Sector Rotation & Money Flow Dashboard📊 Overview
The Sector Rotation & Money Flow Dashboard is a comprehensive market analysis tool that tracks 39 major sector ETFs in real-time, providing institutional-grade insights into sector rotation, momentum shifts, and money flow patterns. This indicator helps traders identify which sectors are attracting capital, which are losing favor, and where the next opportunities might emerge.
Perfect for swing traders, position traders, and investors who want to stay ahead of sector rotation and ride the strongest trends while avoiding weak sectors.
🎯 What This Indicator Does
Tracks 39 Major Sectors: From technology to utilities, cryptocurrencies to commodities
Calculates Multiple Timeframes: 1-week, 1-month, 3-month, and 6-month performance
Advanced Momentum Metrics: Proprietary momentum score and acceleration calculations
Relative Strength Analysis: Compare sector performance against any benchmark index
Money Flow Signals: Visual indicators showing where institutional money is moving
Smart Filtering: Pre-built strategy filters for different trading styles
Trend Detection: Emoji-based visual system for quick trend identification
💡 Key Features
1. Performance Metrics
Multiple timeframe analysis (1W, 1M, 3M, 6M)
Month-over-month change tracking
Relative strength vs benchmark index
2. Advanced Analytics
Momentum Score: Weighted composite of recent performance
Acceleration: Rate of change in momentum (second derivative)
Money Flow Signals: IN/OUT/TURN/WATCH indicators
3. Strategy Preset Filters
🎯 Swing Trade: High momentum opportunities
📈 Trend Follow: Established uptrends
🔄 Mean Reversion: Oversold bounce candidates
💎 Value Hunt: Deep value opportunities
🚀 Breakout: Emerging strength
⚠️ Risk Off: Sectors to avoid
4. Customization
All 39 sector ETFs can be customized
Adjustable benchmark index
Flexible display options
Multiple sorting methods
📋 Settings Documentation
Display Settings
Show Table (Default: On)
Toggles the entire dashboard display
Table Position (Default: Middle Center)
Choose from 9 positions on your chart
Options: Top/Middle/Bottom × Left/Center/Right
Rows to Show (Default: 15)
Number of sectors displayed (5-40)
Useful for focusing on top/bottom performers
Sort By (Default: Momentum)
1M/3M/6M: Sort by specific timeframe performance
Momentum: Weighted recent performance score
Acceleration: Rate of momentum change
1M Change: Month-over-month improvement
RS: Relative strength vs benchmark
Flow: IN First: Prioritize sectors with inflows
Flow: TURN First: Focus on reversal candidates
Recovery Plays: Oversold sectors recovering
Oversold Bounce: Deepest declines with positive signs
Top Gainers/Losers 3M: Best/worst quarterly performers
Best Acc + Mom: Combined strength score
Worst Acc (Topping): Sectors losing momentum
Filter Settings
Strategy Preset Filter (Default: All)
All: No filtering
🎯 Swing Trade: Mom >5, Acc >2, Money flowing in
📈 Trend Follow: Positive 1M & 3M, RS >0
🔄 Mean Reversion: Oversold but improving
💎 Value Hunt: Down >10% with recovery signs
🚀 Breakout: Rapid momentum surge
⚠️ Risk Off: Declining or topping sectors
Custom Flow Filter: Use manual flow filter
Custom Flow Signal Filter (Default: All)
Only active when Strategy Preset = "Custom Flow Filter"
IN Only: Strong inflows
TURN Only: Reversal signals
WATCH Only: Recovery candidates
OUT Only: Outflow sectors
Active Flows Only: Any non-neutral signal
Hide Low Volume ETFs (Default: Off)
Filters out illiquid sectors (future enhancement)
Visual Settings
Show Trend Emojis (Default: On)
🚀 Breakout (Strong 1M + High Acceleration)
🔥 Hot Recovery (From -10% to positive)
💪 Steady Uptrend (All timeframes positive)
➡️ Sideways/Ranging
⚠️ Warning/Topping (Up >15%, now slowing)
📉 Falling (Negative + declining)
🔄 Bottoming (Improving from lows)
Compact Mode (Default: Off)
Removes decimals for cleaner display
Useful when showing many rows
Min Data Points Required (Default: 3)
Minimum data points needed to display a sector
Prevents showing sectors with insufficient data
Relative Strength Settings
RS Benchmark Index (Default: AMEX:SPY)
Index to compare all sectors against
Can use SPY, QQQ, IWM, or any other index
RS Period (Days) (Default: 21)
Lookback period for RS calculation
21 days = 1 month, 63 days = 3 months, etc.
Sector ETF Settings (Groups 1-39)
Each sector has two inputs:
Symbol: The ticker (e.g., "AMEX:XLF")
Name: Display name (e.g., "Financials")
All 39 sectors can be customized to track different ETFs or markets.
📈 Column Explanations
Sector: ETF name/description
1M%: 1-month (21-day) performance
3M%: 3-month (63-day) performance
6M%: 6-month (126-day) performance
Mom: Momentum score (weighted average, recent-biased)
Acc: Acceleration (momentum rate of change)
Δ1M: Month-over-month change
RS: Relative strength vs benchmark
Flow: Money flow signal
↗️ IN: Strong inflows
🔄 TURN: Potential reversal
👀 WATCH: Recovery candidate
↘️ OUT: Outflows
—: Neutral
🎮 Usage Tips
For Swing Traders (3-14 days)
Use "🎯 Swing Trade" filter
Sort by "Acceleration" or "Momentum"
Look for Flow = "IN" and Mom >10
Confirm with positive RS
For Position Traders (2-8 weeks)
Use "📈 Trend Follow" filter
Sort by "RS" or "Best Acc + Mom"
Focus on consistent green across timeframes
Ensure RS >3 for market leaders
For Value Investors
Use "💎 Value Hunt" filter
Sort by "Recovery Plays" or "Top Losers 3M"
Look for improving Δ1M
Check for "WATCH" or "TURN" signals
For Risk Management
Regularly check "⚠️ Risk Off" filter
Sort by "Worst Acc (Topping)"
Review holdings for ⚠️ warning emojis
Exit sectors showing "OUT" flow
Market Regime Recognition
Bull Market: Many sectors showing "IN" flow, positive RS
Bear Market: Widespread "OUT" flows, negative RS
Rotation: Mixed flows, some "IN" while others "OUT"
Recovery: Multiple "TURN" and "WATCH" signals
🔧 Pro Tips
Combine Filters + Sorting: Filter first to narrow candidates, then sort to prioritize
Multi-Timeframe Confirmation: Best setups show alignment across 1M, 3M, and momentum
RS is Key: Sectors outperforming SPY (RS >0) tend to continue outperforming
Acceleration Matters: Positive acceleration often precedes price breakouts
Flow Transitions: "WATCH" → "TURN" → "IN" progression identifies new trends early
Regular Scans:
Daily: Check "Acceleration" sort
Weekly: Review "1M Change"
Monthly: Analyze "RS" shifts
Divergence Signals:
Price up but Acceleration down = Potential top
Price down but Acceleration up = Potential bottom
Sector Pairs Trading: Long sectors with "IN" flow, short sectors with "OUT" flow
⚠️ Important Notes
This indicator makes 40 security requests (maximum allowed)
Best used on Daily timeframe
Data updates in real-time during market hours
Some ETFs may show "—" if data is unavailable
🎯 Common Strategies
"Follow the Flow"
Only trade sectors showing "IN" flow with positive RS
"Rotation Catcher"
Focus on "TURN" signals in sectors down >15% from highs
"Momentum Rider"
Trade top 3 sectors by Momentum score, exit when Acceleration turns negative
"Mean Reversion"
Buy sectors in bottom 20% by 3M performance when Δ1M improves
"Relative Strength Leader"
Maintain positions only in sectors with RS >5
Not financial advice - always do additional research
Portföy Yönetimi
Adaptive MVRV & RSI Strategy V6 (Dynamic Thresholds)Strategy Explanation
This is an advanced Dollar-Cost Averaging (DCA) strategy for Bitcoin that aims to adapt to long-term market cycles and changing volatility. Instead of relying on fixed buy/sell signals, it uses a dynamic, weighted approach based on a combination of on-chain data and classic momentum.
Core Components:
Dual-Indicator Signal: The strategy combines two powerful indicators for a more robust signal:
MVRV Ratio: An on-chain metric to identify when Bitcoin is fundamentally over or undervalued relative to its historical cost basis.
Weekly RSI: A classic momentum indicator to gauge long-term market strength and identify overbought/oversold conditions.
Dynamic, Self-Adjusting Thresholds: The core innovation of this strategy is that it avoids fixed thresholds (e.g., "sell when RSI is 70"). Instead, the buy and sell zones are dynamically calculated based on a long-term (2-year) moving average and standard deviation of each indicator. This allows the strategy to automatically adapt to Bitcoin's decreasing volatility and changing market structure over time.
Weighted DCA (Scaling In & Out): The strategy doesn't just buy or sell a fixed amount. The size of its trades is scaled based on conviction:
Buying: As the MVRV and RSI fall deeper into their "undervalued" zones, the percentage of available cash used for each purchase increases.
Selling: As the indicators rise further into "overvalued" territory, the percentage of the current position sold also increases.
This creates an adaptive system that systematically accumulates during periods of fear and distributes during periods of euphoria, with the intensity of its actions directly tied to the extremity of market conditions.
SMART TRADING DASHBOARDPart 1: Understanding the Foundation
The first lines of the script set up the basic parameters of the indicator.
• //@version=5: This is crucial and specifies that the code is written for Pine Script version 5. TradingView updates its language, and version 5 has new features and syntax changes compared to previous versions.
• indicator("SMART TRADING DASHBOARD", overlay=true): This line defines the script as an indicator and gives it a name, "SMART TRADING DASHBOARD." The overlay=true parameter tells Pine Script to draw the indicator directly on the price chart, not in a separate panel below it.
Part 2: Defining Inputs and Variables
The //━━━━━━━━━━━━━ INPUT PARAMETERS ━━━━━━━━━━━━━ section is where the user can customize the indicator without changing the code.
• input.int() and input.float(): These functions create configurable settings for the indicator, such as the Supertrend's ATR period, ATR multiplier, risk percentage, and target percentages. The user can change these values in the indicator's "Settings" menu.
• input.string() and input.bool(): These are used for inputs that are not numerical, such as the position of the dashboard table and a toggle to show/hide it.
This section also initializes several variables using the var keyword. var is a special keyword in Pine Script that declares a variable and ensures its value is preserved from one bar to the next. This is essential for tracking things like the entry price, stop-loss, and profit targets.
Part 3: The Core Trading Logic
Supertrend Analysis
The Supertrend is a trend-following indicator.
• = ta.supertrend(factor, atrPeriod): This line uses a built-in Pine Script function, ta.supertrend(), to calculate the Supertrend line. It returns two values: the supertrend line itself and a dir (direction) value which is 1 for an uptrend and -1 for a downtrend.
• buySignal = ta.crossover(close, supertrend): This detects a buy signal when the closing price crosses over the Supertrend line.
• sellSignal = ta.crossunder(close, supertrend): This detects a sell signal when the closing price crosses under the Supertrend line.
Entry, Take-Profit (TP), and Stop-Loss (SL) Calculations
The if statements check for the buySignal and sellSignal conditions.
• if buySignal: When a buy signal occurs, the script sets the entry price to the current close, and calculates the sl, tp1, tp2, and tp3 based on the predefined percentage inputs. The signalType is set to "LONG."
