Bitcoin Power Law [LuxAlgo]The Bitcoin Power Law tool is a representation of Bitcoin prices first proposed by Giovanni Santostasi, Ph.D. It plots BTCUSD daily closes on a log10-log10 scale, and fits a linear regression channel to the data.
This channel helps traders visualise when the price is historically in a zone prone to tops or located within a discounted zone subject to future growth.
🔶 USAGE
Giovanni Santostasi, Ph.D. originated the Bitcoin Power-Law Theory; this implementation places it directly on a TradingView chart. The white line shows the daily closing price, while the cyan line is the best-fit regression.
A channel is constructed from the linear fit root mean squared error (RMSE), we can observe how price has repeatedly oscillated between each channel areas through every bull-bear cycle.
Excursions into the upper channel area can be followed by price surges and finishing on a top, whereas price touching the lower channel area coincides with a cycle low.
Users can change the channel areas multipliers, helping capture moves more precisely depending on the intended usage.
This tool only works on the daily BTCUSD chart. Ticker and timeframe must match exactly for the calculations to remain valid.
🔹 Linear Scale
Users can toggle on a linear scale for the time axis, in order to obtain a higher resolution of the price, (this will affect the linear regression channel fit, making it look poorer).
🔶 DETAILS
One of the advantages of the Power Law Theory proposed by Giovanni Santostasi is its ability to explain multiple behaviors of Bitcoin. We describe some key points below.
🔹 Power-Law Overview
A power law has the form y = A·xⁿ , and Bitcoin’s key variables follow this pattern across many orders of magnitude. Empirically, price rises roughly with t⁶, hash-rate with t¹² and the number of active addresses with t³.
When we plot these on log-log axes they appear as straight lines, revealing a scale-invariant system whose behaviour repeats proportionally as it grows.
🔹 Feedback-Loop Dynamics
Growth begins with new users, whose presence pushes the price higher via a Metcalfe-style square-law. A richer price pool funds more mining hardware; the Difficulty Adjustment immediately raises the hash-rate requirement, keeping profit margins razor-thin.
A higher hash rate secures the network, which in turn attracts the next wave of users. Because risk and Difficulty act as braking forces, user adoption advances as a power of three in time rather than an unchecked S-curve. This circular causality repeats without end, producing the familiar boom-and-bust cadence around the long-term power-law channel.
🔹 Scale Invariance & Predictions
Scale invariance means that enlarging the timeline in log-log space leaves the trajectory unchanged.
The same geometric proportions that described the first dollar of value can therefore extend to a projected million-dollar bitcoin, provided no catastrophic break occurs. Institutional ETF inflows supply fresh capital but do not bend the underlying slope; only a persistent deviation from the line would falsify the current model.
🔹 Implications
The theory assigns scarcity no direct role; iterative feedback and the Difficulty Adjustment are sufficient to govern Bitcoin’s expansion. Long-term valuation should focus on position within the power-law channel, while bubbles—sharp departures above trend that later revert—are expected punctuations of an otherwise steady climb.
Beyond about 2040, disruptive technological shifts could alter the parameters, but for the next order of magnitude the present slope remains the simplest, most robust guide.
Bitcoin behaves less like a traditional asset and more like a self-organising digital organism whose value, security, and adoption co-evolve according to immutable power-law rules.
🔶 SETTINGS
🔹 General
Start Calculation: Determine the start date used by the calculation, with any prior prices being ignored. (default - 15 Jul 2010)
Use Linear Scale for X-Axis: Convert the horizontal axis from log(time) to linear calendar time
🔹 Linear Regression
Show Regression Line: Enable/disable the central power-law trend line
Regression Line Color: Choose the colour of the regression line
Mult 1: Toggle line & fill, set multiplier (default +1), pick line colour and area fill colour
Mult 2: Toggle line & fill, set multiplier (default +0.5), pick line colour and area fill colour
Mult 3: Toggle line & fill, set multiplier (default -0.5), pick line colour and area fill colour
Mult 4: Toggle line & fill, set multiplier (default -1), pick line colour and area fill colour
🔹 Style
Price Line Color: Select the colour of the BTC price plot
Auto Color: Automatically choose the best contrast colour for the price line
Price Line Width: Set the thickness of the price line (1 – 5 px)
Show Halvings: Enable/disable dotted vertical lines at each Bitcoin halving
Halvings Color: Choose the colour of the halving lines
Penunjuk dan strategi
CoffeeShopCrypto Supertrend Liquidity EngineMost SuperTrend indicators use fixed ATR multipliers that ignore context—forcing traders to constantly tweak settings that rarely adapt well across timeframes or assets.
This Supertrend is a nodd to and a more completion of the work
done by Olivier Seban ( @olivierseban )
This version replaces guesswork with an adaptive factor based on prior session volatility, dynamically adjusting stops to match current conditions. It also introduces liquidity-aware zones, real-time strength histograms, and a visual control panel—making your stoploss smarter, more responsive, and aligned with how the market actually moves.
📏 The Multiplier Problem & Adaptive Factor Solution
Traditional SuperTrend indicators rely on fixed ATR multipliers—often arbitrary numbers like 1.5, 2, or 3. The issue? No logical basis ties these values to actual market conditions. What works on a 5-minute Nasdaq chart fails on a daily EUR/USD chart. Traders spend hours tweaking multipliers per asset, timeframe, or volatility phase—and still end up with stoplosses that are either too tight or too loose. Worse, the market doesn’t care about your setting—it behaves according to underlying volatility, not your parameter.
This version fixes that by automating the multiplier selection entirely. It uses a 4-zone model based on the current ATR relative to the previous session’s ATR, dynamically adjusting the SuperTrend factor to match current volatility. It eliminates guesswork, adapts to the asset and timeframe, and ensures you’re always using a context-aware stoploss—one that evolves with the market instead of fighting it.
ATR EXAMPLE
Let’s say prior session ATR = 2.00
Now suppose current ATR = 0.32
This places us in Zone 1 (Very Low Volatility)
It doesn’t imply "overbought" or "oversold" — it tells you the market is moving very little, which often means:
Lower risk | Smaller stops | Smaller opportunities (and losses)
🔁 Liquidity Zones vs. Arbitrary Pullbacks
The standard SuperTrend stop loss line often looks like price “barely misses it” before continuing its trend. Traders call this "stop hunting," but what’s really happening is liquidity collection—price pulls back into a zone rich in orders before continuing. The problem? The old SuperTrend doesn’t show this zone. It only draws the outer limit, leaving no visual cue for where entries or continuation moves might realistically originate.
This script introduces 2 levels in the Liquidity Zone. One for Support and one for Stophunts, which draw dynamically between the current price and the SuperTrend line. These levels reflect where the market is most likely to revisit before resuming the trend. By visualizing the area just above the Supertrend stop loss, you can anticipate pullbacks, spot ideal re-entries, and avoid premature exits. This bridges the gap between mechanical stoploss logic and real-world liquidity behavior.
⏳ Prior Session ATR vs. Live ATR
Using real-time ATR to determine movement potential is like driving by looking in your rearview mirror. It’s reactive, not predictive. Traders often base decisions on live ATR, unaware that today’s range is still unfolding —creating volatility mismatches between what’s calculated and what actually matters. Since ATR reflects range, calculating it mid-session gives an incomplete and misleading picture of true volatility.
Instead, this system uses the ATR from the previous session , anchoring your volatility assumptions in a fully-formed price structure . It tells you how far price moved in the last full market phase—be it London, New York, or Tokyo—giving you a more reliable gauge of expected range today. This is a smarter way to estimate how far price could move rather than how far it has moved.
The Smoothing function will take the ATR, Support, Resistance, Stophunt Levels, and the Moving Avearage and smooth them by the calculation you choose.
It will also plot a moving average on your chart against closing prices by the smoothing function you choose.
🧭 Scalping vs. Trending Modes
The market moves in at least 4 phases. Trending, Ranging, Consolidation, Distribution.
Every trader has a different style —some scalp low-volatility moves during off-hours, while others ride macro trends across days. The problem with classic SuperTrend? It treats every market condition the same. A fixed system can’t possibly provide proper stoploss spacing for both a fast scalp and a long-term swing. Traders are forced to rebuild their system every time the market changes character or the session shifts.
This version solves that with a simple toggle:
Scalping or Trend Mode . With one switch, it inverts the logic of the adaptive factor to either tighten or loosen your trailing stops. During low-liquidity hours or consolidation phases, Scalping Mode offers snug stoplosses. During expansion or clear directional bias.
Trend Mode lets the trade breathe. This is flexibility built directly into the logic—not something you have to recalibrate manually.
📉 Histogram Oscillator for Move Strength
In legacy indicators, there’s no built-in way to gauge when the move is losing power . Traders rely on price action or momentum indicators to guess if a trend is fading. But this adds clutter, lag, and often contradiction. The classic SuperTrend doesn’t offer insight into how strong or weak the current trend leg is—only whether price has crossed a line.
This version includes a Trending Liquidity Histogram —a histogram that shows whether the liquidity in the SuperTrend zone is expanding or compressing. When the bars weaken or cross toward zero, it signals liquidity exhaustion . This early warning gives you time to prep for reversals or anticipate pullbacks. It even adapts visually depending on your trading mode, showing color-coded signals for scalping vs. trending behavior. It's both a strength gauge and a trade timing tool—built into your stoploss logic.
Histogram in Scalping Mode
Histogram in Trending Mode
📊 Visual Table for Real-Time Clarity
A major issue with custom indicators is opacity —you don’t always know what settings or values are currently being used. Even worse, if your dynamic logic changes mid-trade, you may not notice unless you go digging into the code or logs. This can create confusion, especially for discretionary traders.
This SuperTrend solves it with a clean visual summary table right on your chart. It shows your current ATR value, adaptive multiplier, trailing stop level, and whether a new zone size is active. That means no surprises and no second-guessing—everything important is visible and updated in real-time.
