Inside/Multiple Inside Bars Detector by Yasser R.01Multiple Inside Bars Trading System
Detects multiple inside bar patterns with visual alerts, breakout signals, and risk management levels (1:2 RR ratio). Identifies high-probability trading setups.
This indicator scans for consecutive inside bar patterns (2+ bars forming within a 'mother bar'), which often precede strong breakouts. When detected, it:
1. Draws clear reference lines showing the mother bar's high/low
2. Alerts when price breaks either level
3. Automatically calculates 1:2 risk-reward stop loss and take profit levels
4. Displays entry points with trade details
Ideal for swing traders, the system helps identify consolidation periods before potential trend continuations. Works best on 4H/Daily timeframes.
#InsideBars #BreakoutTrading #RiskManagement #SwingTrading #PriceAction
Professional-grade inside bar detector that:
✅ Identifies single AND multiple inside bar setups
✅ Provides clean visual references (lines/labels)
✅ Generates breakout signals with calculated RR levels
✅ Self-cleaning - removes old setups automatically
Use alongside trend analysis for best results. Customizable in Settings.
Göstergeler ve stratejiler
Dr. Jones TSI DivergenceSimple TSI indicator with signal line. Publishing for my trading group to be able to utilize. It's really cool.
Multi-Indicator Switch PanelAdaptive Entry Toolkit
This powerful indicator combines three high-quality trading systems into one modular, easy-to-use panel. Each system is independently toggleable, enabling full customization based on your trading style and market conditions.
📦 INCLUDED MODULES:
1. RSI Pullback Signals
Identifies momentum exhaustion and possible reversal zones using the Relative Strength Index.
Conditions tuned to detect when RSI pulls back after reaching oversold (for buys) or overbought (for sells) levels.
Highlights potential early entry points in trending markets.
2. Double EMA Pullback
Detects pullbacks in strong trends using a fast and slow EMA (default: 50 & 200).
Buy/Sell signals generated when price crosses back over the faster EMA in the direction of the larger trend.
Great for trend continuation entries.
🧠 ADVANCED FEATURES
Fully customizable inputs for each module
Alerts for every signal (RSI pullback, EMA cross, breakout from suppression)
Minimalistic and lightweight for real-time use
Overlay-based for clean integration with your price chart
🧰 Best Used For:
Anticipating breakouts
Trend continuation setups
Low-volatility squeeze detection
Confluence-based entries
[TupTrader] prev candle of Opening session✅ Session Key Levels + Daily Zones
This smart indicator automatically marks the key levels from the previous candle before the opening of each main trading session — Asia, London, and New York — along with the previous daily candle levels. These levels are critical for price reaction, support/resistance, and session-based breakouts or reversals.
🧠 What does it do?
Detects and plots the previous candle before each session (Asia, London, New York)
Automatically draws:
High/Low/Open/Close of that candle
Optional body/fibonacci levels (25%, 50%, 75% or 23.6%, 38.2%, 61.8%)
Box zones to visualize the session range
Highlights the previous daily OHLC and key levels
🚨 Built-in alerts for touches on key session and daily levels
Fully customizable: colors, font size, levels visibility, and session times
💡 How to Use It?
Scalping or Intraday: Look for price reactions around session levels.
Breakout Strategy: Wait for price to break session highs/lows with volume.
Reversals: Watch for fakeouts around previous session or daily zones.
Use it with trend tools (e.g., EMA or structure) for confluence.
These levels act like a roadmap of market structure and liquidity. Perfect for day traders, scalpers, and session-based traders.
Grothendieck-Teichmüller Geometric SynthesisDskyz's Grothendieck-Teichmüller Geometric Synthesis (GTGS)
THEORETICAL FOUNDATION: A SYMPHONY OF GEOMETRIES
The 🎓 GTGS is built upon a revolutionary premise: that market dynamics can be modeled as geometric and topological structures. While not a literal academic implementation—such a task would demand computational power far beyond current trading platforms—it leverages core ideas from advanced mathematical theories as powerful analogies and frameworks for its algorithms. Each component translates an abstract concept into a practical market calculation, distinguishing GTGS by identifying deeper structural patterns rather than relying on standard statistical measures.
1. Grothendieck-Teichmüller Theory: Deforming Market Structure
The Theory : Studies symmetries and deformations of geometric objects, focusing on the "absolute" structure of mathematical spaces.
Indicator Analogy : The calculate_grothendieck_field function models price action as a "deformation" from its immediate state. Using the nth root of price ratios (math.pow(price_ratio, 1.0/prime)), it measures market "shape" stretching or compression, revealing underlying tensions and potential shifts.
2. Topos Theory & Sheaf Cohomology: From Local to Global Patterns
The Theory : A framework for assembling local properties into a global picture, with cohomology measuring "obstructions" to consistency.
Indicator Analogy : The calculate_topos_coherence function uses sine waves (math.sin) to represent local price "sections." Summing these yields a "cohomology" value, quantifying price action consistency. High values indicate coherent trends; low values signal conflict and uncertainty.
3. Tropical Geometry: Simplifying Complexity
The Theory : Transforms complex multiplicative problems into simpler, additive, piecewise-linear ones using min(a, b) for addition and a + b for multiplication.
