Volatility Risk PremiumTHE INSURANCE PREMIUM OF THE STOCK MARKET
Every day, millions of investors face a fundamental question that has puzzled economists for decades: how much should protection against market crashes cost? The answer lies in a phenomenon called the Volatility Risk Premium, and understanding it may fundamentally change how you interpret market conditions.
Think of the stock market like a neighborhood where homeowners buy insurance against fire. The insurance company charges premiums based on their estimates of fire risk. But here is the interesting part: insurance companies systematically charge more than the actual expected losses. This difference between what people pay and what actually happens is the insurance premium. The same principle operates in financial markets, but instead of fire insurance, investors buy protection against market volatility through options contracts.
The Volatility Risk Premium, or VRP, measures exactly this difference. It represents the gap between what the market expects volatility to be (implied volatility, as reflected in options prices) and what volatility actually turns out to be (realized volatility, calculated from actual price movements). This indicator quantifies that gap and transforms it into actionable intelligence.
THE FOUNDATION
The academic study of volatility risk premiums began gaining serious traction in the early 2000s, though the phenomenon itself had been observed by practitioners for much longer. Three research papers form the backbone of this indicator's methodology.
Peter Carr and Liuren Wu published their seminal work "Variance Risk Premiums" in the Review of Financial Studies in 2009. Their research established that variance risk premiums exist across virtually all asset classes and persist over time. They documented that on average, implied volatility exceeds realized volatility by approximately three to four percentage points annualized. This is not a small number. It means that sellers of volatility insurance have historically collected a substantial premium for bearing this risk.
Tim Bollerslev, George Tauchen, and Hao Zhou extended this research in their 2009 paper "Expected Stock Returns and Variance Risk Premia," also published in the Review of Financial Studies. Their critical contribution was demonstrating that the VRP is a statistically significant predictor of future equity returns. When the VRP is high, meaning investors are paying substantial premiums for protection, future stock returns tend to be positive. When the VRP collapses or turns negative, it often signals that realized volatility has spiked above expectations, typically during market stress periods.
Gurdip Bakshi and Nikunj Kapadia provided additional theoretical grounding in their 2003 paper "Delta-Hedged Gains and the Negative Market Volatility Risk Premium." They demonstrated through careful empirical analysis why volatility sellers are compensated: the risk is not diversifiable and tends to materialize precisely when investors can least afford losses.
HOW THE INDICATOR CALCULATES VOLATILITY
The calculation begins with two separate measurements that must be compared: implied volatility and realized volatility.
For implied volatility, the indicator uses the CBOE Volatility Index, commonly known as the VIX. The VIX represents the market's expectation of 30-day forward volatility on the S&P 500, calculated from a weighted average of out-of-the-money put and call options. It is often called the "fear gauge" because it rises when investors rush to buy protective options.
Realized volatility requires more careful consideration. The indicator offers three distinct calculation methods, each with specific advantages rooted in academic literature.
The Close-to-Close method is the most straightforward approach. It calculates the standard deviation of logarithmic daily returns over a specified lookback period, then annualizes this figure by multiplying by the square root of 252, the approximate number of trading days in a year. This method is intuitive and widely used, but it only captures information from closing prices and ignores intraday price movements.
The Parkinson estimator, developed by Michael Parkinson in 1980, improves efficiency by incorporating high and low prices. The mathematical formula calculates variance as the sum of squared log ratios of daily highs to lows, divided by four times the natural logarithm of two, times the number of observations. This estimator is theoretically about five times more efficient than the close-to-close method because high and low prices contain additional information about the volatility process.
The Garman-Klass estimator, published by Mark Garman and Michael Klass in 1980, goes further by incorporating opening, high, low, and closing prices. The formula combines half the squared log ratio of high to low prices minus a factor involving the log ratio of close to open. This method achieves the minimum variance among estimators using only these four price points, making it particularly valuable for markets where intraday information is meaningful.
THE CORE VRP CALCULATION
Once both volatility measures are obtained, the VRP calculation is straightforward: subtract realized volatility from implied volatility. A positive result means the market is paying a premium for volatility insurance. A negative result means realized volatility has exceeded expectations, typically indicating market stress.
The raw VRP signal receives slight smoothing through an exponential moving average to reduce noise while preserving responsiveness. The default smoothing period of five days balances signal clarity against lag.
INTERPRETING THE REGIMES
The indicator classifies market conditions into five distinct regimes based on VRP levels.
The EXTREME regime occurs when VRP exceeds ten percentage points. This represents an unusual situation where the gap between implied and realized volatility is historically wide. Markets are pricing in significantly more fear than is materializing. Research suggests this often precedes positive equity returns as the premium normalizes.
The HIGH regime, between five and ten percentage points, indicates elevated risk aversion. Investors are paying above-average premiums for protection. This often occurs after market corrections when fear remains elevated but realized volatility has begun subsiding.
The NORMAL regime covers VRP between zero and five percentage points. This represents the long-term average state of markets where implied volatility modestly exceeds realized volatility. The insurance premium is being collected at typical rates.
The LOW regime, between negative two and zero percentage points, suggests either unusual complacency or that realized volatility is catching up to implied volatility. The premium is shrinking, which can precede either calm continuation or increased stress.
The NEGATIVE regime occurs when realized volatility exceeds implied volatility. This is relatively rare and typically indicates active market stress. Options were priced for less volatility than actually occurred, meaning volatility sellers are experiencing losses. Historically, deeply negative VRP readings have often coincided with market bottoms, though timing the reversal remains challenging.
TERM STRUCTURE ANALYSIS
Beyond the basic VRP calculation, sophisticated market participants analyze how volatility behaves across different time horizons. The indicator calculates VRP using both short-term (default ten days) and long-term (default sixty days) realized volatility windows.
Under normal market conditions, short-term realized volatility tends to be lower than long-term realized volatility. This produces what traders call contango in the term structure, analogous to futures markets where later delivery dates trade at premiums. The RV Slope metric quantifies this relationship.
When markets enter stress periods, the term structure often inverts. Short-term realized volatility spikes above long-term realized volatility as markets experience immediate turmoil. This backwardation condition serves as an early warning signal that current volatility is elevated relative to historical norms.
The academic foundation for term structure analysis comes from Scott Mixon's 2007 paper "The Implied Volatility Term Structure" in the Journal of Derivatives, which documented the predictive power of term structure dynamics.
MEAN REVERSION CHARACTERISTICS
One of the most practically useful properties of the VRP is its tendency to mean-revert. Extreme readings, whether high or low, tend to normalize over time. This creates opportunities for systematic trading strategies.
The indicator tracks VRP in statistical terms by calculating its Z-score relative to the trailing one-year distribution. A Z-score above two indicates that current VRP is more than two standard deviations above its mean, a statistically unusual condition. Similarly, a Z-score below negative two indicates VRP is unusually low.
Mean reversion signals trigger when VRP reaches extreme Z-score levels and then shows initial signs of reversal. A buy signal occurs when VRP recovers from oversold conditions (Z-score below negative two and rising), suggesting that the period of elevated realized volatility may be ending. A sell signal occurs when VRP contracts from overbought conditions (Z-score above two and falling), suggesting the fear premium may be excessive and due for normalization.
These signals should not be interpreted as standalone trading recommendations. They indicate probabilistic conditions based on historical patterns. Market context and other factors always matter.
MOMENTUM ANALYSIS
The rate of change in VRP carries its own information content. Rapidly rising VRP suggests fear is building faster than volatility is materializing, often seen in the early stages of corrections before realized volatility catches up. Rapidly falling VRP indicates either calming conditions or rising realized volatility eating into the premium.
The indicator tracks VRP momentum as the difference between current VRP and VRP from a specified number of bars ago. Positive momentum with positive acceleration suggests strengthening risk aversion. Negative momentum with negative acceleration suggests intensifying stress or rapid normalization from elevated levels.
PRACTICAL APPLICATION
For equity investors, the VRP provides context for risk management decisions. High VRP environments historically favor equity exposure because the market is pricing in more pessimism than typically materializes. Low or negative VRP environments suggest either reducing exposure or hedging, as markets may be underpricing risk.
For options traders, understanding VRP is fundamental to strategy selection. Strategies that sell volatility, such as covered calls, cash-secured puts, or iron condors, tend to profit when VRP is elevated and compress toward its mean. Strategies that buy volatility tend to profit when VRP is low and risk materializes.
For systematic traders, VRP provides a regime filter for other strategies. Momentum strategies may benefit from different parameters in high versus low VRP environments. Mean reversion strategies in VRP itself can form the basis of a complete trading system.
LIMITATIONS AND CONSIDERATIONS
No indicator provides perfect foresight, and the VRP is no exception. Several limitations deserve attention.
The VRP measures a relationship between two estimates, each subject to measurement error. The VIX represents expectations that may prove incorrect. Realized volatility calculations depend on the chosen method and lookback period.
Mean reversion tendencies hold over longer time horizons but provide limited guidance for short-term timing. VRP can remain extreme for extended periods, and mean reversion signals can generate losses if the extremity persists or intensifies.
The indicator is calibrated for equity markets, specifically the S&P 500. Application to other asset classes requires recalibration of thresholds and potentially different data sources.
Historical relationships between VRP and subsequent returns, while statistically robust, do not guarantee future performance. Structural changes in markets, options pricing, or investor behavior could alter these dynamics.
STATISTICAL OUTPUTS
The indicator presents comprehensive statistics including current VRP level, implied volatility from VIX, realized volatility from the selected method, current regime classification, number of bars in the current regime, percentile ranking over the lookback period, Z-score relative to recent history, mean VRP over the lookback period, realized volatility term structure slope, VRP momentum, mean reversion signal status, and overall market bias interpretation.
Color coding throughout the indicator provides immediate visual interpretation. Green tones indicate elevated VRP associated with fear and potential opportunity. Red tones indicate compressed or negative VRP associated with complacency or active stress. Neutral tones indicate normal market conditions.
ALERT CONDITIONS
The indicator provides alerts for regime transitions, extreme statistical readings, term structure inversions, mean reversion signals, and momentum shifts. These can be configured through the TradingView alert system for real-time monitoring across multiple timeframes.
REFERENCES
Bakshi, G., and Kapadia, N. (2003). Delta-Hedged Gains and the Negative Market Volatility Risk Premium. Review of Financial Studies, 16(2), 527-566.
Bollerslev, T., Tauchen, G., and Zhou, H. (2009). Expected Stock Returns and Variance Risk Premia. Review of Financial Studies, 22(11), 4463-4492.
Carr, P., and Wu, L. (2009). Variance Risk Premiums. Review of Financial Studies, 22(3), 1311-1341.
Garman, M. B., and Klass, M. J. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53(1), 67-78.
Mixon, S. (2007). The Implied Volatility Term Structure of Stock Index Options. Journal of Empirical Finance, 14(3), 333-354.
Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53(1), 61-65.
在腳本中搜尋"reversal"
Kurtosis with Skew Crossover Focused OscillatorDescription:
This indicator highlights Skewness/Kurtosis crossovers for short-term trading:
Green upward arrows: Skew crosses above Kurtosis → potential long signal.
Red downward arrows: Skew crosses below Kurtosis → potential short signal.
Yellow upward arrows: Extreme negative skew (skew ≤ -1.7) → potential oversold/reversal opportunity.
Oscillator Pane:
Orange = Skewness (smoothed)
Blue = Kurtosis (adjusted, smoothed)
Zero line = visual reference
Usage:
Primarily for 2–5 minute charts, highlighting statistical anomalies and potential short-term reversals that can be used in conjunction with OBV and/or CVD
Arrows signal potential entries based on skew/kurt dynamics.
Potential ideas???????
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Add Supporting Market Context
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Currently, signals are purely based on skew/kurt crossovers. Adding supporting indicators could improve reliability:
Volume / CVD: Identify when crossovers occur with real buying/selling pressure.
Wick Imbalance: Detect forced moves in price structure.
Volatility Regime (Parkinson / ATR): Filter signals during high volatility spikes or compressions.
Experimentation: Try weighting these supporting signals to dynamically confirm or filter skew/kurt crossovers and see if false signals decrease on 2–5 minute charts.
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Dynamic Thresholds & Scaling
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Right now, the extreme skew signal is triggered at a fixed level (skew ≤ -1.7). Future improvements could include:
Adaptive thresholds: Scale extreme skew levels based on recent standard deviation or intraday volatility.
Kurtosis thresholds: Introduce a cutoff for kurtosis to identify “fat-tail” events.
Experimentation: Backtest different adaptive thresholds for both skew and kurt, and see how it affects the precision vs. frequency of signals.
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Multi-Timeframe or Combined Oscillator
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Skew/kurt signals could be combined across multiple intraday timeframes (e.g., 1-min, 3-min, 5-min) to improve confirmation.
Create a composite oscillator that blends short-term and slightly longer-term skew/kurt values to reduce noise.
Experimentation: Compare a single timeframe approach vs multi-timeframe composite, and measure signal reliability and lag.
I'm leaving this open so anyone can experiment with it as this project may be on the backburner, but these are my thoughts so far
NoProcess PivotsNoProcess Pivots
Visualize the structural framework of price action with NoProcess Pivots, a precision tool for multi-timeframe confluence trading.
Pivots are mathematically derived levels where price statistically finds support, resistance, or equilibrium. Institutional order flow respects these levels as key decision points where liquidity pools form and inefficiencies seek rebalancing.
NoProcess Pivots displays historical pivot ranges as period-bounded zones across Daily, Weekly, and Quarterly timeframes—allowing you to observe how price has respected or violated these levels over time. By projecting ±33% extensions beyond R1/S1, traders can identify targets, retracement levels, and key reversal points.
Cross-reference pivots across multiple timeframes to find confluence zones where Daily, Weekly, and Quarterly levels stack. These high-conviction areas offer the clearest setups for entries and exits.
Features:
Multi-timeframe pivots: Daily, Weekly, Quarterly
Historical levels with adjustable depth
Period-bounded zones
±33% extensions
Adaptive light/dark mode table
Real-time Δ PP percentage
Pivot cross alerts
Built for traders who respect the math behind the markets.
US Market Long Horizon Momentum Summary in one paragraph
US Market Long Horizon Momentum is a trend following strategy for US index ETFs and futures built around a single eighteen month time series momentum measure. It helps you stay long during persistent bull regimes and step aside or flip short when long term momentum turns negative.
Scope and intent
• Markets. Large cap US equity indices, liquid US index ETFs, index futures
• Timeframes. 4h/ Daily charts
• Default demo used in the publication. SPY on 4h timeframe chart
• Purpose. Provide a minimal long bias index timing model that can reduce deep drawdowns and capture major cycles without parameter mining
• Limits. This is a strategy. Orders are simulated on standard candles only
Originality and usefulness
• Unique concept or fusion. One unscaled multiple month log return of an external benchmark symbol drives all entries and exits, with optional volatility targeting as a single risk control switch.
• Failure mode addressed. Fully passive buy and hold ignores the sign of long horizon momentum and can sit through multi year drawdowns. This script offers a way to step down risk in prolonged negative momentum without chasing short term noise.
• Testability. All parameters are visible in Inputs and the momentum series is plotted so users can verify every regime change in the Tester and on price history.
• Portable yardstick. The log return over a fixed window is a unit that can be applied to any liquid symbol with daily data.
Method overview in plain language
The method looks at how far the benchmark symbol has moved in log return terms over an eighteen month window in our example. If that long horizon return is positive the strategy allows a long stance on the traded symbol. If it is negative and shorts are enabled the strategy can flip short, otherwise it goes flat. There is an optional realised volatility estimate on the traded symbol that can scale position size toward a target annual volatility, but in the default configuration the model uses unit leverage and only the sign of momentum matters.
Base measures
Return basis. The core yardstick is the natural log of close divided by the close eighteen months ago on the benchmark symbol. Daily log returns of the traded symbol feed the realised volatility estimate when volatility targeting is enabled.
Components
• Component one Momentum eighteen months. Log of benchmark close divided by its close mom_lookback bars ago. Its sign defines the trend regime. No extra smoothing is applied beyond the long window itself.
• Component two Realised volatility optional. Standard deviation of daily log returns on the traded symbol over sixty three days. Annualised by the square root of 252. Used only when volatility targeting is enabled.
• Optional component Volatility targeting. Converts target annual volatility and realised volatility into a leverage factor clipped by a maximum leverage setting.
Fusion rule
The model uses a simple gate. First compute the sign of eighteen month log momentum on the benchmark symbol. Optionally compute leverage from volatility. The sign decides whether the strategy wants to be long, short, or flat. Leverage only rescales position size when enabled and does not change direction.
Signal rule
• Long suggestion. When eighteen month log momentum on the benchmark symbol is greater than zero, the strategy wants to be long.