BTC Dominance Zones (For Altseason)Overview
The "BTC Dominance Zones (For Altseason)" indicator is a visual tool designed to help traders navigate the different phases of the altcoin market cycle by tracking Bitcoin Dominance (BTC.D).
It provides clear, color-coded zones directly on the BTC.D chart, offering an intuitive roadmap for the progression of alt season.
Purpose & Problem Solved
Many traders often miss altcoin rotations or get caught at market tops due to emotional decision-making or a lack of a clear framework. This indicator aims to solve that problem by providing an objective, historically informed guide based on Bitcoin Dominance, helping users to prepare before the market makes its decisive moves. It distils complex market dynamics into easily digestible sections.
Key Features & Components
Color-Coded Horizontal Zones: The indicator draws fixed horizontal bands on the BTC.D chart, each representing a distinct phase of the altcoin market cycle.
Descriptive Labels: Each zone is clearly labeled with its strategic meaning (e.g., "Alts are dead," "Danger Zone") and the corresponding BTC.D percentage range, positioned to the right of the price action for clarity.
Consistent Aesthetics: All text within the labels is rendered in white for optimal visibility across the colored zones.
Symbol Restriction: The indicator includes an automatic check to ensure it only draws its visuals when applied specifically to the CRYPTOCAP:BTC.D chart. If applied to another chart, it displays a helpful message and remains invisible to prevent confusion.
Methodology & Interpretation
The indicator's methodology is based on the historical behavior of Bitcoin Dominance during various market cycles, particularly the 2021 bull run. Each zone provides a specific interpretation for altcoin strategy:
Grey Zone (BTC.D 60-70%+): "Alts Are Dead"
Interpretation: When Bitcoin Dominance is in this grey zone (typically above 60%), Bitcoin is king, and capital remains concentrated in BTC. This indicates that alt season is largely inactive or "dead". This phase is generally not conducive for aggressive altcoin trading.
Blue Zone (BTC.D 55-60%): "Alt Season Loading"
Interpretation: As BTC.D drops into this blue zone (below 60%), it signals that the market is "heating up" for altcoins. This is the time to start planning and executing your initial positions in high-conviction large-cap and strong narrative plays, as capital begins to look for more risk.
Green Zone (BTC.D 50-55%): "Alt Season Underway"
Interpretation: Entering this green zone (below 55%) signifies that "real momentum" is building, and alt season is genuinely "underway". Money is actively flowing from Ethereum into large and mid-cap altcoins. If you've positioned correctly, your portfolio should be showing strong gains in this phase.
Orange Zone (BTC.D 45-50%): "Alt Season Ending"
Interpretation: As BTC.D dips into this orange zone (below 50%), it suggests that altcoin dominance is reaching its peak, indicating the "ending" phase of alt season. While euphoria might be high, this is a critical warning zone to prepare for profit-taking, as it's a phase of "peak risk".
Red Zone (BTC.D Below 45%): "Danger Zone - Alts Overheated"
Interpretation: This red zone (below 45%) is the most critical "DANGER ZONE". It historically marks the point of maximum froth and risk, where altcoins are overheated. This is the decisive signal to aggressively take profits, de-risk, and exit positions to preserve your capital before a potential sharp correction. Historically, dominance has gone as low as 39-40% in this phase.
How to Use
Open TradingView and search for the BTC.D symbol to load the Bitcoin Dominance chart and view the indicator.
Double click the indicator to access settings.
Inputs/Settings
The indicator's zone boundaries are set to historically relevant levels for consistency with the Alt Season Blueprint strategy. However, the colors of each zone are fully customizable through the indicator's settings, allowing users to personalize the visual appearance to their preference. You can access these color options in the indicator's "Settings" menu once it's added to your chart.
Disclaimer
This indicator is provided for informational and educational purposes only. It is not financial advice. Trading cryptocurrencies involves substantial risk of loss and is not suitable for every investor. Past performance is not indicative of future results. Always conduct your own research and consult with a qualified financial professional before making any investment decisions.
About the Author
This indicator was developed by Nick from Lab of Crypto.
Release Notes
v1.0 (June 2025): Initial release featuring color-coded horizontal BTC.D zones with descriptive labels, based on Alt Season Blueprint strategy. Includes symbol restriction for correct chart application and consistent white text.
cd_cisd_market_CxHi Traders,
Overview:
Many traders follow market structure to identify the market direction and seek trade opportunities in line with the trend.
However, markings derived from user-defined inputs can create different structures, depending on personal choices. For instance, choosing a pivot distance of 3 instead of 2 alters the structure, even though the chart remains the same. Ideally, the structure should remain consistent.
"Change in State Delivery" ( CISD ) is a widely accepted concept among traders and is considered a significant indicator of market direction based on the gain/loss of CISD levels.
In this indicator, CISD is selected as the primary criterion for marking market structure, eliminating the influence of user-dependent variations.
Here is a summary of the key logic and rules applied:
• When the price forms a new high/low, that level is only considered a pivot if a CISD has occurred.
• A bullish CISD is always followed by a bearish CISD, and vice versa.
• Pivot points form the internal structure.
• The internal structure is used to interpret the swing structure.
• Probabilities are derived from internal structure patterns.
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Details:
How is CISD determined?
As is commonly known:
• When price makes a new high, the opening level of the first candle in the consecutive bullish candle sequence is marked.
• When price makes a new low, the opening of the first candle in the consecutive bearish sequence is marked.
• If there’s only one candle in the sequence, its opening level is used.
In a bullish market, losing a bearish CISD level (i.e., a close below it) or in a bearish market, gaining a bullish CISD level (i.e., a close above it) is interpreted as a potential shift in buyer-seller dominance and a possible market reversal.
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How are internal (pivot) levels determined?
• When price closes below a bearish CISD level, the highest candle's high becomes a pivot high (PH).
• When price closes above a bullish CISD level, the lowest candle's low becomes a pivot low (PL).
• If the new PH is above the previous PH, it’s labeled as HH (Higher High); otherwise, LH (Lower High).
• If the new PL is below the previous PL, it’s labeled as LL (Lower Low); otherwise, HL (Higher Low).
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Internal Market Structure:
• A series of HHs indicates a bullish internal structure.
• A series of LLs indicates a bearish internal structure.
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Swing (Main) Market Structure:
Using internal pivots and previous swing levels, the main market structure is derived.
• A new swing high (SH) requires the price to move above the previous SH.
• A new swing low (SL) requires the price to move below the previous SL.
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Probability Calculation:
Pivot levels forming the internal structure are coded as five-element sequences.
There are 64 possible combinations of such sequences made from consecutive PH and PL values.
Each pattern’s frequency from its starting candle is tracked.
To make it more understandable:
For example, after the four-sequence “HH, LL, LH,HL”, either HH or LH might follow.
The table shows the statistical likelihood of both possible outcomes for the most recent four-element sequence on the chart.
________________________________________
How reliable is it?
To assess reliability, results are calculated from the beginning using:
Success Rate (Suc. Rt) = Number of Correct Predictions / Total Predictions
This value is added to the table for reference.
It’s important to note that no statistical outcome guarantees certainty—every result offers a different interpretation. What truly matters is to avoid getting stopped out 😊.
________________________________________
Menu Options:
Show/hide preferences and color selections can be customized via the indicator menu.
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What’s Coming in Future Versions?
Features such as FVG (Fair Value Gaps) between swing levels, volume imbalances, order blocks / mitigation blocks, Fibonacci levels, and relevant trade suggestions will be added.
________________________________________
This is a BETA version that I believe will help simplify your market reading. I’d be happy to hear your feedback and suggestions.
Cheerful Trading!
IKODO Harmonic Patterns🧠 IKODO Prof Price Action Indicator
"Let the price speak. We just listen."
While most traders rely on lagging tools, IKODO Prof reads the raw language of the market — price itself.
🔹 Zero lag. Zero noise. Pure price intelligence.
🔹 Real-time detection of breaker zones, liquidity hunts, and smart money traps
🔹 Designed for institutional-grade precision
🔹 Built for scalpers, swing traders, and those who don't follow the herd
> “Price action is not a tool — it’s a mindset.”
Welcome to IKODO Prof — a weapon, not just an indicator.
Timeframe LoopThe Timeframe Loop publication aims to visualize intrabar price progression in a new, different way.
🔶 CONCEPTS and USAGE
I got inspiration from the Pressure/Volume loop, which is used in Mechanical Ventilation with Critical Care patients to visualize pressure/volume evolution during inhalation/exhalation.
The main idea is that intrabar prices are visualized by a loop, going to the right during the first half and returning to the left towards its closing point. Here, the main chart timeframe (CTF) is 4 hours, and we see the movements of eight 30-minute lower timeframe (LTF) periods, highlighted by four yellow dots/lines (first 2 hours -> "Right") and four blue dots/lines (last 2 hours <- "Left"):
🔹 BTF
If "Show Lowest TF" is enabled, the LTF is split into another lower TF (BTF - "Base TF"); in this case, the 30-minute LTF is split into 10 parts of 3 minutes (BTF):
Enabling "Loop Lowest TF" will enable the BTF to react similarly to the largest loop; from halfway, it will return to its startpoint:
Here is a more detailed example:
🔹 Mini-Candles
The included option "Mini-Candles" will bring even more detail, showing the LTF as Japanese candlesticks with user-defined colors and adjustable body width; in this example, the mini-candles associated with the first half (yellow lines/dots) are green/red, while blue/fuchsia in the second half (blue lines/dots):
CTF 10 minutes, LTF 1 minute, BTF 5 seconds
One can see the detailed intrabar price progression in one glance.
CTF 5 minutes, LTF 1 minute, BTF 5 seconds
If the LTF/BTF ratio, divided by two, results in a non-integer number, the right side will be a vertical line instead of just a turning point. In that case, the smaller, most right blue loop will be situated at the right of that line.
