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Time & Sales (Tape) - TableWhat it is A compact Time & Sales (tape) visualizer rendered as a table on your chart. It reconstructs prints from a lower timeframe (e.g., 1-second) or, optionally, streams them in Live Tick mode. Each row shows Date, Time, Side (▲ up / ▼ down), Last, and Volume (or Price Volume), with a dynamic scale bar summarizing buy vs sell activity across the visible rows. Note for newcomers This is not a “go-to” oscillator or a signal generator. It’s an old-school tape reader that shows the market’s pulse in real time—prints, pace, and the balance between buying and selling. It’s descriptive, not predictive. Use it to observe flow and context on top of your existing method (price/volume/structure), not as a stand-alone “magic wand.” What it shows Date: user-selectable format (YYYY-MM-DD, DD-MM-YYYY, MM-DD, DD-MM). Time: formatted in the selected timezone (New York - UTC-5/-4 by default). Side: ▲ when last price increased vs the previous print, ▼ when it decreased. Last: last traded price (formatted to symbol tick size). Volume: either raw Volume or Price Volume (= HLC3 × Volume). Dynamic Scale Bar: stacked “blocks” showing the proportion of buy vs sell volume over the current table window. How it works When Implement By Timeframe Aggregates prints from a user-selected lower timeframe (using request.security_lower_tf). If the chosen timeframe is invalid (i.e., higher than the chart), it automatically falls back to 1S. The table is rebuilt on the last bar update. When Implement By Live Tick Streams prints only while the chart is in real time (using barstate.isrealtime). This mode does not backfill historical ticks; it appends as new ticks arrive. Inputs Timezone: maps timestamps to your chosen session/region. Date Format: YYYY-MM-DD, DD-MM-YYYY, MM-DD, or DD-MM. Table Location / Size: choose where the table sits and its font size. Show Dynamic Volume Scale Bar: toggle the header scale bar. Implement By: Timeframe (lower-TF reconstruction) or Live Tick. Timeframe: lower-TF source (e.g., 1S). Falls back automatically if invalid. Length (№ of Rows): number of rows displayed. Size Type Volume = raw exchange volume. Price Volume = HLC3 × Volume (emphasizes prints at higher prices). Repainting Does not repaint historical rows. Timeframe mode reconstructs from completed lower-TF bars and draws a fixed history window. Live Tick mode only adds rows in real time; no historical backfill. The table itself is redrawn on the last bar update for layout, but previously printed rows aren’t revised with future data. Notes & limitations Lower-TF aggregation depends on data availability for your symbol/plan. If you request very granular timeframes far back in history, data may be limited by platform constraints. For performance on long histories, reduce Length (№ of Rows) or use Timeframe mode with 1S/5S as appropriate. “Price Volume” is a derived metric, not exchange-reported volume. Use it if you want to weight volume by price (HLC3). Use case This module provides a fast, at-a-glance tape read directly on the chart: spot bursts of buys vs sells, see the pace and size of flow, and reference exact date/time and last price without leaving your layout. It’s designed as a supporting tool for discretionary execution and for documenting trade ideas that reference tape behavior. Credits Timezone helper: n00btraders/Timezone (imported library). Built for Pine v6. Disclosure This script is an educational visualization. It does not place trades and is not financial or investment advice.
Pine Script® göstergesi
The_Smart_Tiger tarafından
10
Angle Market Structure [BigBeluga]🔵 OVERVIEW Angle Market Structure is a smart pivot-based tool that dynamically adapts to price action by accelerating breakout and breakdown detection. It draws market structure levels based on pivot highs/lows and gradually adjusts those levels closer to price using an angle threshold. Upon breakout, the indicator projects deviation zones with labeled levels (+1, +2, +3 or −1, −2, −3) to track price extension beyond structure. 🔵 CONCEPTS Adaptive Market Structure: Uses pivots to define structure levels, which dynamically angle closer to price over time to capture breakouts sooner. Breakout Acceleration: Pivot high levels decrease and pivot low levels increase each bar using a user-defined angle (based on ATR), improving reactivity. Deviation Zones: Once a breakout or breakdown occurs, 3 deviation levels are projected to show how far price extends beyond the breakout point. Count Labels: Each successful structure break is numbered sequentially, giving traders insight into momentum and trend persistence. Visual Clarity: The script uses colored pivot points, trend lines, and extension labels for easy structural interpretation. 🔵 FEATURES Calculates pivot highs and lows using a customizable length. Applies an angle modifier (ATR-based) to gradually pull levels closer to price. Plots breakout and breakdown lines in distinct colors with automatic extension. Shows deviation zones (+1, +2, +3 or −1, −2, −3) after breakout with customizable size. Color-coded labels for trend break count (bullish or bearish). Dynamic label sizing and theme-aware colors. Smart label positioning to avoid chart clutter. Built-in limit for deviation zones to maintain clarity and performance. 🔵 HOW TO USE Use pivot-based market structure to identify breakout and breakdown zones. Watch for crossover (up) or crossunder (down) events as trend continuation or reversal signals. Observe +1/+2/+3 or -1/-2/-3 levels for overextension opportunities or trailing stop ideas. Use breakout count as a proxy for trend strength—multiple counts suggest momentum. Combine with volume or order flow tools for higher confidence entries at breakout points. Adjust the angle setting to fine-tune sensitivity based on market volatility. 🔵 CONCLUSION Angle Market Structure enhances traditional pivot-based analysis by introducing breakout acceleration and structured deviation tracking. It’s a powerful tool for traders seeking a cleaner, faster read on market structure and momentum strength—especially during impulsive price moves or structural transitions.
Pine Script® göstergesi
BigBeluga tarafından
12
Dynamic Swing Anchored VWAP (Zeiierman)█ Overview Dynamic Swing Anchored VWAP (Zeiierman) is a price–volume tool that anchors VWAP at fresh swing highs/lows and then adapts its responsiveness as conditions change. Instead of one static VWAP that drifts away over time, this indicator re-anchors at meaningful structure points (swings). It computes a decayed, volume-weighted average that can speed up in volatile markets and slow down during quiet periods. Blending swing structure with an adaptive VWAP engine creates a fair-value path that stays aligned with current price behavior, making retests, pullbacks, and mean reversion opportunities easier to spot and trade. █ How It Works ⚪ Swing Anchor Engine The script scans for swing highs/lows using your Swing Period. When market direction flips (new pivot confirmed), the indicator anchors a new VWAP at that pivot and starts tracking from there. ⚪ Adaptive VWAP Core From each anchor , VWAP is computed using a decay model (recent price×volume matters more; older data matters less). Adaptive Price Tracking lets you set the base responsiveness in “bars.” Lower = more reactive, higher = smoother. Volatility Adjustment (ATR vs Avg ATR) can automatically speed up the VWAP during spikes and slow it during compression, so the line stays relevant to live conditions. █ Why This Adaptive Approach Beats a Simple VWAP Standard VWAP is cumulative from the anchor point. As time passes and volume accumulates, it often drifts far from current price, especially in prolonged trends or multi-session moves. That drift makes retests rare and unreliable. Dynamic Swing Anchored VWAP solves this in two ways: ⚪ Event-Driven Anchoring (Swings): By restarting at fresh swing highs/lows, the VWAP reference reflects today’s structure. You get frequent, meaningful retests because the anchor stays near the action. ⚪ Adaptive Responsiveness (Volatility-Aware): Markets don’t move at one speed. When volatility expands, a fixed VWAP lags; when volatility contracts, it can overreact to noise. Here, the “tracking speed” can auto-adjust using ATR vs its average. High Volatility → faster tracking: VWAP hugs price more tightly, preserving retest relevance. Low Volatility → smoother tracking: VWAP filters chop and stays stable. Result: A VWAP that follows price more accurately, creating plenty of credible retest opportunities and more trustworthy mean-reversion/continuation reads than a simple, ever-growing VWAP. █ How to Use ⚪ S wing-Aware Fair Value Use the VWAP as a dynamic fair-value guide that restarts at key structural pivots. Pullbacks to the VWAP after impulsive moves often provide retest entries. ⚪ Trend Trading In trends, the adaptive VWAP will ride closer to price, offering continuation pullbacks. █ Settings Swing Period: Number of bars to confirm swing highs/lows. Larger = bigger, cleaner pivots (slower); smaller = more frequent pivots (noisier). Adaptive Price Tracking: Sets the base reaction speed (in bars). Lower = faster, tighter to price; higher = smoother, slower. Adapt APT by ATR ratio: When ON, the tracking speed auto-adjusts with market volatility (ATR vs its own average). High vol → faster; low vol → calmer. Volatility Bias: Controls how strongly volatility affects the speed. >1 = stronger effect; <1 = lighter touch. ----------------- 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.