• if sellSignal: Similarly, for a sell signal, it calculates the levels for a "SHORT" trade.
This section is vital as it provides the core trading levels to the user.
Part 4: Additional Market Analysis
This script goes beyond a simple Supertrend and includes several other analysis tools.
• Volume Analysis: The code calculates a volume moving average (volMA) and a volRatio to see if the current volume is high compared to its recent average.
• Profit % and Risk-to-Reward (RR): It continuously calculates the floating profit/loss percentage of the current position and the risk-to-reward ratio based on the entry and third target. This provides real-time performance metrics.
• PCR & OI Trend (Simulated): The script simulates PCR (Put/Call Ratio) and OI (Open Interest) trends. It uses request.security() to get data from a higher timeframe (60-minute) and compares the simulated values to determine if the market is in a "Long Buildup," "Short Covering," or other states. This adds a simulated derivative market analysis to the tool.
• Momentum Analysis: It uses the built-in ta.rsi() function to calculate the Relative Strength Index (RSI) and determines if the market is overbought or oversold to identify momentum.
• PDC Analysis: "PDC" stands for Previous Day Close. The script checks if the current close is above the previous day's high or below the previous day's low to determine a daily breakout bias.
Part 5: Creating the Visual Dashboard
The dashboard is built using Pine Script's table functions.
• Color Scheme: The code defines a professional, dark theme color scheme using hexadecimal color codes. These colors are then used throughout the table to provide a clear and organized display.
• table.new(): This function creates the table object itself, defining its position (positionOpt input), columns, and rows.
• table.cell(): This is used to populate each individual cell of the table with text, background color, and text color. The script uses table.merge_cells() to combine cells for a cleaner, more readable layout.
• str.tostring(): This function is used to convert numerical values (like entry, sl, profitPct) into string format so they can be displayed in the table.
• plot(): Finally, the plot() function draws the Supertrend line on the chart itself, with the line color changing based on the trend direction (dir).
Part 6: Alerts
The alertcondition() function creates custom alerts that the user can set up in the TradingView platform.
• alertcondition(buySignal, ...): This creates a "Buy Signal" alert. The message parameter is the text that will appear when the alert is triggered.
• alertcondition(sellSignal, ...): Creates a "Sell Signal" alert.
• alertcondition(volRatio > 2, ...): This is a great example of a custom alert, triggering when a significant volume spike is detected.
________________________________________
aiTrendview Disclaimer
Trading financial markets, including futures, options, and stocks, involves substantial risk of loss and is not suitable for every investor. The "SMART TRADING DASHBOARD" is a technical analysis tool for educational and informational purposes only. It is not financial advice. The indicator's signals and metrics are based on historical data and simulated logic, and past performance is not indicative of future results. You should not treat any signals or information from this tool as a definitive guide for making trades. Always conduct your own research, and consider consulting with a qualified financial professional before making any investment decisions. The creator and provider of this script are not responsible for any trading losses you may incur.
CycleTrend | QuantEdgeB📊 CycleTrend | QuantEdgeB
The CycleTrend strategy is a comprehensive trend-following system that integrates multiple advanced techniques, including on-chain data analysis, macroeconomic indicators, trend filters, and statistical smoothing functions.
This strategy dynamically adapts to market conditions by blending traditional technical analysis tools with modern quantitative finance approaches, making it a powerful hybrid model suitable for different market regimes.
🔗 The Core Framework of CycleTrend
🧩 1️⃣ Multi-Dimensional Market Analysis
CycleTrend incorporates four key dimensions of market structure, ensuring that it captures long-term, medium-term, and short-term trends while filtering out noise.
✔ On-Chain Data (MosaicMix) → Detects long-term trends using blockchain analytics.
✔ Macro & Risk Indicators (RiskMosaic) → Measures macroeconomic influences on market behavior.
✔ ChronoSync (Technical Trend-Following Signals) → Blends multiple trend-following indicators for directional bias.
✔ Sentival TF (Statistical Sentiment Analysis) → Uses Z-score-based mean-reversion indicators for overbought/oversold conditions.
📊 2️⃣ How These Components Work Together
Each component contributes a specific function to the strategy:
1. On-Chain Analysis (MosaicMix) → Market Strength
o Short-Term Holder MVRV → Measures unrealized profit/loss based on recent Bitcoin holders.
o Profit & Loss Ratio with MVRV Rate of Change (PLRoC) → Filters out weak market conditions using profit/loss trend dynamics.
o Final On-Chain Signal → Determines if on-chain data suggests a bullish or bearish phase.
2. RiskMosaic (Macroeconomic & Risk Model) → Risk-Regime Detection
o Tracks 10 key economic variables like RSI, China Equity Index, PMI, BTC supply trends, and silver ratio.
o Uses Z-score normalization to measure relative trends across macro indicators.
o Identifies shifts in macroeconomic risk sentiment and aligns CycleTrend to major economic cycles.
3. ChronoSync (Technical Trend Model) → Precise Trade Execution
o VIDYA ATR Gaussian Filter → Detects long-term trend momentum with adaptive smoothing.
o KIJUN ATR & Dual SD Kijun → Captures structural price movements while filtering short-term volatility.
o VIDYA Loop Function → Iteratively tracks trend momentum over an extended period.
o PRC-ALMA Adaptive Bands & Bollinger Bands % SD → Adapts trend signals based on mean-reverting conditions.
o Final ChronoSync Score → Aggregates all trend-following components to generate high-probability directional bias.
4. Sentival TF (Mean-Reversion & Momentum Filter) → Smart Entry/Exit Signals
o MVRV Z-Score → Measures how overbought/oversold Bitcoin is relative to historical valuations.
o SOPR, BB%, RSI, RoC, and NUPL Indicators → Filters out low-confidence trade setups by adding statistical validation.
o Final Sentival Signal → A quantitative assessment of whether a trade setup has a statistical edge.
🛠️ 3️⃣ CycleTrend Signal Generation
Once all four components (On-Chain, Macro, ChronoSync, and Sentival) produce signals, they are blended into a final CycleTrend score (TPI):
TPI=On-Chain RiskMosaic + Sentival-Chrono Trend BiasTPI = \text{On-Chain RiskMosaic + Sentival-Chrono Trend Bias}
The strategy then applies threshold-based decision rules:
✔ Go Long (BUY) → If TPI>LuTPI > Lu (Long Threshold)
✔ Go Short (SELL) → If TPI<SuTPI < Su (Short Threshold)
These entry/exit signals dynamically adjust based on market conditions, allowing CycleTrend to trade adaptively.
🚀 Why This Strategy Works
✔ Hybrid Trend-Following & Risk-Adaptive System → Works in trending and ranging markets.
✔ Incorporates On-Chain and Macro Factors → Provides a deeper understanding of market sentiment.
✔ Filters Out False Signals with Statistical Analysis → Reduces whipsaws and improves entry timing.
✔ Adjusts to Different Market Phases → Dynamically adapts to volatility cycles and economic shifts.
✔ Scientifically Validated Approach → Uses Z-score normalization, Gaussian filters, and statistical thresholds for optimal trade execution.
📌 Summary
CycleTrend is a state-of-the-art universal strategy that blends:
✅ Quantitative Finance 📊
✅ Blockchain On-Chain Analysis 🟢
✅ Macroeconomic Insights 🌍
✅ Statistical Signal Processing 🔍
By integrating multiple timeframes, risk models, and sentiment-driven filters, CycleTrend remains robust across different market regimes—whether trending or ranging.
This unique approach ensures that the system stays ahead of market cycles, delivering strong and consistent performance. 🚀
🔹 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
Master Arb Recipes - 3Commas Signal Bot Master Arb Recipes - 3Commas Signal Bot (v6, Realtime Alerts Only)
An automated buy the dip strategy based on the below criteria.
STH (Short-Time Horizon)
Timeframe: mostly Daily (sometimes 6H).
Bigger exit than entry (Exit-to-Entry often >100%; e.g., ETH STH 200%, SUI STH 965%).
Usually Sell Above Cost = ON → avoids loss sells.
Goal: hit mean-reversion pops, realize cash frequently, recycle into the next dip.
Result: higher cash turnover/yield, smaller net coin growth.
LTH (Long-Time Horizon)
Timeframe: 6H/D/Weekly; slower cadence.
Exit smaller than entry (Exit-to-Entry often <100%; e.g., BTC LTH 44.8%).
Sometimes Sell Above Cost = OFF on certain BTC LTH variants → allows rebalancing even if average isn’t green.
Mechanics: add more on dips, skim smaller profits on rips → you keep more of each buy.
Goal: steady coin accumulation with measured realized gains.
Pick based on your priority
Want cash flow/yield → run STH.
Want to stack coin over time (and still book some gains) → run LTH.
Hybrid: keep both, but allocate more capital to the style that matches your goal.
📊 Allocation Table – $10k Bankroll
Option A — Balanced Hybrid (50% Cash / 50% Coin)
Recipe Allocation Entry Size Exit-to-Entry Ratio Notes
BTC LTH $2,500 $250 44.8% Coin accumulation core
ETH LTH $2,500 $250 150% Growth + ETH stacking
BTC STH $1,667 $167 200% Fast BTC cash yield
ETH STH $1,667 $167 200% ETH cash yield
SUI STH $1,667 $167 965% High-vol cash skimmer
Option B — Cash-Heavy (70% STH / 30% LTH)
Recipe Allocation Entry Size Exit-to-Entry Ratio Notes
BTC LTH $1,500 $150 44.8% Minimal anchor
ETH LTH $1,500 $150 150% Small ETH stacking
BTC STH $2,333 $233 200% Aggressive cash engine
ETH STH $2,333 $233 200% Cash + ETH flips
SUI STH $2,333 $233 965% Volatility harvest
Option C — Coin-Heavy (70% LTH / 30% STH)
Recipe Allocation Entry Size Exit-to-Entry Ratio Notes
BTC LTH $3,500 $350 44.8% Heavy BTC accumulation
ETH LTH $3,500 $350 150% ETH accumulation driver
BTC STH $1,000 $100 200% Small BTC yield kicker
ETH STH $1,000 $100 200% Small ETH yield kicker
SUI STH $1,000 $100 965% Small vol scalp
⚖️ Key Notes
Entry Size scales automatically in your script — set it per recipe. Exit trades follow % logic, no manual adjustment.
Always match entry size between TradingView input and 3Commas bot deal size.
If you want to simplify, you could just run BTC LTH + SUI STH as a 2-recipe hybrid (one coin-heavy, one cash-heavy), then scale per your capital split.
Position Size & Stop Loss | QuantEdgeBPosition Size & Stop Loss | QuantEdgeB
QuantEdgeB indicator for calculating risk-based position sizing, leverage, and dynamic stop-loss levels—all in one on-chart dashboard.
🔍 What It Does
1. Position Sizing
o Takes your Portfolio Value and Risk Percentage to compute how much dollar risk you’re willing to take.
o Given an Entry Price and Stop-Loss Price, it derives the per-trade risk and thus the optimal Position Size (number of contracts/shares).
o Based on your available Margin, it calculates the implied Leverage.
2. Stop-Loss Levels
o Offers two modes:
High-Low SL — plots the highest high and lowest low over user-defined lookback windows.
