock analysis and offers additional hover info such as sector rotation suggestions. Customizable sustainable levels, growth input, and inflation input. Phase background coloring.
⚠ DISCLAIMER: Not financial advice. Not a trading system. DYOR. I am not affiliated with Medium, Macro Ops, iShares, or Merrill Lynch.
About the Author: I am a patent-holding inventor, a futures trader, a hobby PineScripter, and a former FINRA Registered Representative.
Delta Agnostic Correlation CoefficientVisually see how well a symbol tracks another's movements, without taking price deltas into account.
For example, a 1% move on the index and a 5% move on the target will return a DCC value of 1. An index move of 0.5% on the index and a 10% move on the target will also return a DCC value of 1. The same happens for downward moves.
The SMA value can be set to smooth the curve. A larger value creates a smoother curve.
Gold24fx IndicatorGold24fx
Class : hybrid – trend oscillator
Trading type : scalping
Time frame : 5 min
Purpose : detection of optimal buy entry points
Level of aggressiveness : high
Indicator « Gold24fx » was developed for scalping trading in Gold market. It can be used to define optimal buy entry points when the bullish sentiments prevail.
Indicator « Gold24fx » is based on unique author algorithm. It allows to provide quantitative assessments of current market sentiments as well as to visualize them. Also «Gold24fx» can detect divergences between current market price and fair value of the Gold for a specific moment of time. Local undervaluation of the Gold is a reason to generate a buy signal in situation when market is controlled by the bulls.
Thus Indicator «Gold24fx» provides sufficient data to the trader for the successful trading in the Gold market.
Structure of the indicator
Indicator consists of the following elements:
- Market sentiments curve – is presented with 3 color gammas: blue color (bullish sentiments are dominating), red color (bearish sentiments are dominating), green color (flat is present in the market),
- Red cross on the curve – a signal in favor of contraindications for buy trades. Can be related with bearish sentiments in the market or local overbought of the Gold;
- Green triangle on the curve – is a buy signal for short term Gold trades. It appears when the bullish market sentiments are prevail and asset is temporarily undervalued.
Rules of trading
Rules of trading are very simple. Blue color of the curve evidences in favor of bullish market sentiments. When the buy signal appears (green triangle on the curve) long position in Gold should be opened.
Fluxion Oscillator [Kodexius]Fluxion Oscillator is a multi dimensional momentum and flow toolkit designed to highlight exhaustion, reversals and confluence in a very compact way. The script combines a normalized trend oscillator, volume sensitive money movement, a volatility gauge and a visual confluence gauge that all sit in a single pane.
Instead of focusing on a single signal, Fluxion looks at the interaction between price, momentum and volume. The core oscillator tracks the relationship between a fast and a slow response of price, then rescales it into a stable 0 to 100 band. A companion flow line tracks how actively price is being supported or pressured by volume. On top of that, a volatility based gauge and an overbought or oversold reversal layer help highlight when moves are stretched and vulnerable.
The result is an environment where you can quickly see:
-When momentum is expanding or fading
-When price swings are supported or rejected by volume
-Where local tops or bottoms can be forming through divergence
-How strong the current push is in the context of recent volatility
-A compact gauge that visually ranks the current state from “minimum” to “maximum” pressure
It is not a trading system by itself, but a framework that makes it much easier to build rules and confluence around your own strategy.
⭐ Features
Normalized Fluxion Oscillator
Core oscillator built from the difference between a fast and a slow smoothing of the chosen source.
Automatically normalized into a bounded range so it behaves consistently across symbols and timeframes.
Dual line structure: the main line and a signal line, making crossovers easy to read.
Dynamic fill that shifts color depending on whether the main line is above or below the signal line.
Bullish and Bearish Crosses
Visual circles highlighting when the main oscillator crosses its signal line upward or downward.
Bullish crosses emphasize potential momentum ignition after downside pressure.
Bearish crosses emphasize potential cooling of momentum after upside pressure.
Money Flow Layer
Separate line that blends price and volume over a configurable lookback.
Smoothed to reduce noise and plotted around a central balance level.
Colored region that clearly shows whether buying pressure or selling pressure dominates.
Divergence Detection Suite
Automatic detection of regular bullish and regular bearish divergences between price and the normalized oscillator.
Optional hidden bullish and hidden bearish divergences for continuation setups.
Uses pivot based swing points so the lines attach to meaningful highs and lows instead of random wiggles.
All divergence types can be toggled independently so you can keep the chart as clean as you like.
Volatility and Positioning Gauge
A compact gauge that evaluates where the current price sits relative to a volume weighted average and its recent typical fluctuation.
Colors shift as price moves from neutral to stretched zones in either direction.
Background highlighting above and below the oscillator scale to reflect when this gauge is in an extreme region.
Helps quickly see whether you are buying into strength after a large extension or stepping in near value.
Reversal Signals With Volume Confirmation
A higher time sensitivity reversal metric based on a 0 to 100 scale of recent price changes.
Signals are only highlighted when there is also a short burst in volume, so quiet market noise is reduced.
Bearish reversal markers appear in the upper region, bullish markers in the lower region, giving a clear visual “top” and “bottom” feel.
Confluence Gauge
Right side grid composed of horizontal bands, from “Min” at the bottom to “Max” at the top.
Each band reflects a segment of a smoothed, range based momentum reading that tracks how far price has advanced within its recent 0 to 100 window.
The currently active band is highlighted in green for bullish momentum or red for bearish momentum, depending on the relationship between fast and slow lines within that range.
A pointer and labels make it obvious where the current environment sits relative to the full range of possible conditions.
Divergence Core
Users can define the pivot length to control how strict and how far apart swing points should be.
High Customization
Adjustable lookback lengths for the core oscillator, signal smoothing and normalization.
Separate controls for money flow length and smoothing.
Optional toggles for each divergence type so you can focus only on the structures you care about.
⭐ Calculations
This section explains conceptually how Fluxion works without exposing the full underlying formula details. The goal is to help you understand what each component represents and how it behaves, so you can use it more effectively.
Fluxion Oscillator Core
The foundation of the indicator is the difference between two smoothed versions of the selected price source. One reacts more quickly to new price information, the other reacts more slowly.
When the fast curve is above the slow curve, the oscillator becomes positive, signaling that short term action is advancing faster than the background trend. When the fast curve is below the slow curve, it becomes negative, indicating short term weakness.
This raw difference is then normalized over a rolling window. The highest and lowest values in that window are used to rescale the oscillator into a 0 to 100 band. This produces a stable, comparable scale across markets and timeframes.
A secondary smoothing of the oscillator creates the signal line. The interaction between the main line and this signal is used to color the fill region and locate cross events.
Money Flow Construction
The money flow line is based on how price closes within its candle range combined with the traded volume. Up candles with strong closes and high volume contribute positively, while down candles with weak closes and high volume contribute negatively.
These contributions are aggregated over a configurable period to create a net “pressure” measure. The result represents how aggressively participants have been positioning over that window, not just whether price went up or down.
The line is then smoothed to reduce micro noise and plotted around a central balance level, here set at 50. Values above the balance zone suggest net positive pressure, values below suggest net negative pressure.
An additional internal threshold is used to detect when this pressure stays on one side of the balance area long enough to be considered an “overflow,” which helps detect sustained accumulation or distribution phases.
Volatility and Positioning Gauge
The gauge computes a volume weighted average price over a user defined period. This gives more weight to prices at which more volume was traded.
It then evaluates how far the current price is from that volume weighted center, relative to the typical price variation around it. This creates a standardized distance measure that tells you how stretched price is from its recent fair zone.
When the distance becomes significantly positive, the market is considered extended upward. When it becomes significantly negative, it is extended downward. Intermediate thresholds are used to create “warning” and “extreme” zones.
Background fills at the top and bottom of the panel change based on this standardized distance, visually indicating when the market is moving into overextended territory that often precedes mean reversion or at least slowing of the move.
Reversal Metric With Volume Filter
A separate 0 to 100 style momentum score is calculated over a mid length window. It evaluates recent gains and losses in price to produce a relative strength measure of the current move.
Upper and lower thresholds on this score are used to mark areas where price action is historically stretched to the upside or downside.
This alone would generate many signals, so a volume based filter is added. Reversal markers are only displayed when this momentum score is in an extreme area and volume has shown a short term pickup.
This combination gives more weight to reversals that occur during active trading, where trapped positions and forced unwinds are more likely.
Divergence Engine
The divergence logic scans for swing highs and swing lows in the normalized oscillator and in price. Swing points are defined by requiring a certain number of bars on both sides of the pivot, which you can configure via the divergence length input.
For regular bullish divergence:
Price makes a lower low, indicating apparent weakness.
The oscillator makes a higher low over the same general region, indicating that internal momentum is actually improving.
If both conditions are met within a valid bar distance, a bullish divergence line is drawn from the prior oscillator pivot to the new one.
For regular bearish divergence:
Price makes a higher high, suggesting continued strength.
The oscillator makes a lower high, showing that underlying momentum is waning.
The engine checks that both pivot structures appear within an allowed time frame, then draws a bearish line between the oscillator peaks.
Hidden divergences are handled in a similar way, except the direction of price and oscillator swings is reversed, which makes them suitable for trend continuation contexts instead of reversal contexts.
Confluence Gauge
The grid on the right converts a smoothed, range based momentum reading into ten equal bands. This momentum reading looks at where the current value sits between the lowest and highest readings of a recent window, then rescales it into a 0 to 100 scale.
That 0 to 100 value is divided into ten slices of ten points each. For example, 0 to 10 is the lowest band, 90 to 100 is the top band.
The algorithm then checks whether the fast component of this reading is above or below its slower companion. If fast is above slow, it is treated as bullish pressure and the active band is colored in green. If fast is below slow, it is treated as bearish pressure and the active band is colored in red.
A pointer label is placed alongside the active band and “Max” and “Min” markers are drawn above and below the grid. This creates a compact visual where you can quickly gauge if the current state is closer to the lower boundary of recent conditions or to the upper boundary, along with its directional bias.
Normalization And Scaling
Several internal components use rolling highest and lowest values to transform raw readings into normalized percentages. This includes the main oscillator and the range based momentum used by the confluence gauge.
The key idea is to express conditions relative to what has recently been possible on that instrument and timeframe instead of using absolute fixed thresholds. This makes Fluxion adaptive and more robust when switching between assets with different volatility profiles.
Macro Return ForecastWhen the macro environment was similar, what annualized return did the market usually deliver next?
Before using the indicator, make sure your chart is set to any US-market symbol (SPX, QQQ, DIA, etc.).
This requirement is simple: the indicator pulls macro series from US data (yields, TIPS, credit spreads, breadth of US indices).
Because these series are independent from the chart’s price series, the chart symbol itself does not affect the internal calculations.
Any US symbol works, and the output of the model will be identical as long as you are on a US asset with daily, weekly or monthly timeframe.
The plotted price does not matter: the macro engine is fully exogenous to the chart symbol.
1. What the indicator does relative to selected assets
In the settings you choose which market you want to analyze:
- S&P500
- Nasdaq or NQ100
- Dow Jones
- Russell 2000
- US-wide (VTI)
- S&P500 sectors (XLF, XLY, XLP, etc.)
For each one, the indicator loads:
- Its internal breadth series (percentage of constituents above MA200)
- Its price history to compute forward log-returns at multiple horizons
- Its regime position relative to its own MA200 (for bull/bear filtering)
This means the tool is not tied to the chart symbol you display.
If your chart is SPX but the indicator setting is “S&P500 Technology”, the expected return projection is computed for the Technology sector using its own data, not the chart’s data.
You can therefore:
- Visualize macro-driven expected returns for any major US index or sector.
- Compare how different parts of the market historically reacted to similar macro states.
- Switch assets instantly to see which segment historically behaved better in comparable macro conditions.
