Thirdeyechart Volume Gold//@version=6
indicator("MT5 Style Quotes – Custom Pair Table", overlay=true, max_labels_count=500)
// ==== USER INPUTS ====
// Masukkan pair tambahan kat sini (pisahkan dengan koma)
extraPairsInput = input.string("XAUUSD,XAUJPY,USDJPY,EURJPY", "Custom Pairs (comma separated)")
// Convert input → array
string extraPairs = str.split(extraPairsInput, ",")
// Function kira % change
f_change(sym, tf) =>
o = request.security(sym, tf, open)
c = request.security(sym, tf, close)
pct = ((c - o) / o) * 100
pct
// Table setup
rowCount = array.size(extraPairs) + 1
var tbl = table.new(position.top_right, 4, rowCount, border_width=1)
// Header row
table.cell(tbl, 0, 0, "Symbol", bgcolor=color.new(color.white, 90))
table.cell(tbl, 1, 0, "Day %", bgcolor=color.new(color.white, 90))
table.cell(tbl, 2, 0, "H1 %", bgcolor=color.new(color.white, 90))
table.cell(tbl, 3, 0, "H4 %", bgcolor=color.new(color.white, 90))
// Loop setiap pair
for i = 0 to array.size(extraPairs)-1
sym = str.trim(array.get(extraPairs, i))
day = f_change(sym, "D")
h1 = f_change(sym, "60")
h4 = f_change(sym, "240")
col_day = day >= 0 ? color.blue : color.red
col_h1 = h1 >= 0 ? color.blue : color.red
col_h4 = h4 >= 0 ? color.blue : color.red
table.cell(tbl, 0, i+1, sym)
table.cell(tbl, 1, i+1, str.tostring(day, format.percent), text_color=col_day)
table.cell(tbl, 2, i+1, str.tostring(h1, format.percent), text_color=col_h1)
table.cell(tbl, 3, i+1, str.tostring(h4, format.percent), text_color=col_h4)
Indicateurs et stratégies
MoneyLine FusionMoneyLine Fusion is an advanced multi-indicator system combining linear regression trend analysis with dynamic volatility bands, Fisher Transform oscillator signals, and Aroon momentum filters. The core "Money Line" uses linear regression to identify trend direction, surrounded by ATR-based bands that dynamically expand during strong trends (powered by ADX). Buy and sell signals trigger when the Fisher Transform crosses extreme levels (-2.0/+2.0) while Aroon conditions confirm momentum exhaustion or reversal. The indicator features an enhanced scoring system integrating MACD, DMI, MFI, and RSI to provide a comprehensive market sentiment gauge. Visual feedback includes color-coded trend bands, scoring bars at chart bottom, and an info panel displaying Money Line value, volatility percentile, RSI levels, and composite TA score. Signal filtering prevents duplicate entries within 5 bars, and all parameters (Fisher/Aroon periods, signal thresholds, band multipliers) are fully customizable. Ideal for swing traders seeking high-probability entries with multi-timeframe confluence and clear visual confirmation.
Position Sizer (FinPip)Position Sizer (FinPip)
The Position Sizer (FinPip) indicator is a crucial, all-in-one risk management tool designed to calculate the precise trade size required to limit your risk to a predetermined percentage of your total account capital.
This indicator helps you consistently execute sound risk management, regardless of the instrument's volatility or the trade's price levels.
Key Features:
Calculates Position Size: Based on your configurable Account Capital, desired Risk Percentage (default 2.5%), and the price distance between your Entry and Stop-Loss levels.
Visual Trade Planning: Plots three clear levels directly on the chart for easy visualization:
Entry Price (Blue)
Stop-Loss Price (SL) (Red)
Profit Target (Lime Green, calculated using the Reward:Risk Ratio).
Custom Risk Management: Easily adjust the Risk Percentage and the Reward:Risk Ratio (default 4.0) in the indicator's settings.
Heads-Up Display (HUD): A clean, fixed table in the bottom-left corner of the chart clearly displays all calculated metrics, including your Required Position Size (in units/shares/contracts), Risk Amount, and Potential Profit.
How to Use:
Enter your Account Capital and desired Risk % in the settings panel.
Set your desired Entry Price and Stop-Loss Price.
The indicator immediately calculates and displays the exact number of units you need to trade to maintain your risk limit.
ICT FVG & Swing Detector Basic by Trader Riaz//@version=6
indicator("ICT FVG & Swing Detector Basic by Trader Riaz", overlay=true)
// Display toggles for Bullish FVGs
show_bull_fvg = input.bool(true, "Show Bullish FVGs?")
// Input settings for Bullish FVGs
fvg_bull_count = input.int(1, "Number of Bullish FVGs to show", minval=1)
// Color settings for Bullish FVGs
bullish_fvg_color = input.color(color.green, "Bullish FVG Color")
// Bullish FVG Extend Options
bull_fvg_extendGroup = "Bullish FVG Extend Options"
bull_fvg_extendOption = input.string("Default", "Bullish FVG Extend Option", options= , group=bull_fvg_extendGroup)
bull_fvg_extendCandles = input.int(8, "Bullish FVG Extend Candles (Limited Only)", minval=1, maxval=100, step=1, group=bull_fvg_extendGroup)
// Display toggles for Bearish FVGs
show_bear_fvg = input.bool(true, "Show Bearish FVGs?")
// Input settings for Bearish FVGs
fvg_bear_count = input.int(1, "Number of Bearish FVGs to show", minval=1)
// Color settings for Bearish FVGs
bearish_fvg_color = input.color(color.red, "Bearish FVG Color")
// Bearish FVG Extend Options
bear_fvg_extendGroup = "Bearish FVG Extend Options"
bear_fvg_extendOption = input.string("Default", "Bearish FVG Extend Option", options= , group=bear_fvg_extendGroup)
bear_fvg_extendCandles = input.int(8, "Bearish FVG Extend Candles (Limited Only)", minval=1, maxval=100, step=1, group=bear_fvg_extendGroup)
// Display toggles for Swing Highs
show_swing_high = input.bool(true, "Show Swing Highs?")
// Input settings for Swing Highs
swing_high_count = input.int(2, "Number of Swing Highs to show", minval=1)
// Color settings for Swing Highs
swing_high_color = input.color(color.green, "Swing High Line & Label Color")
// Swing High Extend Options
swing_high_extendGroup = "Swing High Extend Options"
swing_high_extendOption = input.string("Default", "Swing High Extend Option", options= , group=swing_high_extendGroup)
swing_high_extendCandles = input.int(8, "Swing High Extend Candles (Limited Only)", minval=1, maxval=100, step=1, group=swing_high_extendGroup)
// Display toggles for Swing Lows
show_swing_low = input.bool(true, "Show Swing Lows?")
// Input settings for Swing Lows
swing_low_count = input.int(2, "Number of Swing Lows to show", minval=1)
// Color settings for Swing Lows
swing_low_color = input.color(color.red, "Swing Low Line & Label Color")
// Swing Low Extend Options
swing_low_extendGroup = "Swing Low Extend Options"
swing_low_extendOption = input.string("Default", "Swing Low Extend Option", options= , group=swing_low_extendGroup)
swing_low_extendCandles = input.int(8, "Swing Low Extend Candles (Limited Only)", minval=1, maxval=100, step=1, group=swing_low_extendGroup)
// Target Settings
showNextTarget = input.bool(true, "Show Next Target")
nextTargetHighColor = input.color(color.red, "Next Target High Color")
nextTargetLowColor = input.color(color.red, "Next Target Low Color")
// === Time Calculation ===
// Calculate one bar duration in milliseconds
barDuration = time - time
// Define reasonable extension period (4 bars into future)
extensionPeriod = barDuration * 4
// Arrays to store values with timestamps
var bull_fvg_data = array.new(0)
var bear_fvg_data = array.new(0)
var swing_high_data = array.new(0)
var swing_low_data = array.new(0)
var bull_fvg_labels = array.new(0)
var bear_fvg_labels = array.new(0)
var swing_high_labels = array.new(0)
var swing_low_labels = array.new(0)
var bull_fvg_midlines = array.new(0)
var bear_fvg_midlines = array.new(0)
var bull_fvg_tops = array.new(0)
var bull_fvg_bottoms = array.new(0)
var bear_fvg_tops = array.new(0)
var bear_fvg_bottoms = array.new(0)
// Get the last bar index
last_bar = last_bar_index + 3
// Function to determine right boundary based on extend option
get_right_boundary(option, extend_candles, default_right) =>
if option == "None"
bar_index - 2
else if option == "Limited"
bar_index - 2 + extend_candles
else
default_right
// Bullish FVG Detection
if high < low and show_bull_fvg
right_bar = get_right_boundary(bull_fvg_extendOption, bull_fvg_extendCandles, last_bar)
new_box = box.new(left=bar_index-2,
top=low,
right=right_bar,
bottom=high ,
bgcolor=color.new(bullish_fvg_color, 90),
border_color=bullish_fvg_color)
bull_mid = (low + high ) / 2
new_midline = line.new(bar_index-2, bull_mid, right_bar, bull_mid,
color=color.new(bullish_fvg_color, 50),
style=line.style_dashed)
new_label = label.new(right_bar-1, bull_mid, "Bullish FVG",
color=color.new(bullish_fvg_color, 100),
textcolor=bullish_fvg_color,
style=label.style_none,
textalign=text.align_right,
size=size.small)
array.unshift(bull_fvg_data, new_box)
array.unshift(bull_fvg_midlines, new_midline)
array.unshift(bull_fvg_labels, new_label)
array.unshift(bull_fvg_tops, low)
array.unshift(bull_fvg_bottoms, high )
if array.size(bull_fvg_data) > fvg_bull_count
box.delete(array.pop(bull_fvg_data))
line.delete(array.pop(bull_fvg_midlines))
label.delete(array.pop(bull_fvg_labels))
array.pop(bull_fvg_tops)
array.pop(bull_fvg_bottoms)
// Bearish FVG Detection
if low > high and show_bear_fvg
right_bar = get_right_boundary(bear_fvg_extendOption, bear_fvg_extendCandles, last_bar)
new_box = box.new(left=bar_index-2,
top=low ,
right=right_bar,
bottom=high,
bgcolor=color.new(bearish_fvg_color, 90),
border_color=bearish_fvg_color)
bear_mid = (low + high) / 2
new_midline = line.new(bar_index-2, bear_mid, right_bar, bear_mid,
color=color.new(bearish_fvg_color, 50),
style=line.style_dashed)
new_label = label.new(right_bar-1, bear_mid, "Bearish FVG",
color=color.new(bearish_fvg_color, 100),
