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อินดิเคเตอร์และกลยุทธ์

Signs of the Times [LucF]█  OVERVIEW This oscillator calculates the directional strength of bars using a primitive weighing mechanism based on a small number of what I consider to be fundamental properties of a bar. It does not consider the amplitude of price movements, so can be used as a complement to momentum-based oscillators. It thus belongs to the same family of indicators as my Bar Balance , ​Volume Ticks , Efficient work , ​Volume Buoyancy or my Delta ​Volume indicators. █  CONCEPTS The calculations underlying Signs of the Times (SOTT) use a simple, oft-explored concept: measure bar attributes, assign a weight to them, and aggregate results to provide an evaluation of a bar's directional strength. Bull and bear weights are added independently, then subtracted and divided by the maximum possible weight, so the final calculation looks like this: (up - ​dn) / weightRange SOTT has a zero centerline and oscillates between +1 and -1. Ten elementary properties are evaluated. Most carry a weight of one, a few are doubly weighted. All properties are evaluated using only the current bar's values or by comparing its values to those of the preceding bar. The bull conditions follow; their inverse applies to bear conditions:  Weight of 1  • Bar's close is greater than the bar's open (bar is considered to be of "up" polarity)  • Rising open  • Rising high  • Rising low  • Rising close  • Bar is up and its body size is greater than that of the previous bar  • Bar is up and its body size is greater than the combined size of wicks  Weight of 2  • Gap to the upside  • Efficient Work when it is positive  • Bar is up and ​volume is greater than that of the previous bar (this only kicks in if ​volume is actually available on the chart's data feed) Except for the Efficient Work weight, which is a +1 to -1 float value multiplied by 2, all weights are discrete; either zero or the full weight of 1 or 2 is generated. This will cause any gap, for example, to generate a weight of +2 or -2, regardless of the gap's size. That is the reason why the oscillator is oblivious to the amplitude of price movements. You can see the code used to calculate SOTT in my ta library 's `sott()` function. █  HOW TO USE THE INDICATOR No videos explain this indicator and none are planned; reading this description or the script's code is the only way to understand what Signs of the Times does. Load the indicator on an active chart (see here if you don't know how). The default configuration displays:  • An Arnaud-Legoux moving average of length 20 of the instant SOTT value. This is the signal line.  • A fill between the MA and the centerline.  • Levels at arbitrary values of +0.3 and -0.3.  • A channel between the signal line and its MA (a simple MA of length 20), which can be one of four colors:   • Bull (green): The signal line is above its MA.   • Strong bull (lime): The bull condition is fulfilled and the signal line is above the centerline.   • Bear (red): The signal line is below its MA.   • Strong bear (pink): The bear condition is fulfilled and the signal line is below the centerline. The script's "Inputs" tab allows you to:  • Choose a higher timeframe to calculate the indicator's values. This can be useful to get a wider perspective of the indicator's values.   If you elect to use a higher timeframe, make sure that your chart's timeframe is always lower than the higher timeframe you specified,   as calculating on a timeframe lower than the chart's does not make much sense because the indicator is then displaying only the value of the last intrabar in the chart bar.  • Specify the type of MA used to produce the signal line. Use a length of 1 or the Data Window to see the instant value of SOTT. It is quite noisy, thus the need to average it.  • Specify the type of MA applied to the signal line. The idea here is to provide context to the signal.  • Control the display and colors of the lines and fills. The first pane of this publication's chart shows the default setup. The second one shows only a monochrome signal line. Using the "Style" tab of the indicator's settings, you can change the type and width of the lines, and the level values. █  INTERPRETATION Remember that Signs of the Times evaluates directional bar strength — not price movement. Its highs and lows do not reflect price, but the strength of chart bars. The fact that SOTT knows nothing of how far price moves or of trends is easy to forget. As such, I think SOTT is best used as a confirmation tool. Chart movements may appear to be easy to read when looking at historical bars, but when you have to make go-no-go decisions on the last bar, the landscape often becomes murkier. By providing a quantitative evaluation of the strength of the last few bars, which is not always easily discernible by simply looking at them, SOTT aims to help you decide if the short-term past favors the bets you are considering. Can SOTT predict the future? Of course not. While SOTT uses completely different calculations than classical momentum oscillators, its profile shares many of their characteristics. This could lead one to infer that directional bar strength correlates with price movement, which could in turn lead one to conclude that indicators such as this one are useless, or that they can be useful tools to confirm momentum oscillators or other models of price movement. The call is, of course, up to you. You can try, for example, to compare a Wilder MA of SOTT to an RSI of the same length. One key difference with momentum oscillators is that SOTT is much less sensitive to large price movements. The default Arnaud-Legoux MA used for the signal line makes it quite active; you can use a more quiet SMA or EMA if you prefer to tone it down. In systems where it can be useful to only enter or exit on short-term strength, an average of SOTT values over the last 3 to 5 bars can be used as a more quiet filter than a momentum oscillator would. █  NOTES My publications often go through a long gestation period where I use them on my charts or in systems before deciding if they are worth a publication. With an incubation period of more than three years, Signs of the Times holds the record. The properties SOTT currently evaluates result from the systematic elimination of contaminants over that lengthy period of time. It was long because of my usual, slow gear, but also because I had to try countless combinations of conditions before realizing that, contrary to my intuition, best results were achieved by:  • Keeping the number of evaluated properties to the absolute minimum.  • Limiting the evaluation's ​scope to the current and preceding bar.  • Choosing properties that, in my view, were unmistakably indicative of ​bullish/​bearish conditions. Repainting As most oscillators, the indicator provides live realtime values that will recalculate with chart updates. It will thus repaint in real time, but not on historical values. To learn more about repainting, see the Pine Script™ User Manual's page on the subject .
อินดิเคเตอร์ Pine Script®
โดย LucF
34
Estimated Time At Price [Kioseff Trading]Hello! This script uses the same formula as the recently released "Volume Delta" script to ascertain lower timeframe values. Instead, this script looks to estimate the approximate time spent at price blocks; all time estimates are in minute.second format. The image above shows functionality. Time spent at price levels/blocks are estimated in duration. The highest estimated block is the highlighted level and a POC line is extended right until violated. Colors, the presence of POC lines and whether they're removed subsequent violation are all configurable. As show in the image above, the data is displayable in an additional format. When select the "non-classic" format shown above - precise price levels are calculated and the estimated time spent at those levels is summed and displayed right of the current bar. The off-colored level (yellow in the example) denotes the price level encompassing the highest *estimated* time spent. You can deselect the neon effect and choose to have the script recalculate after any conceivable amount of time has passed. The script can also calculate for the most current bar should you configure it to do so. That's all! (for now). A quick/easy script building off an existing foundation. If you've any ideas for features and ways to "spice up" this script please let me know (: I'll gladly incorporate requests. Thank you!
อินดิเคเตอร์ Pine Script®
โดย KioseffTrading
23
Volume Profile, Pivot Anchored by DGTVolume Profile (also known as Price by Volume ) is an charting study that displays trading activity over a specified time period at specific price levels. It is plotted as a horizontal histogram on the finacial isntrumnet's chart that highlights the trader's interest at specific price levels. Specified time period with Pivots Anchored Volume Profile is determined by the Pivot Levels, where the Pivot Points High Low indicator is used and presented with this Custom indicator Finally, Volume Weighted Colored Bars indicator is presneted with the study Different perspective of Volume Profile applications; Anchored to Session, Week, Month etc : Anchored-Volume-Profile Custom Range, Interactive : Volume-Profile-Custom-Range Fixed Range with Volume Indicator : Volume-Profile-Fixed-Range Combined with Support and Resistance Indicator : Price-Action-Support-Resistance and Volume-Profile Combined with Supply and Demand Zones, Interactive : Supply-Demand-and-Equilibrium-Zones Disclaimer : Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
อินดิเคเตอร์ Pine Script®
โดย dgtrd
248
Fair Value MSThis indicator introduces rigid rules to familiar concepts to better capture and visualize Market Structure and Areas of Support and Resistance in a way that is both rule-based and reactive to market movements. Typical "Market Structure" or "Zig-Zag" methods determine swing points based on fixed thresholds (length or percentage). While this does provide rigid structure, the results may be lagging or confusing due to the timing, since it is fixed to static parameters. I believe the concept of Fair Value Gaps can solve this problem. As you will notice, there are no length settings in this indicator. > FVG Market Structure Fair Value Gaps are a well known concept used to indicate directional intent, forming when price moves aggressively in one direction, leaving behind an imbalance between buyers and sellers. While the term FVG was popularized by ICT, the underlying concept predates them, known historically as imbalances, inefficiencies, or liquidity voids in institutional trading. Note: For simplicity, in this indicator they'll be called FVGs. By reading into this, we are able to clearly and rigidly define market structure simply by "looking" at the chart, using objective price events rather than subjective interpretation, or lengths. By using FVGs to determine structure direction, the length, and speed of identification lies entirely on the market. If an FVG Down occurs immediately after a New Higher High forms, it is reasonable to assume there was a seller at that point, so the script would indicate a New Swing High. The script is NOT stuck, waiting for a % retrace, or # bars to pass to identify it as such. Sometimes the market is in a steady trend in a single direction and no FVGs form; therefore, no structure forms. -> Why would we try to impose structure on a clear trend? Ultimately, the FVG Structure Method uses real reactions from the market to determine Market structure, and is not fixed to specific parameters. As with other market structure indicators, "Market Structure Breaks" are still identifiable when price moves outside the most recent swing points. These are helpful to indicate larger direction. In the following section you will see how these help us determine when we should start the search for an "Area of Interest (AOI)". > Areas of Interest (AOIs) "Area of Interest (AOI)" is a generalized term, and could refer to many types of zones you might recognize under different names. While the AOIs in this indicator are specialized in their own way, I have chosen to simply use the term "Area of Interest" because it’s more important to understand how they behave and why they exist than to focus on what they’re called. The goal of an AOI is to point out reasonable areas where buyers or sellers may be staging, as is typical with support and resistance. In order to reasonably identify these areas, we look for cause and effect relationships. When considering these relationships, it's easier to understand the placement of the points to define each zone. (Buyer Examples) Cause: Strong Buyers step in at Swing Low Effect: Fair Value Gap Forms Cause: Sustained Buying Pressure Effect: Market Structure Breaks In this example, The zone is drawn from the Swing Low, to the Bottom of the FVG closest to the swing point. In theory, the participation at the swing point was strong and aggressive enough to create the FVG imbalance. Which then found acceptance and continued into a Market Structure Break. So with these AOIs, we are trying to locate the aggressive Buyers or Sellers which were positioned BEFORE the FVG. These Zones are intended to act as areas to look for reactions from market participants, to judge where price may be going. When revisiting these zones, we look for a reaction or a break, to further provide us information to if the buyers or sellers are still there. As seen in the screenshot above, The information we gain is not from the creation of these zones, but from the behavior we witness when these zones are revisited. Technical Note: In this indicator, Market Structure Breaks are only considered when price closes outside the recent swing points. Wicks are not considered as confirmation, therefore are not used to detect structural breaks. Inside each AOI you can optionally display a readout of the volume which accumulated during the time starting at the swing point and going until the closing bar of the FVG. Note: We are counting volume until the closing bar of the FVG since the FVG is a 3 bar formation, and aggressive volume is required throughout to create the imbalance. There are multiple FVGs that typically occur in a single direction, but we do not look to every single one to be indicative of structure, only the first FVG in the opposite direction of the previous direction (which is determined by previous FVGs) You will probably notice, the AOIs do not form from the closest swing or FVG to the break, this is because we are targeting larger directional changes to draw these AOIs from. Since they do not always happen perfectly every time, the AOI formation waits for an FVG to occur AND a Market structure break to happen. One without the other will result in no Zone displaying. > Reflection Lines While they may seem slightly redundant, Reflection Lines serve as reminders of previous support and resistance pivots. They are drawn at the same Pivots where and AOI is formed, and extend beyond the mitigation of the AOI. These lines are often points of price to look for "Support Flips", a re-test pattern where price trades through previous support (or resistance) then returns to it and rejects, continuing into a larger move or trend. Their namesake is based on the behavior of price, "reflecting" at these levels. The Reflection lines are simple and change color based on price's location. If price is above, we would typically look to a reflection line in with support in mind. As a basic filter, these lines use an average price to determine their color, this way they will not change their color as frequently in choppy situations. > Session Start/End Lines For analysis purposes and trade review, it is helpful to analyze with context. For that reason, I have implemented start and end session lines into the indicator, these are helpful when reviewing historical charts to not provide additional context. By default, they are set to the NYSE Session, but can be changed to fit any needs. These lines are not advanced, and simply draw a line as the chart passes the start and end of the sessions. It's very likely that you may need to adjust the session for your specific needs. Note: The Timezone can be adjusted within the code if needed. By Default, the indicator uses "America/New_York" Timezone. > Conclusion If you’ve ever felt like your structure tools were confusing or lagging, drawing zones too late, or zones that simply don't make sense, this should feel like a breath of fresh air. By removing arbitrary length settings and instead using FVGs to define structure and as a basis for AOIs, you're getting a more accurate look at what price is doing and where it's reacting from. This indicator is rule-based, reactive, and aims to keep things logical without fluff or false confidence. Enjoy!
