CVD Complete Volume Analysis ProCVD Complete Volume Analysis Pro | Order Flow & Absorption
Introduction:
In the world of modern trading, Price is the advertisement, but Volume is the fuel. However, standard volume indicators on TradingView are often insufficient. They tell you how much was traded, but they don’t tell you how it was traded.
Was that large volume spike aggressive buying driving the trend? or was it a "buying frenzy" hitting a wall of passive limit orders (absorption)?
The CVD Complete Volume Analysis Pro (v5) is an advanced institutional-grade Order Flow engine. By utilizing 1-second intrabar data, this indicator reconstructs the "Tick Rule" to separate Aggressive (Market) orders from Passive (Limit) orders. It calculates Cumulative Volume Delta (CVD), detects Absorption/Distribution anomalies, and utilizes an embedded Logistic Regression model to predict daily directional bias.
This is not just an indicator; it is a complete Order Flow Dashboard designed to aid and support complex footprint charts for the everyday trader.
🏗️ How It Works: The "Micro-Structure" Engine
Most volume indicators on TradingView look at the close of a 1-minute or 5-minute bar to guess the volume direction. This script goes deeper.
1. The 1-Second Granularity
Using TradingView's request.security_lower_tf capability, this script pulls 1-second resolution data regardless of the chart timeframe you are on.
It analyzes the price movement every second.
It applies the "Tick Rule": If price moves up, volume is classified as Buy. If price moves down, volume is classified as Sell.
This allows for a highly accurate reconstruction of Buying vs. Selling pressure that standard indicators miss.
2. The "Cluster" Concept
The script aggregates these 1-second data points into Clusters.
Default: 60 seconds (1 minute) per cluster.
This creates a normalized "Heartbeat" of the market, allowing us to compare the efficiency of volume over fixed time windows, removing the noise of time-based chart distortions.
3. The "Passive" Detection Logic (The Core Feature)
This is the most powerful aspect of the tool. It calculates the relationship between Effort (CVD) and Result (Price Move).
The Baseline: The script calculates a rolling statistical baseline (Standard Deviation) of how much price should move for a given amount of Delta.
Absorption (Hidden Buying): If we see massive Aggressive Selling (Negative CVD) but price refuses to drop (or drops significantly less than the statistical model predicts), the script identifies this as Passive Buying.
Distribution (Hidden Selling): If we see massive Aggressive Buying (Positive CVD) but price refuses to rise, the script identifies this as Passive Selling.
📊 The Dashboard Breakdown
The on-screen dashboard is your command center. It updates in real-time to provide a snapshot of the market's internal mechanics.
Section 1: Flow Analysis
This section analyzes the current session's behavior.
Flow Type: Categorizes the market state using algorithmic logic.
Aggressive Buying/Selling: The market is trending, and aggressive participants are winning.
Strong Accumulation/Distribution: A reversal signal. Aggressive participants are trapped, and passive whales are absorbing order flow.
Flow vs. Price: Detects divergences instantly.
Bullish Divergence: Net Flow is Positive, but Price is down (indicates manipulation or temporary suppression).
Bearish Divergence: Net Flow is Negative, but Price is up (indicates a "trap" move).
Section 2: Volume Breakdown
A detailed ledger of the day's activity.
Aggressive Buy/Sell: Market orders executing at the ask/bid. This represents "Impatience."
Passive Buy/Sell: The estimated volume of Limit Orders absorbing the aggressive flow. This represents "Intent."
Net Flow: The mathematical sum of all buy pressure minus sell pressure.
Section 3: Net Positioning (Multi-Day)
Markets don't happen in a vacuum. This section looks back (default 5 days) to see the accumulated inventory.
Bias: Are we in a multi-day accumulation or distribution phase?
Activity Type:
High Hidden Activity: Indicates a fighting market with heavy limit orders (choppy/reversal prone).
Mostly Aggressive: Indicates a trending market with low resistance.
Section 4: Predictive Model (Machine Learning)
The script features an embedded Logistic Regression Model.
It trains on the last N days of Flow Data (CVD, Net Aggressive, Net Passive, Passive Ratios).
It outputs a Probability Score (0% to 100%) regarding the likelihood of an UP close for the current session.
Note: This is a probability model based on order flow history, not a guarantee. Use it as a bias confirmation tool.
🧠 Educational: How to Trade With This
Strategy 1: The "Absorption" Reversal
Context: Price hits a major resistance level.
Look at the Dashboard: You want to see "Flow Type" switch to "Strong Distribution".
The Logic: Price is rising, and aggressive buyers are hitting the ask. However, the script detects that for every buy order, a passive seller is absorbing it. Price stops moving up despite high volume.
The Trigger: When Price creates a lower low on the chart while the dashboard shows Distribution, this is a high-probability short entry.
Strategy 2: The Flow Divergence
Context: Price is trending down.
Look at the Dashboard: Price is making new lows, but the "Net Flow" is turning Green (Positive), or the "Cum CVD" is sloping upwards.
The Logic: This is "Effort vs. Result." Sellers are exhausted. They are pushing price down, but the net flow is shifting to buyers.
The Trigger: Enter Long on the first structure break.
Strategy 3: Trend Continuation
Context: Market is opening or breaking a range.
Look at the Dashboard: You want "Full Alignment."
Signals: "Flow Type" says Aggressive Buying, Net Flow is Positive, and the Predictive Model shows >60% Bullish Probability.
The Logic: There is no passive resistance. Aggressive buyers are pushing price up freely.
The Trigger: Buy pullbacks.
⚙️ Settings & Configuration
Cluster Size: The number of 1-second bars to group together.
Use 60 (1 min) for Scalping.
Use 300 (5 min) for Day Trading.
Average Length: The baseline for statistical calculations. Higher numbers = smoother baselines but slower adaptation.
Detection Settings:
Passive Multiplier: Adjusts the sensitivity of the absorption estimation. 1.0 is standard. Increase to 1.5 if you only want to see extreme anomalies.
Daily Tracking:
History Days: How many days of data to display in the table. Note: Due to TradingView data limits, keeping this between 3-5 days ensures the most stability.
⚠️ Important Technical Limitations
Please read this section carefully to understand the constraints of the Pine Script environment:
Data Depth (The 100k Limit): TradingView limits request.security_lower_tf to approximately 100,000 intrabars.
This means the script can typically only "see" the last 3 to 5 days of true 1-second data.
If you set History Days or Training Days too high (e.g., 20 days), the script may return 0 values for older dates because the high-resolution data simply doesn't exist on the server.
Approximation of Ticks: While 1-second data is extremely precise, it is still an aggregation. In extremely high-volatility events (like CPI releases), multiple ticks happen inside one second. The script attributes the volume of that second based on the close relative to the open/prev close. It is the best approximation possible on TradingView, but not a replacement for Level 3 Tick Data feeds.
Calculation Time: This is a heavy script. On lower-end devices or when loading on many charts simultaneously, you may experience a "Calculation took too long" warning. If this happens, reduce the History Days to 3.
🛡️ Disclaimer
No Repainting: This indicator uses strict historical referencing and does not repaint closed clusters.
Not Financial Advice: This tool provides data visualization. Order flow is a subjective art. Always manage your risk.
Author's Note:
I built this tool because I wanted the power of Order Flow footprint charts without the visual clutter. By using statistical baselines to detect passive liquidity, we can finally see the "invisible hand" of the market directly on our TradingView charts. I hope this adds value to your trading.
👍 If you find this script useful, please leave a Boost and a Comment below!
Search in scripts for "THE SCRIPT"
Bullish Engulfing at Daily Support (Pivot Low) - R Target (v6)1. What this strategy really is (in human terms)
This strategy is not about predicting the market.
It’s about waiting for proof that buyers are stepping in at a price where they already should.
Think of it like this:
“I only buy when price falls into a known ‘floor’ and buyers visibly take control.”
That’s it.
Everything in the script enforces that idea.
2. The two ingredients (nothing else)
Ingredient #1: Daily Support (the location)
Support is an area where price previously fell and then reversed upward.
In the script:
Support is defined as the most recent confirmed daily swing low
A swing low means:
Price went down
Stopped
Then went up enough to prove that buyers defended that level
This matters because:
You’re not guessing where support might be
You’re using a level where buyers already proved themselves
“At support” doesn’t mean exact
Markets don’t bounce off perfect lines.
So the script allows a small zone (the “support tolerance”):
Example: 0.5% tolerance
If support is at 100
Anywhere between ~99.5–100.5 counts
This prevents missing good trades just because price was off by a few ticks.
Ingredient #2: Bullish Engulfing Candle (the trigger)
This is the confirmation.
A bullish engulfing candle means:
Sellers were in control
Buyers stepped in hard enough to fully overpower them
The bullish candle’s body “swallows” the previous candle
Psychologically, it says:
“Sellers tried, failed, and buyers just took control.”
That’s why this candle works only at support.
A bullish engulfing in the middle of nowhere means nothing.
3. Why daily timeframe matters
The daily chart:
Filters out noise
Reflects decisions made by institutions, not random scalpers
Produces fewer but higher-quality signals
That’s why:
The script uses daily data
You typically get very few trades per month
Most days: no trade
That “boredom” is the edge.
4. When a trade is taken (exact conditions)
A trade happens only if ALL are true:
Price drops into a recent daily support zone
A bullish engulfing candle forms on the daily chart
Risk is clearly defined (entry, stop, target)
If any one is missing → no trade
5. How risk is controlled (this is crucial)
The stop loss (where you admit you’re wrong)
The stop is placed:
Below the support level
Or below the low of the engulfing candle
With a small ATR buffer so normal noise doesn’t stop you out
Meaning:
“If price breaks below this area, buyers were wrong. I’m out.”
No hoping. No moving stops. No exceptions.
Position sizing (why this strategy survives losing streaks)
Each trade risks a fixed % of your account (default 1%).
So:
Big stop = smaller position
Small stop = larger position
This keeps every trade equal in risk, not equal in size.
That’s professional behavior.
6. The take-profit logic (why 2.8R matters)
Instead of guessing targets:
The strategy uses a multiple of risk (R)
Example:
Risk = $1
Target = $2.80
You can lose many times and still come out ahead.
This is why:
Win rate ≈ 60% is more than enough
Even 40–45% could still work if discipline is perfect
7. Why patience is the real edge (not the pattern)
The bullish engulfing is common.
Bullish engulfing at daily support is rare.
Most people fail because they:
Trade engulfings everywhere
Ignore location
Lower standards when bored
Add “just one more indicator”
Your edge is:
Saying no 95% of the time
Taking only trades that look obvious after they work
8. How to use this strategy effectively (rules to follow)
Rule 1: Only take “clean” setups
Skip trades when:
Support is messy or unclear
Price is chopping sideways
The engulfing candle is tiny
The market is news-chaotic (earnings, FOMC, etc.)
If you have to convince yourself, skip it.
Rule 2: One trade at a time
This strategy works best when:
You’re not stacked in multiple correlated trades
You treat each setup like it matters
Quality > quantity.
Rule 3: Journal screenshots, not just numbers
After each trade, save:
Daily chart screenshot
Support level marked
Entry / stop / target
After 50–100 trades, patterns jump out:
Best tolerance %
Best stop buffer
Markets that behave well vs poorly
That’s how the original trader refined it.
Rule 4: Expect boredom and drawdowns
You will have:
Weeks with zero trades
Clusters of losses
Long flat periods
That’s normal.
If you “fix” it by adding more trades:
You destroy the edge.
9. Who this strategy is perfect for
This fits you if:
You don’t want screen addiction
You prefer process over excitement
You’re okay being wrong often
You want something you can execute for years
It is not for:
Scalpers
Indicator collectors
People who need action every day
10. The mindset shift (the real lesson of that story)
The money didn’t come from bullish engulfings.
It came from:
Defining one repeatable behavior
Removing everything else
Trusting math + patience
Doing nothing most of the time
If you want, next we can:
Walk through real example trades bar-by-bar
Optimize settings for a specific market you trade
Add filters that increase quality without adding complexity
Crypto Professional Suite V2.0 [R2D2]Here is the complete professional documentation and strategy guide for your Crypto Pro Suite indicator. This guide is designed to help you install the tool correctly, understand its features, and utilize it to maximize your trading returns.
Crypto Pro Suite: The Professional Crypto Trader's All-In-One Toolkit
1. Introduction
The Crypto Pro Suite is a high-performance TradingView indicator designed to consolidate the five most critical technical analysis tools into a single, clean overlay.
Instead of cluttering your screen with multiple sub-charts (panes), this suite integrates Moving Averages, Bollinger Bands, and Fibonacci Levels directly onto the price action. It transforms "oscillator" data (RSI and MACD) into actionable Buy/Sell signals on your candles and a real-time Status Dashboard.
Key Features:
Zero Clutter: No bottom panes required; maximizes screen real estate for price analysis.
Real-Time Dashboard: Instant readout of Trend, RSI status, and MACD momentum.
Dynamic Overlay: Indicators move fluidly with your candles.
2. Installation Instructions
Follow these steps precisely to ensure the "Overlay" feature functions correctly.
Clean Slate: If you have any previous version of this script on your chart, remove it now (click the 'X' next to the indicator name).
Open Editor: Click the Pine Editor tab at the bottom of your TradingView screen.
Paste Code: Delete any existing text and paste the Final Polished Script provided in the previous response.
Save & Add: Click Save, then click Add to chart.
Note: By adding it fresh, TradingView forces the script to lock onto the price candles rather than a separate pane.
Verify: You should see colored lines (MAs, Bollinger Bands) directly on top of your candlesticks and a Dashboard in the top right.
3. Using the Script: Settings & Customization
Access the settings by clicking the Gear Icon next to the indicator name.
Dashboard: You can toggle the info panel On/Off or change its size (Tiny to Large) to fit your screen resolution.
Toggle Control: Each of the 5 indicators has a "Show" checkbox. You can turn off noise (e.g., hide Bollinger Bands) when you only want to focus on Trend (MAs).
Inputs:
MAs: Defaults are 50/200 (Classic Golden Cross setup).
RSI: Default is 14 length, 70/30 limits.
Fibs: Default lookback is 200 bars. Increase this number to find Support/Resistance over a longer timeframe.
4. Maximizing Returns: Strategy & Examples
To maximize returns, professionals do not use indicators in isolation. They look for Confluence—where multiple indicators signal the same direction simultaneously.
A. Moving Averages (Trend Filter)
The Setup: The script plots a Fast MA (Yellow) and Slow MA (Blue).
Strategy:
Golden Cross (Buy): When the Yellow line crosses above the Blue line. This signals the start of a long-term bull trend.
Death Cross (Sell): When the Yellow line crosses below the Blue line.
Pro Tip: Never go long (Buy) if price is significantly below the Blue (Slow) MA.
B. Relative Strength Index (RSI)
The Setup: Instead of a line graph, you will see labels on the candles: "RSI Buy" (Green) or "RSI Sell" (Red).
Strategy:
Mean Reversion: If you see an "RSI Sell" label, the asset is Overbought. This is often a signal to take profit, not necessarily to short.
The Dip Buy: Look for an "RSI Buy" label occurring during a general uptrend (price above Slow MA). This indicates a healthy pullback that is ready to bounce.
C. MACD (Momentum)
The Setup: Green Triangles (Buy) and Red Triangles (Sell) appear above/below candles.
Strategy:
Trend Confirmation: Use MACD to confirm the Moving Average. If price crosses above the MA and you get a Green MACD Triangle, the breakout has high momentum and is likely to succeed.
Exit Signal: If you are in a Long trade and see a Red MACD Triangle, momentum is fading. Consider tightening your stop-loss.
D. Bollinger Bands (Volatility)
The Setup: A shaded teal channel surrounding the price.
Strategy:
The Squeeze: When the bands get very narrow, a massive move is coming. Wait for the breakout.
Walking the Bands: In a strong crypto bull run, price will hug the Upper Band. If price closes outside the band and then immediately closes inside it, it is a reversal signal (Sell).
E. Fibonacci Retracement (Support/Resistance)
The Setup: Dynamic horizontal lines (0%, 23.6%, 38.2%, 50%, 61.8%, 100%) based on recent highs/lows.
Strategy:
The Golden Pocket: The most powerful buy zone in crypto is between the 0.5 and 0.618 lines.
Execution: If price falls to the 0.618 line and you see an "RSI Buy" or MACD Green Triangle appear at that exact level, this is a high-probability entry.
5. The "Perfect Trade" Example
Putting it all together for maximum profit.
Context: The Dashboard says "MA Trend: Bullish."
Trigger: Price pulls back down and touches the 0.618 Fibonacci line.
Confirmation 1: Price is also touching the Lower Bollinger Band (acting as dynamic support).
Confirmation 2: An "RSI Buy" label appears on the candle.
Action: BUY.
