TRharmonic Fib & Pi Bands
# TRharmonic Fibonacci & Pi Bands - Technical Guide
## Theoretical Framework
The indicator is built with dynamic volatility bands based on different Fibonacci ratios (φ = 1.618, φ² = 2.618, φ³ = 4.236, φ⁴ = 6.854) and the mathematical constant π (3.14159) as deviation multipliers. The calculation of the central tendency occurs through using 9 different types of moving averages each (with specific mathematically properties) that are designed for market cycles.
## Moving Average Specifications
**Classical Averages**: Classic Averages are calculated as: Simple MA – arithmetic mean; Exponential MA – weighted, with exponentially decreasing weights, α = 2/(n+1); Smoothed MA (Wilder's RMA) – smoothed using α = 1/n to suppress noise effect; Triangular MA - double-smoothed SMMA(SMMA(x, ⌈(n+1)/2⌉), ⌊(n+1)/2⌋).
Advanced Averages: The Hull MA diminishes delay using the formula WMA(2×WMA(n/2) - WMA(n), √n). Kernel-based estimators utilize Epanechnikov (‘parabolic’, 1-u²) and Gaussian (‘exponential’, exp(-½(i/σ )²) kernel functions. Tis would correspond to the fi new’ expectation via its statistical mean which, for rate dominated data (multiplicative) is the Harmonic = n/Σ(1/xᵢ) (Geometric = exp(Σ log(xᵢ)/n), for multiplicative).
## Band Construction Methodology
Bands are derived from the basis ± (ATR × Fibonacci/Pi multiplier × width coefficient), where ATR undergoes 200-period calculation followed by 100-period RMA smoothing for stability. Each resultant band is further refined via Hull MA to eliminate discontinuities while preserving responsiveness.
## Projection Algorithm
Future band trajectories are extrapolated using MA-type-specific mathematical models. Linear extrapolation applies to SMA; exponential decay characterizes EMA/RMA projections; Hull MA incorporates second-derivative acceleration terms (x + vt + ½at²). Harmonic and Geometric projections operate in reciprocal (1/x) and logarithmic (log x) domains respectively, ensuring mathematical consistency with their computational foundations.
## Interpretation Guidelines
Positioning relative to the basis above it indicates bullishness, while sub-basis positioning suggests bearishness. Heavy band penetration (Fib 3-4 zones) indicates a potential change in market direction or mean reversion. The π band becomes an intermediate reference line between Fib 2 and Fib 3, which often works as a sort of dynamic S/R level because of its commonality in cyclic phenomenon.
## Liquidity Sweep Detection
The former highlights swing pivots beyond Fib 3 bands (according to Smart Money Concepts, institutional liquidity harvesting), reminiscent of possible reversals and thus deserving more attention analysis-wise.
دورات
Clear TICK [YH]Clear TICK is a lightweight, at-a-glance market breadth indicator designed to display the NYSE TICK ( USI:TICK ) feed (TradingView symbol `USI:TICK`) in a floating “status window” on your chart. Its primary purpose is to give you an immediate read on intraday buying versus selling pressure by showing a single, continuously updating TICK value labeled as `TICK: `. Rather than plotting a full oscillator pane, it keeps the chart clean while still providing actionable breadth context—particularly useful for index traders (SPX/ES, NQ, YM) and anyone timing entries/exits around internal market strength.
To reduce noise and avoid overreacting to single, transient spikes, the displayed value is the **simple moving average (SMA) of the last N TICK samples**, where **N is configurable**. By default, **N = 3**, meaning the indicator smooths the raw TICK feed with a short SMA to provide a steadier signal while remaining responsive. The indicator then applies threshold logic to categorize conditions as **Neutral** when the smoothed TICK is between **-150 and 150**, **Bullish** when it is **greater than 150**, and **Bearish** when it is **less than -150**. These categories drive the background/foreground styling of the floating window, making regime changes immediately visible.
All visual styling is configurable in the indicator settings. You can set the **window position** (Top Right by default, with Top Left / Bottom Right / Bottom Left options) and customize both **background and foreground (text) colors** independently for Neutral, Bullish, and Bearish states. The defaults are: **Neutral** = light gray background with **white** text, **Bullish** = light green background with **black** text, and **Bearish** = red background with **white** text. This ensures the text remains readable across states while preserving a clear visual association between regime and color.
Typical use cases include validating breakouts (bullish breadth confirmation when TICK is persistently above the bullish threshold), filtering mean reversion entries (e.g., avoiding long fades when breadth remains strongly bullish), and timing risk management decisions (e.g., tightening stops when the indicator flips bearish during a long). Because it is smoothed, it is well-suited to traders who want breadth confirmation without constant flicker. For faster sensitivity, reduce N (e.g., 1–2); for a calmer, more “regime” oriented read, increase N (e.g., 5–10), depending on your trading timeframe and tolerance for noise.
Your feedback is appreciated!
Happy trading,
Yuval Haspel.