อินดิเคเตอร์ Pine Script®
โดย SamRecio
10
Risk Distribution HistogramStatistical risk visualization and analysis tool for any ticker 📊 The Risk Distribution Histogram visualizes the statistical distribution of different risk metrics for any financial instrument. It converts risk data into histograms with quartile-based color coding, so that traders can understand their risk, tail-risks, exposure patterns and make data-driven decisions based on empirical evidence rather than assumptions. The indicator supports multiple risk calculation methods, each designed for different aspects of market analysis, from general volatility assessment to tail risk analysis. Risk Measurement Methods Standard Deviation Captures raw daily price volatility by measuring the dispersion of price movements. Ideal for understanding overall market conditions and timing volatility-based strategies. Use case: Options trading and volatility analysis. Average True Range (ATR) Measures true range as a percentage of price, accounting for gaps and limit moves. Valuable for position sizing across different price levels. Use case: Position sizing and stop-loss placement. The chart above illustrates how ATR statistical distribution can be used by looking at the ATR % of price distribution. For example, 90% of the movements are below 5%. Downside Deviation Only considers negative price movements, making it ideal for checking downside risk and capital protection rather than capturing upside volatility. Use case: Downside protection strategies and stop losses. Drawdown Analysis Tracks peak-to-trough declines, providing insight into maximum loss potential during different market conditions. Use case: Risk management and capital preservation. The chart above illustrates tale risk for the asset (TQQQ), showing that it is possible to have drawdowns higher than 20%. Entropy-Based Risk (EVaR) Uses information theory to quantify market uncertainty. Higher entropy values indicate more unpredictable price action, valuable for detecting regime changes. Use case: Advanced risk modeling and tail-risk. VIX Histogram Incorporates the market's fear index directly into analysis, showing how current volatility expectations compare to historical patterns. The CAPITALCOM:VIX histogram is independent from the ticker on the chart. Use case: Volatility trading and market timing. Visual Features The histogram uses quartile-based color coding that immediately shows where current risk levels stand relative to historical patterns: Green (Q1): Low Risk (0-25th percentile) Yellow (Q2): Medium-Low Risk (25-50th percentile) Orange (Q3): Medium-High Risk (50-75th percentile) Red (Q4): High Risk (75-100th percentile) The data table provides detailed statistics, including: Count Distribution: Historical observations in each bin PMF: Percentage probability for each risk level CDF: Cumulative probability up to each level Current Risk Marker: Shows your current position in the distribution Trading Applications When current risk falls into upper quartiles (Q3 or Q4), it signals conditions are riskier than 50-75% of historical observations. This guides position sizing and portfolio adjustments. Key applications: Position sizing based on empirical risk distributions Monitoring risk regime changes over time Comparing risk patterns across timeframes Risk distribution analysis improves trade timing by identifying when market conditions favor specific strategies. Enter positions during low-risk periods (Q1) Reduce exposure in high-risk periods (Q4) Use percentile rankings for dynamic stop-loss placement Time volatility strategies using distribution patterns Detect regime shifts through distribution changes Compare current conditions to historical benchmarks Identify outlier events in tail regions Validate quantitative models with empirical data Configuration Options Data Collection Lookback Period: Control amount of historical data analyzed Date Range Filtering: Focus on specific market periods Sample Size Validation: Automatic reliability warnings Histogram Customization Bin Count: 10-50 bins for different detail levels Auto/Manual Bin Width: Optimize for your data range Visual Preferences: Custom colors and font sizes Implementation Guide Start with Standard Deviation on daily charts for the most intuitive introduction to distribution-based risk analysis. Method Selection: Begin with Standard Deviation Setup: Use daily charts with 20-30 bins Interpretation: Focus on quartile transitions as signals Monitoring: Track distribution changes for regime detection The tool provides comprehensive statistics including mean, standard deviation, quartiles, and current position metrics like Z-score and percentile ranking. Enjoy, and please let me know your feedback! 😊🥂
อินดิเคเตอร์ Pine Script®
โดย HenriqueCentieiro
2
Crowding model ║ BullVision🔬 Overview The Crypto Crowding Model Pro is a sophisticated analytical tool designed to visualize and quantify market conditions across multiple cryptocurrencies. By leveraging Relative Strength Index (RSI) and Z-score calculations, this indicator provides traders with an intuitive and detailed snapshot of current crypto market dynamics, highlighting areas of extreme momentum, crowded trades, and potential reversal points. ⚙️ Key Concepts 📊 RSI and Z-Score Analysis RSI (Relative Strength Index) evaluates the momentum and strength of each cryptocurrency, identifying overbought or oversold conditions. Z-Score Normalization measures each asset's current price deviation relative to its historical average, identifying statistically significant extremes. 🎯 Crowding Analytics An integrated analytics panel provides real-time crowding metrics, quantifying market sentiment into four distinct categories: 🔥 FOMO (Fear of Missing Out): High momentum, potential exhaustion. ❄️ Fear: Low momentum, potential reversal or consolidation. 📈 Recovery: Moderate upward momentum after a downward trend. 💪 Strength: Stable bullish conditions with sustained momentum. 🖥️ Visual Scatter Plot Assets are plotted on a dynamic scatter plot, positioning each cryptocurrency according to its RSI and Z-score. Color coding, symbol shapes, and sizes help quickly identify main market segments (BTC, ETH, TOTAL, OTHERS) and individual asset conditions. 🧩 Quadrant Classification Assets are categorized into four quadrants based on their momentum and deviation: Overbought Extended: High RSI and positive Z-score. Recovery Phase: Low RSI but positive Z-score. Oversold Compressed: Low RSI and negative Z-score. Strong Consolidation: High RSI but negative Z-score. 🔧 User Customization 🎨 Visual Settings Bar Scale: Adjust the scatter plot visual scale. Asset Visibility: Optionally display key market benchmarks (TOTAL, BTC, ETH, OTHERS). Gradient Background: Enhances visual interpretation of asset clusters. Crowding Analytics Panel: Toggle the analytics panel on/off. 📊 Indicator Parameters RSI Length: Defines the calculation period for RSI. Z-score Lookback: Historical lookback period for normalization. Crowding Alert Threshold: Sets alert sensitivity for crowded market conditions. 🎯 Zone Settings Quadrant Labels: Displays descriptive labels for each quadrant. Danger Zones: Highlights extreme RSI levels indicative of heightened market risk. 📈 Visual Output Dynamic Scatter Plot: Visualizes asset positioning clearly and intuitively. Gradient and Grid: Professional gridlines and subtle gradient backgrounds assist visual assessment. Danger Zone Highlights: Visually indicates RSI extremes to warn of potential market turning points. Crowding Analytics Panel: Real-time summary of market sentiment and asset distribution. 🔍 Use Cases This indicator is particularly beneficial for traders and analysts looking to: Identify crowded trades and potential reversal points. Quickly assess overall market sentiment and individual asset strength. Integrate a robust momentum analysis into broader technical or fundamental strategies. Enhance market timing and improve risk management decisions. ⚠️ Important Notes This indicator does not provide explicit buy or sell signals. It is intended solely for informational, analytical, and educational purposes. Past performance and signals are not indicative of future market results. Always combine with additional tools and analysis as part of comprehensive decision-making.
อินดิเคเตอร์ Pine Script®
โดย BullVisionCapital
12
Dynamic Gap Probability ToolDynamic Gap Probability Tool measures the percentage gap between price and a chosen moving average, then analyzes your chart history to estimate the likelihood of the next candle moving up or down. It dynamically adjusts its sample size to ensure statistical robustness while focusing on the exact deviation level. Originality and Value: • Combines gap-based analysis with dynamic sample aggregation to balance precision and reliability. • Automatically extends the sample when exact matches are scarce, avoiding misleading signals on rare extreme moves. • Provides real “next-candle” probabilities based on historical occurrences rather than fixed thresholds or untested heuristics. • Adds value by giving traders an evidence-based edge: you see how similar past deviations actually played out. How It Works: 1. Calculate gap = (close – moving average) / moving average * 100. 2. Round the absolute gap to nearest percent (X%). 3. Count historical bars where gap ≥ X% above or ≤ –X% below. 4. If exact X% count is below the minimum occurrences threshold, include gaps at X+1%, X+2%, etc., until threshold is reached. 5. Compute “next-candle” green vs. red probabilities from the aggregated sample. 6. Display current gap, sample size, green probability, and red probability in a table. Inputs: • Moving Average Type (SMA, EMA, WMA, VWMA, HMA, SMMA, TMA) • Moving Average Period (default 200) • Minimum Occurrences Threshold (default 50) • Table position and styling options Examples: • If price is 3% above the 200-period SMA and 120 occurrences ≥3% are found, with 84 green next candles (70%) and 36 red (30%), the script displays “3% | 120 | 70% green | 30% red.” • If price is 8% below the SMA but only 20 exact matches exist, the script will include 9% and 10% gaps until it reaches 50 samples, then calculate probabilities from that broader set. Why It’s Useful: • Mean-reversion traders see green-probability signals at extreme overbought or oversold levels. • Trend-followers identify continuation likelihood when red probability is high. • Risk managers gauge reliability by inspecting sample size before acting on any signal. Limitations: • Historical probabilities do not guarantee future performance. • Results depend on timeframe and symbol, backtest with your data before trading. • Use realistic slippage and commission when overlaying on strategy scripts.
อินดิเคเตอร์ Pine Script®
โดย Julien_Eche
12
EVaR Indicator and Position SizingThe Problem: Financial markets consistently show "fat-tailed" distributions where extreme events occur with higher frequency than predicted by normal distributions (Gaussian or even log-normal). These fat tails manifest in sudden price crashes, volatility spikes, and black swan events that traditional risk measures like volatility can underestimate. Standard deviation and conventional VaR calculations assume normally distributed returns, leaving traders vulnerable to severe drawdowns during market stress. Cryptocurrencies and volatile instruments display particularly pronounced fat-tailed behavior, with extreme moves occurring 5-10 times more frequently than normal distribution models would predict. This reality demands a more sophisticated approach to risk measurement and position sizing. The Solution: Entropic Value at Risk (EVAR) EVaR addresses these limitations by incorporating principles from statistical mechanics and information theory through Tsallis entropy. This advanced approach captures the non-linear dependencies and power-law distributions characteristic of real financial markets. Entropy is more adaptive than standard deviations and volatility measures. I was inspired to create this indicator after reading the paper " The End of Mean-Variance? Tsallis Entropy Revolutionises Portfolio Optimisation in Cryptocurrencies " by by Sana Gaied Chortane and Kamel Naoui. Key advantages of EVAR over traditional risk measures: Superior tail risk capture: More accurately quantifies the probability of extreme market moves Adaptability to market regimes: Self-calibrates to changing volatility environments Non-parametric flexibility: Makes less assumptions about the underlying return distribution Forward-looking risk assessment: Better anticipates potential market changes (just look at the charts :) Mathematically, EVAR is defined as: EVAR_α(X) = inf_{z>0} {z * log(1/α * M_X(1/z))} Where the moment-generating function is calculated using q-exponentials rather than conventional exponentials, allowing precise modeling of fat-tailed behavior. Technical Implementation This indicator implements EVAR through a q-exponential approach from Tsallis statistics: Returns Calculation: Price returns are calculated over the lookback period Moment Generating Function: Approximated using q-exponentials to account for fat tails EVAR Computation: Derived from the MGF and confidence parameter Normalization: Scaled to for intuitive visualization Position Sizing: Inversely modulated based on normalized EVAR The q-parameter controls tail sensitivity—higher values (1.5-2.0) increase the weighting of extreme events in the calculation, making the model more conservative during potentially turbulent conditions. Indicator Components 1. EVAR Risk Visualization Dynamic EVAR Plot: Color-coded from red to green normalized risk measurement (0-1) Risk Thresholds: Reference lines at 0.3, 0.5, and 0.7 delineating risk zones 2. Position Sizing Matrix Risk Assessment: Current risk level and raw EVAR value Position Recommendations: Percentage allocation, dollar value, and quantity Stop Parameters: Mathematically derived stop price with percentage distance Drawdown Projection: Maximum theoretical loss if stop is triggered Interpretation and Application The normalized EVAR reading provides a probabilistic risk assessment: < 0.3: Low risk environment with minimal tail concerns 0.3-0.5: Moderate risk with standard tail behavior 0.5-0.7: Elevated risk with increased probability of significant moves > 0.7: High risk environment with substantial tail risk present Position sizing is automatically calculated using an inverse relationship to EVAR, contracting during high-risk periods and expanding during low-risk conditions. This is a counter-cyclical approach that ensures consistent risk exposure across varying market regimes, especially when the market is hyped or overheated. Parameter Optimization For optimal risk assessment across market conditions: Lookback Period: Determines the historical window for risk calculation Q Parameter: Controls tail sensitivity (higher values increase conservatism) Confidence Level: Sets the statistical threshold for risk assessment For cryptocurrencies and highly volatile instruments, a q-parameter between 1.5-2.0 typically provides the most accurate risk assessment because it helps capturing the fat-tailed behavior characteristic of these markets. You can also increase the q-parameter for more conservative approaches. Practical Applications Adaptive Risk Management: Quantify and respond to changing tail risk conditions Volatility-Normalized Positioning: Maintain consistent exposure across market regimes Black Swan Detection: Early identification of potential extreme market conditions Portfolio Construction: Apply consistent risk-based sizing across diverse instruments This indicator is my own approach to entropy-based risk measures as an alterative to volatility and standard deviations and it helps with fat-tailed markets. Enjoy!
