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10-2 Year Treasury Yield Spread by zdmreLong-term bond yield reflects inflation. Short-term bond yields are tools used to predict Fed's interest rate policy. Spread between the two represents four cycles of an economy.
1. Growth
Short-term yield rises as interest rates rise. Spread narrows.
2. Slow growth
Central bank raises interest rates faster and short-term yield exceeds long-term yield. Spread turns negative.
3. Recession
High interest rates lead to more defaults. Inflation caps consumption. Central bank lowers interest rate to stimulate the economy and short-term yield falls. Spread widens.
4. Recovery
Central bank continues easing. Spread remains wide and yield curve remains steep.
0 = Recession Risk
2.6 = Recovery Plan
DYOR
6 Figures Scalping 2x MACD10-11-2019
This script plots a double MACD in a new indicator pane
The default settings:
Pink = STD MACD , settings 12-26-9
Green - Fast MACD, settings 5-15-1
The MACD settings can be changed in the indicators setting window
10/20/50/100/200 SMA'sMultiple MA's to get a good feel for momentum and interim supports and resistances
Moving Average x10 (SMA, EMA)10 configurable Simple and Exponential moving averages combined in one indicator
SMA RIBBON10 SMA's arranged in a ribbon. Color coded depending on price close. Free to use, open source. As seen in some charts.
10Y Bond Yield Spread (beta)10-Year Bond Yield Spread using Quandl data
See also:
- seekingalpha.com
- www.babypips.com
- www.forexfactory.com
10 Simple & 6 Exponential Moving Averages (w/ 18 day,week,month)* This is for the trader who wants tons of moving averages on their chart from one indicator
* Using the options, you should be able ot turn off some of them if the screen is too noisy for you
* You should also be able to change colors and thickness of the bars
* The thicker bars are for longer term averages
* This version is similar to my other script except it adds the 18 day, 18 week, and 18 Month SMa
* I added them after watching ira Epstein's YouTube videos
* Let me know if there are any bugs or things that need to be change
Quantum Market Analyzer X7Quantum Market Analyzer X7 - Complete Study Guide
Table of Contents
1. Overview
2. Indicator Components
3. Signal Interpretation
4. Live Market Analysis Guide
5. Best Practices
6. Limitations and Considerations
7. Risk Disclaimer
________________________________________
Overview
The Quantum Market Analyzer X7 is a comprehensive multi-timeframe technical analysis indicator that combines traditional and modern analytical methods. It aggregates signals from multiple technical indicators across seven key analysis categories to provide traders with a consolidated view of market sentiment and potential trading opportunities.
Key Features:
• Multi-Indicator Analysis: Combines 20+ technical indicators
• Real-Time Dashboard: Professional interface with customizable display
• Signal Aggregation: Weighted scoring system for overall market sentiment
• Advanced Analytics: Includes Order Block detection, Supertrend, and Volume analysis
• Visual Progress Indicators: Easy-to-read progress bars for signal strength
________________________________________
Indicator Components
1. Oscillators Section
Purpose: Identifies overbought/oversold conditions and momentum changes
Included Indicators:
• RSI (14): Relative Strength Index - momentum oscillator
• Stochastic (14): Compares closing price to price range
• CCI (20): Commodity Channel Index - cycle identification
• Williams %R (14): Momentum indicator similar to Stochastic
• MACD (12,26,9): Moving Average Convergence Divergence
• Momentum (10): Rate of price change
• ROC (9): Rate of Change
• Bollinger Bands (20,2): Volatility-based indicator
Signal Interpretation:
• Strong Buy (6+ points): Multiple oscillators indicate oversold conditions
• Buy (2-5 points): Moderate bullish momentum
• Neutral (-1 to 1 points): Balanced conditions
• Sell (-2 to -5 points): Moderate bearish momentum
• Strong Sell (-6+ points): Multiple oscillators indicate overbought conditions
2. Moving Averages Section
Purpose: Determines trend direction and strength
Included Indicators:
• SMA: 10, 20, 50, 100, 200 periods
• EMA: 10, 20, 50 periods
Signal Logic:
• Price >2% above MA = Strong Buy (+2)
• Price above MA = Buy (+1)
• Price below MA = Sell (-1)
• Price >2% below MA = Strong Sell (-2)
Signal Interpretation:
• Strong Buy (6+ points): Price well above multiple MAs, strong uptrend
• Buy (2-5 points): Price above most MAs, bullish trend
• Neutral (-1 to 1 points): Mixed MA signals, consolidation
• Sell (-2 to -5 points): Price below most MAs, bearish trend
• Strong Sell (-6+ points): Price well below multiple MAs, strong downtrend
3. Order Block Analysis
Purpose: Identifies institutional support/resistance levels and breakouts