• Short suggestion. When that log momentum is less than zero and shorts are allowed, the strategy wants to be short. If shorts are disabled it stays flat instead.
• Wait state. When the log momentum is exactly zero or history is not long enough the strategy stays flat.
• In position. In practice the strategy sits IN LONG while the sign stays positive and flips to IN SHORT or flat only when the sign changes.
Inputs with guidance
Setup
• Momentum Lookback (months). Controls the horizon of the log return on the benchmark symbol. Typical range 6 to 24 months. Raising it makes the model slower and more selective. Lowering it makes it more reactive and sensitive to medium term noise.
• Symbol. External symbol used for the momentum calculation, SPY by default. Changing it lets you time other indices or run signals from a benchmark while trading a correlated instrument.
Logic
• Allow Shorts. When true the strategy will open short positions during negative momentum regimes. When false it will stay flat whenever momentum is negative. Practical setting is tied to whether you use a margin account or an ETF that supports shorting.
Internal risk parameters (not exposed as inputs in this version) are:
• Target Vol (annual). Target annual volatility for volatility targeting, default 0.2.
• Vol Lookback (days). Window for realised volatility, default 63 trading days.
• Max Leverage. Cap on leverage when volatility targeting is enabled, default 2.
Usage recipes
Swing continuation
• Signal timeframe. Use the daily chart.
• Benchmark symbol. Leave at SPY for US equity index exposure.
• Momentum lookback. Eighteen months as a default, with twelve months as an alternative preset for a faster swing bias.
Properties visible in this publication
• Initial capital. 100000
• Base currency. USD
• Default order size method. 5% of the total capital in this example
• Pyramiding. 0
• Commission. 0.03 percent
• Slippage. 3 ticks
• Process orders on close. On
• Bar magnifier. Off
• Recalculate after order is filled. Off
• Calc on every tick. Off
• All request.security calls use lookahead = barmerge.lookahead_off
Realism and responsible publication
The strategy is for education and research only. It does not claim any guaranteed edge or future performance. All results in Strategy Tester are hypothetical and depend on the data vendor, costs, and slippage assumptions. Intrabar motion is not modeled inside daily bars so extreme moves and gaps can lead to fills that differ from live trading. The logic is built for standard candles and should not be used on synthetic chart types for execution decisions.
Performance is sensitive to regime structure in the US equity market, which may change over time. The strategy does not protect against single day crash risk inside bars and does not model gap risk explicitly. Past behavior of SPY and the momentum effect does not guarantee future persistence.
Honest limitations and failure modes
• Long sideways regimes with small net change over eighteen months can lead to whipsaw around the zero line.
• Very sharp V shaped reversals after deep declines will often be missed because the model waits for momentum to turn positive again.
• The sample size in a full SPY history is small because regime changes are infrequent, so any test must be interpreted as indicative rather than statistically precise.
• The model is highly dependent on the chosen lookback. Users should test nearby values and validate that behavior is qualitatively stable.
Legal
Education and research only. Not investment advice. You are responsible for your own decisions. Always test on historical data and in simulation with realistic costs before any live use.
Round Number Analyzer v3Round Number Analyzer v3 is an indicator designed to analyze how price interacts with round number levels (levels spaced at fixed intervals in points or pips).
The indicator does not generate entry/exit signals, but provides detailed statistics to better understand market dynamics around these key levels.
✨ Key Features
Cross Counting: detects every time the price crosses a round number level (up = Long, down = Short).
Continuations & Reversals: classifies each cross as:
Continuation: the move continues in the same direction as the previous sequence.
Reversal: the move changes direction compared to the previous sequence.
Sequence Classification (L1…L5+): each level is labelled based on its position within the consecutive cross sequence:
L1 = first level of the sequence,
L2 = second consecutive,
…
L5+ = fifth or higher.
Comprehensive Stats Table (top right corner):
Total crosses (Long, Short, Totals).
Total continuations + breakdown by L1…L5+.
Total reversals + breakdown by L1…L5+.
Percentages calculated against the proper denominator, displayed directly inside the cells next to the absolute values.
Date range of analysis (user-defined).
Customizable Step: Works in both points and pips, making the indicator suitable for indices and forex.
⚙️ Main Inputs
Start date / End date → sets the analysis period.
Step mode → Points or Pips.
Step value → distance between round levels.
Pip size → pip size (default = 0.0001, typical for forex).
📈 How to Interpret
A high continuation percentage after L1–L2 suggests the market tends to extend multiple times beyond the first breakout levels.
Higher reversal percentages at advanced levels (L4–L5+) may signal trend exhaustion.
The analysis helps estimate the probability of continuation or reversal depending on how many consecutive levels have already been crossed.
🔎 Practical Applications
Support for breakout or mean-reversion strategies.
Comparative analysis across different markets (e.g. indices vs forex) or different time periods.
📝 Notes
The indicator is timeframe-robust, as it accounts for multiple steps within the same candle, ensuring results do not depend on the selected timeframe (except for TradingView’s historical data limits).
It does not provide automatic trading signals, but serves as a quantitative analysis tool to refine your strategies.
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Round Number Analyzer v3 è un indicatore pensato per analizzare come il prezzo interagisce con i livelli di round number (livelli a distanza fissa in punti o pips).
L’indicatore non genera segnali di ingresso/uscita, ma fornisce statistiche dettagliate utili per comprendere la dinamica del mercato attorno a questi livelli.
✨ Funzionalità principali
Conteggio dei Cross: rileva ogni volta che il prezzo attraversa un livello round (verso l’alto = Long, verso il basso = Short).
Continuations & Reversals: classifica ogni attraversamento come:
Continuation: il movimento prosegue nella stessa direzione della sequenza precedente.
Reversal: il movimento inverte la direzione rispetto alla sequenza precedente.
Classificazione per sequenza (L1…L5+): ogni livello è etichettato in base alla sua posizione nella sequenza di cross consecutivi:
L1 = primo livello della sequenza,
L2 = secondo consecutivo,
…
L5+ = quinto o superiore.
Statistiche complete in tabella (in alto a destra):
Cross totali (Long, Short, Totals).
Continuations totali + breakdown per L1…L5+.
Reversals totali + breakdown per L1…L5+.
Percentuali calcolate sul denominatore corretto, mostrate direttamente dentro le celle accanto ai valori assoluti.
Date range di analisi (impostabile dall’utente).
Step personalizzabile: puoi lavorare sia in punti che in pips, così l’indicatore è adatto sia per indici che per forex.
⚙️ Input principali
Start date / End date → imposta l’intervallo temporale di analisi.
Step mode → punti o pips.
Step value → ampiezza tra i livelli round.
Pip size → dimensione del pip (default = 0.0001, tipico per il forex).
📈 Come interpretarlo
Una percentuale di continuation molto alta dopo L1–L2 indica che il mercato tende a proseguire più volte oltre i primi livelli di breakout.
Percentuali di reversal più elevate nei livelli avanzati (L4–L5+) possono suggerire esaurimento della spinta.
L’analisi permette di stimare la probabilità che un movimento in corso continui o si inverta in base a quanti livelli sono già stati attraversati consecutivamente.
🔎 Applicazioni pratiche
Supporto per strategie di breakout o mean reversion.
Analisi comparativa tra mercati (es. indici vs forex) o tra periodi temporali diversi.
📝 Note
L’indicatore è timeframe-robust: il conteggio tiene conto di multipli step dentro la stessa candela, così i risultati non dipendono dal timeframe scelto (salvo i limiti di caricamento storico di TradingView).
Non fornisce segnali operativi automatici, ma è un tool di analisi quantitativa per affinare le proprie strategie.
Mean Reversion Indictor, Based on Standard Deviations Description:
The Reversal Candle Mean Reversion Indicator is designed for traders seeking to identify potential reversal points in the market based on key price action and volatility. This indicator combines price action analysis (sweeping prior highs or lows) with mean reversion theory, highlighting opportunities where the price tests or touches a moving average's standard deviation bands.
By focusing on these moments of price extremes, the indicator helps traders spot bullish and bearish reversal signals when the price retraces from volatile movements. These conditions often signal a return to the mean—an ideal setup for reversal traders who thrive on fading exaggerated price moves.
How It Works:
1. Price Action Reversal Signal:
* Bullish Reversal: The indicator flags a bullish signal when the current candle's low sweeps the prior candle's low, and the candle closes higher than the prior candle's close.
* Bearish Reversal: The indicator flags a bearish signal when the current candle's high sweeps the prior candle's high, and the candle closes lower than the prior candle's close.
2. Mean Reversion Confirmation:
* Mean Reversion Signal is triggered when the price touches or tests the upper or lower bands, calculated using a user-selected moving average (SMA, EMA, WMA, VWMA, or Hull MA) and standard deviation.
* The indicator combines price action and volatility, providing stronger reversal signals when the price reaches an extreme distance from the moving average.
3. Customization Options:
* Moving Average Type: Choose from SMA, EMA, WMA, VWMA, or Hull MA.
* Moving Average Length: Adjust the length of the moving average (default: 20).
* Standard Deviation Multiplier: Set the number of standard deviations for the volatility bands (default: 2.0).
* Custom Candle Colors: Choose custom colors for bullish and bearish reversal candles to easily spot signals.
How to Use for Trading Reversals:
1. Identify Extremes:
* Watch for candles where the price tests or touches the standard deviation bands. These are key moments when the price has moved significantly from the moving average, indicating a potential overbought or oversold condition.
2. Look for Reversals:
* When the price tests a band and simultaneously forms a bullish reversal pattern (sweeping the prior low and closing higher), it signals a potential mean reversion to the upside.
* When the price tests a band and forms a bearish reversal pattern (sweeping the prior high and closing lower), it signals a potential mean reversion to the downside.
3. Entry Points:
* Long Trades: Enter a long trade after a bullish signal appears (green candle) near the lower band, indicating a likely price reversal back towards the mean.
* Short Trades: Enter a short trade after a bearish signal appears (red candle) near the upper band, indicating a likely price pullback.
4. Exit Strategy:
* Set a profit target at the moving average (the mean) or a specific price level based on your strategy.
* Consider using a trailing stop to capture additional profit in case of a stronger reversal beyond the mean.
5. Risk Management:
* Place stops just below the low of the bullish reversal candle or just above the high of the bearish reversal candle to manage risk efficiently.
Geometric Trend Angle [AstroHub]This script, "Geometric Trend Angle," is designed to identify trend reversals based on the geometric angle of the price chart. Here's a detailed explanation of its originality, functionality, and usage:
Originality and Usefulness:
The uniqueness of this script lies in its approach to trend reversal detection through the calculation of the geometric trend angle. Unlike traditional methods, this script combines the analysis of the angle of the price movement with specific conditions for identifying potential trend reversals.
How it Works:
Length and Trend Angle: The user sets the "Length" parameter, determining the period for calculating the trend angle. The script then computes the trend angle, representing the change in prices over the specified period.
Trend Reversal: The script identifies potential trend reversals when the trend angle changes from positive to negative, and the current closing price is higher than the previous closing price.
Green Reversal: Additionally, the script looks for instances where the trend angle changes from negative to positive, and the current closing price is lower than the previous closing price, indicating a potential reversal to the downside.
Graphical Representation: The script visually highlights the identified reversal points on the chart with labels ("Trend Reversal" and "Green Reversal") and draws a line from the reversal point for better visualization.
Alerts: Traders are alerted to potential trend reversals and green reversals, allowing for timely responses to changing market dynamics.
How to Use:
Apply the script to your TradingView chart.
Customize the "Length" parameter based on your preference and analysis.
Observe the colored candles and graphical elements to identify potential trend reversals.
Pay attention to alerts for timely notifications of reversal signals.
Conclusion:
The "Geometric Trend Angle" script provides a unique perspective on trend reversals, combining geometric angle analysis with specific conditions for improved accuracy. Traders can use it as part of their overall analysis to make informed decisions in the dynamic market environment.
OBV with MA & Bollinger Bands by Marius1032OBV with MA & Bollinger Bands by Marius1032
This script adds customizable moving averages and Bollinger Bands to the classic OBV (On Balance Volume) indicator. It helps identify volume-driven momentum and trend strength.
Features:
OBV-based trend tracking
Optional smoothing: SMA, EMA, RMA, WMA, VWMA
Optional Bollinger Bands with SMA
Potential Combinations and Trading Strategies:
Breakouts: Look for price breakouts from the Bollinger Bands, and confirm with a rising OBV for an uptrend or falling OBV for a downtrend.
Trend Reversals: When the price touches a Bollinger Band, examine the OBV for divergence. A bullish divergence (price lower low, OBV higher low) near the lower band could signal a reversal.
Volume Confirmation: Use OBV to confirm the strength of the trend indicated by Bollinger Bands. For example, if the BBs indicate an uptrend and OBV is also rising, it reinforces the bullish signal.
1. On-Balance Volume (OBV):
Purpose: OBV is a momentum indicator that uses volume flow to predict price movements.
Calculation: Volume is added on up days and subtracted on down days.
Interpretation: Rising OBV suggests potential upward price movement. Falling OBV suggests potential lower prices.
Divergence: Divergence between OBV and price can signal potential trend reversals.
2. Moving Average (MA):
Purpose: Moving Averages smooth price fluctuations and help identify trends.
Combination with OBV: Pairing OBV with MAs helps confirm trends and identify potential reversals. A crossover of the OBV line and its MA can signal a trend reversal or continuation.
3. Bollinger Bands (BB):
Purpose: BBs measure market volatility and help identify potential breakouts and trend reversals.
Structure: They consist of a moving average (typically 20-period) and two standard deviation bands.
Combination with OBV: Combining BBs with OBV allows for a multifaceted approach to market analysis. For example, a stock hitting the lower BB with a rising OBV could indicate accumulation and a potential upward reversal.
Created by: Marius1032
UVR Crypto TrendINDICATOR OVERVIEW: UVR CRYPTO TREND
The UVR Crypto Trend indicator is a custom-built tool designed specifically for cryptocurrency markets, utilizing advanced volatility, momentum, and trend-following techniques. It aims to identify trend reversals and provide buy and sell signals by analyzing multiple factors, such as price volatility(UVR), RSI (Relative Strength Index), CMF (Chaikin Money Flow), and EMA (Exponential Moving Average). The indicator is optimized for CRYPTO MARKETS only.
KEY FEATURES AND HOW IT WORKS
Volatility Analysis with UVR
The UVR (Ultimate Volatility Rate) is a proprietary calculation that measures market volatility by comparing significant price extremes and smoothing the data over time.
Purpose: UVR aims to reduce noise in low-volatility environments and highlight significant movements during higher-volatility periods. While it strives to improve filtering in low-volatility conditions, it does not guarantee perfect performance, making it a balanced and adaptable tool for dynamic markets like cryptocurrency.
HOW UVR (ULTIMATE VOLATILITY RATE) IS CALCULATED
UVR is calculated using a method that ensures precise measurement of market volatility by comparing price extremes across consecutive candles:
Volatility Components:
Two values are calculated to represent potential price fluctuations:
The absolute difference between the current candle's high and the previous candle's low:
Volatility Component 1=∣High−Low ∣
The absolute difference between the previous candle's high and the current candle's low:
Volatility Component 2=∣High −Low∣
Volatility Ratio:
The larger of the two components is selected as the Volatility Ratio, ensuring UVR captures the most significant movement:
Volatility Ratio=max(Volatility Component 1,Volatility Component 2)
Smoothing with SMMA:
To stabilize the volatility calculation, the Volatility Ratio is smoothed using a Smoothed Moving Average (SMMA) over a user-defined period (e.g., 14 candles):
UVR=(UVR(Previous)×(Period−1)+Volatility Ratio)/Period
This calculation ensures UVR adapts dynamically to market conditions, focusing on significant price movements while filtering out noise.
RSI FOR MOMENTUM DETECTION
RSI (Relative Strength Index) identifies overbought and oversold conditions.
Trend Confirmation at the 50 Level
RSI values crossing above 50 signal the potential start of an upward trend.
RSI values crossing below 50 indicate the potential start of a downward trend.
Key Reversals at Extreme Levels
RSI detects trend reversals at overbought (>70) and oversold (<30) levels.
For example:
Overbought Trend Reversal: RSI >70 followed by bearish price action signals a potential downtrend.
Oversold Trend Reversal: RSI <30 with bullish confirmation signals a potential uptrend.
Rare Extreme RSI Readings
Extreme levels, such as RSI <12 (oversold) or RSI >88 (overbought), are used to identify rare yet powerful reversals.
---HOW IT DIFFERS FROM OTHER INDICATORS---
Using UVR High and Low Values
The Ultimate Volatility Rate (UVR) focuses on analyzing the high and low price ranges of the market to measure volatility.
Unlike traditional trend indicators that rely primarily on momentum or moving average crossovers, UVR leverages price extremes to better identify trend reversals.
