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Master Portfolio Lab PRO [The Quant Science]The Master Portfolio Lab PRO is an advanced quantitative analysis terminal designed to transform TradingView into a powerful multi-asset portfolio management engine. Developed with institutional-grade calculation logic, this tool allows you to simulate, monitor, and analyze the combined performance of 12 customizable assets within a single, dynamic environment. In a world where trading is often hyper-focused on a single ticker, the Master Lab enables you to level up: stop looking at the tree and start managing the forest. 🧪 USAGE The script is designed for traders and investors looking to validate asset allocation strategies or monitor their real-market exposure against a specific benchmark. 🧬 How to configure it: Asset Allocation: Enter your desired tickers (Crypto, Stocks, Forex, or Commodities) and assign a percentage weight to each slot. Ensure the total weight equals 100%. Capital Configuration: Choose from predefined capital profiles (from $1k to $1M) or set a custom capital amount for precise simulations. Costs & Fees: Set a "Portfolio Fee" to reflect transaction costs and generate a realistic, non-theoretical equity curve. Benchmark Comparison: Select a reference index (e.g., S&P 500 or Bitcoin) to measure the Alpha generated by your active management. 🧪 DETAILS 🧬 Multi-Mode Analysis Engine The script offers four independent visualization modes, instantly switchable via the settings menu: Cumulative (%): Comparative analysis between the portfolio's percentage return and the benchmark. Equity ($): Monetary monitoring of net liquidity and cash growth. SMA Ribbon: Identification of the portfolio's trend regime using moving averages applied directly to the equity curve. Volatility: Real-time monitoring of portfolio "thermal stress" via smoothed Standard Deviation (WMA). 🧬 Alpha-Glow Logic The system utilizes a high-fidelity visual architecture based on dynamic gradients. When the portfolio outperforms the benchmark (Positive Alpha), the fill area illuminates, providing immediate psychological feedback on the quality of your management. 🧬 Real-Time Dashboard An integrated table in the bottom-right corner processes live data to provide: Net Value: Current portfolio value, including PnL and costs. Return %: Total return from the selected starting anchor point. Alpha vs Index: The "holy grail" of trading—exactly how much value you are adding compared to a passive investment. 🧪 SETTINGS 🧬 Capital Configuration 1) Fixed Capital: Toggle quick selectors for standard account sizes. 2) Custom Capital: Manual input for simulating specific real-world accounts. 🧬 Date Period Analysis Allows you to set a precise start date (Day/Month/Year) to analyze portfolio performance during specific macroeconomic events or historical cycles. The Master Portfolio Lab PRO was born from the need to overcome TradingView's native limitations in multi-symbol management. By utilizing normalization techniques and iterative Rate of Change (ROC) calculations, we have created a framework capable of simulating an entire investment fund with surgical precision.
Индикатор Pine Script®
от thequantscience
Risk Management & Position size calculator (FinPip)# Risk Management & Position size calculator (FinPip) **Size your trades by risk.** Set your capital, risk %, entry and stop-loss — the indicator gives you position size in units, a take-profit level from your reward:risk ratio, and a clear summary on the chart. Works on any symbol (stocks, forex, crypto, futures). Pine Script v6 · Mozilla Public License 2.0 --- ## How to add to your chart 1. In TradingView, open **Indicators** (or search **“Risk Management”** / **“Finpip Risk Manager”**). 2. Select **Risk Management & Position size calculator (FinPip)** and add it to the chart. 3. Open **Settings** (gear on the indicator label) to set your capital, risk %, entry price, and stop-loss price. --- ## What you get - **Entry, Stop-Loss & Take-Profit** — Drawn as horizontal lines on the chart (direction is Long if Entry > SL, Short if Entry < SL). - **Position size** — Number of units (shares, lots, contracts) so that if price hits your SL, you lose only your chosen risk amount. - **Info panel** — Capital, Risk %, Risk $, Reward $, R:R, Size (units), Risk/Unit, Entry/SL/TP prices, and direction. You can move the panel to any corner and change text size. --- ## Settings (inputs) **Risk & Capital** - **Total Account Capital ($)** — Capital used for risk (default 10000). - **Risk per Trade (%)** — % of capital to risk on this trade (default 2.5%). **Trade Levels** - **Entry Price** — Your planned or actual entry. - **Stop-Loss Price** — Exit price if the trade goes against you. **Profit Target** - **Reward:Risk Ratio** — Target as multiple of risk (e.g. 4 = 4R). TP is placed at Entry ± (SL distance × R:R). **Display** - **Show Info Panel** — On/off for the metrics table. - **Round to Whole Shares/Units** — On for stocks/shares (whole numbers); off for forex/fractional. - **Panel Position** — Top Left, Top Right, Bottom Left, Bottom Right. - **Text Size** — Tiny / Small / Normal / Large. - **Show Level Labels** — Price labels for Entry, SL, TP on the right. **Colors** — Customize Entry, SL, TP lines and panel colors. --- ## How it’s calculated - **Risk $** = Capital × (Risk % ÷ 100) - **Risk per unit** = |Entry − Stop-Loss| - **Position size** = Risk $ ÷ Risk per unit (optionally rounded down to whole units) - **Take-profit** = Entry + (Risk per unit × R:R) for Long, or Entry − (…) for Short - **Reward $** = Risk $ × R:R --- ## If something looks wrong - **“Invalid setup — Entry and SL must be different prices”** — Your Entry and Stop-Loss are the same; change one of them. - **“0 units (rounds to 0)”** — Risk $ is too small for the distance to SL with whole units. Increase capital or risk %, or turn off **Round to Whole Shares/Units** to see fractional size. --- *Published on TradingView. Open source (MPL 2.0).*
Индикатор Pine Script®
от finpipcom
Tribute To Peter LynchTribute To Peter Lynch - Fund Manager Simulator "Know what you own, and know why you own it." - Peter Lynch WHO WAS PETER LYNCH? Peter Lynch is one of the greatest investors who ever lived. From 1977 to 1990, he managed the Fidelity Magellan Fund and turned it from $18 million into $14 billion in assets. He averaged a 29.2% annual return for 13 straight years, making Magellan the best-performing mutual fund in the world. What made Lynch different? He believed regular people could beat Wall Street. He bought stocks in companies he understood - he'd ask minivan owners at movie theaters what they thought of their Chrysler, he'd notice the local factory hiring more people, he'd pay attention to which donut shop had the longest line. He called this your "investor's edge" - the things you already know from your daily life that Wall Street analysts sitting in their offices can't see. Lynch famously said: "If you can tell your stock story to a fifth grader and they understand it, you've got a good one. The more complicated the story, the more likely it is to fall apart." He also had the stomach for downturns. During his 13 years at Magellan, the market dropped 10% or more NINE times, and Magellan fell right along with it every single time. But he never panicked. He knew that behind every stock is a company, and if the company is doing well, the stock will eventually follow. WHAT THIS SCRIPT DOES This is a fund manager simulator built entirely around Peter Lynch's investing philosophy. It's not a technical indicator. There are no moving averages, no RSI, no MACD. Lynch was a fundamental investor - he cared about what a company earns, how fast it's growing, how much debt it has, and whether the story makes sense. The script classifies any stock into one of Lynch's 6 categories, scores it on a 0-100 Lynch Score, evaluates the fundamental story, and generates buy/sell signals based purely on the same criteria Lynch used at Magellan. THE 6 STOCK CATEGORIES Lynch believed you can't treat all stocks the same. A 50% gain on a Slow Grower is fantastic and probably time to sell. The same 50% on a Fast Grower could be just the beginning of a 10-bagger. He broke every stock into 6 categories: 1. FAST GROWERS (20%+ earnings growth) Companies growing earnings fast, typically small to mid-sized. These are the potential 10-baggers. Lynch looked for PEG ratios below 1.0 and used his "baseball inning" concept - you want to buy these in innings 2-5, when the formula is proven but there's still a long runway ahead. Think of Walmart when it only had a few hundred stores, or Microsoft 3 years after its IPO. 2. SLOW GROWERS (3-8% earnings growth) Large, mature companies that grow about as fast as the overall economy. You buy these for the dividend. Lynch looked for steady, rising dividends with a low payout ratio (so the dividend is safe). If the dividend yield drops or the payout ratio gets too high, it's time to move on. 3. STALWARTS (10-14% earnings growth) Big, solid companies that aren't going to disappear but aren't going to triple overnight either. Lynch's rule: take your 30-50% gain and rotate the money into another Stalwart. These are your portfolio's defense - they hold up in recessions and won't go bankrupt. 4. CYCLICALS (tied to the economic cycle) Companies in industries like autos, airlines, steel, and chemicals that boom and bust with the economy. Here's Lynch's counterintuitive trick: buy cyclicals when the PE is HIGH (that means earnings are at the trough and about to recover) and sell when the PE is LOW (earnings have peaked and are about to fall). This is the opposite of how PE works for every other category. 5. TURNAROUNDS (beaten down, potential recovery) Companies in deep trouble that might recover. Lynch always checked: does this company have enough cash to survive? Is there a real plan (new management, cost cutting, selling bad divisions)? He said don't buy on hope - wait for actual evidence the turnaround is working. But when they work, the upside can be enormous. 6. ASSET PLAYS (hidden value) Companies sitting on assets the market doesn't see or doesn't value - real estate, patents, brand names, cash on the balance sheet. Lynch's example: Disney after it opened Epcot. Growth slowed, but the company was sitting on the Disney name, all that Florida land, and a library of characters worth billions that were carried on the books for nothing. BAR REPLAY - WHERE THIS SCRIPT REALLY SHINES This script was built with TradingView's Bar Replay feature in mind. Bar Replay is what transforms this from a backtest into a fund manager simulator. HOW TO USE BAR REPLAY: 1. Add the script to your chart 2. Pick any stock and classify it (Fast Grower, Stalwart, etc.) 3. Set your fundamental inputs if using Manual mode, or let Auto mode pull TradingView financial data 4. Click the Bar Replay button on your toolbar (the clock icon with a rewind arrow) 5. Pick a starting date - maybe go back 5 years, or 10, or start right before a crash 6. Press Play and watch the simulation unfold bar by bar WHAT YOU'LL SEE IN BAR REPLAY: - The Lynch Score updates in real time as fundamentals change each quarter - The Story Status shifts: "Growth Accelerating" might become "Growth Slowing" as the company matures - Buy and sell signals fire when Lynch's criteria are met - The P&L tracker shows your position gain in real time - The Peter Lynch portrait in the corner changes color based on how your portfolio is doing: -- Equity rising: Lynch appears in bright GREEN against a deep BLUE background, getting more vivid as gains grow -- Heavy drawdown: Lynch turns CRIMSON RED against a BLACK background -- Stop loss hit: The portrait FLASHES RED as a warning -- Neutral: Lynch appears in CYAN/TEAL against dark navy - The daily Lynch wisdom quote rotates, keeping you in his mindset BAR REPLAY SCENARIOS TO TRY: - Start replay on Apple (AAPL) in 2003 as a Turnaround - watch it reclassify into a Fast Grower - Replay Ford (F) through 2008-2012 as a Cyclical - see Lynch's high-PE-buy rule in action - Run Coca-Cola (KO) from 1990 as a Stalwart - practice the 30-50% rotate rule - Try any stock through the 2020 COVID crash - Lynch survived 9 crashes at Magellan, now you can experience one SETTINGS GUIDE - EXPLAINED SIMPLY Think of the settings like customizing a video game character before you play. Here's what each group does: GROUP 1: STOCK TYPE "What kind of company is this?" Pick one of the 6 categories. This changes ALL the rules the script uses. Baseball Inning: Where is this company in its life? - Innings 1-3 = Young, lots of room to grow (like a kid) - Innings 4-6 = Middle age, proving itself - Innings 7-9 = Old, running out of gas Early innings get a score bonus. Late innings get a penalty. GROUP 2: FUNDAMENTAL DATA "Where do the numbers come from?" - Auto mode: TradingView pulls real financial data (EPS, revenue, debt) automatically. Best for stocks. - Manual mode: You type in the numbers yourself. Use this for crypto, forex, or if Auto data looks wrong. GROUP 3: PEG & VALUATION "How do I know if a stock is cheap or expensive?" PEG = Price-to-Earnings divided by Growth rate. - PEG of 1.0 = fairly priced (PE matches growth rate) - PEG below 1.0 = cheap (you're paying less than what the growth is worth) - PEG above 2.0 = expensive The Buy Threshold (default 1.0) means: "only buy if PEG is at or below this number." The Sell Threshold (default 2.0) means: "sell if PEG goes above this number." GROUP 4: BALANCE SHEET HEALTH "Is this company in good financial shape?" - Max Debt/Equity: How much debt is too much? Default 0.50 means total debt shouldn't be more than half of the company's net worth. Lynch always checked the balance sheet. - Min Cash Ratio: Does the company have enough cash? Important for Turnarounds. - Max Payout Ratio: Is the company paying out too much of its earnings as dividends? Above 80% means the dividend might get cut. GROUP 5: CATEGORY RULES "Special rules for each stock type." These are the specific thresholds for each of the 6 categories. The defaults are based on Lynch's actual criteria from his books. Examples: - Fast Grower Min Growth 20%: Lynch