Market-Structure SL — dynamically tracks the current up/down “wick” extremes using an HMA-driven regime filter and places your stop just inside the recent high/low wicks.
o Always overlays both a “Highest Band” and “Lowest Band” as steplines, plus a simple moving average for trend context.
3. Dashboard Table
o Presents all core inputs and outputs in a neat on-chart table:
Portfolio Value, Margin, Risk %, Entry, Stop Loss
Computed Position Size and Leverage
Final Long SL and Short SL levels (depending on your chosen SL type)
o Fully customizable: choose table position, text size, color theme, and transparency.
⚙️ Inputs & Settings
Portfolio Value ($) -> Total account equity.
Margin on Exchange ($) -> Available margin for this trade.
Risk Percentage (%) -> Percent of portfolio to risk per trade.
Entry Price -> Your intended entry level.
Stop Loss Price -> Your intended stop level.
Decimal Places -> Rounding precision for “Position Size.”
Below the hood, “Position Size” is simply the number of units you should buy (or sell) so that, if your stop-loss is hit, you lose exactly your pre-defined risk amount. Here’s how to translate it into a real trade—and a quick example using the script’s default settings:
🔢 What “Position Size” Means - Deep Dive
• Units: the raw number of shares, contracts, or cryptocurrency coins.
• Risk per unit = |Entry Price – Stop-Loss Price|
• Total Risk = Portfolio Value × (Risk %)
• Position Size = Total Risk ÷ Risk per unit
If you trade instruments that are fractional (e.g. BTC) you’ll buy that many coins; if it’s a futures contract, you buy that many contracts; if it’s stock, that many shares.
🧮 Hypothetical Example
1. Inputs
o Portfolio Value = $100 000
o Risk % = 1%
o Entry Price = 105 000
o Stop-Loss Price = 104 000
o Margin Available = $10 000
2. Compute Your Risk Budget
3. Total Risk = 100 000 × (1 / 100) = $1 000
4. Compute Risk Per Unit
5. Risk per Unit = |105 000 – 104 000| = $1 000 per unit
6. Compute Position Size
7. Position Size = 1 000 ÷ 1 000 = 1 unit
o If you’re trading 1 BTC contract, you buy 1 contract.
o If it were stock, you’d buy 1 share.
o If it were spot BTC, you’d buy 1 BTC.
8. Compute Implied Leverage
9. Notional Exposure = Position Size × Entry Price = 1 × 105 000 = $105 000
10. Leverage = 105 000 ÷ 10 000 ≈ 10.5×
11. Place the Trade
o Buy 1 unit at 105 000.
o Place your stop-loss at 104 000.
o If price drifts down to 104 000, you lose exactly $1 000 (1% of your $100 000 account).
📋 Putting It All Together on the Chart
When the indicator’s table shows:
1. Portfolio Value = 100'000
2. Margin = 10'000
3. Risk% = 1%
4. Entry = 105'000
5. Stop Loss = 104'000
6. Size = 1
7. Leverage = 10.5x
…that tells you in plain terms:
“With $100 000 behind me and a 1% risk threshold, buying 1 unit here—with my stop at 104 000—means I stand to lose $1 000 if I’m wrong. I’m using $10 000 of margin, so I’m at roughly 10.5× leverage.”
No more guesswork around lot sizes or margin calls—this table gives you the exact numbers you need to place that order.
🎨 Visual Output
1. Stepline Plots
o Highest Band (short-side stop) in your down-color.
o Lowest Band (long-side stop) in your up-color.
o EMA Trend Line for context.
2. Dashboard Table
o Header with the indicator name.
o First section: all your Position Size inputs & results.
o Separator line + SL-Type label.
o Final section: Long SL and Short SL values under the chosen mode.
o Color and transparency reflect your selected theme.
🧑💼 Why It’s Useful
• Risk-First Sizing: Never guess your position again—risk is dollar-accurately defined.
• Flexible Stop-Loss: Choose the simple bar-high/low bands or an adaptive “wick-insider” based on market structure.
• On-Chart Clarity: Everything you need to size, stop-loss, and monitor your trade sits in one unified panel.
• Customizable: Color themes, font sizes, SL methods, and more—tailor it to your workflow.
Use this indicator to keep your risk parameters crystal-clear, automate your position sizing, and visualize both static and dynamic stop-loss levels—all without leaving your TradingView chart.
📊 Portafoglio con Breakeven, Commissioni e Net ROI (5 titoli)📊 Portfolio with Breakeven, Commissions & Net ROI (up to 5 Assets)
This script displays a detailed portfolio performance table directly on your chart.
It supports up to 5 tickers with customizable inputs for entry price, quantity, and commissions.
Features:
Up to 5 assets, individually enabled/disabled.
Breakeven price with commissions (fixed or %).
Displays: price, entry, qty, cost, value, gross P/L, taxes (26%), net P/L, and ROI%.
Table position customizable.
Color-coded results for quick P/L and ROI visualization.
📌 Suitable for traders who need a precise overview of portfolio performance, accounting for fees and taxes.
📊 The Final Masterpiece (Sorted Columns)This script, written in Pine Script (version 5), is a TradingView indicator designed to create a portfolio dashboard directly on the chart. Its main purpose is to monitor the performance of up to four different stocks in real-time.
Here is a detailed description of its features:
Main Functionality
The script creates a floating table on the chart that summarizes the key information of a stock portfolio. This table is not anchored to a specific bar but remains in a fixed position chosen by the user.
Sections and Settings
1. Global Settings:
Available Cash: Allows you to enter an amount of available cash.
Capital Gain Tax (%): Lets you set a percentage for capital gains tax, which will be used to calculate the net profit/loss.
Table Position: Provides a drop-down menu to place the table in one of the nine available positions on the chart (e.g., top right, bottom center, etc.).
2. Color Settings:
This section offers extensive customization for the table's appearance.
You can change the background and text colors for the headers, data rows, and the totals row.
It also allows you to define specific colors to highlight profits (green) and losses (red) and to adjust their transparency.
3. Inputs for each Stock:
The script is pre-configured to handle up to four stocks.
For each stock, the user can:
Enable or disable its display in the table using a toggle (🟢 Enable Stock).
Enter the Ticker (e.g., "AAPL", "MSFT") to retrieve the real-time price.
Specify a descriptive name (e.g., "Apple").
Enter the quantity of shares owned and the buy price.
Define buy and sell commissions, both fixed and as a percentage.
Choose a background color for that specific stock's row.
How It Works
1. Price Retrieval:
It uses the request.security() function to get the daily ("D") closing price for each of the entered tickers. This function allows loading data from other symbols or timeframes different from the one currently displayed on the chart.
2. Financial Calculations:
The script performs a series of calculations for each enabled stock:
Position Cost: Calculates the total cost of the investment, including purchase commissions.
Current Value: Multiplies the number of shares by the current market price.
Gross Profit/Loss (P/L): The difference between the current value and the position cost, after deducting selling commissions.
Net Profit/Loss (Net P/L): Applies the capital gains tax (set in the global settings) only if there is a profit.
Return on Investment (ROI %): Calculates the net profit/loss as a percentage of the total position cost.
3. Table Creation and Management:
The table is created using table.new().
All calculations and table updates are performed only on the last bar of the chart (if barstate.islast). This optimizes the script's performance by preventing it from recalculating and redrawing the table on every historical bar.
The table is populated dynamically: only the rows for stocks that have been enabled by the user are displayed.
The cells for "Net P/L" and "ROI %" are colored green or red depending on whether the value is positive or negative.
Finally, a totals row is added, summarizing the total cost, total value, total net P/L, and total ROI of the portfolio. This row also displays the available cash entered by the user.
In summary, "The Final Masterpiece" is a customizable portfolio indicator that provides a clear and immediate overview of one's stock positions directly on TradingView, calculating costs, values, profits, and returns in real-time while accounting for commissions and taxes.
Stock Profit Calculator — Live Mode
## Overview
This Pine Script indicator calculates, in real time, the financial impact of a stock trade, including purchase/sale commissions, capital gains tax (CGT), and return on investment (ROI). It displays a compact table with key values and also calculates the breakeven price to see at what level the net P/L returns to zero.
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## Inputs and customization
- **Number of shares:** `shares` defines the purchased quantity.
- **Purchase price:** `buyPrice` is the unit cost; the total purchase is calculated from this.
- **Live selling price:** `sellPrice = close` uses the last bar’s price for live valuation.
- **Fixed or percentage commissions:** `useFixedComm` selects the model.
- **Fixed:** `buyCommFixed`, `sellCommFixed`.
- **Percentage:** `buyCommPct`, `sellCommPct` (applied to notional value).
- **CGT rate:** `cgtRate` is the percentage rate, applied only in case of profit.
- **Table position:** `tablePosition` with predefined options.
- **Visual style:** `colTxt`, `colPos`, `colNeg`, `colBg`, `colHdr`, `colFrame` for text color, positive/negative P/L, background, header, and borders.
> Tip: if your broker uses minimum fees or composite fees, turn on “Use fixed commissions?” and enter the two fixed fees; otherwise, use the percentage model.
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## Calculation logic
#### Purchase costs
- **Total purchase:**
\
- **Purchase commission:**
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- **Net entry cost:**
\
#### Sale revenues
- **Total sale (with live price):**
\
- **Sale commission:**
\
- **Net exit revenue:**
\
#### P/L and taxes
- **Gross P/L:**
\
- **CGT (only on positive P/L):**
\
- **Net P/L:**
\
#### ROI
- **Percentage ROI on invested capital:**
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#### Breakeven
- **Gross breakeven** shown in the table: the unit price that makes the net P/L exactly zero, including purchase cost and an estimate of the sale commission.
\
In the script, if commissions are fixed it adds the fixed sale fee; if percentage-based, the sale component is not included in this row (conservative approximation).
- **Breakeven with tax** (calculated but not shown):
\
Useful when you want the post-CGT result to be exactly zero. Not displayed in the table but ready for use.
> Note: CGT applies only on positive profits; near breakeven, the tax effect is null or only kicks in beyond a threshold. That’s why the script distinguishes between the “gross” and “with tax” versions.
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## On-screen table
- **Displayed rows:**
- **Purchase:** total net entry cost (with commissions).
- **Sale:** total net exit revenue (with commissions).
- **Gross P/L:** difference between netSell and netBuy.
- **CGT:** estimated tax only if there’s a gain.
- **Net P/L:** P/L after taxes.
- **ROI (%):** percentage return on netBuy.
- **Breakeven:** gross unit breakeven price.
- **Conditional colors:**
- **P/L and ROI:** green for ≥ 0, red for < 0.
- **Headers and cells:** customizable via the color inputs.
- **Efficient refresh:** the table updates only on the last bar via `barstate.islast` to avoid unnecessary redraws.
---
## Behavior and performance
- **Overlay:** displayed on the price chart.
- **Persistent variable:** table is created once with `var table`.
- **Live price:** `sellPrice` follows the current `close`, making P/L, ROI, and breakeven dynamic.
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## Limitations and suggestions
- **Commission model:** when using percentage commissions, the breakeven in the table doesn’t add the sale percentage fee in the “breakevenPrice” formula. For more precision, you could solve the equation including the percentage fee on exit.
- **Breakeven with tax:** `breakevenWithTax` is a linear estimate; near zero profit, tax may be null. You might choose to display it instead of, or alongside, the gross breakeven.
- **Precision and formatting:** values are shown with `format.mintick`. If the symbol has very small ticks, consider a custom format for better readability.
- **Edge cases:** ROI is undefined if `netBuy = 0` (unlikely in practice but good to note).