CNE - Efficient Swing Structure + MomentumThe CNE Efficient Swing Structure and Momentum indicator is a sophisticated technical analysis tool designed to quantify the strength and exhaustion of price movements relative to genuine market structure rather than arbitrary time constraints. Unlike traditional oscillators that reset based on a fixed number of candles, this indicator anchors its calculations to confirmed structural pivots. The foundation of the system is a volatility-adaptive swing detection algorithm that utilizes the Average True Range (ATR) to filter out insignificant noise. A trend change is only registered when price retraces against the current direction by a user-defined multiple of the ATR, ensuring that the tool remains locked onto the prevailing trend until a statistically significant reversal occurs. This mechanism allows the trader to view momentum as a cumulative force continuously building from a verified low or high, providing a pure view of the current leg's intensity.
Once a structural anchor is established, the indicator calculates the "Pivot-to-Pivot" momentum, displaying the percent change from the start of the trend to the current price. This creates a zero-based oscillator where the zero line represents the structural origin—the absolute bottom of the current uptrend or the absolute top of the current downtrend. To contextualize this raw data, the script overlays dynamic statistical bands based on standard deviations. These bands function similarly to Bollinger Bands but are applied to the momentum of the swing itself. When the momentum histogram pushes into the outer deviation bands, specifically beyond two standard deviations, it signals that the current move is statistically overextended relative to the asset's recent volatility profile. This helps traders distinguish between a healthy, sustainable trend and a climactic move that is prone to a mean-reversion snapback.
A critical feature of this system is its ability to visualize the "average extension" of market moves, providing an immediate benchmark for trade management and target setting. The indicator plots two distinct sets of lines for both upward extensions and downward drawdowns without relying on heavy historical arrays, ensuring optimal computational efficiency. The first is a solid step-line representing the historical average of all past swings, serving as a long-term baseline for what constitutes a "normal" move. The second is a dotted marker representing a recency-weighted average, heavily biased toward the last five swings. By comparing these two lines, a trader can instantly gauge the changing market regime; if the recent weighted average is expanding away from the historical baseline, volatility is increasing, whereas a contracting recent average suggests the market is entering a period of compression.
Finally, the indicator integrates automated divergence detection based on structural flips rather than simple candle-to-candle comparisons. It records the peak momentum value of every completed trend leg and compares it to the peak of the previous leg in the same direction. If price makes a new structural high but the momentum oscillator fails to surpass the peak of the previous uptrend leg, a bearish divergence is flagged. Conversely, if price pushes to a new structural low with weaker downside momentum than the prior drop, a bullish divergence is highlighted. This combination of volatility-filtered structure, statistical deviation bands, efficiency-optimized extension targets, and structural divergence creates a comprehensive framework for assessing the probability of trend continuation versus reversal.
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Push3 V5Divergence in trading charts signals potential trend reversals by showing a mismatch between price action and an oscillator indicator. There are two main types: regular divergence (predicting trend exhaustion) and hidden divergence (suggesting trend continuation). Regular bearish divergence occurs when price makes a higher high, but the indicator (like RSI or MACD) forms a lower high, indicating weakening upward momentum. Conversely, regular bullish divergence appears when price prints a lower low, but the indicator forms a higher low, hinting at slowing downward momentum. Hidden bearish divergence happens during a pullback in an uptrend where price makes a higher low, but the indicator shows a lower low, suggesting the uptrend will resume. Hidden bullish divergence occurs in a downtrend pullback where price forms a lower high, but the indicator makes a higher high, implying the downtrend will continue. Divergence is a powerful warning tool, but it should always be confirmed with other analysis techniques, as acting on it alone can lead to false signals.
Strategy Battle: Lump Sum vs. DCA vs. Dip BuyingSummary This indicator is a "Strategy Battle" simulator designed to answer the ultimate investing question: Is it better to invest immediately, Dollar Cost Average (DCA), or wait for a market crash?
Unlike standard back-testers, this script simulates a realistic "High-Yield Savings" environment. It acknowledges that cash sitting on the sidelines is not dead money—it earns interest (e.g., 3-5%) while waiting for a buying opportunity. This levels the playing field and allows for a fair comparison between being fully invested vs. keeping "dry powder" for a crash.
The script compares 4 distinct strategies simultaneously on your chart, starting with a fresh yearly budget every January 1st.
he 4 Strategies
🔵 Option 1: Lump Sum (The "Set & Forget")
Takes the entire yearly budget and invests it all on the first trading day of the year.
Pros: Maximizes "time in the market."
Cons: vulnerable to buying at immediate peaks.
🟠 Option 2: DCA (The "Steady Earner")
Splits the yearly budget into 12 equal parts.
Invests monthly regardless of price.
The "Fairness" Twist: The money waiting to be spent sits in the cash pile and accumulates interest until it is deployed.
🟢 Option 3: Regression Sniper (The "Math Hunter")
Keeps the entire budget in cash (earning interest).
Watches a dynamic Linear Regression Channel.
Trigger: If the price drops below the channel, it goes "All-In," deploying all accumulated cash and interest immediately to buy the dip.
🔴 Option 4: Manual Sniper (The "Trend Hunter")
Keeps the entire budget in cash (earning interest).
Watches a User-Defined Growth Line (e.g., a straight line growing at 10% per year).
Trigger: If the price drops below this specific valuation line, it goes "All-In."
Detailed Settings & Options
💰 Money Settings
Yearly Budget ($): The amount of fresh capital injected into the simulation every January 1st.