Pine Script® göstergesi
Zeiierman tarafından
48
Trading Holidays and Expiry CalendarTrading Holiday and Expiry Calendar This indicator displays calendar for current and next 2 months. Calendar marks ‘CRITICAL DAYS’ on the calendar. ‘CRITICAL DAYS’: Trading Days Trading Holidays Weekends Expiry Days Out of these ‘Expiry Days’ are marked based on User input and rest of the days are derived and marked automatically. Why this indicator: Most of the Pine Script developers find it tedious (rather difficult or impossible) to find future holidays and expiry days. This indicator exactly does that based on a simple input parameter Use cases: Calendar view of 3 months Trading days along with identification off critical activity days Pine Script developers can extract code/functions from the indicator and use it for building indicators and strategies Chart Snapshot
Pine Script® göstergesi
Sharad_Gaikwad tarafından
3
Drawdown Distribution Analysis (DDA) ACADEMIC FOUNDATION AND RESEARCH BACKGROUND The Drawdown Distribution Analysis indicator implements quantitative risk management principles, drawing upon decades of academic research in portfolio theory, behavioral finance, and statistical risk modeling. This tool provides risk assessment capabilities for traders and portfolio managers seeking to understand their current position within historical drawdown patterns. The theoretical foundation of this indicator rests on modern portfolio theory as established by Markowitz (1952), who introduced the fundamental concepts of risk-return optimization that continue to underpin contemporary portfolio management. Sharpe (1966) later expanded this framework by developing risk-adjusted performance measures, most notably the Sharpe ratio, which remains a cornerstone of performance evaluation in financial markets. The specific focus on drawdown analysis builds upon the work of Chekhlov, Uryasev and Zabarankin (2005), who provided the mathematical framework for incorporating drawdown measures into portfolio optimization. Their research demonstrated that traditional mean-variance optimization often fails to capture the full risk profile of investment strategies, particularly regarding sequential losses. More recent work by Goldberg and Mahmoud (2017) has brought these theoretical concepts into practical application within institutional risk management frameworks. Value at Risk methodology, as comprehensively outlined by Jorion (2007), provides the statistical foundation for the risk measurement components of this indicator. The coherent risk measures framework developed by Artzner et al. (1999) ensures that the risk metrics employed satisfy the mathematical properties required for sound risk management decisions. Additionally, the focus on downside risk follows the framework established by Sortino and Price (1994), while the drawdown-adjusted performance measures implement concepts introduced by Young (1991). MATHEMATICAL METHODOLOGY The core calculation methodology centers on a peak-tracking algorithm that continuously monitors the maximum price level achieved and calculates the percentage decline from this peak. The drawdown at any time t is defined as DD(t) = (P(t) - Peak(t)) / Peak(t) × 100, where P(t) represents the asset price at time t and Peak(t) represents the running maximum price observed up to time t. Statistical distribution analysis forms the analytical backbone of the indicator. The system calculates key percentiles using the ta.percentile_nearest_rank() function to establish the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of the historical drawdown distribution. This approach provides a complete picture of how the current drawdown compares to historical patterns. Statistical significance assessment employs standard deviation bands at one, two, and three standard deviations from the mean, following the conventional approach where the upper band equals μ + nσ and the lower band equals μ - nσ. The Z-score calculation, defined as Z = (DD - μ) / σ, enables the identification of statistically extreme events, with thresholds set at |Z| > 2.5 for extreme drawdowns and |Z| > 3.0 for severe drawdowns, corresponding to confidence levels exceeding 99.4% and 99.7% respectively. ADVANCED RISK METRICS The indicator incorporates several risk-adjusted performance measures that extend beyond basic drawdown analysis. The Sharpe ratio calculation follows the standard formula Sharpe = (R - Rf) / σ, where R represents the annualized return, Rf represents the risk-free rate, and σ represents the annualized volatility. The system supports dynamic sourcing of the risk-free rate from the US 10-year Treasury yield or allows for manual specification. The Sortino ratio addresses the limitation of the Sharpe ratio by focusing exclusively on downside risk, calculated as Sortino = (R - Rf) / σd, where σd represents the downside deviation computed using only negative returns. This measure provides a more accurate assessment of risk-adjusted performance for strategies that exhibit asymmetric return distributions. The Calmar ratio, defined as Annual Return divided by the absolute value of Maximum Drawdown, offers a direct measure of return per unit of drawdown risk. This metric proves particularly valuable for comparing strategies or assets with different risk profiles, as it directly relates performance to the maximum historical loss experienced. Value at Risk calculations provide quantitative estimates of potential losses at specified confidence levels. The 95% VaR corresponds to the 5th percentile of the drawdown distribution, while the 99% VaR corresponds to the 1st percentile. Conditional VaR, also known as Expected Shortfall, estimates the average loss in the worst 5% of scenarios, providing insight into tail risk that standard VaR measures may not capture. To enable fair comparison across assets with different volatility characteristics, the indicator calculates volatility-adjusted drawdowns using the formula Adjusted DD = Raw DD / (Volatility / 20%). This normalization allows for meaningful comparison between high-volatility assets like cryptocurrencies and lower-volatility instruments like government bonds. The Risk Efficiency Score represents a composite measure ranging from 0 to 100 that combines the Sharpe ratio and current percentile rank to provide a single metric for quick asset assessment. Higher scores indicate superior risk-adjusted performance relative to historical patterns. COLOR SCHEMES AND VISUALIZATION The indicator implements eight distinct color themes designed to accommodate different analytical preferences and market contexts. The EdgeTools theme employs a corporate blue palette that matches the design system used throughout the edgetools.org platform, ensuring visual consistency across analytical tools. The Gold theme specifically targets precious metals analysis with warm tones that complement gold chart analysis, while the Quant theme provides a grayscale scheme suitable for analytical environments that prioritize clarity over aesthetic appeal. The Behavioral theme incorporates psychology-based color coding, using green to represent greed-driven market conditions and red to indicate fear-driven environments. Additional themes include Ocean, Fire, Matrix, and Arctic schemes, each designed for specific market conditions or user preferences. All themes function effectively with both dark and light mode trading platforms, ensuring accessibility across different user interface configurations. PRACTICAL APPLICATIONS Asset allocation and portfolio construction represent primary use cases for this analytical framework. When comparing multiple assets such as Bitcoin, gold, and the S&P 500, traders can examine Risk Efficiency Scores to identify instruments offering superior risk-adjusted performance. The 95% VaR provides worst-case scenario comparisons, while volatility-adjusted drawdowns enable fair comparison despite varying volatility profiles. The practical decision framework suggests that assets with Risk Efficiency Scores above 70 may be suitable for aggressive portfolio allocations, scores between 40 and 70 indicate moderate allocation potential, and scores below 40 suggest defensive positioning or avoidance. These thresholds should be adjusted based on individual risk tolerance and market conditions. Risk management and position sizing applications utilize the current percentile rank to guide allocation decisions. When the current drawdown ranks above the 75th percentile of historical data, indicating that current conditions are better than 75% of historical periods, position increases may be warranted. Conversely, when percentile rankings fall below the 25th percentile, indicating elevated risk conditions, position reductions become advisable. Institutional portfolio monitoring applications include hedge fund risk dashboard implementations where multiple strategies can be monitored simultaneously. Sharpe ratio tracking identifies deteriorating risk-adjusted performance across strategies, VaR monitoring ensures portfolios remain within established risk limits, and drawdown duration tracking provides valuable information for investor reporting requirements. Market timing applications combine the statistical analysis with trend identification techniques. Strong buy signals may emerge when risk levels register as "Low" in conjunction with established uptrends, while extreme risk levels combined with downtrends may indicate exit or hedging opportunities. Z-scores exceeding 3.0 often signal statistically oversold conditions that may precede trend reversals. STATISTICAL SIGNIFICANCE AND VALIDATION The indicator provides 