Industry Indices Comparison

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © kruskakli
//@version=6
// @description Industry Sector Performance Analyzer
//
// PURPOSE:
// This indicator provides real-time analysis of major industry sectors' performance relative to the broader market,
// helping traders and investors identify sector rotation, market leadership, and potential trading opportunities.
//
// KEY FEATURES:
// 1. Comprehensive Sector Coverage:
//    - Tracks 11 major S&P 500 sector ETFs: Technology (XLK), Financials (XLF), Energy (XLE), Healthcare (XLV),
//      Industrials (XLI), Basic Materials (XLB), Consumer Staples (XLP), Consumer Discretionary (XLY),
//      Real Estate (XLRE), Utilities (XLU), and Telecommunications (XLC)
//    - Compares each sector against a benchmark index (default: SPY)
//
// 2. Performance Metrics:
//    - Current Price: Real-time price for each sector ETF
//    - Performance %: Price change over selected timeframe (1D/1W/1M/3M)
//    - Relative Strength: Sector performance vs benchmark (outperform/underperform)
//    - Trend Indicator: Visual markers (▲ up, ▼ down, − neutral)
//    - Volume Analysis: Trading volume in millions of shares
//
// 3. Visual Features:
//    - Color-coded performance (Green = outperform, Red = underperform)
//    - Customizable table position (4 corner options)
//    - Adjustable text size (Tiny/Small/Normal)
//    - Clean, organized data presentation
//
// REFERENCES & THEORY:
// 1. Market Trend Analysis:
//    - Dow Theory (Charles Dow): Markets move in trends; primary, secondary, and minor
//    - Reference: "Technical Analysis of Stock Trends" by Robert D. Edwards and John Magee
//    - Link: https://www.investopedia.com/terms/d/dowtheory.asp
//
// 2. Sector Rotation Model:
//    - Sam Stovall's Sector Rotation: Sectors move in predictable patterns through economic cycles
//    - Reference: "Standard & Poor's Guide to Sector Investing" by Sam Stovall
//    - Economic Cycle Stages:
//      * Early Recession: Consumer Staples, Utilities, Healthcare
//      * Full Recession: Consumer Staples, Utilities
//      * Early Recovery: Technology, Basic Materials, Industrials
//      * Full Recovery: Energy, Materials, Industrials
//    - Link: https://www.investopedia.com/articles/trading/05/020305.asp
//
// 3. Relative Strength Theory:
//    - Developed by J. Welles Wilder Jr.
//    - Reference: "New Concepts in Technical Trading Systems"
//    - Principle: Strong sectors tend to stay strong; momentum persists
//    - Link: https://www.investopedia.com/terms/r/relativestrength.asp
//
// 4. Volume Analysis:
//    - Richard Wyckoff's Market Theory
//    - Reference: "Studies in Tape Reading" by Richard D. Wyckoff
//    - Importance of volume confirmation in trend analysis
//    - Link: https://www.investopedia.com/articles/active-trading/070715/making-money-wyckoff-way.asp
//
// 5. Modern Applications:
//    - "Intermarket Analysis" by John J. Murphy
//    - "Trading with Intermarket Analysis" by John J. Murphy
//    - Explains relationships between different market sectors
//    - Link: https://www.investopedia.com/terms/i/intermarket-analysis.asp
//
// HOW IT WORKS:
// 1. Data Collection:
//    - Fetches real-time price and volume data for each sector ETF
//    - Calculates performance over user-selected timeframe
//    - Computes relative performance against benchmark
//
// 2. Analysis:
//    - Determines sector trends based on price movement
//    - Identifies outperforming/underperforming sectors
//    - Monitors volume patterns for confirmation
//
// 3. Display:
//    - Organizes data in an easy-to-read table format
//    - Updates continuously during market hours
//    - Highlights important metrics through color coding
//
// PRACTICAL APPLICATIONS:
// 1. Sector Rotation Strategy:
//    - Identify which sectors are leading/lagging
//    - Spot early signs of sector rotation
//    - Time sector-based trades
//
// 2. Risk Management:
//    - Monitor sector exposure
//    - Identify market trends through sector performance
//    - Validate broader market moves
//
// 3. Portfolio Management:
//    - Guide sector allocation decisions
//    - Identify potential sector opportunities
//    - Track sector momentum
//
// USAGE INSTRUCTIONS:
// 1. Setup:
//    - Add indicator to your chart
//    - Select preferred benchmark (default: SPY)
//    - Choose comparison timeframe (1D/1W/1M/3M)
//    - Adjust table position and text size if needed
//
// 2. Analysis:
//    - Green/Red colors show outperformance/underperformance
//    - Use trend arrows for quick direction reference
//    - Compare volume patterns with price movement
//    - Monitor relative performance vs benchmark
//
// 3. Trading Applications:
//    - Long opportunities: Focus on outperforming sectors
//    - Short opportunities: Consider underperforming sectors
//    - Pair trades: Compare related sectors
//    - Sector rotation: Track performance shifts
//
// CUSTOMIZATION OPTIONS:
// - Benchmark Selection: Choose any market index
// - Timeframe: Select from 1D, 1W, 1M, or 3M comparisons
// - Visual Settings: Adjust table position, text size, and colors
// - Sector Names: Customize display names for each sector
//
// This indicator is ideal for traders and investors who want to:
// - Make informed sector-based trading decisions
// - Understand market dynamics through sector analysis
// - Implement sector rotation strategies
// - Monitor sector-specific opportunities
// - Validate broader market trends