The indicator becomes an analyzer of macro sensitivity, not a chart-dependent indicator.
2. Method overview
The model answers a statistical question:
“When macro conditions looked like they do today, what forward annualized return did this asset usually deliver?”
To do this it combines four macro pillars:
- Market breadth of the selected asset
- Yield curve slope (US 10Y minus 2Y)
- US credit spread (high yield minus gov)
- US real rate (TIPS 10Y)
It normalizes each metric into a 0–100 score, groups similar historical states into bins, and examines what the asset did next across six horizons (from ~9 months to ~5 years).
This produces a historical map connecting macro states to realized forward returns.
It is not a forecast model.
It is a conditional-distribution estimator: it tells you what has historically happened from similar setups.
3. Why this produces useful insights on assets
For any chosen asset (SPX, Nasdaq, sectors…), the indicator computes:
- Its forward return distribution in similar macro states.
- How often these states occurred (n).
- Whether the macro environment that preceded positive returns in the past resembles today’s.
- Whether the asset tends to be more sensitive or more resilient than the broad index under given macro configurations.
- Whether a given sector historically benefited from specific yield-curve, credit or real-rate environments.
This lets you answer questions such as:
- Does this sector usually outperform in an inverted yield curve environment?
- Does the Nasdaq historically recover strongly after breadth collapses?
- How did the S&P500 behave historically when real rates were this high?
- Is today’s credit-spread environment typically associated with positive or negative forward returns for this index?
These insights are not predictions but statistical context backed by past market behavior.
4. Why the technique is robust (and why it matters)
The engine uses strict, non-optimistic data processing:
- Winsorization of returns to neutralize extreme outliers without deleting information.
- Shrinkage estimators to avoid overfitting when bins contain few occurrences.
- Adaptive or static bounds for scaling macro indicators, ensuring comparability across cycles.
- Inverse-variance weighting of horizons with penalties for horizon redundancy.
- HAC-style adjustments to reduce autocorrelation bias in return estimation.
Each method aims to prevent artificial inflation of expected-return values and to keep the estimator stable even in unusual macro states.
This produces a result that is not “optimistic”, not curve-fit, not dependent on chart tricks, and not sensitive to isolated historical anomalies.
5. What you get as a user
A single clean line:
Expected Annual Return (%)
This line reflects how the chosen asset historically performed after macro environments similar to today’s.
The color gradient and confidence indicator (n) show the density of comparable episodes in history.
This makes the output extremely simple to read:
- High, stable expectation: historically supportive macro environment.
- Low or negative expectation: historically weaker environments.
- Low confidence: the macro state is rare and historical comparisons are limited.
The tool therefore adds context, not signals.
It helps you understand the environment the asset is currently in, based on how markets behaved in similar conditions across US market history.
Trapper Absolute PriceActionThe Trapper Absolute PriceAction (TAPA) indicator is a custom, momentum-based oscillator designed to help traders visually read shifts in bullish and bearish price strength — with no reliance on volume or external data.
TAPA calculates and smooths both bullish and bearish momentum using multiple methods (RSI, Stochastic, or ADX) and compares their relative strength in real time. The result is a clean dual-line oscillator with color-coded histograms that highlight which side of the market currently has control.
It was built to give traders a sniper-level precision tool for detecting early momentum shifts before they appear clearly on price charts, allowing confirmation or invalidation of setups faster than with lagging indicators.
How It Works
Momentum Strength Calculation
The script measures directional price movement across the chosen mode (RSI, Stochastic, or ADX).
These values are smoothed twice using a selectable moving average type (WMA, EMA, SMA, ALMA, HMA, etc.).
Bullish & Bearish Curves
The green line represents smoothed bullish momentum (SmthBulls).
The orange/red line represents smoothed bearish momentum (SmthBears).
Histogram Strength Visualization
The distance between the two curves forms a color-coded histogram.
Green/Lime bars indicate growing bullish control, while Orange/Red bars show bearish dominance.
A gray neutral zone reflects indecision or range-bound conditions.
Signal Triggers
BUY 🐂 appears when the green line crosses up through the orange — signaling a bullish momentum flip.
SELL 🐻 appears when the green line crosses down through the orange — signaling bearish control.
Alerts can be enabled directly in TradingView through the BUY (🐂) or SELL (🐻) alert conditions for automated notifications or integrations.
How to Use
1. Confirm Early Momentum Shifts
When a crossover appears, check that the histogram color supports the move (green shades for bullish, red/orange for bearish).
Avoid signals when both lines are tangled and the histogram alternates gray, that usually indicates consolidation or low volatility.
2. Validate with Higher-Timeframe Structure
TAPA is most powerful when aligned with trend structure from higher timeframes.
Example: A bullish crossover on the 1-hour timeframe, while the daily TAPA shows the green line already rising, can confirm momentum alignment before entry.
3. Combine with Support/Resistance
Mark your key support and resistance zones (manual or using your “Trapper S&R PRO” indicator).
Look for a TAPA bullish crossover occurring at a major support zone, that’s often the start of a reversal move.
4. Multi-Mode Analysis
Experiment with “Indicator Method” in the inputs:
RSI Mode - smoother and responsive for swing trading.
Stochastic Mode - better for short-term entries and exits.
ADX Mode - captures trending momentum on strong breakouts.
Examples
Bullish Example:
Price forms a higher low on the chart while TAPA’s green line crosses up through orange with a lime/green histogram. That’s a strong early signal that momentum is reversing before price confirms on structure.
Bearish Example:
Price rallies into resistance, then TAPA shows a red histogram and a bearish cross (green dropping under orange). That’s typically a high-probability short signal once structure breaks.
What Makes TAPA Different
No Volume Dependency: Focuses purely on price behavior, not volume spikes or anomalies.
Multi-Mode Engine: Switch between RSI, Stochastic, or ADX-style momentum math instantly.
Customizable Visuals: Editable histogram color layers (weak/strong bull/bear, neutral) and line color control.
Sniper Labeling System: Clean, minimal BUY/SELL cues at each verified crossover.
Alert-Ready: Built-in conditions allow for TradingView alerts, webhooks, or bot automation.
Modernized Core: Rebuilt in Pine v6 with optimized performance and compliance to TradingView standards.
TAPA is designed to filter out the noise and show what truly drives a move — the shift in control between buyers and sellers.
Best Pairing Indicators
To get maximum clarity and confluence:
Trapper Support & Resistance PRO
Helps identify key zones where momentum flips from TAPA have the most impact. A bullish crossover at a defined support level often marks an early trend reversal.
Trapper Volume Trigger
While TAPA doesn’t use volume internally, pairing it with a volume-based trigger confirms that momentum shifts have institutional participation.
Simple Moving Averages (5, 20, or 50)
Overlay short and mid-term SMAs on your chart to confirm directional bias. A bullish TAPA cross that aligns with SMA-5 crossing above SMA-20 increases reliability.
Disclaimer
This indicator is provided for educational and analytical purposes only.
It does not constitute financial advice or a recommendation to buy or sell any security.
Always conduct your own due diligence and practice proper risk management before trading any strategy.
© 2025 RAMS-offthecharts | “Read • Analyze • Mark • Snipe.”
TAPA is part of the RAMS ecosystem of tactical market tools, designed for traders who focus on precision, discipline, and momentum awareness.
[boitl] Trendfilter🧭 Trend Filter – Curve View (1D / 1H + M15 Check)
A multi-timeframe trend filter that blends daily, hourly, and 15-minute data into a smooth, color-coded curve displayed in a separate panel.
It visualizes both trend direction and strength while accounting for overextension, providing a reliable “context indicator” for entries and filters.
🔍 Concept
The indicator evaluates three timeframes:
1D (Daily) → SMA200 for long-term trend bias
1H (Hourly) → EMA50 for medium-term confirmation
15M (Intraday) → EMA20 + ATR to detect overextension or mean reversion zones
It computes a continuous trend score between −1 and +1:
+1 → Strong bullish alignment (D1 & H1 both up)
−1 → Strong bearish alignment (D1 & H1 both down)
≈ 0 → Neutral, conflicting, or overextended conditions
The score is smoothed and normalized for a clean visual curve —
green for bullish, red for bearish, with dynamic transparency based on strength.
⚙️ Logic Overview
Timeframe Indicator Purpose
1D SMA200 Long-term trend direction
1H EMA50 Medium-term confirmation
15M EMA20 + ATR Overextension control
Alignment between D1 and H1 defines clear trend bias
Conflicts between them reduce the trend score
M15 overextension (price far from EMA20) softens the signal further
The result is a responsive trend-strength oscillator, ideal for multi-timeframe setups.
🧩 Use Cases
As a trend filter for strategies (e.g. allow entries only if score > 0.3 or < −0.3)
As a visual confirmation of higher-timeframe direction
To avoid trades during conflict or exhaustion
💡 Visualization
Single curve (area plot):
Green = bullish bias
Red = bearish bias
Transparency increases with weaker trend
Background colors:
🟠 Orange → D1/H1 conflict
🔴 Light red → M15 overextension active
Optional: binary alignment line (+1 / 0 / −1) for simplified display
⚙️ Parameters
Proximity to EMA20 (M15) = X×ATR → defines “near” condition
Overextension threshold = X×ATR → sets exhaustion boundary
EMA smoothing → reduces noise for a smoother score
Toggle overextension impact on/off
Triple Gaussian Smoothed Ribbon [BOSWaves]Triple Gaussian Smoothed Ribbon – Adaptive Gaussian Framework
Overview
The Triple Gaussian Smoothed Ribbon is a next-generation market visualization framework built on the principles of Gaussian filtering - a mathematical model from digital signal processing designed to remove noise while preserving the integrity of the underlying trend.
Unlike conventional moving averages that suffer from phase lag and overreaction to volatility spikes, Gaussian smoothing produces a symmetrical, low-lag curve that isolates meaningful directional shifts with exceptional clarity.
Developed under the Adaptive Gaussian Framework, this indicator extends the classical Gaussian model into a multi-stage smoothing and visualization system. By layering three progressive Gaussian filters and rendering their interactions as a gradient-based ribbon field, it translates market energy into a coherent, visually structured trend environment. Each ribbon layer represents a progressively smoothed component of price motion, producing a high-fidelity gradient field that evolves in sync with real-time trend strength and momentum.
The result is a uniquely fluid trend and reversal detection system - one that feels organic, adapts seamlessly across timeframes, and reveals hidden transitions in market structure long before traditional indicators confirm them.
Theoretical Foundation
The Gaussian filter, derived from the Gaussian function developed by Carl Friedrich Gauss in 1809, operates on the principle of weighted symmetry, assigning higher importance to central price data while tapering influence toward historical extremes following a bell-curve distribution. This symmetrical design minimizes phase distortion and smooths without introducing lag spikes — a stark contrast to exponential or linear filters that sacrifice temporal accuracy for responsiveness.
By cascading three Gaussian stages in sequence, the indicator creates a multi-frequency decomposition of price action:
The first stage captures immediate trend transitions.
The second absorbs mid-term volatility ripples.
The third stabilizes structural directionality.
The final composite ribbon reflects the market’s dominant frequency - a smoothed yet reactive trend spine - while an independent, heavier Gaussian smoothing serves as a reference layer to gauge whether the primary motion leads or lags relative to broader market structure.
This multi-layered Gaussian framework effectively replicates the behavior of a signal-processing filter bank: isolating meaningful cyclical movements, suppressing random noise, and revealing phase shifts with minimal delay.
How It Works
Triple Gaussian Core
Price data is passed through three successive Gaussian smoothing stages, each refining the trend further and removing higher-frequency distortions.
The result is a fluid, continuously adaptive baseline that responds naturally to directional changes without overshooting or flattening key inflection points.
