Morning ORB FVG Trigger✅ Overview
Morning ORB FVG Trigger is a complete intraday trading framework built around:
A Morning Opening Range Breakout (ORB)
The first Fair Value Gap (FVG) after that breakout
Strict risk management and position sizing
Optional HTF trend filter (Daily / Weekly / Monthly)
Optional Daily ATR filter to avoid extreme days
The script is designed for futures / indices / FX on intraday charts up to 15 minutes and for traders who want a clean, mechanical entry framework with clear risk.
🧠 Core idea
Define a morning opening range (e.g. 09:30–09:45).
Wait for a clean breakout above/below that range.
After the breakout, wait for the first FVG in breakout direction,
confirmed by the next candle (no immediate full reclaim).
Use a chosen stop logic + R:R factor to build risk/reward boxes.
Calculate position size based on your account risk.
(Optional) Only take trades:
In the direction of the HTF EMA trend (D/W/M).
On days where the morning range is within a band of the Daily ATR.
You can also disable all signals/boxes and use the script just as a visual ORB tool.
⏰ 1. ORB / Morning Range
Inputs (Main section)
Morning Range Session
Time window of the opening range in exchange time
Example: 09:30–09:45 for a 15-minute ORB.
You can type custom ranges (e.g. 09:30–09:35 for a 5-minute ORB).
Risk/Reward (TP factor)
Multiplier for the take-profit distance relative to the stop.
2.0 = TP is 2× the stop distance
1.5 = TP is 1.5× the stop distance
Show ORB range
If enabled, draws:
ORB high/low lines
ORB labels (e.g. 15min ORB high / low)
Optional midline
Extend ORB lines to the right (bars)
How many bars to extend the ORB high/low horizontally beyond the ORB itself.
Trade box width (bars)
Horizontal width (in bars) of:
Red risk box (entry–stop)
Green reward box (entry–TP)
Implementation details
The ORB is always calculated on 1-minute data internally, so it stays precise even on 5m/15m charts.
The script only works on intraday timeframes up to 15 minutes.
📦 2. FVG Block
Group: “FVG”
Threshold %
Minimum size of an FVG in % of price.
0 = every FVG
Higher values = only larger gaps
Auto threshold (from volatility)
If enabled, the minimum FVG size is derived from historical volatility
instead of a fixed percentage.
Allow breakout FVG partly inside ORB
Off (default): the FVG must lie fully outside the ORB.
On: the breakout FVG itself may still overlap the ORB a bit,
as long as it is the first one attached to the breakout move.
Enable FVG entry signals, boxes & alerts
On: full system – FVG detection, entry labels, risk/TP boxes, alerts.
Off: no entries, no risk/TP boxes, no alerts.
You only get the ORB and (optionally) the HTF dashboard, so you can trade your own setups.
Entry mode
Entry mode (Mid / Edge / NextOpen)
Mid – Entry at the midpoint of the FVG.
Edge – Long at the upper FVG edge, short at the lower FVG edge.
NextOpen – No limit order in the gap. Entry is placed at the next bar open after FVG confirmation.
Edge offset (ticks)
Additional offset for Edge entries:
Long:
+ticks = a bit above the FVG (more conservative)
-ticks = deeper into the FVG (more aggressive)
Short:
+ticks = a bit below the FVG
-ticks = deeper into the FVG
FVG detection logic
Uses a LuxAlgo-style 3-candle FVG pattern (gap between candle 1 and 3).
Only one FVG is taken: the first valid FVG after the ORB breakout in breakup direction.
The FVG candle is the middle bar; the script:
Detects the FVG on the previous bar.
Waits for the current bar to confirm it:
Bullish: current low must stay above the lower FVG boundary
Bearish: current high must stay below the upper FVG boundary
Only then an entry signal is generated.
🛑 3. Stop Logic
Group: “Stop Logic”
Stop mode (PrevBar / Pivot / FVG Candle)
PrevBar – Stop at the low/high of the candle before the FVG
(tight/aggressive).
FVG Candle – Stop at the low/high of the FVG candle itself
(medium).
Pivot – Stop at the most recent swing high/low
using pivotLeft / pivotRight pivots (more conservative).
Ticks (stop buffer)
Offset (in ticks) from the selected stop level.
> 0 = further away (more room, more risk)
< 0 = closer (tighter stop)
Pivot left / Pivot right
Number of candles left/right to define a swing high/low
when using Pivot stop mode.
Typical intraday values: 2–3.
The script also sanity-checks the stop:
if the calculated stop would be invalid (e.g. above entry in a long), it moves it by a minimal distance (2 ticks) to keep a valid risk.
📈 4. HTF Trend Filter (Daily / Weekly / Monthly)
Group: “HTF Trend Filter”
Enable HTF trend filter
If enabled, trades are only allowed:
Long when at least 2 of D/W/M closes are above their EMA
Short when at least 2 of D/W/M closes are below their EMA
EMA length (D/W/M)
EMA length for all three higher timeframes (Daily, Weekly, Monthly).
This helps focus entries in the direction of the dominant higher-timeframe trend.
📊 5. ATR Filter (Daily)
Group: “ATR Filter (Daily)”
Use daily ATR filter
If enabled, the height of the ORB (ORB high – ORB low) must be within
a band of the Daily ATR to allow any signals.
Daily ATR length
ATR period on the Daily timeframe.
Min ORB size vs ATR
Lower bound:
Example: 0.3 → ORB must be at least 0.3 × Daily ATR
0.0 = no minimum.
Max ORB size vs ATR
Upper bound:
Example: 1.5 → ORB must be ≤ 1.5 × Daily ATR
0.0 = no maximum.
If the ORB is too small (choppy) or too large (exhausted move), no breakout or FVG signal will be generated on that day.
🧭 6. HTF Dashboard & Signal Labels
Group: “HTF Trend Dashboard”
Show HTF dashboard
Draws a small label at the top of the chart showing:
HTF Trend (EMA X)
D: UP/FLAT/DOWN
W: UP/FLAT/DOWN
M: UP/FLAT/DOWN
Dashboard position
Top Right, Top Center, Top Left – places the dashboard at the top.
Over Risk Info – no top dashboard; instead, the HTF trend info is shown as a label near the risk box when a new signal appears.
Lookback (bars) for top anchor
How many bars to use to determine the top price level for dashboard placement.
Show HTF trend above risk box on signal
Only relevant if Dashboard position = Over Risk Info.
When enabled, a small HTF label appears near the risk box for each new trade.
Signal label vertical offset (ticks)
Vertical spacing between risk info label and HTF label.
Minimum spacing HTF/Risk (ticks)
Ensures a minimum vertical distance so the two labels don’t overlap.
HTF signal label X offset (bars)
Horizontal offset (left/right) relative to the risk info label.
⏳ 7. ORB–FVG Filters (Session & Time Window)
Group: “ORB FVG Filter”
Only same session day
If enabled, FVG entries are only allowed on the same calendar day
as the ORB. When the date changes, all state & drawings are reset.
Limit hours after ORB
Enables a time window after the ORB end.
Trading window after ORB (hours)
Length of that window in hours.
Example: 2.0 → FVG signals only in the first 2 hours after ORB end.
💰 8. Risk Management & Position Sizing
Group: “Risk Management”
Calculate position size
If enabled, the script computes suggested mini and micro contract size for you.
Account size
Your trading account size (in account currency).
Risk mode
Percent – risk is a % of account size (Account risk %).
Fixed amount – risk is a fixed dollar amount (Fixed risk ($)).
Account risk %
Risk per trade as a percentage of account size (e.g. 1.0 for 1%).
Fixed risk ($)
Fixed risk per trade in dollars when using Fixed amount mode.
Micro factor (vs mini)
How much a micro contract is worth relative to a mini.
Example:
0.1 → one micro moves 1/10 of one mini.
Risk Info label
For each new trade, a label is shown above the boxes with:
Stop distance in price and $ risk per mini
Max risk allowed for the trade
Suggested mini and micro size
Text like:
Suggested: 2 mini
Suggested: 5 micro
or Suggested: no trade
This makes the script especially useful for prop-firm rules or strict risk discipline.
🎨 9. Visual Style (Boxes, Labels, ORB Lines)
Group: “Box & Label Style (Trade)”
Label font size (Very small, Small, Normal, Large)
Entry label BG / text color
Stop label BG / text color
TP label BG / text color
Risk info BG / text color
Risk box color (entry–stop zone)
Reward box color (entry–TP zone)
Group: “ORB Style”
ORB high line color
ORB low line color
ORB line width
ORB label font size
ORB label background color
ORB label text color
Show ORB midline
ORB midline color / width / style (Solid / Dashed / Dotted)
⚠️ 10. Alerts
Group: “Alerts”
The script defines three alert conditions:
Long entry FVG breakout
Triggered when a new long signal appears.
Short entry FVG breakout
Triggered when a new short signal appears.
FVG entry (long/short)
Generic alert for any new signal (long or short).
To use them:
Add the indicator to the chart.
Open the Alerts dialog → “Condition”.
Select this script and one of the alert conditions.
Set your preferred expiration and notification settings.
Alerts only fire when Enable FVG entry signals, boxes & alerts is on.
🧩 11. How the trading logic flows (summary)
Build ORB on 1-minute data during the selected session.
Optionally reject the day if ORB is outside the ATR bounds.
Wait for a breakout (close above high or below low), respecting HTF trend filter.
After breakout, look for the first valid FVG in that direction:
Outside the ORB (unless breakout FVG allowed inside)
Confirmed by the next candle (no full reclaim)
Once confirmed:
Compute entry, stop, target.
Draw risk/reward boxes and all labels.
Optionally show HTF signal label over the risk info.
Trigger alerts if enabled.
If you disable FVG signals, only steps 1–3 (plus dashboard) are effectively active.
⚠️ 12. Notes & Disclaimer
Script is intended for intraday trading up to 15-minute timeframes.
All signals are mechanical and do not guarantee profitability.
Always backtest and forward-test on your own data before risking real money.
This script is for educational purposes only and is not financial advice.
🚀 Quick-start guide
Add the script to your chart
Use an intraday timeframe ≤ 15 minutes (1m, 3m, 5m, 15m).
Works best on liquid indices, futures, FX and large-cap stocks.
Set the Morning Range
In “Morning Range Session” choose the exchange’s opening window.
Examples
US index futures (CME): 08:30–08:45 or 08:30–08:35
US stocks (NYSE/Nasdaq): 09:30–09:45 or 09:30–09:35
The ORB is always calculated on 1-minute data internally, so the range stays accurate on higher intraday charts.
Keep the default filters at first
HTF Trend Filter: ON
EMA length = 20
This will only allow trades in the direction of the dominant D/W/M trend.
ATR Filter: OFF (optional; you can enable later once you’re comfortable).
Use the full trade system
In the FVG group leave
“Enable FVG entry signals, boxes & alerts” = ON
Entry mode: Mid
Stop mode: FVG Candle or PrevBar
Risk/Reward: 2.0 as a starting point.
Set your risk
Turn on “Calculate position size”.
Enter your Account size and choose either:
Risk mode = Percent (e.g. 1.0 = 1% per trade), or
Risk mode = Fixed amount (e.g. $250 per trade).
The risk info label will show:
Stop distance in price and $/contract
Max allowed risk
Suggested mini and micro contract size.
Enable alerts (optional)
Open the Alerts dialog → Condition: this script.
Choose one of:
Long entry FVG breakout
Short entry FVG breakout
FVG entry (long/short)
Choose “Once per bar” or “Once per bar close”, and your preferred notification type.
Replay & journal
Use the TradingView bar replay tool to step through past days.
Focus on:
How the ORB defines the structure.
How the first confirmed FVG outside the ORB behaves.
Whether the risk/TP levels fit your own style and product.
🎛 Recommended settings & profiles
These are starting points, not rules. Always adapt to the instrument and your own risk tolerance.
1. Conservative / Trend-following
Timeframe: 5m or 15m
Morning Range Session: 15-minute ORB around the cash or futures open
FVG
Threshold %: 0.05–0.1 (filter out very small gaps)
Auto threshold: OFF (keep it simple)
Allow breakout FVG partly inside ORB: OFF
Enable FVG entry signals/boxes/alerts: ON
Entry mode: Mid
Stop Logic
Stop mode: Pivot
Pivot left/right: 2–3
Stop buffer: +1–2 ticks
HTF Trend Filter
Enabled: ON
EMA length: 20
ATR Filter
Enabled: ON
Daily ATR length: 14
Min ORB vs ATR: 0.3–0.4
Max ORB vs ATR: 1.2–1.5
Risk Management
Risk mode: Percent
Account risk: 0.5–1.0%
Idea: Only trade when the higher-timeframe trend supports the move and the opening range is of a “normal” size for the current volatility.
2. Balanced / Intraday directional
Timeframe: 3m or 5m
FVG
Threshold %: 0.02–0.05
Auto threshold: ON (lets the script adapt to volatility)
Allow breakout FVG partly inside ORB: ON
(first breakout FVG may partly sit inside the ORB)
Entry mode: Edge
Edge offset (ticks): 0 or +1
Stop Logic
Stop mode: FVG Candle
Stop buffer: 0–1 ticks
HTF Trend Filter
Enabled: ON
ATR Filter
Enabled: OFF (optional)
Risk Management
Risk mode: Percent
Account risk: 1.0–1.5% (if this fits your plan)
Idea: Slightly more aggressive entries at the gap edge, still aligned with HTF trend, but with more flexibility on ATR.
3. Aggressive / Scalping around the ORB
Timeframe: 1m or 3m
FVG
Threshold %: 0.0–0.02
Auto threshold: ON
Allow breakout FVG partly inside ORB: ON
Entry mode: NextOpen or Edge with a negative offset (deeper into the gap)
Stop Logic
Stop mode: PrevBar
Stop buffer: 0 or -1 tick
HTF Trend Filter
Enabled: OFF (or ON but treat as soft guidance)
ATR Filter
Enabled: OFF
Risk Management
Risk mode: Percent
Account risk: lower, e.g. 0.25–0.5% per trade
Idea: More trades and tighter stops. Best for experienced traders who understand the limitations of scalping and whipsaw risk.
Final reminder
All of these are templates, not guarantees:
Always check how the system behaves on your market and session.
Start on replay and demo before trading real money.
Adjust filters (HTF, ATR, thresholds) until the signals fit your personal approach.
In den Scripts nach "profit" suchen
Bassi's Consolidation Breakout — ULTIMATE PRO + VPOverview
Bassi’s Consolidation Breakout — ULTIMATE PRO + VP is a professional-grade breakout detection system that combines price structure, volume confirmation, volatility compression, and custom volume profile logic.
The indicator automatically detects compressed consolidation zones, confirms breakouts with multi-layer filters, and plots full trade setups including:
Entry level
Stop-loss
TP1, TP2, TP3 (R:R based)
Trend filters + MTF EMA
Retest validation
Volume Profile confirmation (POC / VAH / VAL)
This is one of the most complete breakout frameworks for TradingView.
🔍 Core Concept
The script detects tight consolidation boxes based on:
Price range (% compression)
Lookback period
Minimum required bars
Breakout above/below the box
Once the consolidation ends, breakout signals fire only if they pass all filters.
This focuses your trading on high-probability breakouts only.
🔥 Key Features
1️⃣ Automated Consolidation Box Detection
Draws consolidation boxes dynamically
Identifies tight range compression
Supports advanced range logic for high accuracy
2️⃣ Smart Breakout + Retest Engine
Breakouts and breakdowns require:
Structure break
Minimum breakout expansion (0.15%)
Volume confirmation
Trend (200 EMA) confirmation
Optional retest validation
Optional Volume Profile filter
Each valid breakout prints a signal + full trade setup.
3️⃣ Custom Volume Profile Engine
Fast and lightweight custom-built VP that calculates:
POC (Point of Control)
VAH (Value Area High)
VAL (Value Area Low)
These levels can optionally be used to filter weak breakouts.
4️⃣ Multi-Timeframe Trend Filter
Uses 200 EMA from any selected higher timeframe
Helps avoid counter-trend fakeouts
Fully optional
5️⃣ Automatic Trade Setup Projection
Each breakout generates:
Stop-loss (ATR × multiplier)
TP1 (R:R)
TP2 (R:R)
TP3 (optional)
Clean signal labels
Only keeps the last 2 signals to maintain clarity
6️⃣ Alerts Included
Alerts fire instantly when a valid breakout occurs:
“Bassi LONG + VP”
“Bassi SHORT + VP”
Alerts include ticker + entry price.
📘 Usage Guide & Trading Rules
✔ Recommended Trading Steps
1. Wait for a confirmed consolidation box
Box must be narrow
Must meet minimum bar requirement
2. Wait for a confirmed breakout signal
Signal requires:
Breakout above/below box
Volume confirmation
Trend & MTF confirmation if enabled
Optional retest
Optional VP filter (close outside VAH/VAL)
3. Follow the projected setup
The script prints:
Entry
SL
TP1 / TP2 / TP3
Target lines extend automatically.
📖 How to Use the Script (Trading Rules)
1️⃣ Long Entry Rules
Enter Long when:
Price breaks above trend confirmation level
Momentum signal turns bullish
Candle closes above trigger line
Volatility filter is satisfied
Exit Long:
TP1/TP2/TP3 levels
Reversal signal
Trailing stop hit
2️⃣ Short Entry Rules
Enter Short when:
Price breaks below trend confirmation level
Momentum signal turns bearish
Candle closes below trigger line
Volatility filter is satisfied
Exit Short:
TP1/TP2/TP3 levels
Trend reversal
Trailing stop hit
✔ Recommended Markets
Crypto
Forex
Indices
Futures
Stocks
Works on all timeframes from 1-minute to daily.
✔ Best Practice
Avoid taking signals against HTF trend
Prefer signals that break away from VAH/VAL
Use TP1 to secure partial profits
Move SL to breakeven after TP1 if desired
Always follow personal risk management
👤 Author
Created by: Mahdi Bassi
Professional trader & systems designer
Focused on structural, volume-based and volatility-based strategies.
⚠️ Disclaimer
This script is for educational purposes only.
No indicator can guarantee profits.
Always use proper risk management and trade responsibly.
Session Open Range, Breakout & Trap Framework - TrendPredator OBSession Open Range, Breakout & Trap Framework — TrendPredator Open Box
Stacey Burke’s trading approach combines concepts from George Douglas Taylor, Tony Crabel, Steve Mauro, and Robert Schabacker. His framework focuses on reading price behaviour across daily templates and identifying how markets move through recurring cycles of expansion, contraction, and reversal. While effective, much of this analysis requires real-time interpretation of session-based behaviour, which can be demanding for traders working on lower intraday timeframes.
The TrendPredator indicators formalize parts of this methodology by introducing mechanical rules for multi-timeframe bias tracking and session structure analysis. They aim to present the key elements of the system—bias, breakouts, fakeouts, and range behaviour—in a consistent and objective way that reduces discretionary interpretation.
The Open Box indicator focuses specifically on the opening behaviour of major trading sessions. It builds on principles found in classical Open Range Breakout (ORB) techniques described by Tony Crabel, where a defined time window around the session open forms a structural reference range. Price behaviour relative to this range—breaking out, failing back inside, or expanding—can highlight developing session bias, potential trap formation, and directional conviction.
This indicator applies these concepts throughout the major equity sessions. It automatically maps the session’s initial range (“Open Box”) and tracks how price interacts with it as liquidity and volatility increase. It also incorporates related structural references such as:
* the first-hour high and low of the futures session
* the exact session open level
* an anchored VWAP starting at the session open
* automated expansion levels projected from the Open Box
In combination, these components provide a unified view of early session activity, including breakout attempts, fakeouts, VWAP reactions, and liquidity targeting. The Open Box offers a structured lens for observing how price transitions through the major sessions (Asia → London → New York) and how these behaviours relate to higher-timeframe bias defined in the broader TrendPredator framework.
