Guapoxtrades Goal Setting & Mission PanelGT Mission Panel™ - By Guapo Trades®
Purpose- Accountability Framework towards the operator mission
Overview
The GT Mission Panel™ is a trader accountability and goal-management system designed to keep your long-term objectives visible while you operate in the short-term environment of the financial markets.
Many traders spend years developing technical skills but struggle with consistency, discipline, emotional control, and maintaining focus on their larger purpose.
The GT Mission Panel™ was created to help bridge that gap.
Rather than acting as a trading signal generator, this tool serves as a daily reminder of:
Who you are
What you're building
Why you're trading
What rules you must follow
What goals still remain ahead
The objective is simple:
Keep your mission visible when emotions are trying to take control.
Core Features
Mission Statement Integration
Create a personalized mission statement that remains visible directly on your chart.
Your mission becomes part of your trading environment rather than something forgotten in a notebook.
Purpose Reminder:
Storing the deeper reason behind your journey.
Whether your objective is:
Financial freedom
Family security
Business ownership
Legacy building
Personal growth
The purpose remains visible while decisions are being made.
Personal Creed System
Display your personal trading creed directly on your charts.
Examples:
Trade like a professional.
Protect capital first.
Discipline over emotion.
Process over outcome.
Your creed becomes a constant reinforcement mechanism.
Goal Tracking Framework & Monitor progress toward major milestones.
Examples:
Emergency Fund
LLC Formation
Capital Goals
Business Expansion
Family Trust Development
Track progress while maintaining focus on long-term execution.
Progress Meter
Automatically calculates completion percentage based on completed goals.
This creates a visual representation of progress rather than relying solely on memory.
Rule Reminder Engine
Display critical trading rules directly on your chart.
Examples:
Never risk more than 2%.
No revenge trading.
Trade only high-quality setups.
Trade with a clear mind.
When discipline is most needed, your rules remain visible.
Identity Reinforcement
The GT Mission Panel™ encourages traders to align daily actions with the person they are trying to become.
Instead of focusing solely on profit and loss, traders are reminded to think in terms of:
Process
Discipline
Consistency
Character
Long-term execution
Decision Accountability Question
The panel includes a powerful self-assessment checkpoint:
"Does this trade serve the mission?"
This simple question can help prevent impulsive decisions and emotional trades.
Why GT Mission Panel™?
Most traders lose money because of:
Emotional decision making
Lack of discipline
Goal drift
Overtrading
Revenge trading
Impatience
Very few tools address the psychological side of trading.
The GT Mission Panel™ was built specifically to keep traders focused on the behaviors required for long-term success.
Best Used For
✔ New Traders
✔ Experienced Traders
✔ Goal-Oriented Individuals
✔ Accountability Systems
✔ Trading Psychology Development
✔ Risk Management Reinforcement
✔ Habit Formation
✔ Business Owners
✔ Personal Growth Frameworks
✔ Long-Term Wealth Building
Educational Purpose
The GT Mission Panel™ is not designed to predict market direction or generate trade signals.
Its purpose is to serve as a personal accountability and organizational framework that helps traders remain aligned with their long-term objectives and trading rules.
The indicator is intended to support discipline, consistency, and decision-making processes through visual reminders and goal tracking.
Visual Optimization
GT Mission Panel™ was designed primarily for dark-theme chart layouts.
Panel colors, transparency levels, and contrast settings were optimized specifically for dark backgrounds to maximize readability and reduce visual clutter.
Users utilizing light-themed charts may wish to customize the appearance through:
Settings → Panel Transparency
Additional visual settings can be adjusted to better match personal chart preferences.
Free Community Edition
The GT Mission Panel™ is provided free of charge as part of the Guapo Trades® commitment to trader development and education.
While many tools focus on finding trades, the GT Mission Panel™ focuses on helping traders become the type of person capable of consistently executing them.
About Guapo Trades®
Guapo Trades® develops professional trading tools focused on:
Market Structure
Liquidity
Probability
Risk Management
Trading Psychology
Trader Education
Transform Market Complexity Into Actionable Information.
Disclaimer- Educational Use Only
GT Mission Panel™ is provided solely for educational and informational purposes. This indicator does not provide financial advice, investment advice, trading advice, or recommendations to buy or sell any financial instrument.
This tool is intended exclusively as a personal organization, accountability, goal-setting, and educational framework.
Trading financial markets involves substantial risk and may not be suitable for all investors. Past performance does not guarantee future results.
Users remain solely responsible for their trading decisions, risk management practices, and financial outcomes.
Always perform your own independent analysis before entering any position.
© Guapo Trades® | Professional Trading Tools Built for Serious Traders Indicator
Indicator
ALEX Liquidity & Elasticity Index (ALEX)🚀 ALEX Liquidity & Elasticity Index (ALEX)
The ALEX Liquidity & Elasticity Index is an institutional-grade, quantitative market microstructure oscillator designed to measure liquidity stress, price elasticity, and hidden supply-demand imbalances in real time. Built on a fusion of Amihud illiquidity modeling, volatility-normalized divergence engines, and adaptive Gaussian smoothing systems, this indicator engineered by @gunebak4n isolates the true underlying behavior of market liquidity flow rather than raw price movement.
Unlike traditional oscillators that rely purely on momentum or overbought/oversold assumptions, ALEX reconstructs market behavior through a multi-layered statistical architecture that combines price impact sensitivity, volume efficiency, and divergence pressure into a single bounded regime signal.
The result is a clean, adaptive, and institutionally relevant liquidity spectrum that highlights when markets are expanding aggressively, compressing into accumulation phases, or transitioning into unstable imbalance regimes.
💡 Key Features
🧠 Liquidity Stress Engine (Amihud Core Model):
ALEX measures the relationship between price returns and dollar volume efficiency to estimate real-time market illiquidity. This allows detection of hidden execution stress that is not visible on standard price charts.
📊 Divergence Pressure System:
By comparing price momentum (ROC) against volume momentum, ALEX detects structural imbalance between buyers and sellers. This divergence layer acts as the core driver of regime classification.
🔬 Adaptive Gaussian Smoothing Architecture:
Multi-stage Gaussian filtering removes microstructure noise while preserving macro trend integrity. This ensures the signal reflects structural liquidity shifts rather than short-term volatility spikes.
⚡ Dynamic Regime Normalization:
All raw outputs are transformed into a bounded oscillator (-100 to +100) using adaptive scaling logic. This allows consistent interpretation across different market conditions and assets.
🛡️ Volatility-Responsive Threshold System:
Upper and lower extreme zones automatically adjust based on realized volatility. This ensures that signal sensitivity adapts to changing market regimes rather than relying on static levels.