Adaptive Ribbon Architecture
The indicator visualizes its internal dynamics through a five-layer gradient ribbon. Each layer represents a progressively delayed Gaussian curve, creating a color field that dynamically shifts between bullish and bearish tones.
Expanding ribbons indicate accelerating momentum and trend conviction.
Compressing ribbons reflect consolidation and volatility contraction.
The smooth color gradient provides a real-time depiction of energy buildup or dissipation within the trend, making it visually clear when the market is entering a state of expansion, transition, or exhaustion.
Momentum-Weighted Opacity
Ribbon transparency adjusts according to normalized momentum strength.
As trend force builds, colors intensify and layers become more opaque, signifying conviction.
When momentum wanes, ribbons fade - an early visual cue for potential reversals or pauses in trend continuation.
Candle Gradient Integration
Optional candle coloring ties the chart’s candles to the prevailing Gaussian gradient, allowing traders to view raw price action and smoothed wave dynamics as a unified system.
This integration produces a visually coherent chart environment that communicates directional intent instantly.
Signal Detection Logic
Directional cues emerge when the smoother, broader Gaussian curve crosses the faster-reacting Gaussian line, marking structural inflection points in the filtered trend.
Bullish shifts : short-term momentum transitions upward through the long-term baseline after a localized trough.
Bearish shifts : momentum declines through the baseline following a local peak.
To maintain integrity in choppy markets, the framework applies a trend-strength and separation filter, which blocks weak or overlapping conditions where movement lacks conviction.
Interpretation
The Triple Gaussian Smoothed Ribbon provides a layered, intuitive read on market structure:
Trend Continuation : Expanding ribbons with deep color intensity confirm directional strength.
Reversal Phases : Color gradients flip direction, indicating a phase shift or exhaustion point.
Compression Zones : Tight, pale ribbons reveal equilibrium phases often preceding breakouts.
Momentum Divergence : Fading color intensity despite continued price movement signals weakening conviction.
These transitions mirror the natural ebb and flow of market energy - captured through the Gaussian filter’s ability to represent smooth curvature without distortion.
Strategy Integration
Trend Following
Engage during strong directional expansions. When ribbons widen and color gradients intensify, the trend is accelerating with high confidence.
Reversal Identification
Monitor for full gradient inversion and fading momentum opacity. These conditions often precede transitional phases and early reversals.
Breakout Anticipation
Flat, compressed ribbons signal low volatility and energy buildup. A sudden gradient expansion with renewed opacity confirms breakout initiation.
Multi-Timeframe Alignment
Use higher timeframes to establish directional bias and lower timeframes for entry during compression-to-expansion transitions.
Technical Implementation Details
Triple Gaussian Stack : Sequential smoothing stages produce low-lag, high-purity signals.
Adaptive Ribbon Rendering : Five-layer Gaussian visualization for gradient-based trend depth.
Momentum Normalization : Opacity dynamically tied to trend strength and volatility context.
Consolidation Filter : Suppresses false signals in low-energy or range-bound conditions.
Integrated Candle Mode : Optional color synchronization with underlying gradient flow.
Alert System : Built-in notifications for bullish and bearish transitions.
This structure blends the precision of digital signal processing with the readability of visual market analysis, creating a clean but information-rich framework.
Optimal Application Parameters
Asset Recommendations
Cryptocurrency : Higher smoothing and sigma for stability under volatility.
Forex : Balanced parameters for cycle identification and reduced noise.
Equities : Moderate Gaussian length for responsive yet stable trend reads.
Indices & Futures : Longer smoothing periods for structural confirmation.
Timeframe Recommendations
Scalping (1 - 5m) : Use shorter smoothing for fast reactivity.
Intraday (15m - 1h) : Mid-length Gaussian chain for balance.
Swing (4h - 1D) : Prioritize clarity and opacity-driven trend phases.
Position (Daily - Weekly) : Longer smoothing to capture macro rhythm.
Performance Characteristics
Most Effective In :
Trending markets with recurring volatility cycles.
Transitional phases where early directional confirmation is crucial.
Less Effective In:
Ultra-low volume markets with erratic tick data.
Random, micro-chop conditions with no structural flow.
Integration Guidelines
Pair with volatility or volume expansion tools for enhanced breakout confirmation.
Use ribbon compression to anticipate volatility shifts.
Align entries with gradient expansion in the dominant color direction.
Scale position size relative to opacity strength and ribbon width.
Disclaimer
The Triple Gaussian Smoothed Ribbon – Adaptive Gaussian Framework is designed as a signal visualization and trend interpretation tool, not a standalone trading system. Its accuracy depends on appropriate parameter tuning, contextual confirmation, and disciplined risk management. It should be applied as part of a comprehensive technical or algorithmic trading strategy.
Smoothise RSI indicateur BETA V1 020925H2231**Indicator Description: "Smoothise RSI indicateur BETA V1 020925H2231"**
**General Functionality:**
This advanced indicator analyzes multi-timeframe momentum using smoothed RSI values across different periods (M1 to H1) to identify trends and potential reversal points.
**Key Features:**
**📊 Multi-Timeframe Fusion:**
- Combines data from 6 different timeframes (M1, M5, M15, M30, M45, H1)
- Each timeframe has customizable smoothing parameters
- Intelligent M1 compression to fit within the M5/M15 cloud
**🎨 Clear Visualization:**
- Distinct colored curves for each timeframe
- Colored clouds between M5/M15 and M30/M45 curves
- Blue background to identify Thursdays
- RSI level lines at 20, 50, and 80
**⚡ Trading Signals:**
- **Crossover triangles**: Visual signals for M5/H1 and M5/M15 crossovers
- **Dynamic colors**: Clouds change color according to trend (green bullish, red bearish, purple neutral)
- **Special green cloud**: Activates when M5 drops below 20 RSI
**🎯 Advanced Customization:**
- Individual settings for each SMA (length and smoothing)
- Complete style options: colors, thickness, triangle sizes
- Total visual customization of all elements
**💡 Practical Use:**
- Identify multi-timeframe trends
- Spot convergences/divergences between timeframes
- Detect potential reversal points
- Confirm signals with multiple timeframes
**Target Audience:**
Experienced traders looking for multi-timeframe momentum analysis with advanced and customizable visualization.
*Ideal for swing trading and day trading across all asset classes.*
Seasonality - Multiple Timeframes📊 Seasonality - Multiple Timeframes
🎯 What This Indicator Does
This advanced seasonality indicator analyzes historical price patterns across multiple configurable timeframes and projects future seasonal behavior based on statistical averages. Unlike simple seasonal overlays, this indicator provides gap-resistant architecture specifically designed for commodity futures markets and other instruments with contract rolls.
🔧 Key Features
Multiple Timeframe Analysis
Three Independent Timeframes: Configure separate historical periods (e.g., 5Y, 10Y, 15Y) for comprehensive analysis
Individual Control: Enable/disable historical lines and projections independently for each timeframe
Color Customization: Distinct colors for historical patterns and future projections
Advanced Architecture
Gap-Resistant Design: Handles missing data and contract rolls in futures markets seamlessly
Calendar-Day Normalization: Uses 365-day calendar system for accurate seasonal comparisons
Outlier Filtering: Automatically excludes extreme price movements (>10% daily changes)
Roll Detection: Identifies and excludes contract roll periods to maintain data integrity
Real-Time Projections
Forward-Looking Analysis: Projects seasonal patterns into the future based on remaining calendar days
Configurable Projection Length: Adjust forecast period from 10 to 150 bars
Data Interpolation: Optional gap-filling for smoother seasonal curves
📈 How It Works
Data Collection Process
The indicator collects daily price returns for each calendar day (1-365) over your specified historical periods. For each timeframe, it:
Calculates daily returns while excluding roll periods and outliers
Accumulates these returns by calendar day across multiple years
Computes average seasonal performance from January 1st to current date
Projects remaining seasonal pattern based on historical averages
🎯 Designed For
Primary Use Cases
Commodity Futures Trading: Corn, soybeans, coffee, sugar, cocoa, natural gas, crude oil
Seasonal Strategy Development: Identify optimal entry/exit timing based on historical patterns
Pattern Validation: Confirm seasonal tendencies across different time horizons
Market Timing: Compare current performance against historical seasonal expectations
Trading Applications
Trend Confirmation: Use multiple timeframes to validate seasonal direction
Risk Assessment: Understand seasonal volatility patterns
Position Sizing: Adjust exposure based on seasonal performance consistency
Calendar Spread Analysis: Identify seasonal price relationships
⚙️ Configuration Guide
Timeframe Setup
Configure each timeframe independently:
Years: Set historical lookback period (1-20 years)
Historical Display: Show/hide the seasonal pattern line
Projection Display: Enable/disable future seasonal projection
Colors: Customize line colors for visual clarity
Display Options
Current YTD: Compare actual year-to-date performance
Info Table: Detailed performance comparison across timeframes
Projection Bars: Control forward-looking projection length
Fill Gaps: Interpolate missing data points for smoother curves
Debug Features
Enable debug mode to validate data quality:
Data Point Counts: Verify sufficient historical data per calendar day
Roll Detection Status: Monitor contract roll identification
Empty Days Analysis: Identify potential data gaps
Calculation Verification: Debug seasonal price computations
📊 Interpretation Guidelines
Strong Seasonal Signal
All three timeframes align in the same direction
Current price follows seasonal expectation
Sufficient data points (>3 years minimum per timeframe)
Seasonal Divergence
Different timeframes show conflicting patterns
Recent years deviate from longer-term averages
Current price significantly above/below seasonal expectation
Data Quality Indicators
Green Status: Adequate data across all calendar days
Red Warnings: Insufficient data or excessive gaps
Roll Detection: Proper handling of futures contract changes
⚠️ Important Considerations
Data Requirements
Minimum History: At least 3-5 years for reliable seasonal analysis
Continuous Data: Best results with daily continuous contract data
Market Hours: Designed for traditional market session data
Limitations
Past Performance: Historical patterns don't guarantee future results
Market Changes: Structural shifts can alter traditional seasonal patterns
External Factors: Weather, geopolitics, and policy changes affect seasonal behavior
Contract Rolls: Some data gaps may occur during futures roll periods
🔍 Technical Specifications
Performance Optimizations
Array Management: Efficient data storage using Pine Script arrays
Gap Handling: Robust price calculation with fallback mechanisms
Memory Usage: Optimized for large historical datasets (max_bars_back = 4000)
Real-Time Updates: Live calculation updates as new data arrives
Calculation Accuracy
Outlier Filtering: Excludes daily moves >10% to prevent data distortion
Roll Detection: 8% threshold for identifying contract changes
Data Validation: Multiple checks for price continuity and data integrity
🚀 Getting Started
Add to Chart: Apply indicator to your desired futures contract or commodity
Configure Timeframes: Set historical periods (recommend 5Y, 10Y, 15Y)
Enable Projections: Turn on future seasonal projections for forward guidance
Validate Data: Use debug mode initially to ensure sufficient historical data
Interpret Patterns: Compare current price action against seasonal expectations
💡 Pro Tips
Multiple Confirmations: Use all three timeframes for stronger signal validation
Combine with Technicals: Integrate seasonal analysis with technical indicators
Monitor Divergences: Pay attention when current price deviates from seasonal pattern
Adjust for Volatility: Consider seasonal volatility patterns for position sizing
Regular Updates: Recalibrate settings annually to maintain relevance
---
This indicator represents years of development focused on commodity market seasonality. It provides institutional-grade seasonal analysis previously available only to professional trading firms.
DVWAP Spread -WinCAlgoDynamic VWAP Spread Oscillator
This indicator transforms the relationship between two adaptive VWAP curves into an oscillator format, making trend analysis more precise and intuitive.