Core Features
Open Box (Session Structure)
The indicator defines an initial session range beginning at the selected session open. This “Open Box” represents a fixed time window—commonly the first 30 minutes, or any user-defined duration—that serves as a structural reference for analysing early session behaviour.
The range highlights whether price remains inside the box, breaks out, or rejects the boundaries, providing a consistent foundation for interpreting early directional tendencies and recognising breakout, continuation, or fakeout characteristics.
How it works:
* At the session open, the indicator calculates the high and low over the specified time window.
* This range is plotted as the initial structure of the session.
* Price behaviour at the boundaries can illustrate emerging bias or potential trap formation.
* An optional secondary range (e.g., 15-minute high/low) can be enabled to capture early volatility with additional precision.
Inputs / Options:
* Session specifications (Tokyo, London, New York)
* Open Box start and end times (e.g., equity open + first 30 minutes, or any custom length)
* Open Box colour and label settings
* Formatting options for Open Box high and low lines
* Optional secondary range per session (e.g., 15-minute high/low)
* Forward extension of Open Box high/low lines
* Number of historic Open Boxes to display
Session VWAPs
The indicator plots VWAPs for each major trading session—Asia, London, and New York—anchored to their respective session opens. These session-specific VWAPs assist in tracking how value develops through the day and how price interacts with session-based volume distributions.
How it works:
* At each session open, a VWAP is anchored to the open price.
* The VWAP updates throughout the session as new volume and price data arrive.
* Deviations above or below the VWAP may indicate balance, imbalance, or directional control.
* Viewed together, session VWAPs help identify transitions in value across sessions.
Inputs / Options:
* Enable or disable VWAP per session
* Adjustable anchor and end times (optionally to end of day)
* Line styling and label settings
* Number of historic VWAPs to draw
First Hour High/Low Extensions
The indicator marks the high and low formed during the first hour of each session. These reference points often function as early control levels and provide context for assessing whether the session is establishing bias, consolidating, or exhibiting reversal behaviour.
How it works:
* After the session starts, the indicator records the highest and lowest prices during the first hour.
* These levels are plotted and extended across the session.
* They provide a visual reference for observing reactions, targets, or rejection zones.
Inputs / Options:
* Enable or disable for each session
* Line style, colour, and label visibility
* Number of historic sessions displayed
EQO Levels (Equity Open)
The indicator plots the opening price of each configured session. These “Equity Open” levels represent short-term reference points that can attract price early in the session.
Once the level is revisited after the Open Box has formed, it is automatically cut to avoid clutter. If not revisited, the line remains as an untested reference, similar to a naked point of control.
How it works:
* At session open, the open price is recorded.
* The level is plotted as a local reference.
* If price interacts with the level after the Open Box completes, the line is cut.
* Untested EQOs extend forward until interacted with.
Inputs / Options:
* Enable/disable per session
* Line style and label settings
* Optional extension into the next day
* Option for cutting vs. hiding on revisit
* Number of historic sessions displayed
OB Range Expansions (Automatic)
Range expansions are calculated from the height of the Open Box. These levels provide structured reference zones for identifying potential continuation or exhaustion areas within a session.
How it works:
* After the Open Box is formed, multiples of the range (e.g., 1×, 2×, 3×) are projected.
* These expansion levels are plotted above and below the range.
* Price reactions near these areas can illustrate continuation, hesitation, or potential reversal.
Inputs / Options:
* Enable or disable per session
* Select number of multiples
* Line style, colour, and label settings
* Extension length into the session
Stacey Burke 12-Candle Window Marker
The indicator can highlight the 12-candle window often referenced in Stacey Burke’s session methodology. This window represents the key active period of each session where breakout attempts, volatility shifts, and reversal signatures often occur.
How it works:
* A configurable window (default 12 candles) is highlighted from each session open.
* This window acts as a guide for observing active session behaviour.
* It remains visible throughout the session for structural context.
Inputs / Options:
* Enable/disable per session
* Configurable window duration (default: 3 hours)
* Colour and transparency controls
Concept and Integration
The Open Box is built around the same multi-timeframe logic that underpins the broader TrendPredator framework.
While higher-timeframe tools track bias and setups across the H8–D–W–M levels, the Open Box focuses on the H1–M30 domain to define session structure and observe how early intraday behaviour aligns with higher-timeframe conditions.
The indicator integrates with the TrendPredator FO (Breakout, Fakeout & Trend Switch Detector), which highlights microstructure signals on lower timeframes (M15/M5). Together they form a layered workflow:
* Higher timeframes: context, bias, and developing setups
* TrendPredator OB: intraday and intra-session structure
* TrendPredator FO: microstructure confirmation (e.g., FOL/FOH, switches)
This alignment provides a structured way to observe how daily directional context interacts with intraday behaviour.
See the public open source indicator TP FO here (click on it for access):
Practical Application
Before Session Open
* Review previous session Open Box, Open level, and VWAPs
* Assess how higher-timeframe bias aligns with potential intraday continuation or reversal
* Note untested EQO levels or VWAPs that may function as liquidity attractors
During Session Open
* Observe behaviour around the first-hour high/low and higher-timeframe reference levels
* Monitor how the M15 and 30-minute ranges close
* Track reactions relative to the session open level and the session VWAP
After the Open Box completes
* Assess price interaction with Open Box boundaries and first-hour levels
* Use microstructure signals (e.g., FOH/FOL, switches) for potential confirmation
* Refer to expansion levels as reference zones for management or target setting
After Session
* Review how price behaved relative to the Open Box, EQO levels, VWAPs, and expansion zones
* Analyse breakout attempts, fakeouts, and whether intraday structure aligned with the broader daily move
Example Workflow and Trade
1. Higher-timeframe analysis signals a Daily Fakeout Low Continuation (bullish context).
2. The New York session forms an Open Box; price breaks above and holds above the first-hour high.
3. A Fakeout Low + Switch Bar appears on M5 (via FO), after retesting the session VWAP triggering the entry.
4. 1x expansion level serves as reference targets for take profit.
Relation to the TrendPredator Ecosystem
The Open Box is part of the TrendPredator Indicator Family, designed to apply multi-timeframe logic consistently across:
* higher-timeframe context and setups
* intraday and session structure (OB)
* microstructure confirmation (FO)
Together, these modules offer a unified structure for analysing how daily and intraday cycles interact.
Disclaimer
This indicator is for educational purposes only and does not guarantee profits.
It does not provide buy or sell signals but highlights structural and behavioural areas for analysis.
Users are solely responsible for their trading decisions and outcomes.
Dumb Money Flow - Retail Panic & FOMO# Dumb Money Flow (DMF) - Retail Panic & FOMO
## 🌊 Overview
**Dumb Money Flow (DMF)** is a powerful **contrarian indicator** designed to track the emotional state of the retail "herd." It identifies moments of extreme **Panic** (irrational selling) and **FOMO** (irrational buying) by analyzing on-chain data, volume anomalies, and price velocity.
In crypto markets, retail traders often buy the top (FOMO) and sell the bottom (Panic). This indicator helps you do the opposite: **Buy when the herd is fearful, and Sell when the herd is greedy.**
---
## 🧠 How It Works
The indicator combines multiple data points into a single **Sentiment Index** (0-100), normalized over a 90-day period to ensure it always uses the full range of the chart.
### 1. Panic Index (Bearish Sentiment)
Tracks signs of capitulation and fear. High values contribute to the **Panic Zone**.
* **Exchange Inflows:** Spikes in funds moving to exchanges (preparing to sell).
* **Volume Spikes:** High volume during price drops (panic selling).
* **Price Crash (ROC):** Rapid, emotional price drops over 3 days.
* **Volatility (ATR):** High market nervousness and instability.
### 2. FOMO Index (Bullish Sentiment)
Tracks signs of euphoria and greed. High values contribute to the **FOMO Zone**.
* **Exchange Outflows:** Funds moving to cold storage (HODLing/Greed).
* **Profitable Addresses:** When >90% of holders are in profit, tops often form.
* **Parabolic Rise:** Rapid, unsustainable price increases.
---
## 🎨 Visual Guide
The indicator uses a distinct color scheme to highlight extremes:
* **🟢 Dark Green Zone (> 80): Extreme FOMO**
* **Meaning:** The crowd is euphoric. Risk of a correction is high.
* **Action:** Consider taking profits or looking for short entries.
* **🔴 Dark Burgundy Zone (< 20): Extreme Panic**
* **Meaning:** The crowd is capitulating. Prices may be oversold.
* **Action:** Look for buying opportunities (catching the knife with confirmation).
* **🔵 Light Blue Line:**
* The smoothed moving average of the sentiment, helpful for seeing the trend direction.
---
## 🛠️ How to Use (Trading Strategies)
### 1. Contrarian Reversals (The Primary Strategy)
* **Buy Signal:** Wait for the line to drop deep into the **Burgundy Panic Zone (< 20)** and then start curling up. This indicates that the worst of the selling pressure is over.
* **Sell Signal:** Wait for the line to spike into the **Green FOMO Zone (> 80)** and then start curling down. This suggests buying exhaustion.
### 2. Divergences
* **Bullish Divergence:** Price makes a **Lower Low**, but the DMF Indicator makes a **Higher Low** (less panic on the second drop). This is a strong reversal signal.
* **Bearish Divergence:** Price makes a **Higher High**, but the DMF Indicator makes a **Lower High** (less FOMO/buying power on the second peak).
### 3. Trend Confirmation (Midline Cross)
* **Crossing 50 Up:** Sentiment is shifting from Fear to Greed (Bullish).
* **Crossing 50 Down:** Sentiment is shifting from Greed to Fear (Bearish).
---
## ⚙️ Settings
* **Data Source:** Defaults to `INTOTHEBLOCK` for on-chain data.
* **Crypto Asset:** Auto-detects BTC/ETH, but can be forced.
* **Normalization Period:** Default 90 days. Determines the "window" for defining what is considered "Extreme" relative to recent history.
* **Weights:** You can customize how much each factor (Volume, Inflows, Price) contributes to the index.
---
**Disclaimer:** This indicator is for educational purposes only. "Dumb Money" analysis is a probability tool, not a crystal ball. Always manage your risk.
**Indicator by:** @iCD_creator
**Version:** 1.0
**Pine Script™ Version:** 6
---
## Updates & Support
For questions, suggestions, or bug reports, please comment below or message the author.
**Like this indicator? Leave a 👍 and share your feedback!**
Fibonacci Degree System This Pine Script creates a sophisticated technical analysis tool that combines Fibonacci retracements with a degree-based cycle system. Here's a comprehensive breakdown:
Core Concept
The indicator maps price movements onto a 360-degree circular framework, treating market cycles like geometric angles. It creates a visual "mesh" where Fibonacci ratios intersect in both price (horizontal) and time (vertical) dimensions.
How It Works
1. Finding Reference Points
The script looks back over a specified period (default 100 bars) to identify:
Highest High: The peak price point
Lowest Low: The trough price point
Time Locations: Exactly which bars these extremes occurred on
These two points form the boundaries of your analysis window.
2. Creating the Fibonacci Grid
Horizontal Lines (Price Levels):
The script divides the price range between high and low into seven key Fibonacci ratios:
0% (Low) - Bottom boundary in red
23.6% - Minor retracement in orange
38.2% - Shallow retracement in yellow
50% - Midpoint in lime green
61.8% - Golden ratio in aqua (most significant)
78.6% - Deep retracement in blue
100% (High) - Top boundary in purple
Each line represents a potential support/resistance level where price might react.
Vertical Lines (Time Cycles):
The same Fibonacci ratios are applied to the time dimension between the high and low bars. If your high and low are 50 bars apart, vertical lines appear at:
Bar 0 (0%)
Bar 12 (23.6%)
Bar 19 (38.2%)
Bar 25 (50%)
Bar 31 (61.8%)
Bar 39 (78.6%)
Bar 50 (100%)
These suggest when price might make significant moves.
3. The Degree Mapping System
The innovative feature maps the time progression to degrees:
0° = Start point (0% time)
85° = 23.6% through the cycle
138° = 38.2% through the cycle
180° = Midpoint (50%)
222° = 61.8% through the cycle (golden angle)
283° = 78.6% through the cycle
360° = Complete cycle (100%)
This treats market movements as circular patterns, similar to how planets orbit or pendulums swing.
Visual Output
When you apply this indicator, you'll see:
A rectangular mesh extending beyond your high-low range (by 150% default)
Color-coded horizontal lines showing price Fibonacci levels
Matching vertical lines showing time Fibonacci intervals
Price labels on the right showing percentage levels
Degree labels at the bottom showing the angular position in the cycle
Intersection points creating a grid of potentially significant price-time coordinates
Trading Application
Traders use this to identify:
Support/Resistance Zones: Where horizontal and vertical lines intersect
Time Targets: When price might reverse (at vertical Fibonacci times)
Cycle Completion: When approaching 360°, a new cycle may begin
Harmonic Patterns: Geometric relationships between price and time
Customization Features
The script offers extensive control:
Lookback period: Adjust cycle length (10-500 bars)
Mesh extension: How far to project the grid forward
Visual toggles: Show/hide horizontal lines, vertical lines, labels
Styling: Line thickness, style (solid/dashed/dotted), colors
Label positioning: Fine-tune text placement for readability
The intersection at 61.8% time and 61.8% price at 222° becomes a key target zone.
This tool essentially converts the abstract concept of market cycles into a concrete, visual geometric framework that traders can analyze and act upon.
DISCLAIMER: This information is provided for educational purposes only and should not be considered financial, investment, or trading advice.
No guarantee of profits: Past performance and theoretical models do not guarantee future results. Trading and investing involve substantial risk of loss.
Not a recommendation: This script illustration does not constitute a recommendation to buy, sell, or hold any financial instrument.
Do your own research: Always conduct thorough independent research and consider consulting with a qualified financial advisor before making any trading decisions.
Dimensional Resonance ProtocolDimensional Resonance Protocol
🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION
The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states.
The Complexity Hypothesis:
Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics.
Takens' Embedding Theorem (1981):
A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation.
Mathematical Foundation:
From single observable x(t), create embedding vectors:
X(t) =
Where:
• d = Embedding dimension (default 5)
• τ = Time delay (default 3 bars)
• x(t) = Price or return at time t
Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series.
Why This Matters:
Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect:
• Stable regions : Predictable behavior (trade opportunities)
• Chaotic regions : Unpredictable behavior (avoid trading)
• Critical transitions : Phase changes between regimes
Phase Space Magnitude Calculation:
phase_magnitude = sqrt(Σ ² for i = 0 to d-1)
This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation.
📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA)
Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states?
Recurrence Plot Foundation:
A recurrence occurs when two phase space points are closer than threshold ε:
R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0
This creates a binary matrix showing when the system revisits similar states.
Key RQA Metrics:
1. Recurrence Rate (RR):
RR = (Number of recurrent points) / (Total possible pairs)
• RR near 0: System never repeats (highly stochastic)
• RR = 0.1-0.3: Moderate recurrence (tradeable patterns)
• RR > 0.5: System stuck in attractor (ranging market)
• RR near 1: System frozen (no dynamics)
Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck.
2. Determinism (DET):
Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths).
DET = (Recurrence points on diagonals) / (Total recurrence points)
• DET < 0.3: Random dynamics
• DET = 0.3-0.7: Moderate determinism (patterns with noise)
• DET > 0.7: Strong determinism (technical patterns reliable)
Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck).
Threshold Selection (ε):
Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns.
🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT
Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns.
Algorithm (Bandt & Pompe, 2002):
1. Take overlapping windows of length n (default n=4)
2. For each window, record the rank order pattern
Example: → pattern (ranks from lowest to highest)
3. Count frequency of each possible pattern
4. Calculate Shannon entropy of pattern distribution
Mathematical Formula:
H_perm = -Σ p(π) · ln(p(π))
Where π ranges over all n! possible permutations, p(π) is the probability of pattern π.
Normalized to :
H_norm = H_perm / ln(n!)
Interpretation:
• H < 0.3 : Very ordered, crystalline structure (strong trending)
• H = 0.3-0.5 : Ordered regime (tradeable with patterns)
• H = 0.5-0.7 : Moderate complexity (mixed conditions)
• H = 0.7-0.85 : Complex dynamics (challenging to trade)
• H > 0.85 : Maximum entropy (nearly random, avoid)
Entropy Regime Classification:
DRP classifies markets into five entropy regimes:
• CRYSTALLINE (H < 0.3): Maximum order, persistent trends
• ORDERED (H < 0.5): Clear patterns, momentum strategies work
• MODERATE (H < 0.7): Mixed dynamics, adaptive required
• COMPLEX (H < 0.85): High entropy, mean reversion better
• CHAOTIC (H ≥ 0.85): Near-random, minimize trading
Why Permutation Entropy?
Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy:
• Works directly on time series
• Robust to monotonic transformations
• Computationally efficient
• Captures temporal structure, not just distribution
• Immune to outliers (uses ranks, not values)
⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY
The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos.
Physical Meaning:
Two trajectories starting infinitely close will diverge at exponential rate e^(λt):
Distance(t) ≈ Distance(0) × e^(λt)
Interpretation:
• λ > 0 : Positive Lyapunov exponent = CHAOS
- Small errors grow exponentially
- Long-term prediction impossible
- System is sensitive, unpredictable
- AVOID TRADING
• λ ≈ 0 : Near-zero = CRITICAL STATE
- Edge of chaos
- Transition zone between order and disorder
- Moderate predictability
- PROCEED WITH CAUTION
• λ < 0 : Negative Lyapunov exponent = STABLE
- Small errors decay
- Trajectories converge
- System is predictable
- OPTIMAL FOR TRADING
Estimation Method:
DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars):
For each bar i in window:
δ₀ = |x - x | (initial separation)
δ₁ = |x - x | (previous separation)
if δ₁ > 0:
ratio = δ₀ / δ₁
log_ratios += ln(ratio)
λ ≈ average(log_ratios)
Stability Classification:
• STABLE : λ < 0 (negative growth rate)
• CRITICAL : |λ| < 0.1 (near neutral)
• CHAOTIC : λ > 0.2 (strong positive growth)
Signal Filtering:
By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down.
📐 HIGUCHI FRACTAL DIMENSION
Fractal dimension measures self-similarity and complexity of the price trajectory.
Theoretical Background:
A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve):
• D ≈ 1.0 : Smooth, persistent trending
• D ≈ 1.5 : Random walk (Brownian motion)
• D ≈ 2.0 : Highly irregular, space-filling
Higuchi Method (1988):
For a time series of length N, construct k different curves by taking every k-th point:
L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k)
For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k):
D = slope of log(L) vs log(1/k)
Market Interpretation:
• D < 1.35 : Strong trending, persistent (Hurst > 0.5)
- TRENDING regime
- Momentum strategies favored
- Breakouts likely to continue
• D = 1.35-1.45 : Moderate persistence
- PERSISTENT regime
- Trend-following with caution
- Patterns have meaning
• D = 1.45-1.55 : Random walk territory
- RANDOM regime
- Efficiency hypothesis holds
- Technical analysis least reliable
• D = 1.55-1.65 : Anti-persistent (mean-reverting)
- ANTI-PERSISTENT regime
- Oscillator strategies work
- Overbought/oversold meaningful
• D > 1.65 : Highly complex, choppy
- COMPLEX regime
- Avoid directional bets
- Wait for regime change
Signal Filtering:
Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning.
🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW
Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer.
Schreiber's Definition (2000):
Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future:
TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past)
Where H is Shannon entropy.
Key Properties:
1. Directional : TE(X→Y) ≠ TE(Y→X) in general
2. Non-linear : Detects complex causal relationships
3. Model-free : No assumptions about functional form
4. Lag-independent : Captures delayed causal effects
Three Causal Flows Measured:
1. Volume → Price (TE_V→P):
Measures how much volume patterns predict price changes.
• TE > 0 : Volume provides predictive information about price
- Institutional participation driving moves
- Volume confirms direction
- High reliability
• TE ≈ 0 : No causal flow (weak volume/price relationship)
- Volume uninformative
- Caution on signals
• TE < 0 (rare): Suggests price leading volume
- Potentially manipulated or thin market
2. Volatility → Momentum (TE_σ→M):
Does volatility expansion predict momentum changes?
• Positive TE : Volatility precedes momentum shifts
- Breakout dynamics
- Regime transitions
3. Structure → Price (TE_S→P):
Do support/resistance patterns causally influence price?
• Positive TE : Structural levels have causal impact
- Technical levels matter
- Market respects structure
Net Causal Flow:
Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P
• Net > +0.1 : Bullish causal structure
• Net < -0.1 : Bearish causal structure
• |Net| < 0.1 : Neutral/unclear causation
Causal Gate:
For signal confirmation, NEXUS requires:
• Buy signals : TE_V→P > 0 AND Net_Flow > 0.05
• Sell signals : TE_V→P > 0 AND Net_Flow < -0.05
This ensures volume is actually driving price (causal support exists), not just correlated noise.
Implementation Note:
Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner.
🌊 HILBERT PHASE COHERENCE
Phase coherence measures synchronization across market dimensions using Hilbert transform analysis.
Hilbert Transform Theory:
For a signal x(t), the Hilbert transform H (t) creates an analytic signal:
z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t))
Where:
• A(t) = Instantaneous amplitude
• φ(t) = Instantaneous phase
Instantaneous Phase:
φ(t) = arctan(H (t) / x(t))
The phase represents where the signal is in its natural cycle—analogous to position on a unit circle.
Four Dimensions Analyzed:
1. Momentum Phase : Phase of price rate-of-change
2. Volume Phase : Phase of volume intensity
3. Volatility Phase : Phase of ATR cycles
4. Structure Phase : Phase of position within range
Phase Locking Value (PLV):
For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization:
PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩|
Where ⟨·⟩ is time average over window.
Interpretation:
• PLV = 0 : Completely random phase relationship (no synchronization)
• PLV = 0.5 : Moderate phase locking
• PLV = 1 : Perfect synchronization (phases locked)
Pairwise PLV Calculations:
• PLV_momentum-volume : Are momentum and volume cycles synchronized?
• PLV_momentum-structure : Are momentum cycles aligned with structure?
• PLV_volume-structure : Are volume and structural patterns in phase?
Overall Phase Coherence:
Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3
Signal Confirmation:
Emergence signals require coherence ≥ threshold (default 0.70):
• Below 0.70: Dimensions not synchronized, no coherent market state
• Above 0.70: Dimensions in phase, coherent behavior emerging
Coherence Direction:
The summed phase angles indicate whether synchronized dimensions point bullish or bearish:
Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure)
• Direction > 0 : Phases pointing upward (bullish synchronization)
• Direction < 0 : Phases pointing downward (bearish synchronization)
🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT
The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence.
Eight Components with Weights:
1. Phase Coherence (20%):
Direct contribution: coherence × 0.20
Measures dimensional synchronization.
2. Entropy Regime (15%):
Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0
Rewards low entropy (ordered, predictable states).
3. Lyapunov Stability (12%):
• λ < 0 (stable): +0.12
• |λ| < 0.1 (critical): +0.08
• λ > 0.2 (chaotic): +0.0
Requires stable, predictable dynamics.
4. Fractal Dimension Trending (12%):
Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0
Rewards trending fractal structure (D < 1.45).
5. Dimensional Resonance (12%):
Contribution: |dimensional_resonance| × 0.12
Measures alignment across momentum, volume, structure, volatility dimensions.
6. Causal Flow Strength (9%):
Contribution: |net_causal_flow| × 0.09
Rewards strong causal relationships.
7. Phase Space Embedding (10%):
Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0
Rewards strong trajectory in reconstructed phase space.
8. Recurrence Quality (10%):
Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8
Rewards deterministic patterns with moderate recurrence.
Total Emergence Score:
E = Σ(components) ∈
Capped at 1.0 maximum.
Emergence Direction:
Separate calculation determining bullish vs bearish:
• Dimensional resonance sign
• Net causal flow sign
• Phase magnitude correlation with momentum
Signal Threshold:
Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals.
Why Emergence Matters:
Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order.
These are the highest-probability trade opportunities because the entire system is resonating in the same direction.
🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE
DRP generates two tiers of signals with different requirements:
TIER 1: EMERGENCE SIGNALS (Primary)
Requirements:
1. Emergence score ≥ threshold (default 0.75)
2. Phase coherence ≥ threshold (default 0.70)
3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish)
4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction
5. Stability zone (if enabled): λ < 0 or |λ| < 0.1
6. Price confirmation: Close > open (bulls) or close < open (bears)
7. Cooldown satisfied: bars_since_signal ≥ cooldown_period
EMERGENCE BUY:
• All above conditions met with bullish direction
• Market has achieved coherent bullish state
• Multiple dimensions synchronized upward
EMERGENCE SELL:
• All above conditions met with bearish direction
• Market has achieved coherent bearish state
• Multiple dimensions synchronized downward
Premium Emergence:
When signal_quality (emergence_score × phase_coherence) > 0.7:
• Displayed as ★ star symbol
• Highest conviction trades
• Maximum dimensional alignment
Standard Emergence:
When signal_quality 0.5-0.7:
• Displayed as ◆ diamond symbol
• Strong signals but not perfect alignment
TIER 2: RESONANCE SIGNALS (Secondary)
Requirements:
1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish)
2. Fractal dimension < 1.5 (trending/persistent regime)
3. Price confirmation matches direction
4. NOT in chaotic regime (λ < 0.2)
5. Cooldown satisfied
6. NO emergence signal firing (resonance is fallback)
RESONANCE BUY:
• Dimensional alignment without full emergence
• Trending fractal structure
• Moderate conviction
RESONANCE SELL:
• Dimensional alignment without full emergence
• Bearish resonance with trending structure
• Moderate conviction
Displayed as small ▲/▼ triangles with transparency.
Signal Hierarchy:
IF emergence conditions met:
Fire EMERGENCE signal (★ or ◆)
ELSE IF resonance conditions met:
Fire RESONANCE signal (▲ or ▼)
ELSE:
No signal
Cooldown System:
After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions.
Cooldown tracks using bar_index:
bars_since_signal = current_bar_index - last_signal_bar_index
cooldown_ok = bars_since_signal >= cooldown_period
🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY
DRP provides rich visual feedback across four distinct layers:
LAYER 1: COHERENCE FIELD (Background)
Colored background intensity based on phase coherence:
• No background : Coherence < 0.5 (incoherent state)
• Faint glow : Coherence 0.5-0.7 (building coherence)
• Stronger glow : Coherence > 0.7 (coherent state)
Color:
• Cyan/teal: Bullish coherence (direction > 0)
• Red/magenta: Bearish coherence (direction < 0)
• Blue: Neutral coherence (direction ≈ 0)
Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible.
LAYER 2: STABILITY/CHAOS ZONES
Background color indicating Lyapunov regime:
• Green tint (95% transparent): λ < 0, STABLE zone
- Safe to trade
- Patterns meaningful
• Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone
- Edge of chaos
- Moderate risk
• Red tint (85% transparent): λ > 0.2, CHAOTIC zone
- Avoid trading
- Unpredictable behavior
LAYER 3: DIMENSIONAL RIBBONS
Three EMAs representing dimensional structure:
• Fast ribbon : EMA(8) in cyan/teal (fast dynamics)
• Medium ribbon : EMA(21) in blue (intermediate)
• Slow ribbon : EMA(55) in red/magenta (slow dynamics)
Provides visual reference for multi-scale structure without cluttering with raw phase space data.
LAYER 4: CAUSAL FLOW LINE
A thicker line plotted at EMA(13) colored by net causal flow:
• Cyan/teal : Net_flow > +0.1 (bullish causation)
• Red/magenta : Net_flow < -0.1 (bearish causation)
• Gray : |Net_flow| < 0.1 (neutral causation)
Shows real-time direction of information flow.
EMERGENCE FLASH:
Strong background flash when emergence signals fire:
• Cyan flash for emergence buy
• Red flash for emergence sell
• 80% transparency for visibility without obscuring price
📊 COMPREHENSIVE DASHBOARD
Real-time monitoring of all complexity metrics:
HEADER:
• 🌀 DRP branding with gold accent
CORE METRICS:
EMERGENCE:
• Progress bar (█ filled, ░ empty) showing 0-100%
• Percentage value
• Direction arrow (↗ bull, ↘ bear, → neutral)
• Color-coded: Green/gold if active, gray if low
COHERENCE:
• Progress bar showing phase locking value
• Percentage value
• Checkmark ✓ if ≥ threshold, circle ○ if below
• Color-coded: Cyan if coherent, gray if not
COMPLEXITY SECTION:
ENTROPY:
• Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC)
• Numerical value (0.00-1.00)
• Color: Green (ordered), gold (moderate), red (chaotic)
LYAPUNOV:
• State (STABLE/CRITICAL/CHAOTIC)
• Numerical value (typically -0.5 to +0.5)
• Status indicator: ● stable, ◐ critical, ○ chaotic
• Color-coded by state
FRACTAL:
• Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX)
• Dimension value (1.0-2.0)
• Color: Cyan (trending), gold (random), red (complex)
PHASE-SPACE:
• State (STRONG/ACTIVE/QUIET)
• Normalized magnitude value
• Parameters display: d=5 τ=3
CAUSAL SECTION:
CAUSAL:
• Direction (BULL/BEAR/NEUTRAL)
• Net flow value
• Flow indicator: →P (to price), P← (from price), ○ (neutral)
V→P:
• Volume-to-price transfer entropy
• Small display showing specific TE value
DIMENSIONAL SECTION:
RESONANCE:
• Progress bar of absolute resonance
• Signed value (-1 to +1)
• Color-coded by direction
RECURRENCE:
• Recurrence rate percentage
• Determinism percentage display
• Color-coded: Green if high quality
STATE SECTION:
STATE:
• Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING
• Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning)
• Color-coded by state
SIGNALS:
• E: count of emergence signals
• R: count of resonance signals
⚙️ KEY PARAMETERS EXPLAINED
Phase Space Configuration:
• Embedding Dimension (3-10, default 5): Reconstruction dimension
- Low (3-4): Simple dynamics, faster computation
- Medium (5-6): Balanced (recommended)
- High (7-10): Complex dynamics, more data needed
- Rule: d ≥ 2D+1 where D is true dimension
• Time Delay (τ) (1-10, default 3): Embedding lag
- Fast markets: 1-2
- Normal: 3-4
- Slow markets: 5-10
- Optimal: First minimum of mutual information (often 2-4)
• Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity
- Tight (0.01-0.05): Very similar states only
- Medium (0.08-0.15): Balanced
- Loose (0.20-0.50): Liberal matching
Entropy & Complexity:
• Permutation Order (3-7, default 4): Pattern length
- Low (3): 6 patterns, fast but coarse
- Medium (4-5): 24-120 patterns, balanced
- High (6-7): 720-5040 patterns, fine-grained
- Note: Requires window >> order! for stability
• Entropy Window (15-100, default 30): Lookback for entropy
- Short (15-25): Responsive to changes
- Medium (30-50): Stable measure
- Long (60-100): Very smooth, slow adaptation
• Lyapunov Window (10-50, default 20): Stability estimation window
- Short (10-15): Fast chaos detection
- Medium (20-30): Balanced
- Long (40-50): Stable λ estimate
Causal Inference:
• Enable Transfer Entropy (default ON): Causality analysis
- Keep ON for full system functionality
• TE History Length (2-15, default 5): Causal lookback
- Short (2-4): Quick causal detection
- Medium (5-8): Balanced
- Long (10-15): Deep causal analysis
• TE Discretization Bins (4-12, default 6): Binning granularity
- Few (4-5): Coarse, robust, needs less data
- Medium (6-8): Balanced
- Many (9-12): Fine-grained, needs more data
Phase Coherence:
• Enable Phase Coherence (default ON): Synchronization detection
- Keep ON for emergence detection
• Coherence Threshold (0.3-0.95, default 0.70): PLV requirement
- Loose (0.3-0.5): More signals, lower quality
- Balanced (0.6-0.75): Recommended
- Strict (0.8-0.95): Rare, highest quality
• Hilbert Smoothing (3-20, default 8): Phase smoothing
- Low (3-5): Responsive, noisier
- Medium (6-10): Balanced
- High (12-20): Smooth, more lag
Fractal Analysis:
• Enable Fractal Dimension (default ON): Complexity measurement
- Keep ON for full analysis
• Fractal K-max (4-20, default 8): Scaling range
- Low (4-6): Faster, less accurate
- Medium (7-10): Balanced
- High (12-20): Accurate, slower
• Fractal Window (30-200, default 50): FD lookback
- Short (30-50): Responsive FD
- Medium (60-100): Stable FD
- Long (120-200): Very smooth FD
Emergence Detection:
• Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence
- Sensitive (0.5-0.65): More signals
- Balanced (0.7-0.8): Recommended
- Strict (0.85-0.95): Rare signals
• Require Causal Gate (default ON): TE confirmation
- ON: Only signal when causality confirms
- OFF: Allow signals without causal support
• Require Stability Zone (default ON): Lyapunov filter
- ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical)
- OFF: Allow signals in chaotic regimes (risky)
• Signal Cooldown (1-50, default 5): Minimum bars between signals
- Fast (1-3): Rapid signal generation
- Normal (4-8): Balanced
- Slow (10-20): Very selective
- Ultra (25-50): Only major regime changes
Signal Configuration:
• Momentum Period (5-50, default 14): ROC calculation
• Structure Lookback (10-100, default 20): Support/resistance range
• Volatility Period (5-50, default 14): ATR calculation
• Volume MA Period (10-50, default 20): Volume normalization
Visual Settings:
• Customizable color scheme for all elements
• Toggle visibility for each layer independently
• Dashboard position (4 corners) and size (tiny/small/normal)
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: System Familiarization (Week 1)
Goal: Understand complexity metrics and dashboard interpretation
Setup:
• Enable all features with default parameters
• Watch dashboard metrics for 500+ bars
• Do NOT trade yet
Actions:
• Observe emergence score patterns relative to price moves
• Note coherence threshold crossings and subsequent price action
• Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC)
• Correlate Lyapunov state with signal reliability
• Track which signals appear (emergence vs resonance frequency)
Key Learning:
• When does emergence peak? (usually before major moves)
• What entropy regime produces best signals? (typically ORDERED or MODERATE)
• Does your instrument respect stability zones? (stable λ = better signals)
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to instrument characteristics
Requirements:
• Understand basic dashboard metrics from Phase 1
• Have 1000+ bars of history loaded
Embedding Dimension & Time Delay:
• If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2)
• If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5)
• Sweet spot: 4-8 emergence signals per 100 bars
Coherence Threshold:
• Check dashboard: What's typical coherence range?
• If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65
• If coherence often >0.80: Can raise threshold to 0.75-0.80
• Goal: Signals fire during top 20-30% of coherence values
Emergence Threshold:
• If too few signals: Lower to 0.65-0.70
• If too many signals: Raise to 0.80-0.85
• Balance with coherence threshold—both must be met
Phase 3: Signal Quality Assessment (Weeks 3-4)
Goal: Verify signals have edge via paper trading
Requirements:
• Parameters optimized per Phase 2
• 50+ signals generated
• Detailed notes on each signal
Paper Trading Protocol:
• Take EVERY emergence signal (★ and ◆)
• Optional: Take resonance signals (▲/▼) separately to compare
• Use simple exit: 2R target, 1R stop (ATR-based)
• Track: Win rate, average R-multiple, maximum consecutive losses
Quality Metrics:
• Premium emergence (★) : Should achieve >55% WR
• Standard emergence (◆) : Should achieve >50% WR
• Resonance signals : Should achieve >45% WR
• Overall : If <45% WR, system not suitable for this instrument/timeframe
Red Flags:
• Win rate <40%: Wrong instrument or parameters need major adjustment
• Max consecutive losses >10: System not working in current regime
• Profit factor <1.0: No edge despite complexity analysis
Phase 4: Regime Awareness (Week 5)
Goal: Understand which market conditions produce best signals
Analysis:
• Review Phase 3 trades, segment by:
- Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC)
- Lyapunov state (STABLE vs CRITICAL vs CHAOTIC)
- Fractal regime (TRENDING vs RANDOM vs COMPLEX)
Findings (typical patterns):
• Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal
• Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal
• Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these)
Optimization:
• If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70
• If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic
• If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate with minimal capital at risk
Requirements:
• Paper trading shows: WR >48%, PF >1.2, max DD <20%
• Understand complexity metrics intuitively
• Know which regimes work best from Phase 4
Setup:
• 10-20% of intended position size
• Focus on premium emergence signals (★) only initially
• Proper stop placement (1.5-2.0 ATR)
Execution Notes:
• Emergence signals can fire mid-bar as metrics update
• Use alerts for signal detection
• Entry on close of signal bar or next bar open
• DO NOT chase—if price gaps away, skip the trade
Comparison:
• Your live results should track within 10-15% of paper results
• If major divergence: Execution issues (slippage, timing) or parameters changed
Phase 6: Full Deployment (Month 3+)
Goal: Scale to full size over time
Requirements:
• 30+ micro live trades
• Live WR within 10% of paper WR
• Profit factor >1.1 live
• Max drawdown <15%
• Confidence in parameter stability
Progression:
• Months 3-4: 25-40% intended size
• Months 5-6: 40-70% intended size
• Month 7+: 70-100% intended size
Maintenance:
• Weekly dashboard review: Are metrics stable?
• Monthly performance review: Segmented by regime and signal type
• Quarterly parameter check: Has optimal embedding/coherence changed?
Advanced:
• Consider different parameters per session (high vs low volatility)
• Track phase space magnitude patterns before major moves
• Combine with other indicators for confluence
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Phase Space Revelation:
Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D.
The Permutation Entropy Discovery:
Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering.
The Lyapunov Filter Breakthrough:
Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself.
The Transfer Entropy Challenge:
Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters.
The Phase Coherence Insight:
Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason.
The Eight-Component Emergence Formula:
Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it.
The Cooldown Necessity:
Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude.
• NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty.
• NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data.
• NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections.
• NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off.
Core Assumptions:
1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly).
2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong.
3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise).
4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets).
5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive).
6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT).
Performs Best On:
• ES, NQ, RTY (major US index futures - high liquidity, clean volume data)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis)
• Liquid commodities: CL (crude oil), GC (gold), NG (natural gas)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure)
• Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis
• Exotic forex pairs - erratic spreads, thin volume
• Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis
• Pre-market/after-hours - gappy, thin, different dynamics
• Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions
• Highly manipulated instruments - spoofing and layering create false coherence
Known Weaknesses:
• Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed.
• Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument.
• Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable.
• Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive.
• False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through.
• Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this.
• Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless.
Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals.
Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size.
The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it.
Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe.
The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic.
Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations.
Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
📁 DOCUMENTATION
The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research:
Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory.
Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics.
Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data.
Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions.
Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales.
Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory.
The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?).
This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor.
Multiple oscillator-type calculations contribute to dimensional analysis:
Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection.
Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized.
Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed.
Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional.
The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states.
While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space.
Volatility analysis permeates the system:
ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score.
Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility).
Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory.
Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable.
Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions.
The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator.
CLOSING STATEMENT
DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence .
This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity.
When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance .
These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state.
Most indicators see shadows on the wall. DRP reconstructs the cave.
"In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Simple Grid Trading v1.0 [PUCHON]Simple Grid Trading v1.0
Overview
This is a Long-Only Grid Trading Strategy developed in Pine Script v6 for TradingView. It is designed to profit from market volatility by placing a series of Buy Limit orders at predefined price levels. As the price drops, the strategy accumulates positions. As the price rises, it sells these positions at a profit.
Features
Grid Types : Supports both Arithmetic (equal price spacing) and Geometric (equal percentage spacing) grids.
Flexible Order Management : Uses strategy.order for precise control and prevents duplicate orders at the same level.
Performance Dashboard : A real-time table displaying key metrics like Capital, Cashflow, and Drawdown.
Advanced Metrics : Includes Max Drawdown (MaxDD) , Avg Monthly Return , and CAGR calculations.
Customizable : Fully adjustable price range, grid lines, and lot size.
Dashboard Metrics
The dashboard (default: Bottom Right) provides a quick snapshot of the strategy's performance:
Initial Capital : The starting capital defined in the strategy settings.
Lot Size : The fixed quantity of assets purchased per grid level.
Avg. Profit per Grid : The average realized profit for each closed trade.
Cashflow : The total realized net profit (closed trades only).
MaxDD : Maximum Drawdown . The largest percentage drop in equity (realized + unrealized) from a peak.
Avg Monthly Return : The average percentage return generated per month.
CAGR : Compound Annual Growth Rate . The mean annual growth rate of the investment over the specified time period.
Strategy Settings (Inputs)
Grid Settings
Upper Price : The highest price level for the grid.
Lower Price : The lowest price level for the grid.
Number of Grid Lines : The total number of levels (lines) in the grid.
Grid Type :
Arithmetic: Distance between lines is fixed in price terms (e.g., $10, $20, $30).
Geometric: Distance between lines is fixed in percentage terms (e.g., 1%, 2%, 3%).
Lot Size : The fixed amount of the asset to buy at each level.
Dashboard Settings
Show Dashboard : Toggle to hide/show the performance table.
Position : Choose where the dashboard appears on the chart (e.g., Bottom Right, Top Left).
How It Works
Initialization : On the first bar, the script calculates the price levels based on your Upper/Lower price and Grid Type.
Entry Logic :
The strategy places Buy Limit orders at every grid level below the current price.
It checks if a position already exists at a specific level to avoid "stacking" multiple orders on the same line.
Exit Logic :
For every Buy order, a corresponding Sell Limit (Take Profit) order is placed at the next higher grid level.
MaxDD Calculation :
The script continuously tracks the highest equity peak.
It calculates the drawdown on every bar (including intra-bar movements) to ensure accuracy.
Displayed as a percentage (e.g., 5.25%).
Disclaimer
This script is for educational and backtesting purposes only. Grid trading involves significant risk, especially in strong trending markets where the price may move outside your grid range. Always use proper risk management.
NIFTY Options Breakout StrategyThis strategy trades NIFTY 50 Options (CALL & PUT) using 5-minute breakout logic, strict trend filters, expiry-based symbol validation, and a dynamic trailing-profit engine.
1️⃣ Entry Logic
Only trades NIFTY 50 options, filtered automatically by symbol.
Trades only between 10:00 AM – 2:15 PM (5m bars).
Breakout trigger:
Price enters the buy breakout zone (high of last boxLookback bars ± buffer).
Trend filter:
Price must be above EMA50 or EMA200,
AND EMA50 ≥ EMA100 (to avoid weak conditions).
Optional strengthening:
EMA20>EMA50 OR EMA50>EMA100 recent cross can be enforced.
Higher-timeframe trend check:
EMA50 > EMA200 (bullish regime only).
Start trading options only after expiry–2 months (auto-parsed).
2️⃣ One Trade Per Day
Maximum 1 long trade per day.
No shorting (long-only strategy).
3️⃣ Risk Management — SL, TP & Trailing
Includes three types of exits:
🔹 A) Hard SL/TP
Hard Stop-Loss: -15%
Hard Take-Profit: +40%
🔹 B) Step-Ladder Trailing Profit
As the option price rises, trailing activates:
Max Profit Reached Exit Trigger When Falls To
≥ 35% ≤ 30%
≥ 30% ≤ 25%
≥ 25% ≤ 20%
≥ 20% ≤ 15%
≥ 15% ≤ 10%
≥ 5% ≤ 0%
🔹 C) Loss-Recovery Exit
If loss reaches –10% but then recovers to 0%, exit at breakeven.
4️⃣ Trend-Reversal Exit
If price closes below 5m EMA50, the long is exited instantly.
5️⃣ Optional Intraday Exit
EOD square-off at 3:15 PM.
6️⃣ Alerts for Automation
The strategy provides alerts for:
BUY entry
TP/SL/Trailing exit
EMA50 reversal exit
EOD exit
Oracle Protocol — Arch Public (Testing Clone) Oracle Protocol — Arch Public Series (testing clone)
This model implements the Arch Public Oracle structure: a systematic accumulation-and-distribution engine built around a dynamic Accumulation Cost Base (ACB), strict profit-gate exit logic, and a capital-bounded flywheel reinvestment system.
It is designed for transparent execution, deterministic behaviour, and rule-based position management.
Core Function Set
1. Accumulation Framework (ACB-Driven)
The accumulation engine evaluates market movement against defined entry conditions, including:
Percentage-based entry-drop triggers
Optional buy-below-ACB mode
Capital-gated entries tied to available ledger balance
Fixed-dollar and min-dollar entry rules (as seen in Arch public materials)
Automated sizing through flywheel capital
Range-bounded ledger for controlled backtesting input
Each qualifying buy updates the live ACB, maintains the internal ledger, and forms the next reference point for exit evaluation.
No forecasting mechanisms are included.
2. Profit-Gate Exit System
Exits are governed by the standard Arch public approach:
A sealed ACB reference for threshold evaluation
Optional live-ACB visibility
Profit-gate triggers defined per asset class
Candle-confirmation integration (“ProfitGate + Candle” mode)
Distribution only when the smallest active threshold is met
This provides a consistent cadence with published Arch diagrams and PDFs.
3. Once-Per-Rally Governance
After a distribution, the algorithm locks until price retraces below the most recent accumulation base.
Only after re-arming can the next profit gate activate.
This prevents over-frequency selling and aligns with the public-domain Oracle behaviour.
4. Quiet-Bars & Threshold Cluster Control
A volatility-stabilisation layer prevents multiple exits from micro-fluctuations or transient spikes.
This ensures clean execution during fast markets and high volatility.
5. Flywheel Reinvestment
Distribution proceeds automatically return to the capital pool where permitted, creating a closed system of:
Entry sizing
Exit proceeds
Ledger-managed capital state
All sizing respects capital boundaries and does not breach dollar floors or overrides.
6. Automation Hooks and Integration
The script exposes:
3Commas-compatible JSON sizing
Entry/exit signalling via alertcondition()
Deterministic event reporting suitable for external automation
This allows consistent deployment across automated execution environments.
7. Visual Tooling
Optional displays include:
Live ACB line
Exit-guide markers
Capital, state, and ledger panels
Realized/unrealized outcome tracking based on internal logic only
Visual components do not influence execution rules.
Operating Notes
This model is rule-based, deterministic, and non-predictive.
It executes only according to the explicit thresholds, capital limits, and state transitions defined within the script.
No discretionary or forward-looking logic is included.
(CRT) MTF Candle Range Theory Model# 🚀 **CASH Pro MTF – Candle Range Theory (CRT) Indicator**
**The Smart Money ICT Setup Detector** 🔥
Hey Traders!
Here is the **ultimate Pine Script indicator** that automatically detects one of the most powerful Smart Money / ICT setups: **Candle Range Theory (CRT)**
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### What is Candle Range Theory – CRT?
**CRT** is a high-probability price action model based on **liquidity grabs** and **range expansion**.
Price loves to:
1️⃣ Raid the low/high of the previous candle (take stop-losses)
2️⃣ Then reverse and run to the opposite side of the range (or beyond)
When this happens near a **key higher-timeframe level**, magic happens!
### Bullish CRT Model
- Price touches a **strong HTF support**
- Previous candle closes near that support
- Next candle **sweeps the low** (grabs liquidity)
- Current candle **closes above the raided low AND breaks the high** of the sweep candle
**Result → Aggressive bullish move expected!**
**Entry:** On close above the high (or on retest + MSS)
**Stop Loss:** Below the swept low
**Take Profit:** CRT High or next liquidity pool
### Bearish CRT Model
- Price touches a **strong HTF resistance**
- Previous candle closes near resistance
- Next candle **sweeps the high** (grabs buy stops)
- Current candle **closes below the raided high AND breaks the low** of the sweep candle
**Result → Strong bearish expansion!**
**Entry:** On close below the low
**Stop Loss:** Above the swept high
**Take Profit:** CRT Low or next downside liquidity
This whole setup can form in **just 3 candles**… or sometimes more if price consolidates after the sweep.
---
### Why This Indicator is Special
This is **NOT** a simple 3-candle pattern scanner!
This is a **true CRT + MTF confluence beast** with:
- **Multi-Timeframe Confirmation** (default 4H – fully customizable)
- **Built-in RSI Filter** (avoid fake moves in overbought/oversold)
- **Day-2 High/Low Levels** automatically drawn (the exact CRT range!)
- **Clean “LONG” / “SHORT” labels** right on the candle (no ugly arrows or offset)
- **Background highlight** on signal
- **Fully grouped inputs** – super clean settings panel
---
### Features at a Glance
| Feature | Included |
|--------------------------------|----------|
| Higher Timeframe Confirmation | Yes |
| RSI Overbought/Oversold Filter | Yes |
| Day-2 High/Low Lines + Labels | Yes |
| Clean Text Signals (no offset) | Yes |
| Background Highlight | Yes |
| Fully Customizable Colors & Text| Yes |
| Works on All Markets & TFs | Yes |
---
### How to Use
1. Add the indicator to your chart
2. Wait for a **LONG** or **SHORT** label to appear
3. Confirm price is near a **key HTF level** (order block, FVG, etc.)
4. Enter on close or retest (your choice)
5. Manage risk with the drawn Day-2 levels
**Pro Tip:** Combine with ICT Market Structure Shift (MSS) or Fair Value Gaps for even higher accuracy!
Vital Wave 20-50Simplicity is almost always the most effective approach, and here I’m giving you a trend-following system that exploits the bullish bias of traditional markets and their trending nature, with very basic rules.
Rules (long entries only)
• Market entry: When the EMA 20 crosses above the EMA 50 (from below)
• Main market exit: When the EMA 20 crosses below the EMA 50 (from above)
• Fixed Stop Loss: Placed at the price level of the Lower Bollinger Band at the moment the trade is entered.
In my strategy, the primary exit is when the EMA 20 crosses below the EMA 50. However, this crossover can sometimes take a while to occur, and in the meantime the price may have already dropped significantly. The Stop Loss based on the Lower Bollinger Band is designed to limit losses in case the market moves sharply against the position without giving the bearish crossover signal in time. Having two exit conditions makes the strategy much more robust in terms of risk management.
Risk Management:
• Initial capital: $10,000
• Position size: 10% of available capital per trade
• Commissions: 0.1% on traded volume
• Stop Loss: Based on the Lower Bollinger Band
• Take Profit / Exit: When EMA 20 crosses below EMA 50
Recommended Markets:
XAUUSD (OANDA) (Daily)
Period: January 3, 1833 – November 23, 2025
Total Profit & Loss: +$6,030.62 USD (+57.57%)
Maximum Drawdown: $541.53 USD (3.83%)
Total Trades: 136
Winning Trades (Win Rate): 36.03% (49/136)
Profit Factor: 2.483
XAUUSD (OANDA) (12-hour)
Period: March 19, 2006 – November 23, 2025
Total Profit & Loss: +$1,209.56 USD (+11.89%)
Maximum Drawdown: $384.58 USD (3.61%)
Total Trades: 97
Winning Trades (Win Rate): 35.05% (34/97)
Profit Factor: 1.676
XAUUSD (OANDA) (8-hour)
Period: March 19, 2006 – November 23, 2025
Total Profit & Loss: +$1,179.36 USD (+11.81%)
Maximum Drawdown: $246.88 USD (2.32%)
Total Trades: 147
Winning Trades (Win Rate): 31.97% (47/147)
Profit Factor: 1.626
Tesla (NASDAQ) (4-hour)
Period: June 29, 2010 – November 23, 2025
Total Profit & Loss (Absolute): +$11,687.90 USD (+116.88%)
Maximum Drawdown: $922.05 USD (6.50%)
Total Trades: 68
Winning Trades (Win Rate): 39.71% (27/68)
Profit Factor: 4.156
Tesla (NASDAQ) (3-hour)
Total Profit & Loss: +$11,522.33 USD (+115.22%)
Maximum Drawdown: $1,247.60 USD (8.80%)
Total Trades: 114
Winning Trades: 33.33% (38/114)
Profit Factor: 2.811
Additional Recommendations
(These assets have shown good trending behavior with the same strategy across multiple timeframes):
• NVDA (15 min, 30 min, 1h, 2h, 3h, 4h, 6h, 8h, 12h, Daily)
• NFLX (1h, 2h, 3h, 4h, 6h, 8h, 12h, Daily)
• MA (1h, 2h, 3h, 4h, 6h, 8h, 12h, Daily)
• META (1h, 2h, 3h, 4h, 6h, 8h, 12h, Daily)
• AAPL (1h, 2h, 3h, 4h, 6h, 8h, 12h, Daily)
• SPY (12h, Daily)
About the Code
The user can modify:
• EMA periods (20 and 50 by default)
• Bollinger Bands length (20 periods)
• Standard deviation (2.0)
Visualization
• EMA 20: Blue line
• EMA 50: Red line
• Green background when EMA20 > EMA50 (bullish trend)
• Red background when EMA20 < EMA50 (bearish trend)
Important Note:
We can significantly increase the profit factor and overall profitability by risking a fixed percentage per trade instead of a fixed amount. This would prevent losses from fluctuating with changes in volatility.
This could be implemented by reducing position size or adjusting leverage based on the volatility percentage required for each trade, but I’m not sure if this is fully possible in Pine Script. In my other script, “ Golden Cross 50/200 EMA ,” I go deeper into this topic and provide examples.
I hope you enjoy this contribution. Best regards!
Hash Momentum IndicatorHash Momentum Indicator
Overview
The Hash Momentum Indicator provides real-time momentum-based trading signals with visual entry/exit markers and automatic risk management levels. This is the indicator version of the popular Hash Momentum Strategy, designed for traders who want signal alerts without backtesting functionality.
Perfect for: Live trading, automation via alerts, multi-indicator setups, and clean chart visualization.
What Makes This Indicator Special
1. Pure Momentum-Based Signals
Captures price acceleration in real-time - not lagging moving average crossovers. Enters when momentum exceeds a dynamic ATR-based threshold, catching moves as they begin accelerating.
2. Automatic Risk Management Visualization
Every signal automatically displays:
Entry level (white dashed line)
Stop loss level (red line)
Take profit target (green line)
Partial TP levels (dotted green lines)
3. Smart Trade Management
Trade Cooldown: Prevents overtrading by enforcing waiting period between signals
EMA Trend Filter: Only trades with the trend (optional)
Session Filters: Trade only during Tokyo/London/New York sessions (optional)
Weekend Toggle: Avoid low-liquidity weekend periods (optional)
4. Clean Visual Design
🟢 Tiny green dot = Long entry signal
🔴 Tiny red dot = Short entry signal
🔵 Blue X = Long exit
🟠 Orange X = Short exit
No cluttered labels or dashboard - just clean signals
5. Professional Alerts Ready
Set up TradingView alerts for:
Long signals
Short signals
Long exits
Short exits
How It Works
Step 1: Calculate Momentum
Momentum = Current Price - Price
Normalized by standard deviation for consistency
Must exceed ATR × Threshold to trigger
Step 2: Confirm Acceleration
Momentum must be increasing (positive momentum change)
Price must be moving in signal direction
Step 3: Apply Filters
EMA Filter: Long only above EMA, short only below EMA (if enabled)
Session Filter: Check if in allowed trading session (if enabled)
Weekend Filter: Block signals on Sat/Sun (if enabled)
Cooldown: Ensure minimum bars passed since last signal
Step 4: Generate Signal
All conditions met = Entry signal fires
Lines automatically drawn for entry, stop, and targets
Step 5: Exit Detection
Opposite momentum detected = Exit signal
Stop loss or take profit hit = Exit signal
Lines removed from chart
⚙️ Settings Guide
Core Strategy
Momentum Length (Default: 13)
Number of bars for momentum calculation. Higher values = stronger signals but fewer trades.
Aggressive: 10
Balanced: 13
Conservative: 18-24
Momentum Threshold (Default: 2.25)
ATR multiplier for signal generation. Higher values = only trade the biggest momentum moves.
Aggressive: 2.0
Balanced: 2.25
Conservative: 2.5-3.0
Risk:Reward Ratio (Default: 2.5)
Your target profit as a multiple of your risk. With 2.2% stop and 2.5 R:R, your target is 5.5% profit.
Conservative: 3.0+ (need 25% win rate to profit)
Balanced: 2.5 (need 29% win rate to profit)
Aggressive: 2.0 (need 33% win rate to profit)
Wick-RSI-CandleBody_SEZERthis strategy is ideal to recognize peaks for both long and short positions in 1h and 4h periods. for quick response and faster trade, please use 15m period but keep in mind targeting lower profits. otherwise you may lose your profit.
Mirror Blocks: StrategyMirror Blocks is an educational structural-wave model built around a unique concept:
the interaction of mirrored weighted moving averages (“blocks”) that reflect shifts in market structure as price transitions between layered symmetry zones.
Rather than attempting to “predict” markets, the Mirror Blocks framework visualizes how price behaves when it expands away from, contracts toward, or flips across stacked WMA structures. These mirrored layers form a wave-like block system that highlights transitional zones in a clean, mechanical way.
This strategy version allows you to study how these structural transitions behave in different environments and on different timeframes.
The goal is understanding wave structure, not generating signals.
How It Works
Mirror Blocks builds three mirrored layers:
Top Block (Structural High Symmetry)
Base Block (Neutral Wave)
Bottom Block (Structural Low Symmetry)
The relative position of these blocks — and how price interacts with them — helps visualize:
Compression and expansion
Reversal zones
Wave stability
Momentum transitions
Structure flips
A structure is considered bullish-stack aligned when:
Top > Base > Bottom
and bearish-stack aligned when:
Bottom > Base > Top
These formations create the core of the Mirror Blocks wave engine.
What the Strategy Version Adds
This version includes:
Long Only, Short Only, or Long & Short modes
Adjustable symmetry distance (Mirror Distance)
Configurable WMA smoothing length
Optional trend filter using fast/slow MA comparison
ENTER / EXIT / LONG / SHORT labels for structural transitions
Fixed stop-loss controls for research
A clean, transparent structure with no hidden components
It is optimized for educational chart study, not automated signals.