said a Fast Grower needs at least 20% earnings growth - Stalwart Rotate Target 40%: Lynch rotated Stalwarts after 30-50% gains - Cyclical Trough PE 25: Buy cyclicals when PE is HIGH (counterintuitive!) GROUP 6: STRATEGY SETTINGS "Should the Lynch Score control the trades?" - Score Gate OFF (default): The Lynch Score is just a meter showing conviction. Trades fire based on category rules alone. - Score Gate ON: The Lynch Score must be above the entry threshold to buy and below the exit threshold to sell. This adds an extra filter. GROUP 7: RISK MANAGEMENT "How much can I lose on one trade?" - Stop Loss: If the stock drops this % from your entry price, sell automatically. Default 15%. - Take Profit: If the stock rises this % from your entry price, sell automatically. Default 100% (a double). GROUP 8: DISPLAY "What do I see on the chart?" - Show/hide the Lynch portrait, the dashboard panel, and buy/sell signals - Customize your bull/bear/neutral colors THE DASHBOARD The top-right panel shows everything Lynch would want to see at a glance: - LYNCH SCORE: A 0-100 conviction meter with a visual bar. Built from 5 sub-scores: -- PEG Score (0-25): Is the stock cheap relative to its growth? -- Earnings Score (0-25): Is earnings growth meeting the target for this category? -- Balance Sheet Score (0-20): How healthy is the financial structure? -- Margin Score (0-15): Are profit margins good and expanding? -- Dividend Score (0-15): Is the dividend policy appropriate for this category? - STORY STATUS: The current fundamental narrative in plain English -- "Growth Accelerating", "Trough - Buy Zone", "Turnaround Succeeding", etc. - SIGNAL: The specific reason behind the current buy or sell signal - P&L: When in a trade, shows your unrealized gain/loss and warns Stalwart holders when approaching the rotate target - VALUATION: PEG ratio, PE ratio, and fair value comparison - EARNINGS: EPS, EPS growth rate, and revenue growth vs. category targets - BALANCE SHEET: Debt/equity ratio and net cash per share - MARGINS: Profit margin trend (expanding or contracting) - DIVIDENDS: Yield, payout ratio, and sustainability assessment - DAILY WISDOM: A rotating quote from Peter Lynch to keep you grounded RESEARCH & SOURCES This script was built using the following primary sources, all studied extensively: 1. "One Up On Wall Street" by Peter Lynch with John Rothchild (1989) share.google Lynch's first book laying out his complete investment philosophy - the 6 categories, PEG ratio, the "story" framework, and the idea that everyday investors have edges over professionals. 2. "Beating the Street" by Peter Lynch with John Rothchild (1993) share.google Lynch's second book where he walks through his actual portfolio decisions at Magellan, explains his mutual fund strategy, and shows step-by-step how he picks stocks. 328 pages of real-world application. 3. "The Peter Lynch Playbook" compiled by Mayur Jain (@mjbaldbard) share.google A comprehensive set of notes summarizing both Lynch books plus interview snippets. Covers portfolio allocation percentages, category-specific checklists, the 2-minute drill for each stock type, risk/reward profiles, and sell criteria. This document was instrumental in translating Lynch's qualitative approach into quantifiable rules. 4. Peter Lynch's "Stock Shop" Video Consultation (Fidelity Archives) youtu.be A full-length video presentation where Lynch personally explains his approach to categorizing stocks, building stories, understanding PE ratios, evaluating balance sheets, and managing risk. Key quotes transcribed and used to calibrate the scoring engine and story status logic. IMPORTANT NOTES - This is a STRATEGY script, not an indicator. It generates actual backtestable trades. - The script is purely fundamental. No technical analysis. Lynch didn't use charts to make decisions. - Auto mode requires stocks with TradingView financial data. For other instruments, use Manual mode. - The Lynch Score is a CONVICTION METER by default, not a trade gate. Enable Score Gate in settings if you want it to filter trades. - All portrait coloring is non-repainting. Colors only update on confirmed (closed) bars. - Start with Bar Replay. That's where the magic happens. Watch the story unfold bar by bar, just like Lynch did at his desk at Fidelity every morning. "The key organ is the stomach, not the brain." - Peter Lynch
Стратегия Pine Script®
от Beck_Mullen
Top 40 Best Performing Nasdaq Stocks with Advanced Stats ScreenWelcome to the CustomQuantLabs Advanced Stats Screener. This dashboard is designed for traders who need more than just price action—it provides a comprehensive, institutional-grade view of the "Top 40" performing assets in the Nasdaq (or any watchlist of your choice) at a single glance. Instead of flipping through 40 different charts, this screener aggregates Performance Metrics and Advanced Statistical Risk Models into one clean, heatmap-style dashboard. It helps you instantly identify outliers, trend leaders, and potential mean-reversion setups. Key Features 1. Multi-Timeframe Performance Heatmap Instantly spot momentum. The dashboard tracks returns across 5 key timeframes, color-coded with a dynamic heatmap (Bright Green for leaders, Bright Red for laggards): Week% (Short-term momentum) Month% & Quarter% (Medium-term trend) 6M% & 12M% (Long-term secular trend) 2. Institutional Risk Metrics (Advanced Stats) We go beyond simple percentage changes. This screener calculates complex statistical formulas for every single ticker in real-time: Kelly Criterion (%): A money management formula used to determine optimal position size based on win probability and return ratio. A higher Kelly % suggests a statistically stronger "edge" based on recent history. Sharpe Ratio: Measures risk-adjusted return. How much return are you getting for every unit of risk? (Values > 1.0 are generally considered good). Sortino Ratio: Similar to Sharpe, but only penalizes downside volatility. This is crucial for distinguishing between "good volatility" (upside pumps) and "bad volatility" (crashes). Z-Score: A mean-reversion metric. It measures how many standard deviations the current price is from its 20-day mean. High Positive Z-Score (>2): Price may be overextended to the upside. Low Negative Z-Score (<-2): Price may be oversold. Volatility (%): A dynamic measure of the asset's daily range, helping you gauge the "personality" of the stock before entering. Customization & Settings Fully Customizable Watchlist: While pre-loaded with top Nasdaq performers (like NVDA, AMD, PLTR, MU), you can easily edit the "Symbols" input in the settings to track Crypto, Forex, or your own custom stock portfolio. Smart Theme Detection: Includes a toggle for Dark Mode (ProjectSyndicate style) and Light Mode (Clean white style). Compact Mode: You can toggle specific columns on or off to fit the table on smaller screens. How to Use Add the script to your chart. Open Settings (Gear Icon). Paste your list of 40 tickers into the "Ticker List" text area (separated by commas). Use the Z-Score to find overbought/oversold setups and the Relative Strength (Week/Month) to find breakout candidates. Disclaimer: This tool is for informational purposes only. The "Top 40" list requires manual updating if the market leaders change. All statistical metrics (Kelly, Sharpe, etc.) are based on historical data and do not guarantee future performance. Built by CustomQuantLabs.
Индикатор Pine Script®
от CustomQuantLabs
BTC - Satoshis Altcoin Graveyard OVERVIEW The Satoshi's Altcoin Graveyard (SAG) is a macro-statistical engine designed to solve the problem of Survivorship Bias . It is a well-known phenomenon in the crypto markets that the "Top 10" list is in a constant state of flux. If you look at historical data from CoinMarketCap (CMC) year by year, you will see a revolving door of projects that once seemed "too big to fail" disappearing into obscurity. Meanwhile, Bitcoin has remained the undisputed #1 since inception. While most traders have a "gut feeling" that Altcoins eventually depreciate against Bitcoin, I believe in measuring it and drawing it on a chart for better visibility. By locking in specific "Cohorts" of market leaders from the past, we can track their inevitable decay through the Satoshi Sieve . THE 13-COIN STATISTICAL BUCKET To ensure an objective, non-biased audit, each cohort (we look at 2018, 2020 and 2022) is constructed using a fixed market-cap methodology from the snapshot date (excluding stablecoins): • The Core: The Top 10 non-stablecoin assets at that time by Marketcap. • The Risk Alpha: Representative samples from the Top #25, #50, and #100 ranks. (By including lower-ranked "riskier" alts, we capture the full statistical decay of the market, not just the "Blue Chips.") TECHNICAL ARCHITECTURE This script is engineered to push the boundaries of the Pine Script engine. TradingView enforces a hard limit of 40 unique data requests . By tracking 3 cohorts of 13 assets plus the Bitcoin base, this indicator utilizes exactly 40/40 requests , providing the maximum possible data density in a single chart window. THE SPS CONCEPT (Survival Probability Score) The SPS measures the Breadth of Survival . It answers: "How many coins from this year (the year of the snapshot) are actually outperforming BTC?" We use a binary logic system to determine if a coin is "Winning" or "Losing" against the only benchmark that matters: Bitcoin. • The Status Formula: Status = Current_Alt_BTC_Ratio >= Entry_Alt_BTC_Ratio ? 1 : 0 . This means: Every single day, at the Daily Close , the script compares the current Alt/BTC ratio to the fixed ratio from the snapshot date. If the coin is worth more in Bitcoin today than it was back then, it is assigned a "1" (a Win). If it has lost value against Bitcoin, it gets a "0" (a Loss). • The SPS Line: SPS Line = (Sum of 'Wins' / 13) * 100 This means: We add up all the "Winners" for that specific day and turn it into a percentage. For example, if the Aqua line is at 7.69% on your chart, it confirms that on that day , exactly 1 out of the 13 coins was successfully beating Bitcoin, while the other 12 were underperforming. THE PERFORMANCE MATRIX In the top-right corner, we provide a Weighted Portfolio Simulation . This answers the financial question: "If I swapped 1 BTC into an equal-weight basket of these 13 coins on the snapshot day, what is my BTC value today?". • Value < 1.0 BTC: You lost purchasing power compared to holding Bitcoin. • Value > 1.0 BTC: You successfully achieved "Alpha" over the benchmark. HOW TO READ THE CHART • The Waterfall: Lines generally trend downward as the "Satoshi Sieve" filters out assets that cannot maintain their BTC-relative value. • Dynamic Winners: We dynamically print the names of the current survivors at the tip of each line. If a cohort shows "None," the graveyard is full. HOW TO READ THE MATRIX • The BTC Target: Any portfolio value in the matrix below 1.0 BTC represents a failed altcoin rotation. • Class of 2018: A portfolio value near 0.15 BTC at the current date, means a 85% loss rate. • Class of 2020: A portfolio value near 0.77 BTC at the current date, means an approx 20 % loss rate. • Class of 2022: A portfolio value near 0.31 BTC at the current date, means an approx 70% loss rate. DIFFERENCE FROM AN ALTCOIN INDEX Standard Altcoin Indexes (like my ALSI Index ) "rebalance" by removing losers and adding new winners. This is deceptive. The Altcoin Graveyard never rebalances . It forces you to watch the "losers" decay, providing a realistic look at the long-term opportunity cost of "Buy and Hold" for anything other than Bitcoin. CONCLUSION The data revealed by the Satoshi Sieve leads to a singular, sobering "Lesson Learned": Picking the right coin to outperform Bitcoin is not just difficult—it is statistically improbable over a long-term horizon. While the "Risk-Reward" of altcoins is often marketed as having higher upside, the Altcoin Graveyard proves that for the vast majority of assets, the reward does not justify the risk of total portfolio erosion in BTC terms. • The Mathematical Odds: If you picked a Top 10 coin in 2018, your chance of outperforming BTC today is effectively 0%. • The Rotation Trap: Most investors "HODL" these assets into the graveyard, hoping for a return to previous ATHs that never comes because the liquidity has already moved on to the next "Class" of winners. The final conclusion is clear: Diversification into altcoins is often just a slow-motion transfer of wealth back to Bitcoin. If you cannot identify the 1-out-of-13 that survives the Sieve, your best risk-adjusted move has historically been to simply hold the benchmark. DISCLAIMER This script is for educational purposes only. It does not constitute financial advice. It is a mathematical study of historical opportunity cost and survivorship bias. Tags bitcoin, btc, satoshis graveyard, altseason, dominance, total3, rotation, cycle, index, alsi, Rob Maths, robmaths
Индикатор Pine Script®
от Rob_Maths
