Tìm kiếm tập lệnh với " TABLE "
Multiple Indicator 50EMA Cross AlertsHere’s a screener including Symbol, Price, TSI, and 50 ema cross in a table output.
The 50 Exponential Moving Average is a trend indicator
You can find bullish momentum when the 50 ema crossed over or a bearish momentum when the 50 ema crossed under we are looking to take advantage by trading the reversion of these trends.
True strength index (TSI) is a trend momentum indicator
Readings are bullish when the True Strength Index shows positive values
Readings are bearish when the indicator displays negative values.
When a value is above 20, we look for selling overbought opportunity and when the value is under 20, we look for buying oversold opportunity.
You can select the pair of your choice in the settings.
Make sure to create an alert and choose any alerts then an alert will trigger when a price cross under or cross over the 50 ema for every pair separately.
This allow the user to verify if there is a trade set up or not.
Disclaimer
This post and the script don’t provide any financial advice.
Performance Table From OpenThis indicator plots the percentage performance from the open of up to 20 different customizable tickers.
Enjoy!
[Nic] Intraday Vix LabelsPrints intraday percent change of VIX9D, VVIX, PCC, and any other arbitrary symbol on a table for quick reference.
BTC Futures BasisShows various basis percentages in a table and plots historical basis. Also has an alert function for backwardation events. Useful for tracking bullish/bearish sentiment in BTC futures markets.
*Currently displays March and June futures for the following exchanges: Bitmex, Binance, Deribit, Okex, and FTX
Also displays CME Continuous Next Contract. All of the symbols are customizable.
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Market-wide backwardation usually occurs during a heavy sell-off (such as a liquidation cascade).
**For getting alerts of backwardation events, I recommend creating an alert on the 1 minute chart with the condition "Any alert() function call". Alert level is customizable as well.
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*NOTE!! : Futures contracts expire (obviously), so the contract symbols will need to be updated periodically. I will try to keep them updated going into the future.
**NOTE2!! : The alert() function does not track the CME contract. This is to avoid false triggers.
SPY Sub-Sector Daily Money Flow TableThis calculates the dollar volume per candlestick (2nd row) and cumulative (3rd row) of the entire trading day for each subsector of the SPY.
The 'Total' column is the total of all the subsectors combined. It is calculated separately from SPY volume.
The money flow is calculated with (open+close)/2 which means different timeframes yield different results and won't be especially accurate day-by-day. This is useful to quickly see rotation and possible divergences.
Enjoy!
PreMarketStatsThe idea is to catch pre market information (or other relevant data), that basically consists of a single number, in a table instead of using a plot that takes up space in the chart. In this example, I added pre market volume and pre market change in %. Where the second one is as well available in the details tab of the stock, it is not available if this tab is closed or during replays.
[CLX][#01] Animation - Price Ticker (Marquee)This indicator displays a classic animated price ticker overlaid on the user’s current chart. It is possible to fully customize it or to select one of the predefined styles.
A detailed description will follow in the next few days.
Used Pinescript technics:
- varip (view/animation)
- tulip instance (config/codestructur)
- table (view/position)
By the way, for me, one of the coolest animated effects is by Duyck
We hope you enjoy it! 🎉
CRYPTOLINX - jango_blockchained 😊👍
Disclaimer:
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely.
The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script.
Probability TableThe script is inspired by user NickbarComb, I suggested checking out his Price Convergence script.
Basically, this script plots a table containing the probability of the current candle closing either higher or lower based on user-define past period.
Hope that it will be helpful.
MTF Price/Volume % [Anan]Hello friends,
This is a multi-timeframe table with these features:
Display price change percentage compared with the last timeframe candle close.
Display price change percentage compared with the last timeframe candle close MA.
Displays change percentage compared with the last timeframe candle volume.
Displays change percentage compared with the last timeframe candle volume MA.
Change type/length of MA for Price/Volume.
Full control of Panel position and size.
Full control of displaying any row or column.
Average Daily Range TableThis is the last script to complete Vladimir Poltoratskiy's setup found in his books.
Poltoratskiy argues that you should not take any fractal corridors higher than 50% of the Average Daily Range. To be honest, even 40% is a lot, because then, your target will be 160% ADR away from your entry and one "fracture" just can't be enough to predict moves this big.
I chose a table to visually represent the indicator because it doesn't change its value during the day. It takes far less room on the chart.
There are also two simple moving averages. You may use the as an indicator if the relative volatility as of late is extremely low and in that case, perhaps, expect an increase in the coming days. They are applied to the Average Daily Range, not one day range!
PAC newThis indicator will alert you when a candle goes above or below the price action channel (PAC) but only on the first or second candle after a colour change in candle.
When price is above the price action channel that is a bullish sign, when price is below the PAC that is a bearish sign.
The idea is that a sudden change in price is a cause to investigate further price action moving in that direction so the indicator aims to identify reversal
Scalping strategy that works on 5 min chart and aims to gain 10 pips. Do not act on every signal. Further investigation is required, for example by looking at RSI oversolf and overbought levels. For example, at an oversold area, a buy signal is more valid
Table: Forex Central Bank Interest RatesThis tool shows CB Interest Rates for USD, JPY, CAD, CHF, EUR, GBP, NZD, AUD - basically all the majors.
Use override and input your own value if it is changed and I haven't updated the script yet.
Month/Month Percentage % Change, Historical; Seasonal TendencyTable of monthly % changes in Average Price over the last 10 years (or the 10 yrs prior to input year).
Useful for gauging seasonal tendencies of an asset; backtesting monthly volatility and bullish/bearish tendency.
~~User Inputs~~
Choose measure of average: sma(close), sma(ohlc4), vwap(close), vwma(close).
Show last 10yrs, with 10yr average % change, or to just show single year.
Chose input year; with the indicator auto calculating the prior 10 years.
Choose color for labels and size for labels; choose +Ve value color and -Ve value color.
Set 'Daily bars in month': 21 for Forex/Commodities/Indices; 30 for Crypto.
Set precision: decimal places
~~notes~~
-designed for use on Daily timeframe (tradingview is buggy on monthly timeframe calculations, and less precise on weekly timeframe calculations).
-where Current month of year has not occurred yet, will print 9yr average.
-calculates the average change of displayed month compared to the previous month: i.e. Jan22 value represents whole of Jan22 compared to whole of Dec21.
-table displays on the chart over the input year; so for ES, with 2010 selected; shows values from 2001-2010, displaying across 2010-2011 on the chart.
-plots on seperate right hand side scale, so can be shrunk and dragged vertically.
-thanks to @gabx11 for the suggestion which inspired me to write this
Koalafied Risk ManagementTables and labels/lines showing trade levels and risk/reward. Use to manage trade risk compared to portfolio size.
Initial design optimised for tickers denominated against USD.
Multi-Session High/Low Trackertable that shows rth eth and full weekly range high and low with range difference from high and low
Table ATH and DayQuotes in the middle of a chartJust important things at a glance ..
AlltimeHigh and Daily High/Low
Liquidity Trend & Squeeze RadarThe Liquidity Trend & Squeeze Radar is a comprehensive trading system designed to visualize the three most critical components of price action: Trend, Volatility, and Momentum. The core philosophy of this tool is to identify periods of market "compression" (low volatility), where energy builds up, and then signal when that energy is released (expansion) for a potential breakout trade. It combines an EMA Cloud for trend direction with a TTM-style Squeeze indicator and a linear regression momentum filter.
Key Components
Trend Cloud (Structure) This component identifies the overall market bias. It uses a Fast EMA and a Slow EMA to create a shaded "Cloud."
Uptrend: The Fast EMA is above the Slow EMA. The Cloud is shaded green (default).
Downtrend: The Fast EMA is below the Slow EMA. The Cloud is shaded red (default).
Usage: Generally, traders should look to take Long signals only when the Trend Cloud is bullish and Short signals when the Trend Cloud is bearish.