This approach ensures fewer false signals during low-volatility phases and more accurate trend detection during high-volatility conditions.
UVR as the Core Component
The indicator is fundamentally built around UVR as the primary filter, while supporting tools like RSI (momentum detection), CMF (volume confirmation), and EMA (trend validation) complement its functionality.
By integrating these additional components, the indicator provides a multidimensional analysis rather than relying solely on a single approach.
Dynamic Adaptation to Volatility
UVR dynamically adjusts to market conditions, striving to improve filtering in low-volatility phases. While not flawless, this approach minimizes false signals and adapts more effectively to varying levels of market activity.
Trend Clouds for Visual Guidance
UVR-based dynamic clouds visually mark high and low price areas, highlighting potential consolidation or retracement zones.
These clouds serve as guides for setting stop-loss or take-profit levels, offering clear risk management strategies.
BUY AND SELL SIGNAL LOGIC
BUY CONDITIONS
Momentum-Based Buy-Entry
RSI >50, CMF >0, and the close price is above EMA50.
The price difference between open and close exceeds a threshold based on UVR.
Oversold Reversal
RSI <30 and CMF >0 with a strong bullish candle (close > open and UVR-based sensitivity filter).
Breakout Confirmation
The price breaks above a previously identified resistance, with conditions for RSI and CMF supporting the breakout.
Reversal from Oversold RSI Extreme
RSI <12 on the previous candle with a strong rebound on the current candle with UVR confirmation filter.
SELL CONDITIONS
Momentum-Based Sell-Entry
RSI <50, CMF <0, and the close price is below EMA50.
The price difference between open and close exceeds the UVR threshold.
Overbought Reversal
RSI >70 with bearish price action (open > close and UVR-based sensitivity filter).
Breakdown Confirmation
The price breaks below a previously identified support, with RSI and CMF supporting the breakdown.
Reversal from Overbought RSI Extreme
RSI >88 on the previous candle with a bearish confirmation on the current candle with UVR confirmation filter.
BUY AND SELL SIGNALS VISUALIZATION
The UVR Crypto Trend Indicator visually represents buy and sell conditions using dynamic plots, making it easier for traders to interpret and act on the signals. Below is an explanation of the visual representation:
Buy Signals and Visualization
Signal Trigger:
A buy signal is generated when one of the defined Buy Conditions is met (e.g., RSI >50, CMF >0, price above EMA50).
Visual Representation:
A blue upward arrow appears at the candle where the buy condition is triggered.
A blue cloud forms above the price candles, representing the strength of the bullish trend. The cloud dynamically adapts to market volatility, using the UVR calculation to mark support zones or consolidation levels.
Purpose of the Blue Cloud:
It acts as a visual guide for price movements and stay horizontal when the trend is not moving up
Sell Signals and Visualization
Signal Trigger:
A sell signal is generated when one of the defined Sell Conditions is met (e.g., RSI <50, CMF <0, price below EMA50).
Visual Representation:
A red downward arrow appears at the candle where the sell condition is triggered.
A red cloud forms below the price candles, representing the strength of the bearish trend. Like the blue cloud, it uses the UVR calculation to dynamically mark resistance zones or potential retracement levels.
Purpose of the Red Cloud:
It acts as a visual guide for price movements and stay horizontal when the trend is not moving down.
CONCLUSION
The UVR Crypto Trend indicator provides a powerful tool for trend reversal detection by combining volatility analysis, momentum confirmation, and trend-following techniques. Its unique use of the Ultimate Volatility Rate (UVR) as a core element, supported by proven indicators like RSI, CMF, and EMA, ensures reliable and actionable signals tailored for the crypto market's dynamic nature. By leveraging UVR’s high and low price range analysis, it achieves a level of precision that traditional indicators lack, making it a high-performing system for cryptocurrency traders.
BX-Volume Trend and OscillatorBX-Volume Trend and Oscillator (VTO)
This is my second indicator. I created this indicator for myself. I was inspired by the indicators created by Bjorgum, Duyck and QuantTherapy and decided to create multiple indicators that either work well combined with their indicators or something new that applies some of their indicator concepts. I decided to share this because I believe in learning and earing together as a community. I will later share the rest of the indicators I have created. If you guys have any questions or suggestions write them.
The BX-Volume Trend and Oscillator (VTO) is a comprehensive trading indicator designed to help traders identify trends, momentum shifts, and potential reversals by analyzing volume and price action through various metrics. This indicator combines relative volume analysis with custom Xtrender oscillators and moving averages to provide valuable insights into market behavior.
Image: BX-Volume Trend and Oscillator (VTO)
Features:
Relative Volume Analysis: Measures the current volume relative to the average volume over a specified period, helping traders understand if the current trading activity is unusually high or low.
Short-Term Xtrender Oscillator: This oscillator analyzes the difference between two short-term Exponential Moving Averages (EMAs) and smooths it with a custom RSI, highlighting short-term trends and potential reversal points.
Long-Term Xtrender Oscillator: Similar to the short-term oscillator but uses longer-term EMAs and RSI for identifying more sustained trends and shifts.
T3 Moving Average: A smoothed version of the Xtrender oscillator that helps in detecting trend changes more clearly.
Volume Trend Plot: Shows the smoothed relative volume to understand how trading activity aligns with the trend.
Visual Indicators: Uses colors and shapes to highlight significant changes and trends, such as circles to mark potential reversal points.
How to Use the Indicator
Analyze Relative Volume:
Relative Volume Plot: The smoothed relative volume is displayed in white, helping you assess if current trading volumes are above or below the historical average.
High Relative Volume: Indicates strong trading interest, which could support or contradict the prevailing trend.
Image above: is set to daily timeframe
Monitor Short-Term Xtrender Oscillator
Short-Term Xtrender: Plotted as a column histogram with colors changing from green to red based on the oscillator's movement and momentum. Green and lime colors indicate bullish trends, while maroon and red suggest bearish conditions.
Smoothed Short-Term Xtrender (T3): Plotted as a line that adjusts color based on the short-term Xtrender's trend. The line changes color to match the histogram's color, providing a clearer view of momentum shifts.
Reversal Markers: Small circles indicate potential short-term trend reversals, where changes in the T3 moving average suggest shifts in momentum.
Assess Long-Term Xtrender Oscillator:
Long-Term Xtrender: Plotted as a histogram, with color changes similar to the short-term Xtrender. It shows longer-term trends and shifts.
Color Indicators: Lime and green colors suggest an uptrend, while red and maroon indicate a downtrend.
Look for Zero Line Crossings:
The zero line serves as a reference point. Crossings above the zero line may indicate bullish trends, while crossings below may signal bearish trends.
Image above: is set to daily timeframe, and it showcases the Short-Term Xtrender (T3) applied.
Image above: is set to 8hr timeframe: Using the lower timeframe you can spot better details of pullbacks and potential reversals.
Example of Use:
Identify Trend and Momentum: Use the combination of the short-term and long-term Xtrender oscillators to gauge the prevailing market trend. For instance, if both oscillators are above zero and showing upward momentum, it suggests a strong bullish trend.
Spot Reversals: Observe the short-term Xtrender and its smoothed T3 version. If the T3 line changes direction and crosses through previous peaks and troughs, it could signal a potential reversal.
Volume Confirmation: Check the relative volume and its smoothed version to confirm the strength of price movements. Significant changes in volume can validate the trends indicated by the Xtrender oscillators.
By combining these elements, the BX-Volume Trend and Oscillator (VTO) provides a holistic view of market dynamics, helping traders make more informed decisions based on trend strength, potential reversals, and volume activity.
Lastly, my Scripts/Indicators/Ideas /Systems that I share are only for educational purposes!
Goldmine Wealth Builder - DKK/SKKGoldmine Wealth Builder
Version 1.0
Introduction to Long-Term Investment Strategies: DKK, SKK1 and SKK2
In the dynamic realm of long-term investing, the DKK, SKK1, and SKK2 strategies stand as valuable pillars. These strategies, meticulously designed to assist investors in building robust portfolios, combine the power of Super Trend, RSI (Relative Strength Index), Exponential Moving Averages (EMAs), and their crossovers. By providing clear alerts and buy signals on a daily time frame, they equip users with the tools needed to make well-informed investment decisions and navigate the complexities of the financial markets. These strategies offer a versatile and structured approach to both conservative and aggressive investment, catering to the diverse preferences and objectives of investors.
Each part of this strategy provides a unique perspective and approach to the accumulation of assets, making it a versatile and comprehensive method for investors seeking to optimize their portfolio performance. By diligently applying this multi-faceted approach, investors can make informed decisions and effectively capitalize on potential market opportunities.
DKK Strategy for ETFs and Funds:
The DKK system is a strategy designed for accumulating ETFs and Funds as long-term investments in your portfolio. It simplifies the process of identifying trend reversals and opportune moments to invest in listed ETFs and Funds, particularly during bull markets. Here's a detailed explanation of the DKK system:
Objective: The primary aim of the DKK system is to build a long-term investment portfolio by focusing on ETFs and Funds. It facilitates the identification of stocks that are in the process of reversing their trends, allowing investors to benefit from upward price movements in these financial instruments.
Stock Selection Criteria: The DKK system employs specific criteria for selecting ETFs and Funds:
• 200EMA (Exponential Moving Average): The system monitors whether the prices of ETFs and Funds are consistently below the 200-day Exponential Moving Average. This is considered an indicator of weakness, especially on a daily time frame.
• RSI (Relative Strength Index): The system looks for an RSI value of less than 40. An RSI below 40 is often seen as an indication of a weak or oversold condition in a financial instrument.
Alert Signal: Once the DKK system identifies ETFs and Funds meeting these criteria, it provides an alert signal:
• Red Upside Triangle Sign: This signal is automatically generated on the daily chart of ETFs and Funds. It serves as a clear indicator to investors that it's an opportune time to accumulate these financial instruments for long-term investment.
It's important to note that the DKK system is specifically designed for ETFs and Funds, so it should be applied to these types of investments. Additionally, it's recommended to track index ETFs and specific types of funds, such as REITs (Real Estate Investment Trusts) and INVITs (Infrastructure Investment Trusts), in line with the DKK system's approach. This strategy simplifies the process of identifying investment opportunities within this asset class, particularly during periods of market weakness.
SKK1 Strategy for Conservative Stock Investment:
The SKK 1 system is a stock investment strategy tailored for conservative investors seeking long-term portfolio growth with a focus on stability and prudent decision-making. This strategy is meticulously designed to identify pivotal market trends and stock price movements, allowing investors to make informed choices and capitalize on upward market trends while minimizing risk. Here's a comprehensive overview of the SKK 1 system, emphasizing its suitability for conservative investors:
Objective: The primary objective of the SKK 1 system is to accumulate stocks as long-term investments in your portfolio while prioritizing capital preservation. It offers a disciplined approach to pinpointing potential entry points for stocks, particularly during market corrections and trend reversals, thereby enabling you to actively participate in bullish market phases while adopting a conservative risk management stance.
Stock Selection Criteria: The SKK 1 system employs a stringent set of criteria to select stocks for investment:
• Correction Mode: It identifies stocks that have undergone a correction, signifying a decline in stock prices from their recent highs. This conservative approach emphasizes the importance of seeking stocks with a history of stability.
• 200EMA (Exponential Moving Average): The system diligently analyses daily stock price movements, specifically looking for stocks that have fallen to or below the 200-day Exponential Moving Average. This indicator suggests potential overselling and aligns with a conservative strategy of buying low.
Trend Reversal Confirmation: The SKK 1 system doesn't merely pinpoint stocks in correction mode; it takes an extra step to confirm a trend reversal. It employs the following indicators:
• Short-term Downtrends Reversal: This aspect focuses on identifying the reversal of short-term downtrends in stock prices, observed through the transition of the super trend indicator from the red zone to the green zone. This cautious approach ensures that the trend is genuinely shifting.
• Super Trend Zones: These zones are crucial for assessing whether a stock is in a bullish or bearish trend. The system consistently monitors these zones to confirm a potential trend reversal.
Alert & Buy Signals: When the SKK 1 system identifies stocks that have reached a potential bottom and are on the verge of a trend reversal, it issues vital alert signals, aiding conservative investors in prudent decision-making:
• Orange Upside Triangle Sign: This signal serves as a cautious heads-up, indicating that a stock may be poised for a trend reversal. It advises investors to prepare funds for potential investment without taking undue risks.
• Green Upside Triangle Sign: This is the confirmation of a trend reversal, signifying a robust buy signal. Conservative investors can confidently enter the market at this point, accumulating stocks for a long-term investment, secure in the knowledge that the trend is in their favor.
In summary, the SKK 1 system is a systematic and conservative approach to stock investing. It excels in identifying stocks experiencing corrections and ensures that investors act when there's a strong indication of a trend reversal, all while prioritizing capital preservation and risk management. This strategy empowers conservative investors to navigate the intricacies of the stock market with confidence, providing a calculated and stable path toward long-term portfolio growth.
Note: The SKK1 strategy, known for its conservative approach to stock investment, also provides an option to extend its methodology to ETFs and Funds for those investors who wish to accumulate assets more aggressively. By enabling this feature in the settings, you can harness the SKK1 strategy's careful criteria and signal indicators to accumulate aggressive investments in ETFs and Funds.
This flexible approach acknowledges that even within a conservative strategy, there may be opportunities for more assertive investments in assets like ETFs and Funds. By making use of this option, you can strike a balance between a conservative stance in your stock portfolio while exploring an aggressive approach in other asset classes. It offers the versatility to cater to a variety of investment preferences, ensuring that you can adapt your strategy to suit your financial goals and risk tolerance.
SKK 2 Strategy for Aggressive Stock Investment:
The SKK 2 strategy is designed for those who are determined not to miss significant opportunities within a continuous uptrend and seek a way to enter a trend that doesn't present entry signals through the SKK 1 strategy. While it offers a more aggressive entry approach, it is ideal for individuals willing to take calculated risks to potentially reap substantial long-term rewards. This strategy is particularly suitable for accumulating stocks for aggressive long-term investment. Here's a detailed description of the SKK 2 strategy:
Objective: The primary aim of the SKK 2 strategy is to provide an avenue for investors to identify short-term trend reversals and seize the opportunity to enter stocks during an uptrend, thereby capitalizing on a sustained bull run. It acknowledges that there may not always be clear entry signals through the SKK 1 strategy and offers a more aggressive alternative.
Stock Selection Criteria: The SKK 2 strategy utilizes a specific set of criteria for stock selection:
1. 50EMA (Exponential Moving Average): It targets stocks that are trading below the 50-day Exponential Moving Average. This signals a short-term reversal from the top and indicates that the stock is in a downtrend.
2. RSI (Relative Strength Index): The strategy considers stocks with an RSI of less than 40, which is an indicator of weakness in the stock.
Alert Signals: The SKK 2 strategy provides distinct alert signals that facilitate entry during an aggressive reversal:
• Red Downside Triangle Sign: This signal is triggered when the stock is below the 50EMA and has an RSI of less than 40. It serves as a clear warning of a short-term reversal from the top and a downtrend, displayed on the daily chart.
• Purple Upside Triangle Sign: This sign is generated when a reversal occurs through a bullish candle, and the RSI is greater than 40. It signifies the stock has bottomed out from a short-term downtrend and is now reversing. This purple upside triangle serves as an entry signal on the chart, presenting an attractive opportunity to accumulate stocks during a strong bullish phase, offering a chance to seize a potentially favorable long-term investment.
In essence, the SKK 2 strategy caters to aggressive investors who are willing to take calculated risks to enter stocks during a continuous uptrend. It focuses on identifying short-term reversals and provides well-defined signals for entry. While this strategy is more aggressive in nature, it has the potential to yield substantial rewards for those who are comfortable with a higher level of risk and are looking for opportunities to build a strong long-term portfolio.
Introduction to Strategy Signal Information Chart
This chart provides essential information on strategy signals for DKK, SKK1, and SKK2. By quickly identifying "Buy" and "Alert" signals for each strategy, investors can efficiently gauge market conditions and make informed decisions to optimize their investment portfolios.
In Conclusion
These investment strategies, whether conservative like DKK and SKK1 or more aggressive like SKK2, offer a range of options for investors to navigate the complex world of long-term investments. The combination of Super Trend, RSI, and EMAs with their crossovers provides clear signals on a daily time frame, empowering users to make well-informed decisions and potentially capitalize on market opportunities. Whether you're looking for stability or are ready to embrace more risk, these strategies have something to offer for building and growing your investment portfolio.
VWMA True Range | Lyro RSVWMA True Range | Lyro RS
This script is a hybrid technical analysis tool designed to identify trends and spot potential reversals. It employs a consensus-based system that uses multiple smoothed, Volume-Weighted Moving Averages (VWMA) to generate both trend-following and counter-trend signals.