How It Works:
• Detects historical levels where large orders were placed
• Monitors price behavior around these levels
• Identifies breakouts from established order blocks
Signal Types:
• BULLISH BRK (+2): Breakout above resistance order block
• BEARISH BRK (-2): Breakdown below support order block
• ABOVE SUP (+1): Price holding above support
• BELOW RES (-1): Price rejected at resistance
• NEUTRAL (0): No significant order block interaction
4. Supertrend Analysis
Purpose: Trend following indicator based on Average True Range
Parameters:
• ATR Period: 10 (default)
• ATR Multiplier: 6.0 (default)
Signal Types:
• BULLISH (+2): Price above Supertrend line
• BEARISH (-2): Price below Supertrend line
• NEUTRAL (0): Transition period
5. Trendline/Channel Analysis
Purpose: Identifies trend channels and breakout patterns
Components:
• Dynamic trendline calculation using pivot points
• Channel width based on historical volatility
• Breakout detection algorithm
Signal Types:
• UPPER BRK (+2): Breakout above upper channel
• LOWER BRK (-2): Breakdown below lower channel
• ABOVE MID (+1): Price above channel midline
• BELOW MID (-1): Price below channel midline
6. Volume Analysis
Purpose: Confirms price movements with volume data
Components:
• Volume spikes detection
• On Balance Volume (OBV)
• Volume Price Trend (VPT)
• Money Flow Index (MFI)
• Accumulation/Distribution Line
Signal Calculation: Multiple volume indicators are combined to determine institutional activity and confirm price movements.
________________________________________
Signal Interpretation
Overall Summary Signals
The indicator aggregates all component signals into an overall market sentiment:
Signal Score Range Interpretation Action
STRONG BUY 10+ Overwhelming bullish consensus Consider long positions
BUY 4-9 Moderate to strong bullish bias Look for long opportunities
NEUTRAL -3 to 3 Mixed signals, consolidation Wait for clearer direction
SELL -4 to -9 Moderate to strong bearish bias Look for short opportunities
STRONG SELL -10+ Overwhelming bearish consensus Consider short positions
Progress Bar Interpretation
• Filled bars indicate signal strength
• Green bars: Bullish signals
• Red bars: Bearish signals
• More filled bars = stronger conviction
________________________________________
Live Market Analysis Guide
Step 1: Initial Assessment
1. Check Overall Summary: Start with the main signal
2. Verify with Component Analysis: Ensure signals align
3. Look for Divergences: Identify conflicting signals
Step 2: Timeframe Analysis
1. Set Appropriate Timeframe: Use 1H for intraday, 4H/1D for swing trading
2. Multi-Timeframe Confirmation: Check higher timeframes for trend context
3. Entry Timing: Use lower timeframes for precise entry points
Step 3: Signal Confirmation Process
For Buy Signals:
1. Oscillators: Look for oversold conditions (RSI <30, Stoch <20)
2. Moving Averages: Price should be above key MAs
3. Order Blocks: Confirm bounce from support levels
4. Volume: Check for accumulation patterns
5. Supertrend: Ensure bullish trend alignment
For Sell Signals:
1. Oscillators: Look for overbought conditions (RSI >70, Stoch >80)
2. Moving Averages: Price should be below key MAs
3. Order Blocks: Confirm rejection at resistance levels
4. Volume: Check for distribution patterns
5. Supertrend: Ensure bearish trend alignment
Step 4: Risk Management Integration
1. Signal Strength Assessment: Stronger signals = larger position size
2. Stop Loss Placement: Use Order Block levels for stops
3. Take Profit Targets: Based on channel analysis and resistance levels
4. Position Sizing: Adjust based on signal confidence
________________________________________
Best Practices
Entry Strategies
1. High Conviction Entries: Wait for STRONG BUY/SELL signals
2. Confluence Trading: Look for multiple components aligning
3. Breakout Trading: Use Order Block and Trendline breakouts
4. Trend Following: Align with Supertrend direction
Risk Management
1. Never Risk More Than 2% Per Trade: Regardless of signal strength
2. Use Stop Losses: Place at invalidation levels
3. Scale Positions: Stronger signals warrant larger (but still controlled) positions
4. Diversification: Don't rely solely on one indicator
Market Conditions
1. Trending Markets: Focus on Supertrend and MA signals
2. Range-Bound Markets: Emphasize Oscillator and Order Block signals
3. High Volatility: Reduce position sizes, widen stops
4. Low Volume: Be cautious of breakout signals
Common Mistakes to Avoid
1. Signal Chasing: Don't enter after signals have already moved significantly
2. Ignoring Context: Consider overall market conditions
3. Overtrading: Wait for high-quality setups
4. Poor Risk Management: Always use appropriate position sizing
________________________________________
Limitations and Considerations
Technical Limitations