Indicator Analogy : The calculate_tropical_metric function applies tropical_add(a, b) => math.min(a, b) to identify the "lowest energy" state among recent price points, pinpointing critical support levels non-linearly.
4. Motivic Cohomology & Non-Commutative Geometry
The Theory : Studies deep arithmetic and quantum-like properties of geometric spaces.
Indicator Analogy : The motivic_rank and spectral_triple functions compute weighted sums of historical prices to capture market "arithmetic complexity" and "spectral signature." Higher values reflect structured, harmonic price movements.
5. Perfectoid Spaces & Homotopy Type Theory
The Theory : Abstract fields dealing with p-adic numbers and logical foundations of mathematics.
Indicator Analogy : The perfectoid_conv and type_coherence functions analyze price convergence and path identity, assessing the "fractal dust" of price differences and price path cohesion, adding fractal and logical analysis.
The Combination is Key : No single theory dominates. GTGS ’s Unified Field synthesizes all seven perspectives into a comprehensive score, ensuring signals reflect deep structural alignment across mathematical domains.
🎛️ INPUTS: CONFIGURING THE GEOMETRIC ENGINE
The GTGS offers a suite of customizable inputs, allowing traders to tailor its behavior to specific timeframes, market sectors, and trading styles. Below is a detailed breakdown of key input groups, their functionality, and optimization strategies, leveraging provided tooltips for precision.
Grothendieck-Teichmüller Theory Inputs
🧬 Deformation Depth (Absolute Galois) :
What It Is : Controls the depth of Galois group deformations analyzed in market structure.
How It Works : Measures price action deformations under automorphisms of the absolute Galois group, capturing market symmetries.
Optimization :
Higher Values (15-20) : Captures deeper symmetries, ideal for major trends in swing trading (4H-1D).
Lower Values (3-8) : Responsive to local deformations, suited for scalping (1-5min).
Timeframes :
Scalping (1-5min) : 3-6 for quick local shifts.
Day Trading (15min-1H) : 8-12 for balanced analysis.
Swing Trading (4H-1D) : 12-20 for deep structural trends.
Sectors :
Stocks : Use 8-12 for stable trends.
Crypto : 3-8 for volatile, short-term moves.
Forex : 12-15 for smooth, cyclical patterns.
Pro Tip : Increase in trending markets to filter noise; decrease in choppy markets for sensitivity.
🗼 Teichmüller Tower Height :
What It Is : Determines the height of the Teichmüller modular tower for hierarchical pattern detection.
How It Works : Builds modular levels to identify nested market patterns.
Optimization :
Higher Values (6-8) : Detects complex fractals, ideal for swing trading.
Lower Values (2-4) : Focuses on primary patterns, faster for scalping.
Timeframes :
Scalping : 2-3 for speed.
Day Trading : 4-5 for balanced patterns.
Swing Trading : 5-8 for deep fractals.
Sectors :
Indices : 5-8 for robust, long-term patterns.
Crypto : 2-4 for rapid shifts.
Commodities : 4-6 for cyclical trends.
Pro Tip : Higher towers reveal hidden fractals but may slow computation; adjust based on hardware.
🔢 Galois Prime Base :
What It Is : Sets the prime base for Galois field computations.
How It Works : Defines the field extension characteristic for market analysis.
Optimization :
Prime Characteristics :
2 : Binary markets (up/down).
3 : Ternary states (bull/bear/neutral).
5 : Pentagonal symmetry (Elliott waves).
7 : Heptagonal cycles (weekly patterns).
11,13,17,19 : Higher-order patterns.
Timeframes :
Scalping/Day Trading : 2 or 3 for simplicity.
Swing Trading : 5 or 7 for wave or cycle detection.
Sectors :
Forex : 5 for Elliott wave alignment.
Stocks : 7 for weekly cycle consistency.
Crypto : 3 for volatile state shifts.
Pro Tip : Use 7 for most markets; 5 for Elliott wave traders.
Topos Theory & Sheaf Cohomology Inputs
🏛️ Temporal Site Size :
What It Is : Defines the number of time points in the topological site.
How It Works : Sets the local neighborhood for sheaf computations, affecting cohomology smoothness.
Optimization :
Higher Values (30-50) : Smoother cohomology, better for trends in swing trading.
Lower Values (5-15) : Responsive, ideal for reversals in scalping.
Timeframes :
Scalping : 5-10 for quick responses.
Day Trading : 15-25 for balanced analysis.
Swing Trading : 25-50 for smooth trends.
Sectors :
Stocks : 25-35 for stable trends.
Crypto : 5-15 for volatility.
Forex : 20-30 for smooth cycles.
Pro Tip : Match site size to your average holding period in bars for optimal coherence.
📐 Sheaf Cohomology Degree :
What It Is : Sets the maximum degree of cohomology groups computed.
How It Works : Higher degrees capture complex topological obstructions.
Optimization :
Degree Meanings :
1 : Simple obstructions (basic support/resistance).
2 : Cohomological pairs (double tops/bottoms).
3 : Triple intersections (complex patterns).
4-5 : Higher-order structures (rare events).