textcolor=bearish_fvg_color,
style=label.style_none,
textalign=text.align_right,
size=size.small)
array.unshift(bear_fvg_data, new_box)
array.unshift(bear_fvg_midlines, new_midline)
array.unshift(bear_fvg_labels, new_label)
array.unshift(bear_fvg_tops, low )
array.unshift(bear_fvg_bottoms, high)
if array.size(bear_fvg_data) > fvg_bear_count
box.delete(array.pop(bear_fvg_data))
line.delete(array.pop(bear_fvg_midlines))
label.delete(array.pop(bear_fvg_labels))
array.pop(bear_fvg_tops)
array.pop(bear_fvg_bottoms)
// Swing High Detection
is_swing_high = high < high and high > high
if is_swing_high and show_swing_high
right_bar = get_right_boundary(swing_high_extendOption, swing_high_extendCandles, last_bar + 3)
new_line = line.new(bar_index - 1, high , right_bar, high ,
color=swing_high_color)
new_label = label.new(right_bar, high , "Swing High",
color=color.new(color.white, 30),
style=label.style_label_left,
textcolor=swing_high_color,
size=size.tiny)
array.unshift(swing_high_data, new_line)
array.unshift(swing_high_labels, new_label)
if array.size(swing_high_data) > swing_high_count
line.delete(array.pop(swing_high_data))
label.delete(array.pop(swing_high_labels))
// Swing Low Detection
is_swing_low = low > low and low < low
if is_swing_low and show_swing_low
right_bar = get_right_boundary(swing_low_extendOption, swing_low_extendCandles, last_bar + 3)
new_line = line.new(bar_index -1, low , right_bar, low ,
color=swing_low_color)
new_label = label.new(right_bar, low , "Swing Low",
color=color.new(color.white, 30),
style=label.style_label_left,
textcolor=swing_low_color,
size=size.tiny)
array.unshift(swing_low_data, new_line)
array.unshift(swing_low_labels, new_label)
if array.size(swing_low_data) > swing_low_count
line.delete(array.pop(swing_low_data))
label.delete(array.pop(swing_low_labels))
// Clean up if toggles are turned off
if not show_bull_fvg and array.size(bull_fvg_data) > 0
for i = 0 to array.size(bull_fvg_data) - 1
box.delete(array.get(bull_fvg_data, i))
line.delete(array.get(bull_fvg_midlines, i))
label.delete(array.get(bull_fvg_labels, i))
array.clear(bull_fvg_data)
array.clear(bull_fvg_midlines)
array.clear(bull_fvg_labels)
array.clear(bull_fvg_tops)
array.clear(bull_fvg_bottoms)
if not show_bear_fvg and array.size(bear_fvg_data) > 0
for i = 0 to array.size(bear_fvg_data) - 1
box.delete(array.get(bear_fvg_data, i))
line.delete(array.get(bear_fvg_midlines, i))
label.delete(array.get(bear_fvg_labels, i))
array.clear(bear_fvg_data)
array.clear(bear_fvg_midlines)
array.clear(bear_fvg_labels)
array.clear(bear_fvg_tops)
array.clear(bear_fvg_bottoms)
// === Swing High/Low Detection ===
var float swingHighs = array.new()
var int swingHighTimes = array.new()
var float swingLows = array.new()
var int swingLowTimes = array.new()
var line swingHighLines = array.new()
var label swingHighLabels = array.new()
var line swingLowLines = array.new()
var label swingLowLabels = array.new()
isSwingHigh = high > high and high > high
isSwingLow = low < low and low < low
if isSwingHigh
array.unshift(swingHighs, high )
array.unshift(swingHighTimes, time )
if isSwingLow
array.unshift(swingLows, low )
array.unshift(swingLowTimes, time )
// === Next Target Detection ===
var line currentTargetLine = na
var label currentTargetLabel = na
if showNextTarget
if not na(currentTargetLine)
line.delete(currentTargetLine)
if not na(currentTargetLabel)
label.delete(currentTargetLabel)
priceRising = close > open
priceFalling = close < open
// Use slightly longer extension for targets
targetExtension = barDuration * 8
if priceRising and array.size(swingHighs) > 0
for i = 0 to array.size(swingHighs) - 1
target = array.get(swingHighs, i)
targetTime = array.get(swingHighTimes, i)
if target > close
currentTargetLine := line.new(
x1=targetTime, y1=target,
x2=time + targetExtension, y2=target,
color=nextTargetHighColor, width=2,
style=line.style_dashed,
xloc=xloc.bar_time)
currentTargetLabel := label.new(
x=time + targetExtension, y=target,
text="Potential Target", size=size.tiny,
style=label.style_label_left,
color=nextTargetHighColor,
textcolor=color.white,
xloc=xloc.bar_time)
break
else if priceFalling and array.size(swingLows) > 0
for i = 0 to array.size(swingLows) - 1
target = array.get(swingLows, i)
targetTime = array.get(swingLowTimes, i)
if target < close
currentTargetLine := line.new(
x1=targetTime, y1=target,
x2=time + targetExtension, y2=target,
color=nextTargetLowColor, width=2,
style=line.style_dashed,
xloc=xloc.bar_time)
currentTargetLabel := label.new(
x=time + targetExtension, y=target,
text="Potential Target", size=size.tiny,
style=label.style_label_left,
color=nextTargetLowColor,
textcolor=color.white,
xloc=xloc.bar_time)
break
EMA 50/200 Strategy Signals - Axis.BhpEMA 50/200 Strategy
Plots EMA 50 and EMA 200
Displays arrows when a crossover occurs (signals)
Creates alerts for TradingView
Works for crypto, forex, and stocks
SuperEMA RSI Strategy [wjdtks255]Indicator Description and Trading Guide for “SuperEMA RSI Strategy ”
Indicator Name: SuperEMA RSI Strategy
Description
The SuperEMA RSI Strategy combines fast and slow Exponential Moving Averages (EMA) to identify key trend changes, enhanced with the Relative Strength Index (RSI) to filter momentum strength. It plots clear buy and sell signals based on EMA crossovers while highlighting trend zones with colored backgrounds for quick visual reference. This multi-timeframe compatible indicator is suitable for stocks, cryptocurrencies, and other markets.
How to Use the SuperEMA RSI Strategy
Buy Signal:
Enter a long position when the short-term EMA crosses above the long-term EMA, indicated by a green circle below the price bar. Confirm the trend with a green background and consider RSI values showing sufficient momentum (typically RSI above 50).
Sell Signal:
Enter a short position when the short-term EMA crosses below the long-term EMA, indicated by a red circle above the price bar. Confirm the bearish trend with a red background and verify momentum weakening (typically RSI below 50).
Trend Confirmation:
Use the background color-shaded zones to identify prevailing trend strength and avoid counter-trend trades.
RSI Application:
Optionally enable RSI display to avoid overbought/oversold entries by watching for levels above 70 (overbought) or below 30 (oversold).
Additional Tips
Adjust EMA and RSI parameters to suit different timeframes or asset volatility.
Combine this indicator with volume analysis and other technical tools for enhanced signal reliability.
Implement well-defined stop-loss and take-profit levels to manage risk effectively.
Bitcoin Macro Fair Value [Structural]//@version=6
indicator("Bitcoin Macro Fair Value ", overlay=true)
// --- Model Coefficients (Derived from Python Analysis 2019-2025) ---
intercept = input.float(3.156434, "Intercept")
c_m2 = input.float(0.132827, "Real M2 Coef")
c_corp = input.float(0.742593, "Corp Spread Coef")
c_hy = input.float(-0.617968, "HY Spread Coef")
c_dxy = input.float(0.009772, "DXY Coef")
c_real30 = input.float(0.713311, "Real 30Y Coef")
c_be30 = input.float(-1.059273, "Breakeven 30Y Coef")
c_slope = input.float(0.402220, "Slope 10Y-2Y Coef")
// --- Data Fetching ---
m2 = request.security("FRED:M2SL", "M", close)
cpi = request.security("FRED:CPIAUCSL", "M", close)
real_m2 = m2 / cpi
corp = request.security("FRED:BAMLC0A0CM", "D", close)
hy = request.security("FRED:BAMLH0A0HYM2", "D", close)
dxy = request.security("TVC:DXY", "D", close)
real30 = request.security("FRED:DFII30", "D", close)
nom30 = request.security("FRED:DGS30", "D", close)
be30 = nom30 - real30
nom10 = request.security("FRED:DGS10", "D", close)
nom2 = request.security("FRED:DGS2", "D", close)
slope = nom10 - nom2
// --- Calculation ---
log_fv = intercept + (c_m2 * real_m2) + (c_corp * corp) + (c_hy * hy) + (c_dxy * dxy) + (c_real30 * real30) + (c_be30 * be30) + (c_slope * slope)
fair_value = math.exp(log_fv)
plot(fair_value, "Macro Fair Value", color=color.new(color.blue, 0), linewidth=2)
PSP - Precision Swing Point (NQ / ES / YM)Inspired by Gxt
A very simple indicator detecting PSP between NQ ES and YM.
ORB + Fair Value Gaps (FVG/iFVG) Suite with Daily 50% MidlineA complete smart-money–focused price-action toolkit combining the New York Open Range Breakout (ORB), ICT-style Fair Value Gaps, Inverted FVGs, and a dynamic Daily 50% Midline.
Designed for traders who want a clean, fast, and highly visual way to track liquidity, imbalances, and intraday directional bias.
📌 Key Features
1. NY Session ORB (09:30–09:45 New York Time)
Automatically plots:
ORB High
ORB Low
Labels for ORB high/low
Optional 5-minute chart restriction
Lines extend forward for easy reference
Used to identify breakout conditions, liquidity sweeps, and directional bias into the morning session.
📌 2. ICT-Style Fair Value Gaps (FVGs)
Full automated detection of bullish & bearish FVGs based on the classic 3-candle displacement structure:
Bullish FVG: high < low
Bearish FVG: low > high
Each FVG is drawn as a box with:
Custom colour
Custom border style (solid, dashed, dotted)
Automatic extension to the right until filled
Optional size text showing the gap in points (font size/colour adjustable)
Adjustable max lookback for performance
📌 3. Inverted FVGs (iFVGs)
Once price fully fills an FVG, it automatically becomes an iFVG, shown with:
Custom iFVG colour
Custom border style
Extension to the right
Once price trades through the zone from the opposite side, the iFVG is considered “consumed” and:
It stops extending
And optionally auto-deletes based on user settings
This makes it easy to track meaningful imbalances that turn into liquidity pockets.