อินดิเคเตอร์ Pine Script®
โดย HenriqueCentieiro
34
Divergence Screener [Trendoscope®]🎲Overview The Divergence Screener is a powerful TradingView indicator designed to detect and visualize bullish and bearish divergences, including hidden divergences, between price action and a user-selected oscillator. Built with flexibility in mind, it allows traders to customize the oscillator type, trend detection method, and other parameters to suit various trading strategies. The indicator is non-overlay, displaying divergence signals directly on the oscillator plot, with visual cues such as lines and labels on the chart for easy identification. This indicator is ideal for traders seeking to identify potential reversal or continuation signals based on price-oscillator divergences. It supports multiple oscillators, trend detection methods, and alert configurations, making it versatile for different markets and timeframes. 🎲Features 🎯Customizable Oscillator Selection Built-in Oscillators : Choose from a variety of oscillators including RSI, CCI, CMO, COG, MFI, ROC, Stochastic, and WPR. External Oscillator Support : Users can input an external oscillator source, allowing integration with custom or third-party indicators. Configurable Length : Adjust the oscillator’s period (e.g., 14 for RSI) to fine-tune sensitivity. 🎯Divergence Detection The screener identifies four types of divergences: Bullish Divergence : Price forms a lower low, but the oscillator forms a higher low, signaling potential upward reversal. Bearish Divergence : Price forms a higher high, but the oscillator forms a lower high, indicating potential downward reversal. Bullish Hidden Divergence : Price forms a higher low, but the oscillator forms a lower low, suggesting trend continuation in an uptrend. Bearish Hidden Divergence : Price forms a lower high, but the oscillator forms a higher high, suggesting trend continuation in a downtrend. 🎯Flexible Trend Detection The indicator offers three methods to determine the trend context for divergence detection: Zigzag : Uses zigzag pivots to identify trends based on higher highs (HH), higher lows (HL), lower highs (LH), and lower lows (LL). MA Difference : Calculates the trend based on the difference in a moving average (e.g., SMA, EMA) between divergence pivots. External Trend Signal : Allows users to input an external trend signal (positive for uptrend, negative for downtrend) for custom trend analysis. 🎯Zigzag-Based Pivot Analysis Customizable Zigzag Length : Adjust the zigzag length (default: 13) to control the sensitivity of pivot detection. Repaint Option : Choose whether divergence lines repaint based on the latest data or wait for confirmed pivots, balancing responsiveness and reliability. 🎯Visual and Alert Features Divergence Visualization : Divergence lines are drawn between price pivots and oscillator pivots, color-coded for easy identification: Bullish Divergence : Green Bearish Divergence : Red Bullish Hidden Divergence : Lime Bearish Hidden Divergence : Orange Labels and Tooltips : Labels (e.g., “D” for divergence, “H” for hidden) appear on price and oscillator pivots, with tooltips providing detailed information such as price/oscillator values, ratios, and pivot directions. Alerts : Configurable alerts for each divergence type (bullish, bearish, bullish hidden, bearish hidden) trigger on bar close, ensuring timely notifications. 🎲 How It Works 🎯Oscillator Calculation The indicator calculates the selected oscillator (or uses an external source) and plots it on the chart. Oscillator values are stored in a map for reference during divergence calculations. 🎯Pivot Detection A zigzag algorithm identifies pivots in the oscillator data, with configurable length and repainting options. Price and oscillator pivots are compared to detect divergences based on their direction and ratio. 🎯Divergence Identification The indicator compares price and oscillator pivot directions (HH, HL, LH, LL) to identify divergences. Trend context is determined using the selected method (Zigzag, MA Difference, or External). Divergences are classified as bullish, bearish, bullish hidden, or bearish hidden based on price-oscillator relationships and trend direction. 🎯Visualization and Alerts Valid divergences are drawn as lines connecting price and oscillator pivots, with corresponding labels. Alerts are triggered for allowed divergence types, providing detailed information via tooltips. 🎯Validation Divergence lines are validated to ensure no intermediate bars violate the divergence condition, enhancing signal reliability. 🎲 Usage Instructions as Indicator 🎯Add to Chart: Add the “Divergence Screener ” to your TradingView chart. The indicator appears in a separate pane below the price chart, plotting the oscillator and divergence signals. 🎯Configure Settings: Adjust the oscillator type and length to match your trading style. Select a trend detection method and configure related parameters (e.g., MA type/length or external signal). Set the zigzag length and repainting preference. Enable/disable alerts for specific divergence types. I🎯nterpret Signals: Bullish Divergence (Green) : Look for potential buy opportunities in a downtrend. Bearish Divergence (Red) : Consider sell opportunities in an uptrend. Bullish Hidden Divergence (Lime) : Confirm continuation in an uptrend. Bearish Hidden Divergence (Orange): Confirm continuation in a downtrend. Use tooltips on labels to review detailed pivot and divergence information. 🎯Set Alerts: Create alerts for each divergence type to receive notifications via TradingView’s alert system. Alerts include detailed text with price, oscillator, and divergence information. 🎲 Example Scenarios as Indicator 🎯 With External Oscillator (Use MACD Histogram as Oscillator) In order to use MACD as an oscillator for divergence signal instead of the built in options, follow these steps. Load MACD Indicator from Indicator library From Indicator settings of Divergence Screener, set Use External Oscillator and select MACD Histograme from the dropdown You can now see that the oscillator pane shows the data of selected MACD histogram and divergence signals are generated based on the external MACD histogram data. 🎯 With External Trend Signal (Supertrend Ladder ATR) Now let's demonstrate how to use external direction signals using Supertrend Ladder ATR indicator. Please note that in order to use the indicator as trend source, the indicator should return positive integer for uptrend and negative integer for downtrend. Steps are as follows: Load the desired trend indicator. In this example, we are using Supertrend Ladder ATR From the settings of Divergence Screener, select "External" as Trend Detection Method Select the trend detection plot Direction from the dropdown. You can now see that the divergence signals will rely on the new trend settings rather than the built in options. 🎲 Using the Script with Pine Screener The primary purpose of the Divergence Screener is to enable traders to scan multiple instruments (e.g., stocks, ETFs, forex pairs) for divergence signals using TradingView’s Pine Screener, facilitating efficient comparison and identification of trading opportunities. To use the Divergence Screener as a screener, follow these steps: Add to Favorites : Add the Divergence Screener to your TradingView favorites to make it available in the Pine Screener. Create a Watchlist : Build a watchlist containing the instruments (e.g., stocks, ETFs, or forex pairs) you want to scan for divergences. Access Pine Screener : Navigate to the Pine Screener via TradingView’s main menu: Products -> Screeners -> Pine, or directly visit tradingview.com/pine-screener/. Select Watchlist : Choose the watchlist you created from the Watchlist dropdown in the Pine Screener interface. Choose Indicator : Select Divergence Screener from the Choose Indicator dropdown. Configure Settings : Set the desired timeframe (e.g., 1 hour, 1 day) and adjust indicator settings such as oscillator type, zigzag length, or trend detection method as needed. Select Filter Criteria : Select the condition on which the watchlist items needs to be filtered. Filtering can only be done on the plots defined in the script. Run Scan : Press the Scan button to display divergence signals across the selected instruments. The screener will show which instruments exhibit bullish, bearish, bullish hidden, or bearish hidden divergences based on the configured settings. 🎲 Limitations and Possible Future Enhancements Limitations are Custom input for oscillator and trend detection cannot be used in pine screener. Pine screener has max 500 bars available. Repaint option is by default enabled. When in repaint mode expect the early signal but the signals are prone to repaint. Possible future enhancements Add more built-in options for oscillators and trend detection methods so that dependency on external indicators is limited Multi level zigzag support
อินดิเคเตอร์ Pine Script®
โดย Trendoscope
21
Logarithmic Moving Average (LMA) [QuantAlgo]🟢 Overview The Logarithmic Moving Average (LMA) uses advanced logarithmic weighting to create a dynamic trend-following indicator that prioritizes recent price action while maintaining statistical significance. Unlike traditional moving averages that use linear or exponential weights, this indicator employs logarithmic decay functions to create a more sophisticated price averaging system that adapts to market volatility and momentum conditions. The indicator displays a smoothed signal line that oscillates around zero, with positive values indicating bullish momentum and negative values indicating bearish momentum. The signal incorporates trend quality assessment, momentum confirmation, and multiple filtering mechanisms to help traders and investors identify trend continuation and reversal opportunities across different timeframes and asset classes. 🟢 How It Works The indicator's core innovation lies in its logarithmic weighting system, where weights are calculated using the formula: w = 1.0 / math.pow(math.log(i + steepness), 2) The steepness parameter controls how aggressively recent data is prioritized over historical data, creating a dynamic weight decay that can be fine-tuned for different trading styles. This logarithmic approach provides more nuanced weight distribution compared to exponential moving averages, offering better responsiveness while maintaining stability. The LMA calculation combines multiple sophisticated components. First, it calculates the logarithmic weighted average of closing prices. Then it measures the slope of this average over a 10-period lookback: lmaSlope = (lma - lma ) / lma * 100 The system also incorporates trend quality assessment using R-squared correlation analysis of log-transformed prices, measuring how well the price data fits a linear trend model over the specified period. The final signal generation uses the formula: signal = lmaSlope * (0.5 + rSquared * 0.5) which combines the LMA slope with trend quality weighting. When momentum confirmation is enabled, the indicator calculates annualized log-return momentum and applies a multiplier when the momentum direction aligns with the signal direction, strengthening confirmed signals while filtering out weak or counter-trend movements. 🟢 How to Use 1. Signal Interpretation and Threshold Zones Positive Values (Above Zero): LMA slope indicating bullish momentum with upward price trajectory relative to logarithmic baseline Negative Values (Below Zero): LMA slope indicating bearish momentum with downward price trajectory relative to logarithmic baseline Zero Line Crosses: Signal transitions between bullish and bearish regimes, indicating potential trend changes Long Entry Threshold Zone: Area above positive threshold (default 0.5) indicating confirmed bullish signals suitable for long positions Short Entry Threshold Zone: Area below negative threshold (default -0.5) indicating confirmed bearish signals suitable for short positions Extreme Values: Signals exceeding ±1.0 represent strong momentum conditions with higher probability of continuation 2. Momentum Confirmation and Visual Analysis Signal Color Intensity: Gradient coloring shows signal strength, with brighter colors indicating stronger momentum Bar Coloring: Optional price bar coloring matches signal direction for quick visual trend identification Position Labels: Real-time position classification (Bullish/Bearish/Neutral) displayed on the latest bar Momentum Weight Factor: When short-term log-return momentum aligns with LMA signal direction, the signal receives additional weight confirmation Trend Quality Component: R-squared values weight the signal strength, with higher correlation indicating more reliable trend conditions 3. Examples: Preconfigured Settings Default: Universally applicable configuration balanced for medium-term investing and general trading across multiple timeframes and asset classes. Scalping: Highly responsive setup with shorter period and higher steepness for ultra-short-term trades on 1-15 minute charts, optimized for quick momentum shifts. Swing Trading: Extended period with moderate steepness and increased smoothing for multi-day positions, designed to filter noise while capturing larger price swings on 1-4 hour and daily charts. Trend Following: Maximum smoothing with lower steepness for established trend identification, generating fewer but more reliable signals optimal for daily and weekly timeframes. Mean Reversion: Shorter period with high steepness for counter-trend strategies, more sensitive to extreme moves and reversal opportunities in ranging market conditions.
อินดิเคเตอร์ Pine Script®
โดย QuantAlgo
16
Volumatic Support/Resistance Levels [BigBeluga]🔵 OVERVIEW A smart volume-powered tool for identifying key support and resistance zones—enhanced with real-time volume histogram fills and high-volume markers. Volumatic Support/Resistance Levels detects structural levels from swing highs and lows, and wraps them in dynamic histograms that reflect the relative volume strength around those zones. It highlights the strongest price levels not just by structure—but by the weight of market participation. 🔵 CONCEPTS Price Zones: Support and resistance levels are drawn from recent price pivots, while volume is used to visually enhance these zones with filled histograms and highlight moments of peak activity using markers. Histogram Fill = Activity Zone: The width and intensity of each filled zone adjusts to recent volume bursts. High-Volume Alerts: Circle markers highlight moments of volume dominance directly on the levels—revealing pressure points of support/resistance. Clean Visual Encoding: Red = resistance zones, green = support zones, orange = high-volume bars. 🔵 FEATURES Detects pivot-based resistance (highs) and support (lows) using a customizable range length. Wraps these levels in volume-weighted bands that expand/contract based on percentile volume. Color fill intensity increases with rising volume pressure, creating a live histogram feel. When volume > user-defined threshold , the indicator adds circle markers at the top and bottom of that price level zone. Bar coloring highlights the candles that generated this high-volume behavior (orange by default). Adjustable settings for all thresholds and colors, so traders can dial in volume sensitivity. 🔵 HOW TO USE Identify volume-confirmed resistance and support zones for potential reversal or breakout setups. Focus on levels with intense histogram fill and circle markers —they indicate strong participation. Use bar coloring to track when key activity started and align it with broader market context. Works well in combination with order blocks, trend indicators, or liquidity zones. Ideal for day traders, scalpers, and volume-sensitive setups. 🔵 CONCLUSION Volumatic Support/Resistance Levels elevates traditional support and resistance logic by anchoring it in volume context. Instead of relying solely on price action, it gives traders insight into where real conviction lies—by mapping how aggressively the market defended or rejected key levels. It's a visual, reactive, and volume-conscious upgrade to your structural toolkit.
อินดิเคเตอร์ Pine Script®
โดย BigBeluga
10
True Close – Institutional Trading Sessions (Zeiierman)█ Overview True Close – Institutional Trading Sessions (Zeiierman) is a professional-grade session mapping tool designed to help traders align with how institutions perceive the market’s true close. Unlike the textbook “daily close” used by retail traders, institutional desks often anchor their risk management, execution benchmarks, and exposure metrics to the first hour of the next session. This indicator visualizes that logic directly on your chart — drawing session boxes, true close levels, and time-aligned labels across Sydney, Tokyo, London, and New York. It highlights the first hour of each session, projects the institutional closing price, and builds a live dashboard that tells you which sessions are active, which are in the critical opening phase, and what levels matter most right now. More than just a visual tool, this indicator embeds institutional rhythm directly into your workflow — giving you a window into where big players finalize yesterday’s business, rebalance exposure, and execute delayed orders. It’s not just about painting sessions on your chart — it’s about adopting the mindset of those who truly move the market. Institutions don’t settle risk at the bell; they complete it in the next session. This tool lets you see that transition in real time, giving you an edge that goes beyond candles and indicators. █ How It Works ⚪ Session Detection Engine Each session is identified by its own time block (e.g., 09:00–17:30 for London). Once a session opens: A full-session box is drawn to track its range. The first hour is highlighted separately. Once the first hour completes, the true close line is plotted, representing the price institutions often treat as the "real" close of the prior day. ⚪ Institutional True Close Logic The script captures the close of the first hour, not the end of the day. This line becomes a static reference across your chart, letting you visualize how price interacts with that institutional anchor: Rejections from it show where yesterday's flow is respected. Breaks through it may indicate that today's flows are rewriting the narrative. ⚪ Dynamic Dashboard Table A live table appears in the corner of your screen, showing: Each session's active status Whether we’re inside the first hour The current “true close” price if available Each cell comes with advanced tooltips giving institutional context, flow dynamics, and market microstructure insights — from rebalancing spillovers to VWAP/TWAP lag effects. █ How to Use ⚪ Use the First-Hour Line as Your Institutional Anchor Treat it like the price level that big funds care about. Watch how the price behaves around level. Fades, re-tests, or continuation moves often occur as the market finishes recapping yesterday’s leftover orders. ⚪ Structure Entries Around the Session Context Are you inside the first hour? Expect more volatility, more decisive flow. After the first session hour, expect fading liquidity as the market slows down and awaits the next session to open. █ Settings UTC Offset – Select your preferred time zone; all sessions adjust accordingly. Session Toggles – Enable/disable Sydney, Tokyo, London, or NY. Box Display Options – Show/hide session background, first-hour fill, borders. True Close Line Controls – Enable line, label, and customize width & color. Execution Hour Labels – Optional toggle for first-hour label placement. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. 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.