Understanding the Indicator's Components
The indicator plots a main line on a separate pane and provides visual alerts directly on the chart.
The Main Line: This line represents a smoothed average of momentum scores derived from multiple VWMAs. Its direction and value are the foundation of the analysis.
Signal Generation: The tool provides two distinct types of signals:
Trend Signals: These trend-following signals ("⬆️Long" / "⬇️Short") activate when the indicator's consensus reaches a pre-set strength threshold, indicating sustained momentum in one direction.
Reversal Signals: These counter-trend alerts ("📈Oversold" / "📉Overbought") trigger when the main line breaks a previous period's level, hinting at exhaustion and a potential short-term reversal.
Visual Alerts:
Colored Background: The indicator's background highlights during strong trend signals for added visual emphasis.
Chart Shapes: Small circles appear on the main chart to mark where potential reversals are detected.
Colored Candles: You can choose to color the price candles to reflect the current trend signal.
Information Table: A compact table provides an at-a-glance summary of all currently active signals.
Suggested Use and Interpretation
Here are a few ways to incorporate this indicator into your analysis:
Following the Trend: Use the "Long" or "Short" trend signals to align your trades with the prevailing market momentum.
Spotting Reversals: Watch for "Oversold" or "Overbought" reversal signals, often accompanied by chart shapes, to identify potential market turning points.
Combining Signals: Use the primary trend signal for context and look for reversal signals that may indicate a pullback within the larger trend, potentially offering favorable entry points.
Customization Options:
You can tailor the indicator's behavior and appearance through several settings:
Core Settings: Adjust the Calculation Period and Smooth Length to make the main line more or less responsive to price movements.
Signal Thresholds: Fine-tune the Long threshold and Short threshold to control how easily trend signals are triggered.
Visual Settings: Toggle various visual elements like the indicator band, candle coloring, and the information table on or off.
Table Settings: Customize where the information table appears and its size to suit your chart layout.
⚠️Disclaimer
This indicator is a tool for technical analysis and does not guarantee future results. It should be used as part of a comprehensive trading strategy that includes other analysis techniques and strict risk management. The creators are not responsible for any financial decisions made based on its signals.
RSI of Accumulation/DistributionHow to Use the RSI of Accumulation/Distribution Indicator:
1. Identify Overbought/Oversold Conditions:
Overbought: When the RSI of the ADL is above 70, it indicates that the asset may be overbought and could be due for a pullback or correction.
Oversold: When the RSI of the ADL is below 30, it suggests that the asset may be oversold and could be poised for a rebound.
2. Look for Divergences:
Bullish Divergence: If the price is making lower lows while the RSI of the ADL is making higher lows, it can signal a potential reversal to the upside.
Bearish Divergence: If the price is making higher highs while the RSI of the ADL is making lower highs, it can indicate a potential reversal to the downside.
3. Confirm Trend Strength:
Use the RSI of the ADL to confirm the strength of a trend. For example, if the RSI is consistently above 50 during an uptrend, it suggests strong buying pressure and the trend is likely to continue.
Conversely, if the RSI is consistently below 50 during a downtrend, it indicates strong selling pressure and the trend is likely to persist.
4. Monitor for Reversals:
When the RSI of the ADL crosses above 50, it can signal a potential bullish reversal.
When the RSI of the ADL crosses below 50, it can signal a potential bearish reversal.
Is It Worth It?
The RSI of the Accumulation/Distribution Line can be a valuable tool for traders looking to gain insights into market momentum and trend strength. Here are a few reasons why it might be worth considering:
1. Volume and Price Combination: By combining price action (RSI) with volume-based analysis (ADL), this indicator provides a more comprehensive view of market dynamics.
2. Divergence Detection: It helps identify divergences between price and volume, which can be early signals of potential reversals.
3. Trend Confirmation: It offers additional confirmation of trend strength and potential reversal points, helping traders make more informed decisions.
However, like any indicator, it's important to use it in conjunction with other analysis methods and not rely on it solely for trading decisions. Backtesting the indicator on historical data and combining it with other technical analysis tools can improve its effectiveness.
Feel free to test the script in TradingView and see how it performs in different market conditions. If you have any specific questions or need further assistance, let me know! 😊
Volume Trend Swing Points | viResearchVolume Trend Swing Points | viResearch
Conceptual Foundation and Innovation
The "Volume Trend Swing Points" script is designed to identify pivotal swing points in market trends by leveraging the Price Volume Trend (PVT) indicator. This unique approach combines price and volume movements to highlight moments when a market may experience a significant trend reversal. By detecting the highest and lowest points of the PVT over customizable periods, this script aims to provide traders with valuable insights into potential bullish or bearish market behavior.
The simplicity of the script, combined with its use of the PVT, offers an effective way for traders to anticipate key market swings based on both price and volume momentum.
Technical Composition and Calculation
The core of the "Volume Trend Swing Points" script is built around the Price Volume Trend (PVT) indicator, which adjusts price changes according to trading volume. The script focuses on identifying the highest and lowest values of the PVT over user-defined lookback periods:
Price Volume Trend (PVT): The PVT is used to calculate the momentum of price movements, taking volume into account. By incorporating both price and volume, the PVT offers a more dynamic and responsive indicator of trend direction compared to price alone.
Swing Point Detection: The script identifies the highest and lowest PVT values over user-defined lookback periods (x for highs and y for lows). When the current PVT matches either the highest or lowest value, it signals a potential trend reversal or continuation, depending on whether the high or low is detected.
Entry and Exit Signals: A long signal (bullish) is generated when the current PVT matches the highest value over the lookback period, while a short signal (bearish) is generated when the current PVT matches the lowest value. These signals can be visualized with alerts and background colors.
Features and User Inputs
The "Volume Trend Swing Points" script allows traders to customize several parameters to better suit their trading strategies and market conditions:
Lookback Periods (x and y): The script allows for two customizable lookback periods—one for detecting the highest PVT and another for the lowest. Adjusting these values can help refine the sensitivity of the swing points.
Bar Coloring: The script includes an optional setting to color the bars based on detected bullish or bearish trends, making it easier to visualize potential market shifts.
Background Colors: The background color changes dynamically based on whether a high or low swing point is detected, providing traders with a clear visual indication of potential trend reversals.
Alerts: The script includes alert conditions for both long and short signals, enabling traders to set notifications for when potential swing points are detected.
Practical Applications
The "Volume Trend Swing Points" script is ideal for traders who focus on price and volume dynamics when making trading decisions. Its application is particularly useful in the following scenarios:
Detecting Trend Reversals: By identifying the highest and lowest PVT values over a given period, the script can help traders spot potential reversal points, allowing for more timely entries or exits.
Confirming Trend Continuations: When the PVT continues to match the highest or lowest values, it may indicate that the trend is likely to continue, helping traders maintain their positions with greater confidence.
Volume-Based Trend Analysis: Since the script uses the PVT, it is particularly effective in markets where volume plays a significant role in driving price movements, offering insights that go beyond simple price-based indicators.
Advantages and Strategic Value
This script enhances traditional trend analysis by incorporating both price and volume through the PVT, providing a more comprehensive view of market momentum. The customizable lookback periods allow traders to adapt the script to different assets and timeframes, making it a versatile tool for swing trading and trend-following strategies.
The visual cues provided by bar coloring and background shading help traders quickly identify potential market shifts, improving decision-making speed and accuracy.
Summary and Usage Tips
The "Volume Trend Swing Points" script is a straightforward yet powerful tool for identifying market reversals and trend continuations based on both price and volume. By adjusting the lookback periods, traders can fine-tune the script to better suit their trading style and the assets they are monitoring. The visual and alert features further enhance the script's usability, making it easy to incorporate into a trading strategy.
Remember to backtest the script across various market conditions to better understand its performance. Past performance is not necessarily indicative of future results, so using this script in conjunction with other technical tools is recommended for optimal decision-making.
Curved Smart Money Concepts Probability (Zeiierman)█ Overview
The Curved Smart Money Concepts Probability indicator, developed by Zeiierman, is a sophisticated trading tool designed to leverage the principles of Smart Money trading. This indicator identifies key market structure points and adapts to changing market conditions, providing traders with actionable insights into market trends and potential reversals. The trading tool stands out due to its unique curved structure and advanced probability features, which enhance its effectiveness and usability for traders.
█ How It Works
The indicator operates by analyzing market data to identify pivotal moments where institutional investors might be influencing price movements. It employs a combination of adaptive trend lengths, multipliers for sensitivity adjustments, and pivot periods to accurately capture market structure shifts. The indicator calculates upper and lower bands based on adaptive sizes and identifies zones of overbought (premium) and oversold (discount) conditions.
Key Features of Probability Calculations
The Curved Smart Money Concepts Probability indicator integrates sophisticated probability calculations to enhance trading decision-making:
Win/Loss Tracking: The indicator tracks the number of successful (win) and unsuccessful (loss) trades based on the identified market structure points (ChoCH, SMS, BMS). This provides a historical context of the indicator's performance.
Probability Percentages: For each market structure point (ChoCH, SMS, BMS), the indicator calculates the probability of the next move being successful or not. This is presented as a percentage, giving traders a quantifiable measure of confidence in the signals.
Dynamic Adaptation: The probability calculations adapt to market conditions by considering the frequency and success rate of the signals, allowing traders to adjust their strategies based on the indicator’s historical accuracy.
Visual Representation: Probabilities are displayed on the chart, helping traders quickly assess the likelihood of future price movements based on past performance.
Key benefits of the Curved Structure
The Curved Smart Money Concepts Probability indicator features a unique curved structure that offers several advantages over traditional linear structures:
Noise Reduction: The curved structure smooths out short-term market fluctuations, reducing the noise often seen in linear structures. This helps traders focus on the true trend direction rather than getting distracted by minor price movements.
Adaptive Sensitivity: The curved structure adjusts its sensitivity based on market conditions. This means it can effectively capture both short-term and long-term trends by dynamically adapting to changes in market volatility, something linear structures struggle with.
Enhanced Trend Detection: By providing a more gradual transition between market phases, the curved structure helps in identifying trends more accurately. This is particularly useful in volatile markets where linear structures might give false signals due to their rigid nature.
Improved Market Structure Analysis: The curved structure's ability to adapt and smooth out irregularities provides a clearer picture of the overall market structure. This clarity is essential for identifying premium and discount zones, as well as mid-range support and resistance levels, which are crucial for effective ICT Smart Money Trading.
█ Terminology
ChoCH (Change of Character): Indicates a potential reversal in market direction. It is identified when the price breaks a significant high or low, suggesting a shift from a bullish to bearish trend or vice versa.
SMS (Smart Money Shift): Represents the transition phase in market structure where smart money begins accumulating or distributing assets. It typically follows a BMS and indicates the start of a new trend.
BMS (Bullish/Bearish Market Structure): Confirms the trend direction. Bullish Market Structure (BMS) confirms an uptrend, while Bearish Market Structure (BMS) confirms a downtrend. It is characterized by a series of higher highs and higher lows (bullish) or lower highs and lower lows (bearish).
Premium: A zone where the price is considered overbought. It is calculated as the upper range of the current market structure and indicates a potential area for selling or shorting.
Mid Range: The midpoint between the high and low of the market structure. It often acts as a support or resistance level, helping traders identify potential reversal or continuation points.
Discount: A zone where the price is considered oversold. It is calculated as the lower range of the current market structure and indicates a potential area for buying or going long.
█ How to Use
Identifying Trends and Reversals: Traders can use the indicator to identify the overall market trend and potential reversal points. By observing the ChoCH, SMS, and BMS signals, traders can gauge whether the market is transitioning into a new trend or continuing the current trend.
Example Strategies
⚪ Trend Following Strategy:
Identify the current market trend using BMS signals.
Enter a trade in the direction of the trend when the price retraces to the mid-range zone.
Set a stop-loss just below the mid-range (for long trades) or above the mid-range (for short trades).
Take profit in the premium/discount zone or when a ChoCH signal indicates a potential reversal.
⚪ Reversal Strategy:
Wait for a ChoCH signal to identify a potential market reversal.
Enter a trade in the direction of the new trend as indicated by the SMS signal.
Set a stop-loss just beyond the recent high (for short trades) or low (for long trades).
Take profit when the price reaches the premium or discount zone opposite to the entry.
█ Settings
Curved Trend Length: Determines the length of the trend used to calculate the adaptive size of the structure. Adjusting this length allows traders to capture either longer-term trends (for smoother curves) or short-term trends (for more reactive curves).
Curved Multiplier: Scales the adjustment factors for the upper and lower bands. Increasing the multiplier widens the bands, reducing sensitivity to price changes. Decreasing it narrows the bands, making the structure more responsive.
Pivot Period: Sets the period for capturing trends. A higher period captures broader trends, while a lower period focuses on short-term trends.
Response Period: Adjusts the structure’s responsiveness. A low value focuses on short-term changes, while a high value smoothens the structure.
Premium/Discount Range: Allows toggling between displaying the active range or previous range to analyze real-time or historical levels.
Structure Candles: Enables the display of curved structure candles on the chart, providing a modified view of price action.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Kashif_MFI+RSI+BBMerging Money Flow Index (MFI), Relative Strength Index (RSI), and Bollinger Bands in TradingView can offer traders a comprehensive view of market conditions, providing insights into potential price reversals, overbought or oversold conditions, and potential trend changes. Here are some benefits of combining these indicators:
Confirmation of Overbought and Oversold Conditions:
MFI and RSI are both oscillators that measure overbought and oversold conditions. When MFI and RSI readings are high (above their respective overbought levels), and the price is near or above the upper Bollinger Band, it may suggest that the asset is overextended and a reversal could be imminent. Conversely, when MFI and RSI readings are low (below their respective oversold levels) and the price is near or below the lower Bollinger Band, it may indicate potential buying opportunities.
Divergence Analysis:
Traders often look for divergences between price action and MFI/RSI. If the price is making new highs, but MFI/RSI is not confirming these highs (bearish divergence), it could signal weakening momentum and a possible reversal. Combining this analysis with Bollinger Bands can add another layer of confirmation, especially if the price is touching or exceeding the upper Bollinger Band during this divergence.
Volatility Confirmation:
Bollinger Bands provide a measure of volatility by expanding and contracting based on price volatility. If the bands are widening, it indicates increased volatility. Combining this information with MFI and RSI readings can help traders assess the strength of a trend. For example, during a strong uptrend, if MFI and RSI are high and Bollinger Bands are expanding, it may suggest a sustained bullish trend.
Identifying Trend Reversals:
The combination of MFI, RSI, and Bollinger Bands can be useful in identifying potential trend reversals. For instance, if MFI and RSI are in overbought conditions and the price is significantly above the upper Bollinger Band, it may signal that the trend is reaching an extreme and could reverse. Conversely, if MFI and RSI are in oversold conditions and the price is near or below the lower Bollinger Band, it may suggest that selling pressure is exhausted, and a reversal might be in play.
Comprehensive Market Assessment:
By merging these indicators, traders get a more comprehensive view of market conditions. They can assess both momentum (MFI and RSI) and volatility (Bollinger Bands) simultaneously, helping them make more informed trading decisions.
It's important to note that no single indicator or combination of indicators guarantees accurate predictions in trading. Traders should use these tools as part of a broader analysis and consider other factors such as fundamental analysis, market trends, and risk management.
COT IndexTHE HIDDEN INTELLIGENCE IN FUTURES MARKETS
What if you could see what the smartest players in the futures markets are doing before the crowd catches on? While retail traders chase momentum indicators and moving averages, obsess over Japanese candlestick patterns, and debate whether the RSI should be set to fourteen or twenty-one periods, institutional players leave footprints in the sand through their mandatory reporting to the Commodity Futures Trading Commission. These footprints, published weekly in the Commitment of Traders reports, have been hiding in plain sight for decades, available to anyone with an internet connection, yet remarkably few traders understand how to interpret them correctly. The COT Index indicator transforms this raw institutional positioning data into actionable trading signals, bringing Wall Street intelligence to your trading screen without requiring expensive Bloomberg terminals or insider connections.
The uncomfortable truth is this: Most retail traders operate in a binary world. Long or short. Buy or sell. They apply technical analysis to individual positions, constrained by limited capital that forces them to concentrate risk in single directional bets. Meanwhile, institutional traders operate in an entirely different dimension. They manage portfolios dynamically weighted across multiple markets, adjusting exposure based on evolving market conditions, correlation shifts, and risk assessments that retail traders never see. A hedge fund might be simultaneously long gold, short oil, neutral on copper, and overweight agricultural commodities, with position sizes calibrated to volatility and portfolio Greeks. When they increase gold exposure from five percent to eight percent of portfolio allocation, this rebalancing decision reflects sophisticated analysis of opportunity cost, risk parity, and cross-market dynamics that no individual chart pattern can capture.