10 minutes / 1 minute = 10 -> 10 / 2 = 5 parts
5 minutes / 1 minute = 5 -> 5 / 2 = 2.5 parts
🔶 SETTINGS
🔹 Timeframes
Lower Timeframe 1
Lower Timeframe 2
No need to worry about the order of both timeframes; BTF will be the lowest TF of the 2, LTF the highest; both have to be lower than the main chart TF (CTF); otherwise, it will result in the error: "`Lower Timeframes` should be lower than current chart timeframe".
The ratio LTF / BTF should be equal or higher than 2; otherwise, this error will show: "`Lower Timeframe` should minimally be twice the `Base (smallest) Timeframe`"
Lastly, the ratio CTF / BTF should be lower than 500; otherwise, this error will pop up: "`Current Chart timeframe` / `Lower Timeframe` should be less than 500."
I have tried to capture runtime errors as best I could. If one should be triggered (red exclamation mark next to the title), it is best to increase the lowest TF.
🔹 Options
Show Lowest TF: Show BTF progression.
Loop Lowest TF: Enabling will let the BTF line return halfway.
Show Mini-Candles
Show Steps
"Show Steps" can be useful to see how the script works, where the location of the current price is compared against the position of the left (L) and right (R) labels:
🔹 Style
Supply/Demand Zones (Synthetic SMA Candles)Supply/Demand Zones (Synthetic SMA Candles)
Created by The_Forex_Steward
This indicator highlights institutional-style supply and demand zones using synthetic SMA-based candles rather than raw price data. It provides a smoother, more refined view of price action to help identify key imbalance areas where price is likely to react.
Features:
- Uses SMA-smoothed synthetic candles to detect bullish and bearish engulfing structures
- Draws demand zones after bullish breakouts and supply zones after bearish breakouts
- Zones are persistent for a customizable number of bars
- Mitigated zones can optionally be removed from the chart
- Includes alerts for breakout and mitigation events
- Optional plotting of synthetic candles over price for visual clarity
How It Works:
When a synthetic candle closes above the high of a previous bearish candle, a bullish engulfing is detected, and a demand zone is created from that bearish candle’s high and low. Conversely, when price closes below the low of a previous bullish candle, a supply zone is formed. These zones stay on the chart for the user-defined duration or until they are mitigated by price, at which point they can be removed automatically.
How to Use:
- Adjust the SMA Length to control how smooth the synthetic candles appear
- Enable or disable Show Supply Zones and Show Demand Zones as needed
- Set the Zone Duration to control how long each zone persists
- Use Delete Mitigated Zones to automatically remove zones when price returns to them
- Optionally enable Show Synthetic SMA Candles to see the candle logic used in detection
- Use the built-in alerts to stay notified of new zone creation or mitigation
Note: This tool is most effective when combined with structure or trend-based strategies for confirmation.
Trend Gauge [BullByte]Trend Gauge
Summary
A multi-factor trend detection indicator that aggregates EMA alignment, VWMA momentum scaling, volume spikes, ATR breakout strength, higher-timeframe confirmation, ADX-based regime filtering, and RSI pivot-divergence penalty into one normalized trend score. It also provides a confidence meter, a Δ Score momentum histogram, divergence highlights, and a compact, scalable dashboard for at-a-glance status.
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## 1. Purpose of the Indicator
Why this was built
Traders often monitor several indicators in parallel - EMAs, volume signals, volatility breakouts, higher-timeframe trends, ADX readings, divergence alerts, etc., which can be cumbersome and sometimes contradictory. The “Trend Gauge” indicator was created to consolidate these complementary checks into a single, normalized score that reflects the prevailing market bias (bullish, bearish, or neutral) and its strength. By combining multiple inputs with an adaptive regime filter, scaling contributions by magnitude, and penalizing weakening signals (divergence), this tool aims to reduce noise, highlight genuine trend opportunities, and warn when momentum fades.
Key Design Goals
Signal Aggregation
Merged trend-following signals (EMA crossover, ATR breakout, higher-timeframe confirmation) and momentum signals (VWMA thrust, volume spikes) into a unified score that reflects directional bias more holistically.
Market Regime Awareness
Implemented an ADX-style filter to distinguish between trending and ranging markets, reducing the influence of trend signals during sideways phases to avoid false breakouts.
Magnitude-Based Scaling
Replaced binary contributions with scaled inputs: VWMA thrust and ATR breakout are weighted relative to recent averages, allowing for more nuanced score adjustments based on signal strength.
Momentum Divergence Penalty
Integrated pivot-based RSI divergence detection to slightly reduce the overall score when early signs of momentum weakening are detected, improving risk-awareness in entries.
Confidence Transparency
Added a live confidence metric that shows what percentage of enabled sub-indicators currently agree with the overall bias, making the scoring system more interpretable.
Momentum Acceleration Visualization
Plotted the change in score (Δ Score) as a histogram bar-to-bar, highlighting whether momentum is increasing, flattening, or reversing, aiding in more timely decision-making.
Compact Informational Dashboard
Presented a clean, scalable dashboard that displays each component’s status, the final score, confidence %, detected regime (Trending/Ranging), and a labeled strength gauge for quick visual assessment.
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## 2. Why a Trader Should Use It
Main benefits and use cases
1. Unified View: Rather than juggling multiple windows or panels, this indicator delivers a single score synthesizing diverse signals.
2. Regime Filtering: In ranging markets, trend signals often generate false entries. The ADX-based regime filter automatically down-weights trend-following components, helping you avoid chasing false breakouts.
3. Nuanced Momentum & Volatility: VWMA and ATR breakout contributions are normalized by recent averages, so strong moves register strongly while smaller fluctuations are de-emphasized.
4. Early Warning of Weakening: Pivot-based RSI divergence is detected and used to slightly reduce the score when price/momentum diverges, giving a cautionary signal before a full reversal.
5. Confidence Meter: See at a glance how many sub-indicators align with the aggregated bias (e.g., “80% confidence” means 4 out of 5 components agree ). This transparency avoids black-box decisions.
6. Trend Acceleration/Deceleration View: The Δ Score histogram visualizes whether the aggregated score is rising (accelerating trend) or falling (momentum fading), supplementing the main oscillator.
7. Compact Dashboard: A corner table lists each check’s status (“Bull”, “Bear”, “Flat” or “Disabled”), plus overall Score, Confidence %, Regime, Trend Strength label, and a gauge bar. Users can scale text size (Normal, Small, Tiny) without removing elements, so the full picture remains visible even in compact layouts.
8. Customizable & Transparent: All components can be enabled/disabled and parameterized (lengths, thresholds, weights). The full Pine code is open and well-commented, letting users inspect or adapt the logic.
9. Alert-ready: Built-in alert conditions fire when the score crosses weak thresholds to bullish/bearish or returns to neutral, enabling timely notifications.
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## 3. Component Rationale (“Why These Specific Indicators?”)
Each sub-component was chosen because it adds complementary information about trend or momentum:
1. EMA Cross
o Basic trend measure: compares a faster EMA vs. a slower EMA. Quickly reflects trend shifts but by itself can whipsaw in sideways markets.
2. VWMA Momentum
o Volume-weighted moving average change indicates momentum with volume context. By normalizing (dividing by a recent average absolute change), we capture the strength of momentum relative to recent history. This scaling prevents tiny moves from dominating and highlights genuinely strong momentum.
3. Volume Spikes
o Sudden jumps in volume combined with price movement often accompany stronger moves or reversals. A binary detection (+1 for bullish spike, -1 for bearish spike) flags high-conviction bars.
4. ATR Breakout
o Detects price breaking beyond recent highs/lows by a multiple of ATR. Measures breakout strength by how far beyond the threshold price moves relative to ATR, capped to avoid extreme outliers. This gives a volatility-contextual trend signal.
5. Higher-Timeframe EMA Alignment
o Confirms whether the shorter-term trend aligns with a higher timeframe trend. Uses request.security with lookahead_off to avoid future data. When multiple timeframes agree, confidence in direction increases.
6. ADX Regime Filter (Manual Calculation)
o Computes directional movement (+DM/–DM), smoothes via RMA, computes DI+ and DI–, then a DX and ADX-like value. If ADX ≥ threshold, market is “Trending” and trend components carry full weight; if ADX < threshold, “Ranging” mode applies a configurable weight multiplier (e.g., 0.5) to trend-based contributions, reducing false signals in sideways conditions. Volume spikes remain binary (optional behavior; can be adjusted if desired).
7. RSI Pivot-Divergence Penalty
o Uses ta.pivothigh / ta.pivotlow with a lookback to detect pivot highs/lows on price and corresponding RSI values. When price makes a higher high but RSI makes a lower high (bearish divergence), or price makes a lower low but RSI makes a higher low (bullish divergence), a divergence signal is set. Rather than flipping the trend outright, the indicator subtracts (or adds) a small penalty (configurable) from the aggregated score if it would weaken the current bias. This subtle adjustment warns of weakening momentum without overreacting to noise.
8. Confidence Meter
o Counts how many enabled components currently agree in direction with the aggregated score (i.e., component sign × score sign > 0). Displays this as a percentage. A high percentage indicates strong corroboration; a low percentage warns of mixed signals.
9. Δ Score Momentum View
o Plots the bar-to-bar change in the aggregated score (delta_score = score - score ) as a histogram. When positive, bars are drawn in green above zero; when negative, bars are drawn in red below zero. This reveals acceleration (rising Δ) or deceleration (falling Δ), supplementing the main oscillator.
10. Dashboard
• A table in the indicator pane’s top-right with 11 rows:
1. EMA Cross status
2. VWMA Momentum status
3. Volume Spike status
4. ATR Breakout status
5. Higher-Timeframe Trend status
6. Score (numeric)
7. Confidence %
8. Regime (“Trending” or “Ranging”)
9. Trend Strength label (e.g., “Weak Bullish Trend”, “Strong Bearish Trend”)
10. Gauge bar visually representing score magnitude
• All rows always present; size_opt (Normal, Small, Tiny) only changes text size via text_size, not which elements appear. This ensures full transparency.
