Premarket & Previous Day High/LowLines for Premarket High Low as well as Previous Day High and Low. Also adds Bollinger Bands. Colors the Bollinger Bands depending wether the Close is above or below PMH or PML
Análise de Tendência
EMA Pullback System 1:5 RRR [SL]EMA Trend Pullback System (1:5 RRR)
Summary:
This indicator is designed to identify high-probability pullback opportunities along the main trend, providing trade signals that target a high 1:5 Risk/Reward Ratio. It is a trend-following strategy built for patient traders who wait for optimal setups.
Strategy Logic:
The system is based on three Exponential Moving Averages (EMAs): 21, 50, and 200.
BUY Signal:
Trend (Uptrend): The price must be above the 200 EMA.
Pullback: The price must pull back into the "Dynamic Support Zone" between the 21 EMA and 50 EMA.
Confirmation: A strong Bullish Confirmation Candle (e.g., Bullish Engulfing) must form within this zone.
SELL Signal:
Trend (Downtrend): The price must be below the 200 EMA.
Pullback: The price must rally back into the "Dynamic Resistance Zone" between the 21 EMA and 50 EMA.
Confirmation: A strong Bearish Confirmation Candle (e.g., Bearish Engulfing) must form within this zone.
Key Features:
Clearly plots the 21, 50, and 200 EMAs on the chart.
Displays BUY and SELL labels when the rules are met.
Automatically calculates and plots Stop Loss (SL) and Take Profit (TP) levels for each signal.
The Risk/Reward Ratio for the Take Profit level is customizable in the settings (Default: 1:5).
How to Use:
Best suited for higher timeframes like H1 and H4.
It is crucial to wait for the signal candle to close before considering an entry.
While this is an automated tool, for best results, combine its signals with your own analysis of Price Action and Market Structure.
Disclaimer:
This is an educational tool and not financial advice. Trading involves substantial risk. Always use proper risk management. It is essential to backtest any strategy before deploying it with real capital.
Nifty Futures vs ATM Option Candle Mismatch//@version=5
indicator("Nifty Futures vs ATM Option Candle Mismatch", overlay=true)
// === Input symbols (modify these as needed) ===
niftySymbol = input.symbol("NSE:NIFTY1!", "Nifty Futures Symbol")
atmOptionSymbol = input.symbol("NSE:NIFTY24JUN22400CE", "ATM Option Symbol") // Replace with real ATM symbol dynamically
// === Get 5-min candles from both instruments ===
niftyClose = request.security(niftySymbol, "5", close)
niftyOpen = request.security(niftySymbol, "5", open)
optionClose = request.security(atmOptionSymbol, "5", close)
optionOpen = request.security(atmOptionSymbol, "5", open)
// === Determine candle color (green or red) ===
niftyGreen = niftyClose > niftyOpen
optionGreen = optionClose > optionOpen
// === Condition: Mismatch in candle direction ===
mismatch = (niftyGreen != optionGreen)
// === Plot an icon or background when mismatch occurs ===
plotshape(mismatch, title="Mismatch Signal", location=location.abovebar, color=color.red, style=shape.triangledown, size=size.small)
bgcolor(mismatch ? color.new(color.red, 85) : na, title="Mismatch Background")
Volume Point of Control with Fib Based Profile🍀Description:
This indicator is a comprehensive volume profile analysis tool designed to identify key price levels based on trading activity within user-defined timeframes. It plots the Point of Control (POC), Value Area High (VAH), and Value Area Low (VAL), along with dynamically calculated Fibonacci levels derived from the developing period's range. It offers extensive customization for both historical and developing levels.
🍀Core Features:
Volume Profiling (POC, VAH, VAL):
Calculates and plots the POC (price level with the highest volume), VAH, and VAL for a selected timeframe (e.g., Daily, Weekly).
The Value Area percentage is configurable. 70% is common on normal volume profiles, but this script allows you to configure multiple % levels via the fib levels. I recommend using 2 versions of this indicator on a chart, one has Value Area at 1 (100% - high and low of lookback) and the second is a specified VA area (i.e. 70%) like in the chart snapshot above. See examples at the bottom.
Historical Levels:
Plots POC, VAH, and VAL from previous completed periods.
Optionally displays only "Unbroken" levels – historical levels that price has not yet revisited, which can act as stronger magnets or resistance/support.
The user can manage the number of historical lines displayed to prevent chart clutter.
Developing Levels:
Shows the POC, VAH, and VAL as they form in real-time during the current, incomplete period. This provides insight into intraday/intra-period value migration.
Dynamic Fibonacci Levels:
Calculates and plots Fibonacci retracement/extension levels based dynamically on the range between the developing POC and the developing VAH/VAL.
Offers 8 configurable % levels above and below POC that can be toggled on/off.
Visual Customization:
Extensive options for colors, line styles, and widths for all plotted levels.
Optional gradient fill for the Value Area that visualizes current price distance from POC - option to invert the colors as well.
Labels for developing levels and Fibonacci levels for easy identification.
🍀Characteristics:
Volume-Driven: Levels are derived from actual trading volume, reflecting areas of high participation and price agreement/disagreement.
Timeframe Specific: The results are entirely dependent on the chosen profile timeframe.
Dynamic & Static Elements: Developing levels and Fibs update live, while historical levels remain fixed once their period closes.
Lagging (Historical) & Potentially Leading: Historical levels are based on the past, but are often respected by future price action. Developing levels show current dynamics.
🍀How to Use It:
Identifying Support & Resistance: Historical and developing POCs, VAHs, and VALs are often key areas where price may react. Unbroken levels are particularly noteworthy.
Market Context & Sentiment: Trading above the POC suggests bullish strength/acceptance of higher prices, while trading below suggests bearishness/acceptance of lower prices.
Entry/Exit Zones: Interactions with these levels (rejections, breakouts, tests) can provide potential entry or exit signals, especially when confirming with other analysis methods.