อินดิเคเตอร์ Pine Script®
โดย Zeiierman
31
Open Interest Footprint IQ [TradingIQ]Hello Traders! Th e Open Interest Footprint IQ indicator is an advanced visualization tool designed for cryptocurrency markets. It provides a granular, real-time breakdown of open interest changes across different price levels, allowing traders to see how aggressive market participation is distributed within each bar. Unlike standard footprint charts that rely solely on volume, this indicator offers unique insights by focusing on the interaction between price action and changes in open interest (OI) — a leading metric often used to infer trader intent and positioning. How it works The Open Interest Footprint IQ processes lower timeframe price and open interest data to build a footprint-style chart that shows how traders are positioning themselves within each candle. Here’s a breakdown of the process: 1. Granular OI & Price Sampling The script retrieves lower-timeframe data (1-minute, 1-second, or 1-tick, based on your setting). For each candle, it captures: High and low prices Price change direction Change in open interest (OI) 2. Classifying Trader Behavior For each lower-timeframe segment, the indicator determines the type of positioning occurring based on price movement and OI change: If price is moving up and open interest is increasing, it suggests that long positions are being opened. This is considered a "Longs Opening" event, labeled as UU (Up/Up). If price is moving up but open interest is decreasing, it indicates that short positions are being closed. This is referred to as UD (Up/Down), or "Shorts Closing." If price is moving down and open interest is increasing, it signals that short positions are being opened. This is known as DU (Down/Up), or "Shorts Opening." If price is moving down while open interest is also decreasing, it means that long positions are being closed. This is labeled as DD (Down/Down), or "Longs Closing." These are stored in separate arrays and displayed at specific price levels. It is particularly useful for identifying: Where longs or shorts are opening/closing positions Stacked imbalances (indicative of potential absorption or exhaustion) Value area zones and POC (Point of Control) based on OI, not volume This footprint runs on your choice of sub-bar granularity and is ideal for high-frequency trading, scalping, and entries based on order flow dynamics. Key Features Footprint Visualization At each price level within a candle: Long/short opening and closing behavior is broken down. Delta (net open interest change) is displayed both numerically and color-coded. Optional gradient coloring shows intensity and type of flow (longs/shorts opened/closed). Cumulative or per-bar reset modes allow you to track OI evolution over time. The image above explains the information that each Footprint box shows across a candlestick! Each footprint box shows: OI Delta OI Delta % Longs Opened (LO) Longs Closed (LC) Shorts Opened (SO) Shorts Closed (SC) The image above explains the color-coding feature of the indicator. Boxes are color coded to show which position action dominated at the price area. For this example: Green boxes = Long positions being opened dominated Purple boxes = Long positions being closed dominated Red boxes = Short positions being opened dominated Yellow boxes = Short positions being closed dominated All colors are customizable. Additionally, for traders who are only interested in whether OI increased/decreased, a "two-color" option is available in the settings. For the two-color option, footprint boxes can be one of two colors. Showing whether OI increased or decreased at the level. Cumulative Levels Open Interest Footprint IQ contains a "Cumulative Levels" feature that tracks/stores open interest change at tick levels over time, rather than resetting per bar. With the "Cumulative Levels" feature enabled, traders can see open interest changes persist across all candlesticks. This feature is useful for determining whether longs opening, longs closing, shorts opening, or shorts closing are dominating at particular price areas over time rather than on a single bar. A useful feature to see if shorts/longs are favoring certain price throughout the day, week, month, etc. Input Settings Explained Granularity (Dropdown: Granularity) Options: 1-Minute, 1-Second, 1-Tick Determines how finely the script samples the lower timeframe data to construct the footprint. For precision: 1-Tick = Highest accuracy, but more resource-intensive. 1-Second/1-Minute = Suitable for broader or more zoomed-out analysis. Tick Level Distance (Tick Level Distance (0 = Auto)) Defines the vertical spacing between levels in the footprint chart. If 0, the script uses an automatic calculation based on ATR to adapt to volatility. Set a manual value (e.g., 5) to control the height granularity of each level in ticks. Cumulative Levels (Toggle) If enabled, the footprint builds cumulatively over time, rather than resetting per candle. Use case: Visualize ongoing buildup of OI activity across a session or day. Cumulative Levels Reset TF (Timeframe) Sets the reset interval for the cumulative view (e.g., reset daily, hourly, etc.) Works only when Cumulative Levels is enabled. Delta Box Display Settings Show Delta Percentage Toggles the display of the percentage change in OI across the footprint level. Helpful to gauge how aggressive positioning is relative to total OI at that level. Show Longs/Shorts (Opened/Closed) Show Longs Opened: Displays OI increase in up candles (price ↑, OI ↑). Show Longs Closed: Displays OI decrease in down candles (price ↓, OI ↓). Show Shorts Opened: OI increase in down candles (price ↓, OI ↑). Show Shorts Closed: OI decrease in up candles (price ↑, OI ↓). These behaviors are color-coded to give traders instant context: Blue-green for longs opening. Purple for longs closing. Red for shorts opening. Yellow for shorts closing. Value Area & POC Value Area % (Value Area %) Controls how much cumulative open interest is used to define the value area. Example: 70% means the smallest range of prices that contains 70% of total OI in that bar will be marked. Helps identify zones of interest, support/resistance, and institutional levels. The image above explains how to identify the VAH/VAL/POC shown by Open Interest Footprint IQ. VAH = Upper 🞂 POC = ● VAL = Lower 🞂 Imbalances Imbalance Percentage Defines the minimum delta % required at a level to be marked as an imbalance. If the net open interest change at a level exceeds this threshold, a visual marker appears. Stacked Imbalance Count If the number of consecutive imbalance levels meets this count, a “Stacked Imbalance” alert will trigger. This can signal aggressive buying or selling pressure, potential breakout zones, or institutional absorption. Color Settings Longs Opened / Closed, Shorts Opened / Closed Customize the color palette for each order flow behavior. These colors appear in the background gradient of the footprint boxes. Up/Down Only Mode Toggle to override all behavior-based colors with a single Up Color and Down Color. Useful if you prefer a simple bull/bear view. Up Color / Down Color If "Up/Down Only" is enabled, these two colors are used to represent all net positive or negative deltas. Special Notes Crypto only: This script works only with crypto tickers on TradingView. For other assets (stocks, futures), a warning message will appear instead. OI data must be available from the exchange (many perpetual pairs support this). If the footprint is too small or invisible, increase your tick level spacing in the settings. Alerts When a stacked imbalance is detected, an alert is fired ("Stacked Imbalance"). This feature is useful for automated systems, bots, or simply staying informed of potential trade setups. And that's all for now! If you have any questions or features you'd like to see feel free to share them in the comments below! Thank you traders!
อินดิเคเตอร์ Pine Script®
โดย Trading-IQ
14
Zigzag CandlesCan't deny that I am obsessed with zigzags. Been doing some crazy experiments with it and have many more in pipeline. I believe zigzag can be used to derive better trend following methods. Here is an attempt to visualize zigzag as candlesticks. Next steps probably to derive moving average, atr (although there was an attempt of AZR made earlier) and probably supertrend too ;) Input parameters include ZigzagLength (to calculate zigzag) and CandleSize (number of zigzag pivots in each candle) CandleSize can be 3 or more. Every time we collect pivots which are equal to CandleSize, we derive one candle. And when we derive a candle, we remove all old pivots except the last one. Becauase, the last pivot acts as open to the next bar and is required. Body of the candle tells the start and end zigzag pivot in the range. And Wicks signify highest and lowest pivots in the range. High and Low wicks are placed at the pivot where high and lows are formed. Hence, you can see them at different positions each time. Thanks to @RicardoSantos for suggesting boxes for candles - while I was trying to achieve this with plotbar
อินดิเคเตอร์ Pine Script®
โดย Trendoscope
13
Tape [LucF]█  OVERVIEW This script prints an ersatz of a trading console's "tape" section to the right of your chart. It displays the time, price and volume​  of each update of the chart's feed. It also calculates volume​ delta for the bar. As it calculates from realtime information, it will not display information on historical bars. █  FEATURES Calculations Each new line in the tape displays the last price/volume​ update from the TradingView feed that's building your chart. These updates do not necessarily correspond to ticks from the originating broker/exchange's matching engine. Multiple broker/exchange ticks are often aggregated in one  chart update. The script first determines if price has moved up or down since the last update. The polarity of the price change, in turn, determines the polarity of the volume​​ for that specific update. If price does not move between consecutive updates, then the last known polarity is used. Using this method, we can calculate a running volume​ delta accumulation for the bar, which becomes the bar's final volume​ delta value when the bar closes (you can inspect values of elapsed realtime bars in the Data Window or the indicator's values). Note that these values will all reset if the script re-executes because of a change in inputs or a chart refresh. While this method of calculating volume​ delta is not perfect, it is currently the most precise way of calculating volume​ delta available on TradingView at the moment. Calculating more precise results would require scripts to have access to bid/ask levels from any chart timeframe. Charts at seconds timeframes do use exchange/broker ticks when the feeds you are using allow for it, and this indicator will run on them, but tick data is not yet available from higher timeframes, for now. Also note that the method used in this script is far superior to the intrabar inspection technique used on historical bars in my other "Delta Volume​" indicators. This is because volume​ delta here is calculated from many more realtime updates than the available intrabars in history. Inputs You can use the script's inputs to configure:  • The number of lines displayed in the tape.  • If new lines appear at the top or bottom.  • If you want to hide lines with low volume​.  • The precision of volume​ values.  • The size of the text and the colors used to highlight either the tape's text or background.  • The position where you want the tape on your chart.  • Conditions triggering three different markers. Display Deltas are shown at the bottom of the tape. They are reset on each bar. Time delta displays the time elapsed since the beginning of the bar, on intraday timeframes only. Contrary to the price change display by TradingView at the top left of charts, which is calculated from the close of the previous bar, the price delta in the tape is calculated from the bar's open, because that's the information used in the calculation of volume​ delta. The time will become orange when volume​ delta's polarity diverges from that of the bar. The volume  delta value represents the current, cumulative value for the bar. Its color reflects its polarity. When new realtime bars appear on the chart, a ↻ symbol will appear before the volume​ value in tape lines. Markers There are three types of markers you can choose to display:  • Marker 1 on volume​ bumps. A bump is defined as two consecutive and increasing/decreasing plus/minus delta volume​  values,   when no divergence between the polarity of delta volume​ and the bar occurs on the second bar.  • Marker 2 on volume​ delta for the bar exceeding a limit of your choice when there is no divergence between the polarity of delta volume​ and the bar. These trigger at the bar's close.  • Marker 3 on tape lines with volume​ exceeding a threshold. These trigger in realtime. Be sure to set a threshold high enough so that it doesn't generate too many alerts.   These markers will only display briefly under the bar, but another marker appears next to the relevant line in the tape. The marker conditions are used to trigger alerts configured on the script. Alert messages will mention the marker(s) that triggered the specific alert event, along with the relevant volume​ value that triggered the marker. If more than one marker triggers a single alert, they will overprint under the bar, which can make it difficult to distinguish them. For more detailed on-chart analysis of realtime volume​ delta, see my Delta Volume Realtime Action . █  NOTES FOR CODERS This script showcases two new Pine features:  • Tables, which allow Pine programmers to display tabular information in fixed locations of the chart. The tape uses this feature.   See the Pine User Manual's page on Tables for more information.  • varip -type variables which we can use to save values between realtime updates.   See the " Using `varip` variables " publication by PineCoders for more information.
อินดิเคเตอร์ Pine Script®
โดย LucF
99
Circular Candlestick ChartAn original (but impractical) way to represent a candlestick chart using circles arc. The most recent candles are further away from the circle origin. Note that OHLC values follow a clockwise direction. A higher arc length would indicate candles with a higher body or wick range. The Length settings determine the number of past candles to be included in the circular candlestick chart. The Width setting control the width of the circular chart. The Spacing setting controls the space between each arcs. Finally, the Precision settings allow obtaining a more precise representation of candles, with lower values returning more precise results, however, more precision requires a higher amount of lines. Settings are quite hard to adjust, using a higher length might require a lower spacing value. Additionally, the script includes two pointers indicating the location of the 75 (in blue) and 25 (in orange) percentiles. This allows obtaining an estimate of the current market sentiment, with the most recent arcs laying closer to the 75 percentile pointer indicating an up-trend. This new way to represent candlesticks might be useful to more easily identify candles clusters or to find new price patterns. Who knows, we know that new ways to see prices always stimulate traders imagination. See you next year.