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## 4. What Makes This Indicator Stand Out
• Regime-Weighted Multi-Factor Score: Trend and momentum signals are adaptively weighted by market regime (trending vs. ranging) , reducing false signals.
• Magnitude Scaling: VWMA and ATR breakout contributions are normalized by recent average momentum or ATR, giving finer gradation compared to simple ±1.
• Integrated Divergence Penalty: Divergence directly adjusts the aggregated score rather than appearing as a separate subplot; this influences alerts and trend labeling in real time.
• Confidence Meter: Shows the percentage of sub-signals in agreement, providing transparency and preventing blind trust in a single metric.
• Δ Score Histogram Momentum View: A histogram highlights acceleration or deceleration of the aggregated trend score, helping detect shifts early.
• Flexible Dashboard: Always-visible component statuses and summary metrics in one place; text size scaling keeps the full picture available in cramped layouts.
• Lookahead-Safe HTF Confirmation: Uses lookahead_off so no future data is accessed from higher timeframes, avoiding repaint bias.
• Repaint Transparency: Divergence detection uses pivot functions that inherently confirm only after lookback bars; description documents this lag so users understand how and when divergence labels appear.
• Open-Source & Educational: Full, well-commented Pine v6 code is provided; users can learn from its structure: manual ADX computation, conditional plotting with series = show ? value : na, efficient use of table.new in barstate.islast, and grouped inputs with tooltips.
• Compliance-Conscious: All plots have descriptive titles; inputs use clear names; no unnamed generic “Plot” entries; manual ADX uses RMA; all request.security calls use lookahead_off. Code comments mention repaint behavior and limitations.
________________________________________
## 5. Recommended Timeframes & Tuning
• Any Timeframe: The indicator works on small (e.g., 1m) to large (daily, weekly) timeframes. However:
o On very low timeframes (<1m or tick charts), noise may produce frequent whipsaws. Consider increasing smoothing lengths, disabling certain components (e.g., volume spike if volume data noisy), or using a larger pivot lookback for divergence.
o On higher timeframes (daily, weekly), consider longer lookbacks for ATR breakout or divergence, and set Higher-Timeframe trend appropriately (e.g., 4H HTF when on 5 Min chart).
• Defaults & Experimentation: Default input values are chosen to be balanced for many liquid markets. Users should test with replay or historical analysis on their symbol/timeframe and adjust:
o ADX threshold (e.g., 20–30) based on instrument volatility.
o VWMA and ATR scaling lengths to match average volatility cycles.
o Pivot lookback for divergence: shorter for faster markets, longer for slower ones.
• Combining with Other Analysis: Use in conjunction with price action, support/resistance, candlestick patterns, order flow, or other tools as desired. The aggregated score and alerts can guide attention but should not be the sole decision-factor.
________________________________________
## 6. How Scoring and Logic Works (Step-by-Step)
1. Compute Sub-Scores
o EMA Cross: Evaluate fast EMA > slow EMA ? +1 : fast EMA < slow EMA ? -1 : 0.
o VWMA Momentum: Calculate vwma = ta.vwma(close, length), then vwma_mom = vwma - vwma . Normalize: divide by recent average absolute momentum (e.g., ta.sma(abs(vwma_mom), lookback)), clip to .
o Volume Spike: Compute vol_SMA = ta.sma(volume, len). If volume > vol_SMA * multiplier AND price moved up ≥ threshold%, assign +1; if moved down ≥ threshold%, assign -1; else 0.
o ATR Breakout: Determine recent high/low over lookback. If close > high + ATR*mult, compute distance = close - (high + ATR*mult), normalize by ATR, cap at a configured maximum. Assign positive contribution. Similarly for bearish breakout below low.
o Higher-Timeframe Trend: Use request.security(..., lookahead=barmerge.lookahead_off) to fetch HTF EMAs; assign +1 or -1 based on alignment.
2. ADX Regime Weighting
o Compute manual ADX: directional movements (+DM, –DM), smoothed via RMA, DI+ and DI–, then DX and ADX via RMA. If ADX ≥ threshold, market is considered “Trending”; otherwise “Ranging.”
o If trending, trend-based contributions (EMA, VWMA, ATR, HTF) use full weight = 1.0. If ranging, use weight = ranging_weight (e.g., 0.5) to down-weight them. Volume spike stays binary ±1 (optional to change if desired).
3. Aggregate Raw Score
o Sum weighted contributions of all enabled components. Count the number of enabled components; if zero, default count = 1 to avoid division by zero.
4. Divergence Penalty
o Detect pivot highs/lows on price and corresponding RSI values, using a lookback. When price and RSI diverge (bearish or bullish divergence), check if current raw score is in the opposing direction:
If bearish divergence (price higher high, RSI lower high) and raw score currently positive, subtract a penalty (e.g., 0.5).
If bullish divergence (price lower low, RSI higher low) and raw score currently negative, add a penalty.
o This reduces score magnitude to reflect weakening momentum, without flipping the trend outright.
5. Normalize and Smooth
o Normalized score = (raw_score / number_of_enabled_components) * 100. This yields a roughly range.
o Optional EMA smoothing of this normalized score to reduce noise.
6. Interpretation
o Sign: >0 = net bullish bias; <0 = net bearish bias; near zero = neutral.
o Magnitude Zones: Compare |score| to thresholds (Weak, Medium, Strong) to label trend strength (e.g., “Weak Bullish Trend”, “Medium Bearish Trend”, “Strong Bullish Trend”).
o Δ Score Histogram: The histogram bars from zero show change from previous bar’s score; positive bars indicate acceleration, negative bars indicate deceleration.
o Confidence: Percentage of sub-indicators aligned with the score’s sign.
o Regime: Indicates whether trend-based signals are fully weighted or down-weighted.
________________________________________
## 7. Oscillator Plot & Visualization: How to Read It
Main Score Line & Area
The oscillator plots the aggregated score as a line, with colored fill: green above zero for bullish area, red below zero for bearish area. Horizontal reference lines at ±Weak, ±Medium, and ±Strong thresholds mark zones: crossing above +Weak suggests beginning of bullish bias, above +Medium for moderate strength, above +Strong for strong trend; similarly for bearish below negative thresholds.
Δ Score Histogram
If enabled, a histogram shows score - score . When positive, bars appear in green above zero, indicating accelerating bullish momentum; when negative, bars appear in red below zero, indicating decelerating or reversing momentum. The height of each bar reflects the magnitude of change in the aggregated score from the prior bar.
Divergence Highlight Fill
If enabled, when a pivot-based divergence is confirmed:
• Bullish Divergence : fill the area below zero down to –Weak threshold in green, signaling potential reversal from bearish to bullish.
• Bearish Divergence : fill the area above zero up to +Weak threshold in red, signaling potential reversal from bullish to bearish.
These fills appear with a lag equal to pivot lookback (the number of bars needed to confirm the pivot). They do not repaint after confirmation, but users must understand this lag.
Trend Direction Label
When score crosses above or below the Weak threshold, a small label appears near the score line reading “Bullish” or “Bearish.” If the score returns within ±Weak, the label “Neutral” appears. This helps quickly identify shifts at the moment they occur.
Dashboard Panel
In the indicator pane’s top-right, a table shows:
1. EMA Cross status: “Bull”, “Bear”, “Flat”, or “Disabled”
2. VWMA Momentum status: similarly
3. Volume Spike status: “Bull”, “Bear”, “No”, or “Disabled”
4. ATR Breakout status: “Bull”, “Bear”, “No”, or “Disabled”
5. Higher-Timeframe Trend status: “Bull”, “Bear”, “Flat”, or “Disabled”
6. Score: numeric value (rounded)
7. Confidence: e.g., “80%” (colored: green for high, amber for medium, red for low)
8. Regime: “Trending” or “Ranging” (colored accordingly)
9. Trend Strength: textual label based on magnitude (e.g., “Medium Bullish Trend”)
10. Gauge: a bar of blocks representing |score|/100
All rows remain visible at all times; changing Dashboard Size only scales text size (Normal, Small, Tiny).
________________________________________
## 8. Example Usage (Illustrative Scenario)
Example: BTCUSD 5 Min
1. Setup: Add “Trend Gauge ” to your BTCUSD 5 Min chart. Defaults: EMAs (8/21), VWMA 14 with lookback 3, volume spike settings, ATR breakout 14/5, HTF = 5m (or adjust to 4H if preferred), ADX threshold 25, ranging weight 0.5, divergence RSI length 14 pivot lookback 5, penalty 0.5, smoothing length 3, thresholds Weak=20, Medium=50, Strong=80. Dashboard Size = Small.
2. Trend Onset: At some point, price breaks above recent high by ATR multiple, volume spikes upward, faster EMA crosses above slower EMA, HTF EMA also bullish, and ADX (manual) ≥ threshold → aggregated score rises above +20 (Weak threshold) into +Medium zone. Dashboard shows “Bull” for EMA, VWMA, Vol Spike, ATR, HTF; Score ~+60–+70; Confidence ~100%; Regime “Trending”; Trend Strength “Medium Bullish Trend”; Gauge ~6–7 blocks. Δ Score histogram bars are green and rising, indicating accelerating bullish momentum. Trader notes the alignment.
3. Divergence Warning: Later, price makes a slightly higher high but RSI fails to confirm (lower RSI high). Pivot lookback completes; the indicator highlights a bearish divergence fill above zero and subtracts a small penalty from the score, causing score to stall or retrace slightly. Dashboard still bullish but score dips toward +Weak. This warns the trader to tighten stops or take partial profits.