This portfolio reweighting activity, multiplied across hundreds of institutional participants, manifests in the aggregate positioning data published weekly by the CFTC. The Commitment of Traders report does not show individual trades or strategies. It shows the collective footprint of how actual commercial hedgers and large speculators have allocated their capital across different markets. When mining companies collectively increase forward gold sales to hedge thirty percent more production than last quarter, they are not reacting to a moving average crossover. They are making strategic allocation decisions based on production forecasts, cost structures, and price expectations derived from operational realities invisible to outside observers. This is portfolio management in action, revealed through positioning data rather than price charts.
If you want to understand how institutional capital actually flows, how sophisticated traders genuinely position themselves across market cycles, the COT report provides a rare window into that hidden world. But understand what you are getting into. This is not a tool for scalpers seeking confirmation of the next five-minute move. This is not an oscillator that flashes oversold at market bottoms with convenient precision. COT analysis operates on a timescale measured in weeks and months, revealing positioning shifts that precede major market turns but offer no precision timing. The data arrives three days stale, published only once per week, capturing strategic positioning rather than tactical entries.
If you need instant gratification, if you trade intraday moves, if you demand mechanical signals with ninety percent accuracy, close this document now. COT analysis rewards patience, position sizing discipline, and tolerance for being early. It punishes impatience, overleveraging, and the expectation that any single indicator can substitute for market understanding.
The premise is deceptively simple. Every Tuesday, large traders in futures markets must report their positions to the CFTC. By Friday afternoon, this data becomes public. Academic research spanning three decades has consistently shown that not all market participants are created equal. Some traders consistently profit while others consistently lose. Some anticipate major turning points while others chase trends into exhaustion. Bessembinder and Chan (1992) demonstrated in their seminal study that commercial hedgers, those with actual exposure to the underlying commodity or financial instrument, possess superior forecasting ability compared to speculators. Their research, published in the Journal of Finance, found statistically significant predictive power in commercial positioning, particularly at extreme levels. This finding challenged the efficient market hypothesis and opened the door to a new approach to market analysis based on positioning rather than price alone.
Think about what this means. Every week, the government publishes a report showing you exactly how the most informed market participants are positioned. Not their opinions. Not their predictions. Their actual money at risk. When agricultural producers collectively hold their largest short hedge in five years, they are not making idle speculation. They are locking in prices for crops they will harvest, informed by private knowledge of weather conditions, soil quality, inventory levels, and demand expectations invisible to outside observers. When energy companies aggressively hedge forward production at current prices, they reveal information about expected supply that no analyst report can capture. This is not technical analysis based on past prices. This is not fundamental analysis based on publicly available data. This is behavioral analysis based on how the smartest money is actually positioned, how institutions allocate capital across portfolios, and how those allocation decisions shift as market conditions evolve.
WHY SOME TRADERS KNOW MORE THAN OTHERS
Building on this foundation, Sanders, Boris and Manfredo (2004) conducted extensive research examining the behaviour patterns of different trader categories. Their work, which analyzed over a decade of COT data across multiple commodity markets, revealed a fascinating dynamic that challenges much of what retail traders are taught. Commercial hedgers consistently positioned themselves against market extremes, buying when speculators were most bearish and selling when speculators reached peak bullishness. The contrarian positioning of commercials was not random noise but rather reflected their superior information about supply and demand fundamentals. Meanwhile, large speculators, primarily hedge funds and commodity trading advisors, exhibited strong trend-following behaviour that often amplified market moves beyond fundamental values. Small traders, the retail participants, consistently entered positions late in trends, frequently near turning points, making them reliable contrary indicators.
Wang (2003) extended this research by demonstrating that the predictive power of commercial positioning varies significantly across different commodity sectors. His analysis of agricultural commodities showed particularly strong forecasting ability, with commercial net positions explaining up to fifteen percent of return variance in subsequent weeks. This finding suggests that the informational advantages of hedgers are most pronounced in markets where physical supply and demand fundamentals dominate, as opposed to purely financial markets where information asymmetries are smaller. When a corn farmer hedges six months of expected harvest, that decision incorporates private observations about rainfall patterns, crop health, pest pressure, and local storage capacity that no distant analyst can match. When an oil refinery hedges crude oil purchases and gasoline sales simultaneously, the spread relationships reveal expectations about refining margins that reflect operational realities invisible in public data.
The theoretical mechanism underlying these empirical patterns relates to information asymmetry and different participant motivations. Commercial hedgers engage in futures markets not for speculative profit but to manage business risks. An agricultural producer selling forward six months of expected harvest is not making a bet on price direction but rather locking in revenue to facilitate financial planning and ensure business viability. However, this hedging activity necessarily incorporates private information about expected supply, inventory levels, weather conditions, and demand trends that the hedger observes through their commercial operations (Irwin and Sanders, 2012). When aggregated across many participants, this private information manifests in collective positioning.
Consider a gold mining company deciding how much forward production to hedge. Management must estimate ore grades, recovery rates, production costs, equipment reliability, labor availability, and dozens of other operational variables that determine whether locking in prices at current levels makes business sense. If the industry collectively hedges more aggressively than usual, it suggests either exceptional production expectations or concern about sustaining current price levels or combination of both. Either way, this positioning reveals information unavailable to speculators analyzing price charts and economic data. The hedger sees the physical reality behind the financial abstraction.
Large speculators operate under entirely different incentives and constraints. Commodity Trading Advisors managing billions in assets typically employ systematic, trend-following strategies that respond to price momentum rather than fundamental supply and demand. When crude oil rallies from sixty dollars to seventy dollars per barrel, these systems generate buy signals. As the rally continues to eighty dollars, position sizes increase. The strategy works brilliantly during sustained trends but becomes a liability at reversals. By the time oil reaches ninety dollars, trend-following funds are maximally long, having accumulated positions progressively throughout the rally. At this point, they represent not smart money anticipating further gains but rather crowded money vulnerable to reversal. Sanders, Boris and Manfredo (2004) documented this pattern across multiple energy markets, showing that extreme speculator positioning typically marked late-stage trend exhaustion rather than early-stage trend development.
Small traders, the retail participants who fall below reporting thresholds, display the weakest forecasting ability. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns, meaning their aggregate positioning served as a reliable contrary indicator. The explanation combines several factors. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, entering trends after mainstream media coverage when institutional participants are preparing to exit. Perhaps most importantly, they trade with emotion, buying into euphoria and selling into panic at precisely the wrong times.
At major turning points, the three groups often position opposite each other with commercials extremely bearish, large speculators extremely bullish, and small traders piling into longs at the last moment. These high-divergence environments frequently precede increased volatility and trend reversals. The insiders with business exposure quietly exit as the momentum traders hit maximum capacity and retail enthusiasm peaks. Within weeks, the reversal begins, and positions unwind in the opposite sequence.
FROM RAW DATA TO ACTIONABLE SIGNALS
The COT Index indicator operationalizes these academic findings into a practical trading tool accessible through TradingView. At its core, the indicator normalizes net positioning data onto a zero to one hundred scale, creating what we call the COT Index. This normalization is critical because absolute position sizes vary dramatically across different futures contracts and over time. A commercial trader holding fifty thousand contracts net long in crude oil might be extremely bullish by historical standards, or it might be quite neutral depending on the context of total market size and historical ranges. Raw position numbers mean nothing without context. The COT Index solves this problem by calculating where current positioning stands relative to its range over a specified lookback period, typically two hundred fifty-two weeks or approximately five years of weekly data.
The mathematical transformation follows the methodology originally popularized by legendary trader Larry Williams, though the underlying concept appears in statistical normalization techniques across many fields. For any given trader category, we calculate the highest and lowest net position values over the lookback period, establishing the historical range for that specific market and trader group. Current positioning is then expressed as a percentage of this range, where zero represents the most bearish positioning ever seen in the lookback window and one hundred represents the most bullish extreme. A reading of fifty indicates positioning exactly in the middle of the historical range, suggesting neither extreme optimism nor pessimism relative to recent history (Williams and Noseworthy, 2009).
This index-based approach allows for meaningful comparison across different markets and time periods, overcoming the scaling problems inherent in analyzing raw position data. A commercial index reading of eighty-five in gold carries the same interpretive meaning as an eighty-five reading in wheat or crude oil, even though the absolute position sizes differ by orders of magnitude. This standardization enables systematic analysis across entire futures portfolios rather than requiring market-specific expertise for each contract.
The lookback period selection involves a fundamental tradeoff between responsiveness and stability. Shorter lookback periods, perhaps one hundred twenty-six weeks or approximately two and a half years, make the index more sensitive to recent positioning changes. However, it also increases noise and produces more false signals. Longer lookback periods, perhaps five hundred weeks or approximately ten years, create smoother readings that filter short-term noise but become slower to recognize regime changes. The indicator settings allow users to adjust this parameter based on their trading timeframe, risk tolerance, and market characteristics.
UNDERSTANDING CFTC DATA STRUCTURES
The indicator supports both Legacy and Disaggregated COT report formats, reflecting the evolution of CFTC reporting standards over decades of market development. Legacy reports categorize market participants into three broad groups: commercial traders (hedgers with underlying business exposure), non-commercial traders (large speculators seeking profit without commercial interest), and non-reportable traders (small speculators below reporting thresholds). Each category brings distinct motivations and information advantages to the market (CFTC, 2020).
The Disaggregated reports, introduced in September 2009 for physical commodity markets, provide finer granularity by splitting participants into five categories (CFTC, 2009). Producer and merchant positions capture those actually producing, processing, or merchandising the physical commodity. Swap dealers represent financial intermediaries facilitating derivative transactions for clients. Managed money includes commodity trading advisors and hedge funds executing systematic or discretionary strategies. Other reportables encompasses diverse participants not fitting the main categories. Small traders remain as the fifth group, representing retail participation.
This enhanced categorization reveals nuances invisible in Legacy reports, particularly distinguishing between different types of institutional capital and their distinct behavioural patterns. The indicator automatically detects which report type is appropriate for each futures contract and adjusts the display accordingly.
Importantly, Disaggregated reports exist only for physical commodity futures. Agricultural commodities like corn, wheat, and soybeans have Disaggregated reports because clear producer, merchant, and swap dealer categories exist. Energy commodities like crude oil and natural gas similarly have well-defined commercial hedger categories. Metals including gold, silver, and copper also receive Disaggregated treatment (CFTC, 2009). However, financial futures such as equity index futures, Treasury bond futures, and currency futures remain available only in Legacy format. The CFTC has indicated no plans to extend Disaggregated reporting to financial futures due to different market structures and participant categories in these instruments (CFTC, 2020).
THE BEHAVIORAL FOUNDATION
Understanding which trader perspective to follow requires appreciation of their distinct trading styles, success rates, and psychological profiles. Commercial hedgers exhibit anticyclical behaviour rooted in their fundamental knowledge and business imperatives. When agricultural producers hedge forward sales during harvest season, they are not speculating on price direction but rather locking in revenue for crops they will harvest. Their business requires converting volatile commodity exposure into predictable cash flows to facilitate planning and ensure survival through difficult periods. Yet their aggregate positioning reveals valuable information because these hedging decisions incorporate private information about supply conditions, inventory levels, weather observations, and demand expectations that hedgers observe through their commercial operations (Bessembinder and Chan, 1992).
Consider a practical example from energy markets. Major oil companies continuously hedge portions of forward production based on price levels, operational costs, and financial planning needs. When crude oil trades at ninety dollars per barrel, they might aggressively hedge the next twelve months of production, locking in prices that provide comfortable profit margins above their extraction costs. This hedging appears as short positioning in COT reports. If oil rallies further to one hundred dollars, they hedge even more aggressively, viewing these prices as exceptional opportunities to secure revenue. Their short positioning grows increasingly extreme. To an outside observer watching only price charts, the rally suggests bullishness. But the commercial positioning reveals that the actual producers of oil find these prices attractive enough to lock in years of sales, suggesting skepticism about sustaining even higher levels. When the eventual reversal occurs and oil declines back to eighty dollars, the commercials who hedged at ninety and one hundred dollars profit while speculators who chased the rally suffer losses.
Large speculators or managed money traders operate under entirely different incentives and constraints. Their systematic, momentum-driven strategies mean they amplify existing trends rather than anticipate reversals. Trend-following systems, the most common approach among large speculators, by definition require confirmation of trend through price momentum before entering positions (Sanders, Boris and Manfredo, 2004). When crude oil rallies from sixty dollars to eighty dollars per barrel over several months, trend-following algorithms generate buy signals based on moving average crossovers, breakouts, and other momentum indicators. As the rally continues, position sizes increase according to the systematic rules.
However, this approach becomes a liability at turning points. By the time oil reaches ninety dollars after a sustained rally, trend-following funds are maximally long, having accumulated positions progressively throughout the move. At this point, their positioning does not predict continued strength. Rather, it often marks late-stage trend exhaustion. The psychological and mechanical explanation is straightforward. Trend followers by definition chase price momentum, entering positions after trends establish rather than anticipating them. Eventually, they become fully invested just as the trend nears completion, leaving no incremental buying power to sustain the rally. When the first signs of reversal appear, systematic stops trigger, creating a cascade of selling that accelerates the downturn.
Small traders consistently display the weakest track record across academic studies. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns in his analysis across multiple commodity markets. This result means that whatever small traders collectively do, the opposite typically proves profitable. The explanation for small trader underperformance combines several factors documented in behavioral finance literature. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, learning about commodity trends through mainstream media coverage that arrives after institutional participants have already positioned. Perhaps most importantly, retail traders are more susceptible to emotional decision-making, buying into euphoria and selling into panic at precisely the wrong times (Tharp, 2008).
SETTINGS, THRESHOLDS, AND SIGNAL GENERATION
The practical implementation of the COT Index requires understanding several key features and settings that users can adjust to match their trading style, timeframe, and risk tolerance. The lookback period determines the time window for calculating historical ranges. The default setting of two hundred fifty-two bars represents approximately one year on daily charts or five years on weekly charts, balancing responsiveness with stability. Conservative traders seeking only the most extreme, highest-probability signals might extend the lookback to five hundred bars or more. Aggressive traders seeking earlier entry and willing to accept more false positives might reduce it to one hundred twenty-six bars or even less for shorter-term applications.
The bullish and bearish thresholds define signal generation levels. Default settings of eighty and twenty respectively reflect academic research suggesting meaningful information content at these extremes. Readings above eighty indicate positioning in the top quintile of the historical range, representing genuine extremes rather than temporary fluctuations. Conversely, readings below twenty occupy the bottom quintile, indicating unusually bearish positioning (Briese, 2008).
However, traders must recognize that appropriate thresholds vary by market, trader category, and personal risk tolerance. Some futures markets exhibit wider positioning swings than others due to seasonal patterns, volatility characteristics, or participant behavior. Conservative traders seeking high-probability setups with fewer signals might raise thresholds to eighty-five and fifteen. Aggressive traders willing to accept more false positives for earlier entry could lower them to seventy-five and twenty-five.
The key is maintaining meaningful differentiation between bullish, neutral, and bearish zones. The default settings of eighty and twenty create a clear three-zone structure. Readings from zero to twenty represent bearish territory where the selected trader group holds unusually bearish positions. Readings from twenty to eighty represent neutral territory where positioning falls within normal historical ranges. Readings from eighty to one hundred represent bullish territory where the selected trader group holds unusually bullish positions.
The trading perspective selection determines which participant group the indicator follows, fundamentally shaping interpretation and signal meaning. For counter-trend traders seeking reversal opportunities, monitoring commercial positioning makes intuitive sense based on the academic research discussed earlier. When commercials reach extreme bearish readings below twenty, indicating unprecedented short positioning relative to recent history, they are effectively betting against the crowd. Given their informational advantages demonstrated by Bessembinder and Chan (1992), this contrarian stance often precedes major bottoms.
Trend followers might instead monitor large speculator positioning, but with inverted logic compared to commercials. When managed money reaches extreme bullish readings above eighty, the trend may be exhausting rather than accelerating. This seeming paradox reflects their late-cycle participation documented by Sanders, Boris and Manfredo (2004). Sophisticated traders thus use speculator extremes as fade signals, entering positions opposite to speculator consensus.
Small trader monitoring serves primarily as a contrary indicator for all trading styles. Extreme small trader bullishness above seventy-five or eighty typically warns of retail FOMO at market tops. Extreme small trader bearishness below twenty or twenty-five often marks capitulation bottoms where the last weak hands have sold.
VISUALIZATION AND USER INTERFACE
The visual design incorporates multiple elements working together to facilitate decision-making and maintain situational awareness during active trading. The primary COT Index line plots in bold with adjustable line width, defaulting to two pixels for clear visibility against busy price charts. An optional glow effect, controlled by a simple toggle, adds additional visual prominence through multiple plot layers with progressively increasing transparency and width.