indicator("Industry Indices Comparison", overlay=true)

// Color constants
GREEN = color.new(#26a69a, 0)
RED = color.new(#ef5350, 0)
GRAY = color.new(#787B86, 0)
TEXT_COLOR = color.new(#FFFFFF, 0)

// Input parameters
// Benchmark selection
benchmarkSymbol = input.symbol("SPY", "Benchmark Index", group="Index Selection")

// Industry indices selection
index1Symbol = input.symbol("XLK", "Technology Index", group="Index Selection")
index2Symbol = input.symbol("XLF", "Financials Index", group="Index Selection")
index3Symbol = input.symbol("XLE", "Energy Index", group="Index Selection")
index4Symbol = input.symbol("XLV", "Healthcare Index", group="Index Selection")
index5Symbol = input.symbol("XLI", "Industrials Index", group="Index Selection")
index6Symbol = input.symbol("XLB", "Materials Index", group="Index Selection")
index7Symbol = input.symbol("XLP", "Consumer Staples Index", group="Index Selection")
index8Symbol = input.symbol("XLY", "Consumer Discretionary Index", group="Index Selection")
index9Symbol = input.symbol("XLRE", "Real Estate Index", group="Index Selection")
index10Symbol = input.symbol("XLU", "Utilities Index", group="Index Selection")
index11Symbol = input.symbol("XLC", "Telecommunications Index", group="Index Selection")

// Industry names
index1Name = input.string("Technology", "Technology Name", group="Industry Names")
index2Name = input.string("Financials", "Financials Name", group="Industry Names")
index3Name = input.string("Energy", "Energy Name", group="Industry Names")
index4Name = input.string("Healthcare", "Healthcare Name", group="Industry Names")
index5Name = input.string("Industrials", "Industrials Name", group="Industry Names")
index6Name = input.string("Materials", "Materials Name", group="Industry Names")
index7Name = input.string("Consumer Staples", "Consumer Staples Name", group="Industry Names")
index8Name = input.string("Consumer Discretionary", "Consumer Discretionary Name", group="Industry Names")
index9Name = input.string("Real Estate", "Real Estate Name", group="Industry Names")
index10Name = input.string("Utilities", "Utilities Name", group="Industry Names")
index11Name = input.string("Telecommunications", "Telecommunications Name", group="Industry Names")

