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Tape [LucF]█  OVERVIEW This script prints an ersatz of a trading console's "tape" section to the right of your chart. It displays the time, price and volume​  of each update of the chart's feed. It also calculates volume​ delta for the bar. As it calculates from realtime information, it will not display information on historical bars. █  FEATURES Calculations Each new line in the tape displays the last price/volume​ update from the TradingView feed that's building your chart. These updates do not necessarily correspond to ticks from the originating broker/exchange's matching engine. Multiple broker/exchange ticks are often aggregated in one  chart update. The script first determines if price has moved up or down since the last update. The polarity of the price change, in turn, determines the polarity of the volume​​ for that specific update. If price does not move between consecutive updates, then the last known polarity is used. Using this method, we can calculate a running volume​ delta accumulation for the bar, which becomes the bar's final volume​ delta value when the bar closes (you can inspect values of elapsed realtime bars in the Data Window or the indicator's values). Note that these values will all reset if the script re-executes because of a change in inputs or a chart refresh. While this method of calculating volume​ delta is not perfect, it is currently the most precise way of calculating volume​ delta available on TradingView at the moment. Calculating more precise results would require scripts to have access to bid/ask levels from any chart timeframe. Charts at seconds timeframes do use exchange/broker ticks when the feeds you are using allow for it, and this indicator will run on them, but tick data is not yet available from higher timeframes, for now. Also note that the method used in this script is far superior to the intrabar inspection technique used on historical bars in my other "Delta Volume​" indicators. This is because volume​ delta here is calculated from many more realtime updates than the available intrabars in history. Inputs You can use the script's inputs to configure:  • The number of lines displayed in the tape.  • If new lines appear at the top or bottom.  • If you want to hide lines with low volume​.  • The precision of volume​ values.  • The size of the text and the colors used to highlight either the tape's text or background.  • The position where you want the tape on your chart.  • Conditions triggering three different markers. Display Deltas are shown at the bottom of the tape. They are reset on each bar. Time delta displays the time elapsed since the beginning of the bar, on intraday timeframes only. Contrary to the price change display by TradingView at the top left of charts, which is calculated from the close of the previous bar, the price delta in the tape is calculated from the bar's open, because that's the information used in the calculation of volume​ delta. The time will become orange when volume​ delta's polarity diverges from that of the bar. The volume  delta value represents the current, cumulative value for the bar. Its color reflects its polarity. When new realtime bars appear on the chart, a ↻ symbol will appear before the volume​ value in tape lines. Markers There are three types of markers you can choose to display:  • Marker 1 on volume​ bumps. A bump is defined as two consecutive and increasing/decreasing plus/minus delta volume​  values,   when no divergence between the polarity of delta volume​ and the bar occurs on the second bar.  • Marker 2 on volume​ delta for the bar exceeding a limit of your choice when there is no divergence between the polarity of delta volume​ and the bar. These trigger at the bar's close.  • Marker 3 on tape lines with volume​ exceeding a threshold. These trigger in realtime. Be sure to set a threshold high enough so that it doesn't generate too many alerts.   These markers will only display briefly under the bar, but another marker appears next to the relevant line in the tape. The marker conditions are used to trigger alerts configured on the script. Alert messages will mention the marker(s) that triggered the specific alert event, along with the relevant volume​ value that triggered the marker. If more than one marker triggers a single alert, they will overprint under the bar, which can make it difficult to distinguish them. For more detailed on-chart analysis of realtime volume​ delta, see my Delta Volume Realtime Action . █  NOTES FOR CODERS This script showcases two new Pine features:  • Tables, which allow Pine programmers to display tabular information in fixed locations of the chart. The tape uses this feature.   See the Pine User Manual's page on Tables for more information.  • varip -type variables which we can use to save values between realtime updates.   See the " Using `varip` variables " publication by PineCoders for more information.
Chỉ báo Pine Script®
của LucF
99
Crash Stats 15m (ETH) — X% | prev RTH min(VWAP, Close)# Crash Stats 15m (ETH) — X% Drawdown Event Analyzer A 15-minute indicator that scans up to the last 5 years to find **crash events** where the close falls by at least **X%** relative to the **lower of** the prior day’s **RTH VWAP** and **RTH close**. It then measures recovery and follow-through behavior, tags the market regime around each event, and summarizes everything in a table. --- ## What the script detects **Crash event (trigger):** * On a 15-minute bar, `close <= refPrice * (1 - X%)`. * `refPrice = min(previous RTH VWAP, previous RTH close)`. * First touch only: subsequent bars below the threshold on the same trading day are ignored. * Extended hours (ETH) are supported; if ETH is off, the script safely infers the previous RTH reference. **Per-event measurements** 1. **Time to “turn up”** – first close **above the event-anchored AVWAP** (AVWAP cumulated from the trigger bar onward). 2. **Time to recover the reference price** – first close ≥ `refPrice`. 3. **Time to recover Y% above the crash-day average price** – first close ≥ `crashDayVWAP * (1+Y%)`. 4. **Post-crash lowest price & timing** – the lowest low and how long after the event it occurs, within a user-defined horizon (default 10 trading days, approximated in calendar days). 5. **Intraday RTH low timing** – on the crash day’s RTH session, when did the day’s intraday low occur, and **was it on the first 15-minute bar**? 6. **First 15-minute low of the RTH day** – recorded for context. All durations are shown as **D days H hours M minutes**. --- ## Regime tagging (A / B) For each event the script classifies the surrounding trend using daily closes: * Let `r6m = (prevClose – close_6mAgo) / close_6mAgo`, `r12m = (prevClose – close_12mAgo) / close_12mAgo`. * **A**: both `r6m > 0` and `r12m > 0` (uptrend across 6m & 12m). * **B**: one positive, one negative, and `r6m + r12m ≥ 0` (mixed but net non-negative). * Otherwise: **—**. This helps separate selloffs in strong uptrends (A) from mixed regimes (B) and others. --- ## Inputs * **X — Crash threshold (%)**: default 5. * **Y — Recovery above crash-day average (%)**: default 5. * **Lookback years**: default 5 (bounded by data availability). * **Horizon for post-crash lowest (trading days)**: default 10 (approximated as calendar days). * **RTH session**: default `09:30–16:00` (exchange timezone). * **Show markers**: plot labels on triggers. * **Rows to display**: last N events in the table. --- ## Table columns * Index, **Trigger time**, **Drop %**, **Ref price**, **Regime (A/B/—)** * **Time to turn up** (above anchored AVWAP) * **Time to ref price**, **Time to day VWAP + Y%** * **Window lowest price**, **Time to window low** * **RTH first-15m low**, **RTH lowest time**, **Was RTH low on first 15m?** * **Crash-day VWAP** --- ## How to use 1. **Set chart to 15-minute** and **enable extended hours** for equities (recommended). 2. Keep defaults (**X=5%, Y=5%**) to start; tighten to 3–4% for more frequent events on less volatile symbols. 3. For non-US symbols or futures, adjust the **RTH session** if needed. 4. Read the table (top-right) for per-event diagnostics and aggregate averages (bottom row). --- ## Notes & implementation details * Works whether ETH is on or off. If ETH is off, the script back-fills “previous RTH” references at the next RTH open and uses the prior daily close as a fallback. * The “turn up” definition uses **event-anchored AVWAP**, a robust, price–volume anchor widely used for post-shock mean reversion analysis. * Events are **de-duplicated**: only one event per trading day (per target RTH cycle). * Lookback is limited by your plan and the data vendor. The script requests deep history (`max_bars_back=50000`), but availability varies by symbol. * Durations use minute precision and are rendered as **days–hours–minutes** for readability. --- ## Quick troubleshooting * **No events found**: lower **X%**, enable **ETH**, or ensure sufficient history is loaded (scroll back, or briefly switch to a higher timeframe to force deeper backfill, then return to 15m). * **RTH boundaries off**: check the **RTH session** input matches the venue. * **Few rows in table**: increase **Rows to display**. --- ## Typical use cases * Back-test how fast different symbols tend to stabilize after a sharp gap-down or intraday shock. * Compare recovery behavior across regimes **A / B** for sizing and risk timing. * Build playbooks: e.g., if the RTH low occurs on the first 15m bar X% of the time, plan entries accordingly. --- ## Changelog * **v1.0**: Initial public release with crash detection, anchored-AVWAP reversal, reference & VWAP+Y recovery timers, regime tagging, window-low timing, RTH low timing, and first-15m low capture.
Chỉ báo Pine Script®
của lvyaofeng19920413
Scientific Correlation Testing FrameworkScientific Correlation Testing Framework - Comprehensive Guide Introduction to Correlation Analysis What is Correlation? Correlation is a statistical measure that describes the degree to which two assets move in relation to each other. Think of it like measuring how closely two dancers move together on a dance floor. Perfect Positive Correlation (+1.0): Both dancers move in perfect sync, same direction, same speed Perfect Negative Correlation (-1.0): Both dancers move in perfect sync but in opposite directions Zero Correlation (0): The dancers move completely independently of each other In financial markets, correlation helps us understand relationships between different assets, which is crucial for: Portfolio diversification Risk management Pairs trading strategies Hedging positions Market analysis Why This Script is Special This script goes beyond simple correlation calculations by providing: Two different correlation methods (Pearson and Spearman) Statistical significance testing to ensure results are meaningful Rolling correlation analysis to track how relationships change over time Visual representation for easy interpretation Comprehensive statistics table with detailed metrics Deep Dive into the Script's Components 1. Input Parameters Explained- Symbol Selection: This allows you to select the second asset to compare with the chart's primary asset Default is Apple (NASDAQ:AAPL), but you can change this to any symbol Example: If you're viewing a Bitcoin chart, you might set this to "NASDAQ:TSLA" to see if Bitcoin and Tesla are correlated Correlation Window (60): This is the number of periods used to calculate the main correlation Larger values (e.g., 100-500) provide more stable, long-term correlation measures Smaller values (e.g., 10-50) are more responsive to recent price movements 60 is a good balance for most daily charts (about 3 months of trading days) Rolling Correlation Window (20): A shorter window to detect recent changes in correlation This helps identify when the relationship between assets is strengthening or weakening Default of 20 is roughly one month of trading days Return Type: This determines how price changes are calculated Simple Returns: (Today's Price - Yesterday's Price) / Yesterday's Price Easy to understand: "The asset went up 2% today" Log Returns: Natural logarithm of (Today's Price / Yesterday's Price) More mathematically elegant for statistical analysis Better for time-additive properties (returns over multiple periods) Less sensitive to extreme values. Confidence Level (95%): This determines how certain we want to be about our results 95% confidence means we accept a 5% chance of being wrong (false positive) Higher confidence (e.g., 99%) makes the test more strict Lower confidence (e.g., 90%) makes the test more lenient 95% is the standard in most scientific research Show Statistical Significance: When enabled, the script will test if the correlation is statistically significant or just due to random chance. Display options control what you see on the chart: Show Pearson/Spearman/Rolling Correlation: Toggle each correlation type on/off Show Scatter Plot: Displays a scatter plot of returns (limited to recent points to avoid performance issues) Show Statistical Tests: Enables the detailed statistics table Table Text Size: Adjusts the size of text in the statistics table 2.Functions explained- calcReturns(): This function calculates price returns based on your selected method: Log Returns: Formula: ln(Price_t / Price_t-1) Example: If a stock goes from $100 to $101, the log return is ln(101/100) = ln(1.01) ≈ 0.00995 or 0.995% Benefits: More symmetric, time-additive, and better for statistical modeling Simple Returns: Formula: (Price_t - Price_t-1) / Price_t-1 Example: If a stock goes from $100 to $101, the simple return is (101-100)/100 = 0.01 or 1% Benefits: More intuitive and easier to understand rankArray(): This function calculates the rank of each value in an array, which is used for Spearman correlation: How ranking works: The smallest value gets rank 1 The second smallest gets rank 2, and so on For ties (equal values), they get the average of their ranks Example: For values Sorted: Ranks: (the two 2s tie for ranks 1 and 2, so they both get 1.5) Why this matters: Spearman correlation uses ranks instead of actual values, making it less sensitive to outliers and non-linear relationships. pearsonCorr(): This function calculates the Pearson correlation coefficient: Mathematical Formula: r = (nΣxy - ΣxΣy) / √ Where x and y are the two variables, and n is the sample size What it measures: The strength and direction of the linear relationship between two variables Values range from -1 (perfect negative linear relationship) to +1 (perfect positive linear relationship) 0 indicates no linear relationship Example: If two stocks have a Pearson correlation of 0.8, they have a strong positive linear relationship When one stock goes up, the other tends to go up in a fairly consistent proportion spearmanCorr(): This function calculates the Spearman rank correlation: How it works: Convert each value in both datasets to its rank Calculate the Pearson correlation on the ranks instead of the original values What it measures: The strength and direction of the monotonic relationship between two variables A monotonic relationship is one where as one variable increases, the other either consistently increases or decreases It doesn't require the relationship to be linear When to use it instead of Pearson: When the relationship is monotonic but not linear When there are significant outliers in the data When the data is ordinal (ranked) rather than interval/ratio Example: If two stocks have a Spearman correlation of 0.7, they have a strong positive monotonic relationship When one stock goes up, the other tends to go up, but not necessarily in a straight-line relationship tStatistic(): This function calculates the t-statistic for correlation: Mathematical Formula: t = r × √((n-2)/(1-r²)) Where r is the correlation coefficient and n is the sample size What it measures: How many standard errors the correlation is away from zero Used to test the null hypothesis that the true correlation is zero Interpretation: Larger absolute t-values indicate stronger evidence against the null hypothesis Generally, a t-value greater than 2 (in absolute terms) is considered statistically significant at the 95% confidence level criticalT() and pValue(): These functions provide approximations for statistical significance testing: criticalT(): Returns the critical t-value for a given degrees of freedom (df) and significance level The critical value is the threshold that the t-statistic must exceed to be considered statistically significant Uses approximations since Pine Script doesn't have built-in statistical distribution functions pValue(): Estimates the p-value for a given t-statistic and degrees of freedom The p-value is the probability of observing a correlation as strong as the one calculated, assuming the true correlation is zero Smaller p-values indicate stronger evidence against the null hypothesis Standard interpretation: p < 0.01: Very strong evidence (marked with **) p < 0.05: Strong evidence (marked with *) p ≥ 0.05: Weak evidence, not statistically significant stdev(): This function calculates the standard deviation of a dataset: Mathematical Formula: σ = √(Σ(x-μ)²/(n-1)) Where x is each value, μ is the mean, and n is the sample size What it measures: The amount of variation or dispersion in a set of values A low standard deviation indicates that the values tend to be close to the mean A high standard deviation indicates that the values are spread out over a wider range Why it matters for correlation: Standard deviation is used in calculating the correlation coefficient It also provides information about the volatility of each asset's returns Comparing standard deviations helps understand the relative riskiness of the two assets. 