MFI Candle Trend🎯 Purpose:
The MFI Candle Trend is a custom TradingView indicator that transforms the Money Flow Index (MFI) into candle-style visuals using various smoothing and transformation techniques. Rather than displaying MFI as a line, this script generates synthetic candles from MFI values, helping traders visualize money flow trends, strength, and potential reversals with more clarity.
📌 Trend strength can be analyzed based on buying and selling pressures in the trend direction.
🧩 How It Works:
Calculates MFI values for open, high, low, and close prices.
Applies optional smoothing using the user-selected moving average (EMA, SMA, WMA, etc.).
Transforms the smoothed MFI data into synthetic candles using a selected method:
Normal: Uses raw MFI data
Heikin-Ashi: Applies HA transformation to MFI
Linear: Uses linear regression on MFI values
Rational Quadratic: Applies advanced rational quadratic filtering via an external kernel library
Colors candles based on MFI momentum:
Cyan: Strong positive MFI movement
Red: Strong negative MFI movement
⚙️ Key Inputs:
Method:
The type of smoothing method to apply to MFI
Options: None, EMA, SMA, SMMA (RMA), WMA, VWMA, HMA, Mode
Length:
Period for both the MFI and smoothing calculation
Candle:
Selects the transformation mode for generating synthetic candles
Options: Normal, Heikin-Ashi, Linear, Rational Quadratic
Rational Quadratic:
Adjusts the depth of smoothing for the Rational Quadratic filter (applies only if selected)
📊 Outputs:
Synthetic MFI Candlesticks:
Plotted using the smoothed and transformed MFI values.
Dynamic Coloring:
Cyan when MFI momentum is increasing
Red when MFI momentum is decreasing
Horizontal Lines:
80: Overbought zone
20: Oversold zone
🧠 Why Use This Indicator?
Unlike traditional MFI indicators that use a line plot, this tool gives traders:
A candle-based visualization of money flow momentum
Enhanced trend and reversal detection using color-coded MFI candles
A choice of smoothing filters and transformations for noise reduction
A powerful combination of momentum and structure-based analysis
To combine volume and price strength into a single chart element
❗Important Note:
This indicator is for educational and analytical purposes only. It does not constitute financial advice. Always use proper risk management and validate with additional tools or analysis.
Educational
JPMorgan G7 Volatility IndexThe JPMorgan G7 Volatility Index: Scientific Analysis and Professional Applications
Introduction
The JPMorgan G7 Volatility Index (G7VOL) represents a sophisticated metric for monitoring currency market volatility across major developed economies. This indicator functions as an approximation of JPMorgan's proprietary volatility indices, providing traders and investors with a normalized measurement of cross-currency volatility conditions (Clark, 2019).
Theoretical Foundation
Currency volatility is fundamentally defined as "the statistical measure of the dispersion of returns for a given security or market index" (Hull, 2018, p.127). In the context of G7 currencies, this volatility measurement becomes particularly significant due to the economic importance of these nations, which collectively represent more than 50% of global nominal GDP (IMF, 2022).
According to Menkhoff et al. (2012, p.685), "currency volatility serves as a global risk factor that affects expected returns across different asset classes." This finding underscores the importance of monitoring G7 currency volatility as a proxy for global financial conditions.
Methodology
The G7VOL indicator employs a multi-step calculation process:
Individual volatility calculation for seven major currency pairs using standard deviation normalized by price (Lo, 2002)
- Weighted-average combination of these volatilities to form a composite index
- Normalization against historical bands to create a standardized scale
- Visual representation through dynamic coloring that reflects current market conditions
The mathematical foundation follows the volatility calculation methodology proposed by Bollerslev et al. (2018):
Volatility = σ(returns) / price × 100
Where σ represents standard deviation calculated over a specified timeframe, typically 20 periods as recommended by the Bank for International Settlements (BIS, 2020).
Professional Applications
Professional traders and institutional investors employ the G7VOL indicator in several key ways:
1. Risk Management Signaling
According to research by Adrian and Brunnermeier (2016), elevated currency volatility often precedes broader market stress. When the G7VOL breaches its high volatility threshold (typically 1.5 times the 100-period average), portfolio managers frequently reduce risk exposure across asset classes. As noted by Borio (2019, p.17), "currency volatility spikes have historically preceded equity market corrections by 2-7 trading days."
2. Counter-Cyclical Investment Strategy
Low G7 volatility periods (readings below the lower band) tend to coincide with what Shin (2017) describes as "risk-on" environments. Professional investors often use these signals to increase allocations to higher-beta assets and emerging markets. Campbell et al. (2021) found that G7 volatility in the lowest quintile historically preceded emerging market outperformance by an average of 3.7% over subsequent quarters.
3. Regime Identification
The normalized volatility framework enables identification of distinct market regimes:
- Readings above 1.0: Crisis/high volatility regime
- Readings between -0.5 and 0.5: Normal volatility regime
- Readings below -1.0: Unusually calm markets
According to Rey (2015), these regimes have significant implications for global monetary policy transmission mechanisms and cross-border capital flows.
Interpretation and Trading Applications
G7 currency volatility serves as a barometer for global financial conditions due to these currencies' centrality in international trade and reserve status. As noted by Gagnon and Ihrig (2021, p.423), "G7 currency volatility captures both trade-related uncertainty and broader financial market risk appetites."
Professional traders apply this indicator in multiple contexts:
- Leading indicator: Research from the Federal Reserve Board (Powell, 2020) suggests G7 volatility often leads VIX movements by 1-3 days, providing advance warning of broader market volatility.
- Correlation shifts: During periods of elevated G7 volatility, cross-asset correlations typically increase what Brunnermeier and Pedersen (2009) term "correlation breakdown during stress periods." This phenomenon informs portfolio diversification strategies.
- Carry trade timing: Currency carry strategies perform best during low volatility regimes as documented by Lustig et al. (2011). The G7VOL indicator provides objective thresholds for initiating or exiting such positions.
References
Adrian, T. and Brunnermeier, M.K. (2016) 'CoVaR', American Economic Review, 106(7), pp.1705-1741.
Bank for International Settlements (2020) Monitoring Volatility in Foreign Exchange Markets. BIS Quarterly Review, December 2020.
Bollerslev, T., Patton, A.J. and Quaedvlieg, R. (2018) 'Modeling and forecasting (un)reliable realized volatilities', Journal of Econometrics, 204(1), pp.112-130.
Borio, C. (2019) 'Monetary policy in the grip of a pincer movement', BIS Working Papers, No. 706.
Brunnermeier, M.K. and Pedersen, L.H. (2009) 'Market liquidity and funding liquidity', Review of Financial Studies, 22(6), pp.2201-2238.
Campbell, J.Y., Sunderam, A. and Viceira, L.M. (2021) 'Inflation Bets or Deflation Hedges? The Changing Risks of Nominal Bonds', Critical Finance Review, 10(2), pp.303-336.
Clark, J. (2019) 'Currency Volatility and Macro Fundamentals', JPMorgan Global FX Research Quarterly, Fall 2019.
Gagnon, J.E. and Ihrig, J. (2021) 'What drives foreign exchange markets?', International Finance, 24(3), pp.414-428.
Hull, J.C. (2018) Options, Futures, and Other Derivatives. 10th edn. London: Pearson.
International Monetary Fund (2022) World Economic Outlook Database. Washington, DC: IMF.
Lo, A.W. (2002) 'The statistics of Sharpe ratios', Financial Analysts Journal, 58(4), pp.36-52.
Lustig, H., Roussanov, N. and Verdelhan, A. (2011) 'Common risk factors in currency markets', Review of Financial Studies, 24(11), pp.3731-3777.
Menkhoff, L., Sarno, L., Schmeling, M. and Schrimpf, A. (2012) 'Carry trades and global foreign exchange volatility', Journal of Finance, 67(2), pp.681-718.