Portfolio TrackerDescription The Portfolio Tracker is a utility dashboard designed for traders who need to monitor the performance of a multi-asset portfolio directly from a single chart layout. While TradingView provides excellent charting for individual symbols, tracking the combined Profit & Loss (PnL) of a basket of 20 different securities (stocks, crypto, forex, or indices) usually requires switching tabs, using external spreadsheets, or logging into multiple exchange accounts. This script solves that problem by allowing users to manually input their position details into a customizable table. It fetches real-time price data for each symbol and calculates the individual and total portfolio performance, including commission costs. Why This Tool is Useful This indicator was built to address specific pain points for active traders: Consolidated View: Instead of checking 20 different charts to see how your positions are doing, you get a single, real-time snapshot of your entire portfolio's health on one screen. Risk Management: By seeing the "Total PnL" and "Total Investment" in one place, traders can better understand their overall market exposure, rather than focusing on single winning or losing trades. Flexible Accounting: The ability to switch between "Unit Price" and "Total Cost" inputs accommodates different trading styles—whether you are a scalper entering a single price or an investor averaging down with a specific total capital allocation. CRITICAL: Input Logic & Warnings To ensure accurate PnL calculations, users must understand the relationship between Quantity and Cost, especially when using "Total Cost (Manual)" mode. The Golden Rule: Your Input Cost must always match the Total Quantity entered. Example Scenario: Imagine you buy 2 BTC at a price of $90,000 each. Correct Entry: You must enter Quantity: 2 and Cost: 180000 ($90k x 2). Result: If BTC drops to $85k, your Portfolio Value is $170k. The script correctly shows a PnL of -$10,000. Result: If BTC rises to $95k, your Portfolio Value is $190k. The script correctly shows a PnL of +$10,000. Incorrect Entry: If you enter Quantity: 2 but leave Cost at 90000 (the unit price). Result: The script thinks you bought 2 BTC for a total of only $90k. It will instantly show a massive, incorrect profit because the math implies you bought 2 coins for the price of 1. Please double-check your inputs. The script includes a "Sanity Check" feature to help catch these errors, but accurate data entry is the user's responsibility. Key Features & Benefits Multi-Asset Tracking (20 Slots): Monitor up to 20 different tickers simultaneously. Real-Time Valuation: Uses request.security() to fetch the current market price for every symbol in the list. Your PnL updates with every tick of the market. Flexible Cost Basis Modes: Auto-Calc Mode: Enter Entry Price and Quantity. (Best for simple, single-entry trades). Manual Cost Mode: Enter Total Invested Amount. (Best for averaged-down positions). Advanced Commission Handling: Supports both Global and Individual commission rates. This provides a realistic "Net PnL" by factoring in fees on both the entry (cost basis) and the theoretical exit (current value). Input Safety ("Sanity Check"): A logic check that compares the user's input against the current market value. If a user switches to "Total Cost" mode but leaves a small "Unit Price" value in the input field, the script flags the row to prevent irrational PnL percentages (e.g., >100,000%). Clean & Customizable UI: The table can be positioned in 9 different locations, and inputs are hidden from the chart status line to keep the visual workspace clean. How It Works The script operates using a systematic loop that processes user inputs through a series of mathematical validations: Data Acquisition: The script collects all 20 user inputs and utilizes request.security() to fetch the real-time close price for every non-empty symbol in the list. Cost Basis Calculation: In Auto-Calc Mode: The script calculates Raw Cost = Quantity * Input Price. In Manual Mode: The script takes the Input Value directly as the Raw Cost. "Round-Trip" Commission Modeling: Entry Cost: Raw Cost * (1 + Commission%) (Fees increase your breakeven). Exit Value: (Quantity * Current Price) * (1 - Commission%) (Fees reduce your payout). Net PnL: Exit Value - Entry Cost. Sanity Check Algorithm: Before displaying data, the script compares the Input Cost against the Gross Market Value (Qty * Price). If the Input Cost is less than a user-defined threshold (default 1%) of the Market Value, it triggers a warning, assuming the user forgot to update the field to a "Total Cost" figure. Disclaimer This script is for informational and educational purposes only. It is a tool to assist in tracking hypothetical or real positions based on manual user inputs and standard TradingView data feeds. It should not be relied upon as a primary accounting ledger or tax reporting tool. Past performance is not indicative of future results. Trading involves risk. Always verify your PnL against your actual exchange or broker statements.
Индикатор Pine Script®
от abgthecoder
1
Account GuardianAccount Guardian: Dynamic Risk/Reward Overlay Introduction Account Guardian is an open-source indicator for TradingView designed to help traders evaluate trade setups before entering positions. It automatically calculates Risk-to-Reward ratios based on market structure, displays visual Stop Loss and Take Profit zones, and provides real-time position sizing recommendations. The indicator addresses a fundamental question every trader should ask before entering a trade: "Does this setup make mathematical sense?" Account Guardian answers this question visually and numerically, helping traders avoid impulsive entries with poor risk profiles. Core Functionality Account Guardian performs four primary functions: Detects swing highs and swing lows to identify logical stop loss placement levels Calculates Risk-to-Reward ratios for both long and short setups in real-time Displays visual SL/TP zones on the chart for immediate trade planning Computes position sizing based on your account size and risk tolerance The goal is to provide traders with instant feedback on whether a potential trade meets their minimum risk/reward criteria before committing capital. How It Works Swing Detection The indicator uses pivot point detection to identify recent swing highs and swing lows on the chart. These swing points serve as logical areas for stop loss placement: For Long Trades: The most recent swing low becomes the stop loss level. Price breaking below this level would invalidate the bullish thesis. For Short Trades: The most recent swing high becomes the stop loss level. Price breaking above this level would invalidate the bearish thesis. The swing detection lookback period is configurable, allowing you to adjust sensitivity based on your trading timeframe and style. It automatically adjusts the tp and sl when it is applied to your chart so it is always moving up and down! Risk/Reward Calculation Once swing levels are identified, the indicator calculates: Entry Price: Current close price (where you would enter) Stop Loss: Recent swing low (for longs) or swing high (for shorts) Risk: Distance from entry to stop loss Take Profit: Entry plus (Risk × Target Multiplier) R:R Ratio: Reward divided by Risk The R:R ratio is then evaluated against your configured thresholds to determine if the setup is valid, marginal, or poor. Visual Elements SL/TP Zones When enabled, the indicator draws colored boxes on the chart showing: Red Zone: Stop Loss area - the region between your entry and stop loss Green/Gold/Red Zone: Take Profit area - colored based on R:R quality The color coding provides instant visual feedback: Green: R:R meets or exceeds your "Good R:R" threshold (default 3:1) Gold: R:R meets minimum threshold but below "Good" (between 2:1 and 3:1) Red: R:R below minimum threshold - setup should be avoided Swing Point Markers Small circles mark detected swing points on the chart: Green circles: Swing lows (potential support / long SL levels) Red circles: Swing highs (potential resistance / short SL levels) Dashboard Panel The dashboard in the top-right corner displays comprehensive trade planning information: R:R Row: Current Risk-to-Reward ratio for long and short setups Status Row: VALID, OK, BAD, or N/A based on R:R thresholds Stop Loss Row: Exact price level for stop loss placement Take Profit Row: Exact price level for take profit placement Pos Size Row: Recommended position size based on your risk parameters Risk $ Row: Dollar amount at risk per trade Position Sizing Logic The indicator calculates position size using the formula: Position Size = Risk Amount / Risk per Unit Where: Risk Amount = Account Size × (Risk Percentage / 100) Risk per Unit = Entry Price - Stop Loss Price For example, with a $10,000 account risking 1% per trade ($100), if your entry is at 100 and stop loss at 98 (risk of 2 per unit), your position size would be 50 units. Input Parameters Swing Detection: Swing Lookback: Number of bars to look back for pivot detection (default: 10). Higher values find more significant swing points but may be slower to update. Target Multiplier: Multiplier applied to risk to calculate take profit distance (default: 2). A value of 2 means TP is 2× the distance of SL from entry. Risk/Reward Thresholds: Minimum R:R: Minimum acceptable Risk-to-Reward ratio (default: 2.0). Setups below this show as "BAD" in red. Good R:R: Threshold for excellent setups (default: 3.0). Setups at or above this show as "VALID" in green. Account Settings: Account Size ($): Your trading account size in dollars (default: 10,000). Used for position sizing calculations. Risk Per Trade (%): Percentage of account to risk per trade (default: 1.0%). Professional traders typically risk 0.5-2% per trade. Display: Show SL/TP Zones: Toggle visibility of the colored zone boxes on chart (default: enabled) Show Dashboard: Toggle visibility of the information panel (default: enabled) Analyze Direction: Choose to analyze Long only, Short only, or Both directions (default: Both) How to Use This Indicator Basic Workflow: Add the indicator to your chart Configure your account size and risk percentage in the settings Set your minimum and good R:R thresholds based on your trading rules Look at the dashboard to see current R:R for potential long and short entries Only consider trades where the status shows "VALID" or at minimum "OK" Use the displayed SL and TP levels for your order placement Use the position size recommendation to determine lot/contract size Interpreting the Dashboard: VALID (Green): Excellent setup - R:R meets your "Good" threshold. This is the ideal scenario for taking a trade. OK (Gold): Acceptable setup - R:R meets minimum but isn't optimal. Consider taking if other confluence factors align. BAD (Red): Poor setup - R:R below minimum threshold. Avoid this trade or wait for better entry. N/A (Gray): Cannot calculate - usually means no valid swing point detected yet. Best Practices: Use this indicator as a filter, not a signal generator. It tells you IF a trade makes sense, not WHEN to enter. Combine with your existing entry strategy - use Account Guardian to validate setups from other analysis. Adjust the swing lookback based on your timeframe. Lower timeframes may need smaller lookback values. Be honest with your account size input - accurate position sizing requires accurate inputs. Consider the target multiplier carefully. Higher multipliers mean larger potential reward but lower probability of hitting TP. Alerts The indicator includes four alert conditions: Good Long Setup: Triggers when long R:R reaches or exceeds your "Good R:R" threshold Good Short Setup: Triggers when short R:R reaches or exceeds your "Good R:R" threshold Bad Long Setup: Triggers when long R:R falls below your minimum threshold Bad Short Setup: Triggers when short R:R falls below your minimum threshold These alerts can help you monitor multiple charts and get notified when favorable setups appear. Technical Implementation The indicator is built using Pine Script v6 and includes: Pivot-based swing detection using ta.pivothigh() and ta.pivotlow() Dynamic box drawing for visual SL/TP zones Table-based dashboard for clean information display Color-coded visual feedback system Persistent variable tracking for swing levels Code Structure: // Swing Detection float swingHi = ta.pivothigh(high, swingLen, swingLen) float swingLo = ta.pivotlow(low, swingLen, swingLen) // R:R Calculation for Long float longSL = recentSwingLo float longRisk = entry - longSL float longTP = entry + (longRisk * targetMult) float longRR = (longTP - entry) / longRisk // Position Sizing float riskAmount = accountSize * (riskPct / 100) float posSize = riskAmount / longRisk Limitations The indicator uses historical swing points which may not always represent optimal SL placement for your specific strategy Position sizing assumes you can trade fractional units - adjust accordingly for instruments with minimum lot sizes R:R calculations assume linear price movement and don't account for gaps or slippage The indicator doesn't predict price direction - it only evaluates the mathematical viability of a setup Swing detection has inherent lag due to the lookback period required for pivot confirmation Recommended Settings by Trading Style Scalping (1-5 minute charts): Swing Lookback: 5-8 Target Multiplier: 1-2 Minimum R:R: 1.5 Good R:R: 2.0 Day Trading (15-60 minute charts): Swing Lookback: 8-12 Target Multiplier: 2 Minimum R:R: 2.0 Good R:R: 3.0 Swing Trading (4H-Daily charts): Swing Lookback: 10-20 Target Multiplier: 2-3 Minimum R:R: 2.5 Good R:R: 4.0 Why Risk/Reward Matters Many traders focus solely on win rate, but profitability depends on the combination of win rate AND risk/reward ratio. Consider these scenarios: 50% win rate with 1:1 R:R = Breakeven (before costs) 50% win rate with 2:1 R:R = Profitable 40% win rate with 3:1 R:R = Profitable 60% win rate with 1:2 R:R = Losing money Account Guardian helps ensure you only take trades where the math works in your favor, even if you're wrong more often than you're right. Disclaimer This indicator is provided for educational and informational purposes only. It is not intended as financial, investment, trading, or any other type of advice or recommendation. Trading involves substantial risk of loss and is not suitable for all investors. The calculations provided by this indicator are based on historical price data and mathematical formulas that may not accurately predict future price movements. Position sizing recommendations are estimates based on user inputs and should be verified before placing actual trades. Always consider factors such as leverage, margin requirements, and broker-specific rules when determining actual position sizes. The Risk-to-Reward ratios displayed are theoretical calculations based on swing point detection. Actual trade outcomes will vary based on market conditions, execution quality, and other factors not captured by this indicator. Past performance does not guarantee future results. Users should thoroughly test any trading approach in a demo environment before risking real capital. The authors and publishers of this indicator are not responsible for any losses or damages arising from its use. Always consult with a qualified financial advisor before making investment decisions.
