CCI with Volume Weighted EMA Here is an attempt to improve on the CCI using a volume weighted ema which is then plugged into the CCI formula.
Use:
The CCI with VW EMA is an oscillator that gives readings between -100 and +100. The usual use is to 'go long' with values over +100 and short on values less than -100.
Another use of this oscillator is a countertrend indicator where one sells at crosses under +100 and buys on crosses over -100.
Поиск скриптов по запросу "马斯克+100万"
Multi-Functional Fisher Transform MTF with MACDL TRIGGERWhat this indicator gives you is a true signal when price is exhausted and ready for a fast turnaround. Fisher Transform is set for multi-time frame and also allows the user to change the length. This way a user can compare two or more time spans and lengths to look for these MACDL divergent triggers after a Fisher exhaustion. With so many indicators, it's probably best to merge these indicators and change the Fisher and Trigger colors so you can still have a look at price action (remember to scale right after merger). I've noticed from time to time when you have Fisher 34 100 and 300 up and running on two different time frames such as 5 and 15 min charts, with MACDL triggers on the 100/300 or 34/100 you get a high probability trade trigger. However, there are rare exceptions such as when price moves in a parabolic state up or down for a long period where this indication does not work. Ideally this indicator works best in a sideways market or slow rising/descending moving market.
This indicator was worked on by Glaz, nmike and myself
LazyBear also introduced the MACDL indicator
CCI Crossover AlertThis very simple indicator will give you a blue background where the CCI crossed from below -100 to above -100, and a red background where it crossed from above 100 to below 100.
Craig Cobb Cradle Strategy//@version=5
strategy("Craig Cobb Cradle Strategy", overlay=true, margin_long=100, margin_short=100)
// ─────────────────────────────────────────────
// Inputs
// ─────────────────────────────────────────────
emaLenFast = input.int(10, "Fast EMA")
emaLenSlow = input.int(20, "Slow EMA")
smallCandlePct = input.float(0.35, "Small candle max size (% of ATR)")
// MACD Inputs
fastLength = input.int(12)
slowLength = input.int(26)
signalLength = input.int(9)
// ─────────────────────────────────────────────
// EMAs (Cradle Zone)
// ─────────────────────────────────────────────
ema10 = ta.ema(close, emaLenFast)
ema20 = ta.ema(close, emaLenSlow)
plot(ema10, color=color.yellow, title="10 EMA")
plot(ema20, color=color.orange, title="20 EMA")
inCradleZone = low <= ema10 and high >= ema20 or low <= ema20 and high >= ema10
// Trend alignment
bullTrend = ema10 > ema20
bearTrend = ema10 < ema20
// ─────────────────────────────────────────────
// MACD + signal line
// ─────────────────────────────────────────────
macd = ta.ema(close, fastLength) - ta.ema(close, slowLength)
signal = ta.ema(macd, signalLength)
hist = macd - signal
// MACD confluence: Signal line moving in trend direction
macdBull = signal > signal // signal rising
macdBear = signal < signal // signal falling
// ─────────────────────────────────────────────
// Small Cradle Candle (the trigger candle)
// Candle must be small relative to ATR
// ─────────────────────────────────────────────
atr = ta.atr(14)
candleSize = high - low
smallCandle = candleSize <= atr * smallCandlePct
// Bullish small candle
bullishCandle = close > open and smallCandle
// Bearish small candle
bearishCandle = close < open and smallCandle
// ─────────────────────────────────────────────
// Cradle Entry Conditions
// ─────────────────────────────────────────────
longEntry = bullTrend and inCradleZone and bullishCandle and macdBull
shortEntry = bearTrend and inCradleZone and bearishCandle and macdBear
// ─────────────────────────────────────────────
// Plot signals
// ─────────────────────────────────────────────
plotshape(longEntry, title="Long Cradle", style=shape.labelup, color=color.lime, text="Cradle Long")
plotshape(shortEntry, title="Short Cradle", style=shape.labeldown, color=color.red, text="Cradle Short")
// ─────────────────────────────────────────────
// Alerts
// ─────────────────────────────────────────────
alertcondition(longEntry, title="Cradle Long Alert", message="Cradle long setup detected")
alertcondition(shortEntry, title="Cradle Short Alert", message="Cradle short setup detected")
// Strategy entry (optional)
if longEntry
strategy.entry("Long", strategy.long)
if shortEntry
strategy.entry("Short", strategy.short)
Dresteghamat-Multi timeframe Regime & Exhaustion**Dresteghamat-Multi timeframe Regime & Exhaustion**
This script is a custom decision-support dashboard that aggregates volatility, momentum, and structural data across multiple timeframes to filter market noise. It addresses the problem of "Analysis Paralysis" by automating the correlation between lower timeframe momentum and higher timeframe structure using a weighted scoring algorithm.
### 🔧 Methodology & Calculation Logic
The core engine does not simply overlay indicators; it normalizes their outputs into a unified score (-100 to +100). The logic is hidden (Protected) to preserve the proprietary weighting algorithm, but the underlying concepts are as follows:
**1. Adaptive Timeframe Selection (Context Engine)**
Instead of static monitoring, the script detects the user's current chart timeframe (`timeframe.multiplier`) and dynamically assigns two relevant Higher Timeframes (HTF) as anchors.
* *Logic:* If Current TF < 5min, the script analyzes 15m and 1H data. If Current TF < 1H, it shifts to 4H and Daily data. This ensures the analysis is contextually relevant.
**2. Regime & Volatility Filter (ATR Based)**
We use the Average True Range (ATR) to determine the market regime (Trend vs. Range).
* **Calculation:** We compare the current Swing Range (High-Low lookback) against a smoothed ATR. A high Ratio (> 2.0) indicates a Trend Regime, activating Trend-Following logic. A low ratio dampens the signals.
**3. Directional Bias (Structure + Flow)**
Direction is not determined by a single crossover. It is a fusion of:
* **Swing Structure:** Using `ta.pivothigh/low` to identify Higher Highs/Lower Lows.
* **Volume Flow:** Calculating the cumulative delta of candle bodies over a lookback period.
* **Micro-Bias:** A short-term (default 5-bar) momentum filter to detect immediate order flow changes.
**4. Exhaustion Logic (Mean Reversion Warning)**
To prevent buying at tops, the script calculates an "Exhaustion Score" based on:
* **RSI Divergence:** Detecting discrepancies between price peaks and momentum.
* **Volatility Extension:** Identifying when price has deviated significantly from its volatility mean (VRSD logic).
* **Volume Anomalies:** Detecting low volume on new highs (Supply absorption).
### 📊 How to Read the Dashboard
The table displays the raw status of each timeframe. The **"MODE"** row is the output of the algorithmic decision tree:
* **BUY/SELL ONLY:** Generated when the Current TF momentum aligns with the dynamically selected HTF structure AND the Exhaustion Score is below the threshold (default 70).
* **PULLBACK:** Triggered when the HTF Structure is bullish, but Current Momentum is bearish (indicating a corrective phase).
* **HTF EXHAUST:** A safety warning triggered when the HTF Volatility or RSI metrics hit extreme levels, overriding any entry signals.
* **WAIT:** Default state when volatility is low (Range Regime) or signals conflict.
### ⚠️ Disclaimer
This tool provides algorithmic analysis based on historical price action and volatility metrics. It does not guarantee future results.
Dresteghamat-Multi timeframe Regime & Exhaustion**Dresteghamat-Multi timeframe Regime & Exhaustion**
This script is a custom decision-support dashboard that aggregates volatility, momentum, and structural data across multiple timeframes to filter market noise. It addresses the problem of "Analysis Paralysis" by automating the correlation between lower timeframe momentum and higher timeframe structure using a weighted scoring algorithm.
### 🔧 Methodology & Calculation Logic
The core engine does not simply overlay indicators; it normalizes their outputs into a unified score (-100 to +100). The logic is hidden (Protected) to preserve the proprietary weighting algorithm, but the underlying concepts are as follows:
**1. Adaptive Timeframe Selection (Context Engine)**
Instead of static monitoring, the script detects the user's current chart timeframe (`timeframe.multiplier`) and dynamically assigns two relevant Higher Timeframes (HTF) as anchors.
* *Logic:* If Current TF < 5min, the script analyzes 15m and 1H data. If Current TF < 1H, it shifts to 4H and Daily data. This ensures the analysis is contextually relevant.
**2. Regime & Volatility Filter (ATR Based)**
We use the Average True Range (ATR) to determine the market regime (Trend vs. Range).
* **Calculation:** We compare the current Swing Range (High-Low lookback) against a smoothed ATR. A high Ratio (> 2.0) indicates a Trend Regime, activating Trend-Following logic. A low ratio dampens the signals.
**3. Directional Bias (Structure + Flow)**
Direction is not determined by a single crossover. It is a fusion of:
* **Swing Structure:** Using `ta.pivothigh/low` to identify Higher Highs/Lower Lows.
* **Volume Flow:** Calculating the cumulative delta of candle bodies over a lookback period.
* **Micro-Bias:** A short-term (default 5-bar) momentum filter to detect immediate order flow changes.
**4. Exhaustion Logic (Mean Reversion Warning)**
To prevent buying at tops, the script calculates an "Exhaustion Score" based on:
* **RSI Divergence:** Detecting discrepancies between price peaks and momentum.
* **Volatility Extension:** Identifying when price has deviated significantly from its volatility mean (VRSD logic).
* **Volume Anomalies:** Detecting low volume on new highs (Supply absorption).
### 📊 How to Read the Dashboard
The table displays the raw status of each timeframe. The **"MODE"** row is the output of the algorithmic decision tree:
* **BUY/SELL ONLY:** Generated when the Current TF momentum aligns with the dynamically selected HTF structure AND the Exhaustion Score is below the threshold (default 70).
* **PULLBACK:** Triggered when the HTF Structure is bullish, but Current Momentum is bearish (indicating a corrective phase).
* **HTF EXHAUST:** A safety warning triggered when the HTF Volatility or RSI metrics hit extreme levels, overriding any entry signals.
* **WAIT:** Default state when volatility is low (Range Regime) or signals conflict.
### ⚠️ Disclaimer
This tool provides algorithmic analysis based on historical price action and volatility metrics. It does not guarantee future results.
BOSS_DELTA_XRAYBOSS DELTA XRAY is a momentum-classification system designed to quantify short-term rate-of-change (ΔROC) behavior using a structured, 7-zone intensity model. The indicator measures 5-bar ROC and maps it into clearly defined thresholds to identify acceleration, deceleration, and momentum degradation with high precision.
The goal of BOSS DELTA XRAY is to provide a continuous, color-coded representation of momentum strength to support trade management, continuation assessment, and early detection of weakening trend velocity. This makes it suitable for intraday trading, momentum confirmation, and exit-timing decisions.
Mathematical Basis
The core metric is a 5-period Rate of Change:
𝑅
𝑂
𝐶
5
=
𝐶
𝑙
𝑜
𝑠
𝑒
−
𝐶
𝑙
𝑜
𝑠
𝑒
5
𝐶
𝑙
𝑜
𝑠
𝑒
5
×
100
ROC
5
=
Close
5
Close−Close
5
×100
This 5-bar ΔROC value is compared against three threshold tiers on both positive and negative sides, creating a symmetric 7-zone classification.
Zone Definitions (Absolute ROC%)
Zone ROC Threshold Classification Color
+3 / –3 > 0.20% High-Intensity Momentum Bright Green
+2 / –2 0.10%–0.20% Moderate Momentum Light Green
+1 / –1 0.05%–0.10% Low Momentum Yellow
0 < 0.05% Neutral / No Significant Δ Tan
The system applies the same structure to positive and negative ROC, maintaining symmetry for upward and downward momentum events.
Indicator Output
A continuously-colored histogram representing real-time ΔROC magnitude.
Color transitions reflect zone boundaries, enabling rapid interpretation of momentum intensity.
A zero-line reference is included for structural orientation.
Intended Use Cases
BOSS DELTA XRAY is designed for:
Momentum verification during trend continuation setups
Exit timing, identifying when momentum begins to degrade
Filtering low-energy environments where continuation probability decreases
Monitoring momentum integrity on breakdowns, pullbacks, and retracement legs
Confirming trade validity based on sustained ΔROC structure
Key Advantages
Objective classification of short-term trend velocity
Fast identification of momentum failure or deceleration
High clarity in intraday environments where momentum shifts rapidly
Supports disciplined, systematic trade management
Minimizes discretionary interpretation by relying on defined ΔROC thresholds
Technical Summary
BOSS DELTA XRAY provides a mathematically precise and visually interpretable momentum framework. By quantifying short-term rate-of-change into discrete operational zones, the indicator enables traders to assess momentum strength, weakness, and transition states with consistency and reliability.