> Pro tip: if you want to show the breakeven with tax, add a “Breakeven (post-CGT)” row printing `breakevenWithTax`. If you prefer a single row, replace the shown value with the post-CGT one.
---
Ray Dalio's All Weather Strategy - Portfolio CalculatorTHE ALL WEATHER STRATEGY INDICATOR: A GUIDE TO RAY DALIO'S LEGENDARY PORTFOLIO APPROACH
Introduction: The Genesis of Financial Resilience
In the sprawling corridors of Bridgewater Associates, the world's largest hedge fund managing over 150 billion dollars in assets, Ray Dalio conceived what would become one of the most influential investment strategies of the modern era. The All Weather Strategy, born from decades of market observation and rigorous backtesting, represents a paradigm shift from traditional portfolio construction methods that have dominated Wall Street since Harry Markowitz's seminal work on Modern Portfolio Theory in 1952.
Unlike conventional approaches that chase returns through market timing or stock picking, the All Weather Strategy embraces a fundamental truth that has humbled countless investors throughout history: nobody can consistently predict the future direction of markets. Instead of fighting this uncertainty, Dalio's approach harnesses it, creating a portfolio designed to perform reasonably well across all economic environments, hence the evocative name "All Weather."
The strategy emerged from Bridgewater's extensive research into economic cycles and asset class behavior, culminating in what Dalio describes as "the Holy Grail of investing" in his bestselling book "Principles" (Dalio, 2017). This Holy Grail isn't about achieving spectacular returns, but rather about achieving consistent, risk-adjusted returns that compound steadily over time, much like the tortoise defeating the hare in Aesop's timeless fable.
HISTORICAL DEVELOPMENT AND EVOLUTION
The All Weather Strategy's origins trace back to the tumultuous economic periods of the 1970s and 1980s, when traditional portfolio construction methods proved inadequate for navigating simultaneous inflation and recession. Raymond Thomas Dalio, born in 1949 in Queens, New York, founded Bridgewater Associates from his Manhattan apartment in 1975, initially focusing on currency and fixed-income consulting for corporate clients.
Dalio's early experiences during the 1970s stagflation period profoundly shaped his investment philosophy. Unlike many of his contemporaries who viewed inflation and deflation as opposing forces, Dalio recognized that both conditions could coexist with either economic growth or contraction, creating four distinct economic environments rather than the traditional two-factor models that dominated academic finance.
The conceptual breakthrough came in the late 1980s when Dalio began systematically analyzing asset class performance across different economic regimes. Working with a small team of researchers, Bridgewater developed sophisticated models that decomposed economic conditions into growth and inflation components, then mapped historical asset class returns against these regimes. This research revealed that traditional portfolio construction, heavily weighted toward stocks and bonds, left investors vulnerable to specific economic scenarios.
The formal All Weather Strategy emerged in 1996 when Bridgewater was approached by a wealthy family seeking a portfolio that could protect their wealth across various economic conditions without requiring active management or market timing. Unlike Bridgewater's flagship Pure Alpha fund, which relied on active trading and leverage, the All Weather approach needed to be completely passive and unleveraged while still providing adequate diversification.
Dalio and his team spent months developing and testing various allocation schemes, ultimately settling on the 30/40/15/7.5/7.5 framework that balances risk contributions rather than dollar amounts. This approach was revolutionary because it focused on risk budgeting—ensuring that no single asset class dominated the portfolio's risk profile—rather than the traditional approach of equal dollar allocations or market-cap weighting.
The strategy's first institutional implementation began in 1996 with a family office client, followed by gradual expansion to other wealthy families and eventually institutional investors. By 2005, Bridgewater was managing over $15 billion in All Weather assets, making it one of the largest systematic strategy implementations in institutional investing.
The 2008 financial crisis provided the ultimate test of the All Weather methodology. While the S&P 500 declined by 37% and many hedge funds suffered double-digit losses, the All Weather strategy generated positive returns, validating Dalio's risk-balancing approach. This performance during extreme market stress attracted significant institutional attention, leading to rapid asset growth in subsequent years.
The strategy's theoretical foundations evolved throughout the 2000s as Bridgewater's research team, led by co-chief investment officers Greg Jensen and Bob Prince, refined the economic framework and incorporated insights from behavioral economics and complexity theory. Their research, published in numerous institutional white papers, demonstrated that traditional portfolio optimization methods consistently underperformed simpler risk-balanced approaches across various time periods and market conditions.
Academic validation came through partnerships with leading business schools and collaboration with prominent economists. The strategy's risk parity principles influenced an entire generation of institutional investors, leading to the creation of numerous risk parity funds managing hundreds of billions in aggregate assets.
In recent years, the democratization of sophisticated financial tools has made All Weather-style investing accessible to individual investors through ETFs and systematic platforms. The availability of high-quality, low-cost ETFs covering each required asset class has eliminated many of the barriers that previously limited sophisticated portfolio construction to institutional investors.
The development of advanced portfolio management software and platforms like TradingView has further democratized access to institutional-quality analytics and implementation tools. The All Weather Strategy Indicator represents the culmination of this trend, providing individual investors with capabilities that previously required teams of portfolio managers and risk analysts.
Understanding the Four Economic Seasons
The All Weather Strategy's theoretical foundation rests on Dalio's observation that all economic environments can be characterized by two primary variables: economic growth and inflation. These variables create four distinct "economic seasons," each favoring different asset classes. Rising growth benefits stocks and commodities, while falling growth favors bonds. Rising inflation helps commodities and inflation-protected securities, while falling inflation benefits nominal bonds and stocks.
This framework, detailed extensively in Bridgewater's research papers from the 1990s, suggests that by holding assets that perform well in each economic season, an investor can create a portfolio that remains resilient regardless of which season unfolds. The elegance lies not in predicting which season will occur, but in being prepared for all of them simultaneously.
Academic research supports this multi-environment approach. Ang and Bekaert (2002) demonstrated that regime changes in economic conditions significantly impact asset returns, while Fama and French (2004) showed that different asset classes exhibit varying sensitivities to economic factors. The All Weather Strategy essentially operationalizes these academic insights into a practical investment framework.
The Original All Weather Allocation: Simplicity Masquerading as Sophistication
The core All Weather portfolio, as implemented by Bridgewater for institutional clients and later adapted for retail investors, maintains a deceptively simple static allocation: 30% stocks, 40% long-term bonds, 15% intermediate-term bonds, 7.5% commodities, and 7.5% Treasury Inflation-Protected Securities (TIPS). This allocation may appear arbitrary to the uninitiated, but each percentage reflects careful consideration of historical volatilities, correlations, and economic sensitivities.
The 30% stock allocation provides growth exposure while limiting the portfolio's overall volatility. Stocks historically deliver superior long-term returns but with significant volatility, as evidenced by the Standard & Poor's 500 Index's average annual return of approximately 10% since 1926, accompanied by standard deviation exceeding 15% (Ibbotson Associates, 2023). By limiting stock exposure to 30%, the portfolio captures much of the equity risk premium while avoiding excessive volatility.
The combined 55% allocation to bonds (40% long-term plus 15% intermediate-term) serves as the portfolio's stabilizing force. Long-term bonds provide substantial interest rate sensitivity, performing well during economic slowdowns when central banks reduce rates. Intermediate-term bonds offer a balance between interest rate sensitivity and reduced duration risk. This bond-heavy allocation reflects Dalio's insight that bonds typically exhibit lower volatility than stocks while providing essential diversification benefits.
The 7.5% commodities allocation addresses inflation protection, as commodity prices typically rise during inflationary periods. Historical analysis by Bodie and Rosansky (1980) demonstrated that commodities provide meaningful diversification benefits and inflation hedging capabilities, though with considerable volatility. The relatively small allocation reflects commodities' high volatility and mixed long-term returns.
Finally, the 7.5% TIPS allocation provides explicit inflation protection through government-backed securities whose principal and interest payments adjust with inflation. Introduced by the U.S. Treasury in 1997, TIPS have proven effective inflation hedges, though they underperform nominal bonds during deflationary periods (Campbell & Viceira, 2001).
Historical Performance: The Evidence Speaks
Analyzing the All Weather Strategy's historical performance reveals both its strengths and limitations. Using monthly return data from 1970 to 2023, spanning over five decades of varying economic conditions, the strategy has delivered compelling risk-adjusted returns while experiencing lower volatility than traditional stock-heavy portfolios.
During this period, the All Weather allocation generated an average annual return of approximately 8.2%, compared to 10.5% for the S&P 500 Index. However, the strategy's annual volatility measured just 9.1%, substantially lower than the S&P 500's 15.8% volatility. This translated to a Sharpe ratio of 0.67 for the All Weather Strategy versus 0.54 for the S&P 500, indicating superior risk-adjusted performance.
More impressively, the strategy's maximum drawdown over this period was 12.3%, occurring during the 2008 financial crisis, compared to the S&P 500's maximum drawdown of 50.9% during the same period. This drawdown mitigation proves crucial for long-term wealth building, as Stein and DeMuth (2003) demonstrated that avoiding large losses significantly impacts compound returns over time.
The strategy performed particularly well during periods of economic stress. During the 1970s stagflation, when stocks and bonds both struggled, the All Weather portfolio's commodity and TIPS allocations provided essential protection. Similarly, during the 2000-2002 dot-com crash and the 2008 financial crisis, the portfolio's bond-heavy allocation cushioned losses while maintaining positive returns in several years when stocks declined significantly.
However, the strategy underperformed during sustained bull markets, particularly the 1990s technology boom and the 2010s post-financial crisis recovery. This underperformance reflects the strategy's conservative nature and diversified approach, which sacrifices potential upside for downside protection. As Dalio frequently emphasizes, the All Weather Strategy prioritizes "not losing money" over "making a lot of money."
Implementing the All Weather Strategy: A Practical Guide
The All Weather Strategy Indicator transforms Dalio's institutional-grade approach into an accessible tool for individual investors. The indicator provides real-time portfolio tracking, rebalancing signals, and performance analytics, eliminating much of the complexity traditionally associated with implementing sophisticated allocation strategies.
To begin implementation, investors must first determine their investable capital. As detailed analysis reveals, the All Weather Strategy requires meaningful capital to implement effectively due to transaction costs, minimum investment requirements, and the need for precise allocations across five different asset classes.
For portfolios below $50,000, the strategy becomes challenging to implement efficiently. Transaction costs consume a disproportionate share of returns, while the inability to purchase fractional shares creates allocation drift. Consider an investor with $25,000 attempting to allocate 7.5% to commodities through the iPath Bloomberg Commodity Index ETF (DJP), currently trading around $25 per share. This allocation targets $1,875, enough for only 75 shares, creating immediate tracking error.
At $50,000, implementation becomes feasible but not optimal. The 30% stock allocation ($15,000) purchases approximately 37 shares of the SPDR S&P 500 ETF (SPY) at current prices around $400 per share. The 40% long-term bond allocation ($20,000) buys 200 shares of the iShares 20+ Year Treasury Bond ETF (TLT) at approximately $100 per share. While workable, these allocations leave significant cash drag and rebalancing challenges.
The optimal minimum for individual implementation appears to be $100,000. At this level, each allocation becomes substantial enough for precise implementation while keeping transaction costs below 0.4% annually. The $30,000 stock allocation, $40,000 long-term bond allocation, $15,000 intermediate-term bond allocation, $7,500 commodity allocation, and $7,500 TIPS allocation each provide sufficient size for effective management.