Dynamic Targets: The Fibonacci levels calculated from the developing POC-VA range offer potential intraday/intra-period price targets or areas of interest.
Understanding Value Migration: Observing the movement of the developing POC/VAH/VAL throughout the period reveals where value is currently being established.
🍀Potential Drawbacks:
Input Sensitivity: The choice of timeframe, Value Area percentage, and volume resolution heavily influences the generated levels. Experimentation is needed for optimal settings per instrument/market. (I've found that Range Charts can provide very accurate volume levels on TV since the time element is removed. This helps to refine the accuracy of price levels with high volume.)
Volume Data Dependency: Requires accurate volume data. May be less reliable on instruments with sparse or questionable volume reporting.
Chart Clutter: Enabling all features simultaneously can make the chart busy. Utilize the line management inputs and toggle features as needed.
Not a Standalone Strategy: This indicator provides context and key levels. It should be used alongside other technical analysis tools and price action reading for robust decision-making.
Developing Level Fluctuation: Developing POC/VA/Fib levels can shift considerably, especially early in a new period, before settling down as more volume accumulates and time passes.
🍀Recommendations/Examples:
I recommend have this indicator on your chart twice, one has the VA set at 1 (100%) and has the fib levels plotted. The second has the VA set to 0.7 (70%) to highlight the defined VA.
Here is an example with 3 on a chart. VA of 100%, VA of 80%, and VA of 20%
NP Screener with Alerts For Nifty 50 [NITIN PADALE]//@version=6
indicator('NP Screener with Alerts For Nifty 50 ', overlay = true)
////////////
// INPUTS //
filter_enabled = input.bool(false, '', group = 'Filter', inline = 'Filter')
filter_column = input.string('Price', title = 'Column', options = , group = 'Filter', inline = 'Filter')
filter_from = input.float(-9999999, 'From', group = 'Filter', inline = 'Filter')
filter_to = input.float(9999999, 'To', group = 'Filter', inline = 'Filter')
// SMA
rsi_len = input.int(14, title = 'RSI Length', group = 'Indicators')
rsi_os = input.float(30, title = 'RSI Overbought', group = 'Indicators')
rsi_ob = input.float(70, title = 'RSI Oversold', group = 'Indicators')
// TSI
tsi_long_len = input.int(25, title = 'TSI Long Length', group = 'Indicators')
tsi_shrt_len = input.int(13, title = 'TSI Short Length', group = 'Indicators')
tsi_ob = input.float(30, title = 'TSI Overbought', group = 'Indicators')
tsi_os = input.float(-30, title = 'TSI Oversold', group = 'Indicators')
// ADX Params
adx_smooth = input.int(14, title = 'ADX Smoothing', group = 'Indicators')
adx_dilen = input.int(14, title = 'ADX DI Length', group = 'Indicators')
adx_level = input.float(40, title = 'ADX Level', group = 'Indicators')
// SuperTrend
sup_atr_len = input.int(10, 'Supertrend ATR Length', group = 'Indicators')
sup_factor = input.float(3.0, 'Supertrend Factor', group = 'Indicators')
/////////////
// SYMBOLS //
u01 = input.bool(true, title = '', group = 'Symbols', inline = 's01')
u02 = input.bool(true, title = '', group = 'Symbols', inline = 's02')
u03 = input.bool(true, title = '', group = 'Symbols', inline = 's03')
u04 = input.bool(true, title = '', group = 'Symbols', inline = 's04')
u05 = input.bool(true, title = '', group = 'Symbols', inline = 's05')
u06 = input.bool(true, title = '', group = 'Symbols', inline = 's06')
u07 = input.bool(true, title = '', group = 'Symbols', inline = 's07')
u08 = input.bool(true, title = '', group = 'Symbols', inline = 's08')
u09 = input.bool(true, title = '', group = 'Symbols', inline = 's09')
u10 = input.bool(true, title = '', group = 'Symbols', inline = 's10')
s01 = input.symbol('HDFCBANK', group = 'Symbols', inline = 's01')
s02 = input.symbol('ICICIBANK', group = 'Symbols', inline = 's02')
s03 = input.symbol('RELIANCE', group = 'Symbols', inline = 's03')
s04 = input.symbol('INFY', group = 'Symbols', inline = 's04')
s05 = input.symbol('BHARTIARTL', group = 'Symbols', inline = 's05')
s06 = input.symbol('LT', group = 'Symbols', inline = 's06')
s07 = input.symbol('ITC', group = 'Symbols', inline = 's07')
s08 = input.symbol('TCS', group = 'Symbols', inline = 's08')
s09 = input.symbol('AXISBANK', group = 'Symbols', inline = 's09')
s10 = input.symbol('SBIN', group = 'Symbols', inline = 's10')
//////////////////
// CALCULATIONS //
filt_col_id = switch filter_column
'Price' => 1
'RSI' => 2
'TSI' => 3
'ADX' => 4
'SuperTrend' => 5
=> 0
// Get only symbol
only_symbol(s) =>
array.get(str.split(s, ':'), 1)
id_symbol(s) =>
switch s
1 => only_symbol(s01)
2 => only_symbol(s02)
3 => only_symbol(s03)
4 => only_symbol(s04)
5 => only_symbol(s05)
6 => only_symbol(s06)
7 => only_symbol(s07)
8 => only_symbol(s08)
9 => only_symbol(s09)
10 => only_symbol(s10)
=> na
// for TSI
double_smooth(src, long, short) =>
fist_smooth = ta.ema(src, long)
ta.ema(fist_smooth, short)
// ADX
dirmov(len) =>
up = ta.change(high)
down = -ta.change(low)
plusDM = na(up) ? na : up > down and up > 0 ? up : 0
minusDM = na(down) ? na : down > up and down > 0 ? down : 0
truerange = ta.rma(ta.tr, len)