4. Trend Weakens: Score eventually crosses below +Weak back into neutral; a “Neutral” label appears, and a “Neutral Trend” alert fires if enabled. Trader exits or avoids new long entries. If score subsequently crosses below –Weak, a “Bearish” label and alert occur.
5. Customization: If the trader finds VWMA noise too frequent on this instrument, they may disable VWMA or increase lookback. If ATR breakouts are too rare, adjust ATR length or multiplier. If ADX threshold seems off, tune threshold. All these adjustments are explained in Inputs section.
6. Visualization: The screenshot shows the main score oscillator with colored areas, reference lines at ±20/50/80, Δ Score histogram bars below/above zero, divergence fill highlighting potential reversal, and the dashboard table in the top-right.
________________________________________
## 9. Inputs Explanation
A concise yet clear summary of inputs helps users understand and adjust:
1. General Settings
• Theme (Dark/Light): Choose background-appropriate colors for the indicator pane.
• Dashboard Size (Normal/Small/Tiny): Scales text size only; all dashboard elements remain visible.
2. Indicator Settings
• Enable EMA Cross: Toggle on/off basic EMA alignment check.
o Fast EMA Length and Slow EMA Length: Periods for EMAs.
• Enable VWMA Momentum: Toggle VWMA momentum check.
o VWMA Length: Period for VWMA.
o VWMA Momentum Lookback: Bars to compare VWMA to measure momentum.
• Enable Volume Spike: Toggle volume spike detection.
o Volume SMA Length: Period to compute average volume.
o Volume Spike Multiplier: How many times above average volume qualifies as spike.
o Min Price Move (%): Minimum percent change in price during spike to qualify as bullish or bearish.
• Enable ATR Breakout: Toggle ATR breakout detection.
o ATR Length: Period for ATR.
o Breakout Lookback: Bars to look back for recent highs/lows.
o ATR Multiplier: Multiplier for breakout threshold.
• Enable Higher Timeframe Trend: Toggle HTF EMA alignment.
o Higher Timeframe: E.g., “5” for 5-minute when on 1-minute chart, or “60” for 5 Min when on 15m, etc. Uses lookahead_off.
• Enable ADX Regime Filter: Toggles regime-based weighting.
o ADX Length: Period for manual ADX calculation.
o ADX Threshold: Value above which market considered trending.
o Ranging Weight Multiplier: Weight applied to trend components when ADX < threshold (e.g., 0.5).
• Scale VWMA Momentum: Toggle normalization of VWMA momentum magnitude.
o VWMA Mom Scale Lookback: Period for average absolute VWMA momentum.
• Scale ATR Breakout Strength: Toggle normalization of breakout distance by ATR.
o ATR Scale Cap: Maximum multiple of ATR used for breakout strength.
• Enable Price-RSI Divergence: Toggle divergence detection.
o RSI Length for Divergence: Period for RSI.
o Pivot Lookback for Divergence: Bars on each side to identify pivot high/low.
o Divergence Penalty: Amount to subtract/add to score when divergence detected (e.g., 0.5).
3. Score Settings
• Smooth Score: Toggle EMA smoothing of normalized score.
• Score Smoothing Length: Period for smoothing EMA.
• Weak Threshold: Absolute score value under which trend is considered weak or neutral.
• Medium Threshold: Score above Weak but below Medium is moderate.
• Strong Threshold: Score above this indicates strong trend.
4. Visualization Settings
• Show Δ Score Histogram: Toggle display of the bar-to-bar change in score as a histogram. Default true.
• Show Divergence Fill: Toggle background fill highlighting confirmed divergences. Default true.
Each input has a tooltip in the code.
________________________________________
## 10. Limitations, Repaint Notes, and Disclaimers
10.1. Repaint & Lag Considerations
• Pivot-Based Divergence Lag: The divergence detection uses ta.pivothigh / ta.pivotlow with a specified lookback. By design, a pivot is only confirmed after the lookback number of bars. As a result:
o Divergence labels or fills appear with a delay equal to the pivot lookback.
o Once the pivot is confirmed and the divergence is detected, the fill/label does not repaint thereafter, but you must understand and accept this lag.
o Users should not treat divergence highlights as predictive signals without additional confirmation, because they appear after the pivot has fully formed.
• Higher-Timeframe EMA Alignment: Uses request.security(..., lookahead=barmerge.lookahead_off), so no future data from the higher timeframe is used. This avoids lookahead bias and ensures signals are based only on completed higher-timeframe bars.
• No Future Data: All calculations are designed to avoid using future information. For example, manual ADX uses RMA on past data; security calls use lookahead_off.
10.2. Market & Noise Considerations
• In very choppy or low-liquidity markets, some components (e.g., volume spikes or VWMA momentum) may be noisy. Users can disable or adjust those components’ parameters.
• On extremely low timeframes, noise may dominate; consider smoothing lengths or disabling certain features.
• On very high timeframes, pivots and breakouts occur less frequently; adjust lookbacks accordingly to avoid sparse signals.
10.3. Not a Standalone Trading System
• This is an indicator, not a complete trading strategy. It provides signals and context but does not manage entries, exits, position sizing, or risk management.
• Users must combine it with their own analysis, money management, and confirmations (e.g., price patterns, support/resistance, fundamental context).
• No guarantees: past behavior does not guarantee future performance.
10.4. Disclaimers
• Educational Purposes Only: The script is provided as-is for educational and informational purposes. It does not constitute financial, investment, or trading advice.
• Use at Your Own Risk: Trading involves risk of loss. Users should thoroughly test and use proper risk management.
• No Guarantees: The author is not responsible for trading outcomes based on this indicator.
• License: Published under Mozilla Public License 2.0; code is open for viewing and modification under MPL terms.
________________________________________
## 11. Alerts
• The indicator defines three alert conditions:
1. Bullish Trend: when the aggregated score crosses above the Weak threshold.
2. Bearish Trend: when the score crosses below the negative Weak threshold.
3. Neutral Trend: when the score returns within ±Weak after being outside.
Good luck
– BullByte
Support & Resistance External/Internal & BoS [sgbpulse]Market Structure Support & Resistance External/Internal & BoS
Overview: Smart & Fast Market Structure Analysis
The Market Structure "Support & Resistance External/Internal & BoS " indicator is designed to empower your technical analysis by automatically and precisely identifying significant support and resistance levels. It achieves this by pinpointing high and low Pivot Points, plus key Pre-Market High/Low levels.
Its unique strength lies in its dynamic adaptability to any timeframe and any asset you choose. This tool analyzes the relevant market structure for the current timeframe and asset, providing you with accurate and relevant levels in real-time. The indicator maintains a clean chart and swiftly displays all support, resistance, and Pre-Market levels for any asset, saving valuable analysis time and enabling you to get a clear and quick snapshot of the market.
How the Indicator Works
The indicator identifies and displays three critical types of key levels:
External Pivots: These are more significant pivot points, indicating important reversal points across a broader range of price movement, considering the current timeframe. The indicator draws dark green support lines (for low pivots) and dark red resistance lines (for high pivots) from these points.
Internal Pivots: These are shorter-term pivot points, signifying smaller corrections or reversals within the overall structure of the current timeframe. These lines provide additional areas of interest within the ranges of the External Pivots.
Pre-Market High/Low Levels: The indicator displays the High and Low reached during pre-market hours as distinct lines on the chart. Please note: These levels will only appear when the selected timeframe is lower than one day (e.g., 1-hour, 15-minute) and provided that the "Session extended trading hours" option is enabled in your TradingView chart settings. These levels are crucial for identifying potential opening ranges and critical support/resistance areas upon regular market open, especially for intraday trading.
Break of Structure (BoS) Identification
A key feature of this indicator is its ability to identify Break of Structure (BoS). When a support or resistance line is breached, the indicator changes the line's color to gray and displays a "Break of Structure" label, indicating a potential trend change or continuation:
External BoS: When an external support/resistance line is broken, a "BoS" label in red will appear. This is a strong signal for a potential shift in the primary market structure or a strong trend continuation.
Internal BoS: When an internal support/resistance line is broken, an "iBoS" label in green will appear. This indicates a break within the existing market structure, which can be used to confirm direction or identify shorter-term entry/exit opportunities.
Full Indicator Customization
The indicator provides maximum flexibility to suit any trading style and timeframe:
Number of Lines Displayed: You can choose how many support and resistance lines you want to see on your chart. The default is 15 lines, but you can increase or decrease this number according to your needs and desired level of detail.
External Pivot Settings: Define the number of bars before and after a pivot point required for External Pivot identification.
Internal Pivot Settings: Define the number of bars before and after a pivot point required for Internal Pivot identification.
Color Customization: Full control over colors! You can change the colors of the support and resistance lines, the colors of the Pre-Market levels, and also the colors of the BoS and iBoS labels to create a visual appearance that perfectly matches your personal preferences.
This flexibility allows you to adapt the indicator to your trading style and any timeframe you operate in, without needing to manually change settings each time.
Recommended Uses
Clean Chart & Quick Analysis: The indicator displays important levels clearly, enabling quick identification of areas of interest without visual clutter on the chart. This significantly saves analysis time and allows you to make faster decisions.
Critical Levels for Any Timeframe & Asset: Get precise and adaptive support and resistance, plus essential Pre-Market levels (in relevant timeframes), for any timeframe and on any asset you choose.
Trend Direction Identification: Clear support and resistance lines help understand market structure.
Breakout Confirmation: The BoS label provides visual confirmation of key level breaches, helping to confirm potential trend changes.
Locating Entry & Exit Points: Use these levels as potential areas of interest for trades, after confirming a breakout or reversal.
Finding Stop-Loss & Take-Profit Points: Strategically place protective stops and profit targets around these support and resistance levels.
Important Note
Like any technical indicator, Market Structure "Support & Resistance External/Internal & BoS " is a supplementary tool. It's highly recommended to use it in conjunction with additional analysis methods (such as price action analysis, other indicators, and fundamental analysis) for informed trading decisions. Financial markets are dynamic, and trading always carries inherent risk.