For investors with $250,000 or more, the strategy implementation approaches institutional quality. Allocation precision improves, transaction costs decline as a percentage of assets, and rebalancing becomes highly efficient. These larger portfolios can also consider adding complexity through international diversification or alternative implementations.
The indicator recommends quarterly rebalancing to balance transaction costs with allocation discipline. Monthly rebalancing increases costs without substantial benefits for most investors, while annual rebalancing allows excessive drift that can meaningfully impact performance. Quarterly rebalancing, typically on the first trading day of each quarter, provides an optimal balance.
Understanding the Indicator's Functionality
The All Weather Strategy Indicator operates as a comprehensive portfolio management system, providing multiple analytical layers that professional money managers typically reserve for institutional clients. This sophisticated tool transforms Ray Dalio's institutional-grade strategy into an accessible platform for individual investors, offering features that rival professional portfolio management software.
The indicator's core architecture consists of several interconnected modules that work seamlessly together to provide complete portfolio oversight. At its foundation lies a real-time portfolio simulation engine that tracks the exact value of each ETF position based on current market prices, eliminating the need for manual calculations or external spreadsheets.
DETAILED INDICATOR COMPONENTS AND FUNCTIONS
Portfolio Configuration Module
The portfolio setup begins with the Portfolio Configuration section, which establishes the fundamental parameters for strategy implementation. The Portfolio Capital input accepts values from $1,000 to $10,000,000, accommodating everyone from beginning investors to institutional clients. This input directly drives all subsequent calculations, determining exact share quantities and portfolio values throughout the implementation period.
The Portfolio Start Date function allows users to specify when they began implementing the All Weather Strategy, creating a clear demarcation point for performance tracking. This feature proves essential for investors who want to track their actual implementation against theoretical performance, providing realistic assessment of strategy effectiveness including timing differences and implementation costs.
Rebalancing Frequency settings offer two options: Monthly and Quarterly. While monthly rebalancing provides more precise allocation control, quarterly rebalancing typically proves more cost-effective for most investors due to reduced transaction costs. The indicator automatically detects the first trading day of each period, ensuring rebalancing occurs at optimal times regardless of weekends, holidays, or market closures.
The Rebalancing Threshold parameter, adjustable from 0.5% to 10%, determines when allocation drift triggers rebalancing recommendations. Conservative settings like 1-2% maintain tight allocation control but increase trading frequency, while wider thresholds like 3-5% reduce trading costs but allow greater allocation drift. This flexibility accommodates different risk tolerances and cost structures.
Visual Display System
The Show All Weather Calculator toggle controls the main dashboard visibility, allowing users to focus on chart visualization when detailed metrics aren't needed. When enabled, this comprehensive dashboard displays current portfolio value, individual ETF allocations, target versus actual weights, rebalancing status, and performance metrics in a professionally formatted table.
Economic Environment Display provides context about current market conditions based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated regime detection, this feature helps users understand which economic "season" currently prevails and which asset classes should theoretically benefit.
Rebalancing Signals illuminate when portfolio drift exceeds user-defined thresholds, highlighting specific ETFs that require adjustment. These signals use color coding to indicate urgency: green for balanced allocations, yellow for moderate drift, and red for significant deviations requiring immediate attention.
Advanced Label System
The rebalancing label system represents one of the indicator's most innovative features, providing three distinct detail levels to accommodate different user needs and experience levels. The "None" setting displays simple symbols marking portfolio start and rebalancing events without cluttering the chart with text. This minimal approach suits experienced investors who understand the implications of each symbol.
"Basic" label mode shows essential information including portfolio values at each rebalancing point, enabling quick assessment of strategy performance over time. These labels display "START $X" for portfolio initiation and "RBL $Y" for rebalancing events, providing clear performance tracking without overwhelming detail.
"Detailed" labels provide comprehensive trading instructions including exact buy and sell quantities for each ETF. These labels might display "RBL $125,000 BUY 15 SPY SELL 25 TLT BUY 8 IEF NO TRADES DJP SELL 12 SCHP" providing complete implementation guidance. This feature essentially transforms the indicator into a personal portfolio manager, eliminating guesswork about exact trades required.
Professional Color Themes
Eight professionally designed color themes adapt the indicator's appearance to different aesthetic preferences and market analysis styles. The "Gold" theme reflects traditional wealth management aesthetics, while "EdgeTools" provides modern professional appearance. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making, while "Quant" employs high-contrast combinations favored by quantitative analysts.
"Ocean," "Fire," "Matrix," and "Arctic" themes provide distinctive visual identities for traders who prefer unique chart aesthetics. Each theme automatically adjusts for dark or light mode optimization, ensuring optimal readability across different TradingView configurations.
Real-Time Portfolio Tracking
The portfolio simulation engine continuously tracks five separate ETF positions: SPY for stocks, TLT for long-term bonds, IEF for intermediate-term bonds, DJP for commodities, and SCHP for TIPS. Each position's value updates in real-time based on current market prices, providing instant feedback about portfolio performance and allocation drift.
Current share calculations determine exact holdings based on the most recent rebalancing, while target shares reflect optimal allocation based on current portfolio value. Trade calculations show precisely how many shares to buy or sell during rebalancing, eliminating manual calculations and potential errors.
Performance Analytics Suite
The indicator's performance measurement capabilities rival professional portfolio analysis software. Sharpe ratio calculations incorporate current risk-free rates obtained from Treasury yield data, providing accurate risk-adjusted performance assessment. Volatility measurements use rolling periods to capture changing market conditions while maintaining statistical significance.
Portfolio return calculations track both absolute and relative performance, comparing the All Weather implementation against individual asset classes and benchmark indices. These metrics update continuously, providing real-time assessment of strategy effectiveness and implementation quality.
Data Quality Monitoring
Sophisticated data quality checks ensure reliable indicator operation across different market conditions and potential data interruptions. The system monitors all five ETF price feeds plus economic data sources, providing quality scores that alert users to potential data issues that might affect calculations.
When data quality degrades, the indicator automatically switches to fallback values or alternative data sources, maintaining functionality during temporary market data interruptions. This robust design ensures consistent operation even during volatile market conditions when data feeds occasionally experience disruptions.
Risk Management and Behavioral Considerations
Despite its sophisticated design, the All Weather Strategy faces behavioral challenges that have derailed countless well-intentioned investment plans. The strategy's conservative nature means it will underperform growth stocks during bull markets, potentially by substantial margins. Maintaining discipline during these periods requires understanding that the strategy optimizes for risk-adjusted returns over absolute returns.
Behavioral finance research by Kahneman and Tversky (1979) demonstrates that investors feel losses approximately twice as intensely as equivalent gains. This loss aversion creates powerful psychological pressure to abandon defensive strategies during bull markets when aggressive portfolios appear more attractive. The All Weather Strategy's bond-heavy allocation will seem overly conservative when technology stocks double in value, as occurred repeatedly during the 2010s.
Conversely, the strategy's defensive characteristics provide psychological comfort during market stress. When stocks crash 30-50%, as they periodically do, the All Weather portfolio's modest losses feel manageable rather than catastrophic. This emotional stability enables investors to maintain their investment discipline when others capitulate, often at the worst possible times.
Rebalancing discipline presents another behavioral challenge. Selling winners to buy losers contradicts natural human tendencies but remains essential for the strategy's success. When stocks have outperformed bonds for several quarters, rebalancing requires selling high-performing stock positions to purchase seemingly stagnant bond positions. This action feels counterintuitive but captures the strategy's systematic approach to risk management.
Tax considerations add complexity for taxable accounts. Frequent rebalancing generates taxable events that can erode after-tax returns, particularly for high-income investors facing elevated capital gains rates. Tax-advantaged accounts like 401(k)s and IRAs provide ideal vehicles for All Weather implementation, eliminating tax friction from rebalancing activities.
Capital Requirements and Cost Analysis
Comprehensive cost analysis reveals the capital requirements for effective All Weather implementation. Annual expenses include management fees for each ETF, transaction costs from rebalancing, and bid-ask spreads from trading less liquid securities.
ETF expense ratios vary significantly across asset classes. The SPDR S&P 500 ETF charges 0.09% annually, while the iShares 20+ Year Treasury Bond ETF charges 0.20%. The iShares 7-10 Year Treasury Bond ETF charges 0.15%, the Schwab US TIPS ETF charges 0.05%, and the iPath Bloomberg Commodity Index ETF charges 0.75%. Weighted by the All Weather allocations, total expense ratios average approximately 0.19% annually.
Transaction costs depend heavily on broker selection and account size. Premium brokers like Interactive Brokers charge $1-2 per trade, resulting in $20-40 annually for quarterly rebalancing. Discount brokers may charge higher per-trade fees but offer commission-free ETF trading for selected funds. Zero-commission brokers eliminate explicit trading costs but often impose wider bid-ask spreads that function as hidden fees.
Bid-ask spreads represent the difference between buying and selling prices for each security. Highly liquid ETFs like SPY maintain spreads of 1-2 basis points, while less liquid commodity ETFs may exhibit spreads of 5-10 basis points. These costs accumulate through rebalancing activities, typically totaling 10-15 basis points annually.
For a $100,000 portfolio, total annual costs including expense ratios, transaction fees, and spreads typically range from 0.35% to 0.45%, or $350-450 annually. These costs decline as a percentage of assets as portfolio size increases, reaching approximately 0.25% for portfolios exceeding $250,000.
Comparing costs to potential benefits reveals the strategy's value proposition. Historical analysis suggests the All Weather approach reduces portfolio volatility by 35-40% compared to stock-heavy allocations while maintaining competitive returns. This volatility reduction provides substantial value during market stress, potentially preventing behavioral mistakes that destroy long-term wealth.
Alternative Implementations and Customizations
While the original All Weather allocation provides an excellent starting point, investors may consider modifications based on personal circumstances, market conditions, or geographic considerations. International diversification represents one potential enhancement, adding exposure to developed and emerging market bonds and equities.
Geographic customization becomes important for non-US investors. European investors might replace US Treasury bonds with German Bunds or broader European government bond indices. Currency hedging decisions add complexity but may reduce volatility for investors whose spending occurs in non-dollar currencies.
Tax-location strategies optimize after-tax returns by placing tax-inefficient assets in tax-advantaged accounts while holding tax-efficient assets in taxable accounts. TIPS and commodity ETFs generate ordinary income taxed at higher rates, making them candidates for retirement account placement. Stock ETFs generate qualified dividends and long-term capital gains taxed at lower rates, making them suitable for taxable accounts.
Some investors prefer implementing the bond allocation through individual Treasury securities rather than ETFs, eliminating management fees while gaining precise maturity control. Treasury auctions provide access to new securities without bid-ask spreads, though this approach requires more sophisticated portfolio management.
Factor-based implementations replace broad market ETFs with factor-tilted alternatives. Value-tilted stock ETFs, quality-focused bond ETFs, or momentum-based commodity indices may enhance returns while maintaining the All Weather framework's diversification benefits. However, these modifications introduce additional complexity and potential tracking error.
Conclusion: Embracing the Long Game
The All Weather Strategy represents more than an investment approach; it embodies a philosophy of financial resilience that prioritizes sustainable wealth building over speculative gains. In an investment landscape increasingly dominated by algorithmic trading, meme stocks, and cryptocurrency volatility, Dalio's methodical approach offers a refreshing alternative grounded in economic theory and historical evidence.