BOSS_DELTA_XRAYBOSS DELTA XRAY is a momentum-classification system designed to quantify short-term rate-of-change (ΔROC) behavior using a structured, 7-zone intensity model. The indicator measures 5-bar ROC and maps it into clearly defined thresholds to identify acceleration, deceleration, and momentum degradation with high precision.
The goal of BOSS DELTA XRAY is to provide a continuous, color-coded representation of momentum strength to support trade management, continuation assessment, and early detection of weakening trend velocity. This makes it suitable for intraday trading, momentum confirmation, and exit-timing decisions.
Mathematical Basis
The core metric is a 5-period Rate of Change:
𝑅
𝑂
𝐶
5
=
𝐶
𝑙
𝑜
𝑠
𝑒
−
𝐶
𝑙
𝑜
𝑠
𝑒
5
𝐶
𝑙
𝑜
𝑠
𝑒
5
×
100
ROC
5
=
Close
5
Close−Close
5
×100
This 5-bar ΔROC value is compared against three threshold tiers on both positive and negative sides, creating a symmetric 7-zone classification.
Zone Definitions (Absolute ROC%)
Zone ROC Threshold Classification Color
+3 / –3 > 0.20% High-Intensity Momentum Bright Green
+2 / –2 0.10%–0.20% Moderate Momentum Light Green
+1 / –1 0.05%–0.10% Low Momentum Yellow
0 < 0.05% Neutral / No Significant Δ Tan
The system applies the same structure to positive and negative ROC, maintaining symmetry for upward and downward momentum events.
Indicator Output
A continuously-colored histogram representing real-time ΔROC magnitude.
Color transitions reflect zone boundaries, enabling rapid interpretation of momentum intensity.
A zero-line reference is included for structural orientation.
Intended Use Cases
BOSS DELTA XRAY is designed for:
Momentum verification during trend continuation setups
Exit timing, identifying when momentum begins to degrade
Filtering low-energy environments where continuation probability decreases
Monitoring momentum integrity on breakdowns, pullbacks, and retracement legs
Confirming trade validity based on sustained ΔROC structure
Key Advantages
Objective classification of short-term trend velocity
Fast identification of momentum failure or deceleration
High clarity in intraday environments where momentum shifts rapidly
Supports disciplined, systematic trade management
Minimizes discretionary interpretation by relying on defined ΔROC thresholds
Technical Summary
BOSS DELTA XRAY provides a mathematically precise and visually interpretable momentum framework. By quantifying short-term rate-of-change into discrete operational zones, the indicator enables traders to assess momentum strength, weakness, and transition states with consistency and reliability.
NormalizedIndicatorsNormalizedIndicators - Comprehensive Trend Normalization Library
Overview
This Pine Script™ library provides an extensive collection of normalized trend-following indicators and calculation functions for technical analysis. The main advantage of this library lies in its unified signal output: All trend indicators are normalized to a standardized format where 1 represents a bullish signal, -1 represents a bearish signal, and 0 (where applicable) represents a neutral signal.
This normalization enables traders to seamlessly combine different indicators, create consensus signals, and develop complex multi-indicator strategies without worrying about different scales and interpretations.
📊 Categories
The library is divided into two main categories:
1. Trend-Following Indicators
2. Calculation Indicators
🔄 Trend-Following Indicators
Stationary Indicators
These oscillate around a fixed value and are not bound to price.
BBPct() - Bollinger Bands Percent
Source: Algoalpha X Sushiboi77
Parameters:
Length: Period for Bollinger Bands
Factor: Standard deviation multiplier
Source: Price source (typical: close)
Logic: Calculates the position of price within the Bollinger Bands as a percentage
Signal:
1 (bullish): when positionBetweenBands > 50
-1 (bearish): when positionBetweenBands ≤ 50
Special Feature: Uses an array to store historical standard deviations for additional analysis
RSI() - Relative Strength Index
Source: TradingView
Parameters:
len: RSI period
src: Price source
smaLen: Smoothing period for RSI
Logic: Classic RSI with additional SMA smoothing
Signal:
1 (bullish): RSI-SMA > 50
-1 (bearish): RSI-SMA < 50
0 (neutral): RSI-SMA = 50
Non-Stationary Indicators
These follow price movement and have no fixed boundaries.
NorosTrendRibbonSMA() & NorosTrendRibbonEMA()
Source: ROBO_Trading
Parameters:
Length: Moving average and channel period
Source: Price source
Logic: Creates a price channel based on the highest/lowest MA value over a specified period
Signal:
1 (bullish): Price breaks above upper band
-1 (bearish): Price breaks below lower band
0 (neutral): Price within channel (maintains last state)
Difference: SMA version uses simple moving averages, EMA version uses exponential
TrendBands()
Source: starlord_xrp
Parameters: src (price source)
Logic: Uses 12 EMAs (9-30 period) and checks if all are rising or falling simultaneously
Signal:
1 (bullish): All 12 EMAs are rising
-1 (bearish): All 12 EMAs are falling
0 (neutral): Mixed signals
Special Feature: Very strict conditions - extremely strong trend filter
Vidya() - Variable Index Dynamic Average
Source: loxx
Parameters:
source: Price source
length: Main period
histLength: Historical period for volatility calculation
Logic: Adaptive moving average that adjusts to volatility
Signal:
1 (bullish): VIDYA is rising
-1 (bearish): VIDYA is falling
VZO() - Volume Zone Oscillator
Parameters:
source: Price source
length: Smoothing period
volumesource: Volume data source
Logic: Combines price and volume direction, calculates the ratio of directional volume to total volume
Signal:
1 (bullish): VZO > 14.9
-1 (bearish): VZO < -14.9
0 (neutral): VZO between -14.9 and 14.9
TrendContinuation()
Source: AlgoAlpha
Parameters:
malen: First HMA period
malen1: Second HMA period
theclose: Price source
Logic: Uses two Hull Moving Averages for trend assessment with neutrality detection
Signal:
1 (bullish): Uptrend without divergence
-1 (bearish): Downtrend without divergence
0 (neutral): Trend and longer MA diverge
LeonidasTrendFollowingSystem()
Source: LeonidasCrypto
Parameters:
src: Price source
shortlen: Short EMA period
keylen: Long EMA period
Logic: Simple dual EMA crossover system
Signal:
1 (bullish): Short EMA < Key EMA
-1 (bearish): Short EMA ≥ Key EMA
ysanturtrendfollower()
Source: ysantur
Parameters:
src: Price source
depth: Depth of Fibonacci weighting
smooth: Smoothing period
bias: Percentage bias adjustment
Logic: Complex system with Fibonacci-weighted moving averages and bias bands
Signal:
1 (bullish): Weighted MA > smoothed MA (with upward bias)
-1 (bearish): Weighted MA < smoothed MA (with downward bias)
0 (neutral): Within bias zone
TRAMA() - Trend Regularity Adaptive Moving Average
Source: LuxAlgo
Parameters:
src: Price source
length: Adaptation period
Logic: Adapts to trend regularity - accelerates in stable trends, slows in consolidations
Signal:
1 (bullish): Price > TRAMA
-1 (bearish): Price < TRAMA
0 (neutral): Price = TRAMA
HullSuite()
Source: InSilico
Parameters:
_length: Base period
src: Price source
_lengthMult: Length multiplier
Logic: Uses Hull Moving Average with lagged comparisons for trend determination
Signal:
1 (bullish): Current Hull > Hull 2 bars ago
-1 (bearish): Current Hull < Hull 2 bars ago
0 (neutral): No change
STC() - Schaff Trend Cycle
Source: shayankm (described as "Better MACD")
Parameters:
length: Cycle period
fastLength: Fast MACD period
slowLength: Slow MACD period
src: Price source
Logic: Combines MACD concepts with stochastic normalization for early trend signals
Signal:
1 (bullish): STC is rising
-1 (bearish): STC is falling
🧮 Calculation Indicators
These functions provide specialized mathematical calculations for advanced analysis.
LCorrelation() - Long-term Correlation
Creator: unicorpusstocks
Parameters:
Input: First time series
Compare: Second time series
Logic: Calculates the average of correlations across 6 different periods (30, 60, 90, 120, 150, 180)
Returns: Correlation value between -1 and 1
Application: Long-term relationship analysis between assets, markets, or indicators
MCorrelation() - Medium-term Correlation
Creator: unicorpusstocks
Parameters:
Input: First time series
Compare: Second time series
Logic: Calculates the average of correlations across 6 different periods (15, 30, 45, 60, 75, 90)
Returns: Correlation value between -1 and 1
Application: Medium-term relationship analysis with higher sensitivity
assetBeta() - Beta Coefficient
Creator: unicorpusstocks
Parameters:
measuredSymbol: The asset to be measured
baseSymbol: The reference asset (e.g., market index)
Logic:
Calculates Beta across 4 different time horizons (50, 100, 150, 200 periods)
Beta = Correlation × (Asset Standard Deviation / Market Standard Deviation)
Returns the average of all 4 Beta values
Returns: Beta value (typically 0-2, can be higher/lower)
Interpretation:
Beta = 1: Asset moves in sync with the market
Beta > 1: Asset more volatile than market
Beta < 1: Asset less volatile than market
Beta < 0: Asset moves inversely to the market
💡 Usage Examples
Example 1: Multi-Indicator Consensus
pinescriptimport unicorpusstocks/MyIndicatorLibrary/1 as lib
// Combine multiple indicators
signal1 = lib.BBPct(20, 2.0, close)
signal2 = lib.RSI(14, close, 5)
signal3 = lib.TRAMA(close, 50)
// Consensus signal: At least 2 of 3 must agree
consensus = (signal1 + signal2 + signal3)
strongBuy = consensus >= 2
strongSell = consensus <= -2
Example 2: Correlation-Filtered Trading
pinescriptimport unicorpusstocks/MyIndicatorLibrary/1 as lib
// Only trade when strong correlation with market exists
spy = request.security("SPY", timeframe.period, close)
correlation = lib.MCorrelation(close, spy)
trendSignal = lib.NorosTrendRibbonEMA(50, close)
// Only bullish signals with positive correlation
tradeBuy = trendSignal == 1 and correlation > 0.5
tradeSell = trendSignal == -1 and correlation > 0.5
Example 3: Beta-Adjusted Position Sizing
pinescriptimport unicorpusstocks/MyIndicatorLibrary/1 as lib
spy = request.security("SPY", timeframe.period, close)
beta = lib.assetBeta(close, spy)
// Adjust position size based on Beta
basePositionSize = 100
adjustedSize = basePositionSize / beta // Less size with high Beta
⚙️ Technical Details
Normalization Standard
Bullish: 1
Bearish: -1
Neutral: 0 (only for selected indicators)
Advantages of Normalization
Simple Aggregation: Signals can be added/averaged
Consistent Interpretation: No confusion about different scales
Strategy Development: Simplified logic for backtesting
Combinability: Seamlessly mix different indicator types
Performance Considerations
All functions are optimized for Pine Script v5
Proper use of var for state management
Efficient array operations where needed
Minimal recursive calls
📋 License
This code is subject to the Mozilla Public License 2.0. More details at: mozilla.org
🎯 Use Cases
This library is ideal for:
Quantitative Traders: Systematic strategy development with unified signals
Multi-Timeframe Analysis: Consensus across different timeframes
Portfolio Managers: Beta and correlation analysis for diversification
Algo Traders: Machine learning with standardized features
Retail Traders: Simplified signal interpretation without deep technical knowledge
🔧 Installation
pinescriptimport unicorpusstocks/MyIndicatorLibrary/1
Then use the functions with your chosen alias:
pinescriptlib.BBPct(20, 2.0, close)
lib.RSI(14, close, 5)
// etc.
⚠️ Important Notes
All indicators are lagging, as is typical for trend-following indicators
Signals should be combined with additional analysis (volume, support/resistance, etc.)
Backtesting is recommended before starting live trading with these signals
Different assets and timeframes may require different parameter optimizations
This library provides a solid foundation for professional trading system design with the flexibility to develop your own complex strategies while abstracting away technical complexity.
Sentiment Heatmap with EMA Sentiment Heatmap with EMA Let’s build a script mini-LuxAlgo-style sentiment heatmap Enhanced Simple Sentiment Heatmap + Right-Side Legend Automatic legend on the right side
Just like professional indicators:
MAX GREED
GREED
NEUTRAL
FEAR
MAX FEAR
✔ Legend stays updated on the last bar
It moves automatically as price moves.