Timeframes :
Scalping/Day Trading : 1-2 for simplicity.
Swing Trading : 3 for complex patterns.
Sectors :
Indices : 2-3 for robust patterns.
Crypto : 1-2 for rapid shifts.
Commodities : 3-4 for cyclical events.
Pro Tip : Degree 3 is optimal for most trading; higher degrees for research or rare event detection.
🌐 Grothendieck Topology :
What It Is : Chooses the Grothendieck topology for the site.
How It Works : Affects how local data integrates into global patterns.
Optimization :
Topology Characteristics :
Étale : Finest topology, captures local-global principles.
Nisnevich : A1-invariant, good for trends.
Zariski : Coarse but robust, filters noise.
Fpqc : Faithfully flat, highly sensitive.
Sectors :
Stocks : Zariski for stability.
Crypto : Étale for sensitivity.
Forex : Nisnevich for smooth trends.
Indices : Zariski for robustness.
Timeframes :
Scalping : Étale for precision.
Swing Trading : Nisnevich or Zariski for reliability.
Pro Tip : Start with Étale for precision; switch to Zariski in noisy markets.
Unified Field Configuration Inputs
⚛️ Field Coupling Constant :
What It Is : Sets the interaction strength between geometric components.
How It Works : Controls signal amplification in the unified field equation.
Optimization :
Higher Values (0.5-1.0) : Strong coupling, amplified signals for ranging markets.
Lower Values (0.001-0.1) : Subtle signals for trending markets.
Timeframes :
Scalping : 0.5-0.8 for quick, strong signals.
Swing Trading : 0.1-0.3 for trend confirmation.
Sectors :
Crypto : 0.5-1.0 for volatility.
Stocks : 0.1-0.3 for stability.
Forex : 0.3-0.5 for balance.
Pro Tip : Default 0.137 (fine structure constant) is a balanced starting point; adjust up in choppy markets.
📐 Geometric Weighting Scheme :
What It Is : Determines the framework for combining geometric components.
How It Works : Adjusts emphasis on different mathematical structures.
Optimization :
Scheme Characteristics :
Canonical : Equal weighting, balanced.
Derived : Emphasizes higher-order structures.
Motivic : Prioritizes arithmetic properties.
Spectral : Focuses on frequency domain.
Sectors :
Stocks : Canonical for balance.
Crypto : Spectral for volatility.
Forex : Derived for structured moves.
Indices : Motivic for arithmetic cycles.
Timeframes :
Day Trading : Canonical or Derived for flexibility.
Swing Trading : Motivic for long-term cycles.
Pro Tip : Start with Canonical; experiment with Spectral in volatile markets.
Dashboard and Visual Configuration Inputs
📋 Show Enhanced Dashboard, 📏 Size, 📍 Position :
What They Are : Control dashboard visibility, size, and placement.
How They Work : Display key metrics like Unified Field , Resonance , and Signal Quality .
Optimization :
Scalping : Small size, Bottom Right for minimal chart obstruction.
Swing Trading : Large size, Top Right for detailed analysis.
Sectors : Universal across markets; adjust size based on screen setup.
Pro Tip : Use Large for analysis, Small for live trading.
📐 Show Motivic Cohomology Bands, 🌊 Morphism Flow, 🔮 Future Projection, 🔷 Holographic Mesh, ⚛️ Spectral Flow :
What They Are : Toggle visual elements representing mathematical calculations.
How They Work : Provide intuitive representations of market dynamics.
Optimization :
Timeframes :
Scalping : Enable Morphism Flow and Spectral Flow for momentum.
Swing Trading : Enable all for comprehensive analysis.
Sectors :
Crypto : Emphasize Morphism Flow and Future Projection for volatility.
Stocks : Focus on Cohomology Bands for stable trends.
Pro Tip : Disable non-essential visuals in fast markets to reduce clutter.
🌫️ Field Transparency, 🔄 Web Recursion Depth, 🎨 Mesh Color Scheme :
What They Are : Adjust visual clarity, complexity, and color.
How They Work : Enhance interpretability of visual elements.
Optimization :
Transparency : 30-50 for balanced visibility; lower for analysis.
Recursion Depth : 6-8 for balanced detail; lower for older hardware.
Color Scheme :
Purple/Blue : Analytical focus.
Green/Orange : Trading momentum.
Pro Tip : Use Neon Purple for deep analysis; Neon Green for active trading.
⏱️ Minimum Bars Between Signals :
What It Is : Minimum number of bars required between consecutive signals.
How It Works : Prevents signal clustering by enforcing a cooldown period.
Optimization :
Higher Values (10-20) : Fewer signals, avoids whipsaws, suited for swing trading.
Lower Values (0-5) : More responsive, allows quick reversals, ideal for scalping.
Timeframes :
Scalping : 0-2 bars for rapid signals.
Day Trading : 3-5 bars for balance.
Swing Trading : 5-10 bars for stability.
Sectors :
Crypto : 0-3 for volatility.
Stocks : 5-10 for trend clarity.
Forex : 3-7 for cyclical moves.
Pro Tip : Increase in choppy markets to filter noise.
