PnL TrackerThis script allows you to manually input the details for up to 64 unique positions in the settings, each requiring a Symbol, Average Cost, and Quantity (Qty).
Key Features:
Average Cost Line: Plots a horizontal line on the chart corresponding to your recorded Average Cost for the security currently being viewed.
Real-Time PnL Label: A dynamic label attached to the Average Cost line provides an instant summary of your PnL in both percentage and currency for the last visible bar.
Detailed PnL Box: Displays a consolidated, easy-to-read table in the bottom-right corner of the chart, clearly showing:
The Symbol and Quantity of your position.
Your Average Cost.
The current PnL in percentage (%) and base currency (e.g., USD, EUR).
Visibility Controls: Toggles in the settings allow you to show or hide the Average Cost line and the PnL summary box independently.
This tool is perfect for actively managing and visualizing your multi-asset portfolio positions without leaving your main trading chart. Simply enter your positions in the indicator's settings, and the script will automatically track the PnL for the symbol matching the current chart.
Statistics
PnL PortfolioThis script allows you to input the details for up to 20 active positions across various trading pairs or markets. Stop manually calculating your trades—get instant, real-time feedback on your performance.
Key Features:
Multi-Pair Tracking: Monitor up to 20 unique symbols simultaneously.
Required Inputs: Easily define the Symbol, Entry Price, and Position Quantity (size) for each trade in the indicator settings.
Real-Time PnL: Instantly calculates and displays two critical metrics based on the current market price:
% PnL (Percentage Profit/Loss)
Absolute Profit/Loss (in currency)
Color-Coded Feedback: The PnL columns are color-coded (green/teal for profit, red/maroon for loss) for immediate visual confirmation of your trade health.
Customizable Layout: Choose where the dashboard table appears on your chart (top-left, top-right, bottom-left, or bottom-right) to keep your trading view clean.
This is an essential overlay for any trader managing multiple active positions and needing a consolidated, easy-to-read overview.
Squeeze Backtest by Shaqi v2.0Script to backtest price squeeze's. Works on long and short directions
Delta Volume Heatmap🔥 Delta Volume Heatmap
The Delta Volume Heatmap visualizes the real-time strength of per-bar delta volume — highlighting the imbalance between buying and selling pressure.
Each column’s color intensity reflects how strong the delta volume deviates from its moving average and standard deviation.
🟩 Green tones = Buy-dominant activity (bullish imbalance)
🟥 Red tones = Sell-dominant activity (bearish imbalance)
This tool helps traders quickly identify:
Abnormal volume spikes
Absorption or exhaustion zones
Potential reversal or continuation signals
First Passage Time - Distribution AnalysisThe First Passage Time (FPT) Distribution Analysis indicator is a sophisticated probabilistic tool that answers one of the most critical questions in trading: "How long will it take for price to reach my target, and what are the odds of getting there first?"
Unlike traditional technical indicators that focus on what might happen, this indicator tells you when it's likely to happen.
Mathematical Foundation: First Passage Time Theory
What is First Passage Time?
First Passage Time (FPT) is a concept in stochastic processes that measures the time it takes for a random process to reach a specific threshold for the first time. Originally developed in physics and mathematics, FPT has applications in:
Quantitative Finance: Option pricing, risk management, and algorithmic trading
Neuroscience: Modeling neural firing patterns
Biology: Population dynamics and disease spread
Engineering: Reliability analysis and failure prediction
The Mathematics Behind It
This indicator uses Geometric Brownian Motion (GBM), the same stochastic model used in the Black-Scholes option pricing formula:
dS = μS dt + σS dW
Where:
S = Asset price
μ = Drift (trend component)
σ = Volatility (uncertainty component)
dW = Wiener process (random walk)
Through Monte Carlo simulation, the indicator runs 1,000+ price path simulations to statistically determine:
When each threshold (+X% or -X%) is likely to be hit
Which threshold is hit first (directional bias)
How often each scenario occurs (probability distribution)
🎯 How This Indicator Works
Core Algorithm Workflow:
Calculate Historical Statistics
Measures recent price volatility (standard deviation of log returns)
Calculates drift (average directional movement)
Annualizes these metrics for meaningful comparison
Run Monte Carlo Simulations
Generates 1,000+ random price paths based on historical behavior
Tracks when each path hits the upside (+X%) or downside (-X%) threshold
Records which threshold was hit first in each simulation
Aggregate Statistical Results
Calculates percentile distributions (10th, 25th, 50th, 75th, 90th)
Computes "first hit" probabilities (upside vs downside)
Determines average and median time-to-target
Visual Representation
Displays thresholds as horizontal lines
Shows gradient risk zones (purple-to-blue)
Provides comprehensive statistics table
📈 Use Cases
1. Options Trading
Selling Options: Determine if your strike price is likely to be hit before expiration
Buying Options: Estimate probability of reaching profit targets within your time window
Time Decay Management: Compare expected time-to-target vs theta decay
Example: You're considering selling a 30-day call option 5% out of the money. The indicator shows there's a 72% chance price hits +5% within 12 days. This tells you the trade has high assignment risk.
2. Swing Trading
Entry Timing: Wait for higher probability setups when directional bias is strong
Target Setting: Use median time-to-target to set realistic profit expectations
Stop Loss Placement: Understand probability of hitting your stop before target
Example: The indicator shows 85% upside probability with median time of 3.2 days. You can confidently enter long positions with appropriate position sizing.
3. Risk Management
Position Sizing: Larger positions when probability heavily favors one direction
Portfolio Allocation: Reduce exposure when probabilities are near 50/50 (high uncertainty)
Hedge Timing: Know when to add protective positions based on downside probability
Example: Indicator shows 55% upside vs 45% downside—nearly neutral. This signals high uncertainty, suggesting reduced position size or wait for better setup.
4. Market Regime Detection
Trending Markets: High directional bias (70%+ one direction)
Range-bound Markets: Balanced probabilities (45-55% both directions)
Volatility Regimes: Compare actual vs theoretical minimum time
Example: Consistent 90%+ bullish bias across multiple timeframes confirms strong uptrend—stay long and avoid counter-trend trades.
First Hit Rate (Most Important!)
Shows which threshold is likely to be hit FIRST:
Upside %: Probability of hitting upside target before downside
Downside %: Probability of hitting downside target before upside
These always sum to 100%
⚠️ Warning: If you see "Low Hit Rate" warning, increase this parameter!
Advanced Parameters
Drift Mode
Allows you to explore different scenarios:
Historical: Uses actual recent trend (default—most realistic)
Zero (Neutral): Assumes no trend, only volatility (symmetric probabilities)
50% Reduced: Dampens trend effect (conservative scenario)
Use Case: Switch to "Zero (Neutral)" to see what happens in a pure volatility environment, useful for range-bound markets.
Distribution Type
Percentile: Shows 10%, 25%, 50%, 75%, 90% levels (recommended for most users)
Sigma: Shows standard deviation levels (1σ, 2σ)—useful for statistical analysis
⚠️ Important Limitations & Best Practices
Limitations
Assumes GBM: Real markets have fat tails, jumps, and regime changes not captured by GBM
Historical Parameters: Uses recent volatility/drift—may not predict regime shifts
No Fundamental Events: Cannot predict earnings, news, or macro shocks
Computational: Runs only on last bar—doesn't give historical signals
Remember: Probabilities are not certainties. Use this indicator as part of a comprehensive trading plan with proper risk management.
Created by: Henrique Centieiro. feedback is more than welcome!