What it shows:
Spread Value: The difference between Fast VWAP and Smoothed VWAP
Dynamic Coloring: Intensity increases as the spread moves away from zero
Zero Line: The neutral point where both VWAP curves converge
How to interpret:
Above Zero (Green): Fast VWAP > Smoothed VWAP → Bullish bias
Below Zero (Red): Fast VWAP < Smoothed VWAP → Bearish bias
Distance from Zero: Shows the strength of the current trend
Zero Crossovers: Potential trend change signals
📌 Usage Ideas:
Trend Filter: Take long trades only when oscillator is positive, shorts when negative
Momentum Gauge: Larger spread values indicate stronger trend momentum
Divergence Analysis: Look for divergences between price and oscillator for reversal signals
Overbought/Oversold: Extreme values may indicate potential mean reversion opportunities
Zero Line Bounces: Use zero line as dynamic support/resistance for entries
Parameters:
Period: Controls the lookback period for adaptive calculations
Adjustment Step: Fine-tunes the adaptive smoothing sensitivity
Fast Response: Adjusts how quickly the fast VWAP responds to price changes
Source: Price input for VWAP calculation (default: HLC3)
Big Mo’s Glaskugel — Macro Drawdown Risk (v1.1.2)What it does / what you see
An at-a-glance drawdown-risk oscillator that blends several macro US signals.
• A smooth, color-blended line (green→orange→red) shows the scaled risk score (0–100).
• Subtle shading marks “re-steepen warning windows” (starts when the yield curve re-steepens after an inversion; ends on normalization/cool-down).
• A compact status table summarizes: overall risk level, Yield Curve (10y–3m), Credit Stress (Baa–10y), Economy (LEI), and Valuation (CAPE).
Data used & why
Yield Curve (10y–3m) — FRED:T10Y3M. Inversions and subsequent re-steepens often precede recessions/equity drawdowns.
Credit Stress — FRED:BAA10Y vs its 1-year average (deviation in bps). Widening credit spreads flag tightening financial conditions.
Economy (LEI) — ECONOMICS:USLEI. 6-month annualized growth below a cutoff highlights macro deterioration.
Valuation (CAPE) — SHILLER_PE_RATIO_MONTH. Elevated valuations can amplify downside risk.
VIX spikes — optional boost that recognizes sudden risk repricings.
Important disclaimer
This is not a reliable or predictive indicator in all regimes. No guarantees or warranties of any kind are provided. It is not financial advice. Signals can be early, late, or wrong.
That said, it leans on well-studied warning factors (yield-curve dynamics, credit spreads, LEI weakness, valuation extremes) that have flagged major market downturns in the past.
Key customization / tweaks
Weights for each component (Yield, Credit, LEI, VIX, CAPE).
Thresholds: yield inversion months, re-steepen lookback, credit-stress bps, LEI cutoff, CAPE level, VIX spike levels.
Re-steepen boost: enable/disable, base points, half-life decay.
Shading behavior: cool-down bars to “unwarn,” max warning duration, only shade when risk ≠ green.
Scaling & smoothing: dynamic rolling max, EMA length, yellow/red thresholds.
Status table: position, and a snapshot mode to view values at a chosen historical time.
Forecasting Quadratic Regression [UPDATED V6] Forecasting Quadratic Regression applies a second-degree polynomial regression model to price data, offering a non-linear alternative to traditional linear regression. By fitting a quadratic curve of the form:
y=a+bx+cx2
the indicator captures both directional trend and curvature, allowing traders to detect momentum shifts earlier than with straight-line models.
🔹 Core Features
Fits a quadratic regression curve to user-defined lookback periods
Extends the fitted curve forward to generate forecast projections
Calculates slope curvature to highlight trend acceleration or deceleration
Adapts dynamically as new bars are added
🔹 Trading Applications
Identify potential reversal zones when the curve inflects (2nd derivative sign change)
Forecast near-term mean reversion targets or extended trend continuations
Filter trades by measuring momentum curvature rather than linear slope
Visualize higher-order structure in price beyond standard regression lines
⚠️ Note: This model is statistical and assumes past curvature informs short-term future price paths. It should be combined with confirmation signals (volume, oscillators, support/resistance) to reduce false inflection points.
Advanced Market TheoryADVANCED MARKET THEORY (AMT)
This is not an indicator. It is a lens through which to see the true nature of the market.
Welcome to the definitive application of Auction Market Theory. What you have before you is the culmination of decades of market theory, fused with state-of-the-art data analysis and visual engineering. It is an institutional-grade intelligence engine designed for the serious trader who seeks to move beyond simplistic indicators and understand the fundamental forces that drive price.
This guide is your complete reference. Read it. Study it. Internalize it. The market is a complex story, and this tool is the language with which to read it.
PART I: THE GRAND THEORY - A UNIVERSE IN AN AUCTION
To understand the market, you must first understand its purpose. The market is a mechanism of discovery, organized by a continuous, two-way auction.
This foundational concept was pioneered by the legendary trader J. Peter Steidlmayer at the Chicago Board of Trade in the 1980s. He observed that beneath the chaotic facade of ticking prices lies a beautifully organized structure. The market's primary function is not to go up or down, but to facilitate trade by seeking a price level that encourages the maximum amount of interaction between buyers and sellers. This price is "value."
The Organizing Principle: The Normal Distribution
Over any given period, the market's activity will naturally form a bell curve (a normal distribution) turned on its side. This is the blueprint of the auction.
The Point of Control (POC): This is the peak of the bell curve—the single price level where the most trade occurred. It represents the point of maximum consensus, the "fairest price" as determined by the market participants. It is the gravitational center of the session.
The Value Area (VA): This is the heart of the bell curve, typically containing 70% of the session's activity (one standard deviation). This is the zone of "accepted value." Prices within this area are considered fair and are where the market is most comfortable conducting business.
The Extremes: The thin areas at the top and bottom of the curve are the "unfair" prices. These are levels where one side of the auction (buyers at the top, sellers at the bottom) was shut off, and trade was quickly rejected. These are areas of emotional trading and excess.
The Narrative of the Day: Balance vs. Imbalance
Every trading session is a story of the market's search for value.
Balance: When the market rotates and builds a symmetrical, bell-shaped profile, it is in a state of balance . Buyers and sellers are in agreement, and the market is range-bound.
Imbalance: When the market moves decisively away from a balanced area, it is in a state of imbalance . This is a trend. The market is actively seeking new information and a new area of value because the old one was rejected.
Your Purpose as a Trader
Your job is to read this story in real-time. Are we in balance or imbalance? Is the auction succeeding or failing at these new prices? The Advanced Market Theory engine is your Rosetta Stone to translate this complex narrative into actionable intelligence.
PART II: THE AMT ENGINE - AN EVOLUTION IN MARKET VISION
A standard market profile tool shows you a picture. The AMT Engine gives you the architect's full schematics, the engineer's stress tests, and the psychologist's behavioral analysis, all at once.
This is what makes it the Advanced Market Theory. We have fused the timeless principles with layers of modern intelligence:
TRINITY ANALYSIS: You can view the market through three distinct lenses. A Volume Profile shows where the money traded. A TPO (Time) Profile shows where the market spent its time. The revolutionary Hybrid Profile fuses both, giving you a complete picture of market conviction—marrying volume with duration.
AUTOMATED STRUCTURAL DECODING: The engine acts as your automated analyst, identifying critical structural phenomena in real-time:
Poor Highs/Lows: Weak auction points that signal a high probability of reversal.
Single Prints & Ledges: Footprints of rapid, aggressive market moves and areas of strong institutional acceptance.
Day Type Classification: The engine analyzes the session's personality as it develops ("Trend Day," "Normal Day," etc.), allowing you to adapt your strategy to the market's current character.
MACRO & MICRO FUSION: Via the Composite Profile , the engine merges weeks of data to reveal the major institutional battlegrounds that govern long-term price action. You can see the daily skirmish and the multi-month war on a single chart.
ORDER FLOW INTELLIGENCE: The ultimate advancement is the integrated Cumulative Volume Delta (CVD) engine. This moves beyond structure to analyze the raw aggression of buyers versus sellers. It is your window into the market's soul, automatically detecting critical Divergences that often precede major trend shifts.
ADAPTIVE SIGNALING: The engine's signal generation is not static; it is a thinking system. It evaluates setups based on a multi-factor Confluence Score , understands the market Regime (e.g., High Volatility), and adjusts its own confidence ( Probability % ) based on the complete context.
This is not a tool that gives you signals. This is a tool that gives you understanding .
PART III: THE VISUAL KEY - A LEXICON OF MARKET STRUCTURE
Every element on your chart is a piece of information. This is your guide to reading it fluently.
--- THE CORE ARCHITECTURE ---
The Profile Histogram: The primary visual on the left of each session. Its shape is the story. A thin profile is a trend; a fat, symmetrical profile is balance.
Blue Box : The zone of accepted, "fair" value. The heart of the session's business.
Bright Orange Line & Label : The Point of Control. The gravitational center. The price of maximum consensus. The most significant intraday level.
Dashed Blue Lines & Labels : The boundaries of value. Critical inflection points where the market decides to either remain in balance or seek value elsewhere.
Dashed Cyan Lines & Labels : The major, long-term structural levels derived from weeks of data. These are institutional reference points and carry immense weight. Treat them as primary support and resistance.
Dashed Orange Lines & Labels : Marks a Poor or Unfinished Auction . These represent emotional, weak extremes and are high-probability targets for future price action.
Diamond Markers : Mark Single Prints , which are footprints of aggressive, one-sided moves that left a "liquidity vacuum." Price is often drawn back to these levels to "repair" the poor structure.
Arrow Markers : Mark Ledges , which are areas of strong horizontal acceptance. They often act as powerful support/resistance in the future.
Dotted Gray Lines & Labels : The projected daily range based on multiples of the Initial Balance . Use them to set realistic profit targets and gauge the day's potential.
--- THE SIGNAL SUITE ---
Colored Triangles : These are your high-probability entry signals. The color is a strategic playbook:
Gold Triangle : ELITE Signal. An A+ setup with overwhelming confluence. This is the highest quality signal the engine can produce.
Yellow Triangle : FADE Signal. A counter-trend setup against an exhausted move at a structural extreme.
Cyan Triangle : BREAKOUT Signal. A momentum setup attempting to capitalize on a breakout from the value area.
Purple Triangle : ROTATION Signal. A mean-reversion setup within the value area, typically from one edge towards the POC.
Magenta Triangle : LIQUIDITY Signal. A sophisticated setup that identifies a "stop run" or liquidity sweep.
Percentage Number: The engine's calculated probability of success . This is not a guarantee, but a data-driven confidence score.
Dotted Gray Line: The signal's Entry Price .
Dashed Green Lines: The calculated Take Profit Targets .
Dashed Red Line: The calculated Stop Loss level.
PART IV: THE DASHBOARD - YOUR STRATEGIC COMMAND CENTER
The dashboard is your real-time intelligence briefing. It synthesizes all the engine's analysis into a clear, concise, and constantly updating summary.
--- CURRENT SESSION ---
POC, VAH, VAL: The live values for the core structure.
Profile Shape: Is the current auction top-heavy ( b-shaped ), bottom-heavy ( P-shaped ), or balanced ( D-shaped )?
VA Width: Is the value area expanding (trending) or contracting (balancing)?
Day Type: The engine's judgment on the day's personality. Use this to select the right strategy.
IB Range & POC Trend: Key metrics for understanding the opening sentiment and its evolution.
--- CVD ANALYSIS ---
Session CVD: The raw order flow. Is there more net buying or selling pressure in this session?
CVD Trend & DIVERGENCE: This is your order flow intelligence. Is the order flow confirming the price action? If "DIVERGENCE" flashes, it is a critical, high-alert warning of a potential reversal.
--- MARKET METRICS ---
Volume, ATR, RSI: Your standard contextual metrics, providing a quick read on activity, volatility, and momentum.