Hardcoded Parameters
Tropical, Motivic, Spectral, Perfectoid, Homotopy Inputs : Fixed to optimize performance but influence calculations (e.g., tropical_degree=4 for support levels, perfectoid_prime=5 for convergence).
Optimization : Experiment with codebase modifications if advanced customization is needed, but defaults are robust across markets.
🎨 ADVANCED VISUAL SYSTEM: TRADING IN A GEOMETRIC UNIVERSE
The GTTMTSF ’s visuals are direct representations of its mathematics, designed for intuitive and precise trading decisions.
Motivic Cohomology Bands :
What They Are : Dynamic bands ( H⁰ , H¹ , H² ) representing cohomological support/resistance.
Color & Meaning : Colors reflect energy levels ( H⁰ tightest, H² widest). Breaks into H¹ signal momentum; H² touches suggest reversals.
How to Trade : Use for stop-loss/profit-taking. Band bounces with Dashboard confirmation are high-probability setups.
Morphism Flow (Webbing) :
What It Is : White particle streams visualizing market momentum.
Interpretation : Dense flows indicate strong trends; sparse flows signal consolidation.
How to Trade : Follow dominant flow direction; new flows post-consolidation signal trend starts.
Future Projection Web (Fractal Grid) :
What It Is : Fibonacci-period fractal projections of support/resistance.
Color & Meaning : Three-layer lines (white shadow, glow, colored quantum) with labels showing price, topological class, anomaly strength (φ), resonance (ρ), and obstruction ( H¹ ). ⚡ marks extreme anomalies.
How to Trade : Target ⚡/● levels for entries/exits. High-anomaly levels with weakening Unified Field are reversal setups.
Holographic Mesh & Spectral Flow :
What They Are : Visuals of harmonic interference and spectral energy.
How to Trade : Bright mesh nodes or strong Spectral Flow warn of building pressure before price movement.
📊 THE GEOMETRIC DASHBOARD: YOUR MISSION CONTROL
The Dashboard translates complex mathematics into actionable intelligence.
Unified Field & Signals :
FIELD : Master value (-10 to +10), synthesizing all geometric components. Extreme readings (>5 or <-5) signal structural limits, often preceding reversals or continuations.
RESONANCE : Measures harmony between geometric field and price-volume momentum. Positive amplifies bullish moves; negative amplifies bearish moves.
SIGNAL QUALITY : Confidence meter rating alignment. Trade only STRONG or EXCEPTIONAL signals for high-probability setups.
Geometric Components :
What They Are : Breakdown of seven mathematical engines.
How to Use : Watch for convergence. A strong Unified Field is reliable when components (e.g., Grothendieck , Topos , Motivic ) align. Divergence warns of trend weakening.
Signal Performance :
What It Is : Tracks indicator signal performance.
How to Use : Assesses real-time performance to build confidence and understand system behavior.
🚀 DEVELOPMENT & UNIQUENESS: BEYOND CONVENTIONAL ANALYSIS
The GTTMTSF was developed to analyze markets as evolving geometric objects, not statistical time-series.
Why This Is Unlike Anything Else :
Theoretical Depth : Uses geometry and topology, identifying patterns invisible to statistical tools.
Holistic Synthesis : Integrates seven deep mathematical frameworks into a cohesive Unified Field .
Creative Implementation : Translates PhD-level mathematics into functional Pine Script , blending theory and practice.
Immersive Visualization : Transforms charts into dynamic geometric landscapes for intuitive market understanding.
The GTTMTSF is more than an indicator; it’s a new lens for viewing markets, for traders seeking deeper insight into hidden order within chaos.
" Where there is matter, there is geometry. " - Johannes Kepler
— Dskyz , Trade with insight. Trade with anticipation.
HOG Trifecta HOG Trifecta
📊 Overview
HOG Trifecta is a real-time market monitor that blends three core elements of price action — trend, momentum, and volume positioning — into one clean directional output. Built for tactical traders, it cuts through the noise and highlights when the market is ready to move or stay neutral.