Powell, J. (2020) Monetary Policy and Price Stability. Speech at Jackson Hole Economic Symposium, August 27, 2020.
Rey, H. (2015) 'Dilemma not trilemma: The global financial cycle and monetary policy independence', NBER Working Paper No. 21162.
Shin, H.S. (2017) 'The bank/capital markets nexus goes global', Bank for International Settlements Speech, January 15, 2017.
Bloomberg Financial Conditions Index (Proxy)The Bloomberg Financial Conditions Index (BFCI): A Proxy Implementation
Financial conditions indices (FCIs) have become essential tools for economists, policymakers, and market participants seeking to quantify and monitor the overall state of financial markets. Among these measures, the Bloomberg Financial Conditions Index (BFCI) has emerged as a particularly influential metric. Originally developed by Bloomberg L.P., the BFCI provides a comprehensive assessment of stress or ease in financial markets by aggregating various market-based indicators into a single, standardized value (Hatzius et al., 2010).
The original Bloomberg Financial Conditions Index synthesizes approximately 50 different financial market variables, including money market indicators, bond market spreads, equity market valuations, and volatility measures. These variables are normalized using a Z-score methodology, weighted according to their relative importance to overall financial conditions, and then aggregated to produce a composite index (Carlson et al., 2014). The resulting measure is centered around zero, with positive values indicating accommodative financial conditions and negative values representing tighter conditions relative to historical norms.
As Angelopoulou et al. (2014) note, financial conditions indices like the BFCI serve as forward-looking indicators that can signal potential economic developments before they manifest in traditional macroeconomic data. Research by Adrian et al. (2019) demonstrates that deteriorating financial conditions, as measured by indices such as the BFCI, often precede economic downturns by several months, making these indices valuable tools for predicting changes in economic activity.
Proxy Implementation Approach
The implementation presented in this Pine Script indicator represents a proxy of the original Bloomberg Financial Conditions Index, attempting to capture its essential features while acknowledging several significant constraints. Most critically, while the original BFCI incorporates approximately 50 financial variables, this proxy version utilizes only six key market components due to data accessibility limitations within the TradingView platform.
These components include:
Equity market performance (using SPY as a proxy for S&P 500)
Bond market yields (using TLT as a proxy for 20+ year Treasury yields)
Credit spreads (using the ratio between LQD and HYG as a proxy for investment-grade to high-yield spreads)
Market volatility (using VIX directly)
Short-term liquidity conditions (using SHY relative to equity prices as a proxy)
Each component is transformed into a Z-score based on log returns, weighted according to approximated importance (with weights derived from literature on financial conditions indices by Brave and Butters, 2011), and aggregated into a composite measure.
Differences from the Original BFCI
The methodology employed in this proxy differs from the original BFCI in several important ways. First, the variable selection is necessarily limited compared to Bloomberg's comprehensive approach. Second, the proxy relies on ETFs and publicly available indices rather than direct market rates and spreads used in the original. Third, the weighting scheme, while informed by academic literature, is simplified compared to Bloomberg's proprietary methodology, which may employ more sophisticated statistical techniques such as principal component analysis (Kliesen et al., 2012).
These differences mean that while the proxy BFCI captures the general direction and magnitude of financial conditions, it may not perfectly replicate the precision or sensitivity of the original index. As Aramonte et al. (2013) suggest, simplified proxies of financial conditions indices typically capture broad movements in financial conditions but may miss nuanced shifts in specific market segments that more comprehensive indices detect.
Practical Applications and Limitations
Despite these limitations, research by Arregui et al. (2018) indicates that even simplified financial conditions indices constructed from a limited set of variables can provide valuable signals about market stress and future economic activity. The proxy BFCI implemented here still offers significant insight into the relative ease or tightness of financial conditions, particularly during periods of market stress when correlations among financial variables tend to increase (Rey, 2015).
In practical applications, users should interpret this proxy BFCI as a directional indicator rather than an exact replication of Bloomberg's proprietary index. When the index moves substantially into negative territory, it suggests deteriorating financial conditions that may precede economic weakness. Conversely, strongly positive readings indicate unusually accommodative financial conditions that might support economic expansion but potentially also signal excessive risk-taking behavior in markets (López-Salido et al., 2017).
The visual implementation employs a color gradient system that enhances interpretation, with blue representing neutral conditions, green indicating accommodative conditions, and red signaling tightening conditions—a design choice informed by research on optimal data visualization in financial contexts (Few, 2009).
References
Adrian, T., Boyarchenko, N. and Giannone, D. (2019) 'Vulnerable Growth', American Economic Review, 109(4), pp. 1263-1289.
Angelopoulou, E., Balfoussia, H. and Gibson, H. (2014) 'Building a financial conditions index for the euro area and selected euro area countries: what does it tell us about the crisis?', Economic Modelling, 38, pp. 392-403.
Aramonte, S., Rosen, S. and Schindler, J. (2013) 'Assessing and Combining Financial Conditions Indexes', Finance and Economics Discussion Series, Federal Reserve Board, Washington, D.C.
Arregui, N., Elekdag, S., Gelos, G., Lafarguette, R. and Seneviratne, D. (2018) 'Can Countries Manage Their Financial Conditions Amid Globalization?', IMF Working Paper No. 18/15.
Brave, S. and Butters, R. (2011) 'Monitoring financial stability: A financial conditions index approach', Economic Perspectives, Federal Reserve Bank of Chicago, 35(1), pp. 22-43.
Carlson, M., Lewis, K. and Nelson, W. (2014) 'Using policy intervention to identify financial stress', International Journal of Finance & Economics, 19(1), pp. 59-72.
Few, S. (2009) Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press, Oakland, CA.
Hatzius, J., Hooper, P., Mishkin, F., Schoenholtz, K. and Watson, M. (2010) 'Financial Conditions Indexes: A Fresh Look after the Financial Crisis', NBER Working Paper No. 16150.
Kliesen, K., Owyang, M. and Vermann, E. (2012) 'Disentangling Diverse Measures: A Survey of Financial Stress Indexes', Federal Reserve Bank of St. Louis Review, 94(5), pp. 369-397.
López-Salido, D., Stein, J. and Zakrajšek, E. (2017) 'Credit-Market Sentiment and the Business Cycle', The Quarterly Journal of Economics, 132(3), pp. 1373-1426.
Rey, H. (2015) 'Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence', NBER Working Paper No. 21162.
RSI - SECUNDARIO - mauricioofsousaSecondary RSI – MGO
Reading the rhythm behind the price action
The Secondary RSI is a specialized oscillator developed as part of the MGO (Matriz Gráficos ON) methodology. It works as a refined strength filter, designed to complement traditional RSI readings by isolating the true internal rhythm of price action and reducing the influence of market noise.
While the standard RSI measures price momentum, the Secondary RSI focuses on identifying breaks in oscillatory balance—the moments when the market shifts from accumulation to distribution or from compression to expansion.
🎯 What the Secondary RSI highlights:
Internal imbalances in energy between buyers and sellers
Micro-divergences not visible on standard RSI
Areas of price fatigue or overextension that often precede reversals
Confirmation zones for MGO oscillatory events (RPA, RPB, RBA, RBB)
📊 Recommended use:
Combine with the Primary RSI for dual-layer validation
Use as a noise-reduction tool before entering trends
Ideal in medium timeframes (12H / 4H) where oscillatory patterns form clearly
🧠 How it works:
The Secondary RSI recalculates the momentum signal using a block-based interpretation (aligned with the MGO structure) instead of simply following raw candle data. It adapts to the periodic nature of price behavior and provides the trader with a more stable and reliable measure of true market strength.
RSI - PRIMARIO -mauricioofsousa
MGO Primary – Matriz Gráficos ON
The Blockchain of Trading applied to price behavior
The MGO Primary is the foundation of Matriz Gráficos ON — an advanced graphical methodology that transforms market movement into a logical, predictable, and objective sequence, inspired by blockchain architecture and periodic oscillatory phenomena.