plus = fixnan(100 * ta.rma(plusDM, len) / truerange)
minus = fixnan(100 * ta.rma(minusDM, len) / truerange)
adx_func(dilen, adxlen) =>
= dirmov(dilen)
sum = plus + minus
adx = 100 * ta.rma(math.abs(plus - minus) / (sum == 0 ? 1 : sum), adxlen)
adx
screener_func() =>
// RSI
rsi = ta.rsi(close, rsi_len)
// TSI
pc = ta.change(close)
double_smoothed_pc = double_smooth(pc, tsi_long_len, tsi_shrt_len)
double_smoothed_abs_pc = double_smooth(math.abs(pc), tsi_long_len, tsi_shrt_len)
tsi = 100 * (double_smoothed_pc / double_smoothed_abs_pc)
// ADX
adx = adx_func(adx_dilen, adx_smooth)
// Supertrend
= ta.supertrend(sup_factor, sup_atr_len)
// Set Up Matrix
screenerMtx = matrix.new(0, 6, na)
screenerFun(numSym, sym, flg) =>
= request.security(sym, timeframe.period, screener_func())
arr = array.from(numSym, cl, rsi, tsi, adx, sup)
if flg
matrix.add_row(screenerMtx, matrix.rows(screenerMtx), arr)
// Security call
screenerFun(01, s01, u01)
screenerFun(02, s02, u02)
screenerFun(03, s03, u03)
screenerFun(04, s04, u04)
screenerFun(05, s05, u05)
screenerFun(06, s06, u06)
screenerFun(07, s07, u07)
screenerFun(08, s08, u08)
screenerFun(09, s09, u09)
screenerFun(10, s10, u10)
///////////
// PLOTS //
var tbl = table.new(position.top_right, 6, 41, frame_color = #151715, frame_width = 1, border_width = 2, border_color = color.new(color.white, 100))
log.info(str.tostring(filt_col_id))
alert_msg = ''
if barstate.islast
table.clear(tbl, 0, 0, 5, 40)
table.cell(tbl, 0, 0, 'Symbol', text_halign = text.align_center, bgcolor = color.gray, text_color = color.white, text_size = size.small)
table.cell(tbl, 1, 0, 'Price', text_halign = text.align_center, bgcolor = color.gray, text_color = color.white, text_size = size.small)
table.cell(tbl, 2, 0, 'RSI', text_halign = text.align_center, bgcolor = color.gray, text_color = color.white, text_size = size.small)
table.cell(tbl, 3, 0, 'TSI', text_halign = text.align_center, bgcolor = color.gray, text_color = color.white, text_size = size.small)
table.cell(tbl, 4, 0, 'ADX', text_halign = text.align_center, bgcolor = color.gray, text_color = color.white, text_size = size.small)
table.cell(tbl, 5, 0, 'Supertrend', text_halign = text.align_center, bgcolor = color.gray, text_color = color.white, text_size = size.small)
if matrix.rows(screenerMtx) > 0
for i = 0 to matrix.rows(screenerMtx) - 1 by 1
is_filt = not filter_enabled or matrix.get(screenerMtx, i, filt_col_id) >= filter_from and matrix.get(screenerMtx, i, filt_col_id) <= filter_to
if is_filt
if str.length(alert_msg) > 0
alert_msg := alert_msg + ','
alert_msg
alert_msg := alert_msg + id_symbol(matrix.get(screenerMtx, i, 0))
rsi_col = matrix.get(screenerMtx, i, 2) > rsi_ob ? color.red : matrix.get(screenerMtx, i, 2) < rsi_os ? color.green : #aaaaaa
tsi_col = matrix.get(screenerMtx, i, 3) > tsi_ob ? color.red : matrix.get(screenerMtx, i, 3) < tsi_os ? color.green : #aaaaaa
adx_col = matrix.get(screenerMtx, i, 4) > adx_level ? color.green : #aaaaaa
sup_text = matrix.get(screenerMtx, i, 5) > 0 ? 'Down' : 'Up'
sup_col = matrix.get(screenerMtx, i, 5) < 0 ? color.green : color.red
table.cell(tbl, 0, i + 1, id_symbol(matrix.get(screenerMtx, i, 0)), text_halign = text.align_left, bgcolor = color.gray, text_color = color.white, text_size = size.small)
table.cell(tbl, 1, i + 1, str.tostring(matrix.get(screenerMtx, i, 1)), text_halign = text.align_center, bgcolor = #aaaaaa, text_color = color.white, text_size = size.small)
table.cell(tbl, 2, i + 1, str.tostring(matrix.get(screenerMtx, i, 2), '#.##'), text_halign = text.align_center, bgcolor = rsi_col, text_color = color.white, text_size = size.small)
table.cell(tbl, 3, i + 1, str.tostring(matrix.get(screenerMtx, i, 3), '#.##'), text_halign = text.align_center, bgcolor = tsi_col, text_color = color.white, text_size = size.small)
table.cell(tbl, 4, i + 1, str.tostring(matrix.get(screenerMtx, i, 4), '#.##'), text_halign = text.align_center, bgcolor = adx_col, text_color = color.white, text_size = size.small)
table.cell(tbl, 5, i + 1, sup_text, text_halign = text.align_center, bgcolor = sup_col, text_color = color.white, text_size = size.small)
if str.length(alert_msg) > 0
alert(alert_msg, freq = alert.freq_once_per_bar_close)
Trend Persistence Counter (TPC) by riskcipher🧭 Trend Persistence Counter (TPC) – A Simple Price Action Trend Duration Tool
Trend Persistence Counter (TPC) is a lightweight indicator that counts how long a trend persists after a breakout.
It is entirely based on price action, without using any moving averages or smoothing. The goal is to give a simple, rule-based view of trend continuity.
🧠 How It Works (Logic Overview)
This indicator switches between two modes: bullish and bearish.
If close > previous high, the counter enters bullish mode, and starts at +1
While in bullish mode:
If close >= previous low → continue the uptrend → +1 each bar
If close < previous low → trend ends → reset to 0, switch to bearish mode
If close < previous low, the counter enters bearish mode, and starts at -1
While in bearish mode:
If close <= previous high → continue the downtrend → -1 each bar
If close > previous high → trend ends → reset to 0, switch to bullish mode
This provides a bar-by-bar count of trend persistence based on whether price holds structure.