Cash Interest Rate (%): The annual interest rate earned on uninvested cash (compounded monthly). This is crucial for accurately simulating the "opportunity cost" of holding cash.
⚙️ Sniper Settings (Option 3)
Channel Baseline Length: How far back the math looks to determine the "fair value" curve.
Vertical Shift (%): Move the buy zone up or down. Negative numbers (e.g., -5) make the strategy more conservative, waiting for deeper crashes.
Source: Defaults to Low to catch market wicks and intraday crashes.
📈 Manual Line Settings (Option 4)
Start Price ($): The valuation of the asset at the start of the simulation (Jan 1, Start Year).
Yearly Growth (%): The expected "fair" growth rate of the asset (e.g., S&P 500 average is ~10%).
Vertical Shift (%): Slide the manual line up or down to fine-tune your buy signal.
👁️ Visual Settings
Show Buy Price: Displays the exact dollar amount invested and the stock price at the moment of the buy on the chart labels.
Show Lump Sum Markers: Adds a Blue label at the start of every year to visualize the Lump Sum entry.
Show DCA Markers: Adds small Orange labels for every monthly buy.
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Gold Seasonality Pro [Sultan]Discover high-probability monthly cycles with the Sultan Seasonality Dashboard. This tool analyzes years of historical data to reveal the bullish and bearish tendencies of any asset, specifically optimized for Gold (XAUUSD) traders.
The Power of Time-Based Edge In trading, "When" is just as important as "Where." The Sultan Seasonality Dashboard Pro is a data-mining tool that scans historical monthly closes to identify recurring seasonal patterns. By understanding how an asset has performed over the last 10–20 years during a specific month, traders can align their bias with long-term institutional cycles.
How It Works (Logic & Originality) Unlike static charts, this script uses a dynamic String-Passing Engine inside a request.security call. This allows the script to fetch Monthly (1M) data even if you are viewing a 5-minute or 15-minute chart.
Win Rate Calculation: It calculates the percentage of times a month closed higher (Bull %) vs. lower (Bear %).
Average Displacement: It shows the average percentage move for each month, helping you set realistic monthly targets.
Zero-Repaint Technology: Built with lookahead=barmerge.lookahead_off to ensure that historical data stays accurate and is not influenced by future prices.
Features:
Any Timeframe Compatibility: Works on any chart without losing the monthly context.
Custom Lookback: Analyze the last 5, 10, or even 20 years of history.
Real-Time Highlight: The dashboard automatically highlights the current month so you can quickly see the historical odds for your active trades.
How to use:
If Jan shows a 70% Bullish tendency and you are in a long trade, you have historical probability on your side.
Use the Avg Move to gauge if the current month’s volatility is normal or exhausted.
Weekly Financial Liquidity Index (With Overlay, Corr, Shift)Skylark Digital Assets — Weekly Financial Liquidity Proxy (WFLI) (Overlay) + Rolling Correlation + Lead/Lag Shift
The Weekly Financial Liquidity Proxy (WFLI) is a macro-liquidity regime gauge designed to sit directly on your price chart (overlay). It compresses a diversified set of “liquidity-sensitive” markets into a single weekly signal, then lets you quantify how tightly your current ticker has been moving with liquidity via a rolling correlation, and phase-align the relationship using lead/lag shifting in both weeks and months.
What you’ll see
WFLI line (overlay): Plotted on the main chart so you can visually compare liquidity conditions with price action.
Rolling correlation: A continuously updating correlation reading showing how strongly your current symbol is tracking WFLI over the chosen lookback window.
Lead/Lag shift (weeks + months): Offsets WFLI forward/backward to help align real-world phase differences (because different assets respond to liquidity on different timelines).
How to use it
Regime filter:
Rising WFLI tends to align with risk-on / expanding liquidity backdrops.
Falling WFLI tends to align with risk-off / tightening liquidity backdrops.
Confirmation & divergence:
If price is breaking out while WFLI is deteriorating, treat it as a potential fragility / divergence signal.
If WFLI turns up before price stabilizes, it can help identify early shifts in conditions.
Correlation as a “relationship strength” meter:
High positive correlation = the asset has recently been behaving like a liquidity follower.
Low/unstable correlation = the asset is being driven by idiosyncratic factors (earnings, sector shocks, narratives, supply events, supply/demand quirks, etc.).
Lead/Lag shift to phase-align:
Use the shift controls to find the offset where correlation is most stable/meaningful.
This is useful when an asset typically reacts after liquidity changes (lagger) or anticipates them (leader).
Using WFLI on the monthly timeframe
Even though this is a weekly liquidity proxy, it’s intentionally useful on the monthly chart as well. Viewing WFLI on the monthly timeframe smooths noise, makes regime shifts more readable, and (in practice) does not reduce efficacy—it simply presents the same underlying signal through a slower lens, which can be ideal for macro alignment and longer-horizon positioning.
Inputs (high level)
Rolling correlation length: Lookback window for the correlation calculation.
Shift controls:
Weeks shift (fine adjustment for weekly relationships)
Months shift (coarse adjustment for longer macro phase drift)
Optional display toggles (if included in your script): show/hide correlation, labels, smoothing, etc.