📌 4. “Show Only After ORB” Filter
Optionally hide all FVGs/iFVGs formed before the ORB completes.
This is especially useful for intraday strategies focused on NY session structure only.
📌 5. Daily 50% Midline (OHLC Midpoint)
A dynamic, always-updating midpoint of the current daily candle:
Mid = (Daily High + Daily Low) / 2
Features:
Custom colour
Dashed styling
Extends left and right as a horizontal ray
Updates live as the daily candle forms
Great for bias filters, mean reversion, and daily liquidity zones.
📌 6. Performance-Optimized (Fast!)
Built with:
Fully configurable max lookback
Memory-efficient arrays
Auto-cleaning of old FVG/iFVG objects
Lightweight daily midline recalculation
This allows extremely fast rendering even on 1-minute charts.
📌 7. Alerts
Includes a clean alert condition:
Price returned to a Fair Value Gap
Works for both bullish and bearish FVG revisits.
🎯 Who This Indicator Is For
This tool is ideal for traders who use:
ICT / SMC concepts
Liquidity-based trading
ORB strategies
Imbalance-driven price action
Intraday or NY session-focused setups
Futures, crypto, forex, and equities
🎁 Summary
This indicator gives you:
A clean ORB framework
Automatic, dynamic FVG and iFVG analysis
Real-time daily candle context
Customizable visuals
Powerful session filtering
Efficient performance
All in one clean, intuitive package built for real-time decision making.
New Day Opening Gaps (1m 3:29-9:15)Precise Day Opening Gap IndicatorThis custom Pine Script indicator is designed for traders who rely on high-precision gap analysis, particularly for markets like the NSE/BSE.🎯 Core Functionality: Precision Gap IdentificationThis indicator calculates and highlights the exact price gap between the previous day's close and the current day's open. Unlike standard gap analysis that relies on higher timeframes, this script ensures accuracy by strictly using 1-minute data:Previous Close: Captured from the 1-minute candle closing at 3:29 PM (15:29).Current Open: Captured from the 1-minute candle opening at 9:15 AM (09:15).The resulting gap zone is plotted and automatically extends to the right, serving as a critical level for current price interaction.✨ Key Features1. Cross-Timeframe PersistenceThe gap markings are calculated based on the 1-minute chart but are displayed correctly and persist across all timeframes, including 5-minute, 15-minute, 1-hour, and even the Daily chart, ensuring consistency no matter how you analyze the price action.2. Controlled Historical ViewAvoid chart clutter with the "Number of Gaps to Show" input. Easily control how many historical day-opening gaps you want to display on your chart, allowing you to focus only on the most recent and relevant levels.3. Full CustomizationCustomize the look and feel to fit your charting style:Gap Zone: Adjust the color and opacity (transparency) of the gap box.Date Label: Toggle the date label display on/off and control its color, background, opacity, and size. The label is optimally placed at the top-right of the gap zone for clear visibility.🛠️ Recommended UseThis tool is perfect for intraday traders looking to:Identify immediate support and resistance zones based on overnight price action.Track where price action reacts to prior day gaps (filling or holding the gap).Maintain a clear visual reference of daily market openings.
Dynamic Equity Allocation Model//@version=6
indicator('Dynamic Equity Allocation Model', shorttitle = 'DEAM', overlay = false, precision = 1, scale = scale.right, max_bars_back = 500)
// DYNAMIC EQUITY ALLOCATION MODEL
// Quantitative framework for dynamic portfolio allocation between stocks and cash.
// Analyzes five dimensions: market regime, risk metrics, valuation, sentiment,
// and macro conditions to generate allocation recommendations (0-100% equity).
//
// Uses real-time data from TradingView including fundamentals (P/E, ROE, ERP),
// volatility indicators (VIX), credit spreads, yield curves, and market structure.
// INPUT PARAMETERS
group1 = 'Model Configuration'
model_type = input.string('Adaptive', 'Allocation Model Type', options = , group = group1, tooltip = 'Conservative: Slower to increase equity, Aggressive: Faster allocation changes, Adaptive: Dynamic based on regime')
use_crisis_detection = input.bool(true, 'Enable Crisis Detection System', group = group1, tooltip = 'Automatic detection and response to crisis conditions')
use_regime_model = input.bool(true, 'Use Market Regime Detection', group = group1, tooltip = 'Identify Bull/Bear/Crisis regimes for dynamic allocation')
group2 = 'Portfolio Risk Management'
target_portfolio_volatility = input.float(12.0, 'Target Portfolio Volatility (%)', minval = 3, maxval = 20, step = 0.5, group = group2, tooltip = 'Target portfolio volatility (Cash reduces volatility: 50% Equity = ~10% vol, 100% Equity = ~20% vol)')
max_portfolio_drawdown = input.float(15.0, 'Maximum Portfolio Drawdown (%)', minval = 5, maxval = 35, step = 2.5, group = group2, tooltip = 'Maximum acceptable PORTFOLIO drawdown (not market drawdown - portfolio with cash has lower drawdown)')
enable_portfolio_risk_scaling = input.bool(true, 'Enable Portfolio Risk Scaling', group = group2, tooltip = 'Scale allocation based on actual portfolio risk characteristics (recommended)')
risk_lookback = input.int(252, 'Risk Calculation Period (Days)', minval = 60, maxval = 504, group = group2, tooltip = 'Period for calculating volatility and risk metrics')
group3 = 'Component Weights (Total = 100%)'
w_regime = input.float(35.0, 'Market Regime Weight (%)', minval = 0, maxval = 100, step = 5, group = group3)
w_risk = input.float(25.0, 'Risk Metrics Weight (%)', minval = 0, maxval = 100, step = 5, group = group3)
w_valuation = input.float(20.0, 'Valuation Weight (%)', minval = 0, maxval = 100, step = 5, group = group3)
w_sentiment = input.float(15.0, 'Sentiment Weight (%)', minval = 0, maxval = 100, step = 5, group = group3)
w_macro = input.float(5.0, 'Macro Weight (%)', minval = 0, maxval = 100, step = 5, group = group3)
group4 = 'Crisis Detection Thresholds'
crisis_vix_threshold = input.float(40, 'Crisis VIX Level', minval = 30, maxval = 80, group = group4, tooltip = 'VIX level indicating crisis conditions (COVID peaked at 82)')
crisis_drawdown_threshold = input.float(15, 'Crisis Drawdown Threshold (%)', minval = 10, maxval = 30, group = group4, tooltip = 'Market drawdown indicating crisis conditions')
crisis_credit_spread = input.float(500, 'Crisis Credit Spread (bps)', minval = 300, maxval = 1000, group = group4, tooltip = 'High yield spread indicating crisis conditions')
group5 = 'Display Settings'
show_components = input.bool(false, 'Show Component Breakdown', group = group5, tooltip = 'Display individual component analysis lines')
show_regime_background = input.bool(true, 'Show Dynamic Background', group = group5, tooltip = 'Color background based on allocation signals')
show_reference_lines = input.bool(false, 'Show Reference Lines', group = group5, tooltip = 'Display allocation percentage reference lines')
show_dashboard = input.bool(true, 'Show Analytics Dashboard', group = group5, tooltip = 'Display comprehensive analytics table')
show_confidence_bands = input.bool(false, 'Show Confidence Bands', group = group5, tooltip = 'Display uncertainty quantification bands')
smoothing_period = input.int(3, 'Smoothing Period', minval = 1, maxval = 10, group = group5, tooltip = 'Smoothing to reduce allocation noise')
background_intensity = input.int(95, 'Background Intensity (%)', minval = 90, maxval = 99, group = group5, tooltip = 'Higher values = more transparent background')
// Styling Options
color_scheme = input.string('EdgeTools', 'Color Theme', options = , group = 'Appearance', tooltip = 'Professional color themes')
use_dark_mode = input.bool(true, 'Optimize for Dark Theme', group = 'Appearance')
main_line_width = input.int(3, 'Main Line Width', minval = 1, maxval = 5, group = 'Appearance')
// DATA RETRIEVAL
// Market Data
sp500 = request.security('SPY', timeframe.period, close)
sp500_high = request.security('SPY', timeframe.period, high)
sp500_low = request.security('SPY', timeframe.period, low)
sp500_volume = request.security('SPY', timeframe.period, volume)
// Volatility Indicators
vix = request.security('VIX', timeframe.period, close)
vix9d = request.security('VIX9D', timeframe.period, close)
vxn = request.security('VXN', timeframe.period, close)
// Fixed Income and Credit
us2y = request.security('US02Y', timeframe.period, close)
us10y = request.security('US10Y', timeframe.period, close)
us3m = request.security('US03MY', timeframe.period, close)
hyg = request.security('HYG', timeframe.period, close)