This indicator replaces emotional candlestick reading with a mathematical interpretation of price blocks, cycles, and frequency. Its mission is to eliminate noise, anticipate reversals, and clearly show where capital is entering or exiting the market.
What MGO Primary detects:
Oscillatory phenomena that reveal the true behavior of orders in the book:
RPA – Breakout of Bullish Pivot
RPB – Breakout of Bearish Pivot
RBA – Sharp Bullish Breakout
RBB – Sharp Bearish Breakout
Rhythmic patterns that repeat in medium timeframes (especially on 12H and 4H)
Wave and block frequency, highlighting critical entry and exit zones
Validation through Primary and Secondary RSI, measuring the real strength behind movements
Who is this indicator for:
Traders seeking statistical clarity and visual logic
Operators who want to escape the subjectivity of candlesticks
Anyone who values technical precision with operational discipline
Recommended use:
Ideal timeframes: 12H (high precision) and 4H (moderate intensity)
Recommended assets: indices (e.g., NASDAQ), liquid stocks, and futures
Combine with: structured risk management and macro context analysis
Real-world performance:
The MGO12H achieved a 92% accuracy rate in 2025 on the NASDAQ, outperforming the average performance of major global quantitative strategies, with a net score of over 6,200 points for the year.
CRT Finder (WanHakimFX)📈 Liquidity Grab Indicator with MTF Confluence & Alerts
🔍 Overview:
The Liquidity Grab Indicator is designed to detect precise moments when price sweeps liquidity — either by wicking below recent lows (bullish LQH) or above recent highs (bearish LQL) — followed by a clear rejection. It combines this logic with multi-timeframe confirmation and trend filters, making it a powerful tool for identifying high-probability reversal setups.
⚙️ How It Works:
✅ Liquidity Sweep Logic (LQH / LQL)
Bullish (LQH):
Current candle wicks below the previous low
Closes above the previous candle body
Confirms potential bullish reversal
Bearish (LQL):
Current candle wicks above the previous high
Closes below the previous candle body
Confirms potential bearish reversal
✅ Additional Conditions:
Must occur during London or New York sessions.
Requires trend confluence:
LQH = Price must be above SMMA 60/100/200
LQL = Price must be below SMMA 60/100/200
🧠 Multi-Timeframe Confluence:
The indicator scans for LQH/LQL sweeps across:
Daily
4H
1H
30M
15M
If a sweep occurs on any of these timeframes, an alert is triggered and a triangle marker appears on the chart for real-time visual confluence.
📊 Visual Features:
Green/Red labels for active timeframe sweeps.
Dotted wick lines to show liquidity zones from the previous candle.
Colored triangle markers for MTF sweep alerts.
🛠 Strategy Usage:
This indicator is best used as a trigger tool in a confluence-based strategy:
Use higher-timeframe MTF LQH/LQL markers for directional bias.
Wait for matching sweep on your entry timeframe (e.g., M1/M5).
Enter on confirmation candle or break of structure.
Target imbalances, FVGs, or previous highs/lows.
Risk-managed entries using sweep candle's high/low as stop.
📢 Alerts:
✅ Bullish Sweep (LQH) on any timeframe
✅ Bearish Sweep (LQL) on any timeframe
IU Three Line Strike Candlestick PatternIU Three Line Strike Candlestick Pattern
This indicator identifies the Three Line Strike candlestick pattern — a rare yet powerful 4-bar reversal setup that captures exhaustion and momentum shifts at the end of strong trends.
Pattern Logic:
The Three Line Strike is a 4-candle pattern that typically signals a sharp reversal after a sustained directional move. This script detects both bullish and bearish variations using strict criteria to ensure accuracy.
Bullish Three Line Strike:
* Previous three candles must be bearish (red)
* Each of these candles must close progressively lower (indicating a strong downtrend)
* The current candle must:
* Be bullish (green)
* Open below the prior close
* Completely engulf the previous three candles by closing above the first candle's open
* And make a higher high than the last 3 bars — confirming a strong reversal
* Once confirmed, a green shaded box is drawn around the 4-bar zone to highlight the pattern
Bearish Three Line Strike:
* Previous three candles must be bullish (green)
* Each must close progressively higher (indicating a strong uptrend)
* The current candle must:
* Be bearish (red)
* Open above the prior close
* Completely engulf the prior three candles by closing below the first candle's open
* And make a lower low than the last 3 bars — confirming downside strength
* A red shaded box is plotted around the 4-bar formation to emphasize the reversal zone
Why this is unique:
Most candlestick tools focus on 1–2 bar patterns. The Three Line Strike goes a step further by combining trend exhaustion (3 same-colored candles) with a full reversal engulfing candle. This pattern is both rare and highly expressive of sentiment shift, making it a standout signal for discretionary and algorithmic traders alike.
How users can benefit:
* High-probability setups: Filters out weak signals using multi-bar confirmation logic
* Clear visual cues: Dynamic shaded boxes and labels make spotting reversals effortless
* Cross-timeframe compatible: Works on intraday and higher timeframes across all markets
* Real-time alerts: Get notified instantly when a bullish or bearish setup forms
This indicator is a valuable addition for traders who want to capture key reversals backed by strong multi-bar price action logic. Whether you are a price action purist or a pattern-based strategist, the IU Three Line Strike gives you a reliable edge.
Disclaimer:
This script is for educational purposes only and does not constitute financial advice. Trading involves risk, and past performance is not indicative of future results. Always do your own research and consult with a licensed financial advisor before making trading decisions.
NIFTY Option Chain Table with Custom CE/PE Price FiltersThis Pine Script creates a powerful and visually organized option chain dashboard for NIFTY Index Options, showing 10 Call Options (CE) and 10 Put Options (PE), with real-time prices updated on a 5-minute chart.
You can filter and view only the most relevant option contracts based on your preferred price ranges, helping you make quick decisions for scalping, intraday, or positional trades.
🔍 How It Works:
You manually select up to 10 Call Option symbols and 10 Put Option symbols from NSE (e.g., NIFTY240530C18000, NIFTY240530P18000, etc.).
Keep that time options this are old options in defalt so there will be a error
The script fetches the real-time close price of each option using the request.security() function.
You define the minimum and maximum price range separately for Calls and Puts.
The script filters out any options that fall outside of your desired price range.
Only a limited number of matching options (as set by you) are displayed in the table for both Calls and Puts.
The table is shown at your preferred location on the chart (Bottom Right, Top Left, etc.).
✅ Features:
🔟 Supports exactly 10 CE and 10 PE options for tracking.
📈 Live price updates pulled directly from the chart timeframe (5-min).
🎯 Custom price filters for CE and PE (separate inputs).
📊 Show only the top X number of contracts that meet your filter criteria.
🧱 Vertical layout with clear headers and color-coded sections (green for Calls, red for Puts).
🎛️ Position the table wherever it's most convenient on your chart.
⚡ Helps you quickly spot low premium or range-bound options during the day.
📌 Use Case:
Ideal for:
Option scalpers and day traders who want to focus only on options within a specific price zone.
Traders who want to monitor multiple strikes simultaneously without clutter.
Users building custom NIFTY strategies based on option premiums.
Strike Price selection by GoldenJetThis script is designed to assist options traders in selecting appropriate strike prices based on the latest prices of two financial instruments. It retrieves the latest prices, rounds them to the nearest significant value, and calculates potential strike prices for both call and put options. The results are displayed in a customizable table, allowing traders to quickly see the relevant strike prices for their trading decisions.
The strike prices shown are In-The-Money (ITM), which helps options traders in several ways:
Saving from Theta Decay: On expiry day, ITM options experience less time decay (Theta), which can help preserve the option's value.
Capturing Good Points: ITM options have a higher Delta, meaning they move more in line with the underlying asset's price. This can help traders capture a good amount of points as the underlying asset's price changes.