A twenty-one period exponential moving average overlays the index line, providing trend context for positioning changes. When the index crosses above its moving average, it signals accelerating bullish sentiment among the selected trader group regardless of whether absolute positioning is extreme. Conversely, when the index crosses below its moving average, it signals deteriorating sentiment and potentially the beginning of a reversal in positioning trends.
The EMA provides a dynamic reference line for assessing positioning momentum. When the index trades far above its EMA, positioning is not only extreme in absolute terms but also building with momentum. When the index trades far below its EMA, positioning is contracting or reversing, which may indicate weakening conviction even if absolute levels remain elevated.
The data table positioned at the top right of the chart displays eleven metrics for each trader category, transforming the indicator from a simple index calculation into an analytical dashboard providing multidimensional market intelligence. Beyond the COT Index itself, users can monitor positioning extremity, which measures how unusual current levels are compared to historical norms using statistical techniques. The extremity metric clarifies whether a reading represents the ninety-fifth or ninety-ninth percentile, with values above two standard deviations indicating genuinely exceptional positioning.
Market power quantifies each group's influence on total open interest. This metric expresses each trader category's net position as a percentage of total market open interest. A commercial entity holding forty percent of total open interest commands significantly more influence than one holding five percent, making their positioning signals more meaningful.
Momentum and rate of change metrics reveal whether positions are building or contracting, providing early warning of potential regime shifts. Position velocity measures the rate of change in positioning changes, effectively a second derivative providing even earlier insight into inflection points.
Sentiment divergence highlights disagreements between commercial and speculative positioning. This metric calculates the absolute difference between normalized commercial and large speculator index values. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals.
The table also displays concentration metrics when available, showing how positioning is distributed among the largest handful of traders in each category. High concentration indicates a few dominant players controlling most of the positioning, while low concentration suggests broad-based participation across many traders.
THE ALERT SYSTEM AND MONITORING
The alert system, comprising five distinct alert conditions, enables systematic monitoring of dozens of futures markets without constant screen watching. The bullish and bearish COT signal alerts trigger when the index crosses user-defined thresholds, indicating the selected trader group has reached extreme positioning worthy of attention. These alerts fire in real-time as new weekly COT data publishes, typically Friday afternoon following the Tuesday measurement date.
Extreme positioning alerts fire at ninety and ten index levels, representing the top and bottom ten percent of the historical range, warning of particularly stretched readings that historically precede reversals with high probability. When commercials reach a COT Index reading below ten, they are expressing their most bearish stance in the entire lookback period.
The data staleness alert notifies users when COT reports have not updated for more than ten days, preventing reliance on outdated information for trading decisions. Government shutdowns or federal holidays can interrupt the normal Friday publication schedule. Using stale signals while believing them current creates dangerous false confidence.
The indicator's watermark information display positioned in the bottom right corner provides essential context at a glance. This persistent display shows the symbol and timeframe, the COT report date timestamp, days since last update, and the current signal state. A trader analyzing a potential short entry in crude oil can glance at the watermark to instantly confirm positioning context without interrupting analysis flow.
LIMITATIONS AND REALISTIC EXPECTATIONS
Practical application requires understanding both the indicator's considerable strengths and inherent limitations. COT data inherently lags price action by three days, as Tuesday positions are not published until Friday afternoon. This delay means the indicator cannot catch rapid intraday reversals or respond to surprise news events. Traders using the COT Index for timing entries must accept this latency and focus on swing trading and position trading timeframes where three-day lags matter less than in day trading or scalping.
The weekly publication schedule similarly makes the indicator unsuitable for short-term trading strategies requiring immediate feedback. The COT Index works best for traders operating on weekly or longer timeframes, where positioning shifts measured in weeks and months align with trading horizon.
Extreme COT readings can persist far longer than typical technical indicators suggest, testing the patience and capital reserves of traders attempting to fade them. When crude oil enters a sustained bull market driven by genuine supply disruptions, commercial hedgers may maintain bearish positioning for many months as prices grind higher. A commercial COT Index reading of fifteen indicating extreme bearishness might persist for three months while prices continue rallying before finally reversing. Traders without sufficient capital and risk tolerance to weather such drawdowns will exit prematurely, precisely when the signal is about to work (Irwin and Sanders, 2012).
Position sizing discipline becomes paramount when implementing COT-based strategies. Rather than risking large percentages of capital on individual signals, successful COT traders typically allocate modest position sizes across multiple signals, allowing some to take time to mature while others work more quickly.
The indicator also cannot overcome fundamental regime changes that alter the structural drivers of markets. If gold enters a true secular bull market driven by monetary debasement, commercial hedgers may remain persistently bearish as mining companies sell forward years of production at what they perceive as favorable prices. Their positioning indicates valuation concerns from a production cost perspective, but cannot stop prices from rising if investment demand overwhelms physical supply-demand balance.
Similarly, structural changes in market participation can alter the meaning of positioning extremes. The growth of commodity index investing in the two thousands brought massive passive long-only capital into futures markets, fundamentally changing typical positioning ranges. Traders relying on COT signals without recognizing this regime change would have generated numerous false bearish signals during the commodity supercycle from 2003 to 2008.
The research foundation supporting COT analysis derives primarily from commodity markets where the commercial hedger information advantage is most pronounced. Studies specifically examining financial futures like equity indices and bonds show weaker but still present effects. Traders should calibrate expectations accordingly, recognizing that COT analysis likely works better for crude oil, natural gas, corn, and wheat than for the S&P 500, Treasury bonds, or currency futures.
Another important limitation involves the reporting threshold structure. Not all market participants appear in COT data, only those holding positions above specified minimums. In markets dominated by a few large players, concentration metrics become critical for proper interpretation. A single large trader accounting for thirty percent of commercial positioning might skew the entire category if their individual circumstances are idiosyncratic rather than representative.
GOLD FUTURES DURING A HYPOTHETICAL MARKET CYCLE
Consider a practical example using gold futures during a hypothetical but realistic market scenario that illustrates how the COT Index indicator guides trading decisions through a complete market cycle. Suppose gold has rallied from fifteen hundred to nineteen hundred dollars per ounce over six months, driven by inflation concerns following aggressive monetary expansion, geopolitical uncertainty, and sustained buying by Asian central banks for reserve diversification.
Large speculators, operating primarily trend-following strategies, have accumulated increasingly bullish positions throughout this rally. Their COT Index has climbed progressively from forty-five to eighty-five. The table display shows that large speculators now hold net long positions representing thirty-two percent of total open interest, their highest in four years. Momentum indicators show positive readings, indicating positions are still building though at a decelerating rate. Position velocity has turned negative, suggesting the pace of position building is slowing.
Meanwhile, commercial hedgers have responded to the rally by aggressively selling forward production and inventory. Their COT Index has moved inversely to price, declining from fifty-five to twenty. This bearish commercial positioning represents mining companies locking in forward sales at prices they view as attractive relative to production costs. The table shows commercials now hold net short positions representing twenty-nine percent of total open interest, their most bearish stance in five years. Concentration metrics indicate this positioning is broadly distributed across many commercial entities, suggesting the bearish stance reflects collective industry view rather than idiosyncratic positioning by a single firm.
Small traders, attracted by mainstream financial media coverage of gold's impressive rally, have recently piled into long positions. Their COT Index has jumped from forty-five to seventy-eight as retail investors chase the trend. Television financial networks feature frequent segments on gold with bullish guests. Internet forums and social media show surging retail interest. This retail enthusiasm historically marks late-stage trend development rather than early opportunity.
The COT Index indicator, configured to monitor commercial positioning from a contrarian perspective, displays a clear bearish signal given the extreme commercial short positioning. The table displays multiple confirming metrics: positioning extremity shows commercials at the ninety-sixth percentile of bearishness, market power indicates they control twenty-nine percent of open interest, and sentiment divergence registers sixty-five, indicating massive disagreement between commercial hedgers and large speculators. This divergence, the highest in three years, places the market in the historically high-risk category for reversals.
The interpretation requires nuance and consideration of context beyond just COT data. Commercials are not necessarily predicting an imminent crash. Rather, they are hedging business operations at what they collectively view as favorable price levels. However, the data reveals they have sold unusually large quantities of forward production, suggesting either exceptional production expectations for the year ahead or concern about sustaining current price levels or combination of both. Combined with extreme speculator positioning indicating a crowded long trade, and small trader enthusiasm confirming retail FOMO, the confluence suggests elevated reversal risk even if the precise timing remains uncertain.
A prudent trader analyzing this situation might take several actions based on COT Index signals. Existing long positions could be tightened with closer stop losses. Profit-taking on a portion of long exposure could lock in gains while maintaining some participation. Some traders might initiate modest short positions as portfolio hedges, sizing them appropriately for the inherent uncertainty in timing reversals. Others might simply move to the sidelines, avoiding new long entries until positioning normalizes.
The key lesson from case study analysis is that COT signals provide probabilistic edges rather than deterministic predictions. They work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five percent win rate with proper risk management produces substantial profits over time, yet still means forty-five percent of signals will be premature or wrong. Traders must embrace this probabilistic reality rather than seeking the impossible goal of perfect accuracy.
INTEGRATION WITH TRADING SYSTEMS
Integration with existing trading systems represents a natural and powerful use case for COT analysis, adding a positioning dimension to price-based technical approaches or fundamental analytical frameworks. Few traders rely exclusively on a single indicator or methodology. Rather, they build systems that synthesize multiple information sources, with each component addressing different aspects of market behavior.
Trend followers might use COT extremes as regime filters, modifying position sizing or avoiding new trend entries when positioning reaches levels historically associated with reversals. Consider a classic trend-following system based on moving average crossovers and momentum breakouts. Integration of COT analysis adds nuance. When large speculator positioning exceeds ninety or commercial positioning falls below ten, the regime filter recognizes elevated reversal risk. The system might reduce position sizing by fifty percent for new signals during these high-risk periods (Kaufman, 2013).
Mean reversion traders might require COT signal confluence before fading extended moves. When crude oil becomes technically overbought and large speculators show extreme long positioning above eighty-five, both signals confirm. If only technical indicators show extremes while positioning remains neutral, the potential short signal is rejected, avoiding fades of trends with underlying institutional support (Kaufman, 2013).
Discretionary traders can monitor the indicator as a continuous awareness tool, informing bias and position sizing without dictating mechanical entries and exits. A discretionary trader might notice commercial positioning shifting from neutral to progressively more bullish over several months. This trend informs growing positive bias even without triggering mechanical signals.
Multi-timeframe analysis represents another powerful integration approach. A trader might use daily charts for trade execution and timing while monitoring weekly COT positioning for strategic context. When both timeframes align, highest-probability opportunities emerge.
Portfolio construction for futures traders can incorporate COT signals as an additional selection criterion. Markets showing strong technical setups AND favorable COT positioning receive highest allocations. Markets with strong technicals but neutral or unfavorable positioning receive reduced allocations.
ADVANCED METRICS AND INTERPRETATION
The metrics table transforms simple positioning data into multidimensional market intelligence. Position extremity, calculated as the absolute deviation from the historical mean normalized by standard deviation, helps identify truly unusual readings versus routine fluctuations. A reading above two standard deviations indicates ninety-fifth percentile or higher extremity. Above three standard deviations indicates ninety-ninth percentile or higher, genuinely rare positioning that historically precedes major events with high probability.
Market power, expressed as a percentage of total open interest, reveals whose positioning matters most from a mechanical market impact perspective. Consider two scenarios in gold futures. In scenario one, commercials show a COT Index reading of fifteen while their market power metric shows they hold net shorts representing thirty-five percent of open interest. This is a high-confidence bearish signal. In scenario two, commercials also show a reading of fifteen, but market power shows only eight percent. While positioning is extreme relative to this category's normal range, their limited market share means less mechanical influence on price.
The rate of change and momentum metrics highlight whether positions are accelerating or decelerating, often providing earlier warnings than absolute levels alone. A COT Index reading of seventy-five with rapidly building momentum suggests continued movement toward extremes. Conversely, a reading of eighty-five with decelerating or negative momentum indicates the positioning trend is exhausting.
Position velocity measures the rate of change in positioning changes, effectively a second derivative. When velocity shifts from positive to negative, it indicates that while positioning may still be growing, the pace of growth is slowing. This deceleration often precedes actual reversal in positioning direction by several weeks.
Sentiment divergence calculates the absolute difference between normalized commercial and large speculator index values. When commercials show extreme bearish positioning at twenty while large speculators show extreme bullish positioning at eighty, the divergence reaches sixty, representing near-maximum disagreement. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The mechanism is intuitive. Extreme divergence indicates the informed hedgers and momentum-following speculators have positioned opposite each other with conviction. One group will prove correct and profit while the other proves incorrect and suffers losses. The resolution of this disagreement through price movement often involves volatility.
The table also displays concentration metrics when available. High concentration indicates a few dominant players controlling most of the positioning within a category, while low concentration suggests broad-based participation. Broad-based positioning more reliably reflects collective market intelligence and industry consensus. If mining companies globally all independently decide to hedge aggressively at similar price levels, it suggests genuine industry-wide view about price valuations rather than circumstances specific to one firm.
DATA QUALITY AND RELIABILITY
The CFTC has maintained COT reporting in various forms since the nineteen twenties, providing nearly a century of positioning data across multiple market cycles. However, data quality and reporting standards have evolved substantially over this long period. Modern electronic reporting implemented in the late nineteen nineties and early two thousands significantly improved accuracy and timeliness compared to earlier paper-based systems.
Traders should understand that COT reports capture positions as of Tuesday's close each week. Markets remain open three additional days before publication on Friday afternoon, meaning the reported data is three days stale when received. During periods of rapid market movement or major news events, this lag can be significant. The indicator addresses this limitation by including timestamp information and staleness warnings.
The three-day lag creates particular challenges during extreme volatility episodes. Flash crashes, surprise central bank interventions, geopolitical shocks, and other high-impact events can completely transform market positioning within hours. Traders must exercise judgment about whether reported positioning remains relevant given intervening events.
Reporting thresholds also mean that not all market participants appear in disaggregated COT data. Traders holding positions below specified minimums aggregate into the non-reportable or small trader category. This aggregation affects different markets differently. In highly liquid contracts like crude oil with thousands of participants, reportable traders might represent seventy to eighty percent of open interest. In thinly traded contracts with only dozens of active participants, a few large reportable positions might represent ninety-five percent of open interest.
Another data quality consideration involves trader classification into categories. The CFTC assigns traders to commercial or non-commercial categories based on reported business purpose and activities. However, this process is not perfect. Some entities engage in both commercial and speculative activities, creating ambiguity about proper classification. The transition to Disaggregated reports attempted to address some of these ambiguities by creating more granular categories.
COMPARISON WITH ALTERNATIVE APPROACHES
Several alternative approaches to COT analysis exist in the trading community beyond the normalization methodology employed by this indicator. Some analysts focus on absolute position changes week-over-week rather than index-based normalization. This approach calculates the change in net positioning from one week to the next. The emphasis falls on momentum in positioning changes rather than absolute levels relative to history. This method potentially identifies regime shifts earlier but sacrifices cross-market comparability (Briese, 2008).
Other practitioners employ more complex statistical transformations including percentile rankings, z-score standardization, and machine learning classification algorithms. Ruan and Zhang (2018) demonstrated that machine learning models applied to COT data could achieve modest improvements in forecasting accuracy compared to simple threshold-based approaches. However, these gains came at the cost of interpretability and implementation complexity.
The COT Index indicator intentionally employs a relatively straightforward normalization methodology for several important reasons. First, transparency enhances user understanding and trust. Traders can verify calculations manually and develop intuitive feel for what different readings mean. Second, academic research suggests that most of the predictive power in COT data comes from extreme positioning levels rather than subtle patterns requiring complex statistical methods to detect. Third, robust methods that work consistently across many markets and time periods tend to be simpler rather than more complex, reducing the risk of overfitting to historical data. Fourth, the complexity costs of implementation matter for retail traders without programming teams or computational infrastructure.
PSYCHOLOGICAL ASPECTS OF COT TRADING
Trading based on COT data requires psychological fortitude that differs from momentum-based approaches. Contrarian positioning signals inherently mean betting against prevailing market sentiment and recent price action. When commercials reach extreme bearish positioning, prices have typically been rising, sometimes for extended periods. The price chart looks bullish, momentum indicators confirm strength, moving averages align positively. The COT signal says bet against all of this. This psychological difficulty explains why COT analysis remains underutilized relative to trend-following methods.
Human psychology strongly predisposes us toward extrapolation and recency bias. When prices rally for months, our pattern-matching brains naturally expect continued rally. The recent price action dominates our perception, overwhelming rational analysis about positioning extremes and historical probabilities. The COT signal asking us to sell requires overriding these powerful psychological impulses.