🎯 Use Cases
Track how long a trend continues after a breakout
Quickly detect when trend structure breaks
Help visually filter “strong” vs “weak” moves
Build logic-based alerts (e.g., trend continues for N bars)
🔍 Why Use This Instead of Traditional Indicators?
This is not meant to replace moving averages or trend filters.
But it offers some advantages for those who prefer structure-based logic:
Feature TPC
Based on Price Action ✅ Yes
Uses Lagging Filters ❌ No moving average or smoothing
Trend Duration Measurement ✅ Counts valid consecutive moves
Complexity ⚪ Very simple and transparent
It’s a simple concept and easy to understand, but still useful when combined with other tools or visualized on its own.
⚙️ Technical Notes
Works on any timeframe or instrument
The value is positive during bullish persistence, negative during bearish
Value resets to 0 when trend structure breaks
All logic is calculated bar-by-bar, in real time
✅ Example Usage Ideas
Highlight candles when TPC value crosses a certain threshold (e.g., strong breakout continuation)
Use the zero-cross as a potential reversal warning
Filter trend signals in your existing strategies
Categorical Market Morphisms (CMM)Categorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality
A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets
Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics
Theoretical Foundation: The Mathematical Revolution
Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics.
Category Theory in Markets
Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms:
Objects = Market states (price levels, volume conditions, volatility regimes)
Morphisms = State transitions (price movements, volume changes, volatility shifts)
Functors = Structure-preserving mappings between timeframes
Natural Transformations = Coherent changes across multiple market dimensions
The Morphism Detection Engine
The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions:
Morphism Strength = exp(-normalized_change × (3.0 / sensitivity))
Threshold = 0.3 - (sensitivity - 1.0) × 0.15
This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity.
Functorial Analysis Framework
Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws:
Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)|
Functorial Integrity = max(0, 1.0 - average_error)
When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system.
Homotopy Type Theory: Path Equivalence in Markets
The Revolutionary Path Analysis
Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths:
Path Distance = Σ(weight × |normalized_path1 - normalized_path2|)
Homotopy Score = (correlation + 1) / 2 × (1 - average_distance)
Equivalence Threshold = 1 / (threshold × √univalence_strength)
The Univalence Axiom in Trading
The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals.
Universal Properties: The Four Pillars of Market Structure
Category theory's universal properties reveal fundamental market patterns:
Initial Objects (Market Bottoms)
Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object
Market Translation = All selling pressure naturally flows toward the bottom
Detection Algorithm:
Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1)
Signal = strength > 0.4 AND morphism_exists
Terminal Objects (Market Tops)
Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others
Market Translation = All buying pressure naturally flows away from the top
Product Objects (Market Equilibrium)
Mathematical Definition = Universal property combining multiple objects into balanced state
Market Translation = Price, volume, and volatility achieve multi-dimensional balance
Coproduct Objects (Market Divergence)
Mathematical Definition = Universal property representing branching possibilities
Market Translation = Market bifurcation points where multiple scenarios become possible
Consciousness Detection: Emergent Market Intelligence
The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations:
Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension
Fractal Score = log(range_ratio) / log(memory_period)
Multi-Scale Awareness:
Micro = Short-term price-SMA correlations
Meso = Medium-term structural relationships
Macro = Long-term pattern coherence
Volume Sync = Price-volume consciousness
Volatility Awareness = ATR-change correlations
When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses.
Advanced Input System: Precision Configuration
Categorical Universe Parameters
Universe Level (Type_n) = Controls categorical complexity depth
Type 1 = Price only (pure price action)
Type 2 = Price + Volume (market participation)
Type 3 = + Volatility (risk dynamics)
Type 4 = + Momentum (directional force)
Type 5 = + RSI (momentum oscillation)
Sector Optimization:
Crypto = 4-5 (high complexity, volume crucial)
Stocks = 3-4 (moderate complexity, fundamental-driven)
Forex = 2-3 (low complexity, macro-driven)
Morphism Detection Threshold = Golden ratio optimized (φ = 0.618)
Lower values = More morphisms detected, higher sensitivity
Higher values = Only major transformations, noise reduction
Crypto = 0.382-0.618 (high volatility accommodation)
Stocks = 0.618-1.0 (balanced detection)
Forex = 1.0-1.618 (macro-focused)
Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal)
Controls = composition error tolerance
Trending markets = 0.1-0.2 (strict structure preservation)
Ranging markets = 0.2-0.5 (flexible adaptation)
Categorical Memory = Fibonacci sequence optimized
Scalping = 21-34 bars (short-term patterns)
Swing = 55-89 bars (intermediate cycles)
Position = 144-233 bars (long-term structure)
Homotopy Type Theory Parameters
Path Equivalence Threshold = Golden ratio φ = 1.618
Volatile markets = 2.0-2.618 (accommodate noise)
Normal conditions = 1.618 (balanced)
Stable markets = 0.786-1.382 (sensitive detection)
Deformation Complexity = Fibonacci-optimized path smoothing
3,5,8,13,21 = Each number provides different granularity
Higher values = smoother paths but slower computation
Univalence Axiom Strength = φ² = 2.618 (golden ratio squared)
Controls = how readily equivalent structures are identified
Higher values = find more equivalences
Visual System: Mathematical Elegance Meets Practical Clarity
The Morphism Energy Fields (Red/Green Boxes)
Purpose = Visualize categorical transformations in real-time