✔ Trend EMA included (optional) 9 EMA → White
20 EMA → Red
50 EMA → Yellow
100 EMA → Blue
200 EMA → Purple Alerts (e.g., “Max Fear – Buy Zone”)
✔ Liquidity line / support-resistance auto zones Full sentiment heatmap (Greed → Fear)
✔ Right-side legend like LuxAlgo
✔ All 5 EMAs added (my colors): EMA trend cloud (9/20, 20/50, 50/200)
Buy/Sell circles based on sentiment reversals Right-side legend: MAX GREED / GREED / NEUTRAL / FEAR / MAX FEAR
5 EMAs:
9 → White
20 → Red
50 → Yellow
100 → Blue
200 → Purple
Sav Fx Dynamic P & D°//@version=5
indicator("Sav Fx Dynamic P & D°", overlay = true, max_boxes_count = 50, max_labels_count = 2, max_lines_count = 10)
// Global Settings (visible)
customLineColor = input.color(#000000, "True Open", group = "Global Settings")
// Input for custom sessionTypeText size and width
sessionTypeTextSize = input.string("small", "Session Type Text Size", options= , group="Text Settings")
// On/Off switches for each open line
show90MinuteCycleOpen = input.bool(true, "90 Minute Cycle Open", group="Open Lines")
showTrueNewYorkOpen = input.bool(true, "True New York Open", group="Open Lines")
showTrueDayOpen = input.bool(true, "True Day Open", group="Open Lines")
showTrueWeekOpen = input.bool(true, "True Week Open", group="Open Lines")
showTrueMonthOpen = input.bool(false, "True Month Open", group="Open Lines")
IsTime(h, m, timezone) =>
not na(time) and hour(time, timezone) == h and minute(time, timezone) == m
IsSession(sess, timezone) =>
not na(time(timeframe.period, sess, timezone))
is6_00Session = IsSession("0600-0730", "America/New_York")
is7_30Session = IsSession("0730-0900", "America/New_York")
is9_00Session = IsSession("0900-1030", "America/New_York")
is10_30Session = IsSession("1030-1200", "America/New_York")
var MOPLine = line.new(na, na, na, na, color = customLineColor, width = 1, style = line.style_dashed)
var MOPLabel = label.new(na, na, text = "True Day Open", color = color.rgb(120, 123, 134, 100), textcolor = customLineColor, size = size.small, style = label.style_label_left)
var float trueDayOpen = na
if showTrueDayOpen
if IsTime(0, 0, "America/New_York")
line.set_xy1(MOPLine, bar_index, open)
line.set_xy2(MOPLine, bar_index, open)
label.set_xy(MOPLabel, bar_index, open)
trueDayOpen := open
if barstate.islast
line.set_x2(MOPLine, bar_index + 20)
label.set_x(MOPLabel, bar_index + 20)
else
line.delete(MOPLine)
label.delete(MOPLabel)
var NYTrueOpenLine = line.new(na, na, na, na, color = customLineColor, width = 1, style = line.style_dashed)
var NYTrueOpenLabel = label.new(na, na, text = "True New York Open", color = color.rgb(105, 130, 218, 100), textcolor = customLineColor, size = size.small, style = label.style_label_left)
var float NYTrueOpen = na
if showTrueNewYorkOpen
if IsTime(1, 30, "America/New_York") or IsTime(7, 30, "America/New_York") or IsTime(13, 30, "America/New_York")
line.set_xy1(NYTrueOpenLine, bar_index, open)
line.set_xy2(NYTrueOpenLine, bar_index, open)
label.set_xy(NYTrueOpenLabel, bar_index, open)
NYTrueOpen := open
if IsTime(1, 30, "America/New_York")
label.set_text(NYTrueOpenLabel, "True London Open")
if IsTime(7, 30, "America/New_York")
label.set_text(NYTrueOpenLabel, "True New York Open")
if IsTime(13, 30, "America/New_York")
label.set_text(NYTrueOpenLabel, "True PM Session Open")
if barstate.islast
line.set_x2(NYTrueOpenLine, bar_index + 20)
label.set_x(NYTrueOpenLabel, bar_index + 20)
else
line.delete(NYTrueOpenLine)
label.delete(NYTrueOpenLabel)
var lookahead_bars = 20
var MondayLine = line.new(na, na, na, na, color = customLineColor, width = 1, style = line.style_dashed)
var MondayLabel = label.new(na, na, text = timeframe.isintraday and timeframe.multiplier >= 5 ? "True week Open" : "", color = #9b27b000, textcolor = customLineColor, size = size.small, style = label.style_label_left)
if showTrueWeekOpen
if dayofweek == dayofweek.monday and IsTime(18, 0, "America/New_York")
line.set_xy1(MondayLine, bar_index, close)
line.set_xy2(MondayLine, bar_index, close)
label.set_xy(MondayLabel, bar_index, close)
if barstate.islast
line.set_x2(MondayLine, bar_index + lookahead_bars)
label.set_x(MondayLabel, bar_index + lookahead_bars)
else
line.delete(MondayLine)
label.delete(MondayLabel)
var ninetyMinuteCycleLine = line.new(na, na, na, na, color = customLineColor, width = 1, style = line.style_dashed)
var ninetyMinuteCycleLabel = label.new(na, na, text = "90 Minute Cycle True Open", color = #4caf4f00, textcolor = customLineColor, size = size.small, style = label.style_label_left)
if show90MinuteCycleOpen
if IsTime(3, 23, "America/New_York") or IsTime(9, 23, "America/New_York") or IsTime(15, 23, "America/New_York")
line.set_xy1(ninetyMinuteCycleLine, bar_index, open)
line.set_xy2(ninetyMinuteCycleLine, bar_index, open)
label.set_xy(ninetyMinuteCycleLabel, bar_index, open)
if IsTime(3, 23, "America/New_York")
label.set_text(ninetyMinuteCycleLabel, "03:23 Cycle True Open")
if IsTime(9, 23, "America/New_York")
label.set_text(ninetyMinuteCycleLabel, "09:23 Cycle True Open")
if IsTime(15, 23, "America/New_York")
label.set_text(ninetyMinuteCycleLabel, "15:23 Cycle True Open")
if barstate.islast
line.set_x2(ninetyMinuteCycleLine, bar_index + lookahead_bars)
label.set_x(ninetyMinuteCycleLabel, bar_index + lookahead_bars)
else
line.delete(ninetyMinuteCycleLine)
label.delete(ninetyMinuteCycleLabel)
var monthOpenLine = line.new(na, na, na, na, color = customLineColor, width = 1, style = line.style_dashed)
var monthOpenLabel = label.new(na, na, text = "True Month Open", color = #ff990000, textcolor = customLineColor, size = size.small, style = label.style_label_left)
isSecondWeekSunday = dayofweek == dayofweek.sunday and (dayofmonth >= 8 and dayofmonth <= 14)
if showTrueMonthOpen
if isSecondWeekSunday and IsTime(18,0, "America/New_York")
line.set_xy1(monthOpenLine, bar_index, close)
line.set_xy2(monthOpenLine, bar_index + lookahead_bars, close)
label.set_xy(monthOpenLabel, bar_index, close)
if barstate.islast
line.set_x2(monthOpenLine, bar_index + lookahead_bars)
label.set_x(monthOpenLabel, bar_index + lookahead_bars)
else
line.delete(monthOpenLine)
label.delete(monthOpenLabel)
directionalBias = "N/A"
if is6_00Session or is7_30Session or is9_00Session or is10_30Session
directionalBias := open > NYTrueOpen ? "Bullish" : "Bearish"
var directionalBiasLabel = label.new(na, na, text = "Directional Bias: " + directionalBias, color = na, textcolor = customLineColor, size = size.normal, style = label.style_label_left)
if barstate.islast
label.set_x(directionalBiasLabel, bar_index + lookahead_bars)
label.set_text(directionalBiasLabel, "Directional Bias: " + directionalBias)
var float WeekOpen = na
if dayofweek == dayofweek.monday and IsTime(18, 0, "America/New_York")
WeekOpen := close
if showTrueWeekOpen
line.set_xy1(MondayLine, bar_index, close)
line.set_xy2(MondayLine, bar_index, close)
label.set_xy(MondayLabel, bar_index, close)
// New table for static session type display
var sessionTable = table.new(position.bottom_right, 1, 1, bgcolor = #b9b9bab8)
// Update the table.cell function call
if barstate.islast and not na(trueDayOpen) and not na(NYTrueOpen) and not na(WeekOpen)
var string sessionTypeText = syminfo.ticker + " Dead Zone"
var color sessionColor = color.rgb(126, 126, 126, 65)
// Check conditions and set session type text and color accordingly
if close < trueDayOpen and close < NYTrueOpen and close < WeekOpen
sessionTypeText := syminfo.ticker + " Week Discount"
sessionColor := #ba4b4b59
else if close > trueDayOpen and close > NYTrueOpen and close > WeekOpen
sessionTypeText := syminfo.ticker + " Week Premium"
sessionColor := #4b56ba5a
else if close < trueDayOpen and close < NYTrueOpen and close > WeekOpen
sessionTypeText := syminfo.ticker + " Day Discount & Week Dead Zone"
sessionColor := #ba4b4b59
else if close > trueDayOpen and close > NYTrueOpen and close < WeekOpen
sessionTypeText := syminfo.ticker + " Day premium & Week Dead Zone"
sessionColor := #4b56ba5a
// Using only size input for session type text
table.cell(sessionTable, 0, 0, sessionTypeText, bgcolor = sessionColor, text_color = color.black, text_size = sessionTypeTextSize)
SP500 Session Gap Fade StrategySummary in one paragraph
SPX Session Gap Fade is an intraday gap fade strategy for index futures, designed around regular cash sessions on five minute charts. It helps you participate only when there is a full overnight or pre session gap and a valid intraday session window, instead of trading every open. The original part is the gap distance engine which anchors both stop and optional target to the previous session reference close at a configurable flat time, so every trade’s risk scales with the actual gap size rather than a fixed tick stop.
Scope and intent
• Markets. Primarily index futures such as ES, NQ, YM, and liquid index CFDs that exhibit overnight gaps and regular cash hours.
• Timeframes. Intraday timeframes from one minute to fifteen minutes. Default usage is five minute bars.
• Default demo used in the publication. Symbol CME:ES1! on a five minute chart.
• Purpose. Provide a simple, transparent way to trade opening gaps with a session anchored risk model and forced flat exit so you are not holding into the last part of the session.
• Limits. This is a strategy. Orders are simulated on standard candles only.
Originality and usefulness
• Unique concept or fusion. The core novelty is the combination of a strict “full gap” entry condition with a session anchored reference close and a gap distance based TP and SL engine. The stop and optional target are symmetric multiples of the actual gap distance from the previous session’s flat close, rather than fixed ticks.
• Failure mode it addresses. Fixed sized stops do not scale when gaps are unusually small or unusually large, which can either under risk or over risk the account. The session flat logic also reduces the chance of holding residual positions into late session liquidity and news.
• Testability. All key pieces are explicit in the Inputs: session window, minutes before session end, whether to use gap exits, whether TP or SL are active, and whether to allow candle based closes and forced flat. You can toggle each component and see how it changes entries and exits.
• Portable yardstick. The main unit is the absolute price gap between the entry bar open and the previous session reference close. tp_mult and sl_mult are multiples of that gap, which makes the risk model portable across contracts and volatility regimes.
Method overview in plain language
The strategy first defines a trading session using exchange time, for example 08:30 to 15:30 for ES day hours. It also defines a “flat” time a fixed number of minutes before session end. At the flat bar, any open position is closed and the bar’s close price is stored as the reference close for the next session. Inside the session, the strategy looks for a full gap bar relative to the prior bar: a gap down where today’s high is below yesterday’s low, or a gap up where today’s low is above yesterday’s high. A full gap down generates a long entry; a full gap up generates a short entry. If the gap risk engine is enabled and a valid reference close exists, the strategy measures the distance between the entry bar open and that reference close. It then sets a stop and optional target as configurable multiples of that gap distance and manages them with strategy.exit. Additional exits can be triggered by a candle color flip or by the forced flat time.
Base measures
• Range basis. The main unit is the absolute difference between the current entry bar open and the stored reference close from the previous session flat bar. That value is used as a “gap unit” and scaled by tp_mult and sl_mult to build the target and stop.