Volatility Radar (The Squeeze) This logic detects when the market enters a period of low volatility. It calculates this by comparing Bollinger Bands (Expansion) against Keltner Channels (Average Range).
Squeeze Active: When the Bollinger Bands narrow and go inside the Keltner Channels, a "Squeeze" is active. This is represented by gray dots plotted along the Fast EMA and gray-colored price candles.
Usage: Do not trade during a Squeeze. This indicates indecision and chop. Treat this as a "Wait" signal while potential energy builds.
Momentum Filter (Hidden Logic) While the Squeeze is active, the script calculates the underlying momentum using Linear Regression. This predicts the likely direction of the breakout before it happens. This data is displayed in the Dashboard.
Breakout Signals (Fire) When the Squeeze condition ends (volatility expands), the script checks the Momentum filter.
Bullish Breakout: If the Squeeze ends and Momentum is positive, a triangle pointing up is plotted below the bar.
Bearish Breakout: If the Squeeze ends and Momentum is negative, a triangle pointing down is plotted above the bar.
Status Dashboard A table located in the top-right corner provides a real-time summary of the market state without needing to interpret the chart visuals manually. It lists the current Trend direction, Volatility state (Squeeze vs. Expansion), and Momentum value (Positive vs. Negative).
How to Trade This Indicator
Step 1: Identify the Trend Observe the background Cloud. Ensure you are trading in the direction of the dominant flow. If the Cloud is green, favor Longs. If red, favor Shorts.
Step 2: Wait for the Squeeze Look for the gray dots to appear on the moving average line and for the candles to turn gray. This indicates the market is resting and building energy. During this phase, you are stalking the trade. Avoid entering positions while the gray dots remain visible.
Step 3: The Breakout (The Trigger) Wait for the gray dots to disappear. This means the Squeeze has "Fired."
Long Entry: Look for a Triangle Up signal. Ideally, this should occur when the Trend Cloud is green.
Short Entry: Look for a Triangle Down signal. Ideally, this should occur when the Trend Cloud is red.
Step 4: Confirmation Check the Dashboard table. High-probability trades occur when all three metrics align (e.g., Trend is BULL, Volatility is EXPANSION, and Momentum is POSITIVE).
Settings Guide
Trend Structure:
Fast/Slow EMA Length: Adjusts the sensitivity of the Trend Cloud. Higher numbers effectively smooth out noise but react slower to trend changes.
Show Trend Cloud: Toggles the shaded area between EMAs on or off.
Volatility Radar:
Bollinger/Keltner Settings: These define the Squeeze sensitivity.
Keltner Mult: The most important setting. The default is 1.5. Lowering this to 1.0 will make the Squeeze harder to trigger (requiring extreme compression), leading to fewer but potentially more explosive signals.
Momentum:
Momentum Length: The lookback period for the linear regression calculation used to determine breakout direction.
Visuals:
Colorize Candles: Paints the price bars based on the current state (Gray for Squeeze, Green/Red for Trend).
Show Dashboard: Toggles the visibility of the data table.
Disclaimer This indicator and guide are for educational and informational purposes only. They do not constitute financial, investment, or trading advice. Trading in financial markets involves a significant risk of loss and is not suitable for every investor. Past performance of any trading system or methodology is not necessarily indicative of future results. The user assumes all responsibility for any trades made using this tool. Always use proper risk management.
Daily Protocol [#] by @shulktrades@shulktrades aka Shulkins aka shulk
Open Source Script created from the code “ICT Everything” by @coldbrewrosh
*Adjusted Key Times and Standard Deviations to better fit my model*
Daily Protocol Indicator - Description
Overview
The Daily Protocol indicator is a comprehensive trading tool designed to help traders identify and visualize key intraday time-based levels and sessions for forex and other markets. This indicator automatically marks important market opening times, session ranges, and standard deviation projections to assist in technical analysis and trade planning.
Key Features
Time-Based Vertical Lines
Midnight Open (00:00): Marks the start of each trading day
News Open (08:30): Highlights the typical economic news release time
NY AM Open (09:30): Identifies the US equity market opening
Opening Price Lines
Midnight Opening Price: Tracks the price at market day start
News Opening Price (08:30): Shows the price level at news time
NY AM Opening Price (09:30): Displays equity market open price
Afternoon Opening Price (13:30): Optional afternoon session marker
Weekly & Monthly Opening Prices: Higher timeframe reference levels
Session Range Boxes
London Session (LNDN): 02:00-05:00 range with optional standard deviation projections
Asian Session (ASIA): 20:00-00:00 range with optional standard deviation projections
NY AM Session (NYAM): 09:30-10:00 range with optional standard deviation projections
Standard Deviation Projections
Automatically calculates and displays 1-4 standard deviation levels based on session ranges
Configurable directional display (both sides, upside only, or downside only)
Helps identify potential target zones and reversal areas
Additional Features
Day of Week Labels: Clearly marks each trading day
Customizable Timezone: Supports multiple global timezones
Historical Lines Toggle: Option to show/hide past levels for cleaner charts
Terminus Settings: Control how far price lines extend into the future
Color Customization: Full control over all colors, line styles, and widths
Range Statistics Table: Displays current session ranges and pip counts (forex only)
Best Use Cases
Identifying overnight key times and structure before market opens for NY
Planning entries around major session times
Setting targets using standard deviation projections
Understanding market structure through time-based analysis
Backtesting strategies based on session behavior
Timeframe Recommendation
Works best on intraday timeframes (1-60 minutes). The indicator automatically hides on timeframes above 61 minutes to prevent chart clutter.
Customization
Extensive settings allow traders to:
Enable/disable any individual component
Adjust all colors, line styles, and widths
Control label display and formatting
Set timezone preferences
Configure standard deviation calculations
Choose which sessions and times to display
This indicator is ideal for traders who follow time-based trading methodologies and want to incorporate structured intraday analysis into their workflow.
Smart Adaptive Double Patterns [The_lurker]Smart Adaptive Double Patterns
This is an advanced technical indicator that combines two of the strongest and most renowned classical price reversal patterns:
✅ Double Bottom Pattern — a bullish reversal pattern that forms after a downtrend
✅ Double Top Pattern — a bearish reversal pattern that forms after an uptrend
The indicator does not merely detect patterns — it provides a fully integrated, intelligent system that includes:
✅ Precise quality scoring for each pattern using 5 technical criteria
✅ Automatic price target calculation at three levels (Conservative, Balanced, Aggressive)
✅ Multi-layer dynamic filtering to avoid false signals
✅ Live pattern tracking from formation to target achievement or failure
✅ Comprehensive alert system covering all possible trading scenarios
🎯 Why Is This Indicator Unique?