In essence, this tool simplifies the process of determining strike prices, making it easier for traders to make informed decisions and potentially improve their trading outcomes.
UNITED TRADING COMMUNITY WaterMarkWATER MARK indicator. Will allow you to improve the order of the entries you need on the chart.
1. Name and date for the traded instrument
2. Watermarks to protect your charts (in the center and around the perimeter of the chart)
3. The new "notes" option will allow you to keep focus on the factors that are important to you on the chart.
Very flexible settings for any notes, labels, watermarks on the chart that are important to you.
Индикатор WATER MARK . Даст возможность вам улучшить порядок нужных вам записей на графике.
1. Название и дата для торгуемого инструмента
2. Водные знаки для защиты ваших графиков ( в центре и по периметру графика)
3. Новая опция "заметки" позволит вам держать фокус на важных для вас факторах на графике.
Очень гибкая настройка , любых значимых для вас заметок , лейблов , вотермарк на графике.
ZMVZMV-STRATEGY
Z – Zero-Based Thinking
At the core of the ZMV-STRATEGY lies zero-based thinking: the practice of assessing actions, projects, or goals as if starting from scratch. This principle encourages:
Eliminating outdated assumptions
Prioritizing current relevance over historical momentum
Making decisions based on present and future potential, not sunk costs
M – Momentum Mapping
Momentum is essential for sustained progress. The "M" emphasizes:
Identifying key areas where traction exists
Mapping energy flows within a team, project, or market
Leveraging small wins to catalyze exponential growth
V – Value Alignment
Finally, the “V” represents value alignment, which ensures that:
Every move aligns with core values and purpose
Stakeholders are engaged through shared vision
Ethical, meaningful impact is prioritized alongside metrics
Correlation Drift📈 Correlation Drift
The Correlation Drift indicator is designed to detect shifts in market momentum by analyzing the relationship between correlation and price lag. It combines the principles of correlation analysis and lag factor measurement to provide a unique perspective on trend alignment and momentum shifts.
🔍 Core Concept:
The indicator calculates the Correlation vs PLF Ratio, which measures the alignment between an asset’s price movement and a chosen benchmark (e.g., BTCUSD). This ratio reflects how well the asset’s momentum matches the market trend while accounting for price lag.
📊 How It Works:
Correlation Calculation:
The script calculates the correlation between the asset and the selected benchmark over a specified period.
A higher correlation indicates that the asset’s price movements are in sync with the benchmark.
Price Lag Factor (PLF) Calculation:
The PLF measures the difference between long-term and short-term price momentum, dynamically scaled by recent volatility.
It highlights potential overextensions or lags in the asset’s price movements.
Combining Correlation and PLF:
The Correlation vs PLF Ratio combines these metrics to detect momentum shifts relative to the trend.
The result is a dynamic, smoothed histogram that visualizes whether the asset is leading or lagging behind the trend.
💡 How to Interpret:
Positive Values (Green/Aqua Bars):
Indicates bullish alignment with the trend.
Aqua: Rising bullish momentum, suggesting continuation.
Teal: Decreasing bullish momentum, signaling caution.
Negative Values (Purple/Fuchsia Bars):
Indicates bearish divergence from the trend.
Fuchsia: Falling bearish momentum, indicating increasing pressure.
Purple: Rising bearish momentum, suggesting potential reversal.
Clipping for Readability:
Values are clipped between -3 and +3 to prevent outliers from compressing the histogram.
This ensures clear visualization of typical momentum shifts while still marking extreme cases.
🚀 Best Practices:
Use Correlation Drift as a confirmation tool in conjunction with trend indicators (e.g., moving averages) to identify momentum alignment or divergence.
Look for transitions from positive to negative (or vice versa) as signals of potential trend shifts.
Combine with volume analysis to strengthen confidence in breakout or breakdown signals.
⚠️ Key Features:
Customizable Settings: Adjust the correlation length, PLF length, and smoothing factor to fine-tune the indicator for different market conditions.
Visual Gradient: The histogram changes color based on the strength and direction of the ratio, making it easy to identify shifts at a glance.
Zero Line Reference: Clearly distinguishes between bullish and bearish momentum zones.
🔧 Recommended Settings:
Correlation Length: 14 (for short to medium-term analysis)
PLF Length: 50 (to smooth out noise while capturing trend shifts)
Smoothing Factor: 3 (for enhanced clarity without excessive lag)
Benchmark Symbol: BTCUSD (or another relevant market indicator)
By providing a quantitative measure of trend alignment while accounting for price lag, the Correlation Drift indicator helps traders make more informed decisions during periods of momentum change. Whether you are trading crypto, forex, or equities, this tool can be a powerful addition to your momentum-based trading strategies.
⚠️ Disclaimer:
The Correlation Drift indicator is a technical analysis tool designed to aid in identifying potential shifts in market momentum and trend alignment. It is intended for informational and educational purposes only and should not be considered as financial advice or a recommendation to buy, sell, or hold any financial instrument.
Trading financial instruments, including cryptocurrencies, involves significant risk and may result in the loss of your capital. Past performance is not indicative of future results. Always conduct thorough research and seek advice from a certified financial professional before making any trading decisions.
The developer (RWCS_LTD) is not responsible for any trading losses or adverse outcomes resulting from the use of this indicator. Users are encouraged to test and validate the indicator in a simulated environment before applying it to live trading. Use at your own risk.
KingJakesFx CRTThis TradingView indicator is a comprehensive tool that identifies and marks significant high and low points of Candle Range Type (CRT) candles. Its standout feature is the ability to visualize these key levels across multiple timeframes, allowing traders to maintain awareness of important price zones even when analyzing shorter timeframes.
The indicator extends high and low lines into the future, creating dynamic support and resistance levels that help anticipate potential price reactions. With extensive customization options, users can tailor the visual appearance of lines, labels, and alerts to match their trading setup and preferences.
Perfect for traders who analyze multiple timeframes and want to maintain awareness of significant price levels, this indicator combines powerful technical analysis with flexible visual customization to enhance any trading strategy.
Goldman Sachs Risk Appetite ProxyRisk appetite indicators serve as barometers of market psychology, measuring investors' collective willingness to engage in risk-taking behavior. According to Mosley & Singer (2008), "cross-asset risk sentiment indicators provide valuable leading signals for market direction by capturing the underlying psychological state of market participants before it fully manifests in price action."
The GSRAI methodology aligns with modern portfolio theory, which emphasizes the importance of cross-asset correlations during different market regimes. As noted by Ang & Bekaert (2002), "asset correlations tend to increase during market stress, exhibiting asymmetric patterns that can be captured through multi-asset sentiment indicators."
Implementation Methodology
Component Selection
Our implementation follows the core framework outlined by Goldman Sachs research, focusing on four key components:
Credit Spreads (High Yield Credit Spread)
As noted by Duca et al. (2016), "credit spreads provide a market-based assessment of default risk and function as an effective barometer of economic uncertainty." Higher spreads generally indicate deteriorating risk appetite.
Volatility Measures (VIX)
Baker & Wurgler (2006) established that "implied volatility serves as a direct measure of market fear and uncertainty." The VIX, often called the "fear gauge," maintains an inverse relationship with risk appetite.
Equity/Bond Performance Ratio (SPY/IEF)
According to Connolly et al. (2005), "the relative performance of stocks versus bonds offers significant insight into market participants' risk preferences and flight-to-safety behavior."
Commodity Ratio (Oil/Gold)
Baur & McDermott (2010) demonstrated that "gold often functions as a safe haven during market turbulence, while oil typically performs better during risk-on environments, making their ratio an effective risk sentiment indicator."
Standardization Process
Each component undergoes z-score normalization to enable cross-asset comparisons, following the statistical approach advocated by Burdekin & Siklos (2012). The z-score transformation standardizes each variable by subtracting its mean and dividing by its standard deviation: Z = (X - μ) / σ
This approach allows for meaningful aggregation of different market signals regardless of their native scales or volatility characteristics.