lqd = request.security('LQD', timeframe.period, close)
tlt = request.security('TLT', timeframe.period, close)
// Safe Haven Assets
gold = request.security('GLD', timeframe.period, close)
usd = request.security('DXY', timeframe.period, close)
yen = request.security('JPYUSD', timeframe.period, close)
// Financial data with fallback values
get_financial_data(symbol, fin_id, period, fallback) =>
data = request.financial(symbol, fin_id, period, ignore_invalid_symbol = true)
na(data) ? fallback : data
// SPY fundamental metrics
spy_earnings_per_share = get_financial_data('AMEX:SPY', 'EARNINGS_PER_SHARE_BASIC', 'TTM', 20.0)
spy_operating_earnings_yield = get_financial_data('AMEX:SPY', 'OPERATING_EARNINGS_YIELD', 'FY', 4.5)
spy_dividend_yield = get_financial_data('AMEX:SPY', 'DIVIDENDS_YIELD', 'FY', 1.8)
spy_buyback_yield = get_financial_data('AMEX:SPY', 'BUYBACK_YIELD', 'FY', 2.0)
spy_net_margin = get_financial_data('AMEX:SPY', 'NET_MARGIN', 'TTM', 12.0)
spy_debt_to_equity = get_financial_data('AMEX:SPY', 'DEBT_TO_EQUITY', 'FY', 0.5)
spy_return_on_equity = get_financial_data('AMEX:SPY', 'RETURN_ON_EQUITY', 'FY', 15.0)
spy_free_cash_flow = get_financial_data('AMEX:SPY', 'FREE_CASH_FLOW', 'TTM', 100000000)
spy_ebitda = get_financial_data('AMEX:SPY', 'EBITDA', 'TTM', 200000000)
spy_pe_forward = get_financial_data('AMEX:SPY', 'PRICE_EARNINGS_FORWARD', 'FY', 18.0)
spy_total_debt = get_financial_data('AMEX:SPY', 'TOTAL_DEBT', 'FY', 500000000)
spy_total_equity = get_financial_data('AMEX:SPY', 'TOTAL_EQUITY', 'FY', 1000000000)
spy_enterprise_value = get_financial_data('AMEX:SPY', 'ENTERPRISE_VALUE', 'FY', 30000000000)
spy_revenue_growth = get_financial_data('AMEX:SPY', 'REVENUE_ONE_YEAR_GROWTH', 'TTM', 5.0)
// Market Breadth Indicators
nya = request.security('NYA', timeframe.period, close)
rut = request.security('IWM', timeframe.period, close)
// Sector Performance
xlk = request.security('XLK', timeframe.period, close)
xlu = request.security('XLU', timeframe.period, close)
xlf = request.security('XLF', timeframe.period, close)
// MARKET REGIME DETECTION
// Calculate Market Trend
sma_20 = ta.sma(sp500, 20)
sma_50 = ta.sma(sp500, 50)
sma_200 = ta.sma(sp500, 200)
ema_10 = ta.ema(sp500, 10)
// Market Structure Score
trend_strength = 0.0
trend_strength := trend_strength + (sp500 > sma_20 ? 1 : -1)
trend_strength := trend_strength + (sp500 > sma_50 ? 1 : -1)
trend_strength := trend_strength + (sp500 > sma_200 ? 2 : -2)
trend_strength := trend_strength + (sma_50 > sma_200 ? 2 : -2)
// Volatility Regime
returns = math.log(sp500 / sp500 )
realized_vol_20d = ta.stdev(returns, 20) * math.sqrt(252) * 100
realized_vol_60d = ta.stdev(returns, 60) * math.sqrt(252) * 100
ewma_vol = ta.ema(math.pow(returns, 2), 20)
realized_vol = math.sqrt(ewma_vol * 252) * 100
vol_premium = vix - realized_vol
// Drawdown Calculation
running_max = ta.highest(sp500, risk_lookback)
current_drawdown = (running_max - sp500) / running_max * 100
// Regime Score
regime_score = 0.0
// Trend Component (40%)
if trend_strength >= 4
regime_score := regime_score + 40
regime_score
else if trend_strength >= 2
regime_score := regime_score + 30
regime_score
else if trend_strength >= 0
regime_score := regime_score + 20
regime_score
else if trend_strength >= -2
regime_score := regime_score + 10
regime_score
else
regime_score := regime_score + 0
regime_score
// Volatility Component (30%)
if vix < 15
regime_score := regime_score + 30
regime_score
else if vix < 20
regime_score := regime_score + 25
regime_score
else if vix < 25
regime_score := regime_score + 15
regime_score
else if vix < 35
regime_score := regime_score + 5
regime_score
else
regime_score := regime_score + 0
regime_score
// Drawdown Component (30%)
if current_drawdown < 3
regime_score := regime_score + 30
regime_score
else if current_drawdown < 7
regime_score := regime_score + 20
regime_score
else if current_drawdown < 12
regime_score := regime_score + 10
regime_score
else if current_drawdown < 20
regime_score := regime_score + 5
regime_score
else
regime_score := regime_score + 0
regime_score
// Classify Regime
market_regime = regime_score >= 80 ? 'Strong Bull' : regime_score >= 60 ? 'Bull Market' : regime_score >= 40 ? 'Neutral' : regime_score >= 20 ? 'Correction' : regime_score >= 10 ? 'Bear Market' : 'Crisis'
// RISK-BASED ALLOCATION
// Calculate Market Risk
parkinson_hl = math.log(sp500_high / sp500_low)
parkinson_vol = parkinson_hl / (2 * math.sqrt(math.log(2))) * math.sqrt(252) * 100
garman_klass_vol = math.sqrt((0.5 * math.pow(math.log(sp500_high / sp500_low), 2) - (2 * math.log(2) - 1) * math.pow(math.log(sp500 / sp500 ), 2)) * 252) * 100
market_volatility_20d = math.max(ta.stdev(returns, 20) * math.sqrt(252) * 100, parkinson_vol)
market_volatility_60d = ta.stdev(returns, 60) * math.sqrt(252) * 100
market_drawdown = current_drawdown
// Initialize risk allocation
risk_allocation = 50.0
if enable_portfolio_risk_scaling
// Volatility-based allocation
vol_based_allocation = target_portfolio_volatility / math.max(market_volatility_20d, 5.0) * 100
vol_based_allocation := math.max(0, math.min(100, vol_based_allocation))
// Drawdown-based allocation
dd_based_allocation = 100.0
if market_drawdown > 1.0
dd_based_allocation := max_portfolio_drawdown / market_drawdown * 100
dd_based_allocation := math.max(0, math.min(100, dd_based_allocation))
dd_based_allocation
// Combine (conservative)
risk_allocation := math.min(vol_based_allocation, dd_based_allocation)
// Dynamic adjustment
current_equity_estimate = 50.0
estimated_portfolio_vol = current_equity_estimate / 100 * market_volatility_20d
estimated_portfolio_dd = current_equity_estimate / 100 * market_drawdown
vol_utilization = estimated_portfolio_vol / target_portfolio_volatility
dd_utilization = estimated_portfolio_dd / max_portfolio_drawdown
risk_utilization = math.max(vol_utilization, dd_utilization)
risk_adjustment_factor = 1.0
if risk_utilization > 1.0
risk_adjustment_factor := math.exp(-0.5 * (risk_utilization - 1.0))
risk_adjustment_factor := math.max(0.5, risk_adjustment_factor)
risk_adjustment_factor
else if risk_utilization < 0.9
risk_adjustment_factor := 1.0 + 0.2 * math.log(1.0 / risk_utilization)
risk_adjustment_factor := math.min(1.3, risk_adjustment_factor)
risk_adjustment_factor
risk_allocation := risk_allocation * risk_adjustment_factor
risk_allocation
else
vol_scalar = target_portfolio_volatility / math.max(market_volatility_20d, 10)
vol_scalar := math.min(1.5, math.max(0.2, vol_scalar))
drawdown_penalty = 0.0
if current_drawdown > max_portfolio_drawdown
drawdown_penalty := (current_drawdown - max_portfolio_drawdown) / max_portfolio_drawdown
drawdown_penalty := math.min(1.0, drawdown_penalty)
drawdown_penalty
risk_allocation := 100 * vol_scalar * (1 - drawdown_penalty)
risk_allocation
risk_allocation := math.max(0, math.min(100, risk_allocation))
// VALUATION ANALYSIS
// Valuation Metrics
actual_pe_ratio = spy_earnings_per_share > 0 ? sp500 / spy_earnings_per_share : spy_pe_forward
actual_earnings_yield = nz(spy_operating_earnings_yield, 0) > 0 ? spy_operating_earnings_yield : 100 / actual_pe_ratio
total_shareholder_yield = spy_dividend_yield + spy_buyback_yield
// Equity Risk Premium (multi-method calculation)
method1_erp = actual_earnings_yield - us10y
method2_erp = actual_earnings_yield + spy_buyback_yield - us10y
payout_ratio = spy_dividend_yield > 0 and actual_earnings_yield > 0 ? spy_dividend_yield / actual_earnings_yield : 0.4
sustainable_growth = spy_return_on_equity * (1 - payout_ratio) / 100
method3_erp = spy_dividend_yield + sustainable_growth * 100 - us10y
implied_growth = spy_revenue_growth * 0.7
method4_erp = total_shareholder_yield + implied_growth - us10y
equity_risk_premium = method1_erp * 0.35 + method2_erp * 0.30 + method3_erp * 0.20 + method4_erp * 0.15
ev_ebitda_ratio = spy_enterprise_value > 0 and spy_ebitda > 0 ? spy_enterprise_value / spy_ebitda : 15.0
debt_equity_health = spy_debt_to_equity < 1.0 ? 1.2 : spy_debt_to_equity < 2.0 ? 1.0 : 0.8
// Valuation Score
base_valuation_score = 50.0
if equity_risk_premium > 4
base_valuation_score := 95
base_valuation_score
else if equity_risk_premium > 3
base_valuation_score := 85
base_valuation_score
else if equity_risk_premium > 2
base_valuation_score := 70
base_valuation_score
else if equity_risk_premium > 1
base_valuation_score := 55
base_valuation_score
else if equity_risk_premium > 0
base_valuation_score := 40
base_valuation_score
else if equity_risk_premium > -1
base_valuation_score := 25
base_valuation_score
else
base_valuation_score := 10
base_valuation_score
growth_adjustment = spy_revenue_growth > 10 ? 10 : spy_revenue_growth > 5 ? 5 : 0
margin_adjustment = spy_net_margin > 15 ? 5 : spy_net_margin < 8 ? -5 : 0