Notes & limitations
Correlation is not causation. Treat it as a diagnostic for behavioral alignment, not a guarantee.
Lead/lag is non-stationary: relationships can compress/expand across cycles and volatility regimes.
Built for context and structure, not as a standalone entry/exit system.
Educational use only — not financial advice.
SAT LevelsThis indicator shows the following:
HTF:
- Yearly Range (+ Previous Year) and its Eq Levels
- Quarterly Range (+ Previous Quarter) and its Eq Levels
- Monthly Range (+ Previous Month) and its Eq Levels
- Weekly Range (+ Previous Week) and its Eq Levels
Intraday Levels:
- Yesterday's Range (+ Day Before Yesterday 'DBY') and its Eq Levels
- Premarket Range (4am - 9.29am)
- 1min range (9.30 candle)
- 5min range (9.30-9.34)
- 15min range (9.30-9.44)
Z-Score Momentum Dashboard Z-Score Momentum Dashboard: A Comprehensive Technical Analysis Framework
Understanding the Z-Score Momentum Dashboard
The Z-Score Momentum Dashboard represents a sophisticated evolution in technical analysis indicators, designed to synthesize multiple analytical frameworks into a singular, coherent probabilistic assessment of market conditions. At its core, this indicator is a multi-dimensional analytical engine that processes price action, volume dynamics, cyclical patterns, and statistical anomalies to generate standardized z-scores that measure how far current market behavior deviates from established norms. Unlike traditional single-metric indicators that examine price through one lens, this dashboard constructs a comprehensive probabilistic model by weighting and combining six distinct analytical domains: Ehlers bandpass filtering for cycle detection, momentum calculations across multiple timeframes, mean reversion tendencies, trend strength measurements, volatility regime analysis, and volume confirmation signals.
The indicator operates by first calculating individual scores across each of these six domains, normalizing them into comparable z-score formats, then applying user-configurable weights to create a composite probability score that estimates the likelihood of upward price movement. This probability undergoes statistical transformation through hyperbolic tangent functions to ensure bounded outputs between zero and one, which are then compared against historical baselines to generate the final z-score reading. The z-score itself becomes the primary signal, indicating not just direction but the statistical significance of the current market state relative to recent history. When the z-score exceeds predefined thresholds, it suggests the market has entered a regime that statistically differs from the baseline, implying either strong momentum continuation or potential exhaustion depending on accompanying contextual indicators.
The dashboard visualization provides traders with immediate access to critical information through a comprehensive table display that shows historical z-scores over the past five days, current probability assessments, trend classification, momentum measurements, acceleration metrics, and distance from moving averages. This multi-temporal perspective allows traders to observe not just the current state but the trajectory of change, identifying whether momentum is building, plateauing, or reversing. The indicator also generates regime classifications such as "PARABOLIC EXT," "OVERSOLD," "STRONG MOM," and "NEUTRAL," which combine z-score readings with price extension metrics to characterize the current market environment. These classifications directly inform suggested actions, ranging from "Ride trend w/ stops" during strong momentum periods to "Watch for reversal" during oversold conditions with increasing momentum, providing traders with contextually appropriate strategic guidance.
The Special Nature of This Analytical Approach
What distinguishes the Z-Score Momentum Dashboard from conventional technical indicators is its fundamental philosophical approach to market analysis, which embraces probabilistic thinking rather than deterministic prediction. Most traditional indicators generate binary signals or directional recommendations based on threshold crossovers or pattern recognition, implicitly suggesting certainty about future price movement. This dashboard, in contrast, explicitly models uncertainty by generating probability distributions and measuring statistical significance, acknowledging that markets are stochastic systems where edge comes from systematic bias rather than predictive certainty. By converting diverse technical signals into standardized z-scores, the indicator creates a common language for comparing fundamentally different types of market information, whether that information comes from price momentum, volume patterns, or cyclical oscillations.
The pseudo-machine learning architecture embedded within the indicator represents another distinctive feature that elevates it beyond standard technical analysis tools. While Pine Script limitations prevent the implementation of actual neural networks or gradient-boosted decision trees, the indicator approximates ensemble learning principles by treating each analytical domain as a separate "model" whose outputs are weighted and combined. Users can adjust these weights based on their market beliefs or through backtesting optimization, effectively training the indicator to emphasize whichever analytical dimensions prove most predictive in their specific trading context. This flexibility means the same indicator can be configured for mean-reversion trading in range-bound markets by increasing mean reversion weights, or for momentum trading in trending markets by emphasizing trend and momentum components, making it adaptable across varying market regimes without requiring entirely different analytical tools.
The integration of John Ehlers' digital signal processing concepts, particularly the bandpass filtering and super smoother functions, introduces engineering-grade analytical precision to financial market analysis. Ehlers' work translates aerospace and telecommunications signal processing mathematics into trading applications, allowing the indicator to isolate specific cyclical frequencies within price action while filtering out noise. This is fundamentally different from simple moving averages or oscillators that indiscriminately smooth price data; bandpass filters can extract the 10-day cycle component separately from the 20-day cycle component, identifying when multiple cycles align or diverge. The inclusion of these sophisticated filters alongside more conventional tools creates a hybrid analytical framework that combines the mathematical rigor of quantitative finance with the practical market wisdom embedded in traditional technical analysis.