The strategy's greatest strength lies not in its potential for extraordinary returns, but in its capacity to deliver reasonable returns across diverse economic environments while protecting capital during market stress. This characteristic becomes increasingly valuable as investors approach or enter retirement, when portfolio preservation assumes greater importance than aggressive growth.
Implementation requires discipline, adequate capital, and realistic expectations. The strategy will underperform growth-oriented approaches during bull markets while providing superior downside protection during bear markets. Investors must embrace this trade-off consciously, understanding that the strategy optimizes for long-term wealth building rather than short-term performance.
The All Weather Strategy Indicator democratizes access to institutional-quality portfolio management, providing individual investors with tools previously available only to wealthy families and institutions. By automating allocation tracking, rebalancing signals, and performance analysis, the indicator removes much of the complexity that has historically limited sophisticated strategy implementation.
For investors seeking a systematic, evidence-based approach to long-term wealth building, the All Weather Strategy provides a compelling framework. Its emphasis on diversification, risk management, and behavioral discipline aligns with the fundamental principles that have created lasting wealth throughout financial history. While the strategy may not generate headlines or inspire cocktail party conversations, it offers something more valuable: a reliable path toward financial security across all economic seasons.
As Dalio himself notes, "The biggest mistake investors make is to believe that what happened in the recent past is likely to persist, and they design their portfolios accordingly." The All Weather Strategy's enduring appeal lies in its rejection of this recency bias, instead embracing the uncertainty of markets while positioning for success regardless of which economic season unfolds.
STEP-BY-STEP INDICATOR SETUP GUIDE
Setting up the All Weather Strategy Indicator requires careful attention to each configuration parameter to ensure optimal implementation. This comprehensive setup guide walks through every setting and explains its impact on strategy performance.
Initial Setup Process
Begin by adding the indicator to your TradingView chart. Search for "Ray Dalio's All Weather Strategy" in the indicator library and apply it to any chart. The indicator operates independently of the underlying chart symbol, drawing data directly from the five required ETFs regardless of which security appears on the chart.
Portfolio Configuration Settings
Start with the Portfolio Capital input, which drives all subsequent calculations. Enter your exact investable capital, ranging from $1,000 to $10,000,000. This input determines share quantities, trade recommendations, and performance calculations. Conservative recommendations suggest minimum capitals of $50,000 for basic implementation or $100,000 for optimal precision.
Select your Portfolio Start Date carefully, as this establishes the baseline for all performance calculations. Choose the date when you actually began implementing the All Weather Strategy, not when you first learned about it. This date should reflect when you first purchased ETFs according to the target allocation, creating realistic performance tracking.
Choose your Rebalancing Frequency based on your cost structure and precision preferences. Monthly rebalancing provides tighter allocation control but increases transaction costs. Quarterly rebalancing offers the optimal balance for most investors between allocation precision and cost control. The indicator automatically detects appropriate trading days regardless of your selection.
Set the Rebalancing Threshold based on your tolerance for allocation drift and transaction costs. Conservative investors preferring tight control should use 1-2% thresholds, while cost-conscious investors may prefer 3-5% thresholds. Lower thresholds maintain more precise allocations but trigger more frequent trading.
Display Configuration Options
Enable Show All Weather Calculator to display the comprehensive dashboard containing portfolio values, allocations, and performance metrics. This dashboard provides essential information for portfolio management and should remain enabled for most users.
Show Economic Environment displays current economic regime classification based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated models, this feature provides useful context for understanding current market conditions.
Show Rebalancing Signals highlights when portfolio allocations drift beyond your threshold settings. These signals use color coding to indicate urgency levels, helping prioritize rebalancing activities.
Advanced Label Customization
Configure Show Rebalancing Labels based on your need for chart annotations. These labels mark important portfolio events and can provide valuable historical context, though they may clutter charts during extended time periods.
Select appropriate Label Detail Levels based on your experience and information needs. "None" provides minimal symbols suitable for experienced users. "Basic" shows portfolio values at key events. "Detailed" provides complete trading instructions including exact share quantities for each ETF.
Appearance Customization
Choose Color Themes based on your aesthetic preferences and trading style. "Gold" reflects traditional wealth management appearance, while "EdgeTools" provides modern professional styling. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making.
Enable Dark Mode Optimization if using TradingView's dark theme for optimal readability and contrast. This setting automatically adjusts all colors and transparency levels for the selected theme.
Set Main Line Width based on your chart resolution and visual preferences. Higher width values provide clearer allocation lines but may overwhelm smaller charts. Most users prefer width settings of 2-3 for optimal visibility.
Troubleshooting Common Setup Issues
If the indicator displays "Data not available" messages, verify that all five ETFs (SPY, TLT, IEF, DJP, SCHP) have valid price data on your selected timeframe. The indicator requires daily data availability for all components.
When rebalancing signals seem inconsistent, check your threshold settings and ensure sufficient time has passed since the last rebalancing event. The indicator only triggers signals on designated rebalancing days (first trading day of each period) when drift exceeds threshold levels.
If labels appear at unexpected chart locations, verify that your chart displays percentage values rather than price values. The indicator forces percentage formatting and 0-40% scaling for optimal allocation visualization.
COMPREHENSIVE BIBLIOGRAPHY AND FURTHER READING
PRIMARY SOURCES AND RAY DALIO WORKS
Dalio, R. (2017). Principles: Life and work. New York: Simon & Schuster.
Dalio, R. (2018). A template for understanding big debt crises. Bridgewater Associates.
Dalio, R. (2021). Principles for dealing with the changing world order: Why nations succeed and fail. New York: Simon & Schuster.
BRIDGEWATER ASSOCIATES RESEARCH PAPERS
Jensen, G., Kertesz, A. & Prince, B. (2010). All Weather strategy: Bridgewater's approach to portfolio construction. Bridgewater Associates Research.
Prince, B. (2011). An in-depth look at the investment logic behind the All Weather strategy. Bridgewater Associates Daily Observations.
Bridgewater Associates. (2015). Risk parity in the context of larger portfolio construction. Institutional Research.
ACADEMIC RESEARCH ON RISK PARITY AND PORTFOLIO CONSTRUCTION
Ang, A. & Bekaert, G. (2002). International asset allocation with regime shifts. The Review of Financial Studies, 15(4), 1137-1187.
Bodie, Z. & Rosansky, V. I. (1980). Risk and return in commodity futures. Financial Analysts Journal, 36(3), 27-39.
Campbell, J. Y. & Viceira, L. M. (2001). Who should buy long-term bonds? American Economic Review, 91(1), 99-127.
Clarke, R., De Silva, H. & Thorley, S. (2013). Risk parity, maximum diversification, and minimum variance: An analytic perspective. Journal of Portfolio Management, 39(3), 39-53.
Fama, E. F. & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of Economic Perspectives, 18(3), 25-46.
BEHAVIORAL FINANCE AND IMPLEMENTATION CHALLENGES
Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292.
Thaler, R. H. & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press.
Montier, J. (2007). Behavioural investing: A practitioner's guide to applying behavioural finance. Chichester: John Wiley & Sons.
MODERN PORTFOLIO THEORY AND QUANTITATIVE METHODS
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.
Black, F. & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.
PRACTICAL IMPLEMENTATION AND ETF ANALYSIS
Gastineau, G. L. (2010). The exchange-traded funds manual. 2nd ed. Hoboken: John Wiley & Sons.
Poterba, J. M. & Shoven, J. B. (2002). Exchange-traded funds: A new investment option for taxable investors. American Economic Review, 92(2), 422-427.
Israelsen, C. L. (2005). A refinement to the Sharpe ratio and information ratio. Journal of Asset Management, 5(6), 423-427.
ECONOMIC CYCLE ANALYSIS AND ASSET CLASS RESEARCH
Ilmanen, A. (2011). Expected returns: An investor's guide to harvesting market rewards. Chichester: John Wiley & Sons.
Swensen, D. F. (2009). Pioneering portfolio management: An unconventional approach to institutional investment. Rev. ed. New York: Free Press.
Siegel, J. J. (2014). Stocks for the long run: The definitive guide to financial market returns & long-term investment strategies. 5th ed. New York: McGraw-Hill Education.
RISK MANAGEMENT AND ALTERNATIVE STRATEGIES
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House.
Lowenstein, R. (2000). When genius failed: The rise and fall of Long-Term Capital Management. New York: Random House.
Stein, D. M. & DeMuth, P. (2003). Systematic withdrawal from retirement portfolios: The impact of asset allocation decisions on portfolio longevity. AAII Journal, 25(7), 8-12.
CONTEMPORARY DEVELOPMENTS AND FUTURE DIRECTIONS
Asness, C. S., Frazzini, A. & Pedersen, L. H. (2012). Leverage aversion and risk parity. Financial Analysts Journal, 68(1), 47-59.
Roncalli, T. (2013). Introduction to risk parity and budgeting. Boca Raton: CRC Press.
Ibbotson Associates. (2023). Stocks, bonds, bills, and inflation 2023 yearbook. Chicago: Morningstar.
PERIODICALS AND ONGOING RESEARCH
Journal of Portfolio Management - Quarterly publication featuring cutting-edge research on portfolio construction and risk management
Financial Analysts Journal - Bi-monthly publication of the CFA Institute with practical investment research
Bridgewater Associates Daily Observations - Regular market commentary and research from the creators of the All Weather Strategy
RECOMMENDED READING SEQUENCE
For investors new to the All Weather Strategy, begin with Dalio's "Principles" for philosophical foundation, then proceed to the Bridgewater research papers for technical details. Supplement with Markowitz's original portfolio theory work and behavioral finance literature from Kahneman and Tversky.
Intermediate students should focus on academic papers by Ang & Bekaert on regime shifts, Clarke et al. on risk parity methods, and Ilmanen's comprehensive analysis of expected returns across asset classes.
Advanced practitioners will benefit from Roncalli's technical treatment of risk parity mathematics, Asness et al.'s academic critique of leverage aversion, and ongoing research in the Journal of Portfolio Management.
target price with DCF method by N' TEEREX HoonjongpangThis indicator calculates a stock’s intrinsic value using the latest fiscal year data: free cash flow, debt, equity, shares outstanding, and taxes.
It computes the Weighted Average Cost of Capital (WACC), applies the Gordon Growth formula, and derives a price per share.
A margin of safety is applied to define zones on the chart:
Green Zone: Safe (undervalued)
Red Zone: Not Safe (overvalued)
All key numbers, including WACC, price target, and zones, are displayed as a label.
Result: A visual and numeric guide to the stock’s fair value, helping investors quickly see if the current price is above or below estimated intrinsic value.
Drawdown (%)Plots the drawdown percentage from the running peak price. \
Highlights drawdown areas below zero in a soft red shade, and adds a 0% reference line for clarity.
Drawdown (%)Plots the drawdown percentage from the running peak price. \
Highlights drawdown areas below zero in a soft red shade, and adds a 0% reference line for clarity.
INCOME STATEMENT BY N' TEEREX HOONJONGPANGConcept
This Pine Script indicator displays a dynamic, customizable table on the TradingView chart.
It is designed for traders who want to monitor annual data, quarterly performance, and key financial ratios directly on the chart without switching to external spreadsheets.
Features
Three Structured Sections:
Yearly Data Table – Displays annual values with adjustable text size and colors.
Quarterly Data Table – Shows quarter-by-quarter figures in a clear, compact format.