VWAP Deviation Oscillator [BackQuant]VWAP Deviation Oscillator
Introduction
The VWAP Deviation Oscillator turns VWAP context into a clean, tradeable oscillator that works across assets and sessions. It adapts to your workflow with four VWAP regimes plus two rolling modes, and three deviation metrics: Percent, Absolute, and Z-Score. Colored zones, optional standard deviation rails, and flexible plot styles make it fast to read for both trend following and mean reversion.
What it does
This tool measures how far price is from a chosen VWAP and expresses that gap as an oscillator. You can view the deviation as raw price units, percent, or standardized Z-Score. The plot can be a histogram or a line with optional fills and sigma bands, so you can quickly spot polarity shifts, overbought and oversold conditions, and strength of extension.
VWAP modes track a session VWAP that resets (4H, Daily, Weekly) or a rolling VWAP that updates continuously over a fixed number of bars or days.
Deviation modes let you choose the lens: Percent, Absolute, or Z-Score. Each highlights different aspects of stretch and mean pressure.
Visual encoding uses a 10-zone color palette to grade the magnitude of deviation on both sides of zero.
Volatility guards compute mode-specific sigma so thresholds are stable even when volatility compresses.
Why this works
VWAP is a high signal anchor used by institutions to gauge fair participation. Deviations around VWAP cluster in regimes: mild oscillations within a band, decisive pushes that signal imbalance, and standardized extremes that often precede either continuation or snapback. Expressing that distance as a single time series adds clarity: bias is the oscillator’s sign, risk context is its magnitude, and regime is the way it behaves around sigma lines.
How to use it
Trend following
Favor the side of the zero line. Bullish when the oscillator is above zero and making higher swing highs. Bearish when below zero and making lower swing lows. Use +1 sigma and +2 sigma in your mode as strength tiers. Pullbacks that hold above zero in uptrends, or below zero in downtrends, are often continuation entries.
Mean reversion
Fade stretched readings when structure supports it. Look for tests of +2 sigma to +3 sigma that fail to progress and roll back toward zero, or the mirror on the downside. Z-Score mode is best when you want standardized gates across assets. Percent mode is intuitive for intraday scalps where a given percent stretch tends to mean revert.
Session playbook
Use Daily or Weekly VWAP for intraday or swing context. Rolling modes help when the asset lacks clean session boundaries or when you want a continuous anchor that adapts to liquidity shifts.
Key settings
VWAP computation
VWAP Mode = 4 Hours, Daily, Weekly, Rolling (Bars), Rolling (Days). Session modes reset the VWAP when a new session begins. Rolling modes compute VWAP over a fixed trailing window.
Rolling (Lookback: Bars) controls the trailing bar count when using Rolling (Bars).
Rolling (Lookback: Days) converts days to bars at runtime and uses that trailing span.
Use Close instead of HLC3 switches the price reference. HLC3 is smoother. Close makes the anchor track settlement more tightly.
Deviation measurement
Deviation Mode
Percent : 100 * (Price / VWAP - 1). Good for uniform scaling across instruments.
Absolute : Price - VWAP. Good when price units themselves matter.
Z-Score : Standardizes the absolute residual by its own mean and standard deviation over Z/Std Window . Ideal for cross-asset comparability and regime studies.
Z/Std Window sets the mean and standard deviation window for Z-Score mode.
Volatility controls
Percent Mode Volatility Lookback estimates sigma for percent deviations.
Absolute Mode Volatility Lookback estimates sigma for absolute deviations.
Minimum Sigma Guard (pct pts) prevents the percent sigma from collapsing to near zero in extremely quiet markets.
Visualization
Plot Type = Histogram or Line. Histogram emphasizes impulse and polarity changes. Line emphasizes trend waves and divergences.
Positive Color / Negative Color define the palette for line mode. Histogram uses a 10-bucket gradient automatically.
Show Standard Deviations plots symmetric rails at ±1, ±2, ±3 sigma in the current mode’s units.
Fill Line Oscillator and Fill Opacity add a soft bias band around zero for line mode.
Line Width affects both the oscillator and the sigma rails.
Reading the zones
The oscillator’s color and height map deviation to nine graded buckets on each side of zero, with deeper greens above and deeper reds below. In Percent and Absolute modes, those buckets are scaled by their mode-specific sigma. In Z-Score mode the bucket edges are fixed at 0.5, 1.0, 2.0, and 2.8.
0 to +1 sigma weak positive bias, usually rotational.
+1 to +2 sigma constructive impulse. Pullbacks that hold above zero often continue.
+2 to +3 sigma strong expansion. Watch for either trend continuation or exhaustion tells.
Beyond +3 sigma statistical extreme. Requires structure to avoid fading too soon.
Mirror logic applies on the negative side.
Suggested workflows
Trend continuation checklist
Pick a session VWAP that matches your timeframe, for example Daily for intraday or Weekly for position trades.
Wait for the oscillator to hold the correct side of zero and for a sequence of higher swing lows in the oscillator (uptrend) or lower swing highs (downtrend).
Buy pullbacks that stabilize between zero and +1 sigma in an uptrend. Sell rallies that stabilize between zero and -1 sigma in a downtrend.
Use the next sigma band or a prior price swing as your target reference.
Mean reversion checklist
Switch to Z-Score mode for standardized thresholds.
Identify tests of ±2 sigma to ±3 sigma that fail to extend while price meets support or resistance.
Enter on a polarity change through the prior histogram bar or a small hook in line mode.
Fade back to zero or to the opposite inner band, then reassess.
Notes on the three modes
Percent is easy to reason about when you care about proportional stretch. It is well suited to intraday and multi-asset dashboards.
Absolute tracks cash distance from VWAP. This is useful when instruments have tight ticks and you plan risk in price units.
Z-Score standardizes the residual and is best for quant studies, cross-asset comparisons, and threshold research that must be scale invariant.
What the alerts can tell you
Polarity changes at zero can mark the start or end of a leg.
Crosses of ±1 sigma identify overbought or oversold in the current mode’s units.
Zone changes signal an upgrade or downgrade in deviation strength.
Troubleshooting and edge cases
If your instrument has long flat periods, keep Minimum Sigma Guard above zero in Percent mode so the rails do not vanish.
In Rolling modes, very short windows will respond quickly but can whip around. Session modes smooth this by resetting at well known boundaries.
If Z-Score looks erratic, increase Z/Std Window to stabilize the estimate of mean and sigma for the residual.
Final thoughts
VWAP is the anchor. The deviation oscillator is the narrative. By separating bias, magnitude, and regime into a simple stream you can execute faster and review cleaner. Pick the VWAP mode that matches your horizon, choose the deviation lens that matches your risk framework, and let the color graded zones guide your decisions.
Performance-based Asset Weighting(MTF)**Performance-Based Asset Weighting (MTF/Symbol Free Setting)**
#### Overview
This indicator is a tool that visualizes the relative strength of performance (price change rate) as “weight (allocation ratio)” for **four user-defined stocks**.
By setting any specified past point in time as the baseline (where all symbols are equally weighted at 25%), it aims to provide an intuitive understanding of which symbols outperformed others and attracted capital, or underperformed and saw capital outflows.
**【Default Settings and Application Scenario: Pension Fund Rebalancing Analysis】**
The default settings reference the basic portfolio of Japan's Government Pension Investment Fund (GPIF), configuring four major asset classes: domestic equities, foreign equities, domestic bonds, and foreign bonds. It is known that when market fluctuations cause deviations from this equal-weighted ratio, rebalancing occurs to restore the original ratio (selling assets whose weight has increased and buying assets whose weight has decreased).
Analyzing using this default setting can serve as a reference point for considering **“whether rebalancing sales (or purchases) by pension funds and similar entities are likely to occur in the future.”**
**【Important: Usage Notes】**
The weights shown by this indicator are **theoretical reference values** calculated solely based on performance from the specified start date. Even if large investors conduct significant rebalancing (asset buying/selling) during the period, those transactions themselves are not reflected in this chart's calculations.