1. Lagging Nature: All technical indicators are based on historical data
2. False Signals: No indicator is 100% accurate
3. Market Regime Changes: Indicators may perform differently in various market conditions
4. Whipsaws: Possible in choppy, sideways markets
Optimal Use Cases
1. Trending Markets: Performs best in clear trending environments
2. Medium to High Volatility: Requires sufficient price movement for signals
3. Liquid Markets: Works best with adequate volume and tight spreads
4. Multiple Timeframe Analysis: Most effective when used across different timeframes
When to Use Caution
1. Major News Events: Fundamental analysis may override technical signals
2. Market Opens/Closes: Higher volatility can create false signals
3. Low Volume Periods: Signals may be less reliable
4. Holiday Trading: Reduced participation affects signal quality
________________________________________
Risk Disclaimer
IMPORTANT LEGAL DISCLAIMER FROM aiTrendview
WARNING: TRADING INVOLVES SUBSTANTIAL RISK OF LOSS
This Quantum Market Analyzer X7 indicator ("the Indicator") is provided for educational and informational purposes only. By using this indicator, you acknowledge and agree to the following terms:
No Investment Advice
• The Indicator does NOT constitute investment advice, financial advice, or trading recommendations
• All signals generated are based on historical price data and mathematical calculations
• Past performance does not guarantee future results
• No representation is made that any account will achieve profits or losses similar to those shown
Risk Acknowledgment
• TRADING CARRIES SUBSTANTIAL RISK: You may lose some or all of your invested capital
• LEVERAGE AMPLIFIES RISK: Margin trading can result in losses exceeding your initial investment
• MARKET VOLATILITY: Financial markets are inherently unpredictable and volatile
• TECHNICAL ANALYSIS LIMITATIONS: No technical indicator is infallible or guarantees profitable trades
User Responsibility
• YOU ARE SOLELY RESPONSIBLE for all trading decisions and their consequences
• CONDUCT YOUR OWN RESEARCH: Always perform independent analysis before making trading decisions
• CONSULT PROFESSIONALS: Seek advice from qualified financial advisors
• RISK MANAGEMENT: Implement appropriate risk management strategies
No Warranties
• The Indicator is provided "AS IS" without warranties of any kind
• aiTrendview makes no representations about the accuracy, reliability, or suitability of the Indicator
• Technical glitches, data feed issues, or calculation errors may occur
• The Indicator may not work as expected in all market conditions
Limitation of Liability
• aiTrendview SHALL NOT BE LIABLE for any direct, indirect, incidental, or consequential damages
• This includes but is not limited to: trading losses, missed opportunities, data inaccuracies, or system failures
• MAXIMUM LIABILITY is limited to the amount paid for the indicator (if any)
Code Usage and Distribution
• This indicator is published on TradingView in accordance with TradingView's house rules
• UNAUTHORIZED MODIFICATION or redistribution of this code is prohibited
• Users may not claim ownership of this intellectual property
• Commercial use requires explicit written permission from aiTrendview
Compliance and Regulations
• VERIFY LOCAL REGULATIONS: Ensure compliance with your jurisdiction's trading laws
• Some trading strategies may not be suitable for all investors
• Tax implications of trading are your responsibility
• Report trading activities as required by law
Specific Risk Factors
1. False Signals: The Indicator may generate incorrect buy/sell signals
2. Market Gaps: Overnight gaps can invalidate technical analysis
3. Fundamental Events: News and economic data can override technical signals
4. Liquidity Risk: Some markets may have insufficient liquidity
5. Technology Risk: Platform failures or connectivity issues may prevent order execution
Professional Trading Warning
• THIS IS NOT PROFESSIONAL TRADING SOFTWARE: Not intended for institutional or professional trading
• NO REGULATORY APPROVAL: This indicator has not been approved by any financial regulatory authority
• EDUCATIONAL PURPOSE: Designed primarily for learning technical analysis concepts
FINAL WARNING
NEVER INVEST MONEY YOU CANNOT AFFORD TO LOSE
Trading financial instruments involves significant risk. The majority of retail traders lose money. Before using this indicator in live trading:
1. Practice on paper/demo accounts extensively
2. Start with small position sizes
3. Develop a comprehensive trading plan
4. Implement strict risk management rules
5. Continuously educate yourself about market dynamics
By using the Quantum Market Analyzer X7, you acknowledge that you have read, understood, and agree to this disclaimer. You assume full responsibility for all trading decisions and their outcomes.