📉 Liquidity Regime Classification:
ALEX dynamically categorizes market conditions into:
* Liquidity Vacuum (Aggressive Expansion / Stress Imbalance)
* Accumulation Zone (Absorption / Passive Buy Pressure)
* Trending Imbalance (Directional Continuation Phase)
* Balanced Regime (Neutral Equilibrium State)
🔬 Mathematical Logic and Structure
ALEX is constructed as a multi-layer liquidity-state transformation system that converts raw market microstructure into a bounded regime oscillator.
The model begins by defining market impact through an adapted Amihud Illiquidity framework, where price movement is evaluated relative to available trading volume. This creates the foundational liquidity stress variable, representing how “expensive” price movement is in current market conditions.
From there, the system applies statistical normalization using a rolling Z-score transformation. This step converts raw liquidity stress into a standardized deviation measure, allowing comparison across different volatility regimes.
To capture hidden structural imbalance, ALEX introduces a divergence engine between price momentum and volume momentum. When price acceleration is not confirmed by volume expansion, the system registers negative structural support; when both expand in sync, liquidity confirmation strengthens directional conviction.
These two components—liquidity stress and structural divergence—are then fused into a single composite pressure field. This field represents the core behavioral signal of the market: whether movement is liquidity-driven, absorption-based, or structurally supported.
To eliminate microstructure noise, the composite signal is passed through a multi-stage Gaussian smoothing process, followed by a median aggregation layer. This ensures that only macro-level liquidity transitions are preserved while short-term distortions are filtered out.
Finally, the processed signal is mapped into a bounded oscillator space (-100 to +100) using adaptive range normalization based on recent historical extremes. This transforms raw statistical pressure into a stable regime classification framework, enabling consistent interpretation across all market conditions.
In essence, ALEX does not track price. It reconstructs the *cost of price movement under current liquidity conditions* and translates it into a structured regime map of accumulation, expansion, imbalance, and equilibrium.
🛠️ How to Use
1. Liquidity Vacuum Zones (Red Extremes):
Indicates aggressive expansion of price impact relative to available liquidity. Often associated with exhaustion moves, breakout failures, or forced liquidations.
2. Accumulation Zones (Green Extremes):
Suggests absorption of sell pressure and hidden institutional accumulation. Typically observed before structural bullish expansions.
3. Trending Regime (Mid Zones):
When the system enters transitional imbalance, directional continuation becomes dominant. Momentum strategies perform optimally in this phase.
4. Balanced Regime (Neutral Zone):
Indicates equilibrium between supply and demand. Price action becomes mean-reverting and low edge for directional trades.
🎛️ Settings
* Calculation Window (34): Core statistical sensitivity of liquidity modeling
* Median Smoothing Length (7): Outlier suppression for price/volume data
* Signal Smoothing Length (21): Macro trend stabilization layer
* Scaling Lookback (60): Normalization horizon for oscillator compression
* Volatility Sensitivity (1.5): Controls adaptive threshold expansion
📌 Credits and Origins
This indicator is engineered by @gunebak4n, combining modern market microstructure theory, liquidity impact modeling, and adaptive statistical filtering techniques into a unified analytical framework.
ALEX is designed for professional traders, quant-style discretionary operators, and algorithmic systems that require a clean representation of liquidity-driven market behavior rather than purely price-based momentum interpretation.
⚠️ Disclaimer
All outputs generated by the ALEX Liquidity & Elasticity Index are probabilistic in nature. The indicator does not predict outcomes or guarantee performance. It is a decision-support tool intended for structural market analysis only. Proper risk management and execution discipline remain mandatory at all times.
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SteveFi Trade CockpitThe dashboard has 15 rows:
| Row | Purpose |
| ----------- | ---------------------------------------------------- |
| **ACTION** | Final decision output |
| **AGG** | Weighted overall score using all 13 major attributes |
| **EWave** | Elliott/Wolfe/Stoch/RSI-style timing score |
| **CANSLIM** | CANSLIM-style technical quality score |
| **RS** | Relative strength vs QQQ |
| **52W** | Distance from 52-week high |
| **Vol** | Current volume vs average volume |
| **Accum** | Accumulation vs distribution pressure |
| **Pivot** | Whether price is near a breakout/pivot area |
| **MR** | Mean reversion / extension risk |
| **Trend** | Overall trend structure |
| **Stoch** | Momentum reset/exhaustion |
| **Price** | Price action confirmation |
| **INV** | Invalidation level |
| **Target** | Potential upside target |
The color coding is:
| Color | Meaning |
| ------------- | ---------------------------------------------- |
| **Green** | Positive / bullish |
| **Orange** | Watch / caution / constructive but not perfect |
| **Dark gray** | Neutral / waiting |
| **Red** | Negative / risk / failure |
---
# 1. ACTION
This is the **main output**.
Possible values:
| ACTION | Meaning |
| ------------------ | --------------------------------------------------------------------------------- |
| **BUY TRIGGER** | Aggregate score is strong, price confirms, not invalidated, and not too stretched |
| **BUY WATCH** | Setup is building, but not fully confirmed |
| **TRIM / CAUTION** | Momentum may be rolling over or price is stretched |
| **WAIT** | Some positives exist, but not enough for action |
| **NO TRADE** | Weak overall setup |
| **INV** | Invalidation level broke. Setup failed. |
The action row is based mostly on **AGG**, not just EWave.
Current logic:
```text
BUY TRIGGER =
AGG >= 80
AND Price is Confirmed
AND not invalidated
AND MR is not Stretched
```
If you turn on:
```text
Require CANSLIM Filter for BUY TRIGGER
```
Then BUY TRIGGER also requires a minimum CANSLIM-quality pass.
That is the stricter version.
---
# 2. AGG
**AGG** is the most important score now.
It is the weighted aggregate score based on all major dashboard factors. This is the “whole chart quality” score.
Current weighting:
| Attribute | Weight |
| ----------- | -----: |
| **CANSLIM** | 21% |
| **RS** | 13% |
| **Price** | 12% |
| **Trend** | 10% |
| **Pivot** | 10% |
| **52W** | 8% |
| **EWave** | 8% |
| **Vol** | 6% |
| **MR** | 5% |
| **Accum** | 3% |
| **Stoch** | 2% |
| **INV** | 1% |
| **Target** | 1% |
This weighting intentionally makes the dashboard **less wave-heavy**.
The thinking is:
> Great stocks with strong RS and confirmed price action matter more than a possible wave count.
AGG grades:
| AGG Score | Grade | Meaning |
| -----------: | ----- | ---------------------------------- |
| **85+** | A+ | Excellent |
| **80–84** | A | Strong / trigger zone if confirmed |
| **70–79** | B | Good watchlist setup |
| **60–69** | C | Developing but not there yet |
| **50–59** | D | Weak / wait |
| **Under 50** | Weak | No trade |
How I’d use it:
| AGG | Interpretation |
| -----------: | ------------------------------------------------- |
| **80+** | Potential action zone |
| **70–79** | Watch closely |
| **60–69** | Setup forming, still early |
| **Under 60** | Probably ignore unless you have a separate thesis |
---
# 3. EWave
**EWave** is the Elliott/Wolfe-style timing score.
It uses:
| EWave Component | Points |
| --------------------------------------------- | -----: |
| Bullish trend context | 20 |
| Fib pullback / possible Wave 2 or Wave 4 zone | 20 |
| RSI bullish divergence | 20 |
| Stoch reset | 15 |
| Wolfe-like structure | 10 |
| Price action confirmation | 10 |
Maximum is **95**.