3.Getting Price Data- price1: The closing price of the primary asset (the chart you're viewing) price2: The closing price of the secondary asset (the one you selected in the input parameters) Returns are used instead of raw prices because: Returns are typically stationary (mean and variance stay constant over time) Returns normalize for price levels, allowing comparison between assets of different values Returns represent what investors actually care about: percentage changes in value 4.Information Table- Creates a table to display statistics Only shows on the last bar to avoid performance issues Positioned in the top right of the chart Has 2 columns and 15 rows Populating the Table The script then populates the table with various statistics: Header Row: "Metric" and "Value" Sample Information: Sample size and return type Pearson Correlation: Value, t-statistic, p-value, and significance Spearman Correlation: Value, t-statistic, p-value, and significance Rolling Correlation: Current value Standard Deviations: For both assets Interpretation: Text description of the correlation strength The table uses color coding to highlight important information: Green for significant positive results Red for significant negative results Yellow for borderline significance Color-coded headers for each section => Practical Applications and Interpretation How to Interpret the Results Correlation Strength 0.0 to 0.3 (or 0.0 to -0.3): Weak or no correlation The assets move mostly independently of each other Good for diversification purposes 0.3 to 0.7 (or -0.3 to -0.7): Moderate correlation The assets show some tendency to move together (or in opposite directions) May be useful for certain trading strategies but not extremely reliable 0.7 to 1.0 (or -0.7 to -1.0): Strong correlation The assets show a strong tendency to move together (or in opposite directions) Can be useful for pairs trading, hedging, or as a market indicator Statistical Significance p < 0.01: Very strong evidence that the correlation is real Marked with ** in the table Very unlikely to be due to random chance p < 0.05: Strong evidence that the correlation is real Marked with * in the table Unlikely to be due to random chance p ≥ 0.05: Weak evidence that the correlation is real Not marked in the table Could easily be due to random chance Rolling Correlation The rolling correlation shows how the relationship between assets changes over time If the rolling correlation is much different from the long-term correlation, it suggests the relationship is changing This can indicate: A shift in market regime Changing fundamentals of one or both assets Temporary market dislocations that might present trading opportunities Trading Applications 1. Portfolio Diversification Goal: Reduce overall portfolio risk by combining assets that don't move together Strategy: Look for assets with low or negative correlations Example: If you hold tech stocks, you might add some utilities or bonds that have low correlation with tech 2. Pairs Trading Goal: Profit from the relative price movements of two correlated assets Strategy: Find two assets with strong historical correlation When their prices diverge (one goes up while the other goes down) Buy the underperforming asset and short the outperforming asset Close the positions when they converge back to their normal relationship Example: If Coca-Cola and Pepsi are highly correlated but Coca-Cola drops while Pepsi rises, you might buy Coca-Cola and short Pepsi 3. Hedging Goal: Reduce risk by taking an offsetting position in a negatively correlated asset Strategy: Find assets that tend to move in opposite directions Example: If you hold a portfolio of stocks, you might buy some gold or government bonds that tend to rise when stocks fall 4. Market Analysis Goal: Understand market dynamics and interrelationships Strategy: Analyze correlations between different sectors or asset classes Example: If tech stocks and semiconductor stocks are highly correlated, movements in one might predict movements in the other If the correlation between stocks and bonds changes, it might signal a shift in market expectations 5. Risk Management Goal: Understand and manage portfolio risk Strategy: Monitor correlations to identify when diversification benefits might be breaking down Example: During market crises, many assets that normally have low correlations can become highly correlated (correlation convergence), reducing diversification benefits Advanced Interpretation and Caveats Correlation vs. Causation Important Note: Correlation does not imply causation Example: Ice cream sales and drowning incidents are correlated (both increase in summer), but one doesn't cause the other Implication: Just because two assets move together doesn't mean one causes the other to move Solution: Look for fundamental economic reasons why assets might be correlated Non-Stationary Correlations Problem: Correlations between assets can change over time Causes: Changing market conditions Shifts in monetary policy Structural changes in the economy Changes in the underlying businesses Solution: Use rolling correlations to monitor how relationships change over time Outliers and Extreme Events Problem: Extreme market events can distort correlation measurements Example: During a market crash, many assets may move in the same direction regardless of their normal relationship Solution: Use Spearman correlation, which is less sensitive to outliers Be cautious when interpreting correlations during extreme market conditions Sample Size Considerations Problem: Small sample sizes can produce unreliable correlation estimates Rule of Thumb: Use at least 30 data points for a rough estimate, 60+ for more reliable results Solution: Use the default correlation length of 60 or higher Be skeptical of correlations calculated with small samples Timeframe Considerations Problem: Correlations can vary across different timeframes Example: Two assets might be positively correlated on a daily basis but negatively correlated on a weekly basis Solution: Test correlations on multiple timeframes Use the timeframe that matches your trading horizon Look-Ahead Bias Problem: Using information that wouldn't have been available at the time of trading Example: Calculating correlation using future data Solution: This script avoids look-ahead bias by using only historical data Best Practices for Using This Script 1. Appropriate Parameter Selection Correlation Window: For short-term trading: 20-50 periods For medium-term analysis: 50-100 periods For long-term analysis: 100-500 periods Rolling Window: Should be shorter than the main correlation window Typically 1/3 to 1/2 of the main window Return Type: For most applications: Log Returns (better statistical properties) For simplicity: Simple Returns (easier to interpret) 2. Validation and Testing Out-of-Sample Testing: Calculate correlations on one time period Test if they hold in a different time period Multiple Timeframes: Check if correlations are consistent across different timeframes Economic Rationale: Ensure there's a logical reason why assets should be correlated 3. Monitoring and Maintenance Regular Review: Correlations can change, so review them regularly Alerts: Set up alerts for significant correlation changes Documentation: Keep notes on why certain assets are correlated and what might change that relationship 4. Integration with Other Analysis Fundamental Analysis: Combine correlation analysis with fundamental factors Technical Analysis: Use correlation analysis alongside technical indicators Market Context: Consider how market conditions might affect correlations Conclusion This Scientific Correlation Testing Framework provides a comprehensive tool for analyzing relationships between financial assets. By offering both Pearson and Spearman correlation methods, statistical significance testing, and rolling correlation analysis, it goes beyond simple correlation measures to provide deeper insights. For beginners, this script might seem complex, but it's built on fundamental statistical concepts that become clearer with use. Start with the default settings and focus on interpreting the main correlation lines and the statistics table. As you become more comfortable, you can adjust the parameters and explore more advanced applications. Remember that correlation analysis is just one tool in a trader's toolkit. It should be used in conjunction with other forms of analysis and with a clear understanding of its limitations. When used properly, it can provide valuable insights for portfolio construction, risk management, and pair trading strategy development.
Chỉ báo Pine Script®
của kingshukghosh71
Candle Size MonitorCandle Size Monitor – Description Update 27.10.25 Objective Volatility Assessment The Candle Size Monitor helps traders assess actual market movement—regardless of whether candles appear visually large or small on the chart. It supports evaluating whether your planned trade structure (e.g., stop-loss, take-profit) aligns with current volatility. Key Features Volatility Analysis: Calculates the average candle size (difference between high and low) over a user-defined number of candles. Identifies the largest candle in the selected period. Displays results in a compact table on the chart. Exchange Rate Integration (optional): Shows the current USD-EUR exchange rate (formatted with German-style comma and four decimal places). Useful for traders in USD-denominated markets who apply EUR-based risk management rules. Customizable Display: Text Size: Small, medium, or large. Colors: Customizable text and background colors. Table Position: Top/bottom left/right. Number of Candles: User-defined (default: 20). Dynamic Updates: The table updates with each new bar. The exchange rate is fetched in real-time from OANDA:EURUSD. Settings and Translations Settings Anzahl Kerzen → Number of Candles (Number of candles for calculation, default: 20). Textgröße → Text Size (Text size in the table: small, medium, large). Textfarbe → Text Color (Text color, default: white). Hintergrundfarbe → Background Color (Background color of the table, default: black). Position → Position (Table position: Top Left, Top Right, Bottom Left, Bottom Right). Wechselkurs anzeigen (USD → EUR) → Show Exchange Rate (USD → EUR) (Option to display the exchange rate). Table Contents and Translations The table displays the following information (with German formatting): Ø Größe (N): English: "Avg Size (N): " (Average candle size over the last N candles). Example: "Ø Größe (20): 15,3" → "Avg Size (20): 15.3". Größte Kerze: English: "Largest Candle: " (Largest candle size in the selected period). Example: "Größte Kerze: 42,7" → "Largest Candle: 42.7". 1 USD = € (only if enabled) English: "1 USD = EUR" (Current USD-EUR exchange rate, formatted with a comma). Example: "1 USD = 0,9234 €" → "1 USD = 0.9234 EUR".
Chỉ báo Pine Script®
của kp_eu_de
ema200 plus Description: This advanced indicator displays Exponential Moving Averages (EMA) across multiple timeframes to help traders identify trend direction and strength across different market perspectives. Key Features: Multi-Timeframe EMA Analysis: Plots 200-period EMA on four different timeframes: 30-minute, 1-hour, 4-hour, and Daily Each timeframe is displayed with distinct colors for easy visual identification Visual Elements: Chart Lines: Four colored EMA lines plotted directly on the price chart Price Labels: Clear labels showing each EMA's current value at the latest bar Color-coded Table: Comprehensive data table showing price position relative to each EMA Trend Identification: Bullish Signal: When price closes above an EMA (green background in table) Bearish Signal: When price closes below an EMA (dark background in table) Helps identify confluence when multiple timeframes align in direction Customizable Settings: Adjustable EMA length (default: 200 periods) Customizable line width and offset Flexible table positioning (top/middle/bottom, left/center/right) Configurable table cell size and text appearance Swing traders analyzing multiple timeframes Position traders looking for trend confirmation Technical analysts seeking confluence across time horizons This indicator provides a comprehensive view of market trends across different time perspectives, helping traders make more informed decisions based on multi-timeframe analysis. This indicator does not provide trading advice. It is for educational and informational purposes only. **指标名称：多时间框架200 EMA** **描述：** 这款高级指标在多个时间框架上显示指数移动平均线（EMA），帮助交易者识别不同市场视角下的趋势方向和强度。 **主要特点：** 1. **多时间框架EMA分析：** - 在四个不同时间框架上绘制200周期EMA：30分钟、1小时、4小时和日线 - 每个时间框架使用独特颜色显示，便于视觉识别 2. **视觉元素：** - **图表线：** 在价格图表上直接绘制四条彩色EMA线 - **价格标签：** 清晰显示最新K线处各EMA的当前值 - **颜色编码表格：** 综合数据表格显示价格相对于各EMA的位置 3. **趋势识别：** - **看涨信号：** 当价格收于EMA上方时（表格中显示绿色背景） - **看跌信号：** 当价格收于EMA下方时（表格中显示深色背景） - 帮助识别多个时间框架方向一致时的共振信号 4. **可自定义设置：** - 可调整EMA长度（默认：200周期） - 可自定义线宽和偏移量 - 灵活的表格定位（上/中/下，左/中/右） - 可配置表格单元格大小和文本外观 **适合人群：** - 分析多时间框架的摆动交易者 - 寻求趋势确认的头寸交易者 - 寻找不同时间维度共振信号的技术分析师
Chỉ báo Pine Script®
của Backspace1998
Smart MACD Volume Trader# Smart MACD Volume Trader ## Overview Smart MACD Volume Trader is an enhanced momentum indicator that combines the classic MACD (Moving Average Convergence Divergence) oscillator with an intelligent high-volume filter. This combination significantly reduces false signals by ensuring that trading signals are only generated when price momentum is confirmed by substantial volume activity. The indicator supports over 24 different instruments including major and exotic forex pairs, precious metals (gold and silver), energy commodities (crude oil, natural gas), and industrial metals (copper). For forex and commodity traders, the indicator automatically maps to CME and COMEX futures contracts to provide accurate institutional-grade volume data. ## Originality and Core Concept Traditional MACD indicators generate signals based solely on price momentum, which can result in numerous false signals during low-activity periods or ranging markets. This indicator addresses this critical weakness by introducing a volume confirmation layer with automatic institutional volume integration. **What makes this approach original:** - Signals are triggered only when MACD crossovers coincide with elevated volume activity - Implements a lookback mechanism to detect volume spikes within recent bars - Automatically detects and maps 24+ forex pairs and commodities to their corresponding CME and COMEX futures contracts - Provides real institutional volume data for forex pairs where spot volume is unreliable - Combines two independent market dimensions (price momentum and volume) into a single, actionable signal - Includes intelligent asset detection that works across multiple exchanges and ticker formats **The underlying principle:** Volume validates price movement. When institutional money enters the market, it creates volume signatures. By requiring high volume confirmation and using actual institutional volume data from futures markets, this indicator filters out weak price movements and focuses on trades backed by genuine market participation. The automatic futures mapping ensures that forex and commodity traders always have access to the most accurate volume data available, without manual configuration. ## How It Works ### MACD Component The indicator calculates MACD using standard methodology: 1. **Fast EMA (default: 12 periods)** - Tracks short-term price momentum 2. **Slow EMA (default: 26 periods)** - Tracks longer-term price momentum 3. **MACD Line** - Difference between Fast EMA and Slow EMA 4. **Signal Line (default: 9-period SMA)** - Smoothed average of MACD line **Crossover signals:** - **Bullish:** MACD line crosses above Signal line (momentum turning positive) - **Bearish:** MACD line crosses below Signal line (momentum turning negative) ### Volume Filter Component The volume filter adds an essential confirmation layer: 1. **Volume Moving Average** - Calculates exponential MA of volume (default: 20 periods) 2. **High Volume Threshold** - Multiplies MA by ratio (default: 2.0x or 200%) 3. **Volume Detection** - Identifies bars where current volume exceeds threshold 4. **Lookback Period** - Checks if high volume occurred in recent bars (default: 5 bars) **Signal logic:** - Buy/Sell signals only trigger when BOTH conditions are met: - MACD crossover/crossunder occurs - High volume detected within lookback period ### Automatic CME Futures Integration For forex traders, spot FX volume data can be unreliable or non-existent. This indicator solves this problem by automatically detecting forex pairs and mapping them to corresponding CME futures contracts with real institutional volume data. **Supported Major Forex Pairs (7):** - EURUSD → CME:6E1! (Euro FX Futures) - GBPUSD → CME:6B1! (British Pound Futures) - AUDUSD → CME:6A1! (Australian Dollar Futures) - USDJPY → CME:6J1! (Japanese Yen Futures) - USDCAD → CME:6C1! (Canadian Dollar Futures) - USDCHF → CME:6S1! (Swiss Franc Futures) - NZDUSD → CME:6N1! (New Zealand Dollar Futures) **Supported Exotic Forex Pairs (4):** - USDMXN → CME:6M1! (Mexican Peso Futures) - USDRUB → CME:6R1! (Russian Ruble Futures) - USDBRL → CME:6L1! (Brazilian Real Futures) - USDZAR → CME:6Z1! (South African Rand Futures) **Supported Cross Pairs (6):** - EURJPY → CME:6E1! (Uses Euro Futures) - GBPJPY → CME:6B1! (Uses British Pound Futures) - EURGBP → CME:6E1! (Uses Euro Futures) - AUDJPY → CME:6A1! (Uses Australian Dollar Futures) - EURAUD → CME:6E1! (Uses Euro Futures) - GBPAUD → CME:6B1! (Uses British Pound Futures) **Supported Precious Metals (2):** - Gold (XAUUSD, GOLD) → COMEX:GC1! (Gold Futures) - Silver (XAGUSD, SILVER) → COMEX:SI1! (Silver Futures) **Supported Energy Commodities (3):** - WTI Crude Oil (USOIL, WTIUSD) → NYMEX:CL1! (Crude Oil Futures) - Brent Oil (UKOIL) → NYMEX:BZ1! (Brent Crude Futures) - Natural Gas (NATGAS) → NYMEX:NG1! (Natural Gas Futures) **Supported Industrial Metals (1):** - Copper (COPPER) → COMEX:HG1! (Copper Futures) **How the automatic detection works:** The indicator intelligently identifies the asset type by analyzing: 1. Exchange name (FX, OANDA, TVC, COMEX, NYMEX, etc.) 2. Currency pair pattern (6-letter codes like EURUSD, GBPUSD) 3. Commodity identifiers (XAU for gold, XAG for silver, OIL for crude) When a supported instrument is detected, the indicator automatically switches to the corresponding futures contract for volume analysis. For stocks, cryptocurrencies, and other assets, the indicator uses the native volume data from the current chart. **Visual feedback:** An information table appears in the top-right corner of the MACD pane showing: - Current chart symbol - Exchange name - Currency pair or asset name - Volume source being used (highlighted in orange for futures, yellow for native volume) - Current high volume status This provides complete transparency about which data source the indicator is using for its volume analysis. ## How to Use ### Basic Setup 1. Add the indicator to your chart 2. The indicator displays in a separate pane (MACD) and overlay (signals/volume bars) 3. Default settings work well for most assets, but can be customized ### Signal Interpretation ### Visual Signals **Visual Signals:** - **Green "BUY" label** - Bullish