⚙️ How It Works
• Scores five key signals:
• EMA 9/21 crossover for directional trend
• RSI > 50 or < 50 for momentum bias
• MACD histogram for momentum expansion (WAE-style logic)
• Price relative to EMA 50 as a volume anchor
• ADX-powered trend strength confirmation
• Combines the signals into a score that determines a single bias:
BULLISH, NEUTRAL, or BEARISH
• Displays a floating, color-coded label above price for instant clarity
• Optional background shading tied to sentiment (toggleable)
🎯 Inputs
• Show Label — toggle the sentiment word on/off
• Show Background — toggle chart shading based on bias
✅ Benefits
• Monitors trend, momentum, and volume in real time
• Tells you when conditions align for directional setups
• Avoids false signals with NEUTRAL states
• Fully self-contained — no external dependencies
• Lightweight and fast for daily or intraday use
📈 Use Cases
• Entry confirmation in trend strategies
• Swing trade bias filter
• Anchor higher timeframe sentiment for lower timeframe entries
⚠️ Notes
• Score thresholds:
+2 or more → BULLISH
−2 or less → BEARISH
−1 to +1 → NEUTRAL
• Built using only standard Pine Script tools
CEYLON Golden Indicator Buy & SellDesigned to provide traders with clear, high-probability trading signals, this indicator helps you identify key market levels
Log Regression Oscillator (caN)fi(ki)=>'ra'
// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © fikira
//@version=6
indicator('Log Regression Oscillator', max_bars_back=5000, max_labels_count=500, max_lines_count=500, overlay=false)
________________________________________________________________________________________________________________________ ='
⎞ Settings ⎛
(__--------__) '
cGREEN = #089981, cRED = #F23645, cGRAY = #757a79
threshold = input.int (300 , minval=150)
proactive = input.bool (false )
GRE = input.color(cGREEN , 'Bull' , group='Style' )
RED = input.color(cRED , 'Bear' , group='Style' )
GRY = input.color(cGRAY , 'Unconfirmed Bull/Bear' , group='Style' )
showDsh = input.bool ( true , 'Show Dashboard' , group='Dashboard' )
dshLoc = str.replace(str.lower(input.string('Top Right', 'Location', group='Dashboard', options= )), ' ', '_')
txtSize = str.lower(input.string('Normal' , 'Size' , group='Dashboard', options= ) )
________________________________________________________________________________________________________________________ :='
⎞ Constants and general variables ⎛
(__-------------------------------__) '
INV = color(na)
n = bar_index
________________________________________________________________________________________________________________________ :='
⎞ Functions ⎛
(__---------__) '
dot(x, y)=>
if x.size() > 1 and y.size() > 1
m1 = matrix.new()
m2 = matrix.new()
m1.add_col(m1.columns(), y)
m2.add_row(m2.rows (), x)
m1.mult (m2)
.eigenvalues()
.sum()
//Closed form solution to best fit log function
log_reg(log_x, log_x2, log_y) =>
sum_log_x = log_x . sum()
sum_y = log_y . sum()
sum_log_x_y = dot(log_x ,log_y)
sum_log_x_sq = log_x2 . sum()
n_ = log_x .size()
//Closed-form solutions for a and b
a = (n_ * sum_log_x_y - sum_log_x * sum_y)
/ (n_ * sum_log_x_sq - math.pow(sum_log_x , 2))
b = ( sum_y - a * sum_log_x ) / n_
//Variables declared for draw()
var arrayarr = array.new(4, na)
proActH = false, proActL = false
var lastHi = 0., var lastLi = 0.
draw(aTop_x, aTop_x2, aTop_y, aBot_x, aBot_x2, aBot_y, top_points, prc_points, btm_points, refit) =>
var label labH = na, var label labL = na
vTop = 0.
vBtm = 0.
if refit
top_points.clear(), prc_points.clear(), btm_points.clear()
= log_reg(aTop_x, aTop_x2, aTop_y), arr.set(0, a_top), arr.set(1, b_top)
= log_reg(aBot_x, aBot_x2, aBot_y), arr.set(2, a_btm), arr.set(3, b_btm)
for i = 0 to n
top = math.exp(a_top * math.log(i) + b_top)
btm = math.exp(a_btm * math.log(i) + b_btm)
avg = math.avg(top, btm)
if i == n
vTop := top
vBtm := btm
ix = n - i
if ix < 4999
hi = high
lo = low
cl = close
getC = hi > avg ? hi : lo < avg ? lo : cl
prc_points.push(chart.point.from_index(i, 100 * math.max(-1.5, math.min(1.5, (getC - btm) / (top - btm)))))
for lab in label.all
lab.delete()
firstH = proactive ? true : false
firstL = proactive ? true : false
color colH = na, color colL = na
sz = prc_points.size()
if aTop_x.size() > 0
for i = aTop_x.size() -1 to 0
idx = int(math.exp(aTop_x.get(i)))
if idx < sz and idx > n - 5000 and idx >= 0
if firstH
if aTop_x.last() != lastHi
colH := GRY
firstH := false
else
colH := RED
else
colH := RED
top = math.exp(a_top * math.log(idx) + b_top)
btm = math.exp(a_btm * math.log(idx) + b_btm)
label.new(idx , 100 *
math.max(-1.5, math.min(1.5, (high - btm)
/ (top - btm)
) ), '●', textcolor = colH, color=INV, size=8)
if aBot_x.size() > 0
for i = aBot_x.size() -1 to 0
idx = int(math.exp(aBot_x.get(i)))
if idx < sz and idx > n - 5000 and idx >= 0
if firstL
if aBot_x.last() != lastLi
colL := GRY
firstL := false
else
colL := GRE
else
colL := GRE
top = math.exp(a_top * math.log(idx) + b_top)
btm = math.exp(a_btm * math.log(idx) + b_btm)
label.new(idx , 100 *
math.max(-1.5, math.min(1.5, (low - btm)
/ (top - btm)
) ), '●', textcolor = colL, color=INV, size=8
, style = label.style_label_up)
else
top = math.exp(arr.get(0) * math.log(n) + arr.get(1))
btm = math.exp(arr.get(2) * math.log(n) + arr.get(3))
avg = math.avg(top, btm)
vTop := top
vBtm := btm
hi = high, lo = low, cl = close