Therefore, please understand that the actual portfolio ratios may differ. **Use this solely as a rough guideline. **
#### Key Features
* **Freely configure the 4 assets for analysis:** You can freely set any 4 assets (stocks, indices, currencies, cryptocurrencies, etc.) you wish to compare via the settings screen.
* **Performance-based weight calculation:** Rather than simple price composition ratios, it calculates each asset's price change since the specified start date as a “performance index” and displays each asset's proportion of the total sum.
* **Freely set analysis start date:** You can set any desired starting point for analysis, such as “after the XX shock” or “after earnings announcements,” using the calendar.
* **Multi-Timeframe (MTF) Support:** Independently of the timeframe displayed on the chart, you can freely select the timeframe (e.g., 1-hour, 4-hour, daily) used by the indicator for calculations.
#### Calculation Principle
This indicator calculates weights in the following three steps:
1. **Obtaining the Base Price**
Obtain the closing price for each of the four stocks on the user-set “Start Date for Weight Calculation.” This becomes the **base price** for analysis.
2. **Calculating the Performance Index**
Divide the current price of each stock by the **base price** obtained in Step 1 to calculate the “Performance Index”.
`Performance Index = Current Price ÷ Base Date Price`
This quantifies how many times the current performance has increased compared to the base date performance, which is set to “1”.
3. **Calculating Weights**
Sum the “Performance Indexes” of the four stocks. Then, calculate the percentage contribution of each stock's Performance Index to this total sum and plot it on the chart.
`Weight (%) = (Individual Performance Index ÷ Total Performance Index of 4 Stocks) × 100`
Using this logic, on the analysis start date, all stocks' performance indices are set to “1”, so the weights start equally at 25%.
#### Usage
* **Application Example 1: Market Sentiment Analysis (Using Default Settings)**
Analyze using the default asset classes. By observing the relative strength between “Equities” and “Bonds”, you can assess whether the market is risk-on or risk-off.
* **Application Example 2: Sector/Theme Strength Analysis**
Configure settings for groups like “Top 4 semiconductor stocks” or “4 GAFAM stocks.” Setting the start date to the beginning of the year or earnings season allows you to instantly compare which stocks within the same sector are performing best.
* **Application Example 3: Cryptocurrency Power Map Analysis**
By setting major cryptocurrencies like “BTC, ETH, SOL, ADA,” you can analyze which currencies are attracting market capital.
**【About Legend Display】**
Due to Pine Script specification constraints, the legend on the chart will display fixed names: **“Stock 1” to “Stock 4”. **
Please note that the symbol you entered for “Symbol 1” in the settings corresponds to the “Symbol 1” line on the chart.
#### Settings
* **Symbol 1 to Symbol 4:** Set the four symbols you wish to analyze.
* **Timeframe for Calculation:** Select the timeframe the indicator references when calculating weights.
* **Start Date for Weight Calculation:** This serves as the base date for comparing performance.
#### Disclaimer
This script is solely a tool to assist with market analysis and does not recommend buying or selling any specific financial instruments. Please make all final investment decisions at your own discretion.
-------------------------------------------------------------------------------------------------------------------
**Performance-based Asset Weighting（MTF・シンボル自由設定）**
#### 概要
このインジケーターは、**ユーザーが自由に設定した4つの銘柄**について、パフォーマンス（騰落率）の相対的な強さを「ウェイト（構成比率）」として可視化するツールです。
指定した過去の任意の時点を基準（全銘柄が均等な25%）として、そこからどの銘柄のパフォーマンスが他の銘柄を上回り、資金が向かっているのか、あるいは下回っているのかを直感的に把握することを目的としています。
**【デフォルト設定と活用シナリオ：年金基金のリバランス考察】**
デフォルト設定では、日本の年金積立金管理運用独立行政法人（GPIF）の基本ポートフォリオを参考に、主要4資産クラス（国内株式, 外国株式, 国内債券, 外国債券）が設定されています。市場の変動によってこの均等な比率に乖離が生じると、元の比率に戻すためのリバランス（比率が増えた資産を売り、減った資産を買う）が行われることが知られています。
このデフォルト設定で分析することで、**「今後、年金基金などによるリバランスの売り（買い）が発生する可能性があるか」を考察するための、一つの目安として利用できます。**
**【重要：利用上の注意点】**
このインジケーターが示すウェイトは、あくまで指定した開始日からのパフォーマンスのみを基に算出した**理論上の参考値**です。実際に大口投資家などが途中で大規模なリバランス（資産の売買）を行ったとしても、その取引自体はこのチャートの計算には反映されません。
そのため、実際のポートフォリオ比率とは異なる可能性があることをご理解の上、**あくまで大まかな目安としてご活用ください。**
#### 主な特徴
* **分析対象の4銘柄を自由に設定可能:** 設定画面から、比較したい4つの銘柄（株式、指数、為替、仮想通貨など）を自由に設定できます。
* **パフォーマンス基準のウェイト計算:** 単純な価格の構成比ではなく、指定した開始日からの各銘柄の騰落を「パフォーマンス指数」として算出し、その合計に占める各銘柄の割合を表示します。
* **分析開始日の自由な設定:** 「〇〇ショック後」「決算発表後」など、分析したい任意の時点をカレンダーから設定できます。
* **マルチタイムフレーム（MTF）対応:** チャートに表示している時間足とは別に、インジケーターが計算に使う時間足（1時間足、4時間足、日足など）を自由に選択できます。
#### 計算の原理
このインジケーターは、以下の3ステップでウェイトを算出しています。
1. **基準価格の取得**
ユーザーが設定した「ウェイト計算の開始日」における、4つの各銘柄の終値を取得し、これを分析の**基準価格**とします。
2. **パフォーマンス指数の算出**
現在の各銘柄の価格を、ステップ1で取得した**基準価格**で割ることで、「パフォーマンス指数」を算出します。
`パフォーマンス指数 = 現在の価格 ÷ 基準日の価格`
これにより、基準日のパフォーマンスを「1」とした場合、現在のパフォーマンスが何倍になっているかが数値化されます。
3. **ウェイトの算出**
4つの銘柄の「パフォーマンス指数」の合計値を算出します。そして、合計値に占める各銘柄のパフォーマンス指数の割合（%）を計算し、チャートに描画します。
`ウェイト (%) = (個別のパフォーマンス指数 ÷ 4銘柄のパフォーマンス指数の合計) × 100`
このロジックにより、分析開始日には全銘柄のパフォーマンス指数が「1」となるため、ウェイトは均等に25%からスタートします。
#### 使用方法
* **応用例1：市場のセンチメント分析（デフォルト設定利用）**
デフォルト設定の資産クラスで分析し、「株式」と「債券」の力関係を見ることで、市場がリスクオンなのかリスクオフなのかを判断する材料になります。
* **応用例2：セクター・テーマ別の強弱分析**
設定画面で、例えば「半導体関連の主要4銘柄」や「GAFAMの4銘柄」などを設定します。開始日を年初や決算時期に設定することで、同セクター内でどの銘柄が最もパフォーマンスが良いかを一目で比較できます。
* **応用例3：仮想通貨の勢力図分析**
「BTC, ETH, SOL, ADA」など、主要な仮想通貨を設定することで、市場の資金がどの通貨に向かっているのかを分析できます。
**【凡例の表示について】**
Pine Scriptの仕様上の制約により、チャート上の凡例は**「銘柄1」〜「銘柄4」という固定名で表示されます。**
お手数ですが、設定画面でご自身が「銘柄1」に入力したシンボルが、チャート上の「銘柄1」のラインに対応する、という形でご覧ください。
#### 設定項目
* **銘柄1〜銘柄4:** 分析したい4つのシンボルをそれぞれ設定します。
* **計算に使う時間足:** インジケーターがウェイトを計算する際に参照する時間足を選択します。
* **ウェイト計算の開始日:** パフォーマンスを比較する上での基準日となります。
#### 免責事項
このスクリプトはあくまで市場分析を補助するためのツールであり、特定の金融商品の売買を推奨するものではありません。投資の最終的な判断は、ご自身の責任において行ってください。
ATT Numbers Header (Movable)For anybody that trades with ATT (Advanced Time Technique) And can't remember the numbers and want's to have them on their chart at all time with full customizability as well this indicator is for you.