MACD crossover confirmed by high volume - **Red "SELL" label** - Bearish MACD crossunder confirmed by high volume - **Green/Red candles** - Highlight bars with volume exceeding the threshold - **Light green/red background** - Emphasizes signal bars on the chart **Information Table:** A detailed information table appears in the top-right corner of the MACD pane, providing real-time transparency about the indicator's operation: - **Chart:** Current symbol being analyzed - **Exchange:** The exchange or data feed being used - **Pair:** The currency pair or asset name extracted from the ticker - **Volume From:** The actual symbol used for volume analysis - Orange color indicates CME or COMEX futures are being used (automatic institutional volume) - Yellow color indicates native volume from the chart symbol is being used - Hover tooltip shows whether automatic futures mapping is active - **High Volume:** Current status showing YES (green) when volume exceeds threshold, NO (gray) otherwise This table ensures complete transparency and allows you to verify that the correct volume source is being used for your analysis. **Volume Analysis:** - Gray histogram bars = Normal volume - Red histogram bars = High volume (exceeds threshold) - Green line = Volume moving average baseline **MACD Analysis:** - Blue line = MACD line (momentum indicator) - Orange line = Signal line (trend confirmation) - Gray dotted line = Zero line (bullish above, bearish below) ### Parameter Customization **MACD Parameters:** - Adjust Fast/Slow EMA lengths for different sensitivities - Shorter periods = More signals, faster response - Longer periods = Fewer signals, less noise **Volume Parameters:** - **Volume MA Period:** Higher values smooth volume analysis - **High Volume Ratio:** Lower values (1.5x) = More signals; Higher values (3.0x) = Fewer, stronger signals - **Volume Lookback Bars:** Controls how recent the volume spike must be **Direction Filters:** - **Only Buy Signals:** Enables long-only strategy mode - **Only Sell Signals:** Enables short-only strategy mode ### Alert Configuration The indicator includes three alert types: 1. **Buy Signal Alert** - Triggers when bullish signal appears 2. **Sell Signal Alert** - Triggers when bearish signal appears 3. **High Volume Alert** - Triggers when volume exceeds threshold To set up alerts: 1. Click the indicator name → "Add alert on Smart MACD Volume Trader" 2. Select desired alert condition 3. Configure notification method (popup, email, webhook, etc.) ## Trading Strategy Guidelines ### Best Practices **Recommended markets:** - Liquid stocks (large-cap, high daily volume) - Major forex pairs (EURUSD, GBPUSD, USDJPY, AUDUSD, USDCAD, USDCHF, NZDUSD) - Exotic forex pairs (USDMXN, USDRUB, USDBRL, USDZAR) - Cross pairs (EURJPY, GBPJPY, EURGBP, AUDJPY, EURAUD, GBPAUD) - Precious metals (Gold, Silver with automatic COMEX futures mapping) - Energy commodities (Crude Oil, Natural Gas with automatic NYMEX futures mapping) - Industrial metals (Copper with automatic COMEX futures mapping) - Major cryptocurrency pairs - Index futures and ETFs **Timeframe recommendations:** - **Day trading:** 5-minute to 15-minute charts - **Swing trading:** 1-hour to 4-hour charts - **Position trading:** Daily charts **Risk management:** - Use signals as entry confirmation, not standalone strategy - Combine with support/resistance levels - Consider overall market trend direction - Always use stop-loss orders ### Strategy Examples **Trend Following Strategy:** 1. Identify overall trend using higher timeframe (e.g., daily chart) 2. Trade only in trend direction 3. Use "Only Buy" filter in uptrends, "Only Sell" in downtrends 4. Enter on signal, exit on opposite signal or at resistance/support **Volume Breakout Strategy:** 1. Wait for consolidation period (low volume, tight MACD range) 2. Enter when signal appears with high volume (confirms breakout) 3. Target previous swing highs/lows 4. Stop loss below/above recent consolidation **Forex Scalping Strategy (with automatic CME futures):** 1. The indicator automatically detects forex pairs and uses CME futures volume 2. Trade during active sessions only (use session filter) 3. Focus on quick profits (10-20 pips) 4. Exit at opposite signal or profit target **Commodities Trading Strategy (Gold, Silver, Oil):** 1. The indicator automatically maps to COMEX and NYMEX futures contracts 2. Trade during high-liquidity sessions (overlap of major markets) 3. Use the high volume confirmation to identify institutional entry points 4. Combine with key support and resistance levels for entries 5. Monitor the information table to confirm futures volume is being used (orange color) 6. Exit on opposite MACD signal or at predefined profit targets ## Why This Combination Works ### The Volume Advantage Studies consistently show that price movements accompanied by high volume are more likely to continue, while low-volume movements often reverse. This indicator leverages this principle by requiring volume confirmation. **Key benefits:** 1. **Reduced False Signals:** Eliminates MACD whipsaws during low-volume consolidation 2. **Confirmation Bias:** Two independent indicators (price momentum + volume) agreeing 3. **Institutional Alignment:** High volume often indicates institutional participation 4. **Trend Validation:** Volume confirms that price momentum has "conviction" ### Statistical Edge By combining two uncorrelated signals (MACD crossovers and volume spikes), the indicator creates a higher-probability setup than either signal alone. The lookback mechanism ensures signals aren't missed if volume spike slightly precedes the MACD cross. ## Supported Exchanges and Automatic Detection The indicator includes intelligent asset detection that works across multiple exchanges and ticker formats: **Forex Exchanges (Automatic CME Mapping):** - FX (TradingView forex feed) - OANDA - FXCM - SAXO - FOREXCOM - PEPPERSTONE - EASYMARKETS - FX_IDC **Commodity Exchanges (Automatic COMEX/NYMEX Mapping):** - TVC (TradingView commodity feed) - COMEX (directly) - NYMEX (directly) - ICEUS **Other Asset Classes (Native Volume):** - Stock exchanges (NASDAQ, NYSE, AMEX, etc.) - Cryptocurrency exchanges (BINANCE, COINBASE, KRAKEN, etc.) - Index providers (SP, DJ, etc.) The detection algorithm analyzes three factors: 1. Exchange prefix in the ticker symbol 2. Pattern matching for currency pairs (6-letter codes) 3. Commodity identifiers in the symbol name This ensures accurate automatic detection regardless of which data feed or exchange you use for charting. The information table in the top-right corner always displays which volume source is being used, providing complete transparency. ## Technical Details **Calculations:** - MACD Fast MA: EMA(close, fastLength) - MACD Slow MA: EMA(close, slowLength) - MACD Line: Fast MA - Slow MA - Signal Line: SMA(MACD Line, signalLength) - Volume MA: Exponential MA of volume - High Volume: Current volume >= Volume MA × Ratio **Signal logic:** ``` Buy Signal = (MACD crosses above Signal) AND (High volume in last N bars) Sell Signal = (MACD crosses below Signal) AND (High volume in last N bars) ``` ## Parameters Reference | Parameter | Default | Description | |-----------|---------|-------------| | Volume Symbol | Blank | Manual override for volume source (leave blank for automatic detection) | | Use CME Futures | False | Legacy option (automatic detection is now built-in) | | Alert Session | 1530-2200 | Active session time range for alerts | | Timezone | UTC+1 | Timezone for alert sessions | | Volume MA Period | 20 | Number of periods for volume moving average | | High Volume Ratio | 2.0 | Volume threshold multiplier (2.0 = 200% of average) | | Volume Lookback | 5 | Number of bars to check for high volume confirmation | | MACD Fast Length | 12 | Fast EMA period for MACD calculation | | MACD Slow Length | 26 | Slow EMA period for MACD calculation | | MACD Signal Length | 9 | Signal line SMA period | | Only Buy | False | Filter to show only bullish signals | | Only Sell | False | Filter to show only bearish signals | | Show Signals | True | Display buy and sell labels on chart | ## Optimization Tips **For volatile markets (crypto, small caps):** - Increase High Volume Ratio to 2.5-3.0 - Reduce Volume Lookback to 3-4 bars - Consider faster MACD settings (8, 17, 9) **For stable markets (large-cap stocks, bonds):** - Decrease High Volume Ratio to 1.5-1.8 - Increase Volume MA Period to 30-50 - Use standard MACD settings **For forex (with automatic CME futures):** - The indicator automatically uses CME futures when forex pairs are detected - Set appropriate trading session based on your timezone - Use Volume Lookback of 5-7 bars - Consider session-based alerts only - Monitor the information table to verify correct futures mapping **For commodities (Gold, Silver, Oil, Copper):** - The indicator automatically maps to COMEX and NYMEX futures - Increase High Volume Ratio to 2.0-2.5 for metals - Use slightly higher Volume MA Period (25-30) for smoother analysis - Trade during active market hours for best volume data - The information table will show the futures contract being used (orange highlight) ## Limitations and Considerations **What this indicator does NOT do:** - Does not predict future price direction - Does not guarantee profitable trades - Does not replace proper risk management - Does not work well in extremely low-volume conditions **Market conditions to avoid:** - Pre-market and after-hours sessions (low volume) - Major news events (volatile, unpredictable volume) - Holidays and low-liquidity periods - Extremely low float stocks ## Conclusion Smart MACD Volume Trader represents a significant evolution of the traditional MACD indicator by combining volume confirmation with automatic institutional volume integration. This dual-confirmation approach significantly improves signal quality by filtering out low-conviction price movements and ensuring traders work with accurate volume data. The indicator's automatic detection and mapping system supports over 24 instruments across forex, commodities, and metals markets. By intelligently switching to CME and COMEX futures contracts when appropriate, the indicator provides forex and commodity traders with the same quality of volume data that stock traders naturally have access to. This indicator is particularly valuable for traders who want to: - Align their entries with institutional money flow - Avoid getting trapped in false breakouts - Trade forex pairs with reliable volume data - Access accurate volume information for gold, silver, and energy commodities - Combine momentum and volume analysis in a single, streamlined tool Whether you are day trading stocks, swing trading forex pairs, or positioning in commodities markets, this indicator provides a robust framework for identifying high-probability momentum trades backed by genuine institutional participation. The automatic futures mapping works seamlessly across all supported instruments, requiring no manual configuration or expertise in futures markets. --- ## Support and Updates This indicator is actively maintained and updated based on user feedback and market conditions. For questions about implementation or custom modifications, please use the comments section below. **Disclaimer:** This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always conduct your own analysis and risk management before trading.
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của Smart-Day-Trader
Squeeze Hour Frequency [CHE]Squeeze Hour Frequency (ATR-PR) — Standalone — Tracks daily squeeze occurrences by hour to reveal time-based volatility patterns Summary This indicator identifies periods of unusually low volatility, defined as squeezes, and tallies their frequency across each hour of the day over historical trading sessions. By aggregating counts into a sortable table, it helps users spot hours prone to these conditions, enabling better scheduling of trading activity to avoid or target specific intraday regimes. Signals gain robustness through percentile-based detection that adapts to recent volatility history, differing from fixed-threshold methods by focusing on relative lowness rather than absolute levels, which reduces false positives in varying market environments. Motivation: Why this design? Traders often face uneven intraday volatility, with certain hours showing clustered low-activity phases that precede or follow breakouts, leading to mistimed entries or overlooked calm periods. The core idea of hourly squeeze frequency addresses this by binning low-volatility events into 24 hourly slots and counting distinct daily occurrences, providing a historical profile of when squeezes cluster. This reveals time-of-day biases without relying on real-time alerts, allowing proactive adjustments to session focus. What’s different vs. standard approaches? - Reference baseline: Classical volatility tools like simple moving average crossovers or fixed ATR thresholds, which flag squeezes uniformly across the day. - Architecture differences: - Uses persistent arrays to track one squeeze per hour per day, preventing overcounting within sessions. - Employs custom sorting on ratio arrays for dynamic table display, prioritizing top or bottom performers. - Handles timezones explicitly to ensure consistent binning across global assets. - Practical effect: Charts show a persistent table ranking hours by squeeze share, making intraday patterns immediately visible—such as a top hour capturing over 20 percent of total events—unlike static overlays that ignore temporal distribution, which matters for avoiding low-liquidity traps in crypto or forex. How it works (technical) The indicator first computes a rolling volatility measure over a specified lookback period. It then derives a relative ranking of the current value against recent history within a window of bars. A squeeze is flagged when this ranking falls below a user-defined cutoff, indicating the value is among the lowest in the recent sample. On each bar, the local hour is extracted using the selected timezone. If a squeeze occurs and the bar has price data, the count for that hour increments only if no prior mark exists for the current day, using a persistent array to store the last marked day per hour. This ensures one tally per unique trading day per slot. At the final bar, arrays compile counts and ratios for all 24 hours, where the ratio represents each hour's share of total squeezes observed. These are sorted ascending or descending based on display mode, and the top or bottom subset populates the table. Background shading highlights live squeezes in red for visual confirmation. Initialization uses zero-filled arrays for counts and negative seeds for day tracking, with state persisting across bars via variable declarations. No higher timeframe data is pulled, so there is no repaint risk from external fetches; all logic runs on confirmed bars. Parameter Guide ATR Length — Controls the lookback for the volatility measure, influencing sensitivity to short-term fluctuations; shorter values increase responsiveness but add noise, longer ones smooth for stability — Default: 14 — Trade-offs/Tips: Use 10-20 for intraday charts to balance quick detection with fewer false squeezes; test on historical data to avoid over-smoothing in trending markets. Percentile Window (bars) — Sets the history depth for ranking the current volatility value, affecting how "low" is defined relative to past; wider windows emphasize long-term norms — Default: 252 — Trade-offs/Tips: 100-300 bars suit daily cycles; narrower for fast assets like crypto to catch recent regimes, but risks instability in sparse data. Squeeze threshold (PR < x) — Defines the cutoff for flagging low relative volatility, where values below this mark a squeeze; lower thresholds tighten detection for rarer events — Default: 10.0 — Trade-offs/Tips: 5-15 percent for conservative signals reducing false positives; raise to 20 for more frequent highlights in high-vol environments, monitoring for increased noise. Timezone — Specifies the reference for hourly binning, ensuring alignment with market sessions — Default: Exchange — Trade-offs/Tips: Set to "America/New_York" for US assets; mismatches can skew counts, so verify against chart timezone. Show Table — Toggles the results display, essential for reviewing frequencies — Default: true — Trade-offs/Tips: Disable on mobile for performance; pair with position tweaks for clean overlays. Pos — Places the table on the chart pane — Default: Top Right — Trade-offs/Tips: Bottom Left avoids candle occlusion on volatile charts. Font — Adjusts text readability in the table — Default: normal — Trade-offs/Tips: Tiny for dense views, large for emphasis on key hours. Dark — Applies high-contrast colors for visibility — Default: true — Trade-offs/Tips: Toggle false in light themes to prevent washout. Display — Filters table rows to focus on extremes or full list — Default: All — Trade-offs/Tips: Top 3 for quick scans of risky hours; Bottom 3 highlights safe low-squeeze periods. Reading & Interpretation Red background shading appears on bars meeting the squeeze condition, signaling current low relative volatility. The table lists hours as "H0" to "H23", with columns for daily squeeze counts, percentage share of total squeezes (summing to 100 percent across hours), and an arrow marker on the top hour. A summary row above details the peak count, its share, and the leading hour. A label at the last bar recaps total days observed, data-valid days, and top hour stats. Rising shares indicate clustering, suggesting regime persistence in that slot. Practical Workflows & Combinations - Trend following: Scan for hours with low squeeze shares to enter during stable regimes; confirm with higher highs or lower lows on the 15-minute chart, avoiding top-share hours post-news like tariff announcements. - Exits/Stops: Tighten stops in high-share hours to guard against sudden vol spikes; use the table to shift to conservative sizing outside peak squeeze times. - Multi-asset/Multi-TF: Defaults work across crypto pairs on 5-60 minute timeframes; for stocks, widen percentile window to 500 bars. Combine with volume oscillators—enter only if squeeze count is below average for the asset. Behavior, Constraints & Performance Logic executes on closed bars, with live bars updating counts provisionally but finalizing on confirmation; table refreshes only at the last bar, avoiding intrabar flicker. No security calls or higher timeframes, so no repaint from external data. Resources include a 5000-bar history limit, loops up to 24 iterations for sorting and totals, and arrays sized to 24 elements; labels and table are capped at 500 each for efficiency. Known limits: Skips hours without bars (e.g., weekends), assumes uniform data availability, and may undercount in sparse sessions; timezone shifts can alter profiles without warning. Sensible Defaults & Quick Tuning Start with ATR Length at 14, Percentile Window at 252, and threshold at 10.0 for broad crypto use. If too many squeezes flag (noisy table), raise threshold to 15.0 and narrow window to 100 for stricter relative lowness. For sluggish detection in calm markets, drop ATR Length to 10 and threshold to 5.0 to capture subtler dips. In high-vol assets, widen window to 500 and threshold to 20.0 for stability. What this indicator is—and isn’t This is a historical frequency tracker and visualization layer for intraday volatility patterns, best as a filter in multi-tool setups. It is not a standalone signal generator, predictive model, or risk manager—pair it with price action, news filters, and position sizing rules. Disclaimer The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context. Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved. By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk. Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts. Best regards and happy trading Chervolino Thanks to Duyck for the ma sorter