Stop Loss: Place just below the 100% Fib line.
Take Profit: Sell half at the 0% Fib line (recent high) and let the rest ride.
Multi-indicator Signal Builder [Skyrexio]Overview
Multi-Indicator Signal Builder is a versatile, all-in-one script designed to streamline your trading workflow by combining multiple popular technical indicators under a single roof.
It features a single-entry, single-exit logic, intrabar stop-loss/take-profit handling, an optional time filter, a visually accessible condition table, and a built-in statistics label.
Traders can choose any combination of 12+ indicators (RSI, Ultimate Oscillator, Bollinger %B, Moving Averages, ADX, Stochastic, MACD, PSAR, MFI, CCI, Heikin Ashi, and a “TV Screener” placeholder) to form entry or exit conditions.
This script aims to simplify strategy creation and analysis , making it a powerful toolkit for technical traders.
Indicators Overview
RSI (Relative Strength Index)
Measures recent price changes to evaluate overbought or oversold conditions on a 0–100 scale.
Ultimate Oscillator (UO)
Uses weighted averages of three different timeframes, aiming to confirm price momentum while avoiding false divergences.
Bollinger %B
Expresses price relative to Bollinger Bands, indicating whether price is near the upper band (overbought) or lower band (oversold).
Moving Average (MA)
Smooths price data over a specified period. The script supports both SMA and EMA to help identify trend direction and potential crossovers.
ADX (Average Directional Index)
Gauges the strength of a trend (0–100). Higher ADX signals stronger momentum, while lower ADX indicates a weaker trend.
Stochastic
Compares a closing price to a price range over a given period to identify momentum shifts and potential reversals.
MACD (Moving Average Convergence/Divergence)
Tracks the difference between two EMAs plus a signal line, commonly used to spot momentum flips through crossovers.
PSAR (Parabolic SAR)
Plots a trailing stop-and-reverse dot that moves with the trend. Often used to signal potential reversals when price crosses PSAR.
MFI (Money Flow Index)
Similar to RSI but incorporates volume data. A reading above 80 can suggest overbought conditions, while below 20 may indicate oversold.
CCI (Commodity Channel Index)
Identifies cyclical trends or overbought/oversold levels by comparing current price to an average price over a set timeframe.
Heikin Ashi
A type of candlestick charting that filters out market noise. The script uses a streak-based approach (multiple consecutive bullish or bearish bars) to gauge mini-trends.
TV Screener
A placeholder condition designed to integrate external buy/sell logic (like a TradingView “Buy” or “Sell” rating). Users can override or reference external signals if desired.
Unique Features
Multi-Indicator Entry and Exit
You can selectively enable any subset of 12+ classic indicators, each with customizable parameters and conditions. A position opens only if all enabled entry conditions are met, and it closes only when all enabled exit conditions are satisfied, helping reduce false triggers.
Single-Entry / Single-Exit with Intrabar SL/TP
The script supports a single position at a time. Once a position is open, it monitors intrabar to see if the price hits your stop-loss or take-profit levels before the bar closes, making results more realistic for fast-moving markets.
Time Window Filter
Users may specify a start/end date range during which trades are allowed, making it convenient to focus on specific market cycles for backtesting or live trading.
Condition Table and Statistics
A table at the bottom of the chart lists all active entry/exit indicators. Upon each closed trade, an integrated statistics label displays net profit, total trades, win/loss count, average and median PnL, etc.
Seamless Alerts and Automation
• Configure alerts in TradingView using “Any alert() function call.”
• The script sends JSON alert messages you can route to your own webhook.
• The indicator can be integrated with Skyrexio alert bots to automate execution on major cryptocurrency exchanges.
Optional MA/PSAR Plots
For added visual clarity, optionally plot the chosen moving averages or PSAR on the chart to confirm signals without stacking multiple indicators.
Methodology
Multi-Indicator Entry Logic
When multiple entry indicators are enabled (e.g., RSI + Stochastic + MACD), the script requires all signals to align before generating an entry. Each indicator can be set for crossovers, crossunders, thresholds (above/below), etc. This “AND” logic aims to filter out low-confidence triggers.
Single-Entry Intrabar SL/TP
• One Position At a Time: Once an entry signal triggers, a trade opens at the bar’s close.
• Intrabar Checks: Stop-loss and take-profit levels (if enabled) are monitored on every tick. If either is reached, the position closes immediately, without waiting for the bar to end.
Exit Logic
All Conditions Must Agree: If the trade is still open (SL/TP not triggered), then all enabled exit indicators must confirm a closure before the script exits on the bar’s close.
Time Filter
Optional Trading Window: You can activate a date/time range to constrain entries and exits strictly to that interval.
Justification of Methodology
Indicator Confluence: Combining multiple tools (RSI, MACD, etc.) can reduce noise and false signals.
Intrabar SL/TP: Capturing real-time spikes or dips provides a more precise reflection of typical live trading scenarios.
Single-Entry Model: Straightforward for both manual and automated tracking (especially important in bridging to bots).
Custom Date Range: Helps refine backtesting for specific market conditions or to avoid known irregular data periods.
How to Use
Add the Script to Your Chart
• In TradingView, open Indicators , search for “Multi-indicator Signal Builder” .
• Click to add it to your chart.
Configure Inputs
• Time Filter: Set a start and end date for trades.
• Alerts Messages: Input any JSON or text payload needed by your external service or bot.
• Entry Conditions: Enable and configure any indicators (e.g., RSI, MACD) for a confluence-based entry.
• Close Conditions: Enable exit indicators, along with optional SL (negative %) and TP (positive %) levels.
Set Up Alerts
• In TradingView, select “Create Alert” → Condition = “Any alert() function call” → choose this script.
• Entry Alert: Triggers on the script’s entry signal.
• Close Alert: Triggers on the script’s close signal (or if SL/TP is hit).
• Skyrexio Alert Bots: You can route these alerts via webhook to Skyrexio alert bots to automate order execution on major crypto exchanges (or any other supported broker).
Visual Reference
• A condition table at the bottom summarizes active signals.
• Statistics Label updates automatically as trades are closed, showing PnL stats and distribution metrics.
Backtesting Guidelines
Symbol/Timeframe: Works on multiple assets and timeframes; always do thorough testing.
Realistic Costs: Adjust commissions and potential slippage to match typical exchange conditions.
Risk Management: If using the built-in stop-loss/take-profit, set percentages that reflect your personal risk tolerance.
Longer Test Horizons: Verify performance across diverse market cycles to gauge reliability.
Example of statistic calculation
Test Period: 2023-01-01 to 2025-12-31
Initial Capital: $1,000
Commission: 0.1%, Slippage ~5 ticks
Trade Count: 680 (varies by strategy conditions)
Win rate: 75.44% (varies by strategy conditions)
Net Profit: +90.14% (varies by strategy conditions)
Disclaimer
This indicator is provided strictly for informational and educational purposes.
It does not constitute financial or trading advice.
Past performance never guarantees future results.
Always test thoroughly in demo environments before using real capital.
Enjoy exploring the Multi-Indicator Signal Builder! Experiment with different indicator combinations and adjust parameters to align with your trading preferences, whether you trade manually or link your alerts to external automation services. Happy trading and stay safe!
Options Chain Table [Enhanced]The primary purpose of this script is Unusual Options Activity (UOA) Detection.Identifying "Whales": Traders use it to spot when large institutions or "smart money" are aggressively buying Calls (betting price goes up) or Puts (betting price goes down).Contextualizing Volume: Instead of just showing raw volume (e.g., "10,000 contracts traded"), it calculates a Ratio. If the average volume is 1,000 and today's volume is 10,000, that is a 10x Spike, which is highly significant.0DTE & Short-Term Trading: It is optimized for analyzing the "Active Expiration" (often the current day for SPX/NDX), making it useful for 0DTE (Zero Days to Expiration) strategies.2. Key Features & VisualsThe script overlays a table on your chart with the following columns:ColumnDescriptionCall AvgThe historical average volume (Moving Average) for the Call option.Call RatioThe "Spike Factor." calculated as $ NSE:CURRENT Volume / Average Volume$$. High ratios turn Green.Call VolThe actual volume traded today for that Call strike.StrikeThe Strike Price of the option (e.g., 5800). The "At-The-Money" (ATM) strike is highlighted Blue.Put VolThe actual volume traded today for that Put strike.Put RatioThe "Spike Factor" for Puts. High ratios turn Red/Fuchsia.Put AvgThe historical average volume (Moving Average) for the Put option.3. How It Works (Technical Breakdown)This script uses advanced Pine Script techniques to bypass some of TradingView's limitations regarding options data.A. Dynamic Symbol ConstructionTradingView does not have a simple function to "get the option chain." This script manually constructs the ticker symbol for each option contract using the OPRA format:Format: OPRA:ROOT Example: OPRA:SPXW251226C5800 (SPX Weekly, Dec 26, 2025, Call, 5800 Strike).B. Tuple Fetching (Optimization)TradingView limits scripts to 40 request.security calls. To display 11 rows of data (which would normally require 44 calls: Call Vol, Call MA, Put Vol, Put MA per row), the script uses Tuple Fetching. It requests the Volume and the Moving Average in a single request, cutting the data usage in half and allowing the table to load faster without errors.C. Spike LogicIt calculates a moving average (EMA or SMA) of the volume over a set lookback period (default 20 bars).Medium Spike (M): Volume is > 2x the average.Large Spike (L): Volume is > 3.5x the average.Extreme Spike (E): Volume is > 5x the average.4. How to Use It (User Guide)To use this script effectively, you must configure the "Inputs" correctly, as it cannot always guess the correct expiration dates automatically.Add to Chart: Add the script to a chart (works best on indices like SPX, NDX, SPY, QQQ).Set the Center Price (Crucial):In the settings, look for "Manual Center Price".Input the current price of the asset (e.g., if SPX is at 5815, enter 5815).Why? The script generates strikes around this number. If you leave it 0, it might try to use the close price, which can be buggy during pre-market or if data is delayed.Set the Expiration (DTE):The script attempts to default to "Today," but for best results, manually enter the date in YYMMDD format in the "Manual DTE" field.Example: For December 26, 2025, enter 251226.Read the Alerts:The script allows you to set alerts in TradingView."Any Spike → CALL": Tells you a Call option just had a massive volume spike."Any Spike → PUT": Tells you a Put option just had a massive volume spike.5. Strategy ExampleA trader using this script might see the following scenario:Market: SPX is trading sideways at 5800.Signal: The table flashes a bright green cell on the 5850 Call with a ratio of "E 6.2x" (Extreme, 6.2 times normal volume).Interpretation: Someone is aggressively buying out-of-the-money Calls. The trader might interpret this as a bullish signal (Gamma exposure increasing at 5850) and enter a long position, expecting the price to be magnetized toward 5850.
Verified Astro-Table SimplifiedThis script, titled the **Financial Astrological Ephemeris Table**, is designed to be a high-precision astronomical dashboard for TradingView. Unlike standard indicators that rely on price formulas, this script serves as a **digital bridge** between professional Swiss Ephemeris data and your trading chart.
Here is a detailed breakdown of what the script provides and how to maximize its utility.
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**1. What the Script Provides**
**A. 100% Ephemeris Synchronization**
Most "Astro" indicators in TradingView use "mean motion" math, which drifts over time. This script uses **Static Switch Logic**. By hard-coding the data from the Swiss Ephemeris, the script ensures that the degrees you see on your chart match the physical reality of the sky.
* **Sun & Moon**: Accurate to the degree for the current period.
* **Saturn & Outer Planets**: Corrects the "sign drift" found in other scripts, keeping Saturn in its true position (late Pisces for 2025).
**B. Sign & Degree Tracking**
The script translates raw longitude (0–360°) into the traditional 12-sign zodiac format (`Sign` + `Degree`). This allows you to immediately identify where planets are transiting relative to key price levels.
**C. The Sun-Relative House System**
The script calculates an **Equal House System** based on the Sun's current position.
* This treats the Sun as the "Rising" point for the day's dashboard, showing you how other planets are "angled" relative to the Sun's current solar light.
**D. Stability and Performance**
Because the script uses `barstate.islast`, it only calculates for the most recent candle. This prevents "Runtime Errors" and ensures your TradingView platform remains fast and responsive, even on low-powered laptops.
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**2. How to Use it Effectively**
**A. Identifying Confluence with Price**
Watch for "Degree Hits." If the table shows **Saturn at 25° Pisces** and your asset is hitting a major resistance level at a number ending in **25** (or a harmonic like 2.50), it signifies a moment of "Astro-Price Confluence." These are often high-probability reversal points.
**B. Customizing the Visual Experience**
You can tailor the dashboard to your specific chart layout via the **Settings (Gear Icon)**:
* **Position**: Move the table to any corner (Top Right, Bottom Left, etc.) so it doesn't block your price action.
* **Transparency**: Adjust the "Background Color" to make the table more subtle or more prominent.
* **Text Size**: If you trade on a mobile device, set the text to "Normal." If you use a 4K monitor, set it to "Tiny" to save space.
**C. Managing the "Switch" Data**
To keep the script accurate for the long term, I will update the `get_pdf_lon` block once a month (or once a year) with the new coordinates from the Swiss Ephemeris.
**D. Directional Trading (The "Dir" Column)**
The script includes a "Direction" column. Use this to track if a planet is **Direct (D)** or **Retrograde (Rx)**.
**Strategy**: If a planet is listed as "D," its influence is considered "forward-moving" and predictable. If you update the code to show "Rx," expect the market sectors associated with that planet to experience "re-evaluations" or delays.
---
### Summary of Benefits for the User
1. **Eliminates Guesswork**: You no longer have to flip between an Ephemeris and TradingView; the data is on your screen.
2. **Historical Analysis**: You can manually change the data in the script to a historical date to see exactly how the "Astro-Weather" looked during a previous market crash or rally.
ODTE Layman Signals 📌 Script Name
Layman Options Signals – Structured BUY CALL / BUY PUT with SL & TP
📖 Overview
This indicator is a complete, finished intraday trading system designed to simplify options trading (including 0DTE and weekly options) by converting price action and market structure into clear, actionable signals.
The script performs all analysis in the background and displays only what the trader needs to execute consistently:
BUY CALL or BUY PUT
Predefined Stop Loss (SL)
Two Take Profit levels (TP1 and TP2)
Trade status and levels displayed in a live status box
The focus of this tool is execution discipline, not prediction.
🧠 Core Concepts Used (What Makes This Script Original)
This script combines multiple price-action concepts into a single, rule-based framework:
1️⃣ Opening Range Breakout (ORB)
The script calculates the opening range high and low using the first X minutes of the regular session.
Trades are only allowed above ORB high for CALLs and below ORB low for PUTs.
This filters low-quality trades during early chop.
2️⃣ Market Structure Confirmation
CALL trades require higher highs and higher lows
PUT trades require lower lows and lower highs
This prevents trading against structure.
3️⃣ Retest & Liquidity Sweep Validation
Breakouts are validated using:
ORB retests (price accepts above/below the range)
Liquidity sweeps (false breakouts that trap traders)
This helps reduce fake breakouts.
4️⃣ Volatility-Aware Risk Management
Stop losses are placed using market structure + ATR buffer
This avoids stops being placed at obvious levels.
5️⃣ Multi-Target Trade Management
TP1 = partial profit (risk reduction)
TP2 = runner target (trend continuation)
After TP1, stop loss can move to breakeven (optional)
6️⃣ Discipline Controls
Only one active trade at a time
Cooldown period after a stop loss
Prevents over-trading and revenge trading
📊 What the Indicator Displays
The script plots the following directly on the chart:
Entry level
Stop Loss (SL)
Take Profit 1 (TP1)
Take Profit 2 (TP2)
Opening Range High & Low
It also includes a Status Box that always shows one of the following states:
WAIT
BUY CALL
BUY PUT
IN TRADE
COOLDOWN
This allows traders to understand the current state at a glance without reading code.
▶️ How to Use the Indicator
Recommended Timeframes
1-minute or 2-minute charts
Intraday use only
Entry Rules
When BUY CALL appears → Buy an ATM or slightly ITM call
When BUY PUT appears → Buy an ATM or slightly ITM put
Risk Management
Exit immediately if price hits the SL line
Take partial profits at TP1
Hold remaining position for TP2 if conditions allow
When Status Shows WAIT or COOLDOWN
No trade should be taken
⚙️ Recommended Instruments
SPY / QQQ
Liquid large-cap stocks
Intraday options (0DTE / weeklies)
⚠️ Important Disclaimer
This script is provided for educational purposes only.
It is not financial advice
It does not guarantee profits
It does not place trades automatically
Options trading involves significant risk
Always test using paper trading or small size before live use.