95% confidence intervals around current drawdown levels using the standard formula CI = μ ± 1.96σ. This statistical framework enables users to assess whether current conditions fall within normal market variation or represent statistically significant departures from historical patterns. Risk level classification employs a dynamic assessment system based on percentile ranking within the historical distribution. Low risk designation applies when current drawdowns perform better than 50% of historical data, moderate risk encompasses the 25th to 50th percentile range, high risk covers the 10th to 25th percentile range, and extreme risk applies to the worst 10% of historical drawdowns. Sample size considerations play a crucial role in statistical reliability. For daily data, the system requires a minimum of 252 trading days (approximately one year) but performs better with 500 or more observations. Weekly data analysis benefits from at least 104 weeks (two years) of history, while monthly data requires a minimum of 60 months (five years) for reliable statistical inference. IMPLEMENTATION BEST PRACTICES Parameter optimization should consider the specific characteristics of different asset classes. Equity analysis typically benefits from 500-day lookback periods with 21-day smoothing, while cryptocurrency analysis may employ 365-day lookback periods with 14-day smoothing to account for higher volatility patterns. Fixed income analysis often requires longer lookback periods of 756 days with 34-day smoothing to capture the lower volatility environment. Multi-timeframe analysis provides hierarchical risk assessment capabilities. Daily timeframe analysis supports tactical risk management decisions, weekly analysis informs strategic positioning choices, and monthly analysis guides long-term allocation decisions. This hierarchical approach ensures that risk assessment occurs at appropriate temporal scales for different investment objectives. Integration with complementary indicators enhances the analytical framework. Trend indicators such as RSI and moving averages provide directional bias context, volume analysis helps confirm the severity of drawdown conditions, and volatility measures like VIX or ATR assist in market regime identification. ALERT SYSTEM AND AUTOMATION The automated alert system monitors five distinct categories of risk events. Risk level changes trigger notifications when drawdowns move between risk categories, enabling proactive risk management responses. Statistical significance alerts activate when Z-scores exceed established threshold levels of 2.5 or 3.0 standard deviations. New maximum drawdown alerts notify users when historical maximum levels are exceeded, indicating entry into uncharted risk territory. Poor risk efficiency alerts trigger when the composite risk efficiency score falls below 30, suggesting deteriorating risk-adjusted performance. Sharpe ratio decline alerts activate when risk-adjusted performance turns negative, indicating that returns no longer compensate for the risk undertaken. TRADING STRATEGIES Conservative risk parity strategies can be implemented by monitoring Risk Efficiency Scores across a diversified asset portfolio. Monthly rebalancing maintains equal risk contribution from each asset, with allocation reductions triggered when risk levels reach "High" status and complete exits executed when "Extreme" risk levels emerge. This approach typically results in lower overall portfolio volatility, improved risk-adjusted returns, and reduced maximum drawdown periods. Tactical asset rotation strategies compare Risk Efficiency Scores across different asset classes to guide allocation decisions. Assets with scores exceeding 60 receive overweight allocations, while assets scoring below 40 receive underweight positions. Percentile rankings provide timing guidance for allocation adjustments, creating a systematic approach to asset allocation that responds to changing risk-return profiles. Market timing strategies with statistical edges can be constructed by entering positions when Z-scores fall below -2.5, indicating statistically oversold conditions, and scaling out when Z-scores exceed 2.5, suggesting overbought conditions. The 95% VaR serves as a stop-loss reference point, while trend confirmation indicators provide additional validation for position entry and exit decisions. LIMITATIONS AND CONSIDERATIONS Several statistical limitations affect the interpretation and application of these risk measures. Historical bias represents a fundamental challenge, as past drawdown patterns may not accurately predict future risk characteristics, particularly during structural market changes or regime shifts. Sample dependence means that results can be sensitive to the selected lookback period, with shorter periods providing more responsive but potentially less stable estimates. Market regime changes can significantly alter the statistical parameters underlying the analysis. During periods of structural market evolution, historical distributions may provide poor guidance for future expectations. Additionally, many financial assets exhibit return distributions with fat tails that deviate from normal distribution assumptions, potentially leading to underestimation of extreme event probabilities. Practical limitations include execution risk, where theoretical signals may not translate directly into actual trading results due to factors such as slippage, timing delays, and market impact. Liquidity constraints mean that risk metrics assume perfect liquidity, which may not hold during stressed market conditions when risk management becomes most critical. Transaction costs are not incorporated into risk-adjusted return calculations, potentially overstating the attractiveness of strategies that require frequent trading. Behavioral factors represent another limitation, as human psychology may override statistical signals, particularly during periods of extreme market stress when disciplined risk management becomes most challenging. TECHNICAL IMPLEMENTATION Performance optimization ensures reliable operation across different market conditions and timeframes. All technical analysis functions are extracted from conditional statements to maintain Pine Script compliance and ensure consistent execution. Memory efficiency is achieved through optimized variable scoping and array usage, while computational speed benefits from vectorized calculations where possible. Data quality requirements include clean price data without gaps or errors that could distort distribution analysis. Sufficient historical data is essential, with a minimum of 100 bars required and 500 or more preferred for reliable statistical inference. Time alignment across related assets ensures meaningful comparison when conducting multi-asset analysis. The configuration parameters are organized into logical groups to enhance usability. Core settings include the Distribution Analysis Period (100-2000 bars), Drawdown Smoothing Period (1-50 bars), and Price Source selection. Advanced metrics settings control risk-free rate sourcing, either from live market data or fixed rate specification, along with toggles for various risk-adjusted metric calculations. Display options provide flexibility in visual presentation, including color theme selection from eight available schemes, automatic dark mode optimization, and control over table display, position lines, percentile bands, and standard deviation overlays. These options ensure that the indicator can be adapted to different analytical workflows and visual preferences. CONCLUSION The Drawdown Distribution Analysis indicator provides risk management tools for traders seeking to understand their current position within historical risk patterns. By combining established statistical methodology with practical usability features, the tool enables evidence-based risk assessment and portfolio optimization decisions. The implementation draws upon established academic research while providing practical features that address real-world trading requirements. Dynamic risk-free rate integration ensures accurate risk-adjusted performance calculations, while multiple color schemes accommodate different analytical preferences and use cases. Academic compliance is maintained through transparent methodology and acknowledgment of limitations. The tool implements peer-reviewed statistical techniques while clearly communicating the constraints and assumptions underlying the analysis. This approach ensures that users can make informed decisions about the appropriate application of the risk assessment framework within their broader trading and investment processes. BIBLIOGRAPHY Artzner, P., Delbaen, F., Eber, J.M. and Heath, D. (1999) 'Coherent Measures of Risk', Mathematical Finance, 9(3), pp. 203-228. Chekhlov, A., Uryasev, S. and Zabarankin, M. (2005) 'Drawdown Measure in Portfolio Optimization', International Journal of Theoretical and Applied Finance, 8(1), pp. 13-58. Goldberg, L.R. and Mahmoud, O. (2017) 'Drawdown: From Practice to Theory and Back Again', Journal of Risk Management in Financial Institutions, 10(2), pp. 140-152. Jorion, P. (2007) Value at Risk: The New Benchmark for Managing Financial Risk. 3rd edn. New York: McGraw-Hill. Markowitz, H. (1952) 'Portfolio Selection', Journal of Finance, 7(1), pp. 77-91. Sharpe, W.F. (1966) 'Mutual Fund Performance', Journal of Business, 39(1), pp. 119-138. Sortino, F.A. and Price, L.N. (1994) 'Performance Measurement in a Downside Risk Framework', Journal of Investing, 3(3), pp. 59-64. Young, T.W. (1991) 'Calmar Ratio: A Smoother Tool', Futures, 20(1), pp. 40-42.