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của chervolino
Cumulative Returns by Session [BackQuant]Cumulative Returns by Session What this is This tool breaks the trading day into three user-defined sessions and tracks how much each session contributes to return, volatility, and volume. It then aggregates results over a rolling window so you can see which session has been pulling its weight, how streaky each session has been, and how sessions relate to one another through a compact correlation heatmap. We’ve also given the functionality for the user to use a simplified table, just by switching off all settings they are not interested in. How it works 1) Session segmentation You define APAC, EU, and US sessions with explicit hours and time zones. The script detects when each session starts and ends on every intraday bar and records its open, intraday high and low, close, and summed volume. 2) Per-session math At each session end the script computes: Return — either Percent: (Close−Open)÷Open×100(Close − Open) ÷ Open × 100(Close−Open)÷Open×100 or Points: (Close−Open)(Close − Open)(Close−Open), based on your selection. Volatility — either Range: (High−Low)÷Open×100(High − Low) ÷ Open × 100(High−Low)÷Open×100 or ATR scaled by price: ATR÷Open×100ATR ÷ Open × 100ATR÷Open×100. Volume — total volume transacted during that session. 3) Storage and lookback Each day’s three session stats are stored as a row. You choose how many recent sessions to keep in memory. The script then: Builds cumulative returns for APAC, EU, US across the lookback. Computes averages, win rates, and a Sharpe-like ratio avgreturn÷avgvolatilityavg return ÷ avg volatilityavgreturn÷avgvolatility per session. Tracks streaks of positive or negative sessions to show momentum. Tracks drawdowns on cumulative returns to show worst runs from peak. Computes rolling means over a short window for short-term drift. 4) Correlation heatmap Using the stored arrays of session returns, the script calculates Pearson correlations between APAC–EU, APAC–US, and EU–US, and colors the matrix by strength and sign so you can spot coupling or decoupling at a glance. What it plots Three lines: cumulative return for APAC, EU, US over the chosen lookback. Zero reference line for orientation. A statistics table with cumulative %, average %, positive session rate, and optional columns for volatility, average volume, max drawdown, current streak, return-to-vol ratio, and rolling average. A small correlation heatmap table showing APAC, EU, US cross-session correlations. How to use it Pick the asset — leave Custom Instrument empty to use the chart symbol, or point to another symbol for cross-asset studies. Set your sessions and time zones — defaults approximate APAC, EU, and US hours, but you can align them to exchange times or your workflow. Choose calculation modes — Percent vs Points for return, Range vs ATR for volatility. Points are convenient for futures and fixed-tick assets, Percent is comparable across symbols. Decide the lookback — more sessions smooths lines and stats; fewer sessions makes the tool more reactive. Toggle analytics — add volatility, volume, drawdown, streaks, Sharpe-like ratio, rolling averages, and the correlation table as needed. Why session attribution helps Different sessions are driven by different flows. Asia often sets the overnight tone, Europe adds liquidity and direction changes, and the US session can dominate range expansion. Separating contributions by session helps you: Identify which session has been the main driver of net trend. Measure whether volatility or volume is concentrated in a specific window. See if one session’s gains are consistently given back in another. Adapt tactics: fade during a mean-reverting session, press during a trending session. Reading the tables Cumulative % — sum of session returns over the lookback. The sign and slope tell you who is carrying the move. Avg Return % and Positive Sessions % — direction and hit rate. A low average but high hit rate implies many small moves; the reverse implies occasional big swings. Avg Volatility % — typical intrabars range for that session. Compare with Avg Return to judge efficiency. Return/Vol Ratio — return per unit of volatility. Higher is better for stability. Max Drawdown % — worst cumulative give-back within the lookback. A quick way to spot riskiness by session. Current Streak — consecutive up or down sessions. Useful for mean-reversion or regime awareness. Rolling Avg % — short-window drift indicator to catch recent turnarounds. Correlation matrix — green clusters indicate sessions tending to move together; red indicates offsetting behavior. Settings overview Basic Number of Sessions — how many recent days to include. Custom Instrument — analyze another ticker while staying on your current chart. Session Configuration and Times Enable or hide APAC, EU, US rows. Set hours per session and the specific time zone for each. Calculation Methods Return Calculation — Percent or Points. Volatility Calculation — Range or ATR; ATR Length when applicable. Advanced Analytics Correlation, Drawdown, Momentum, Sharpe-like ratio, Rolling Statistics, Rolling Period. Display Options and Colors Show Statistics Table and its position. Toggle columns for Volatility and Volume. Pick individual colors for each session line and row accents. Common applications Session bias mapping — find which window tends to trend in your market and plan exposure accordingly. Strategy scheduling — allocate attention or risk to the session with the best return-to-vol ratio. News and macro awareness — see if correlation rises around central bank cycles or major data releases. Cross-asset monitoring — set the Custom Instrument to a driver (index future, DXY, yields) to see if your symbol reacts in a particular session. Notes This indicator works on intraday charts, since sessions are defined within a day. If you change session clocks or time zones, give the script a few bars to accumulate fresh rows. Percent vs Points and Range vs ATR choices affect comparability across assets, so be consistent when comparing symbols. Session context is one of the simplest ways to explain a messy tape. By separating the day into three windows and scoring each one on return, volatility, and consistency, this tool shows not just where price ended up but when and how it got there. Use the cumulative lines to spot the steady driver, read the table to judge quality and risk, and glance at the heatmap to learn whether the sessions are amplifying or canceling one another. Adjust the hours to your market and let the data tell you which session deserves your focus.
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của BackQuant
ATAI Volume analysis with price action V 1.00ATAI Volume Analysis with Price Action 1. Introduction 1.1 Overview ATAI Volume Analysis with Price Action is a composite indicator designed for TradingView. It combines per‑side volume data —that is, how much buying and selling occurs during each bar—with standard price‑structure elements such as swings, trend lines and support/resistance. By blending these elements the script aims to help a trader understand which side is in control, whether a breakout is genuine, when markets are potentially exhausted and where liquidity providers might be active. The indicator is built around TradingView’s up/down volume feed accessed via the TradingView/ta/10 library. The following excerpt from the script illustrates how this feed is configured: import TradingView/ta/10 as tvta // Determine lower timeframe string based on user choice and chart resolution string lower_tf_breakout = use_custom_tf_input ? custom_tf_input : timeframe.isseconds ? "1S" : timeframe.isintraday ? "1" : timeframe.isdaily ? "5" : "60" // Request up/down volume (both positive) = tvta.requestUpAndDownVolume(lower_tf_breakout) Lower‑timeframe selection. If you do not specify a custom lower timeframe, the script chooses a default based on your chart resolution: 1 second for second charts, 1 minute for intraday charts, 5 minutes for daily charts and 60 minutes for anything longer. Smaller intervals provide a more precise view of buyer and seller flow but cover fewer bars. Larger intervals cover more history at the cost of granularity. Tick vs. time bars. Many trading platforms offer a tick / intrabar calculation mode that updates an indicator on every trade rather than only on bar close. Turning on one‑tick calculation will give the most accurate split between buy and sell volume on the current bar, but it typically reduces the amount of historical data available. For the highest fidelity in live trading you can enable this mode; for studying longer histories you might prefer to disable it. When volume data is completely unavailable (some instruments and crypto pairs), all modules that rely on it will remain silent and only the price‑structure backbone will operate. Figure caption, Each panel shows the indicator’s info table for a different volume sampling interval. In the left chart, the parentheses “(5)” beside the buy‑volume figure denote that the script is aggregating volume over five‑minute bars; the center chart uses “(1)” for one‑minute bars; and the right chart uses “(1T)” for a one‑tick interval. These notations tell you which lower timeframe is driving the volume calculations. Shorter intervals such as 1 minute or 1 tick provide finer detail on buyer and seller flow, but they cover fewer bars; longer intervals like five‑minute bars smooth the data and give more history. Figure caption, The values in parentheses inside the info table come directly from the Breakout — Settings. The first row shows the custom lower-timeframe used for volume calculations (e.g., “(1)”, “(5)”, or “(1T)”) 2. Price‑Structure Backbone Even without volume, the indicator draws structural features that underpin all other modules. These features are always on and serve as the reference levels for subsequent calculations. 2.1 What it draws • Pivots: Swing highs and lows are detected using the pivot_left_input and pivot_right_input settings. A pivot high is identified when the high recorded pivot_right_input bars ago exceeds the highs of the preceding pivot_left_input bars and is also higher than (or equal to) the highs of the subsequent pivot_right_input bars; pivot lows follow the inverse logic. The indicator retains only a fixed number of such pivot points per side, as defined by point_count_input, discarding the oldest ones when the limit is exceeded. • Trend lines: For each side, the indicator connects the earliest stored pivot and the most recent pivot (oldest high to newest high, and oldest low to newest low). When a new pivot is added or an old one drops out of the lookback window, the line’s endpoints—and therefore its slope—are recalculated accordingly. • Horizontal support/resistance: The highest high and lowest low within the lookback window defined by length_input are plotted as horizontal dashed lines. These serve as short‑term support and resistance levels. • Ranked labels: If showPivotLabels is enabled the indicator prints labels such as “HH1”, “HH2”, “LL1” and “LL2” near each pivot. The ranking is determined by comparing the price of each stored pivot: HH1 is the highest high, HH2 is the second highest, and so on; LL1 is the lowest low, LL2 is the second lowest. In the case of equal prices the newer pivot gets the better rank. Labels are offset from price using ½ × ATR × label_atr_multiplier, with the ATR length defined by label_atr_len_input. A dotted connector links each label to the candle’s wick. 2.2 Key settings • length_input: Window length for finding the highest and lowest values and for determining trend line endpoints. A larger value considers more history and will generate longer trend lines and S/R levels. • pivot_left_input, pivot_right_input: Strictness of swing confirmation. Higher values require more bars on either side to form a pivot; lower values create more pivots but may include minor swings. • point_count_input: How many pivots are kept in memory on each side. When new pivots exceed this number the oldest ones are discarded. • label_atr_len_input and label_atr_multiplier: Determine how far pivot labels are offset from the bar using ATR. Increasing the multiplier moves labels further away from price. • Styling inputs for trend lines, horizontal lines and labels (color, width and line style). Figure caption, The chart illustrates how the indicator’s price‑structure backbone operates. In this daily example, the script scans for bars where the high (or low) pivot_right_input bars back is higher (or lower) than the preceding pivot_left_input bars and higher or lower than the subsequent pivot_right_input bars; only those bars are marked as pivots. These pivot points are stored and ranked: the highest high is labelled “HH1”, the second‑highest “HH2”, and so on, while lows are marked “LL1”, “LL2”, etc. Each label is offset from the price by half of an ATR‑based distance to keep the chart clear, and a dotted connector links the label to the actual candle. The red diagonal line connects the earliest and latest stored high pivots, and the green line does the same for low pivots; when a new pivot is added or an old one drops out of the lookback window, the end‑points and slopes adjust accordingly. Dashed horizontal lines mark the highest high and lowest low within the current lookback window, providing visual support and resistance levels. Together, these elements form the structural backbone that other modules reference, even when volume data is unavailable. 3. Breakout Module 3.1 Concept This module confirms that a price break beyond a recent high or low is supported by a genuine shift in buying or selling pressure. It requires price to clear the highest high (“HH1”) or lowest low (“LL1”) and, simultaneously, that the winning side shows a significant volume spike, dominance and ranking. Only when all volume and price conditions pass is a breakout labelled. 3.2 Inputs • lookback_break_input : This controls the number of bars used to compute moving averages and percentiles for volume. A larger value smooths the averages and percentiles but makes the indicator respond more slowly. • vol_mult_input : The “spike” multiplier; the current buy or sell volume must be at least this multiple of its moving average over the lookback window to qualify as a breakout. • rank_threshold_input (0–100) : Defines a volume percentile cutoff: the current buyer/seller volume must be in the top (100−threshold)%(100−threshold)% of all volumes within the lookback window. For example, if set to 80, the current volume must be in the top 20 % of the lookback distribution. • ratio_threshold_input (0–1) : Specifies the minimum share of total volume that the buyer (for a bullish breakout) or seller (for bearish) must hold on the current bar; the code also requires that the cumulative buyer volume over the lookback window exceeds the seller volume (and vice versa for bearish cases). • use_custom_tf_input / custom_tf_input : When enabled, these inputs override the automatic choice of lower timeframe for up/down volume; otherwise the script selects a sensible default based on the chart’s timeframe. • Label appearance settings : Separate options control the ATR-based offset length, offset multiplier, label size and colors for bullish and bearish breakout labels, as well as the connector style and width. 3.3 Detection logic 1. Data preparation : Retrieve per‑side volume from the lower timeframe and take absolute values. Build rolling arrays of the last lookback_break_input values to compute simple moving averages (SMAs), cumulative sums and percentile ranks for buy and sell volume. 2. Volume spike: A spike is flagged when the current buy (or, in the bearish case, sell) volume is at least vol_mult_input times its SMA over the lookback window. 3. Dominance test: The buyer’s (or seller’s) share of total volume on the current bar must meet or exceed ratio_threshold_input. In addition, the cumulative sum of buyer volume over the window must exceed the cumulative sum of seller volume for a bullish breakout (and vice versa for bearish). A separate requirement checks the sign of delta: for bullish breakouts delta_breakout must be non‑negative; for bearish breakouts it must be non‑positive. 4. Percentile rank: The current volume must fall within the top (100 – rank_threshold_input) percent of the lookback distribution—ensuring that the spike is unusually large relative to recent history. 5. Price test: For a bullish signal, the closing price must close above the highest pivot (HH1); for a bearish signal, the close must be below the lowest pivot (LL1). 