Contact: For questions about this disclaimer or the indicator, contact aiTrendview through official TradingView channels only.
________________________________________
This study guide and indicator are published on TradingView in compliance with TradingView's community guidelines and house rules. All users must adhere to TradingView's terms of service when using this indicator.
Document Version: 1.0
Last Updated: September 2025
Publisher: aiTrendview
________________________________________
Disclaimer from aiTrendview.com
The content provided in this blog post is for educational and training purposes only. It is not intended to be, and should not be construed as, financial, investment, or trading advice. All charting and technical analysis examples are for illustrative purposes. Trading and investing in financial markets involve substantial risk of loss and are not suitable for every individual. Before making any financial decisions, you should consult with a qualified financial professional to assess your personal financial situation.
Wick Pressure Zones [BigBeluga]
The Wick Pressure Zones indicator highlights areas where extreme wick activity occurred, signaling strong buy or sell pressure. By measuring unusually long upper or lower wicks and mapping them into gradient volume zones , the tool helps traders identify levels where liquidity was absorbed, leaving behind footprints of supply and demand imbalances. These zones often act as support, resistance, or liquidity sweep magnets .
🔵 CONCEPTS
Extreme Wicks : Large upper or lower shadows indicate aggressive rejection — upper wicks suggest selling pressure, lower wicks suggest buying pressure.
Volumatic Gradient Zones : From each detected wick, the indicator projects a layered gradient zone, proportional to the wick’s size, showing where most pressure occurred.
Liquidity Footprints : These zones mark levels where significant buy/sell volume was executed, often becoming reaction points on future retests.
Automatic Expiration : Zones persist until price decisively trades through them, after which they are cleared to keep the chart clean.
🔵 FEATURES
Automatic Wick Detection : Identifies extreme upper and lower wick events using percentile filtering and Realative Strength Index.
Gradient Zone Visualization : Builds a 10-layer zone from the wick top/bottom, shading intensity according to pressure strength.
Volume Labels : Each zone is annotated with the bar’s volume at the origin point for added context.
Dynamic Zone Extension : Zones extend to the right as long as they remain relevant; once price closes through them, they are removed.
Support & Resistance Mapping : Upper wick zones (red) behave like supply/resistance, lower wick zones (green) like demand/support.
Clutter Control : Limits the number of active zones (default 10) to keep charts responsive.
Background Highlighting : Optional background shading when new wick zones appear (red for sell, green for buy).
🔵 HOW TO USE
Look for Upper Wick Zones (red) : Indicate strong selling pressure; watch for resistance, reversals, or liquidity sweeps above.
Look for Lower Wick Zones (green) : Indicate strong buying pressure; watch for support or liquidity sweeps below.
Trade Retests : When price returns to a zone, expect a reaction (bounce or rejection) due to leftover liquidity.
Combine with Context : Align wick pressure zones with HTF support/resistance, order blocks, or volume profile for stronger signals.
Use Volume Labels : High-volume wicks indicate more significant liquidity events, making the zone more likely to act as a strong reaction point.