Pine Script® göstergesi
EdgeTools tarafından
8
FunctionADFLibrary "FunctionADF" Augmented Dickey-Fuller test (ADF), The ADF test is a statistical method used to assess whether a time series is stationary – meaning its statistical properties (like mean and variance) do not change over time. A time series with a unit root is considered non-stationary and often exhibits non-mean-reverting behavior, which is a key concept in technical analysis. Reference: - - rtmath.net - en.wikipedia.org adftest(data, n_lag, conf)   : Augmented Dickey-Fuller test for stationarity.   Parameters:      data (array) : Data series.      n_lag (int) : Maximum lag.      conf (string) : Confidence Probability level used to test for critical value, (`90%`, `95%`, `99%`).   Returns: `adf` The test statistic. \ `crit` Critical value for the test statistic at the 10 % levels. \ `nobs` Number of observations used for the ADF regression and calculation of the critical values.
Pine Script® kütüphanesi
RicardoSantos tarafından
6
Adaptive Market Profile – Auto Detect & Dynamic Activity ZonesAdaptive Market Profile is an advanced indicator that automatically detects and displays the most relevant trend channel and market profile for any asset and timeframe. Unlike standard regression channel tools, this script uses a fully adaptive approach to identify the optimal period, providing you with the channel that best fits the current market dynamics. The calculation is based on maximizing the statistical significance of the trend using Pearson’s R coefficient, ensuring that the most relevant trend is always selected. Within the selected channel, the indicator generates a dynamic market profile, breaking the price range into configurable zones and displaying the most active areas based on volume or the number of touches. This allows you to instantly identify high-activity price levels and potential support/resistance zones. The “most active lines” are plotted in real-time and always stay parallel to the channel, dynamically adapting to market structure. Key features: - Automatic detection of the optimal regression period: The script scans a wide range of lengths and selects the channel that statistically represents the strongest trend. - Dynamic market profile: Visualizes the distribution of volume or price touches inside the trend channel, with customizable section count. - Most active zones: Highlights the most traded or touched price levels as dynamic, parallel lines for precise support/resistance reading. - Manual override: Optionally, users can select their own channel period for full control. - Supports both linear and logarithmic charts: Simple toggle to match your chart scaling. Use cases: - Trend following and channel trading strategies. - Quick identification of dynamic support/resistance and liquidity zones. - Objective selection of the most statistically significant trend channel, without manual guesswork. - Suitable for all assets and timeframes (crypto, stocks, forex, futures). Originality: This script goes beyond basic regression channels by integrating dynamic profile analysis and fully adaptive period detection, offering a comprehensive tool for modern technical analysts. The combination of trend detection, market profile, and activity zone mapping is unique and not available in TradingView built-ins. Instructions: Add Adaptive Market Profile to your chart. By default, the script automatically detects the optimal channel period and displays the corresponding regression channel with dynamic profile and activity zones. If you prefer manual control, disable “Auto trend channel period” and set your preferred period. Adjust profile settings as needed for your asset and timeframe. For questions, suggestions, or further customization, contact Julien Eche (@Julien_Eche) directly on TradingView.
Pine Script® göstergesi
Julien_Eche tarafından
15
Trading Report Generator from CSVMany people use the Trading Panel. Unfortunately, it doesn't have a Performance Report. However, TradingView has strategies, and they have a Performance Report :-D What if we combine the first and second? It's easy! This script is a special strategy that parses transactions in csv format from Paper Trading (and it will also work for other brokers) and “plays” them. As a result, we get a Performance Report for a specific instrument based on our real trades in Paper or another broker. How to use it : First, we need to get a CSV file with transactions. To do this, go to the Trading Panel and connect the desired broker. Select the History tab, then the Filled sub-tab, and configure the columns there, leaving only: Side, Qty, Fill Price, Closing Time. After that, open the Export data dialog, select History, and click Export. Open the downloaded CSV file in a regular text editor (Notepad or similar). It will contain a text like this: Symbol,Side,Qty,Fill Price,Closing Time FX:EURUSD,Buy,1000,1.0938700000000001,2023-04-05 14:29:23 COINBASE:ETHUSD,Sell,1,1332.05,2023-01-11 17:41:33 CME_MINI:ESH2023,Sell,1,3961.75,2023-01-11 17:30:40 CME_MINI:ESH2023,Buy,1,3956.75,2023-01-11 17:08:53 Next select all the text (Ctrl+A) and copy it to the clipboard. Now apply the "Trading Report Generator from CSV" strategy to the chart with the desired symbol and TF, open the settings/input dialog, paste the contents of the clipboard into the single text input field of the strategy, and click Ok. That's it. In the Strategy Tester, we see a detailed Performance Report based on our real transactions. P.S. The CSV file may contain transactions for different instruments, for example, you may have transactions for CRYPTO:BTCUSD and NASDAQ:AAPL. To view the report is based on CRYPTO:BTCUSD trades, simply change the symbol on the chart to CRYPTO:BTCUSD. To view the report is based on NASDAQ:AAPL trades, simply change the symbol on the chart to NASDAQ:AAPL. No changes to the strategy are required. How it works : At the beginning of the calculation, we parse the csv once, create trade objects (Trade) and sort them in chronological order. Next, on each bar, we check whether we have trades for the time period of the next bar. If there are, we place a limit order for each trade, with limit price == Fill Price of the trade. Here, we assume that if the trade is real, its execution price will be within the bar range, and the Pine strategy engine will execute this order at the specified limit price.
Pine Script® stratejisi
adolgov tarafından
4
Smart Money Breakout Channels [AlgoAlpha]🟠 OVERVIEW This script draws breakout detection zones called “Smart Money Breakout Channels” based on volatility-normalized price movement and visualizes them as dynamic boxes with volume overlays. It identifies temporary accumulation or distribution ranges using a custom normalized volatility metric and tracks when price breaks out of those zones—either upward or downward. Each channel represents a structured range where smart money may be active, helping traders anticipate key breakouts with added context from volume delta, up/down volume, and a visual gradient gauge for momentum bias. 🟠 CONCEPTS The script calculates normalized price volatility by measuring the standard deviation of price mapped to a scale using the highest and lowest prices over a set lookback period. When normalized volatility reaches a local low and flips upward, a boxed channel is drawn between the highest and lowest prices in that zone. These boxes persist until price breaks out, either with a strong candle close (configurable) or by touching the boundary. Volume analysis enhances interpretation by rendering delta bars inside the box, showing volume distribution during the channel. Additionally, a real-time visual “gauge” shows where volume delta sits within the channel range, helping users spot pressure imbalances. 🟠 FEATURES Automatic detection and drawing of breakout channels based on volatility-normalized price pivots. Optional nested channels to allow multiple simultaneous zones or a clean single-zone view. Gradient-filled volume gauge with dynamic pointer to show current delta pressure within the box. Three volume visualization modes: raw volume, comparative up/down volume, and delta. Alerts for new channel creation and confirmed bullish or bearish breakouts. 🟠 USAGE Apply the indicator to any chart. Wait for a new breakout box to form—this occurs when volatility behavior shifts and a stable range emerges. Once a box appears, monitor price relative to its boundaries. A breakout above suggests bullish continuation, below suggests bearish continuation; signals are stronger when “Strong Closes Only” is enabled. Watch the internal volume candles to understand where buy/sell pressure is concentrated during the box. Use the gauge on the right to interpret whether net pressure is building upward or downward before breakout to anticipate the direction. Use alerts to catch breakout events without needing to monitor the chart constantly 🚨.