The indicator design attempts to support the required psychological discipline through several features. Clear threshold markers and signal states reduce ambiguity about when signals trigger. When the commercial index crosses below twenty, the signal is explicit and unambiguous. The background shifts to red, the signal label displays bearish, and alerts fire. This explicitness helps traders act on signals rather than waiting for additional confirmation that may never arrive.
The metrics table provides analytical justification for contrarian positions, helping traders maintain conviction during inevitable periods of adverse price movement. When a trader enters short positions based on extreme commercial bearish positioning but prices continue rallying for several weeks, doubt naturally emerges. The table display provides reassurance. Commercial positioning remains extremely bearish. Divergence remains high. The positioning thesis remains intact even though price action has not yet confirmed.
Alert functionality ensures traders do not miss signals due to inattention while also not requiring constant monitoring that can lead to emotional decision-making. Setting alerts for COT extremes enables a healthier relationship with markets. When meaningful signals occur, alerts notify them. They can then calmly assess the situation and execute planned responses.
However, no indicator design can completely overcome the psychological difficulty of contrarian trading. Some traders simply cannot maintain short positions while prices rally. For these traders, COT analysis might be better employed as an exit signal for long positions rather than an entry signal for shorts.
Ultimately, successful COT trading requires developing comfort with probabilistic thinking rather than certainty-seeking. The signals work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five or sixty percent win rate with proper risk management produces substantial profits over years, yet still means forty to forty-five percent of signals will be premature or wrong. COT analysis provides genuine edge, but edge means probability advantage, not elimination of losing trades.
EDUCATIONAL RESOURCES AND CONTINUOUS LEARNING
The indicator provides extensive built-in educational resources through its documentation, detailed tooltips, and transparent calculations. However, mastering COT analysis requires study beyond any single tool or resource. Several excellent resources provide valuable extensions of the concepts covered in this guide.
Books and practitioner-focused monographs offer accessible entry points. Stephen Briese published The Commitments of Traders Bible in two thousand eight, offering detailed breakdowns of how different markets and trader categories behave (Briese, 2008). Briese's work stands out for its empirical focus and market-specific insights. Jack Schwager includes discussion of COT analysis within the broader context of market behavior in his book Market Sense and Nonsense (Schwager, 2012). Perry Kaufman's Trading Systems and Methods represents perhaps the most rigorous practitioner-focused text on systematic trading approaches including COT analysis (Kaufman, 2013).
Academic journal articles provide the rigorous statistical foundation underlying COT analysis. The Journal of Futures Markets regularly publishes research on positioning data and its predictive properties. Bessembinder and Chan's earlier work on systematic risk, hedging pressure, and risk premiums in futures markets provides theoretical foundation (Bessembinder, 1992). Chang's examination of speculator returns provides historical context (Chang, 1985). Irwin and Sanders provide essential skeptical perspective in their two thousand twelve article (Irwin and Sanders, 2012). Wang's two thousand three article provides one of the most empirical analyses of COT data across multiple commodity markets (Wang, 2003).
Online resources extend beyond academic and book-length treatments. The CFTC website provides free access to current and historical COT reports in multiple formats. The explanatory materials section offers detailed documentation of report construction, category definitions, and historical methodology changes. Traders serious about COT analysis should read these official CFTC documents to understand exactly what they are analyzing.
Commercial COT data services such as Barchart provide enhanced visualization and analysis tools beyond raw CFTC data. TradingView's educational materials, published scripts library, and user community provide additional resources for exploring different approaches to COT analysis.
The key to mastering COT analysis lies not in finding a single definitive source but rather in building understanding through multiple perspectives and information sources. Academic research provides rigorous empirical foundation. Practitioner-focused books offer practical implementation insights. Direct engagement with data through systematic backtesting develops intuition about how positioning dynamics manifest across different market conditions.
SYNTHESIZING KNOWLEDGE INTO PRACTICE
The COT Index indicator represents the synthesis of academic research, trading experience, and software engineering into a practical tool accessible to retail traders equipped with nothing more than a TradingView account and willingness to learn. What once required expensive data subscriptions, custom programming capabilities, statistical software, and institutional resources now appears as a straightforward indicator requiring only basic parameter selection and modest study to understand. This democratization of institutional-grade analysis tools represents a broader trend in financial markets over recent decades.
Yet technology and data access alone provide no edge without understanding and discipline. Markets remain relentlessly efficient at eliminating edges that become too widely known and mechanically exploited. The COT Index indicator succeeds only when users invest time learning the underlying concepts, understand the limitations and probability distributions involved, and integrate signals thoughtfully into trading plans rather than applying them mechanically.
The academic research demonstrates conclusively that institutional positioning contains genuine information about future price movements, particularly at extremes where commercial hedgers are maximally bearish or bullish relative to historical norms. This informational content is neither perfect nor deterministic but rather probabilistic, providing edge over many observations through identification of higher-probability configurations. Bessembinder and Chan's finding that commercial positioning explained modest but significant variance in future returns illustrates this probabilistic nature perfectly (Bessembinder and Chan, 1992). The effect is real and statistically significant, yet it explains perhaps ten to fifteen percent of return variance rather than most variance. Much of price movement remains unpredictable even with positioning intelligence.
The practical implication is that COT analysis works best as one component of a trading system rather than a standalone oracle. It provides the positioning dimension, revealing where the smart money has positioned and where the crowd has followed, but price action analysis provides the timing dimension. Fundamental analysis provides the catalyst dimension. Risk management provides the survival dimension. These components work together synergistically.
The indicator's design philosophy prioritizes transparency and education over black-box complexity, empowering traders to understand exactly what they are analyzing and why. Every calculation is documented and user-adjustable. The threshold markers, background coloring, tables, and clear signal states provide multiple reinforcing channels for conveying the same information.
This educational approach reflects a conviction that sustainable trading success comes from genuine understanding rather than mechanical system-following. Traders who understand why commercial positioning matters, how different trader categories behave, what positioning extremes signify, and where signals fit within probability distributions can adapt when market conditions change. Traders mechanically following black-box signals without comprehension abandon systems after normal losing streaks.
The research foundation supporting COT analysis comes primarily from commodity markets where commercial hedger informational advantages are most pronounced. Agricultural producers hedging crops know more about supply conditions than distant speculators. Energy companies hedging production know more about operating costs than financial traders. Metals miners hedging output know more about ore grades than index funds. Financial futures markets show weaker but still present effects.
The journey from reading this documentation to profitable trading based on COT analysis involves several stages that cannot be rushed. Initial reading and basic understanding represents the first stage. Historical study represents the second stage, reviewing past market cycles to observe how positioning extremes preceded major turning points. Paper trading or small-size real trading represents the third stage to experience the psychological challenges. Refinement based on results and personal psychology represents the fourth stage.
Markets will continue evolving. New participant categories will emerge. Regulatory structures will change. Technology will advance. Yet the fundamental dynamics driving COT analysis, that different market participants have different information, different motivations, and different forecasting abilities that manifest in their positioning, will persist as long as futures markets exist. While specific thresholds or optimal parameters may shift over time, the core logic remains sound and adaptable.
The trader equipped with this indicator, understanding of the theory and evidence behind COT analysis, realistic expectations about probability rather than certainty, discipline to maintain positions through adverse volatility, and patience to allow signals time to develop possesses genuine edge in markets. The edge is not enormous, markets cannot allow large persistent inefficiencies without arbitraging them away, but it is real, measurable, and exploitable by those willing to invest in learning and disciplined application.
REFERENCES
Bessembinder, H. (1992) Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies, 5(4), pp. 637-667.
Bessembinder, H. and Chan, K. (1992) The profitability of technical trading rules in the Asian stock markets, Pacific-Basin Finance Journal, 3(2-3), pp. 257-284.
Briese, S. (2008) The Commitments of Traders Bible: How to Profit from Insider Market Intelligence. Hoboken: John Wiley & Sons.
Chang, E.C. (1985) Returns to speculators and the theory of normal backwardation, Journal of Finance, 40(1), pp. 193-208.
Commodity Futures Trading Commission (CFTC) (2009) Explanatory Notes: Disaggregated Commitments of Traders Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Commodity Futures Trading Commission (CFTC) (2020) Commitments of Traders: About the Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Irwin, S.H. and Sanders, D.R. (2012) Testing the Masters Hypothesis in commodity futures markets, Energy Economics, 34(1), pp. 256-269.
Kaufman, P.J. (2013) Trading Systems and Methods. 5th edn. Hoboken: John Wiley & Sons.
Ruan, Y. and Zhang, Y. (2018) Forecasting commodity futures prices using machine learning: Evidence from the Chinese commodity futures market, Applied Economics Letters, 25(12), pp. 845-849.
Sanders, D.R., Boris, K. and Manfredo, M. (2004) Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports, Energy Economics, 26(3), pp. 425-445.
Schwager, J.D. (2012) Market Sense and Nonsense: How the Markets Really Work and How They Don't. Hoboken: John Wiley & Sons.
Tharp, V.K. (2008) Super Trader: Make Consistent Profits in Good and Bad Markets. New York: McGraw-Hill.
Wang, C. (2003) The behavior and performance of major types of futures traders, Journal of Futures Markets, 23(1), pp. 1-31.
Williams, L.R. and Noseworthy, M. (2009) The Right Stock at the Right Time: Prospering in the Coming Good Years. Hoboken: John Wiley & Sons.
FURTHER READING
For traders seeking to deepen their understanding of COT analysis and futures market positioning beyond this documentation, the following resources provide valuable extensions:
Academic Journal Articles:
Fishe, R.P.H. and Smith, A. (2012) Do speculators drive commodity prices away from supply and demand fundamentals?, Journal of Commodity Markets, 1(1), pp. 1-16.
Haigh, M.S., Hranaiova, J. and Overdahl, J.A. (2007) Hedge funds, volatility, and liquidity provision in energy futures markets, Journal of Alternative Investments, 9(4), pp. 10-38.
Kocagil, A.E. (1997) Does futures speculation stabilize spot prices? Evidence from metals markets, Applied Financial Economics, 7(1), pp. 115-125.
Sanders, D.R. and Irwin, S.H. (2011) The impact of index funds in commodity futures markets: A systems approach, Journal of Alternative Investments, 14(1), pp. 40-49.
Books and Practitioner Resources:
Murphy, J.J. (1999) Technical Analysis of the Financial Markets: A Guide to Trading Methods and Applications. New York: New York Institute of Finance.
Pring, M.J. (2002) Technical Analysis Explained: The Investor's Guide to Spotting Investment Trends and Turning Points. 4th edn. New York: McGraw-Hill.
Federal Reserve and Research Institution Publications:
Federal Reserve Banks regularly publish working papers examining commodity markets, futures positioning, and price discovery mechanisms. The Federal Reserve Bank of San Francisco and Federal Reserve Bank of Kansas City maintain active research programs in this area.
Online Resources:
The CFTC website provides free access to current and historical COT reports, explanatory materials, and regulatory documentation.
Barchart offers enhanced COT data visualization and screening tools.
TradingView's community library contains numerous published scripts and educational materials exploring different approaches to positioning analysis.
Kairos BarakahTrade with precision during high-probability windows using this advanced Pine Script indicator, designed specifically for Indian Standard Time (IST). The tool identifies key reversal opportunities within a user-defined trading session, combining time-based reference levels, sequence-validated signals, and multi-factor win probability analysis for confident decision-making.
Key Features
1. Time-Based Reference Levels
Automatically sets high/low reference levels at a customizable start time (default: 19:00 IST).
Active trading window with adjustable duration (default: 135 minutes).
Clear visual reference lines for easy tracking.
2. Intelligent Signal Generation
Initial Signals:
Buy (B): Triggered when price closes above the reference high.
Sell (S): Triggered when price closes below the reference low.
Reversal Signals (R):
Valid only after an initial signal, ensuring proper sequence.
Buy Reversal: Price closes above reference high (after a Sell signal).
Sell Reversal: Price closes below reference low (after a Buy signal).
3. Multi-Dimensional Win Probability
Body Strength: Measures candle conviction (body size / total range).
Volume Confirmation: Compares current volume to 20-period average.
Trend Alignment: Uses EMA crosses (9/21) and RSI (14) for momentum.
Composite Score: Weighted blend of all factors, color-coded for quick interpretation:
🟢 >70%: High-confidence signal.
🟠 40-69%: Moderate confidence.
🔴 <40%: Weak signal.
4. Professional Visualization
Clean labels (B/S/R) at signal points.
Real-time reference table showing levels, active signal, and probabilities.
Customizable alerts for all signal types.
Why Use This Indicator?
IST-Optimized: Tailored for Indian market hours.
Rules-Based Reversals: Avoids false signals with strict sequence checks.
Data-Driven Confidence: Win probability metrics reduce guesswork.
Flexible Setup: Adjust time windows and parameters to fit your strategy.
BySq - Market PsychologyThe script I provided is a Market Psychology Index indicator for TradingView, which focuses on three key psychological market phases:
FOMO (Fear of Missing Out)
Panic Selling
Reversal
This indicator uses volume, price changes, and specific time periods to gauge market sentiment. Let me break it down:
1. Input Parameters:
FOMO Period: Defines how many bars (candles) the FOMO index will consider for its calculation.
Panic Period: Defines the period to evaluate Panic Selling.
Reversal Period: Defines the period to evaluate potential price reversals.
You can adjust these periods based on your analysis preferences. The default for each period is 14.
2. FOMO Index:
The FOMO Index aims to capture the "fear of missing out" behavior in the market.
It uses volume and price change:
Volume is compared to the Simple Moving Average (SMA) of volume over the specified period.
Price change is calculated as the percentage change in price compared to the previous bar.
If both volume and price change indicate strong upward movement, the FOMO index spikes.
3. Panic Selling Index:
The Panic Selling Index captures when traders are selling out of fear, often in a rapid or irrational way.
Similar to the FOMO Index, it considers volume and price change:
It uses volume and compares it to the SMA of volume for the panic period.
Price change is negative, meaning it considers only price drops.
When there is high volume coupled with significant price drops, it signals panic selling.
4. Reversal Index:
The Reversal Index aims to detect potential trend reversals in the market.
This index also considers volume and price change:
It focuses on upward price movement and compares volume to its SMA.
If there’s strong upward price movement along with increasing volume, it signals the possibility of a price reversal.
5. Graphical Output:
Histograms are drawn on the chart for each of the three indices:
FOMO is shown in green (indicating the presence of FOMO) and red (when the index is low).
Panic Selling is shown in orange.
Reversal is shown in purple.
The Zero Line (horizontal dotted line) helps identify when any of the indices is positive or negative.
6. Labels:
Labels for each index are shown on the chart at the relevant bar when the index spikes.
FOMO is labeled "FOMO" in green when it spikes.
Panic Selling is labeled "Panic Selling" in orange when it spikes.
Reversal is labeled "Reversal" in purple when it spikes.
Additionally, period labels show above the chart, indicating the specific periods (FOMO, Panic, and Reversal periods) currently being applied. This provides clarity on what time frame each index is analyzing.
7. How to Use:
FOMO: High values may indicate that traders are buying out of fear of missing out on a rally, suggesting a potentially overheated market.
Panic Selling: High values could suggest irrational selling behavior or capitulation, potentially marking the bottom of a downtrend.
Reversal: High values signal the potential for a market reversal, where the price could change direction due to increased volume and upward movement.
8. Visual Appearance:
The indicator’s histograms change colors based on the level of market sentiment detected. The color-coded approach provides an easy-to-read visual representation of different psychological phases in the market.
The horizontal zero line allows easy differentiation between positive and negative values.
Summary:
This script combines the psychology of the market (FOMO, Panic Selling, and Reversal) into a set of indicators that help traders identify potential turning points or emotional states in the market. By focusing on volume and price change, the script attempts to give a clear picture of market sentiment and possible future movements.
Probability Grid [LuxAlgo]The Probability Grid tool allows traders to see the probability of where and when the next reversal would occur, it displays a 10x10 grid and/or dashboard with the probability of the next reversal occurring beyond each cell or within each cell.
🔶 USAGE
By default, the tool displays deciles (percentiles from 0 to 90), users can enable, disable and modify each percentile, but two of them must always be enabled or the tool will display an error message alerting of it.
The use of the tool is quite simple, as shown in the chart above, the further the price moves on the grid, the higher the probability of a reversal.
In this case, the reversal took place on the cell with a probability of 9%, which means that there is a probability of 91% within the square defined by the last reversal and this cell.
🔹 Grid vs Dashboard
The tool can display a grid starting from the last reversal and/or a dashboard at three predefined locations, as shown in the chart above.