1️⃣ High Detection Accuracy
Unlike traditional indicators that rely on simple rules, this one applies 5 strict structural conditions to confirm pattern validity:
A clear trend must precede the pattern
High symmetry between the two bottoms or two tops
No break of critical price levels during formation
Logical spacing between key points
Technical confirmation from ADX, ATR, and Volume
2️⃣ Advanced Quality Scoring System
Each pattern is scored out of 100 based on 5 weighted criteria:
Symmetry (30%): How closely the two bottoms or tops match
Trend Strength (20%): Strength of the prior trend
Volume Behavior (20%): Trading activity at critical points
Pattern Depth (15%): Vertical distance between neckline and bottom/top
Structural Integrity (15%): Full compliance with structural rules
3️⃣ Smart Target Management
Separate targets for bullish (Double Bottom) and bearish (Double Top) patterns
Separate projections for success and failure cases
Multiple options: Conservative (0.618) / Balanced (1.0) / Aggressive (1.618)
Live tracking with dynamic moving lines
4️⃣ Professional Failure Handling
Failed patterns are not ignored — they are turned into counter-trend opportunities:
Failed Double Bottom → triggers a bearish signal with downside targets
Failed Double Top → triggers a bullish signal with upside targets
Automatic color change for clear visual distinction
5️⃣ Full Customization Flexibility
Enable/disable each pattern independently
22+ adjustable settings
Unique colors for each pattern and quality level
Full bilingual support (Arabic / English)
📐 Pattern Details
🟦 Double Bottom Pattern
Sequence of points:
🔹 Point 1: Peak marking the start of a strong downtrend
🔹 Point 2 (Bottom 1): First low — first key bounce
🔹 Point 3: Intermediate high — forms the neckline (resistance)
🔹 Point 4 (Bottom 2): Second low — should closely match Bottom 1
🔹 Point 5: Breakout point — pattern confirmation
Mandatory Conditions:
✅ Clear downtrend before Point 2
✅ Bottoms 2 & 4 nearly identical (≤1.5% difference by default)
✅ Point 3 higher than both bottoms
✅ Neither bottom is broken during formation
✅ Sufficient time between points (≥10 candles by default)
✅ Success Scenario
→ Price breaks above the neckline (Point 3)
→ Point 5 is plotted at breakout candle
→ Dashed vertical line drawn from Point 5 to target
→ Horizontal dashed line tracks price toward target
→ Dashboard shows: Pattern Type | Quality | Rating | Target | Status
→ When target hits: line turns green + ✅ appears
🎯 Target Calculation
Pattern Height = Point 3 − Point 4
• Conservative: Point 3 + (Height × 0.618 × Quality Factor)
• Balanced: Point 3 + (Height × 1.0 × Quality Factor)
• Aggressive: Point 3 + (Height × 1.618 × Quality Factor)
❌ Failure Scenario
→ Price breaks below both Bottom 1 or Bottom 2 before neckline breakout
Visual Changes:
All lines turn red
Red ✖ appears at breakdown candle
Neckline stops expanding
Red dashed vertical line from breakdown point to bearish target
Red horizontal tracking line follows price
Dashboard updates to:
⚠ Failed Bottom – Bearish
→ Shows new bearish target
→ Indicates target mode for failure case
→ Status: Bearish Reversal
→ Fully red display
🟥 Double Top Pattern
Sequence of points:
🔹 Point 1: Trough marking the start of a strong uptrend
🔹 Point 2 (Top 1): First peak — first key resistance
🔹 Point 3: Intermediate low — forms the neckline (support)
🔹 Point 4 (Top 2): Second peak — should closely match Top 1
🔹 Point 5: Breakdown point — pattern confirmation
Mandatory Conditions:
✅ Clear uptrend before Point 2
✅ Tops 2 & 4 nearly identical (≤1.5% difference by default)
✅ Point 3 lower than both tops
✅ Neither top is breached during formation
✅ Sufficient time between points (≥10 candles by default)
✅ Success Scenario
→ Price breaks below the neckline (Point 3)
→ Point 5 is plotted at breakdown candle
→ Dashed vertical line drawn to target
→ Horizontal tracking line moves with price
→ Dashboard updates accordingly
→ Green line + ✅ on hit
🎯 Target Calculation
Pattern Height = Point 4 − Point 3
• Conservative: Point 3 − (Height × 0.618 × Quality Factor)
• Balanced: Point 3 − (Height × 1.0 × Quality Factor)
• Aggressive: Point 3 − (Height × 1.618 × Quality Factor)
❌ Failure Scenario
→ Price breaks above either Top 1 or Top 2 before neckline breakdown
Visual Changes:
All lines turn cyan (light blue)
Cyan ✖ appears at breakout candle
Neckline stops expanding
Cyan dashed vertical line to bullish target
Cyan horizontal tracking line follows price
Dashboard updates to:
⚠ Failed Top – Bullish
→ Shows new bullish target
→ Indicates target mode for failure case
→ Status: Bullish Reversal
→ Fully cyan display
🎯 Upside Target (after Double Top failure)
Max Top = max(Point 2, Point 4)
Height = Max Top − Point 3
• Conservative: Max Top + (Height × 0.618)
• Balanced: Max Top + (Height × 1.0)
• Aggressive: Max Top + (Height × 1.618)
📊 Quality Scoring System (0–100)
1️⃣ Symmetry (30%)
Measures price match between the two bottoms or two tops.
High score (25–30): Near-perfect symmetry → very strong pattern
Medium (15–24): Good match → reliable signal
Low (5–14): Weak symmetry → use caution
Zero: No symmetry → invalid pattern
2️⃣ Trend Strength (20%)
Uses ADX and DI indicators.
20 pts: Strong trend confirmed (e.g., ADX ≥ 20 + correct DI alignment)
10 pts: Trend filter disabled
6 pts: Weak or sideways trend
3️⃣ Volume Behavior (20%)
Declining volume on second touch is a positive sign (shows exhaustion).
15–20 pts: Clear volume drop → strong signal
10 pts: Neutral volume
6 pts: Rising volume → higher risk of continuation
4️⃣ Pattern Depth (15%)
Deeper patterns = stronger reversals.
12–15 pts: Deep → high reversal power
8–11 pts: Medium → acceptable
<8 pts: Shallow → weak signal
5️⃣ Structural Integrity (15%)
Checks logical structure (e.g., Point 1 > Point 3 in Double Bottom).
12–15 pts: Ideal structure
8–11 pts: Minor flaws
<8 pts: Poor setup
📈 Final Quality Rating & Colors
• 85–100 → ⭐ Excellent
→ Double Bottom: Cyan #00BCD4
→ Double Top: Light Red #FF5252
• 75–84 → ✨ Very Good
• 65–74 → ✓ Good
• 60–64 → ○ Acceptable
→ All use Amber #FFC107
• <60 → ❌ Rejected (not shown)
→ Gray #9E9E9E
🔧 Dynamic Filters
1️⃣ ATR Filter (Volatility Check)
Rejects patterns in abnormally high volatility periods.
→ If current ATR > 1.8 × 50-period ATR MA → pattern rejected
✅ Recommended for crypto, small caps
❌ Optional for calm markets (gold, bonds)
2️⃣ ADX Filter (Trend Confirmation)
Ensures a real trend exists before the pattern.
→ If ADX < 14 (70% of default 20) → pattern rejected
✅ Strongly recommended (keep ON)
3️⃣ Volume Filter (Behavior Validation)
Not used to reject patterns, but strongly affects quality score.
✅ Best for liquid markets (Forex majors, large stocks)
❌ Optional for illiquid assets
⚙️ Key Settings Explained
🔘 General Settings
• Language: Arabic / English
• Show Previous Patterns: Yes / No
→ “No” keeps chart clean; “Yes” for historical review
🔘 Pattern Selection
• Enable Double Bottom: ✅ / ❌
• Enable Double Top: ✅ / ❌
→ Use combinations:
✅✅ → Full reversal scanning
✅❌ → Long setups only
❌✅ → Short setups only
❌❌ → Indicator OFF
🔘 Detection Parameters
• Pivots Left (1–20): Higher = more reliable, fewer patterns
• Pivots Right (1–20): Lower = faster signals
• Min Width (5–100): Min candles between Bottom/Top 1 & 2
• Tolerance % (0.1%–5%): Max allowed price difference
• Min Arm (5–50): Min candles between pivot & neckline
• Min Trend (5–50): Min candles in prior trend
• Trend Lookback (50–500): How far back to detect trend start
• Extension Multiplier (1.0–5.0): How long to wait for breakout
🔘 Quality Settings
• Min Quality Score (0–100):
→ Conservative: 75–85
→ Balanced: 60–70
→ Flexible: 50–55
• Custom Weights: Adjust based on market (e.g., increase Volume weight in Forex)
🔘 Target Settings
• Bottom Bullish Target: Conservative / Balanced / Aggressive
• Bottom Bearish Target: (used on failure)
• Top Bearish Target: Conservative / Balanced / Aggressive
• Top Bullish Target: (used on failure)
🔘 Visual Settings
• Label Size: Small / Normal / Large / Huge
• Pattern Colors: Fully customizable
• Table: Show/Hide + Size (Small/Normal/Large) + Position (Top-Right / Top-Left / Bottom-Right / Bottom-Left)
• Fill Transparency: 70%–95% (default: 85%)
🔔 Alert System (8 Independent Alerts)
📌 Double Bottom Alerts
Bullish Breakout → “Double Bottom Breakout – Bullish!”