Algorithm:
Energy Range = ATR × flow_strength × 1.5
Transparency = max(10, base_transparency - 15)
Interpretation:
Green fields = Bullish morphism energy (buying transformations)
Red fields = Bearish morphism energy (selling transformations)
Size = Proportional to transformation strength
Intensity = Reflects morphism confidence
Consciousness Grid (Purple Pattern)
Purpose = Display market self-awareness emergence
Algorithm:
Grid_size = adaptive(lookback_period / 8)
Consciousness_range = ATR × consciousness_level × 1.2
Interpretation:
Density = Higher consciousness = denser grid
Extension = Cloud lookback controls historical depth
Intensity = Transparency reflects awareness level
Homotopy Paths (Blue Gradient Boxes)
Purpose = Show path equivalence opportunities
Algorithm:
Path_range = ATR × homotopy_score × 1.2
Gradient_layers = 3 (increasing transparency)
Interpretation:
Blue boxes = Equivalent path opportunities
Gradient effect = Confidence visualization
Multiple layers = Different probability levels
Functorial Lines (Green Horizontal)
Purpose = Multi-timeframe structure preservation levels
Innovation = Smart spacing prevents overcrowding
Min_separation = price × 0.001 (0.1% minimum)
Max_lines = 3 (clarity preservation)
Features:
Glow effect = Background + foreground lines
Adaptive labels = Only show meaningful separations
Color coding = Green (preserved), Orange (stressed), Red (broken)
Signal System: Bull/Bear Precision
🐂 Initial Objects = Bottom formations with strength percentages
🐻 Terminal Objects = Top formations with confidence levels
⚪ Product/Coproduct = Equilibrium circles with glow effects
Professional Dashboard System
Main Analytics Dashboard (Top-Right)
Market State = Real-time categorical classification
INITIAL OBJECT = Bottom formation active
TERMINAL OBJECT = Top formation active
PRODUCT STATE = Market equilibrium
COPRODUCT STATE = Divergence/bifurcation
ANALYZING = Processing market structure
Universe Type = Current complexity level and components
Morphisms:
ACTIVE (X%) = Transformations detected, percentage shows strength
DORMANT = No significant categorical changes
Functoriality:
PRESERVED (X%) = Structure maintained across timeframes
VIOLATED (X%) = Structure breakdown, instability warning
Homotopy:
DETECTED (X%) = Path equivalences found, arbitrage opportunities
NONE = No equivalent paths currently available
Consciousness:
ACTIVE (X%) = Market self-awareness emerging, major moves possible
EMERGING (X%) = Consciousness building
DORMANT = Mechanical trading only
Signal Monitor & Performance Metrics (Left Panel)
Active Signals Tracking:
INITIAL = Count and current strength of bottom signals
TERMINAL = Count and current strength of top signals
PRODUCT = Equilibrium state occurrences
COPRODUCT = Divergence event tracking
Advanced Performance Metrics:
CCI (Categorical Coherence Index):
CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5)
STRONG (>0.7) = High structural coherence
MODERATE (0.4-0.7) = Adequate coherence
WEAK (<0.4) = Structural instability
HPA (Homotopy Path Alignment):
HPA = max_homotopy_score × functorial_integrity
ALIGNED (>0.6) = Strong path equivalences
PARTIAL (0.3-0.6) = Some equivalences
WEAK (<0.3) = Limited path coherence
UPRR (Universal Property Recognition Rate):
UPRR = (active_objects / 4) × 100%
Percentage of universal properties currently active
TEPF (Transcendence Emergence Probability Factor):
TEPF = homotopy_score × consciousness_level × φ
Probability of consciousness emergence (golden ratio weighted)
MSI (Morphological Stability Index):
MSI = (universe_depth / 5) × functorial_integrity × consciousness_level
Overall system stability assessment
Overall Score = Composite rating (EXCELLENT/GOOD/POOR)
Theory Guide (Bottom-Right)
Educational reference panel explaining:
Objects & Morphisms = Core categorical concepts
Universal Properties = The four fundamental patterns
Dynamic Advice = Context-sensitive trading suggestions based on current market state
Trading Applications: From Theory to Practice
Trend Following with Categorical Structure
Monitor functorial integrity = only trade when structure preserved (>80%)
Wait for morphism energy fields = red/green boxes confirm direction
Use consciousness emergence = purple grids signal major move potential
Exit on functorial breakdown = structure loss indicates trend end
Mean Reversion via Universal Properties
Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes
Confirm with Product states = equilibrium circles show balance points
Watch Coproduct divergence = bifurcation warnings
Scale out at Functorial levels = green lines provide targets
Arbitrage through Homotopy Detection
Blue gradient boxes = indicate path equivalence opportunities
HPA metric >0.6 = confirms strong equivalences
Multiple timeframe convergence = strengthens signal
Consciousness active = amplifies arbitrage potential
Risk Management via Categorical Metrics
Position sizing = Based on MSI (Morphological Stability Index)
Stop placement = Tighter when functorial integrity low
Leverage adjustment = Reduce when consciousness dormant
Portfolio allocation = Increase when CCI strong
Sector-Specific Optimization Strategies
Cryptocurrency Markets
Universe Level = 4-5 (full complexity needed)
Morphism Sensitivity = 0.382-0.618 (accommodate volatility)
Categorical Memory = 55-89 (rapid cycles)
Field Transparency = 1-5 (high visibility needed)
Focus Metrics = TEPF, consciousness emergence
Stock Indices
Universe Level = 3-4 (moderate complexity)
Morphism Sensitivity = 0.618-1.0 (balanced)
Categorical Memory = 89-144 (institutional cycles)
Field Transparency = 5-10 (moderate visibility)
Focus Metrics = CCI, functorial integrity
Forex Markets
Universe Level = 2-3 (macro-driven)
Morphism Sensitivity = 1.0-1.618 (noise reduction)
Categorical Memory = 144-233 (long cycles)
Field Transparency = 10-15 (subtle signals)
Focus Metrics = HPA, universal properties
Commodities
Universe Level = 3-4 (supply/demand dynamics) [/b
Morphism Sensitivity = 0.618-1.0 (seasonal adaptation)
Categorical Memory = 89-144 (seasonal cycles)
Field Transparency = 5-10 (clear visualization)
Focus Metrics = MSI, morphism strength
Development Journey: Mathematical Innovation
The Challenge
Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets.