อินดิเคเตอร์ Pine Script®
โดย alexgrover
20
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE) ## Advanced Multi-Dimensional Mathematical Analysis System *Where Quantum Mathematics Meets Market Structure* --- ## 🎓 THEORETICAL FOUNDATION The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry. ### 🌊 Multi-Dimensional Volatility with Jump Detection **Hawkes Process Implementation:** The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects. **Mathematical Foundation:** ``` Intensity λ(t) = μ + Σ α(t - Tᵢ) ``` Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99). **Mahalanobis Distance Calculation:** The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions: - **Dimension 1:** Price volatility (standard deviation of returns) - **Dimension 2:** Volume volatility (normalized volume fluctuations) - **Dimension 3:** Range volatility (high-low spread variations) - **Dimension 4:** Correlation volatility (price-volume relationship changes) - **Dimension 5:** Microstructure volatility (intrabar positioning analysis) This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches. ### 📐 Hurst Exponent Regime Detection **Fractal Market Hypothesis Integration:** The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification: - **H > 0.6:** Trending (persistent) markets - momentum strategies optimal - **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal - **H ≈ 0.5:** Random walk markets - breakout strategies preferred **Adaptive R/S Analysis:** Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions: ``` H = log(R/S) / log(n) ``` Where R is the range of cumulative deviations and S is the standard deviation over period n. **Dynamic Regime Classification:** The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods. ### 🔄 Transfer Entropy Analysis **Information Flow Quantification:** Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements: ``` TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log ``` **Causality Detection:** - **Volume → Price:** Indicates accumulation/distribution phases - **Price → Volume:** Suggests retail participation or momentum chasing - **Balanced Flow:** Market equilibrium or transition periods The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows. --- ## 🔧 COMPREHENSIVE INPUT SYSTEM ### Core Parameters Group **Primary Analysis Window (10-100, Default: 50)** The fundamental lookback period affecting all calculations. Optimization by timeframe: - **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements) - **15 minute-1 hour:** 30-50 (balanced responsiveness and stability) - **4 hour-daily:** 50-100 (smooth signals, reduced noise) - **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60 **Signal Sensitivity (0.1-2.0, Default: 0.7)** Master control affecting all threshold calculations: - **Conservative (0.3-0.6):** High-quality signals only, fewer false positives - **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles - **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering **Signal Generation Mode:** - **Aggressive:** Any component signals (highest frequency) - **Confluence:** 2+ components agree (balanced approach) - **Conservative:** All 3 components align (highest quality) ### Volatility Jump Detection Group **Volatility Dimensions (2-5, Default: 3)** Determines the mathematical space complexity: - **2D:** Price + Volume volatility (suitable for clean markets) - **3D:** + Range volatility (optimal for most conditions) - **4D:** + Correlation volatility (advanced multi-asset analysis) - **5D:** + Microstructure volatility (maximum sensitivity) **Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)** Standard deviations required for volatility jump classification: - **Cryptocurrency:** 2.0-2.5σ (naturally volatile) - **Stock Indices:** 2.5-3.0σ (moderate volatility) - **Forex Major Pairs:** 3.0-3.5σ (typically stable) - **Commodities:** 2.0-3.0σ (varies by commodity) **Jump Clustering Decay (0.5-0.99, Default: 0.85)** Hawkes process memory parameter: - **0.5-0.7:** Fast decay (jumps treated as independent) - **0.8-0.9:** Moderate clustering (realistic market behavior) - **0.95-0.99:** Strong clustering (crisis/event-driven markets) ### Hurst Exponent Analysis Group **Calculation Method Options:** - **Classic R/S:** Original Rescaled Range (fast, simple) - **Adaptive R/S:** Dynamic windowing (recommended for trading) - **DFA:** Detrended Fluctuation Analysis (best for noisy data) **Trending Threshold (0.55-0.8, Default: 0.60)** Hurst value defining persistent market behavior: - **0.55-0.60:** Weak trend persistence - **0.65-0.70:** Clear trending behavior - **0.75-0.80:** Strong momentum regimes **Mean Reversion Threshold (0.2-0.45, Default: 0.40)** Hurst value defining anti-persistent behavior: - **0.35-0.45:** Weak mean reversion - **0.25-0.35:** Clear ranging behavior - **0.15-0.25:** Strong reversion tendency ### Transfer Entropy Parameters Group **Information Flow Analysis:** - **Price-Volume:** Classic flow analysis for accumulation/distribution - **Price-Volatility:** Risk flow analysis for sentiment shifts - **Multi-Timeframe:** Cross-timeframe causality detection **Maximum Lag (2-20, Default: 5)** Causality detection window: - **2-5 bars:** Immediate causality (scalping) - **5-10 bars:** Short-term flow (day trading) - **10-20 bars:** Structural flow (swing trading) **Significance Threshold (0.05-0.3, Default: 0.15)** Minimum entropy for signal generation: - **0.05-0.10:** Detect subtle information flows - **0.10-0.20:** Clear causality only - **0.20-0.30:** Very strong flows only --- ## 🎨 ADVANCED VISUAL SYSTEM ### Tensor Volatility Field Visualization **Five-Layer Resonance Bands:** The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength: - **Core Layer (Purple):** Primary tensor field with highest intensity - **Layer 2 (Neutral):** Secondary mathematical resonance - **Layer 3 (Info Blue):** Tertiary harmonic frequencies - **Layer 4 (Warning Gold):** Outer field boundaries - **Layer 5 (Success Green):** Maximum field extension **Field Strength Calculation:** ``` Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity) ``` The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility. **Radiation Line Network:** During active tensor states, the system projects directional radiation lines showing field energy distribution: - **8 Directional Rays:** Complete angular coverage - **Tapering Segments:** Progressive transparency for natural visual flow - **Pulse Effects:** Enhanced visualization during volatility jumps ### Dimensional Portal System **Portal Mathematics:** Dimensional portals visualize regime transitions using category theory principles: - **Green Portals (◉):** Trending regime detection (appear below price for support) - **Red Portals (◎):** Mean-reverting regime (appear above price for resistance) - **Yellow Portals (○):** Random walk regime (neutral positioning) **Tensor Trail Effects:** Each portal generates 8 trailing particles showing mathematical momentum: - **Large Particles (●):** Strong mathematical signal - **Medium Particles (◦):** Moderate signal strength - **Small Particles (·):** Weak signal continuation - **Micro Particles (˙):** Signal dissipation ### Information Flow Streams **Particle Stream Visualization:** Transfer entropy creates flowing particle streams indicating information direction: - **Upward Streams:** Volume leading price (accumulation phases) - **Downward Streams:** Price leading volume (distribution phases) - **Stream Density:** Proportional to information flow strength **15-Particle Evolution:** Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent. ### Fractal Matrix Grid System **Multi-Timeframe Fractal Levels:** The system calculates and displays fractal highs/lows across five Fibonacci periods: - **8-Period:** Short-term fractal structure - **13-Period:** Intermediate-term patterns - **21-Period:** Primary swing levels - **34-Period:** Major structural levels - **55-Period:** Long-term fractal boundaries **Triple-Layer Visualization:** Each fractal level uses three-layer rendering: - **Shadow Layer:** Widest, darkest foundation (width 5) - **Glow Layer:** Medium white core line (width 3) - **Tensor Layer:** Dotted mathematical overlay (width 1) **Intelligent Labeling System:** Smart spacing prevents label overlap using ATR-based minimum distances. Labels include: - **Fractal Period:** Time-based identification - **Topological Class:** Mathematical complexity rating (0, I, II, III) - **Price Level:** Exact fractal price - **Mahalanobis Distance:** Current mathematical field strength - **Hurst Exponent:** Current regime classification - **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡) ### Wick Pressure Analysis **Rejection Level Mathematics:** The system analyzes candle wick patterns to project future pressure zones: - **Upper Wick Analysis:** Identifies selling pressure and resistance zones - **Lower Wick Analysis:** Identifies buying pressure and support zones - **Pressure Projection:** Extends lines forward based on mathematical probability **Multi-Layer Glow Effects:** Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart. ### Enhanced Regime Background **Dynamic Intensity Mapping:** Background colors reflect mathematical regime strength: - **Deep Transparency (98% alpha):** Subtle regime indication - **Pulse Intensity:** Based on regime strength calculation - **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random) **Smoothing Integration:** Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts. ### Color Scheme System **Six Professional Themes:** - **Dark (Default):** Professional trading environment optimization - **Light:** High ambient light conditions - **Classic:** Traditional technical analysis appearance - **Neon:** High-contrast visibility for active trading - **Neutral:** Minimal distraction focus - **Bright:** Maximum visibility for complex setups Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences. --- ## 📊 INSTITUTIONAL-GRADE DASHBOARD ### Tensor Field Status Section **Field Strength Display:** Real-time Mahalanobis distance calculation with dynamic emoji indicators: - **⚡ (Lightning):** Extreme field strength (>1.5× threshold) - **● (Solid Circle):** Strong field activity (>1.0× threshold) - **○ (Open Circle):** Normal field state **Signal Quality Rating:** Democratic algorithm assessment: - **ELITE:** All 3 components aligned (highest probability) - **STRONG:** 2 components aligned (good probability) - **GOOD:** 1 component active (moderate probability) - **WEAK:** No clear component signals **Threshold and Anomaly Monitoring:** - **Threshold Display:** Current mathematical threshold setting - **Anomaly Level (0-100%):** Combined volatility and volume spike measurement - **>70%:** High anomaly (red warning) - **30-70%:** Moderate anomaly (orange caution) - **<30%:** Normal conditions (green confirmation) ### Tensor State Analysis Section **Mathematical State Classification:** - **↑ BULL (Tensor State +1):** Trending regime with bullish bias - **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias - **◈ SUPER (Tensor State 0):** Random walk regime (neutral) **Visual State Gauge:** Five-circle progression showing tensor field polarity: - **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment - **⚪⚪🟡⚪⚪:** Neutral/transitional state - **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment **Trend Direction and Phase Analysis:** - **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification - **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength) - **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength) - **😴 CALM:** Low information flow (<1.0 flow strength) ### Trading Signals Section **Real-Time Signal Status:** - **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability - **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability - **Components (X/3):** Active algorithmic components - **Mode Display:** Current signal generation mode **Signal Strength Visualization:** Color-coded component count: - **Green:** 3/3 components (maximum confidence) - **Aqua:** 2/3 components (good confidence) - **Orange:** 1/3 components (moderate confidence) - **Gray:** 0/3 components (no signals) ### Performance Metrics Section **Win Rate Monitoring:** Estimated win rates based on signal quality with emoji indicators: - **🔥 (Fire):** ≥60% estimated win rate - **👍 (Thumbs Up):** 45-59% estimated win rate - **⚠️ (Warning):** <45% estimated win rate **Mathematical Metrics:** - **Hurst Exponent:** Real-time fractal dimension (0.000-1.000) - **Information Flow:** Volume/price leading indicators - **📊 VOL:** Volume leading price (accumulation/distribution) - **💰 PRICE:** Price leading volume (momentum/speculation) - **➖ NONE:** Balanced information flow - **Volatility Classification:** - **🔥 HIGH:** Above 1.5× jump threshold - **📊 NORM:** Normal volatility range - **😴 LOW:** Below 0.5× jump threshold ### Market Structure Section (Large Dashboard) **Regime Classification:** - **📈 TREND:** Hurst >0.6, momentum strategies optimal - **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal - **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred **Mathematical Field Analysis:** - **Dimensions:** Current volatility space complexity (2D-5D) - **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00) - **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal) ### Settings Summary Section (Large Dashboard) **Active Configuration Display:** - **Sensitivity:** Current master sensitivity setting - **Lookback:** Primary analysis window - **Theme:** Active color scheme - **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA) **Dashboard Sizing Options:** - **Small:** Essential metrics only (mobile/small screens) - **Normal:** Balanced information density (standard desktop) - **Large:** Maximum detail (multi-monitor setups) **Position Options:** - **Top Right:** Standard placement (avoids price action) - **Top Left:** Wide chart optimization - **Bottom Right:** Recent price focus (scalping) - **Bottom Left:** Maximum price visibility (swing trading) --- ## 🎯 SIGNAL GENERATION LOGIC ### Multi-Component Convergence System **Component Signal Architecture:** The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing: **Volatility Component:** - **Jump Detection:** Mahalanobis distance threshold breach - **Hawkes Intensity:** Self-exciting process activation (>0.2) - **Multi-dimensional:** Considers all volatility dimensions simultaneously **Hurst Regime Component:** - **Trending Markets:** Price above SMA-20 with positive momentum - **Mean-Reverting Markets:** Price at Bollinger Band extremes - **Random Markets:** Bollinger squeeze breakouts with directional confirmation **Transfer Entropy Component:** - **Volume Leadership:** Information flow from volume to price - **Volume Spike:** Volume 110%+ above 20-period average - **Flow Significance:** Above entropy threshold with directional bias ### Democratic Signal Weighting **Signal Mode Implementation:** - **Aggressive Mode:** Any single component triggers signal - **Confluence Mode:** Minimum 2 components must agree - **Conservative Mode:** All 3 components must align **Momentum Confirmation:** All signals require momentum confirmation: - **Long Signals:** RSI >50 AND price >EMA-9 - **Short Signals:** RSI <50 AND price 0.6):** - **Increase Sensitivity:** Catch momentum continuation - **Lower Mean Reversion Threshold:** Avoid counter-trend signals - **Emphasize Volume Leadership:** Institutional accumulation/distribution - **Tensor Field Focus:** Use expansion for trend continuation - **Signal Mode:** Aggressive or Confluence for trend following **Range-Bound Markets (Hurst <0.4):** - **Decrease Sensitivity:** Avoid false breakouts - **Lower Trending Threshold:** Quick regime recognition - **Focus on Price Leadership:** Retail sentiment extremes - **Fractal Grid Emphasis:** Support/resistance trading - **Signal Mode:** Conservative for high-probability reversals **Volatile Markets (High Jump Frequency):** - **Increase Hawkes Decay:** Recognize event clustering - **Higher Jump Threshold:** Avoid noise signals - **Maximum Dimensions:** Capture full volatility complexity - **Reduce Position Sizing:** Risk management adaptation - **Enhanced Visuals:** Maximum information for rapid decisions **Low Volatility Markets (Low Jump Frequency):** - **Decrease Jump Threshold:** Capture subtle movements - **Lower Hawkes Decay:** Treat moves as independent - **Reduce Dimensions:** Simplify analysis - **Increase Position Sizing:** Capitalize on compressed volatility - **Minimal Visuals:** Reduce distraction in quiet markets --- ## 🚀 ADVANCED TRADING STRATEGIES ### The Mathematical Convergence Method **Entry Protocol:** 1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels 2. **Tensor Field Confirmation:** Verify field expansion supporting direction 3. **Portal Signal:** Wait for dimensional portal appearance 4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals 5. **Component Consensus:** Confirm 2+ components agree in Confluence mode **Example Implementation:** - Price approaching 21-period fractal high - Tensor field expanding upward (bullish mathematical alignment) - Green portal appears below price (trending regime confirmation) - ELITE quality signal with 3/3 components active - Enter long position with stop below fractal level **Risk Management:** - **Stop Placement:** Below/above fractal level that generated signal - **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size) - **Profit Targets:** Next fractal level or tensor field resistance ### The Regime Transition Strategy **Regime Change Detection:** 1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds 2. **Portal Color Change:** Regime transitions show different portal colors 3. **Background Intensity:** Increasing regime background intensity 4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis) **Trading Implementation:** - **Trending Transitions:** Trade momentum breakouts, follow trend - **Mean Reversion Transitions:** Trade range boundaries, fade extremes - **Random Transitions:** Trade breakouts with tight stops **Advanced Techniques:** - **Multi-Timeframe:** Confirm regime on higher timeframe - **Early Entry:** Enter on regime transition rather than confirmation - **Regime Strength:** Larger positions during strong regime signals ### The Information Flow Momentum Strategy **Flow Detection Protocol:** 1. **Monitor Transfer Entropy:** Watch for significant information flow shifts 2. **Volume Leadership:** Strong edge when volume leads price 3. **Flow Acceleration:** Increasing flow strength indicates momentum 4. **Directional Confirmation:** Ensure flow aligns with intended trade direction **Entry Signals:** - **Volume → Price Flow:** Enter during accumulation/distribution phases - **Price → Volume Flow:** Enter on momentum confirmation breaks - **Flow Reversal:** Counter-trend entries when flow reverses **Optimization:** - **Scalping:** Use immediate flow detection (2-5 bar lag) - **Swing Trading:** Use structural flow (10-20 bar lag) - **Multi-Asset:** Compare flow between correlated assets ### The Tensor Field Expansion Strategy **Field Mathematics:** The tensor field expansion indicates mathematical pressure building in market structure: **Expansion Phases:** 1. **Compression:** Field contracts, volatility decreases 2. **Tension Building:** Mathematical pressure accumulates 3. **Expansion:** Field expands rapidly with directional movement 4. **Resolution:** Field stabilizes at new equilibrium **Trading Applications:** - **Compression Trading:** Prepare for breakout during field contraction - **Expansion Following:** Trade direction of field expansion - **Reversion Trading:** Fade extreme field expansion - **Multi-Dimensional:** Consider all field layers for confirmation ### The Hawkes Process Event Strategy **Self-Exciting Jump Trading:** Understanding that market shocks cluster and create follow-on opportunities: **Jump Sequence Analysis:** 1. **Initial Jump:** First volatility jump detected 2. **Clustering Phase:** Hawkes intensity remains elevated 3. **Follow-On Opportunities:** Additional jumps more likely 4. **Decay Period:** Intensity gradually decreases **Implementation:** - **Jump Confirmation:** Wait for mathematical jump confirmation - **Direction Assessment:** Use other components for direction - **Clustering Trades:** Trade subsequent moves during high intensity - **Decay Exit:** Exit positions as Hawkes intensity decays ### The Fractal Confluence System **Multi-Timeframe Fractal Analysis:** Combining fractal levels across different periods for high-probability zones: **Confluence Zones:** - **Double Confluence:** 2 fractal levels align - **Triple Confluence:** 3+ fractal levels cluster - **Mathematical Confirmation:** Tensor field supports the level - **Information Flow:** Transfer entropy confirms direction **Trading Protocol:** 1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR 2. **Mathematical Support:** Verify tensor field alignment 3. **Signal Quality:** Wait for STRONG or ELITE signal 4. **Risk Definition:** Use fractal level for stop placement 5. **Profit Targeting:** Next major fractal confluence zone --- ## ⚠️ COMPREHENSIVE RISK MANAGEMENT ### Mathematical Position Sizing **Mahalanobis Distance Integration:** Position size should inversely correlate with mathematical field strength: ``` Position Size = Base Size × (Threshold / Mahalanobis Distance) ``` **Risk Scaling Matrix:** - **Low Field Strength (<2.0):** Standard position sizing - **Moderate Field Strength (2.0-3.0):** 75% position sizing - **High Field Strength (3.0-4.0):** 50% position sizing - **Extreme Field Strength (>4.0):** 25% position sizing or no trade ### Signal Quality Risk Adjustment **Quality-Based Position Sizing:** - **ELITE Signals:** 100% of planned position size - **STRONG Signals:** 75% of planned position size - **GOOD Signals:** 50% of planned position size - **WEAK Signals:** No position or paper trading only **Component Agreement Scaling:** - **3/3 Components:** Full position size - **2/3 Components:** 75% position size - **1/3 Components:** 50% position size or skip trade ### Regime-Adaptive Risk Management **Trending Market Risk:** - **Wider Stops:** Allow for trend continuation - **Trend Following:** Trade with regime direction - **Higher Position Size:** Trend probability advantage - **Momentum Stops:** Trail stops based on momentum indicators **Mean-Reverting Market Risk:** - **Tighter Stops:** Quick exits on trend continuation - **Contrarian Positioning:** Trade against extremes - **Smaller Position Size:** Higher reversal failure rate - **Level-Based Stops:** Use fractal levels for stops **Random Market Risk:** - **Breakout Focus:** Trade only clear breakouts - **Tight Initial Stops:** Quick exit if breakout fails - **Reduced Frequency:** Skip marginal setups - **Range-Based Targets:** Profit targets at range boundaries ### Volatility-Adaptive Risk Controls **High Volatility Periods:** - **Reduced Position Size:** Account for wider price swings - **Wider Stops:** Avoid noise-based exits - **Lower Frequency:** Skip marginal setups - **Faster Exits:** Take profits more quickly **Low Volatility Periods:** - **Standard Position Size:** Normal risk parameters - **Tighter Stops:** Take advantage of compressed ranges - **Higher Frequency:** Trade more setups - **Extended Targets:** Allow for compressed volatility expansion ### Multi-Timeframe Risk Alignment **Higher Timeframe Trend:** - **With Trend:** Standard or increased position size - **Against Trend:** Reduced position size or skip - **Neutral Trend:** Standard position size with tight management **Risk Hierarchy:** 1. **Primary:** Current timeframe signal quality 2. **Secondary:** Higher timeframe trend alignment 3. **Tertiary:** Mathematical field strength 4. **Quaternary:** Market regime classification --- ## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS ### Advanced Mathematical Concepts **Tensor Analysis in Markets:** The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture. **Information Theory Applications:** Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior. **Fractal Geometry in Trading:** The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure. **Stochastic Process Theory:** The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects. ### Learning Progressive Complexity **Beginner Mathematical Concepts:** - **Volatility Dimensions:** Understanding multi-dimensional analysis - **Regime Classification:** Learning market personality types - **Signal Democracy:** Algorithmic consensus building - **Visual Mathematics:** Interpreting mathematical concepts visually **Intermediate Mathematical Applications:** - **Mahalanobis Distance:** Statistical distance in multi-dimensional space - **Rescaled Range Analysis:** Fractal dimension measurement - **Information Entropy:** Quantifying uncertainty and causality - **Field Theory:** Understanding mathematical fields in market context **Advanced Mathematical Integration:** - **Tensor Field Dynamics:** Multi-dimensional market force analysis - **Stochastic Self-Excitation:** Event clustering and memory effects - **Categorical Composition:** Mathematical signal combination theory - **Topological Market Analysis:** Understanding market shape and connectivity ### Practical Mathematical Intuition **Developing Market Mathematics Intuition:** The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of: - **How markets exhibit mathematical structure beneath apparent randomness** - **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches** - **How information flows through markets in measurable, predictable ways** - **Why mathematical models provide probabilistic edges rather than certainties** --- ## 🔬 IMPLEMENTATION AND OPTIMIZATION ### Getting Started Protocol **Phase 1: Observation (Week 1)** 1. **Apply with defaults:** Use standard settings on your primary trading timeframe 2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams 3. **Monitor dashboard:** Observe how metrics change with market conditions 4. **No trading:** Focus entirely on pattern recognition and understanding **Phase 2: Pattern Recognition (Week 2-3)** 1. **Identify signal patterns:** Note what market conditions produce different signal qualities 2. **Regime correlation:** Observe how Hurst regimes affect signal performance 3. **Visual confirmation:** Learn to read tensor field expansion and portal signals 4. **Component analysis:** Understand which components drive signals in different markets **Phase 3: Parameter Optimization (Week 4-5)** 1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument 2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe 3. **Sensitivity adjustment:** Balance signal frequency with quality 4. **Visual customization:** Optimize colors and intensity for your trading environment **Phase 4: Live Implementation (Week 6+)** 1. **Paper trading:** Test signals with hypothetical trades 2. **Small position sizing:** Begin with minimal risk during learning phase 3. **Performance tracking:** Monitor actual vs. expected signal performance 4. **Continuous optimization:** Refine settings based on real performance data ### Performance Monitoring System **Signal Quality Tracking:** - **ELITE Signal Win Rate:** Track highest quality signals separately - **Component Performance:** Monitor which components provide best signals - **Regime Performance:** Analyze performance across different market regimes - **Timeframe Analysis:** Compare performance across different session times **Mathematical Metric Correlation:** - **Field Strength vs. Performance:** Higher field strength should correlate with better performance - **Component Agreement vs. Win Rate:** More component agreement should improve win rates - **Regime Alignment vs. Success:** Trading with mathematical regime should outperform ### Continuous Optimization Process **Monthly Review Protocol:** 1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown 2. **Parameter Assessment:** Evaluate if current settings remain optimal 3. **Market Adaptation:** Adjust for changes in market character or volatility 4. **Component Weighting:** Consider if certain components should receive more/less emphasis **Quarterly Deep Analysis:** 1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid 2. **Regime Distribution:** Analyze time spent in different market regimes 3. **Signal Evolution:** Track how signal characteristics change over time 4. **Correlation Analysis:** Monitor correlations between different mathematical components --- ## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS ### Revolutionary Mathematical Integration **First-Ever Implementations:** 1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility 2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes 3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation 4. **Democratic Component Voting:** First algorithmic consensus system for signal generation 5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels ### Advanced Visualization Innovations **Mathematical Visualization Breakthroughs:** - **Tensor Field Radiation:** Visual representation of mathematical field energy - **Dimensional Portal System:** Category theory visualization for regime transitions - **Information Flow Streams:** Real-time visual display of market information transfer - **Multi-Layer Fractal Grid:** Intelligent spacing and projection system - **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength ### Practical Trading Innovations **Trading System Advances:** - **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence - **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality - **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering - **Component Performance Tracking:** Real-time monitoring of algorithmic component success - **Field-Strength Position Sizing:** Mathematical volatility integration for risk management --- ## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS ### Mathematical Model Limitations **Understanding Model Boundaries:** While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations: - **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements - **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely - **Model Risk:** Complex models can fail during unprecedented market conditions - **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions ### Proper Implementation Guidelines **Risk Management Requirements:** - **Never Risk More Than 2% Per Trade:** Regardless of signal quality - **Diversification Mandatory:** Don't rely solely on mathematical signals - **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence - **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters **Realistic Expectations:** - **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes - **Learning Curve Required:** Complex mathematical concepts require time to master - **Market Adaptation Necessary:** Parameters must evolve with changing market conditions - **Continuous Education Important:** Understanding underlying mathematics improves application ### Ethical Trading Considerations **Market Impact Awareness:** - **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants - **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure - **Sharing Knowledge:** Consider educational contributions to trading community - **Fair Market Participation:** Use mathematical advantages responsibly within market framework ### Professional Development Path **Skill Development Sequence:** 1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application 2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals 3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights 4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution --- ## 🔮 CONCLUSION The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability. ### Mathematical Innovation Legacy This indicator establishes new paradigms in technical analysis: - **Tensor analysis for market volatility understanding** - **Stochastic self-excitation for event clustering prediction** - **Information theory for causality-based trade generation** - **Democratic algorithmic consensus for signal quality enhancement** - **Mathematical field visualization for intuitive market understanding** ### Practical Trading Revolution Beyond mathematical innovation, the TMAE transforms practical trading: - **Quality-rated signals replace binary buy/sell decisions** - **Regime-adaptive strategies automatically optimize for market personality** - **Multi-dimensional risk management integrates mathematical volatility measures** - **Visual mathematical concepts make complex analysis immediately interpretable** - **Educational value creates lasting improvement in trading understanding** ### Future-Proof Design The mathematical foundations ensure lasting relevance: - **Universal mathematical principles transcend market evolution** - **Multi-dimensional analysis adapts to new market structures** - **Regime detection automatically adjusts to changing market personalities** - **Component democracy allows for future algorithmic additions** - **Mathematical visualization scales with increasing market complexity** ### Commitment to Excellence The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value. ### Trading with Mathematical Precision In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals. By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics. ### Final Perspective Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure. Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior. Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE. *"In mathematics, you don't understand things. You just get used to them." - John von Neumann* *With the TMAE, mathematical market understanding becomes not just possible, but intuitive.* — Dskyz, Trade with insight. Trade with anticipation.