Bullish Target Hit → “Bullish Target Achieved!”
Failure (Bearish) → “Double Bottom Failed – Bearish!”
Bearish Target Hit → “Bearish Target Achieved (Failure)!”
📌 Double Top Alerts
Bearish Breakdown → “Double Top Breakdown – Bearish!”
Bearish Target Hit → “Bearish Target Achieved!”
Failure (Bullish) → “Double Top Failed – Bullish!”
Bullish Target Hit → “Bullish Target Achieved (Failure)!”
Each alert can be enabled/disabled independently and supports pop-ups, emails, or webhooks.
⚠️ Disclaimer:
This indicator is for educational and analytical purposes only. It does not constitute financial, investment, or trading advice. Use it in conjunction with your own strategy and risk management. Neither TradingView nor the developer is liable for any financial decisions or losses.
DarkPool's Gann High Low Activator DarkPool's Gann High-Low Activator.
It enhances the traditional trend-following logic by integrating Heikin Ashi smoothing, Multi-Timeframe (MTF) analysis, and volatility filtering. It is designed to filter out market noise and provide clearer trend signals during volatile conditions.
Underlying Concepts
Heikin Ashi Smoothing: Standard price candles can produce erratic signals due to wicks and short-term volatility. This script includes a "Calculation Mode" setting that allows the Gann logic to run on Heikin Ashi average prices. This smoothes out price data, helping traders stay in trends longer by ignoring temporary pullbacks.
Gann High-Low Logic: The core algorithm tracks the Simple Moving Average (SMA) of Highs and Lows over a user-defined period.
Bullish Trend: Price closes above the trailing SMA of Highs.
Bearish Trend: Price closes below the trailing SMA of Lows.
Volatility & Trend Filtering: To reduce false signals during sideways markets, this tool employs two filters:
ADX Filter (Choppiness): Uses the Average Directional Index to detect low-volatility environments. If the ADX is below the defined threshold (default 20), the indicator identifies the market as "choppy" and suppresses signals to preserve capital.
EMA Filter (Baseline): An optional Exponential Moving Average filter ensures trades are only taken in the direction of the longer-term trend (e.g., Longs only above the 200 EMA).
Features
Dual Calculation Modes: Switch between Standard price logic and Heikin Ashi smoothing logic.
Multi-Timeframe (MTF): Calculate the trend based on a higher timeframe (e.g., 4-Hour) while viewing a lower timeframe chart (e.g., 15-Minute).
Automated JSON Alerts: Generates machine-readable JSON alert payloads compatible with external trading bots and webhooks.
Live Dashboard: A data table displaying the current Trend State, Calculation Mode, ADX Value, and risk percentage.
How to Use
Buy Signal: Generated when the trend flips Bullish, provided the ADX indicates sufficient momentum and the price satisfies the EMA filter (if enabled).
Sell Signal: Generated when the trend flips Bearish, subject to the same momentum and trend filters.
Neutral State (Gray Cloud): When the cloud fill turns gray, the market is in consolidation. It is recommended to avoid entering new positions during this state.
Trailing Stop: The Gann Line serves as a dynamic trailing stop-loss level. A close beyond this line invalidates the current trend.
Settings Configuration
Calculation Mode: Select "Standard" for raw price action or "Heikin Ashi" for smoothed trend following.
Gann Length: Lower values (3-5) are suitable for short-term scalping; higher values (10+) are better for swing trading.
MTF Mode: Enable to lock the calculation to a specific higher timeframe.
ADX Threshold: Adjust based on asset volatility. Recommended: 20-25 for Crypto, 15-20 for Forex/Indices.
Disclaimer
This source code and the information presented here are for educational purposes only. This script does not constitute financial advice, trading recommendations, or a solicitation to buy or sell any financial instruments. Trading in financial markets involves a high degree of risk and may not be suitable for all investors. Past performance is not indicative of future results. The author assumes no responsibility for any losses incurred while using this indicator. Use this tool at your own discretion and risk.
Universal Ratio Trend Matrix [InvestorUnknown]The Universal Ratio Trend Matrix is designed for trend analysis on asset/asset ratios, supporting up to 40 different assets. Its primary purpose is to help identify which assets are outperforming others within a selection, providing a broad overview of market trends through a matrix of ratios. The indicator automatically expands the matrix based on the number of assets chosen, simplifying the process of comparing multiple assets in terms of performance.
Key features include the ability to choose from a narrow selection of indicators to perform the ratio trend analysis, allowing users to apply well-defined metrics to their comparison.
Drawback: Due to the computational intensity involved in calculating ratios across many assets, the indicator has a limitation related to loading speed. TradingView has time limits for calculations, and for users on the basic (free) plan, this could result in frequent errors due to exceeded time limits. To use the indicator effectively, users with any paid plans should run it on timeframes higher than 8h (the lowest timeframe on which it managed to load with 40 assets), as lower timeframes may not reliably load.
Indicators:
RSI_raw: Simple function to calculate the Relative Strength Index (RSI) of a source (asset price).
RSI_sma: Calculates RSI followed by a Simple Moving Average (SMA).
RSI_ema: Calculates RSI followed by an Exponential Moving Average (EMA).
CCI: Calculates the Commodity Channel Index (CCI).
Fisher: Implements the Fisher Transform to normalize prices.
Utility Functions:
f_remove_exchange_name: Strips the exchange name from asset tickers (e.g., "INDEX:BTCUSD" to "BTCUSD").
f_remove_exchange_name(simple string name) =>
string parts = str.split(name, ":")
string result = array.size(parts) > 1 ? array.get(parts, 1) : name
result
f_get_price: Retrieves the closing price of a given asset ticker using request.security().
f_constant_src: Checks if the source data is constant by comparing multiple consecutive values.
Inputs:
General settings allow users to select the number of tickers for analysis (used_assets) and choose the trend indicator (RSI, CCI, Fisher, etc.).
Table settings customize how trend scores are displayed in terms of text size, header visibility, highlighting options, and top-performing asset identification.
The script includes inputs for up to 40 assets, allowing the user to select various cryptocurrencies (e.g., BTCUSD, ETHUSD, SOLUSD) or other assets for trend analysis.
Price Arrays:
Price values for each asset are stored in variables (price_a1 to price_a40) initialized as na. These prices are updated only for the number of assets specified by the user (used_assets).