roe_adjustment = spy_return_on_equity > 20 ? 5 : spy_return_on_equity < 10 ? -5 : 0
valuation_score = base_valuation_score + growth_adjustment + margin_adjustment + roe_adjustment
valuation_score := math.max(0, math.min(100, valuation_score * debt_equity_health))
// SENTIMENT ANALYSIS
// VIX Term Structure
vix_term_structure = vix9d > 0 ? vix / vix9d : 1
backwardation = vix_term_structure > 1.05
steep_backwardation = vix_term_structure > 1.15
// Safe Haven Flows
gold_momentum = ta.roc(gold, 20)
dollar_momentum = ta.roc(usd, 20)
yen_momentum = ta.roc(yen, 20)
treasury_momentum = ta.roc(tlt, 20)
safe_haven_flow = gold_momentum * 0.3 + treasury_momentum * 0.3 + dollar_momentum * 0.25 + yen_momentum * 0.15
// Advanced Sentiment Analysis
vix_percentile = ta.percentrank(vix, 252)
vix_zscore = (vix - ta.sma(vix, 252)) / ta.stdev(vix, 252)
vix_momentum = ta.roc(vix, 5)
vvix_proxy = ta.stdev(vix_momentum, 20) * math.sqrt(252)
risk_reversal_proxy = (vix - realized_vol) / realized_vol
// Sentiment Score
base_sentiment = 50.0
vix_adjustment = 0.0
if vix_zscore < -1.5
vix_adjustment := 40
vix_adjustment
else if vix_zscore < -0.5
vix_adjustment := 20
vix_adjustment
else if vix_zscore < 0.5
vix_adjustment := 0
vix_adjustment
else if vix_zscore < 1.5
vix_adjustment := -20
vix_adjustment
else
vix_adjustment := -40
vix_adjustment
term_structure_adjustment = backwardation ? -15 : steep_backwardation ? -30 : 5
vvix_adjustment = vvix_proxy > 2.0 ? -10 : vvix_proxy < 1.0 ? 10 : 0
sentiment_score = base_sentiment + vix_adjustment + term_structure_adjustment + vvix_adjustment
sentiment_score := math.max(0, math.min(100, sentiment_score))
// MACRO ANALYSIS
// Yield Curve
yield_spread_2_10 = us10y - us2y
yield_spread_3m_10 = us10y - us3m
// Credit Conditions
hyg_return = ta.roc(hyg, 20)
lqd_return = ta.roc(lqd, 20)
tlt_return = ta.roc(tlt, 20)
hyg_duration = 4.0
lqd_duration = 8.0
tlt_duration = 17.0
hyg_log_returns = math.log(hyg / hyg )
lqd_log_returns = math.log(lqd / lqd )
hyg_volatility = ta.stdev(hyg_log_returns, 20) * math.sqrt(252)
lqd_volatility = ta.stdev(lqd_log_returns, 20) * math.sqrt(252)
hyg_yield_proxy = -math.log(hyg / hyg ) * 100
lqd_yield_proxy = -math.log(lqd / lqd ) * 100
tlt_yield = us10y
hyg_spread = (hyg_yield_proxy - tlt_yield) * 100
lqd_spread = (lqd_yield_proxy - tlt_yield) * 100
hyg_distance = (hyg - ta.lowest(hyg, 252)) / (ta.highest(hyg, 252) - ta.lowest(hyg, 252))
lqd_distance = (lqd - ta.lowest(lqd, 252)) / (ta.highest(lqd, 252) - ta.lowest(lqd, 252))
default_risk_proxy = 2.0 - (hyg_distance + lqd_distance)
credit_spread = hyg_spread * 0.5 + (hyg_volatility - lqd_volatility) * 1000 * 0.3 + default_risk_proxy * 200 * 0.2
credit_spread := math.max(50, credit_spread)
credit_market_health = hyg_return > lqd_return ? 1 : -1
flight_to_quality = tlt_return > (hyg_return + lqd_return) / 2
// Macro Score
macro_score = 50.0
yield_curve_score = 0
if yield_spread_2_10 > 1.5 and yield_spread_3m_10 > 2
yield_curve_score := 40
yield_curve_score
else if yield_spread_2_10 > 0.5 and yield_spread_3m_10 > 1
yield_curve_score := 30
yield_curve_score
else if yield_spread_2_10 > 0 and yield_spread_3m_10 > 0
yield_curve_score := 20
yield_curve_score
else if yield_spread_2_10 < 0 or yield_spread_3m_10 < 0
yield_curve_score := 10
yield_curve_score
else
yield_curve_score := 5
yield_curve_score
credit_conditions_score = 0
if credit_spread < 200 and not flight_to_quality
credit_conditions_score := 30
credit_conditions_score
else if credit_spread < 400 and credit_market_health > 0
credit_conditions_score := 20
credit_conditions_score
else if credit_spread < 600
credit_conditions_score := 15
credit_conditions_score
else if credit_spread < 1000
credit_conditions_score := 10
credit_conditions_score
else
credit_conditions_score := 0
credit_conditions_score
financial_stability_score = 0
if spy_debt_to_equity < 0.5 and spy_return_on_equity > 15
financial_stability_score := 20
financial_stability_score
else if spy_debt_to_equity < 1.0 and spy_return_on_equity > 10
financial_stability_score := 15
financial_stability_score
else if spy_debt_to_equity < 1.5
financial_stability_score := 10
financial_stability_score
else
financial_stability_score := 5
financial_stability_score
macro_score := yield_curve_score + credit_conditions_score + financial_stability_score
macro_score := math.max(0, math.min(100, macro_score))
// CRISIS DETECTION
crisis_indicators = 0
if vix > crisis_vix_threshold
crisis_indicators := crisis_indicators + 1
crisis_indicators
if vix > 60
crisis_indicators := crisis_indicators + 2
crisis_indicators
if current_drawdown > crisis_drawdown_threshold
crisis_indicators := crisis_indicators + 1
crisis_indicators
if current_drawdown > 25
crisis_indicators := crisis_indicators + 1
crisis_indicators
if credit_spread > crisis_credit_spread
crisis_indicators := crisis_indicators + 1
crisis_indicators
sp500_roc_5 = ta.roc(sp500, 5)
tlt_roc_5 = ta.roc(tlt, 5)
if sp500_roc_5 < -10 and tlt_roc_5 < -5
crisis_indicators := crisis_indicators + 2
crisis_indicators
volume_spike = sp500_volume > ta.sma(sp500_volume, 20) * 2
sp500_roc_1 = ta.roc(sp500, 1)
if volume_spike and sp500_roc_1 < -3
crisis_indicators := crisis_indicators + 1
crisis_indicators
is_crisis = crisis_indicators >= 3
is_severe_crisis = crisis_indicators >= 5
// FINAL ALLOCATION CALCULATION
// Convert regime to base allocation
regime_allocation = market_regime == 'Strong Bull' ? 100 : market_regime == 'Bull Market' ? 80 : market_regime == 'Neutral' ? 60 : market_regime == 'Correction' ? 40 : market_regime == 'Bear Market' ? 20 : 0
// Normalize weights
total_weight = w_regime + w_risk + w_valuation + w_sentiment + w_macro
w_regime_norm = w_regime / total_weight
w_risk_norm = w_risk / total_weight
w_valuation_norm = w_valuation / total_weight
w_sentiment_norm = w_sentiment / total_weight
w_macro_norm = w_macro / total_weight
// Calculate Weighted Allocation
weighted_allocation = regime_allocation * w_regime_norm + risk_allocation * w_risk_norm + valuation_score * w_valuation_norm + sentiment_score * w_sentiment_norm + macro_score * w_macro_norm
// Apply Crisis Override
if use_crisis_detection
if is_severe_crisis
weighted_allocation := math.min(weighted_allocation, 10)
weighted_allocation
else if is_crisis
weighted_allocation := math.min(weighted_allocation, 25)
weighted_allocation
// Model Type Adjustment
model_adjustment = 0.0
if model_type == 'Conservative'
model_adjustment := -10
model_adjustment
else if model_type == 'Aggressive'
model_adjustment := 10
model_adjustment
else if model_type == 'Adaptive'
recent_return = (sp500 - sp500 ) / sp500 * 100
if recent_return > 5
model_adjustment := 5
model_adjustment
else if recent_return < -5
model_adjustment := -5
model_adjustment
// Apply adjustment and bounds
final_allocation = weighted_allocation + model_adjustment
final_allocation := math.max(0, math.min(100, final_allocation))
// Smooth allocation
smoothed_allocation = ta.sma(final_allocation, smoothing_period)
// Calculate portfolio risk metrics (only for internal alerts)
actual_portfolio_volatility = smoothed_allocation / 100 * market_volatility_20d
actual_portfolio_drawdown = smoothed_allocation / 100 * current_drawdown
// VISUALIZATION
// Color definitions
var color primary_color = #2196F3
var color bullish_color = #4CAF50
var color bearish_color = #FF5252
var color neutral_color = #808080
var color text_color = color.white
var color bg_color = #000000
var color table_bg_color = #1E1E1E
var color header_bg_color = #2D2D2D
switch color_scheme // Apply color scheme
'Gold' =>
primary_color := use_dark_mode ? #FFD700 : #DAA520
bullish_color := use_dark_mode ? #FFA500 : #FF8C00
bearish_color := use_dark_mode ? #FF5252 : #D32F2F
neutral_color := use_dark_mode ? #C0C0C0 : #808080
text_color := use_dark_mode ? color.white : color.black
bg_color := use_dark_mode ? #000000 : #FFFFFF
table_bg_color := use_dark_mode ? #1A1A00 : #FFFEF0
header_bg_color := use_dark_mode ? #2D2600 : #F5F5DC
header_bg_color
'EdgeTools' =>
primary_color := use_dark_mode ? #4682B4 : #1E90FF
bullish_color := use_dark_mode ? #4CAF50 : #388E3C
bearish_color := use_dark_mode ? #FF5252 : #D32F2F
neutral_color := use_dark_mode ? #708090 : #696969
text_color := use_dark_mode ? color.white : color.black
bg_color := use_dark_mode ? #000000 : #FFFFFF
table_bg_color := use_dark_mode ? #0F1419 : #F0F8FF
header_bg_color := use_dark_mode ? #1E2A3A : #E6F3FF
header_bg_color
'Behavioral' =>
primary_color := #808080
bullish_color := #00FF00
bearish_color := #8B0000
neutral_color := #FFBF00
text_color := use_dark_mode ? color.white : color.black
bg_color := use_dark_mode ? #000000 : #FFFFFF