🎯 Who This Script Is For
✔ Traders who want clear rules
✔ Traders who prefer price action over indicators
✔ Options traders who value risk management
✔ Users who want less chart clutter and more discipline
❌ Not intended for swing trading
❌ Not intended for automated trading systems
🧩 Final Notes
This is a complete, finished indicator, not a test or experimental script.
All logic is deterministic, non-repainting, and designed for real-time use.
The philosophy behind this tool is simple:
Good trading comes from structure, discipline, and risk control — not prediction.
MP SESSIONS, DST, OTTHere’s a clear description you can use for this script (for yourself or as a TradingView “Indicator Description”):
---
### MP SESSIONS, DST, OTT – What this indicator does
This script is a **multi-session market timing tool** that:
1. **Draws full trading sessions on the chart** (Asia, Sydney, Tokyo, Shanghai, Europe, London, New York, NYSE)
2. **Automatically adjusts for Daylight Saving Time (DST)** for Sydney, London, and New York
3. **Shows a live info table** with session times, DST status, and whether each session is currently open or closed
4. **Adds optional custom “OTT” vertical lines** at user-defined intraday times (for your own models, killzones, or time blocks)
---
### Main Features (high level)
#### 1. Market mode & time zone handling
* **Market Mode**:
* `Forex`
* `Stock`
* `User Custom` (you type your own session ranges)
* `TFlab suggestion` (predefined “optimized” session times)
* **Time Zone Mode**:
* `UTC`
* `Session Local Time` (local exchange time: Sydney, Tokyo, London, New York etc.)
* `Your Time Zone` (converts to the user-selected TZ, e.g. `UTC-4:00`)
* Handles separate time zones for:
* Asia, Sydney, Tokyo, Shanghai, Europe, London, New York, NYSE
* Has logic to **recalculate session start/end depending on DST** and the chosen mode.
---
#### 2. Daylight Saving Time (DST) engine
The function `DST_Detector`:
* Calculates when DST **starts and ends** for:
* `Australia/Sydney`
* `Europe/London`
* `America/New_York`
* Detects the correct Sunday (2nd, 4th, etc.) for start/end using day-of-week and week counts.
* Returns `'Active'` or `'Inactive'` for each region.
* These values are then used to **shift the sessions** (e.g. New York 13:00–21:00 vs 12:00–20:00 in UTC).
The script can also **draw vertical lines** on the chart when DST starts/ends and label them:
* “Sydney DST Started / Ended”
* “London DST Started / Ended”
* “New York DST Started / Ended”
---
#### 3. Session timing & sessions on the chart
The function `Market_TimeZone_Calculator`:
* Based on **Market Mode** + **Time Zone Mode** + **DST state**, it returns:
* Time ranges for: Sydney, Tokyo, Shanghai, Asia (combined), Europe, London, New York, NYSE
* These ranges are in `"HHMM-HHMM"` format.
Then the script:
* Converts these to `time()` conditions using the proper time zone
* Creates boolean series like `On_sesAsia`, `On_sesEurope`, `On_sesNewYork`, etc., which are **1 when the session is open and 0 when closed**.
---
#### 4. Session high/low boxes & labels
The function `LowHighSessionDetector`:
* Tracks **high and low of each session** while it’s active.
* When a new session starts:
* Resets and starts recording the session high/low.
* While session is active:
* Updates `High` with the max of current bar high and previous session high.
* Updates `Low` with the min of current bar low and previous session low.
* When the session is "on":
* Draws a **box** from session low to high (`box.new`) and extends it to the right as long as the session continues.
* Places a **label with session name** (Asia, London, New York, etc.) near the high:
* Style depends on the session (down/right/left).
You have visibility toggles per session:
* `Asia Session`, `Sydney Session`, `Tokyo Session`, `Shanghai Session`, `Europe Session`, `London Session`, `New York Session`, `NYSE` (for TFlab mode).
So you visually see:
* A shaded box for each session
* The full H/L range for that session
* A text label with the session name.
---
#### 5. Info table
The indicator builds a **table in a corner of the chart** showing:
* Header:
* “FOREX Session”, “Stock Market Trading Hours”, “User Custom Session”, or “TFlab suggestion” depending on mode.
* Columns:
1. Session name (Asia, Sydney, Tokyo, Shanghai, Europe, London, New York, NYSE)
2. DST status for that region (“Active 🌞 / Inactive 🍂 / Not Observed”)
3. Session **start time**
4. Session **end time**
5. Current **status** (“Open / Closed”, with green/red background)
The function `SplitFunction`:
* Parses the `"HHMM-HHMM"` strings for each session.
* Converts them into:
* Either raw times (if viewing in UTC/session local)
* Or converted times in **Your Time Zone** using `timestamp` and `hour/ minute` with `YourTZ`.
* Returns formatted `Start` and `End` strings like `9:30`, `13:00`, etc.
So the table is effectively a **live session schedule** that:
* Auto-adjusts to DST
* Can show times in your own time zone
* Shows which session is open right now.
---
#### 6. OTT vertical lines (custom intraday markers)
At the bottom, there is an **OTT section** which lets you draw up to **three sets of vertical lines** at specific times:
* Each OTT block has:
* Enable toggle (`Enable OTT 1/2/3`)
* Start hour & minute
* End hour & minute
* Color
* Global OTT settings:
* Line style: `Solid / Dashed / Dotted`
* Line width
* Toggle: “Show OTT Labels?”
Logic:
* `is_ott_time()` checks if current bar’s `hour` and `minute` match the OTT input time.
* `draw_ott()`:
* When the bar time matches, draws a **vertical line** through the candle from low to high (`extend.both`).
* Optionally adds a label above the bar, like `"OTT1 Start"`, `"OTT1 End"`, etc.
Use cases:
* Marking **open/close of your trading session**
* Defining **killzones**, news times, or custom model windows
* Visual anchors for your intraday routine (NY open, 10 AM candle, etc.)
---
### TL;DR
This indicator is a **session toolkit + DST engine + time markers**:
* **Visually paints the main global sessions** with boxes and labels.
* **Handles DST automatically** for Sydney, London, New York.
* **Shows a live table** with session times, DST status, and open/closed status in your time zone.
* **Adds up to three configurable vertical time markers (OTT)** for custom session windows or key times.
If you want, I can also write a **short version** (2–3 sentences) for the TradingView “Description” field.
Trinity Real Move Detector DashboardRelease Notes (critical)
1. This code "will" require tweaks for different timeframes to the multiplier, do not assume the data in the table is accurate, cross check it with the Trinity Real Move Detector or another ATR tool, to validate the values in the table and ensure you have set the correct values.
2. I mention this below. But please understand that pine code has a limitation in the number of security calls (40 request.security() calls per script). This code is on the limit of that threshold and I would encourage developers to see if they can find a way around this to improve the script and release further updates.
What do we have...
The Trinity Real Move Detector Dashboard is a powerful TradingView indicator designed to scan multiple assets at once and show when each one has genuine short-term volatility "energy" — the kind that makes directional options trades (especially 0DTE or short-dated) have a high probability of follow-through, and can be used for swing trading as well. It combines a simple ATR-based volatility filter with a SuperTrend-style bias to tell you not only if the market is "awake" but also in which direction the momentum is leaning.
At its core, the indicator calculates the current ATR on your chosen timeframe and compares it to a user-defined percentage of the asset's daily ATR. When the short-term ATR spikes above that threshold, it signals "enough energy" — meaning the underlying is moving with real force rather than choppy noise. The SuperTrend logic then determines bullish or bearish bias, so the status shows "BULLISH ENERGY" (green) or "BEARISH ENERGY" (red) when energy is on, or "WAIT" when it's not. It also counts how many bars the energy has been active and shows the current ATR vs threshold for quick visual confirmation.
The dashboard displays all this in a clean table with columns for Symbol, Multiplier, Current ATR, Threshold, Status, Bars Active, and Bias (UP/DOWN). It's perfect for 3-minute charts but works on any timeframe — just adjust the multiplier based on the hints in the settings.
Editing symbols and multipliers is straightforward and user-friendly. In the indicator settings, you'll see numbered inputs like "1. Symbol - NVDA" and "1. Multiplier". To change an asset, simply type the new ticker in the symbol field (e.g., replace "NVDA" with "TSLA", "AVGO", or "ADAUSD"). You can also adjust the multiplier for each asset individually in the corresponding "Multiplier" field to make it more or less sensitive — lower numbers give more signals, higher numbers give stricter, higher-quality ones. This lets you customize the dashboard to your watchlist without any coding. For example, if you switch to a 4-hour chart or a slower-moving stock like AVGO, you may need to raise the multiplier (e.g., to 0.3–0.4) to avoid false "bullish" signals during minor bounces in a larger downtrend.
One important note about the multiplier and timeframes: the default values are optimized for fast intraday charts (like 3-minute or 5-minute). On higher timeframes (15-minute, 1-hour, 4-hour, or daily), the SuperTrend bias can be too sensitive with low multipliers (1.0 default in the code), leading to situations like the AVGO 4-hour example — where price is clearly downtrending, but the dashboard shows "BULLISH ENERGY" because the tight bands flip on small bounces. To fix this, you need to manually increase the multiplier for that asset (or all assets) in the settings. For 4-hour or daily charts, 0.25–0.35 is often better to match smoother SuperTrend indicators like Trinity. Always test on your timeframe and asset — crypto usually needs slightly lower multipliers than stocks due to higher volatility.
TradingView has a hard limit of 40 request.security() calls per script. Each asset in the dashboard requires several calls (current ATR, daily ATR, SuperTrend components, etc.), so with the full ATR-based bias, you can safely monitor about 6–8 assets before hitting the limit. Adding more symbols increases the number of calls and will trigger the "too many securities" error. This is a platform restriction to prevent excessive server load, and there's no official way around it in a single script. Some advanced coders use tricks like caching or lower-timeframe requests to squeeze in a few more, but for reliability, sticking to 6–8 assets is recommended. If you need more, the common workaround is to create two separate indicators (e.g., one for stocks, one for crypto) and add both to the same chart.
Overall, this dashboard gives you a professional-grade multi-asset scanner that filters out low-energy noise and highlights real momentum opportunities across stocks and crypto — all in one glance. It's especially valuable for options traders who want to avoid theta decay on weak moves and only strike when the market has true fuel. By tweaking the per-symbol multipliers in the settings, you can perfectly adapt it to any timeframe or asset behavior, avoiding issues like the AVGO false bullish signal on higher timeframes.
Liquidity Entry Triggers (4-Model System) | WarRoomXYZLiquidity Entry Triggers is an open-source, price-action-based analytical framework designed to highlight recurring institutional liquidity behaviors that appear across all liquid markets.
The script focuses on how and where liquidity is taken, rather than attempting to predict direction using oscillators or lagging indicators.
It is optimized for XAUUSD, FX pairs, indices, and crypto , particularly on 1m–15m timeframes where session behavior and liquidity reactions are most visible.
This tool is not a buy/sell signal generator .
It provides contextual entry zones based on structural liquidity logic, allowing traders to apply their own execution rules.
Core Philosophy
Markets move because of:
•Trapped traders
•Forced liquidations
•Session-based liquidity cycles
•Reactions at prior institutional participation zones
This script visualizes four repeatable entry triggers that emerge from those mechanisms.
🔹 1. Failed Breakout / Trapped Trader Model
When price breaks a clearly defined range high or low, breakout traders often enter expecting continuation.
If price fails to hold outside the range and closes back inside, those traders become trapped.
The script detects:
•Breaks beyond recent highs/lows
•Immediate rejection back into the range
•Structural failure of momentum
These conditions frequently lead to mean reversion or reversal moves as trapped traders exit and fuel movement in the opposite direction.
Markers are plotted at the point of failure to highlight potential trap zones.
🔹 2. Liquidation Flush Detection
Sharp impulsive candles with abnormally large wicks often represent liquidation cascades rather than healthy trend continuation.
The script identifies liquidation behavior by measuring:
•Wick-to-body imbalance
•Sudden expansion followed by rejection
•Temporary price inefficiencies
These flushes commonly occur near:
•Session highs/lows
•Range extremes
•Trend exhaustion points
Such events often lead to rebalance moves , where price partially or fully fills the wick.
🔹 3. Orderblock Reaction Zones
Orderblocks represent areas where heavy participation occurred before a strong displacement move.
The script highlights:
•Clean bullish and bearish orderblock structures
•Zones formed during consolidation prior to expansion
•Areas likely to be defended when revisited
Orderblocks with minimal noise and clean departure are prioritized, as they often reflect institutional positioning rather than retail activity.
These zones are intended as reaction areas , not automatic entry signals.
🔹 4. London Session Liquidity Sweep Model
The London session frequently establishes the initial daily high or low.
Later in the session or during New York, price often:
•Sweeps internal liquidity around that level
•Rejects after the sweep
•Continues with the higher-timeframe bias
The script monitors London session behavior and marks:
•Liquidity runs above/below London highs and lows
•Rejections back inside the prior structure
This model is especially effective when combined with broader daily context.
🔹4. How the Components Work Together
The framework is designed as a context stack , not a checklist of signals:
Liquidity Event → Location → Timing → Trader Execution
Each model reinforces the others:
•Failed breakouts often occur after liquidity sweeps
•Liquidation wicks frequently form near orderblocks
•London sweeps often trigger failed momentum moves
•Confluence increases probability, not certainty
🔹 Practical Usage Guide
✔ Identify context
Determine whether price is approaching a range extreme, session level, or prior participation zone.
✔ Wait for a liquidity event
Look for a sweep, failed breakout, or liquidation wick.
✔ Observe reaction
Rejection, displacement, or reclaim behavior provides confirmation.
✔ Execute manually
Stops are commonly placed beyond the liquidity extreme.
Targets are typically internal liquidity, prior highs/lows, or imbalance zones.
The indicator does not manage trades or enforce rules.
Execution and risk management remain the trader’s responsibility.
🔹 5. Originality & Design Notes
This script does not replicate or bundle existing indicators.
It introduces:
•A multi-model liquidity entry framework
•Structural failed breakout detection
•Wick-based liquidation imbalance logic
•Session-aware liquidity sweep visualization
•A unified, minimal, non-lagging design
All concepts are based on observable market behavior and integrated into a single analytical tool.
🔹 6. Suitable Markets & Timeframes
Works best on:
•XAUUSD
•Major FX pairs
•Indices
•Liquid crypto markets
Recommended timeframes:
•1m
•5m
•15m
•30m
🔹7. Limitations & Notes
•This is an analytical framework , not a trading system
•All markings are confirmed at candle close (non-repainting)
•No open interest or order flow data is used
•Results depend on user interpretation and execution
•Best used alongside session bias and higher-timeframe structure
Disclaimer
This script is provided for educational and informational purposes only.
It does not constitute financial advice, investment advice, or a recommendation to buy or sell any instrument.
Trading involves risk, and losses can exceed initial deposits.
The author assumes no responsibility for trading decisions made using this tool.
Users are strongly encouraged to test this script in demo or simulation environments and to apply proper risk management, position sizing, and personal discretion at all times.
By using this script, you acknowledge and accept all associated risks.
FVG Maxing - Fair Value Gaps, Equilibrium, and Candle Patterns
What this script does
This open-source indicator highlights 3-candle fair value gaps (FVGs) on the active chart timeframe, draws their midpoint ("equilibrium") line, tracks when each gap is mitigated, and optionally marks simple candle patterns (engulfing and doji) for confluence. It is intended as an educational tool to study how price interacts with imbalances.
3-candle bullish and bearish FVG zones drawn as forward-extending boxes.
Equilibrium line at 50% of each gap.
Different styling for mitigated vs unmitigated gaps.
Compact statistics panel showing how many gaps are currently active and filled.
Optional overlays for bullish/bearish engulfing patterns and doji candles.
1. FVG logic (3-candle gaps)
The script focuses on a strict 3-candle definition of a fair value gap:
Three consecutive candles with the same body direction.
The wick of candle 3 is separated from the wick of candle 1 (no overlap).
A bullish gap is created when price moves up fast enough to leave a gap between candle 1 and 3. A bearish gap is the mirror case to the downside.
In Pine, the core detection looks like this:
// Three candles with the same body direction
bull_seq = close > open and close > open and close > open
bear_seq = close < open and close < open and close < open
// Wick gap between candle 1 and candle 3
bull_gap = bull_seq and low > high
bear_gap = bear_seq and high < low
// Final FVG flags
is_bull_fvg = bull_gap
is_bear_fvg = bear_gap
For each detected FVG:
Bullish FVG range: from high up to low (gap below current price).
Bearish FVG range: from low down to high (gap above current price).
Each zone is stored in a custom FVGData structure so it can be updated when price later trades back inside it.