Intended Purpose
Mirror Blocks is meant to help traders:
Study structural transitions
Understand symmetry-based wave models
Explore how price interacts with mirrored layers
Examine reversals and expansions from a mechanical perspective
Conduct long and short backtesting for research
Develop a deeper sense of market rhythm
This is not a prediction model.
It is a visual and structural framework for understanding movement.
Backtesting Disclaimer
Backtest results can vary depending on:
Slippage settings
Commission settings
Timeframe
Asset volatility
Structural sensitivity parameters
Past performance does not guarantee future results.
Use this as a research tool only.
Warnings & Compliance
This script is educational.
It is not financial advice.
It does not provide signals.
It does not promise profitability.
The purpose is to help visualize structure, not predict price.
The strategy features are simply here to help users study how structural transitions behave under various conditions.
License
Released under the Michael Culpepper Gratitude License (2025).
Use and modify freely for education and research with attribution.
No resale.
No promises of profitability.
Purpose is understanding, not signals.
Hash Momentum Strategy# Hash Momentum Strategy
## 📊 Overview
The **Hash Momentum Strategy** is a professional-grade momentum trading system designed to capture strong directional price movements with precision timing and intelligent risk management. Unlike traditional EMA crossover strategies, this system uses momentum acceleration as its primary signal, resulting in earlier entries and better risk-to-reward ratios.
---
## ⚡ What Makes This Strategy Unique
### 1. Momentum-Based Entry System
Most strategies rely on lagging indicators like moving average crossovers. This strategy captures momentum *acceleration* - entering when price movement is gaining strength, not after the move has already happened.
### 2. Programmable Risk-to-Reward
Set your exact R:R ratio (1:2, 1:2.5, 1:3, etc.) and the strategy automatically calculates stop loss and take profit levels. No more guessing or manual calculations.
### 3. Smart Partial Profit Taking
Lock in profits at multiple stages:
- **First TP**: Take 50% off at 2R
- **Second TP**: Take 40% off at 2.5R
- **Final TP**: Let 10% ride to maximum target
This approach locks in gains while letting winners run.
### 4. Dynamic Momentum Threshold
Uses ATR (Average True Range) multiplied by your threshold setting to adapt to market volatility. Volatile markets = higher threshold. Quiet markets = lower threshold.
### 5. Trade Cooldown System
Prevents overtrading and revenge trading by enforcing a cooldown period between trades. Configurable from 1-24 bars.
### 6. Optional Session & Weekend Filters
Filter trades by Tokyo, London, and New York sessions. Optional weekend-off toggle to avoid low-liquidity periods.
---
## 🎯 How It Works
### Signal Generation
**STEP 1: Calculate Momentum**
- Momentum = Current Price - Price
- Check if Momentum > ATR × Threshold Multiplier
- Momentum must be accelerating (positive change in momentum)
**STEP 2: Confirm with EMA Trend Filter**
- Long: Price must be above EMA
- Short: Price must be below EMA
**STEP 3: Check Filters**
- Not in cooldown period
- Valid session (if enabled)
- Not weekend (if enabled)
**STEP 4: ENTRY SIGNAL TRIGGERED**
### Risk Management Example
**Example Long Trade:**
- Entry: $100
- Stop Loss: $97.80 (2.2% risk)
- Risk Amount: $2.20
**Take Profit Levels:**
- TP1: $104.40 (2R = $4.40) → Close 50%
- TP2: $105.50 (2.5R = $5.50) → Close 40%
- Final: $105.50 (2.5R) → Close remaining 10%
---
## ⚙️ Settings Guide
### Core Strategy
**Momentum Length** (Default: 13)
Number of bars for momentum calculation. Higher = stronger but fewer signals.
**Momentum Threshold** (Default: 2.25)
ATR multiplier. Higher = only trade biggest moves.
**Use EMA Trend Filter** (Default: ON)
Only long above EMA, short below EMA.
**EMA Length** (Default: 28)
Period for trend-confirming EMA.
### Filters
**Use Trading Session Filter** (Default: OFF)
Restrict trading to specific sessions.
**Tokyo Session** (Default: OFF)
Trade during Asian hours (00:00-09:00 JST).
**London Session** (Default: OFF)
Trade during European hours (08:00-17:00 GMT).
**New York Session** (Default: OFF)
Trade during US hours (08:00-17:00 EST).
**Weekend Off** (Default: OFF)
Disable trading on Saturdays and Sundays.
### Risk Management
**Stop Loss %** (Default: 2.2)
Fixed percentage stop loss from entry.
**Risk:Reward Ratio** (Default: 2.5)
Your target reward as multiple of risk.
**Use Partial Profit Taking** (Default: ON)
Take profits in stages.
**First TP R:R** (Default: 2.0)
First target as multiple of risk.
**First TP Size %** (Default: 50)
Percentage of position to close at TP1.
**Second TP R:R** (Default: 2.5)
Second target as multiple of risk.
**Second TP Size %** (Default: 40)
Percentage of position to close at TP2.
### Trade Management
**Use Trade Cooldown** (Default: ON)
Prevent overtrading.
**Cooldown Bars** (Default: 6)
Bars to wait after closing a trade.
---
## 🎨 Visual Elements
### Chart Indicators
🟢 **Green Dot** (below bar) = Long entry signal
🔴 **Red Dot** (above bar) = Short entry signal
🔵 **Blue X** (above bar) = Long position closed
🟠 **Orange X** (below bar) = Short position closed
**EMA Line** = Trend direction (green when bullish, red when bearish)
**White Line** = Entry price
**Red Line** = Stop loss level
**Green Lines** = Take profit levels (TP1, TP2, Final)
### Dashboard
When not in real-time mode, a dashboard displays:
- Current position (LONG/SHORT/FLAT)
- Entry price
- Stop loss price
- Take profit price
- R:R ratio
- Current momentum strength
- Total trades
- Win rate
- Net profit %
---
## 📈 Recommended Settings by Timeframe
### 1-Hour Timeframe (Default)
- Momentum Length: 13
- Momentum Threshold: 2.25
- EMA Length: 28
- Stop Loss: 2.2%
- R:R Ratio: 2.5
- Cooldown: 6 bars
### 4-Hour Timeframe
- Momentum Length: 24-36
- Momentum Threshold: 2.5
- EMA Length: 50
- Stop Loss: 3-4%
- R:R Ratio: 2.0-2.5
- Cooldown: 6-8 bars
### 15-Minute Timeframe
- Momentum Length: 8-10
- Momentum Threshold: 2.0
- EMA Length: 20
- Stop Loss: 1.5-2%
- R:R Ratio: 2.0
- Cooldown: 4-6 bars
---
## 🔧 Optimization Tips
### Want More Trades?
- Decrease Momentum Threshold (2.0 instead of 2.25)
- Decrease Momentum Length (10 instead of 13)
- Decrease Cooldown Bars (4 instead of 6)
### Want Higher Quality Trades?
- Increase Momentum Threshold (2.5-3.0)
- Increase Momentum Length (18-24)
- Increase Cooldown Bars (8-10)
### Want Lower Drawdown?
- Increase Cooldown Bars
- Use tighter stop loss
- Enable session filters (trade only high-liquidity sessions)
- Enable Weekend Off
### Want Higher Win Rate?
- Increase R:R Ratio (may reduce total profit)
- Increase Momentum Threshold (fewer but stronger signals)
- Use longer EMA for trend confirmation
---
## 📊 Performance Expectations
Based on typical backtesting results:
- **Win Rate**: 35-45%
- **Profit Factor**: 1.5-2.0
- **Risk:Reward**: 1:2.5 (configurable)
- **Max Drawdown**: 10-20%
- **Trades/Month**: 8-15 (1H timeframe)
**Note:** Win rate may appear low, but with 2.5:1 R:R, you only need ~29% win rate to break even. The strategy aims for quality over quantity.
---
## 🎓 Strategy Logic Explained
### Why Momentum > EMA Crossover?
**EMA Crossover Problems:**
- Signals lag behind price
- Late entries = poor R:R
- Many false signals in ranging markets
**Momentum Advantages:**
- Catches moves as they start accelerating
- Earlier entries = better R:R
- Adapts to volatility via ATR
### Why Partial Profit Taking?
**Without Partial TPs:**
- All-or-nothing approach
- Winners often turn to losers
- High stress watching open positions
**With Partial TPs:**
- Lock in 50% at first target
- Reduce risk to breakeven
- Let remainder ride for bigger gains
- Lower psychological pressure
### Why Trade Cooldown?
**Without Cooldown:**
- Revenge trading after losses
- Overtrading in choppy markets
- Emotional decision-making
**With Cooldown:**
- Forces discipline
- Waits for new setup to develop
- Reduces transaction costs
- Better signal quality
---
## ⚠️ Important Notes
1. **This is a momentum strategy, not an EMA strategy**
The EMA only confirms trend direction. Momentum generates the actual signals.
2. **Backtest thoroughly before live trading**
Past performance ≠ future results. Test on your specific asset and timeframe.
3. **Use proper position sizing**
Risk 1-2% of account per trade maximum. The strategy uses 100% equity by default (adjust in Properties).
4. **Dashboard auto-hides in real-time**
Clean chart for live trading. Visible during backtesting.
5. **Customize for your trading style**
All settings are fully adjustable. No single "best" configuration.
---
## 🚀 Quick Start Guide
1. **Add to Chart**: Apply to your preferred asset and timeframe
2. **Keep Defaults**: Start with default settings
3. **Backtest**: Review historical performance
4. **Paper Trade**: Test with simulated money first
5. **Go Live**: Start small and scale up
---
## 💡 Pro Tips
**Tip 1: Combine Timeframes**
Use higher timeframe (4H) for trend direction, lower timeframe (1H) for entries.
**Tip 2: Avoid News Events**
Major news can cause whipsaws. Consider manual intervention during high-impact events.
**Tip 3: Monitor Momentum Strength**
Dashboard shows momentum in sigma (σ). Values >1.0σ indicate very strong momentum.
**Tip 4: Adjust for Volatility**
In high-volatility markets, increase threshold and stop loss. In quiet markets, decrease them.
**Tip 5: Review Losing Trades**
Check if losses are hitting stop loss or reversing. Adjust stop accordingly.
---
## 📝 Changelog
**v1.0** - Initial Release
- Momentum-based signal generation
- EMA trend filter
- Programmable R:R ratio
- Partial profit taking (3 stages)
- Trade cooldown system
- Session filters (Tokyo/London/New York)
- Weekend off toggle
- Smart dashboard (auto-hides in real-time)
- Clean visual design
---
## 🙏 Credits
Developed by **Hash Capital Research**
If you find this strategy useful, please give it a like and share with others!
---
## ⚖️ Disclaimer
This strategy is for educational purposes only. Trading involves substantial risk of loss and is not suitable for all investors. Past performance is not indicative of future results. Always do your own research and consult with a qualified financial advisor before trading.
---
## 📬 Feedback
Have suggestions or found a bug? Leave a comment below! I'm continuously improving this strategy based on community feedback.
---
**Happy Trading! 🚀📈**
Swing Point PnL PressureThis indicator visualizes the cumulative profit potential of bulls and bears based on recent swing highs and lows — offering a unique lens into trend maturity, sentiment imbalance, and exhaustion risk.
🟢 Bull PnL rises as price moves above prior swing lows — reflecting unrealized gains for long positions
🔴 Bear PnL rises as price drops below prior swing highs — capturing short-side profitability
Over time, these curves diverge during strong trends, revealing which side is in control. But when they converge, it often signals that the dominant side is losing steam — a potential turning point where profit-taking, traps, or reversals may emerge.
This tool doesn’t predict tops or bottoms — it tracks the emotional and financial pressure building on each side of the market. Use it to:
Spot trend exhaustion before price confirms it
Identify profit parity zones where sentiment may flip
Time accumulation or distribution phases with greater confidence
Whether you’re swing trading or analyzing macro structure, this indicator helps you see what price alone can’t: who’s winning, who’s trapped, and who’s about to give up.
Quantum Market Analyzer X7Quantum Market Analyzer X7 - Complete Study Guide
Table of Contents
1. Overview
2. Indicator Components
3. Signal Interpretation
4. Live Market Analysis Guide
5. Best Practices
6. Limitations and Considerations
7. Risk Disclaimer
________________________________________
Overview
The Quantum Market Analyzer X7 is a comprehensive multi-timeframe technical analysis indicator that combines traditional and modern analytical methods. It aggregates signals from multiple technical indicators across seven key analysis categories to provide traders with a consolidated view of market sentiment and potential trading opportunities.
Key Features:
• Multi-Indicator Analysis: Combines 20+ technical indicators
• Real-Time Dashboard: Professional interface with customizable display
• Signal Aggregation: Weighted scoring system for overall market sentiment
• Advanced Analytics: Includes Order Block detection, Supertrend, and Volume analysis
• Visual Progress Indicators: Easy-to-read progress bars for signal strength
________________________________________
Indicator Components
1. Oscillators Section
Purpose: Identifies overbought/oversold conditions and momentum changes
Included Indicators:
• RSI (14): Relative Strength Index - momentum oscillator
• Stochastic (14): Compares closing price to price range
• CCI (20): Commodity Channel Index - cycle identification
• Williams %R (14): Momentum indicator similar to Stochastic
• MACD (12,26,9): Moving Average Convergence Divergence
• Momentum (10): Rate of price change
• ROC (9): Rate of Change
• Bollinger Bands (20,2): Volatility-based indicator
Signal Interpretation:
• Strong Buy (6+ points): Multiple oscillators indicate oversold conditions
• Buy (2-5 points): Moderate bullish momentum
• Neutral (-1 to 1 points): Balanced conditions
• Sell (-2 to -5 points): Moderate bearish momentum
• Strong Sell (-6+ points): Multiple oscillators indicate overbought conditions
2. Moving Averages Section
Purpose: Determines trend direction and strength
Included Indicators:
• SMA: 10, 20, 50, 100, 200 periods
• EMA: 10, 20, 50 periods
Signal Logic:
• Price >2% above MA = Strong Buy (+2)
• Price above MA = Buy (+1)
• Price below MA = Sell (-1)
• Price >2% below MA = Strong Sell (-2)
Signal Interpretation:
• Strong Buy (6+ points): Price well above multiple MAs, strong uptrend
• Buy (2-5 points): Price above most MAs, bullish trend
• Neutral (-1 to 1 points): Mixed MA signals, consolidation
• Sell (-2 to -5 points): Price below most MAs, bearish trend
• Strong Sell (-6+ points): Price well below multiple MAs, strong downtrend
3. Order Block Analysis
Purpose: Identifies institutional support/resistance levels and breakouts
How It Works:
• Detects historical levels where large orders were placed
• Monitors price behavior around these levels
• Identifies breakouts from established order blocks
Signal Types:
• BULLISH BRK (+2): Breakout above resistance order block
• BEARISH BRK (-2): Breakdown below support order block
• ABOVE SUP (+1): Price holding above support
• BELOW RES (-1): Price rejected at resistance
• NEUTRAL (0): No significant order block interaction
4. Supertrend Analysis
Purpose: Trend following indicator based on Average True Range
Parameters:
• ATR Period: 10 (default)
• ATR Multiplier: 6.0 (default)
Signal Types:
• BULLISH (+2): Price above Supertrend line
• BEARISH (-2): Price below Supertrend line
• NEUTRAL (0): Transition period
5. Trendline/Channel Analysis
Purpose: Identifies trend channels and breakout patterns
Components:
• Dynamic trendline calculation using pivot points
• Channel width based on historical volatility
• Breakout detection algorithm
Signal Types:
• UPPER BRK (+2): Breakout above upper channel
• LOWER BRK (-2): Breakdown below lower channel
• ABOVE MID (+1): Price above channel midline
• BELOW MID (-1): Price below channel midline
6. Volume Analysis
Purpose: Confirms price movements with volume data
Components:
• Volume spikes detection
• On Balance Volume (OBV)
• Volume Price Trend (VPT)
• Money Flow Index (MFI)
• Accumulation/Distribution Line
Signal Calculation: Multiple volume indicators are combined to determine institutional activity and confirm price movements.
________________________________________
Signal Interpretation
Overall Summary Signals
The indicator aggregates all component signals into an overall market sentiment:
Signal Score Range Interpretation Action
STRONG BUY 10+ Overwhelming bullish consensus Consider long positions
BUY 4-9 Moderate to strong bullish bias Look for long opportunities
NEUTRAL -3 to 3 Mixed signals, consolidation Wait for clearer direction
SELL -4 to -9 Moderate to strong bearish bias Look for short opportunities
STRONG SELL -10+ Overwhelming bearish consensus Consider short positions
Progress Bar Interpretation
• Filled bars indicate signal strength
• Green bars: Bullish signals
• Red bars: Bearish signals
• More filled bars = stronger conviction
________________________________________
Live Market Analysis Guide
Step 1: Initial Assessment
1. Check Overall Summary: Start with the main signal
2. Verify with Component Analysis: Ensure signals align
3. Look for Divergences: Identify conflicting signals
Step 2: Timeframe Analysis
1. Set Appropriate Timeframe: Use 1H for intraday, 4H/1D for swing trading
2. Multi-Timeframe Confirmation: Check higher timeframes for trend context
3. Entry Timing: Use lower timeframes for precise entry points
Step 3: Signal Confirmation Process.
For Buy Signals:
1. Oscillators: Look for oversold conditions (RSI <30, Stoch <20)
2. Moving Averages: Price should be above key MAs
3. Order Blocks: Confirm bounce from support levels
4. Volume: Check for accumulation patterns
5. Supertrend: Ensure bullish trend alignment.
For Sell Signals:
1. Oscillators: Look for overbought conditions (RSI >70, Stoch >80)
2. Moving Averages: Price should be below key MAs
3. Order Blocks: Confirm rejection at resistance levels
4. Volume: Check for distribution patterns
5. Supertrend: Ensure bearish trend alignment.
Step 4: Risk Management Integration
1. Signal Strength Assessment: Stronger signals = larger position size
2. Stop Loss Placement: Use Order Block levels for stops
3. Take Profit Targets: Based on channel analysis and resistance levels
4. Position Sizing: Adjust based on signal confidence
________________________________________
Best Practices
Entry Strategies
1. High Conviction Entries: Wait for STRONG BUY/SELL signals
2. Confluence Trading: Look for multiple components aligning
3. Breakout Trading: Use Order Block and Trendline breakouts
4. Trend Following: Align with Supertrend direction.
Risk Management
1. Never Risk More Than 2% Per Trade: Regardless of signal strength
2. Use Stop Losses: Place at invalidation levels
3. Scale Positions: Stronger signals warrant larger (but still controlled) positions
4. Diversification: Don't rely solely on one indicator.
Market Conditions
1. Trending Markets: Focus on Supertrend and MA signals
2. Range-Bound Markets: Emphasize Oscillator and Order Block signals
3. High Volatility: Reduce position sizes, widen stops
4. Low Volume: Be cautious of breakout signals.
Common Mistakes to Avoid
1. Signal Chasing: Don't enter after signals have already moved significantly
2. Ignoring Context: Consider overall market conditions
3. Overtrading: Wait for high-quality setups
4. Poor Risk Management: Always use appropriate position sizing
________________________________________
Limitations and Considerations
Technical Limitations
1. Lagging Nature: All technical indicators are based on historical data
2. False Signals: No indicator is 100% accurate
3. Market Regime Changes: Indicators may perform differently in various market conditions
4. Whipsaws: Possible in choppy, sideways markets.
Optimal Use Cases
1. Trending Markets: Performs best in clear trending environments
2. Medium to High Volatility: Requires sufficient price movement for signals
3. Liquid Markets: Works best with adequate volume and tight spreads
4. Multiple Timeframe Analysis: Most effective when used across different timeframes.
When to Use Caution
1. Major News Events: Fundamental analysis may override technical signals
2. Market Opens/Closes: Higher volatility can create false signals
3. Low Volume Periods: Signals may be less reliable
4. Holiday Trading: Reduced participation affects signal quality
________________________________________
Risk Disclaimer
IMPORTANT LEGAL DISCLAIMER FROM aiTrendview
WARNING: TRADING INVOLVES SUBSTANTIAL RISK OF LOSS
This Quantum Market Analyzer X7 indicator ("the Indicator") is provided for educational and informational purposes only. By using this indicator, you acknowledge and agree to the following terms:
No Investment Advice
• The Indicator does NOT constitute investment advice, financial advice, or trading recommendations
• All signals generated are based on historical price data and mathematical calculations
• Past performance does not guarantee future results
• No representation is made that any account will achieve profits or losses similar to those shown.