6. Labeling: When all conditions above are satisfied, the indicator prints “Breakout ↑” above the bar (bullish) or “Breakout ↓” below the bar (bearish). Labels are offset using half of an ATR‑based distance and linked to the candle with a dotted connector. Figure caption, (Breakout ↑ example) , On this daily chart, price pushes above the red trendline and the highest prior pivot (HH1). The indicator recognizes this as a valid breakout because the buyer‑side volume on the lower timeframe spikes above its recent moving average and buyers dominate the volume statistics over the lookback period; when combined with a close above HH1, this satisfies the breakout conditions. The “Breakout ↑” label appears above the candle, and the info table highlights that up‑volume is elevated relative to its 11‑bar average, buyer share exceeds the dominance threshold and money‑flow metrics support the move. Figure caption, In this daily example, price breaks below the lowest pivot (LL1) and the lower green trendline. The indicator identifies this as a bearish breakout because sell‑side volume is sharply elevated—about twice its 11‑bar average—and sellers dominate both the bar and the lookback window. With the close falling below LL1, the script triggers a Breakout ↓ label and marks the corresponding row in the info table, which shows strong down volume, negative delta and a seller share comfortably above the dominance threshold. 4. Market Phase Module (Volume Only) 4.1 Concept Not all markets trend; many cycle between periods of accumulation (buying pressure building up), distribution (selling pressure dominating) and neutral behavior. This module classifies the current bar into one of these phases without using ATR , relying solely on buyer and seller volume statistics. It looks at net flows, ratio changes and an OBV‑like cumulative line with dual‑reference (1‑ and 2‑bar) trends. The result is displayed both as on‑chart labels and in a dedicated row of the info table. 4.2 Inputs • phase_period_len: Number of bars over which to compute sums and ratios for phase detection. • phase_ratio_thresh : Minimum buyer share (for accumulation) or minimum seller share (for distribution, derived as 1 − phase_ratio_thresh) of the total volume. • strict_mode: When enabled, both the 1‑bar and 2‑bar changes in each statistic must agree on the direction (strict confirmation); when disabled, only one of the two references needs to agree (looser confirmation). • Color customisation for info table cells and label styling for accumulation and distribution phases, including ATR length, multiplier, label size, colors and connector styles. • show_phase_module: Toggles the entire phase detection subsystem. • show_phase_labels: Controls whether on‑chart labels are drawn when accumulation or distribution is detected. 4.3 Detection logic The module computes three families of statistics over the volume window defined by phase_period_len: 1. Net sum (buyers minus sellers): net_sum_phase = Σ(buy) − Σ(sell). A positive value indicates a predominance of buyers. The code also computes the differences between the current value and the values 1 and 2 bars ago (d_net_1, d_net_2) to derive up/down trends. 2. Buyer ratio: The instantaneous ratio TF_buy_breakout / TF_tot_breakout and the window ratio Σ(buy) / Σ(total). The current ratio must exceed phase_ratio_thresh for accumulation or fall below 1 − phase_ratio_thresh for distribution. The first and second differences of the window ratio (d_ratio_1, d_ratio_2) determine trend direction. 3. OBV‑like cumulative net flow: An on‑balance volume analogue obv_net_phase increments by TF_buy_breakout − TF_sell_breakout each bar. Its differences over the last 1 and 2 bars (d_obv_1, d_obv_2) provide trend clues. The algorithm then combines these signals: • For strict mode , accumulation requires: (a) current ratio ≥ threshold, (b) cumulative ratio ≥ threshold, (c) both ratio differences ≥ 0, (d) net sum differences ≥ 0, and (e) OBV differences ≥ 0. Distribution is the mirror case. • For loose mode , it relaxes the directional tests: either the 1‑ or the 2‑bar difference needs to agree in each category. If all conditions for accumulation are satisfied, the phase is labelled “Accumulation” ; if all conditions for distribution are satisfied, it’s labelled “Distribution” ; otherwise the phase is “Neutral” . 4.4 Outputs • Info table row : Row 8 displays “Market Phase (Vol)” on the left and the detected phase (Accumulation, Distribution or Neutral) on the right. The text colour of both cells matches a user‑selectable palette (typically green for accumulation, red for distribution and grey for neutral). • On‑chart labels : When show_phase_labels is enabled and a phase persists for at least one bar, the module prints a label above the bar ( “Accum” ) or below the bar ( “Dist” ) with a dashed or dotted connector. The label is offset using ATR based on phase_label_atr_len_input and phase_label_multiplier and is styled according to user preferences. Figure caption, The chart displays a red “Dist” label above a particular bar, indicating that the accumulation/distribution module identified a distribution phase at that point. The detection is based on seller dominance: during that bar, the net buyer-minus-seller flow and the OBV‑style cumulative flow were trending down, and the buyer ratio had dropped below the preset threshold. These conditions satisfy the distribution criteria in strict mode. The label is placed above the bar using an ATR‑based offset and a dashed connector. By the time of the current bar in the screenshot, the phase indicator shows “Neutral” in the info table—signaling that neither accumulation nor distribution conditions are currently met—yet the historical “Dist” label remains to mark where the prior distribution phase began. Figure caption, In this example the market phase module has signaled an Accumulation phase. Three bars before the current candle, the algorithm detected a shift toward buyers: up‑volume exceeded its moving average, down‑volume was below average, and the buyer share of total volume climbed above the threshold while the on‑balance net flow and cumulative ratios were trending upwards. The blue “Accum” label anchored below that bar marks the start of the phase; it remains on the chart because successive bars continue to satisfy the accumulation conditions. The info table confirms this: the “Market Phase (Vol)” row still reads Accumulation, and the ratio and sum rows show buyers dominating both on the current bar and across the lookback window. 5. OB/OS Spike Module 5.1 What overbought/oversold means here In many markets, a rapid extension up or down is often followed by a period of consolidation or reversal. The indicator interprets overbought (OB) conditions as abnormally strong selling risk at or after a price rally and oversold (OS) conditions as unusually strong buying risk after a decline. Importantly, these are not direct trade signals; rather they flag areas where caution or contrarian setups may be appropriate. 5.2 Inputs • minHits_obos (1–7): Minimum number of oscillators that must agree on an overbought or oversold condition for a label to print. • syncWin_obos: Length of a small sliding window over which oscillator votes are smoothed by taking the maximum count observed. This helps filter out choppy signals. • Volume spike criteria: kVolRatio_obos (ratio of current volume to its SMA) and zVolThr_obos (Z‑score threshold) across volLen_obos. Either threshold can trigger a spike. • Oscillator toggles and periods: Each of RSI, Stochastic (K and D), Williams %R, CCI, MFI, DeMarker and Stochastic RSI can be independently enabled; their periods are adjustable. • Label appearance: ATR‑based offset, size, colors for OB and OS labels, plus connector style and width. 5.3 Detection logic 1. Directional volume spikes: Volume spikes are computed separately for buyer and seller volumes. A sell volume spike (sellVolSpike) flags a potential OverBought bar, while a buy volume spike (buyVolSpike) flags a potential OverSold bar. A spike occurs when the respective volume exceeds kVolRatio_obos times its simple moving average over the window or when its Z‑score exceeds zVolThr_obos. 2. Oscillator votes: For each enabled oscillator, calculate its overbought and oversold state using standard thresholds (e.g., RSI ≥ 70 for OB and ≤ 30 for OS; Stochastic %K/%D ≥ 80 for OB and ≤ 20 for OS; etc.). Count how many oscillators vote for OB and how many vote for OS. 3. Minimum hits: Apply the smoothing window syncWin_obos to the vote counts using a maximum‑of‑last‑N approach. A candidate bar is only considered if the smoothed OB hit count ≥ minHits_obos (for OverBought) or the smoothed OS hit count ≥ minHits_obos (for OverSold). 4. Tie‑breaking: If both OverBought and OverSold spike conditions are present on the same bar, compare the smoothed hit counts: the side with the higher count is selected; ties default to OverBought. 5. Label printing: When conditions are met, the bar is labelled as “OverBought X/7” above the candle or “OverSold X/7” below it. “X” is the number of oscillators confirming, and the bracket lists the abbreviations of contributing oscillators. Labels are offset from price using half of an ATR‑scaled distance and can optionally include a dotted or dashed connector line. Figure caption, In this chart the overbought/oversold module has flagged an OverSold signal. A sell‑off from the prior highs brought price down to the lower trend‑line, where the bar marked “OverSold 3/7 DeM” appears. This label indicates that on that bar the module detected a buy‑side volume spike and that at least three of the seven enabled oscillators—in this case including the DeMarker—were in oversold territory. The label is printed below the candle with a dotted connector, signaling that the market may be temporarily exhausted on the downside. After this oversold print, price begins to rebound towards the upper red trend‑line and higher pivot levels. Figure caption, This example shows the overbought/oversold module in action. In the left‑hand panel you can see the OB/OS settings where each oscillator (RSI, Stochastic, Williams %R, CCI, MFI, DeMarker and Stochastic RSI) can be enabled or disabled, and the ATR length and label offset multiplier adjusted. On the chart itself, price has pushed up to the descending red trendline and triggered an “OverBought 3/7” label. That means the sell‑side volume spiked relative to its average and three out of the seven enabled oscillators were in overbought territory. The label is offset above the candle by half of an ATR and connected with a dashed line, signaling that upside momentum may be overextended and a pause or pullback could follow. 6. Buyer/Seller Trap Module 6.1 Concept A bull trap occurs when price appears to break above resistance, attracting buyers, but fails to sustain the move and quickly reverses, leaving a long upper wick and trapping late entrants. A bear trap is the opposite: price breaks below support, lures in sellers, then snaps back, leaving a long lower wick and trapping shorts. This module detects such traps by looking for price structure sweeps, order‑flow mismatches and dominance reversals. It uses a scoring system to differentiate risk from confirmed traps. 6.2 Inputs • trap_lookback_len: Window length used to rank extremes and detect sweeps. • trap_wick_threshold: Minimum proportion of a bar’s range that must be wick (upper for bull traps, lower for bear traps) to qualify as a sweep. • trap_score_risk: Minimum aggregated score required to flag a trap risk. (The code defines a trap_score_confirm input, but confirmation is actually based on price reversal rather than a separate score threshold.) • trap_confirm_bars: Maximum number of bars allowed for price to reverse and confirm the trap. If price does not reverse in this window, the risk label will expire or remain unconfirmed. • Label settings: ATR length and multiplier for offsetting, size, colours for risk and confirmed labels, and connector style and width. Separate settings exist for bull and bear traps. • Toggle inputs: show_trap_module and show_trap_labels enable the module and control whether labels are drawn on the chart. 6.3 Scoring logic The module assigns points to several conditions and sums them to determine whether a trap risk is present. For bull traps, the score is built from the following (bear traps mirror the logic with highs and lows swapped): 1. Sweep (2 points): Price trades above the high pivot (HH1) but fails to close above it and leaves a long upper wick at least trap_wick_threshold × range. For bear traps, price dips below the low pivot (LL1), fails to close below and leaves a long lower wick. 2. Close break (1 point): Price closes beyond HH1 or LL1 without leaving a long wick. 3. Candle/delta mismatch (2 points): The candle closes bullish yet the order flow delta is negative or the seller ratio exceeds 50%, indicating hidden supply. Conversely, a bearish close with positive delta or buyer dominance suggests hidden demand. 4. Dominance inversion (2 points): The current bar’s buyer volume has the highest rank in the lookback window while cumulative sums favor sellers, or vice versa. 5. Low‑volume break (1 point): Price crosses the pivot but total volume is below its moving average. The total score for each side is compared to trap_score_risk. If the score is high enough, a “Bull Trap Risk” or “Bear Trap Risk” label is drawn, offset from the candle by half of an ATR‑scaled distance using a dashed outline. If, within trap_confirm_bars, price reverses beyond the opposite level—drops back below the high pivot for bull traps or rises above the low pivot for bear traps—the label is upgraded to a solid “Bull Trap” or “Bear Trap” . In this version of the code, there is no separate score threshold for confirmation: the variable trap_score_confirm is unused; confirmation depends solely on a successful price reversal within the specified number of bars. Figure caption, In this example the trap module has flagged a Bear Trap Risk. Price initially breaks below the most recent low pivot (LL1), but the bar closes back above that level and leaves a long lower wick, suggesting a failed push lower. Combined with a mismatch between the candle direction and the order flow (buyers regain control) and a reversal in volume dominance, the aggregate score exceeds the risk threshold, so a dashed “Bear Trap Risk” label prints beneath the bar. The green and red trend lines mark the current low and high pivot trajectories, while the horizontal dashed lines show the highest and lowest values in the lookback window. If, within the next few bars, price closes decisively above the support, the risk label would upgrade to a solid “Bear Trap” label. Figure caption, In this example the trap module has identified both ends of a price range. Near the highs, price briefly pushes above the descending red trendline and the recent pivot high, but fails to close there and leaves a noticeable upper wick. That combination of a sweep above resistance and order‑flow mismatch generates a Bull Trap Risk label with a dashed outline, warning that the upside break may not hold. At the opposite extreme, price later dips below the green trendline and the labelled low pivot, then quickly snaps back and closes higher. The long lower wick and subsequent price reversal upgrade the previous bear‑trap risk into a confirmed Bear Trap (solid label), indicating that sellers were caught on a false breakdown. Horizontal dashed lines mark the highest high and lowest low of the lookback window, while the red and green diagonals connect the earliest and latest pivot highs and lows to visualize the range. 7. Sharp Move Module 7.1 Concept Markets sometimes display absorption or climax behavior—periods when one side steadily gains the upper hand before price breaks out with a sharp move. This module evaluates several order‑flow and volume conditions to anticipate such moves. Users can choose how many conditions must be met to flag a risk and how many (plus a price break) are required for confirmation. 7.2 Inputs • sharp Lookback: Number of bars in the window used to compute moving averages, sums, percentile ranks and reference levels. • sharpPercentile: Minimum percentile rank for the current side’s volume; the current buy (or sell) volume must be greater than or equal to this percentile of historical volumes over the lookback window. • sharpVolMult: Multiplier used in the volume climax check. The current side’s volume must exceed this multiple of its average to count as a climax. • sharpRatioThr: Minimum dominance ratio (current side’s volume relative to the opposite side) used in both the instant and cumulative dominance checks. • sharpChurnThr: Maximum ratio of a bar’s range to its ATR for absorption/churn detection; lower values indicate more absorption (large volume in a small range). • sharpScoreRisk: Minimum number of conditions that must be true to print a risk label. • sharpScoreConfirm: Minimum number of conditions plus a price break required for confirmation. • sharpCvdThr: Threshold for cumulative delta divergence versus price change (positive for bullish accumulation, negative for bearish distribution). • Label settings: ATR length (sharpATRlen) and multiplier (sharpLabelMult) for positioning labels, label size, colors and connector styles for bullish and bearish sharp moves. • Toggles: enableSharp activates the module; show_sharp_labels controls whether labels are drawn. 7.3 Conditions (six per side) For each side, the indicator computes six boolean conditions and sums them to form a score: 1. Dominance (instant and cumulative): – Instant dominance: current buy volume ≥ sharpRatioThr × current sell volume. – Cumulative dominance: sum of buy volumes over the window ≥ sharpRatioThr × sum of sell volumes (and vice versa for bearish checks). 2. Accumulation/Distribution divergence: Over the lookback window, cumulative delta rises by at least sharpCvdThr while price fails to rise (bullish), or cumulative delta falls by at least sharpCvdThr while price fails to fall (bearish). 3. Volume climax: The current side’s volume is ≥ sharpVolMult × its average and the product of volume and bar range is the highest in the lookback window. 4. Absorption/Churn: The current side’s volume divided by the bar’s range equals the highest value in the window and the bar’s range divided by ATR ≤ sharpChurnThr (indicating large volume within a small range). 5. Percentile rank: The current side’s volume percentile rank is ≥ sharp Percentile. 6. Mirror logic for sellers: The above checks are repeated with buyer and seller roles swapped and the price break levels reversed. Each condition that passes contributes one point to the corresponding side’s score (0 or 1). Risk and confirmation thresholds are then applied to these scores. 