อินดิเคเตอร์ Pine Script®
โดย DskyzInvestments
17
SIP Evaluator and Screener [Trendoscope®]The SIP Evaluator and Screener is a Pine Script indicator designed for TradingView to calculate and visualize Systematic Investment Plan (SIP) returns across multiple investment instruments. It is tailored for use in TradingView's screener, enabling users to evaluate SIP performance for various assets efficiently. 🎲 How SIP Works A Systematic Investment Plan (SIP) is an investment strategy where a fixed amount is invested at regular intervals (e.g., monthly or weekly) into a financial instrument, such as stocks, mutual funds, or ETFs. The goal is to build wealth over time by leveraging the power of compounding and mitigating the impact of market volatility through disciplined, consistent investing. Here’s a breakdown of how SIPs function: Regular Investments : In an SIP, an investor commits to investing a fixed sum at predefined intervals, regardless of market conditions. This consistency helps inculcate a habit of saving and investing. Cost Averaging : By investing a fixed amount regularly, investors purchase more units when prices are low and fewer units when prices are high. This approach, known as dollar-cost averaging, reduces the average cost per unit over time and mitigates the risk of investing a large amount at a peak price. Compounding Benefits : Returns generated from the invested amount (e.g., capital gains or dividends) are reinvested, leading to exponential growth over the long term. The longer the investment horizon, the greater the potential for compounding to amplify returns. Dividend Reinvestment : In some SIPs, dividends received from the underlying asset can be reinvested to purchase additional units, further enhancing returns. Taxes on dividends, if applicable, may reduce the reinvested amount. Flexibility and Accessibility : SIPs allow investors to start with small amounts, making them accessible to a wide range of individuals. They also offer flexibility in terms of investment frequency and the ability to adjust or pause contributions. In the context of the SIP Evaluator and Screener , the script simulates an SIP by calculating the number of units purchased with each fixed investment, factoring in commissions, dividends, taxes and the chosen price reference (e.g., open, close, or average prices). It tracks the cumulative investment, equity value, and dividends over time, providing a clear picture of how an SIP would perform for a given instrument. This helps users understand the impact of regular investing and make informed decisions when comparing different assets in TradingView’s screener. It offers insights into key metrics such as total invested amount, dividends received, equity value, and the number of installments, making it a valuable resource for investors and traders interested in understanding long-term investment outcomes. 🎲 Key Features Customizable Investment Parameters: Users can define the recurring investment amount, price reference (e.g., open, close, HL2, HLC3, OHLC4), and whether fractional quantities are allowed. Commission Handling: Supports both fixed and percentage-based commission types, adjusting calculations accordingly. Dividend Reinvestment: Optionally reinvests dividends after a user-specified period, with the ability to apply tax on dividends. Time-Bound Analysis: Allows users to set a start year for the analysis, enabling historical performance evaluation. Flexible Dividend Periods: Dividends can be evaluated based on bars, days, weeks, or months. Visual Outputs: Plots key metrics like total invested amount, dividends, equity value, and remainder, with customizable display options for clarity in the data window and chart. 🎲 Using the script as an indicator on Tradingview Supercharts In order to use the indicator on charts, do the following. Load the instrument of your choice - Preferably a stable stocks, ETFs. Chose monthly timeframe as lower timeframes are insignificant in this type of investment strategy Load the indicator SIP Evaluator and Screener and set the input parameters as per your preference. Indicator plots, investment value, dividends and equity on the chart. 🎲 Visualizations Installments : Displays the number of SIP installments (gray line, visible in the data window). Invested Amount : Shows the cumulative amount invested, excluding reinvested dividends (blue area plot). Dividends : Tracks total dividends received (green area plot). Equity : Represents the current market value of the investment based on the closing price (purple area plot). Remainder : Indicates any uninvested cash after each installment (gray line, visible in the data window). 🎲 Deep dive into the settings The SIP Evaluator and Screener offers a range of customizable settings to tailor the Systematic Investment Plan (SIP) simulation to your preferences. Below is an explanation of each setting, its purpose, and how it impacts the analysis: 🎯 Duration Start Year (Default: 2020) : Specifies the year from which the SIP calculations begin. When Start Year is enabled via the timebound option, the script only considers data from the specified year onward. This is useful for analyzing historical SIP performance over a defined period. If disabled, the script uses all available data. Timebound (Default: False) : A toggle to enable or disable the Start Year restriction. When set to False, the SIP calculation starts from the earliest available data for the instrument. 🎯 Investment Recurring Investment (Default: 1000.0) : The fixed amount invested in each SIP installment (e.g., $1000 per period). This represents the regular contribution to the SIP and directly influences the total invested amount and quantity purchased. Allow Fractional Qty (Default: True) : When enabled, the script allows the purchase of fractional units (e.g., 2.35 shares). If disabled, only whole units are purchased (e.g., 2 shares), with any remaining funds carried forward as Remainder. This setting impacts the precision of investment allocation. Price Reference (Default: OPEN): Determines the price used for purchasing units in each SIP installment. Options include: OPEN : Uses the opening price of the bar. CLOSE : Uses the closing price of the bar. HL2 : Uses the average of the high and low prices. HLC3 : Uses the average of the high, low, and close prices. OHLC4 : Uses the average of the open, high, low, and close prices. This setting affects the cost basis of each purchase and, consequently, the total quantity and equity value. 🎯 Commission Commission (Default: 3) : The commission charged per SIP installment, expressed as either a fixed amount (e.g., $3) or a percentage (e.g., 3% of the investment). This reduces the amount available for purchasing units. Commission Type (Default: Fixed) : Specifies how the commission is calculated: Fixed ($) : A flat fee is deducted per installment (e.g., $3). Percentage (%) : A percentage of the investment amount is deducted as commission (e.g., 3% of $1000 = $30). This setting affects the net amount invested and the overall cost of the SIP. 🎯 Dividends Apply Tax On Dividends (Default: False) : When enabled, a tax is applied to dividends before they are reinvested or recorded. The tax rate is set via the Dividend Tax setting. Dividend Tax (Default: 47) : The percentage of tax deducted from dividends if Apply Tax On Dividends is enabled (e.g., 47% tax reduces a $100 dividend to $53). This reduces the amount available for reinvestment or accumulation. Reinvest Dividends After (Default: True, 2) : When enabled, dividends received are reinvested to purchase additional units after a specified period (e.g., 2 units of time, defined by Dividends Availability). If disabled, dividends are tracked but not reinvested. Reinvestment increases the total quantity and equity over time. Dividends Availability (Default: Bars) : Defines the time unit for evaluating when dividends are available for reinvestment. Options include: Bars : Based on the number of chart bars. Weeks : Based on weeks. Months : Based on months (approximated as 30.5 days). This setting determines the timing of dividend reinvestment relative to the Reinvest Dividends After period. 🎯 How Settings Interact These settings work together to simulate a realistic SIP. For example, a $1000 recurring investment with a 3% commission and fractional quantities enabled will calculate the number of units purchased at the chosen price reference after deducting the commission. If dividends are reinvested after 2 months with a 47% tax, the script fetches dividend data, applies the tax, and adds the net dividend to the investment amount for that period. The Start Year and Timebound settings ensure the analysis aligns with the desired timeframe, while the Dividends Availability setting fine-tunes dividend reinvestment timing. By adjusting these settings, users can model different SIP scenarios, compare performance across instruments in TradingView’s screener, and gain insights into how commissions, dividends, and price references impact long-term returns. 🎲 Using the script with Pine Screener The main purpose of developing this script is to use it with Tradingview Pine Screener so that multiple ETFs/Funds can be compared. In order to use this as a screener, the following things needs to be done. Add SIP Evaluator and Screener to your favourites (Required for it to be added in pine screener) Create a watch list containing required instruments to compare Open pine screener from Tradingview main menu Products -> Screeners -> Pine or simply load the URL - www.tradingview.com Select the watchlist created from Watchlist dropdown. Chose the SIP Evaluator and Screener from the "Choose Indicator" dropdown Set timeframe to 1 month and update settings as required. Press scan to display collected data on the screener. 🎲 Use Case This indicator is ideal for educational purposes, allowing users to experiment with SIP strategies across different instruments. It can be applied in TradingView’s screener to compare SIP performance for stocks, ETFs, or other assets, helping users understand how factors like commissions, dividends, and price references impact returns over time.
อินดิเคเตอร์ Pine Script®
โดย Trendoscope
2
Wavelet-Trend ML Integration [Alpha Extract]Alpha-Extract Volatility Quality Indicator The Alpha-Extract Volatility Quality (AVQ) Indicator provides traders with deep insights into market volatility by measuring the directional strength of price movements. This sophisticated momentum-based tool helps identify overbought and oversold conditions, offering actionable buy and sell signals based on volatility trends and standard deviation bands. 🔶 CALCULATION The indicator processes volatility quality data through a series of analytical steps: Bar Range Calculation: Measures true range (TR) to capture price volatility. Directional Weighting: Applies directional bias (positive for bullish candles, negative for bearish) to the true range. VQI Computation: Uses an exponential moving average (EMA) of weighted volatility to derive the Volatility Quality Index (VQI). Smoothing: Applies an additional EMA to smooth the VQI for clearer signals. Normalization: Optionally normalizes VQI to a -100/+100 scale based on historical highs and lows. Standard Deviation Bands: Calculates three upper and lower bands using standard deviation multipliers for volatility thresholds. Signal Generation: Produces overbought/oversold signals when VQI reaches extreme levels (±200 in normalized mode). Formula: Bar Range = True Range (TR) Weighted Volatility = Bar Range × (Close > Open ? 1 : Close < Open ? -1 : 0) VQI Raw = EMA(Weighted Volatility, VQI Length) VQI Smoothed = EMA(VQI Raw, Smoothing Length) VQI Normalized = ((VQI Smoothed - Lowest VQI) / (Highest VQI - Lowest VQI) - 0.5) × 200 Upper Band N = VQI Smoothed + (StdDev(VQI Smoothed, VQI Length) × Multiplier N) Lower Band N = VQI Smoothed - (StdDev(VQI Smoothed, VQI Length) × Multiplier N) 🔶 DETAILS Visual Features: VQI Plot: Displays VQI as a line or histogram (lime for positive, red for negative). Standard Deviation Bands: Plots three upper and lower bands (teal for upper, grayscale for lower) to indicate volatility thresholds. Reference Levels: Horizontal lines at 0 (neutral), +100, and -100 (in normalized mode) for context. Zone Highlighting: Overbought (⋎ above bars) and oversold (⋏ below bars) signals for extreme VQI levels (±200 in normalized mode). Candle Coloring: Optional candle overlay colored by VQI direction (lime for positive, red for negative). Interpretation: VQI ≥ 200 (Normalized): Overbought condition, strong sell signal. VQI 100–200: High volatility, potential selling opportunity. VQI 0–100: Neutral bullish momentum. VQI 0 to -100: Neutral bearish momentum. VQI -100 to -200: High volatility, strong bearish momentum. VQI ≤ -200 (Normalized): Oversold condition, strong buy signal. 🔶 EXAMPLES Overbought Signal Detection: When VQI exceeds 200 (normalized), the indicator flags potential market tops with a red ⋎ symbol. Example: During strong uptrends, VQI reaching 200 has historically preceded corrections, allowing traders to secure profits. Oversold Signal Detection: When VQI falls below -200 (normalized), a lime ⋏ symbol highlights potential buying opportunities. Example: In bearish markets, VQI dropping below -200 has marked reversal points for profitable long entries. Volatility Trend Tracking: The VQI plot and bands help traders visualize shifts in market momentum. Example: A rising VQI crossing above zero with widening bands indicates strengthening bullish momentum, guiding traders to hold or enter long positions. Dynamic Support/Resistance: Standard deviation bands act as dynamic volatility thresholds during price movements. Example: Price reversals often occur near the third standard deviation bands, providing reliable entry/exit points during volatile periods. 🔶 SETTINGS Customization Options: VQI Length: Adjust the EMA period for VQI calculation (default: 14, range: 1–50). Smoothing Length: Set the EMA period for smoothing (default: 5, range: 1–50). Standard Deviation Multipliers: Customize multipliers for bands (defaults: 1.0, 2.0, 3.0). Normalization: Toggle normalization to -100/+100 scale and adjust lookback period (default: 200, min: 50). Display Style: Switch between line or histogram plot for VQI. Candle Overlay: Enable/disable VQI-colored candles (lime for positive, red for negative). The Alpha-Extract Volatility Quality Indicator empowers traders with a robust tool to navigate market volatility. By combining directional price range analysis with smoothed volatility metrics, it identifies overbought and oversold conditions, offering clear buy and sell signals. The customizable standard deviation bands and optional normalization provide precise context for market conditions, enabling traders to make informed decisions across various market cycles.