Signal Integration
The four standardized components are equally weighted and combined to form a composite score. This democratic weighting approach is supported by Rapach et al. (2010), who found that "simple averaging often outperforms more complex weighting schemes in financial applications due to estimation error in the optimization process."
The final index is scaled to a 0-100 range, with:
Values above 70 indicating "Risk-On" market conditions
Values below 30 indicating "Risk-Off" market conditions
Values between 30-70 representing neutral risk sentiment
Limitations and Differences from Original Implementation
Proprietary Components
The original Goldman Sachs indicator incorporates additional proprietary elements not publicly disclosed. As Goldman Sachs Global Investment Research (2019) notes, "our comprehensive risk appetite framework incorporates proprietary positioning data and internal liquidity metrics that enhance predictive capability."
Technical Limitations
Pine Script v6 imposes certain constraints that prevent full replication:
Structural Limitations: Functions like plot, hline, and bgcolor must be defined in the global scope rather than conditionally, requiring workarounds for dynamic visualization.
Statistical Processing: Advanced statistical methods used in the original model, such as Kalman filtering or regime-switching models described by Ang & Timmermann (2012), cannot be fully implemented within Pine Script's constraints.
Data Availability: As noted by Kilian & Park (2009), "the quality and frequency of market data significantly impacts the effectiveness of sentiment indicators." Our implementation relies on publicly available data sources that may differ from Goldman Sachs' institutional data feeds.
Empirical Performance
While a formal backtest comparison with the original GSRAI is beyond the scope of this implementation, research by Froot & Ramadorai (2005) suggests that "publicly accessible proxies of proprietary sentiment indicators can capture a significant portion of their predictive power, particularly during major market turning points."
References
Ang, A., & Bekaert, G. (2002). "International Asset Allocation with Regime Shifts." Review of Financial Studies, 15(4), 1137-1187.
Ang, A., & Timmermann, A. (2012). "Regime Changes and Financial Markets." Annual Review of Financial Economics, 4(1), 313-337.
Baker, M., & Wurgler, J. (2006). "Investor Sentiment and the Cross-Section of Stock Returns." Journal of Finance, 61(4), 1645-1680.
Baur, D. G., & McDermott, T. K. (2010). "Is Gold a Safe Haven? International Evidence." Journal of Banking & Finance, 34(8), 1886-1898.
Burdekin, R. C., & Siklos, P. L. (2012). "Enter the Dragon: Interactions between Chinese, US and Asia-Pacific Equity Markets, 1995-2010." Pacific-Basin Finance Journal, 20(3), 521-541.
Connolly, R., Stivers, C., & Sun, L. (2005). "Stock Market Uncertainty and the Stock-Bond Return Relation." Journal of Financial and Quantitative Analysis, 40(1), 161-194.
Duca, M. L., Nicoletti, G., & Martinez, A. V. (2016). "Global Corporate Bond Issuance: What Role for US Quantitative Easing?" Journal of International Money and Finance, 60, 114-150.
Froot, K. A., & Ramadorai, T. (2005). "Currency Returns, Intrinsic Value, and Institutional-Investor Flows." Journal of Finance, 60(3), 1535-1566.
Goldman Sachs Global Investment Research (2019). "Risk Appetite Framework: A Practitioner's Guide."
Kilian, L., & Park, C. (2009). "The Impact of Oil Price Shocks on the U.S. Stock Market." International Economic Review, 50(4), 1267-1287.
Mosley, L., & Singer, D. A. (2008). "Taking Stock Seriously: Equity Market Performance, Government Policy, and Financial Globalization." International Studies Quarterly, 52(2), 405-425.
Oppenheimer, P. (2007). "A Framework for Financial Market Risk Appetite." Goldman Sachs Global Economics Paper.
Rapach, D. E., Strauss, J. K., & Zhou, G. (2010). "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy." Review of Financial Studies, 23(2), 821-862.
Intraday Fibs RetracementFibonacci (Fibs) levels are often used by traders as a way to find support and resistance, based on the Fibonacci sequence. These levels are widely used in technical analysis to identify potential reversal points in the price of an asset.
Fibs retracement draws lines at these Fibs level between a significant high and low point on a price chart.
What it shows:
This indicator will automatically draw Fibs Retracement Levels on your chart without any manual work.
It is designed to be used for day trading, especially in scenarios where a ticker gaps up/down large compared to the prior day close. (i.e. scenario where the difference of day's open and prior day close is large)
The drawing will happen on each trading day the moment trading hours open, and will NOT draw during pre-market and post-market.
User can see the line of each Fibs level, labelled with the Fib percentage and price value for the corresponding levels.
User will specify a start and end point of Fibs and based on the choice the indicator will automatically compute the other user defined Fibs levels and display on the chart.
How to use it:
The Fib levels drawn can be a potential support and resistance zone. Therefore in scenario where you already have a position and are approaching one of these levels it could be a point to close out some or all the position as you are approaching a resistance. On the other hand when price do approach these levels you could enter a position for a reversal trade. These are few ways to use the indicator but there are other ways that can be used, which can be found out by researching "Fibonacci (Fibs) Retracement".
In the example on the chart you can see a price bounce from the 0.7886 Fibs level on this particular day, where the price gapped up and was coming down after market hours opened.
Key settings:
1. Fibs Retracement Start and end Point: User selects where the Fibs levels should be drawn.
Available Options are:
Start Points:
Market Open
Market Open High (Dependent on the time frame you are on)
Pre-market High
Day's High
End Points:
Previous Day Close
Previous Day Low
Previous Day High
Pre-market Low (Current Day)
Day's Low
2. Custom Fib Levels: User can manually enter the Fib levels they want to see. (Max 9)
Default values are: 0,0.236,0.382,0.5,0.618,0.786,1,1.618,2.618.
3. Display settings: User can specify the line colour, thickness and style.
4. Label Setting: User can choose to turn on/off the labels for the each Fibs Level. Label will show the fib percentage and the corresponding price. User can also choose the location of the labels, defined by an offset from the current candle.
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If anything is not clear please let me know!
Statistical Reliability Index (SRI)Statistical Reliability Index (SRI)
The Statistical Reliability Index (SRI) is a professional financial analysis tool designed to assess the statistical stability and reliability of market conditions. It combines advanced statistical methods to gauge whether current market trends are statistically consistent or prone to erratic behavior. This allows traders to make more informed decisions when navigating trending and choppy markets.
Key Concepts:
1. Extrapolation of Cumulative Distribution Functions (CDF)
What is CDF?
A Cumulative Distribution Function (CDF) is a statistical tool that models the probability of a random variable falling below a certain value.
How it’s used in SRI:
The SRI utilizes the 95th percentile CDF of recent returns to estimate the likelihood of extreme price movements. This helps identify when a market is experiencing statistically significant changes, crucial for forecasting potential breakouts or breakdowns.
Weight in SRI:
The weight of the CDF extrapolation can be adjusted to emphasize its impact on the overall reliability index, allowing customization based on the trader's preference for tail risk analysis.
2. Bias Factor (BF)
What is the Bias Factor?
The Bias Factor measures the ratio of the current market price to the expected mean price calculated over a defined period. It represents the deviation from the typical price level.
How it’s used in SRI:
A higher bias factor indicates that the current price significantly deviates from the historical average, suggesting a potential mean reversion or trend exhaustion.
Weight in SRI:
Adjusting the Bias Factor weight lets users control how much this deviation influences the SRI, balancing between momentum trading and mean reversion strategies.
3. Coefficient of Variation (CV)
What is CV?
The Coefficient of Variation (CV) is a statistical measure that expresses the ratio of the standard deviation to the mean. It indicates the relative variability of asset returns, helping gauge the risk-to-return consistency.
How it’s used in SRI:
A lower CV indicates more stable and predictable price behavior, while a higher CV signals increased volatility. The SRI incorporates the inverse of the normalized CV to reflect price stability positively.