Components
• Component one: Gap Direction. Detects full gap up or full gap down by comparing the current high and low to the previous bar’s high and low. Gap down signals a long fade, gap up signals a short fade. There is no smoothing; it is a strict structural condition.
• Component two: Session Window. Only allows entries when the current time is within the configured session window. It also defines a flat time before the session end where positions are forced flat and the reference close is updated.
• Component three: Gap Distance Risk Engine. Computes the absolute distance between the entry open and the stored reference close. The stop and optional target are placed as entry ± gap_distance × multiplier so that risk scales with gap size.
• Optional component: Candle Exit. If enabled, a bullish bar closes short positions and a bearish bar closes long positions, which can shorten holding time when price reverses quickly inside the session.
• Session windows. Session logic uses the exchange time of the chart symbol. When changing symbols or venues, verify that the session time string still matches the new instrument’s cash hours.
Fusion rule
All gates are hard conditions rather than weighted scores. A trade can only open if the session window is active and the full gap condition is true. The gap distance engine only activates if a valid reference close exists and use_gap_risk is on. TP and SL are controlled by separate booleans so you can use SL only, TP only, or both. Long and short are symmetric by construction: long trades fade full gap downs, short trades fade full gap ups with mirrored TP and SL logic.
Signal rule
• Long entry. Inside the active session, when the current bar shows a full gap down relative to the previous bar (current high below prior low), the strategy opens a long position. If the gap risk engine is active, it places a gap based stop below the entry and an optional target above it.
• Short entry. Inside the active session, when the current bar shows a full gap up relative to the previous bar (current low above prior high), the strategy opens a short position. If the gap risk engine is active, it places a gap based stop above the entry and an optional target below it.
• Forced flat. At the configured flat time before session end, any open position is closed and the close price of that bar becomes the new reference close for the following session.
• Candle based exit. If enabled, a bearish bar closes longs, and a bullish bar closes shorts, regardless of where TP or SL sit, as long as a position is open.
What you will see on the chart
• Markers on entry bars. Standard strategy entry markers labeled “long” and “short” on the gap bars where trades open.
• Exit markers. Standard exit markers on bars where either the gap stop or target are hit, or where a candle exit or forced flat close occurs. Exit IDs “long_gap” and “short_gap” label gap based exits.
• Reference levels. Horizontal lines for the current long TP, long SL, short TP, and short SL while a position is open and the gap engine is enabled. They update when a new trade opens and disappear when flat.
• Session background. This version does not add background shading for the session; session logic runs internally based on time.
• No on chart table. All decisions are visible through orders and exit levels. Use the Strategy Tester for performance metrics.
Inputs with guidance
Session Settings
• Trading session (sess). Session window in exchange time. Typical value uses the regular cash session for each contract, for example “0830-1530” for ES. Adjust if your broker or symbol uses different hours.
• Minutes before session end to force exit (flat_before_min). Minutes before the session end where positions are forced flat and the reference close is stored. Typical range is 15 to 120. Raising it closes trades earlier in the day; lowering it allows trades later in the session.
Gap Risk
• Enable gap based TP/SL (use_gap_risk). Master switch for the gap distance exit engine. Turning it off keeps entries and forced flat logic but removes automatic TP and SL placement.
• Use TP limit from gap (use_gap_tp). Enables gap based profit targets. Typical values are true for structured exits or false if you want to manage exits manually and only keep a stop.
• Use SL stop from gap (use_gap_sl). Enables gap based stop losses. This should normally remain true so that each trade has a defined initial risk in ticks.
• TP multiplier of gap distance (tp_mult). Multiplier applied to the gap distance for the target. Typical range is 0.5 to 2.0. Raising it places the target further away and reduces hit frequency.
• SL multiplier of gap distance (sl_mult). Multiplier applied to the gap distance for the stop. Typical range is 0.5 to 2.0. Raising it widens the stop and increases risk per trade; lowering it tightens the stop and may increase the number of small losses.
Exit Controls
• Exit with candle logic (use_candle_exit). If true, closes shorts on bullish candles and longs on bearish candles. Useful when you want to react to intraday reversal bars even if TP or SL have not been reached.
• Force flat before session end (use_forced_flat). If true, guarantees you are flat by the configured flat time and updates the reference close. Turn this off only if you understand the impact on overnight risk.
Filters
There is no separate trend or volatility filter in this version. All trades depend on the presence of a full gap bar inside the session. If you need extra filtering such as ATR, volume, or higher timeframe bias, they should be added explicitly and documented in your own fork.
Usage recipes
Intraday conservative gap fade
• Timeframe. Five minute chart on ES regular session.
• Gap risk. use_gap_risk = true, use_gap_tp = true, use_gap_sl = true.
• Multipliers. tp_mult around 0.7 to 1.0 and sl_mult around 1.0.
• Exits. use_candle_exit = false, use_forced_flat = true. Focus on the structured TP and SL around the gap.
Intraday aggressive gap fade
• Timeframe. Five minute chart.
• Gap risk. use_gap_risk = true, use_gap_tp = false, use_gap_sl = true.
• Multipliers. sl_mult around 0.7 to 1.0.
• Exits. use_candle_exit = true, use_forced_flat = true. Entries fade full gaps, stops are tight, and candle color flips flatten trades early.
Higher timeframe gap tests
• Timeframe. Fifteen minute or sixty minute charts on instruments with regular gaps.
• Gap risk. Keep use_gap_risk = true. Consider slightly higher sl_mult if gaps are structurally wider on the higher timeframe.
• Note. Expect fewer trades and be careful with sample size; multi year data is recommended.
Properties visible in this publication
• On average our risk for each position over the last 200 trades is 0.4% with a max intraday loss of 1.5% of the total equity in this case of 100k $ with 1 contract ES. For other assets, recalculations and customizations has to be applied.
• Initial capital. 100 000.
• Base currency. USD.
• Default order size method. Fixed with size 1 contract.
• Pyramiding. 0.
• Commission. Flat 2 USD per order in the Strategy Tester Properties. (2$ buying + 2$selling)
• Slippage. One tick in the Strategy Tester Properties.
• Process orders on close. ON.
Realism and responsible publication
• No performance claims are made. Past results do not guarantee future outcomes.
• Costs use a realistic flat commission and one tick of slippage per trade for ES class futures.
• Default sizing with one contract on a 100 000 reference account targets modest per trade risk. In practice, extreme slippage or gap through events can exceed this, so treat the one and a half percent risk target as a design goal, not a guarantee.
• All orders are simulated on standard candles. Shapes can move while a bar is forming and settle on bar close.
Honest limitations and failure modes
• Economic releases, thin liquidity, and limit conditions can break the assumptions behind the simple gap model and lead to slippage or skipped fills.
• Symbols with very frequent or very large gaps may require adjusted multipliers or alternative risk handling, especially in high volatility regimes.
• Very quiet periods without clean gaps will produce few or no trades. This is expected behavior, not a bug.
• Session windows follow the exchange time of the chart. Always confirm that the configured session matches the symbol.
• When both the stop and target lie inside the same bar’s range, the TradingView engine decides which is hit first based on its internal intrabar assumptions. Without bar magnifier, tie handling is approximate.
Legal
Education and research only. This strategy is not investment advice. You remain responsible for all trading decisions. Always test on historical data and in simulation with realistic costs before considering any live use.
Multi EMA + Golden Trio Crossover (Bullish & Bearish) by SKL📌 Multi EMA + Golden Trio Crossover (Bullish & Bearish) — by SKL
This indicator plots six key Exponential Moving Averages (EMA 5, 13, 26, 50, 100, 200) and highlights powerful momentum shift signals through the Golden Trio Crossover — a unique setup where EMA 5 crosses both EMA 13 and EMA 26 in the same candle .
It works for both bullish and bearish conditions, making it suitable for intraday, swing, and positional trading.
🔍 What is the Golden Trio Crossover?
A Golden Trio Crossover occurs when:
Bullish: EMA 5 crosses ** above ** EMA 13 *and* EMA 26 in the same candle
Bearish: EMA 5 crosses ** below ** EMA 13 *and* EMA 26 in the same candle
This triple-confirmation crossover often signals:
Early trend reversals
Strong continuation breakouts
Momentum shift points
📈 What This Indicator Includes
1. Six EMA Lines
EMA 5 – Blue
EMA 13 – Green
EMA 26 – Orange
EMA 50 – Black
EMA 100 – Gray
EMA 200 – Red
These EMAs help traders track trend direction, strength, and structure.
🌟 Visual Highlights
Green background → Bullish Golden Trio
Red background → Bearish Golden Trio
Label markers on each signal
“BULL GCO”
“BEAR GCO”
🔔 Alerts Included
You can enable alerts for:
Bullish Golden Trio Crossover
Bearish Golden Trio Crossover
Useful for breakout traders, scalpers, and swing traders.
🎯 How Traders Use This Indicator
Identify early trend shifts
Spot high-probability breakout candles
Confirm entries with multi-EMA confluence
Combine with volume, price action, or RSI for even stronger setups
📌 Notes
Works on all timeframes
Works on all asset classes (Stocks, Indices, Crypto, Forex, Commodities)
Fully automatic signal detection
Frequency Momentum Oscillator [QuantAlgo]🟢 Overview
The Frequency Momentum Oscillator applies Fourier-based spectral analysis principles to price action to identify regime shifts and directional momentum. It calculates Fourier coefficients for selected harmonic frequencies on detrended price data, then measures the distribution of power across low, mid, and high frequency bands to distinguish between persistent directional trends and transient market noise. This approach provides traders with a quantitative framework for assessing whether current price action represents meaningful momentum or merely random fluctuations, enabling more informed entry and exit decisions across various asset classes and timeframes.
🟢 How It Works
The calculation process removes the dominant trend from price data by subtracting a simple moving average, isolating cyclical components for frequency analysis:
detrendedPrice = close - ta.sma(close , frequencyPeriod)
The detrended price series undergoes frequency decomposition through Fourier coefficient calculation across the first 8 harmonics. For each harmonic frequency, the algorithm computes sine and cosine components across the lookback window, then derives power as the sum of squared coefficients:
for k = 1 to 8
cosSum = 0.0
sinSum = 0.0
for n = 0 to frequencyPeriod - 1
angle = 2 * math.pi * k * n / frequencyPeriod
cosSum := cosSum + detrendedPrice * math.cos(angle)
sinSum := sinSum + detrendedPrice * math.sin(angle)
power = (cosSum * cosSum + sinSum * sinSum) / frequencyPeriod
Power measurements are aggregated into three frequency bands: low frequencies (harmonics 1-2) capturing persistent cycles, mid frequencies (harmonics 3-4), and high frequencies (harmonics 5-8) representing noise. Each band's power normalizes against total spectral power to create percentage distributions:
lowFreqNorm = totalPower > 0 ? (lowFreqPower / totalPower) * 100 : 33.33
highFreqNorm = totalPower > 0 ? (highFreqPower / totalPower) * 100 : 33.33
The normalized frequency components undergo exponential smoothing before calculating spectral balance as the difference between low and high frequency power:
smoothLow = ta.ema(lowFreqNorm, smoothingPeriod)
smoothHigh = ta.ema(highFreqNorm, smoothingPeriod)
spectralBalance = smoothLow - smoothHigh
Spectral balance combines with price momentum through directional multiplication, producing a composite signal that integrates frequency characteristics with price direction:
momentum = ta.change(close , frequencyPeriod/2)
compositeSignal = spectralBalance * math.sign(momentum)
finalSignal = ta.ema(compositeSignal, smoothingPeriod)
The final signal oscillates around zero, with positive values indicating low-frequency dominance coupled with upward momentum (trending up), and negative values indicating either high-frequency dominance (choppy market) or downward momentum (trending down).
🟢 How to Use This Indicator
→ Long/Short Signals: the indicator generates long signals when the smoothed composite signal crosses above zero (indicating low-frequency directional strength dominates) and short signals when it crosses below zero (indicating bearish momentum persistence).
→ Upper and Lower Reference Lines: the +25 and -25 reference lines serve as threshold markers for momentum strength. Readings beyond these levels indicate strong directional conviction, while oscillations between them suggest consolidation or weakening momentum. These references help traders distinguish between strong trending regimes and choppy transitional periods.
→ Preconfigured Presets: three optimized configurations are available with Default (32, 3) offering balanced responsiveness, Fast Response (24, 2) designed for scalping and intraday trading, and Smooth Trend (40, 5) calibrated for swing trading and position trading with enhanced noise filtration.