getC = hi > avg ? hi : lo < avg ? lo : cl
prc_points.push(chart.point.from_index(n, 100 * math.max(-1.5, math.min(1.5, (getC - btm) / (top - btm)))))
for poly in polyline.all
poly.delete()
if barstate.islast
labH.delete(), labH := label.new(n, 100, str.tostring(vTop, format.mintick), color=color.new(chart.fg_color, 85), textcolor=RED, style=label.style_label_lower_left, size=12)
labL.delete(), labL := label.new(n, 0, str.tostring(vBtm, format.mintick), color=color.new(chart.fg_color, 85), textcolor=GRE, style=label.style_label_upper_left, size=12)
polyline.new(prc_points.size() >= 5000 ? prc_points.slice(prc_points.size()-4999, prc_points.size()-1) : prc_points, line_color=chart.fg_color)
________________________________________________________________________________________________________________________ :='
⎞ Variables ⎛
(__---------__) '
//bool trigerring fit
refit = false
var top_points = array.new(0)
var prc_points = array.new(0)
var btm_points = array.new(0)
//Variables arrays
var peaks_y = array.new(0)
var peaks_x = array.new(0)
var peaks_x2 = array.new(0)
var btms_y = array.new(0)
var btms_x = array.new(0)
var btms_x2 = array.new(0)
var tb = table.new(dshLoc, 4, 8
, bgcolor = #1e222d
, border_color = #373a46
, border_width = 1
, frame_color = #373a46
, frame_width = 1)
________________________________________________________________________________________________________________________ :='
⎞ Exec ⎛
(__----__) '
//Top Bottom detection
max = ta.max(high)
var min = low
min := max == high ? low
: math.min(low , min)
barsmax = ta.barssince(high == max)
barsmin = ta.barssince(low == min)
if barsmax == threshold
nmax = n-barsmax
if peaks_x .size() > 0 and peaks_x.last() != lastHi
peaks_y .set(-1, math.log( max) )
peaks_x .set(-1, math.log(nmax) )
peaks_x2.set(-1, math.pow(math.log(nmax), 2))
else
peaks_y .push( math.log(max) )
peaks_x .push( math.log(nmax) )
peaks_x2.push( math.pow(math.log(nmax), 2))
lastHi := math.log(nmax)
refit := true
else
min := math.min(low , min)
if barsmin == threshold
nmin = n-barsmin
if btms_x .size() > 0 and btms_x.last() != lastLi
btms_y .set(-1, math.log(min) )
btms_x .set(-1, math.log(nmin) )
btms_x2 .set(-1, math.pow(math.log(nmin), 2))
else
btms_y .push( math.log( min) )
btms_x .push( math.log(nmin) )
btms_x2.push( math.pow(math.log(nmin), 2))
lastLi := math.log(nmin)
refit := true
chMax = ta.change(max) , chMin = ta.change(min)
if (chMax != 0 or chMin != 0) and proactive and not refit and n > threshold
= log_reg(peaks_x, peaks_x2, peaks_y)
= log_reg( btms_x, btms_x2, btms_y)
top = math.exp(a_top * math.log(n) + b_top)
btm = math.exp(a_btm * math.log(n) + b_btm)
if 100 * ((high - btm) / (top - btm)) > 90
if peaks_x.last() == lastHi
peaks_y .push(math.log(max))
peaks_x .push(math.log(n))
peaks_x2.push(math.log(n)
*math.log(n))
else
peaks_y .set(-1, math.log(max))
peaks_x .set(-1, math.log(n))
peaks_x2.set(-1, math.log(n)
* math.log(n))
arr.set(0, a_top), arr.set(1, b_top)
arr.set(2, a_btm), arr.set(3, b_btm)
refit := true
proActH := true
if 100 * ((low - btm) / (top - btm)) < 10
if btms_x.last() == lastLi
btms_y .push(math.log(min))
btms_x .push(math.log(n))
btms_x2.push(math.log(n)
*math.log(n))
else
btms_y .set(-1, math.log(min))
btms_x .set(-1, math.log(n))
btms_x2.set(-1, math.log(n)
* math.log(n))
arr.set(0, a_top), arr.set(1, b_top)
arr.set(2, a_btm), arr.set(3, b_btm)
refit := true
proActL := true
enough = peaks_x.size() > 1 and btms_x.size() > 1
if enough
draw(peaks_x, peaks_x2, peaks_y, btms_x, btms_x2, btms_y, top_points, prc_points, btm_points, refit)
else
if barstate.islast
txt = ''
if peaks_x.size() < 2
txt += str.format('{0} Top Swing', peaks_x.size())
if btms_x .size() < 2
if txt != ''
txt += ', '
txt += str.format('{0} Bottom Swing', btms_x .size())
txt += ' Change "Threshold" or timeframe for more Swings'
tb.cell(0, 0, txt, text_color=chart.fg_color, text_size=txtSize)
________________________________________________________________________________________________________________________ :='
⎞ Plot ⎛
(__----__) '
plot(n%2==0? 30 : na,'30' , color=color.new(chart.fg_color, 50), style=plot.style_linebr, display=display.pane)
plot(n%2==0? 70 : na,'70' , color=color.new(chart.fg_color, 50), style=plot.style_linebr, display=display.pane)
_100 = plot(100, 'na(100)', display=display.none)
_70 = plot( 70, 'na(70)' , display=display.none)
_60 = plot( 60, 'na(60)' , display=display.none)
_50 = plot( 50, 'na(50)' , display=display.none)
_40 = plot( 40, 'na(40)' , display=display.none)
_30 = plot( 30, 'na(30)' , display=display.none)
_00 = plot( 0, 'na(0)' , display=display.none)
fill(_100, _70, 100, 70, color.new(RED, 50), INV)
fill( _60, _50, 60, 50, INV, color.new(chart.fg_color, 85))
fill( _50, _40, 50, 40, color.new(chart.fg_color, 85), INV)
fill( _30, _00, 30, 0, INV, color.new(GRE, 75))
________________________________________________________________________________________________________________________ :='
⎞ End ⎛
(__---__) '
Boring Candles by The School of Dalal StreetThis indicator highlights the "boring" candles. These are candles where the body is less than 50% in length as compared to the high and low length. This allows us to quickly find the lower timeframe demand/supply without switching the chart timeframe. The use case is to quickly find our targets based on lower time frames.