Индикатор Pine Script®
от officialjackofalltrades
Volatility Targeting: Single Asset [BackQuant]Volatility Targeting: Single Asset An educational example that demonstrates how volatility targeting can scale exposure up or down on one symbol, then applies a simple EMA cross for long or short direction and a higher timeframe style regime filter to gate risk. It builds a synthetic equity curve and compares it to buy and hold and a benchmark. Important disclaimer This script is a concept and education example only . It is not a complete trading system and it is not meant for live execution. It does not model many real world constraints, and its equity curve is only a simplified simulation. If you want to trade any idea like this, you need a proper strategy() implementation, realistic execution assumptions, and robust backtesting with out of sample validation. Single asset vs the full portfolio concept This indicator is the single asset, long short version of the broader volatility targeted momentum portfolio concept. The original multi asset concept and full portfolio implementation is here: That portfolio script is about allocating across multiple assets with a portfolio view. This script is intentionally simpler and focuses on one symbol so you can clearly see how volatility targeting behaves, how the scaling interacts with trend direction, and what an equity curve comparison looks like. What this indicator is trying to demonstrate Volatility targeting is a risk scaling framework. The core idea is simple: If realized volatility is low relative to a target, you can scale position size up so the strategy behaves like it has a stable risk budget. If realized volatility is high relative to a target, you scale down to avoid getting blown around by the market. Instead of always being 1x long or 1x short, exposure becomes dynamic. This is often used in risk parity style systems, trend following overlays, and volatility controlled products. This script combines that risk scaling with a simple trend direction model: Fast and slow EMA cross determines whether the strategy is long or short. A second, longer EMA cross acts as a regime filter that decides whether the system is ACTIVE or effectively in CASH. An equity curve is built from the scaled returns so you can visualize how the framework behaves across regimes. How the logic works step by step 1) Returns and simple momentum The script uses log returns for the base return stream: ret = log(price / price ) It also computes a simple momentum value: mom = price / price - 1 In this version, momentum is mainly informational since the directional signal is the EMA cross. The lookback input is shared with volatility estimation to keep the concept compact. 2) Realized volatility estimation Realized volatility is estimated as the standard deviation of returns over the lookback window, then annualized: vol = stdev(ret, lookback) * sqrt(tradingdays) The Trading Days/Year input controls annualization: 252 is typical for traditional markets. 365 is typical for crypto since it trades daily. 3) Volatility targeting multiplier Once realized vol is estimated, the script computes a scaling factor that tries to push realized volatility toward the target: volMult = targetVol / vol This is then clamped into a reasonable range: Minimum 0.1 so exposure never goes to zero just because vol spikes. Maximum 5.0 so exposure is not allowed to lever infinitely during ultra low volatility periods. This clamp is one of the most important “sanity rails” in any volatility targeted system. Without it, very low volatility regimes can create unrealistic leverage. 4) Scaled return stream The per bar return used for the equity curve is the raw return multiplied by the volatility multiplier: sr = ret * volMult Think of this as the return you would have earned if you scaled exposure to match the volatility budget. 5) Long short direction via EMA cross Direction is determined by a fast and slow EMA cross on price: If fast EMA is above slow EMA, direction is long. If fast EMA is below slow EMA, direction is short. This produces dir as either +1 or -1. The scaled return stream is then signed by direction: avgRet = dir * sr So the strategy return is volatility targeted and directionally flipped depending on trend. 6) Regime filter: ACTIVE vs CASH A second EMA pair acts as a top level regime filter: If fast regime EMA is above slow regime EMA, the system is ACTIVE. If fast regime EMA is below slow regime EMA, the system is considered CASH, meaning it does not compound equity. This is designed to reduce participation in long bear phases or low quality environments, depending on how you set the regime lengths. By default it is a classic 50 and 200 EMA cross structure. Important detail, the script applies regime_filter when compounding equity, meaning it uses the prior bar regime state to avoid ambiguous same bar updates. 7) Equity curve construction The script builds a synthetic equity curve starting from Initial Capital after Start Date . Each bar: If regime was ACTIVE on the previous bar, equity compounds by (1 + netRet). If regime was CASH, equity stays flat. Fees are modeled very simply as a per bar penalty on returns: netRet = avgRet - (fee_rate * avgRet) This is not realistic execution modeling, it is just a simple turnover penalty knob to show how friction can reduce compounded performance. Real backtesting should model trade based costs, spreads, funding, and slippage. Benchmark and buy and hold comparison The script pulls a benchmark symbol via request.security and builds a buy and hold equity curve starting from the same date and initial capital. The buy and hold curve is based on benchmark price appreciation, not the strategy’s asset price, so you can compare: Strategy equity on the chart symbol. Buy and hold equity for the selected benchmark instrument. By default the benchmark is TVC:SPX, but you can set it to anything, for crypto you might set it to BTC, or a sector index, or a dominance proxy depending on your study. What it plots If enabled, the indicator plots: Strategy Equity as a line, colored by recent direction of equity change, using Positive Equity Color and Negative Equity Color . Buy and Hold Equity for the chosen benchmark as a line. Optional labels that tag each curve on the right side of the chart. This makes it easy to visually see when volatility targeting and regime gating change the shape of the equity curve relative to a simple passive hold. Metrics table explained If Show Metrics Table is enabled, a table is built and populated with common performance statistics based on the simulated daily returns of the strategy equity curve after the start date. These include: Net Profit (%) total return relative to initial capital. Max DD (%) maximum drawdown computed from equity peaks, stored over time. Win Rate percent of positive return bars. Annual Mean Returns (% p/y) mean daily return annualized. Annual Stdev Returns (% p/y) volatility of daily returns annualized. Variance of annualized returns. Sortino Ratio annualized return divided by downside deviation, using negative return stdev. Sharpe Ratio risk adjusted return using the risk free rate input. Omega Ratio positive return sum divided by negative return sum. Gain to Pain total return sum divided by absolute loss sum. CAGR (% p/y) compounded annual growth rate based on time since start date. Portfolio Alpha (% p/y) alpha versus benchmark using beta and the benchmark mean. Portfolio Beta covariance of strategy returns with benchmark returns divided by benchmark variance. Skewness of Returns actually the script computes a conditional value based on the lower 5 percent tail of returns, so it behaves more like a simple CVaR style tail loss estimate than classic skewness. Important note, these are calculated from the synthetic equity stream in an indicator context. They are useful for concept exploration, but they are not a substitute for professional backtesting where trade timing, fills, funding, and leverage constraints are accurately represented. How to interpret the system conceptually Vol targeting effect When volatility rises, volMult falls, so the strategy de risks and the equity curve typically becomes smoother. When volatility compresses, volMult rises, so the system takes more exposure and tries to maintain a stable risk budget. This is why volatility targeting is often used as a “risk equalizer”, it can reduce the “biggest drawdowns happen only because vol expanded” problem, at the cost of potentially under participating in explosive upside if volatility rises during a trend. Long short directional effect Because direction is an EMA cross: In strong trends, the direction stays stable and the scaled return stream compounds in that trend direction. In choppy ranges, the EMA cross can flip and create whipsaws, which is where fees and regime filtering matter most. Regime filter effect The 50 and 200 style filter tries to: Keep the system active in sustained up regimes. Reduce exposure during long down regimes or extended weakness. It will always be late at turning points, by design. It is a slow filter meant to reduce deep participation, not to catch bottoms. Common applications This script is mainly for understanding and research, but conceptually, volatility targeting overlays are used for: Risk budgeting normalize risk so your exposure is not accidentally huge in high vol regimes. System comparison see how a simple trend model behaves with and without vol scaling. Parameter exploration test how target volatility, lookback length, and regime lengths change the shape of equity and drawdowns. Framework building as a reference blueprint before implementing a proper strategy() version with trade based execution logic. Tuning guidance Lookback lower values react faster to vol shifts but can create unstable scaling, higher values smooth scaling but react slower to regime changes. Target volatility higher targets increase exposure and drawdown potential, lower targets reduce exposure and usually lower drawdowns, but can under perform in strong trends. Signal EMAs tighter EMAs increase trade frequency, wider EMAs reduce churn but react slower. Regime EMAs slower regime filters reduce false toggles but will miss early trend transitions. Fees if you crank this up you will see how sensitive higher turnover parameter sets are to friction. Final note This is a compact educational demonstration of a volatility targeted, long short single asset framework with a regime gate and a synthetic equity curve. If you want a production ready implementation, the correct next step is to convert this concept into a strategy() script, add realistic execution and cost modeling, test across multiple timeframes and market regimes, and validate out of sample before making any decision based on the results.
Индикатор Pine Script®
от BackQuant
2
DCA Ladder CalculatorThis script is a DCA (Dollar-Cost Averaging) Ladder Calculator with Risk & Leverage Management baked in. It’s designed for both LONG and SHORT positions, and helps you: 🎯 Strategically scale into positions across multiple entry points 🔐 Control risk exposure via defined capital allocation ⚖️ Utilize leverage responsibly — for efficiency, not destruction 🧮 Visualize risk, stop loss level, and entry distribution 🔁 Adapt to trend reversals or key zones, especially when combined with reversal indicators or higher timeframe signals 🧠 How It Works This tool takes a capital allocation approach to building a ladder of positions: 1. You define: - Portfolio value - Risk per trade (as %) - Leverage - Number of DCA levels - Entry multiplier (e.g. 1x, 2x, 4x...) 2. The script then: - Calculates total margin to risk = Portfolio × Risk % - Calculates total leveraged position size = Margin × Leverage - Distributes entries according to exponential weights (1x, 2x, 4x...), totaling 7 for 3 levels - Calculates per-entry: - Entry price (based on price zone spacing) - Multiplier - Exact margin per entry - Leverage per entry (margin × leverage) - Computes: - Average entry price (margin-weighted) - Approximate stop loss level based on recent ATR and price structure - % drawdown to SL - Total margin and position size 3. Displays all this in a clean on-chart table. 📈 How to Use It 1. Apply the indicator to a chart (default: 1D — ideal for clean zones). 2. Configure your: - Portfolio Value (total trading capital) - Risk per Trade (%) (your acceptable loss) - Leverage (exchange or strategy-based) - DCA Levels (e.g. 3 = anchor + 2 entries) - Multiplier (typically 2.0 for doubling) 3. Choose LONG or SHORT mode depending on direction. 4. The table will show: - Entry price ladder - Margin used per entry - Total position size - Approx. stop loss (where your full risk is defined) Use in conjunction with price action, S/R zones, trendline breaks, volume divergence, or reversal indicators. ✅ Best Practices for Using This Tool - Leverage is a tool, not a weapon. Use it to scale smartly — not recklessly. - Use fewer, higher-conviction entries. Don’t blindly ladder; combine with price structure and signals. - Stick to your risk percent. Never risk more than you can afford to lose. Let this calculator enforce discipline. - Combine with other confirmation tools, like RSI divergence, momentum shifts, OB zones, etc. - Avoid martingale-style over-exposure. This is not a gambling tool — it’s for capital efficiency. 🛡️ What This Tool Does NOT Do - This is not a trade signal indicator. - It does not place trades or auto-manage positions. - It does not replace personal responsibility or strategy — it's a tool to help apply structure. ⚠️ Disclaimer This script is for educational and informational purposes only. It does not constitute financial advice, nor is it a recommendation to buy or sell any financial instrument. Always consult a licensed financial advisor before making investment decisions. Use of leverage involves high risk and can lead to substantial losses. The author and publisher assume no liability for any trading losses resulting from use of this script.
Индикатор Pine Script®
от RWCS_LTD
1
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.