Trend scores for each asset are stored in separate arrays
// declare price variables as "na"
var float price_a1 = na, var float price_a2 = na, var float price_a3 = na, var float price_a4 = na, var float price_a5 = na
var float price_a6 = na, var float price_a7 = na, var float price_a8 = na, var float price_a9 = na, var float price_a10 = na
var float price_a11 = na, var float price_a12 = na, var float price_a13 = na, var float price_a14 = na, var float price_a15 = na
var float price_a16 = na, var float price_a17 = na, var float price_a18 = na, var float price_a19 = na, var float price_a20 = na
var float price_a21 = na, var float price_a22 = na, var float price_a23 = na, var float price_a24 = na, var float price_a25 = na
var float price_a26 = na, var float price_a27 = na, var float price_a28 = na, var float price_a29 = na, var float price_a30 = na
var float price_a31 = na, var float price_a32 = na, var float price_a33 = na, var float price_a34 = na, var float price_a35 = na
var float price_a36 = na, var float price_a37 = na, var float price_a38 = na, var float price_a39 = na, var float price_a40 = na
// create "empty" arrays to store trend scores
var a1_array = array.new_int(40, 0), var a2_array = array.new_int(40, 0), var a3_array = array.new_int(40, 0), var a4_array = array.new_int(40, 0)
var a5_array = array.new_int(40, 0), var a6_array = array.new_int(40, 0), var a7_array = array.new_int(40, 0), var a8_array = array.new_int(40, 0)
var a9_array = array.new_int(40, 0), var a10_array = array.new_int(40, 0), var a11_array = array.new_int(40, 0), var a12_array = array.new_int(40, 0)
var a13_array = array.new_int(40, 0), var a14_array = array.new_int(40, 0), var a15_array = array.new_int(40, 0), var a16_array = array.new_int(40, 0)
var a17_array = array.new_int(40, 0), var a18_array = array.new_int(40, 0), var a19_array = array.new_int(40, 0), var a20_array = array.new_int(40, 0)
var a21_array = array.new_int(40, 0), var a22_array = array.new_int(40, 0), var a23_array = array.new_int(40, 0), var a24_array = array.new_int(40, 0)
var a25_array = array.new_int(40, 0), var a26_array = array.new_int(40, 0), var a27_array = array.new_int(40, 0), var a28_array = array.new_int(40, 0)
var a29_array = array.new_int(40, 0), var a30_array = array.new_int(40, 0), var a31_array = array.new_int(40, 0), var a32_array = array.new_int(40, 0)
var a33_array = array.new_int(40, 0), var a34_array = array.new_int(40, 0), var a35_array = array.new_int(40, 0), var a36_array = array.new_int(40, 0)
var a37_array = array.new_int(40, 0), var a38_array = array.new_int(40, 0), var a39_array = array.new_int(40, 0), var a40_array = array.new_int(40, 0)
f_get_price(simple string ticker) =>
request.security(ticker, "", close)
// Prices for each USED asset
f_get_asset_price(asset_number, ticker) =>
if (used_assets >= asset_number)
f_get_price(ticker)
else
na
// overwrite empty variables with the prices if "used_assets" is greater or equal to the asset number
if barstate.isconfirmed // use barstate.isconfirmed to avoid "na prices" and calculation errors that result in empty cells in the table
price_a1 := f_get_asset_price(1, asset1), price_a2 := f_get_asset_price(2, asset2), price_a3 := f_get_asset_price(3, asset3), price_a4 := f_get_asset_price(4, asset4)
price_a5 := f_get_asset_price(5, asset5), price_a6 := f_get_asset_price(6, asset6), price_a7 := f_get_asset_price(7, asset7), price_a8 := f_get_asset_price(8, asset8)
price_a9 := f_get_asset_price(9, asset9), price_a10 := f_get_asset_price(10, asset10), price_a11 := f_get_asset_price(11, asset11), price_a12 := f_get_asset_price(12, asset12)
price_a13 := f_get_asset_price(13, asset13), price_a14 := f_get_asset_price(14, asset14), price_a15 := f_get_asset_price(15, asset15), price_a16 := f_get_asset_price(16, asset16)
price_a17 := f_get_asset_price(17, asset17), price_a18 := f_get_asset_price(18, asset18), price_a19 := f_get_asset_price(19, asset19), price_a20 := f_get_asset_price(20, asset20)
price_a21 := f_get_asset_price(21, asset21), price_a22 := f_get_asset_price(22, asset22), price_a23 := f_get_asset_price(23, asset23), price_a24 := f_get_asset_price(24, asset24)
price_a25 := f_get_asset_price(25, asset25), price_a26 := f_get_asset_price(26, asset26), price_a27 := f_get_asset_price(27, asset27), price_a28 := f_get_asset_price(28, asset28)
price_a29 := f_get_asset_price(29, asset29), price_a30 := f_get_asset_price(30, asset30), price_a31 := f_get_asset_price(31, asset31), price_a32 := f_get_asset_price(32, asset32)
price_a33 := f_get_asset_price(33, asset33), price_a34 := f_get_asset_price(34, asset34), price_a35 := f_get_asset_price(35, asset35), price_a36 := f_get_asset_price(36, asset36)
price_a37 := f_get_asset_price(37, asset37), price_a38 := f_get_asset_price(38, asset38), price_a39 := f_get_asset_price(39, asset39), price_a40 := f_get_asset_price(40, asset40)
Universal Indicator Calculation (f_calc_score):
This function allows switching between different trend indicators (RSI, CCI, Fisher) for flexibility.
It uses a switch-case structure to calculate the indicator score, where a positive trend is denoted by 1 and a negative trend by 0. Each indicator has its own logic to determine whether the asset is trending up or down.
// use switch to allow "universality" in indicator selection
f_calc_score(source, trend_indicator, int_1, int_2) =>
int score = na
if (not f_constant_src(source)) and source > 0.0 // Skip if you are using the same assets for ratio (for example BTC/BTC)
x = switch trend_indicator
"RSI (Raw)" => RSI_raw(source, int_1)
"RSI (SMA)" => RSI_sma(source, int_1, int_2)
"RSI (EMA)" => RSI_ema(source, int_1, int_2)
"CCI" => CCI(source, int_1)
"Fisher" => Fisher(source, int_1)
y = switch trend_indicator
"RSI (Raw)" => x > 50 ? 1 : 0
"RSI (SMA)" => x > 50 ? 1 : 0
"RSI (EMA)" => x > 50 ? 1 : 0
"CCI" => x > 0 ? 1 : 0
"Fisher" => x > x ? 1 : 0
score := y
else
score := 0
score
Array Setting Function (f_array_set):
This function populates an array with scores calculated for each asset based on a base price (p_base) divided by the prices of the individual assets.
It processes multiple assets (up to 40), calling the f_calc_score function for each.
// function to set values into the arrays
f_array_set(a_array, p_base) =>
array.set(a_array, 0, f_calc_score(p_base / price_a1, trend_indicator, int_1, int_2))
array.set(a_array, 1, f_calc_score(p_base / price_a2, trend_indicator, int_1, int_2))
array.set(a_array, 2, f_calc_score(p_base / price_a3, trend_indicator, int_1, int_2))
array.set(a_array, 3, f_calc_score(p_base / price_a4, trend_indicator, int_1, int_2))
array.set(a_array, 4, f_calc_score(p_base / price_a5, trend_indicator, int_1, int_2))
array.set(a_array, 5, f_calc_score(p_base / price_a6, trend_indicator, int_1, int_2))
array.set(a_array, 6, f_calc_score(p_base / price_a7, trend_indicator, int_1, int_2))
array.set(a_array, 7, f_calc_score(p_base / price_a8, trend_indicator, int_1, int_2))
array.set(a_array, 8, f_calc_score(p_base / price_a9, trend_indicator, int_1, int_2))
array.set(a_array, 9, f_calc_score(p_base / price_a10, trend_indicator, int_1, int_2))
array.set(a_array, 10, f_calc_score(p_base / price_a11, trend_indicator, int_1, int_2))
array.set(a_array, 11, f_calc_score(p_base / price_a12, trend_indicator, int_1, int_2))
array.set(a_array, 12, f_calc_score(p_base / price_a13, trend_indicator, int_1, int_2))
array.set(a_array, 13, f_calc_score(p_base / price_a14, trend_indicator, int_1, int_2))
array.set(a_array, 14, f_calc_score(p_base / price_a15, trend_indicator, int_1, int_2))
array.set(a_array, 15, f_calc_score(p_base / price_a16, trend_indicator, int_1, int_2))
array.set(a_array, 16, f_calc_score(p_base / price_a17, trend_indicator, int_1, int_2))
array.set(a_array, 17, f_calc_score(p_base / price_a18, trend_indicator, int_1, int_2))
array.set(a_array, 18, f_calc_score(p_base / price_a19, trend_indicator, int_1, int_2))
array.set(a_array, 19, f_calc_score(p_base / price_a20, trend_indicator, int_1, int_2))
array.set(a_array, 20, f_calc_score(p_base / price_a21, trend_indicator, int_1, int_2))
array.set(a_array, 21, f_calc_score(p_base / price_a22, trend_indicator, int_1, int_2))
array.set(a_array, 22, f_calc_score(p_base / price_a23, trend_indicator, int_1, int_2))
array.set(a_array, 23, f_calc_score(p_base / price_a24, trend_indicator, int_1, int_2))
array.set(a_array, 24, f_calc_score(p_base / price_a25, trend_indicator, int_1, int_2))
array.set(a_array, 25, f_calc_score(p_base / price_a26, trend_indicator, int_1, int_2))
array.set(a_array, 26, f_calc_score(p_base / price_a27, trend_indicator, int_1, int_2))
array.set(a_array, 27, f_calc_score(p_base / price_a28, trend_indicator, int_1, int_2))
array.set(a_array, 28, f_calc_score(p_base / price_a29, trend_indicator, int_1, int_2))
array.set(a_array, 29, f_calc_score(p_base / price_a30, trend_indicator, int_1, int_2))
array.set(a_array, 30, f_calc_score(p_base / price_a31, trend_indicator, int_1, int_2))
array.set(a_array, 31, f_calc_score(p_base / price_a32, trend_indicator, int_1, int_2))
array.set(a_array, 32, f_calc_score(p_base / price_a33, trend_indicator, int_1, int_2))
array.set(a_array, 33, f_calc_score(p_base / price_a34, trend_indicator, int_1, int_2))
array.set(a_array, 34, f_calc_score(p_base / price_a35, trend_indicator, int_1, int_2))
array.set(a_array, 35, f_calc_score(p_base / price_a36, trend_indicator, int_1, int_2))
array.set(a_array, 36, f_calc_score(p_base / price_a37, trend_indicator, int_1, int_2))
array.set(a_array, 37, f_calc_score(p_base / price_a38, trend_indicator, int_1, int_2))
array.set(a_array, 38, f_calc_score(p_base / price_a39, trend_indicator, int_1, int_2))
array.set(a_array, 39, f_calc_score(p_base / price_a40, trend_indicator, int_1, int_2))
a_array
Conditional Array Setting (f_arrayset):
This function checks if the number of used assets is greater than or equal to a specified number before populating the arrays.