The dashboard's temporal analysis capabilities provide another layer of analytical depth rarely found in standalone indicators. By displaying five days of historical z-scores alongside current readings, the interface enables pattern recognition at the signal level rather than just the price level. Traders can observe whether z-scores are trending, oscillating, or demonstrating divergent behavior relative to price action. For instance, if price continues making new highs while z-scores decline, this suggests deteriorating statistical support for the advance despite superficial price strength, providing early warning of potential reversals. Similarly, rising z-scores during price consolidation indicate building statistical pressure that may soon manifest as directional movement. This meta-analytical capability transforms the indicator from a simple signal generator into a comprehensive framework for understanding the statistical character of market behavior.
Algorithmic Superiority and Technical Advantages
The algorithmic architecture of the Z-Score Momentum Dashboard demonstrates several technical advantages that contribute to its analytical power and practical utility. The normalization of disparate technical indicators into standardized z-scores solves a fundamental problem in multi-factor analysis: how to combine indicators with different scales and units into a coherent composite signal. A momentum reading measured in price points cannot be directly compared to an RSI reading measured on a 0-100 scale, nor to a volume ratio measured as a multiplier. By converting each measure into a z-score representing standard deviations from its respective mean, the indicator creates dimensional consistency, ensuring that each component contributes proportionally to the final composite score based on its statistical deviation rather than its nominal value.
The use of adaptive baselines through rolling statistical windows provides robustness against regime changes and non-stationary market behavior. Rather than comparing current readings against fixed historical values or statically defined overbought/oversold levels, the indicator continuously recalculates mean and standard deviation estimates over the user-defined baseline period. This approach automatically adjusts to changing volatility regimes, market cycles, and structural shifts in price behavior. During high-volatility periods, the standard deviation increases, requiring larger absolute deviations to generate extreme z-scores, appropriately raising the bar for signal generation. Conversely, during low-volatility periods, smaller absolute movements can generate significant z-scores, maintaining signal sensitivity across diverse market conditions.
The composite probability calculation employs mathematically sound transformation functions rather than arbitrary scaling. After weighting and combining individual z-scores into a composite score, the indicator applies hyperbolic tangent transformation to convert the unbounded composite score into a bounded probability estimate between zero and one. The tanh function was chosen specifically because its sigmoid-shaped curve smoothly compresses extreme values while maintaining sensitivity around the center, preventing outlier distortion while preserving information about moderate deviations. This is superior to linear scaling or simple threshold clamping, which can create artificial discontinuities or lose information about the magnitude of extreme readings. The subsequent z-score calculation on this probability distribution creates a second-order statistical metric that measures not just "is probability high?" but "is probability statistically significantly higher than typical?" This layered statistical approach provides more nuanced information than single-stage calculations.
The incorporation of acceleration metrics alongside momentum measurements adds a crucial dimension to the analytical framework. While momentum measures the first derivative of the z-score (rate of change), acceleration measures the second derivative (rate of change of the rate of change), identifying inflection points where momentum itself shifts. Markets often reverse not when momentum reaches zero but when acceleration reverses, as this indicates the rate of momentum decay is accelerating even while momentum remains positive. By explicitly calculating and displaying acceleration, the indicator provides early warning of potential trend exhaustion before momentum fully dissipates. This mathematical sophistication mirrors concepts from physics and calculus, applying them to financial market dynamics in ways that enhance predictive capability.
The multi-timeframe momentum analysis embedded within the indicator examines price changes over five, ten, and twenty periods, capturing different temporal scales of market behavior. Short-term momentum captures immediate price action and trading range dynamics, while longer-term momentum reflects sustained directional bias and major trend development. By combining these timeframes into a weighted average before calculating z-scores, the indicator synthesizes information across temporal scales, avoiding the myopia of single-timeframe analysis. This approach recognizes that market structure exists simultaneously at multiple frequencies, and robust signals often emerge when momentum aligns across timeframes, while divergences between timeframes can signal pending reversals or consolidations.
Predictive Power Through Cyclical Analysis
The integration of cyclical analysis into the Z-Score Momentum Dashboard represents one of its most powerful predictive capabilities, leveraging the empirical observation that financial markets exhibit periodic behavior driven by fundamental economic cycles, seasonal patterns, trader psychology, and technical feedback loops. The Ehlers bandpass filters implemented in the indicator specifically isolate cyclical components at 10, 15, and 20-day periods, frequencies that correspond to common trading cycles including bi-weekly, monthly, and quarterly rhythms in market activity. By extracting these specific frequency bands and measuring their slope, the indicator identifies when cycles are aligned in the same directional phase versus when they are diverging, with aligned cycles providing stronger predictive signals than single-frequency readings.
Cyclical analysis offers predictive power because cycles, by definition, have characteristic wavelengths that enable forecasting of future turning points based on the current phase. If the indicator detects that the 10-day cycle is in a trough phase while the 20-day cycle is also declining, it can anticipate that the shorter cycle should begin turning upward before the longer cycle, potentially creating a bullish divergence or early reversal signal. Conversely, when a shorter cycle reaches a peak while longer cycles continue rising, this suggests the current rally may consolidate before the longer-cycle momentum can drive new highs. This phase relationship analysis transforms cyclical information from descriptive to predictive, allowing traders to position ahead of probable turning points rather than merely reacting to them.