🔵 CONCLUSION
The Wick Pressure Zones is a powerful way to visualize hidden liquidity and aggressive rejections. By mapping extreme wick events into dynamic, volume-annotated zones, it shows traders where the market absorbed heavy buy/sell pressure. These levels frequently act as magnets or turning points, making them valuable for timing entries, stop placement, or fade strategies.
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.
ICT Killzones and Sessions W/ Silver Bullet + MacrosForex and Equity Session Tracker with Killzones, Silver Bullet, and Macro Times
This Pine Script indicator is a comprehensive timekeeping tool designed specifically for ICT traders using any time-based strategy. It helps you visualize and keep track of forex and equity session times, kill zones, macro times, and silver bullet hours.
Features:
Session and Killzone Lines:
Green: London Open (LO)
White: New York (NY)
Orange: Australian (AU)
Purple: Asian (AS)
Includes AM and PM session markers.
Dotted/Striped Lines indicate overlapping kill zones within the session timeline.
Customization Options:
Display sessions and killzones in collapsed or full view.
Hide specific sessions or killzones based on your preferences.
Customize colors, texts, and sizes.
Option to hide drawings older than the current day.
Automatic Updates:
The indicator draws all lines and boxes at the start of a new day.
Automatically adjusts time-based boxes according to the New York timezone.
Killzone Time Windows (for indices):
London KZ: 02:00 - 05:00
New York AM KZ: 07:00 - 10:00
New York PM KZ: 13:30 - 16:00
Silver Bullet Times:
03:00 - 04:00
10:00 - 11:00
14:00 - 15:00
Macro Times:
02:33 - 03:00
04:03 - 04:30
08:50 - 09:10
09:50 - 10:10
10:50 - 11:10
11:50 - 12:50
Latest Update:
January 15:
Added option to automatically change text coloring based on the chart.
Included additional optional macro times per user request:
12:50 - 13:10
13:50 - 14:15
14:50 - 15:10
15:50 - 16:15
Usage:
To maximize your experience, minimize the pane where the script is drawn. This minimizes distractions while keeping the essential time markers visible. The script is designed to help traders by clearly annotating key trading periods without overwhelming their charts.
Originality and Justification:
This indicator uniquely integrates various time-based strategies essential for ICT traders. Unlike other indicators, it consolidates session times, kill zones, macro times, and silver bullet hours into one comprehensive tool. This allows traders to have a clear and organized view of critical trading periods, facilitating better decision-making.
Credits:
This script incorporates open-source elements with significant improvements to enhance functionality and user experience.
Forex and Equity Session Tracker with Killzones, Silver Bullet, and Macro Times
This Pine Script indicator is a comprehensive timekeeping tool designed specifically for ICT traders using any time-based strategy. It helps you visualize and keep track of forex and equity session times, kill zones, macro times, and silver bullet hours.
Features:
Session and Killzone Lines:
Green: London Open (LO)
White: New York (NY)
Orange: Australian (AU)
Purple: Asian (AS)
Includes AM and PM session markers.
Dotted/Striped Lines indicate overlapping kill zones within the session timeline.
Customization Options:
Display sessions and killzones in collapsed or full view.
Hide specific sessions or killzones based on your preferences.
Customize colors, texts, and sizes.
Option to hide drawings older than the current day.
Automatic Updates:
The indicator draws all lines and boxes at the start of a new day.
Automatically adjusts time-based boxes according to the New York timezone.
Killzone Time Windows (for indices):
London KZ: 02:00 - 05:00
New York AM KZ: 07:00 - 10:00
New York PM KZ: 13:30 - 16:00
Silver Bullet Times:
03:00 - 04:00
10:00 - 11:00
14:00 - 15:00
Macro Times:
02:33 - 03:00
04:03 - 04:30
08:50 - 09:10
09:50 - 10:10
10:50 - 11:10
11:50 - 12:50
Latest Update:
January 15:
Added option to automatically change text coloring based on the chart.
Included additional optional macro times per user request:
12:50 - 13:10
13:50 - 14:15
14:50 - 15:10
15:50 - 16:15
ICT Sessions and Kill Zones
What They Are:
ICT Sessions: These are specific times during the trading day when market activity is expected to be higher, such as the London Open, New York Open, and the Asian session.
Kill Zones: These are specific time windows within these sessions where the probability of significant price movements is higher. For example, the New York AM Kill Zone is typically from 8:30 AM to 11:00 AM EST.