Weight in SRI:
By adjusting the CV weight, users can prioritize consistent price movements over erratic volatility, aligning the indicator with risk tolerance and strategy preferences.
Interpreting the SRI:
1. SRI Plot:
The SRI plot dynamically changes color to reflect market conditions:
Aqua Line: Indicates uptrend stability, signaling statistically consistent upward movements.
Fuchsia Line: Indicates downtrend stability, where statistically reliable downward movements are present.
The overlay background shifts between colors:
Aqua Background: Signifies statistical stability, where trends are historically consistent.
Fuchsia Background: Indicates statistical instability, often associated with trend uncertainty.
Yellow Background: Marks choppy periods, where statistical data suggests that market conditions are not conducive to reliable trading.
2. SRI Volatility Plot:
Displays the volatility of the SRI itself to detect when the indicator is stable or unstable:
Blue Area Fill: Signifies that the SRI is stable, indicating trending conditions.
Yellow Area Fill: Represents choppy or unstable SRI movements, suggesting sideways or unreliable market conditions.
A Chop Threshold Line (dotted yellow) highlights the maximum acceptable SRI volatility before the market is considered too unpredictable.
3. Stability Assessment:
Stable Trend (No Chop):
The SRI is smooth and consistent, often accompanied by aqua or fuchsia lines.
Volatility remains below the chop threshold, indicating a low-risk, trend-following environment.
Chop Mode:
The SRI becomes erratic, and the volatility plot spikes above the threshold.
Marked by a yellow shaded background, indicating uncertain and non-trending conditions.
[Trend Identification:
Use the color-coded SRI line and background to determine uptrend or downtrend reliability.
Be cautious when the SRI volatility plot shows yellow, as this signals trading conditions may not be reliable.
Practical Use Cases:
Trend Confirmation:
Utilize the SRI plot color and background to confirm whether a detected trend is statistically reliable.
Chop Mode Filtering:
During yellow chop periods, it is advisable to reduce trading activity or adopt range-bound strategies.
Strategy Filter:
Combine the SRI with trend-following indicators (like moving averages) to enhance entry and exit accuracy.
Volatility Monitoring:
Pay attention to the SRI volatility plot, as spikes often precede erratic price movements or trend reversals.
Disclaimer:
The Statistical Reliability Index (SRI) is a technical analysis tool designed to aid in market stability assessment and trend validation. It is not intended as a standalone trading signal generator. While the SRI can help identify statistically reliable trends, it is essential to incorporate additional technical and fundamental analysis to make well-informed trading decisions.
Trading and investing involve substantial risk, and past performance does not guarantee future results. Always use risk management practices and consult with a financial advisor to tailor strategies to your individual risk profile and objectives.
Position Size Calculatorusing the settings you can edit your portfolio balance and desired risk, helps you calculate everything required about position sizing and helps you NOT lose more than intended + 10% deviation on top of that.
TCP | Money Management indicator | Crypto Version📌 TCP | Money Management Indicator | Crypto Version
A robust, multi-target risk and capital management indicator tailored for crypto traders. Whether you're trading spot, perpetual futures, or leverage tokens, this tool empowers you with precise control over risk, reward, and position sizing—directly on your chart. Eliminate guesswork and trade with confidence.
🔰 Introduction: Master Your Capital, Master Your Trades
Poor money management is the number one reason traders lose their accounts, even with solid strategies. The TCP Money Management Indicator, built by Trade City Pro (TCP), solves this problem by providing a structured, rule-based approach to capital allocation.
Want to dive deeper into the concept of money management? Check out our comprehensive tutorial on TradingView, " TradeCityPro Academy: Money Management ", to understand the principles that power this indicator and transform your trading mindset.
This indicator equips you to:
• Calculate optimal position sizes based on your capital, risk percentage, and leverage
• Set up to 5 customizable take-profit targets with partial close percentages
• Access real-time metrics like Risk-to-Reward (R/R), USD profit, and margin usage
• Trade with discipline, avoiding emotional or inconsistent decisions
💸 Money Management Formula
The indicator uses a professional capital allocation model:
Position Size = (Capital × Risk %) ÷ (Stop Loss % × Leverage)
From this, it calculates:
• Total risk amount in USD
• Optimal position size for your trade
• Margin required for each take-profit target
• Adjusted R/R for each target, accounting for partial position closures
🛠 How to Use
Enter Trade Parameters: Input your capital, risk %, leverage, entry price, and stop-loss price.
Set Take-Profit Targets: Enable 1 to 5 take-profit levels and specify the percentage of the position to close at each.
Real-Time Calculations: The indicator automatically computes:
• R/R ratio for each target
• Profit in USD for each partial close
• Margin used per target (in % and USD)
Visualize Your Trade:
• Price levels for entry, stop-loss, and take-profits are plotted on the chart.
• A dynamic info panel on the left side displays all key metrics.
🔄 Dynamic Adjustments: As each take-profit target is hit and a portion of the position is closed, the indicator recalculates the remaining position size, expected profit, R/R, and margin for subsequent targets. This ensures accuracy and reflects real-world trade behavior.
📊 Table Overview
The left-side panel provides a clear snapshot:
• Trade Setup: Capital, entry price, stop-loss, risk amount, and position size
• Per Target: Percentage closed, R/R, profit in USD, and margin used
• Summary: Total expected profit across all targets
⚙️ Settings Panel
• Total Capital ($): Your account size for the trade
• Risk per Trade (%): The percentage of capital you’re willing to risk
• Leverage: The leverage applied to the trade
• Entry/Stop-Loss Prices: Define your trade’s risk zone
• Take-Profit Targets (1–5): Set price levels and percentage to close at each
🔍 Use Case Example
Imagine you have $1,000 capital, risking 1%, using 10x leverage:
• Entry: $100 | Stop-Loss: $95
• TP1: $110 (close 50%) | TP2: $115 (close 50%)
The indicator calculates the exact position size, profit at each target, and margin allocation in real time, with all metrics displayed on the chart.
✅ Why Traders Love It
• Precision: No more manual calculations or guesswork
• Versatility: Works on all crypto pairs (BTC, ETH, altcoins, etc.)
• Flexibility: Perfect for scalping, swing trading, or futures strategies
• Universal: Compatible with all timeframes
• Transparency: Fully manual, with clear and reliable outputs
🧩 Built by Trade City Pro (TCP)
Developed by TCP, a trusted name in trading tools, used by over 150,000 traders worldwide. This indicator is coded in Pine Script v5, ensuring compatibility with TradingView’s platform.
🧾 Final Notes
• No Auto-Trading: This is a manual tool for disciplined traders
• No Repainting: All calculations are accurate and non-repainting
• Tested: Rigorously validated across major crypto pairs
• Publish-Ready: Built for seamless use on TradingView
🔗 Resources
• Money Management Tutorial: Learn the fundamentals of capital management with our detailed guide: TradeCityPro Academy: Money Management
• TradingView Profile: Explore more tools by TCP on TradingView
Order Block with BoSHere’s a professional and concise description you can use for publishing your **TradingView script** titled **"Order Block with BoS"**:
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### 📌 **Description for TradingView Publication:**
**"Order Block with Break of Structure (BoS)"** is a powerful price action-based indicator designed to identify potential reversal zones and momentum shifts using **Order Block** detection combined with **Break of Structure (BoS)** confirmation.
### 🔍 **Key Features:**
* **Order Block Detection**: Highlights bullish and bearish order blocks using precise candle structure logic.
* **Break of Structure (BoS)**: Confirms structural breaks above swing highs or below swing lows to validate potential trend continuation or reversal.
* **Dynamic ATR Filter**: Uses a 14-period ATR with dynamic thresholds to confirm significant moves, filtering out weak breakouts.
* **Visual Aids**:
* Color-coded **boxes** to mark detected Order Blocks.
* **Arrows** at BoS confirmation points when ATR confirms strong momentum.