table_bg_color := use_dark_mode ? #1A1A1A : #F8F8F8
header_bg_color := use_dark_mode ? #2D2D2D : #E8E8E8
header_bg_color
'Quant' =>
primary_color := #808080
bullish_color := #FFA500
bearish_color := #8B0000
neutral_color := #4682B4
text_color := use_dark_mode ? color.white : color.black
bg_color := use_dark_mode ? #000000 : #FFFFFF
table_bg_color := use_dark_mode ? #0D0D0D : #FAFAFA
header_bg_color := use_dark_mode ? #1A1A1A : #F0F0F0
header_bg_color
'Ocean' =>
primary_color := use_dark_mode ? #20B2AA : #008B8B
bullish_color := use_dark_mode ? #00CED1 : #4682B4
bearish_color := use_dark_mode ? #FF4500 : #B22222
neutral_color := use_dark_mode ? #87CEEB : #2F4F4F
text_color := use_dark_mode ? #F0F8FF : #191970
bg_color := use_dark_mode ? #001F3F : #F0F8FF
table_bg_color := use_dark_mode ? #001A2E : #E6F7FF
header_bg_color := use_dark_mode ? #002A47 : #CCF2FF
header_bg_color
'Fire' =>
primary_color := use_dark_mode ? #FF6347 : #DC143C
bullish_color := use_dark_mode ? #FFD700 : #FF8C00
bearish_color := use_dark_mode ? #8B0000 : #800000
neutral_color := use_dark_mode ? #FFA500 : #CD853F
text_color := use_dark_mode ? #FFFAF0 : #2F1B14
bg_color := use_dark_mode ? #2F1B14 : #FFFAF0
table_bg_color := use_dark_mode ? #261611 : #FFF8F0
header_bg_color := use_dark_mode ? #3D241A : #FFE4CC
header_bg_color
'Matrix' =>
primary_color := use_dark_mode ? #00FF41 : #006400
bullish_color := use_dark_mode ? #39FF14 : #228B22
bearish_color := use_dark_mode ? #FF073A : #8B0000
neutral_color := use_dark_mode ? #00FFFF : #008B8B
text_color := use_dark_mode ? #C0FF8C : #003300
bg_color := use_dark_mode ? #0D1B0D : #F0FFF0
table_bg_color := use_dark_mode ? #0A1A0A : #E8FFF0
header_bg_color := use_dark_mode ? #112B11 : #CCFFCC
header_bg_color
'Arctic' =>
primary_color := use_dark_mode ? #87CEFA : #4169E1
bullish_color := use_dark_mode ? #00BFFF : #0000CD
bearish_color := use_dark_mode ? #FF1493 : #8B008B
neutral_color := use_dark_mode ? #B0E0E6 : #483D8B
text_color := use_dark_mode ? #F8F8FF : #191970
bg_color := use_dark_mode ? #191970 : #F8F8FF
table_bg_color := use_dark_mode ? #141B47 : #F0F8FF
header_bg_color := use_dark_mode ? #1E2A5C : #E0F0FF
header_bg_color
// Transparency settings
bg_transparency = use_dark_mode ? 85 : 92
zone_transparency = use_dark_mode ? 90 : 95
band_transparency = use_dark_mode ? 70 : 85
table_transparency = use_dark_mode ? 80 : 15
// Allocation color
alloc_color = smoothed_allocation >= 80 ? bullish_color : smoothed_allocation >= 60 ? color.new(bullish_color, 30) : smoothed_allocation >= 40 ? primary_color : smoothed_allocation >= 20 ? color.new(bearish_color, 30) : bearish_color
// Dynamic background
var color dynamic_bg_color = na
if show_regime_background
if smoothed_allocation >= 70
dynamic_bg_color := color.new(bullish_color, background_intensity)
dynamic_bg_color
else if smoothed_allocation <= 30
dynamic_bg_color := color.new(bearish_color, background_intensity)
dynamic_bg_color
else if smoothed_allocation > 60 or smoothed_allocation < 40
dynamic_bg_color := color.new(primary_color, math.min(99, background_intensity + 2))
dynamic_bg_color
bgcolor(dynamic_bg_color, title = 'Allocation Signal Background')
// Plot main allocation line
plot(smoothed_allocation, 'Equity Allocation %', color = alloc_color, linewidth = math.max(1, main_line_width))
// Reference lines (static colors for hline)
hline_bullish_color = color_scheme == 'Gold' ? use_dark_mode ? #FFA500 : #FF8C00 : color_scheme == 'EdgeTools' ? use_dark_mode ? #4CAF50 : #388E3C : color_scheme == 'Behavioral' ? #00FF00 : color_scheme == 'Quant' ? #FFA500 : color_scheme == 'Ocean' ? use_dark_mode ? #00CED1 : #4682B4 : color_scheme == 'Fire' ? use_dark_mode ? #FFD700 : #FF8C00 : color_scheme == 'Matrix' ? use_dark_mode ? #39FF14 : #228B22 : color_scheme == 'Arctic' ? use_dark_mode ? #00BFFF : #0000CD : #4CAF50
hline_bearish_color = color_scheme == 'Gold' ? use_dark_mode ? #FF5252 : #D32F2F : color_scheme == 'EdgeTools' ? use_dark_mode ? #FF5252 : #D32F2F : color_scheme == 'Behavioral' ? #8B0000 : color_scheme == 'Quant' ? #8B0000 : color_scheme == 'Ocean' ? use_dark_mode ? #FF4500 : #B22222 : color_scheme == 'Fire' ? use_dark_mode ? #8B0000 : #800000 : color_scheme == 'Matrix' ? use_dark_mode ? #FF073A : #8B0000 : color_scheme == 'Arctic' ? use_dark_mode ? #FF1493 : #8B008B : #FF5252
hline_primary_color = color_scheme == 'Gold' ? use_dark_mode ? #FFD700 : #DAA520 : color_scheme == 'EdgeTools' ? use_dark_mode ? #4682B4 : #1E90FF : color_scheme == 'Behavioral' ? #808080 : color_scheme == 'Quant' ? #808080 : color_scheme == 'Ocean' ? use_dark_mode ? #20B2AA : #008B8B : color_scheme == 'Fire' ? use_dark_mode ? #FF6347 : #DC143C : color_scheme == 'Matrix' ? use_dark_mode ? #00FF41 : #006400 : color_scheme == 'Arctic' ? use_dark_mode ? #87CEFA : #4169E1 : #2196F3
hline(show_reference_lines ? 100 : na, '100% Equity', color = color.new(hline_bullish_color, 70), linestyle = hline.style_dotted, linewidth = 1)
hline(show_reference_lines ? 80 : na, '80% Equity', color = color.new(hline_bullish_color, 40), linestyle = hline.style_dashed, linewidth = 1)
hline(show_reference_lines ? 60 : na, '60% Equity', color = color.new(hline_bullish_color, 60), linestyle = hline.style_dotted, linewidth = 1)
hline(50, '50% Balanced', color = color.new(hline_primary_color, 50), linestyle = hline.style_solid, linewidth = 2)
hline(show_reference_lines ? 40 : na, '40% Equity', color = color.new(hline_bearish_color, 60), linestyle = hline.style_dotted, linewidth = 1)
hline(show_reference_lines ? 20 : na, '20% Equity', color = color.new(hline_bearish_color, 40), linestyle = hline.style_dashed, linewidth = 1)
hline(show_reference_lines ? 0 : na, '0% Equity', color = color.new(hline_bearish_color, 70), linestyle = hline.style_dotted, linewidth = 1)
// Component plots
plot(show_components ? regime_allocation : na, 'Regime', color = color.new(#4ECDC4, 70), linewidth = 1)
plot(show_components ? risk_allocation : na, 'Risk', color = color.new(#FF6B6B, 70), linewidth = 1)
plot(show_components ? valuation_score : na, 'Valuation', color = color.new(#45B7D1, 70), linewidth = 1)
plot(show_components ? sentiment_score : na, 'Sentiment', color = color.new(#FFD93D, 70), linewidth = 1)
plot(show_components ? macro_score : na, 'Macro', color = color.new(#6BCF7F, 70), linewidth = 1)
// Confidence bands
upper_band = plot(show_confidence_bands ? math.min(100, smoothed_allocation + ta.stdev(smoothed_allocation, 20)) : na, color = color.new(neutral_color, band_transparency), display = display.none, title = 'Upper Band')
lower_band = plot(show_confidence_bands ? math.max(0, smoothed_allocation - ta.stdev(smoothed_allocation, 20)) : na, color = color.new(neutral_color, band_transparency), display = display.none, title = 'Lower Band')
fill(upper_band, lower_band, color = show_confidence_bands ? color.new(neutral_color, zone_transparency) : na, title = 'Uncertainty')
// DASHBOARD
if show_dashboard and barstate.islast
var table dashboard = table.new(position.top_right, 2, 20, border_width = 1, bgcolor = color.new(table_bg_color, table_transparency))
table.clear(dashboard, 0, 0, 1, 19)
// Header
header_color = color.new(header_bg_color, 20)
dashboard_text_color = text_color
table.cell(dashboard, 0, 0, 'DEAM', text_color = dashboard_text_color, bgcolor = header_color, text_size = size.normal)
table.cell(dashboard, 1, 0, model_type, text_color = dashboard_text_color, bgcolor = header_color, text_size = size.normal)
// Core metrics
table.cell(dashboard, 0, 1, 'Equity Allocation', text_color = dashboard_text_color, text_size = size.small)
table.cell(dashboard, 1, 1, str.tostring(smoothed_allocation, '##.#') + '%', text_color = alloc_color, text_size = size.small)
table.cell(dashboard, 0, 2, 'Cash Allocation', text_color = dashboard_text_color, text_size = size.small)
cash_color = 100 - smoothed_allocation > 70 ? bearish_color : primary_color
table.cell(dashboard, 1, 2, str.tostring(100 - smoothed_allocation, '##.#') + '%', text_color = cash_color, text_size = size.small)
// Signal
signal_text = 'NEUTRAL'
signal_color = primary_color
if smoothed_allocation >= 70
signal_text := 'BULLISH'
signal_color := bullish_color
signal_color
else if smoothed_allocation <= 30
signal_text := 'BEARISH'
signal_color := bearish_color
signal_color
table.cell(dashboard, 0, 3, 'Signal', text_color = dashboard_text_color, text_size = size.small)
table.cell(dashboard, 1, 3, signal_text, text_color = signal_color, text_size = size.small)
// Market Regime
table.cell(dashboard, 0, 4, 'Regime', text_color = dashboard_text_color, text_size = size.small)
regime_color_display = market_regime == 'Strong Bull' or market_regime == 'Bull Market' ? bullish_color : market_regime == 'Neutral' ? primary_color : market_regime == 'Crisis' ? bearish_color : bearish_color