Индикатор Pine Script®
от BackQuant
Crypto Index Price# Crypto Index Price - Indicator Description ## 📊 What is this indicator? **Crypto Index Price** is an indicator for creating your own cryptocurrency index based on an equal-weighted portfolio. It allows you to track the overall dynamics of the cryptocurrency market through a composite index of selected assets. ## 🎯 Key Features - **Up to 20 assets in the index** — create an index from any trading pairs - **Equal-weighted methodology** — each asset has the same weight in the index - **Moving average** — optional trend filter for the index - **Flexible visualization settings** — customizable colors and line thickness ## 📈 How to Use The indicator is displayed in a separate pane below the chart and shows: 1. **Blue line** — crypto index value 2. **Orange line** (optional) — moving average of the index ### Trading Applications: - **Identify overall market trend** — if the index is rising, most coins are in an uptrend - **Divergences** — divergence between your asset and the index may signal local opportunities - **Signal confirmation** — use the index to confirm trading decisions on individual coins - **Market condition filter** — trade longs when index is above MA, shorts when below ## ⚙️ Settings ### Assets (Symbols) - **Asset 1-10** — main cryptocurrencies (default: BTC, ETH, BNB, SOL, XRP, ADA, AVAX, LINK, DOGE, TRX) - **Asset 11-20** — additional slots for index expansion ### Visual Parameters - **Index line color** — main line color (default: blue) - **Line width** — from 1 to 5 pixels - **Show moving average** — enable/disable MA - **MA period** — moving average calculation period (default: 20) - **MA color** — moving average line color (default: orange) ## 💡 Recommendations - For a top coins index, use 5-10 largest cryptocurrencies by market cap - For an altcoin index, add medium and small coins from your sector - Use MA to filter false signals and identify the global trend - Compare individual asset behavior with the index to find anomalies ## ⚠️ Important The indicator uses equal-weighted methodology — each coin contributes equally regardless of price or market cap. This differs from cap-weighted indices and may provide a different market perspective. --- *This indicator is intended for analysis and is not trading advice. Always conduct your own analysis before making trading decisions.* ---
Индикатор Pine Script®
от AlekMel
Portfolio Strategy TesterThe Portfolio Strategy Tester is an institutional-grade backtesting framework that evaluates the performance of trend-following strategies on multi-asset portfolios. It enables users to construct custom portfolios of up to 30 assets and apply moving average crossover strategies across individual holdings. The model features a clear, color-coded table that provides a side-by-side comparison between the buy-and-hold portfolio and the portfolio using the risk management strategy, offering a comprehensive assessment of both approaches relative to the benchmark. Portfolios are constructed by entering each ticker symbol in the menu, assigning its respective weight, and reviewing the total sum of individual weights displayed at the top left of the table. For strategy selection, users can choose between Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), Moving Average Convergence Divergence (MACD), and Volume-Weighted Moving Average (VWMA). Moving average lengths are defined in the menu and apply only to strategy-enabled assets. To accurately replicate real-world portfolio conditions, users can choose between daily, weekly, monthly, or quarterly rebalancing frequencies and decide whether cash is held or redistributed. Daily rebalancing maintains constant portfolio weights, while longer intervals allow natural drift. When cash positions are not allowed, capital from bearish assets is automatically redistributed proportionally among bullish assets, ensuring the portfolio remains fully invested at all times. The table displays a comprehensive set of widely used institutional-grade performance metrics: CAGR = Compounded annual growth rate of returns. Volatility = Annualized standard deviation of returns. Sharpe = CAGR per unit of annualized standard deviation. Sortino = CAGR per unit of annualized downside deviation. Calmar = CAGR relative to maximum drawdown. Max DD = Largest peak-to-trough decline in value. Beta (β) = Sensitivity of returns relative to benchmark returns. Alpha (α) = Excess annualized risk-adjusted returns relative to benchmark. Upside = Ratio of average return to benchmark return on up days. Downside = Ratio of average return to benchmark return on down days. Tracking = Annualized standard deviation of returns versus benchmark. Turnover = Average sum of absolute changes in weights per year. Cumulative returns are displayed on each label as the total percentage gain from the selected start date, with green indicating positive returns and red indicating negative returns. In the table, baseline metrics serve as the benchmark reference and are always gray. For portfolio metrics, green indicates outperformance relative to the baseline, while red indicates underperformance relative to the baseline. For strategy metrics, green indicates outperformance relative to both the baseline and the portfolio, red indicates underperformance relative to both, and gray indicates underperformance relative to either the baseline or portfolio. Metrics such as Volatility, Tracking Error, and Turnover ratio are always displayed in gray as they serve as descriptive measures. In summary, the Portfolio Strategy Tester is a comprehensive backtesting tool designed to help investors evaluate different trend-following strategies on custom portfolios. It enables real-world simulation of both active and passive investment approaches and provides a full set of standard institutional-grade performance metrics to support data-driven comparisons. While results are based on historical performance, the model serves as a powerful portfolio management and research framework for developing, validating, and refining systematic investment strategies.
Индикатор Pine Script®
от QuantitativeAlpha
Portfolio Simulator & BacktesterMulti-asset portfolio simulator with different metrics and ratios, DCA modeling, and rebalancing strategies. Core Features Portfolio Construction Up to 5 assets with customizable weights (must total 100%) Support for any tradable symbol: stocks, ETFs, crypto, indices, commodities Real-time validation of allocations Dollar Cost Averaging Monthly or Quarterly contributions Applies to both portfolio and benchmark for fair comparison Model real-world investing behavior Rebalancing Four strategies: None, Monthly, Quarterly, Yearly Automatic rebalancing to target weights Transaction cost modeling (customizable fee %) Key Metrics Table CAGR: Annualized compound return (S&P 500 avg: ~10%) Alpha: Excess return vs. benchmark (positive = outperformance) Sharpe Ratio: Return per unit of risk (>1.0 is good, >2.0 excellent) Sortino Ratio: Like Sharpe but only penalizes downside (better metric) Calmar Ratio: CAGR / Max Drawdown (>1.0 good, >2.0 excellent) Max Drawdown: Largest peak-to-trough decline Win Rate: % of positive days (doesn't indicate profitability) Visualization Dual-chart comparison - Portfolio vs. Benchmark Dollar or percentage view toggle Customizable colors and line width Two tables: Statistics + Asset Allocation Adjustable table position and text size 🚀 Quick Start Guide Enter 1-5 ticker symbols (e.g., SPY, QQQ, TLT, GLD, BTCUSD) Make sure percentage weights total 100% Choose date range (ensure chart shows full period - zoom out!) Configure DCA and rebalancing (optional) Select benchmark (default: SPX) Analyze results in statistics table 💡 Pro Tips Chart data matters: Load SPY or your longest-history asset as main chart If you select an asset that was not available for the selected period, the chart will not show up! E.g. BTCUSD data: Only available from ~2017 onwards. Transaction fees: 0.1% default (adjust to match your broker) ⚠️ Important Notes Requires visible chart data (zoom out to show full date range) Limited by each asset's historical data availability Transaction fees and costs are modeled, but taxes/slippage are not Past performance ≠ future results Use for research and education only, not financial advice Let me know if you have any suggestions to improve this simulator.
Индикатор Pine Script®
от HenriqueCentieiro
2
Normalized Portfolio TrackerThis script lets you create, visualize, and track a custom portfolio of up to 15 assets directly on TradingView. It calculates a synthetic "portfolio index" by combining multiple tickers with user-defined weights, automatically normalizing them so the total allocation always equals 100%. All assets are scaled to a common starting point, allowing you to compare your portfolio’s performance versus any benchmark like SPY, QQQ, or BTC. 🚀 Goal This script helps traders and investors: • Understand the combined performance of their portfolio. • Normalize diverse assets into a single synthetic chart . • Make portfolio-level insights without relying on external spreadsheets. 🎯 Use Cases • Backtest your portfolio allocations directly on the chart. • Compare your portfolio vs. benchmarks like SPY, QQQ, BTC. • Track thematic baskets (commodities, EV supply chain, regional ETFs). • Visualize how each component contributes to overall performance. 📊 Features • Weighted Portfolio Performance : Combines selected assets into a synthetic value series. • Base Price Alignment : Each asset is normalized to its starting price at the chosen date. • Dynamic Portfolio Table : Displays symbols, normalized weights (%), equivalent shares (based on each asset’s start price, sums to 100 shares), and a total row that always sums to 100%. • Multi-Asset Support : Works with stocks, ETFs, indices, crypto, or any TradingView-compatible symbol. ⚙️ Configuration Flexible Portfolio Setup • Add up to 15 assets with custom weight inputs. • You can enter any arbitrary numbers (e.g. 30, 15, 55). • The script automatically normalizes all weights so the total allocation always equals 100%. Start Date Selection • Choose any custom start date to normalize all assets. • The portfolio value is then scaled relative to the main chart symbol, so you can directly compare portfolio performance against benchmarks like SPY or QQQ. Chart Styles • Candlestick chart • Heikin Ashi chart • Line chart Custom Display • Adjustable colors and line widths • Optionally display asset list, normalized weights, and equivalent shares ⚙️ How It Works • Fetch OHLC data for each asset. • Normalizes weights internally so totals = 100%. • Stores each asset’s base price at the selected start date. • Calculates equivalent “shares” for each allocation. • Builds a synthetic portfolio value series by summing weighted contributions. • Renders as Candlestick, Heikin Ashi, or Line chart. • Adds a portfolio info table for clarity. ⚠️ Notes • This script is for visualization only . It does not place trades or auto-rebalance. • Weight inputs are automatically normalized, so you don’t need to enter exact percentages.
Индикатор Pine Script®
от dehypnosis
Correlation Heatmap Matrix [TradingFinder] 20 Assets Variable🔵 Introduction Correlation is one of the most important statistical and analytical metrics in financial markets, data mining, and data science. It measures the strength and direction of the relationship between two variables. The correlation coefficient always ranges between +1 and -1 : a perfect positive correlation (+1) means that two assets or currency pairs move together in the same direction and at a constant ratio, a correlation of zero (0) indicates no clear linear relationship, and a perfect negative correlation (-1) means they move in exactly opposite directions. While the Pearson Correlation Coefficient is the most common method for calculation, other statistical methods like Spearman and Kendall are also used depending on the context. In financial market analysis, correlation is a key tool for Forex, the Stock Market, and the Cryptocurrency Market because it allows traders to assess the price relationship between currency pairs, stocks, or coins. For example, in Forex, EUR/USD and GBP/USD often have a high positive correlation; in stocks, companies from the same sector such as Apple and Microsoft tend to move similarly; and in crypto, most altcoins show a strong positive correlation with Bitcoin. Using a Correlation Heatmap in these markets visually displays the strength and direction of these relationships, helping traders make more accurate decisions for risk management and strategy optimization. 🟣 Correlation in Financial Markets In finance, correlation refers to measuring how closely two assets move together over time. These assets can be stocks, currency pairs, commodities, indices, or cryptocurrencies. The main goal of correlation analysis in trading is to understand these movement patterns and use them for risk management, trend forecasting, and developing trading strategies. 🟣 Correlation Heatmap A correlation heatmap is a visual tool that presents the correlation between multiple assets in a color-coded table. Each cell shows the correlation coefficient between two assets, with colors indicating its strength and direction. Warm colors (such as red or orange) represent strong negative correlation, cool colors (such as blue or cyan) represent strong positive correlation, and mid-range tones (such as yellow or green) indicate correlations that are close to neutral. 🟣 Practical Applications in Markets Forex : Identify currency pairs that move together or in opposite directions, avoid overexposure to similar trades, and spot unusual divergences. Crypto : Examine the dependency of altcoins on Bitcoin and find independent movers for portfolio diversification. Stocks : Detect relationships between stocks in the same industry or find outliers that move differently from their sector. 🟣 Key Uses of Correlation in Trading Risk management and diversification: Select assets with low or negative correlation to reduce portfolio volatility. Avoiding overexposure: Prevent opening multiple positions on highly correlated assets. Pairs trading: Exploit temporary deviations between historically correlated assets for arbitrage opportunities. Intermarket analysis: Study the relationships between different markets like stocks, currencies, commodities, and bonds. Divergence detection: Spot when two typically correlated assets move apart as a possible trend change signal. Market forecasting: Use correlated asset movements to anticipate others’ behavior. Event reaction analysis: Evaluate how groups of assets respond to economic or political events. ❗ Important Note It’s important to note that correlation does not imply causation — it only reflects co-movement between assets. Correlation is also dynamic and can change over time, which is why analyzing it across multiple timeframes provides a more accurate picture. Combining correlation heatmaps with other analytical tools can significantly improve the precision of trading decisions. 🔵 How to Use The Correlation Heatmap Matrix indicator is designed to analyze and manage the relationships between multiple assets at once. After adding the tool to your chart, start by selecting the assets you want to compare (up to 20). Then, choose the Correlation Period that fits your trading strategy. Shorter periods (e.g., 20 bars) are more sensitive to recent price movements, making them suitable for short-term trading, while longer periods (e.g., 100 or 200 bars) provide a broader view of correlation trends over time. The indicator outputs a color-coded matrix where each cell represents the correlation between two assets. Warm colors like red and orange signal strong negative correlation, while cool colors like blue and cyan indicate strong positive correlation. Mid-range tones such as yellow or green suggest correlations that are close to neutral. This visual representation makes it easy to spot market patterns at a glance. One of the most valuable uses of this tool is in portfolio risk management. Portfolios with highly correlated assets are more vulnerable to market swings. By using the heatmap, traders can find assets with low or negative correlation to reduce overall risk. Another key benefit is preventing overexposure. For example, if EUR/USD and GBP/USD have a high positive correlation, opening trades on both is almost like doubling the position size on one asset, increasing risk unnecessarily. The heatmap makes such relationships clear, helping you avoid them. The indicator is also useful for pairs trading, where a trader identifies assets that are usually correlated but have temporarily diverged — a potential arbitrage or mean-reversion opportunity. Additionally, the tool supports intermarket analysis, allowing traders to see how movements in one market (e.g., crude oil) may impact others (e.g., the Canadian dollar). Divergence detection is another advantage: if two typically aligned assets suddenly move in opposite directions, it could signal a major trend shift or a news-driven move. Overall, the Correlation Heatmap Matrix is not just an analytical indicator but also a fast, visual alert system for monitoring multiple markets at once. This is particularly valuable for traders in fast-moving environments like Forex and crypto. 🔵 Settings 🟣 Logic Correlation Period : Number of bars used to calculate correlation between assets. 🟣 Display Table on Chart : Enable/disable displaying the heatmap directly on the chart. Table Size : Choose the table size (from very small to very large). Table Position : Set the table location on the chart (top, middle, or bottom in various alignments). 🟣 Symbol Custom Select Market : Choose the market type (Forex, Stocks, Crypto, or Custom). Symbol 1 to Symbol 20: In custom mode, you can define up to 20 assets for correlation calculation. 🔵 Conclusion The Correlation Heatmap Matrix is a powerful tool for analyzing correlations across multiple assets in Forex, crypto, and stock markets. By displaying a color-coded table, it visually conveys both the strength and direction of correlations — warm colors for strong negative correlation, cool colors for strong positive correlation, and mid-range tones such as yellow or green for near-zero or neutral correlation. This helps traders select assets with low or negative correlation for diversification, avoid overexposure to similar trades, identify arbitrage and pairs trading opportunities, and detect unusual divergences between typically aligned assets. With support for custom mode and up to 20 symbols, it offers high flexibility for different trading strategies, making it a valuable complement to technical analysis and risk management.