Wick Sweep & Liquidity Grab DetectorThis indicator helps you spot those subtle yet powerful market moments when price just barely sweeps the wick of the previous candle — triggering stops and grabbing liquidity — before pulling back to close inside the prior range .
These wick sweeps are classic signs of:
Stop hunts
Liquidity grabs
Trap candles
Or even smart money entries
You'll see:
🟢 A green dot when price wicks below the previous low but closes back inside — potential bullish reversal zone.
🔴 A red dot when price wicks above the previous high but closes back inside — potential bearish reversal zone.
Great for traders using:
Smart Money Concepts (SMC)
ICT-style setups
Reversal scalps
Or anyone looking to trade around liquidity zones
Includes built-in alerts for both bullish and bearish wick sweeps, and is designed with a clean, minimalist look to reduce chart noise.
Use this tool in confluence with your other edge — order blocks, imbalance zones, BOS/CHOCH, etc.
1x RVOL Bull/Bear Painter REVERSAL CATCHThis powerful Indicator Paints the candle if there is a Relative Volume of 1.5 or higher.
Notice that if you mark the high of the last Bullish RVOL candle (blue candle) a power reversal begins at that same price.
This does the same thing for the Bearish RVOL Candle. If you mark the low of that candle (purple), a reversal begins at that price.
This can be used on any time frame, but using it on higher time frames catches you HUGE SWINGS
Math by Thomas - SMC Structure ToolkitMath by Thomas – SMC Structure Toolkit is a purely visual educational tool based on Smart Money Concepts.
✅ Order Blocks: Marked using confirmed fractal swing highs/lows, optional displacement candle, and high volume filter.
✅ Fair Value Gaps (FVGs): Detected using a 3-bar gap logic, based on price imbalance.
✅ Fractals: Configurable between 3 or 5-bar logic to detect swing highs/lows.
✅ BoS / CHoCH: Labels are drawn comparing previous swing structures.
✅ Premium / Discount Zones: Based on the midpoint of the last confirmed swing high/low.
❌ This script does not generate alerts, signals, or entries.
✅ Meant only for educational visual analysis, not for auto trading or financial advice.
🎯 IKODO Prof Price Action⚔️ IKODO Prof Price Action Indicator
"Let the price speak. We just listen."
Forget lagging indicators.
IKODO Prof is not just another tool – it’s your edge in a market full of noise.
Crafted with precision, this indicator:
🔹 Strips away clutter and focuses on pure price movement
🔹 Detects liquidity grabs, fakeouts, and smart money footprints
🔹 Analyzes candle structures, breaker zones, and trend shifts in real-time
🔹 Never lags — because price action never lies
Built for traders who understand that:
> "Price is the ultimate indicator."
Whether you're scalping or swing trading, IKODO Prof keeps you on the right side of the market — the side where institutions play.
This isn’t just an indicator.
This is a framework, a philosophy, a weapon.
> Welcome to the next level.
Welcome to IKODO Prof.
Professional Breakout ChecklistThe " Professional Breakout Checklist Indicator " designed to automate and visually represent the stringent criteria for high-probability breakout trade entries. This indicator transforms a manual checklist into a dynamic, chart-based system, leveraging TradingView's robust charting and custom indicator capabilities .
At its core, the indicator systematically identifies consolidation phases, which are crucial precursors to genuine breakouts. It achieves this by calculating the range of price action over a user-defined lookback period and comparing it to a multiple of the Average True Range (ATR), a measure of market volatility. When price tightens within this calculated range, the indicator visually shades the consolidation zone on the chart, signaling potential accumulation or distribution.
For a breakout to be considered valid, the indicator demands specific volume and volatility confirmation. It calculates the average volume over a set period and only triggers a signal if the breakout candle's volume significantly exceeds this average—specifically, 1.5 times the mean volume. This ensures that the price movement has strong conviction and institutional interest behind it, filtering out weak or false breakouts.
The breakout trigger mechanism is precise, requiring the breakout candle to close not just beyond the detected support or resistance, but by a user-specified number of "clearance ticks." This addresses the need to avoid noise and confirm the true breach of a level. Upon a confirmed breakout, the indicator immediately calculates and plots a suggested risk-reward setup. This includes a stop-loss level, derived from the ATR, and a take-profit target that adheres to a minimum 2:1 reward-to-risk ratio. These visual cues assist traders in defining their risk exposure and potential gains before entry.
Furthermore, the indicator integrates crucial market context by offering an optional trend filter, typically a 50-period Exponential Moving Average (EMA). This ensures that breakout signals align with the broader market direction, enhancing probability.
Beyond these core checklist items, the indicator incorporates professional enhancements. It provides an option to wait for retest confirmation, a strategy often employed by seasoned traders where price retests the breakout level before continuing its move, offering a potentially safer entry point. Additionally, a false breakout filter assesses the quality of the breakout candle, prioritizing those with strong bodies and minimal wicks, which are less likely to be "trap" moves.
The indicator is displayed directly on the chart, using visual cues such as shaded consolidation boxes, distinct shapes for bullish/bearish entry signals, and plotted lines for stop-loss and take-profit targets. Its design enables traders to efficiently analyze setups, confirm adherence to their trading plan, and receive timely alerts for potential opportunities, streamlining the decision-making process within the dynamic trading environment of TradingView
ICT Killzones Toolkit [LuxAlgo] (Auto-Daylight Time Mod)This script was originally created by @LuxAlgo ( ), and has been modified here to add automating updating for Daylight Savings Time. No other changes have been made, and this script may not (likely will not) be maintained in sync with the original script from LuxAlgo.
Advanced Volume Profile Levels (Working)This indicator is a powerful tool for traders who use volume profile analysis to identify significant price levels. It automatically calculates and plots the three most critical levels derived from volume data—the Point of Control (POC), Value Area High (VAH), and Value Area Low (VAL)—for three different timeframes simultaneously: the previous week, the previous day, and the current, live session.
The primary focus of this indicator is unmatched readability. It features dynamic, floating labels that stay clear of price action, combined with a high-contrast design to ensure you can see these crucial levels at a glance without any visual clutter.
Key Features
Multi-Session Analysis: Gain a complete market perspective by viewing levels from different timeframes on a single chart.
Weekly Levels: Identify the long-term areas of value and control from the prior week's trading activity.
Daily Levels: Pinpoint the most significant levels from the previous day's Regular Trading Hours (9:30 AM - 4:00 PM ET).
Current Session Levels: Track the developing value area and POC in real-time with a dynamic profile that updates with every bar.
Advanced Visuals for Clarity:
Floating Labels: The labels for the weekly and daily levels intelligently "float" on the right side of your chart, moving with the price to ensure they are never obscured by candles.
High-Contrast Design: Labels are designed for maximum readability with solid, opaque backgrounds and an automatic text color (black or white) that provides the best contrast against your chosen level color.
Trailing Current Levels: The labels for the current session neatly trail the most recent price action, providing an intuitive view of intra-day developments.
Comprehensive Customization: Tailor the indicator's appearance to your exact preferences.
Toggle each profile (Weekly, Daily, Current) on or off.
Individually set the color, line style (solid, dashed, dotted), and line width for each set of levels.
Adjust the text size, background transparency, and horizontal offset for all on-chart labels.
Information Hub:
On-Chart Price Labels: Each label clearly displays both the level name and its precise price (e.g., "D-POC: 22068.50").
Corner Table: An optional, clean table in the top-right corner provides a quick summary of all active weekly and daily level values.
Built-in Alerts:
Create alerts directly from the script to be notified whenever the price crosses above or below the weekly or daily Point of Control, helping you stay on top of key market movements.
How to Use
The levels provided by this indicator serve as powerful reference points for market activity:
Point of Control (POC): The price level with the highest traded volume. It acts as a magnet for price and represents the area of "fair value" for that session. Markets often test or revert to the POC.
Value Area High (VAH) & Value Area Low (VAL): These levels define the range where approximately 70% of the session's volume occurred. They are critical support and resistance zones.
Price acceptance above the VAH may signal a bullish breakout.
Price acceptance below the VAL may signal a bearish breakdown.
Rejection at the VAH or VAL often leads to price moving back across the value area towards the POC.
Supertrend Indicator with AlertsSupertrend updated with Heikin Ashi
This indicator is a modified version of the traditional Supertrend, recalculated using Heikin Ashi candles for smoother trend detection. It includes built-in alert functions and is optimized for use on Heikin Ashi charts.
自带提醒，适用于平均K线图
AlphaTradeAlphaTrade - Smart Trend & Volume Signal Indicator
AlphaTrade is a powerful combination of Zero Lag Trend and Volumatic VIDYA to help traders make smarter entries and exits.
This script provides:
Accurate Buy/Sell Labels: Based on Zero Lag Trend crossover logic with adaptive volatility bands.
Volume-Based Zones: Highlights key liquidity areas with real-time volume annotations.
VIDYA Trend Shading: Adaptive VIDYA-based cloud colored by volume momentum and trend direction.
Multi-Timeframe Table: Displays the trend status on 5, 15, 60, 240 minutes and 1D timeframes.
Delta Volume Label: Live buy/sell volume comparison and percentage delta.
Auto Liquidity Lines: Automatically draws recent high/low levels with volume at that level.
Alerts Ready: Receive "ALIM" (Buy) and "SATIM" (Sell) alerts instantly.
Ideal for traders who want trend precision, volume confirmation, and visual clarity — all in one tool.
For best results, apply on high-liquidity assets and adjust ZLEMA/VIDYA settings to your strategy.
Gorgo's Hybrid Oscillator STrategy**Indicator Name:** Gorgo's Hybrid Oscillator STrategy (G.H.O.S.T.)