Dashboard examples:
| EWave | Meaning |
| ----------------------- | ------------------------ |
| **75+ / A** | Strong wave/timing setup |
| **60–74 / B Watch** | Setup is building |
| **40–59 / C Early** | Early or incomplete |
| **Under 40 / No Trade** | Weak timing setup |
Important: **EWave is only 8% of AGG now.**
That means it helps with timing, but it does not dominate the decision.
Best interpretation:
> EWave tells you whether the pullback/structure/timing looks constructive.
It does **not** mean “this is definitely Wave 3.”
---
# 4. CANSLIM
**CANSLIM** is a technical approximation of CANSLIM-style stock quality.
It does not use IBD’s proprietary EPS, sales, or fund ownership data. PulseWire cannot reliably recreate that from Pine.
Instead, it uses technical leadership proxies:
| CANSLIM Component | Points |
| ---------------------------- | -----: |
| RS vs benchmark | 20 |
| RS line near/new high | 15 |
| Distance from 52-week high | 15 |
| Volume vs average | 15 |
| Accumulation vs distribution | 15 |
| Pivot/base status | 20 |
Maximum is **100**.
Grades:
| CANSLIM Score | Grade | Meaning |
| ------------: | ----- | -------------------------------------- |
| **80+** | A | Strong CANSLIM-style technical profile |
| **65–79** | B | Good |
| **50–64** | C | Mixed |
| **Under 50** | Weak | Not acting like a leader |
This is now the **largest weight in AGG** at **21%**.
That’s intentional.
The dashboard is saying:
> Stock quality and leadership come first. Timing comes second.
---
# 5. RS
**RS** means relative strength versus the benchmark, currently defaulted to **QQQ**.
It compares the stock’s performance to QQQ over the selected lookback period.
Possible values:
| RS | Meaning |
| ---------- | ------------------------------------------------------------------------- |
| **Strong** | Stock is outperforming QQQ and its RS line is above its RS moving average |
| **OK** | Stock is outperforming QQQ, but not as strongly |
| **Weak** | Stock is underperforming QQQ |
Why this matters:
CANSLIM-style trading wants **leaders**, not laggards.
A stock with **RS Strong** is showing better demand than the market.
Best read:
| RS | Interpretation |
| ------------------ | ------------------------ |
| **Strong / green** | Good leader behavior |
| **OK / orange** | Acceptable but not elite |
| **Weak / red** | Caution |
---
# 6. 52W
**52W** shows how far the stock is from its 52-week high.
Example:
```text
-6.1%
```
Means the stock is 6.1% below its 52-week high.
Interpretation:
| 52W Distance | Meaning |
| ---------------: | ----------------- |
| **0% to -5%** | Very strong |
| **-5% to -15%** | Good / acceptable |
| **-15% to -25%** | Caution |
| **Below -25%** | Weak / damaged |
Why it matters:
CANSLIM usually prefers stocks near highs, not stocks buried far below highs.
A stock near highs is often showing institutional demand.
---
# 7. Vol
**Vol** shows current volume as a percentage of average volume.
Example:
```text
89%
```
Means current volume is 89% of the average volume.
Interpretation:
| Vol | Meaning |
| -------------- | --------------------------------------- |
| **140%+** | Strong demand / breakout-quality volume |
| **100–139%** | Decent |
| **Under 100%** | Light / not confirmed |
Why it matters:
A breakout or strong move is more convincing when volume expands.
For CANSLIM-style buying, strong volume gives confirmation that bigger buyers may be involved.
One note: on intraday bars or during the trading day, volume can look low until the day completes. On daily charts, interpret it best near/after the close.
---
# 8. Accum
**Accum** tries to detect whether the stock has more accumulation or distribution pressure.
It compares:
* Up days on above-average volume
* Down days on above-average volume
Possible values:
| Accum | Meaning |
| ----------- | ----------------------------------------------------- |
| **Accum** | More heavy-volume up days than heavy-volume down days |
| **Neutral** | No clear edge |
| **Distrib** | More heavy-volume down days than up days |
Why it matters:
CANSLIM cares about institutional accumulation.
A stock can look okay on price, but if it is under distribution, the setup is lower quality.
---
# 9. Pivot
**Pivot** tells you where price is relative to a recent breakout/base area.
It uses the recent high over the base lookback period as a rough pivot reference.
Possible values:
| Pivot | Meaning |
| --------------- | ---------------------------------------------- |
| **Breakout** | Price broke above pivot with strong volume |
| **Near Pivot** | Price is within about 5% below the pivot |
| **Above Pivot** | Price is above pivot, but volume is not strong |
| **Extended** | Price is more than about 5% above pivot |
| **Base Low** | Price is not near a useful pivot area |
Why it matters:
The best setups are often near proper buy areas.
For your dashboard:
| Pivot | Interpretation |
| ----------------------- | ------------------ |
| **Breakout / green** | Strong |
| **Near Pivot / orange** | Watch closely |
| **Extended / red** | Be careful chasing |
| **Base Low / gray** | Not actionable yet |
---
# 10. MR
**MR** means mean reversion / extension risk.
It measures how far price is from the fast EMA using ATR.
Example:
```text
Extended 1.73x ATR
```
Means price is 1.73 ATR above the fast EMA.
Possible values:
| MR | Meaning |
| ------------- | ---------------------------------------------------------- |
| **Discount** | Price pulled back below fast EMA but remains above mid EMA |
| **Normal** | Price is within normal range of fast EMA |
| **Extended** | Price is above normal range |
| **Stretched** | Price is too far extended |
| **Weak** | Price is below mid EMA |
| **n/a** | Not enough data |
Why this matters:
This row helps prevent chasing.
A stock can be strong but too extended for a fresh entry.
Important trigger rule:
```text
BUY TRIGGER cannot fire if MR = Stretched
```
That’s a guardrail.
Best read:
| MR | Interpretation |
| --------------------- | ------------------------ |
| **Discount / green** | Pullback zone |
| **Normal / neutral** | Healthy |
| **Extended / orange** | Be careful |
| **Stretched / red** | Probably not a fresh buy |
| **Weak / red** | Structure may be broken |
---
# 11. Trend
**Trend** gives the broader moving-average structure.
Possible values:
| Trend | Meaning |
| --------------- | ------------------------------------------------------------------- |
| **Bull Strong** | Price above mid EMA, fast EMA above mid EMA, mid EMA above long SMA |
| **Bull Basic** | Price and fast trend are above mid EMA |
| **Neutral** | Mixed |
| **Bear** | Price and averages are bearish |
This row is important because you generally want to buy pullbacks in strong trends, not fight broken charts.