table.cell(dashboard, 1, 4, market_regime, text_color = regime_color_display, text_size = size.small)
// VIX
table.cell(dashboard, 0, 5, 'VIX Level', text_color = dashboard_text_color, text_size = size.small)
vix_color_display = vix < 20 ? bullish_color : vix < 30 ? primary_color : bearish_color
table.cell(dashboard, 1, 5, str.tostring(vix, '##.##'), text_color = vix_color_display, text_size = size.small)
// Market Drawdown
table.cell(dashboard, 0, 6, 'Market DD', text_color = dashboard_text_color, text_size = size.small)
market_dd_color = current_drawdown < 5 ? bullish_color : current_drawdown < 10 ? primary_color : bearish_color
table.cell(dashboard, 1, 6, '-' + str.tostring(current_drawdown, '##.#') + '%', text_color = market_dd_color, text_size = size.small)
// Crisis Detection
table.cell(dashboard, 0, 7, 'Crisis Detection', text_color = dashboard_text_color, text_size = size.small)
crisis_text = is_severe_crisis ? 'SEVERE' : is_crisis ? 'CRISIS' : 'Normal'
crisis_display_color = is_severe_crisis or is_crisis ? bearish_color : bullish_color
table.cell(dashboard, 1, 7, crisis_text, text_color = crisis_display_color, text_size = size.small)
// Real Data Section
financial_bg = color.new(primary_color, 85)
table.cell(dashboard, 0, 8, 'REAL DATA', text_color = dashboard_text_color, bgcolor = financial_bg, text_size = size.small)
table.cell(dashboard, 1, 8, 'Live Metrics', text_color = dashboard_text_color, bgcolor = financial_bg, text_size = size.small)
// P/E Ratio
table.cell(dashboard, 0, 9, 'P/E Ratio', text_color = dashboard_text_color, text_size = size.small)
pe_color = actual_pe_ratio < 18 ? bullish_color : actual_pe_ratio < 25 ? primary_color : bearish_color
table.cell(dashboard, 1, 9, str.tostring(actual_pe_ratio, '##.#'), text_color = pe_color, text_size = size.small)
// ERP
table.cell(dashboard, 0, 10, 'ERP', text_color = dashboard_text_color, text_size = size.small)
erp_color = equity_risk_premium > 2 ? bullish_color : equity_risk_premium > 0 ? primary_color : bearish_color
table.cell(dashboard, 1, 10, str.tostring(equity_risk_premium, '##.##') + '%', text_color = erp_color, text_size = size.small)
// ROE
table.cell(dashboard, 0, 11, 'ROE', text_color = dashboard_text_color, text_size = size.small)
roe_color = spy_return_on_equity > 20 ? bullish_color : spy_return_on_equity > 10 ? primary_color : bearish_color
table.cell(dashboard, 1, 11, str.tostring(spy_return_on_equity, '##.#') + '%', text_color = roe_color, text_size = size.small)
// D/E Ratio
table.cell(dashboard, 0, 12, 'D/E Ratio', text_color = dashboard_text_color, text_size = size.small)
de_color = spy_debt_to_equity < 0.5 ? bullish_color : spy_debt_to_equity < 1.0 ? primary_color : bearish_color
table.cell(dashboard, 1, 12, str.tostring(spy_debt_to_equity, '##.##'), text_color = de_color, text_size = size.small)
// Shareholder Yield
table.cell(dashboard, 0, 13, 'Dividend+Buyback', text_color = dashboard_text_color, text_size = size.small)
yield_color = total_shareholder_yield > 4 ? bullish_color : total_shareholder_yield > 2 ? primary_color : bearish_color
table.cell(dashboard, 1, 13, str.tostring(total_shareholder_yield, '##.#') + '%', text_color = yield_color, text_size = size.small)
// Component Scores
component_bg = color.new(neutral_color, 80)
table.cell(dashboard, 0, 14, 'Components', text_color = dashboard_text_color, bgcolor = component_bg, text_size = size.small)
table.cell(dashboard, 1, 14, 'Scores', text_color = dashboard_text_color, bgcolor = component_bg, text_size = size.small)
table.cell(dashboard, 0, 15, 'Regime', text_color = dashboard_text_color, text_size = size.small)
regime_score_color = regime_allocation > 60 ? bullish_color : regime_allocation < 40 ? bearish_color : primary_color
table.cell(dashboard, 1, 15, str.tostring(regime_allocation, '##'), text_color = regime_score_color, text_size = size.small)
table.cell(dashboard, 0, 16, 'Risk', text_color = dashboard_text_color, text_size = size.small)
risk_score_color = risk_allocation > 60 ? bullish_color : risk_allocation < 40 ? bearish_color : primary_color
table.cell(dashboard, 1, 16, str.tostring(risk_allocation, '##'), text_color = risk_score_color, text_size = size.small)
table.cell(dashboard, 0, 17, 'Valuation', text_color = dashboard_text_color, text_size = size.small)
val_score_color = valuation_score > 60 ? bullish_color : valuation_score < 40 ? bearish_color : primary_color
table.cell(dashboard, 1, 17, str.tostring(valuation_score, '##'), text_color = val_score_color, text_size = size.small)
table.cell(dashboard, 0, 18, 'Sentiment', text_color = dashboard_text_color, text_size = size.small)
sent_score_color = sentiment_score > 60 ? bullish_color : sentiment_score < 40 ? bearish_color : primary_color
table.cell(dashboard, 1, 18, str.tostring(sentiment_score, '##'), text_color = sent_score_color, text_size = size.small)
table.cell(dashboard, 0, 19, 'Macro', text_color = dashboard_text_color, text_size = size.small)
macro_score_color = macro_score > 60 ? bullish_color : macro_score < 40 ? bearish_color : primary_color
table.cell(dashboard, 1, 19, str.tostring(macro_score, '##'), text_color = macro_score_color, text_size = size.small)
// ALERTS
// Major allocation changes
alertcondition(smoothed_allocation >= 80 and smoothed_allocation < 80, 'High Equity Allocation', 'Equity allocation reached 80% - Bull market conditions')
alertcondition(smoothed_allocation <= 20 and smoothed_allocation > 20, 'Low Equity Allocation', 'Equity allocation dropped to 20% - Defensive positioning')
// Crisis alerts
alertcondition(is_crisis and not is_crisis , 'CRISIS DETECTED', 'Crisis conditions detected - Reducing equity allocation')
alertcondition(is_severe_crisis and not is_severe_crisis , 'SEVERE CRISIS', 'Severe crisis detected - Maximum defensive positioning')
// Regime changes
regime_changed = market_regime != market_regime
alertcondition(regime_changed, 'Regime Change', 'Market regime has changed')
// Risk management alerts
risk_breach = enable_portfolio_risk_scaling and (actual_portfolio_volatility > target_portfolio_volatility * 1.2 or actual_portfolio_drawdown > max_portfolio_drawdown * 1.2)
alertcondition(risk_breach, 'Risk Breach', 'Portfolio risk exceeds target parameters')
// USAGE
// The indicator displays a recommended equity allocation percentage (0-100%).
// Example: 75% allocation = 75% stocks, 25% cash/bonds.
//
// The model combines market regime analysis (trend, volatility, drawdowns),
// risk management (portfolio-level targeting), valuation metrics (P/E, ERP),
// sentiment indicators (VIX term structure), and macro factors (yield curve,
// credit spreads) into a single allocation signal.
//
// Crisis detection automatically reduces exposure when multiple warning signals
// converge. Alerts available for major allocation shifts and regime changes.
//
// Designed for SPY/S&P 500 portfolio allocation. Adjust component weights and
// risk parameters in settings to match your risk tolerance.
View in Pine
UT Bot + Smart Money Concepts [LuxAlgo]UT Bot + Smart Money Concepts , BUY SELL INDICATOR and support and resistance
3B / 3S System + 99 EMA + Camarilla Pivots3B / 3S System + 99 EMA + Camarilla Pivots, EMA5 above 2 candles buy or SELL
Pivot Points Standard + 9/20/50/200 EMA by NK//@version=6
indicator("Pivot Points Standard + 9/20/50/200 EMA", "Pivots+EMA", overlay=true, max_lines_count=500, max_labels_count=500)
// --- EMA calculations and plots
ema9 = ta.ema(close, 9)
ema20 = ta.ema(close, 20)
ema50 = ta.ema(close, 50)
ema200 = ta.ema(close, 200)
plot(ema9, color=color.green, linewidth=2, title="EMA 9")
plot(ema20, color=color.red, linewidth=2, title="EMA 20")
plot(ema50, color=color.new(color.blue, 0), linewidth=2, title="EMA 50") // dark blue
plot(ema200, color=color.black, linewidth=2, title="EMA 200")
// --- Pivots Inputs
pivotTypeInput = input.string(title="Type", defval="Traditional", options= )
pivotAnchorInput = input.string(title="Pivots Timeframe", defval="Auto", options= )
maxHistoricalPivotsInput = input.int(title="Number of Pivots Back", defval=15, minval=1, maxval=200, display = display.data_window)
isDailyBasedInput = input.bool(title="Use Daily-based Values", defval=true, display = display.data_window, tooltip="When this option is unchecked, Pivot Points will use intraday data while calculating on intraday charts. If Extended Hours are displayed on the chart, they will be taken into account during the pivot level calculation. If intraday OHLC values are different from daily-based values (normal for stocks), the pivot levels will also differ.")