* Optional **dashed BoS lines** to show where price broke structure.
### ⚙️ **Customizable Inputs**:
* `Swing Length`: Defines the sensitivity of swing high/low detection.
* `Show Break of Structure`: Toggle on/off BoS confirmation lines.
* `Candle Lookback`: Number of historical candles to consider.
This indicator is ideal for traders who incorporate **smart money concepts**, **market structure analysis**, or **institutional order flow** strategies.
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Would you like me to help write the **strategy** version of this or translate the description into another language for international audiences?
cc AJGB Candle Range Finder with TableOverview:
The "cc AJGB Candle Range Finder with Table" is a versatile Pine Script indicator designed to identify and visualize price ranges within the 1 minute charts based on UTC+2 Time Zone. Unlike traditional range indicators, it offers three unique calculation methods to define ranges based on minute and hour interactions, displays ranges as boxes with labeled point values, and summarizes average range sizes in a customizable table. This tool is ideal for analyzing price ranges of specific time based ranges.
Features:
Customizable Time Range: Users specify a start and end minute (0-59) to define the range period (e.g., 29th to 35th minute).
Three Calculation Methods:
Minute Only: Uses the minute of each bar to identify ranges (e.g., matches user-specified minutes).
Minute - Hour: Adjusts the minute by subtracting the hour, allowing for dynamic range detection across hourly cycles.
Minute + Hour: Combines minute and hour values for a unique range calculation, useful for specific intraday patterns.
Visual Output: Draws boxes around detected ranges, with labels showing the start/end minutes and range size in points.
Summary Table: Displays the average range size (in points) for each method, with customizable position, colors, and text size.
How It Works:
The indicator evaluates each bar’s timestamp in (UTC+2 ONLY) to match user-specified minutes using one or more selected methods. When a start minute is detected, it tracks the high and low prices until the end minute, drawing a box to highlight the range and labeling it with the range size in points. A table summarizes the average range size for each method, helping traders assess typical price movements during the specified period.
Market Analysis: Compare range sizes across different methods to understand intraday volatility patterns.
Settings Customization: Adjust colors, table position, and label sizes to suit your chart preferences.
Settings:
Range to Find: Set start and end minutes.
Range Selection: Enable/disable each method and customize colors.
Range Label Size: Choose label size (Tiny to Huge).
Table Settings: Configure table position (Top, Bottom, Left, Right), sub-position, text size, and colors.
Notes:
Only works on 1 minute charts
The indicator works best using Start Times that are lower than the End Times.
Ensure the chart is set to UTC+2 Time Zone for accurate range detection.
Why It’s Unique:
Unlike standard range indicators that focus on sessions or fixed periods, this tool allows precise minute-based range detection with three distinct calculation methods, offering flexibility for data gathering. The interactive table provides quick insights into average range sizes.
ADX and DI - Trader FelipeADX and DI - Trader Felipe
This indicator combines the Average Directional Index (ADX) and the Directional Indicators (DI+ and DI-) to help traders assess market trends and their strength. It is designed to provide a clear view of whether the market is in a trending phase (either bullish or bearish) and helps identify potential entry and exit points.
What is ADX and DI?
DI+ (Green Line):
DI+ measures the strength of upward (bullish) price movements. When DI+ is above DI-, it signals that the market is experiencing upward momentum.
DI- (Red Line):
DI- measures the strength of downward (bearish) price movements. When DI- is above DI+, it suggests that the market is in a bearish phase, with downward momentum.
ADX (Blue Line):
ADX quantifies the strength of the trend, irrespective of whether it is bullish or bearish. The higher the ADX, the stronger the trend:
ADX > 20: Indicates a trending market (either up or down).
ADX < 20: Indicates a weak or sideways market with no clear trend.
Threshold Line (Gray Line):
This horizontal line, typically set at 20, represents the threshold for identifying whether the market is trending or not. If ADX is above 20, the market is considered to be in a trend. If ADX is below 20, it suggests that the market is not trending and is likely in a consolidation phase.
Summary of How to Use the Indicator:
Trend Confirmation: Use ADX > 20 to confirm a trending market. If ADX is below 20, avoid trading.
Long Entry: Enter a long position when DI+ > DI- and ADX > 20.
Short Entry: Enter a short position when DI- > DI+ and ADX > 20.
Avoid Sideways Markets: Do not trade when ADX is below 20. Look for other strategies for consolidation phases.
Exit Strategy: Exit the trade if ADX starts to decline or if the DI lines cross in the opposite direction.
Combine with Other Indicators: Use additional indicators like RSI, moving averages, or support/resistance to filter and confirm signals.
Yearly History Calendar-Aligned Price up to 10 Years)Overview
This indicator helps traders compare historical price patterns from the past 10 calendar years with the current price action. It overlays translucent lines (polylines) for each year’s price data on the same calendar dates, providing a visual reference for recurring trends. A dynamic table at the top of the chart summarizes the active years, their price sources, and history retention settings.
Key Features
Historical Projections
Displays price data from the last 10 years (e.g., January 5, 2023 vs. January 5, 2024).
Price Source Selection
Choose from Open, Low, High, Close, or HL2 ((High + Low)/2) for historical alignment.
The selected source is shown in the legend table.
Bulk Control Toggles
Show All Years : Display all 10 years simultaneously.
Keep History for All : Preserve historical lines on year transitions.
Hide History for All : Automatically delete old lines to update with current data.
Individual Year Settings
Toggle visibility for each year (-1 to -10) independently.
Customize color and line width for each year.
Control whether to keep or delete historical lines for specific years.
Visual Alignment Aids
Vertical lines mark yearly transitions for reference.
Polylines are semi-transparent for clarity.
Dynamic Legend Table
Shows active years, their price sources, and history status (On/Off).
Updates automatically when settings change.
How to Use
Configure Settings
Projection Years : Select how many years to display (1–10).
Price Source : Choose Open, Low, High, Close, or HL2 for historical alignment.
History Precision : Set granularity (Daily, 60m, or 15m).
Daily (D) is recommended for long-term analysis (covers 10 years).
60m/15m provides finer precision but may only cover 1–3 years due to data limits.
Adjust Visibility & History
Show Year -X : Enable/disable specific years for comparison.
Keep History for Year -X : Choose whether to retain historical lines or delete them on new year transitions.
Bulk Controls
Show All Years : Display all 10 years at once (overrides individual toggles).
Keep History for All / Hide History for All : Globally enable/disable history retention for all years.
Customize Appearance
Line Width : Adjust polyline thickness for better visibility.
Colors : Assign unique colors to each year for easy identification.
Interpret the Legend Table
The table shows:
Year : Label (e.g., "Year -1").
Source : The selected price type (e.g., "Close", "HL2").
Keep History : Indicates whether lines are preserved (On) or deleted (Off).
Tips for Optimal Use
Use Daily Timeframes for Long-Term Analysis :
Daily (1D) allows 10+ years of data. Smaller timeframes (60m/15m) may have limited historical coverage.
Compare Recurring Patterns :
Look for overlaps between historical polylines and current price to identify potential support/resistance levels.
Customize Colors & Widths :
Use contrasting colors for years you want to highlight. Adjust line widths to avoid clutter.
Leverage Global Toggles :
Enable Show All Years for a quick overview. Use Keep History for All to maintain continuity across transitions.
Example Workflow
Set Up :
Select Projection Years = 5.
Choose Price Source = Close.
Set History Precision = 1D for long-term data.
Customize :
Enable Show Year -1 to Show Year -5.
Assign distinct colors to each year.
Disable Keep History for All to ensure lines update on year transitions.
Analyze :
Observe how the 2023 close prices align with 2024’s price action.
Use vertical lines to identify yearly boundaries.
Common Questions
Why are some years missing?
Ensure the chart has sufficient historical data (e.g., daily charts cover 10 years, 60m/15m may only cover 1–3 years).
How do I update the data?
Adjust the Price Source or toggle years/history settings. The legend table updates automatically.