Regime: The engine's assessment of the broad market environment: High Volatility (favor breakouts), Low Volatility (favor mean reversion), or Normal .
--- PROFILE STATS, COMPOSITE, & STRUCTURE ---
These sections give you a quick quantitative summary of the profile structure, the major long-term Composite levels, and any active Poor Structures.
--- SIGNAL TYPES & ACTIVE SIGNAL ---
A permanent key to the signal colors and their meanings, along with the full details of the most recent active signal: its Type , Probability , Entry , Stop , and Target .
PART V: THE INPUTS MENU - CALIBRATING YOUR LENS
This engine is designed to be calibrated to your specific needs as a trader. Every input is a lever. This is not a "one size fits all" tool. The extensive tooltips are your built-in user manual, but here are the key areas of focus:
--- MARKET PROFILE ENGINE ---
Profile Mode: This is the most fundamental choice. Volume is the standard for price-based support and resistance. TPO is for analyzing time-based acceptance. Hybrid is the professional's choice, fusing both for a complete picture.
Profile Resolution: This is your zoom lens. Lower values for scalping and intraday precision. Higher values for a cleaner, big-picture view suitable for swing trading.
Composite Sessions: Your timeframe for macro analysis. 5-10 sessions for a weekly view; 20-30 sessions for a monthly, structural view.
--- SESSION & VALUE AREA ---
These settings must be configured correctly for your specific asset. The Session times are critical. The Initial Balance should reflect the key opening period for your market (60 minutes is standard for equities).
--- SIGNAL ENGINE & RISK MANAGEMENT ---
Signal Mode: THIS IS YOUR PERSONAL RISK PROFILE. Set it to Conservative to see only the absolute best A+ setups. Use Elite or Balanced for a standard approach. Use Aggressive only if you are an experienced scalper comfortable with managing more frequent, lower-probability setups.
ATR Multipliers: This suite gives you full, dynamic control over your risk/reward parameters. You can precisely define your initial stop loss distance and profit targets based on the market's current volatility.
A FINAL WORD FROM THE ARCHITECT
The creation of this engine was a journey into the very heart of market dynamics. It was born from a frustrating truth: that the most profound market theories were often confined to books and expensive institutional platforms, inaccessible to the modern retail trader. The goal was to bridge that gap.
The challenge was monumental. Making each discrete system—the volume profile, the TPO counter, the composite engine, the CVD tracker, the signal generator, the dynamic dashboard—work was a task in itself. But the true struggle, the frustrating, painstaking process that consumed countless hours, was making them work in unison . It was about ensuring the CVD analysis could intelligently inform the signal engine, that the day type classification could adjust the probability scores, and that the composite levels could provide context to the intraday structure, all in a seamless, real-time dance of data.
This engine is the result of that relentless pursuit of integration. It is built on the belief that a trader's greatest asset is not a signal, but clarity . It was designed to clear the noise, to organize the chaos, and to present the elegant, underlying logic of the market auction so that you can make better, more informed, and more confident decisions.
It is now in your hands. Use it not as a crutch, but as a lens. See the market for what it truly is.
"The market can remain irrational longer than you can remain solvent."
- John Maynard Keynes
DISCLAIMER
This script is an advanced analytical tool provided for informational and educational purposes only. It is not financial advice. All trading involves substantial risk, and past performance is not indicative of future results. The signals, probabilities, and metrics generated by this indicator do not constitute a recommendation to buy or sell any financial instrument. You, the user, are solely responsible for all trading decisions, risk management, and outcomes. Use this tool to supplement your own analysis and trading strategy.
PUBLISHING CATEGORIES
Volume Profile
Market Profile
Order Flow
Fractal Manipulation Projections [keypoems]Fractal Manipulation Projections 0-30 minutes
This study draws statistical hourly rails that help visualize how far price normally travels during the first half‑hour of each hour.
How it works
On the first bar of every clock hour (New York time) the script records the hourly open.
It then looks up the historical mean (μ) and standard deviations (σ) of (open - low for bearish| high - open for bullish candles) of the first 5 / 10 / 15 / 20 / 25 / 30‑minute candle that followed that open.
Lines are plotted at ±0.5 σ, ±1 σ and ±1.5 σ above and below the open; optional polylines or smooth curves can connect equal‑σ levels.
A small on‑chart table shows the current ±1.5 σ ranges for quick reference.
Data set
Pre‑computed distributions were built from 1‑minute CME Nasdaq‑100 futures (NQ1!) data:
2020‑present for all other hours (default).
2010‑present for the 02:00 hour (optional toggle).
No external data or HTTP requests are used; the script is fully self‑contained.
Inputs
Select which time‑slices (5 m … 30 m) and which σ levels to draw.
Choose straight or Catmull‑Rom curves, colors, line styles, and how many past hours (1‑6) remain visible.
Intended use
These projections do not predict direction or supply trade signals; they simply show where price would lie if it moved a typical ±σ distance from the hourly open. Use them as a contextual volatility gauge alongside your own strategy.
For educational purposes only. Nothing in this script constitutes financial advice. Past performance‑based statistics do not guarantee future results.
OptionHawk1. What makes the script original?
• Unique concept: It integrates a Keltner based custom supertrend with a multi-EMA energy visualization, ATR based multi target management, and on chart options (CALL/PUT) trade signals—creating a toolkit not found in typical public scripts.
• Innovative use: Instead of off the shelf indicators, it reinvents them:
• Keltner bands used as dynamic Supertrend triggers.
• Fifteen EMAs layered for “energy” zones (bullish/bearish heatmaps).
• ATR dynamically scales multi-TP levels and stop loss.
These are creatively fused into a unified signal and automation engine.
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2. What value does it provide to traders?
• Clear entries & exits: Labels for entry price/time, five TP levels, and SL structure eliminate guesswork.
• Visualization & automation: Real-time bar coloring and energy overlays allow quick momentum reads.
• Targeted to common pain points: Many traders struggle with manual TP/SL and entry timing—this automates that process.
• Ready for real use: Just plug into intraday (e.g., 5 min) or swing setups; no manual calculations. Signals are actionable out of the box.
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3. Why invite only (worth paying)?
• Proprietary fusion: Public indicators like Supertrend or EMA are common—but your layered use, ATR based scaling, and label logic are exclusive.
• Auto-generated options format: Unique labeling for CALL/PUT, with graphical on chart signals, isn’t offered freely elsewhere.
• Time-saver & edge-provider: Saves traders hours of configuration and enhances consistency—worth the subscription cost over piecing together mash ups.
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4. How does it work?
• Signal backbone: Custom supertrend uses Keltner bands crossing with close for direction, filtered by trend direction EMAs.
• Multi time logic: Trend defined by crossover of price over dynamic SMA thresholds built from ATR.
• Energy bar-colors/EMAs: 15 fast EMAs color-coded green/red to instantly show momentum.
• Entry logic: “Bull” when close crosses above supertrend; “Bear” when crosses below.
• Risk management: SL set at previous bar; up to 5 ATR scaled targets (or percentage based).
• Options formatted alerts: CALL/PUT labels with ₹¬currency values, embedded timestamp, SL/TP all printed on the chart.
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5. How should traders use it?
• Best markets & timeframes: Ideal for intraday / low timeframe (1 15m) setups and 1 hour swing trades in equities, indices, options.
• Conditions: Works best in trending or volatility driven sessions—visible via Keltner bands and EMA energy alignment.
• Recommended combo: Use alongside volume filters or broader cycles; when supertrend & energy EMAs align, validation is stronger.
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6. Proof of effectiveness?
• On chart visuals: Entry/exit labels, confirmed labels, TP and SL markers make past hits obvious.
• Real trade examples: Highlighted both bull & bear setups with full profit realization or SL hits.
• Performance is paint tested: Easy to showcase historic signals across multiple tickers.
• Data-backed: Users can export chart data to calculate win rate and avg return per trade.
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Summary Pitch:
OptionHawk offers a holistic, execution-ready trading tool:
1. Proprietary blend of Keltner-supertrend and layered EMAs—beyond standard scripts.
2. Automates entries, multi-tier targets, SL, and options-format labels.
3. Visual energy overlays for quick momentum readings.
4. Use-tested in intraday and swing markets.
5. Installs on chart and works immediately—no setup complexity.
It's not a public indicator package; it's a self-contained, plug and play trade catalyst—worth subscribing for active traders seeking clarity, speed, and structure in their decision-making.
6. While OptionHawk is designed for clarity and structure, no script can predict the market. Always use with discretion and proper risk management.
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OptionHawk: A Comprehensive Trend-Following & Volatility-Adaptive Trading System
The "OptionHawk" script is a sophisticated trading tool designed to provide clear, actionable signals for options trading by combining multiple technical indicators and custom logic. It aims to offer a holistic view of market conditions, identifying trend direction, momentum, and potential entry/exit points with dynamic stop-loss and take-profit levels.
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1. Why These Specific Indicators and Code Elements?
The "OptionHawk" script is a strategic fusion of the Supertrend indicator (modified with Keltner Channels), a multi-EMA "Energy" ribbon, dynamic trend lines (based on SMA and ATR), a 100-period Trend Filter EMA, and comprehensive trade management logic (SL/TP). My reason and motivation for this mashup stem from a desire to create a robust system that accounts for various market aspects often overlooked by individual indicators:
• Supertrend with Keltner Channels: The standard Supertrend is effective for trend identification but can sometimes generate whipsaws in volatile or ranging markets. By integrating Keltner Channels into the Supertrend calculation, the volatility measure becomes more adaptive, using the (high - low) range within the Keltner Channel for its ATR-like component. This aims to create a more responsive yet less prone-to-false-signals Supertrend.
• Multi-EMA "Energy" Ribbon: This visually striking element, composed of 15 EMAs, provides a quick glance at short-to-medium term momentum and potential support/resistance zones. When these EMAs are stacked and moving in one direction, it indicates strong "energy" behind the trend, reinforcing the signals from other indicators.
• Dynamic Trend Lines (SMA + ATR): These lines offer a visual representation of support and resistance that adapts to market volatility. Unlike static trend lines, their ATR-based offset ensures they remain relevant across different market conditions and asset classes, providing context for price action relative to the underlying trend.
• 100-Period Trend Filter EMA: A longer-period EMA acts as a higher-timeframe trend filter. This is crucial for confirming the direction identified by the faster-acting Supertrend, helping to avoid trades against the prevailing broader trend.
• Comprehensive Trade Management Logic: The script integrates automated calculation and display of stop-loss (SL) and multiple take-profit (TP) levels, along with trade confirmation and "TP Hit" labels. This is critical for practical trading, providing immediate, calculated risk-reward parameters that individual indicators typically don't offer.
This combination is driven by the need for a multi-faceted approach to trading that goes beyond simple signal generation to include trend confirmation, volatility adaptation, and essential risk management.
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2. What Problem or Need Does This Mashup Solve?
This mashup addresses several critical gaps that existing individual indicators often fail to fill:
• Reliable Trend Identification in Volatile Markets: While Supertrend is good, it can be late or whipsaw. Integrating Keltner Channels helps it adapt to changing volatility, providing more reliable trend signals.
• Confirmation of Signals: A common pitfall of relying on a single indicator is false signals. "OptionHawk" uses the multi-EMA "Energy" ribbon and the 100-period EMA to confirm the trend identified by the Keltner-Supertrend, reducing false entries.
• Dynamic Support/Resistance & Trend Context: Static support and resistance levels can quickly become irrelevant. The dynamic SMA + ATR trend lines provide continually adjusting zones that reflect the current market's true support and resistance, giving traders a better understanding of price action within the trend.
• Integrated Risk and Reward Management: Most indicators just give entry signals. This script goes a significant step further by automatically calculating and displaying clear stop-loss and up to five take-profit levels (either ATR-based or percentage-based). This is a vital component for structured trading, allowing traders to pre-define their risk and reward for each trade.