อินดิเคเตอร์ Pine Script®
โดย AlphaExtract
6
Bitcoin Power Law Clock [LuxAlgo]The Bitcoin Power Law Clock is a unique representation of Bitcoin prices proposed by famous Bitcoin analyst and modeler Giovanni Santostasi. It displays a clock-like figure with the Bitcoin price and average lines as spirals, as well as the 12, 3, 6, and 9 hour marks as key points in the cycle. 🔶 USAGE Giovanni Santostasi, Ph.D., is the creator and discoverer of the Bitcoin Power Law Theory. He is passionate about Bitcoin and has 12 years of experience analyzing it and creating price models. As we can see in the above chart, the tool is super intuitive. It displays a clock-like figure with the current Bitcoin price at 10:20 on a 12-hour scale. This tool only works on the 1D INDEX:BTCUSD chart. The ticker and timeframe must be exact to ensure proper functionality. According to the Bitcoin Power Law Theory, the key cycle points are marked at the extremes of the clock: 12, 3, 6, and 9 hours. According to the theory, the current Bitcoin prices are in a frenzied bull market on their way to the top of the cycle. 🔹 Enable/Disable Elements All of the elements on the clock can be disabled. If you disable them all, only an empty space will remain. The different charts above show various combinations. Traders can customize the tool to their needs. 🔹 Auto scale The clock has an auto-scale feature that is enabled by default. Traders can adjust the size of the clock by disabling this feature and setting the size in the settings panel. The image above shows different configurations of this feature. 🔶 SETTINGS 🔹 Price Price: Enable/disable price spiral, select color, and enable/disable curved mode Average: Enable/disable average spiral, select color, and enable/disable curved mode 🔹 Style Auto scale: Enable/disable automatic scaling or set manual fixed scaling for the spirals Lines width: Width of each spiral line Text Size: Select text size for date tags and price scales Prices: Enable/disable price scales on the x-axis Handle: Enable/disable clock handle Halvings: Enable/disable Halvings Hours: Enable/disable hours and key cycle points 🔹 Time & Price Dashboard Show Time & Price: Enable/disable time & price dashboard Location: Dashboard location Size: Dashboard size
อินดิเคเตอร์ Pine Script®
โดย LuxAlgo
12
Advanced Fed Decision Forecast Model (AFDFM)The Advanced Fed Decision Forecast Model (AFDFM) represents a novel quantitative framework for predicting Federal Reserve monetary policy decisions through multi-factor fundamental analysis. This model synthesizes established monetary policy rules with real-time economic indicators to generate probabilistic forecasts of Federal Open Market Committee (FOMC) decisions. Building upon seminal work by Taylor (1993) and incorporating recent advances in data-dependent monetary policy analysis, the AFDFM provides institutional-grade decision support for monetary policy analysis. ## 1. Introduction Central bank communication and policy predictability have become increasingly important in modern monetary economics (Blinder et al., 2008). The Federal Reserve's dual mandate of price stability and maximum employment, coupled with evolving economic conditions, creates complex decision-making environments that traditional models struggle to capture comprehensively (Yellen, 2017). The AFDFM addresses this challenge by implementing a multi-dimensional approach that combines: - Classical monetary policy rules (Taylor Rule framework) - Real-time macroeconomic indicators from FRED database - Financial market conditions and term structure analysis - Labor market dynamics and inflation expectations - Regime-dependent parameter adjustments This methodology builds upon extensive academic literature while incorporating practical insights from Federal Reserve communications and FOMC meeting minutes. ## 2. Literature Review and Theoretical Foundation ### 2.1 Taylor Rule Framework The foundational work of Taylor (1993) established the empirical relationship between federal funds rate decisions and economic fundamentals: rt = r + πt + α(πt - π) + β(yt - y) Where: - rt = nominal federal funds rate - r = equilibrium real interest rate - πt = inflation rate - π = inflation target - yt - y = output gap - α, β = policy response coefficients Extensive empirical validation has demonstrated the Taylor Rule's explanatory power across different monetary policy regimes (Clarida et al., 1999; Orphanides, 2003). Recent research by Bernanke (2015) emphasizes the rule's continued relevance while acknowledging the need for dynamic adjustments based on financial conditions. ### 2.2 Data-Dependent Monetary Policy The evolution toward data-dependent monetary policy, as articulated by Fed Chair Powell (2024), requires sophisticated frameworks that can process multiple economic indicators simultaneously. Clarida (2019) demonstrates that modern monetary policy transcends simple rules, incorporating forward-looking assessments of economic conditions. ### 2.3 Financial Conditions and Monetary Transmission The Chicago Fed's National Financial Conditions Index (NFCI) research demonstrates the critical role of financial conditions in monetary policy transmission (Brave & Butters, 2011). Goldman Sachs Financial Conditions Index studies similarly show how credit markets, term structure, and volatility measures influence Fed decision-making (Hatzius et al., 2010). ### 2.4 Labor Market Indicators The dual mandate framework requires sophisticated analysis of labor market conditions beyond simple unemployment rates. Daly et al. (2012) demonstrate the importance of job openings data (JOLTS) and wage growth indicators in Fed communications. Recent research by Aaronson et al. (2019) shows how the Beveridge curve relationship influences FOMC assessments. ## 3. Methodology ### 3.1 Model Architecture The AFDFM employs a six-component scoring system that aggregates fundamental indicators into a composite Fed decision index: #### Component 1: Taylor Rule Analysis (Weight: 25%) Implements real-time Taylor Rule calculation using FRED data: - Core PCE inflation (Fed's preferred measure) - Unemployment gap proxy for output gap - Dynamic neutral rate estimation - Regime-dependent parameter adjustments #### Component 2: Employment Conditions (Weight: 20%) Multi-dimensional labor market assessment: - Unemployment gap relative to NAIRU estimates - JOLTS job openings momentum - Average hourly earnings growth - Beveridge curve position analysis #### Component 3: Financial Conditions (Weight: 18%) Comprehensive financial market evaluation: - Chicago Fed NFCI real-time data - Yield curve shape and term structure - Credit growth and lending conditions - Market volatility and risk premia #### Component 4: Inflation Expectations (Weight: 15%) Forward-looking inflation analysis: - TIPS breakeven inflation rates (5Y, 10Y) - Market-based inflation expectations - Inflation momentum and persistence measures - Phillips curve relationship dynamics #### Component 5: Growth Momentum (Weight: 12%) Real economic activity assessment: - Real GDP growth trends - Economic momentum indicators - Business cycle position analysis - Sectoral growth distribution #### Component 6: Liquidity Conditions (Weight: 10%) Monetary aggregates and credit analysis: - M2 money supply growth - Commercial and industrial lending - Bank lending standards surveys - Quantitative easing effects assessment ### 3.2 Normalization and Scaling Each component undergoes robust statistical normalization using rolling z-score methodology: Zi,t = (Xi,t - μi,t-n) / σi,t-n Where: - Xi,t = raw indicator value - μi,t-n = rolling mean over n periods - σi,t-n = rolling standard deviation over n periods - Z-scores bounded at ±3 to prevent outlier distortion ### 3.3 Regime Detection and Adaptation The model incorporates dynamic regime detection based on: - Policy volatility measures - Market stress indicators (VIX-based) - Fed communication tone analysis - Crisis sensitivity parameters Regime classifications: 1. Crisis: Emergency policy measures likely 2. Tightening: Restrictive monetary policy cycle 3. Easing: Accommodative monetary policy cycle 4. Neutral: Stable policy maintenance ### 3.4 Composite Index Construction The final AFDFM index combines weighted components: AFDFMt = Σ wi × Zi,t × Rt Where: - wi = component weights (research-calibrated) - Zi,t = normalized component scores - Rt = regime multiplier (1.0-1.5) Index scaled to range for intuitive interpretation. ### 3.5 Decision Probability Calculation Fed decision probabilities derived through empirical mapping: P(Cut) = max(0, (Tdovish - AFDFMt) / |Tdovish| × 100) P(Hike) = max(0, (AFDFMt - Thawkish) / Thawkish × 100) P(Hold) = 100 - |AFDFMt| × 15 Where Thawkish = +2.0 and Tdovish = -2.0 (empirically calibrated thresholds). ## 4. Data Sources and Real-Time Implementation ### 4.1 FRED Database Integration - Core PCE Price Index (CPILFESL): Monthly, seasonally adjusted - Unemployment Rate (UNRATE): Monthly, seasonally adjusted - Real GDP (GDPC1): Quarterly, seasonally adjusted annual rate - Federal Funds Rate (FEDFUNDS): Monthly average - Treasury Yields (GS2, GS10): Daily constant maturity - TIPS Breakeven Rates (T5YIE, T10YIE): Daily market data ### 4.2 High-Frequency Financial Data - Chicago Fed NFCI: Weekly financial conditions - JOLTS Job Openings (JTSJOL): Monthly labor market data - Average Hourly Earnings (AHETPI): Monthly wage data - M2 Money Supply (M2SL): Monthly monetary aggregates - Commercial Loans (BUSLOANS): Weekly credit data ### 4.3 Market-Based Indicators - VIX Index: Real-time volatility measure - S&P; 500: Market sentiment proxy - DXY Index: Dollar strength indicator ## 5. Model Validation and Performance ### 5.1 Historical Backtesting (2017-2024) Comprehensive backtesting across multiple Fed policy cycles demonstrates: - Signal Accuracy: 78% correct directional predictions - Timing Precision: 2.3 meetings average lead time - Crisis Detection: 100% accuracy in identifying emergency measures - False Signal Rate: 12% (within acceptable research parameters) ### 5.2 Regime-Specific Performance Tightening Cycles (2017-2018, 2022-2023): - Hawkish signal accuracy: 82% - Average prediction lead: 1.8 meetings - False positive rate: 8% Easing Cycles (2019, 2020, 2024): - Dovish signal accuracy: 85% - Average prediction lead: 2.1 meetings - Crisis mode detection: 100% Neutral Periods: - Hold prediction accuracy: 73% - Regime stability detection: 89% ### 5.3 Comparative Analysis AFDFM performance compared to alternative methods: - Fed Funds Futures: Similar accuracy, lower lead time - Economic Surveys: Higher accuracy, comparable timing - Simple Taylor Rule: Lower accuracy, insufficient complexity - Market-Based Models: Similar performance, higher volatility ## 6. Practical Applications and Use Cases ### 6.1 Institutional Investment Management - Fixed Income Portfolio Positioning: Duration and curve strategies - Currency Trading: Dollar-based carry trade optimization - Risk Management: Interest rate exposure hedging - Asset Allocation: Regime-based tactical allocation ### 6.2 Corporate Treasury Management - Debt Issuance Timing: Optimal financing windows - Interest Rate Hedging: Derivative strategy implementation - Cash Management: Short-term investment decisions - Capital Structure Planning: Long-term financing optimization ### 6.3 Academic Research Applications - Monetary Policy Analysis: Fed behavior studies - Market Efficiency Research: Information incorporation speed - Economic Forecasting: Multi-factor model validation - Policy Impact Assessment: Transmission mechanism analysis ## 7. Model Limitations and Risk Factors ### 7.1 Data Dependency - Revision Risk: Economic data subject to subsequent revisions - Availability Lag: Some indicators released with delays - Quality Variations: Market disruptions affect data reliability - Structural Breaks: Economic relationship changes over time ### 7.2 Model Assumptions - Linear Relationships: Complex non-linear dynamics simplified - Parameter Stability: Component weights may require recalibration - Regime Classification: Subjective threshold determinations - Market Efficiency: Assumes rational information processing ### 7.3 Implementation Risks - Technology Dependence: Real-time data feed requirements - Complexity Management: Multi-component coordination challenges - User Interpretation: Requires sophisticated economic understanding - Regulatory Changes: Fed framework evolution may require updates ## 8. Future Research Directions ### 8.1 Machine Learning Integration - Neural Network Enhancement: Deep learning pattern recognition - Natural Language Processing: Fed communication sentiment analysis - Ensemble Methods: Multiple model combination strategies - Adaptive Learning: Dynamic parameter optimization ### 8.2 International Expansion - Multi-Central Bank Models: ECB, BOJ, BOE integration - Cross-Border Spillovers: International policy coordination - Currency Impact Analysis: Global monetary policy effects - Emerging Market Extensions: Developing economy applications ### 8.3 Alternative Data Sources - Satellite Economic Data: Real-time activity measurement - Social Media Sentiment: Public opinion incorporation - Corporate Earnings Calls: Forward-looking indicator extraction - High-Frequency Transaction Data: Market microstructure analysis ## References Aaronson, S., Daly, M. C., Wascher, W. L., & Wilcox, D. W. (2019). Okun revisited: Who benefits most from a strong economy? Brookings Papers on Economic Activity, 2019(1), 333-404. Bernanke, B. S. (2015). The Taylor rule: A benchmark for monetary policy? Brookings Institution Blog. Retrieved from www.brookings.edu Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910-945. Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43. Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661-1707. Clarida, R. H. (2019). The Federal Reserve's monetary policy response to COVID-19. Brookings Papers on Economic Activity, 2020(2), 1-52. Clarida, R. H. (2025). Modern monetary policy rules and Fed decision-making. American Economic Review, 115(2), 445-478. Daly, M. C., Hobijn, B., Şahin, A., & Valletta, R. G. (2012). A search and matching approach to labor markets: Did the natural rate of unemployment rise? Journal of Economic Perspectives, 26(3), 3-26. Federal Reserve. (2024). Monetary Policy Report. Washington, DC: Board of Governors of the Federal Reserve System. Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. National Bureau of Economic Research Working Paper, No. 16150. Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of Monetary Economics, 50(5), 983-1022. Powell, J. H. (2024). Data-dependent monetary policy in practice. Federal Reserve Board Speech. Jackson Hole Economic Symposium, Federal Reserve Bank of Kansas City. Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214. Yellen, J. L. (2017). The goals of monetary policy and how we pursue them. Federal Reserve Board Speech. University of California, Berkeley. --- Disclaimer: This model is designed for educational and research purposes only. Past performance does not guarantee future results. The academic research cited provides theoretical foundation but does not constitute investment advice. Federal Reserve policy decisions involve complex considerations beyond the scope of any quantitative model. Citation: EdgeTools Research Team. (2025). Advanced Fed Decision Forecast Model (AFDFM) - Scientific Documentation. EdgeTools Quantitative Research Series
อินดิเคเตอร์ Pine Script®
โดย EdgeTools
5
Timeframe LoopThe Timeframe Loop publication aims to visualize intrabar price progression in a new, different way. 🔶 CONCEPTS and USAGE I got inspiration from the Pressure/Volume loop, which is used in Mechanical Ventilation with Critical Care patients to visualize pressure/volume evolution during inhalation/exhalation. The main idea is that intrabar prices are visualized by a loop, going to the right during the first half and returning to the left towards its closing point. Here, the main chart timeframe (CTF) is 4 hours, and we see the movements of eight 30-minute lower timeframe (LTF) periods, highlighted by four yellow dots/lines (first 2 hours -> "Right") and four blue dots/lines (last 2 hours <- "Left"): 🔹 BTF If "Show Lowest TF" is enabled, the LTF is split into another lower TF (BTF - "Base TF"); in this case, the 30-minute LTF is split into 10 parts of 3 minutes (BTF): Enabling "Loop Lowest TF" will enable the BTF to react similarly to the largest loop; from halfway, it will return to its startpoint: Here is a more detailed example: 🔹 Mini-Candles The included option "Mini-Candles" will bring even more detail, showing the LTF as Japanese candlesticks with user-defined colors and adjustable body width; in this example, the mini-candles associated with the first half (yellow lines/dots) are green/red, while blue/fuchsia in the second half (blue lines/dots): CTF 10 minutes, LTF 1 minute, BTF 5 seconds One can see the detailed intrabar price progression in one glance. CTF 5 minutes, LTF 1 minute, BTF 5 seconds If the LTF/BTF ratio, divided by two, results in a non-integer number, the right side will be a vertical line instead of just a turning point. In that case, the smaller, most right blue loop will be situated at the right of that line. 10 minutes / 1 minute = 10 -> 10 / 2 =    5 parts    5 minutes / 1 minute =   5 ->   5 / 2 = 2.5 parts 🔶 SETTINGS 🔹 Timeframes Lower Timeframe 1 Lower Timeframe 2 No need to worry about the order of both timeframes; BTF will be the lowest TF of the 2, LTF the highest; both have to be lower than the main chart TF (CTF); otherwise, it will result in the error: "`Lower Timeframes` should be lower than current chart timeframe". The ratio LTF / BTF should be equal or higher than 2; otherwise, this error will show: "`Lower Timeframe` should minimally be twice the `Base (smallest) Timeframe`" Lastly, the ratio CTF / BTF should be lower than 500; otherwise, this error will pop up: "`Current Chart timeframe` / `Lower Timeframe` should be less than 500." I have tried to capture runtime errors as best I could. If one should be triggered (red exclamation mark next to the title), it is best to increase the lowest TF. 🔹 Options Show Lowest TF: Show BTF progression. Loop Lowest TF: Enabling will let the BTF line return halfway. Show Mini-Candles Show Steps "Show Steps" can be useful to see how the script works, where the location of the current price is compared against the position of the left (L) and right (R) labels: 🔹 Style
อินดิเคเตอร์ Pine Script®
โดย fikira
5