Pine Script® göstergesi
AlgoAlpha tarafından
53
Trend Range Detector (Zeiierman)█ Overview Trend Range Detector (Zeiierman) is a market structure tool that identifies and tracks periods of price compression by forming adaptive range boxes based on volatility and price movement. When prices remain stable within a defined band, the script dynamically draws a range box; when prices break out of that structure, the box highlights the breakout in real-time. By combining a volatility-based envelope with a custom weighted centerline, this tool filters out noise and isolates truly stable zones — providing a clean framework for traders who focus on accumulation, distribution, breakout anticipation, and reversion opportunities. Whether you're range trading, spotting trend consolidations, or looking for volatility contractions before major moves, the Trend Range Detector gives you a mathematically adaptive, visually intuitive structure that maps the heartbeat of the market. █ How It Works ⚪ Range Formation Engine The core of this indicator revolves around two conditions: Distance Filter: The maximum distance between all recent closes and a dynamic centerline must remain within a volatility envelope. Volatility Envelope: Based on an ATR(2000) multiplied by a user-defined factor to account for broader market volatility trends. If both conditions are satisfied over the most recent length bars, a range box is drawn to visually anchor the zone. ⚪ Dynamic Breakout Coloring When price breaks out of the top or bottom of the active range box, the box color shifts in real-time: Blue Boxes represent areas where price has remained within a defined volatility envelope over a sustained number of bars. These zones reflect stable, low-volatility periods, often associated with consolidation, equilibrium, or market indecision. Green Boxes for bullish breakouts. Red Boxes for bearish breakdowns. This allows traders to visually spot transitions from consolidation to expansion phases without relying on lagging signals. █ Why Use a Weighted Close Instead of SMA? A standard Simple Moving Average (SMA) treats all past closes equally, which works well in theory, but not in dynamic, fast-shifting markets. In this script, we replace the traditional SMA with a speed-weighted average that reflects how aggressively the market has moved bar-to-bar. ⚪ Here's why it matters: Bars with higher momentum (larger price differences between closes) are given more weight. Slow, sideways candles (typical in noise or low volume) contribute less to the calculated centerline. This method creates a more accurate snapshot of market behavior, especially during volatile phases. As a result, the indicator adapts to market conditions more effectively, helping traders identify real consolidation zones, not just average lines distorted by flat bars or noise. █ How to Use ⚪ Range Detection Boxes form only when price remains consistently close to the speed-weighted mean. Helps identify sideways zones, consolidations, and low-volatility structures where price is “charging up.” ⚪ Breakout Confirmation Once price exits the top or bottom boundary, the box immediately highlights the direction of the break. Use this signal in conjunction with your own momentum, volume, or trend filters for higher-confidence trades. █ Settings Minimum Range Length: Number of candles required for a valid range to form. Range Width Multiplier: Adjusts the envelope around the weighted average using ATR(2000). Highlight Box Breaks: Enables real-time coloring of breakouts and breakdowns for immediate visual feedback. ----------------- 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.
Pine Script® göstergesi
Zeiierman tarafından
25
BE-Indicator Aggregator toolkit█ Overview: BE-Indicator Aggregator toolkit is a toolkit which is built for those we rely on taking multi-confirmation from different indicators available with the traders. This Toolkit aid's traders in understanding their custom logic for their trade setups and provides the summarized results on how it performed over the past. █ How It Works: Load the external indicator plots in the indicator input setting Provide your custom logic for the trade setup Set your expected SL & TP values █ Legends, Definitions & Logic Building Rules: Building the logic for your trade setup plays a pivotal role in the toolkit, it shall be broken into parts and toolkit aims to understand each of the logical parts of your setup and interpret the outcome as trade accuracy. Toolkit broadly aims to understand 4 types of inputs in "Condition Builder" Comments : Line which starts with single quotation ( ' ) shall be ignored by toolkit while understanding the logic. Note: Blank line space or less than 3 characters are treated equally to comments. Long Condition: Line which starts with " L- " shall be considered for identifying Long setups. Short Condition: Line which starts with " S- " shall be considered for identifying Short setups. Variables: Line which starts with " VAR- " shall be considered as variables. Variables can be one such criteria for Long or short condition. Building Rules: Define all variables first then specify the condition. The usual declare and assign concept of programming. :p) Criteria Rules: Criteria are individual logic for your one parent condition. multiple criteria can be present in one condition. Each parameter should be delimited with ' | ' key and each criteria should be delimited with ' , ' (Comma with a space - IMPORTANT!!!) █ Sample Codes for Conditional Builder: For Trading Long when Open = Low For Trading Short when Open = High with a Red candle 'Long Setup <---- Comment L-O|E|L ' E <- in the above line refers to Equals ' = ' 'Short Setup S-AND:O|E|H, O|G|C ' 2 Criteria for used building one condition. Since, both have to satisfied used "AND:" logic. Understanding of Operator Legends: "E" => Refers to Equals "NE" => Refers to Not Equals "NEOR" => Logical value is Either Comparing value 1 or Comparing value 2 "NEAND" => Logical value is Comparing value 1 And Comparing value 2 "G" => Logical value Greater than Comparing value 1 "GE" => Logical value Greater than and equal to Comparing value 1 "L" => Logical value Lesser than Comparing value 1 "LE" => Logical value Lesser than and equal to Comparing value 1 "B" => Logical value is Between Comparing value 1 & Comparing value 2 "BE" => Logical value is Between or Equal to Comparing value 1 & Comparing value 2 "OSE" => Logical value is Outside of Comparing value 1 & Comparing value 2 "OSI" => Logical value is Outside or Equal to Comparing value 1 & Comparing value 2 "ERR" => Logical value is 'na' "NERR" => Logical value is not 'na' "CO" => Logical value Crossed Over Comparing value 1 "CU" => Logical value Crossed Under Comparing value 1 Understanding of Condition Legends: AND: -> All criteria's to be satisfied for the condition to be True. NAND: -> Output of AND condition shall be Inversed for the condition to be True. OR: -> One of criteria to be satisfied for the condition to be True. NOR: -> Output of OR condition shall be Inversed for the condition to be True. ATLEAST:X: -> At-least X no of criteria to be satisfied for the condition to be True. Note: "X" can be any number NATLEAST:X: -> Output of ATLEAST condition shall be Inversed for the condition to be True WASTRUE:X: -> Single criteria WAS TRUE within X bar in past for the condition to be True. Note: "X" can be any number. ISTRUE:X: -> Single criteria is TRUE since X bar in past for the condition to be True. Note: "X" can be any number. Understanding of Variable Legends: While Condition Supports 8 Types, Variable supports only 6 Types listed below AND: -> All criteria's to be satisfied for the Variable to be True. NAND: -> Output of AND condition shall be Inversed for the Variable to be True. OR: -> One of criteria to be satisfied for the Variable to be True. NOR: -> Output of OR condition shall be Inversed for the Variable to be True. ATLEAST:X: -> At-least X no of criteria to be satisfied for the Variable to be True. Note: "X" can be any number NATLEAST:X: -> Output of ATLEAST condition shall be Inversed for the Variable to be True █ Sample Outputs with Logics: 1. RSI Indicator + Technical Indicator: StopLoss: 2.25 against Reward ratio of 1.75 (3.94 value) Plots Used in Indicator Settings: Source 1:- RSI Source 2:- RSI Based MA Source 3:- Strong Buy Source 4:- Strong Sell Logic Used: For Long Setup : RSI Should be above RSI Based MA, RSI has been Rising when compared to 3 candles ago, Technical Indicator signaled for a Strong Buy on the current candle, however in last 6 candles Technical indicator signaled for Strong Sell. Similarly Inverse for Short Setup. L-AND:ES1|GE|ES2, ES1|G|ES1 L-ES3|E|1 L-OR:ES4 |E|1, ES4 |E|1, ES4 |E|1, ES4 |E|1, ES4 |E|1, ES4 |E|1 S-AND:ES1|LE|ES2, ES1|L|ES1 S-ES4|E|1 S-OR:ES3 |E|1, ES3 |E|1, ES3 |E|1, ES3 |E|1, ES3 |E|1, ES3 |E|1 'Note: Last OR condition can also be written by using WASTRUE definition like below 'L-WASTRUE:6:ES4|E|1 'S-WASTRUE:6:ES3|E|1 Output: 2. Volumatic Support / Resistance Levels : Plots Used in Indicator Settings: Source 1:- Resistance Source 2:- Support Logic Used: For Long Setup : Long Trade on Liquidity Support. For Short Setup : Short Trade on Liquidity Resistance. 'Variable Named "ChkLowTradingAbvSupport" is declared to check if last 3 candles is trading above support line of liquidity. VAR-ChkLowTradingAbvSupport:AND:L|G|ES2, L |G|ES2, L |G|ES2 'Variable Named "ChkCurBarClsdAbv4thBarHigh" is declared to check if current bar closed above the high of previous candle where the Liquidity support is taken (4th Bar). VAR-ChkCurBarClsdAbv4thBarHigh:OR:C|GE|H , L|G|H 'Combining Condition and Variable to Initiate Long Trade Logic L-L |LE|ES2 L-AND:ChkLowTradingAbvSupport, ChkCurBarClsdAbv4thBarHigh VAR-ChkHghTradingBlwRes:AND:H|L|ES1, H |L|ES1, H |L|ES1 VAR-ChkCurBarClsdBlw4thBarLow:OR:C|LE|L , H|L|L S-H |GE|ES1 S-AND:ChkHghTradingBlwRes, ChkCurBarClsdBlw4thBarLow Output 1: Day Trading Version Output 2: Scalper Version Output 3: Position Version