→ Built-in Alerts: the indicator includes three alert conditions for automated monitoring - Long Signal (momentum shifts bullish), Short Signal (momentum shifts bearish), and Signal Change (any directional transition). These alerts enable traders to receive real-time notifications without continuous chart monitoring.
→ Color Customization: four visual themes (Classic green/red, Aqua blue/orange, Cosmic aqua/purple, Custom) allow chart customization for different display environments and personal preferences.
ECG PRICE - mauricioofsousa📉 ECG PRICE – The Price Electrocardiogram
(explained for traders, scientists, and complete beginners)
🔍 1. WHAT IS THE ECG PRICE?
The ECG PRICE protocol is a market-reading system based on the RSI, but with a surgical twist:
👉 You don’t just calculate RSI from price.
👉 You adjust the price using the RSI, and then calculate RSI over this adjusted price.
This creates a filtered, amplified signal that behaves like a heart monitor for price, detecting micro-impulses and subtle market movements long before they show up in the standard RSI.
🧬 2. CORE IDEA
Just like a real ECG amplifies and reveals electrical rhythms hidden inside the heartbeat,
the ECG PRICE amplifies micro-deformations hidden inside the price’s momentum.
It works in three stages:
Compute the regular RSI
Use the RSI to adjust the price (creating an electrocardiographic price)
Compute a second RSI over this modified price
The result is a meta-derived oscillator—more sensitive, more precise, and better at detecting structural changes.
🧩 3. TECHNICAL BREAKDOWN
3.1. First RSI (classic)
The script calculates:
average gains
average losses
relative strength (RS)
and then the standard 0–100 RSI
This is the “normal heart rate monitor” everyone uses.
3.2. Creating the “Adjusted Price”
adjustedPrice = close * (rsi / 100)
This means:
➡️ When RSI is high (strong buying momentum), price is amplified.
➡️ When RSI is low (strong selling momentum), price is compressed.
This converts raw price into a bio-electrical signal, where the price itself is modulated by its own internal momentum.
It’s the financial equivalent of ECG gain adjustment.
3.3. RSI of the Adjusted Price
Now the script calculates a new RSI from this modified price.
That is the actual ECG PRICE.
This second-order oscillator becomes extremely sensitive to:
micro-momentum shifts
early trend fading
volatility shocks
micro-divergences
institutional pressure waves
It reads the electrical pattern behind the price rather than the superficial movement.
🟩🟥 4. Diagnostic Lines of the Protocol
35 (green dotted)
Pre-oversold fatigue zone.
65 (red dotted)
Pre-overbought exhaustion zone.
30 (white solid)
Classic oversold.
70 (white solid)
Classic overbought.
Together they create two diagnostic corridors:
1. Medical corridor (30–70):
Standard RSI clinical range.
2. Electrical corridor (35–65):
The ECG-sensitive zone where micro-shifts appear first.
🧠 5. In Engineering Language (MGO style)
The ECG PRICE is essentially:
A nonlinear second-order oscillator where the RSI feeds back into price, creating a recursive momentum-modulated signal.
It functions like a:
bioinformational modulator
feedback-driven wave processor
impulse amplifier
micro-PID sensitivity enhancer
Very similar to the informational-wave transformations inside the MGO pipeline.
👨⚕️📉 6. Explained for a Total Beginner
Imagine the price is a heart.
The normal RSI shows if the heart is beating fast or slow.
But the ECG PRICE takes that heartbeat…
feeds it back into the heart…
and then measures the new heartbeat.
This creates a much more sensitive exam that detects problems before the normal test would.
💡 7. What It Gives You in Practice
earlier reversal signals
better trend-fatigue detection
clearer micro-divergences
a clean RSI with reduced noise
a smoother momentum curve
advanced behavioral readings before breakouts
It’s an upgrade.
A second-layer RSI that “hears” the inner electrical impulses of price.
Volatility-Targeted Momentum Portfolio [BackQuant]Volatility-Targeted Momentum Portfolio
A complete momentum portfolio engine that ranks assets, targets a user-defined volatility, builds long, short, or delta-neutral books, and reports performance with metrics, attribution, Monte Carlo scenarios, allocation pie, and efficiency scatter plots. This description explains the theory and the mechanics so you can configure, validate, and deploy it with intent.
Table of contents
What the script does at a glance
Momentum, what it is, how to know if it is present
Volatility targeting, why and how it is done here
Portfolio construction modes: Long Only, Short Only, Delta Neutral
Regime filter and when the strategy goes to cash
Transaction cost modelling in this script
Backtest metrics and definitions
Performance attribution chart
Monte Carlo simulation
Scatter plot analysis modes
Asset allocation pie chart
Inputs, presets, and deployment checklist
Suggested workflow
1) What the script does at a glance
Pulls a list of up to 15 tickers, computes a simple momentum score on each over a configurable lookback, then volatility-scales their bar-to-bar return stream to a target annualized volatility.
Ranks assets by raw momentum, selects the top 3 and bottom 3, builds positions according to the chosen mode, and gates exposure with a fast regime filter.
Accumulates a portfolio equity curve with risk and performance metrics, optional benchmark buy-and-hold for comparison, and a full alert suite.
Adds visual diagnostics: performance attribution bars, Monte Carlo forward paths, an allocation pie, and scatter plots for risk-return and factor views.
2) Momentum: definition, detection, and validation
Momentum is the tendency of assets that have performed well to continue to perform well, and of underperformers to continue underperforming, over a specific horizon. You operationalize it by selecting a horizon, defining a signal, ranking assets, and trading the leaders versus laggards subject to risk constraints.
Signal choices . Common signals include cumulative return over a lookback window, regression slope on log-price, or normalized rate-of-change. This script uses cumulative return over lookback bars for ranking (variable cr = price/price - 1). It keeps the ranking simple and lets volatility targeting handle risk normalization.
How to know momentum is present .
Leaders and laggards persist across adjacent windows rather than flipping every bar.
Spread between average momentum of leaders and laggards is materially positive in sample.
Cross-sectional dispersion is non-trivial. If everything is flat or highly correlated with no separation, momentum selection will be weak.
Your validation should include a diagnostic that measures whether returns are explained by a momentum regression on the timeseries.
Recommended diagnostic tool . Before running any momentum portfolio, verify that a timeseries exhibits stable directional drift. Use this indicator as a pre-check: It fits a regression to price, exposes slope and goodness-of-fit style context, and helps confirm if there is usable momentum before you force a ranking into a flat regime.
3) Volatility targeting: purpose and implementation here
Purpose . Volatility targeting seeks a more stable risk footprint. High-vol assets get sized down, low-vol assets get sized up, so each contributes more evenly to total risk.
Computation in this script (per asset, rolling):
Return series ret = log(price/price ).
Annualized volatility estimate vol = stdev(ret, lookback) * sqrt(tradingdays).
Leverage multiplier volMult = clamp(targetVol / vol, 0.1, 5.0).
This caps sizing so extremely low-vol assets don’t explode weight and extremely high-vol assets don’t go to zero.
Scaled return stream sr = ret * volMult. This is the per-bar, risk-adjusted building block used in the portfolio combinations.
Interpretation . You are not levering your account on the exchange, you are rescaling the contribution each asset’s daily move has on the modeled equity. In live trading you would reflect this with position sizing or notional exposure.
4) Portfolio construction modes
Cross-sectional ranking . Assets are sorted by cr over the chosen lookback. Top and bottom indices are extracted without ties.
Long Only . Averages the volatility-scaled returns of the top 3 assets: avgRet = mean(sr_top1, sr_top2, sr_top3). Position table shows per-asset leverages and weights proportional to their current volMult.
Short Only . Averages the negative of the volatility-scaled returns of the bottom 3: avgRet = mean(-sr_bot1, -sr_bot2, -sr_bot3). Position table shows short legs.
Delta Neutral . Long the top 3 and short the bottom 3 in equal book sizes. Each side is sized to 50 percent notional internally, with weights within each side proportional to volMult. The return stream mixes the two sides: avgRet = mean(sr_top1,sr_top2,sr_top3, -sr_bot1,-sr_bot2,-sr_bot3).
Notes .
The selection metric is raw momentum, the execution stream is volatility-scaled returns. This separation is deliberate. It avoids letting volatility dominate ranking while still enforcing risk parity at the return contribution stage.
If everything rallies together and dispersion collapses, Long Only may behave like a single beta. Delta Neutral is designed to extract cross-sectional momentum with low net beta.
5) Regime filter
A fast EMA(12) vs EMA(21) filter gates exposure.
Long Only active when EMA12 > EMA21. Otherwise the book is set to cash.
Short Only active when EMA12 < EMA21. Otherwise cash.
Delta Neutral is always active.
This prevents taking long momentum entries during obvious local downtrends and vice versa for shorts. When the filter is false, equity is held flat for that bar.
6) Transaction cost modelling
There are two cost touchpoints in the script.
Per-bar drag . When the regime filter is active, the per-bar return is reduced by fee_rate * avgRet inside netRet = avgRet - (fee_rate * avgRet). This models proportional friction relative to traded impact on that bar.
Turnover-linked fee . The script tracks changes in membership of the top and bottom baskets (top1..top3, bot1..bot3). The intent is to charge fees when composition changes. The template counts changes and scales a fee by change count divided by 6 for the six slots.
Use case: increase fee_rate to reflect taker fees and slippage if you rebalance every bar or trade illiquid assets. Reduce it if you rebalance less often or use maker orders.
Practical advice .
If you rebalance daily, start with 5–20 bps round-trip per switch on liquid futures and adjust per venue.
For crypto perp microcaps, stress higher cost assumptions and add slippage buffers.
If you only rotate on lookback boundaries or at signals, use alert-driven rebalances and lower per-bar drag.
7) Backtest metrics and definitions
The script computes a standard set of portfolio statistics once the start date is reached.
Net Profit percent over the full test.
Max Drawdown percent, tracked from running peaks.
Annualized Mean and Stdev using the chosen trading day count.
Variance is the square of annualized stdev.
Sharpe uses daily mean adjusted by risk-free rate and annualized.
Sortino uses downside stdev only.
Omega ratio of sum of gains to sum of losses.
Gain-to-Pain total gains divided by total losses absolute.
CAGR compounded annual growth from start date to now.
Alpha, Beta versus a user-selected benchmark. Beta from covariance of daily returns, Alpha from CAPM.
Skewness of daily returns.
VaR 95 linear-interpolated 5th percentile of daily returns.
CVaR average of the worst 5 percent of daily returns.
Benchmark Buy-and-Hold equity path for comparison.
8) Performance attribution
Cumulative contribution per asset, adjusted for whether it was held long or short and for its volatility multiplier, aggregated across the backtest. You can filter to winners only or show both sides. The panel is sorted by contribution and includes percent labels.
9) Monte Carlo simulation
The panel draws forward equity paths from either a Normal model parameterized by recent mean and stdev, or non-parametric bootstrap of recent daily returns. You control the sample length, number of simulations, forecast horizon, visibility of individual paths, confidence bands, and a reproducible seed.
Normal uses Box-Muller with your seed. Good for quick, smooth envelopes.
Bootstrap resamples realized returns, preserving fat tails and volatility clustering better than a Gaussian assumption.
Bands show 10th, 25th, 75th, 90th percentiles and the path mean.
10) Scatter plot analysis
Four point-cloud modes, each plotting all assets and a star for the current portfolio position, with quadrant guides and labels.
Risk-Return Efficiency . X is risk proxy from leverage, Y is expected return from annualized momentum. The star shows the current book’s composite.
Momentum vs Volatility . Visualizes whether leaders are also high vol, a cue for turnover and cost expectations.
Beta vs Alpha . X is a beta proxy, Y is risk-adjusted excess return proxy. Useful to see if leaders are just beta.
Leverage vs Momentum . X is volMult, Y is momentum. Shows how volatility targeting is redistributing risk.
11) Asset allocation pie chart
Builds a wheel of current allocations.
Long Only, weights are proportional to each long asset’s current volMult and sum to 100 percent.
Short Only, weights show the short book as positive slices that sum to 100 percent.
Delta Neutral, 50 percent long and 50 percent short books, each side leverage-proportional.
Labels can show asset, percent, and current leverage.
12) Inputs and quick presets
Core
Portfolio Strategy . Long Only, Short Only, Delta Neutral.
Initial Capital . For equity scaling in the panel.
Trading Days/Year . 252 for stocks, 365 for crypto.
Target Volatility . Annualized, drives volMult.