7.4 Scoring and labels • Risk: If scoreBull ≥ sharpScoreRisk, a “Sharp ↑ Risk” label is drawn above the bar. If scoreBear ≥ sharpScoreRisk, a “Sharp ↓ Risk” label is drawn below the bar. • Confirmation: A risk label is upgraded to “Sharp ↑” when scoreBull ≥ sharpScoreConfirm and the bar closes above the highest recent pivot (HH1); for bearish cases, confirmation requires scoreBear ≥ sharpScoreConfirm and a close below the lowest pivot (LL1). • Label positioning: Labels are offset from the candle by ATR × sharpLabelMult (full ATR times multiplier), not half, and may include a dashed or dotted connector line if enabled. Figure caption, In this chart both bullish and bearish sharp‑move setups have been flagged. Earlier in the range, a “Sharp ↓ Risk” label appears beneath a candle: the sell‑side score met the risk threshold, signaling that the combination of strong sell volume, dominance and absorption within a narrow range suggested a potential sharp decline. The price did not close below the lower pivot, so this label remains a “risk” and no confirmation occurred. Later, as the market recovered and volume shifted back to the buy side, a “Sharp ↑ Risk” label prints above a candle near the top of the channel. Here, buy‑side dominance, cumulative delta divergence and a volume climax aligned, but price has not yet closed above the upper pivot (HH1), so the alert is still a risk rather than a confirmed sharp‑up move. Figure caption, In this chart a Sharp ↑ label is displayed above a candle, indicating that the sharp move module has confirmed a bullish breakout. Prior bars satisfied the risk threshold — showing buy‑side dominance, positive cumulative delta divergence, a volume climax and strong absorption in a narrow range — and this candle closes above the highest recent pivot, upgrading the earlier “Sharp ↑ Risk” alert to a full Sharp ↑ signal. The green label is offset from the candle with a dashed connector, while the red and green trend lines trace the high and low pivot trajectories and the dashed horizontals mark the highest and lowest values of the lookback window. 8. Market‑Maker / Spread‑Capture Module 8.1 Concept Liquidity providers often “capture the spread” by buying and selling in almost equal amounts within a very narrow price range. These bars can signal temporary congestion before a move or reflect algorithmic activity. This module flags bars where both buyer and seller volumes are high, the price range is only a few ticks and the buy/sell split remains close to 50%. It helps traders spot potential liquidity pockets. 8.2 Inputs • scalpLookback: Window length used to compute volume averages. • scalpVolMult: Multiplier applied to each side’s average volume; both buy and sell volumes must exceed this multiple. • scalpTickCount: Maximum allowed number of ticks in a bar’s range (calculated as (high − low) / minTick). A value of 1 or 2 captures ultra‑small bars; increasing it relaxes the range requirement. • scalpDeltaRatio: Maximum deviation from a perfect 50/50 split. For example, 0.05 means the buyer share must be between 45% and 55%. • Label settings: ATR length, multiplier, size, colors, connector style and width. • Toggles : show_scalp_module and show_scalp_labels to enable the module and its labels. 8.3 Signal When, on the current bar, both TF_buy_breakout and TF_sell_breakout exceed scalpVolMult times their respective averages and (high − low)/minTick ≤ scalpTickCount and the buyer share is within scalpDeltaRatio of 50%, the module prints a “Spread ↔” label above the bar. The label uses the same ATR offset logic as other modules and draws a connector if enabled. Figure caption, In this chart the spread‑capture module has identified a potential liquidity pocket. Buyer and seller volumes both spiked above their recent averages, yet the candle’s range measured only a couple of ticks and the buy/sell split stayed close to 50 %. This combination met the module’s criteria, so it printed a grey “Spread ↔” label above the bar. The red and green trend lines link the earliest and latest high and low pivots, and the dashed horizontals mark the highest high and lowest low within the current lookback window. 9. Money Flow Module 9.1 Concept To translate volume into a monetary measure, this module multiplies each side’s volume by the closing price. It tracks buying and selling system money default currency on a per-bar basis and sums them over a chosen period. The difference between buy and sell currencies (Δ$) shows net inflow or outflow. 9.2 Inputs • mf_period_len_mf: Number of bars used for summing buy and sell dollars. • Label appearance settings: ATR length, multiplier, size, colors for up/down labels, and connector style and width. • Toggles: Use enableMoneyFlowLabel_mf and showMFLabels to control whether the module and its labels are displayed. 9.3 Calculations • Per-bar money: Buy $ = TF_buy_breakout × close; Sell $ = TF_sell_breakout × close. Their difference is Δ$ = Buy $ − Sell $. • Summations: Over mf_period_len_mf bars, compute Σ Buy $, Σ Sell $ and ΣΔ$ using math.sum(). • Info table entries: Rows 9–13 display these values as texts like “↑ USD 1234 (1M)” or “ΣΔ USD −5678 (14)”, with colors reflecting whether buyers or sellers dominate. • Money flow status: If Δ$ is positive the bar is marked “Money flow in” ; if negative, “Money flow out” ; if zero, “Neutral”. The cumulative status is similarly derived from ΣΔ.Labels print at the bar that changes the sign of ΣΔ, offset using ATR × label multiplier and styled per user preferences. Figure caption, The chart illustrates a steady rise toward the highest recent pivot (HH1) with price riding between a rising green trend‑line and a red trend‑line drawn through earlier pivot highs. A green Money flow in label appears above the bar near the top of the channel, signaling that net dollar flow turned positive on this bar: buy‑side dollar volume exceeded sell‑side dollar volume, pushing the cumulative sum ΣΔ$ above zero. In the info table, the “Money flow (bar)” and “Money flow Σ” rows both read In, confirming that the indicator’s money‑flow module has detected an inflow at both bar and aggregate levels, while other modules (pivots, trend lines and support/resistance) remain active to provide structural context. In this example the Money Flow module signals a net outflow. Price has been trending downward: successive high pivots form a falling red trend‑line and the low pivots form a descending green support line. When the latest bar broke below the previous low pivot (LL1), both the bar‑level and cumulative net dollar flow turned negative—selling volume at the close exceeded buying volume and pushed the cumulative Δ$ below zero. The module reacts by printing a red “Money flow out” label beneath the candle; the info table confirms that the “Money flow (bar)” and “Money flow Σ” rows both show Out, indicating sustained dominance of sellers in this period. 10. Info Table 10.1 Purpose When enabled, the Info Table appears in the lower right of your chart. It summarises key values computed by the indicator—such as buy and sell volume, delta, total volume, breakout status, market phase, and money flow—so you can see at a glance which side is dominant and which signals are active. 10.2 Symbols • ↑ / ↓ — Up (↑) denotes buy volume or money; down (↓) denotes sell volume or money. • MA — Moving average. In the table it shows the average value of a series over the lookback period. • Σ (Sigma) — Cumulative sum over the chosen lookback period. • Δ (Delta) — Difference between buy and sell values. • B / S — Buyer and seller share of total volume, expressed as percentages. • Ref. Price — Reference price for breakout calculations, based on the latest pivot. • Status — Indicates whether a breakout condition is currently active (True) or has failed. 10.3 Row definitions 1. Up volume / MA up volume – Displays current buy volume on the lower timeframe and its moving average over the lookback period. 2. Down volume / MA down volume – Shows current sell volume and its moving average; sell values are formatted in red for clarity. 3. Δ / ΣΔ – Lists the difference between buy and sell volume for the current bar and the cumulative delta volume over the lookback period. 4. Σ / MA Σ (Vol/MA) – Total volume (buy + sell) for the bar, with the ratio of this volume to its moving average; the right cell shows the average total volume. 5. B/S ratio – Buy and sell share of the total volume: current bar percentages and the average percentages across the lookback period. 6. Buyer Rank / Seller Rank – Ranks the bar’s buy and sell volumes among the last (n) bars; lower rank numbers indicate higher relative volume. 7. Σ Buy / Σ Sell – Sum of buy and sell volumes over the lookback window, indicating which side has traded more. 8. Breakout UP / DOWN – Shows the breakout thresholds (Ref. Price) and whether the breakout condition is active (True) or has failed. 9. Market Phase (Vol) – Reports the current volume‑only phase: Accumulation, Distribution or Neutral. 10. Money Flow – The final rows display dollar amounts and status: – ↑ USD / Σ↑ USD – Buy dollars for the current bar and the cumulative sum over the money‑flow period. – ↓ USD / Σ↓ USD – Sell dollars and their cumulative sum. – Δ USD / ΣΔ USD – Net dollar difference (buy minus sell) for the bar and cumulatively. – Money flow (bar) – Indicates whether the bar’s net dollar flow is positive (In), negative (Out) or neutral. – Money flow Σ – Shows whether the cumulative net dollar flow across the chosen period is positive, negative or neutral. The chart above shows a sequence of different signals from the indicator. A Bull Trap Risk appears after price briefly pushes above resistance but fails to hold, then a green Accum label identifies an accumulation phase. An upward breakout follows, confirmed by a Money flow in print. Later, a Sharp ↓ Risk warns of a possible sharp downturn; after price dips below support but quickly recovers, a Bear Trap label marks a false breakdown. The highlighted info table in the center summarizes key metrics at that moment, including current and average buy/sell volumes, net delta, total volume versus its moving average, breakout status (up and down), market phase (volume), and bar‑level and cumulative money flow (In/Out). 11. Conclusion & Final Remarks This indicator was developed as a holistic study of market structure and order flow. It brings together several well‑known concepts from technical analysis—breakouts, accumulation and distribution phases, overbought and oversold extremes, bull and bear traps, sharp directional moves, market‑maker spread bars and money flow—into a single Pine Script tool. Each module is based on widely recognized trading ideas and was implemented after consulting reference materials and example strategies, so you can see in real time how these concepts interact on your chart. A distinctive feature of this indicator is its reliance on per‑side volume: instead of tallying only total volume, it separately measures buy and sell transactions on a lower time frame. This approach gives a clearer view of who is in control—buyers or sellers—and helps filter breakouts, detect phases of accumulation or distribution, recognize potential traps, anticipate sharp moves and gauge whether liquidity providers are active. The money‑flow module extends this analysis by converting volume into currency values and tracking net inflow or outflow across a chosen window. Although comprehensive, this indicator is intended solely as a guide. It highlights conditions and statistics that many traders find useful, but it does not generate trading signals or guarantee results. Ultimately, you remain responsible for your positions. Use the information presented here to inform your analysis, combine it with other tools and risk‑management techniques, and always make your own decisions when trading.
Chỉ báo Pine Script®
của ata_sabanci
16
%ATR + ΔClose HighlightScript Overview This indicator displays on your chart: Table of the last N bars that passed the ATR-based range filter: Columns: Bar #, High, Range (High–Low), Low Summary row: ATR(N), suggested Stop-Loss (SL = X % of ATR), and the current bar’s range as a percentage of ATR Red badge on the most recent bar showing ΔClose% (the absolute difference between today’s and yesterday’s close, expressed as % of ATR) Background highlights: Blue fill under the most recent bar that met the filter Yellow fill under bars that failed the filter Hidden plots of ATR, %ATR, and ΔClose% (for use in strategies or alerts) All table elements, fills, and plots can be toggled off with a single switch so that only the red ΔClose% badge remains visible. Inputs Setting Description Default Length (bars) Lookback period for ATR and range filter (bars) 5 Upper deviation (%) Upper filter threshold (% of average ATR) 150% Lower deviation (%) Lower filter threshold (% of average ATR) 50% SL as % of ATR Stop-loss distance (% of ATR) 10% Label position Table position relative to bar (“above” or “below”) above Vertical offset (×ATR) Vertical spacing from the bar in ATR units 2.0 Show table & ATR plots Show or hide table, background highlights, and plots true How It Works ATR Calculation & Filtering Computes average True Range over the last N bars. Marks bars whose daily range falls within the specified upper/lower deviation band. Table Construction Gathers up to N most recent bars that passed the filter (or backfills from the most recent pass). Formats each bar’s High, Low, and Range into fixed-width columns for neat alignment. Stop-Loss & Percent Metrics Calculates a recommended SL distance as a percentage of ATR. Computes today’s bar range and ΔClose (absolute change in close) as % of ATR. Chart Display Table: Shows detailed per-bar data and summary metrics. Background fills: Blue for the latest valid bar, yellow for invalid bars. Hidden plots: ATR, %ATR, and ΔClose% (useful for backtesting). Red badge: Always visible on the right side of the last bar, displaying ΔClose%. Tips Disable the table & ATR plots to reduce chart clutter—leave only the red ΔClose% badge for a minimalist volatility alert. Use the hidden ATR fields (plot outputs) in TradingView Strategies or Alerts to automate volatility-based entries/exits. Adjust the deviation band to capture “normal” intraday moves vs. outsized volatility spikes. Load this script on any US market chart (stocks, futures, crypto, etc.) to instantly visualize recent volatility structure, set dynamic SL levels, and highlight today’s price change relative to average true range.
Chỉ báo Pine Script®
của artemtsapkin
PipsHunters Trading ChecklistTitle: PipsHunters Trading Checklist (PHTC) Short Description / Teaser: Enforce trading discipline and never miss a step in your pre-trade analysis with this simple, interactive, on-chart checklist. Full Description: 🚀 Overview The PipsHunters Trading Checklist (PHTC) is a powerful yet simple tool designed to instill discipline and structure into your trading routine. In the heat of the moment, it's easy to forget crucial steps of your analysis, leading to impulsive and low-probability trades. This indicator acts as your personal co-pilot, providing a persistent, on-chart checklist that you must manually complete before taking a trade. This is not an automated signal generator. It is a utility to keep you accountable to your own trading plan. The checklist items are inspired by common concepts in price action and Smart Money Concepts (SMC) methodologies, but they serve any trader who follows a rule-based system. ✨ Key Features Interactive On-Chart Table: Displays a clean, non-intrusive table directly on your chart. Manual Check-off System: You are in full control. Go into the indicator settings and check off each item as you complete your analysis. Real-Time Progress Tracking: The table header shows your progress (e.g., 4/7) and changes color from red to green when all items are checked. Clear Visual Cues: Each item is marked with a ✅ or ❌, and the text color changes to provide an at-a-glance status. "Ready!" Status: A final "READY!" confirmation appears once your entire checklist is complete, giving you the green light to look for an entry based on your strategy. Fully Customizable Position: Place the table in any corner of your chart (Top Left, Top Right, Bottom Left, Bottom Right) to suit your layout. 📋 The Checklist Items Explained The default checklist guides you through a structured, top-down analysis process common in many trading strategies: Seat before 1H: A reminder to be settled and mentally prepared at your desk at least an hour before your target session begins. Avoids rushing and emotional decisions. Check News: Have you checked for high-impact news events that could introduce extreme volatility and invalidate your setup? Mark Day Open: The daily open is a key institutional level. Marking it helps establish the daily bias. Mark LQ Levels: Have you identified key Liquidity (LQ) levels? This includes previous day/week highs and lows, session highs/lows, and other obvious swing points. Wait for Kill Zone: A reminder to be patient and wait for price to trade into a specific, high-probability time window (e.g., London Kill Zone, New York Kill Zone). LQ sweep inside Kill Zone: The core of the setup. Has price swept a key liquidity level within your chosen Kill Zone? Lower TF Confirmations: After the liquidity sweep, have you waited for confirmation on a lower timeframe? This is often a Market Structure Shift (MSS) or Change of Character (CHoCH). 🛠️ How to Use Add the "PipsHunters Trading Checklist" indicator to your chart. Go to the indicator's Settings (click the gear icon ⚙️). As you perform each step of your pre-trade analysis, tick the corresponding checkbox in the Inputs tab. The on-chart table will update instantly to reflect your progress. Only when all 7 items are checked will the table signal "READY!". 🎯 Who Is This For? This indicator is perfect for: SMC / ICT Traders: The checklist items align directly with Smart Money Concepts. New Traders: Helps build the essential habit of a consistent pre-trade routine. Inconsistent Traders: Acts as a guardrail to prevent impulsive, undisciplined entries. Any Rule-Based Trader: Anyone who follows a trading plan can benefit from the structure it provides. Disclaimer: This is a utility tool to aid in discipline and execution. It does not provide financial advice or guarantee profitable trades. All trading involves risk, and you are solely responsible for your own decisions. Trade safe and stay disciplined!