The bandpass filtering approach is particularly valuable because it separates signal from noise more effectively than conventional smoothing techniques. Traditional moving averages suppress both high-frequency noise and the actual signal being measured, creating lag and reducing responsiveness. Bandpass filters, in contrast, selectively attenuate frequencies outside the target band while preserving amplitude and phase information within the band, maintaining the timing and magnitude of the actual cyclical component. This means when the bandpass output changes, it reflects genuine change in the underlying cycle rather than random noise or smoothing artifacts. The z-score normalization of bandpass slopes then measures whether the current cyclical momentum is statistically unusual relative to recent history, identifying periods when cyclical forces are particularly strong or weak.
The integration of Fisher Transform calculations further enhances cyclical predictive power by converting price oscillations into a nearly Gaussian probability distribution. Financial price data typically exhibits non-normal distributions with fat tails and skewness, which violate the assumptions underlying many statistical techniques. The Fisher Transform specifically addresses this by mapping the price data onto a normal distribution where standard statistical inference tools work more reliably. When applied to cyclical data, this transformation makes it possible to accurately assess the statistical significance of cycle phases and turning points, distinguishing between normal cyclical oscillation and statistically significant deviations that may precede major price movements.
The Schaff Trend Cycle component adds another dimension to cyclical analysis by combining MACD calculations with stochastic smoothing to identify trending phases within broader cyclical structures. Markets often exhibit fractal behavior where trends exist within cycles which exist within larger trends. The Schaff indicator specifically addresses this nested structure by detecting when shorter-term trends are emerging within the dominant cycle, providing early identification of trend changes before they become apparent in price action. When the Schaff reading aligns with bandpass filter signals and overall z-score direction, it confirms that multiple analytical perspectives agree on current cyclical phase, increasing confidence in directional predictions.
The Detrended Price Oscillator (DPO) calculation removes trend components to isolate pure cyclical behavior, addressing a common challenge in cyclical analysis where strong trends can mask underlying cycles. By comparing current price to a centered moving average, the DPO reveals cyclical patterns that persist regardless of trend direction, allowing the indicator to maintain cyclical awareness in both trending and ranging markets. This is particularly valuable because cycles often continue operating during trends but become invisible to trend-following indicators, yet these cycles can predict pullbacks, consolidations, and acceleration phases within the larger trend. The incorporation of DPO signals into the composite z-score calculation ensures that cyclical information contributes to the final reading even when dominated by strong directional momentum.
Practical Trading Application and Strategic Implementation
Implementing the Z-Score Momentum Dashboard in practical trading requires understanding both its signal generation logic and the appropriate strategic frameworks for acting on its outputs. The primary trading signal comes from the overall z-score reading relative to the trigger and extreme thresholds, which by default are set at 1.25 and 2.0 respectively. When the z-score exceeds the trigger threshold, it indicates that current market behavior is more than 1.25 standard deviations above the recent baseline, suggesting statistically significant bullish momentum. Traders can interpret this as a regime shift from neutral to bullish conditions, warranting either initiation of long positions or continuation of existing long exposure with trailing stops. The strength of this signal increases when the z-score crosses the extreme threshold, indicating the market has entered a parabolic phase that, while statistically unusual, may represent either climactic buying or unsustainable conditions prone to mean reversion.
The regime classifications provide contextual interpretation that modifies how traders should approach z-score signals. A z-score above the trigger threshold combined with moderate price extension from the 20-period moving average generates a "STRONG MOM" regime classification with the recommended action "Ride trend w/ stops," suggesting that traders should maintain directional exposure while using trailing stop-loss orders to protect profits if momentum reverses. In contrast, a z-score above the trigger threshold but with extreme price extension generates a "PARABOLIC EXT" classification with the action "Mean rev UP expected," warning that despite strong statistical momentum, the price has deviated too far from its moving average and may soon consolidate or reverse toward the mean. This nuanced interpretation prevents traders from blindly chasing extended moves even when z-scores remain elevated.
The trend classification system—identifying RISING, FALLING, BOTTOMING, and TOPPING patterns—provides crucial information about the trajectory of statistical momentum rather than just its current level. A RISING classification indicates that not only is the z-score positive, but it has been consistently increasing over recent periods, suggesting accelerating momentum and increasing statistical support for directional movement. Traders can use this to distinguish between stable momentum that may continue and deteriorating momentum that may reverse, informing position sizing and stop-loss placement decisions. BOTTOMING and TOPPING classifications specifically identify potential inflection points where the direction of z-score movement is changing, generating early reversal signals before z-scores cross back through neutral territory.
For mean reversion traders, the indicator provides exceptional value when z-scores reach extreme negative levels (below -2.0) while showing BOTTOMING trend patterns and positive acceleration. This combination suggests that statistical momentum has reached an extreme oversold condition and is beginning to reverse, creating favorable risk-reward opportunities for counter-trend long positions. The extension metric provides additional confirmation, as extreme negative extension from the moving average creates mechanical pull toward the mean independent of momentum considerations. Traders can enter positions when these factors align, using the moving average as an initial profit target and the z-score returning to neutral as a signal for position closure or transition to trend-following mode.