• Visual Clarity and Actionable Information: Instead of requiring traders to layer multiple indicators manually, "OptionHawk" integrates them into a single, cohesive display with intuitive bar coloring, shape plots, and informative labels. This reduces cognitive load and presents actionable information directly on the chart.
In essence, "OptionHawk" provides a more comprehensive, adaptive, and actionable trading framework than relying on isolated indicators.
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3. How Do the Components Work Together?
The various components of "OptionHawk" interact in a synergistic and often sequential manner to generate signals and manage trades:
• Keltner-Supertrend as the Primary Signal Generator: The supertrend function, enhanced by keltner_channel, is the core of the system. It identifies potential trend reversals and continuation signals (bullish/bearish crosses of the supertrendLine). The sensitivity and factor inputs directly influence how closely the Supertrend follows price and its responsiveness to volatility.
• Multi-EMA "Energy" Ribbon for Momentum and Confirmation: The 15 EMAs (from ema1 to ema15) are plotted to provide a visual representation of short-term momentum. When the price is above these EMAs and they are spread out and pointing upwards, it suggests strong bullish "energy." Conversely, when price is below them and they are pointing downwards, it indicates bearish "energy." This ribbon serves as a simultaneous visual confirmation for the Supertrend signals; a buy signal from Supertrend is stronger if the EMA ribbon is also indicating upward momentum.
• Dynamic Trend Lines for Context and Confirmation: The sma_high and sma_low lines, incorporating ATR, act as dynamic support and resistance. The trend variable, determined by price crossing these lines, provides an overarching directional bias. This component works conditionally with the Supertrend; a bullish Supertrend signal is more potent if the price is also above the sma_high (indicating an uptrend).
• 100-Period Trend Filter EMA for Macro Trend Confirmation: The ema100 acts as a macro trend filter. Supertrend signals are typically considered valid if they align with the direction of the ema100. For example, a "BUY" signal from the Keltner-Supertrend is ideally taken only if the price is also above the ema100, signifying that the smaller trend aligns with the larger trend. This is a conditional filter.
• Trade Confirmation and SL/TP Logic (Sequential and Conditional):
• Once a bull or bear signal is generated by the Keltner-Supertrend, the tradeSignalCall or tradeSignalPut is set to true.
• A confirmation step then occurs for a "BUY" signal, the script checks if the close of the next bar is higher than the entry bar's close. For a "SELL" signal, it checks if the close of the next bar is lower. This is a sequential confirmation step aimed at filtering out weak signals.
• Upon a confirmed signal, the stop-loss (SL) is immediately set based on the previous bar's low (for calls) or high (for puts).
• Multiple take-profit (TP) levels are calculated and stored in arrays. These can be based on a fixed percentage or dynamic ATR multiples, based on user input.
• The TP HIT logic continuously monitors price action simultaneously against these pre-defined target levels, displaying labels when a target is reached. The SL HIT logic similarly monitors for a stop-loss breach.
In summary, the Supertrend generates the initial signal, which is then confirmed by the dynamic trend lines and the 100-period EMA, and visually reinforced by the EMA "Energy" ribbon. The trade management logic then takes over, calculating and displaying vital risk-reward parameters.
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4. What is the Purpose of the Mashup Beyond Simply Merging Code?
The purpose of "OptionHawk" extends far beyond merely combining different indicator codes; it's about creating a structured and informed decision-making process for options trading. The key strategic insights and functionalities added by combining these elements are:
• Enhanced Signal Reliability and Reduced Noise: By requiring multiple indicators to align (e.g., Keltner-Supertrend signal confirmed by EMA trend filter and dynamic trend lines), the script aims to filter out false signals and whipsaws that commonly plague individual indicators. This leads to higher-probability trade setups.
• Adaptive Risk Management: The integration of ATR into both the Supertrend calculation and the dynamic stop-loss/take-profit levels makes the entire system adaptive to current market volatility. This means stop-losses and targets are not static but expand or contract with the market's price swings, promoting more realistic risk management.
• Clear Trade Entry and Exit Framework: The script provides a complete trading plan with each signal: a clear entry point, a precise stop-loss, and multiple cascading take-profit levels. This holistic approach empowers traders to manage their trades effectively from initiation to conclusion, rather than just identifying a potential entry.
• Visual Confirmation of Market Strength: The "Energy" ribbon and dynamic trend lines provide an immediate visual understanding of the market's momentum and underlying trend strength, helping traders gauge conviction behind a signal.
• Improved Backtesting and Analysis: By combining these elements into one script, traders can more easily backtest a comprehensive strategy rather than trying to manually combine signals from multiple overlaying indicators, leading to more accurate strategy analysis.
• Suitability for Options Trading: Options contracts are highly sensitive to price movement and volatility. This script's focus on confirmed trend identification, dynamic volatility adaptation, and precise risk management makes it particularly well-suited for the nuanced demands of options trading, where timing and defined risk are paramount.
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5. What New Functionality or Insight Does Your Script Offer?
"OptionHawk" offers several new functionalities and insights that significantly enhance decision-making, improve accuracy, and provide clearer signals and better timing for traders:
• "Smart" Supertrend: By basing the Supertrend's volatility component on the Keltner Channel's range instead of a simple ATR, the Supertrend becomes more sensitive to price action within its typical bounds while still adapting to broader market volatility. This can lead to earlier and more relevant trend change signals.
• Multi-Confirmation System: The script doesn't just provide a signal; it layers multiple confirmations (Keltner-Supertrend, multi-EMA "Energy" coloration, dynamic trend lines, and the 100-period EMA). This multi-layered validation significantly improves the accuracy of signals by reducing the likelihood of false positives.
• Automated and Dynamic Risk-Reward Display: This is a major functionality enhancement. The automatic calculation and clear display of stop-loss and five distinct take-profit levels (based on either ATR or percentage) directly on the chart, along with "TP HIT" and "SL HIT" labels, streamline the trading process. Traders no longer need to manually calculate these crucial levels, leading to enhanced decision-making and better risk management.
• Visual Trend "Energy" and Momentum: The vibrant coloring of the multi-EMA ribbon based on price relative to the EMA provides an intuitive and immediate visual cue for market momentum and "energy." This offers an insight into the strength of the current move, which isn't available from single EMA plots.
• Post-Signal Confirmation: The "Confirmation" label appearing on the bar after a signal, if the price continues in the signaled direction, adds an extra layer of real-time validation. This helps to improve signal timing by waiting for initial follow-through.
• Streamlined Options Trading Planning: For options traders, having clear entry prices, stop-losses, and multiple target levels directly annotated on the chart is invaluable. It helps in quickly assessing potential premium movements and managing positions effectively.
In essence, "OptionHawk" transitions from a collection of indicators to a semi-automated trading assistant, providing a comprehensive, visually rich, and dynamically adaptive framework for making more informed and disciplined trading decisions.
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Performance & Claims
1. What is the claimed performance of the script or strategy?
Answer: The script does not claim any specific performance metrics (e.g., win rate, profit factor, percentage gains). It's an indicator designed to identify potential buy/sell signals and target/stop-loss levels. The labels it generates ("BUY CALL," "BUY PUT," "TP HIT," "SL HIT") are informational based on its internal logic, not a representation of actual trading outcomes.
2. Is there any proof or backtesting to support this claim?
Answer: No, the provided code does not include any backtesting functionality or historical performance proof. As an indicator, it simply overlays visual signals on the chart. To obtain backtesting results, the logic would need to be implemented as a Pine Script strategy with entry/exit rules and commission/slippage considerations.
3. Are there any unrealistic or exaggerated performance expectations being made?
Answer: The script itself does not make any performance expectations. It avoids quantitative claims. However, if this script were presented to users with implied promises of profit based solely on the visual signals, that would be unrealistic.
4. Have you clearly stated the limitations of the performance data (e.g., “based on backtesting only”)?
Answer: There is no statement of performance data or its limitations because the script doesn't generate performance data.
5. Do you include a disclaimer that past results do not guarantee future performance?
Answer: No, the script does not include any disclaimers about past or future performance. This is typically found in accompanying documentation or marketing materials for a trading system, not within the indicator's code itself.
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Evidence & Transparency
6. How are your performance results measured (e.g., profit factor, win rate, Sharpe ratio)?
Answer: Performance results are not measured by this script. It's an indicator.
7. Are these results reproducible by others using the same script and settings?
Answer: The visual signals and calculated levels (Supertrend line, EMAs, target/SL levels) generated by the script are reproducible on TradingView when applied to the same instrument, timeframe, and with the same input settings. However, the actual trading results (profit/loss) are not generated or reproducible by this indicator.
8. Do you include enough data (charts, equity curves, trade logs) to support your claims?
Answer: No, the script does not include or generate equity curves or trade logs. It provides visual labels on the chart, which can be seen as a form of "data" to support the signal generation, but not the performance claims (as none are made by the code).
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Future Expectations
9. Are you making any predictions about future market performance?
Answer: No, the script does not make any explicit predictions about future market performance. Its signals are based on historical price action and indicator calculations.
10. Have you stated clearly that the future is fundamentally uncertain?
Answer: No, the script does not contain any statements about the uncertainty of the future.
11. Are forward-looking statements presented with caution and appropriate language?
Answer: The script does not contain any forward-looking statements beyond the visual signals it generates based on real-time data.
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Risk & Disclosure
12. Have you disclosed the risks associated with using your script or strategy?
Answer: No, the script does not include any risk disclosures. This is typically found in external documentation.
13. Do you explain that trading involves potential loss as well as gain?
Answer: No, the script does not contain any explanation about the potential for loss in trading.
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Honesty & Integrity
14. Have you avoided hype words like “guaranteed,” “foolproof,” or “no losses”?
Answer: Yes, the script itself avoids these hype words. The language used within the code is technical and describes the indicator's logic.
15. Is your language grounded and realistic rather than promotional?
Answer: Yes, the language within the provided Pine Script code is grounded and realistic as it pertains to the technical implementation of an indicator.
16. Are you leaving out any important details that might mislead users (e.g., selective performance snapshots)?
Answer: From the perspective of the code itself, no, it's not "leaving out" performance details because it's not designed to generate them. However, if this indicator were to be presented as a "strategy" that implies profitability without accompanying disclaimers, backtesting results, and risk disclosures, then that external presentation could be misleading. The script focuses on signal generation and visual representation.
⚠️ Disclaimer:
This indicator is for informational and educational purposes only. It does not guarantee any future results or performance. All trading involves risk. Please assess your own risk tolerance and consult a licensed financial advisor if needed. Past performance does not indicate future returns.
Bear Market Probability Model# Bear Market Probability Model: A Multi-Factor Risk Assessment Framework
The Bear Market Probability Model represents a comprehensive quantitative framework for assessing systemic market risk through the integration of 13 distinct risk factors across four analytical categories: macroeconomic indicators, technical analysis factors, market sentiment measures, and market breadth metrics. This indicator synthesizes established financial research methodologies to provide real-time probabilistic assessments of impending bear market conditions, offering institutional-grade risk management capabilities to retail and professional traders alike.
## Theoretical Foundation
### Historical Context of Bear Market Prediction
Bear market prediction has been a central focus of financial research since the seminal work of Dow (1901) and the subsequent development of technical analysis theory. The challenge of predicting market downturns gained renewed academic attention following the market crashes of 1929, 1987, 2000, and 2008, leading to the development of sophisticated multi-factor models.
Fama and French (1989) demonstrated that certain financial variables possess predictive power for stock returns, particularly during market stress periods. Their three-factor model laid the groundwork for multi-dimensional risk assessment, which this indicator extends through the incorporation of real-time market microstructure data.