Square Root Price Calculator By ABPinescript to Calculate Square root of Price usefull for Gann Lover
% Change & Range (With SMA)- Calculates the % range and change for each candle
- uses SMA over "n" bars to show the average % range and the average % change for green days and red days
- optional standard deviation line (k bands)
Tchwella Stocks Custom WatermarkThis Pine Script v5 indicator adds a customizable watermark to TradingView charts, displaying key stock information while allowing for flexible positioning and formatting.
📌 Features & Functionality:
✅ Custom Positioning:
• Fixed to the top-left corner.
• Adjustable spacing ensures the text is properly aligned.
✅ Displayed Information (Configurable):
• Company Name & Market Cap (Optional: Shows dynamically calculated market cap)
• Stock Ticker & Timeframe
• Industry & Sector
✅ Customization Options:
• Font Size: Huge, Large, Normal, Small
• Text Color & Transparency: Adjustable
• Proper Left Alignment for a clean, structured display
• Vertical Offset Tweaks to move text down for better visibility
✅ Optimized Table Layout:
• Uses table.new() for persistent placement.
• Added an empty row to fine-tune positioning, ensuring the watermark doesn’t overlap key chart areas.
🔧 Use Case:
Designed for traders who want a clear, customizable stock watermark to enhance their charting experience without obstructing price action.
Feb 1
Release Notes
Updated version: now you can decide your location for the watermark
Micha Stocks Custom Watermark (MSWM) – TradingView Script
This Pine Script v5 indicator adds a customizable watermark to TradingView charts, displaying key stock information while allowing for flexible positioning and formatting.
📌 Features & Functionality:
✅ Custom Positioning:
• Fixed to the top-left corner.
• Adjustable spacing ensures the text is properly aligned.
✅ Displayed Information (Configurable):
• Company Name & Market Cap (Optional: Shows dynamically calculated market cap)
• Stock Ticker & Timeframe
• Industry & Sector
✅ Customization Options:
• Font Size: Huge, Large, Normal, Small
• Text Color & Transparency: Adjustable
• Proper Left Alignment for a clean, structured display
• Vertical Offset Tweaks to move text down for better visibility
✅ Optimized Table Layout:
• Uses table.new() for persistent placement.
• Added an empty row to fine-tune positioning, ensuring the watermark doesn’t overlap key chart areas.
🔧 Use Case:
Designed for traders who want a clear, customizable stock watermark to enhance their charting experience without obstructing price action.
Feb 7
Release Notes
Micha Stocks Custom Watermark – Updated Version 🚀
This updated Micha Stocks Custom Watermark script enhances your TradingView experience by adding an ATR-based volatility signal alongside the existing customizable stock watermark.
🆕 New Features & Improvements:
✅ ATR (14-Day) with Dynamic Volatility Indicator
• Displays the ATR value and its percentage relative to price.
• Includes a color-coded volatility signal:
• 🔴 High Volatility (Above user-defined Red Threshold)
• 🟡 Moderate Volatility (Between Red & Yellow Thresholds)
• 🟢 Low Volatility (Below user-defined Yellow Threshold)
✅ Fully Customizable ATR Thresholds
• Users can set their own ATR % levels for Red, Yellow, and Green signals.
✅ Improved Watermark Customization
• Users can still adjust the position, size, and color of the watermark.
• Includes Company Name, Ticker, Market Cap, Industry, and Sector.
• ATR can be turned on/off in settings for flexibility.
🔧 How to Use:
1️⃣ Go to Indicator Settings → Enable or Disable ATR Display
2️⃣ Adjust ATR % Thresholds to fit your volatility preference
3️⃣ Customize Text Position, Color, and Size to match your chart setup
This update makes it easier to quickly assess market volatility while keeping a clean and professional chart layout.
💡 Why Use This Indicator?
• Effortlessly track key stock info without cluttering your chart.
• Quickly identify volatile conditions using ATR percentage signals.
• Adjust settings on the fly to match your trading strategy.
📢 Update Now & Enjoy a Smarter Charting Experience!
Round Number Analyzer v3Round Number Analyzer v3 is an indicator designed to analyze how price interacts with round number levels (levels spaced at fixed intervals in points or pips).
The indicator does not generate entry/exit signals, but provides detailed statistics to better understand market dynamics around these key levels.
✨ Key Features
Cross Counting: detects every time the price crosses a round number level (up = Long, down = Short).
Continuations & Reversals: classifies each cross as:
Continuation: the move continues in the same direction as the previous sequence.
Reversal: the move changes direction compared to the previous sequence.
Sequence Classification (L1…L5+): each level is labelled based on its position within the consecutive cross sequence:
L1 = first level of the sequence,
L2 = second consecutive,
…
L5+ = fifth or higher.
Comprehensive Stats Table (top right corner):
Total crosses (Long, Short, Totals).
Total continuations + breakdown by L1…L5+.
Total reversals + breakdown by L1…L5+.
Percentages calculated against the proper denominator, displayed directly inside the cells next to the absolute values.
Date range of analysis (user-defined).
Customizable Step: Works in both points and pips, making the indicator suitable for indices and forex.
⚙️ Main Inputs
Start date / End date → sets the analysis period.
Step mode → Points or Pips.
Step value → distance between round levels.
Pip size → pip size (default = 0.0001, typical for forex).
📈 How to Interpret
A high continuation percentage after L1–L2 suggests the market tends to extend multiple times beyond the first breakout levels.
Higher reversal percentages at advanced levels (L4–L5+) may signal trend exhaustion.
The analysis helps estimate the probability of continuation or reversal depending on how many consecutive levels have already been crossed.
🔎 Practical Applications
Support for breakout or mean-reversion strategies.
Comparative analysis across different markets (e.g. indices vs forex) or different time periods.
📝 Notes
The indicator is timeframe-robust, as it accounts for multiple steps within the same candle, ensuring results do not depend on the selected timeframe (except for TradingView’s historical data limits).
It does not provide automatic trading signals, but serves as a quantitative analysis tool to refine your strategies.
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Round Number Analyzer v3 è un indicatore pensato per analizzare come il prezzo interagisce con i livelli di round number (livelli a distanza fissa in punti o pips).
L’indicatore non genera segnali di ingresso/uscita, ma fornisce statistiche dettagliate utili per comprendere la dinamica del mercato attorno a questi livelli.
✨ Funzionalità principali
Conteggio dei Cross: rileva ogni volta che il prezzo attraversa un livello round (verso l’alto = Long, verso il basso = Short).
Continuations & Reversals: classifica ogni attraversamento come:
Continuation: il movimento prosegue nella stessa direzione della sequenza precedente.
Reversal: il movimento inverte la direzione rispetto alla sequenza precedente.
Classificazione per sequenza (L1…L5+): ogni livello è etichettato in base alla sua posizione nella sequenza di cross consecutivi:
L1 = primo livello della sequenza,
L2 = secondo consecutivo,
…
L5+ = quinto o superiore.