Best setup:
```text
Trend = Bull Strong
```
---
# 12. Stoch
**Stoch** shows Stochastic momentum condition.
Possible values:
| Stoch | Meaning |
| ------------- | ------------------------------------------------------------ |
| **Reset** | Stoch recently got oversold/reset and is turning back up |
| **K>D** | Momentum is positive, but not necessarily from a fresh reset |
| **Roll Over** | Stoch was overbought and crossed down |
| **No Reset** | No useful signal |
Why this matters:
This came from the original HeidingOut-style method:
> Price action leads, Stoch/RSI confirm.
Best entry setup:
```text
Stoch = Reset
```
Caution setup:
```text
Stoch = Roll Over
```
But Stoch is only **2% of AGG** now because oscillators can be noisy.
---
# 13. Price
**Price** is one of the most important rows.
It checks whether price action has actually confirmed.
It requires:
* Higher-low structure
* Close above fast EMA
* Bullish candle
* Close above prior bar high
Possible values:
| Price | Meaning |
| ------------- | -------------------------------------------- |
| **Confirmed** | Price action supports the setup |
| **Waiting** | Setup may exist, but price has not confirmed |
This is weighted **12%** in AGG, which is high.
Why?
Because a setup without price confirmation is still just a possibility.
A BUY TRIGGER requires:
```text
Price = Confirmed
```
---
# 14. INV
**INV** is the invalidation level.
This is the key structural low from the detected impulse/pullback logic.
Plain English:
> If price closes below this level, the setup is probably broken.
Possible values:
| INV | Meaning |
| ----------- | --------------------------------- |
| Price level | Key invalidation reference |
| n/a | Not enough structure detected yet |
Important:
If price breaks below INV, ACTION becomes:
```text
INV
```
Invalidation overrides everything.
Even if RS is strong and CANSLIM is good, a broken structure means the setup has failed.
---
# 15. Target
**Target** shows the projected upside target and upside percentage.
It uses a **1.618 Fib extension** from the detected impulse.
Example:
```text
145.20 / +12.4%
```
Means:
* Potential target: 145.20
* Upside from current price: +12.4%
This is intentionally weighted only **1%** in AGG.
Why so low?
Because targets are estimates. Useful, but not reliable enough to drive the decision.
Use Target as:
> “Is there enough upside to justify the risk?”
Not as:
> “This will definitely go there.”
---
# How the AGG score thinks
The new AGG is intentionally biased toward objective leadership and confirmation.
Current hierarchy:
## Most important
* CANSLIM
* RS
* Price
* Trend
* Pivot
These answer:
> Is this a quality leader acting correctly?
## Middle importance
* 52W
* EWave
* Vol
* MR
These answer:
> Is it near highs, reasonably timed, supported by volume, and not too stretched?
## Lower importance
* Accum
* Stoch
* INV
* Target
These help, but do not dominate.
---
# How to read a good setup
A very good setup would look like:
| Row | Ideal |
| ------- | -------------------------- |
| ACTION | BUY TRIGGER |
| AGG | 80+ |
| EWave | 60+ |
| CANSLIM | 65+ |
| RS | Strong |
| 52W | Better than -15% |
| Vol | 100%+ or 140%+ on breakout |
| Accum | Accum |
| Pivot | Near Pivot or Breakout |
| MR | Normal or Discount |
| Trend | Bull Strong |
| Stoch | Reset or K>D |
| Price | Confirmed |
| INV | Below nearby structure |
| Target | Positive upside |
That is the kind of dashboard that says:
> This is worth real attention.
---
# How to read your ALAB screenshot
From your screenshot, the dashboard looked roughly like:
| Row | Reading |
| ------- | ------------------ |
| ACTION | WAIT |
| EWave | 40 / C Early |
| CANSLIM | 57 / C |
| RS | Strong |
| 52W | -6.1% |
| Vol | 89% |
| Accum | Accum |
| Pivot | Near Pivot |
| MR | Extended 1.73x ATR |
| Trend | Bull Strong |
| Stoch | K>D |
| Price | Waiting |
| Target | n/a |
That says:
**Good stock. Strong RS. Near highs. In a bullish trend. But not confirmed yet, volume is light, and price is a little extended.**
So **WAIT** makes sense.
It is not saying “bad stock.”
It is saying:
> Good candidate, but not a clean entry yet.
---
# The key dashboard philosophy
This is now the right structure:
## CANSLIM finds the leader
Rows:
* CANSLIM
* RS
* 52W
* Vol
* Accum
* Pivot
## EWave helps timing
Rows:
* EWave
* Stoch
* Price
* MR
## Risk/reward controls the trade
Rows:
* INV
* Target
* MR
## AGG combines it all
AGG answers:
> Overall, is this setup strong enough to act on?
---
# My simple decision rules
Use it like this:
## BUY TRIGGER
Only consider buying when:
* ACTION = BUY TRIGGER
* AGG is 80+
* Price is Confirmed
* MR is not Stretched
* INV is clear
* Target gives enough upside
## BUY WATCH
Put on watchlist when:
* ACTION = BUY WATCH
* RS is Strong
* CANSLIM is at least C/B
* Pivot is Near Pivot
* Price is Waiting
## WAIT
Do nothing when:
* Stock is good, but price is not confirmed
* MR is extended
* Volume is not there
* EWave is early
## TRIM / CAUTION
Consider trimming when:
* Stoch rolls over
* MR becomes Stretched
* Price action weakens after a run
## INV
Respect it.
If INV triggers, the setup is broken. Reassess from scratch.
Indicator
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Indicator
Bitcoin Statistical Forecaster + Power Law [Gabremoku]Bitcoin Statistical Forecaster + Power Law combines two analytical layers into a single BTC-focused overlay.
The first layer is a statistical analog forecaster. It scans historical Bitcoin data and searches for the closest matches to the most recent pattern using a weighted multi-feature distance model based on candle structure, volatility, momentum, trend distance, and structural position relative to the Bitcoin power-law range.
The second layer is a long-term Bitcoin power-law framework built from three structural curves: Floor, Mid-Stair, and Fair Value. These curves are plotted directly from the power-law formula and are not altered by the forecasting engine.
The script is not a simple mashup of two unrelated tools. The power-law layer is used as structural context inside the forecaster itself: it contributes to analog selection, regime comparison, optional forecast anchoring, and optional probability adjustment. The goal is to make historical pattern matching more aware of where price is located inside Bitcoin’s broader long-term structure.
How the forecast works:
The script compares the latest pattern against historical BTC windows.