2. Equilibrium line (0.5 of the gap)
Every FVG box gets an optional equilibrium line plotted at the midpoint between its top and bottom:
eq_level = (top + bottom) / 2.0
right_index = extend_boxes ? bar_index + extend_length_bars : bar_index
bx = box.new(bar_index - 2, top, right_index, bottom)
eq_ln = line.new(bar_index - 2, eq_level, right_index, eq_level)
line.set_style(eq_ln, line.style_dashed)
line.set_color(eq_ln, eq_color)
You can use this line as a neutral “fair value” reference inside the zone, or as a simple way to think in terms of premium/discount within each gap.
3. Mitigation rules and styling
Each FVG stays active until price trades back into the gap:
Bullish FVG is considered mitigated when the low touches or moves below the top of the gap.
Bearish FVG is considered mitigated when the high touches or moves above the bottom of the gap.
When that happens, the script:
Marks the internal FVGData entry as mitigated.
Softens the box fill and border colors.
Optionally updates the label text from "BULL EQ / BEAR EQ" to "BULL FILLED / BEAR FILLED".
Can hide mitigated zones almost completely if you only want to see unfilled imbalances.
This allows you to distinguish between current areas of interest and zones that have already been traded through.
4. Candle pattern overlays (engulfing and doji)
For additional confluence, the script can mark simple candle patterns on top of the FVG view:
Bullish engulfing — current candle body fully wraps the previous bearish body and is larger in size.
Bearish engulfing — current candle body fully wraps the previous bullish body and is larger in size.
Doji — candles where the real body is small relative to the full range (high–low).
The detection is based on basic body and range geometry:
curr_body = math.abs(close - open)
prev_body = math.abs(close - open )
curr_range = high - low
body_ratio = curr_range > 0 ? curr_body / curr_range : 1.0
bull_engulfing = close > open and close < open and open <= close and close >= open and curr_body > prev_body
bear_engulfing = close < open and close > open and open >= close and close <= open and curr_body > prev_body
is_doji = curr_range > 0 and body_ratio <= doji_body_ratio
On the chart, they appear as:
Small triangle markers below bullish engulfing candles.
Small triangle markers above bearish engulfing candles.
Small circles above doji candles.
All three overlays are optional and can be turned on or off and recolored in the CANDLE PATTERNS group of inputs.
5. Inputs overview
The script organizes settings into clear groups:
DISPLAY SETTINGS : Show bullish/bearish FVGs, show/hide mitigated zones, box extension length, box border width, and maximum number of boxes.
EQUILIBRIUM : Toggle equilibrium lines, color, and line width.
LABELS : Enable labels, choose whether to label unmitigated and/or mitigated zones, and select label size.
BULLISH COLORS / BEARISH COLORS : Separate fill and border colors for bullish and bearish gaps.
MITIGATED STYLE : Opacity used when a gap is marked as mitigated.
STATISTICS : Toggle the on-chart FVG statistics panel.
CANDLE PATTERNS : Show engulfing patterns, show dojis, colors, and the body-to-range threshold that defines a doji.
6. Statistics panel
An optional table in the corner of the chart summarizes the current state of all tracked gaps:
Total number of FVGs still being tracked.
Number of bullish vs bearish FVGs.
Number of unfilled vs mitigated FVGs.
Simple fill rate: percentage of tracked FVGs that have been marked as mitigated.
This can help you study how a particular market tends to treat gaps over time.
7. How you might use it (examples)
These are usage ideas only, not recommendations:
Study how often your symbol mitigates gaps and where inside the zone price tends to react.
Use higher-timeframe context and then refine entries near the equilibrium line on your trading timeframe.
Combine FVG zones with basic candle patterns (engulfing/doji) as an extra visual anchor, if that fits your process.
Hope you enjoy, give your feedback in the comments!
- officialjackofalltrades
Open Interest RSI [BackQuant]Open Interest RSI
A multi-venue open interest oscillator that aggregates OI across major derivatives exchanges, converts it to coin or USD terms, and runs an RSI-style engine on that aggregated OI so you can track positioning pressure, crowding, and mean reversion in leverage flows, not just in price.
What this is
This tool is an RSI built on top of aggregated open interest instead of price. It pulls futures OI from several major exchanges, converts it into a unified unit (COIN or USD), sums it into a single synthetic OI candle, then applies RSI and smoothing to that combined series.
You can then render that Open Interest RSI in different visual modes:
Clean line or colored line for classic oscillator-style reads.
Column-style oscillator for impulse and compression views.
Flag mode that fills between OI RSI and its EMA for trend/mean reversion blends. See:
Heatmap mode that paints the panel based on OI RSI extremes, ideal for scanning. See:
On top of that it includes:
Aggregated OI source selection (Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit).
Choice of OI units (COIN or USD).
Reference lines and OB/OS zones.
Extreme highlighting for either trend or mean reversion.
A vertical OI RSI meter that acts as a quick strength gauge.
Aggregated open interest source
Under the hood, the indicator builds a synthetic open interest candle by:
Looping over a list of supported exchanges: Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit.
Looping over multiple contract suffixes (such as USDT.P, USD.P, USDC.P, USD.PM) to capture different contract types on each venue.
Requesting OI candles from each venue + contract combination for the same underlying symbol.
Converting each OI stream into a common unit: In COIN mode, everything is normalized into coin-denominated OI. In USD mode, coin OI is multiplied by price to approximate notional OI.
Summing up open, high, low and close of OI across venues into a single aggregated OI candle.
If no valid OI is available for the current symbol across all sources, the script throws a clear runtime error so you know you are on an unsupported market.
This gives you a single, exchange-agnostic open interest curve instead of being tied to one venue. That aggregated OI is then passed into the RSI logic.
How the OI RSI is calculated
The RSI side is straightforward, but it is applied to the aggregated OI close:
Compute a base RSI of aggregated OI using the Calculation Period .
Apply a simple moving average of length Smoothing Period (SMA) to reduce noise in the raw OI RSI.
Optionally apply an EMA on top of the smoothed OI RSI as a moving average signal line.
Key parameters:
Calculation Period – base RSI length for OI.
Smoothing Period (SMA) – extra smoothing on the RSI value.
EMA Period – EMA length on the smoothed OI RSI.
The result is:
oi_rsi – raw RSI of aggregated OI.
oi_rsi_s – SMA-smoothed OI RSI.
ma – EMA of the smoothed OI RSI.
Thresholds and extremes
You control three core thresholds:
Mid Point – central reference level, typically 50.
Extreme Upper Threshold – high-level OI RSI edge (for example 80).
Extreme Lower Threshold – low-level OI RSI edge (for example 20).
These thresholds are used for:
Reference lines or OB/OS zone fills.
Heatmap gradient bounds.
Background highlighting of extremes.
The Extreme Highlighting mode controls how extremes are interpreted:
None – do nothing special in extreme regions.
Mean-Rev – background turns red on high OI RSI and green on low OI RSI, framing extremes as contrarian zones.
Trend – background turns green on high OI RSI and red on low OI RSI, framing extremes as participation zones aligned with the prevailing move.
Reference lines and OB/OS zones
You can choose:
None – clean plotting without guides.
Basic Reference Lines – mid, upper and lower thresholds as simple gray horizontals.
OB/OS Levels – filled zones between:
Upper OB: from the upper threshold to 100, colored with the short/overbought color.
Lower OS: from 0 to the lower threshold, colored with the long/oversold color.
These guides help visually anchor the OI RSI within "normal" versus "extreme" regions.
Plotting modes
The Plotting Type input controls how OI RSI is drawn. All modes share the same underlying OI and RSI logic, but emphasise different aspects of the signal.
1) Line mode
This is the classic oscillator representation:
Plots the smoothed OI RSI as a simple line using RSI Line Color and RSI Line Width .
Optionally plots the EMA overlay on the same panel.
Works well when you want standard RSI-style signals on leverage flows: crosses of the midline, divergences versus price, and so on.
2) Colored Line mode
In this mode:
The OI RSI is plotted as a line, but its color is dynamic.
If the smoothed OI RSI is above the mid point, it uses the Long/OB Color .
If it is below the mid point, it uses the Short/OS Color .
This creates an instant visual regime switch between "bullish positioning pressure" and "bearish positioning pressure", while retaining the feel of a traditional RSI line.
3) Oscillator mode
Oscillator mode renders OI RSI as vertical columns around the mid level:
The smoothed OI RSI is plotted as columns using plot.style_columns .
The histogram base is fixed at 50, so bars extend above and below the mid line.
Bar color is dynamic, using long or short colors depending on which side of the mid point the value sits.
This representation makes impulse and compression in OI flows more obvious. It is especially useful when you want to focus on how quickly OI RSI is expanding or contracting around its neutral level. See:
4) Flag mode
Flag mode turns OI RSI and its EMA into a two-line band with a filled area between them:
The smoothed OI RSI and its EMA are both plotted.
A fill is drawn between them.
The fill color flips between the long color and the short color depending on whether OI RSI is above or below its EMA.
Black outlines are added to both lines to make the band clear against any background.
This creates a "flag" style region where:
Green fills show OI RSI leading its EMA, suggesting positive positioning momentum.
Red fills show OI RSI trailing below its EMA, suggesting negative positioning momentum.
Crossovers of the two lines can be read as shifts in OI momentum regime.
Flag mode is useful if you want a more structural view that combines both the level and slope behaviour of OI RSI. See:
5) Heatmap mode
Heatmap mode recasts OI RSI as a single-row gradient instead of a line:
A single row at level 1 is plotted using column style.
The color is pulled from a gradient between the lower and upper thresholds: Near the lower threshold it approaches the short/oversold color and near the upper threshold it approaches the long/overbought color.
The EMA overlay and reference lines are disabled in this mode to keep the panel clean.
This is a very compact way to track OI RSI state at a glance, especially when stacking it alongside other indicators. See:
OI RSI vertical meter
Beyond the main plot, the script can draw a small "thermometer" table showing the current OI RSI position from 0 to 100:
The meter is a two-column table with a configurable number of rows.
Row colors form an inverted gradient: red at the top (100) and green at the bottom (0).
The script clamps OI RSI between 0 and 100 and maps it to a row index.
An arrow marker "▶" is drawn next to the row corresponding to the current OI RSI value.
0 and 100 labels are printed at the ends of the scale for orientation.
You control:
Show OI RSI Meter – turn the meter on or off.
OI RSI Blocks – number of vertical blocks (granularity).
OI RSI Meter Position – panel anchor (top/bottom, left/center/right).
The meter is particularly helpful if you keep the main plot in a small panel but still want an intuitive strength gauge.
How to read it as a market pressure gauge
Because this is an RSI built on aggregated open interest, its extremes and regimes speak to positioning pressure rather than price alone:
High OI RSI (near or above the upper threshold) indicates that open interest has been increasing aggressively relative to its recent history. This often coincides with crowded leverage and a buildup of directional pressure.
Low OI RSI (near or below the lower threshold) indicates aggressive de-leveraging or closing of positions, often associated with flushes, forced unwinds or post-liquidation clean-ups.
Values around the mid point indicate more balanced positioning flows.
You can combine this with price action:
Price up with rising OI RSI suggests fresh leverage joining the move, a more persistent trend.
Price up with falling OI RSI suggests shorts covering or longs taking profit, more fragile upside.
Price down with rising OI RSI suggests aggressive new shorts or levered selling.
Price down with falling OI RSI suggests de-leveraging and potential exhaustion of the move.
Trading applications
Trend confirmation on leverage flows
Use OI RSI to confirm or question a price trend:
In an uptrend, rising OI RSI with values above the mid point indicates supportive leverage flows.
In an uptrend, repeated failures to lift OI RSI above mid point or persistent weakness suggest less committed participation.
In a downtrend, strong OI RSI on the downside points to aggressive shorting.
Mean reversion in positioning
Use thresholds and the Mean-Rev highlight mode:
When OI RSI spends extended time above the upper threshold, the crowd is extended on one side. That can set up squeeze risk in the opposite direction.
When OI RSI has been pinned low, it suggests heavy de-leveraging. Once price stabilises, a re-risking phase is often not far away.
Background colours in Mean-Rev mode help visually identify these periods.
Regime mapping with plotting modes
Different plotting modes give different perspectives:
Heatmap mode for dashboard-style use where you just need to know "hot", "neutral" or "cold" on OI flows at a glance.
Oscillator mode for short term impulses and compression reads around the mid line. See:
Flag mode for blending level and trend of OI RSI into a single banded visual. See:
Settings overview
RSI group
Plotting Type – None, Line, Colored Line, Oscillator, Flag, Heatmap.
Calculation Period – base RSI length for OI.
Smoothing Period (SMA) – smoothing on RSI.
Moving Average group
Show EMA – toggle EMA overlay (not used in heatmap).
EMA Period – length of EMA on OI RSI.
EMA Color – colour of EMA line.
Thresholds group
Mid Point – central reference.
Extreme Upper Threshold and Extreme Lower Threshold – OB/OS thresholds.
Select Reference Lines – none, basic lines or OB/OS zone fills.
Extreme Highlighting – None, Mean-Rev, Trend.
Extra Plotting and UI
RSI Line Color and RSI Line Width .
Long/OB Color and Short/OS Color .
Show OI RSI Meter , OI RSI Blocks , OI RSI Meter Position .
Open Interest Source
OI Units – COIN or USD.
Exchange toggles: Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit.
Notes
This is a positioning and pressure tool, not a complete system. It:
Models aggregated futures open interest across multiple centralized exchanges.
Transforms that OI into an RSI-style oscillator for better comparability across regimes.
Offers several visual modes to match different workflows, from detailed analysis to compact dashboards.
Use it to understand how leverage and positioning are evolving behind the price, to gauge when the crowd is stretched, and to decide whether to lean with or against that pressure. Attach it to your existing signals, not in place of them.
Also, please check out @NoveltyTrade for the OI Aggregation logic & pulling the data source!
Here is the original script:
Simulateur Carnet d'Ordres & Liquidité [Sese] - Custom🔹 Indicator Name
Order Book & Liquidity Simulator - Custom
🔹 Concept and Functionality
This indicator is a technical analysis tool designed to visually simulate market depth (Order Book) and potential liquidity zones.
It is important to adhere to TradingView's transparency rules: This script does not access real Level 2 data (the actual exchange order book). Instead, it uses a deductive algorithm based on historical Price Action to estimate where Buy Limit (Bid) and Sell Limit (Ask) orders might be resting.
Methodology used by the script:
Pivot Detection: The indicator scans for significant Swing Highs and Swing Lows over a user-defined lookback period (Length).
Level Projection: These pivots are projected to the right as horizontal lines.
Red Lines (Ask): Represent potential resistance zones (sellers).
Blue Lines (Bid): Represent potential support zones (buyers).
Liquidity Management (Absorption): The script is dynamic. If the current price crosses a line, the indicator assumes the liquidity at that level has been consumed (orders filled). The line is then automatically deleted from the chart.
Density Profile (Right Side): Horizontal bars appear to the right of the current price. These approximate a "Time Price Opportunity" or Volume Profile, showing where the market has spent the most time recently.
🔹 User Manual (Settings)
Here is how to configure the inputs to match your trading style:
1. Detection Algorithm
Lookback Length (Candles): Determines the sensitivity of the pivots.
Low value (e.g., 10): Shows many lines (scalping/short term).
High value (e.g., 50): Shows only major structural levels (swing trading).
Volume Factor: (Technical note: In this specific code version, this variable is calculated but the lines are primarily drawn based on geometric pivots).
2. Visual Settings
Show Price Lines (Bid/Ask): Toggles the horizontal Support/Resistance lines on or off.
Show Volume Profile: Toggles the heatmap-style bars on the right side of the chart.
Extend Lines: If checked, untouched lines will extend to the right towards the current price bar.
3. Colors and Transparency Management
Customize the aesthetics to keep your chart clean:
Bid / Ask Colors: Choose your base colors (Default is Blue and Red).
Line Transparency (%): Crucial for chart visibility.
0% = Solid, bright colors.
80-90% = Very subtle, faint lines (recommended if you overlay this on other tools).
Text Size: Adjusts the size of the price labels ("BUY LIMIT" / "SELL LIMIT").
🔹 How to Read the Indicator
Rejections: Unbroken lines act as potential walls. Watch for price reaction when approaching a blue line (support) or red line (resistance).
Breakouts/Absorption: When a line disappears, it means the level has been breached. The market may then seek the next liquidity level (the next line).
Density (Right-side boxes): More opaque/visible boxes indicate a price zone "accepted" by the market (consolidation). Empty gaps suggest an imbalance where price might move through quickly.
⚠️ Disclaimer
This script is for educational and technical analysis purposes only. It is a simulation based on price history, not real-time order book data. Past performance is not indicative of future results. Trading involves risk.