Индикатор Pine Script®
от TFlab
5
Correlation HeatMap Matrix Data [TradingFinder]🔵 Introduction Correlation is a statistical measure that shows the degree and direction of a linear relationship between two assets. Its value ranges from -1 to +1 : +1 means perfect positive correlation, 0 means no linear relationship, and -1 means perfect negative correlation. In financial markets, correlation is used for portfolio diversification, risk management, pairs trading, intermarket analysis, and identifying divergences. Correlation HeatMap Matrix Data TradingFinder is a Pine Script v6 library that calculates and returns raw correlation matrix data between up to 20 symbols. It only provides the data – it does not draw or render the heatmap – making it ideal for use in other scripts that handle visualization or further analysis. The library uses ta.correlation for fast and accurate calculations. It also includes two helper functions for visual styling : CorrelationColor(corr) : takes the correlation value as input and generates a smooth gradient color, ranging from strong negative to strong positive correlation. CorrelationTextColor(corr) : takes the correlation value as input and returns a text color that ensures optimal contrast over the background color. Library "Correlation_HeatMap_Matrix_Data_TradingFinder" CorrelationColor(corr)   Parameters:      corr (float) CorrelationTextColor(corr)   Parameters:      corr (float) Data_Matrix(Corr_Period, Sym_1, Sym_2, Sym_3, Sym_4, Sym_5, Sym_6, Sym_7, Sym_8, Sym_9, Sym_10, Sym_11, Sym_12, Sym_13, Sym_14, Sym_15, Sym_16, Sym_17, Sym_18, Sym_19, Sym_20)   Parameters:      Corr_Period (int)      Sym_1 (string)      Sym_2 (string)      Sym_3 (string)      Sym_4 (string)      Sym_5 (string)      Sym_6 (string)      Sym_7 (string)      Sym_8 (string)      Sym_9 (string)      Sym_10 (string)      Sym_11 (string)      Sym_12 (string)      Sym_13 (string)      Sym_14 (string)      Sym_15 (string)      Sym_16 (string)      Sym_17 (string)      Sym_18 (string)      Sym_19 (string)      Sym_20 (string) 🔵 How to use Import the library into your Pine Script using the import keyword and its full namespace. Decide how many symbols you want to include in your correlation matrix (up to 20). Each symbol must be provided as a string, for example FX:EURUSD . Choose the correlation period (Corr\_Period) in bars. This is the lookback window used for the calculation, such as 20, 50, or 100 bars. Call Data_Matrix(Corr_Period, Sym_1, ..., Sym_20) with your selected parameters. The function will return an array containing the correlation values for every symbol pair (upper triangle of the matrix plus diagonal). For example : var string Sym_1 = '' , var string Sym_2 = '' , var string Sym_3 = '' , var string Sym_4 = '' , var string Sym_5 = '' , var string Sym_6 = '' , var string Sym_7 = '' , var string Sym_8 = '' , var string Sym_9 = '' , var string Sym_10 = '' var string Sym_11 = '', var string Sym_12 = '', var string Sym_13 = '', var string Sym_14 = '', var string Sym_15 = '', var string Sym_16 = '', var string Sym_17 = '', var string Sym_18 = '', var string Sym_19 = '', var string Sym_20 = '' switch Market 'Forex' => Sym_1 := 'EURUSD' , Sym_2 := 'GBPUSD' , Sym_3 := 'USDJPY' , Sym_4 := 'USDCHF' , Sym_5 := 'USDCAD' , Sym_6 := 'AUDUSD' , Sym_7 := 'NZDUSD' , Sym_8 := 'EURJPY' , Sym_9 := 'EURGBP' , Sym_10 := 'GBPJPY' ,Sym_11 := 'AUDJPY', Sym_12 := 'EURCHF', Sym_13 := 'EURCAD', Sym_14 := 'GBPCAD', Sym_15 := 'CADJPY', Sym_16 := 'CHFJPY', Sym_17 := 'NZDJPY', Sym_18 := 'AUDNZD', Sym_19 := 'USDSEK' , Sym_20 := 'USDNOK' 'Stock' => Sym_1 := 'NVDA' , Sym_2 := 'AAPL' , Sym_3 := 'GOOGL' , Sym_4 := 'GOOG' , Sym_5 := 'META' , Sym_6 := 'MSFT' , Sym_7 := 'AMZN' , Sym_8 := 'AVGO' , Sym_9 := 'TSLA' , Sym_10 := 'BRK.B' ,Sym_11 := 'UNH' , Sym_12 := 'V' , Sym_13 := 'JPM' , Sym_14 := 'WMT' , Sym_15 := 'LLY' , Sym_16 := 'ORCL', Sym_17 := 'HD' , Sym_18 := 'JNJ' , Sym_19 := 'MA' , Sym_20 := 'COST' 'Crypto' => Sym_1 := 'BTCUSD' , Sym_2 := 'ETHUSD' , Sym_3 := 'BNBUSD' , Sym_4 := 'XRPUSD' , Sym_5 := 'SOLUSD' , Sym_6 := 'ADAUSD' , Sym_7 := 'DOGEUSD' , Sym_8 := 'AVAXUSD' , Sym_9 := 'DOTUSD' , Sym_10 := 'TRXUSD' ,Sym_11 := 'LTCUSD' , Sym_12 := 'LINKUSD', Sym_13 := 'UNIUSD', Sym_14 := 'ATOMUSD', Sym_15 := 'ICPUSD', Sym_16 := 'ARBUSD', Sym_17 := 'APTUSD', Sym_18 := 'FILUSD', Sym_19 := 'OPUSD' , Sym_20 := 'USDT.D' 'Custom' => Sym_1 := Sym_1_C , Sym_2 := Sym_2_C , Sym_3 := Sym_3_C , Sym_4 := Sym_4_C , Sym_5 := Sym_5_C , Sym_6 := Sym_6_C , Sym_7 := Sym_7_C , Sym_8 := Sym_8_C , Sym_9 := Sym_9_C , Sym_10 := Sym_10_C ,Sym_11 := Sym_11_C, Sym_12 := Sym_12_C, Sym_13 := Sym_13_C, Sym_14 := Sym_14_C, Sym_15 := Sym_15_C, Sym_16 := Sym_16_C, Sym_17 := Sym_17_C, Sym_18 := Sym_18_C, Sym_19 := Sym_19_C , Sym_20 := Sym_20_C = Corr.Data_Matrix(Corr_period, Sym_1 ,Sym_2 ,Sym_3 ,Sym_4 ,Sym_5 ,Sym_6 ,Sym_7 ,Sym_8 ,Sym_9 ,Sym_10,Sym_11,Sym_12,Sym_13,Sym_14,Sym_15,Sym_16,Sym_17,Sym_18,Sym_19,Sym_20) Loop through or index into this array to retrieve each correlation value for your custom layout or logic. Pass each correlation value to CorrelationColor() to get the corresponding gradient background color, which reflects the correlation’s strength and direction (negative to positive). For example : Corr.CorrelationColor(SYM_3_10) Pass the same correlation value to CorrelationTextColor() to get the correct text color for readability against that background. For example : Corr.CorrelationTextColor(SYM_1_1) Use these colors in a table or label to render your own heatmap or any other visualization you need.
Библиотека Pine Script®
от TFlab
1
Portfolio Tracker ARJO (V-01)Portfolio Tracker ARJO (V-01) This indicator is a user-friendly portfolio tracking tool designed for TradingView charts. It overlays a customizable table on your chart to monitor up to 15 stocks or symbols in your portfolio. It calculates real-time metrics like current market price (CMP), gains/losses, and stoploss breaches, helping you stay on top of your investments without switching between multiple charts. The table uses color-coding for quick visual insights: green for profits, red for losses, and highlights breached stoplosses in red for alerts. It also shows portfolio-wide totals for overall performance. Key Features Supports up to 15 Symbols: Enter stock tickers (e.g., NSE:RELIANCE or BSE:TCS) with details like buy price, date, units, and stoploss. Symbol: The stock ticker and description. Buy Date: When you purchased it. Units: Number of shares/units held. Buy Price: Your entry price. Stop Loss: Your set stoploss level (highlighted in red if breached by CMP). CMP: Current market price (fetched from the chart's timeframe). % Gain/Loss: Percentage change from buy price (color-coded: green for positive, red for negative). Gain/Loss: Total monetary gain/loss based on units. Optional Timeframe Columns: Toggle to show % change over 1 Week (1W), 1 Month (1M), 3 Months (3M), and 6 Months (6M) for historical performance. Portfolio Summary: At the top of the table, see total % gain/loss and absolute gain/loss for your entire portfolio. Visual Customizations: Adjust table position (e.g., Top Right), size, colors for positive/negative values, and intensity cutoff for gradients. Benchmark Index-Based Header: The title row's background color reflects NIFTY's weekly trend (green if above 10-week SMA, red if below) for market context. Benchmark Index-Based Header: The title row's background color reflects NIFTY's weekly trend (green if above 10-week SMA, red if below) for market context. How to Use It: Step-by-Step Guide Add the Indicator to Your Chart: Search for "Portfolio Tracker ARJO (V-01)" in TradingView's indicator library and add it to any chart (preferably Daily timeframe for accuracy). Input Your Portfolio Symbols: Open the indicator settings (gear icon). In the "Symbol 1" to "Symbol 15" groups, fill in: Symbol: Enter the ticker (e.g., NSE:INFY). Year/Month/Day: Select your buy date (e.g., 2024-07-01). Buy Price: Your purchase price per unit. Stoploss: Your exit price if things go south. Units: How many shares you own. Only fill what you need—leave extras blank. The table auto-adjusts to show only entered symbols. Customize the Table (Optional): In "Table settings": Choose position (e.g., Top Right) and size (% of chart). Toggle "Show Timeframe Columns" to add 1W/1M/3M/6M performance. In "Color settings": Pick colors for positive (green) and negative (red) cells. Set "Color intensity cutoff (%)" to control how strong the colors get (e.g., 10% means changes above 10% max out the color). Interpret the Table on Your Chart: The table appears overlaid—scan rows for each symbol's stats. Look at colors: Greener = better gains; redder = bigger losses. Check CMP cell: Red means stoploss breached—consider selling! Portfolio Gain/Loss at the top gives a quick overall health check. For Best Results: Use on a Daily chart to avoid CMP errors (the script will warn if on Weekly/Monthly). Refresh the chart or wait for a new bar if data doesn't update immediately. For Indian stocks, prefix with NSE: or BSE: (e.g., BSE:RELIANCE). This is for tracking only—not trading signals. Combine with your strategy. If no symbols show, ensure inputs are valid (e.g., buy price > 0, valid date). Finally, this tool makes it quite easy for beginners to track their portfolios, while also giving advanced traders powerful and customizable insights. I'd love to hear your feedback—happy trading!