🔶 DETAILS
🔹 Raw Data vs Normalized Data
By default the tool displays the normalized data, this means that instead of using the raw data (price delta between reversals) it uses the returns between each reversal, this is useful to make an apples to apples comparison of all the data in the dataset.
This can be seen in the left side of the chart above (BTCUSD Daily chart) where normalize data is disabled, the percentiles from 0 to 40 overlap and are indistinguishable from each other because the tool uses the raw price delta over the entire bitcoin history, with normalize data enabled as we can see in the right side of the chart we can have a fair comparison of the data over the entire history.
🔹 Probability Beyond or Within Each Cell
Two different probability modes are available, the default mode is Probability Beyond Each Cell, the number displayed in each cell is the probability of the next reversal to be located in the area beyond the cell, for example, if the cell displays 20%, it means that in the area formed by the square starting from the last reversal and ending at the cell, there is an 80% probability and outside that square there is a 20% probability for the location of the next reversal.
The second probability mode is the probability within each cell, this outlines the chance that the next reversal will be within the cell, as we can see on the right chart above, when using deciles as percentiles (default settings), each cell has the same 1% probability for the 10x10 grid.
🔶 SETTINGS
Swing Length: The maximum length in bars used to identify a swing
Maximum Reversals: Maximum number of reversals included in calculations
Normalize Data: Use returns between swings instead of raw price
Probability: Choose between two different probability modes: beyond and inside each cell
Percentiles: Enable/disable each of the ten percentiles and select the percentile number and line style
🔹 Dashboard
Show Dashboard: Enable or disable the dashboard
Position: Choose dashboard location
Size: Choose dashboard size
🔹 Style
Show Grid: Enable or disable the grid
Size: Choose grid text size
Colors: Choose grid background colors
Show Marks: Enable/disable reversal markers
Volume Weighted RSI (VW RSI)The Volume Weighted RSI (VW RSI) is a momentum oscillator designed for TradingView, implemented in Pine Script v6, that enhances the traditional Relative Strength Index (RSI) by incorporating trading volume into its calculation. Unlike the standard RSI, which measures the speed and change of price movements based solely on price data, the VW RSI weights its analysis by volume, emphasizing price movements backed by significant trading activity. This makes the VW RSI particularly effective for identifying bullish or bearish momentum, overbought/oversold conditions, and potential trend reversals in markets where volume plays a critical role, such as stocks, forex, and cryptocurrencies.
Key Features
Volume-Weighted Momentum Calculation:
The VW RSI calculates momentum by comparing the volume associated with upward price movements (up-volume) to the volume associated with downward price movements (down-volume).
Up-volume is the volume on bars where the closing price is higher than the previous close, while down-volume is the volume on bars where the closing price is lower than the previous close.
These volumes are smoothed over a user-defined period (default: 14 bars) using a Running Moving Average (RMA), and the VW RSI is computed using the formula:
\text{VW RSI} = 100 - \frac{100}{1 + \text{VoRS}}
where
\text{VoRS} = \frac{\text{Average Up-Volume}}{\text{Average Down-Volume}}
.
Oscillator Range and Interpretation:
The VW RSI oscillates between 0 and 100, with a centerline at 50.
Above 50: Indicates bullish volume momentum, suggesting that volume on up bars dominates, which may signal buying pressure and a potential uptrend.
Below 50: Indicates bearish volume momentum, suggesting that volume on down bars dominates, which may signal selling pressure and a potential downtrend.
Overbought/Oversold Levels: User-defined thresholds (default: 70 for overbought, 30 for oversold) help identify potential reversal points:
VW RSI > 70: Overbought, indicating a possible pullback or reversal.
VW RSI < 30: Oversold, indicating a possible bounce or reversal.
Visual Elements:
VW RSI Line: Plotted in a separate pane below the price chart, colored dynamically based on its value:
Green when above 50 (bullish momentum).
Red when below 50 (bearish momentum).
Gray when at 50 (neutral).
Centerline: A dashed line at 50, optionally displayed, serving as the neutral threshold between bullish and bearish momentum.
Overbought/Oversold Lines: Dashed lines at the user-defined overbought (default: 70) and oversold (default: 30) levels, optionally displayed, to highlight extreme conditions.
Background Coloring: The background of the VW RSI pane is shaded red when the indicator is in overbought territory and green when in oversold territory, providing a quick visual cue of potential reversal zones.
Alerts:
Built-in alerts for key events:
Bullish Momentum: Triggered when the VW RSI crosses above 50, indicating a shift to bullish volume momentum.
Bearish Momentum: Triggered when the VW RSI crosses below 50, indicating a shift to bearish volume momentum.
Overbought Condition: Triggered when the VW RSI crosses above the overbought threshold (default: 70), signaling a potential pullback.
Oversold Condition: Triggered when the VW RSI crosses below the oversold threshold (default: 30), signaling a potential bounce.
Input Parameters
VW RSI Length (default: 14): The period over which the up-volume and down-volume are smoothed to calculate the VW RSI. A longer period results in smoother signals, while a shorter period increases sensitivity.
Overbought Level (default: 70): The threshold above which the VW RSI is considered overbought, indicating a potential reversal or pullback.
Oversold Level (default: 30): The threshold below which the VW RSI is considered oversold, indicating a potential reversal or bounce.
Show Centerline (default: true): Toggles the display of the 50 centerline, which separates bullish and bearish momentum zones.
Show Overbought/Oversold Lines (default: true): Toggles the display of the overbought and oversold threshold lines.
How It Works
Volume Classification:
For each bar, the indicator determines whether the price movement is upward or downward:
If the current close is higher than the previous close, the bar’s volume is classified as up-volume.
If the current close is lower than the previous close, the bar’s volume is classified as down-volume.
If the close is unchanged, both up-volume and down-volume are set to 0 for that bar.
Smoothing:
The up-volume and down-volume are smoothed using a Running Moving Average (RMA) over the specified period (default: 14 bars) to reduce noise and provide a more stable measure of volume momentum.
VW RSI Calculation:
The Volume Relative Strength (VoRS) is calculated as the ratio of smoothed up-volume to smoothed down-volume.
The VW RSI is then computed using the standard RSI formula, but with volume data instead of price changes, resulting in a value between 0 and 100.
Visualization and Alerts:
The VW RSI is plotted with dynamic coloring to reflect its momentum direction, and optional lines are drawn for the centerline and overbought/oversold levels.
Background coloring highlights overbought and oversold conditions, and alerts notify the trader of significant crossings.
Usage
Timeframe: The VW RSI can be used on any timeframe, but it is particularly effective on intraday charts (e.g., 1-hour, 4-hour) or daily charts where volume data is reliable. Shorter timeframes may require a shorter length for increased sensitivity, while longer timeframes may benefit from a longer length for smoother signals.
Markets: Best suited for markets with significant and reliable volume data, such as stocks, forex, and cryptocurrencies. It may be less effective in markets with low or inconsistent volume, such as certain futures contracts.
Trading Strategies:
Trend Confirmation:
Use the VW RSI to confirm the direction of a trend. For example, in an uptrend, look for the VW RSI to remain above 50, indicating sustained bullish volume momentum, and consider buying on pullbacks when the VW RSI dips but stays above 50.
In a downtrend, look for the VW RSI to remain below 50, indicating sustained bearish volume momentum, and consider selling on rallies when the VW RSI rises but stays below 50.
Overbought/Oversold Conditions:
When the VW RSI crosses above 70, the market may be overbought, suggesting a potential pullback or reversal. Consider taking profits on long positions or preparing for a short entry, but confirm with price action or other indicators.
When the VW RSI crosses below 30, the market may be oversold, suggesting a potential bounce or reversal. Consider entering long positions or covering shorts, but confirm with additional signals.
Divergences:
Look for divergences between the VW RSI and price to spot potential reversals. For example, if the price makes a higher high but the VW RSI makes a lower high, this bearish divergence may signal an impending downtrend.
Conversely, if the price makes a lower low but the VW RSI makes a higher low, this bullish divergence may signal an impending uptrend.
Momentum Shifts:
A crossover above 50 can signal the start of bullish momentum, making it a potential entry point for long trades.
A crossunder below 50 can signal the start of bearish momentum, making it a potential entry point for short trades or an exit for long positions.
Example
On a 4-hour SOLUSDT chart:
During an uptrend, the VW RSI might rise above 50 and stay there, confirming bullish volume momentum. If it approaches 70, it may indicate overbought conditions, as seen near a price peak of 145.08, suggesting a potential pullback.
During a downtrend, the VW RSI might fall below 50, confirming bearish volume momentum. If it drops below 30 near a price low of 141.82, it may indicate oversold conditions, suggesting a potential bounce, as seen in a slight recovery afterward.
A bullish divergence might occur if the price makes a lower low during the downtrend, but the VW RSI makes a higher low, signaling a potential reversal.
Limitations
Lagging Nature: Like the traditional RSI, the VW RSI is a lagging indicator because it relies on smoothed data (RMA). It may not react quickly to sudden price reversals, potentially missing the start of new trends.
False Signals in Ranging Markets: In choppy or ranging markets, the VW RSI may oscillate around 50, generating frequent crossovers that lead to false signals. Combining it with a trend filter (e.g., ADX) can help mitigate this.
Volume Data Dependency: The VW RSI relies on accurate volume data, which may be inconsistent or unavailable in some markets (e.g., certain forex pairs or futures contracts). In such cases, the indicator’s effectiveness may be reduced.
Overbought/Oversold in Strong Trends: During strong trends, the VW RSI can remain in overbought or oversold territory for extended periods, leading to premature exit signals. Use additional confirmation to avoid exiting too early.
Potential Improvements
Smoothing Options: Add options to use different smoothing methods (e.g., EMA, SMA) instead of RMA for the up/down volume calculations, allowing users to adjust the indicator’s responsiveness.
Divergence Detection: Include logic to detect and plot bullish/bearish divergences between the VW RSI and price, providing visual cues for potential reversals.
Customizable Colors: Allow users to customize the colors of the VW RSI line, centerline, overbought/oversold lines, and background shading.
Trend Filter: Integrate a trend strength filter (e.g., ADX > 25) to ensure signals are generated only during strong trends, reducing false signals in ranging markets.
The Volume Weighted RSI (VW RSI) is a powerful tool for traders seeking to incorporate volume into their momentum analysis, offering a unique perspective on market dynamics by emphasizing price movements backed by significant trading activity. It is best used in conjunction with other indicators and price action analysis to confirm signals and improve trading decisions.
Multi-Timeframe Stochastic OverviewPurpose of the Multi-Timeframe Stochastic Indicator:
The Multi-Timeframe Stochastic Indicator provides a consolidated view of market conditions across multiple timeframes (M1, M5, M15, H1) based on the Stochastic Oscillator, a popular technical analysis tool. The main objective is to allow traders to quickly assess momentum and potential trend reversals across different timeframes on a single chart, helping to make informed trading decisions.
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General Purpose of Stochastic Oscillator:
The Stochastic Oscillator measures the relationship between a security's closing price and its price range over a given period, aiming to identify momentum, overbought/oversold levels, and potential reversal points. It works on the assumption that:
1. In uptrends, prices tend to close near their highs.
2. In downtrends, prices tend to close near their lows.
It consists of two lines:
%K (fast line): Represents the raw Stochastic value.
%D (slow line): A moving average of %K, used to smooth the data for better signals.
The indicator is generally used to:
Identify Overbought (price above 80% threshold) and Oversold (price below 20% threshold) conditions.
Spot Bullish and Bearish divergences for potential trend reversals.
Evaluate momentum strength within a trend.
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How This Multi-Timeframe Indicator Enhances Stochastic's Utility:
1. Multi-Timeframe Overview:
The indicator calculates Stochastic values for multiple timeframes (1-minute, 5-minute, 15-minute, and 1-hour) and displays their market conditions (e.g., Bullish, Bearish, Overbought, Oversold, or Indecision) in an organized table format.
This gives traders a broad perspective on short-term, mid-term, and long-term trends simultaneously.
2. Market Condition Summary:
Bullish: Indicates upward momentum (both %K and %D > 50%).
Bearish: Indicates downward momentum (both %K and %D < 50%).
Overbought: Suggests potential trend exhaustion (both %K and %D > 80%).
Oversold: Suggests a potential reversal to the upside (both %K and %D < 20%).
Indecision: Highlights uncertainty when %K and %D are on opposite sides of the 50% level.
3. Quick Decision-Making:
The color-coded table (green for Bullish/Overbought, red for Bearish/Oversold, orange for Indecision) allows traders to quickly identify dominant conditions and momentum alignment across timeframes, helping in trade confirmation.
4. Trend Analysis:
By observing alignment or divergence in market conditions across timeframes, traders can gauge the strength of a trend or anticipate reversals. For example:
If all timeframes show "Bullish," it suggests strong momentum.
If smaller timeframes are "Overbought" while larger ones are "Bearish," it warns of a possible pullback.
5. Customizable Parameters:
The indicator allows customization of Stochastic K, D, smoothing values, and overbought/oversold levels, enabling users to tailor the analysis to specific trading styles or market conditions.
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Use Cases:
1. Scalping:
A scalper can use lower timeframes (e.g., M1, M5) to find overbought/oversold zones for quick trades.
2. Swing Trading:
Swing traders can align smaller timeframes with higher ones (e.g., M15 and H1) to confirm momentum before entering a trade.
3. Trend Reversals:
Overbought or oversold conditions across all timeframes may indicate a major reversal point, helping traders plan exits or countertrend entries.
4. Trend Continuation:
Consistent bullish or bearish conditions across all timeframes confirm the continuation of a trend, providing confidence to hold positions.
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Summary:
This indicator enhances the traditional Stochastic Oscillator by giving a multi-timeframe snapshot of market momentum, overbought/oversold conditions, and trend direction. It enables traders to quickly assess the overall market state, spot opportunities, and make more informed trading decisions.
GL Gann Swing IndicatorIntroduction
The GL Gann Swing Indicator is a versatile tool designed to help traders identify market trends, support and resistance areas, and potential reversals. This indicator applies the principles of Gann Swing Charts, a technique developed by W.D. Gann, which focuses on market swings to determine the overall direction and turning points of price action. Gann Swing Charts are a time-tested method of technical analysis that simplifies price action by focusing on significant highs and lows, thereby eliminating market noise and providing a clearer view of the trend.
By analyzing price action and determining swing directions and turning points, the indicator filters out market noise using four distinct bar types:
Up Bar: Higher High, Higher Low
Down Bar: Lower High, Lower Low
Inside Bar: Lower High, Higher Low
Outside Bar: Higher High, Lower Low
This approach helps traders to:
Identify the primary trend direction.
Determine key support and resistance levels.
Recognize potential reversal points.
Filter out minor price fluctuations that do not affect the overall trend.
Features
Bar Types: Display bar types by checking the Show Bar Type box in the indicator's settings. Up bars appear as green upward-pointing triangles, down bars as red downward-pointing triangles, inside bars as grey circles, and outside bars as blue diamonds. These visual aids help traders quickly identify the type of bar and its significance.
Break Lines: These lines highlight when the price rises above a previous swing high or falls below a prior swing low. Green lines indicate breaks of swing highs, while red lines indicate breaks of swing lows. Break lines are enabled by default but can be turned off in the indicator's settings. Break lines provide visual confirmation of trend continuation or reversal.
Bar Count: Bar counts help determine if a swing is overextended and if a reversal is likely. This feature is off by default but can be enabled in the indicator's settings. Users can set a minimum bar count to focus on significant swings. Analyzing the number of bars in a swing can help traders gauge the strength and potential exhaustion of a trend.
Swing MA (Moving Averages): This feature plots the average of a user-defined number of previous swing highs and lows. Options are available to add two moving averages, allowing for both fast and slow averages. Swing MAs can be enabled in the indicator's settings. These moving averages smooth out the price data, making it easier to identify the underlying trend direction.
Why This Indicator is Useful
The GL Gann Swing Indicator is particularly useful for several reasons:
Trend Identification: By focusing on significant price swings, the indicator helps traders identify the primary trend direction, making it easier to align trades with the overall market movement.
Noise Reduction: The indicator filters out minor price fluctuations, allowing traders to focus on meaningful market movements and avoid being misled by short-term volatility.
Support and Resistance Levels: By highlighting key swing highs and lows, the indicator helps traders identify crucial support and resistance levels, which are essential for making informed trading decisions.
Potential Reversals: The indicator's ability to identify overextended swings and potential reversal points can help traders anticipate market turning points and adjust their strategies accordingly.
Customizability: With options to display bar types, break lines, bar counts, and swing moving averages, traders can customize the indicator to suit their specific trading style and preferences.
By incorporating Gann Swing principles, the GL Gann Swing Indicator offers traders a powerful tool to enhance their technical analysis, improve their trading decisions, and ultimately achieve better trading outcomes.