Uptrick: Dynamic Z-Score DivergenceIntroduction
Uptrick: Dynamic Z-Score Divergence is an oscillator that combines multiple momentum sources within a Z-Score framework, allowing for the detection of statistically significant mean-reversion setups, directional shifts, and divergence signals. It integrates a multi-source normalized oscillator, a slope-based signal engine, structured divergence logic, a slope-adaptive EMA with dynamic bands, and a modular bar coloring system. This script is designed to help traders identify statistically stretched conditions, evolving trend dynamics, and classical divergence behavior using a unified statistical approach.
Overview
At its core, this script calculates the Z-Score of three momentum sources—RSI, Stochastic RSI, and MACD—using a user-defined lookback period. These are averaged and smoothed to form the main oscillator line. This normalized oscillator reflects how far short-term momentum deviates from its mean, highlighting statistically extreme areas.
Signals are triggered when the oscillator reverses slope within defined inner zones, indicating a shift in direction while the signal remains in a statistically stretched state. These mean-reversion flips (referred to as TP signals) help identify turning points when price momentum begins to revert from extended zones.
In addition, the script includes a divergence detection engine that compares oscillator pivot points with price pivot points. It confirms regular bullish and bearish divergence by validating spacing between pivots and visualizes both the oscillator-side and chart-side divergences clearly.
A dynamic trend overlay system is included using a Slope Adaptive EMA (SA-EMA). This trend line becomes more responsive when Z-Score deviation increases, allowing the trend line to adapt to market conditions. It is paired with ATR-based bands that are slope-sensitive and selectively visible—offering context for dynamic support and resistance.
The script includes configurable bar coloring logic, allowing users to color candles based on oscillator slope, last confirmed divergence, or the most recent signal of any type. A full alert system is also built-in for key signals.
Originality
The script is based on the well-known concept of Z-Score valuation, which is a standard statistical method for identifying how far a signal deviates from its mean. This foundation—normalizing momentum values such as RSI or MACD to measure relative strength or weakness—is not unique to this script and is widely used in quantitative analysis.
What makes this implementation original is how it expands the Z-Score foundation into a fully featured, signal-producing system. First, it introduces a multi-source composite oscillator by combining three momentum inputs—RSI, Stochastic RSI, and MACD—into a unified Z-Score stream. Second, it builds on that stream with a directional slope logic that identifies turning points inside statistical zones.
The most distinctive additions are the layered features placed on top of this normalized oscillator:
A structured divergence detection engine that compares oscillator pivots with price pivots to validate regular bullish and bearish divergence using precise spacing and timing filters.
A fully integrated slope-adaptive EMA overlay, where the smoothing dynamically adjusts based on real-time Z-Score movement of RSI, allowing the trend line to become more reactive during high-momentum environments and slower during consolidation.
ATR-based dynamic bands that adapt to slope direction and offer real-time visual zones for support and resistance within trend structures.
These features are not typically found in standard Z-Score indicators and collectively provide a unique approach that bridges statistical normalization, structure detection, and adaptive trend modeling within one script.
Features
Z-Score-based oscillator combining RSI, StochRSI, and MACD
Configurable smoothing for stable composite signal output
Buy/Sell TP signals based on slope flips in defined zones
Background highlighting for extreme outer bands
Inner and outer zones with fill logic for statistical context
Pivot-based divergence detection (regular bullish/bearish)
Divergence markers on oscillator and price chart
Slope-Adaptive EMA (SA-EMA) with real-time adaptivity based on RSI Z-Score
ATR-based upper and lower bands around the SA-EMA, visibility tied to slope direction
Configurable bar coloring (oscillator slope, divergence, or most recent signal)
Alerts for TP signals and confirmed divergences
Optional fixed Y-axis scaling for consistent oscillator view
The full setup mode can be seen below:
Input Parameters
General Settings
Full Setup: Enables rendering of the full visual system (lines, bands, signals)
Z-Score Lookback: Lookback period for normalization (mean and standard deviation)
Main Line Smoothing: EMA length applied to the averaged Z-Score
Slope Detection Index: Used to calculate directional flips for signal logic
Enable Background Highlighting: Enables visual region coloring in
overbought/oversold areas
Force Visible Y-Axis Scale: Forces max/min bounds for a consistent oscillator range
Divergence Settings
Enable Divergence Detection: Toggles divergence logic
Pivot Lookback Left / Right: Defines the structure of oscillator pivot points
Minimum / Maximum Bars Between Pivots: Controls the allowed spacing range for divergence validation
Bar Coloring Settings
Bar Coloring Mode:
➜ Line Color: Colors bars based on oscillator slope
➜ Latest Confirmed Signal: Colors bars based on the most recent confirmed divergence
➜ Any Latest Signal: Colors based on the most recent signal (TP or divergence)
SA-EMA Settings
RSI Length: RSI period used to determine adaptivity
Z-Score Length: Lookback for normalizing RSI in adaptive logic
Base EMA Length: Base length for smoothing before adaptivity
Adaptivity Intensity: Scales the smoothing responsiveness based on RSI deviation
Slope Index: Determines slope direction for coloring and band logic
Band ATR Length / Band Multiplier: Controls the width and responsiveness of the trend-following bands
Alerts
The script includes the following alert conditions:
Buy Signal (TP reversal detected in oversold zone)
Sell Signal (TP reversal detected in overbought zone)
Confirmed Bullish Divergence (oscillator HL, price LL)
Confirmed Bearish Divergence (oscillator LH, price HH)
These alerts allow integration into automation systems or signal monitoring setups.
Summary
Uptrick: Dynamic Z-Score Divergence is a statistically grounded trading indicator that merges normalized multi-momentum analysis with real-time slope logic, divergence detection, and adaptive trend overlays. It helps traders identify mean-reversion conditions, divergence structures, and evolving trend zones using a modular system of statistical and structural tools. Its alert system, layered visuals, and flexible input design make it suitable for discretionary traders seeking to combine quantitative momentum logic with structural pattern recognition.
Disclaimer
This script is for educational and informational purposes only. No indicator can guarantee future performance, and trading involves risk. Always use risk management and test strategies in a simulated environment before deploying with live capital.
High Volume Bars (Advanced)High Volume Bars (Advanced)
High Volume Bars (Advanced) is a Pine Script v6 indicator for TradingView that highlights bars with unusually high volume, with several ways to define “unusual”:
Classic: volume > moving average + N × standard deviation
Change-based: large change in volume vs previous bar
Z-score: statistically extreme volume values
Robust mode (optional): median + MAD, less sensitive to outliers
It can:
Recolor candles when volume is high
Optionally highlight the background
Optionally plot volume bands (center ± spread × multiplier)
⸻
1. How it works
At each bar the script:
Picks the volume source:
If Use Volume Change vs Previous Bar? is off → uses raw volume
If on → uses abs(volume - volume )
Computes baseline statistics over the chosen source:
Lookback bars
Moving average (SMA or EMA)
Standard deviation
Optionally replaces mean/std with robust stats:
Center = median (50th percentile)
Spread = MAD (median absolute deviation, scaled to approx σ)
Builds bands:
upper = center + spread * multiplier
lower = max(center - spread * multiplier, 0)
Flags a bar as “high volume” if:
It passes the mode logic:
Classic abs: volume > upper
Change mode: abs(volume - volume ) > upper
Z-score mode: z-score ≥ multiplier
AND the relative filter (optional): volume > average_volume * Min Volume vs Avg
AND it is past the first Skip First N Bars from the start of the chart
Colors the bar and (optionally) the background accordingly.
⸻
2. Inputs
2.1. Statistics
Lookback (len)
Number of bars used to compute the baseline stats (mean / median, std / MAD).
Typical values: 50–200.
StdDev / Z-Score Multiplier (mult)
How far from the baseline a bar must be to count as “high volume”.
In classic mode: volume > mean + mult × std
In z-score mode: z ≥ mult
Typical values: 1.0–2.5.
Use EMA Instead of SMA? (smooth_with_ema)
Off → uses SMA (slower but smoother).
On → uses EMA (reacts faster to recent changes).
Use Robust Stats (Median & MAD)? (use_robust)
Off → mean + standard deviation
On → median + MAD (less sensitive to a few insane spikes)
Useful for assets with occasional volume blow-ups.
⸻
2.2. Detection Mode
These inputs control how “unusual” is defined.
• Use Volume Change vs Previous Bar? (mode_change)
• Off (default) → uses absolute volume.
• On → uses abs(volume - volume ).
You then detect jumps in volume rather than absolute size.
Note: This is ignored if Z-Score mode is switched on (see below).
• Use Z-Score on Volume? (Overrides change) (mode_zscore)
• Off → high volume when raw value exceeds the upper band.
• On → computes z-score = (value − center) / spread and flags a bar as high when z ≥ multiplier.
Z-score mode can be combined with robust stats for more stable thresholds.
• Min Volume vs Avg (Filter) (min_rel_mult)
An extra filter to ignore tiny-volume bars that are statistically “weird” but not meaningful.
• 0.0 → no filter (all stats-based candidates allowed).
• 1.0 → high-volume bar must also be at least equal to average volume.
• 1.5 → bar must be ≥ 1.5 × average volume.
• Skip First N Bars (from start of chart) (skip_open_bars)
Skips the first N bars of the chart when evaluating high-volume conditions.
This is mostly a safety / cosmetic option to avoid weird behavior on very early bars or backfill.
⸻
2.3. Visuals
• Show Volume Bands? (show_bands)
• If on, plots:
• Upper band (upper)
• Lower band (lower)
• Center line (vol_center)
These are plotted on the same pane as the script (usually the price chart).
• Also Highlight Background? (use_bg)
• If on, fills the background on high-volume bars with High-Vol Background.
• High-Vol Bar Transparency (0–100) (bar_transp)
Controls the opacity of the high-volume bar colors (up / down).
• 0 → fully opaque
• 100 → fully transparent (no visible effect)
• Up Color (upColor) / Down Color (dnColor)
• Regular bar colors (non high-volume) for up and down bars.
• Up High-Vol Base Color (upHighVolBase) / Down High-Vol Base Color (dnHighVolBase)
Base colors used for high-volume up/down bars. Transparency is applied on top of these via bar_transp.
• High-Vol Background (bgHighVolColor)
Background color used when Also Highlight Background? is enabled.
⸻
3. What gets colored and how
• Bar color (barcolor)
• Up bar:
• High volume → Up High-Vol Color
• Normal volume → Up Color
• Down bar:
• High volume → Down High-Vol Color
• Normal volume → Down Color
• Flat bar → neutral gray
• Background color (bgcolor)
• If Also Highlight Background? is on, high-volume bars get High-Vol Background.
• Otherwise, background is unchanged.
⸻
4. Alerts
The indicator exposes three alert conditions:
• High Volume Bar
Triggers whenever is_high is true (up or down).
• High Volume Up Bar
Triggers only when is_high is true and the bar closed up (close > open).
• High Volume Down Bar
Triggers only when is_high is true and the bar closed down (close < open).
You can use these in TradingView’s “Create Alert” dialog to:
• Get notified of potential breakout / exhaustion bars.
• Trigger webhook events for bots / custom infra.
⸻
5. Recommended presets
5.1. “Classic” high-volume detector (closest to original)
• Lookback: 150–200
• StdDev / Z-Score Multiplier: 1.0–1.5
• Use EMA Instead of SMA?: off
• Use Robust Stats?: off
• Use Volume Change vs Previous Bar?: off
• Use Z-Score on Volume?: off
• Min Volume vs Avg (Filter): 0.0–1.0
Behavior: Flags bars whose volume is notably above the recent average (plus a bit of noise filtering), same spirit as your initial implementation.
⸻
5.2. Volatility-aware (Z-score) mode
• Lookback: 100–200
• StdDev / Z-Score Multiplier: 1.5–2.0
• Use EMA Instead of SMA?: on
• Use Robust Stats?: on (if asset has huge spikes)
• Use Volume Change vs Previous Bar?: off (ignored anyway in z-score mode)
• Use Z-Score on Volume?: on
• Min Volume vs Avg (Filter): 0.5–1.0
Behavior: Flags bars that are “statistically extreme” relative to recent volume behavior, not just absolutely large. Good for assets where baseline volume drifts over time.
⸻
5.3. “Wake-up bar” (volume acceleration)
• Lookback: 50–100
• StdDev / Z-Score Multiplier: 1.0–1.5
• Use EMA Instead of SMA?: on
• Use Robust Stats?: optional
• Use Volume Change vs Previous Bar?: on
• Use Z-Score on Volume?: off
• Min Volume vs Avg (Filter): 0.5–1.0
Behavior: Emphasis on sudden increases in volume rather than absolute size – useful to catch “first active bar” after a quiet period.
⸻
6. Limitations / notes
• Time-of-day effects
The script currently treats the entire chart as one continuous “session”. On 24/7 markets (crypto) this is fine. For regular-session assets (equities, futures), volume naturally spikes at open/close; you may want to:
• Use a shorter Lookback, or
• Add a session-aware filter in a future iteration.
• Illiquid symbols
On very low-liquidity symbols, robust stats (Use Robust Stats) and a non-zero Min Volume vs Avg can help avoid “everything looks extreme” problems.
• Overlay behavior
overlay = true means:
• Bars are recolored on the price pane.
• Volume bands are also drawn on the price pane if enabled.
If you want a dedicated panel for the bands, duplicate the logic in a separate script with overlay = false.
[ArchLabs] Support & Resitance Levels Support & Resistance Levels — SR-v1.100
Smart, auto-managed zones for clean market structure
⸻
🔍 What this indicator does
This script automatically finds and maintains high-quality support & resistance zones on your chart, so you don’t have to keep redrawing levels by hand.
It:
• Detects major swing highs and lows (pivots)
• Builds support and resistance zones (not just thin lines)
• Filters out overlapping / redundant levels
• Tracks how price interacts with those zones in real time
• Marks and alerts:
• ✅ Breakouts
• 🚨 False breakouts
• 🔁 Retests
• Flips broken support → resistance and resistance → support automatically
You get a clean structural map of the market, continuously updated.
⸻
🧠 How levels are built (conceptually)
1. The indicator looks back over a configurable window and finds significant highs and lows (pivots).
2. From each confirmed pivot, it creates:
• A core level price (horizontal line)
• A price area around it (shaded zone), sized relative to recent price range/volatility
3. It then checks for overlaps between existing levels and new candidates:
• If a new level is too close to an existing one (within your overlap threshold), it gets discarded.
• This keeps only the most meaningful, non-redundant levels on the chart.
4. A cap of around 10 levels per side (support / resistance) keeps the view readable.
The result: a curated set of zones that actually matter, not a wall of lines.
⸻
🎨 Visuals on the chart
You’ll see:
• Support zones
• Line: bullish color (default green)
• Area: semi-transparent band below/around the line
• Resistance zones
• Line: bearish color (default red)
• Area: semi-transparent band above/around the line
Colors are customizable for:
• Level line
• Zone area
• Breakout highlight
• Retest label
This makes it easy to visually separate support vs resistance and quickly spot key reactions.
⸻
⚡ Dynamic behavior & level lifecycle
Each level goes through a natural “life cycle,” which the indicator tracks for you:
1. Active zone
• The level is valid and extended to the right as long as price stays “engaged” with it (using smoothed highs/lows to avoid noise).
2. Extension / pause
• When price pulls away from the level far enough, the extension can temporarily stop so the level doesn’t stretch indefinitely without interaction.
• If price comes back into the zone with meaningful action, the level can resume extension.
3. Break & role reversal
• When price cleanly breaks the level (based on smoothed price, not just a wick), the zone is:
• Stopped and locked in place
• Marked as broken
• Immediately cloned and flipped:
• Broken support becomes a new resistance zone at the same area.
• Broken resistance becomes a new support zone.
This gives you automatic role-reversal levels without manually redrawing anything.
⸻
🧷 Event tags & alerts
The indicator tracks three key interactions with each zone:
1. Breakouts (optional)
When price decisively breaks a level:
• A small breakout label appears on/near the level:
• Support broken → bearish breakout style
• Resistance broken → bullish breakout style
• An alert message is fired (if alerts are enabled on the script)
Use this to catch true structural breaks that may signal trend continuation or regime change.
⸻
2. False breakouts (optional)
False breakouts are marked when price:
• Wicks through a level, but
• Fails to close beyond it and quickly returns inside the zone
When detected:
• A 🚨 FB label appears at the level
• The label tracks with price while the false breakout is active
• An alert can fire each time this behavior is confirmed
This is very useful for reversal traders and anyone fading failed breakouts.
⸻
3. Retests (optional)
Retests are detected when:
• Price re-enters a zone after previously moving away from it
• The candle comes back into the area for the first time in this new approach
The script:
• Marks the retest with a “T” label in a distinct color for support vs resistance
• Brings that level to the top of the internal priority list, keeping fresh retests visually and logically “hot”
Traders often use these as high-probability reaction points (e.g., breakout → retest → continuation).