Rocky's Dynamic DikFat Supply & Demand ZonesDynamic Supply & Demand Zones
Overview
The Dynamic Supply & Demand Zones indicator identifies key supply and demand levels on your chart by detecting pivot highs and lows. It draws customizable boxes around these zones, helping traders visualize areas where price may react. With flexible display options and dynamic box behavior, this tool is designed to assist in identifying potential support and resistance levels for various trading strategies.
Key Features
Pivot-Based Zones: Automatically detects supply (resistance) and demand (support) zones using pivot highs and lows on the chart’s timeframe.
Dynamic Box Sizing: Boxes shrink when price enters them, reflecting reduced zone strength, and stop adjusting once price fully crosses through.
Customizable Display: Choose to show current-day boxes, historical boxes, or all boxes, with an option to update past box colors dynamically.
Session-Based Extension: Boxes can extend to the current bar or stop at 4:00 PM of the creation day’s 9:30 AM–4:00 PM trading session (ideal for stock markets).
Color Coding: Borders change color based on price position:
Green for demand zones (price above the box).
Red for supply zones (price below the box).
White for neutral zones (price inside the box).
User-Friendly Inputs: Adjust pivot lookback periods, box visibility, extension behavior, and colors via intuitive input settings.
How It Works
Zone Detection: The indicator uses pivot highs and lows to define supply and demand zones, plotting boxes between these levels.
Box Behavior:
Boxes are created when pivot highs and lows are confirmed, with no overlap with the previous box.
When price enters a box, it shrinks to reflect interaction, stopping once price exits completely.
Boxes can extend to the current bar or end at 4:00 PM of the creation day (or next trading day if created after 4:00 PM or on weekends).
Display Options:
Current Only: Shows boxes created on the current day.
Historical Only: Shows boxes from previous days, with optional color updates.
All Boxes: Shows all boxes, with an option to hide historical box color updates.
Performance: Limits the number of boxes to 200 to ensure smooth performance, removing older boxes as needed.
Inputs
Pivot Look Right/Left: Set the number of bars (default: 2) to confirm pivot highs and lows.
What Boxes to Show: Select Current Only, Historical Only, or All Boxes (default: Current Only).
Boxes On/Off: Toggle box visibility (default: on).
Extend Boxes to Current Bar: Choose whether boxes extend to the current bar or stop at 4:00 PM (default: off, stops at 4:00 PM).
Update Past Box Colors: Enable/disable color updates for historical boxes (default: on).
Demand/Supply/Neutral Box Color: Customize border colors (default: green, red, white).
How to Use
Add the indicator to your chart.
Adjust inputs to match your trading style (e.g., pivot lookback, box extension, colors).
Use the boxes to identify potential support (demand) and resistance (supply) zones:
Green-bordered boxes (price above) may act as support.
Red-bordered boxes (price below) may act as resistance.
White-bordered boxes (price inside) indicate active price interaction.
Combine with other analysis tools (e.g., trendlines, indicators) to confirm trade setups.
Monitor box shrinking to gauge zone strength and watch for breakouts when price fully crosses a box.
Understanding Supply and Demand in Stock Trading
In stock trading, supply and demand are fundamental forces driving price movements. Demand refers to the willingness of buyers to purchase a stock at a given price, often creating support levels where buying interest prevents further price declines. Supply represents the willingness of sellers to offload a stock, forming resistance levels where selling pressure halts price increases. These zones are critical because they highlight areas where significant buying or selling activity has occurred, influencing future price behavior.
The importance of supply and demand lies in their ability to reveal where institutional traders, with large orders, have entered or exited the market. Demand zones, often seen at pivot lows, indicate strong buying interest and potential areas for price reversals or bounces. Supply zones, typically at pivot highs, signal heavy selling and possible reversal points for downward moves. By identifying these zones, traders can anticipate where price is likely to stall, reverse, or break out, enabling better entry and exit decisions. This indicator visualizes these zones as dynamic boxes, making it easier to spot high-probability trading opportunities while emphasizing the core market dynamics of supply and demand.
Feedback
This indicator is designed to help traders visualize supply and demand zones effectively. If you have suggestions for improvements, please share your feedback in the comments!
Liquidity Sweep DetectorThe Liquidity Sweep Detector represents a technical analysis tool specifically designed to identify market microstructure patterns typically associated with institutional trading activity. According to Harris (2003), institutional traders frequently employ tactics where they momentarily break through price levels to trigger stop orders before redirecting the market in the opposite direction. This phenomenon, commonly referred to as "stop hunting" or "liquidity sweeping," constitutes a significant aspect of institutional order flow analysis (Osler, 2003). The current implementation provides retail traders with a means to identify these patterns, potentially aligning their trading decisions with institutional movements rather than becoming victims of such strategies.
Osler's (2003) research documents how stop-loss orders tend to cluster around significant price levels, creating concentrations of liquidity. Taylor (2005) argues that sophisticated institutional participants systematically exploit these liquidity clusters by inducing price movements that trigger these orders, subsequently profiting from the ensuing price reaction. The algorithmic detection of such patterns involves several key processes. First, the indicator identifies swing points—local maxima and minima—through comparison with historical price data within a definable lookback period. These swing points correspond to what Bulkowski (2011) describes as "significant pivot points" that frequently serve as liquidity zones where stop orders accumulate.
The core detection algorithm utilizes a multi-stage process to identify potential sweeps. For high sweeps, it monitors when price exceeds a previous swing high by a specified threshold percentage, followed by a bearish candle that closes below the original swing high level. Conversely, for low sweeps, it detects when price drops below a previous swing low by the threshold percentage, followed by a bullish candle closing above the original swing low. As noted by Lo and MacKinlay (2011), these price patterns often emerge when large institutional players attempt to capture liquidity before initiating significant directional moves.
The indicator maintains historical arrays of detected sweep events with their corresponding timestamps, enabling temporal analysis of market behavior following such events. Visual elements include horizontal lines marking sweep levels, background color highlighting for sweep events, and an information table displaying active sweeps with their corresponding price levels and elapsed time since detection. This visualization approach allows traders to quickly identify potential institutional activity without requiring complex interpretation of raw price data.
Parameter customization includes adjustable lookback periods for swing point identification, sweep threshold percentages for signal sensitivity, and display duration settings. These parameters allow traders to adapt the indicator to various market conditions and timeframes, as markets demonstrate different liquidity characteristics across instruments and periods (Madhavan, 2000).
Empirical studies by Easley et al. (2012) suggest that retail traders who successfully identify and act upon institutional liquidity sweeps may achieve superior risk-adjusted returns compared to conventional technical analysis approaches. However, as cautioned by Chordia et al. (2008), such patterns should be considered within broader market context rather than in isolation, as their predictive value varies significantly with overall market volatility and liquidity conditions.
References:
Bulkowski, T. (2011). Encyclopedia of Chart Patterns (2nd ed.). John Wiley & Sons.
Chordia, T., Roll, R., & Subrahmanyam, A. (2008). Liquidity and market efficiency. Journal of Financial Economics, 87(2), 249-268.
Easley, D., López de Prado, M., & O'Hara, M. (2012). Flow Toxicity and Liquidity in a High-frequency World. The Review of Financial Studies, 25(5), 1457-1493.
Harris, L. (2003). Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press.
Lo, A. W., & MacKinlay, A. C. (2011). A Non-Random Walk Down Wall Street. Princeton University Press.
Madhavan, A. (2000). Market microstructure: A survey. Journal of Financial Markets, 3(3), 205-258.
Osler, C. L. (2003). Currency Orders and Exchange Rate Dynamics: An Explanation for the Predictive Success of Technical Analysis. Journal of Finance, 58(5), 1791-1820.
Taylor, M. P. (2005). Official Foreign Exchange Intervention as a Coordinating Signal in the Dollar-Yen Market. Pacific Economic Review, 10(1), 73-82.