Transaction Fees . Per-bar drag and composition change penalty, see the modelling notes above.
Momentum Lookback . Ranking horizon. Shorter is more reactive, longer is steadier.
Start Date . Ensure every symbol has data back to this date to avoid bias.
Benchmark . Used for alpha, beta, and B&H line.
Diagnostics
Metrics, Equity, B&H, Curve labels, Daily return line, Rolling drawdown fill.
Attribution panel. Toggle winners only to focus on what matters.
Monte Carlo mode with Normal or Bootstrap and confidence bands.
Scatter plot type and styling, labels, and portfolio star.
Pie chart and labels for current allocation.
Presets
Crypto Daily, Long Only . Lookback 25, Target Vol 50 percent, Fees 10 bps, Regime filter on, Metrics and Drawdown on. Monte Carlo Bootstrap with Recent 200 bars for bands.
Crypto Daily, Delta Neutral . Lookback 25, Target Vol 50 percent, Fees 15–25 bps, Regime filter always active for this mode. Use Scatter Risk-Return to monitor efficiency and keep the star near upper left quadrants without drifting rightward.
Equities Daily, Long Only . Lookback 60–120, Target Vol 15–20 percent, Fees 5–10 bps, Regime filter on. Use Benchmark SPX and watch Alpha and Beta to keep the book from becoming index beta.
13) Suggested workflow
Universe sanity check . Pick liquid tickers with stable data. Thin assets distort vol estimates and fees.
Check momentum existence . Run on your timeframe. If slope and fit are weak, widen lookback or avoid that asset or timeframe.
Set risk budget . Choose a target volatility that matches your drawdown tolerance. Higher target increases turnover and cost sensitivity.
Pick mode . Long Only for bull regimes, Short Only for sustained downtrends, Delta Neutral for cross-sectional harvesting when index direction is unclear.
Tune lookback . If leaders rotate too often, lengthen it. If entries lag, shorten it.
Validate cost assumptions . Increase fee_rate and stress Monte Carlo. If the edge vanishes with modest friction, refine selection or lengthen rebalance cadence.
Run attribution . Confirm the strategy’s winners align with intuition and not one unstable outlier.
Use alerts . Enable position change, drawdown, volatility breach, regime, momentum shift, and crash alerts to supervise live runs.
Important implementation details mapped to code
Momentum measure . cr = price / price - 1 per symbol for ranking. Simplicity helps avoid overfitting.
Volatility targeting . vol = stdev(log returns, lookback) * sqrt(tradingdays), volMult = clamp(targetVol / vol, 0.1, 5), sr = ret * volMult.
Selection . Extract indices for top1..top3 and bot1..bot3. The arrays rets, scRets, lev_vals, and ticks_arr track momentum, scaled returns, leverage multipliers, and display tickers respectively.
Regime filter . EMA12 vs EMA21 switch determines if the strategy takes risk for Long or Short modes. Delta Neutral ignores the gate.
Equity update . Equity multiplies by 1 + netRet only when the regime was active in the prior bar. Buy-and-hold benchmark is computed separately for comparison.
Tables . Position tables show current top or bottom assets with leverage and weights. Metric table prints all risk and performance figures.
Visualization panels . Attribution, Monte Carlo, scatter, and pie use the last bars to draw overlays that update as the backtest proceeds.
Final notes
Momentum is a portfolio effect. The edge comes from cross-sectional dispersion, adequate risk normalization, and disciplined turnover control, not from a single best asset call.
Volatility targeting stabilizes path but does not fix selection. Use the momentum regression link above to confirm structure exists before you size into it.
Always test higher lag costs and slippage, then recheck metrics, attribution, and Monte Carlo envelopes. If the edge persists under stress, you have something robust.
Market Profile Dominance Analyzer# Market Profile Dominance Analyzer
## 📊 OVERVIEW
**Market Profile Dominance Analyzer** is an advanced multi-factor indicator that combines Market Profile methodology with composite dominance scoring to identify buyer and seller strength across higher timeframes. Unlike traditional volume profile indicators that only show volume distribution, or simple buyer/seller indicators that only compare candle colors, this script integrates six distinct analytical components into a unified dominance measurement system.
This indicator helps traders understand **WHO controls the market** by analyzing price position relative to Market Profile key levels (POC, Value Area) combined with volume distribution, momentum, and trend characteristics.
## 🎯 WHAT MAKES THIS ORIGINAL
### **Hybrid Analytical Approach**
This indicator uniquely combines two separate methodologies that are typically analyzed independently:
1. **Market Profile Analysis** - Calculates Point of Control (POC) and Value Area (VA) using volume distribution across price channels on higher timeframes
2. **Multi-Factor Dominance Scoring** - Weights six independent factors to produce a composite dominance index
### **Six-Factor Composite Analysis**
The dominance score integrates:
- Price position relative to POC (equilibrium assessment)
- Price position relative to Value Area boundaries (acceptance/rejection zones)
- Volume imbalance within Value Area (institutional bias detection)
- Price momentum (directional strength)
- Volume trend comparison (participation analysis)
- Normalized Value Area position (precise location within fair value zone)
### **Adaptive Higher Timeframe Integration**
The script features an intelligent auto-selection system that automatically chooses appropriate higher timeframes based on the current chart period, ensuring optimal Market Profile structure regardless of the trading timeframe being analyzed.
## 💡 HOW IT WORKS
### **Market Profile Construction**
The indicator builds a Market Profile structure on a higher timeframe by:
1. **Session Identification** - Detects new higher timeframe sessions using `request.security()` to ensure accurate period boundaries
2. **Data Accumulation** - Stores high, low, and volume data for all bars within the current higher timeframe session
3. **Channel Distribution** - Divides the session's price range into configurable channels (default: 20 rows)
4. **Volume Mapping** - Distributes each bar's volume proportionally across all price channels it touched
### **Key Level Calculation**
**Point of Control (POC)**
- Identifies the price channel with the highest accumulated volume
- Represents the price level where the most trading activity occurred
- Serves as a magnetic level where price often returns
**Value Area (VA)**
- Starts at POC and expands both upward and downward
- Includes channels until reaching the specified percentage of total volume (default: 70%)
- Expansion algorithm compares adjacent volumes and prioritizes the direction with higher activity
- Defines the "fair value" zone where most market participants agreed to trade
### **Dominance Score Formula**
```
Dominance Score = (price_vs_poc × 10) +
(price_vs_va × 5) +
(volume_imbalance × 0.5) +
(price_momentum × 100) +
(volume_trend × 5) +
(va_position × 15)
```
**Component Breakdown:**
- **price_vs_poc**: +1 if above POC, -1 if below (shows which side of equilibrium)
- **price_vs_va**: +2 if above VAH, -2 if below VAL, 0 if inside VA
- **volume_imbalance**: Percentage difference between upper and lower VA volumes
- **price_momentum**: 5-period SMA of price change (directional acceleration)
- **volume_trend**: Compares 5-period vs 20-period volume averages
- **va_position**: Normalized position within Value Area (-1 to +1)
The composite score is then smoothed using EMA with configurable sensitivity to reduce noise while maintaining responsiveness.
### **Market State Determination**
- **BUYERS Dominant**: Smooth dominance > +10 (bullish control)
- **SELLERS Dominant**: Smooth dominance < -10 (bearish control)
- **NEUTRAL**: Between -10 and +10 (balanced market)
## 📈 HOW TO USE THIS INDICATOR
### **Trend Identification**
- **Green background** indicates buyers are in control - look for long opportunities
- **Red background** indicates sellers are in control - look for short opportunities
- **Gray background** indicates neutral market - consider range-bound strategies
### **Signal Interpretation**
**Buy Signals** (green triangle) appear when:
- Dominance crosses above -10 from oversold conditions
- Previous state was not already bullish
- Suggests shift from seller to buyer control
**Sell Signals** (red triangle) appear when:
- Dominance crosses below +10 from overbought conditions
- Previous state was not already bearish
- Suggests shift from buyer to seller control
### **Value Area Context**
Monitor the information table (top-right) to understand market structure:
- **Price vs POC**: Shows if trading above/below equilibrium
- **Volume Imbalance**: Positive values favor buyers, negative favors sellers
- **Market State**: Current dominant force (BUYERS/SELLERS/NEUTRAL)
### **Multi-Timeframe Strategy**
The auto-timeframe feature analyzes higher timeframe structure:
- On 1-minute charts → analyzes 2-hour structure
- On 5-minute charts → analyzes Daily structure
- On 15-minute charts → analyzes Weekly structure
- On Daily charts → analyzes Yearly structure
This higher timeframe context helps avoid counter-trend trades against the dominant force.
### **Confluence Trading**
Strongest signals occur when multiple factors align:
1. Price above VAH + positive volume imbalance + buyers dominant = Strong bullish setup
2. Price below VAL + negative volume imbalance + sellers dominant = Strong bearish setup
3. Price at POC + neutral state = Potential breakout/breakdown pivot
## ⚙️ INPUT PARAMETERS
- **Higher Time Frame**: Select specific HTF or use 'Auto' for intelligent selection
- **Value Area %**: Percentage of volume contained in VA (default: 70%)
- **Show Buy/Sell Signals**: Toggle signal triangles visibility
- **Show Dominance Histogram**: Toggle histogram display
- **Signal Sensitivity**: EMA period for dominance smoothing (1-20, default: 5)
- **Number of Channels**: Market Profile resolution (10-50, default: 20)
- **Color Settings**: Customize buyer, seller, and neutral colors
## 🎨 VISUAL ELEMENTS
- **Histogram**: Shows smoothed dominance score (green = buyers, red = sellers)
- **Zero Line**: Neutral equilibrium reference
- **Overbought/Oversold Lines**: ±50 levels marking extreme dominance
- **Background Color**: Highlights current market state
- **Information Table**: Displays key metrics (state, dominance, POC relationship, volume imbalance, timeframe, bars in session, total volume)
- **Signal Shapes**: Triangle markers for buy/sell signals
## 🔔 ALERTS
The indicator includes three alert conditions:
1. **Buyers Dominate** - Fires on buy signal crossovers
2. **Sellers Dominate** - Fires on sell signal crossovers
3. **Dominance Shift** - Fires when dominance crosses zero line
## 📊 BEST PRACTICES
### **Timeframe Selection**
- **Scalping (1-5min)**: Focus on 2H-4H dominance shifts
- **Day Trading (15-60min)**: Monitor Daily and Weekly structure
- **Swing Trading (4H-Daily)**: Track Weekly and Monthly dominance
### **Confirmation Strategies**
1. **Trend Following**: Enter in direction of dominance above/below ±20
2. **Reversal Trading**: Fade extreme readings beyond ±50 when diverging with price
3. **Breakout Trading**: Look for dominance expansion beyond ±30 with increasing volume
### **Risk Management**
- Avoid trading during NEUTRAL states (dominance between -10 and +10)
- Use POC levels as logical stop-loss placement
- Consider VAH/VAL as profit targets for mean reversion
## ⚠️ LIMITATIONS & WARNINGS
**Data Requirements**
- Requires sufficient historical data on current chart (minimum 100 bars recommended)
- Lower timeframes may show fewer bars per HTF session initially
- More accurate results after several complete HTF sessions have formed
**Not a Standalone System**
- This indicator analyzes market structure and participant control
- Should be combined with price action, support/resistance, and risk management
- Does not guarantee profitable trades - past dominance does not predict future results
**Repainting Characteristics**
- Higher timeframe levels (POC, VAH, VAL) update as new bars form within the session
- Dominance score recalculates with each new bar
- Historical signals remain fixed, but current session data is developing
**Volume Limitations**
- Uses exchange-provided volume data which varies by instrument type
- Forex and some CFDs use tick volume (not actual transaction volume)
- Most accurate on instruments with reliable volume data (stocks, futures, crypto)
## 🔍 TECHNICAL NOTES
**Performance Optimization**
- Uses `max_bars_back=5000` for extended historical analysis
- Efficient array management prevents memory issues
- Automatic cleanup of session data on new period
**Calculation Method**
- Market Profile uses actual volume distribution, not TPO (Time Price Opportunity)
- Value Area expansion follows traditional Market Profile auction theory
- All calculations occur on the chart's current symbol and timeframe
## 📚 EDUCATIONAL VALUE
This indicator helps traders understand:
- How institutional traders use Market Profile to identify fair value
- The relationship between price, volume, and market acceptance
- Multi-factor analysis techniques for assessing market conditions
- The importance of higher timeframe structure in trade planning
## 🎓 RECOMMENDED READING
To better understand the concepts behind this indicator:
- "Mind Over Markets" by James Dalton (Market Profile foundations)
- "Markets in Profile" by James Dalton (Value Area analysis)
- Volume Profile analysis in institutional trading
## 💬 USAGE TERMS
This indicator is provided as an educational and analytical tool. It does not constitute financial advice, investment recommendations, or trading signals. Users are responsible for their own trading decisions and should conduct their own research and due diligence.