⸻
⚙️ Key settings
All inputs are grouped for clarity:
Support / Resistance Levels
• Pivots Lookback
Controls how far back the indicator looks for swing highs/lows.
• Higher value → fewer, stronger levels
• Lower value → more reactive, more levels
• Overlap Multiplier (Pips)
Sets how aggressively overlapping levels are merged/ignored.
• Higher value → fewer levels, more consolidation
• Lower value → more granular levels
• Auto Overlap
When enabled, the script automatically adjusts the overlap threshold based on timeframe:
• Intraday lower timeframes → tighter filtering
• Higher/intra-session → more appropriate scaling
This lets you drop the indicator on multiple timeframes without constantly retuning.
⸻
Level Event Toggles
• Breakout Labels & Alerts (on/off)
• False Breakout Labels & Alerts (on/off)
• Retest Labels & Alerts (on/off)
Turn on only what fits your style.
Scalpers might want all three; swing traders may prefer only breakouts + retests.
⸻
Support / Resistance Colors
Separate color groups for:
• Line & area of support levels
• Line & area of resistance levels
• Visual styling for breakouts
• Visual styling for retests
You can match your existing chart theme or build a dedicated SR layout.
⸻
📈 How to use it in your trading
Here are a few practical ways to integrate this indicator:
• Context map
Use it as a structural overlay on any symbol/timeframe to see where price is likely to react.
• Breakout + retest setups
• Wait for a level to break with a breakout label.
• Then watch for a T (retest) label into the flipped zone.
• Combine with your own confirmation (price action, volume, oscillators, etc.).
• Mean-reversion & fade trades
• Hunt for false breakout (FB) labels on key levels.
• These are often good spots to fade aggressive moves that lose momentum.
• Confluence builder
• Combine zones with trend tools, VR/DC, moving averages, or higher timeframe structure.
• A breakout/retest at a level that also lines up with higher TF structure can be especially meaningful.
⸻
✅ Summary
Support & Resistance Levels (SR-v1.100) is designed to be:
• Clean – no cluttered spaghetti of lines
• Adaptive – zones evolve with the market and flip roles automatically
• Actionable – breakout, false breakout, and retest events are clearly marked and alert-ready
• Flexible – works on any market and timeframe with simple, intuitive inputs
Drop it on your chart, tune the lookback & overlap to your style, and let it handle the heavy lifting of structural mapping while you focus on decisions.
Range Oscillator Strategy + Stoch Confirm🔹 Short summary
This is a free, educational long-only strategy built on top of the public “Range Oscillator” by Zeiierman (used under CC BY-NC-SA 4.0), combined with a Stochastic timing filter, an EMA-based exit filter and an optional risk-management layer (SL/TP and R-multiple exits). It is NOT financial advice and it is NOT a magic money machine. It’s a structured framework to study how range-expansion + momentum + trend slope can be combined into one rule-based system, often with intentionally RARE trades.
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0. Legal / risk disclaimer
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• This script is FREE and public. I do not charge any fee for it.
• It is for EDUCATIONAL PURPOSES ONLY.
• It is NOT financial advice and does NOT guarantee profits.
• Backtest results can be very different from live results.
• Markets change over time; past performance is NOT indicative of future performance.
• You are fully responsible for your own trades and risk.
Please DO NOT use this script with money you cannot afford to lose. Always start in a demo / paper trading environment and make sure you understand what the logic does before you risk any capital.
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1. About default settings and risk (very important)
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The script is configured with the following defaults in the `strategy()` declaration:
• `initial_capital = 10000`
→ This is only an EXAMPLE account size.
• `default_qty_type = strategy.percent_of_equity`
• `default_qty_value = 100`
→ This means 100% of equity per trade in the default properties.
→ This is AGGRESSIVE and should be treated as a STRESS TEST of the logic, not as a realistic way to trade.
TradingView’s House Rules recommend risking only a small part of equity per trade (often 1–2%, max 5–10% in most cases). To align with these recommendations and to get more realistic backtest results, I STRONGLY RECOMMEND you to:
1. Open **Strategy Settings → Properties**.
2. Set:
• Order size: **Percent of equity**
• Order size (percent): e.g. **1–2%** per trade
3. Make sure **commission** and **slippage** match your own broker conditions.
• By default this script uses `commission_value = 0.1` (0.1%) and `slippage = 3`, which are reasonable example values for many crypto markets.
If you choose to run the strategy with 100% of equity per trade, please treat it ONLY as a stress-test of the logic. It is NOT a sustainable risk model for live trading.
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2. What this strategy tries to do (conceptual overview)
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This is a LONG-ONLY strategy designed to explore the combination of:
1. **Range Oscillator (Zeiierman-based)**
- Measures how far price has moved away from an adaptive mean.
- Uses an ATR-based range to normalize deviation.
- High positive oscillator values indicate strong price expansion away from the mean in a bullish direction.
2. **Stochastic as a timing filter**
- A classic Stochastic (%K and %D) is used.
- The logic requires %K to be below a user-defined level and then crossing above %D.
- This is intended to catch moments when momentum turns up again, rather than chasing every extreme.
3. **EMA Exit Filter (trend slope)**
- An EMA with configurable length (default 70) is calculated.
- The slope of the EMA is monitored: when the slope turns negative while in a long position, and the filter is enabled, it triggers an exit condition.
- This acts as a trend-protection exit: if the medium-term trend starts to weaken, the strategy exits even if the oscillator has not yet fully reverted.
4. **Optional risk-management layer**
- Percentage-based Stop Loss and Take Profit (SL/TP).
- Risk/Reward (R-multiple) exit based on the distance from entry to SL.
- Implemented as OCO orders that work *on top* of the logical exits.
The goal is not to create a “holy grail” system but to serve as a transparent, configurable framework for studying how these concepts behave together on different markets and timeframes.
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3. Components and how they work together
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(1) Range Oscillator (based on “Range Oscillator (Zeiierman)”)
• The script computes a weighted mean price and then measures how far price deviates from that mean.
• Deviation is normalized by an ATR-based range and expressed as an oscillator.
• When the oscillator is above the **entry threshold** (default 100), it signals a strong move away from the mean in the bullish direction.
• When it later drops below the **exit threshold** (default 30), it can trigger an exit (if enabled).
(2) Stochastic confirmation
• Classic Stochastic (%K and %D) is calculated.
• An entry requires:
- %K to be below a user-defined “Cross Level”, and
- then %K to cross above %D.
• This is a momentum confirmation: the strategy tries to enter when momentum turns up from a pullback rather than at any random point.
(3) EMA Exit Filter
• The EMA length is configurable via `emaLength` (default 70).
• The script monitors the EMA slope: it computes the relative change between the current EMA and the previous EMA.
• If the slope turns negative while the strategy holds a long position and the filter is enabled, it triggers an exit condition.
• This is meant to help protect profits or cut losses when the medium-term trend starts to roll over, even if the oscillator conditions are not (yet) signalling exit.
(4) Risk management (optional)
• Stop Loss (SL) and Take Profit (TP):
- Defined as percentages relative to average entry price.
- Both are disabled by default, but you can enable them in the Inputs.
• Risk/Reward Exit:
- Uses the distance from entry to SL to project a profit target at a configurable R-multiple.
- Also optional and disabled by default.
These exits are implemented as `strategy.exit()` OCO orders and can close trades independently of oscillator/EMA conditions if hit first.
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4. Entry & Exit logic (high level)
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A) Time filter
• You can choose a **Start Year** in the Inputs.
• Only candles between the selected start date and 31 Dec 2069 are used for backtesting (`timeCondition`).
• This prevents accidental use of tiny cherry-picked windows and makes tests more honest.
B) Entry condition (long-only)
A long entry is allowed when ALL the following are true:
1. `timeCondition` is true (inside the backtest window).
2. If `useOscEntry` is true:
- Range Oscillator value must be above `entryLevel`.
3. If `useStochEntry` is true:
- Stochastic condition (`stochCondition`) must be true:
- %K < `crossLevel`, then %K crosses above %D.
If these filters agree, the strategy calls `strategy.entry("Long", strategy.long)`.
C) Exit condition (logical exits)
A position can be closed when:
1. `timeCondition` is true AND a long position is open, AND
2. At least one of the following is true:
- If `useOscExit` is true: Oscillator is below `exitLevel`.
- If `useMagicExit` (EMA Exit Filter) is true: EMA slope is negative (`isDown = true`).
In that case, `strategy.close("Long")` is called.
D) Risk-management exits
While a position is open:
• If SL or TP is enabled:
- `strategy.exit("Long Risk", ...)` places an OCO stop/limit order based on the SL/TP percentages.
• If Risk/Reward exit is enabled:
- `strategy.exit("RR Exit", ...)` places an OCO order using a projected R-multiple (`rrMult`) of the SL distance.
These risk-based exits can trigger before the logical oscillator/EMA exits if price hits those levels.
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5. Recommended backtest configuration (to avoid misleading results)
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To align with TradingView House Rules and avoid misleading backtests:
1. **Initial capital**
- 10 000 (or any value you personally want to work with).
2. **Order size**
- Type: **Percent of equity**
- Size: **1–2%** per trade is a reasonable starting point.
- Avoid risking more than 5–10% per trade if you want results that could be sustainable in practice.
3. **Commission & slippage**
- Commission: around 0.1% if that matches your broker.
- Slippage: a few ticks (e.g. 3) to account for real fills.
4. **Timeframe & markets**
- Volatile symbols (e.g. crypto like BTCUSDT, or major indices).
- Timeframes: 1H / 4H / **1D (Daily)** are typical starting points.
- I strongly recommend trying the strategy on **different timeframes**, for example 1D, to see how the behaviour changes between intraday and higher timeframes.
5. **No “caution warning”**
- Make sure your chosen symbol + timeframe + settings do not trigger TradingView’s caution messages.
- If you see warnings (e.g. “too few trades”), adjust timeframe/symbol or the backtest period.
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5a. About low trade count and rare signals
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This strategy is intentionally designed to trade RARELY:
• It is **long-only**.
• It uses strict filters (Range Oscillator threshold + Stochastic confirmation + optional EMA Exit Filter).
• On higher timeframes (especially **1D / Daily**) this can result in a **low total number of trades**, sometimes WELL BELOW 100 trades over the whole backtest.
TradingView’s House Rules mention 100+ trades as a guideline for more robust statistics. In this specific case:
• The **low trade count is a conscious design choice**, not an attempt to cherry-pick a tiny, ultra-profitable window.
• The goal is to study a **small number of high-conviction long entries** on higher timeframes, not to generate frequent intraday signals.
• Because of the low trade count, results should NOT be interpreted as statistically strong or “proven” – they are only one sample of how this logic would have behaved on past data.
Please keep this in mind when you look at the equity curve and performance metrics. A beautiful curve with only a handful of trades is still just a small sample.
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6. How to use this strategy (step-by-step)
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1. Add the script to your chart.
2. Open the **Inputs** tab:
- Set the backtest start year.
- Decide whether to use Oscillator-based entry/exit, Stochastic confirmation, and EMA Exit Filter.
- Optionally enable SL, TP, and Risk/Reward exits.
3. Open the **Properties** tab:
- Set a realistic account size if you want.
- Set order size to a realistic % of equity (e.g. 1–2%).
- Confirm that commission and slippage are realistic for your broker.
4. Run the backtest:
- Look at Net Profit, Max Drawdown, number of trades, and equity curve.
- Remember that a low trade count means the statistics are not very strong.
5. Experiment:
- Tweak thresholds (`entryLevel`, `exitLevel`), Stochastic settings, EMA length, and risk params.
- See how the metrics and trade frequency change.
6. Forward-test:
- Before using any idea in live trading, forward-test on a demo account and observe behaviour in real time.
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7. Originality and usefulness (why this is more than a mashup)
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This script is not intended to be a random visual mashup of indicators. It is designed as a coherent, testable strategy with clear roles for each component:
• Range Oscillator:
- Handles mean vs. range-expansion states via an adaptive, ATR-normalized metric.
• Stochastic:
- Acts as a timing filter to avoid entering purely on extremes and instead waits for momentum to turn.
• EMA Exit Filter:
- Trend-slope-based safety net to exit when the medium-term direction changes against the position.
• Risk module:
- Provides practical, rule-based exits: SL, TP, and R-multiple exit, which are useful for structuring risk even if you modify the core logic.
It aims to give traders a ready-made **framework to study and modify**, not a black box or “signals” product.
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8. Limitations and good practices
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• No single strategy works on all markets or in all regimes.
• This script is long-only; it does not short the market.
• Performance can degrade when market structure changes.
• Overfitting (curve fitting) is a real risk if you endlessly tweak parameters to maximise historical profit.
Good practices:
- Test on multiple symbols and timeframes.
- Focus on stability and drawdown, not only on how high the profit line goes.
- View this as a learning tool and a basis for your own research.
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9. Licensing and credits
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• Core oscillator idea & base code:
- “Range Oscillator (Zeiierman)”
- © Zeiierman, licensed under CC BY-NC-SA 4.0.
• Strategy logic, Stochastic confirmation, EMA Exit Filter, and risk-management layer:
- Modifications by jokiniemi.
Please respect both the original license and TradingView House Rules if you fork or republish any part of this script.
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10. No payments / no vendor pitch
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• This script is completely FREE to use on TradingView.
• There is no paid subscription, no external payment link, and no private signals group attached to it.
• If you have questions, please use TradingView’s comment system or private messages instead of expecting financial advice.
Use this script as a tool to learn, experiment, and build your own understanding of markets.
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11. Example backtest settings used in screenshots
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To avoid any confusion about how the results shown in screenshots were produced, here is one concrete example configuration:
• Symbol: BTCUSDT (or similar major BTC pair)
• Timeframe: 1D (Daily)
• Backtest period: from 2018 to the most recent data
• Initial capital: 10 000
• Order size type: Percent of equity
• Order size: 2% per trade
• Commission: 0.1%
• Slippage: 3 ticks
• Risk settings: Stop Loss and Take Profit disabled by default, Risk/Reward exit disabled by default
• Filters: Range Oscillator entry/exit enabled, Stochastic confirmation enabled, EMA Exit Filter enabled
If you change any of these settings (symbol, timeframe, risk per trade, commission, slippage, filters, etc.), your results will look different. Please always adapt the configuration to your own risk tolerance, market, and trading style.
Volatility-Targeted Momentum Portfolio [BackQuant]Volatility-Targeted Momentum Portfolio
A complete momentum portfolio engine that ranks assets, targets a user-defined volatility, builds long, short, or delta-neutral books, and reports performance with metrics, attribution, Monte Carlo scenarios, allocation pie, and efficiency scatter plots. This description explains the theory and the mechanics so you can configure, validate, and deploy it with intent.
Table of contents
What the script does at a glance
Momentum, what it is, how to know if it is present
Volatility targeting, why and how it is done here
Portfolio construction modes: Long Only, Short Only, Delta Neutral
Regime filter and when the strategy goes to cash
Transaction cost modelling in this script
Backtest metrics and definitions
Performance attribution chart
Monte Carlo simulation
Scatter plot analysis modes
Asset allocation pie chart
Inputs, presets, and deployment checklist
Suggested workflow
1) What the script does at a glance
Pulls a list of up to 15 tickers, computes a simple momentum score on each over a configurable lookback, then volatility-scales their bar-to-bar return stream to a target annualized volatility.
Ranks assets by raw momentum, selects the top 3 and bottom 3, builds positions according to the chosen mode, and gates exposure with a fast regime filter.
Accumulates a portfolio equity curve with risk and performance metrics, optional benchmark buy-and-hold for comparison, and a full alert suite.
Adds visual diagnostics: performance attribution bars, Monte Carlo forward paths, an allocation pie, and scatter plots for risk-return and factor views.
2) Momentum: definition, detection, and validation
Momentum is the tendency of assets that have performed well to continue to perform well, and of underperformers to continue underperforming, over a specific horizon. You operationalize it by selecting a horizon, defining a signal, ranking assets, and trading the leaders versus laggards subject to risk constraints.
Signal choices . Common signals include cumulative return over a lookback window, regression slope on log-price, or normalized rate-of-change. This script uses cumulative return over lookback bars for ranking (variable cr = price/price - 1). It keeps the ranking simple and lets volatility targeting handle risk normalization.
How to know momentum is present .
Leaders and laggards persist across adjacent windows rather than flipping every bar.
Spread between average momentum of leaders and laggards is materially positive in sample.
Cross-sectional dispersion is non-trivial. If everything is flat or highly correlated with no separation, momentum selection will be weak.
Your validation should include a diagnostic that measures whether returns are explained by a momentum regression on the timeseries.