Statistiche complete in tabella (in alto a destra):
Cross totali (Long, Short, Totals).
Continuations totali + breakdown per L1…L5+.
Reversals totali + breakdown per L1…L5+.
Percentuali calcolate sul denominatore corretto, mostrate direttamente dentro le celle accanto ai valori assoluti.
Date range di analisi (impostabile dall’utente).
Step personalizzabile: puoi lavorare sia in punti che in pips, così l’indicatore è adatto sia per indici che per forex.
⚙️ Input principali
Start date / End date → imposta l’intervallo temporale di analisi.
Step mode → punti o pips.
Step value → ampiezza tra i livelli round.
Pip size → dimensione del pip (default = 0.0001, tipico per il forex).
📈 Come interpretarlo
Una percentuale di continuation molto alta dopo L1–L2 indica che il mercato tende a proseguire più volte oltre i primi livelli di breakout.
Percentuali di reversal più elevate nei livelli avanzati (L4–L5+) possono suggerire esaurimento della spinta.
L’analisi permette di stimare la probabilità che un movimento in corso continui o si inverta in base a quanti livelli sono già stati attraversati consecutivamente.
🔎 Applicazioni pratiche
Supporto per strategie di breakout o mean reversion.
Analisi comparativa tra mercati (es. indici vs forex) o tra periodi temporali diversi.
📝 Note
L’indicatore è timeframe-robust: il conteggio tiene conto di multipli step dentro la stessa candela, così i risultati non dipendono dal timeframe scelto (salvo i limiti di caricamento storico di TradingView).
Non fornisce segnali operativi automatici, ma è un tool di analisi quantitativa per affinare le proprie strategie.
Regular Trading Hours Opening Range Gap (RTH ORG)### Regular Trading Hours (RTH) Gap Indicator with Quartile Levels
**Overview**
Discover overnight gaps in index futures like ES, YM, and NQ, or stocks like SPY, with this enhanced Pine Script v6 indicator. It visualizes the critical gap between the previous RTH close (4:15 PM ET for futures, 4:00 PM for SPY) and the next RTH open (9:30 AM ET), helping traders spot potential price sensitivity formed during after-hours trading.
**Key Features**
- **Standard Gap Boxes**: Semi-transparent boxes highlight the gap range, with optional text labels showing day-of-week and "RTH" identifier.
- **Midpoint Line**: A customizable dashed line at the 50% level, with price labels for quick reference.
- **New: Quartile Lines (25% & 75%)**: Dotted lines (default width 1) mark the quarter and three-quarter points within the gap, ideal for finer intraday analysis. Toggle on/off, adjust style/color/width, and add labels.
- **High-Low Gap Variant**: Optional boxes and midlines for gaps between the prior close's high/low and the open's high/low—perfect for wick-based overlaps on lower timeframes (5-min or below recommended).
- **RTH Close Lines**: Extend previous close levels with dotted lines and price tags.
- **Customization Galore**: Extend elements right, limit historical displays (default: 3 gaps), no-plot sessions (e.g., avoid weekends), and time offsets for non-US indices.
**How to Use**
Apply to 15-min or lower charts for best results. Toggle "extend right" for ongoing levels. SPY auto-adjusts for its 4 PM close.
Tested on major indices—enhance your gap trading strategy today! Questions? Drop a comment.
Thanks to twingall for supplying the original code.
Thanks to The Inner Circle Trader (ICT) for the logical and systematic application.
Brownian Motion Probabilistic Forecasting (Time Adaptive)Probabilistic Price Forecast Indicator
Overview
The Probabilistic Price Forecast is an advanced technical analysis tool designed for the TradingView platform. Instead of predicting a single future price, this indicator uses a Monte Carlo simulation to model thousands of potential future price paths, generating a cone of possibilities and calculating the probability of specific outcomes.
This allows traders to move beyond simple price targets and ask more sophisticated questions, such as: "What is the probability that this stock will increase by 5% over the next 24 hours?"
Core Concept: Geometric Brownian Motion
The indicator's forecasting model is built on the principles of Geometric Brownian Motion (GBM) , a widely accepted mathematical model for describing the random movements of financial asset prices. The core idea is that the next price step is a function of the asset's historical trend (drift), its volatility, and a random "shock."
The formula used to project each price step in the simulation is:
next_price = current_price * exp( (μ - (σ²/2))Δt + σZ√(Δt) )
Where:
μ (mu) represents the drift , which is the average historical return.
σ (sigma) represents the volatility , measured by the standard deviation of historical returns.
Z is a random variable from a standard normal distribution, representing the random "shock" or new information affecting the price.
Δt (delta t) is the time step for each projection.
How It Works
The indicator performs a comprehensive analysis on the most recent bar of the chart:
**Historical Analysis**: It first analyzes a user-defined historical period (e.g., the last 240 hours of price data) to calculate the asset's historical drift (μ) and volatility (σ) from its logarithmic returns.
**Monte Carlo Simulation**: It then runs thousands of simulations (e.g., 2000) of future price paths over a specified forecast period (e.g., the next 24 hours). Each path is unique due to the random shock (Z) applied at every step.
**Probability Distribution**: After all simulations are complete, it collects the final price of each path and sorts them to build a probability distribution of potential outcomes.
**Visualization and Signaling**: Finally, it visualizes this distribution on the chart and generates signals based on the user's criteria.
Key Features & Configuration
The indicator is highly configurable, allowing you to tailor its analysis to your specific needs.
Time-Adaptive Periods
The lookback and forecast periods are defined in hours , not bars. The script automatically converts these hour-based inputs into the correct number of bars based on the chart's current timeframe, ensuring the analysis remains consistent across different chart resolutions.
Forecast Quartiles
You can visualize the forecast as a "cone of probability" on the chart. The indicator draws lines and a shaded area representing the price levels for different quartiles (percentiles) of the simulation results. By default, this shows the range between the 25th and 95th percentiles.
Independent Bullish and Bearish Signals
The indicator allows you to set independent criteria for bullish and bearish signals, providing greater flexibility. You can configure:
A bullish signal for an X% confidence of a Y% price increase.
A bearish signal for a W% confidence of a Z% price decrease.
For example, you can set it to alert you for a 90% chance of a 2% drop, while simultaneously looking for a 60% chance of a 10% rally.
How to Interpret the Indicator
The Forecast Cone : The blue shaded area on the chart represents the probable range of future prices. The width of the cone indicates the expected volatility; a wider cone means higher uncertainty. The price labels on the right side of the cone show the calculated percentile levels at the end of the forecast period.
Green Signal Label : A green "UP signal" label appears when the probability of the price increasing by your target percentage exceeds your defined confidence level.
Red Signal Label : A red "DOWN signal" label appears when the probability of the price decreasing by your target percentage exceeds your confidence level.
This tool provides a statistical edge for understanding future possibilities but should be used in conjunction with other analysis techniques.
Aggregated Scores Oscillator [Alpha Extract]A sophisticated risk-adjusted performance measurement system that combines Omega Ratio and Sortino Ratio methodologies to create a comprehensive market assessment oscillator. Utilizing advanced statistical band calculations with expanding and rolling window analysis, this indicator delivers institutional-grade overbought/oversold detection based on risk-adjusted returns rather than traditional price movements. The system's dual-ratio aggregation approach provides superior signal accuracy by incorporating both upside potential and downside risk metrics with dynamic threshold adaptation for varying market conditions.
🔶 Advanced Statistical Framework
Implements dual statistical methodologies using expanding and rolling window calculations to create adaptive threshold bands that evolve with market conditions. The system calculates cumulative statistics alongside rolling averages to provide both historical context and current market regime sensitivity with configurable window parameters for optimal performance across timeframes.