// only set values into arrays for USED assets
f_arrayset(asset_number, a_array, p_base) =>
if (used_assets >= asset_number)
f_array_set(a_array, p_base)
else
na
Main Logic
The main logic initializes arrays to store scores for each asset. Each array corresponds to one asset's performance score.
Setting Trend Values: The code calls f_arrayset for each asset, populating the respective arrays with calculated scores based on the asset prices.
Combining Arrays: A combined_array is created to hold all the scores from individual asset arrays. This array facilitates further analysis, allowing for an overview of the performance scores of all assets at once.
// create a combined array (work-around since pinescript doesn't support having array of arrays)
var combined_array = array.new_int(40 * 40, 0)
if barstate.islast
for i = 0 to 39
array.set(combined_array, i, array.get(a1_array, i))
array.set(combined_array, i + (40 * 1), array.get(a2_array, i))
array.set(combined_array, i + (40 * 2), array.get(a3_array, i))
array.set(combined_array, i + (40 * 3), array.get(a4_array, i))
array.set(combined_array, i + (40 * 4), array.get(a5_array, i))
array.set(combined_array, i + (40 * 5), array.get(a6_array, i))
array.set(combined_array, i + (40 * 6), array.get(a7_array, i))
array.set(combined_array, i + (40 * 7), array.get(a8_array, i))
array.set(combined_array, i + (40 * 8), array.get(a9_array, i))
array.set(combined_array, i + (40 * 9), array.get(a10_array, i))
array.set(combined_array, i + (40 * 10), array.get(a11_array, i))
array.set(combined_array, i + (40 * 11), array.get(a12_array, i))
array.set(combined_array, i + (40 * 12), array.get(a13_array, i))
array.set(combined_array, i + (40 * 13), array.get(a14_array, i))
array.set(combined_array, i + (40 * 14), array.get(a15_array, i))
array.set(combined_array, i + (40 * 15), array.get(a16_array, i))
array.set(combined_array, i + (40 * 16), array.get(a17_array, i))
array.set(combined_array, i + (40 * 17), array.get(a18_array, i))
array.set(combined_array, i + (40 * 18), array.get(a19_array, i))
array.set(combined_array, i + (40 * 19), array.get(a20_array, i))
array.set(combined_array, i + (40 * 20), array.get(a21_array, i))
array.set(combined_array, i + (40 * 21), array.get(a22_array, i))
array.set(combined_array, i + (40 * 22), array.get(a23_array, i))
array.set(combined_array, i + (40 * 23), array.get(a24_array, i))
array.set(combined_array, i + (40 * 24), array.get(a25_array, i))
array.set(combined_array, i + (40 * 25), array.get(a26_array, i))
array.set(combined_array, i + (40 * 26), array.get(a27_array, i))
array.set(combined_array, i + (40 * 27), array.get(a28_array, i))
array.set(combined_array, i + (40 * 28), array.get(a29_array, i))
array.set(combined_array, i + (40 * 29), array.get(a30_array, i))
array.set(combined_array, i + (40 * 30), array.get(a31_array, i))
array.set(combined_array, i + (40 * 31), array.get(a32_array, i))
array.set(combined_array, i + (40 * 32), array.get(a33_array, i))
array.set(combined_array, i + (40 * 33), array.get(a34_array, i))
array.set(combined_array, i + (40 * 34), array.get(a35_array, i))
array.set(combined_array, i + (40 * 35), array.get(a36_array, i))
array.set(combined_array, i + (40 * 36), array.get(a37_array, i))
array.set(combined_array, i + (40 * 37), array.get(a38_array, i))
array.set(combined_array, i + (40 * 38), array.get(a39_array, i))
array.set(combined_array, i + (40 * 39), array.get(a40_array, i))
Calculating Sums: A separate array_sums is created to store the total score for each asset by summing the values of their respective score arrays. This allows for easy comparison of overall performance.
Ranking Assets: The final part of the code ranks the assets based on their total scores stored in array_sums. It assigns a rank to each asset, where the asset with the highest score receives the highest rank.
// create array for asset RANK based on array.sum
var ranks = array.new_int(used_assets, 0)
// for loop that calculates the rank of each asset
if barstate.islast
for i = 0 to (used_assets - 1)
int rank = 1
for x = 0 to (used_assets - 1)
if i != x
if array.get(array_sums, i) < array.get(array_sums, x)
rank := rank + 1
array.set(ranks, i, rank)
Dynamic Table Creation
Initialization: The table is initialized with a base structure that includes headers for asset names, scores, and ranks. The headers are set to remain constant, ensuring clarity for users as they interpret the displayed data.
Data Population: As scores are calculated for each asset, the corresponding values are dynamically inserted into the table. This is achieved through a loop that iterates over the scores and ranks stored in the combined_array and array_sums, respectively.
Automatic Extending Mechanism
Variable Asset Count: The code checks the number of assets defined by the user. Instead of hardcoding the number of rows in the table, it uses a variable to determine the extent of the data that needs to be displayed. This allows the table to expand or contract based on the number of assets being analyzed.
Dynamic Row Generation: Within the loop that populates the table, the code appends new rows for each asset based on the current asset count. The structure of each row includes the asset name, its score, and its rank, ensuring that the table remains consistent regardless of how many assets are involved.