Trading involves substantial risk of loss. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.
US Leverage Overlay — Margin Debt & Total Credit (YoY / Z-score)What this does
An overlay indicator that brings U.S. leverage proxies from FRED onto your main price chart (left axis). Choose between a proxy for investor margin debt or total credit market debt and view them as YoY %, Z-score of YoY, or an Indexed Level so they’re comparable with price without wrecking the scale.
Data sources (FRED symbols)
--- Margin (investor leverage proxy): FRED:BOGZ1FL663067003Q
Brokers & Dealers; Receivables Due from Customers ≈ margin loans (quarterly).
--- TotalCredit (economy-wide leverage): FRED:TCMDO
All sectors; Debt Securities & Loans; Liability (quarterly).
Note: These are quarterly series. The indicator samples monthly and holds values between official prints, so you’ll see step-like updates when new data drops.
Views (pick one in settings)
--- YoY % — 12-month rate of change. Above 0% = leverage expanding; below 0% = contracting.
--- Z-score (YoY) — Standardizes YoY vs. its recent history to flag unusual moves (regime shifts).
--- Indexed Level — 100 × (level / moving average), a compact “above/below trend” view.
How to read quickly
--- Rising YoY % > 0 → leverage expansion (often supportive for risk).
--- Falling YoY % < 0 → deleveraging headwind.
--- Z-score spikes (±2) → unusually fast changes; watch for volatility or policy inflections.
--- Indexed Level crossing down through 100 → slipping below trend.
Inputs
--- Data source: Margin or TotalCredit
--- YoY/Z-score lookbacks and Index baseline length
--- Overlay: overlay=true, scale=scale.left (uses its own left axis by default)
Tips
--- If it spawns in a sub-pane, right-click the label → Move to → Main chart.
--- For context, consider adding related series on separate panes:
FRED:TOTALSL (Consumer Credit), FRED:REVOLSL (Credit Cards),
FRED:BUSLOANS (C&I Loans), FRED:TDSP (Debt Service Ratio).
--- Occasionally FRED returns “Failed to fetch”; re-add or reload fixes it.
Why it’s useful
Equity drawdowns often line up with turns in leverage (households, corporates, or brokers). This overlay gives you a clean, normalized read so you can spot expansion vs. contraction alongside price action.
Compatibility
--- Pine Script® v6
--- Works on any chart timeframe (data internally sampled monthly)
Educational use only — not financial advice.
Multi EMA + Indicators + Mini-Dashboard + Reversals v6📘 Multi EMA + Indicators + Mini-Dashboard + Reversals v6
🧩 Overview
This indicator is a multi-EMA setup that combines trend, momentum, and reversal analysis in a single visual framework.
It integrates four exponential moving averages (EMAs), key oscillators (RSI, MACD, Stochastic, CCI), volatility filtering (ATR), and a dynamic mini-dashboard that summarizes all signals in real time.
Its purpose is to help traders visually confirm trend alignment, filter valid entries, and identify possible trend continuation or reversal points.
It can display buy/sell arrows, detect reversal candles, and issue alerts when trading conditions are met.
⚙️ Core Components
1. Moving Averages (EMA Setup)
EMA1 (fast) and EMA2 (medium) define the short-term trend and trigger bias.
When the price is above both EMAs → bullish bias.
When below → bearish bias.
EMA3 and EMA4 act as trend filters. Their slopes (up or down) confirm overall momentum and help validate signals.
Each EMA has customizable lengths, sources, and colors for up/down trends.
This “EMA stack” is the foundation of the setup — a structured trend-following framework that adapts to market speed and volatility.
2. Momentum and Confirmation Filters
Each indicator can be individually enabled or disabled for flexibility.
RSI: confirms direction (above/below 50).
MACD: detects momentum crossover (MACD > Signal for bullish confirmation).
Stochastic: identifies trend continuation (K > D for longs, K < D for shorts).
CCI: adds trend bias above/below a threshold.
ATR Filter: filters out small, low-volatility candles to reduce noise.
You can activate only the filters that fit your trading plan — for instance, trend traders often use RSI and MACD, while scalpers may rely on Stochastic and ATR.
3. Reversal Detection
The indicator includes an optional Reversal Section that independently detects potential turning points.
It combines multiple configurable criteria:
Candlestick patterns (Bullish Hammer, Shooting Star).
Large Candle filter — detects unusually large bars (relative to close).
Price-to-EMA distance — identifies overextended moves that might revert.
RSI Divergence — detects potential momentum shifts.
RSI Overbought/Oversold zones (70/30 by default).
Doji Candles — sign of indecision.
A bullish or bearish reversal signal appears when enough selected criteria are met.
All sub-modules can be toggled on/off individually, giving you full control over sensitivity.
4. Signal Logic
Buy and sell signals are triggered when EMA alignment and the chosen confirmations agree:
Buy Signal
→ Price above EMA1 & EMA2
→ Confirmations (RSI/MACD/Stoch/CCI/ATR) pass
→ Trend filters (EMA3/EMA4) point upward
Sell Signal
→ Price below EMA1 & EMA2
→ Confirmations align bearishly
→ Trend filters (EMA3/EMA4) slope downward
Reversal signals can appear independently, even against the current EMA trend, depending on your settings.
5. Visual Dashboard
A mini-dashboard appears near the chart showing:
Current trade bias (LONG / SHORT / NEUTRAL)
EMA3 and EMA4 trend directions (↑ / ↓)
Quick visual bars (🟩 / 🟥) for each filter: RSI, MACD, Stoch, ATR, CCI, EMA filters
Reversal criteria status (Doji, RSI divergence, candle size, etc.)
This panel gives you a compact overview of all indicator states at a glance.
The color of the panel changes dynamically — green for bullish, red for bearish, gray for neutral.
6. Alerts
Built-in alerts allow automation or notifications:
Buy Alert
Sell Alert
Reversal Buy
Reversal Sell
You can connect these alerts to TradingView notifications or external bots for semi-automated execution.
💡 How to Use
✅ Trend-Following Setup
Focus on trades in the direction of EMA1 & EMA2.
Confirm with EMA3 & EMA4 trending in the same direction.
Use RSI/MACD/Stoch filters to ensure momentum supports the trade.
Avoid entries when ATR filter indicates low volatility.
🔄 Reversal Setup
Enable the Reversal section for potential tops/bottoms.
Look for reversal buy signals near support zones or after strong downtrends.
Use RSI divergence or Doji + Hammer signals as confirmation.
Combine with key chart areas (supply/demand or previous swing levels).
⚖️ Combination Approach
Trade continuation signals when all EMAs are aligned and filters are green.
Trade reversals only when at a key area (support/resistance) and confirmed by reversal conditions.
Always check higher-timeframe bias before entering a trade.
🧭 Practical Tips
Use different EMA sets for different timeframes:
9/21/50/100 for swing or trend trades.
5/13/34/89 for intraday scalping.
Turn off filters you don’t use to reduce lag.
Always validate signals with price structure, not just indicator alignment.
Practice in replay mode before live trading.
🗺️ Key Chart Confluence (Highly Recommended)
Although the indicator provides structured signals, its best use is in confluence with:
Support and resistance levels
Supply/demand zones
Trendlines and channels
Liquidity pools
Volume clusters
Signals aligned with strong key areas on the chart tend to have greater reliability than isolated indicator triggers.
I use EMA 1 - 20 Open ; EMA 2 - 20 Close ; EMA 3 - 50 ; EMA 4 - 200 or 100 , but that's me...
⚠️ Important Disclaimer
This indicator is a technical tool, not a guarantee of results.
Trading involves risk, and no signal is ever 100% accurate.
Every trader should develop a personal strategy, use proper risk management, and adapt settings to their instrument and timeframe.
Always combine indicator signals with key chart areas, higher-timeframe context, and your own analysis before taking a trade.
Volume HeatMap Divergence [BigBeluga]🔵 OVERVIEW
The Volume HeatMap Divergence is a smart volume visualization tool that overlays normalized volume data directly on the chart. Using a color heatmap from aqua to red, it transforms raw volume into an intuitive scale — highlighting areas of weak to intense market participation. Additionally, it detects volume-based divergences from price to signal potential reversals or exhaustion zones. Combined with clear visual labeling, this tool empowers traders with actionable volume insights.
🔵 CONCEPTS
Normalized Volume Heatmap : Volume is normalized to a 0–100% scale and visually represented as candles below the chart.
float vol = volume / ta.percentile_nearest_rank(volume, 1000, 100) * 100
Bar Coloring : Price candles are dynamically colored based on volume intensity.
Volume Divergence Logic :
Bullish Divergence : Price forms a lower low, but volume forms a higher low.
Bearish Divergence : Price forms a higher high, but volume forms a lower high.
Dynamic Detection Range : Customizable range ensures divergence signals are meaningful and not random.
Volume Labels : Additional info on divergence bars shows both the actual volume and its normalized % score.
🔵 FEATURES
Volume Heatmap Plot : Normalized volume values colored using a smooth gradient from aqua (low) to red (high).
Price Bar Coloring : Candlesticks on the main chart adopt the same heatmap color based on volume.
Divergence Detection :
Bullish divergence with label and low marker
Bearish divergence with label and high marker
Dual Divergence Labels :
On the volume plot : Direction (Bull/Bear), raw volume, and normalized %
On the price chart : Shape labels showing "Bull" or "Bear" at local highs/lows
Custom Inputs :
Divergence range (min & max), pivot detection distance (left/right)
Toggle to show/hide divergence labels, volume, and % text
Clear Bull/Bear Coloring : Fully customizable label and line colors for both bullish and bearish signals.
🔵 HOW TO USE
Use the indicator as an overlay to monitor real-time volume strength using the heatmap color.
Watch for divergence markers:
Bullish divergence: Candle shows higher volume while price makes a new low
Bearish divergence: Candle shows lower volume while price makes a new high
Use the volume info labels to verify the context of divergence:
Actual volume at divergence candle
Normalized % of that volume compared to past 1000 bars
Adjust pivot sensitivity using "Pivot Left" and "Pivot Right" to tune signal frequency and lag with a right pivot length.
Use divergence zones as early warnings for potential reversals or trend shifts.
Disable or customize labels in settings depending on your charting preferences.
🔵 CONCLUSION
Volume HeatMap Divergence merges heatmap-style volume visualization with intelligent divergence detection — giving traders a clean yet powerful edge. By revealing hidden disconnections between price and participation, it helps users spot exhaustion moves or hidden accumulation zones before the market reacts. Whether you’re a scalper, swing trader, or intraday strategist, this tool offers real-time clarity on who’s in control behind the candles.
Daily MA — Higher-Timeframe Daily Moving Average OverlayThis indicator plots a clean, higher-timeframe daily moving average directly on any chart, so you can always see where price sits relative to the daily trend — even while trading on lower timeframes (1m, 5m, etc.).
It’s designed to be:
Simple – a single, configurable daily MA line
Consistent – always anchored to the 1D timeframe
Flexible – choose EMA or SMA and customize line width/color
⸻
What This Indicator Does
Pulls the 1-Day (1D) moving average of the current symbol, regardless of your chart timeframe.
Lets you choose between EMA (Exponential Moving Average) or SMA (Simple Moving Average).
Plots that daily MA as a smooth overlay on your current chart.
Keeps the line visually clean and continuous, making it easy to see daily trend and dynamic support/resistance.
This is not a signals/strategy script. It doesn’t generate buy/sell arrows or backtest logic. It’s a context tool for visualizing the daily trend while you execute your own strategy.
⸻
Why a Daily MA Overlay Is Useful
Traders commonly use a daily moving average to:
Anchor intraday trades to the higher-timeframe trend
Longs when price is holding above the Daily MA
Shorts or caution when price is rejecting from the Daily MA
Identify dynamic support/resistance
Price often reacts around well-watched daily MAs (e.g., 50, 100, 200)
Filter setups
Only take long setups when price is above the daily trend line
Avoid counter-trend trades when price is extended far from the Daily MA
Because this script forces the MA to always be computed on 1D, you don’t have to switch back and forth between intraday and daily charts to keep track of the bigger picture.