It keeps the best analogs according to the selected similarity method.
These analogs are separated into Bull, Central, and Bear groups using the final return at the selected forecast horizon.
For each step in the projection, each scenario path is built from the weighted average of its own analog group, so the paths remain internally coherent instead of mixing bullish and bearish trajectories.
An optional structural bias can softly pull projected prices toward the power-law range over time. This effect fades in progressively across the forecast horizon, so near-term projections are not abruptly distorted.
Scenario probabilities are derived from the same percentile thresholds used to build the Bull, Central, and Bear paths. This keeps the displayed percentages aligned with the projected paths shown on the chart.
The script is designed for daily Bitcoin charts and works best when enough historical data is available. It is a probabilistic context tool, not a prediction guarantee, and it should be used together with risk management and independent market analysis.
Suggested usage:
Use the power-law curves to identify long-term structural position.
Use the forecast paths to estimate how similar historical BTC conditions evolved.
Compare current price location, structural regime, and scenario probabilities before forming a directional bias.
Treat the output as a contextual model, not as a standalone trading signal. Indicator
Indicator
FX Fundamental Strength - Scorecard macro par deviseDescription
FX Fundamental Strength calcule un score de force fondamentale pour 8 devises majeures (USD, EUR, GBP, JPY, CHF, CAD, AUD, NZD) et les classe en temps reel. L'objectif est de fournir un contexte macro clair, pas un signal d'entree.
Pour chaque devise, l'indicateur combine 4 metriques:
Taux directeur (request.economic INTR): plus il est haut, plus la devise est forte.
Inflation annuelle (IRYY): par defaut une inflation elevee est lue comme hawkish (option desactivable).
Momentum du rendement 2 ans: variation du taux 2 ans sur N jours, un rendement qui monte renforce la devise.
Taux de chomage (UR): plus il est bas, plus la devise est forte.
Notation relative
Chaque metrique est notee en relatif: une devise gagne des points selon le nombre d'autres devises qu'elle bat sur cette metrique. Cette approche est robuste aux echelles differentes et aux donnees manquantes (une valeur indisponible vaut 0 point sans casser le calcul). Les 4 scores sont ensuite combines selon des ponderations reglables (par defaut: Taux 40, Momentum 25, Inflation 20, Chomage 15).
Le resultat est un score de 0 a 100 par devise, ou 100 represente la plus forte du panier, avec un classement et un code couleur.
Biais LONG / SHORT
L'indicateur met en avant la devise la plus forte et la plus faible du moment, et propose un biais directionnel: LONG sur la plus forte, SHORT sur la plus faible. Des alertes se declenchent quand la devise la plus forte ou la plus faible change.
Parametres principaux
Ponderations des 4 metriques
Fenetre du momentum 2 ans (jours)
Symboles des rendements 2 ans modifiables (par exemple basculer sur le 10 ans si le 2 ans n'est pas disponible)
Position, taille et affichage du tableau
A noter
C'est un outil de contexte fondamental, a croiser avec ton propre timing (structure de marche, niveaux, methodologie d'execution). L'EUR utilise le Bund allemand comme proxy de rendement. 32 appels de donnees au total (4 metriques x 8 devises), sous la limite Pine. Indicator
Strategy
Indicator
Adaptive Lorentzian Classification [Quantum Algo]Quantum ML Engine — Adaptive Lorentzian Classification
█ OVERVIEW
Quantum ML Engine is a machine-learning classifier that predicts the direction of price over a configurable horizon using an Approximate Nearest Neighbors (ANN) search across historical feature vectors. Instead of relying on a single oscillator, it compares the current bar's "fingerprint" — a vector of up to six normalized features — against thousands of past bars, finds the most similar market conditions, and lets those historical outcomes vote on what is likely to happen next.
By default the engine measures similarity with Lorentzian distance, log(1 + |Δ|), rather than Euclidean distance. Market data is heavily distorted around major events (CPI prints, FOMC, black swans), and Lorentzian distance naturally compresses these outliers — analogous to how mass warps space-time — so a single extreme bar cannot dominate the neighbor selection.
This is an original, fully self-contained implementation written from scratch with zero library imports. The concept of applying Lorentzian distance to kNN classification on charts was pioneered in the open-source work of @jdehorty (Machine Learning: Lorentzian Classification), building on earlier kNN studies by @capissimo. Full credit to both for the foundational research. This script does not reuse their code; it re-derives the approach independently and extends it in the ways described below.
█ WHAT IS DIFFERENT IN THIS IMPLEMENTATION
1 — Time-aligned training set
Each training sample pairs the feature vector recorded AT a given bar with the realized outcome over the following H bars. Features and labels are stored on the same time axis, so the classifier learns from correctly matched cause-and-effect pairs. There is no lookahead: a sample only enters the training set once its outcome is fully realized.
2 — ATR neutral-zone labeling
Historical moves smaller than a configurable multiple of ATR are labeled NEUTRAL instead of long/short. Sideways noise therefore never teaches the model a false directional lesson. Set the multiplier to 0 to disable.
3 — Six engineered features with importance weights
RSI, WaveTrend, CCI, ADX, MFI (volume flow) and Fisher Transform, each normalized to a common 0–1 scale. Every feature slot has its own weight input, so you can tell the engine which dimensions matter more for your market without removing features entirely.
4 — Four selectable distance metrics
Lorentzian (default), Manhattan, Euclidean, and a 50/50 Lorentzian-Manhattan Hybrid. Switching metrics changes the geometry of the neighborhood and is a powerful tuning lever per asset class.
5 — Distance-weighted voting with a confidence score
Closer neighbors vote louder (weight = 1 / (1 + distance)). The agreement between neighbors is expressed as a 0–100% confidence value printed on every bar, and a minimum-confidence gate suppresses low-conviction signals entirely.
6 — Adaptive K
The neighbor count automatically shrinks (up to 40%) when volatility ranks high over the last 100 bars, making the model more reactive in fast markets, and expands back in quiet regimes for stability. Can be disabled for a fixed K.
7 — Sliding training window
The engine always trains on the most recent N bars rather than the oldest bars in chart history, so the model reflects current market structure.
8 — Configurable prediction horizon
The training/holding horizon is an input (1–20 bars) instead of a hardcoded constant.
9 — Three exit modes
Fixed-horizon exits, dynamic kernel-slope exits, and an optional ATR trailing stop with the stop level plotted on the chart.
10 — Higher-timeframe confluence filter
Optionally require price to be above (longs) or below (shorts) an EMA on a higher timeframe of your choice.
█ HOW IT WORKS
1. On every bar, six features are computed and normalized.
2. The bar's feature vector is compared against samples inside the sliding training window, sampled with a minimum chronological spacing (default 4 bars) so neighbors come from distinct market episodes rather than one cluster.