log regression forex and altcoin dom (caN)(0-100 Range)NO REPAİNTİNG
Stablecoin Dominance Indicator
The Stablecoin Dominance Indicator is a powerful tool designed to analyze the relative dominance of stablecoins within the cryptocurrency market. It utilizes a combination of regression analysis and standard deviation to provide valuable insights into market sentiment and potential turning points. This indicator is particularly useful for traders and investors looking to make informed decisions in the dynamic world of cryptocurrencies.
How to Read the Indicator:
The Stablecoin Dominance Indicator comprises three key lines, each serving a specific purpose:
Middle Line (Regression Line):
The middle line represents the Regression Line of stablecoin dominance, acting as a baseline showing the average or mean dominance of stablecoins in the market.
When the stablecoin dominance hovers around this middle line, it suggests a relatively stable market sentiment with no extreme overbought or oversold conditions.
Upper Line (2 Standard Deviations Above Mean):
The upper line, positioned 2 standard deviations above the Regression Line, indicates a significant deviation from the mean.
When stablecoin dominance approaches or surpasses this upper line, it may imply that the cryptocurrency market is experiencing oversold conditions, potentially signaling a market bottom. This is an opportune time for traders to consider increasing their exposure to cryptocurrencies.
Lower Line (2 Standard Deviations Below Mean):
The lower line, positioned 2 standard deviations below the Regression Line, shows a significant deviation in the opposite direction, indicating overbought conditions.
When stablecoin dominance approaches or falls below this lower line, it suggests overbought conditions in the market, possibly indicating a market top. Traders may consider reducing their cryptocurrency holdings or taking profits during this phase.
It's important to note that the Stablecoin Dominance Indicator should be used in conjunction with other analysis tools and strategies.
By understanding and applying the insights provided by this indicator, traders and investors can make more informed decisions in the ever-changing cryptocurrency landscape, potentially enhancing their trading strategies and risk management practices.
Ichimoku AdvancedGreetings. I present to you an improved version of the indicator from LuxAlgo - Ichimoku Theories.
I am grateful to them for the work they have done, since I myself have no experience in programming on Pine Script.
I have supplemented their indicator with such functions as:
Multi-timeframe Tenkan and Kijun lines - you will always know where on the lower timeframe there is a stronger resistance/support.
Ichimoku line formation areas - they can be used as a visualization of the number of bars that appear in the near lines, and for forecasting when the growth of the lines is caused by the fading of candles. They can also be used as measures for setting stop orders.
3-line pattern detector - Marker showing when the price is above/below the lines Tenkan ----> Kijun ----> Senkou A.
Please note that the calculation takes into account the CLOSING price of the candle.
3 Chikou Span lines - for those who use the 3 Chikou Span strategy -9, -26, -52 from the current bar ----> forward.
Points of the expected next direction of the Tenkan, Kijun, Senkou A and B lines and Senkou A and B with 0 offset.
Senkou A and B lines with 0 offset - for visualization of possible resistance/support
Calculation of the angle of inclination of the Ichimoku lines - for better perception of the trend strength. A 90° scale is used for measurement, where 0 is the horizontal position of the line
Measuring the distance from the current price to the Tenkan and Kijun lines - for better interpretation of the next possible price movements
Table - all key points for opening a position are displayed in the table. But please CONSIDER THE CONTENT and THE THEORY OF CYCLES AND WAVES by Goichi Hosoda.
May the take profit be with you!
Luma DCA Tracker (BTC)Luma DCA Tracker (BTC) – User Guide
Function
This indicator simulates a regular Bitcoin investment strategy (Dollar Cost Averaging). It calculates and visualizes:
Accumulated BTC amount
Average entry price
Total amount invested
Current portfolio value
Profit/loss in absolute and percentage terms
Settings
Investment per interval
Fixed amount to be invested at each interval (e.g., 100 USD)
Start date
The date when DCA simulation begins
Investment interval
Choose between:
daily, weekly, every 14 days, or monthly
Show investment data
Displays additional chart lines (total invested, value, profit, etc.)