For trend-following traders, the indicator is most valuable when z-scores remain elevated above the trigger threshold for extended periods with RISING or stable trend patterns and positive momentum readings. This indicates persistent statistical support for the trend rather than a temporary spike, justifying larger position sizes and wider stop-loss placement. The momentum and acceleration metrics help trend followers distinguish between healthy trends with sustained momentum and exhausted trends where momentum is decelerating, allowing for timely exit before reversals occur. When momentum and acceleration both turn negative while z-scores remain positive, it signals that the statistical foundation of the trend is eroding even though the trend nominally persists, prompting trend followers to tighten stops or take partial profits.
The component scores displayed in the dashboard enable advanced traders to perform qualitative analysis of what factors are driving the composite z-score reading. If the composite z-score is positive but the breakdown shows that bandpass and momentum scores are negative while mean reversion scores are strongly positive, this indicates that the bullish reading is driven primarily by oversold mean reversion potential rather than directional momentum. Traders can use this information to adjust their trading approach, perhaps favoring short-term reversal trades over longer-term trend follows. Conversely, if all components show aligned readings, it suggests broad-based agreement across analytical dimensions, increasing confidence in the signal and potentially warranting larger position sizes or longer holding periods.
Integration with broader trading systems can enhance the indicator's effectiveness. Traders might use the z-score as a filter for other strategies, taking long signals from separate systems only when the z-score is positive or trading reversal patterns only when z-scores are extreme. Alternatively, the indicator can serve as a portfolio allocation tool, increasing equity exposure when z-scores are positive and reducing exposure or shifting to defensive positions when z-scores turn negative. The probability estimates can be directly incorporated into Kelly Criterion or other position sizing formulas, scaling position sizes proportionally to the estimated probability of upward movement adjusted for risk-reward ratios of specific trade setups.
Alert conditions built into the indicator provide automated monitoring capabilities, notifying traders when z-scores cross critical thresholds or when trend patterns change from FALLING to BOTTOMING or RISING to TOPPING. These alerts enable traders to monitor multiple instruments without constant chart watching, maintaining awareness of regime changes across a diversified portfolio. The alerts for extreme z-scores specifically warn of potential climactic conditions that may require immediate attention, whether to take profits on existing positions or to prepare for reversal opportunities.
The customization options allow traders to optimize the indicator for specific instruments and market conditions. The baseline period parameter controls the lookback window for calculating statistical norms, with shorter periods making the indicator more responsive to recent conditions at the cost of increased noise, while longer periods provide stability but slower adaptation to regime changes. The weight parameters enable traders to emphasize whichever analytical dimensions prove most predictive in their specific markets, potentially increasing trend weights for strongly trending instruments like technology stocks while increasing mean reversion weights for range-bound commodities or currencies. Through systematic backtesting and forward validation, traders can develop instrument-specific configurations that maximize the indicator's predictive accuracy.
Ultimately, the Z-Score Momentum Dashboard functions most effectively as a comprehensive analytical framework rather than a standalone trading system, providing rich statistical context that enhances decision-making across diverse trading approaches. Whether used for discretionary trade timing, systematic signal generation, risk management, or portfolio allocation, the indicator's multi-dimensional analysis, cyclical awareness, and probabilistic framework offer traders a sophisticated tool for understanding and responding to statistical patterns in market behavior that persist across timeframes, instruments, and market regimes.
QQQ 2025 Bucket ATR (Price & Volume)Work on QQQ, 1-minute timeframe.
Restrict to the year 2025
Breaks the Trading Day into buckets:
9:30–10:30
10:30–11:30
11:30–12:30
12:30–13:30
13:30–14:30
14:30–15:30
15:30–16:00
For each bucket, across all 2025 trading days, compute:
Price ATR-style movement (true range for that bucket)
“ATR” on bucket volume (day-to-day change in total bucket volume)
Average total volume per bucket
OPHANIM MARKET PULSESMT, CISD, IFVG, QUARTERLY. This indicator is made and crefted by ophanim_1 on ig, it is made based on a personal model that has over 75% win rate ratio if well used. please use it alongside killzones not oustside. preferably london or new york. works with every pair
Intervalo de la confianza usando VWMA 5,10,14,55,90,200Varios Itervalos de Confianza usando VWMA
-LOS QUE MANIPULAN LOS MERCADOS, ES COMPRAR DONDE LA VOLATILIDAD ES BAJA, NO HAY RUIDO.
-DESPUES QUE COMPRAN, SU PROXIMO TRABAJO ES CREAR LA VARICIA=FOMO Y MANDAR UNA TARJETA DE INVITACION A LOS INVERSIONISTA MINORITARIOS.
-DESPUES QUE LOS MINORISTA ENTRAN EN CONFIANZA Y VARICIA-FOMO,VENDEN LOS QUE MANIPULAN LOS MERCADOS
-SU ULTIMA ETAPA ES VENDER MAS AGRESIVO PARA CREAR UN MIEDO=FUD Y DARLES EN EL CODO A LOS MINORISTAS PARA QUE SALGAN PERDIENDO.