🔶 Dual Ratio Integration System
Combines Omega Ratio analysis measuring excess returns versus deficit returns with Sortino Ratio calculations focusing on downside deviation for comprehensive risk-adjusted performance assessment. The system applies configurable smoothing to both ratios before aggregation, ensuring stable signal generation while maintaining sensitivity to regime changes.
// Omega Ratio Calculation
Excess_Return = sum((Daily_Return > Target_Return ? Daily_Return - Target_Return : 0), Period)
Deficit_Return = sum((Daily_Return < Target_Return ? Target_Return - Daily_Return : 0), Period)
Omega_Ratio = Deficit_Return ≠ 0 ? (Excess_Return / Deficit_Return) : na
// Sortino Ratio Framework
Downside_Deviation = sqrt(sum((Daily_Return < Target_Return ? (Daily_Return - Target_Return)² : 0), Period) / Period)
Sortino_Ratio = (Mean_Return / Downside_Deviation) * sqrt(Annualization_Factor)
// Aggregated Score
Aggregated_Score = SMA(Omega_Ratio, Omega_SMA) + SMA(Sortino_Ratio, Sortino_SMA)
🔶 Dynamic Band Calculation Engine
Features sophisticated threshold determination using both expanding historical statistics and rolling window analysis to create adaptive overbought/oversold levels. The system incorporates configurable multipliers and sensitivity adjustments to optimize signal timing across varying market volatility conditions with automatic band convergence logic.
🔶 Signal Generation Framework
Generates overbought conditions when aggregated score exceeds adjusted upper threshold and oversold conditions below lower threshold, with neutral zone identification for range-bound markets. The system provides clear binary signal states with background zone highlighting and dynamic oscillator coloring for intuitive market condition assessment.
🔶 Enhanced Visual Architecture
Provides modern dark theme visualization with neon color scheme, dynamic oscillator line coloring based on signal states, and gradient band fills for comprehensive market condition visualization. The system includes zero-line reference, statistical band plots, and background zone highlighting with configurable transparency levels.
snapshot
🔶 Risk-Adjusted Performance Analysis
Utilizes target return parameters for customizable risk assessment baselines, enabling traders to evaluate performance relative to specific return objectives. The system's focus on downside deviation through Sortino analysis provides superior risk-adjusted signals compared to traditional volatility-based oscillators that treat upside and downside movements equally.
🔶 Multi-Timeframe Adaptability
Features configurable calculation periods and rolling windows to optimize performance across various timeframes from intraday to long-term analysis. The system's statistical foundation ensures consistent signal quality regardless of timeframe selection while maintaining sensitivity to market regime changes through adaptive band calculations.
🔶 Performance Optimization Framework
Implements efficient statistical calculations with optimized variable management and configurable smoothing parameters to balance responsiveness with signal stability. The system includes automatic band adjustment mechanisms and rolling window management for consistent performance across extended analysis periods.
This indicator delivers sophisticated risk-adjusted market analysis by combining proven statistical ratios in a unified oscillator framework. Unlike traditional overbought/oversold indicators that rely solely on price movements, the ASO incorporates risk-adjusted performance metrics to identify genuine market extremes based on return quality rather than price volatility alone. The system's adaptive statistical bands and dual-ratio methodology provide institutional-grade signal accuracy suitable for systematic trading approaches across cryptocurrency, forex, and equity markets with comprehensive visual feedback and configurable risk parameters for optimal strategy integration.
Volume Based Sampling [BackQuant]Volume Based Sampling
What this does
This indicator converts the usual time-based stream of candles into an event-based stream of “synthetic” bars that are created only when enough trading activity has occurred . You choose the activity definition:
Volume bars : create a new synthetic bar whenever the cumulative number of shares/contracts traded reaches a threshold.
Dollar bars : create a new synthetic bar whenever the cumulative traded dollar value (price × volume) reaches a threshold.
The script then keeps an internal ledger of these synthetic opens, highs, lows, closes, and volumes, and can display them as candles, plot a moving average calculated over the synthetic closes, mark each time a new sample is formed, and optionally overlay the native time-bars for comparison.
Why event-based sampling matters
Markets do not release information on a clock: activity clusters during news, opens/closes, and liquidity shocks. Event-based bars normalize for that heteroskedastic arrival of information: during active periods you get more bars (finer resolution); during quiet periods you get fewer bars (coarser resolution). Research shows this can reduce microstructure pathologies and produce series that are closer to i.i.d. and more suitable for statistical modeling and ML. In particular:
Volume and dollar bars are a common event-time alternative to time bars in quantitative research and are discussed extensively in Advances in Financial Machine Learning (AFML). These bars aim to homogenize information flow by sampling on traded size or value rather than elapsed seconds.
The Volume Clock perspective models market activity in “volume time,” showing that many intraday phenomena (volatility, liquidity shocks) are better explained when time is measured by traded volume instead of seconds.
Related market microstructure work on flow toxicity and liquidity highlights that the risk dealers face is tied to information intensity of order flow, again arguing for activity-based clocks.
How the indicator works (plain English)
Choose your bucket type
Volume : accumulate volume until it meets a threshold.
Dollar Bars : accumulate close × volume until it meets a dollar threshold.
Pick the threshold rule
Dynamic threshold : by default, the script computes a rolling statistic (mean or median) of recent activity to set the next bucket size. This adapts bar size to changing conditions (e.g., busier sessions produce more frequent synthetic bars).
Fixed threshold : optionally override with a constant target (e.g., exactly 100,000 contracts per synthetic bar, or $5,000,000 per dollar bar).
Build the synthetic bar
While a bucket fills, the script tracks:
o_s: first price of the bucket (synthetic open)
h_s: running maximum price (synthetic high)
l_s: running minimum price (synthetic low)
c_s: last price seen (synthetic close)
v_s: cumulative native volume inside the bucket
d_samples: number of native bars consumed to complete the bucket (a proxy for “how fast” the threshold filled)
Emit a new sample
Once the bucket meets/exceeds the threshold, a new synthetic bar is finalized and stored. If overflow occurs (e.g., a single native bar pushes you past the threshold by a lot), the code will emit multiple synthetic samples to account for the extra activity.
Maintain a rolling history efficiently
A ring buffer can overwrite the oldest samples when you hit your Max Stored Samples cap, keeping memory usage stable.
Compute synthetic-space statistics
The script computes an SMA over the last N synthetic closes and basic descriptors like average bars per synthetic sample, mean and standard deviation of synthetic returns, and more. These are all in event time , not clock time.
Inputs and options you will actually use
Data Settings
Sampling Method : Volume or Dollar Bars.
Rolling Lookback : window used to estimate the dynamic threshold from recent activity.
Filter : Mean or Median for the dynamic threshold. Median is more robust to spikes.
Use Fixed? / Fixed Threshold : override dynamic sizing with a constant target.
Max Stored Samples : cap on synthetic history to keep performance snappy.
Use Ring Buffer : turn on to recycle storage when at capacity.
Indicator Settings
SMA over last N samples : moving average in synthetic space . Because its index is sample count, not minutes, it adapts naturally: more updates in busy regimes, fewer in quiet regimes.
Visuals
Show Synthetic Bars : plot the synthetic OHLC candles.
Candle Color Mode :
Green/Red: directional close vs open
Volume Intensity: opacity scales with synthetic size
Neutral: single color
Adaptive: graded by how large the bucket was relative to threshold
Mark new samples : drop a small marker whenever a new synthetic bar prints.
Comparison & Research
Show Time Bars : overlay the native time-based candles to visually compare how the two sampling schemes differ.