3. A monotonic distance threshold maintains a stable pool of approximate nearest neighbors; when the pool exceeds K, the threshold resets to the 75th-percentile distance, allowing genuinely closer samples to rotate in over time.
4. Neighbors vote long / short / neutral, weighted by proximity. The weighted sum becomes the prediction; the degree of agreement becomes the confidence.
5. The raw signal is then passed through optional filters: volatility regime (recent ATR vs long-run ATR), trend regime (EMA separation normalized by ATR), ADX, EMA/SMA trend, higher-timeframe trend, and a Nadaraya-Watson kernel regression filter (rational quadratic estimate with a Gaussian crossover mode for smoother color transitions).
6. Entries print only when the ML signal, the confidence gate, and all enabled filters agree.
█ SETTINGS GUIDE
General — source, training window size, prediction horizon, neutral-zone width.
ML Engine — K, adaptive K toggle, chronological spacing, distance metric, distance weighting, minimum confidence.
Feature Engineering — feature type, parameters and weight for each of the six slots.
Filters — volatility, regime, ADX, EMA/SMA, higher-timeframe confluence.
Kernel — lookback, relative weighting, regression level, lag, smoothing mode.
Exits — fixed vs dynamic exits, ATR trailing stop and multiplier.
Display — bar colors, prediction labels (value + confidence), dashboard, color compression.
█ DASHBOARD
The on-chart panel shows the live signal, prediction confidence, current adaptive K, volatility and trend regime states, kernel bias, and a calibration win-rate. The calibration statistic simply checks whether price moved in the predicted direction over the horizon after each signal. It exists ONLY to give feedback while tuning features — it is not a backtest, includes no costs or risk management, and must not be treated as a performance claim.
█ USAGE NOTES
— Works on any symbol and timeframe; intraday (15m–4H) and daily charts are typical starting points. Crypto, FX, indices and equities all behave differently — retune the features and metric per market.
— Higher minimum confidence = fewer but more selective signals. Raising chronological spacing diversifies neighbors on lower timeframes.
— Signals are evaluated on bar close. Like any bar-close logic, the in-progress bar can change until it closes.
— Best used as a confluence layer inside a complete trading plan with your own risk management, not as a standalone buy/sell system.
█ CREDITS
Concept inspiration: @jdehorty (Machine Learning: Lorentzian Classification) and @capissimo (kNN implementations). This script is an independent, original implementation with the extensions listed above.
█ DISCLAIMER
This script is provided for educational and informational purposes only. It is not financial advice, and past behavior — including the on-chart calibration statistics — does not guarantee future results. Trading involves substantial risk of loss. Always do your own research and manage risk responsibly. Indicator
Thermodynamic Market Entropy Index (TMEI)🚀 Thermodynamic Market Entropy Index (TMEI)
The Thermodynamic Market Entropy Index (TMEI) is a professional-grade quantitative tool designed to measure structural market order and identify the transition between chaotic consolidation and explosive directional trends. Built upon the physics-derived principles of Shannon Entropy, this indicator processes real-time price distribution and institutional volume flow to map market cycles into a normalized, zero-centered oscillator (-100 to +100).
Unlike standard lagging oscillators, TMEI acts as a dynamic market regime filter, giving traders absolute clarity on whether a market is in a highly organized trend or a random, capital-draining walk (chop).
💡 Key Features
🧠 Information Entropy Core: Using Claude Shannon’s information entropy theory, TMEI discretizes price momentum into 5 distinct thermodynamic states. It continuously measures the randomness of the market, detecting when the market structure is compressing into a high-order trend or expanding into total chaos.
🛡️ The 20% No-Trade Buffer Zone (Compression Ribbon): To eliminate devastating "whipsaws" (false breakouts), TMEI features a mathematically justified ±20 neutral corridor. This zone corresponds directly to the critical RSI 45-55 equilibrium zone, keeping traders completely on the sidelines (Neutral Gray) until institutional momentum gathers enough mass to break out.
📊 Institutional Volume Validation: TMEI does not look at price in a vacuum. It integrates an algorithmic volume filter utilizing smart accumulation/distribution logic. If a breakout occurs on weak or retail-only volume, the indicator suppresses the signal line back toward the Zero Axis, shielding you from fakeouts.
⚙️ Hysteresis-Driven State Engine: Armed with a dual-threshold hysteresis filter, TMEI locks into major macro trends. Once a trend is verified, minor counter-trend pullbacks are filtered out, ensuring you hold winning positions longer without premature exit signals.
🔬 Mathematical Logic and Scaling
TMEI measures the probability matrix of 5 distinct market states over a rolling lookback window. The pure Shannon Entropy measures the total chaos in the system. The index then normalizes this value against maximum possible entropy and subjects it to a directional vector matrix (Net Bias).
Formula: Raw Order = 100 - ((Shannon Entropy / Maximum Entropy) * 100)
Final Score = Raw Order * Directional Sign * Volume Multiplier
This process translates abstract thermodynamic physics into a highly practical, bounded indicator where +100 represents perfect bullish order and -100 represents perfect bearish order.
🛠️ How to Use
1. The Structural Breakout: Monitor the signal line closely as it exits the Gray Compression Ribbon. A definitive close above +20 confirms institutional bullish expansion. A close below -20 confirms structural bearish dominance.
2. The Chaos Neutral Zone (±20): When the line is trapped inside the gray cloud, the market is in a state of maximum entropy (Random Walk/Chop). Strictly avoid directional breakout strategies here.
3. Macro Trend Expansion (±60): Entering the ±60 zones triggers a full background color canvas injection. This highlights that the asset has entered an elite, high-momentum institutional trend corridor.
4. Volume Divergences: If the asset continues to make new structural highs but the TMEI curve prints descending peaks, it indicates a massive decay in volume-price entropy—a highly reliable leading signature of a trend collapse.
🎛️ Settings
Signal Smoothing Period (14): Controls the lookback filter applied via a Weighted Moving Average (WMA) to smooth out high-frequency market noise. Increase to 21 or 25 for ultra-clean macro tracking.
Enable Volume Validation (True): Toggles the institutional smart money filter. Highly recommended for liquid stocks and crypto assets to eliminate low-volume manipulations.
📌 Credits and Origins
This script bridges information theory, statistical thermodynamics, and quantitative technical analysis. It is dedicated to professional traders who demand a purely mathematical, data-driven view of market liquidity, regime verification, and structural mechanics.
Disclaimer: All financial indicators are purely probabilistic models. TMEI is an advanced decision-support architecture and does not guarantee execution success or profits. Always utilize strict risk management protocols and predefined stop-losses. Indicator
Gap Fill Probability for SPY SPX XSPGap Fill Probability Viewer - open gaps with an adaptive fill probability
Gap Fill Probability Viewer automatically finds price gaps on your chart and draws each one as a colored zone - no more drawing rectangles by hand. It then tells you how likely each open gap is to "fill," and exactly how far price has to travel to close it.