// Timeframe selection
timeframeInput = input.string("1M", "Comparison Timeframe", options=["1D", "1W", "1M", "3M"], group="Settings")

// Visual settings
tablePosition = input.string("Top Right", "Table Position", options=["Top Right", "Top Left", "Bottom Right", "Bottom Left"], group="Appearance")
textSize = input.string("Small", "Text Size", options=["Tiny", "Small", "Normal"], group="Appearance")
outperformColor = input.color(GREEN, "Outperform Color", group="Appearance")
underperformColor = input.color(RED, "Underperform Color", group="Appearance")

// Helper functions
getTimeframeOffset() =>
    offset = switch timeframeInput
        "1D" => 1
        "1W" => 7
        "1M" => 30
        "3M" => 90
        => 30  // Default to 1M

getPerformance(sym) =>
    float performance = 0.0
    offset = getTimeframeOffset()
    [currentClose, prevClose] = request.security(sym, timeframe.period, [close, close[offset]])
    if prevClose != 0
        performance := ((currentClose - prevClose) / prevClose) * 100
    performance

// Calculate performances
var benchmarkPerf = 0.0
var index1Perf = 0.0
var index2Perf = 0.0
var index3Perf = 0.0
var index4Perf = 0.0
var index5Perf = 0.0
var index6Perf = 0.0
var index7Perf = 0.0
var index8Perf = 0.0
var index9Perf = 0.0
var index10Perf = 0.0
var index11Perf = 0.0

if barstate.islast
    benchmarkPerf := getPerformance(benchmarkSymbol)
    index1Perf := getPerformance(index1Symbol)
    index2Perf := getPerformance(index2Symbol)
    index3Perf := getPerformance(index3Symbol)
    index4Perf := getPerformance(index4Symbol)
    index5Perf := getPerformance(index5Symbol)
    index6Perf := getPerformance(index6Symbol)
    index7Perf := getPerformance(index7Symbol)
    index8Perf := getPerformance(index8Symbol)
    index9Perf := getPerformance(index9Symbol)
    index10Perf := getPerformance(index10Symbol)
    index11Perf := getPerformance(index11Symbol)

// Calculate relative performances
relativePerf1 = index1Perf - benchmarkPerf
relativePerf2 = index2Perf - benchmarkPerf
relativePerf3 = index3Perf - benchmarkPerf
relativePerf4 = index4Perf - benchmarkPerf
relativePerf5 = index5Perf - benchmarkPerf
relativePerf6 = index6Perf - benchmarkPerf
relativePerf7 = index7Perf - benchmarkPerf
relativePerf8 = index8Perf - benchmarkPerf
relativePerf9 = index9Perf - benchmarkPerf
relativePerf10 = index10Perf - benchmarkPerf
relativePerf11 = index11Perf - benchmarkPerf

// Format numbers
formatPerf(value) =>
    str.tostring(math.round(value, 2)) + "%"

// Table position
var tablePos = switch tablePosition
    "Top Right" => position.top_right
    "Top Left" => position.top_left
    "Bottom Right" => position.bottom_right
    "Bottom Left" => position.bottom_left
    => position.top_right

// Text size
var txtSize = switch textSize
    "Tiny" => size.tiny
    "Small" => size.small
    "Normal" => size.normal
    => size.small

// Create and populate table
var table perfTable = table.new(tablePos, 6, 13, border_width=1)

// Function to determine trend
getTrendSymbol(perf) =>
    perf > 0 ? "▲" : perf < 0 ? "▼" : "−"

// Function to get cell color based on performance
getCellColor(perf) =>
    perf > 0 ? outperformColor : perf < 0 ? underperformColor : GRAY