### Methodological Framework
The model employs a weighted composite scoring methodology based on the theoretical framework established by Campbell and Shiller (1998) for market valuation assessment, extended through the incorporation of high-frequency sentiment and technical indicators as proposed by Baker and Wurgler (2006) in their seminal work on investor sentiment.
The mathematical foundation follows the general form:
Bear Market Probability = Σ(Wi × Ci) / ΣWi × 100
Where:
- Wi = Category weight (i = 1,2,3,4)
- Ci = Normalized category score
- Categories: Macroeconomic, Technical, Sentiment, Breadth
## Component Analysis
### 1. Macroeconomic Risk Factors
#### Yield Curve Analysis
The inclusion of yield curve inversion as a primary predictor follows extensive research by Estrella and Mishkin (1998), who demonstrated that the term spread between 3-month and 10-year Treasury securities has historically preceded all major recessions since 1969. The model incorporates both the 2Y-10Y and 3M-10Y spreads to capture different aspects of monetary policy expectations.
Implementation:
- 2Y-10Y Spread: Captures market expectations of monetary policy trajectory
- 3M-10Y Spread: Traditional recession predictor with 12-18 month lead time
Scientific Basis: Harvey (1988) and subsequent research by Ang, Piazzesi, and Wei (2006) established the theoretical foundation linking yield curve inversions to economic contractions through the expectations hypothesis of the term structure.
#### Credit Risk Premium Assessment
High-yield credit spreads serve as a real-time gauge of systemic risk, following the methodology established by Gilchrist and Zakrajšek (2012) in their excess bond premium research. The model incorporates the ICE BofA High Yield Master II Option-Adjusted Spread as a proxy for credit market stress.
Threshold Calibration:
- Normal conditions: < 350 basis points
- Elevated risk: 350-500 basis points
- Severe stress: > 500 basis points
#### Currency and Commodity Stress Indicators
The US Dollar Index (DXY) momentum serves as a risk-off indicator, while the Gold-to-Oil ratio captures commodity market stress dynamics. This approach follows the methodology of Akram (2009) and Beckmann, Berger, and Czudaj (2015) in analyzing commodity-currency relationships during market stress.
### 2. Technical Analysis Factors
#### Multi-Timeframe Moving Average Analysis
The technical component incorporates the well-established moving average convergence methodology, drawing from the work of Brock, Lakonishok, and LeBaron (1992), who provided empirical evidence for the profitability of technical trading rules.
Implementation:
- Price relative to 50-day and 200-day simple moving averages
- Moving average convergence/divergence analysis
- Multi-timeframe MACD assessment (daily and weekly)
#### Momentum and Volatility Analysis
The model integrates Relative Strength Index (RSI) analysis following Wilder's (1978) original methodology, combined with maximum drawdown analysis based on the work of Magdon-Ismail and Atiya (2004) on optimal drawdown measurement.
### 3. Market Sentiment Factors
#### Volatility Index Analysis
The VIX component follows the established research of Whaley (2009) and subsequent work by Bekaert and Hoerova (2014) on VIX as a predictor of market stress. The model incorporates both absolute VIX levels and relative VIX spikes compared to the 20-day moving average.
Calibration:
- Low volatility: VIX < 20
- Elevated concern: VIX 20-25
- High fear: VIX > 25
- Panic conditions: VIX > 30
#### Put-Call Ratio Analysis
Options flow analysis through put-call ratios provides insight into sophisticated investor positioning, following the methodology established by Pan and Poteshman (2006) in their analysis of informed trading in options markets.
### 4. Market Breadth Factors
#### Advance-Decline Analysis
Market breadth assessment follows the classic work of Fosback (1976) and subsequent research by Brown and Cliff (2004) on market breadth as a predictor of future returns.
Components:
- Daily advance-decline ratio
- Advance-decline line momentum
- McClellan Oscillator (Ema19 - Ema39 of A-D difference)
#### New Highs-New Lows Analysis
The new highs-new lows ratio serves as a market leadership indicator, based on the research of Zweig (1986) and validated in academic literature by Zarowin (1990).
## Dynamic Threshold Methodology
The model incorporates adaptive thresholds based on rolling volatility and trend analysis, following the methodology established by Pagan and Sossounov (2003) for business cycle dating. This approach allows the model to adjust sensitivity based on prevailing market conditions.
Dynamic Threshold Calculation:
- Warning Level: Base threshold ± (Volatility × 1.0)
- Danger Level: Base threshold ± (Volatility × 1.5)
- Bounds: ±10-20 points from base threshold
## Professional Implementation
### Institutional Usage Patterns
Professional risk managers typically employ multi-factor bear market models in several contexts:
#### 1. Portfolio Risk Management
- Tactical Asset Allocation: Reducing equity exposure when probability exceeds 60-70%
- Hedging Strategies: Implementing protective puts or VIX calls when warning thresholds are breached
- Sector Rotation: Shifting from growth to defensive sectors during elevated risk periods
#### 2. Risk Budgeting
- Value-at-Risk Adjustment: Incorporating bear market probability into VaR calculations
- Stress Testing: Using probability levels to calibrate stress test scenarios
- Capital Requirements: Adjusting regulatory capital based on systemic risk assessment
#### 3. Client Communication
- Risk Reporting: Quantifying market risk for client presentations
- Investment Committee Decisions: Providing objective risk metrics for strategic decisions
- Performance Attribution: Explaining defensive positioning during market stress
### Implementation Framework
Professional traders typically implement such models through:
#### Signal Hierarchy:
1. Probability < 30%: Normal risk positioning
2. Probability 30-50%: Increased hedging, reduced leverage
3. Probability 50-70%: Defensive positioning, cash building
4. Probability > 70%: Maximum defensive posture, short exposure consideration
#### Risk Management Integration:
- Position Sizing: Inverse relationship between probability and position size
- Stop-Loss Adjustment: Tighter stops during elevated risk periods
- Correlation Monitoring: Increased attention to cross-asset correlations
## Strengths and Advantages
### 1. Comprehensive Coverage
The model's primary strength lies in its multi-dimensional approach, avoiding the single-factor bias that has historically plagued market timing models. By incorporating macroeconomic, technical, sentiment, and breadth factors, the model provides robust risk assessment across different market regimes.
### 2. Dynamic Adaptability
The adaptive threshold mechanism allows the model to adjust sensitivity based on prevailing volatility conditions, reducing false signals during low-volatility periods and maintaining sensitivity during high-volatility regimes.
### 3. Real-Time Processing
Unlike traditional academic models that rely on monthly or quarterly data, this indicator processes daily market data, providing timely risk assessment for active portfolio management.
### 4. Transparency and Interpretability
The component-based structure allows users to understand which factors are driving risk assessment, enabling informed decision-making about model signals.
### 5. Historical Validation
Each component has been validated in academic literature, providing theoretical foundation for the model's predictive power.
## Limitations and Weaknesses
### 1. Data Dependencies
The model's effectiveness depends heavily on the availability and quality of real-time economic data. Federal Reserve Economic Data (FRED) updates may have lags that could impact model responsiveness during rapidly evolving market conditions.
### 2. Regime Change Sensitivity
Like most quantitative models, the indicator may struggle during unprecedented market conditions or structural regime changes where historical relationships break down (Taleb, 2007).
### 3. False Signal Risk
Multi-factor models inherently face the challenge of balancing sensitivity with specificity. The model may generate false positive signals during normal market volatility periods.
### 4. Currency and Geographic Bias
The model focuses primarily on US market indicators, potentially limiting its effectiveness for global portfolio management or non-USD denominated assets.
### 5. Correlation Breakdown
During extreme market stress, correlations between risk factors may increase dramatically, reducing the model's diversification benefits (Forbes and Rigobon, 2002).
## References
Akram, Q. F. (2009). Commodity prices, interest rates and the dollar. Energy Economics, 31(6), 838-851.
Ang, A., Piazzesi, M., & Wei, M. (2006). What does the yield curve tell us about GDP growth? Journal of Econometrics, 131(1-2), 359-403.
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680.
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636.
Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261-292.
Beckmann, J., Berger, T., & Czudaj, R. (2015). Does gold act as a hedge or a safe haven for stocks? A smooth transition approach. Economic Modelling, 48, 16-24.
Bekaert, G., & Hoerova, M. (2014). The VIX, the variance premium and stock market volatility. Journal of Econometrics, 183(2), 181-192.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1-27.
Campbell, J. Y., & Shiller, R. J. (1998). Valuation ratios and the long-run stock market outlook. The Journal of Portfolio Management, 24(2), 11-26.
Dow, C. H. (1901). Scientific stock speculation. The Magazine of Wall Street.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25(1), 23-49.
Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The Journal of Finance, 57(5), 2223-2261.
Fosback, N. G. (1976). Stock market logic: A sophisticated approach to profits on Wall Street. The Institute for Econometric Research.
Gilchrist, S., & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. American Economic Review, 102(4), 1692-1720.
Harvey, C. R. (1988). The real term structure and consumption growth. Journal of Financial Economics, 22(2), 305-333.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Magdon-Ismail, M., & Atiya, A. F. (2004). Maximum drawdown. Risk, 17(10), 99-102.
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220.
Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18(1), 23-46.
Pan, J., & Poteshman, A. M. (2006). The information in option volume for future stock prices. The Review of Financial Studies, 19(3), 871-908.
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House.
Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Zarowin, P. (1990). Size, seasonality, and stock market overreaction. Journal of Financial and Quantitative Analysis, 25(1), 113-125.
Zweig, M. E. (1986). Winning on Wall Street. Warner Books.
Parsifal.Swing.TrendScoreThe Parsifal.Swing.TrendScore indicator is a module within the Parsifal Swing Suite, which includes a set of swing indicators such as:
• Parsifal Swing TrendScore
• Parsifal Swing Composite
• Parsifal Swing RSI
• Parsifal Swing Flow
Each module serves as an indicator facilitating judgment of the current swing state in the underlying market.
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Background
Market movements typically follow a time-varying trend channel within which prices oscillate. These oscillations—or swings—within the trend are inherently tradable.
They can be approached:
• One-sidedly, aligning with the trend (generally safer), or
• Two-sidedly, aiming to profit from mean reversions as well.
Note: Mean reversions in strong trends often manifest as sideways consolidations, making one-sided trades more stable.
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The Parsifal Swing Suite
The modules aim to provide additional insights into the swing state within a trend and offer various trigger points to assist with entry decisions.
All modules in the suite act as weak oscillators, meaning they fluctuate within a range but are not bounded like true oscillators (e.g., RSI, which is constrained between 0% and 100%).
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The Parsifal.Swing.TrendScore – Specifics
The Parsifal.Swing.TrendScore module combines short-term trend data with information about the current swing state, derived from raw price data and classical technical indicators. It provides an indication of how well the short-term trend aligns with the prevailing swing, based on recent market behavior.
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How Swing.TrendScore Works
The Swing.TrendScore calculates a swing score by collecting data within a bin (i.e., a single candle or time bucket) that signals an upside or downside swing. These signals are then aggregated together with insights from classical swing indicators.
Additionally, it calculates a short-term trend score using core technical signals, including:
• The Z-score of the price's distance from various EMAs
• The slope of EMAs
• Other trend-strength signals from additional technical indicators
These two components—the swing score and the trend score—are then combined to form the Swing.TrendScore indicator, which evaluates the short-term trend in context with swing behavior.
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How to Interpret Swing.TrendScore
The trend component enhances Swing.TrendScore’s ability to provide stronger signals when the short-term trend and swing state align.
It can also override the swing score; for example, even if a mean reversion appears to be forming, a dominant short-term trend may still control the market behavior.
This makes Swing.TrendScore particularly valuable for:
• Short-term trend-following strategies
• Medium-term swing trading
Unlike typical swing indicators, Swing.TrendScore is designed to respond more to medium-term swings rather than short-lived fluctuations.
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Behavior and Chart Representation
The Swing.TrendScore indicator fluctuates within a range, as most of its components are range-bound (though Z-score components may technically extend beyond).