Risk Acknowledgment
• TRADING CARRIES SUBSTANTIAL RISK: You may lose some or all of your invested capital
• LEVERAGE AMPLIFIES RISK: Margin trading can result in losses exceeding your initial investment
• MARKET VOLATILITY: Financial markets are inherently unpredictable and volatile
• TECHNICAL ANALYSIS LIMITATIONS: No technical indicator is infallible or guarantees profitable trades.
User Responsibility
• YOU ARE SOLELY RESPONSIBLE for all trading decisions and their consequences
• CONDUCT YOUR OWN RESEARCH: Always perform independent analysis before making trading decisions
• CONSULT PROFESSIONALS: Seek advice from qualified financial advisors
• RISK MANAGEMENT: Implement appropriate risk management strategies
No Warranties
• The Indicator is provided "AS IS" without warranties of any kind
• aiTrendview makes no representations about the accuracy, reliability, or suitability of the Indicator
• Technical glitches, data feed issues, or calculation errors may occur
• The Indicator may not work as expected in all market conditions.
Limitation of Liability
• aiTrendview SHALL NOT BE LIABLE for any direct, indirect, incidental, or consequential damages
• This includes but is not limited to: trading losses, missed opportunities, data inaccuracies, or system failures
• MAXIMUM LIABILITY is limited to the amount paid for the indicator (if any)
Code Usage and Distribution
• This indicator is published on TradingView in accordance with TradingView's house rules
• UNAUTHORIZED MODIFICATION or redistribution of this code is prohibited
• Users may not claim ownership of this intellectual property
• Commercial use requires explicit written permission from aiTrendview.
Compliance and Regulations
• VERIFY LOCAL REGULATIONS: Ensure compliance with your jurisdiction's trading laws
• Some trading strategies may not be suitable for all investors
• Tax implications of trading are your responsibility
• Report trading activities as required by law
Specific Risk Factors
1. False Signals: The Indicator may generate incorrect buy/sell signals
2. Market Gaps: Overnight gaps can invalidate technical analysis
3. Fundamental Events: News and economic data can override technical signals
4. Liquidity Risk: Some markets may have insufficient liquidity
5. Technology Risk: Platform failures or connectivity issues may prevent order execution.
Professional Trading Warning
• THIS IS NOT PROFESSIONAL TRADING SOFTWARE: Not intended for institutional or professional trading
• NO REGULATORY APPROVAL: This indicator has not been approved by any financial regulatory authority
• EDUCATIONAL PURPOSE: Designed primarily for learning technical analysis concepts
FINAL WARNING
NEVER INVEST MONEY YOU CANNOT AFFORD TO LOSE
Trading financial instruments involves significant risk. The majority of retail traders lose money. Before using this indicator in live trading:
1. Practice on paper/demo accounts extensively
2. Start with small position sizes
3. Develop a comprehensive trading plan
4. Implement strict risk management rules
5. Continuously educate yourself about market dynamics
By using the Quantum Market Analyzer X7, you acknowledge that you have read, understood, and agree to this disclaimer. You assume full responsibility for all trading decisions and their outcomes.
Contact: For questions about this disclaimer or the indicator, contact aiTrendview through official TradingView channels only.
________________________________________
This study guide and indicator are published on TradingView in compliance with TradingView's community guidelines and house rules. All users must adhere to TradingView's terms of service when using this indicator.
Document Version: 1.0
Publisher: aiTrendview
________________________________________
Disclaimer
The content provided in this blog post is for educational and training purposes only. It is not intended to be, and should not be construed as, financial, investment, or trading advice. All charting and technical analysis examples are for illustrative purposes. Trading and investing in financial markets involve substantial risk of loss and are not suitable for every individual. Before making any financial decisions, you should consult with a qualified financial professional to assess your personal financial situation.
Braid Filter StrategyThis strategy is like a sophisticated set of traffic lights and speed limit signs for trading. It only allows a trade when multiple indicators line up to confirm a strong move, giving it its "Braid Filter" name—it weaves together several conditions.
The strategy is set up to use 100% of your account equity (your trading funds) on a trade and does not "pyramid" (it won't add to an existing trade).
1. The Main Trend Check (The Traffic Lights)
The strategy uses three main filters that must agree before it considers a trade.
A. The "Chad Filter" (Direction & Strength)
This is the heart of the strategy, a custom combination of three different Moving AveragesThese averages have fast, medium, and slow settings (3, 7, and 14 periods).
Go Green (Buy Signal): The fastest average is higher than the medium average, AND the three averages are sufficiently separated (not tangled up, which indicates a strong move).
Go Red (Sell Signal): The medium average is higher than the fastest average, AND the three averages are sufficiently separated.
Neutral (Wait): If the averages are tangled or the separation isn't strong enough.
Key Trigger: A primary condition for a signal is when the Chad Filter changes color (e.g., from Red/Grey to Green).
B. The EMA Trend Bars (Secondary Confirmation)
This is a simpler, longer-term filter using a 34-period Exponential Moving Average (EMA). It checks if the current candle's average price is above or below this EMA.
Green Bars: The price is above the 34 EMA (Bullish Trend).
Red Bars: The price is below the 34 EMA (Bearish Trend).
Trades only happen if the signal direction matches the bar color. For a Buy, the bar must be Green. For a Sell, the bar must be Red.
C. ADX/DI Filter (The Speed Limit Sign)
This uses the Average Directional Index (ADX) and Directional Movement Indicators (DI) to check if a trend is actually in motion and getting stronger.
Must-Have Conditions:
The ADX value must be above 20 (meaning there is a trend, not just random movement).
The ADX line must be rising (meaning the trend is accelerating/getting stronger).
The strategy will only trade when the trend is strong and building momentum.
2. The Trading Action (Entry and Exit)
When all three filters (Chad Filter color change, EMA Trend Bar color, and ADX strength/slope) align, the strategy issues a signal, but it doesn't enter immediately.
Entry Strategy (The "Wait-for-Confirmation" Approach):
When a Buy Signal appears, the strategy sets a "Buy Stop" order at the signal candle's closing price.
It then waits for up to 3 candles (Candles Valid for Entry). The price must move up and hit that Buy Stop price within those 3 candles to confirm the move and enter the trade.
A Sell Signal works the same way but uses a "Sell Stop" at the closing price, waiting for the price to drop and hit it.
Risk Management (Stop Loss and Take Profit):
Stop Loss: To manage risk, the strategy finds a recent significant low (for a Buy) or high (for a Sell) over the last 20 candles and places the Stop Loss there. This is a logical place where the current move would be considered "broken" if the price reaches it.
Take Profit: It uses a fixed Risk:Reward Ratio (set to 1.5 by default). This means the potential profit (Take Profit distance) is $1.50 for every $1.00 of risk (Stop Loss distance).
3. Additional Controls
Time Filter: You can choose to only allow trades during specific hours of the day.
Visuals: It shows a small triangle on the chart where the signal happens and colors the background to reflect the Chad Filter's trend (Green/Red/Grey) and the candle bars to show the EMA trend (Lime/Red).
🎯 Summary of the Strategy's Goal
This strategy is designed to capture strong, confirmed momentum moves. It uses a fast, custom indicator ("Chad Filter") to detect the start of a new move, confirms that move with a slower trend filter (34 EMA), and then validates the move's strength with the ADX. By waiting a few candles for the price to hit the entry level, it aims to avoid false signals.
Braid Filter StrategyAnother of TradeIQ's youtube strategies. It looks a little messy but it combines all the indicators into one so there are no extra panes. This strategy is like a sophisticated set of traffic lights and speed limit signs for trading. It only allows a trade when multiple indicators line up to confirm a strong move, giving it its "Braid Filter" name—it weaves together several conditions.
The strategy is set up to use 100% of your account equity (your trading funds) on a trade and does not "pyramid" (it won't add to an existing trade).
1. The Main Trend Check (The Traffic Lights)
The strategy uses three main filters that must agree before it considers a trade.
A. The "Braid Filter" (Direction & Strength)
This is the heart of the strategy, a custom combination of three different Moving Averages
These averages have fast, medium, and slow settings (3, 7, and 14 periods).
Go Green (Buy Signal): The fastest average is higher than the medium average, AND the three averages are sufficiently separated (not tangled up, which indicates a strong move).
Go Red (Sell Signal): The medium average is higher than the fastest average, AND the three averages are sufficiently separated.
Neutral (Wait): If the averages are tangled or the separation isn't strong enough.
Key Trigger: A primary condition for a signal is when the Chad Filter changes color (e.g., from Red/Grey to Green).
B. The EMA Trend Bars (Secondary Confirmation)
This is a simpler, longer-term filter using a 34-period Exponential Moving Average (EMA). It checks if the current candle's average price is above or below this EMA.
Green Bars: The price is above the 34 EMA (Bullish Trend).
Red Bars: The price is below the 34 EMA (Bearish Trend).
Trades only happen if the signal direction matches the bar color. For a Buy, the bar must be Green. For a Sell, the bar must be Red.
C. ADX/DI Filter (The Speed Limit Sign)
This uses the Average Directional Index (ADX) and Directional Movement Indicators (DI) to check if a trend is actually in motion and getting stronger.
Must-Have Conditions:
The ADX value must be above 20 (meaning there is a trend, not just random movement).
The ADX line must be rising (meaning the trend is accelerating/getting stronger).
The strategy will only trade when the trend is strong and building momentum.
2. The Trading Action (Entry and Exit)
When all three filters (Chad Filter color change, EMA Trend Bar color, and ADX strength/slope) align, the strategy issues a signal, but it doesn't enter immediately.
Entry Strategy (The "Wait-for-Confirmation" Approach):
When a Buy Signal appears, the strategy sets a "Buy Stop" order at the signal candle's closing price.
It then waits for up to 3 candles (Candles Valid for Entry). The price must move up and hit that Buy Stop price within those 3 candles to confirm the move and enter the trade.
A Sell Signal works the same way but uses a "Sell Stop" at the closing price, waiting for the price to drop and hit it.
Risk Management (Stop Loss and Take Profit):
Stop Loss: To manage risk, the strategy finds a recent significant low (for a Buy) or high (for a Sell) over the last 20 candles and places the Stop Loss there. This is a logical place where the current move would be considered "broken" if the price reaches it.
Take Profit: It uses a fixed Risk:Reward Ratio (set to 1.5 by default). This means the potential profit (Take Profit distance) is $1.50 for every $1.00 of risk (Stop Loss distance).
3. Additional Controls
Time Filter: You can choose to only allow trades during specific hours of the day.
Visuals: It shows a small triangle on the chart where the signal happens and colors the background to reflect the Chad Filter's trend (Green/Red/Grey) and the candle bars to show the EMA trend (Lime/Red).
🎯 Summary of the Strategy's Goal
This strategy is designed to capture strong, confirmed momentum moves. It uses a fast, custom indicator ("Chad Filter") to detect the start of a new move, confirms that move with a slower trend filter (34 EMA), and then validates the move's strength with the ADX. By waiting a few candles for the price to hit the entry level, it aims to avoid false signals.
MA SMART Angle
### 📊 WHAT IS MA SMART ANGLE?
**MA SMART Angle** is an advanced momentum and trend detection indicator that analyzes the angles (slopes) of multiple moving averages to generate clear, non-repainting BUY and SELL signals.
**Original Concept Credit:** This indicator builds upon the "MA Angles" concept originally created by **JD** (also known as Duyck). The core angle calculation methodology and Jurik Moving Average (JMA) implementation by **Everget** are preserved from the original open-source work. The angle calculation formula was contributed by **KyJ**. This enhanced version is published with respect to the open-source nature of the original indicator.
Original indicator reference: "ma angles - JD" by Duyck
---
## 🎯 ORIGINALITY & VALUE PROPOSITION
### **What Makes This Different from the Original:**
While the original "MA Angles" by **JD** provided excellent angle visualization, it lacked actionable entry signals. **MA SMART Angle** addresses this by adding:
**1. Clear Entry/Exit Signals**
- Explicit BUY/SELL arrows based on angle crossovers, momentum confirmation, and MA alignment
- No guessing when to enter trades - the indicator tells you exactly when conditions align
**2. Non-Repainting Logic**
- All signals use confirmed historical data (shifted by 2 bars minimum)
- Critical for backtesting reliability and live trading confidence
- Original indicator could repaint signals on current bar
**3. Dual Signal System**
- **Simple Mode:** More frequent signals based on angle crossovers + momentum (for active traders)
- **Strict Mode:** Requires full multi-MA alignment + momentum confirmation (for conservative traders)
- Adaptable to different trading styles and risk tolerances
**4. Smart Signal Filtering**
- **Anti-spam cooldown:** Prevents duplicate signals within configurable bar count
- **No-trade zone detection:** Filters out low-conviction sideways markets automatically
- **Multi-timeframe MA alignment:** Ensures all moving averages agree on direction before signaling
**5. Enhanced Visualization**
- Large, clear BUY/SELL arrows with descriptive labels
- Color-coded backgrounds for market states (trending vs. ranging)
- Momentum histogram showing acceleration/deceleration in real-time
- Live status table displaying trend strength, angle value, momentum, and MA alignment
**6. Professional Alert System**
- Four distinct alert conditions: BUY Signal, SELL Signal, Strong BUY, Strong SELL
- Enables automated trade notifications and strategy integration
**7. Modified MA Periods**
- Original used EMA(27), EMA(83), EMA(278)
- Enhanced version uses faster EMA(3), EMA(8), EMA(13) for more responsive signals
- Better suited for modern volatile markets and shorter timeframes
---
## 📐 HOW IT WORKS - TECHNICAL EXPLANATION
### **Core Methodology:**
The indicator calculates angles (slopes) for five key moving averages:
- **JMA (Jurik Moving Average)** - Smooth, lag-reduced trend line (original implementation by **Everget**)
- **JMA Fast** - Responsive momentum indicator with higher power parameter
- **MA27 (EMA 3)** - Primary fast-moving average for signal generation
- **MA83 (EMA 8)** - Medium-term trend confirmation
- **MA278 (EMA 13)** - Slower trend filter
### **Angle Calculation Formula (by KyJ):**
```
angle = arctan((MA - MA ) / ATR(14)) × (180 / π)
```
**Why ATR normalization?**
- Makes angles comparable across different instruments (forex, stocks, crypto)
- Makes angles comparable across different timeframes
- Accounts for volatility - a 10-point move in different assets has different significance
**Angle Interpretation:**
- **> 15°** = Strong trend (momentum accelerating)
- **0° to 15°** = Weak trend (momentum present but moderate)
- **-2° to +2°** = No-trade zone (sideways/choppy market)
- **< -15°** = Strong downtrend
### **Signal Generation Logic:**
#### **BUY Signal Conditions:**
1. MA27 angle crosses above 0° (upward momentum initiates)
2. All three EMAs (3, 8, 13) pointing upward (trend alignment confirmed)
3. Momentum is positive for 2+ bars (acceleration, not deceleration)
4. Angle exceeds minimum threshold (not in no-trade zone)
5. Cooldown period passed (prevents signal spam)
#### **SELL Signal Conditions:**
1. MA27 angle crosses below 0° (downward momentum initiates)
2. All three EMAs pointing downward (downtrend alignment)
3. Momentum is negative for 2+ bars
4. Angle below negative threshold (not in no-trade zone)
5. Cooldown period passed
#### **Strong BUY+ / SELL+ Signals:**
Additional entry opportunities when JMA Fast crosses JMA Slow while maintaining strong directional angle - indicates momentum acceleration within established trend.