Pine Script® göstergesi
TradeWiseWithEase tarafından
17
Synthetic VX3! & VX4! continuous /VX futuresTradingView is missing continuous 3rd and 4th month VIX (/VX) futures, so I decided to try to make a synthetic one that emulates what continuous maturity futures would look like. This is useful for backtesting/historical purposes as it enables traders to see how their further out VX contracts would've performed vs the front month contract. The indicator pulls actual realtime data (if you subscribe to the CBOE data package) or 15 minute delayed data for the VIX spot (the actual non-tradeable VIX index), the continuous front month (VX1!), and the continuous second month (VX2!) continually rolled contracts. Then the indicator's script applies a formula to fairly closely estimate how 3rd and 4th month continuous contracts would've moved. It uses an exponential mean‑reversion to a long‑run level formula using: σ(T) = θ+(σ0−θ)e−kT You can expect it to be off by ~5% or so (in times of backwardation it might be less accurate).
Pine Script® göstergesi
djayjp tarafından
2
Intrabar Efficiency Ratio█   OVERVIEW This indicator displays a directional variant of Perry​ Kaufman's​ Efficiency​ Ratio​, designed to gauge the "efficiency" of intrabar price movement by comparing the sum of movements of the lower timeframe bars composing a chart bar with the respective bar's movement on an average basis. █   CONCEPTS Efficiency Ratio (ER) Efficiency Ratio ​ was first introduced by Perry Kaufman in his 1995 book, titled "Smarter Trading". It is the ratio of absolute price change to the sum of absolute changes on each bar over a period. This tells us how strong the period's trend is relative to the underlying noise. Simply put, it's a measure of price movement efficiency. This ratio is the modulator utilized in Kaufman's​ Adaptive​ Moving​ Average​ (KAMA​), which is essentially an Exponential​ Moving​ Average​ (EMA​) that adapts its responsiveness to movement efficiency. ER's output is bounded between 0 and 1. A value of 0 indicates that the starting price equals the ending price for the period, which suggests that price movement was maximally inefficient. A value of 1 indicates that price had travelled no more than the distance between the starting price and the ending price for the period, which suggests that price movement was maximally efficient. A value between 0 and 1 indicates that price had travelled a distance greater than the distance between the starting price and the ending price for the period. In other words, some degree of noise was present which resulted in reduced efficiency over the period. As an example, let's say that the price of an asset had moved from $15 to $14 by the end of a period, but the sum of absolute changes for each bar of data was $4. ER would be calculated like so: ER = abs(14 - 15)/4 = 0.25 This suggests that the trend was only 25% efficient over the period, as the total distanced travelled by price was four times what was required to achieve the change over the period. Intrabars Intrabars are chart bars at a lower timeframe than the chart's. Each 1H chart bar of a 24x7 market will, for example, usually contain 60 intrabars at the ​LTF of 1min, provided there was market activity during each minute of the hour. Mining information from intrabars can be useful in that it offers traders visibility on the activity inside a chart bar. Lower timeframes (LTFs) A lower timeframe is a timeframe that is smaller than the chart's timeframe. This script determines which ​LTF to use by examining the chart's timeframe. The ​LTF determines how many intrabars are examined for each chart bar; the lower the timeframe, the more intrabars are analyzed, but fewer chart bars can display indicator information because there is a limit to the total number of intrabars that can be analyzed. Intrabar precision The precision of calculations increases with the number of intrabars analyzed for each chart bar. As there is a 100K limit to the number of intrabars that can be analyzed by a script, a trade-off occurs between the number of intrabars analyzed per chart bar and the chart bars for which calculations are possible. Intrabar Efficiency Ratio (IER) Intrabar Efficiency Ratio applies the concept of ER on an intrabar level. Rather than comparing the overall change to the sum of bar changes for the current chart's timeframe over a period, IER compares single bar changes for the current chart's timeframe to the sum of absolute intrabar changes, then applies smoothing to the result. This gives an indication of how efficient changes are on the current chart's timeframe for each bar of data relative to LTF​ bar changes on an average basis. Unlike the standard ER calculation, we've opted to preserve directional information by not taking the absolute value of overall change, thus allowing it to be utilized as a momentum oscillator. However, by taking the absolute value of this oscillator, it could potentially serve as a replacement for ER in the design of adaptive moving averages. Since this indicator preserves directional information, IER can be regarded as similar to the Chande​ Momentum​ Oscillator​ (CMO​) , which was presented in 1994 by Tushar​ Chande​ in "The New Technical Trader". Both CMO​ and ER​ essentially measure the same relationship between trend and noise. CMO simply differs in scale, and considers the direction of overall changes. █   FEATURES Display Three different display types are included within the script:  • Line : Displays the middle length MA of the IER as a line .   Color for this display can be customized via the "Line" portion of the "Visuals" section in the script settings.  • Candles : Displays the non-smooth IER and two moving averages of different lengths as candles .   The `open` and `close` of the candle are the longest and shortest length MAs of the IER respectively.   The `high` and `low` of the candle are the max and min of the IER, longest length MA of the IER, and shortest length MA of the IER respectively.   Colors for this display can be customized via the "Candles" portion of the "Visuals" section in the script settings.  • Circles : Displays three MAs of the IER as circles .   The color of each plot depends on the percent rank of the respective MA over the previous 100 bars.   Different colors are triggered when ranks are below 10%, between 10% and 50%, between 50% and 90%, and above 90%.   Colors for this display can be customized via the "Circles" portion of the "Visuals" section in the script settings. With either display type, an optional information box can be displayed. This box shows the LTF​ that the script is using, the average number of lower timeframe bars per chart bar, and the number of chart bars that contain LTF​ data. Specifying intrabar precision Ten options are included in the script to control the number of intrabars used per chart bar for calculations. The greater the number of intrabars per chart bar, the fewer chart bars can be analyzed. The first five options allow users to specify the approximate amount of chart bars to be covered:  • Least Precise (Most chart bars) : Covers all chart bars by dividing the current timeframe by four.   This ensures the highest level of intrabar precision while achieving complete coverage for the dataset.  • Less Precise (Some chart bars) & More Precise (Less chart bars) : These options calculate a stepped ​LTF in relation to the current chart's timeframe.  • Very precise (2min intrabars) : Uses the second highest quantity of intrabars possible with the 2min ​LTF.  • Most precise (1min intrabars) : Uses the maximum quantity of intrabars possible with the 1min ​LTF. The stepped lower timeframe for "Less Precise" and "More Precise" options is calculated from the current chart's timeframe as follows: Chart Timeframe Lower Timeframe Less Precise More Precise < 1hr 1min 1min < 1D 15min 1min < 1W ​2hr​ 30min > 1W 1D 60min The last five options allow users to specify an approximate fixed number of intrabars to analyze per chart bar. The available choices are 12, 24, 50, 100, and 250. The script will calculate the LTF​ which most closely approximates the specified number of intrabars per chart bar. Keep in mind that due to factors such as the length of a ticker's sessions and rounding of the LTF​, it is not always possible to produce the exact number specified. However, the script will do its best to get as close to the value as possible. Specifying MA type Seven MA types are included in the script for different averaging effects:  • Simple  • Exponential  • Wilder (RMA)  • Weighted  • Volume-Weighted  • Arnaud Legoux with `offset` and `sigma` set to 0.85 and 6 respectively.  • Hull Weighting This script includes the option to weight IER values based on the percent rank of absolute price changes on the current chart's timeframe over a specified period, which can be enabled by checking the "Weigh using relative close changes" option in the script settings. This places reduced emphasis on IER values from smaller changes, which may help to reduce noise in the output. █   FOR Pine Script™ CODERS  • This script imports the recently published lower_ltf library for calculating intrabar statistics and the optimal lower timeframe in relation to the current chart's timeframe.  • This script uses the recently released request.security_lower_tf() Pine Script™ function discussed in this blog post .   It works differently from the usual request.security() in that it can only be used on LTFs, and it returns an array containing one value per intrabar.   This makes it much easier for programmers to access intrabar information.  • This script implements a new recommended best practice for tables which works faster and reduces memory consumption.   Using this new method, tables are declared only once with var , as usual. Then, on the first bar only, we use table.cell() to populate the table.   Finally, table.set_*() functions are used to update attributes of table cells on the last bar of the dataset.   This greatly reduces the resources required to render tables. Look first. Then leap.
Pine Script® göstergesi
TradingView tarafından
9
Strategy█   OVERVIEW This library is a Pine Script™ programmer’s tool containing a variety of strategy-related functions to assist in calculations like profit and loss, stop losses and limits. It also includes several useful functions one can use to convert between units in ticks, price, currency or a percentage of the position's size. █  CONCEPTS The library contains three types of functions: 1 — Functions beginning with `percent` take either a portion of a price, or the current position's entry price and convert it to the value outlined in the function's documentation.   Example: Converting a percent of the current position entry price to ticks, or calculating a percent profit at a given level for the position. 2 — Functions beginning with `tick` convert a tick value to another form.   These are useful for calculating a price or currency value from a specified number of ticks. 3 — Functions containing `Level` are used to calculate a stop or take profit level using an offset in ticks from the current entry price.   These functions can be used to plot stop or take profit levels on the chart, or as arguments to the `limit` and `stop` parameters in strategy.exit() function calls.   Note that these calculated levels flip automatically with the position's bias.   For example, using `ticksToStopLevel()` will calculate a stop level under the entry price for a long position, and above the entry price for a short position. There are also two functions to assist in calculating a position size using the entry's stop and a fixed risk expressed as a percentage of the current account's equity. By varying the position size this way, you ensure that entries with different stop levels risk the same proportion of equity. █  NOTES Example code using some of the library's functions is included at the end of the library. To see it in action, copy the library's code to a new script in the Pine Editor, and “Add to chart”. For each trade, the code displays:  • The entry level in orange.  • The stop level in fuchsia.  • The take profit level in green. The stop and take profit levels automatically flip sides based on whether the current position is long or short. Labels near the last trade's levels display the percentages used to calculate them, which can be changed in the script's inputs. We plot markers for entries and exits because strategy code in libraries does not display the usual markers for them. Look first. Then leap. █  FUNCTIONS percentToTicks(percent) Converts a percentage of the average entry price to ticks.   Parameters:      percent : (series int/float) The percentage of `strategy.position_avg_price` to convert to ticks. 50 is 50% of the entry price.   Returns: (float) A value in ticks. percentToPrice(percent) Converts a percentage of the average entry price to a price.   Parameters:      percent : (series int/float) The percentage of `strategy.position_avg_price` to convert to price. 50 is 50% of the entry price.   Returns: (float) A value in the symbol's quote currency (USD for BTCUSD). percentToCurrency(price, percent) Converts the percentage of a price to money.   Parameters:      price : (series int/float) The symbol's price.      percent : (series int/float) The percentage of `price` to calculate.   Returns: (float) A value in the symbol's currency. percentProfit(exitPrice) Calculates the profit (as a percentage of the position's `strategy.position_avg_price` entry price) if the trade is closed at `exitPrice`.   Parameters:      exitPrice : (series int/float) The potential price to close the position.   Returns: (float) Percentage profit for the current position if closed at the `exitPrice`. priceToTicks(price) Converts a price to ticks.   Parameters:      price : (series int/float) Price to convert to ticks.   Returns: (float) A quantity of ticks. ticksToPrice(price) Converts ticks to a price offset from the average entry price.   Parameters:      price : (series int/float) Ticks to convert to a price.   Returns: (float) A price level that has a distance from the entry price equal to the specified number of ticks. ticksToCurrency(ticks) Converts ticks to money.   Parameters:      ticks : (series int/float) Number of ticks.   Returns: (float) Money amount in the symbol's currency. ticksToStopLevel(ticks) Calculates a stop loss level using a distance in ticks from the current `strategy.position_avg_price` entry price. This value can be plotted on the chart, or used as an argument to the `stop` parameter of a `strategy.exit()` call. NOTE: The stop level automatically flips based on whether the position is long or short.   Parameters:      ticks : (series int/float) The distance in ticks from the entry price to the stop loss level.   Returns: (float) A stop loss level for the current position. ticksToTpLevel(ticks) Calculates a take profit level using a distance in ticks from the current `strategy.position_avg_price` entry price. This value can be plotted on the chart, or used as an argument to the `limit` parameter of a `strategy.exit()` call. NOTE: The take profit level automatically flips based on whether the position is long or short.   Parameters:      ticks : (series int/float) The distance in ticks from the entry price to the take profit level.   Returns: (float) A take profit level for the current position. calcPositionSizeByStopLossTicks(stopLossTicks, riskPercent) Calculates the position size needed to implement a given stop loss (in ticks) corresponding to `riskPercent` of equity.   Parameters:      stopLossTicks : (series int) The stop loss (in ticks) that will be used to protect the position.      riskPercent : (series int/float) The maximum risk level as a percent of current equity (`strategy.equity`).   Returns: (int) A quantity of contracts. calcPositionSizeByStopLossPercent(stopLossPercent, riskPercent, entryPrice) Calculates the position size needed to implement a given stop loss (%) corresponding to `riskPercent` of equity.   Parameters:      stopLossPercent : (series int/float) The stop loss in percent that will be used to protect the position.      riskPercent : (series int/float) The maximum risk level as a percent of current equity (`strategy.equity`).      entryPrice : (series int/float) The entry price of the position.   Returns: (int) A quantity of contracts. exitPercent(id, lossPercent, profitPercent, qty, qtyPercent, comment, when, alertMessage) A wrapper of the `strategy.exit()` built-in which adds the possibility to specify loss & profit in as a value in percent. NOTE: this function may work incorrectly with pyramiding turned on due to the use of `strategy.position_avg_price` in its calculations of stop loss and take profit offsets.   Parameters:      id : (series string) The order identifier of the `strategy.exit()` call.      lossPercent : (series int/float) Stop loss as a percent of the entry price.      profitPercent : (series int/float) Take profit as a percent of the entry price.      qty : (series int/float) Number of contracts/shares/lots/units to exit a trade with. The default value is `na`.      qtyPercent : (series int/float) The percent of the position's size to exit a trade with. If `qty` is `na`, the default value of `qty_percent` is 100.      comment : (series string) Optional. Additional notes on the order.      when : (series bool) Condition of the order. The order is placed if it is true.      alertMessage : (series string) An optional parameter which replaces the {{strategy.order.alert_message}} placeholder when it is used in the "Create Alert" dialog box's "Message" field.