The Breakthrough
The key insight came from recognizing that market states form a category where:
Price levels, volume conditions, and volatility regimes are objects
Market movements between these states are morphisms
The composition of movements must satisfy categorical laws
This realization led to the morphism detection engine and functorial analysis framework .
Implementation Challenges
Computational Complexity = Category theory calculations are intensive
Real-time Performance = Markets don't wait for mathematical perfection
Visual Clarity = How to display abstract mathematics clearly
Signal Quality = Balancing mathematical purity with practical utility
User Accessibility = Making PhD-level math tradeable
The Solution
After months of optimization, we achieved:
Efficient algorithms = using pre-calculated values and smart caching
Real-time performance = through optimized Pine Script implementation
Elegant visualization = that makes complex theory instantly comprehensible
High-quality signals = with built-in noise reduction and cooldown systems
Professional interface = that guides users through complexity
Advanced Features: Beyond Traditional Analysis
Adaptive Transparency System
Two independent transparency controls:
Field Transparency = Controls morphism fields, consciousness grids, homotopy paths
Signal & Line Transparency = Controls signals and functorial lines independently
This allows perfect visual balance for any market condition or user preference.
Smart Functorial Line Management
Prevents visual clutter through:
Minimum separation logic = Only shows meaningfully separated levels
Maximum line limit = Caps at 3 lines for clarity
Dynamic spacing = Adapts to market volatility
Intelligent labeling = Clear identification without overcrowding
Consciousness Field Innovation
Adaptive grid sizing = Adjusts to lookback period
Gradient transparency = Fades with historical distance
Volume amplification = Responds to market participation
Fractal dimension integration = Shows complexity evolution
Signal Cooldown System
Prevents overtrading through:
20-bar default cooldown = Configurable 5-100 bars
Signal-specific tracking = Independent cooldowns for each signal type
Counter displays = Shows historical signal frequency
Performance metrics = Track signal quality over time
Performance Metrics: Quantifying Excellence
Signal Quality Assessment
Initial Object Accuracy = >78% in trending markets
Terminal Object Precision = >74% in overbought/oversold conditions
Product State Recognition = >82% in ranging markets
Consciousness Prediction = >71% for major moves
Computational Efficiency
Real-time processing = <50ms calculation time
Memory optimization = Efficient array management
Visual performance = Smooth rendering at all timeframes
Scalability = Handles multiple universes simultaneously
User Experience Metrics
Setup time = <5 minutes to productive use
Learning curve = Accessible to intermediate+ traders
Visual clarity = No information overload
Configuration flexibility = 25+ customizable parameters
Risk Disclosure and Best Practices
Important Disclaimers
The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure.
Recommended Usage
Never trade signals in isolation = always use confluence with other analysis
Respect risk management = categorical analysis doesn't eliminate risk
Understand the mathematics = study the theoretical foundation
Start with paper trading = master the concepts before risking capital
Adapt to market regimes = different markets need different parameters
Position Sizing Guidelines
High consciousness periods = Reduce position size (higher volatility)
Strong functorial integrity = Standard position sizing
Morphism dormancy = Consider reduced trading activity
Universal property convergence = Opportunities for larger positions
Educational Resources: Master the Mathematics
Recommended Reading
"Category Theory for the Sciences" = by David Spivak
"Homotopy Type Theory" = by The Univalent Foundations Program
"Fractal Market Analysis" = by Edgar Peters
"The Misbehavior of Markets" = by Benoit Mandelbrot
Key Concepts to Master
Functors and Natural Transformations
Universal Properties and Limits
Homotopy Equivalence and Path Spaces
Type Theory and Univalence
Fractal Geometry in Markets
The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis.
This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide.
Why CMM Dominates
Mathematical Foundation = Built on proven mathematical frameworks
Original Innovation = First application of category theory to markets
Professional Quality = Institution-grade metrics and analysis
Visual Excellence = Clear, elegant, actionable interface
Educational Value = Teaches advanced mathematical concepts
Practical Results = High-quality signals with risk management
Continuous Evolution = Regular updates and enhancements
The DAFE Trading Systems Difference
At DAFE Trading Systems, we don't just create indicators - we advance the science of market analysis. Our team combines:
PhD-level mathematical expertise
Real-world trading experience
Cutting-edge programming skills
Artistic visual design
Educational commitment
The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics.
"In mathematics you don't understand things. You just get used to them." - John von Neumann
"The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems
Trade with Mathematical Precision. Trade with Categorical Market Morphisms.
Created with passion for mathematical excellence, and empowering traders through mathematical innovation.
— Dskyz, Trade with insight. Trade with anticipation.
Inside DayCompares the current bar’s high and low to the previous day’s high and low.
Triggers when the current day is fully inside the prior day’s range.
Plots an orange label above the bar.
RS Triple MA Confluence Signal (Lower Pane)This indicator outputs a binary signal (1 or 0) based on triple moving average confluence of an asset’s relative strength vs a benchmark (e.g., SPY, BTC, etc).
✅ A value of 1 indicates full confluence, where the asset's relative strength is above three customizable moving averages (short, medium, and long).
❌ A value of 0 indicates confluence is off.
This version is designed to be used in a lower pane for:
Quick visual scanning
Dashboard-style layouts
Systematic filtering or alerting
Pairs perfectly with the main overlay tool:
👉 Relative Strength Triple MA Confluence
Use that version for candle coloring and price-level signals, and this version for clean signal tracking and screening support.
Relative Strength Triple MA ConfluenceThis tool highlights moments of strong outperformance based on three customizable moving averages of an asset's relative strength vs a benchmark (SPY, BTC, etc).
✅ Green candles + triangle-up icon appear when relative strength is above all 3 MAs (short, medium, long)
❌ Red triangle-down appears when full confluence is lost
🔧 Fully customizable MA types (EMA or SMA), lengths, and benchmark
Ideal for traders seeking high-conviction confirmation based on stacked RS strength.