Chart Elements
Orange line: Average DCA entry price
Grey dots: Entry points based on selected interval
Info box (bottom left): Live summary of all key values
Notes
Purchases are simulated at the closing price of each interval
No fees, slippage, or taxes are included
The indicator is a simulation only and not linked to an actual portfolio
RSI Confluence - 3 Timeframes V1.1RSI Confluence – 3 Timeframes V1.1
RSI Confluence – 3 Timeframes v1.1 is a powerful multi-timeframe momentum indicator that detects RSI alignment across three timeframes. It helps traders identify high-probability reversal or continuation zones where momentum direction is synchronized, offering more reliable entry signals.
✅ Key Features:
📊 3-Timeframe RSI Analysis: Compare RSI values from current, higher, and highest timeframes.
🔁 Customizable Timeframes: Select any combination of timeframes for precision across scalping, swing, or positional trading.
🎯 Overbought/Oversold Zones: Highlights when all RSI values align in extreme zones (e.g., <30 or >70).
🔄 Confluence Filter: Confirms trend reversals or continuations only when all RSIs agree in direction.
📈 Visual Signals: Displays visual cues (such as background color or labels) when multi-timeframe confluence is met.
⚙️ Inputs:
RSI Length: Define the calculation length for RSI.
Timeframe 1 (TF1): Lower timeframe (e.g., current chart)
Timeframe 2 (TF2): Medium timeframe (e.g., 1H or 4H)
Timeframe 3 (TF3): Higher timeframe (e.g., 1D or 1W)
OB/OS Levels: Customizable RSI overbought/oversold thresholds (default: 70/30)
Show Visuals: Toggle for background color or signal markers when confluence conditions are met
📈 Use Cases:
Identify trend continuation when all RSIs support the same direction
Spot strong reversal zones with RSI agreement across TFs
Improve entry accuracy by avoiding false signals on a single timeframe
Suitable for multi-timeframe strategy confirmation
Smart Reversal Signal (Stoch + RSI + EQH/EQL) v1.1📘 Smart Reversal Signal (Stoch + RSI + EQH/EQL)
The Smart Reversal Signal v1.1 is a multi-confirmation reversal indicator that combines momentum and price action signals across timeframes. It is designed to help traders detect high-probability reversal zones based on confluence between stochastic, RSI, and key price structures.
✅ Key Features:
📊 Stochastic Crossover: Detects K and D line crossovers to identify potential overbought/oversold reversal points.
📈 RSI Signal: Confirms momentum exhaustion by checking RSI crossing above/below overbought/oversold levels.
🏛️ EQH/EQL Detection: Identifies Equal Highs (EQH) and Equal Lows (EQL) from higher timeframes as strong reversal zones.
⏱ Multi-Timeframe Lookback: Uses selected timeframe and historical depth to improve signal quality and reduce noise.
🎯 Reversal Alerts: Highlights confluence zones where multiple conditions align for a potential trend reversal.
🌐 Custom Timeframe Support: Analyze signals using data from different timeframes, regardless of current chart.
⚙️ Inputs:
Stochastic Parameters: %K, %D length and smoothing
RSI Parameters: Length, Overbought/Oversold levels
EQH/EQL Settings: Timeframe, Lookback bars
Signal Conditions: Enable/disable RSI and Stoch filter logic
📈 Use Cases:
Catch trend reversals at exhaustion points
Identify smart entry zones near EQH/EQL levels
Combine momentum + structure for higher accuracy
Adaptable for both scalping and swing trading
Touch 30 EMA & 150 EMA - Candle Signal//@version=5
indicator("Touch 30 EMA & 150 EMA - Candle Signal", overlay=true)
// Calculate EMAs
ema30 = ta.ema(close, 30)
ema150 = ta.ema(close, 150)
// Candle types
isGreen = close > open
isRed = close < open
// Candle touches both EMAs (either open-high-low-close range includes both)
touchesBothEMAs = low <= ema30 and high >= ema30 and low <= ema150 and high >= ema150
// Signals
greenArrow = isGreen and touchesBothEMAs
redArrow = isRed and touchesBothEMAs
// Plot arrows
plotshape(greenArrow, title="Green Candle Touch", location=location.belowbar, color=color.green, style=shape.arrowup, size=size.small)
plotshape(redArrow, title="Red Candle Touch", location=location.abovebar, color=color.red, style=shape.arrowdown, size=size.small)
// Plot EMAs for reference
plot(ema30, color=color.orange, title="EMA 30")
plot(ema150, color=color.blue, title="EMA 150")
Auto TrendlinesAuto Trendline – Indicator Description
The Auto Trendline indicator automatically draws trendlines based on recent swing highs and lows using pivot analysis. It helps traders quickly identify short-term and long-term market trends without manual drawing.
✅ Features:
Automatic drawing of trendlines based on pivot points (highs and lows)
Custom timeframe support: Use higher timeframe pivot data while working on lower charts
Trendlines update dynamically as new pivots are formed
Lines extend only to the current bar, keeping the chart clean
⚙️ How It Works:
The indicator detects recent swing highs and lows using pivot strength
Two most recent pivot points are connected to form each trendline:
Uptrend line from two higher lows
Downtrend line from two lower highs
Trendlines are redrawn as new pivots appear