How to Use Them:
Identify the Session: Determine which trading session you are in (London, New York, or Asian).
Focus on Kill Zones: Within that session, focus on the kill zones for potential trade setups. For instance, during the New York session, look for setups between 8:30 AM and 11:00 AM EST.
Silver Bullets
What They Are:
Silver Bullets: These are specific, high-probability trade setups that occur within the kill zones. They are designed to be "one shot, one kill" trades, meaning they aim for precise and effective entries and exits.
How to Use Them:
Time-Based Setup: Look for these setups within the designated kill zones. For example, between 10:00 AM and 11:00 AM for the New York AM session .
Chart Analysis: Start with higher time frames like the 15-minute chart and then refine down to 5-minute and 1-minute charts to identify imbalances or specific patterns .
Macros
What They Are:
Macros: These are broader market conditions and trends that influence your trading decisions. They include understanding the overall market direction, seasonal tendencies, and the Commitment of Traders (COT) reports.
How to Use Them:
Understand Market Conditions: Be aware of the macroeconomic factors and market conditions that could affect price movements.
Seasonal Tendencies: Know the seasonal patterns that might influence the market direction.
COT Reports: Use the Commitment of Traders reports to understand the positioning of large traders and commercial hedgers .
Putting It All Together
Preparation: Understand the macro conditions and review the COT reports.
Session and Kill Zone: Identify the trading session and focus on the kill zones.
Silver Bullet Setup: Look for high-probability setups within the kill zones using refined chart analysis.
Execution: Execute the trade with precision, aiming for a "one shot, one kill" outcome.
By following these steps, you can effectively use ICT sessions, kill zones, silver bullets, and macros to enhance your trading strategy.
Usage:
To maximize your experience, shrink the pane where the script is drawn. This minimizes distractions while keeping the essential time markers visible. The script is designed to help traders by clearly annotating key trading periods without overwhelming their charts.
Originality and Justification:
This indicator uniquely integrates various time-based strategies essential for ICT traders. Unlike other indicators, it consolidates session times, kill zones, macro times, and silver bullet hours into one comprehensive tool. This allows traders to have a clear and organized view of critical trading periods, facilitating better decision-making.
Credits:
This script incorporates open-source elements with significant improvements to enhance functionality and user experience. All credit goes to itradesize for the SB + Macro boxes
Take Profit ModelThis Indicator allows you to define 9 Taking Profit levels between your floor price and a target price you define for 10 selectable Assets and tweak the levels to your preference. It does not do any fancy dynamic calculations, it just draws lines on the chart where you want them so that you have an easy reference for when to take profit (or not).
Example:
So, if your floor price for an asset is e.g. $10 and your target price is $110 (its up to you to define, who knows right, I do not have a crystal ball), You have a range of $100 where you can set your levels as follows
The first level is the Floor price you entered = $10
Formula: Level x (Target - Floor) + Floor = Take Profit level
Levels
0.1 x (110 - 10) + 10 = $20
0.2 x (110 - 10) + 10 = $30
0.3 x (110 - 10) + 10 = $40
0.4 x (110 - 10) + 10 = $50
0.5 x (110 - 10) + 10 = $60
0.6 x (110 - 10) + 10 = $70
0.7 x (110 - 10) + 10 = $80
0.8 x (110 - 10) + 10 = $90
0.9 x (110 - 10) + 10 = $100
And finally the last level is drawn for the target price
Target Price = $110
To change the settings, go to the cog icon of the Indicator, select the assets (Tickers) you have and next enter a value between 0 and 1 (as shown above) for each level, and if you want a different color. Instead of using 0.1-0.9 you e.g. can also use Fibonacci numbers like 0.235, 0.382, 0.618, 0.786 and disable (using the check mark) the rest of the levels. Experiment with this as you see fit.
Make sure that the chart you are looking at in TradingView is the same as you select in the indicator configuration e.g. COINBASE:BTCUSD should be selected as the chart as well as the Ticker in the configuration.
The Start date of the script is configurable (one date across all assets and levels)
The colors of the Levels is configurable (I am colorblind so go wild)
The standard values in the script are just examples, you need to determine the values that apply in your case and do your own research.
Your feedback is most welcome