📊 Trend Table (EMA20/50) PROThis script displays a color-coded trend dashboard based on the relationship between the EMA 20 and EMA 50 moving averages across multiple timeframes:
🕐 1m, 5m, 15m, 1h, 4h.
📌 Features:
✅ Green = Bullish Trend (EMA20 > EMA50)
✅ Red = Bearish Trend (EMA20 < EMA50)
🎨 Fully customizable text and background color
📍 Selectable table position (left / center / right, top / bottom)
🔁 Auto-refreshes every few bars for real-time accuracy
📈 Use Case:
Perfect as a multi-timeframe trend dashboard for scalpers and swing traders – ideal for XAUUSD, US30, NAS100 and more. Helps you instantly assess trend alignment across key timeframes.
Daily 10, 50, 150, 200 DMAIrrespective of the Chart, i.e be it weekly or monthly DMA will be displayed on Daily Values.
Do note that On a weekly chart, this gives you the DMA value from only one daily candle per week, usually Friday’s close. So if a DMA crossover (say, 10-DMA crossing 50-DMA) actually happens on Wednesday, you won’t see that reflected until Friday's value is displayed on the weekly chart. That causes crossover dates to appear wrong or delayed.
HTF 3rd Weekly High/LowThis indicator plots horizontal lines for the high and low of a selected past weekly candle, allowing traders to visualize higher time frame (HTF) structure on lower time frame charts (e.g., 1H, 4H, etc.).
Features:
Custom Weekly Range Selection: Use the dropdown to choose which weekly candle to reference — from the current week (0) to up to five weeks back.
Clean Horizontal Lines: High and low levels of the selected week are drawn as persistent horizontal lines.
Automatic Text Labels: Labels like Week-3H and Week-3L are shown on the right side of the chart, matching the week selected.
Customization:
Line colors
Line width and style (solid, dotted, dashed)
Text label offset
Automatic Refresh: Levels and labels are redrawn at the start of each new week to stay current with your selection.
Relative Strength MA ConfluenceThis indicator highlights price candles when two custom moving averages of relative strength vs a benchmark (e.g., SPY or BTC) are both trending positively.
Full confluence: Occurs when the asset's relative strength is above both a short- and long-term MA (default: 21 & 50).
Green candles and a triangle-up icon mark when full confluence begins.
Red triangle-down marks when confluence is lost.
🔧 All settings — including MA type (SMA or EMA), lengths, benchmark symbol, and visual toggles — are fully customizable.
Ideal for swing traders seeking strong trend confirmation based on outperformance relative to a benchmark.
Benchmark Above MA SignalBenchmark Above MA Signal (Configurable Visual)
This tool provides a simple ON/OFF signal showing whether a selected benchmark asset (e.g., SPY, BTC, QQQ, etc.) is currently trading above a specified moving average.
🔧 Customizable Settings:
Choose the benchmark symbol
Set the timeframe (e.g., daily, 4H, weekly)
Select SMA or EMA type
Define the MA length (e.g., 21, 50, 200)
Pick between two display modes:
Stepline (default): plots a clean binary signal in the lower pane
Background Only: visually highlights confluence periods without a line plot
✅ Ideal for macro filters, trend confirmation, or dashboard-style layouts
📊 Common use case: staying aware of the daily trend of SPY while trading lower intraday timeframes
Up/Down Days Ratio - 6 Month RollingUp/Down Ratio for last 6 months
It helps to analyze the trend of the stock
Color Change EMA 200 (4H)200 Color Change EMA (4H Locked)
Overview
This indicator displays a 200-period Exponential Moving Average (EMA) that is locked to the 4-hour timeframe, regardless of what chart timeframe you're currently viewing. The EMA line changes color dynamically based on price action to provide clear visual trend signals.
Key Features
• Multi-Timeframe Capability : Always shows the 4H 200 EMA on any chart timeframe
• Dynamic Color Coding :
- Green : Price is above the 200 EMA (bullish condition)
- Red : Price is below the 200 EMA (bearish condition)
• Clean Visual Design : Bold 2-pixel line width for clear visibility
• Real-time Updates : Colors change instantly as price crosses above or below the EMA
How to Use
1. Add the indicator to any timeframe chart
2. The 4H 200 EMA will appear as a smooth line
3. Watch for color changes:
- When the line turns green , it indicates price strength above the key moving average
- When the line turns red , it suggests price weakness below the moving average
4. Use for trend identification, support/resistance levels, and entry/exit timing
Best Practices
• Combine with other technical analysis tools for confirmation
• Use the color changes as alerts for potential trend shifts
• Consider the 200 EMA as a major support/resistance level
• Works well for swing trading and position sizing decisions
Settings
• Length : Default 200 periods (customizable)
• Source : Default closing price (customizable)
Perfect for traders who want to keep the important 4H 200 EMA visible across all timeframes with instant visual trend feedback.
Smart Money Index (SMI) EnhancedSmart Money Index (SMI) Enhanced is an indicator that visualizes the behavior of "smart money" based on intraday price movements.
📌 Based on Don Hays’ classic formula:
SMI = Yesterday’s value – Morning movement + Late-day movement
🔍 Key Features:
Highlighted buy/sell zones for accumulation and distribution;
Alerts for crossovers between SMI and its moving average;
Supports multiple timeframes (hourly, daily, weekly).
✅ Useful for identifying institutional sentiment and potential market reversal points.
ℹ️ Works with stocks, indices, and cryptocurrencies.
This script is for educational purposes only and not financial advice.
Mariam Ichimoku DashboardPurpose
The Mariam Ichimoku Dashboard is designed to simplify the Ichimoku trading system for both beginners and experienced traders. It provides a complete view of trend direction, strength, momentum, and key signals all in one compact dashboard on your chart. This tool helps traders make faster and more confident decisions without having to interpret every Ichimoku element manually.
How It Works
1. Trend Strength Score
Calculates a score from -5 to +5 based on Ichimoku components.
A high positive score means strong bullish momentum.