// Function to add index row
addIndexRow(sym, name, perf, relPerf, rowNum) =>
    [price, vol] = request.security(sym, timeframe.period, [close, volume])
    table.cell(perfTable, 0, rowNum, name, text_color=TEXT_COLOR, text_size=txtSize)
    table.cell(perfTable, 1, rowNum, str.tostring(math.round(price, 2)), text_color=TEXT_COLOR, text_size=txtSize)
    table.cell(perfTable, 2, rowNum, formatPerf(perf), text_color=TEXT_COLOR, bgcolor=getCellColor(perf), text_size=txtSize)
    table.cell(perfTable, 3, rowNum, formatPerf(relPerf), text_color=TEXT_COLOR, bgcolor=getCellColor(relPerf), text_size=txtSize)
    table.cell(perfTable, 4, rowNum, getTrendSymbol(perf), text_color=getCellColor(perf), text_size=txtSize)
    table.cell(perfTable, 5, rowNum, str.tostring(math.round(vol/1000000, 1)) + "M", text_color=TEXT_COLOR, text_size=txtSize)

if barstate.islast
    // Headers
    table.cell(perfTable, 0, 0, "Industry", bgcolor=GRAY, text_color=TEXT_COLOR, text_size=txtSize)
    table.cell(perfTable, 1, 0, "Price", bgcolor=GRAY, text_color=TEXT_COLOR, text_size=txtSize)
    table.cell(perfTable, 2, 0, timeframeInput + " %", bgcolor=GRAY, text_color=TEXT_COLOR, text_size=txtSize)
    table.cell(perfTable, 3, 0, "vs " + benchmarkSymbol, bgcolor=GRAY, text_color=TEXT_COLOR, text_size=txtSize)
    table.cell(perfTable, 4, 0, "Trend", bgcolor=GRAY, text_color=TEXT_COLOR, text_size=txtSize)
    table.cell(perfTable, 5, 0, "Volume", bgcolor=GRAY, text_color=TEXT_COLOR, text_size=txtSize)

    // Benchmark row
    [benchPrice, benchVol] = request.security(benchmarkSymbol, timeframe.period, [close, volume])
    table.cell(perfTable, 0, 1, benchmarkSymbol, text_color=TEXT_COLOR, text_size=txtSize)
    table.cell(perfTable, 1, 1, str.tostring(math.round(benchPrice, 2)), text_color=TEXT_COLOR, text_size=txtSize)
    table.cell(perfTable, 2, 1, formatPerf(benchmarkPerf), text_color=TEXT_COLOR, bgcolor=getCellColor(benchmarkPerf), text_size=txtSize)
    table.cell(perfTable, 3, 1, "Benchmark", text_color=TEXT_COLOR, text_size=txtSize)
    table.cell(perfTable, 4, 1, getTrendSymbol(benchmarkPerf), text_color=getCellColor(benchmarkPerf), text_size=txtSize)
    table.cell(perfTable, 5, 1, str.tostring(math.round(benchVol/1000000, 1)) + "M", text_color=TEXT_COLOR, text_size=txtSize)

    // Add industry index rows
    addIndexRow(index1Symbol, index1Name, index1Perf, relativePerf1, 2)
    addIndexRow(index2Symbol, index2Name, index2Perf, relativePerf2, 3)
    addIndexRow(index3Symbol, index3Name, index3Perf, relativePerf3, 4)
    addIndexRow(index4Symbol, index4Name, index4Perf, relativePerf4, 5)
    addIndexRow(index5Symbol, index5Name, index5Perf, relativePerf5, 6)
    addIndexRow(index6Symbol, index6Name, index6Perf, relativePerf6, 7)
    addIndexRow(index7Symbol, index7Name, index7Perf, relativePerf7, 8)
    addIndexRow(index8Symbol, index8Name, index8Perf, relativePerf8, 9)
    addIndexRow(index9Symbol, index9Name, index9Perf, relativePerf9, 10)
    addIndexRow(index10Symbol, index10Name, index10Perf, relativePerf10, 11)
    addIndexRow(index11Symbol, index11Name, index11Perf, relativePerf11, 12)