Chỉ báo Pine Script®
của ntytdr
ATR Plots + OverlayATR Plots + Overlay This tool calculates and displays Average True Range (ATR)-based levels on your chart for any selected timeframe, giving traders a quick visual reference for expected price movement relative to the most recent bar’s open price. It plots guide levels above and below that open and shows how much of the typical ATR-based range has already been covered—all in one interactive table and on-chart overlay. What It Does ATR Calculation: Uses true range data over a user-defined period (default 14), smoothed via RMA, SMA, EMA, or WMA, on the selected timeframe (e.g., 1h, 4h, daily) to calculate the ATR value. Projected Levels: Plots four reference levels relative to the open price of the most recent bar on the chosen timeframe: +100% ATR: Open + ATR +50% ATR: Open + 50% of ATR −50% ATR: Open − 50% of ATR −100% ATR: Open − ATR Coverage %: Tracks high and low prices for the current session on the selected timeframe and calculates what percentage of the ATR has already been covered: Coverage % = (High − Low) ÷ ATR × 100 Interactive Table: Shows the ATR value and current coverage percentage in a customizable table overlay. Position, color scheme, borders, transparency, and an optional empty top row are all adjustable via settings. Customization Options Table Settings: Position the table (top/bottom × left/right). Customize background color, text color, border color, and thickness. Optionally add an empty top row for spacing. Line Settings: Choose color, line style (solid/dotted/dashed), and width. Lines automatically update with each new bar on the selected timeframe, anchored to that bar’s open price. General Inputs: ATR length (number of bars). Smoothing method (RMA, SMA, EMA, WMA). Timeframe selection for ATR calculations (e.g., 15m, 1h, Daily). How to Use It for Trading Measure Volatility: Quickly gauge the expected price movement based on ATR for any timeframe. Identify Overextension: Use the coverage % to see how much of the expected ATR range is already consumed. Plan Entries & Exits: Align trade targets and stops with ATR levels for more objective planning. Visual Reference: Horizontal guide lines and table update automatically as new bars form, keeping information clear and actionable. Ideal For Intraday traders using ATR levels to frame trades. Swing traders wanting ATR-based reference points for larger timeframes. Anyone seeking a volatility-based framework for planning stops, targets, or identifying overextended conditions.
Chỉ báo Pine Script®
của abbadon9
ADR Plots + OverlayADR Plots + Overlay This tool calculates and displays Average Daily Range (ADR) levels on your chart, giving traders a quick visual reference for expected daily price movement. It plots guide levels above and below the daily open and shows how much of the day's typical range has already been covered—all in one interactive table and on-chart overlay. What It Does ADR Calculation: Uses daily high-low differences over a user-defined period (default 14 days), smoothed via RMA, SMA, EMA, or WMA to calculate the average daily range. Projected Levels: Plots four reference levels relative to the current day's open price: +100% ADR: Open + ADR +50% ADR: Open + 50% of ADR −50% ADR: Open − 50% of ADR −100% ADR: Open − ADR Coverage %: Tracks intraday high and low prices to calculate what percentage of the ADR has already been covered for the current session: Coverage % = (High − Low) ÷ ADR × 100 Interactive Table: Shows the ADR value and today's ADR coverage percentage in a customizable table overlay. The table position, colors, border, transparency, and an optional empty top row can all be adjusted via settings. Customization Options Table Settings: Position the table (top/bottom × left/right). Change background color, text color, border color and thickness. Toggle an empty top row for spacing. Line Settings: Choose color, line style (solid/dotted/dashed), and width. Lines automatically reposition each day based on that day's open price and ADR calculation. General Inputs: ADR length (number of days). Smoothing method (RMA, SMA, EMA, WMA). How to Use It for Trading Measure Daily Movement: Instantly know the expected daily price range based on historical volatility. Identify Overextension: Use the coverage % to see if the market has already moved close to or beyond its typical daily range. Plan Entries & Exits: Align trade targets and stops with ADR levels for more objective intraday planning. Visual Reference: Horizontal guide lines and table update automatically as new data comes in, helping traders stay informed without manual calculations. Ideal For Intraday traders tracking daily volatility limits. Swing traders wanting a quick reference for expected price movement per day. Anyone seeking a volatility-based framework for planning targets, stops, or identifying extended market conditions.
Chỉ báo Pine Script®
của abbadon9
HTF Current/Average RangeThe "HTF(Higher Timeframe) Current/Average Range" indicator calculates and displays the current and average price ranges across multiple timeframes, including daily, weekly, monthly, 4 hour, and user-defined custom timeframes. Users can customize the lookback period, table size, timeframe, and font color; with the indicator efficiently updating on the final bar to optimize performance. When the current range surpasses the average range for a given timeframe, the corresponding table cell is highlighted in green, indicating potential maximum price expansion and signaling the possibility of an impending retracement or consolidation. For day trading strategies, the daily average range can serve as a guide, allowing traders to hold positions until the current daily range approaches or meets the average range, at which point exiting the trade may be considered. For scalping strategies, the 15min and 5min average range can be utilized to determine optimal holding periods for fast trades. Other strategies: Intraday Trading - 1h and 4h Average Range Swing Trading - Monthly Average Range Short-term Trading - Weekly Average Range Also using these statistics in accordance with Power 3 ICT concepts, will assist in holding trades to their statistical average range of the chosen HTF candle. CODE The core functionality lies in the data retrieval and table population sections. The request.security function (e.g., = request.security(syminfo.tickerid, "D", , lookahead = barmerge.lookahead_off)) retrieves high and low prices from specified timeframes without lookahead bias, ensuring accurate historical data. These values are used to compute current ranges and average ranges (ta.sma(high - low, avgLength)), which are then displayed in a dynamically generated table starting at (if barstate.islast) using table.new, with conditional green highlighting when the current range is greater than average range, providing a clear visual cue for volatility analysis.
Chỉ báo Pine Script®
của Eightgent7
BARTRADINGPREDV4Please note, that all of the indicators on the chart are working together. I am showing all of the indicators so that you might see the benefits of these indicators working as one. Do your own research. Trade smart. I code tools not advice. So please make decisions based on your trading style and knowledge. Use my scripts freely but please note they are protected by Mozilla. Script Summary: BARTRADINGPREDV4 This Pine Script indicator is a comprehensive trading tool that overlays on your TradingView chart. It combines moving averages, regression channels, volume analysis, RSI filtering, and pattern recognition to assist in making trading decisions. It also provides a forward-looking projection to help anticipate future price movement. Key Features & Logic 1. Moving Averages HMA (High Moving Average): Simple moving average of the high price over a user-defined lookback period. LMA (Low Moving Average): Simple moving average of the low price over the same period. HLMA (High-Low Moving Average): The average of HMA and LMA, providing a midline reference. 2. RSI Filtering Optionally enables a Relative Strength Index (RSI) filter to help avoid trades when the market is not trending strongly. Only allows buy signals if RSI is above 50, and sell signals if RSI is below 50 (if enabled). 3. Signal Generation BUY Signal: Triggered when HL2 (average of OHLC) crosses over LMA and (optionally) RSI > 50. SELL Signal: Triggered when HL2 crosses under HMA and (optionally) RSI < 50. XSB (Extra Strong Buy): HL2 crosses over HMA, is above HLMA, up volume is greater than down volume, and (optionally) RSI > 50. XBS (Extra Strong Sell): HL2 crosses under LMA, is below HLMA, down volume is greater than up volume, and (optionally) RSI < 50. Enable/Disable XSB/XBS: You can turn these signals on or off via script inputs. 4. Take Profit (TP) and Stop Loss (SL) Levels TP and SL are dynamically calculated based on the difference between HMA and LMA, providing contextually relevant exit levels. 5. Regression Channel and Prediction Linear Regression Line: Plots a regression line over the lookback period to show the underlying trend. ATR Channel: Adds an upper and lower channel around the regression line using ATR (Average True Range) for a realistic prediction envelope. Forward Projection: Projects the regression line forward by a user-defined number of bars, visually showing where the trend could extend if current momentum persists. 6. Pattern Recognition Higher Highs/Lows and Lower Highs/Lows: Marks bars where new higher highs/lows or lower highs/lows are set, helping you spot trend continuation or reversal points. 7. Status Table A table shows the current price’s relationship to HMA, HLMA, and LMA, color-coded for quick visual interpretation. User Instructions Inputs Number of Lookback Bars: Sets the period for all moving averages and regression calculations. Prediction Length: (Legacy; not used in current logic.) TURN ON OR OFF XSB/XBS Signal: Toggle extra strong buy/sell signals. Enable RSI Filter: Only allow signals when RSI is in the correct zone. RSI Period: Sets the sensitivity of the RSI filter. Table Position: Choose where the status table appears on your chart. ATR Length & Multiplier: Control the width of the regression prediction channel. Bars Forward (Projection): Number of bars to project the regression line into the future. How to Use Add the script to your TradingView chart. Adjust inputs to suit your asset and timeframe. Interpret signals: BUY (B) and SELL (S): Appear as green/red labels below/above bars. XSB (blue) and XBS (orange): Indicate extra strong buy/sell conditions. HH/HL (green triangles): New higher highs/lows. LH/LL (red triangles): New lower highs/lows. Watch the regression channel: The yellow regression line shows the trend; the shaded band indicates expected volatility. Check the projection: The dashed magenta line projects the regression trend forward, giving a visual target for price continuation. Use the table: Quickly see if price is above or below each moving average. Interpreting the Prediction Aspects Regression Line & Channel Regression Line (Yellow): Represents the best-fit line of price over the lookback period, showing overall trend direction. ATR Channel: The upper and lower bands (yellow, semi-transparent) account for typical volatility, suggesting a range where price is likely to stay if the trend continues. Forward Projection Dashed Magenta Line: Projects the regression line forward by the specified number of bars, using the current slope. This is a trend continuation forecast—not a guarantee, but a statistically reasonable path if current conditions persist. How to use: If price is respecting the regression trend and within the channel, the projection provides a visual target for where price might go in the near future. TP/SL Levels TP (Take Profit): Suggests a price target above the current HL2, based on recent volatility. SL (Stop Loss): Suggests a protective stop below HL2. Best Practices & Warnings No indicator is perfect! Always combine signals with your own analysis and risk management. Regression projection is not a crystal ball: It simply extends the current trend, which can and will change, especially after big news or at support/resistance. Use on liquid, trending assets for best results. Adjust lookback and ATR settings for your market and timeframe. Summary Table Example Price vs HMA vs HLMA vs LMA 43000 +100 +50 -20 Green: Price is above average (bullish). Red: Price is below average (bearish). Yellow: Price is very close to the average (neutral). Final Notes This script is designed to be a multi-tool for trend trading and prediction, combining classic and modern techniques. The forward projection helps visualize possible future price action, while signals and overlays keep you informed of trend shifts and trade opportunities.
Chỉ báo Pine Script®
của ChalkBoardAnalytics
Orthogonal Projections to Latent Structures (O-PLS)Version 0.1 Orthogonal Projections to Latent Structures (O-PLS) Indicator for TradingView This indicator, named "Orthogonal Projections to Latent Structures (O-PLS)", is designed to help traders understand the relevance or predictive power of various market variables on the future close price of the asset it's applied to. Unlike standard correlation coefficients that show a simple linear relationship, O-PLS aims to separate variables into "predictive" (relevant to Y) and "orthogonal" (irrelevant noise) components. This Pine Script indicator provides a simplified proxy of the relevance score derived from O-PLS principles. Purpose of the Indicator The primary purpose of this indicator is to identify which technical factors (such as price, volume, and other indicators) have the strongest relationship with the future price movement of the current trading instrument. By providing a "relevance score" for each input variable, it helps traders focus on the most influential data points, potentially leading to more informed trading decisions. Inputs The indicator offers the following user-definable inputs: * **Lookback Period:** This integer input (default: 100, min: 10, max: 500) determines the number of past bars used to calculate the relevance scores for each variable. A longer lookback period considers more historical data, which can lead to smoother, less reactive scores but might miss recent shifts in variable importance. * **External Asset Symbol:** This symbol input (default: `BINANCE:BTCUSDT`) allows you to specify an external asset (e.g., `BINANCE:ETHUSDT`, `NASDAQ:TSLA`) whose close price will be included in the analysis as an additional variable. This is useful for cross-market analysis to see how other assets influence the current chart. * **Plot Visibility Checkboxes (e.g., "Plot: Open Price Relevance", "Plot: Volume Relevance", etc.):** These boolean checkboxes allow you to toggle the visibility of individual relevance score plots on the chart, helping to declutter the display and focus on specific variables. Outputs The indicator provides two main types of output: Relevance Score Plots: These are lines plotted in a separate pane below the main price chart. Each line corresponds to a specific market variable (Open Price, Close Price, High Price, Low Price, Volume, various RSIs, SMAs, MFI, and the External Asset Close). The value of each line represents the calculated "relevance score" for that variable, typically scaled between 0 and 10. A higher score indicates a stronger predictive relationship with the future close price. Sorted Relevance Table : A table displayed in the top-right corner of the chart provides a clear, sorted list of all analyzed variables and their corresponding relevance scores. The table is sorted in descending order of relevance, making it easy to identify the most influential factors at a glance. Each variable name in the table is colored according to its plot color, and the external asset's name is dynamically displayed without the "BINANCE:" prefix. How to Use the Indicator 1. **Add to Chart:** Apply the "Orthogonal Projections to Latent Structures (O-PLS)" indicator to your desired trading chart (e.g., ETH/USDT). 2. **Adjust Inputs:** * **Lookback Period:** Experiment with different lookback periods to see how the relevance scores change. A shorter period might highlight recent correlations, while a longer one might show more fundamental relationships. * **External Asset Symbol:** If you trade BTC/USDT, you might add ETH/USDT or SPX as an external asset to see its influence. 3. **Analyze Relevance Scores:** * **Plots:** Observe the individual relevance score plots over time. Are certain variables consistently high? Do scores change before significant price moves? * **Table:** Refer to the sorted table on the latest confirmed bar to quickly identify the top-ranked variables. 4. **Incorporate into Strategy:** Use the insights from the relevance scores to: * Prioritize certain indicators or price actions in your trading strategy. For example, if "Volume" has a high relevance score, it suggests volume confirmation is critical for future price moves. * Understand the influence of inter-market relationships (via the External Asset Close). How the Indicator Works The indicator works by performing the following steps on each bar: 1. **Data Fetching:** It gathers historical data for various price components (open, high, low, close), volume, and calculated technical indicators (SMA, RSI, MFI) for the specified `lookback` period. It also fetches the close price of an `External Asset Symbol` . 2. **Standardization (Z-scoring):** All collected raw data series are standardized by converting them into Z-scores. This involves subtracting the mean of each series and dividing by its standard deviation . Standardization is crucial because it brings all variables to a common scale, preventing variables with larger absolute values from disproportionately influencing the correlation calculations. 3. **Correlation Calculation (Proxy for O-PLS Relevance):** The indicator then calculates a simplified form of correlation between each standardized input variable and the standardized future close price (Y variable) . This correlation is a proxy for the relevance that O-PLS would identify. A high absolute correlation indicates a strong linear relationship. 4. **Relevance Scaling:** The calculated correlation values are then scaled to a range of 0 to 10 to provide an easily interpretable "relevance score" . 5. **Output Display:** The relevance scores are presented both as time-series plots (allowing observation of changes over time) and in a real-time sorted table (for quick identification of top factors on the current bar) . How it Differs from Full O-PLS This indicator provides a *simplified proxy* of O-PLS principles rather than a full, mathematically rigorous O-PLS model. Here's why and how it differs: * **Dimensionality Reduction:** A full O-PLS model would involve complex matrix factorization techniques to decompose the independent variables (X) into components that are predictive of Y and components that are orthogonal (unrelated) to Y but still describe X's variance. Pine Script's array capabilities and computational limits make direct implementation of these matrix operations challenging. * **Orthogonal Components:** A true O-PLS model explicitly identifies and removes orthogonal components (noise) from the X data that are unrelated to Y. This indicator, in its simplified form, primarily focuses on the direct correlation (relevance) between each X variable and Y after standardization, without explicitly modeling and separating these orthogonal variations. * **Predictive Model:** A full O-PLS model is ultimately a predictive model that can be used for regression (predicting Y). This indicator, however, focuses solely on **identifying the relevance/correlation of inputs to Y**, rather than building a predictive model for Y itself. It's more of an analytical tool for feature importance than a direct prediction engine. * **Computational Intensity:** Full O-PLS involves Singular Value Decomposition (SVD) or Partial Least Squares (PLS) algorithms, which are computationally intensive. The indicator uses simpler statistical measures (mean, standard deviation, and direct correlation calculation over a lookback window) that are feasible within Pine Script's execution limits. In essence, this Pine Script indicator serves as a practical tool for gaining insights into variable relevance, inspired by the spirit of O-PLS, but adapted for the constraints and common use cases of a TradingView environment.