Индикатор Pine Script®
от ARJO_2019
🧪 Yuri Garcia Smart Money Strategy FULL (Slope Divergence))📣 Yuri Garcia – Smart Money Strategy FULL This is my private Smart Money Concept strategy, designed for my family and community to learn, trade, and grow sustainably. 🔑 How it works: ✅ Volume Cluster Zones: Automatically detects areas where strong buyers or sellers concentrate, acting as dynamic S/R levels. ✅ HTF Institutional Zones (4H): Higher timeframe trend filter ensures you’re always trading in the direction of major flows. ✅ Wick Pullback Filter: Confirms price rejects the zone, catching smart money traps and reversals. ✅ Cumulative Delta (CVD): Confirms whether buyers or sellers are truly in control. ✅ Slope-Based Divergence: Optional hidden divergence between price & CVD to spot reversals others miss. ✅ ATR Dynamic SL/TP: Adapts stop loss and take profit to live volatility with adjustable risk/reward. 🧩 Visual Markers Explained: 🟦 Blue X: Price inside HTF zone 🟨 Yellow X: Price inside Volume Cluster zone 🟧 Orange Circle: Wick pullback detected 🟥 Red Square: CVD confirms order flow strength 🔼 Aqua Triangle Up: Bullish slope divergence 🔽 Purple Triangle Down: Bearish slope divergence 🟢 Green Triangle Up: Final Long Entry confirmed 🔴 Red Triangle Down: Final Short Entry confirmed ⚡ Who is this for? This strategy is best suited for traders who understand smart money concepts, order flow, and want an adaptive framework to trade major assets like BTC, Gold, SP500, NASDAQ, or FX pairs. 🔒 Important Use responsibly, backtest extensively, and combine with solid risk management. This is for educational purposes only. ✨ Credits Built with ❤️ by Yuri Garcia – dedicated to my family & community. ✅ How to use it 1️⃣ Add to chart 2️⃣ Adjust inputs for your asset & timeframe 3️⃣ Enable/disable slope divergence filter to match your style 4️⃣ Set your alerts with built-in conditions
Стратегия Pine Script®
от YGD_CONSULTING_LLC
Risk and Position Sizing📏 Why Position Size Should Be Based on Risk? Let’s say you are ready to lose 1,000 in a trade. Based on your stop loss level, you can calculate how many shares (or quantity) to buy, so that if the stop hits, you only lose that ₹1,000. This is called risk-based position sizing. It makes your trade size dynamic — small when SL is wide, bigger when SL is tight. No more random position sizes — just systematic. Portfolio size is multiplied by the selected risk % to get money risk per trade. This amount is then used to calculate how many shares can be bought for the given stop-loss. So chose your portfolio size in settings. Default Portfolio size is 1,00,000 . You can select your risk % per portfolio in the settings — for example: 0.25% for conservative style 0.5% to 1% for balanced traders 1.25% or 1.5% for aggressive ones (not recommended for beginners) This script will automatically calculate how much quantity you should buy, for each stop-loss scenario. 📈 Progressive & Inverse-Progressive Risk Styles Some traders follow progressive position sizing — they start with small risk when the trend is just starting, and increase the risk % as the trend confirms. Others follow inverse-progressive sizing — they take high risk at early stages of a bull market, and reduce risk as the trend matures (when upside becomes limited). 📌 This script allows you to manually control the risk % in settings, so you can adjust it based on your trading phase and style. 📋 Three SL Scenarios – Choose What Matches Your Style The table shows three different stop-loss conditions, and for each one it calculates: Today’s Low – tightest stop loss Yesterday’s Low – slightly safer, ideal for short-term swing trades EMA Stop (configurable) – gives more breathing room. You can visually compare all 3 in the table and choose whichever fits your strategy and comfort. Also, you can customize: Theme: dark or light Font size Table position (upper/lower corners) 🧠 Designed for traders who take risk management seriously. Let this script handle the math. You focus on execution. Happy Trading! – LensOfChartist
Индикатор Pine Script®
от LensOfChartist
2
40 Ticker Cross-Sectional Z-Scores [BackQuant]40 Ticker Cross-Sectional Z-Scores BackQuant’s 40 Ticker Cross-Sectional Z-Scores is a powerful portfolio management strategy that analyzes the relative performance of up to 40 different assets, comparing them on a cross-sectional basis to identify the top and bottom performers. This indicator computes Z-scores for each asset based on their log returns and evaluates them relative to the mean and standard deviation over a rolling window. The Z-scores represent how far an asset's return deviates from the average, and these values are used to rank the assets, allowing for dynamic asset allocation based on performance. By focusing on the strongest-performing assets and avoiding the weakest, this strategy aims to enhance returns while managing risk. Additionally, by adjusting for standard deviations, the system offers a risk-adjusted method of ranking assets, making it suitable for traders who want to dynamically allocate capital based on performance metrics rather than just price movements. Key Features 1. Cross-Sectional Z-Score Calculation: The system calculates Z-scores for 40 different assets, evaluating their log returns against the mean and standard deviation over a rolling window. This enables users to assess the relative performance of each asset dynamically, highlighting which assets are performing better or worse compared to their historical norms. The Z-score is a useful statistical tool for identifying outliers in asset performance. 2. Asset Ranking and Allocation: The system ranks assets based on their Z-scores and allocates capital to the top performers. It identifies the top and bottom assets, and traders can allocate capital to the top-performing assets, ensuring that their portfolio is aligned with the best performers. Conversely, the bottom assets are removed from the portfolio, reducing exposure to underperforming assets. 3. Rolling Window for Mean and Standard Deviation Calculations: The Z-scores are calculated based on rolling means and standard deviations, making the system adaptive to changing market conditions. This rolling calculation window allows the strategy to adjust to recent performance trends and minimize the impact of outdated data. 4. Mean and Standard Deviation Visualization: The script provides real-time visualizations of the mean (x̄) and standard deviation (σ) of asset returns, helping traders quickly identify trends and volatility in their portfolio. These visual indicators are useful for understanding the current market environment and making more informed allocation decisions. 5. Top & Bottom Performer Tables: The system generates tables that display the top and bottom performers, ranked by their Z-scores. Traders can quickly see which assets are outperforming and underperforming. These tables provide clear and actionable insights, helping traders make informed decisions about which assets to include in their portfolio. 6. Customizable Parameters: The strategy allows traders to customize several key parameters, including: Rolling Calculation Window: Set the window size for the rolling mean and standard deviation calculations. Top & Bottom Tickers: Choose how many of the top and bottom assets to display and allocate capital to. Table Orientation: Select between vertical or horizontal table formats to suit the user’s preference. 7. Forward Test & Out-of-Sample Testing: The system includes out-of-sample forward tests, ensuring that the strategy is evaluated based on real-time performance, not just historical data. This forward testing approach helps validate the robustness of the strategy in dynamic market conditions. 8. Visual Feedback and Alerts: The system provides visual feedback on the current asset rankings and allocations, with dynamic labels and plots on the chart. Additionally, users receive alerts when allocations change, keeping them informed of important adjustments. 9. Risk Management via Z-Scores and Std Dev: The system’s approach to asset selection is based on Z-scores, which normalize performance relative to the historical mean. By incorporating standard deviation, it accounts for the volatility and risk associated with each asset. This allows for more precise risk management and portfolio construction. 10. Note on Mean Reversion Strategy: If you take the inverse of the signals provided by this indicator, the strategy can be used for mean-reversion rather than trend-following. This would involve buying the underperforming assets and selling the outperforming ones. However, it's important to note that this approach does not work well with highly correlated assets, as the relationship between the assets could result in the same directional movement, undermining the effectiveness of the mean-reversion strategy. References www.uts.edu.au onlinelibrary.wiley.com www.cmegroup.com Final Thoughts The 40 Ticker Cross-Sectional Z-Scores strategy offers a data-driven approach to portfolio management, dynamically allocating capital based on the relative performance of assets. By using Z-scores and standard deviations, this strategy ensures that capital is directed to the strongest performers while avoiding weaker assets, ultimately improving the risk-adjusted returns of the portfolio. Whether you’re focused on trend-following or looking to explore mean-reversion strategies, this flexible system can be tailored to suit your investment goals.
Индикатор Pine Script®
от BackQuant
Performance Metrics With Bracketed Rebalacing [BackQuant]Performance Metrics With Bracketed Rebalancing The Performance Metrics With Bracketed Rebalancing script offers a robust method for assessing portfolio performance, integrating advanced portfolio metrics with different rebalancing strategies. With a focus on adaptability, the script allows traders to monitor and adjust portfolio weights, equity, and other key financial metrics dynamically. This script provides a versatile approach for evaluating different trading strategies, considering factors like risk-adjusted returns, volatility, and the impact of portfolio rebalancing. Please take the time to read the following: Key Features and Benefits of Portfolio Methods Bracketed Rebalancing: Bracketed Rebalancing is an advanced strategy designed to trigger portfolio adjustments when an asset's weight surpasses a predefined threshold. This approach minimizes overexposure to any single asset while maintaining flexibility in response to market changes. The strategy is particularly beneficial for mitigating risks that arise from significant asset weight fluctuations. The following image illustrates how this method reacts when asset weights cross the threshold: Daily Rebalancing: Unlike the bracketed method, Daily Rebalancing adjusts portfolio weights every trading day, ensuring consistent asset allocation. This method aims for a more even distribution of portfolio weights, making it a suitable option for traders who prefer less sensitivity to individual asset volatility. Here's an example of Daily Rebalancing in action: No Rebalancing: For traders who prefer a passive approach, the "No Rebalancing" option allows the portfolio to remain static, without any adjustments to asset weights. This method may appeal to long-term investors or those who believe in the inherent stability of their selected assets. Here’s how the portfolio looks when no rebalancing is applied: Portfolio Weights Visualization: One of the standout features of this script is the visual representation of portfolio weights. With adjustable settings, users can track the current allocation of assets in real-time, making it easier to analyze shifts and trends. The following image shows the real-time weight distribution across three assets: Rolling Drawdown Plot: Managing drawdown risk is a critical aspect of portfolio management. The Rolling Drawdown Plot visually tracks the drawdown over time, helping traders monitor the risk exposure and performance relative to the peak equity levels. This feature is essential for assessing the portfolio's resilience during market downturns: Daily Portfolio Returns: Tracking daily returns is crucial for evaluating the short-term performance of the portfolio. The script allows users to plot daily portfolio returns to gain insights into daily profit or loss, helping traders stay updated on their portfolio’s progress: Performance Metrics Net Profit (%): This metric represents the total return on investment as a percentage of the initial capital. A positive net profit indicates that the portfolio has gained value over the evaluation period, while a negative value suggests a loss. It's a fundamental indicator of overall portfolio performance. Maximum Drawdown (Max DD): Maximum Drawdown measures the largest peak-to-trough decline in portfolio value during a specified period. It quantifies the most significant loss an investor would have experienced if they had invested at the highest point and sold at the lowest point within the timeframe. A smaller Max DD indicates better risk management and less exposure to significant losses. Annual Mean Returns (% p/y): This metric calculates the average annual return of the portfolio over the evaluation period. It provides insight into the portfolio's ability to generate returns on an annual basis, aiding in performance comparison with other investment opportunities. Annual Standard Deviation of Returns (% p/y): This measure indicates the volatility of the portfolio's returns on an annual basis. A higher standard deviation signifies greater variability in returns, implying higher risk, while a lower value suggests more stable returns. Variance: Variance is the square of the standard deviation and provides a measure of the dispersion of returns. It helps in understanding the degree of risk associated with the portfolio's returns. Sortino Ratio: The Sortino Ratio is a variation of the Sharpe Ratio that only considers downside risk, focusing on negative volatility. It is calculated as the difference between the portfolio's return and the minimum acceptable return (MAR), divided by the downside deviation. A higher Sortino Ratio indicates better risk-adjusted performance, emphasizing the importance of avoiding negative returns. Sharpe Ratio: The Sharpe Ratio measures the portfolio's excess return per unit of total risk, as represented by standard deviation. It is calculated by subtracting the risk-free rate from the portfolio's return and dividing by the standard deviation of the portfolio's excess return. A higher Sharpe Ratio indicates more favorable risk-adjusted returns. Omega Ratio: The Omega Ratio evaluates the probability of achieving returns above a certain threshold relative to the probability of experiencing returns below that threshold. It is calculated by dividing the cumulative probability of positive returns by the cumulative probability of negative returns. An Omega Ratio greater than 1 indicates a higher likelihood of achieving favorable returns. Gain-to-Pain Ratio: The Gain-to-Pain Ratio measures the return per unit of risk, focusing on the magnitude of gains relative to the severity of losses. It is calculated by dividing the total gains by the total losses experienced during the evaluation period. A higher ratio suggests a more favorable balance between reward and risk. www.linkedin.com Compound Annual Growth Rate (CAGR) (% p/y): CAGR represents the mean annual growth rate of the portfolio over a specified period, assuming the investment has been compounding over that time. It provides a smoothed annual rate of growth, eliminating the effects of volatility and offering a clearer picture of long-term performance. Portfolio Alpha (% p/y): Portfolio Alpha measures the portfolio's performance relative to a benchmark index, adjusting for risk. It is calculated using the Capital Asset Pricing Model (CAPM) and represents the excess return of the portfolio over the expected return based on its beta and the benchmark's performance. A positive alpha indicates outperformance, while a negative alpha suggests underperformance. Portfolio Beta: Portfolio Beta assesses the portfolio's sensitivity to market movements, indicating its exposure to systematic risk. A beta greater than 1 suggests the portfolio is more volatile than the market, while a beta less than 1 indicates lower volatility. Beta is used to understand the portfolio's potential for gains or losses in relation to market fluctuations. Skewness of Returns: Skewness measures the asymmetry of the return distribution. A positive skew indicates a distribution with a long right tail, suggesting more frequent small losses and fewer large gains. A negative skew indicates a long left tail, implying more frequent small gains and fewer large losses. Understanding skewness helps in assessing the likelihood of extreme outcomes. Value at Risk (VaR) 95th Percentile: VaR at the 95th percentile estimates the maximum potential loss over a specified period, given a 95% confidence level. It provides a threshold value such that there is a 95% probability that the portfolio will not experience a loss greater than this amount. Conditional Value at Risk (CVaR): CVaR, also known as Expected Shortfall, measures the average loss exceeding the VaR threshold. It provides insight into the tail risk of the portfolio, indicating the expected loss in the worst-case scenarios beyond the VaR level. These metrics collectively offer a comprehensive view of the portfolio's performance, risk exposure, and efficiency. By analyzing these indicators, investors can make informed decisions, balancing potential returns with acceptable levels of risk. Conclusion The Performance Metrics With Bracketed Rebalancing script provides a comprehensive framework for evaluating and optimizing portfolio performance. By integrating advanced metrics, adaptive rebalancing strategies, and visual analytics, it empowers traders to make informed decisions in managing their investment portfolios. However, it's crucial to consider the implications of rebalancing strategies, as academic research indicates that predictable rebalancing can lead to market impact costs. Therefore, adopting flexible and less predictable rebalancing approaches may enhance portfolio performance and reduce associated costs.