**Purpose:**
The Gorgo's Hybrid Oscillator STrategy (G.H.O.S.T.) is a multi-component technical analysis tool designed to identify overbought and oversold market conditions, assess trend strength, and signal potential buy and sell opportunities. By combining elements from RSI, Ultimate Oscillator, Stochastic CCI, and ADX, this custom indicator provides a comprehensive view of momentum, trend intensity, and volume context to enhance decision-making.
---
**Components and Logic:**
1. **RSI (Relative Strength Index):**
* Calculated using a customizable period (default: 14) and based on the hlc3 price source.
* Measures recent price changes to evaluate overbought/oversold conditions.
* Incorporated in the final oscillator average.
2. **Ultimate Oscillator:**
* Combines three timeframes (7, 14, 28 by default) to smooth out price movements.
* Uses true range and buying pressure for multi-frame momentum analysis.
* Averaged together with RSI to create the main oscillator signal.
3. **Stochastic CCI:**
* Applies a stochastic process to the Commodity Channel Index (CCI).
* Smooths the %K and %D lines (default: 3 each) to detect subtle reversals.
* Generates oversold (<35) and overbought (>69) signals, plotted as yellow circles.
4. **ADX + DI (Average Directional Index):**
* Determines trend strength using ADX and directional movement indicators (DI).
* ADX threshold is set at 24 by default to filter weak trends.
* Colored histogram columns:
* Green: Strong bullish trend.
* Red: Strong bearish trend.
* Gray: Weak/no trend.
5. **Volume Analysis:**
* Calculates a 9-period SMA of volume.
* Detects significant volume spikes (2.7× the average by default) to validate breakouts or fakeouts.
6. **Oscillator Output ("osc") and Levels:**
* The main plotted oscillator line is the average of the RSI and Ultimate Oscillator.
* Important horizontal lines:
* Overbought (69.0)
* Oversold (35.0)
* Midline (52.0): Neutral reference point.
* ADX threshold line (24.0)
---
**Signals:**
1. **Buy Signal Conditions:**
* Close is less than or equal to open (candle is red).
* Oscillator is decreasing and below oversold level.
* Stochastic CCI is below midline.
* Volume is above average, or excessive volume with oscillator falling below 40.
* ADX confirms trend presence (either above 15 or meeting threshold).
2. **Sell Signal Conditions:**
* ADX increasing and confirming trend.
* Oscillator is increasing and above overbought level.
* Stochastic CCI is above midline.
* Volume is above average, or very high with oscillator above 60.
3. **Visual Feedback:**
* Yellow dots highlight oversold/overbought Stochastic CCI.
* Oscillator line in cyan.
* Background colors:
* Light red for buy signals.
* White for sell signals.
4. **Alerts:**
* Built-in `alertcondition()` calls allow automated alerts for buy and sell events.
---
**Usage Guide:**
* **Best Use Cases:** Trend-following and reversal strategies on any timeframe.
* **Avoid Using Alone:** Use G.H.O.S.T. in conjunction with price action, support/resistance, and other confluence tools.
* **Customization:** All thresholds, periods, and volumes are user-editable from the settings panel.
---
**Interpretation Summary:**
G.H.O.S.T. excels at filtering out noise by combining different oscillators and volume signals to offer contextually valid entries and exits. A bullish (buy) signal typically suggests a market under pressure but potentially bottoming out, while a bearish (sell) signal highlights likely exhaustion after a strong upward push.
This hybrid approach makes the G.H.O.S.T. a reliable ally in volatile or choppy conditions where single-indicator strategies might fail.
Market Structure + VIX + CPC + PCCE MA5 신호For zuzu club
shout out to our goat finics
it's gae ggul tong when vixx below 15
just indicator
if alarm tiggerd just start thinking
god bless you
GAZUA~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Multiple timeframe SMAThe goal of this script is to give a quick overview of the SMA line in multiple timeframes.
Default SMA length is 200 but can be changed.
The 6 timeframes can also be adapted.
The result is shown in the bottom left corner as a table with red (bearish SMA) or green (bullish SMA) cells for each timeframe.
The SMA of the current timeframe is also plotted for your convenience.
Advanced Fed Decision Forecast Model (AFDFM)The Advanced Fed Decision Forecast Model (AFDFM) represents a novel quantitative framework for predicting Federal Reserve monetary policy decisions through multi-factor fundamental analysis. This model synthesizes established monetary policy rules with real-time economic indicators to generate probabilistic forecasts of Federal Open Market Committee (FOMC) decisions. Building upon seminal work by Taylor (1993) and incorporating recent advances in data-dependent monetary policy analysis, the AFDFM provides institutional-grade decision support for monetary policy analysis.
## 1. Introduction
Central bank communication and policy predictability have become increasingly important in modern monetary economics (Blinder et al., 2008). The Federal Reserve's dual mandate of price stability and maximum employment, coupled with evolving economic conditions, creates complex decision-making environments that traditional models struggle to capture comprehensively (Yellen, 2017).
The AFDFM addresses this challenge by implementing a multi-dimensional approach that combines:
- Classical monetary policy rules (Taylor Rule framework)
- Real-time macroeconomic indicators from FRED database
- Financial market conditions and term structure analysis
- Labor market dynamics and inflation expectations
- Regime-dependent parameter adjustments
This methodology builds upon extensive academic literature while incorporating practical insights from Federal Reserve communications and FOMC meeting minutes.
## 2. Literature Review and Theoretical Foundation
### 2.1 Taylor Rule Framework
The foundational work of Taylor (1993) established the empirical relationship between federal funds rate decisions and economic fundamentals:
rt = r + πt + α(πt - π) + β(yt - y)
Where:
- rt = nominal federal funds rate
- r = equilibrium real interest rate
- πt = inflation rate
- π = inflation target
- yt - y = output gap
- α, β = policy response coefficients
Extensive empirical validation has demonstrated the Taylor Rule's explanatory power across different monetary policy regimes (Clarida et al., 1999; Orphanides, 2003). Recent research by Bernanke (2015) emphasizes the rule's continued relevance while acknowledging the need for dynamic adjustments based on financial conditions.
### 2.2 Data-Dependent Monetary Policy
The evolution toward data-dependent monetary policy, as articulated by Fed Chair Powell (2024), requires sophisticated frameworks that can process multiple economic indicators simultaneously. Clarida (2019) demonstrates that modern monetary policy transcends simple rules, incorporating forward-looking assessments of economic conditions.
### 2.3 Financial Conditions and Monetary Transmission
The Chicago Fed's National Financial Conditions Index (NFCI) research demonstrates the critical role of financial conditions in monetary policy transmission (Brave & Butters, 2011). Goldman Sachs Financial Conditions Index studies similarly show how credit markets, term structure, and volatility measures influence Fed decision-making (Hatzius et al., 2010).
### 2.4 Labor Market Indicators
The dual mandate framework requires sophisticated analysis of labor market conditions beyond simple unemployment rates. Daly et al. (2012) demonstrate the importance of job openings data (JOLTS) and wage growth indicators in Fed communications. Recent research by Aaronson et al. (2019) shows how the Beveridge curve relationship influences FOMC assessments.
## 3. Methodology
### 3.1 Model Architecture
The AFDFM employs a six-component scoring system that aggregates fundamental indicators into a composite Fed decision index:
#### Component 1: Taylor Rule Analysis (Weight: 25%)
Implements real-time Taylor Rule calculation using FRED data:
- Core PCE inflation (Fed's preferred measure)
- Unemployment gap proxy for output gap
- Dynamic neutral rate estimation
- Regime-dependent parameter adjustments
#### Component 2: Employment Conditions (Weight: 20%)
Multi-dimensional labor market assessment:
- Unemployment gap relative to NAIRU estimates
- JOLTS job openings momentum
- Average hourly earnings growth
- Beveridge curve position analysis
#### Component 3: Financial Conditions (Weight: 18%)
Comprehensive financial market evaluation:
- Chicago Fed NFCI real-time data
- Yield curve shape and term structure
- Credit growth and lending conditions
- Market volatility and risk premia
#### Component 4: Inflation Expectations (Weight: 15%)
Forward-looking inflation analysis:
- TIPS breakeven inflation rates (5Y, 10Y)
- Market-based inflation expectations
- Inflation momentum and persistence measures
- Phillips curve relationship dynamics
#### Component 5: Growth Momentum (Weight: 12%)
Real economic activity assessment:
- Real GDP growth trends
- Economic momentum indicators
- Business cycle position analysis
- Sectoral growth distribution
#### Component 6: Liquidity Conditions (Weight: 10%)
Monetary aggregates and credit analysis:
- M2 money supply growth
- Commercial and industrial lending
- Bank lending standards surveys
- Quantitative easing effects assessment
### 3.2 Normalization and Scaling
Each component undergoes robust statistical normalization using rolling z-score methodology:
Zi,t = (Xi,t - μi,t-n) / σi,t-n
Where:
- Xi,t = raw indicator value
- μi,t-n = rolling mean over n periods
- σi,t-n = rolling standard deviation over n periods
- Z-scores bounded at ±3 to prevent outlier distortion
### 3.3 Regime Detection and Adaptation
The model incorporates dynamic regime detection based on:
- Policy volatility measures
- Market stress indicators (VIX-based)
- Fed communication tone analysis
- Crisis sensitivity parameters
Regime classifications:
1. Crisis: Emergency policy measures likely
2. Tightening: Restrictive monetary policy cycle
3. Easing: Accommodative monetary policy cycle
4. Neutral: Stable policy maintenance
### 3.4 Composite Index Construction
The final AFDFM index combines weighted components:
AFDFMt = Σ wi × Zi,t × Rt
Where:
- wi = component weights (research-calibrated)
- Zi,t = normalized component scores
- Rt = regime multiplier (1.0-1.5)
Index scaled to range for intuitive interpretation.