Pine Script® kütüphanesi
TradingView tarafından
33
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 .
Pine Script® göstergesi
LucF tarafından
33
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!
Pine Script® göstergesi
KioseffTrading tarafından
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
Pine Script® göstergesi
dgtrd tarafından
236
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!
Pine Script® göstergesi
SamRecio tarafından
6
TextLibrary "Text" library to format text in different fonts or cases plus a sort function. 🔸 Credits and Usage This library is inspired by the work of three authors (in chronological order of publication date): Unicode font function - JD - Duyck UnicodeReplacementFunction - wlhm font - kaigouthro 🔹 Fonts Besides extra added font options, the toFont(fromText, font) method uses a different technique. On the first runtime bar (whether it is barstate.isfirst , barstate.islast , or between) regular letters and numbers and mapped with the chosen font. After this, each character is replaced using the build-in key - value pair map function . Also an enum Efont is included. Note: Some fonts are not complete, for example there isn't a replacement for every character in Superscript/Subscript. Example of usage (besides the included table example): import fikira/Text/1 as t i_font = input.enum(t.Efont.Blocks) if barstate.islast sentence = "this sentence contains words" label.new(bar_index, 0, t.toFont(fromText = sentence, font = str.tostring(i_font)), style=label.style_label_lower_right) label.new(bar_index, 0, t.toFont(fromText = sentence, font = "Circled" ), style=label.style_label_lower_left ) label.new(bar_index, 0, t.toFont(fromText = sentence, font = "Wiggly" ), style=label.style_label_upper_right) label.new(bar_index, 0, t.toFont(fromText = sentence, font = "Upside Latin" ), style=label.style_label_upper_left ) 🔹 Cases The script includes a toCase(fromText, case) method to transform text into snake_case, UPPER SNAKE_CASE, kebab-case, camelCase or PascalCase, as well as an enum Ecase . Example of usage (besides the included table example): import fikira/Text/1 as t i_case = input.enum(t.Ecase.camel) if barstate.islast sentence = "this sentence contains words" label.new(bar_index, 0, t.toCase(fromText = sentence, case = str.tostring(i_case)), style=label.style_label_lower_right) label.new(bar_index, 0, t.toCase(fromText = sentence, case = "snake_case" ), style=label.style_label_lower_left ) label.new(bar_index, 0, t.toCase(fromText = sentence, case = "PascalCase" ), style=label.style_label_upper_right) label.new(bar_index, 0, t.toCase(fromText = sentence, case = "SNAKE_CASE" ), style=label.style_label_upper_left ) 🔹 Sort The sort(strings, order, sortByUnicodeDecimalNumbers) method returns a sorted array of strings. strings: array of strings, for example words = array.from("Aword", "beyond", "Space", "salt", "pepper", "swing", "someThing", "otherThing", "12345", "_firstWord") order: "asc" / "desc" (ascending / descending) sortByUnicodeDecimalNumbers: true/false; default = false _____ • sortByUnicodeDecimalNumbers: every Unicode character is linked to a Unicode Decimal number ( wikipedia.org/wiki/List_of_Unicode_characters ), for example: 1 49 2 50 3 51 ... A 65 B 66 ... S 83 ... _ 95 ` 96 a 97 b 98 ... o 111 p 112 q 113 r 114 s 115 ... This means, if we sort without adjusting ( sortByUnicodeDecimalNumbers = true ), in ascending order, the letter b (98 - small) would be after S (83 - Capital). By disabling sortByUnicodeDecimalNumbers , Capital letters are intermediate transformed to str.lower() after which the Unicode Decimal number is retrieved from the small number instead of the capital number. For example S (83) -> s (115), after which the number 115 is used to sort instead of 83. Example of usage (besides the included table example): import fikira/Text/1 as t if barstate.islast aWords = array.from("Aword", "beyond", "Space", "salt", "pepper", "swing", "someThing", "otherThing", "12345", "_firstWord") label.new(bar_index, 0, str.tostring(t.sort(strings= aWords, order = 'asc' , sortByUnicodeDecimalNumbers = false)), style=label.style_label_lower_right) label.new(bar_index, 0, str.tostring(t.sort(strings= aWords, order = 'desc', sortByUnicodeDecimalNumbers = false)), style=label.style_label_lower_left ) label.new(bar_index, 0, str.tostring(t.sort(strings= aWords, order = 'asc' , sortByUnicodeDecimalNumbers = true )), style=label.style_label_upper_right) label.new(bar_index, 0, str.tostring(t.sort(strings= aWords, order = 'desc', sortByUnicodeDecimalNumbers = true )), style=label.style_label_upper_left ) 🔸 Methods/functions method toFont(fromText, font)   toFont : Transforms text into the selected font   Namespace types: series string, simple string, input string, const string   Parameters:      fromText (string)      font (string)   Returns: `fromText` transformed to desired `font` method toCase(fromText, case)   toCase : formats text to snake_case, UPPER SNAKE_CASE, kebab-case, camelCase or PascalCase   Namespace types: series string, simple string, input string, const string   Parameters:      fromText (string)      case (string)   Returns: `fromText` formatted to desired `case` method sort(strings, order, sortByUnicodeDecimalNumbers)   sort : sorts an array of strings, ascending/descending and by Unicode Decimal numbers or not.   Namespace types: array   Parameters:      strings (array)      order (string)      sortByUnicodeDecimalNumbers (bool)   Returns: Sorted array of strings
Pine Script® kütüphanesi
fikira tarafından
2
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! 😊🥂
Pine Script® göstergesi
HenriqueCentieiro tarafından
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.
Pine Script® göstergesi
BullVisionCapital tarafından
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.
Pine Script® göstergesi
Julien_Eche tarafından
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!
Pine Script® göstergesi
HenriqueCentieiro tarafından
21