---
## 🔧 HOW TO USE
### **Recommended Settings by Trading Style:**
**Scalpers / Day Traders:**
- Signal Type: **Simple**
- Minimum Angle: **3-5°**
- Cooldown Bars: **3-5 bars**
- Timeframes: 1m, 5m, 15m
**Swing Traders:**
- Signal Type: **Strict**
- Minimum Angle: **7-10°**
- Cooldown Bars: **8-12 bars**
- Timeframes: 1H, 4H, Daily
**Position Traders:**
- Signal Type: **Strict**
- Minimum Angle: **10-15°**
- Cooldown Bars: **15-20 bars**
- Timeframes: Daily, Weekly
### **Parameter Descriptions:**
**1. Source** (default: OHLC4)
- Price data used for MA calculations
- OHLC4 provides smoothest angles
- Close is more responsive but noisier
**2. Threshold for No-Trade Zones** (default: 2°)
- Angles below this are considered sideways/ranging
- Increase for stricter filtering of choppy markets
- Decrease to allow signals in quieter trending periods
**3. Signal Type** (Simple vs. Strict)
- **Simple:** Angle crossover OR (trend + momentum)
- **Strict:** Angle crossover AND all MAs aligned AND momentum confirmed
- Start with Simple, switch to Strict if too many false signals
**4. Minimum Angle for Signal** (default: 5°)
- Only generate signals when angle exceeds this threshold
- Higher values = stronger trends required
- Lower values = more sensitive to momentum changes
**5. Cooldown Bars** (default: 5)
- Minimum bars between consecutive signals
- Prevents spam during volatile chop
- Scale with your timeframe (higher TF = more bars)
**6. Color Bars** (default: true)
- Colors chart bars based on signal state
- Green = bullish conditions, Red = bearish conditions
- Can disable if you prefer clean price bars
**7. Background Colors**
- **Yellow background** = No-trade zone (low angle, ranging market)
- **Green flash** = BUY signal generated
- **Red flash** = SELL signal generated
- All customizable or can be disabled
---
## 📊 INTERPRETING THE INDICATOR
### **Visual Elements:**
**Main Chart Window:**
- **Thick Lime/Fuchsia Line** = MA27 angle (primary signal line)
- **Medium Green/Red Line** = MA83 angle (trend confirmation)
- **Thin Green/Red Line** = MA278 angle (slow trend filter)
- **Aqua/Orange Line** = JMA Fast (momentum detector)
- **Green/Red Area** = JMA slope (overall trend context)
- **Blue/Purple Histogram** = Momentum (angle acceleration/deceleration)
**Signal Arrows:**
- **Large Green ▲ "BUY"** = Primary buy signal (all conditions met)
- **Small Green ▲ "BUY+"** = Strong momentum buy (JMA fast cross)
- **Large Red ▼ "SELL"** = Primary sell signal (all conditions met)
- **Small Red ▼ "SELL+"** = Strong momentum sell (JMA fast cross)
**Status Table (Top Right):**
- **Angle:** Current MA27 angle in degrees
- **Trend:** Classification (STRONG UP/DOWN, UP/DOWN, FLAT)
- **Momentum:** Acceleration state (ACCEL UP/DN, Up/Down)
- **MAs:** Alignment status (ALL UP/DOWN, Mixed)
- **Zone:** Trading zone status (ACTIVE vs. NO TRADE)
- **Last:** Bars since last signal
### **Trading Strategies:**
**Strategy 1: Pure Signal Following**
- Enter LONG on BUY signal
- Exit on SELL signal
- Use stop-loss at recent swing low/high
- Works best on trending instruments
**Strategy 2: Confirmation with Price Action**
- Wait for BUY signal + bullish candlestick pattern
- Wait for SELL signal + bearish candlestick pattern
- Increases win rate by filtering premature signals
- Recommended for beginners
**Strategy 3: Momentum Acceleration**
- Use BUY+/SELL+ signals for adding to positions
- Only take these in direction of primary signal
- Scalp quick moves during momentum spikes
- For experienced traders
**Strategy 4: Mean Reversion in No-Trade Zones**
- When status shows "NO TRADE", fade extremes
- Wait for angle to exit no-trade zone for reversal
- Contrarian approach for range-bound markets
- Requires tight stops
---
## ⚠️ LIMITATIONS & DISCLAIMERS
**What This Indicator DOES:**
✅ Measures momentum direction and strength via angle analysis
✅ Generates signals when multiple conditions align
✅ Filters out low-conviction sideways markets
✅ Provides visual clarity on trend state
**What This Indicator DOES NOT:**
❌ Predict future price movements with certainty
❌ Guarantee profitable trades (no indicator can)
❌ Work equally well on all instruments/timeframes
❌ Replace proper risk management and position sizing
**Known Limitations:**
- **Lagging Nature:** Like all moving averages, signals occur after momentum begins
- **Whipsaw Risk:** Can generate false signals in volatile, directionless markets
- **Optimization Required:** Parameters need adjustment for different assets
- **Not a Complete System:** Should be combined with risk management, position sizing, and other analysis
**Best Performance Conditions:**
- Strong trending markets (crypto bull runs, stock breakouts)
- Liquid instruments (major forex pairs, large-cap stocks)
- Appropriate timeframe selection (match to trading style)
- Used alongside support/resistance and volume analysis
---
## 🔔 ALERT SETUP
The indicator includes four alert conditions:
**1. BUY SIGNAL**
- Message: "MA SMART Angle: BUY SIGNAL! Angle crossed up with momentum"
- Use for: Primary long entries
**2. SELL SIGNAL**
- Message: "MA SMART Angle: SELL SIGNAL! Angle crossed down with momentum"
- Use for: Primary short entries or long exits
**3. Strong BUY**
- Message: "MA SMART Angle: Strong BUY momentum - JMA fast crossed up"
- Use for: Adding to longs or aggressive entries
**4. Strong SELL**
- Message: "MA SMART Angle: Strong SELL momentum - JMA fast crossed down"
- Use for: Adding to shorts or aggressive exits
**Setting Up Alerts:**
1. Right-click indicator → "Add Alert on MA SMART Angle"
2. Select desired condition from dropdown
3. Choose notification method (popup, email, webhook)
4. Set alert expiration (typically "Once Per Bar Close")
---
## 📚 EDUCATIONAL VALUE
This indicator serves as an excellent learning tool for understanding:
**1. Angle-Based Momentum Analysis**
- Traditional indicators show MA crossovers
- This shows the *rate of change* (velocity) of MAs
- Teaches traders to think in terms of momentum acceleration
**2. Multi-Timeframe Confirmation**
- Shows how fast, medium, and slow MAs interact
- Demonstrates importance of trend alignment
- Helps develop patience for high-probability setups
**3. Signal Quality vs. Quantity Tradeoff**
- Simple mode = more signals, more noise
- Strict mode = fewer signals, higher quality
- Teaches discretionary filtering skills
**4. Market State Recognition**
- Visual distinction between trending and ranging markets
- Helps traders avoid trading choppy conditions
- Develops "market context" awareness
---
## 🔄 DIFFERENCES FROM OTHER MA INDICATORS
**vs. Traditional MA Crossovers:**
- Measures momentum (angle) rather than just price crossing MA
- Provides earlier signals as angles change before price crosses
- Filters better for sideways markets using no-trade zones
**vs. MACD:**
- Uses multiple MAs instead of just two
- ATR normalization makes it universal across instruments
- Visual angle representation more intuitive than histogram
**vs. Supertrend:**
- Not based on ATR bands but on MA slope analysis
- Provides graduated strength indication (not just binary trend)
- Less prone to whipsaw in low volatility
**vs. Original "MA Angles" by JD:**
- Adds explicit entry/exit signals (original had none)
- Implements no-repaint logic for reliability
- Includes signal filtering and quality controls
- Provides dual signal systems (Simple/Strict)
- Enhanced visualization and status monitoring
- Uses faster MA periods (3/8/13 vs 27/83/278) for modern markets
---
## 📖 CODE STRUCTURE (for Pine Script learners)
This indicator demonstrates:
**Advanced Pine Script Techniques:**
- Custom function implementation (JMA, angle calculation)
- Var declarations for stateful tracking
- Table creation for HUD display
- Multi-condition signal logic
- Alert system integration
- Proper use of historical references for no-repaint
**Code Organization:**
- Modular function definitions (JMA, angle)
- Clear separation of concerns (inputs, calculations, plotting, alerts)
- Extensive commenting for maintainability
- Best practices for Pine Script v5
**Learning Resources:**
- Study the JMA function to understand adaptive smoothing
- Examine angle calculation for ATR normalization technique
- Review signal logic for multi-condition confirmation patterns
- Analyze anti-spam filtering for state management
The code is open-source - feel free to study, modify, and improve upon it!
---
## 🙏 CREDITS & ATTRIBUTION
**Original Concepts:**
- **"ma angles - JD" by JD (Duyck)** - Core angle calculation methodology and indicator concept
Original open-source indicator on TradingView Community Scripts
- **JMA (Jurik Moving Average) implementation by Everget** - Smooth, low-lag moving average function
Acknowledged in original JD indicator code
- **Angle Calculation formula by KyJ** - Mathematical formula for converting MA slope to degrees using ATR normalization
Acknowledged in original JD indicator code comments
**Enhancements in This Version:**
- Signal generation logic - Original implementation for this indicator
- No-repaint confirmation system - Original implementation
- Dual signal modes (Simple/Strict) - Original implementation
- Visual enhancements and status table - Original implementation
- Alert system and signal filtering - Original implementation
- Modified MA periods (3/8/13 instead of 27/83/278) - Optimization for modern markets
**Open Source Philosophy:**
This indicator follows the open-source spirit of TradingView and the Pine Script community. The original "ma angles - JD" by JD (Duyck) was published as open-source, enabling this enhanced version. Similarly, this code is published as open-source to allow further community improvements.
---
## ⚡ QUICK START GUIDE
**For New Users:**
1. Add indicator to chart
2. Start with default settings (Simple mode)
3. Wait for BUY signal (green arrow)
4. Observe how price behaves after signal
5. Check status table to understand market state
6. Adjust parameters based on your instrument/timeframe
**For Experienced Traders:**
1. Switch to Strict mode for higher quality signals
2. Increase cooldown bars to reduce frequency
3. Raise minimum angle threshold for stronger trends
4. Combine with your existing strategy for confirmation
5. Set up alerts for desired signal types
6. Backtest on your preferred instruments
---
## 🎓 RECOMMENDED COMBINATIONS
**Works Well With:**
- **Volume Analysis:** Confirm signals with volume spikes
- **Support/Resistance:** Take signals near key levels
- **RSI/Stochastic:** Avoid overbought/oversold extremes
- **ATR:** Size positions based on volatility
- **Price Action:** Wait for candlestick confirmation
**Complementary Indicators:**
- Order Flow / Footprint (for institutional confirmation)
- Volume Profile (for identifying value areas)
- VWAP (for intraday mean reversion reference)
- Fibonacci Retracements (for target setting)
---
## 📈 PERFORMANCE EXPECTATIONS
**Realistic Win Rates:**
- Simple Mode: 45-55% (higher frequency, moderate accuracy)
- Strict Mode: 55-65% (lower frequency, higher accuracy)
- Combined with price action: 60-70%
**Best Asset Classes:**
1. **Cryptocurrencies** (strong trends, clear signals)
2. **Forex Major Pairs** (smooth price action, good angles)
3. **Large-Cap Stocks** (trending behavior, liquid)
4. **Index Futures** (trending instruments)
**Challenging Conditions:**
- Low volatility consolidation periods
- News-driven erratic movements
- Thin/illiquid instruments
- Counter-trending markets
---
## 🛡️ RISK DISCLAIMER
**IMPORTANT LEGAL NOTICE:**
This indicator is for **educational and informational purposes only**. It is **NOT financial advice** and does not constitute a recommendation to buy or sell any financial instrument.
**Trading Risks:**
- Trading carries substantial risk of loss
- Past performance does not guarantee future results
- No indicator can predict market movements with certainty
- You can lose more than your initial investment (especially with leverage)
**User Responsibilities:**
- Conduct your own research and due diligence
- Understand the instruments you trade
- Never risk more than you can afford to lose
- Use proper position sizing and risk management
- Consider consulting a licensed financial advisor
**Indicator Limitations:**
- Signals are based on historical data only
- No guarantee of accuracy or profitability
- Parameters must be optimized for your specific use case
- Results vary significantly by market conditions
By using this indicator, you acknowledge and accept all trading risks. The author is not responsible for any financial losses incurred through use of this indicator.
---
## 📧 SUPPORT & FEEDBACK
**Found a bug?** Please report it in the comments with:
- Chart symbol and timeframe
- Parameter settings used
- Description of unexpected behavior
- Screenshot if possible
**Have suggestions?** Share your ideas for improvements!
**Enjoying the indicator?** Leave a like and follow for updates!
Algorithm Predator - ML-liteAlgorithm Predator - ML-lite
This indicator combines four specialized trading agents with an adaptive multi-armed bandit selection system to identify high-probability trade setups. It is designed for swing and intraday traders who want systematic signal generation based on institutional order flow patterns , momentum exhaustion , liquidity dynamics , and statistical mean reversion .
Core Architecture
Why These Components Are Combined:
The script addresses a fundamental challenge in algorithmic trading: no single detection method works consistently across all market conditions. By deploying four independent agents and using reinforcement learning algorithms to select or blend their outputs, the system adapts to changing market regimes without manual intervention.
The Four Trading Agents
1. Spoofing Detector Agent 🎭
Detects iceberg orders through persistent volume at similar price levels over 5 bars
Identifies spoofing patterns via asymmetric wick analysis (wicks exceeding 60% of bar range with volume >1.8× average)
Monitors order clustering using simplified Hawkes process intensity tracking (exponential decay model)
Signal Logic: Contrarian—fades false breakouts caused by institutional manipulation
Best Markets: Consolidations, institutional trading windows, low-liquidity hours
2. Exhaustion Detector Agent ⚡
Calculates RSI divergence between price movement and momentum indicator over 5-bar window
Detects VWAP exhaustion (price at 2σ bands with declining volume)
Uses VPIN reversals (volume-based toxic flow dissipation) to identify momentum failure
Signal Logic: Counter-trend—enters when momentum extreme shows weakness
Best Markets: Trending markets reaching climax points, over-extended moves
3. Liquidity Void Detector Agent 💧
Measures Bollinger Band squeeze (width <60% of 50-period average)
Identifies stop hunts via 20-bar high/low penetration with immediate reversal and volume spike
Detects hidden liquidity absorption (volume >2× average with range <0.3× ATR)
Signal Logic: Breakout anticipation—enters after liquidity grab but before main move
Best Markets: Range-bound pre-breakout, volatility compression zones
4. Mean Reversion Agent 📊
Calculates price z-scores relative to 50-period SMA and standard deviation (triggers at ±2σ)
Implements Ornstein-Uhlenbeck process scoring (mean-reverting stochastic model)
Uses entropy analysis to detect algorithmic trading patterns (low entropy <0.25 = high predictability)
Signal Logic: Statistical reversion—enters when price deviates significantly from statistical equilibrium
Best Markets: Range-bound, low-volatility, algorithmically-dominated instruments
Adaptive Selection: Multi-Armed Bandit System
The script implements four reinforcement learning algorithms to dynamically select or blend agents based on performance:
Thompson Sampling (Default - Recommended):
Uses Bayesian inference with beta distributions (tracks alpha/beta parameters per agent)
Balances exploration (trying underused agents) vs. exploitation (using proven winners)
Each agent's win/loss history informs its selection probability
Lite Approximation: Uses pseudo-random sampling from price/volume noise instead of true random number generation
UCB1 (Upper Confidence Bound):
Calculates confidence intervals using: average_reward + sqrt(2 × ln(total_pulls) / agent_pulls)
Deterministic algorithm favoring agents with high uncertainty (potential upside)
More conservative than Thompson Sampling
Epsilon-Greedy:
Exploits best-performing agent (1-ε)% of the time
Explores randomly ε% of the time (default 10%, configurable 1-50%)
Simple, transparent, easily tuned via epsilon parameter
Gradient Bandit:
Uses softmax probability distribution over agent preference weights
Updates weights via gradient ascent based on rewards
Best for Blend mode where all agents contribute
Selection Modes:
Switch Mode: Uses only the selected agent's signal (clean, decisive)
Blend Mode: Combines all agents using exponentially weighted confidence scores controlled by temperature parameter (smooth, diversified)
Lock Agent Feature:
Optional manual override to force one specific agent
Useful after identifying which agent dominates your specific instrument
Only applies in Switch mode
Four choices: Spoofing Detector, Exhaustion Detector, Liquidity Void, Mean Reversion
Memory System
Dual-Layer Architecture:
Short-Term Memory: Stores last 20 trade outcomes per agent (configurable 10-50)
Long-Term Memory: Stores episode averages when short-term reaches transfer threshold (configurable 5-20 bars)
Memory Boost Mechanism: Recent performance modulates agent scores by up to ±20%
Episode Transfer: When an agent accumulates sufficient results, averages are condensed into long-term storage
Persistence: Manual restoration of learned parameters via input fields (alpha, beta, weights, microstructure thresholds)
How Memory Works:
Agent generates signal → outcome tracked after 8 bars (performance horizon)
Result stored in short-term memory (win = 1.0, loss = 0.0)
Short-term average influences agent's future scores (positive feedback loop)
After threshold met (default 10 results), episode averaged into long-term storage
Long-term patterns (weighted 30%) + short-term patterns (weighted 70%) = total memory boost
Market Microstructure Analysis
These advanced metrics quantify institutional order flow dynamics:
Order Flow Toxicity (Simplified VPIN):
Measures buy/sell volume imbalance over 20 bars: |buy_vol - sell_vol| / (buy_vol + sell_vol)
Detects informed trading activity (institutional players with non-public information)
Values >0.4 indicate "toxic flow" (informed traders active)
Lite Approximation: Uses simple open/close heuristic instead of tick-by-tick trade classification
Price Impact Analysis (Simplified Kyle's Lambda):
Measures market impact efficiency: |price_change_10| / sqrt(volume_sum_10)
Low values = large orders with minimal price impact ( stealth accumulation )
High values = retail-dominated moves with high slippage
Lite Approximation: Uses simplified denominator instead of regression-based signed order flow
Market Randomness (Entropy Analysis):
Counts unique price changes over 20 bars / 20
Measures market predictability
High entropy (>0.6) = human-driven, chaotic price action
Low entropy (<0.25) = algorithmic trading dominance (predictable patterns)
Lite Approximation: Simple ratio instead of true Shannon entropy H(X) = -Σ p(x)·log₂(p(x))
Order Clustering (Simplified Hawkes Process):
Tracks self-exciting event intensity (coordinated order activity)
Decays at 0.9× per bar, spikes +1.0 when volume >1.5× average
High intensity (>0.7) indicates clustering (potential spoofing/accumulation)
Lite Approximation: Simple exponential decay instead of full λ(t) = μ + Σ α·exp(-β(t-tᵢ)) with MLE
Signal Generation Process
Multi-Stage Validation:
Stage 1: Agent Scoring
Each agent calculates internal score based on its detection criteria
Scores must exceed agent-specific threshold (adjusted by sensitivity multiplier)
Agent outputs: Signal direction (+1/-1/0) and Confidence level (0.0-1.0)
Stage 2: Memory Boost
Agent scores multiplied by memory boost factor (0.8-1.2 based on recent performance)
Successful agents get amplified, failing agents get dampened
Stage 3: Bandit Selection/Blending
If Adaptive Mode ON:
Switch: Bandit selects single best agent, uses only its signal
Blend: All agents combined using softmax-weighted confidence scores
If Adaptive Mode OFF:
Traditional consensus voting with confidence-squared weighting
Signal fires when consensus exceeds threshold (default 70%)
Stage 4: Confirmation Filter
Raw signal must repeat for consecutive bars (default 3, configurable 2-4)
Minimum confidence threshold: 0.25 (25%) enforced regardless of mode
Trend alignment check: Long signals require trend_score ≥ -2, Short signals require trend_score ≤ 2
Stage 5: Cooldown Enforcement
Minimum bars between signals (default 10, configurable 5-15)
Prevents over-trading during choppy conditions
Stage 6: Performance Tracking
After 8 bars (performance horizon), signal outcome evaluated
Win = price moved in signal direction, Loss = price moved against
Results fed back into memory and bandit statistics
Trading Modes (Presets)
Pre-configured parameter sets:
Conservative: 85% consensus, 4 confirmations, 15-bar cooldown
Expected: 60-70% win rate, 3-8 signals/week
Best for: Swing trading, capital preservation, beginners
Balanced: 70% consensus, 3 confirmations, 10-bar cooldown
Expected: 55-65% win rate, 8-15 signals/week
Best for: Day trading, most traders, general use
Aggressive: 60% consensus, 2 confirmations, 5-bar cooldown
Expected: 50-58% win rate, 15-30 signals/week
Best for: Scalping, high-frequency trading, active management
Elite: 75% consensus, 3 confirmations, 12-bar cooldown
Expected: 58-68% win rate, 5-12 signals/week
Best for: Selective trading, high-conviction setups
Adaptive: 65% consensus, 2 confirmations, 8-bar cooldown
Expected: Varies based on learning
Best for: Experienced users leveraging bandit system
How to Use
1. Initial Setup (5 Minutes):
Select Trading Mode matching your style (start with Balanced)
Enable Adaptive Learning (recommended for automatic agent selection)
Choose Thompson Sampling algorithm (best all-around performance)
Keep Microstructure Metrics enabled for liquid instruments (>100k daily volume)
2. Agent Tuning (Optional):
Adjust Agent Sensitivity multipliers (0.5-2.0):
<0.8 = Highly selective (fewer signals, higher quality)
0.9-1.2 = Balanced (recommended starting point)
1.3 = Aggressive (more signals, lower individual quality)
Monitor dashboard for 20-30 signals to identify dominant agent
If one agent consistently outperforms, consider using Lock Agent feature
3. Bandit Configuration (Advanced):
Blend Temperature (0.1-2.0):
0.3 = Sharp decisions (best agent dominates)
0.5 = Balanced (default)
1.0+ = Smooth (equal weighting, democratic)
Memory Decay (0.8-0.99):
0.90 = Fast adaptation (volatile markets)
0.95 = Balanced (most instruments)
0.97+ = Long memory (stable trends)
4. Signal Interpretation:
Green triangle (▲): Long signal confirmed
Red triangle (▼): Short signal confirmed
Dashboard shows:
Active agent (highlighted row with ► marker)
Win rate per agent (green >60%, yellow 40-60%, red <40%)
Confidence bars (█████ = maximum confidence)
Memory size (short-term buffer count)
Colored zones display:
Entry level (current close)
Stop-loss (1.5× ATR)
Take-profit 1 (2.0× ATR)
Take-profit 2 (3.5× ATR)
5. Risk Management:
Never risk >1-2% per signal (use ATR-based stops)
Signals are entry triggers, not complete strategies
Combine with your own market context analysis
Consider fundamental catalysts and news events
Use "Confirming" status to prepare entries (not to enter early)
6. Memory Persistence (Optional):
After 50-100 trades, check Memory Export Panel
Record displayed alpha/beta/weight values for each agent
Record VPIN and Kyle threshold values
Enable "Restore From Memory" and input saved values to continue learning
Useful when switching timeframes or restarting indicator
Visual Components
On-Chart Elements:
Spectral Layers: EMA8 ± 0.5 ATR bands (dynamic support/resistance, colored by trend)
Energy Radiance: Multi-layer glow boxes at signal points (intensity scales with confidence, configurable 1-5 layers)
Probability Cones: Projected price paths with uncertainty wedges (15-bar projection, width = confidence × ATR)
Connection Lines: Links sequential signals (solid = same direction continuation, dotted = reversal)
Kill Zones: Risk/reward boxes showing entry, stop-loss, and dual take-profit targets
Signal Markers: Triangle up/down at validated entry points
Dashboard (Configurable Position & Size):
Regime Indicator: 4-level trend classification (Strong Bull/Bear, Weak Bull/Bear)
Mode Status: Shows active system (Adaptive Blend, Locked Agent, or Consensus)
Agent Performance Table: Real-time win%, confidence, and memory stats
Order Flow Metrics: Toxicity and impact indicators (when microstructure enabled)
Signal Status: Current state (Long/Short/Confirming/Waiting) with confirmation progress
Memory Panel (Configurable Position & Size):
Live Parameter Export: Alpha, beta, and weight values per agent
Adaptive Thresholds: Current VPIN sensitivity and Kyle threshold
Save Reminder: Visual indicator if parameters should be recorded
What Makes This Original
This script's originality lies in three key innovations:
1. Genuine Meta-Learning Framework:
Unlike traditional indicator mashups that simply display multiple signals, this implements authentic reinforcement learning (multi-armed bandits) to learn which detection method works best in current conditions. The Thompson Sampling implementation with beta distribution tracking (alpha for successes, beta for failures) is statistically rigorous and adapts continuously. This is not post-hoc optimization—it's real-time learning.