ESTE CICLOS SE REPITE EN LOS MERCADOS.
Si las personas que operan los mercados tiene sintimentos donde el meido y la varicia entran en el juego de las inversiones y trade, entoces hay que medir como esta su miedo y varicia en diferentes temporaliades.
Que es mejor mediar esta varicia y miedo usando Intervalo de la Confianza usando el VWMA .
AHORA CON ESTA HERRAMIENTA
Ustedes solo tiene que encontrar como esta esta el FOMO o FUD en diferentes temporalidades.
Multiple Confidence Intervals Using VWMA
- Market manipulators buy where volatility is low and there is no noise.
- After they buy, their next step is to create volatility (FOMO) and send an invitation to retail investors.
- Once retail investors gain confidence and experience volatility (FOMO), the market manipulators sell.
- Their final stage is to sell more aggressively to create fear (FUD) and force retailers to lose money.
This cycle repeats itself in the markets. If people who trade the markets experience feelings where fear and greed come into play in their investments and trading, then it's necessary to measure how their fear and greed manifest across different timeframes.
What's the best way to measure this greed and fear using the Confidence Interval with the VWMA?
NOW WITH THIS TOOL
You only need to determine how FOMO or FUD manifests across different timeframes.
CGlimit pro 2026This indicator is designed to assist traders in identifying potential limit entry zones along with confirmation signals based on price behavior and technical conditions. It highlights areas where price may react, helping traders plan entries with a structured and disciplined approach.
The indicator provides both Buy Limit and Sell Limit levels, as well as confirmation signals to improve timing and trade confidence. Users can select from four different signal options, allowing flexibility for conservative or aggressive trading styles.
All signals are generated using predefined logic based on historical price data and market structure. This indicator does not predict future price movement and should be used as a decision-support tool, not as a standalone system.
Key features include multi-timeframe compatibility, customizable signal options, and broad market support including Forex, Crypto, Indices, and Stocks. It is suitable for scalping, day trading, and swing trading when combined with proper risk management.
⚠️ This indicator is intended for educational and analytical purposes only and does not provide financial advice. Trading involves risk, and users are responsible for their own trading decisions.
🟢 Why this will FIX the error
✔️ Description long enough
✔️ Explains what indicator does
✔️ Explains signals (4 options)
✔️ No banned words
✔️ TradingView House Rules compliant
📝 Final Checklist (Before clicking Publish)
✅ Description pasted
✅ Category selected
✅ “I swear to abide by House Rules” ticked
✅ Own chart layout used
✅ Publish Private / Protected (NOT public)
CAP - CSICSI is a Digital Signal Processing (DSP) tool based on the principles of Lars von Thienen’s "Dynamic Cycles." Unlike traditional momentum oscillators, the CSI uses a recursive dual-thrust processor to isolate cyclic price action, helping traders identify hidden rhythms in the market rather than just static overbought or oversold levels.
How to Read the Indicator
This script focuses on four primary technical components:
Dynamic Band Pivots: The indicator calculates a "cyclic memory" (default 34 periods) to create high and low bands. When the CSI moves outside these bands and begins to pivot, it signals a potential cycle exhaustion point.
Momentum Slope: The color-coded area fill identifies the direction of the cycle's slope. A change in slope is often the first warning of a cycle peak or trough.
The Zero Line: The zero line acts as the "equilibrium" point. Position relative to zero helps define whether the current cycle is in a bullish or bearish regime.
Multi-Timeframe Analysis (HTF): The script includes an HTF filter (suggested 5x the chart timeframe) to ensure you are trading in the direction of the dominant macro cycle.
Performance & Testing: The "Trending" Challenge
This indicator has been developed and tested primarily on Futures (ES, NQ, RTY) and US Equities.
Important Note on False Signals: While the CSI "nails" turning points during standard cyclic/swing conditions, users should be aware of "phantom" cycles or false signals during strong trending conditions. In a powerful trend, the indicator may signal a cycle peak while price continues to move linearly, leading to premature exhaustion signals. Filtering these "trend-drifts" is the current focus of development.
Community & Collaboration
This script is an ongoing project. I am making it public to find like-minded traders interested in Lars von Thienen’s work to:
Refine the processor logic for better signal-to-noise ratios during impulsive trends.
Discuss the best "Trend Shields" (Volume, HTF, or Volatility filters) to stay in winners longer.
Share specific settings for different asset classes in the Futures and Equity markets.
Previous Day High/LowIndicator – Previous Day High / Low (PDH/PDL)
This indicator is designed to visually highlight the most important price levels from the previous trading day – the Previous Day High (PDH) and Previous Day Low (PDL) – directly on intraday charts. It provides traders with immediate insight into key support and resistance zones, which often act as reversal points or areas where price reacts significantly. The indicator is built to function reliably across all instruments, including stocks, futures, ETFs, and cryptocurrencies, while accurately reflecting the New York Time (NY) timezone.
rosh PACE PRO Locked Look One Signalpace pro, use wit vwap and s/r , xau, btc good enough to genarate 10% profit a ady, use it ,soon i will make it private