// Automatically extending table based on the number of used assets
var table table = table.new(position.bottom_center, 50, 50, color.new(color.black, 100), color.white, 3, color.white, 1)
if barstate.islast
if not hide_head
table.cell(table, 0, 0, "Universal Ratio Trend Matrix", text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.merge_cells(table, 0, 0, used_assets + 3, 0)
if not hide_inps
table.cell(table, 0, 1,
text = "Inputs: You are using " + str.tostring(trend_indicator) + ", which takes: " + str.tostring(f_get_input(trend_indicator)),
text_color = color.white, text_size = fontSize), table.merge_cells(table, 0, 1, used_assets + 3, 1)
table.cell(table, 0, 2, "Assets", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, 2, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = #010c3b, text_size = fontSize)
table.cell(table, 0, x + 3, text = str.tostring(array.get(assets, x)), text_color = color.white, bgcolor = f_asset_col(array.get(ranks, x)), text_size = fontSize)
for r = 0 to (used_assets - 1)
for c = 0 to (used_assets - 1)
table.cell(table, c + 1, r + 3, text = str.tostring(array.get(combined_array, c + (r * 40))),
text_color = hl_type == "Text" ? f_get_col(array.get(combined_array, c + (r * 40))) : color.white, text_size = fontSize,
bgcolor = hl_type == "Background" ? f_get_col(array.get(combined_array, c + (r * 40))) : na)
for x = 0 to (used_assets - 1)
table.cell(table, x + 1, x + 3, "", bgcolor = #010c3b)
table.cell(table, used_assets + 1, 2, "", bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 1, x + 3, "==>", text_color = color.white)
table.cell(table, used_assets + 2, 2, "SUM", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
table.cell(table, used_assets + 3, 2, "RANK", text_color = color.white, text_size = fontSize, bgcolor = #010c3b)
for x = 0 to (used_assets - 1)
table.cell(table, used_assets + 2, x + 3,
text = str.tostring(array.get(array_sums, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_sum(array.get(array_sums, x), array.get(ranks, x)))
table.cell(table, used_assets + 3, x + 3,
text = str.tostring(array.get(ranks, x)),
text_color = color.white, text_size = fontSize,
bgcolor = f_highlight_rank(array.get(ranks, x)))
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain?
This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions.
How does a Markov Chain work, and how should I read this spheres-and-arrows diagram?
Think of three weather modes: Sunny, Rainy, Cloudy.
Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow).
The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy).
Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step.
Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny.
Now relabel the spheres to markets: Bull, Bear, Neutral.
Spheres: market regimes (uptrend, downtrend, range).
Self‑loop: tendency for the current regime to continue on the next bar.
Arrows: the most common next regime if a switch happens.
How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear).
What states and transitions are shown?
The three market states visualized are:
Bullish (Bull): Upward or strong-market regime.
Bearish (Bear): Downward or weak-market regime.
Neutral: Sideways or range-bound regime.
Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull).
How does the regime detection system work?
You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.).
Values are statistically normalized (Z-scored) over a configurable lookback period.
The normalized outputs are classified into Bull, Bear, or Neutral zones.
If using three indicators, their regime signals are averaged and smoothed for robustness.
How are transition probabilities calculated?
On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions.
The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear).
Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time.
What is unique about the visualization?
3D animated spheres represent each regime and change visually when active.
Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows.
Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed.
All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format.
Can I use custom indicators for regime classification?
Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement.
What does the “Lookback Period” control?
Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt.
How is this different from a Hidden Markov Model (HMM)?
It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust.
How is this Markov Chain different from a Hidden Markov Model (HMM)?
Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable.
Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training.
A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data.
Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune.
HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data.
How does the indicator avoid “repainting” or look-ahead bias?
All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation.
Are there practical tuning tips?
Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability.
Use custom indicators if your asset has unique regime drivers.
Watch for rapid changes in transition probabilities as early warning of a possible regime shift.
Who is this indicator for?
Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models.
Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting:
The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear).
Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions.
Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies.
It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes.
The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting.
Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis.
What's the mathematical foundation behind this?
The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM).
How to use this indicator programmatically?
The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading.
Step-by-Step Integration with Pine Script (input.source)
Add the Markov Chain indicator to your chart.
This must be done first, since your custom script will "pull" the bias signal from the indicator's plot.
In your strategy, create an input using input.source()
Example:
//@version=5
strategy("MC Bias Strategy Example")
mcBias = input.source(close, "MC Bias Source")
After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot).
Use the bias in your trading logic
Example (long only on Bull, flat otherwise):
if mcBias == 1
strategy.entry("Long", strategy.long)
else
strategy.close("Long")
For more advanced workflows, combine mcBias with additional filters or trailing stops.
How does this work behind-the-scenes?
TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source).
The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.).
This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code.
Integrating 45+ Indicators with Your Markov Chain — How & Why
The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots.
// Enhanced Custom Indicators Export - 45 Technical Indicators
// Comprehensive technical analysis suite for advanced market regime detection
//@version=6
indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500)
// |----- Input Parameters -----| //
momentum_group = "Momentum Indicators"
trend_group = "Trend Indicators"
volume_group = "Volume Indicators"
volatility_group = "Volatility Indicators"
oscillator_group = "Oscillator Indicators"
display_group = "Display Settings"
// Common lengths
length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group)
length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group)
length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group)
// Display options
show_table = input.bool(true, "Show Values Table", group=display_group)
table_size = input.string("Small", "Table Size", options= , group=display_group)
// |----- MOMENTUM INDICATORS (15 indicators) -----| //
// 1. RSI (Relative Strength Index)
rsi_14 = ta.rsi(close, length_14)
rsi_centered = rsi_14 - 50
// 2. Stochastic Oscillator
stoch_k = ta.stoch(close, high, low, length_14)
stoch_d = ta.sma(stoch_k, 3)
stoch_centered = stoch_k - 50
// 3. Williams %R
williams_r = ta.stoch(close, high, low, length_14) - 100
// 4. MACD (Moving Average Convergence Divergence)
= ta.macd(close, 12, 26, 9)
// 5. Momentum (Rate of Change)
momentum = ta.mom(close, length_14)
momentum_pct = (momentum / close ) * 100
// 6. Rate of Change (ROC)
roc = ta.roc(close, length_14)
// 7. Commodity Channel Index (CCI)
cci = ta.cci(close, length_20)
// 8. Money Flow Index (MFI)
mfi = ta.mfi(close, length_14)
mfi_centered = mfi - 50
// 9. Awesome Oscillator (AO)
ao = ta.sma(hl2, 5) - ta.sma(hl2, 34)
// 10. Accelerator Oscillator (AC)
ac = ao - ta.sma(ao, 5)
// 11. Chande Momentum Oscillator (CMO)
cmo = ta.cmo(close, length_14)
// 12. Detrended Price Oscillator (DPO)
dpo = close - ta.sma(close, length_20)
// 13. Price Oscillator (PPO)
ppo = ta.sma(close, 12) - ta.sma(close, 26)
ppo_pct = (ppo / ta.sma(close, 26)) * 100
// 14. TRIX
trix_ema1 = ta.ema(close, length_14)
trix_ema2 = ta.ema(trix_ema1, length_14)
trix_ema3 = ta.ema(trix_ema2, length_14)
trix = ta.roc(trix_ema3, 1) * 10000
// 15. Klinger Oscillator
klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55)
// 16. Fisher Transform
fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25
fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2))
// 17. Stochastic RSI
stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14)
stoch_rsi_centered = stoch_rsi - 50
// 18. Relative Vigor Index (RVI)
rvi_num = ta.swma(close - open)
rvi_den = ta.swma(high - low)
rvi = rvi_den != 0 ? rvi_num / rvi_den : 0
// 19. Balance of Power (BOP)
bop = (close - open) / (high - low)
// |----- TREND INDICATORS (10 indicators) -----| //
// 20. Simple Moving Average Momentum
sma_20 = ta.sma(close, length_20)
sma_momentum = ((close - sma_20) / sma_20) * 100
// 21. Exponential Moving Average Momentum
ema_20 = ta.ema(close, length_20)
ema_momentum = ((close - ema_20) / ema_20) * 100
// 22. Parabolic SAR
sar = ta.sar(0.02, 0.02, 0.2)
sar_trend = close > sar ? 1 : -1
// 23. Linear Regression Slope
lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1)
// 24. Moving Average Convergence (MAC)
mac = ta.sma(close, 10) - ta.sma(close, 30)
// 25. Trend Intensity Index (TII)
tii_sum = 0.0
for i = 1 to length_20
tii_sum += close > close ? 1 : 0
tii = (tii_sum / length_20) * 100
// 26. Ichimoku Cloud Components
ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2
ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2
ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1
// 27. MESA Adaptive Moving Average (MAMA)
mama_alpha = 2.0 / (length_20 + 1)
mama = ta.ema(close, length_20)
mama_momentum = ((close - mama) / mama) * 100
// 28. Zero Lag Exponential Moving Average (ZLEMA)
zlema_lag = math.round((length_20 - 1) / 2)
zlema_data = close + (close - close )
zlema = ta.ema(zlema_data, length_20)
zlema_momentum = ((close - zlema) / zlema) * 100
// |----- VOLUME INDICATORS (6 indicators) -----| //