How to read it, step by step
Turn on “Synthetic Bars” and optionally overlay “Time Bars.” You will see that during high-activity bursts, synthetic bars print much faster than time bars.
Watch the synthetic SMA . Crosses in synthetic space can be more meaningful because each update represents a roughly comparable amount of traded information.
Use the “Avg Bars per Sample” in the info table as a regime signal. Falling average bars per sample means activity is clustering, often coincident with higher realized volatility.
Try Dollar Bars when price varies a lot but share count does not; they normalize by dollar risk taken in each sample. Volume Bars are ideal when share count is a better proxy for information flow in your instrument.
Quant finance background and citations
Event time vs. clock time : Easley, López de Prado, and O’Hara advocate measuring intraday phenomena on a volume clock to better align sampling with information arrival. This framing helps explain volatility bursts and liquidity droughts and motivates volume-based bars.
Flow toxicity and dealer risk : The same authors show how adverse selection risk changes with the intensity and informativeness of order flow, further supporting activity-based clocks for modeling and risk management.
AFML framework : In Advances in Financial Machine Learning , event-driven bars such as volume, dollar, and imbalance bars are presented as superior sampling units for many ML tasks, yielding more stationary features and fewer microstructure distortions than fixed time bars. ( Alpaca )
Practical use cases
1) Regime-aware moving averages
The synthetic SMA in event time is not fooled by quiet periods: if nothing of consequence trades, it barely updates. This can make trend filters less sensitive to calendar drift and more sensitive to true participation.
2) Breakout logic on “equal-information” samples
The script exposes simple alerts such as breakout above/below the synthetic SMA . Because each bar approximates a constant amount of activity, breakouts are conditioned on comparable informational mass, not arbitrary time buckets.
3) Volatility-adaptive backtests
If you use synthetic bars as your base data stream, most signal rules become self-paced : entry and exit opportunities accelerate in fast markets and slow down in quiet regimes, which often improves the realism of slippage and fill modeling in research pipelines (pair this indicator with strategy code downstream).
4) Regime diagnostics
Avg Bars per Sample trending down: activity is dense; expect larger realized ranges.
Return StdDev (synthetic) rising: noise or trend acceleration in event time; re-tune risk.
Interpreting the info panel
Method : your sampling choice and current threshold.
Total Samples : how many synthetic bars have been formed.
Current Vol/Dollar : how much of the next bucket is already filled.
Bars in Bucket : native bars consumed so far in the current bucket.
Avg Bars/Sample : lower means higher trading intensity.
Avg Return / Return StdDev : return stats computed over synthetic closes .
Research directions you can build from here
Imbalance and run bars
Extend beyond pure volume or dollar thresholds to imbalance bars that trigger on directional order flow imbalance (e.g., buy volume minus sell volume), as discussed in the AFML ecosystem. These often further homogenize distributional properties used in ML. alpaca.markets
Volume-time indicators
Re-compute classical indicators (RSI, MACD, Bollinger) on the synthetic stream. The premise is that signals are updated by traded information , not seconds, which may stabilize indicator behavior in heteroskedastic regimes.
Liquidity and toxicity overlays
Combine synthetic bars with proxies of flow toxicity to anticipate spread widening or volatility clustering. For instance, tag synthetic bars that surpass multiples of the threshold and test whether subsequent realized volatility is elevated.
Dollar-risk parity sampling for portfolios
Use dollar bars to align samples across assets by notional risk, enabling cleaner cross-asset features and comparability in multi-asset models (e.g., correlation studies, regime clustering). AFML discusses the benefits of event-driven sampling for cross-sectional ML feature engineering.
Microstructure feature set
Compute duration in native bars per synthetic sample , range per sample , and volume multiple of threshold as inputs to state classifiers or regime HMMs . These features are inherently activity-aware and often predictive of short-horizon volatility and trend persistence per the event-time literature. ( Alpaca )
Tips for clean usage
Start with dynamic thresholds using Median over a sensible lookback to avoid outlier distortion, then move to Fixed thresholds when you know your instrument’s typical activity scale.
Compare time bars vs synthetic bars side by side to develop intuition for how your market “breathes” in activity time.
Keep Max Stored Samples reasonable for performance; the ring buffer avoids memory creep while preserving a rolling window of research-grade data.
Total Points Range by exp3rtsThis indicator measures and displays the true intraday movement of a market by approximating tick-level activity using 1-second data aggregation. Instead of only looking at net candle movement, it sums every price change during a session, giving traders a more accurate picture of market effort and volatility.
Total Points Moved (TPM) – Captures the full distance traveled by price, not just the net gain/loss.
Bullish vs. Bearish Movement – Separates upward and downward moves so you can see who dominated the session.
Custom Sessions – Define your own session start/end times and time zone for precise tracking.
End-of-Session Summary – Automatically plots a label at session completion with totals for TPM, bullish, and bearish movement.
Visual Session Highlighting – Background shading makes it easy to see when the chosen session is active.
This tool is useful for:
Understanding the true effort vs. result of price movement
Comparing volatility across sessions
Identifying whether bulls or bears contributed more to market swings
Supporting order flow and tick-based trading strategies
Portfolio Simulator & BacktesterMulti-asset portfolio simulator with different metrics and ratios, DCA modeling, and rebalancing strategies.
Core Features
Portfolio Construction
Up to 5 assets with customizable weights (must total 100%)
Support for any tradable symbol: stocks, ETFs, crypto, indices, commodities
Real-time validation of allocations
Dollar Cost Averaging
Monthly or Quarterly contributions
Applies to both portfolio and benchmark for fair comparison
Model real-world investing behavior
Rebalancing
Four strategies: None, Monthly, Quarterly, Yearly
Automatic rebalancing to target weights
Transaction cost modeling (customizable fee %)
Key Metrics Table
CAGR: Annualized compound return (S&P 500 avg: ~10%)
Alpha: Excess return vs. benchmark (positive = outperformance)
Sharpe Ratio: Return per unit of risk (>1.0 is good, >2.0 excellent)
Sortino Ratio: Like Sharpe but only penalizes downside (better metric)
Calmar Ratio: CAGR / Max Drawdown (>1.0 good, >2.0 excellent)
Max Drawdown: Largest peak-to-trough decline
Win Rate: % of positive days (doesn't indicate profitability)
Visualization
Dual-chart comparison - Portfolio vs. Benchmark
Dollar or percentage view toggle
Customizable colors and line width
Two tables: Statistics + Asset Allocation
Adjustable table position and text size
🚀 Quick Start Guide
Enter 1-5 ticker symbols (e.g., SPY, QQQ, TLT, GLD, BTCUSD)
Make sure percentage weights total 100%
Choose date range (ensure chart shows full period - zoom out!)
Configure DCA and rebalancing (optional)
Select benchmark (default: SPX)
Analyze results in statistics table
💡 Pro Tips
Chart data matters: Load SPY or your longest-history asset as main chart
If you select an asset that was not available for the selected period, the chart will not show up! E.g. BTCUSD data: Only available from ~2017 onwards.
Transaction fees: 0.1% default (adjust to match your broker)
⚠️ Important Notes
Requires visible chart data (zoom out to show full date range)
Limited by each asset's historical data availability
Transaction fees and costs are modeled, but taxes/slippage are not
Past performance ≠ future results
Use for research and education only, not financial advice
Let me know if you have any suggestions to improve this simulator.
AIO Entry Size CalculatorThis indicator is designed to help traders dynamically adjust their position size and drawdown expectations when their trading capital changes.
By entering your original backtest parameters — including base capital, base drawdown percentage, and base position size — along with your current capital, the script calculates two key scenarios:
Lock Drawdown %: Keeps the original drawdown percentage unchanged and calculates the new position size required.