showLabelsInput = input.bool(title="Show Labels", defval=true, group="labels", display = display.data_window)
showPricesInput = input.bool(title="Show Prices", defval=true, group="labels", display = display.data_window)
positionLabelsInput = input.string("Left", "Labels Position", options= , group="labels", display = display.data_window, active = showLabelsInput or showPricesInput)
linewidthInput = input.int(title="Line Width", defval=1, minval=1, maxval=100, group="levels", display = display.data_window)
DEFAULT_COLOR = #FB8C00
showLevel2and3 = pivotTypeInput != "DM"
showLevel4 = pivotTypeInput != "DM" and pivotTypeInput != "Fibonacci"
showLevel5 = pivotTypeInput == "Traditional" or pivotTypeInput == "Camarilla"
pColorInput = input.color(DEFAULT_COLOR, "P ", inline="P", group="levels", display = display.data_window)
pShowInput = input.bool(true, "", inline="P", group="levels", display = display.data_window)
s1ColorInput = input.color(DEFAULT_COLOR, "S1", inline="S1/R1" , group="levels", display = display.data_window)
s1ShowInput = input.bool(true, "", inline="S1/R1", group="levels", display = display.data_window)
r1ColorInput = input.color(DEFAULT_COLOR, " R1", inline="S1/R1", group="levels", display = display.data_window)
r1ShowInput = input.bool(true, "", inline="S1/R1", group="levels", display = display.data_window)
s2ColorInput = input.color(DEFAULT_COLOR, "S2", inline="S2/R2", group="levels", display = display.data_window, active = showLevel2and3)
s2ShowInput = input.bool(true, "", inline="S2/R2", group="levels", display = display.data_window, active = showLevel2and3)
r2ColorInput = input.color(DEFAULT_COLOR, " R2", inline="S2/R2", group="levels", display = display.data_window, active = showLevel2and3)
r2ShowInput = input.bool(true, "", inline="S2/R2", group="levels", display = display.data_window, active = showLevel2and3)
s3ColorInput = input.color(DEFAULT_COLOR, "S3", inline="S3/R3", group="levels", display = display.data_window, active = showLevel2and3)
s3ShowInput = input.bool(true, "", inline="S3/R3", group="levels", display = display.data_window, active = showLevel2and3)
r3ColorInput = input.color(DEFAULT_COLOR, " R3", inline="S3/R3", group="levels", display = display.data_window, active = showLevel2and3)
r3ShowInput = input.bool(true, "", inline="S3/R3", group="levels", display = display.data_window, active = showLevel2and3)
s4ColorInput = input.color(DEFAULT_COLOR, "S4", inline="S4/R4", group="levels", display = display.data_window, active = showLevel4)
s4ShowInput = input.bool(true, "", inline="S4/R4", group="levels", display = display.data_window, active = showLevel4)
r4ColorInput = input.color(DEFAULT_COLOR, " R4", inline="S4/R4", group="levels", display = display.data_window, active = showLevel4)
r4ShowInput = input.bool(true, "", inline="S4/R4", group="levels", display = display.data_window, active = showLevel4)
s5ColorInput = input.color(DEFAULT_COLOR, "S5", inline="S5/R5", group="levels", display = display.data_window, active = showLevel5)
s5ShowInput = input.bool(true, "", inline="S5/R5", group="levels", display = display.data_window, active = showLevel5)
r5ColorInput = input.color(DEFAULT_COLOR, " R5", inline="S5/R5", group="levels", display = display.data_window, active = showLevel5)
r5ShowInput = input.bool(true, "", inline="S5/R5", group="levels", display = display.data_window, active = showLevel5)
type graphicSettings
string levelName
color levelColor
bool showLevel
var graphicSettingsArray = array.from(
graphicSettings.new(" P", pColorInput, pShowInput),
graphicSettings.new("R1", r1ColorInput, r1ShowInput), graphicSettings.new("S1", s1ColorInput, s1ShowInput),
graphicSettings.new("R2", r2ColorInput, r2ShowInput), graphicSettings.new("S2", s2ColorInput, s2ShowInput),
graphicSettings.new("R3", r3ColorInput, r3ShowInput), graphicSettings.new("S3", s3ColorInput, s3ShowInput),
graphicSettings.new("R4", r4ColorInput, r4ShowInput), graphicSettings.new("S4", s4ColorInput, s4ShowInput),
graphicSettings.new("R5", r5ColorInput, r5ShowInput), graphicSettings.new("S5", s5ColorInput, s5ShowInput))
autoAnchor = switch
timeframe.isintraday => timeframe.multiplier <= 15 ? "1D" : "1W"
timeframe.isdaily => "1M"
=> "12M"
pivotTimeframe = switch pivotAnchorInput
"Auto" => autoAnchor
"Daily" => "1D"
"Weekly" => "1W"
"Monthly" => "1M"
"Quarterly" => "3M"
=> "12M"
pivotYearMultiplier = switch pivotAnchorInput
"Biyearly" => 2
"Triyearly" => 3
"Quinquennially" => 5
"Decennially" => 10
=> 1
numOfPivotLevels = switch pivotTypeInput
"Traditional" => 11
"Camarilla" => 11
"Woodie" => 9
"Classic" => 9
"Fibonacci" => 7
"DM" => 3
type pivotGraphic
line pivotLine
label pivotLabel
method delete(pivotGraphic graphic) =>
graphic.pivotLine.delete()
graphic.pivotLabel.delete()
var drawnGraphics = matrix.new()
localPivotTimeframeChange = timeframe.change(pivotTimeframe) and year % pivotYearMultiplier == 0
securityPivotTimeframeChange = timeframe.change(timeframe.period) and year % pivotYearMultiplier == 0
pivotTimeframeChangeCounter(condition) =>
var count = 0
if condition and bar_index > 0
count += 1
count
localPivots = ta.pivot_point_levels(pivotTypeInput, localPivotTimeframeChange)
securityPivotPointsArray = ta.pivot_point_levels(pivotTypeInput, securityPivotTimeframeChange)
securityTimeframe = timeframe.isintraday ? "1D" : timeframe.period
= request.security(syminfo.tickerid, pivotTimeframe, , lookahead = barmerge.lookahead_on)
pivotPointsArray = isDailyBasedInput ? securityPivots : localPivots
affixOldPivots(endTime) =>
if drawnGraphics.rows() > 0
lastGraphics = drawnGraphics.row(drawnGraphics.rows() - 1)
for graphic in lastGraphics
graphic.pivotLine.set_x2(endTime)
if positionLabelsInput == "Right"
graphic.pivotLabel.set_x(endTime)
drawNewPivots(startTime) =>
newGraphics = array.new()
for in pivotPointsArray
levelSettings = graphicSettingsArray.get(index)
if not na(coord) and levelSettings.showLevel
lineEndTime = startTime + timeframe.in_seconds(pivotTimeframe) * 1000 * pivotYearMultiplier
pivotLine = line.new(startTime, coord, lineEndTime, coord, xloc = xloc.bar_time, color=levelSettings.levelColor, width=linewidthInput)
pivotLabel = label.new(x = positionLabelsInput == "Left" ? startTime : lineEndTime,
y = coord,
text = (showLabelsInput ? levelSettings.levelName + " " : "") + (showPricesInput ? "(" + str.tostring(coord, format.mintick) + ")" : ""),
style = positionLabelsInput == "Left" ? label.style_label_right : label.style_label_left,
textcolor = levelSettings.levelColor,
color = #00000000,
xloc=xloc.bar_time)
newGraphics.push(pivotGraphic.new(pivotLine, pivotLabel))
drawnGraphics.add_row(array_id = newGraphics)
if drawnGraphics.rows() > maxHistoricalPivotsInput
oldGraphics = drawnGraphics.remove_row(0)
for graphic in oldGraphics
graphic.delete()
localPivotDrawConditionStatic = not isDailyBasedInput and localPivotTimeframeChange
securityPivotDrawConditionStatic = isDailyBasedInput and securityPivotCounter != securityPivotCounter
var isMultiYearly = array.from("Biyearly", "Triyearly", "Quinquennially", "Decennially").includes(pivotAnchorInput)
localPivotDrawConditionDeveloping = not isDailyBasedInput and time_close == time_close(pivotTimeframe) and not isMultiYearly
securityPivotDrawConditionDeveloping = false
if (securityPivotDrawConditionStatic or localPivotDrawConditionStatic)
affixOldPivots(time)
drawNewPivots(time)
var FIRST_BAR_TIME = time
if (barstate.islastconfirmedhistory and drawnGraphics.columns() == 0)
if not na(securityPivots) and securityPivotCounter > 0
if isDailyBasedInput
drawNewPivots(FIRST_BAR_TIME)
else
runtime.error("Not enough intraday data to calculate Pivot Points. Lower the Pivots Timeframe or turn on the 'Use Daily-based Values' option in the indicator settings.")
else
runtime.error("Not enough data to calculate Pivot Points. Lower the Pivots Timeframe in the indicator settings.")
RSI5vsRSI14_v2//@version=5
indicator("RSI5vsRSI14_v2", shorttitle="RSI5vsRSI14_v2", overlay=false)
plot(ta.rsi(close, 14), title="RSI14", color=color.red)
plot(ta.rsi(close, 5), title="RSI5", color=color.green)
RSI Rate of Change (ROC of RSI)The RSI Rate of Change (ROC of RSI) indicator measures the speed and momentum of changes in the RSI, helping traders identify early trend shifts, strength of price moves, and potential reversals before they appear on the standard RSI.
While RSI shows overbought and oversold conditions, the ROC of RSI reveals how fast RSI itself is rising or falling, offering a deeper view of market momentum.
How the Indicator Works
1. RSI Calculation
The indicator first calculates the classic Relative Strength Index (RSI) using the selected length (default 14). This measures the strength of recent price movements.
2. Rate of Change (ROC) of RSI
Next, it computes the Rate of Change (ROC) of the RSI over a user-defined period.
This shows:
Positive ROC → RSI increasing quickly → strong bullish momentum
Negative ROC → RSI decreasing quickly → strong bearish momentum
ROC crossing above/below 0 → potential early trend shift
What You See on the Chart
Blue Line: RSI
Red Line: ROC of RSI
Grey dotted Zero Line: Momentum reference
Why Traders Use It
The RSI ROC helps you:
Detect momentum reversals early
Spot bullish and bearish accelerations not visible on RSI alone
Identify exhaustion points before RSI reaches extremes
Improve entry/exit precision in trend and swing trading
Validate price breakouts or breakdowns with momentum confirmation
Best For
Swing traders
Momentum traders
Reversal traders
Trend-following systems needing early confirmation signals
EMV// This Pine Script® code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
//@version=5
indicator("EMV", overlay=false)
N = input.int(14, "N")
M = input.int(9, "M")
// ==== VOLUME ====
maVol = ta.sma(volume, N)
VOLUME = maVol / volume
// ==== MID ====
hl = high + low
MID = 100 * (hl - hl ) / hl
// ==== HL_RANGE ====
HL_RANGE = ta.sma(high - low, N)
// ==== EMV ====
EMV_raw = MID * VOLUME * (high - low) / HL_RANGE
EMV = ta.sma(EMV_raw, N)
// ==== MAEMV ====
MAEMV = ta.sma(EMV, M)
plot(EMV, color=color.blue, title="EMV")
plot(MAEMV, color=color.orange, title="MAEMV")
EDU PRO LITE – Divergence + Fake Breakout + CandleThis indicator is created for educational purposes only. It displays EMA, RSI, and the previous day’s high/low to help users understand market trends and price movement. This script does not provide any trading signals, buy/sell recommendations, or entry indications. All trading decisions are entirely outside the scope of this indicator.”
8 EMA Candle Color ChangeableJust a quick indicator that I threw together that shows the 8 EMA and changes the candle colors if the candle closes above or below the EMA.