// 29. On-Balance Volume (OBV)
obv = ta.obv
// 30. Volume Rate of Change (VROC)
vroc = ta.roc(volume, length_14)
// 31. Price Volume Trend (PVT)
pvt = ta.pvt
// 32. Negative Volume Index (NVI)
nvi = 0.0
nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi
// 33. Positive Volume Index (PVI)
pvi = 0.0
pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi
// 34. Volume Oscillator
vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10)
// 35. Ease of Movement (EOM)
eom_distance = high - low
eom_box_height = volume / 1000000
eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0
eom_sma = ta.sma(eom, length_14)
// 36. Force Index
force_index = volume * (close - close )
force_index_sma = ta.sma(force_index, length_14)
// |----- VOLATILITY INDICATORS (10 indicators) -----| //
// 37. Average True Range (ATR)
atr = ta.atr(length_14)
atr_pct = (atr / close) * 100
// 38. Bollinger Bands Position
bb_basis = ta.sma(close, length_20)
bb_dev = 2.0 * ta.stdev(close, length_20)
bb_upper = bb_basis + bb_dev
bb_lower = bb_basis - bb_dev
bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0
bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0
// 39. Keltner Channels Position
kc_basis = ta.ema(close, length_20)
kc_range = ta.ema(ta.tr, length_20)
kc_upper = kc_basis + (2.0 * kc_range)
kc_lower = kc_basis - (2.0 * kc_range)
kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0
// 40. Donchian Channels Position
dc_upper = ta.highest(high, length_20)
dc_lower = ta.lowest(low, length_20)
dc_basis = (dc_upper + dc_lower) / 2
dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0
// 41. Standard Deviation
std_dev = ta.stdev(close, length_20)
std_dev_pct = (std_dev / close) * 100
// 42. Relative Volatility Index (RVI)
rvi_up = ta.stdev(close > close ? close : 0, length_14)
rvi_down = ta.stdev(close < close ? close : 0, length_14)
rvi_total = rvi_up + rvi_down
rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50
// 43. Historical Volatility
hv_returns = math.log(close / close )
hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100
// 44. Garman-Klass Volatility
gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open)
gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100
// 45. Parkinson Volatility
park_vol = math.log(high/low) * math.log(high/low)
parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100
// 46. Rogers-Satchell Volatility
rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open)
rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100
// |----- OSCILLATOR INDICATORS (5 indicators) -----| //
// 47. Elder Ray Index
elder_bull = high - ta.ema(close, 13)
elder_bear = low - ta.ema(close, 13)
elder_power = elder_bull + elder_bear
// 48. Schaff Trend Cycle (STC)
stc_macd = ta.ema(close, 23) - ta.ema(close, 50)
stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10)
stc_d = ta.ema(stc_k, 3)
stc = ta.stoch(stc_d, stc_d, stc_d, 10)
// 49. Coppock Curve
coppock_roc1 = ta.roc(close, 14)
coppock_roc2 = ta.roc(close, 11)
coppock = ta.wma(coppock_roc1 + coppock_roc2, 10)
// 50. Know Sure Thing (KST)
kst_roc1 = ta.roc(close, 10)
kst_roc2 = ta.roc(close, 15)
kst_roc3 = ta.roc(close, 20)
kst_roc4 = ta.roc(close, 30)
kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15)
// 51. Percentage Price Oscillator (PPO)
ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100
ppo_signal = ta.ema(ppo_line, 9)
ppo_histogram = ppo_line - ppo_signal
// |----- PLOT MAIN INDICATORS -----| //
// Plot key momentum indicators
plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1)
plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1)
plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1)
plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1)
plot(cci, title="05_CCI", color=color.green, linewidth=1)
// Plot trend indicators
plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1)
plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1)
plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1)
plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1)
plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1)
// Plot volatility indicators
plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1)
plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1)
plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1)
plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1)
plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1)
// Plot volume indicators
plot(vroc, title="16_VROC", color=color.blue, linewidth=1)
plot(eom_sma, title="17_EOM", color=color.red, linewidth=1)
plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1)
plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1)
plot(obv, title="20_OBV", color=color.purple, linewidth=1)
// Plot additional oscillators
plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1)
plot(cmo, title="22_CMO", color=color.maroon, linewidth=1)
plot(dpo, title="23_DPO", color=color.teal, linewidth=1)
plot(trix, title="24_TRIX", color=color.lime, linewidth=1)
plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1)
// Plot more momentum indicators
plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1)
plot(ac, title="27_AC", color=color.aqua, linewidth=1)
plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1)
plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1)
plot(klinger, title="30_Klinger", color=color.gray, linewidth=1)
// Plot trend continuation
plot(tii, title="31_TII", color=color.blue, linewidth=1)
plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1)
plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1)
plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1)
plot(bop, title="35_BOP", color=color.purple, linewidth=1)
// Plot volume continuation
plot(nvi, title="36_NVI", color=color.navy, linewidth=1)
plot(pvi, title="37_PVI", color=color.maroon, linewidth=1)
plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1)
plot(roc, title="39_ROC", color=color.lime, linewidth=1)
plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1)
// Plot volatility continuation
plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1)
plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1)
plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1)
plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1)
plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1)
// Plot final oscillators
plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1)
plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1)
plot(stc, title="48_STC", color=color.green, linewidth=1)
plot(coppock, title="49_Coppock", color=color.orange, linewidth=1)
plot(kst, title="50_KST", color=color.purple, linewidth=1)
// Plot final indicators
plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1)
plot(pvt, title="52_PVT", color=color.maroon, linewidth=1)
// |----- Reference Lines -----| //
hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1)
hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
// |----- Enhanced Information Table -----| //
if show_table and barstate.islast
table_position = position.top_right
table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal
var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray)
// Headers
table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
// Key Momentum Indicators
table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size)
table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size)
// Key Trend Indicators
table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90))
table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size)
// Key Volatility Indicators
table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90))
table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size)
// Key Volume Indicators
table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90))
table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size)
// Key Oscillators
table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size)
// Summary Statistics
table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70))
table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size)
regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray
regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL"
table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size)
This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator.
How to use this script with the Markov Chain for research and backtesting:
Add the Enhanced Indicator Export to your chart.
Every calculated indicator is available as an individual data stream.
Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option.
In the Markov Chain indicator’s settings, turn ON the custom indicator mode.
For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name.
This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering.
Backtest regime logic directly.
Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns.
The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration.
Export the regime bias for programmatic use.
As described above, use input.source() in your Pine Script strategy or system and link the bias output.
You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes.
With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you.
Optimize your signal generation by backtesting across a universe of meaningful indicator blends.
Enhance risk management with objective, real-time regime boundaries.
Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes.
Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic.
Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem.
References & Further Reading
Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182.
Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384.
Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg.
Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764.
Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC.
On Quantitative Finance and Markov Models:
Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press.
Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press.
TradingView Pine Script Documentation: www.tradingview.com
TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com
GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org
What makes this indicator original and unique?
- On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm.
- Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script.
- Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time.
- Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window.
- Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior.
Integrate your own signals (using input.source())
- In settings, choose the Source . This is powered by input.source() .
- Feed it price, an indicator you compute inside the script, or a custom composite series.
- The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time.
Credits:
Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community.
Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization.
Disclaimer
This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