Lock Position Size: Keeps the original position size unchanged and shows how your drawdown percentage will change.
The results are displayed in a clear, color-coded table, allowing you to instantly see how capital fluctuations affect your risk profile.
With customizable themes (dark/light) and highlight options, it’s an essential tool for risk management, portfolio rebalancing, and position sizing decisions based on real backtest data.
Additionally, this indicator is highly useful when combined with TradingView strategies — helping you interpret backtest results more accurately and translate them into real-world position sizing adjustments as your account balance evolves.
Time Range by exp3rtsTime Window highlights a custom time range directly on your chart, helping you focus on specific market sessions or trading hours.
Key Features:
Highlights a custom time range with a shaded background
Fully adjustable start and end time (hour & minute)
Supports multiple time zones (e.g., GMT, UTC, Europe/Berlin)
Optional market color shading inside the window (bull/bear neutral tone)
Use Cases:
Mark London Open, New York Session, or any session overlap
Focus on high-probability trading hours
Visualize your backtesting timeframe or algo activity window
Track premarket or after-hours activity for futures or indices
Customization:
Set the beginning and end time in your local or exchange time zone
Choose your timezone string (e.g., "GMT", "Etc/UTC", "America/New_York")
Automatically colors candles in the time window for easy visibility
Total Points Moved by exp3rtsThis lightweight utility tracks the total intraday range of price movement, giving you real-time insight into market activity.
It calculates:
🟩 Bullish Points – Total range from bullish candles (close > open)
🟥 Bearish Points – Total range from bearish candles (close < open)
🔁 Total Points Moved (TPM) – Sum of all high–low ranges for the day
Values are pulled from the 1-second chart for high precision and displayed in a compact tag in the top-right corner.
Quantile-Based Adaptive Detection🙏🏻 Dedicated to John Tukey. He invented the boxplot, and I finalized it.
QBAD (Quantile-Based Adaptive Detection) is ‘the’ adaptive (also optionally weighted = ready for timeseries) boxplot with more senseful fences. Instead of hardcoded multipliers for outer fences, I base em on a set of quantile-based asymmetry metrics (you can view it as an ‘algorithmic’ counter part of central & standardized moments). So outer bands are Not hardcoded, not optimized, not cross-validated etc, simply calculated at O(nlogn).
You can use it literally everywhere in any context with any continuous data, in any task that requires statistical control, novelty || outlier detection, without worrying and doubting the sense in arbitrary chosen thresholds. Obviously, given the robust nature of quantiles, it would fit best the cases where data has problems.
The thresholds are:
Basis: the model of the data (median in our case);
Deviations: represent typical spread around basis, together form “value” in general sense;
Extensions: estimate data’s extremums via combination of quantile-based asymmetry metrics without relying on actual blunt min and max, together form “range” / ”frame”. Datapoints outside the frame/range are novelties or outliers;
Limits: based also on quantile asymmetry metrics, estimate the bounds within which values can ‘ever’ emerge given the current data generating process stays the same, together form “field”. Datapoints outside the field are very rare, happen when a significant change/structural break happens in current data-generating process, or when a corrupt datapoint emerges.
…
The first part of the post is for locals xd, the second is for the wanderers/wizards/creators/:
First part:
In terms of markets, mostly u gotta worry about dem instruments that represent crypto & FX assets: it’s either activity hence data sources there are decentralized, or data is fishy.
For a higher algocomplexity cost O(nlong), unlike MBAD that is 0(n), this thing (a control system in fact) works better with ishy data (contaminated with wrong values, incomplete, missing values etc). Read about the “ breakdown point of an estimator ” if you wanna understand it.
Even with good data, in cases when you have multiple instruments that represent the same asset, e.g. CL and BRN futures, and for some reason you wanna skip constructing a proper index of em (while you should), QBAD should be better put on each instrument individually.
Another reason to use this algo-based rather than math-based tool, might be in cases when data quality is all good, but the actual causal processes that generate the data are a bit inconsistent and/or possess ‘increased’ activity in a way. SO in high volatility periods, this tool should provide better.
In terms of built-ins you got 2 weightings: by sequence and by inferred volume delta. The former should be ‘On’ all the time when you work with timeseries, unless for a reason you want to consciously turn it off for a reason. The latter, you gotta keep it ‘On’ unless you apply the tool on another dataset that ain’t got that particular additional dimension.
Ain’t matter the way you gonna use it, moving windows, cumulative windows with or without anchors, that’s your freedom of will, but some stuff stays the same:
Basis and deviations are “value” levels. From process control perspective, if you pls, it makes sense to Not only fade or push based on these levels, but to also do nothing when things are ambiguous and/or don’t require your intervention
Extensions and limits are extreme levels. Here you either push or fade, doing nothing is not an option, these are decisive points in all the meanings
Another important thing, lately I started to see one kind of trend here on tradingview as well and in general in near quant sources, of applying averages, percentiles etc ‘on’ other stationary metrics, so called “indicators”. And I mean not for diagnostic or development reasons, for decision making xd
This is not the evil crime ofc, but hillbilly af, cuz the metrics are stationary it means that you can model em, fit a distribution, like do smth sharper. Worst case you have Bayesian statistics armed with high density intervals and equal tail intervals, and even some others. All this stuff is not hard to do, if u aint’t doing it, it’s on you.
So what I’m saying is it makes sense to apply QBAD on returns ‘of your strategy’, on volume delta, but Not on other metrics that already do calculations over their own moving windows.
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Second part:
Looks like some finna start to have lil suspicions, that ‘maybe’ after all math entities in reality are more like blueprints, while actual representations are physical/mechanical/algorithmic. Std & centralized moments is a math entity that represents location, scale & asymmetry info, and we can use it no problem, when things are legit and consistent especially. Real world stuff tho sometimes deviates from that ideal, so we need smth more handy and real. Add to the mix the algo counter part of means: quantiles.
Unlike the legacy quantile-based asymmetry metrics from the previous century (check quantile skewness & kurtosis), I don’t use arbitrary sets of quantiles, instead we get a binary pattern that is totally geometric & natural (check the code if interested, I made it very damn explicit). In spirit with math based central & standardized moments, each consequent pair is wider empathizing tail info more and more for each higher order metric.
Unlike the classic box plot, where inner thresholds are quartiles and the rest are based on em, here the basis is median (minimises L1), I base inner thresholds on it, and we continue the pattern by basing the further set of levels on the previous set. So unlike the classic box plot, here we have coherency in construction, symmetry.
Another thing to pay attention to, tho for some reason ain’t many talk about it, it’s not conceptually right to think that “you got data and you apply std moments on it”. No, you apply it to ‘centered around smth’ data. That ‘smth’ should minimize L2 error in case of math, L1 error in case of algo, and L0 error in case of learning/MLish/optimizational/whatever-you-cal-it stuff. So in the case of L0, that’s actually the ‘mode’ of KDE, but that’s for another time. Anyways, in case of L2 it’s mean, so we center data around mean, and apply std moments on residuals. That’s the precise way of framing it. If you understand this, suddenly very interesting details like 0th and 1st central moments start to make sense. In case of quantiles, we center data around the median, and do further processing on residuals, same.
Oth moment (I call it init) is always 1, tho it’s interesting to extrapolate backwards the sequence for higher order moments construction, to understand how we actually end up with this zero.
1st moment (I call it bias) of residuals would be zero if you match centering and residuals analysis methods. But for some reason you didn’t do that (e.g centered data around midhinge or mean and applied QBAD on the centered data), you have to account for that bias.
Realizing stuff > understanding stuff
Learning 2981234 human invented fields < realizing the same unified principles how the Universe works
∞