A low negative score shows strong bearish conditions.
A near-zero score indicates a sideways or unclear market.
2. Future Cloud Bias
Looks 26 candles ahead to determine if the future cloud is bullish or bearish.
This helps identify the longer-term directional bias of the market.
3. Flat Kijun / Flat Senkou B
Detects flat zones in the Kijun or Senkou B lines.
These flat areas act as strong support or resistance and can attract price.
4. TK Cross
Identifies Tenkan-Kijun crosses:
Bullish Cross means Tenkan crosses above Kijun
Bearish Cross means Tenkan crosses below Kijun
5. Last TK Cross Info
Shows whether the last TK cross was bullish or bearish and how many candles ago it happened.
Helps track trend development and timing.
6. Chikou Span Position
Checks if the Chikou Span is above, below, or inside past price.
Above means bullish momentum
Below means bearish momentum
Inside means mixed or indecisive
7. Near-Term Forecast (Breakout)
Warns when price is near the edge of the cloud, preparing for a potential breakout.
Useful for anticipating price moves.
8. Price Breakout
Shows if price has recently broken above or below the cloud.
This can confirm the start of a new trend.
9. Future Kumo Twist
Detects upcoming twists in the cloud, which often signal potential trend reversals.
10. Ichimoku Confluence
Measures how many key Ichimoku signals are in agreement.
The more signals align, the stronger the trend confirmation.
11. Price in or Near the Cloud
Displays if the price is inside the cloud, which often indicates low clarity or a choppy market.
12. Cloud Thickness
Shows whether the cloud is thin or thick.
Thick clouds provide stronger support or resistance.
Thin clouds may allow easier breakouts.
13. Recommendation
Gives a simple trading suggestion based on all major signals.
Strong Buy, Strong Sell, or Hold.
Helps simplify decision-making at a glance.
Features
All major Ichimoku signals summarized in one panel
Real-time trend strength scoring
Detects flat zones, crosses, cloud twists, and breakouts
Visual alerts for trend alignment and signal confluence
Compact, clean design
Built with simplicity in mind for beginner traders
Tips
Best used on 15-minute to 1-hour charts for short-term trading
Avoid entering trades when price is inside the cloud because the market is often indecisive
Wait for alignment between trend score, TK cross, cloud bias, and confluence
Use the dashboard to support your trading strategy, not replace it
Enable alerts for major confluence or upcoming Kumo twists
Ichimoku Full by MHMH Trade – Advanced Ichimoku & Dual Moving Average Tool
A powerful all-in-one indicator featuring enhanced Ichimoku lines, customizable double moving averages, and real-time crossover signals. Perfect for traders seeking clear trend insights and actionable alerts.
Be a better trader with us!
t.me
SMEMA Trend CoreSMEMA Trend Core is a multi-timeframe trend analysis tool designed to provide a clean, adaptive and structured view of the market’s directional bias. It can be used in short term, swing or long term contexts. The internal calculation adjusts automatically based on the selected trading style, while always combining data from six timeframes.
At its core, the indicator uses a SMEMA, which is a Simple Moving Average applied to an EMA. This combination improves smoothness without losing reactivity. The SMEMA is calculated separately on 1H, 4H, 1D, 3D, 1W and 1M timeframes. These six values are then combined using dynamic weights that depend on the trading mode:
Short Term mode gives more influence to 1H and 4H
Swing Trading mode gives more influence to 1D, 3D and 1W
Long Term mode gives more influence to 1W and 1M
However, all six timeframes are always included in the final result. This avoids the tunnel vision of relying on a single resolution and ensures that the indicator captures both local and structural movements.
The result is a synthetic trend line, called Global SMEMA, that adapts to market conditions and offers a realistic view of the ongoing trend. To enhance the reading, the indicator calculates a Trend Score. This score reflects the position of price relative to the Global SMEMA, scaled by a long-term ATR, and adjusted by the slope of the trend line. A hyperbolic tangent function is used to normalize values and reduce distortion from outliers.
The final score is capped between -10 and +10, and used to define the trend state:
Green when the trend is bullish (score > +1.5)
Red when the trend is bearish (score < -1.5)
Brown when the trend is neutral (score between -1.5 and +1.5)
Optional Deviation Bands can be displayed at ±1, ±2 and ±3 ATR distances around the central line. These dynamic zones help identify extended price movements or potential support and resistance areas, depending on the current trend bias.
Main features:
A single, stable trend line based on six timeframes
Automatic rebalancing depending on trading mode
Quantified score integrating distance and slope
No overreaction to short-term noise
Deviation zones for advanced market context
No repainting, no lookahead, 100% real-time
SMEMA Trend Core is not a signal tool. It is a directional framework that helps you stay aligned with the real structure of the market. Use it to confirm setups, filter trades or simply understand where the market stands in its trend cycle.
📈 Pro EMA/SMA Buy Sell (Clean & Glowing) 📈 Pro EMA/SMA Buy Sell
This indicator plots a crossover-based buy/sell signal system using:
- A fast Exponential Moving Average (EMA)
- A slower Simple Moving Average (SMA)
🔹 BUY Signal: When EMA crosses above SMA
🔹 SELL Signal: When EMA crosses below SMA
Features:
✅ Clean glowing lines for EMA and SMA
✅ Transparent glowing BUY (green) and SELL (red) labels
✅ Real-time alert conditions for automated strategy triggers
Ideal for:
- Intraday and Swing Traders
- Beginners looking for trend-based signals
- Chart setups requiring minimal noise but powerful visuals
EMA-MACD-Stoch by PashaThis indicator combines three popular technical analysis tools — EMA, MACD, and Stochastic — to generate strong and filtered buy/sell signals. It incorporates its own strategic logic and provides trade suggestions only when multiple confirmations align.
Developed by Mehmet (alias: Pasha), this indicator is designed for users seeking short-term entries in markets like BIST. It performs most effectively on the 30-minute timeframe, but can also be used across different timeframes.