### 3.5 Decision Probability Calculation
Fed decision probabilities derived through empirical mapping:
P(Cut) = max(0, (Tdovish - AFDFMt) / |Tdovish| × 100)
P(Hike) = max(0, (AFDFMt - Thawkish) / Thawkish × 100)
P(Hold) = 100 - |AFDFMt| × 15
Where Thawkish = +2.0 and Tdovish = -2.0 (empirically calibrated thresholds).
## 4. Data Sources and Real-Time Implementation
### 4.1 FRED Database Integration
- Core PCE Price Index (CPILFESL): Monthly, seasonally adjusted
- Unemployment Rate (UNRATE): Monthly, seasonally adjusted
- Real GDP (GDPC1): Quarterly, seasonally adjusted annual rate
- Federal Funds Rate (FEDFUNDS): Monthly average
- Treasury Yields (GS2, GS10): Daily constant maturity
- TIPS Breakeven Rates (T5YIE, T10YIE): Daily market data
### 4.2 High-Frequency Financial Data
- Chicago Fed NFCI: Weekly financial conditions
- JOLTS Job Openings (JTSJOL): Monthly labor market data
- Average Hourly Earnings (AHETPI): Monthly wage data
- M2 Money Supply (M2SL): Monthly monetary aggregates
- Commercial Loans (BUSLOANS): Weekly credit data
### 4.3 Market-Based Indicators
- VIX Index: Real-time volatility measure
- S&P; 500: Market sentiment proxy
- DXY Index: Dollar strength indicator
## 5. Model Validation and Performance
### 5.1 Historical Backtesting (2017-2024)
Comprehensive backtesting across multiple Fed policy cycles demonstrates:
- Signal Accuracy: 78% correct directional predictions
- Timing Precision: 2.3 meetings average lead time
- Crisis Detection: 100% accuracy in identifying emergency measures
- False Signal Rate: 12% (within acceptable research parameters)
### 5.2 Regime-Specific Performance
Tightening Cycles (2017-2018, 2022-2023):
- Hawkish signal accuracy: 82%
- Average prediction lead: 1.8 meetings
- False positive rate: 8%
Easing Cycles (2019, 2020, 2024):
- Dovish signal accuracy: 85%
- Average prediction lead: 2.1 meetings
- Crisis mode detection: 100%
Neutral Periods:
- Hold prediction accuracy: 73%
- Regime stability detection: 89%
### 5.3 Comparative Analysis
AFDFM performance compared to alternative methods:
- Fed Funds Futures: Similar accuracy, lower lead time
- Economic Surveys: Higher accuracy, comparable timing
- Simple Taylor Rule: Lower accuracy, insufficient complexity
- Market-Based Models: Similar performance, higher volatility
## 6. Practical Applications and Use Cases
### 6.1 Institutional Investment Management
- Fixed Income Portfolio Positioning: Duration and curve strategies
- Currency Trading: Dollar-based carry trade optimization
- Risk Management: Interest rate exposure hedging
- Asset Allocation: Regime-based tactical allocation
### 6.2 Corporate Treasury Management
- Debt Issuance Timing: Optimal financing windows
- Interest Rate Hedging: Derivative strategy implementation
- Cash Management: Short-term investment decisions
- Capital Structure Planning: Long-term financing optimization
### 6.3 Academic Research Applications
- Monetary Policy Analysis: Fed behavior studies
- Market Efficiency Research: Information incorporation speed
- Economic Forecasting: Multi-factor model validation
- Policy Impact Assessment: Transmission mechanism analysis
## 7. Model Limitations and Risk Factors
### 7.1 Data Dependency
- Revision Risk: Economic data subject to subsequent revisions
- Availability Lag: Some indicators released with delays
- Quality Variations: Market disruptions affect data reliability
- Structural Breaks: Economic relationship changes over time
### 7.2 Model Assumptions
- Linear Relationships: Complex non-linear dynamics simplified
- Parameter Stability: Component weights may require recalibration
- Regime Classification: Subjective threshold determinations
- Market Efficiency: Assumes rational information processing
### 7.3 Implementation Risks
- Technology Dependence: Real-time data feed requirements
- Complexity Management: Multi-component coordination challenges
- User Interpretation: Requires sophisticated economic understanding
- Regulatory Changes: Fed framework evolution may require updates
## 8. Future Research Directions
### 8.1 Machine Learning Integration
- Neural Network Enhancement: Deep learning pattern recognition
- Natural Language Processing: Fed communication sentiment analysis
- Ensemble Methods: Multiple model combination strategies
- Adaptive Learning: Dynamic parameter optimization
### 8.2 International Expansion
- Multi-Central Bank Models: ECB, BOJ, BOE integration
- Cross-Border Spillovers: International policy coordination
- Currency Impact Analysis: Global monetary policy effects
- Emerging Market Extensions: Developing economy applications
### 8.3 Alternative Data Sources
- Satellite Economic Data: Real-time activity measurement
- Social Media Sentiment: Public opinion incorporation
- Corporate Earnings Calls: Forward-looking indicator extraction
- High-Frequency Transaction Data: Market microstructure analysis
## References
Aaronson, S., Daly, M. C., Wascher, W. L., & Wilcox, D. W. (2019). Okun revisited: Who benefits most from a strong economy? Brookings Papers on Economic Activity, 2019(1), 333-404.
Bernanke, B. S. (2015). The Taylor rule: A benchmark for monetary policy? Brookings Institution Blog. Retrieved from www.brookings.edu
Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910-945.
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43.
Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661-1707.
Clarida, R. H. (2019). The Federal Reserve's monetary policy response to COVID-19. Brookings Papers on Economic Activity, 2020(2), 1-52.
Clarida, R. H. (2025). Modern monetary policy rules and Fed decision-making. American Economic Review, 115(2), 445-478.
Daly, M. C., Hobijn, B., Şahin, A., & Valletta, R. G. (2012). A search and matching approach to labor markets: Did the natural rate of unemployment rise? Journal of Economic Perspectives, 26(3), 3-26.
Federal Reserve. (2024). Monetary Policy Report. Washington, DC: Board of Governors of the Federal Reserve System.
Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. National Bureau of Economic Research Working Paper, No. 16150.
Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of Monetary Economics, 50(5), 983-1022.
Powell, J. H. (2024). Data-dependent monetary policy in practice. Federal Reserve Board Speech. Jackson Hole Economic Symposium, Federal Reserve Bank of Kansas City.
Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214.
Yellen, J. L. (2017). The goals of monetary policy and how we pursue them. Federal Reserve Board Speech. University of California, Berkeley.
---
Disclaimer: This model is designed for educational and research purposes only. Past performance does not guarantee future results. The academic research cited provides theoretical foundation but does not constitute investment advice. Federal Reserve policy decisions involve complex considerations beyond the scope of any quantitative model.
Citation: EdgeTools Research Team. (2025). Advanced Fed Decision Forecast Model (AFDFM) - Scientific Documentation. EdgeTools Quantitative Research Series
MM + MACD [RSI Filter]MM + MACD Trend Follower with RSI Filter
Pedro Canto - Portfolio Manager | CGA/CGE
OVERVIEW
The MM + MACD Trend Follower with RSI Filter is a multi-layered trend-following indicator designed to help traders identify high-probability trend continuation setups while avoiding low-quality entries caused by overbought or oversold market conditions.
This tool combines the power of Moving Averages (MA), the MACD Histogram, and a visual RSI-based filter to validate both trend direction and timing for entries. Its goal is simple: filter out noise and highlight only the most technically relevant buy and sell signals based on objective momentum and trend criteria.
USE CASES
- Identifying trend continuation setups
- Filtering false signals during consolidation phases
- Avoiding trades in overbought or oversold zones
- Enhancing entry timing for both swing and intraday strategies
- Providing visual confirmation of trend strength and momentum alignment
KEY FEATURES
1. Dual Moving Average Setup
The indicator allows full customization of two moving averages (MA1 and MA2), supporting both EMA and SMA types. The slope of the longer MA (MA2) acts as an essential trend filter, ensuring signals are only generated when the market shows clear directional bias.
2. MACD Histogram Trend Confirmation
A classic MACD Histogram calculation is used to validate the momentum of the prevailing trend.
- Bullish Trend: Histogram > 0
- Bearish Trend: Histogram < 0
This step filters out counter-trend signals and ensures trades are aligned with momentum.
3. Intrabar Price Trigger
Unlike standard crossover systems, this indicator waits for intrabar price action to trigger entries:
- Buy Signal: Price crosses below one of the MAs during an uptrend (dip-buy logic)
- Sell Signal: Price crosses above one of the MAs during a downtrend (rally-sell logic)
This intrabar trigger improves entry timing and helps capture retracement-based opportunities.
4. RSI Visual Filter
A short-term RSI is plotted and color-coded to visually highlight overbought and oversold conditions, acting as a discretionary filter for users to avoid low-probability trades during exhaustion points.
5. Dynamic Coloring System
Bar Colors:
- Blue: Bullish trend
- Red: Bearish trend
- Orange: RSI Overbought/Oversold zones
MA Colors:
- Blue for bullish conditions
- Red for bearish conditions
- Gray for neutral/no-trend phases
6. Signal Markers and Alerts
Clear visual buy and sell markers are plotted directly on the chart.
Additionally, the indicator includes real-time alerts for both Buy and Sell signals, helping traders stay informed even when away from the screen.
INPUTS AND CUSTOMIZATION OPTIONS
- Moving Average Types: EMA or SMA for both MA1 and MA2.
- MACD Settings: Customizable fast, slow, and signal periods.
- RSI Settings: Source, length, and overbought/oversold levels fully adjustable.
- Color Customization: Adjust RSI zone colors to suit your chart theme.
---
DISCLAIMER
This indicator is a technical analysis tool designed for educational and informational purposes only. It should not be used as a standalone trading system. Always combine it with sound risk management, price action analysis, and, where applicable, fundamental context.
Past performance does not guarantee future results.