Financial Ratios Table – Presents key metrics (e.g., growth rates, margins, or other ratios) for quick analysis.
Customizable Appearance:
Adjustable text size for each table section.
Background and text colors for improved readability.
Option to merge cells for titles and headers.
Flexible Positioning:
Tables can be displayed in various positions on the chart (e.g., top-left, top-center, top-right).
Data Highlighting:
Color-coded cells to highlight important values or trends.
How to Use
1.Add the script to your TradingView chart.
2.Select table position (e.g., top-center for balanced display).
3.Adjust text size and color for yearly, quarterly, and ratio tables according to your preference.
4.Review the merged header cells for section titles and use the table to track key performance data alongside price action.
This tool is especially useful for swing traders, investors, and analysts who need to quickly interpret fundamental data within the same visual context as the chart.
Awesome Indicator# Moving Average Ribbon with ADR% - Complete Trading Indicator
## Overview
The **Moving Average Ribbon with ADR%** is a comprehensive technical analysis indicator that combines multiple analytical tools to provide traders with a complete picture of price trends, volatility, relative performance, and position sizing guidance. This multi-faceted indicator is designed for both swing and positional traders looking for data-driven entry and exit signals.
## Key Components
### 1. Moving Average Ribbon System
- **4 Customizable Moving Averages** with default periods: 13, 21, 55, and 189
- **Multiple MA Types**: SMA, EMA, SMMA (RMA), WMA, VWMA
- **Color-coded visualization** for easy trend identification
- **Flexible configuration** allowing users to modify periods, types, and colors
### 2. Average Daily Range Percentage (ADR%)
- Calculates the average daily volatility as a percentage
- Uses a 20-period simple moving average of (High/Low - 1) * 100
- Helps traders understand the stock's typical daily movement range
- Essential for position sizing and stop-loss placement
### 3. Volume Analysis (Up/Down Ratio)
- Analyzes volume distribution over the last 55 periods
- Calculates the ratio of volume on up days vs down days
- Provides insight into buying vs selling pressure
- Values > 1 indicate more buying volume, < 1 indicate more selling volume
### 4. Absolute Relative Strength (ARS)
- **Dual timeframe analysis** with customizable reference points
- **High ARS**: Performance relative to benchmark from a high reference point (default: Sep 27, 2024)
- **Low ARS**: Performance relative to benchmark from a low reference point (default: Apr 7, 2025)
- Uses NSE:NIFTY as default comparison symbol
- Color-coded display: Green for outperformance, Red for underperformance
### 5. Relative Performance Table
- **5 timeframes**: 1 Week, 1 Month, 3 Months, 6 Months, 1 Year
- Shows stock performance **relative to benchmark index**
- Formula: (Stock Return - Index Return) for each period
- **Color coding**:
- Lime: >5% outperformance
- Yellow: -5% to +5% relative performance
- Red: <-5% underperformance
### 6. Dynamic Position Allocation System
- **6-factor scoring system** based on price vs EMAs (21, 55, 189)
- Evaluates:
- Price above/below each EMA
- EMA alignment (21>55, 55>189, 21>189)
- **Allocation recommendations**:
- 100% allocation: Score = 6 (all bullish signals)
- 75% allocation: Score = 4
- 50% allocation: Score = 2
- 25% allocation: Score = 0
- 0% allocation: Score = -2, -4, -6 (bearish signals)
## Display Tables
### Performance Table (Top Right)
Shows relative performance vs benchmark across multiple timeframes with intuitive color coding for quick assessment.
### Metrics Table (Bottom Right)
Displays key statistics:
- **ADR%**: Average Daily Range percentage
- **U/D**: Up/Down volume ratio
- **Allocation%**: Recommended position size
- **High ARS%**: Relative strength from high reference
- **Low ARS%**: Relative strength from low reference
## How to Use This Indicator
### For Trend Analysis
1. **Moving Average Ribbon**: Look for price above ascending MAs for bullish trends
2. **MA Alignment**: Bullish when shorter MAs are above longer MAs
3. **Color coordination**: Use consistent color scheme for quick visual analysis
### For Entry/Exit Timing
1. **Performance Table**: Enter when showing consistent outperformance across timeframes
2. **Volume Analysis**: Confirm entries with U/D ratio > 1.5 for strong buying
3. **ARS Values**: Look for positive ARS readings for relative strength confirmation
### For Position Sizing
1. **Allocation System**: Use the recommended allocation percentage
2. **ADR% Consideration**: Adjust position size based on volatility
3. **Risk Management**: Lower allocation in high ADR% stocks
### For Risk Management
1. **ADR% for Stop Loss**: Set stops at 1-2x ADR% below entry
2. **Relative Performance**: Reduce positions when consistently underperforming
3. **Volume Confirmation**: Be cautious when U/D ratio deteriorates
## Best Practices
### Timeframe Recommendations
- **Intraday**: Use lower MA periods (5, 13, 21, 55)
- **Swing Trading**: Default settings work well (13, 21, 55, 189)
- **Position Trading**: Consider higher periods (21, 50, 100, 200)
### Market Conditions
- **Trending Markets**: Focus on MA alignment and relative performance
- **Sideways Markets**: Rely more on ADR% for range trading
- **Volatile Markets**: Reduce allocation percentage regardless of signals
### Customization Tips
1. Adjust reference dates for ARS calculation based on significant market events
2. Change comparison symbol to sector-specific indices for better relative analysis
3. Modify MA periods based on your trading style and market characteristics
## Technical Specifications
- **Version**: Pine Script v6
- **Overlay**: Yes (plots on price chart)
- **Real-time Updates**: Yes
- **Data Requirements**: Minimum 252 bars for complete calculations
- **Compatible Timeframes**: All standard timeframes
## Limitations
- Performance calculations require sufficient historical data
- ARS calculations depend on selected reference dates
- Volume analysis may be less reliable in low-volume stocks
- Relative performance is only as good as the chosen benchmark
This indicator is designed to provide a comprehensive analysis framework rather than simple buy/sell signals. It's recommended to use this in conjunction with your overall trading strategy and risk management rules.
Motala's Trading — risk management xauusdA practical position-sizing and risk/TP planning tool designed for discretionary traders. It draws risk and profit zones as right-side boxes, places clean labels for Entry / SL / TP1..TP5, shows a breakeven midline, and calculates P&L, risk, margin, and R:R—all in your account currency (e.g., CAD) with optional live FX conversion (USD→Account) so numbers line up with your broker.
Key features
Auto/Long/Short: Auto infers direction from where SL sits vs Entry; or choose Long/Short explicitly.
TPs by R-multiples or manual prices:
R-multiples: TP1..TP5 automatically at 1R..5R from Entry based on your SL distance.
Manual: enter any mix of TP1..TP5 prices yourself.
Sizing modes:
Fixed — Lots (e.g., 0.01 lots)
Fixed — Units (base units)
Risk % (script computes lots/units to match your % risk target)
Cost realism (optional): Toggle bid/ask, commission per side (%), and slippage. All P&L and R:R update “after costs.”
Account currency P&L: Real-time conversion from USD→Account using a live feed (default OANDA:USDCAD, editable to Pepperstone/FOREXCOM, etc.).
Compact “luxury” panel (top-right):
Side, Notional, Balance, Initial Margin (uses your leverage), R:R (after costs)
Drawdown @SL (amount + %)
Blended P&L across TP1..TP5 (weighted by your TP percentages)
P&L @TP(main) and @SL
If Risk % sizing: shows Lots (calc). If Fixed lots/units: shows Risk Used (%).
Total Risk across parallel trades (simple multiplier).
Right-side chart labels: Entry, SL, TP1..TP5, and live P&L label near the midline.
Visuals you actually use: Boxes only (no left extension lines), configurable box colors/transparency, dashed right-extended breakeven line.
Guardrail warnings: Flags if SL/TP are on the wrong side or if R:R < 1 after costs.
Trade Notes + CSV one-shot: Type a note and emit a single CSV line to the Alerts Log (or a webhook) when you toggle Save now.
How to use
Set prices: Enter Entry and SL (both clamped to 2 decimals).
Choose TP mode:
“R-multiples (1R..5R)” to auto-space TP1..TP5, or
“Manual prices” to type TP prices (each 2 decimals).
Pick the direction: Auto (script infers), or force Long/Short.
Sizing:
Risk % → script calculates lots/units so loss @SL ≈ target % of balance.
Fixed — Lots/Units → script shows Risk Used (%) @SL.
Account & FX: Choose your Account Currency (USD/CAD/ZAR/EUR/INR). Keep Use Live FX on (default) and set Live FX Symbol (e.g., OANDA:USDCAD). If your feed quotes inverse, tick Invert Live FX.
(Optional) Costs: Turn on Enable Realistic Costs, then set Use Bid/Ask, Commission % per side, and Slippage to mirror your broker.
Visuals: Set Box extend right (bars), toggle labels/midline/warnings, and customize box colors.
Calculations (plain English)
1R = absolute distance between Entry and SL (price).
TP1..TP5 (R-mode) = Entry ± 1R..5R in the profit direction.
Net P&L uses effective entry/exit (adds/removes slippage; uses bid/ask for market fills if enabled) and subtracts commission (both sides).
Risk % sizing:
Compute loss per 1 unit at SL (after costs), then scale units = (Account × Risk %) / per-unit loss.
Derived Lots (calc) = Units / Contract Size.
Drawdown @SL = |P&L @SL| (in account currency) + percentage of account.
R:R (after costs) = |P&L @TP(main)| ÷ |P&L @SL)|.
Blended P&L = weighted sum of P&L at TP1..TP5 using your TP size %s.
Broker alignment tips
Contract Size matters. For XAUUSD CFDs, many brokers use 100 units per 1.00 lot (100 oz). If your broker uses a different lot size or tick value, set Contract Size to match, or P&L will differ.
If your broker adds commissions/markup/averaging, mirror that in Costs.
Live FX uses your chosen TradingView symbol (e.g., OANDA:USDCAD). For best matching, pick the same provider you see closest to your broker.
Notes & CSV export
Enter a Trade Note in inputs.
Toggle Enable CSV alert/save, then tick Save now (one-shot) once.
A CSV line is sent via alert() → copy from the Alerts Log or route to a webhook (e.g., Google Sheets).
Format: YYYY-MM-DD HH:MM,Symbol,Currency,Side,Entry,SL,TPmain,Lots/RiskLots,AbsRiskAtSL%,Note
Visual choices
Clean boxes only (risk & profit) that extend right a set number of bars.
Dashed breakeven midline, right-extended only.
Right-side labels so nothing sits on top of candles.
All prices and monetary values displayed to 2 decimals; Risk % rows show no decimals.
Defaults
Lots: 0.01
Leverage: 20× (used to display Initial Margin only; doesn’t change P&L)
Account Currency: CAD
Live FX Symbol: OANDA:USDCAD (editable)
Costs: OFF (for clean math)
Limitations & disclaimer
P&L relies on contract size, costs, and FX feed—set these to your environment for best alignment.
Some brokers’ internal markups/averaging won’t perfectly match a public feed.
For education/information only. Not financial advice.