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Inputs & Settings
MA Length
Default: 200
Any positive integer (min 1)
Common examples: 50, 100, 200 for trend structure
MA Type
EMA – reacts faster to recent price (default)
SMA – smoother, slower, more “classic” feel
Line Width
Default: 2
Range: 1 to 10
Increase if you want the Daily MA to stand out clearly against other indicators
Color
Default: Purple tone
Fully customizable – pick any color that works with your chart theme
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How to Use It in Your Workflow
Intraday traders (scalpers/day-traders):
Apply the indicator to your 1m/5m/15m charts.
Use the Daily MA as a trend filter :
Only look for long scalps when price is above the Daily MA.
Be more cautious with longs or consider shorts when price is below it.
Swing traders :
Use it on 1H/4H charts to see where price sits relative to a longer-term daily trend.
Watch for:
Pullbacks to the Daily MA in an uptrend as potential demand zones.
Rejections at the Daily MA in a downtrend as potential supply zones.
Risk management & context :
Avoid chasing extended moves far from the Daily MA.
Mark confluence with other tools (support/resistance, volume profile, etc.) around the Daily MA.
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Notes & Limitations
The moving average itself is calculated from daily candles , then displayed on your current timeframe.
This is a visual aid only . It does not guarantee future performance or provide financial advice.
Always combine this indicator with your own analysis, risk management, and trading plan.
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Disclaimer :
This script is provided for educational and informational purposes only. It is not financial advice and does not constitute a recommendation to buy or sell any financial instrument. Always do your own research and trade at your own risk.
Top Finder & Dip Hunter [BackQuant]Top Finder & Dip Hunter
A practical tool to map where price is statistically most likely to exhaust or mean-revert. It builds objective support for dips and resistance for tops from multiple methodologies, then filters raw touches with volume, momentum, trend, and price-action context to surface higher-quality reversal opportunities.
What this does
Draws a Dip Support line and a Top Resistance line using the method you select, or a blended hybrid.
Evaluates each touch/penetration against Quality Filters and assigns a 0–100 composite score.
Prints clean DIP and TOP signals only when depth/extension and quality pass your thresholds.
Optionally annotates the chart with the computed quality score at signal time.
Why it’s useful
Objectivity: Converts vague “looks extended” into rules, reduces discretion creep.
Signal hygiene: Filters raw touches using trend, volume, momentum, and candle structure to avoid obvious traps.
Adaptable regimes: Switch methods, sensitivity, and lookbacks to match choppy vs trending conditions.
How support and resistance are built
Pick one per side, or use “Hybrid.”
Dynamic: Anchors to the extreme of a lookback window, padded by recent ATR, so buffers expand in volatile periods and contract when calm.
Fibonacci: Uses the 0.618/0.786 retracement pair inside the current swing window to target common reaction zones.
Volatility: Uses a moving-average basis with standard-deviation bands to capture statistically stretched moves.
Volume-Weighted: Centers off VWAP and penalizes deviations using dispersion of price around VWAP, helpful on intraday instruments.
Hybrid: A weighted average of the above to smooth out single-method biases.
When a touch becomes a signal
Depth/extension test:
Dips must penetrate their support by at least Min Dip Depth % .
Tops must extend above resistance by at least Min Top Rise % .
Quality Score gate: The composite must clear Min Quality Score . Components:
Trend alignment: Favor dips in bullish regimes and tops in bearish regimes using EMAs and RSI.
Volume confirmation: Reward expansion or spikes versus a 20-period baseline.
RSI context: Prefer oversold for dips, overbought for tops.
Momentum shift: Look for short-term momentum turning in the expected direction.
Candle structure: Reward hammer/shooting-star style responses at the level.
How to use it
Pick your regime:
Range/chop, small caps, mean-revert intraday → Volatility or Volume Weighted .
Cleaner swings/trends → Dynamic or Fibonacci .
Unsure or mixed conditions → Hybrid .
Set windows: Start with Lookback = 50 for both sides. Increase in higher timeframes or slow assets, decrease for fast scalps.
Tune sensitivity: Raise Dip/Top Sensitivity to widen buffers and reduce noise. Lower to be more aggressive.
Gate with quality: Begin with Min Quality Score = 60 . Push to 70–80 for cleaner swing entries, relax to 50–60 for scalps.
Act on first prints: The script only fires on new qualified events. Use the score label to prioritize A-setups.
Typical workflows
Intraday futures/crypto: Volume-Weighted or Volatility methods for both sides, higher Sensitivity , require Volume Filter and Momentum Filter on. Look for DIP during opening drive exhaustion and TOP near late-session fatigue.
Swing equities/FX: Dynamic or Fibonacci with moderate sensitivity. Keep Trend Filter on to only take dips above the 200-EMA and tops below it.
Countertrend scouts: Lower Min Dip Depth % / Min Top Rise % slightly, but raise Min Quality Score to compensate.
Reading the chart
Lines: “Dip Support” and “Top Resistance” are the current actionable rails, lightly smoothed to reduce flicker.
Signals: “DIP” prints below bars when a qualified dip appears, “TOP” prints above for qualified tops.
Scores: Optional labels show the composite at signal time. Favor higher numbers, especially when aligned with higher-timeframe trend.
Background hints: Light highlights mark raw touches meeting depth/extension, even if they fail quality. Treat these as early warnings.
Tuning tips
If you get too many false DIP signals in downtrends, raise Min Dip Depth % and keep Trend Filter on.
If tops appear late in squeezes, lower Top Sensitivity slightly or switch top side to Fibonacci .
On assets with erratic volume, prefer Volatility or Dynamic methods and down-weight the Volume Filter .
For strict systems, increase Min Quality Score and require both Volume and Momentum filters.
What this is not
It is not a blind reversal signal. It’s a structured context tool. Combine with your risk plan and higher-timeframe map.
It is not a guarantee of mean reversion. In strong trends, expect fewer, higher-score opportunities and respect invalidation quickly.
Suggested presets
Scalp preset: Lookback 30–40, Sensitivity 1.2–1.5, Quality ≥ 55, Volume & Momentum filters ON.
Swing preset: Lookback 75–100, Sensitivity 1.0–1.2, Quality ≥ 70, Trend & Volume filters ON.
Chop preset: Volatility/Volume-Weighted methods, Quality ≥ 60, Momentum filter ON, RSI emphasis.
Input quick reference
Dip/Top Method: Choose the model for each side or “Hybrid” to blend.
Lookback: Swing window the levels are built from.
Sensitivity: Scales volatility padding around levels.
Min Dip Depth % / Min Top Rise %: Minimum breach/extension to qualify.
Quality Filters: Trend, Volume, Momentum toggles, plus Min Quality Score gate.
Visuals: Colors and whether to print score labels.
Best practices
Map higher-timeframe trend first, then act on lower-timeframe DIP/TOP in the trend’s favor.
Use the score as triage. Skip mediocre prints into news or at session open unless score is exceptional.
Pre-define stop placement relative to the level you used. If a DIP fails, exit on loss of structure rather than waiting for the next print.
Bottom line: Top Finder & Dip Hunter codifies where reversals are most defensible and only flags the ones with supportive context. Tune the method and filters to your market, then let the score keep your playbook disciplined.
Crypto Correlation Oscillator# Crypto Correlation Oscillator
**Companion indicator for Tri-Align Crypto Trend**
## Overview
The Crypto Correlation Oscillator helps you identify **alpha opportunities** and **market regime changes** by showing how closely your coin follows Bitcoin and other assets over time. It displays rolling correlations as an oscillator in a separate pane below your price chart.
## What It Does
This indicator calculates **Pearson correlations** between different trading pairs on a rolling window (default: 100 bars). Correlations range from **-1.0** (perfect inverse relationship) to **+1.0** (perfect positive relationship), with **0** meaning no correlation.
### The 5 Correlation Lines
1. **Blue (thick line) - Coin vs BTC**: The most important metric
- **High correlation (>0.7)**: Your coin is just following BTC - no independent movement
- **Low correlation (<0.3)**: Your coin has **alpha** - it's moving independently from BTC
- **Negative correlation**: Your coin moves opposite to BTC (rare but powerful)
2. **Purple - Coin/BTC vs BTC**: Inverse relationship check
- **Negative values**: When BTC rises, your coin weakens relative to BTC
- **Positive values**: When BTC rises, your coin strengthens against BTC
3. **Orange - Coin vs Coin/BTC**: Structural consistency check
- Shows how well the Coin/USDT and Coin/BTC pairs maintain their mathematical relationship
- Unusual values can indicate liquidity issues or market inefficiencies
4. **Light Red - Coin vs USDT.D** (optional): Stablecoin dominance correlation
- Shows how your coin correlates with USDT dominance
- Useful for understanding flight-to-safety dynamics
5. **Light Green - Coin vs BTC.D** (optional): Bitcoin dominance correlation
- Shows how your coin correlates with BTC dominance
- Helps identify altcoin season vs BTC dominance cycles
## How to Read It
### Finding Alpha Opportunities
- **Low blue line (<0.3)**: Your coin is decoupled from BTC → potential alpha
- **Blue line dropping**: Coin is gaining independence from BTC
- **Blue line spiking to >0.9**: Coin is a "BTC clone" with no independent movement
### Regime Change Detection
- **Blue line crossing 0.5**: Major shift in correlation behavior
- **Purple line turning negative**: Coin starting to weaken when BTC rises (warning sign)
- **Sharp correlation changes**: Market structure is shifting - adjust strategy
### Visual Zones
- **Blue background**: High correlation zone (>0.7) - coin just following BTC
- **Red background**: Inverse correlation zone (<-0.5) - coin moving opposite to BTC
### Reference Lines
- **+1.0 / -1.0**: Perfect correlation boundaries (dotted gray)
- **+0.5 / -0.5**: Moderate correlation thresholds (dotted gray)
- **0.0**: Zero correlation line (solid gray)
## Dynamic Legend
The legend table (top-right) automatically shows the actual symbol names based on your chart:
- **Example on SOLUSDT**: Shows "SOL vs BTC", "SOL/BTC vs BTC", "SOL vs SOL/BTC", etc.
- **Color boxes**: Match the plot colors for easy identification
- **Live values**: Current correlation numbers update in real-time
- **Tooltips**: Hover over labels for interpretation guidance
## Configuration
### Key Inputs
- **Correlation Lookback** (default: 100): Number of bars for rolling correlation window
- Shorter = more reactive, noisier
- Longer = smoother, slower to detect changes
- **Correlation Smoothing** (default: 5): EMA smoothing period for raw correlations
- Reduces noise while preserving trends
- **Symbol Detection**: Auto-detects symbols from your chart, or use manual overrides
- **Dominance Pairs**: Toggle USDT.D and BTC.D correlations on/off
## Usage Tips
1. **Combine with main Tri-Align indicator**: Use correlation for context, Tri-Align for entry/exit signals
2. **Watch for divergences**: Correlation changing while price moves in sync can signal upcoming shift
3. **Adjust lookback period**: Use shorter (50-70) for day trading, longer (150-200) for position trading
4. **Focus on the blue line**: It's your primary alpha indicator
## Technical Details
- **Calculation**: Pearson correlation coefficient with EMA smoothing
- **Data source**: Close prices from `request.security()` (multi-timeframe capable)
- **Update frequency**: Every bar on your selected timeframe
- **Overlay**: False (displays in separate pane)
## Quick Interpretation Guide
| Blue Line Value | Interpretation | Action |
|----------------|----------------|--------|
| > 0.9 | Coin is a BTC clone | Avoid - no alpha opportunity |
| 0.7 - 0.9 | High correlation | Standard altcoin behavior |
| 0.3 - 0.7 | Moderate correlation | Some independence emerging |
| < 0.3 | Low correlation | **Strong alpha opportunity** |
| < 0 | Inverse correlation | Rare - potential hedge asset |
| Purple Line | Interpretation |
|-------------|----------------|
| Strongly negative | Coin weakens when BTC rises - risky |
| Near zero | Coin/BTC pair moves independently of BTC |
| Positive | Coin strengthens with BTC - ideal |
## Version History
### v1.0 (Initial Release)
- Pearson correlation calculation with configurable lookback
- 5 correlation pairs: Coin vs BTC, Coin/BTC vs BTC, Coin vs Coin/BTC, USDT.D, BTC.D
- EMA smoothing to reduce noise
- Visual zones for high/inverse correlation
- Dynamic legend with symbol name extraction
- Auto-symbol detection matching main Tri-Align indicator