What it shows
Gap zones - every gap-up and gap-down, drawn as a box that extends to the current bar.
Fill probability (P) - an estimate of how likely the gap is to close, shown as a % and color-coded (green = likely, orange = uncertain, red = unlikely).
p-value - the flip side of P: the chance the gap stays open.
Distance to close - how far the current price is from fully closing the gap, in both points and % of the charted symbol.
Stats table - a clean summary for the gap closest to filling.
How the probability works
The estimate adapts to three things research associates with gap fills:
- Size - bigger gaps are harder to fill.
- Distance - the closer price is to the gap, the higher the odds.
- Recency - fresh gaps fill faster; old, stubborn gaps lose momentum.
Handy options
- Auto-hide, fade, or keep gaps once they fill.
- Filter out tiny gaps by % or ATR.
- Detect gaps on wicks or candle bodies.
- Fully tunable probability model and colors.
⚠️ The fill probability is a heuristic estimate for study and education — not a guarantee or financial advice. Calibrate the settings to your own market and timeframe. Indicator
Indicator
Indicator
Indicator
Swing TP/SL UltimateSwing TP/SL Ultimate
PulseWire Pine Script v6 — Overlay Indicator
Short title: TP/SL▲▼ | Version: 2.0 | Type: Overlay Indicator
Overview
Swing TP/SL Ultimate is an advanced Take Profit and Stop Loss analysis tool for PulseWire. Instead of requiring you to manually set fixed TP/SL percentages, it automatically calculates statistically-derived average TP and SL distances by studying every confirmed Swing High and Swing Low in historical price data across multiple timeframes and pivot lengths.
The core idea: the market's own past behaviour tells you how far it typically moves up (TP) and down (SL) from any given point. This indicator collects hundreds of those data points, filters out outliers, and surfaces the most reliable average — displayed as both a percentage and an actual price level on your chart.
Key Features
Multi-length Pivot DetectionSimultaneously scans 3 configurable pivot lengths (Fast / Mid / Slow) to maximise the number of valid swing samples
Multi-TimeFrame (HTF) PivotsImports pivots from a Higher TimeFrame and merges them into
the same statistical buffer
4 Averaging MethodsSimple, EMA-Weighted, Trimmed Mean, and Blended — all computed in parallel
Per-Bar ATR-Style AverageUses every bar's high−open and open−low as additional TP/SL samples for maximum data density
KNN High/Low PredictionA K-Nearest Neighbours model predicts the likely High and Low over the next 5 bars using 10 market features
Buy / Sell SignalsFires when price reaches a confirmed swing extreme, RSI confirms, and optional volume confirmation passes
Scalable DashboardFour size presets (Tiny / Small / Medium / Large) with compact mode for minimal screen space
Dual-format ValuesEvery metric is shown as both % and actual price — e.g. 2.34% (48,250.00)
Opaque Line LabelsLabels sit directly on chart lines with solid dark backgrounds so they block the line rather than overlap it
How It Works
1. Pivot Collection
A Swing High is a bar whose high is the highest of the N bars before it and the N bars after it. PulseWire confirms this N bars after the fact. The indicator runs this detection three times simultaneously with different lengths:
Fast pivot (default N=5) — captures minor swings, many samples
Mid pivot (default N=10) — captures medium swings
Slow pivot (default N=21) — captures major swings, fewer but stronger signals
HTF pivot (default N=10 on 60m) — imported from higher timeframe
Every confirmed pivot feeds into a unified rolling array buffer shared by all four sources.
2. Percentage Calculation
For each confirmed Swing High pivot:
TP% sample = (pivotHigh − confirmClose) / confirmClose × 100
For each confirmed Swing Low pivot:
SL% sample = (confirmClose − pivotLow) / confirmClose × 100
Only positive values are stored. The buffer is capped at Rolling Buffer Size entries — when full, the oldest sample is dropped (FIFO), keeping the average adaptive to the current market regime.
3. Averaging Methods
All four methods run simultaneously. You choose which one drives the final Suggested TP/SL prices:
Simple Average
The arithmetic mean of all samples in the rolling buffer. Straightforward and transparent.
EMA-Weighted Average
A running Exponential Moving Average applied to each new pivot sample as it arrives. Recent pivots are weighted more heavily than older ones, making this method more responsive to regime changes.
α = 2 / (EMA Period + 1)
emaTP = emaTP × (1 − α) + newSample × α
Trimmed Mean (default)
Sorts all buffer samples and removes the top and bottom N% before computing the mean. This eliminates the influence of extreme outlier swings (e.g. a flash crash or a parabolic spike) that would otherwise skew the average.
Default trim: 10% → removes top 10% and bottom 10% of samples
Blended
Averages the Trimmed Mean result with the Per-Bar ATR-style result. Combines the statistical robustness of pivot-based sampling with the high data density of per-bar measurement.
4. Per-Bar ATR-Style Sampling
In addition to pivot-based samples, every single bar contributes:
barTP% = (high − open) / open × 100 ← upside potential per bar
barSL% = (open − low) / open × 100 ← downside risk per bar
An EMA is applied to both series. This gives the indicator a very high sample count (one per bar vs. one per pivot event) and closely mirrors the concept of Average True Range expressed directionally as a percentage.
KNN Prediction
The indicator includes a K-Nearest Neighbours (KNN) machine learning model that predicts how far price is likely to move up and down over the next 5 bars.
Feature Vector (10 dimensions)
#FeatureWhat it captures0RSI (14)Momentum / overbought / oversold1Distance from EMA-50 %Trend deviation2ATR (14) as % of priceVolatility regime3Bollinger Band position %Price within range4Volume vs. 20-bar SMA %Volume spike detection5Candle body size %Bar strength6Upper wick %Rejection at highs7Lower wick %Rejection at lows8Momentum (10-bar) %Short-term trend force9Close position in 20-bar range %Range context
Algorithm
For every bar in the training window (up to 200 bars back, sampled every 2 bars for performance), compute the Euclidean distance between the current bar's feature vector and that historical bar's feature vector.
Select the K nearest historical bars (default K=5).
For each neighbour, look up what actually happened over the next 5 bars: how high did price go, how low did it go.
Weight each neighbour by inverse distance — closer matches contribute more.
The weighted average of future highs and lows becomes the KNN prediction.
The result is plotted as a dotted forward line on the chart and shown in the dashboard.
Buy / Sell Signals
Signals fire only when all conditions are simultaneously true.
Buy Signal (▲ BUY — green label below bar)
ConditionLogicNear Swing Lowclose ≤ lastSwingLow × (1 + avgSL% × 0.5)RSI OversoldRSI ≤ Oversold threshold (default 35)Bullish candleclose > openVolume (optional)volume > 20-bar SMA × 1.2
Sell Signal (▼ SELL — red label above bar)
ConditionLogicNear Swing Highclose ≥ lastSwingHigh × (1 − avgTP% × 0.5)RSI OverboughtRSI ≥ Overbought threshold (default 65)Bearish candleclose < openVolume (optional)volume > 20-bar SMA × 1.2
Note: Signals are contextual alerts, not a complete trading system. Always combine with your own analysis and risk management.