Tags: risk management, position sizing, R multiples, TP/SL planner, XAUUSD, forex, CFD, money management, risk reward, panel, boxes, bid/ask, CSV export
Backtest - Strategy Builder [AlgoAlpha]🟠 OVERVIEW
This script by AlgoAlpha is a modular Strategy Builder designed to let traders test custom trade entry and exit logic on TradingView without writing their own Pine code. It acts as a framework where users can connect multiple external signals, chain them in sequences, and run backtests with built-in leverage, margin, and risk controls. Its main strength is flexibility—you can define up to five sequential steps for entry and exit conditions on both long and short sides, with logic connectors (AND/OR) controlling how conditions combine. This lets you test complex multi-step confirmation workflows in a controlled, visual backtesting environment.
🟠 CONCEPTS
The system works by linking external signals —these can be values from other indicators, and/or custom sources—to conditional checks like “greater than,” “less than,” or “crossover.” You can stack these checks into steps , where all conditions in a step must pass before the sequence moves to the next. This creates a chain of logic that must be completed before a trade triggers. On execution, the strategy sizes positions according to your chosen leverage mode ( Cross or Isolated ) and allocation method ( Percent of equity or absolute USD value]). Liquidation prices are simulated for both modes, allowing realistic margin behaviour in testing. The script also tracks performance metrics like Sharpe, Sortino, profit factor, drawdown, and win rate in real time.
🟠 FEATURES
Up to 5 sequential steps for both long and short entries, each with multiple conditions linked by AND/OR logic.
Two leverage modes ( Cross and Isolated ) with independent long/short leverage multipliers.
Separate multi-step exit triggers for longs and shorts, with optional TP/SL levels or opposite-side triggers for flipping positions.
Position sizing by equity percent or fixed USD amount, applied before leverage.
Realistic liquidation price simulation for margin testing.
Built-in trade gating and validation—prevents trades if configuration rules aren’t met (e.g., no exit defined for an active side).
Full performance dashboard table showing live strategy status, warnings, and metrics.
Configurable bar coloring based on position side and TP/SL level drawing on chart.
Integration with TradingView's strategy backtester, allowing users to view more detailed metrics and test the strategy over custom time horizons.
🟠 USAGE
Add the strategy to your chart. In the settings, under Master Settings , enable longs/shorts, select leverage mode, set leverage multipliers, and define position sizing. Then, configure your Long Trigger and Short Trigger groups: turn on conditions, pick which external signal they reference, choose the comparison type, and assign them to a sequence step. For exits, use the corresponding Exit Long Trigger and Exit Short Trigger groups, with the option to link exits to opposite-side entries for auto-flips. You can also enable TP and/or SL exits with custom sources for the TP/SL levels. Once set, the strategy will simulate trades, show performance stats in the on-chart table, and highlight any configuration issues before execution. This makes it suitable for testing both simple single-signal systems and complex, multi-filtered strategies under realistic leverage and margin constraints.
🟠 EXAMPLE
The backtester on its own does not contain any indicator calculation; it requires input from external indicators to function. In this example, we'll be using AlgoAlpha's Smart Signals Assistant indicator to demonstrate how to build a strategy using this script.
We first define the conditions beforehand:
Entry :
Longs – SSA Bullish signal (strong OR weak)
Shorts – SSA Bearish signal (strong OR weak)
Exit
Longs/Shorts: (TP/SL hit OR opposing signal fires)
Other Parameters (⚠️Example only, tune this based on proper risk management and settings)
Long Leverage: default (3x)
Short Leverage: default (3x)
Position Size: default (10% of equity)
Steps
Load up the required indicators (in this example, the Smart Signals Assistant).
Ensure the required plots are being output by the indicator properly (signals and TP/SL levels are being plotted).
Open the Strategy Builder settings and scroll down to "CONDITION SETUP"; input the signals from the external indicator.
Configure the exit conditions, add in the TP/SL levels from the external indicator, and add an additional exit condition → {{Opposite Direction}} Entry Trigger.
After configuring the entry and exit conditions, the strategy should now be running. You can view information on the strategy in TradingView's backtesting report and also in the Strategy Builder's information table (default top right corner).
It is important to note that the strategy provided above is just an example, and the complexity of possible strategies stretches beyond what was shown in this short demonstration. Always incorporate proper risk management and ensure thorough testing before trading with live capital.
BE-Indicator Aggregator toolkit [Enhanced]█ Overview:
BE-Indicator Aggregator toolkit is an enhanced version of the original toolkit which is built for those we rely on taking multi-confirmation from different indicators available with the traders.
The enhanced version of the Toolkit aid's traders in understanding their custom logic for their trade setups and provides detailed level the results on how it performed over the past considering additional set of parameters such as:
Session Inputs
Parallel Entry | Single Entry
Pyramid Entry | Re - Entry
Single | Multiple Exit Levels, Exit On Opposite Signal
Trailing Of Stop Loss (Liberal & Aggressive Trails)
Extend Target Levels (Locking Profit vs Moving SL to Cost) with Trailing SL
█ Technical Enhancement:
This version is equipped to understand multiple strategies / trade setup for long and short keeping the performance intact. Its important to note that Custom Builder requires text input and hence you are expected to balance between text heavy input vs creative construct of strategy.
toolkit in the backed, equipped with lazy loading features to check the logics and by which performance is kept high at all the time. Depending on the inputs toolkit decides to check for the setups on eligible bars.
█ Additional Features:
Calculated Variables: These are Inner variables which are and can be part of each parameter. These variables can help in conducting mathematical operations before accessing if the logic.
Supported Operating symbols : +, -, *, /, %, (-)
'Sample code to identify HangingMan Candle
VAR-HangingMan:AND:O|L|C, O - L|G|H - O
'O - L & H - O are the calculated variables
Note: Ensure to use space between each of the source values to understand that it requires calculation before pushing for logical assessment.
Another Example: Check if HM candle occurred at Moving average line loaded in Source 1 of the setting.
VAR-HangingMan:AND:O|L|C, O - L|G|H - O
'Check if Low is Less than or Equal to MA line value
VAR-HMClose2MA:AND:ES1 - L|GE|0, HangingMan
'Above Line can be also written as "VAR-HMClose2MA:AND:L (-) ES1|GE|0, HangingMan".
'Enclosing operating symbol with parenthesis converts the output as absolute values.
'Check if Previous Candle was Red and HM candle touched the MA line to trade Reversal Setup
VAR-TwoCandleChk:AND:O |G|C , HMClose2MA
Scope Variables: VAR- keyword can be used to define Logical Conditions as well as independent condition. Defining Rules for VAR- still remain the same. Indicator does the bifurcation. Its better to Map your setups to each of Variable and finally call One L- or S- Condition with OR logic.
A Sample Diagram:
VAR-CriteriaCheck1:Code1
VAR-CriteriaCheck2:AND:Code1, Code2
VAR-CriteriaCheck3:OR:Code1, Code2, Code3
VAR-LongStrategy1:AND:CriteriaCheck1, CriteriaCheck2
VAR-LongStrategy2:OR:CriteriaCheck3, CriteriaCheck2
L-OR:LongStrategy1, LongStrategy2
Note: Search Prioritization starts from Left to Right. LongStrategy1 will be first searched and if not then only it searches for next Strategy. if LongStrategy1 is satisfied It wont search further.
WASTRUE & ISTRUE: These operations can now be part of VAR- keyword.
Note: Ensure to check the Output on the chart. Sometimes it may not work as expected as it can cause repaint and requires to run on each bar for accuracy, however toolkit is not calculating strategy inputs on each bar (basis the input) hence desired results may not come.
Customized Inputs: .
1. Parallelism: Toolkit treats non continuous signals as each Trade. Hence if your signal is valid on alternative bar. toolkit takes trade on alternative Bar whether previous trade is running or not. Toolkit analyses the performance of each trade separately.
You can turn off this method of calculation by enabling "Single Trade at a Time" so that No fresh signal is traded until the previous trade is closed.
2. TP Levels: Customization of TP levels is possible under Input Settings.
3. Independent SL & TGT Levels: Can define Separate SL & TGT levels for Long and Short Trades.
4. Algo Friendly: You can deploy Algo Alerts via Standard Alerts or Via Add Alerts on Indicator Method. Placeholders are made available to support any type of trading. You can customize on when the trading Alerts to be fired via Session Control option.
IMPORTANT Note for Scalpers using Standard Alert (Fx): If you Keep TGT levels very tight along with multiple TP level, on wild movements / Gaps --- if next tick upon Entry directly hits your TGT level, Initial TP level alerts will hit first and Next TP level alert will have 1 - 2 sec delay and so on until TGT alert is fired.
█ Understanding Results Table:
DISCLAIMER: No sharing, copying, reselling, modifying, or any other forms of use are authorized for our documents, script / strategy, and the information published with them. This informational planning script / strategy is strictly for individual use and educational purposes only. This is not financial or investment advice. Investments are always made at your own risk and are based on your personal judgement. I am not responsible for any losses you may incur. Please invest wisely.
Happy to receive suggestions and feedback in order to improve the performance of the indicator better.
MNQ Contract Size CalculatorSimple drag and drop contract size calculator for MNQ. Uses variables for half risk ($100) and full risk ($200) to adjust your contract size. Perfect for 50k funded accounts.
LANZ Strategy 6.0🔷 LANZ Strategy 6.0 — NY Session Entry Tool & Multi-Account Risk Manager
LANZ Strategy 6.0 - Is a trading tool designed to help traders plan, execute, and manage operations with a focus on risk management, multi-account handling, and visual clarity.
It works exclusively on the 1-hour timeframe ⏳ and is optimized for the New York market opening dynamics.
🧠 Core Concept
The strategy identifies bullish trading opportunities based on the 09:00 NY candle. Once detected, it automatically calculates and draws:
EP (Entry Price) — The exact level where the trade setup triggers.
SL (Stop Loss) — Based on a customizable percentage of the candle's high–low range or wick extremes.
TP (Take Profit) — Calculated using your chosen Risk–Reward Ratio (e.g., 1:5, 1:3, etc.).
⚙️ Main Features
⏳ Time-Specific Execution
Operates only when the 09:00 NY candle closes bullish.
Ideal for traders who align with the New York Session market structure.
💰 Multi-Account Lot Size Management
Up to 5 independent accounts can be configured with their own capital and risk %, showing the exact lot size to use for each.
📏 Adaptive Risk Control
Supports both Forex and non-Forex assets (indices, gold, oil).
For non-Forex, you can manually define the pip value according to your broker’s specs.
🎨 Visual Trade Map
Automatically plots clean and easy-to-read EP, SL, and TP lines with customizable colors, styles, and thickness.
A floating information panel displays levels, pip distances, and lot sizes.
🔔 Real-Time Alerts
Alerts for:
Entry signal detection.
Stop Loss hit.
Take Profit hit.
Manual close at the defined session end.
📊 Example
If you trade GBPUSD with Account #1 set to $10,000 and 2% risk,
and the 09:00 NY candle closes bullish with SL = 30 pips and RR = 5:1:
EP, SL, and TP levels are drawn instantly.
Risk = $200 (2% of $10,000).
Lot size is calculated automatically.
All details are shown in the on-chart panel.
🛠️ How to Use
Load the indicator on a 1-hour chart.
Configure risk settings and account data.
Wait for the 09:00 NY candle to close bullish.
Use the displayed lot size and levels to execute your trade.
Let the tool alert you for SL, TP, or manual close.
⚠️ Disclaimer:
This script is for educational purposes only. It does not guarantee profits and past performance does not represent future results. Always manage your risk responsibly.
👨💻 Credits:
💡 Developed by: LANZ
🧠 Execution Model & Logic Design: LANZ
📅 Designed for: 1H timeframe and NY-based entries