Индикатор Pine Script®
от BackQuant
DCA Investment Tracker Pro [tradeviZion]DCA Investment Tracker Pro: Educational DCA Analysis Tool An educational indicator that helps analyze Dollar-Cost Averaging strategies by comparing actual performance with historical data calculations. --- 💡 Why I Created This Indicator As someone who practices Dollar-Cost Averaging, I was frustrated with constantly switching between spreadsheets, calculators, and charts just to understand how my investments were really performing. I wanted to see everything in one place - my actual performance, what I should expect based on historical data, and most importantly, visualize where my strategy could take me over the long term . What really motivated me was watching friends and family underestimate the incredible power of consistent investing. When Napoleon Bonaparte first learned about compound interest, he reportedly exclaimed "I wonder it has not swallowed the world" - and he was right! Yet most people can't visualize how their $500 monthly contributions today could become substantial wealth decades later. Traditional DCA tracking tools exist, but they share similar limitations: Require manual data entry and complex spreadsheets Use fixed assumptions that don't reflect real market behavior Can't show future projections overlaid on actual price charts Lose the visual context of what's happening in the market Make compound growth feel abstract rather than tangible I wanted to create something different - a tool that automatically analyzes real market history, detects volatility periods, and shows you both current performance AND educational projections based on historical patterns right on your TradingView charts. As Warren Buffett said: "Someone's sitting in the shade today because someone planted a tree a long time ago." This tool helps you visualize your financial tree growing over time. This isn't just another calculator - it's a visualization tool that makes the magic of compound growth impossible to ignore. --- 🎯 What This Indicator Does This educational indicator provides DCA analysis tools. Users can input investment scenarios to study: Theoretical Performance: Educational calculations based on historical return data Comparative Analysis: Study differences between actual and theoretical scenarios Historical Projections: Theoretical projections for educational analysis (not predictions) Performance Metrics: CAGR, ROI, and other analytical metrics for study Historical Analysis: Calculates historical return data for reference purposes --- 🚀 Key Features Volatility-Adjusted Historical Return Calculation Analyzes 3-20 years of actual price data for any symbol Automatically detects high-volatility stocks (meme stocks, growth stocks) Uses median returns for volatile stocks, standard CAGR for stable stocks Provides conservative estimates when extreme outlier years are detected Smart fallback to manual percentages when data insufficient Customizable Performance Dashboard Educational DCA performance analysis with compound growth calculations Customizable table sizing (Tiny to Huge text options) 9 positioning options (Top/Middle/Bottom + Left/Center/Right) Theme-adaptive colors (automatically adjusts to dark/light mode) Multiple display layout options Future Projection System Visual future growth projections Timeframe-aware calculations (Daily/Weekly/Monthly charts) 1-30 year projection options Shows projected portfolio value and total investment amounts Investment Insights Performance vs benchmark comparison ROI from initial investment tracking Monthly average return analysis Investment milestone alerts (25%, 50%, 100% gains) Contribution tracking and next milestone indicators --- 📊 Step-by-Step Setup Guide 1. Investment Settings 💰 Initial Investment: Enter your starting lump sum (e.g., $60,000) Monthly Contribution: Set your regular DCA amount (e.g., $500/month) Return Calculation: Choose "Auto (Stock History)" for real data or "Manual" for fixed % Historical Period: Select 3-20 years for auto calculations (default: 10 years) Start Year: When you began investing (e.g., 2020) Current Portfolio Value: Your actual portfolio worth today (e.g., $150,000) 2. Display Settings 📊 Table Sizes: Choose from Tiny, Small, Normal, Large, or Huge Table Positions: 9 options - Top/Middle/Bottom + Left/Center/Right Visibility Toggles: Show/hide Main Table and Stats Table independently 3. Future Projection 🔮 Enable Projections: Toggle on to see future growth visualization Projection Years: Set 1-30 years ahead for analysis Live Example - NASDAQ:META Analysis: Settings shown: $60K initial + $500/month + Auto calculation + 10-year history + 2020 start + $150K current value --- 🔬 Pine Script Code Examples Core DCA Calculations: // Calculate total invested over time months_elapsed = (year - start_year) * 12 + month - 1 total_invested = initial_investment + (monthly_contribution * months_elapsed) // Compound growth formula for initial investment theoretical_initial_growth = initial_investment * math.pow(1 + annual_return, years_elapsed) // Future Value of Annuity for monthly contributions monthly_rate = annual_return / 12 fv_contributions = monthly_contribution * ((math.pow(1 + monthly_rate, months_elapsed) - 1) / monthly_rate) // Total expected value theoretical_total = theoretical_initial_growth + fv_contributions Volatility Detection Logic: // Detect extreme years for volatility adjustment extreme_years = 0 for i = 1 to historical_years yearly_return = ((price_current / price_i_years_ago) - 1) * 100 if yearly_return > 100 or yearly_return < -50 extreme_years += 1 // Use median approach for high volatility stocks high_volatility = (extreme_years / historical_years) > 0.2 calculated_return = high_volatility ? median_of_returns : standard_cagr Performance Metrics: // Calculate key performance indicators absolute_gain = actual_value - total_invested total_return_pct = (absolute_gain / total_invested) * 100 roi_initial = ((actual_value - initial_investment) / initial_investment) * 100 cagr = (math.pow(actual_value / initial_investment, 1 / years_elapsed) - 1) * 100 --- 📊 Real-World Examples See the indicator in action across different investment types: Stable Index Investments: AMEX:SPY (SPDR S&P 500) - Shows steady compound growth with standard CAGR calculations Classic DCA success story: $60K initial + $500/month starting 2020. The indicator shows SPY's historical 10%+ returns, demonstrating how consistent broad market investing builds wealth over time. Notice the smooth theoretical growth line vs actual performance tracking. MIL:VUAA (Vanguard S&P 500 UCITS) - Shows both data limitation and solution approaches Data limitation example: VUAA shows "Manual (Auto Failed)" and "No Data" when default 10-year historical setting exceeds available data. The indicator gracefully falls back to manual percentage input while maintaining all DCA calculations and projections. MIL:VUAA (Vanguard S&P 500 UCITS) - European ETF with successful 5-year auto calculation Solution demonstration: By adjusting historical period to 5 years (matching available data), VUAA auto calculation works perfectly. Shows how users can optimize settings for newer assets. European market exposure with EUR denomination, demonstrating DCA effectiveness across different markets and currencies. NYSE:BRK.B (Berkshire Hathaway) - Quality value investment with Warren Buffett's proven track record Value investing approach: Berkshire Hathaway's legendary performance through DCA lens. The indicator demonstrates how quality companies compound wealth over decades. Lower volatility than tech stocks = standard CAGR calculations used. High-Volatility Growth Stocks: NASDAQ:NVDA (NVIDIA Corporation) - Demonstrates volatility-adjusted calculations for extreme price swings High-volatility example: NVIDIA's explosive AI boom creates extreme years that trigger volatility detection. The indicator automatically switches to "Median (High Vol): 50%" calculations for conservative projections, protecting against unrealistic future estimates based on outlier performance periods. NASDAQ:TSLA (Tesla) - Shows how 10-year analysis can stabilize volatile tech stocks Stable long-term growth: Despite Tesla's reputation for volatility, the 10-year historical analysis (34.8% CAGR) shows consistent enough performance that volatility detection doesn't trigger. Demonstrates how longer timeframes can smooth out extreme periods for more reliable projections. NASDAQ:META (Meta Platforms) - Shows stable tech stock analysis using standard CAGR calculations Tech stock with stable growth: Despite being a tech stock and experiencing the 2022 crash, META's 10-year history shows consistent enough performance (23.98% CAGR) that volatility detection doesn't trigger. The indicator uses standard CAGR calculations, demonstrating how not all tech stocks require conservative median adjustments. Notice how the indicator automatically detects high-volatility periods and switches to median-based calculations for more conservative projections, while stable investments use standard CAGR methods. --- 📈 Performance Metrics Explained Current Portfolio Value: Your actual investment worth today Expected Value: What you should have based on historical returns (Auto) or your target return (Manual) Total Invested: Your actual money invested (initial + all monthly contributions) Total Gains/Loss: Absolute dollar difference between current value and total invested Total Return %: Percentage gain/loss on your total invested amount ROI from Initial Investment: How your starting lump sum has performed CAGR: Compound Annual Growth Rate of your initial investment (Note: This shows initial investment performance, not full DCA strategy) vs Benchmark: How you're performing compared to the expected returns --- ⚠️ Important Notes & Limitations Data Requirements: Auto mode requires sufficient historical data (minimum 3 years recommended) CAGR Limitation: CAGR calculation is based on initial investment growth only, not the complete DCA strategy Projection Accuracy: Future projections are theoretical and based on historical returns - actual results may vary Timeframe Support: Works ONLY on Daily (1D), Weekly (1W), and Monthly (1M) charts - no other timeframes supported Update Frequency: Update "Current Portfolio Value" regularly for accurate tracking --- 📚 Educational Use & Disclaimer This analysis tool can be applied to various stock and ETF charts for educational study of DCA mathematical concepts and historical performance patterns. Study Examples: Can be used with symbols like AMEX:SPY , NASDAQ:QQQ , AMEX:VTI , NASDAQ:AAPL , NASDAQ:MSFT , NASDAQ:GOOGL , NASDAQ:AMZN , NASDAQ:TSLA , NASDAQ:NVDA for learning purposes. EDUCATIONAL DISCLAIMER: This indicator is a study tool for analyzing Dollar-Cost Averaging strategies. It does not provide investment advice, trading signals, or guarantees. All calculations are theoretical examples for educational purposes only. Past performance does not predict future results. Users should conduct their own research and consult qualified financial professionals before making any investment decisions. --- © 2025 TradeVizion. All rights reserved.
Индикатор Pine Script®
от TradeVizion
4
Correlation MA – 15 Assets + Average (Optional)This indicator calculates the moving average of the correlation coefficient between your charted asset and up to 15 user-selected symbols. It helps identify uncorrelated or inversely correlated assets for diversification, pair trading, or hedging. Features: ✅ Compare your current chart against up to 15 assets ✅ Toggle assets on/off individually ✅ Custom correlation and MA lengths ✅ Real-time average correlation line across enabled assets ✅ Horizontal lines at +1, 0, and -1 for easy visual reference Ideal for: Portfolio diversification analysis Finding low-correlation stocks Mean-reversion & pair trading setups Crypto, equities, ETFs To use: set the benchmark chart (e.g. TSLA), choose up to 15 assets, and adjust settings as needed. Look for assets with correlation near 0 or negative values for uncorrelated performance.
Индикатор Pine Script®
от GhostPivot
2