• Historically high or low values may suggest overbought or oversold conditions
• The chart displays:
o A fast curve (orange)
o A slow curve (white)
o A shaded background representing the market state
• Extreme values followed by curve reversals may signal a developing mean reversion
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TrendScore Background Value
The Background Value reflects the combined state of the short-term trend and swing:
• > 0 (shaded green) → Bullish mode: swing and short-term trend both upward
• < 0 (shaded red) → Bearish mode: swing and short-term trend both downward
• The absolute value represents the confidence level in the market mode
Notably, the Background Value can remain positive during short downswings if the short-term trend remains bullish—and vice versa.
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How to Use the Parsifal.Swing.TrendScore
Several change points can act as entry triggers or aids:
• Fast Trigger: change in slope of the fast signal curve
• Trigger: fast line crosses slow line or the slope of the slow signal changes
• Slow Trigger: change in sign of the Background Value
Examples of these trigger points are illustrated in the accompanying chart.
Additionally, market highs and lows aligning with the swing indicator values may serve as pivot points in the evolving price process.
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As always, this indicator should be used in conjunction with other tools and market context in live trading.
While it provides valuable insight and potential entry points, it does not predict future price action.
Instead, it reflects recent tendencies and should be used judiciously.
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Extensions
The aggregation of information—whether derived from bins or technical indicators—is currently performed via simple averaging. However, this can be modified using alternative weighting schemes, based on:
• Historical performance
• Relevance of the data
• Specific market conditions
Smoothing periods used in calculations are also modifiable. In general, the EMAs applied for smoothing can be extended to reflect expectations based on relevance-weighted probability measures.
Since EMAs inherently give more weight to recent data, this allows for adaptive smoothing.
Additionally, EMAs may be further extended to incorporate negative weights, akin to wavelet transform techniques.
Credit Spread Monitor: HY & IG vs US10Y📉 Credit Spread Monitor: HY & IG vs US10Y
This indicator provides a dynamic and visual way to monitor credit spreads relative to the US Treasury benchmark. By comparing High Yield (HY) and Investment Grade (IG) corporate bond yields to the 10-Year US Treasury Yield (US10Y), it helps assess market stress, investor risk appetite, and potential macro turning points.
🔍 What It Does
-Calculates credit spreads:
HY Spread = BAMLH0A0HYM2EY − US10Y
IG Spread = BAMLC0A0CMEY − US10Y
-Detects macro risk regimes using statistical thresholds and yield curve signals:
🔴 HY Spread > +2σ → Potential financial stress
🟠 Inverted Yield Curve + HY Spread > 2% → Recession risk
🟢 HY Spread < 1.5% → Risk-on environment
-Visually highlights conditions with intuitive background colors for fast decision-making.
📊 Data Sources Explained
🔴 High Yield (HY): BAMLH0A0HYM2EY → ICE BofA US High Yield Index Effective Yield
🔵 Investment Grade (IG): BAMLC0A0CMEY → ICE BofA US Corporate Index Effective Yield
⚪ Treasury 10Y: US10Y → 10-Year US Treasury Yield
⚪ Treasury 2Y: US02Y → 2-Year US Treasury Yield (used to detect curve inversion)
✅ This Indicator Is Ideal For:
Macro traders looking to anticipate economic inflection points
Portfolio managers monitoring systemic risk or credit cycles
Fixed-income analysts tracking the cost of corporate borrowing
ETF/Asset allocators identifying shifts between risk-on and risk-off environments
🧠 Why It's Useful
This script helps visualize how tight or loose credit conditions are relative to government benchmarks. Since HY spreads typically widen before major downturns, this tool can provide early warning signals. Similarly, compressed spreads may indicate overheating or complacency in risk markets.
🛠️ Practical Use Case:
You’re managing a multi-asset portfolio. The HY spread jumps above +2σ while the yield curve remains inverted. You decide to reduce exposure to equities and high-yield bonds and rotate into cash or Treasuries as recession risk rises.
📎 Additional Notes
Sourced from FRED (Federal Reserve Economic Data) and TradingView’s bond feeds.
Designed to work best on daily resolution, using open prices to ensure consistency across series with different update timings.
This script is original, not based on built-in or public templates, and intended to offer educational, statistical, and visual insights for serious market participants.
Parsifal.Swing.CompositeThe Parsifal.Swing.Composite indicator is a module within the Parsifal Swing Suite, which includes a set of swing indicators such as:
• Parsifal Swing TrendScore
• Parsifal Swing Composite
• Parsifal Swing RSI
• Parsifal Swing Flow
Each module serves as an indicator facilitating judgment of the current swing state in the underlying market.
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Background
Market movements typically follow a time-varying trend channel within which prices oscillate. These oscillations—or swings—within the trend are inherently tradable.
They can be approached:
• One-sidedly, aligning with the trend (generally safer), or
• Two-sidedly, aiming to profit from mean reversions as well.
Note: Mean reversions in strong trends often manifest as sideways consolidations, making one-sided trades more stable.
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The Parsifal Swing Suite
The modules aim to provide additional insights into the swing state within a trend and offer various trigger points to assist with entry decisions.
All modules in the suite act as weak oscillators, meaning they fluctuate within a range but are not bounded like true oscillators (e.g., RSI, which is constrained between 0% and 100%).
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The Parsifal.Swing.Composite – Specifics
This module consolidates multiple insights into price swing behavior, synthesizing them into an indicator reflecting the current swing state.
It employs layered bagging and smoothing operations based on standard price inputs (OHLC) and classical technical indicators. The module integrates several slightly different sub-modules.
Process overview:
1. Per candle/bin, sub-modules collect directional signals (up/down), with each signal casting a vote.
2. These votes are aggregated via majority counting (bagging) into a single bin vote.
3. Bin votes are then smoothed, typically with short-term EMAs, to create a sub-module vote.
4. These sub-module votes are aggregated and smoothed again to generate the final module vote.
The final vote is a score indicating the module’s assessment of the current swing state. While it fluctuates in a range, it's not a true oscillator, as most inputs are normalized via Z-scores (value divided by standard deviation over a period).
• Historically high or low values correspond to high or low quantiles, suggesting potential overbought or oversold conditions.
• The chart displays a fast (orange) and slow (white) curve against a solid background state.
• Extreme values followed by curve reversals may signal upcoming mean-reversions.
Background Value:
• Value > 0: shaded green → bullish mode
• Value < 0: shaded red → bearish mode
• The absolute value indicates confidence in the mode.
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How to Use the Parsifal.Swing.Composite
Several change points in the indicator serve as potential entry triggers:
• Fast Trigger: change in slope of the fast curve
• Trigger: fast line crossing the slow line or change in the slow curve’s slope
• Slow Trigger: change in sign of the background value
These are illustrated in the introductory chart.
Additionally, market highs and lows aligned with swing values may act as pivot points, support, or resistance levels for evolving price processes.
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As always, supplement this indicator with other tools and market information. While it provides valuable insights and potential entry points, it does not predict future prices. It reflects recent tendencies and should be used judiciously.
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Extensions
All modules in the Parsifal Swing Suite are simple yet adaptable, whether used individually or in combination.
Customization options:
• Weights in EMAs for smoothing are adjustable
• Bin vote aggregation (currently via sum-of-experts) can be modified
• Alternative weighting schemes can be tested
Advanced options:
• Bagging weights may be historical, informational, or relevance-based
• Selection algorithms (e.g., ID3, C4.5, CAT) could replace the current bagging approach
• EMAs may be generalized into expectations relative to relevance-based probability
• Negative weights (akin to wavelet transforms) can be incorporated
Jurik Moving Average (JMA)Overview
Jurik Moving Average (JMA) is an adaptive moving average developed by Mark Jurik, widely regarded as one of the most powerful moving averages available to traders. This implementation provides a direct Pine Script translation of the reverse-engineered JMA algorithm
What Makes JMA Special
Unlike traditional moving averages, JMA adapts to market volatility in real-time. This "triple adaptive" approach allows JMA to:
Reduce lag significantly while maintaining exceptional smoothness
React quickly during trending markets
Filter out noise during consolidation phases
Provide clearer trend signals with fewer whipsaws
The Triple Adaptive Edge
JMA employs a three-stage smoothing process:
Preliminary smoothing via an adaptive EMA
Secondary smoothing using a Kalman filter with phase adjustment
Final smoothing through a unique Jurik adaptive filter
This approach combines with a dynamic volatility-based factor (alpha) that adapts to market conditions, making JMA superior to traditional moving averages in most situations.
Key Parameters
Period : Controls the lookback period (default: 14)
Phase : Adjusts the heaviness of the indicator (-100 to 100, default: 0)
Positive values reduce lag but may cause overshoot
Negative values increase smoothness but reduce responsiveness
Power : Smoothing factor (0.1-0.9, default 0.45)
Higher values create smoother curves
Lower values create more responsive but choppy curves
SMA Strategy Builder: Create & Prove Profitability📄 Pine Script Strategy Description (For Publishing on TradingView)
🎯 Strategy Title:
SMA Strategy Builder: Create & Prove Profitability
✨ Description:
This tool is designed for traders who want to build, customize, and prove their own SMA-based trading strategies. The strategy tracks capital growth in real-time, providing clear evidence of profitability after each trade. Users can adjust key parameters such as SMA period, take profit levels, and initial capital, making it a flexible solution for backtesting and strategy validation.
🔍 Key Features:
✅ SMA-Based Logic:
Core trading logic revolves around the Simple Moving Average (SMA).
SMA period is fully adjustable to suit various trading styles.
🎯 Customizable Take Profit (TP):
User-defined TP percentages per position.
TP line displayed as a Step Line with Breaks for clear segmentation.
Visual 🎯TP label for quick identification of profit targets.
💵 Capital Tracking (Proof of Profitability):
Initial capital is user-defined.
Capital balance updates after each closed trade.
Shows both absolute profit/loss and percentage changes for every position.
Darker green profit labels for better readability and dark red for losses.
📈 Capital Curve (Performance Visualization):
Capital growth curve available (hidden by default, can be enabled via settings).
📏 Dynamic Label Positioning:
Label positions adjust dynamically based on the price range.
Ensures consistent visibility across low and high-priced assets.
⚡ How It Works:
Long Entry:
Triggered when the price crosses above the SMA.
TP level is calculated as a user-defined percentage above the entry price.
Short Entry:
Triggered when the price crosses below the SMA.
TP level is calculated as a user-defined percentage below the entry price.
TP Execution:
Positions close immediately once the TP level is reached (no candle close confirmation needed).
🔔 Alerts:
🟩 Long Signal Alert: When the price crosses above the SMA.
🟥 Short Signal Alert: When the price crosses below the SMA.
🎯 TP Alert: When the TP target is reached.
⚙️ Customization Options:
📅 SMA Period: Choose the moving average period that best fits your strategy.
🎯 Take Profit (%): Adjust TP percentages for flexible risk management.
💵 Initial Capital: Set the starting capital for realistic backtesting.
📈 Capital Curve Toggle: Enable or disable the capital curve to track overall performance.
🌟 Why Use This Tool?
🔧 Flexible Strategy Creation: Adjust core parameters and create tailored SMA-based strategies.
📈 Performance Proof: Capital tracking acts as real proof of profitability after each trade.
🎯 Immediate TP Execution: No waiting for candle closures; profits lock in as soon as targets are hit.
💹 Comprehensive Performance Insights: Percentage-based and absolute capital tracking with dynamic visualization.
🏦 Clean Visual Indicators: Strategy insights made clear with dynamic labeling and adjustable visuals.
⚠️ Disclaimer:
This script is provided for educational and informational purposes only. Trading financial instruments carries risk, and past performance does not guarantee future results. Always perform your own due diligence before making any trading decisions.