Recommended diagnostic tool . Before running any momentum portfolio, verify that a timeseries exhibits stable directional drift. Use this indicator as a pre-check: It fits a regression to price, exposes slope and goodness-of-fit style context, and helps confirm if there is usable momentum before you force a ranking into a flat regime.
3) Volatility targeting: purpose and implementation here
Purpose . Volatility targeting seeks a more stable risk footprint. High-vol assets get sized down, low-vol assets get sized up, so each contributes more evenly to total risk.
Computation in this script (per asset, rolling):
Return series ret = log(price/price ).
Annualized volatility estimate vol = stdev(ret, lookback) * sqrt(tradingdays).
Leverage multiplier volMult = clamp(targetVol / vol, 0.1, 5.0).
This caps sizing so extremely low-vol assets don’t explode weight and extremely high-vol assets don’t go to zero.
Scaled return stream sr = ret * volMult. This is the per-bar, risk-adjusted building block used in the portfolio combinations.
Interpretation . You are not levering your account on the exchange, you are rescaling the contribution each asset’s daily move has on the modeled equity. In live trading you would reflect this with position sizing or notional exposure.
4) Portfolio construction modes
Cross-sectional ranking . Assets are sorted by cr over the chosen lookback. Top and bottom indices are extracted without ties.
Long Only . Averages the volatility-scaled returns of the top 3 assets: avgRet = mean(sr_top1, sr_top2, sr_top3). Position table shows per-asset leverages and weights proportional to their current volMult.
Short Only . Averages the negative of the volatility-scaled returns of the bottom 3: avgRet = mean(-sr_bot1, -sr_bot2, -sr_bot3). Position table shows short legs.
Delta Neutral . Long the top 3 and short the bottom 3 in equal book sizes. Each side is sized to 50 percent notional internally, with weights within each side proportional to volMult. The return stream mixes the two sides: avgRet = mean(sr_top1,sr_top2,sr_top3, -sr_bot1,-sr_bot2,-sr_bot3).
Notes .
The selection metric is raw momentum, the execution stream is volatility-scaled returns. This separation is deliberate. It avoids letting volatility dominate ranking while still enforcing risk parity at the return contribution stage.
If everything rallies together and dispersion collapses, Long Only may behave like a single beta. Delta Neutral is designed to extract cross-sectional momentum with low net beta.
5) Regime filter
A fast EMA(12) vs EMA(21) filter gates exposure.
Long Only active when EMA12 > EMA21. Otherwise the book is set to cash.
Short Only active when EMA12 < EMA21. Otherwise cash.
Delta Neutral is always active.
This prevents taking long momentum entries during obvious local downtrends and vice versa for shorts. When the filter is false, equity is held flat for that bar.
6) Transaction cost modelling
There are two cost touchpoints in the script.
Per-bar drag . When the regime filter is active, the per-bar return is reduced by fee_rate * avgRet inside netRet = avgRet - (fee_rate * avgRet). This models proportional friction relative to traded impact on that bar.
Turnover-linked fee . The script tracks changes in membership of the top and bottom baskets (top1..top3, bot1..bot3). The intent is to charge fees when composition changes. The template counts changes and scales a fee by change count divided by 6 for the six slots.
Use case: increase fee_rate to reflect taker fees and slippage if you rebalance every bar or trade illiquid assets. Reduce it if you rebalance less often or use maker orders.
Practical advice .
If you rebalance daily, start with 5–20 bps round-trip per switch on liquid futures and adjust per venue.
For crypto perp microcaps, stress higher cost assumptions and add slippage buffers.
If you only rotate on lookback boundaries or at signals, use alert-driven rebalances and lower per-bar drag.
7) Backtest metrics and definitions
The script computes a standard set of portfolio statistics once the start date is reached.
Net Profit percent over the full test.
Max Drawdown percent, tracked from running peaks.
Annualized Mean and Stdev using the chosen trading day count.
Variance is the square of annualized stdev.
Sharpe uses daily mean adjusted by risk-free rate and annualized.
Sortino uses downside stdev only.
Omega ratio of sum of gains to sum of losses.
Gain-to-Pain total gains divided by total losses absolute.
CAGR compounded annual growth from start date to now.
Alpha, Beta versus a user-selected benchmark. Beta from covariance of daily returns, Alpha from CAPM.
Skewness of daily returns.
VaR 95 linear-interpolated 5th percentile of daily returns.
CVaR average of the worst 5 percent of daily returns.
Benchmark Buy-and-Hold equity path for comparison.
8) Performance attribution
Cumulative contribution per asset, adjusted for whether it was held long or short and for its volatility multiplier, aggregated across the backtest. You can filter to winners only or show both sides. The panel is sorted by contribution and includes percent labels.
9) Monte Carlo simulation
The panel draws forward equity paths from either a Normal model parameterized by recent mean and stdev, or non-parametric bootstrap of recent daily returns. You control the sample length, number of simulations, forecast horizon, visibility of individual paths, confidence bands, and a reproducible seed.
Normal uses Box-Muller with your seed. Good for quick, smooth envelopes.
Bootstrap resamples realized returns, preserving fat tails and volatility clustering better than a Gaussian assumption.
Bands show 10th, 25th, 75th, 90th percentiles and the path mean.
10) Scatter plot analysis
Four point-cloud modes, each plotting all assets and a star for the current portfolio position, with quadrant guides and labels.
Risk-Return Efficiency . X is risk proxy from leverage, Y is expected return from annualized momentum. The star shows the current book’s composite.
Momentum vs Volatility . Visualizes whether leaders are also high vol, a cue for turnover and cost expectations.
Beta vs Alpha . X is a beta proxy, Y is risk-adjusted excess return proxy. Useful to see if leaders are just beta.
Leverage vs Momentum . X is volMult, Y is momentum. Shows how volatility targeting is redistributing risk.
11) Asset allocation pie chart
Builds a wheel of current allocations.
Long Only, weights are proportional to each long asset’s current volMult and sum to 100 percent.
Short Only, weights show the short book as positive slices that sum to 100 percent.
Delta Neutral, 50 percent long and 50 percent short books, each side leverage-proportional.
Labels can show asset, percent, and current leverage.
12) Inputs and quick presets
Core
Portfolio Strategy . Long Only, Short Only, Delta Neutral.
Initial Capital . For equity scaling in the panel.
Trading Days/Year . 252 for stocks, 365 for crypto.
Target Volatility . Annualized, drives volMult.
Transaction Fees . Per-bar drag and composition change penalty, see the modelling notes above.
Momentum Lookback . Ranking horizon. Shorter is more reactive, longer is steadier.
Start Date . Ensure every symbol has data back to this date to avoid bias.
Benchmark . Used for alpha, beta, and B&H line.
Diagnostics
Metrics, Equity, B&H, Curve labels, Daily return line, Rolling drawdown fill.
Attribution panel. Toggle winners only to focus on what matters.
Monte Carlo mode with Normal or Bootstrap and confidence bands.
Scatter plot type and styling, labels, and portfolio star.
Pie chart and labels for current allocation.
Presets
Crypto Daily, Long Only . Lookback 25, Target Vol 50 percent, Fees 10 bps, Regime filter on, Metrics and Drawdown on. Monte Carlo Bootstrap with Recent 200 bars for bands.
Crypto Daily, Delta Neutral . Lookback 25, Target Vol 50 percent, Fees 15–25 bps, Regime filter always active for this mode. Use Scatter Risk-Return to monitor efficiency and keep the star near upper left quadrants without drifting rightward.
Equities Daily, Long Only . Lookback 60–120, Target Vol 15–20 percent, Fees 5–10 bps, Regime filter on. Use Benchmark SPX and watch Alpha and Beta to keep the book from becoming index beta.
13) Suggested workflow
Universe sanity check . Pick liquid tickers with stable data. Thin assets distort vol estimates and fees.
Check momentum existence . Run on your timeframe. If slope and fit are weak, widen lookback or avoid that asset or timeframe.
Set risk budget . Choose a target volatility that matches your drawdown tolerance. Higher target increases turnover and cost sensitivity.
Pick mode . Long Only for bull regimes, Short Only for sustained downtrends, Delta Neutral for cross-sectional harvesting when index direction is unclear.
Tune lookback . If leaders rotate too often, lengthen it. If entries lag, shorten it.
Validate cost assumptions . Increase fee_rate and stress Monte Carlo. If the edge vanishes with modest friction, refine selection or lengthen rebalance cadence.
Run attribution . Confirm the strategy’s winners align with intuition and not one unstable outlier.
Use alerts . Enable position change, drawdown, volatility breach, regime, momentum shift, and crash alerts to supervise live runs.
Important implementation details mapped to code
Momentum measure . cr = price / price - 1 per symbol for ranking. Simplicity helps avoid overfitting.
Volatility targeting . vol = stdev(log returns, lookback) * sqrt(tradingdays), volMult = clamp(targetVol / vol, 0.1, 5), sr = ret * volMult.
Selection . Extract indices for top1..top3 and bot1..bot3. The arrays rets, scRets, lev_vals, and ticks_arr track momentum, scaled returns, leverage multipliers, and display tickers respectively.
Regime filter . EMA12 vs EMA21 switch determines if the strategy takes risk for Long or Short modes. Delta Neutral ignores the gate.
Equity update . Equity multiplies by 1 + netRet only when the regime was active in the prior bar. Buy-and-hold benchmark is computed separately for comparison.
Tables . Position tables show current top or bottom assets with leverage and weights. Metric table prints all risk and performance figures.
Visualization panels . Attribution, Monte Carlo, scatter, and pie use the last bars to draw overlays that update as the backtest proceeds.
Final notes
Momentum is a portfolio effect. The edge comes from cross-sectional dispersion, adequate risk normalization, and disciplined turnover control, not from a single best asset call.
Volatility targeting stabilizes path but does not fix selection. Use the momentum regression link above to confirm structure exists before you size into it.
Always test higher lag costs and slippage, then recheck metrics, attribution, and Monte Carlo envelopes. If the edge persists under stress, you have something robust.
lower_tfLibrary "lower_tf"
█ OVERVIEW
This library is an enhanced (opinionated) version of the library originally developed by PineCoders contained in lower_tf .
It is a Pine Script® programming tool for advanced lower-timeframe selection and intra-bar analysis.
█ CONCEPTS
Lower Timeframe Analysis
Lower timeframe analysis refers to the analysis of price action and market microstructure using data from timeframes shorter than the current chart period. This technique allows traders and analysts to gain deeper insights into market dynamics, volume distribution, and the price movements occurring within each bar on the chart. In Pine Script®, the request.security_lower_tf() function allows this analysis by accessing intrabar data.
The library provides a comprehensive set of functions for accurate mapping of lower timeframes, dynamic precision control, and optimized historical coverage using request.security_lower_tf().
█ IMPROVEMENTS
The original library implemented ten precision levels. This enhanced version extends that to twelve levels, adding two ultra-high-precision options:
Coverage-Based Precision (Original 5 levels):
1. "Covering most chart bars (least precise)"
2. "Covering some chart bars (less precise)"
3. "Covering fewer chart bars (more precise)"
4. "Covering few chart bars (very precise)"
5. "Covering the least chart bars (most precise)"
Intrabar-Count-Based Precision (Expanded from 5 to 7 levels):
6. "~12 intrabars per chart bar"
7. "~24 intrabars per chart bar"
8. "~50 intrabars per chart bar"
9. "~100 intrabars per chart bar"
10. "~250 intrabars per chart bar"
11. "~500 intrabars per chart bar" ← NEW
12. "~1000 intrabars per chart bar" ← NEW
The key enhancements in this version include:
1. Extended Precision Range: Adds two ultra-high-precision levels (~500 and ~1000 intrabars) for advanced microstructure analysis requiring maximum granularity.
2. Market-Agnostic Implementation: Eliminates the distinction between crypto/forex and traditional markets, removing the mktFactor variable in favor of a unified, predictable approach across all asset classes.
3. Explicit Precision Mapping: Completely refactors the timeframe selection logic using native Pine Script® timeframe properties ( timeframe.isseconds , timeframe.isminutes , timeframe.isdaily , timeframe.isweekly , timeframe.ismonthly ) and explicit multiplier-based lookup tables. The original library used minute-based calculations with market-dependent conditionals that produced inconsistent results. This version provides deterministic, predictable mappings for every chart timeframe, ensuring consistent precision behavior regardless of asset type or market hours.
An example of the differences can be seen side-by-side in the chart below, where the original library is on the left and the enhanced version is on the right:
█ USAGE EXAMPLE
// This Pine Script® code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © andre_007
//@version=6
indicator("lower_tf Example")
import andre_007/lower_tf/1 as LTF
import PineCoders/Time/5 as PCtime
//#region ———————————————————— Example code
// ————— Constants
color WHITE = color.white
color GRAY = color.gray
string LTF1 = "Covering most chart bars (least precise)"
string LTF2 = "Covering some chart bars (less precise)"
string LTF3 = "Covering less chart bars (more precise)"
string LTF4 = "Covering few chart bars (very precise)"
string LTF5 = "Covering the least chart bars (most precise)"
string LTF6 = "~12 intrabars per chart bar"
string LTF7 = "~24 intrabars per chart bar"
string LTF8 = "~50 intrabars per chart bar"
string LTF9 = "~100 intrabars per chart bar"
string LTF10 = "~250 intrabars per chart bar"
string LTF11 = "~500 intrabars per chart bar"
string LTF12 = "~1000 intrabars per chart bar"
string TT_LTF = "This selection determines the approximate number of intrabars analyzed per chart bar. Higher numbers of
intrabars produce more granular data at the cost of less historical bar coverage, because the maximum number of
available intrabars is 200K.
\n\nThe first five options set the lower timeframe based on a specified relative level of chart bar coverage.
The last five options set the lower timeframe based on an approximate number of intrabars per chart bar."
string TAB_TXT = "Uses intrabars at the {0} timeframe.\nAvg intrabars per chart bar:
{1,number,#.#}\nChart bars covered: {2} of {3} ({4,number,#.##}%)"
string ERR_TXT = "No intrabar information exists at the {1}{0}{1} timeframe."
// ————— Inputs
string ltfModeInput = input.string(LTF3, "Intrabar precision", options = , tooltip = TT_LTF)
bool showInfoBoxInput = input.bool(true, "Show information box ")
string infoBoxSizeInput = input.string("normal", "Size ", inline = "01", options = )
string infoBoxYPosInput = input.string("bottom", "↕", inline = "01", options = )
string infoBoxXPosInput = input.string("right", "↔", inline = "01", options = )
color infoBoxColorInput = input.color(GRAY, "", inline = "01")
color infoBoxTxtColorInput = input.color(WHITE, "T", inline = "01")
// ————— Calculations
// @variable A "string" representing the lower timeframe for the data request.
// NOTE:
// This line is a good example where using `var` in the declaration can improve a script's performance.
// By using `var` here, the script calls `ltf()` only once, on the dataset's first bar, instead of redundantly
// evaluating unchanging strings on every bar. We only need one evaluation of this function because the selected
// timeframe does not change across bars in this script.
var string ltfString = LTF.ltf(ltfModeInput, LTF1, LTF2, LTF3, LTF4, LTF5, LTF6, LTF7, LTF8, LTF9, LTF10, LTF11, LTF12)
// @variable An array containing all intrabar `close` prices from the `ltfString` timeframe for the current chart bar.
array intrabarCloses = request.security_lower_tf(syminfo.tickerid, ltfString, close)
// Calculate the intrabar stats.
= LTF.ltfStats(intrabarCloses)
int chartBars = bar_index + 1
// ————— Visuals
// Plot the `avgIntrabars` and `intrabars` series in all display locations.
plot(avgIntrabars, "Average intrabars", color.silver, 6)
plot(intrabars, "Intrabars", color.blue, 2)
// Plot the `chartBarsCovered` and `chartBars` values in the Data Window and the script's status line.
plot(chartBarsCovered, "Chart bars covered", display = display.data_window + display.status_line)
plot(chartBars, "Chart bars total", display = display.data_window + display.status_line)
// Information box logic.
if showInfoBoxInput
// @variable A single-cell table that displays intrabar information.
var table infoBox = table.new(infoBoxYPosInput + "_" + infoBoxXPosInput, 1, 1)
// @variable The span of the `ltfString` timeframe formatted as a number of automatically selected time units.
string formattedLtf = PCtime.formattedNoOfPeriods(timeframe.in_seconds(ltfString) * 1000)
// @variable A "string" containing the formatted text to display in the `infoBox`.
string txt = str.format(
TAB_TXT, formattedLtf, avgIntrabars, chartBarsCovered, chartBars, chartBarsCovered / chartBars * 100, "'"
)
// Initialize the `infoBox` cell on the first bar.
if barstate.isfirst
table.cell(
infoBox, 0, 0, txt, text_color = infoBoxTxtColorInput, text_size = infoBoxSizeInput,
bgcolor = infoBoxColorInput
)
// Update the cell's text on the latest bar.
else if barstate.islast
table.cell_set_text(infoBox, 0, 0, txt)
// Raise a runtime error if no intrabar data is available.
if ta.cum(intrabars) == 0 and barstate.islast
runtime.error(str.format(ERR_TXT, ltfString, "'"))
//#endregion
█ EXPORTED FUNCTIONS
ltf(userSelection, choice1, choice2, ...)