Dashboard
The on-chart table updates on every bar. Its size, position, and content depth are all configurable.
Size Presets
SizeTextRows shownTinytinyCore only (compact)SmallsmallCore + method breakdownMediumnormalCore + breakdown + range statsLargelargeFull: all above + KNN + signal state + HTF status
Dashboard Sections
Always visible
RowLabelDescriptionTitle⬡ TP / SL ULTIMATEActive method + sample countsLive▲ To Swing HIGH% and price distance to last pivot highLive▼ To Swing LOW% and price distance to last pivot lowAvg★ Avg TP (Final)Final averaged TP as % and suggested priceAvg★ Avg SL (Final)Final averaged SL as % and suggested priceAvg⚖ R:R RatioavgTP ÷ avgSL — green ≥2.0, yellow ≥1.0, red <1.0
Small and above
RowLabelDescriptionBreakdownSimple / EMA-Weighted / Trimmed / Per-BarTP% / SL% for each method side by side
Medium and above
RowLabelDescriptionRangeAvg High %Mean upside from all pivot samplesRangeMax / Min High %Best and worst upside seen in bufferRangeAvg Low %Mean downside from all pivot samplesRangeMax Low %Worst downside seen in buffer
Large only
RowLabelDescriptionKNNKNN Pred High +%Predicted upside over next 5 bars (% + price)KNNKNN Pred Low −%Predicted downside over next 5 bars (% + price)SignalRSICurrent RSI value, colour-codedSignalVolume vs SMACurrent volume as % of 20-bar SMASignalActive SignalBUY / SELL / WaitingInfoHTF PivotsWhether HTF pivot collection is active
Chart Visuals
ElementColourDescriptionDashed green lineGreenLast confirmed Swing High levelDashed red lineRedLast confirmed Swing Low levelSolid teal lineTealAvg TP suggested priceSolid orange lineOrangeAvg SL suggested priceDotted purple linePurpleKNN predicted High (forward projection)Dotted fuchsia lineFuchsiaKNN predicted Low (forward projection)▼ triangle above barRedConfirmed pivot high marker▲ triangle below barLimeConfirmed pivot low markerBUY labelLimeBuy signal — all conditions metSELL labelRedSell signal — all conditions met
All lines carry opaque labels (solid dark background matching the line colour) placed directly on the line so the text is legible without the line running through it.
Input Reference
📐 Pivot Detection
InputDefaultRangeDescriptionPivot Length 1 – Fast52–50Left/right bars to confirm a fast swingPivot Length 2 – Mid102–100Left/right bars for mid-grade swingPivot Length 3 – Slow212–200Left/right bars for slow/major swingRolling Buffer Size15020–500Max samples kept per side (TP / SL)Outlier Trim %100–40% of extremes removed from trimmed meanEMA Weight Period142–100Smoothing for EMA-weighted averagePer-Bar EMA Length20010–1000EMA length for per-bar ATR-style average
🌐 Multi-TimeFrame
InputDefaultDescriptionEnable HTF PivotsOnAdds higher-timeframe pivots to the bufferHigher TimeFrame60 (1H)Source timeframe for HTF pivotsHTF Pivot Length10Pivot detection length on the higher timeframe
🤖 KNN Prediction
InputDefaultRangeDescriptionEnable KNNOn—Toggle the KNN engine on/offK Neighbours53–15Number of nearest matches to useKNN Training Window10030–200How many bars back to search
📊 Averaging
InputDefaultOptionsDescriptionPrimary Pivot MethodTrimmed MeanSimple / EMA Weighted / Trimmed Mean / BlendedWhich pivot average drives the final outputSample SourceBlendedPivot Only / Per-Bar Only / BlendedWhat data feeds the final TP/SL
🎯 Signals
InputDefaultDescriptionShow Buy/Sell SignalsOnMaster toggle for signal markersRSI Length14Period for RSI calculationRSI Oversold – Buy35RSI threshold to confirm a buyRSI Overbought – Sell65RSI threshold to confirm a sellVolume ConfirmationOnRequire volume > 1.2× SMA to trigger
🖥 Dashboard
InputDefaultOptionsDescriptionPositionTop RightTop/Bottom × Left/RightCorner placement of the tableDashboard SizeMediumTiny / Small / Medium / LargeControls text size and row countUltra-Compact ModeOff—Shows only title + 6 core rows
🎨 Visuals
InputDefaultDescriptionShow Pivot Level LinesOnDashed lines at last Swing High / LowShow Avg TP/SL LinesOnSolid lines at suggested TP and SL pricesShow KNN Predicted LinesOnDotted forward lines from KNN modelShow Pivot MarkersOnTriangles at confirmed pivot barsShow Line LabelsOnOpaque labels on all chart lines
Recommended Settings by Use Case
Scalping (1m – 5m charts)
Pivot Length 1: 3 Pivot Length 2: 5 Pivot Length 3: 10
HTF Timeframe: 15
Buffer Size: 100 EMA Period: 7 Per-Bar EMA: 50
Primary: EMA Weighted Source: Blended
RSI Oversold: 30 RSI Overbought: 70
Swing Trading (1H – 4H charts)
Pivot Length 1: 5 Pivot Length 2: 10 Pivot Length 3: 21
HTF Timeframe: D
Buffer Size: 150 EMA Period: 14 Per-Bar EMA: 200
Primary: Trimmed Mean Source: Blended
RSI Oversold: 35 RSI Overbought: 65
Position Trading (Daily – Weekly charts)
Pivot Length 1: 10 Pivot Length 2: 20 Pivot Length 3: 50
HTF Timeframe: W
Buffer Size: 200 EMA Period: 21 Per-Bar EMA: 500
Primary: Simple Source: Pivot Only
RSI Oversold: 40 RSI Overbought: 60
Technical Notes
Pine Script version: v6
Type: indicator() with overlay=true
Max lines: 20 | Max labels: 50
MTF data: Uses barmerge.lookahead_off to prevent future-leak
KNN performance: Samples every 2nd bar in the training window; hard-capped at 200 bars to stay within Pine's execution time limits
Global scope rule: All plotshape() calls and var mutations are placed at the global scope as required by Pine Script v6
Changelog
VersionDateChanges1.0InitialBasic pivot avg + dashboard2.0CurrentKNN engine, MTF pivots, 4 avg methods, buy/sell signals, 4 dashboard sizes, opaque line labels, full Pine v6 scope compliance
Built for PulseWire Pine Script v6. For educational and analytical purposes only — not financial advice. Indicator