2. Episodic Memory Architecture with Transfer Learning:
The dual-layer memory system mimics human learning patterns:
Short-term memory captures recent performance (recency bias)
Long-term memory preserves historical patterns (experience)
Automatic transfer mechanism consolidates knowledge
Memory boost creates positive feedback loops (successful strategies become stronger)
This architecture allows the system to adapt without retraining , unlike static ML models that require batch updates.
3. Institutional Microstructure Integration:
Combines retail-focused technical analysis (RSI, Bollinger Bands, VWAP) with institutional-grade microstructure metrics (VPIN, Kyle's Lambda, Hawkes processes) typically found in academic finance literature and professional trading systems, not standard retail platforms. While simplified for Pine Script constraints, these metrics provide insight into informed vs. uninformed trading , a dimension entirely absent from traditional technical analysis.
Mashup Justification:
The four agents are combined specifically for risk diversification across failure modes:
Spoofing Detector: Prevents false breakout losses from manipulation
Exhaustion Detector: Prevents chasing extended trends into reversals
Liquidity Void: Exploits volatility compression (different regime than trending)
Mean Reversion: Provides mathematical anchoring when patterns fail
The bandit system ensures the optimal tool is automatically selected for each market situation, rather than requiring manual interpretation of conflicting signals.
Why "ML-lite"? Simplifications and Approximations
This is the "lite" version due to necessary simplifications for Pine Script execution:
1. Simplified VPIN Calculation:
Academic Implementation: True VPIN uses volume bucketing (fixed-volume bars) and tick-by-tick buy/sell classification via Lee-Ready algorithm or exchange-provided trade direction flags
This Implementation: 20-bar rolling window with simple open/close heuristic (close > open = buy volume)
Impact: May misclassify volume during ranging/choppy markets; works best in directional moves
2. Pseudo-Random Sampling:
Academic Implementation: Thompson Sampling requires true random number generation from beta distributions using inverse transform sampling or acceptance-rejection methods
This Implementation: Deterministic pseudo-randomness derived from price and volume decimal digits: (close × 100 - floor(close × 100)) + (volume % 100) / 100
Impact: Not cryptographically random; may have subtle biases in specific price ranges; provides sufficient variation for agent selection
3. Hawkes Process Approximation:
Academic Implementation: Full Hawkes process uses maximum likelihood estimation with exponential kernels: λ(t) = μ + Σ α·exp(-β(t-tᵢ)) fitted via iterative optimization
This Implementation: Simple exponential decay (0.9 multiplier) with binary event triggers (volume spike = event)
Impact: Captures self-exciting property but lacks parameter optimization; fixed decay rate may not suit all instruments
4. Kyle's Lambda Simplification:
Academic Implementation: Estimated via regression of price impact on signed order flow over multiple time intervals: Δp = λ × Δv + ε
This Implementation: Simplified ratio: price_change / sqrt(volume_sum) without proper signed order flow or regression
Impact: Provides directional indicator of impact but not true market depth measurement; no statistical confidence intervals
5. Entropy Calculation:
Academic Implementation: True Shannon entropy requires probability distribution: H(X) = -Σ p(x)·log₂(p(x)) where p(x) is probability of each price change magnitude
This Implementation: Simple ratio of unique price changes to total observations (variety measure)
Impact: Measures diversity but not true information entropy with probability weighting; less sensitive to distribution shape
6. Memory System Constraints:
Full ML Implementation: Neural networks with backpropagation, experience replay buffers (storing state-action-reward tuples), gradient descent optimization, and eligibility traces
This Implementation: Fixed-size array queues with simple averaging; no gradient-based learning, no state representation beyond raw scores
Impact: Cannot learn complex non-linear patterns; limited to linear performance tracking
7. Limited Feature Engineering:
Advanced Implementation: Dozens of engineered features, polynomial interactions (x², x³), dimensionality reduction (PCA, autoencoders), feature selection algorithms
This Implementation: Raw agent scores and basic market metrics (RSI, ATR, volume ratio); minimal transformation
Impact: May miss subtle cross-feature interactions; relies on agent-level intelligence rather than feature combinations
8. Single-Instrument Data:
Full Implementation: Multi-asset correlation analysis (sector ETFs, currency pairs, volatility indices like VIX), lead-lag relationships, risk-on/risk-off regimes
This Implementation: Only OHLCV data from displayed instrument
Impact: Cannot incorporate broader market context; vulnerable to correlated moves across assets
9. Fixed Performance Horizon:
Full Implementation: Adaptive horizon based on trade duration, volatility regime, or profit target achievement
This Implementation: Fixed 8-bar evaluation window
Impact: May evaluate too early in slow markets or too late in fast markets; one-size-fits-all approach
Performance Impact Summary:
These simplifications make the script:
✅ Faster: Executes in milliseconds vs. seconds (or minutes) for full academic implementations
✅ More Accessible: Runs on any TradingView plan without external data feeds, APIs, or compute servers
✅ More Transparent: All calculations visible in Pine Script (no black-box compiled models)
✅ Lower Resource Usage: <500 bars lookback, minimal memory footprint
⚠️ Less Precise: Approximations may reduce statistical edge by 5-15% vs. academic implementations
⚠️ Limited Scope: Cannot capture tick-level dynamics, multi-order-book interactions, or cross-asset flows
⚠️ Fixed Parameters: Some thresholds hardcoded rather than dynamically optimized
When to Upgrade to Full Implementation:
Consider professional Python/C++ versions with institutional data feeds if:
Trading with >$100K capital where precision differences materially impact returns
Operating in microsecond-competitive environments (HFT, market making)
Requiring regulatory-grade audit trails and reproducibility
Backtesting with tick-level precision for strategy validation
Need true real-time adaptation with neural network-based learning
For retail swing/day trading and position management, these approximations provide sufficient signal quality while maintaining usability, transparency, and accessibility. The core logic—multi-agent detection with adaptive selection—remains intact.
Technical Notes
All calculations use standard Pine Script built-in functions ( ta.ema, ta.atr, ta.rsi, ta.bb, ta.sma, ta.stdev, ta.vwap )
VPIN and Kyle's Lambda use simplified formulas optimized for OHLCV data (see "Lite" section above)
Thompson Sampling uses pseudo-random noise from price/volume decimal digits for beta distribution sampling
No repainting: All calculations use confirmed bar data (no forward-looking)
Maximum lookback: 500 bars (set via max_bars_back parameter)
Performance evaluation: 8-bar forward-looking window for reward calculation (clearly disclosed)
Confidence threshold: Minimum 0.25 (25%) enforced on all signals
Memory arrays: Dynamic sizing with FIFO queue management
Limitations and Disclaimers
Not Predictive: This indicator identifies patterns in historical data. It cannot predict future price movements with certainty.
Requires Human Judgment: Signals are entry triggers, not complete trading strategies. Must be confirmed with your own analysis, risk management rules, and market context.
Learning Period Required: The adaptive system requires 50-100 bars minimum to build statistically meaningful performance data for bandit algorithms.
Overfitting Risk: Restoring memory parameters from one market regime to a drastically different regime (e.g., low volatility to high volatility) may cause poor initial performance until system re-adapts.
Approximation Limitations: Simplified calculations (see "Lite" section) may underperform academic implementations by 5-15% in highly efficient markets.
No Guarantee of Profit: Past performance, whether backtested or live-traded, does not guarantee future performance. All trading involves risk of loss.
Forward-Looking Bias: Performance evaluation uses 8-bar forward window—this creates slight look-ahead for learning (though not for signals). Real-time performance may differ from indicator's internal statistics.
Single-Instrument Limitation: Does not account for correlations with related assets or broader market regime changes.
Recommended Settings
Timeframe: 15-minute to 4-hour charts (sufficient volatility for ATR-based stops; adequate bar volume for learning)
Assets: Liquid instruments with >100k daily volume (forex majors, large-cap stocks, BTC/ETH, major indices)
Not Recommended: Illiquid small-caps, penny stocks, low-volume altcoins (microstructure metrics unreliable)
Complementary Tools: Volume profile, order book depth, market breadth indicators, fundamental catalysts
Position Sizing: Risk no more than 1-2% of capital per signal using ATR-based stop-loss
Signal Filtering: Consider external confluence (support/resistance, trendlines, round numbers, session opens)
Start With: Balanced mode, Thompson Sampling, Blend mode, default agent sensitivities (1.0)
After 30+ Signals: Review agent win rates, consider increasing sensitivity of top performers or locking to dominant agent
Alert Configuration
The script includes built-in alert conditions:
Long Signal: Fires when validated long entry confirmed
Short Signal: Fires when validated short entry confirmed
Alerts fire once per bar (after confirmation requirements met)
Set alert to "Once Per Bar Close" for reliability
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Quasimodo Pattern Strategy Back Test [TradingFinder] QM Trading🔵 Introduction
The QM pattern, also known as the Quasimodo pattern, is one of the popular patterns in price action, and it is often used by technical analysts. The QM pattern is used to identify trend reversals and provides a very good risk-to-reward ratio. One of the advantages of the QM pattern is its high frequency and visibility in charts.
Additionally, due to its strength, it is highly profitable, and as mentioned, its risk-to-reward ratio is very good. The QM pattern is highly popular among traders in supply and demand, and traders also use this pattern.
The Price Action QM pattern, like other Price Action patterns, has two types: Bullish QM and Bearish QM patterns. To identify this pattern, you need to be familiar with its types to recognize it.
🔵 Identifying the QM Pattern
🟣 Bullish QM
In the bullish QM pattern, as you can see in the image below, an LL and HH are formed. As you can see, the neckline is marked as a dashed line. When the price reaches this range, it will start its upward movement.
🟣 Bearish QM
The Price Action QM pattern also has a bearish pattern. As you can see in the image below, initially, an HH and LL are formed. The neckline in this image is the dashed line, and when the LL is formed, the price reaches this neckline. However, it cannot pass it, and the downward trend resumes.
🔵 How to Use
The Quasimodo pattern is one of the clearest structures used to identify market reversals. It is built around the concept of a structural break followed by a pullback into an area of trapped liquidity. Instead of relying on lagging indicators, this pattern focuses purely on price action and how the market reacts after exhausting one side of liquidity. When understood correctly, it provides traders with precise entry points at the transition between trend phases.
🟣 Bullish Quasimodo
A bullish Quasimodo forms after a clear downtrend when sellers start losing control. The market continues to make lower lows until a sudden higher high appears, signaling that buyers are entering with strength. Price then pulls back to retest the previous low, creating what is known as the Quasimodo low.
This area often becomes the final trap for sellers before the market shifts upward. A visible rejection or displacement from this zone confirms bullish momentum. Traders usually place entries near this level, stops below the low, and targets at previous highs or the next resistance zone. Combining the setup with demand zones or Fair Value Gaps increases its accuracy.
🟣 Bearish Quasimodo
A bearish Quasimodo forms near the top of an uptrend when buyers begin to lose strength. The market continues to make higher highs until a sudden lower low breaks the bullish structure, showing that selling pressure is entering the market. Price then retraces upward to retest the previous high, forming the Quasimodo high, where breakout buyers are often trapped.
Once rejection appears at this level, it indicates a likely reversal. Traders can enter short near this area, with stop-losses placed above the high and targets near the next support or previous lows. The setup gains more reliability when aligned with supply zones, SMT divergence, or bearish Fair Value Gaps.
🔵 Setting
Pivot Period : You can use this parameter to use your desired period to identify the QM pattern. By default, this parameter is set to the number 5.
Take Profit Mode : You can choose your desired Take Profit in three ways. Based on the logic of the QM strategy, you can select two Take Profit levels, TP1 and TP2. You can also choose your take profit based on the Reward to Risk ratio. You must enter your desired R/R in the Reward to Risk Ratio parameter.
Stop Loss Refine : The loss limit of the QM strategy is based on its logic on the Head pattern. You can refine it using the ATR Refine option to prevent Stop Hunt. You can enter your desired coefficient in the Stop Loss ATR Adjustment Coefficient parameter.
Reward to Risk Ratio : If you set Take Profit Mode to R/R, you must enter your desired R/R here. For example, if your loss limit is 10 pips and you set R/R to 2, your take profit will be reached when the price is 20 pips away from your entry point.
Stop Loss ATR Adjustment Coefficient : If you set Stop Loss Refine to ATR Refine, you must adjust your loss limit coefficient here. For example, if your buy position's loss limit is at the price of 1000, and your ATR is 10, if you set Stop Loss ATR Adjustment Coefficient to 2, your loss limit will be at the price of 980.
Entry Level Validity : Determines how long the Entry level remains valid. The higher the level, the longer the entry level will remain valid. By default it is 2 and it can be set between 2 and 15.
🔵 Results
The following examples show the backtest results of the Quasimodo (QM) strategy in action. Each image is based on specific settings for the symbol, timeframe, and input parameters, illustrating how the QM logic can generate signals under different market conditions. The detailed configuration for each backtest is also displayed on the image.
⚠ Important Note : Even with identical settings and the same symbol, results may vary slightly across different brokers due to data feed variations and pricing differences.
Default Properties of Backtests :
OANDA:XAUUSD | TimeFrame: 5min | Duration: 1 Year :
BINANCE:BTCUSD | TimeFrame: 5min | Duration: 1 Year :
CAPITALCOM:US30 | TimeFrame: 5min | Duration: 1 Year :
NASDAQ:QQQ | TimeFrame: 5min | Duration: 5 Year :
OANDA:EURUSD | TimeFrame: 5min | Duration: 5 Year :
PEPPERSTONE:US500 | TimeFrame: 5min | Duration: 5 Year :
Hedge Simulation Martingale v1
1. Overview & Strategy Logic
This script implements an automated, multi-position trading strategy that uses a Martingale-inspired approach to manage a series of entries. The core logic is as follows:
Initial Entry: The script enters a trade based on the direction of the previous bar's close. A green bar triggers a Long position; a red bar triggers a Short position.
Profit-Taking: A single, fixed-percentage profit target (Profit Percentage) is set for the entire trade. If reached, all positions are closed for a net profit.
Loss Management (Martingale Logic): If the price moves against the initial position and hits the fixed-percentage stop-loss (Loss Percentage), the script does not exit. Instead, it averages down by adding a new, larger position in the same direction. The size of the new position is determined by multiplying the previous position size by the First Multiplier.
Net Position Management: The script continuously calculates the net average entry price, a new combined profit target, and a new combined stop-loss based on all open positions. The goal is for a single favorable price move to recover all previous losses and hit the profit target.
2. Key Features
Visual Indicators:
Plots the Net Average Entry Price on the chart.
Plots dynamic Profit Target (TP) and Stop-Loss (SL) levels that update as new positions are added.
Displays entry signals (triangles) for the initial Long or Short trade.
Comprehensive Dashboard: A detailed table in the top-right corner shows real-time metrics, including:
Total historical Long/Short volume and PnL.
Current trade's investment, unrealized PnL, and position sizes.
Current position count, direction, and size.
Configurable Parameters:
Profit Percentage: The target profit percentage for the net position.
Loss Percentage: The stop-loss percentage that triggers a new entry.
Initial Position Size: The size of the first position in the series.
First Multiplier: The multiplier applied to the previous position size when averaging down.
Maximum Multiplier: A safety cap (commented out in the code but present) to prevent infinite scaling.
3. Intended Use & Purpose
This script is designed as a position management and tracking tool for traders who are experimenting with or actively using Martingale-style strategies. It is best used to:
Automate the complex calculations of average entry, combined TP/SL, and PnL for multiple entries.
Visually track the status of an ongoing series of positions.
Backtest the viability and risks of such a strategy on historical data.
4. ⚠️ Critical Risk Warning & Disclaimer
THIS STRATEGY CARRIES EXTREME FINANCIAL RISK. USE AT YOUR OWN RISK.
Unlimited Loss Potential: The Martingale strategy is infamous for its potential to generate unlimited losses. By continuously doubling down (or multiplying) on losing positions, a small adverse price move can lead to catastrophic losses that can exceed your account balance.
Margin Calls: The rapidly increasing position size can quickly deplete your margin, leading to a margin call and forced liquidation of all positions at a significant loss.
No Guarantee of Recovery: The assumption that the price will eventually reverse is flawed. A strong, sustained trend can wipe out the entire trading capital.
For Educational/Advanced Use Only: This script is intended for sophisticated traders who fully understand the immense risks involved. It is not a "sure profit" system.
The publisher of this script is not responsible for any financial losses incurred through its use. You are solely responsible for your trading decisions and risk management.
5. How to Use
Apply the Script: Add the script to your chart.
Configure Parameters: Adjust the input parameters according to your risk tolerance and strategy rules. Be extremely cautious with the multiplier and position size.
Monitor the Dashboard: The table will provide all necessary information about the current and historical state of the strategy.
Observe the Levels: Watch the plotted Entry, TP, and SL levels to understand the current market position.
Backtest First: Always test the strategy extensively on historical data before considering it with real capital.
6. Notes
The Maximum Multiplier safety feature is present in the code but is currently commented out. Users are strongly advised to uncomment and set this parameter to act as a final, hard liquidation point.
The script logs key events (trade start, target hit) and export data for further analysis.
This is a complex script and should be thoroughly understood before use.