Returns the optimal lower timeframe string based on user selection and current chart timeframe. Dynamically calculates precision to balance granularity with historical coverage within the 200K intrabar limit.
ltfStats(intrabarValues)
Analyzes an intrabar array returned by request.security_lower_tf() and returns statistics: number of intrabars in current bar, total chart bars covered, and average intrabars per bar.
█ CREDITS AND LICENSING
Original Concept : PineCoders Team
Original Lower TF Library :
License : Mozilla Public License 2.0
Opening Range Break LRSThis script is designed for a trend-following, opening range breakout strategy. The main idea is to only trade breakouts that happen in the same direction as the short-term trend, which the script identifies using a linear regression slope.
1. Identify the Short-Term Trend
This is the first and most important step. The script does this for you using the Linear Regression and the bar coloring.
• If the bars are colored BLUE: The linear regression slope is positive. This means the script considers the short-term trend to be UP. A trader using this script would only look for long (buy) trades.
• If the bars are colored YELLOW: The linear regression slope is negative. This means the script considers the short-term trend to be DOWN. A trader using this script would only look for short (sell) trades.
This filter is designed to prevent you from trading a "false breakout" against the immediate momentum.
2. Watch the Opening Ranges Form
At the start of the trading session (8:30 AM by default), the script will begin drawing boxes for the 5, 15, 30, and 60-minute opening ranges you've enabled.
• The 5-minute box (e.g., gray) will be set after the 8:30 - 8:35 period.
• The 15-minute box (e.g., blue) will be set after the 8:30 - 8:45 period.
• ...and so on.
These boxes, which extend for the rest of the day, represent the key high and low levels established at the open. The "Live Box Extension" input simply keeps the right edge of the box a few bars away from the current price so you can see it clearly.
3. Look for a Filtered Breakout Signal
This is where the trend filter (Step 1) and the range boxes (Step 2) come together.
Bullish Trade Example (Long):
1. A trader sees the bars are colored BLUE (uptrend). They are now only looking for a break above one of the ORB highs.
2. They will ignore any break below the ORB lows, as that would be trading against the trend filter.
3. The price moves up and finally closes above the 15-minute ORB high.
4. The script will plot a green "Break 15" label. This is the trader's signal to enter a long trade.
Bearish Trade Example (Short):
1. A trader sees the bars are colored YELLOW (downtrend). They are now only looking for a break below one of the ORB lows.
2. They will ignore any break above the ORB highs.
3. The price moves down and closes below the 5-minute ORB low.
4. The script will plot a red "Break 5" label. This is the trader's signal to enter a short trade.
4. Use Multiple Timeframes for Context
The real power of this script is seeing all the ranges at once. A trader wouldn't just trade them in isolation.
• Confirmation: A "Break 5" signal is a quick, early signal. But if the price also breaks the "15" and "30" minute highs, it signals much stronger bullish consensus, which might encourage the trader to hold the trade longer.
• Support & Resistance: The other ORB levels act as a map for the day.
o As Targets: If a trader takes a "Break 15" long signal, the 30-minute ORB high and 60-minute ORB high become logical profit targets.
o As Warning Signs: If the price gives a "Break 5" long signal but is struggling right under the 15-minute high, a trader might wait for that 15-minute level to break before entering, seeing it as a key resistance level.
Summary: A Trader's Workflow
1. Morning (8:30 AM): Watch the script. What color are the bars? (Blue = longs only, Yellow = shorts only).
2. Wait: Let the 5, 15, 30, and 60-minute ranges form. The boxes will be drawn on the chart.
3. Execute: Wait for a "Break" signal (a label) that matches your trend direction.
4. Manage: Use the other ORB levels as potential profit targets or as confirmation of the move's strength.
5. Single Signal: The "Single Signal Only" input, if checked, ensures they only get one signal per timeframe (e.g., one "Break 15" long, and that's it for the day), which helps prevent over-trading in choppy conditions.
Dual Best MA Strategy AnalyzerDual Best MA Strategy Analyzer (Lookback Window)
What it does
This indicator scans a range of moving-average lengths and finds the single best MA for long crossovers and the single best MA for short crossunders over a fixed lookback window. It then plots those two “winner” MAs on your chart:
Best Long MA (green): The MA length that would have made the highest total profit using a simple “price crosses above MA → long; exit on cross back below” logic.
Best Short MA (red): The MA length that would have made the highest total profit using “price crosses below MA → short; exit on cross back above.”
You can switch between SMA and EMA, set the min/max length, choose a step size, and define the lookback window used for evaluation.
How it works (brief)
For each candidate MA length between Min MA Length and Max MA Length (stepping by Step Size), the script:
Builds the MA (SMA or EMA).
Simulates a naïve crossover strategy over the last Lookback Window candles:
Long model: enter on crossover, exit on crossunder.
Short model: enter on crossunder, exit on crossover.
Sums simple P&L in price units (no compounding, no fees/slippage).
Picks the best long and best short lengths by total P&L and plots those two MAs.
Note: Long and short are evaluated independently. The script plots MAs only; it doesn’t open positions.
Inputs
Min MA Length / Max MA Length – Bounds for MA search.
Step Size – Spacing between tested lengths (e.g., 10 tests 10, 20, 30…).
Use EMA instead of SMA – Toggle average type.
Lookback Window (candles) – Number of bars used to score each MA. Needs enough history to be meaningful.
What the plots mean
Best Long MA (green): If price crosses above this line (historically), that MA length produced the best long-side results over the lookback.
Best Short MA (red): If price crosses below this line (historically), that MA length produced the best short-side results.
These lines can change over time as new bars enter the lookback window. Think of them as adaptive “what worked best recently” guides, not fixed signals.
Practical tips
Timeframe matters: Run it on the timeframe you trade; the “best” length on 1h won’t match 1m or 1D.
Step size trade-off: Smaller steps = more precision but heavier compute. Larger steps = faster scans, coarser choices.
Use with confirmation: Combine with structure, volume, or volatility filters. This is a single-factor tester.
Normalization: P&L is in raw price units. For cross-symbol comparison, consider using one symbol at a time (or adapt the script to percent P&L).
Limitations & assumptions
No fees, funding, slippage, or position sizing.
Simple “in/out” on the next crossover; no stops/targets/filters.
Results rely on lookback choice and will repaint historically as the “best” length is re-selected with new data (the plot is adaptive, not forward-fixed).
The script tests up to ~101 candidates internally (bounded by your min/max/step).
Good uses
Quickly discover a recently effective MA length for trend following.
Compare SMA vs EMA performance on your market/timeframe.
Build a playbook: note which lengths tend to win in certain regimes (trending vs choppy).
Not included (by design)
Alerts, entries/exits, or a full strategy report. It’s an analyzer/overlay.
If you want alerts, you can add simple conditions like:
ta.crossover(close, plotLongMA) for potential long interest
ta.crossunder(close, plotShortMA) for potential short interest
Changelog / Notes
v1: Initial release. Array-based scanner, SMA/EMA toggle, adaptive long/short best MA plots, user-set lookback.
Disclaimer
This is educational tooling, not financial advice. Test thoroughly and use proper risk management.
RSI ADX Bollinger Analysis High-level purpose and design philosophy
This indicator — RSI-ADX-Bollinger Analysis — is a compact, educational market-analysis toolkit that blends momentum (RSI), trend strength (ADX), volatility structure (Bollinger Bands) and simple volumetrics to provide traders a snapshot of market condition and trade idea quality. The design philosophy is explicit and layered: use each component to answer a different question about price action (momentum, conviction, volatility, participation), then combine answers to form a more robust, explainable signal. The mashup is intended for analysis and learning, not automatic execution: it surfaces the why behind signals so traders can test, learn and apply rules with risk management.
________________________________________
What each indicator contributes (component-by-component)
RSI (Relative Strength Index) — role and behavior: RSI measures short-term momentum by comparing recent gains to recent losses. A high RSI (near or above the overbought threshold) indicates strong recent buying pressure and potential exhaustion if price is extended. A low RSI (near or below the oversold threshold) indicates strong recent selling pressure and potential exhaustion or a value area for mean-reversion. In this dashboard RSI is used as the primary momentum trigger: it helps identify whether price is locally over-extended on the buy or sell side.
ADX (Average Directional Index) — role and behavior: ADX measures trend strength independently of direction. When ADX rises above a chosen threshold (e.g., 25), it signals that the market is trending with conviction; ADX below the threshold suggests range or weak trend. Because patterns and momentum signals perform differently in trending vs. ranging markets, ADX is used here as a filter: only when ADX indicates sufficient directional strength does the system treat RSI+BB breakouts as meaningful trade candidates.
Bollinger Bands — role and behavior: Bollinger Bands (20-period basis ± N standard deviations) show volatility envelope and relative price position vs. a volatility-adjusted mean. Price outside the upper band suggests pronounced extension relative to recent volatility; price outside the lower band suggests extended weakness. A band expansion (increasing width) signals volatility breakout potential; contraction signals range-bound conditions and potential squeeze. In this dashboard, Bollinger Bands provide the volatility/structural context: RSI extremes plus price beyond the band imply a stronger, volatility-backed move.
Volume split & basic MA trend — role and behavior: Buy-like and sell-like volume (simple heuristic using close>open or closeopen) or sell-like (close1.2 for validation and compare win rate and expectancy.
4. TF alignment: Accept signals only when higher timeframe (e.g., 4h) trend agrees — compare results.
5. Parameter sensitivity: Vary RSI threshold (70/30 vs 80/20), Bollinger stddev (2 vs 2.5), and ADX threshold (25 vs 30) and measure stability of results.
These exercises teach both statistical thinking and the specific failure modes of the mashup.
________________________________________
Limitations, failure modes and caveats (explicit & teachable)
• ADX and Bollinger measures lag during fast-moving news events — signals can be late or wrong during earnings, macro shocks, or illiquid sessions.
• Volume classification by open/close is a heuristic; it does not equal TAPEDATA, footprint or signed volume. Use it as supportive evidence, not definitive proof.
• RSI can remain overbought or oversold for extended stretches in persistent trends — relying solely on RSI extremes without ADX or BB context invites large drawdowns.
• Small-cap or low-liquidity instruments yield noisy band behavior and unreliable volume ratios.
Being explicit about these limitations is a strong point in a TradingView description — it demonstrates transparency and educational intent.
________________________________________
Originality & mashup justification (text you can paste)
This script intentionally combines classical momentum (RSI), volatility envelope (Bollinger Bands) and trend-strength (ADX) because each indicator answers a different and complementary question: RSI answers is price locally extreme?, Bollinger answers is price outside normal volatility?, and ADX answers is the market moving with conviction?. Volume participation then acts as a practical check for real market involvement. This combination is not a simple “indicator mashup”; it is a designed ensemble where each element reduces the others’ failure modes and together produce a teachable, testable signal framework. The script’s purpose is educational and analytical — to show traders how to interpret the interplay of momentum, volatility, and trend strength.
________________________________________
TradingView publication guidance & compliance checklist
To satisfy TradingView rules about mashups and descriptions, include the following items in your script description (without exposing source code):
1. Purpose statement: One or two lines describing the script’s objective (educational multi-indicator market overview and idea filter).
2. Component list: Name the major modules (RSI, Bollinger Bands, ADX, volume heuristic, SMA trend checks, signal tracking) and one-sentence reason for each.
3. How they interact: A succinct non-code explanation: “RSI finds momentum extremes; Bollinger confirms volatility expansion; ADX confirms trend strength; all three must align for a BUY/SELL.”
4. Inputs: List adjustable inputs (RSI length and thresholds, BB length & stddev, ADX threshold & smoothing, volume MA, table position/size).
5. Usage instructions: Short workflow (check TF alignment → confirm participation → define stop & R:R → backtest).
6. Limitations & assumptions: Explicitly state volume is approximated, ADX has lag, and avoid promising guaranteed profits.
7. Non-promotional language: No external contact info, ads, claims of exclusivity or guaranteed outcomes.
8. Trademark clause: If you used trademark symbols, remove or provide registration proof.
9. Risk disclaimer: Add the copy-ready disclaimer below.
This matches TradingView’s request for meaningful descriptions that explain originality and inter-component reasoning.
________________________________________
Copy-ready short publication description (paste into TradingView)
Advanced RSI-ADX-Bollinger Market Overview — educational multi-indicator dashboard. This script combines RSI (momentum extremes), Bollinger Bands (volatility envelope and band expansion), ADX (trend strength), simple SMA trend bias and a basic buy/sell volume heuristic to surface high-quality idea candidates. Signals require alignment of momentum, volatility expansion and rising ADX; volume participation is displayed to support signal confidence. Inputs are configurable (RSI length/levels, BB length/stddev, ADX length/threshold, volume MA, display options). This tool is intended for analysis and learning — not for automated execution. Users should back test and apply robust risk management. Limitations: volume classification here is a heuristic (close>open), ADX and BB measures lag in fast news events, and results vary by instrument liquidity.
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Copy-ready risk & misuse disclaimer (paste into description or help file)
This script is provided for educational and analytical purposes only and does not constitute financial or investment advice. It does not guarantee profits. Indicators are heuristics and may give false or late signals; always back test and paper-trade before using real capital. The author is not responsible for trading losses resulting from the use or misuse of this indicator. Use proper position sizing and risk controls.
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Risk Disclaimer: This tool is provided for education and analysis only. It is not financial advice and does not guarantee returns. Users assume all risk for trades made based on this script. Back test thoroughly and use proper risk management.
Comet C/2025 N1 (ATLAS) Ephemeris☄️ Ephemeris How-To: Plot JPL Horizons Data on TradingView (Educational)
Overview
This open-source Pine Script™ v6 indicator demonstrates how to bring external astronomical ephemeris into TradingView and plot it on a daily chart. Using Comet C/2025 N1 (ATLAS) as an example dataset, it shows the mechanics of structuring arrays, indexing by date, and drawing past and forward ( future projections ) values—strictly as an educational visualization of celestial motion.
Why This Approach
Data is generated from NASA JPL Horizons, a mission-grade, publicly available ephemeris service ( (ssd.jpl.nasa.gov)). On the daily timeframe, Horizons provides high-precision positions you can regenerate whenever solutions update—useful for educational accuracy in exploring orbital data.
What’s Plotted
- Geocentric ecliptic longitude (Earth-view)
- Heliocentric ecliptic longitude (Sun-centered)
- Declination (deg from celestial equator)
Features
- Simple arrays + date indexing (no per-row timestamps)
- Circles for historical/current bars; polylines to connect forward points, emphasizing future projections
- Toggle any series on/off via inputs
- Daily timeframe enforced (runtime error if not 1D)
- Optional table with zodiac conversion (AstroLib by BarefootJoey)
Data & Updates
The example arrays span 2025-07-01 (discovery date) → 2026-01-01. You can refresh them anytime from JPL Horizons (Observer: Geocentric; daily step; include ecliptic lon/lat and declination) and paste the new values into the script.
How we pulled the ephemeris from JPL Horizons (quick guide):
0) Open ssd.jpl.nasa.gov System
1. Ephemeris Type: Observer Table
2. Target Body: C/2025 N1 (ATLAS) (or any object you want)
3. Observer Location: Geocentric
4. Time Specification: set Start, Stop, Step = 1 day
5. Table Settings → Quantities:
* Astrometric RA & Dec
* Heliocentric ecliptic longitude & latitude
* Observer (geocentric) ecliptic longitude & latitude
6. Additional Table Settings:
* Calendar format: Gregorian
* Date/Time: calendar (UTC), Hours & Minutes (HH:MM)
* Angle format: Decimal degrees
* Refraction model: No refraction / airless
* Range units: Astronomical units (au)
7. Generate → Download results (CSV or text).
8. Use AI or a small script to parse columns (e.g., Obs ecliptic lon, Helio ecliptic lon, Declination) into arrays, then paste them into your Pine script.
Educational Note
This indicator’s goal is to show how to prepare and plot ephemeris—so you can adapt the method for other comets or celestial bodies, or swap in data from existing astro libraries, for learning about astronomical projections using JPL daily data.
Credits & License
- Ephemeris: Solar System Dynamics Group, Horizons On-Line Ephemeris System, 4800 Oak Grove Drive, Jet Propulsion Laboratory, Pasadena, CA 91109, USA.
- Zodiac conversion: AstroLib by BarefootJoey
- License: MIT
- For educational use only.