Chỉ báo Pine Script®
của VolatilityRob
Momentum Regression [BackQuant]Momentum Regression The Momentum Regression is an advanced statistical indicator built to empower quants, strategists, and technically inclined traders with a robust visual and quantitative framework for analyzing momentum effects in financial markets. Unlike traditional momentum indicators that rely on raw price movements or moving averages, this tool leverages a volatility-adjusted linear regression model (y ~ x) to uncover and validate momentum behavior over a user-defined lookback window. Purpose & Design Philosophy Momentum is a core anomaly in quantitative finance — an effect where assets that have performed well (or poorly) continue to do so over short to medium-term horizons. However, this effect can be noisy, regime-dependent, and sometimes spurious. The Momentum Regression is designed as a pre-strategy analytical tool to help you filter and verify whether statistically meaningful and tradable momentum exists in a given asset. Its architecture includes: Volatility normalization to account for differences in scale and distribution. Regression analysis to model the relationship between past and present standardized returns. Deviation bands to highlight overbought/oversold zones around the predicted trendline. Statistical summary tables to assess the reliability of the detected momentum. Core Concepts and Calculations The model uses the following: Independent variable (x): The volatility-adjusted return over the chosen momentum period. Dependent variable (y): The 1-bar lagged log return, also adjusted for volatility. A simple linear regression is performed over a large lookback window (default: 1000 bars), which reveals the slope and intercept of the momentum line. These values are then used to construct: A predicted momentum trendline across time. Upper and lower deviation bands , representing ±n standard deviations of the regression residuals (errors). These visual elements help traders judge how far current returns deviate from the modeled momentum trend, similar to Bollinger Bands but derived from a regression model rather than a moving average. Key Metrics Provided On each update, the indicator dynamically displays: Momentum Slope (β₁): Indicates trend direction and strength. A higher absolute value implies a stronger effect. Intercept (β₀): The predicted return when x = 0. Pearson’s R: Correlation coefficient between x and y. R² (Coefficient of Determination): Indicates how well the regression line explains the variance in y. Standard Error of Residuals: Measures dispersion around the trendline. t-Statistic of β₁: Used to evaluate statistical significance of the momentum slope. These statistics are presented in a top-right summary table for immediate interpretation. A bottom-right signal table also summarizes key takeaways with visual indicators. Features and Inputs ✅ Volatility-Adjusted Momentum : Reduces distortions from noisy price spikes. ✅ Custom Lookback Control : Set the number of bars to analyze regression. ✅ Extendable Trendlines : For continuous visualization into the future. ✅ Deviation Bands : Optional ±σ multipliers to detect abnormal price action. ✅ Contextual Tables : Help determine strength, direction, and significance of momentum. ✅ Separate Pane Design : Cleanly isolates statistical momentum from price chart. How It Helps Traders 📉 Quantitative Strategy Validation: Use the regression results to confirm whether a momentum-based strategy is worth pursuing on a specific asset or timeframe. 🔍 Regime Detection: Track when momentum breaks down or reverses. Slope changes, drops in R², or weak t-stats can signal regime shifts. 📊 Trade Filtering: Avoid false positives by entering trades only when momentum is both statistically significant and directionally favorable. 📈 Backtest Preparation: Before running costly simulations, use this tool to pre-screen assets for exploitable return structures. When to Use It Before building or deploying a momentum strategy : Test if momentum exists and is statistically reliable. During market transitions : Detect early signs of fading strength or reversal. As part of an edge-stacking framework : Combine with other filters such as volatility compression, volume surges, or macro filters. Conclusion The Momentum Regression indicator offers a powerful fusion of statistical analysis and visual interpretation. By combining volatility-adjusted returns with real-time linear regression modeling, it helps quantify and qualify one of the most studied and traded anomalies in finance: momentum.
Chỉ báo Pine Script®
của BackQuant
3
Trend Gauge [BullByte]Trend Gauge Summary A multi-factor trend detection indicator that aggregates EMA alignment, VWMA momentum scaling, volume spikes, ATR breakout strength, higher-timeframe confirmation, ADX-based regime filtering, and RSI pivot-divergence penalty into one normalized trend score. It also provides a confidence meter, a Δ Score momentum histogram, divergence highlights, and a compact, scalable dashboard for at-a-glance status. ________________________________________ ## 1. Purpose of the Indicator Why this was built Traders often monitor several indicators in parallel - EMAs, volume signals, volatility breakouts, higher-timeframe trends, ADX readings, divergence alerts, etc., which can be cumbersome and sometimes contradictory. The “Trend Gauge” indicator was created to consolidate these complementary checks into a single, normalized score that reflects the prevailing market bias (bullish, bearish, or neutral) and its strength. By combining multiple inputs with an adaptive regime filter, scaling contributions by magnitude, and penalizing weakening signals (divergence), this tool aims to reduce noise, highlight genuine trend opportunities, and warn when momentum fades. Key Design Goals Signal Aggregation Merged trend-following signals (EMA crossover, ATR breakout, higher-timeframe confirmation) and momentum signals (VWMA thrust, volume spikes) into a unified score that reflects directional bias more holistically. Market Regime Awareness Implemented an ADX-style filter to distinguish between trending and ranging markets, reducing the influence of trend signals during sideways phases to avoid false breakouts. Magnitude-Based Scaling Replaced binary contributions with scaled inputs: VWMA thrust and ATR breakout are weighted relative to recent averages, allowing for more nuanced score adjustments based on signal strength. Momentum Divergence Penalty Integrated pivot-based RSI divergence detection to slightly reduce the overall score when early signs of momentum weakening are detected, improving risk-awareness in entries. Confidence Transparency Added a live confidence metric that shows what percentage of enabled sub-indicators currently agree with the overall bias, making the scoring system more interpretable. Momentum Acceleration Visualization Plotted the change in score (Δ Score) as a histogram bar-to-bar, highlighting whether momentum is increasing, flattening, or reversing, aiding in more timely decision-making. Compact Informational Dashboard Presented a clean, scalable dashboard that displays each component’s status, the final score, confidence %, detected regime (Trending/Ranging), and a labeled strength gauge for quick visual assessment. ________________________________________ ## 2. Why a Trader Should Use It Main benefits and use cases 1. Unified View: Rather than juggling multiple windows or panels, this indicator delivers a single score synthesizing diverse signals. 2. Regime Filtering: In ranging markets, trend signals often generate false entries. The ADX-based regime filter automatically down-weights trend-following components, helping you avoid chasing false breakouts. 3. Nuanced Momentum & Volatility: VWMA and ATR breakout contributions are normalized by recent averages, so strong moves register strongly while smaller fluctuations are de-emphasized. 4. Early Warning of Weakening: Pivot-based RSI divergence is detected and used to slightly reduce the score when price/momentum diverges, giving a cautionary signal before a full reversal. 5. Confidence Meter: See at a glance how many sub-indicators align with the aggregated bias (e.g., “80% confidence” means 4 out of 5 components agree ). This transparency avoids black-box decisions. 6. Trend Acceleration/Deceleration View: The Δ Score histogram visualizes whether the aggregated score is rising (accelerating trend) or falling (momentum fading), supplementing the main oscillator. 7. Compact Dashboard: A corner table lists each check’s status (“Bull”, “Bear”, “Flat” or “Disabled”), plus overall Score, Confidence %, Regime, Trend Strength label, and a gauge bar. Users can scale text size (Normal, Small, Tiny) without removing elements, so the full picture remains visible even in compact layouts. 8. Customizable & Transparent: All components can be enabled/disabled and parameterized (lengths, thresholds, weights). The full Pine code is open and well-commented, letting users inspect or adapt the logic. 9. Alert-ready: Built-in alert conditions fire when the score crosses weak thresholds to bullish/bearish or returns to neutral, enabling timely notifications. ________________________________________ ## 3. Component Rationale (“Why These Specific Indicators?”) Each sub-component was chosen because it adds complementary information about trend or momentum: 1. EMA Cross o Basic trend measure: compares a faster EMA vs. a slower EMA. Quickly reflects trend shifts but by itself can whipsaw in sideways markets. 2. VWMA Momentum o Volume-weighted moving average change indicates momentum with volume context. By normalizing (dividing by a recent average absolute change), we capture the strength of momentum relative to recent history. This scaling prevents tiny moves from dominating and highlights genuinely strong momentum. 3. Volume Spikes o Sudden jumps in volume combined with price movement often accompany stronger moves or reversals. A binary detection (+1 for bullish spike, -1 for bearish spike) flags high-conviction bars. 4. ATR Breakout o Detects price breaking beyond recent highs/lows by a multiple of ATR. Measures breakout strength by how far beyond the threshold price moves relative to ATR, capped to avoid extreme outliers. This gives a volatility-contextual trend signal. 5. Higher-Timeframe EMA Alignment o Confirms whether the shorter-term trend aligns with a higher timeframe trend. Uses request.security with lookahead_off to avoid future data. When multiple timeframes agree, confidence in direction increases. 6. ADX Regime Filter (Manual Calculation) o Computes directional movement (+DM/–DM), smoothes via RMA, computes DI+ and DI–, then a DX and ADX-like value. If ADX ≥ threshold, market is “Trending” and trend components carry full weight; if ADX < threshold, “Ranging” mode applies a configurable weight multiplier (e.g., 0.5) to trend-based contributions, reducing false signals in sideways conditions. Volume spikes remain binary (optional behavior; can be adjusted if desired). 7. RSI Pivot-Divergence Penalty o Uses ta.pivothigh / ta.pivotlow with a lookback to detect pivot highs/lows on price and corresponding RSI values. When price makes a higher high but RSI makes a lower high (bearish divergence), or price makes a lower low but RSI makes a higher low (bullish divergence), a divergence signal is set. Rather than flipping the trend outright, the indicator subtracts (or adds) a small penalty (configurable) from the aggregated score if it would weaken the current bias. This subtle adjustment warns of weakening momentum without overreacting to noise. 8. Confidence Meter o Counts how many enabled components currently agree in direction with the aggregated score (i.e., component sign × score sign > 0). Displays this as a percentage. A high percentage indicates strong corroboration; a low percentage warns of mixed signals. 9. Δ Score Momentum View o Plots the bar-to-bar change in the aggregated score (delta_score = score - score ) as a histogram. When positive, bars are drawn in green above zero; when negative, bars are drawn in red below zero. This reveals acceleration (rising Δ) or deceleration (falling Δ), supplementing the main oscillator. 10. Dashboard • A table in the indicator pane’s top-right with 11 rows: 1. EMA Cross status 2. VWMA Momentum status 3. Volume Spike status 4. ATR Breakout status 5. Higher-Timeframe Trend status 6. Score (numeric) 7. Confidence % 8. Regime (“Trending” or “Ranging”) 9. Trend Strength label (e.g., “Weak Bullish Trend”, “Strong Bearish Trend”) 10. Gauge bar visually representing score magnitude • All rows always present; size_opt (Normal, Small, Tiny) only changes text size via text_size, not which elements appear. This ensures full transparency. ________________________________________ ## 4. What Makes This Indicator Stand Out • Regime-Weighted Multi-Factor Score: Trend and momentum signals are adaptively weighted by market regime (trending vs. ranging) , reducing false signals. • Magnitude Scaling: VWMA and ATR breakout contributions are normalized by recent average momentum or ATR, giving finer gradation compared to simple ±1. • Integrated Divergence Penalty: Divergence directly adjusts the aggregated score rather than appearing as a separate subplot; this influences alerts and trend labeling in real time. • Confidence Meter: Shows the percentage of sub-signals in agreement, providing transparency and preventing blind trust in a single metric. • Δ Score Histogram Momentum View: A histogram highlights acceleration or deceleration of the aggregated trend score, helping detect shifts early. • Flexible Dashboard: Always-visible component statuses and summary metrics in one place; text size scaling keeps the full picture available in cramped layouts. • Lookahead-Safe HTF Confirmation: Uses lookahead_off so no future data is accessed from higher timeframes, avoiding repaint bias. • Repaint Transparency: Divergence detection uses pivot functions that inherently confirm only after lookback bars; description documents this lag so users understand how and when divergence labels appear. • Open-Source & Educational: Full, well-commented Pine v6 code is provided; users can learn from its structure: manual ADX computation, conditional plotting with series = show ? value : na, efficient use of table.new in barstate.islast, and grouped inputs with tooltips. • Compliance-Conscious: All plots have descriptive titles; inputs use clear names; no unnamed generic “Plot” entries; manual ADX uses RMA; all request.security calls use lookahead_off. Code comments mention repaint behavior and limitations. ________________________________________ ## 5. Recommended Timeframes & Tuning • Any Timeframe: The indicator works on small (e.g., 1m) to large (daily, weekly) timeframes. However: o On very low timeframes (<1m or tick charts), noise may produce frequent whipsaws. Consider increasing smoothing lengths, disabling certain components (e.g., volume spike if volume data noisy), or using a larger pivot lookback for divergence. o On higher timeframes (daily, weekly), consider longer lookbacks for ATR breakout or divergence, and set Higher-Timeframe trend appropriately (e.g., 4H HTF when on 5 Min chart). • Defaults & Experimentation: Default input values are chosen to be balanced for many liquid markets. Users should test with replay or historical analysis on their symbol/timeframe and adjust: o ADX threshold (e.g., 20–30) based on instrument volatility. o VWMA and ATR scaling lengths to match average volatility cycles. o Pivot lookback for divergence: shorter for faster markets, longer for slower ones. • Combining with Other Analysis: Use in conjunction with price action, support/resistance, candlestick patterns, order flow, or other tools as desired. The aggregated score and alerts can guide attention but should not be the sole decision-factor. ________________________________________ ## 6. How Scoring and Logic Works (Step-by-Step) 1. Compute Sub-Scores o EMA Cross: Evaluate fast EMA > slow EMA ? +1 : fast EMA < slow EMA ? -1 : 0. o VWMA Momentum: Calculate vwma = ta.vwma(close, length), then vwma_mom = vwma - vwma . Normalize: divide by recent average absolute momentum (e.g., ta.sma(abs(vwma_mom), lookback)), clip to . o Volume Spike: Compute vol_SMA = ta.sma(volume, len). If volume > vol_SMA * multiplier AND price moved up ≥ threshold%, assign +1; if moved down ≥ threshold%, assign -1; else 0. o ATR Breakout: Determine recent high/low over lookback. If close > high + ATR*mult, compute distance = close - (high + ATR*mult), normalize by ATR, cap at a configured maximum. Assign positive contribution. Similarly for bearish breakout below low. o Higher-Timeframe Trend: Use request.security(..., lookahead=barmerge.lookahead_off) to fetch HTF EMAs; assign +1 or -1 based on alignment. 2. ADX Regime Weighting o Compute manual ADX: directional movements (+DM, –DM), smoothed via RMA, DI+ and DI–, then DX and ADX via RMA. If ADX ≥ threshold, market is considered “Trending”; otherwise “Ranging.” o If trending, trend-based contributions (EMA, VWMA, ATR, HTF) use full weight = 1.0. If ranging, use weight = ranging_weight (e.g., 0.5) to down-weight them. Volume spike stays binary ±1 (optional to change if desired). 3. Aggregate Raw Score o Sum weighted contributions of all enabled components. Count the number of enabled components; if zero, default count = 1 to avoid division by zero. 4. Divergence Penalty o Detect pivot highs/lows on price and corresponding RSI values, using a lookback. When price and RSI diverge (bearish or bullish divergence), check if current raw score is in the opposing direction:  If bearish divergence (price higher high, RSI lower high) and raw score currently positive, subtract a penalty (e.g., 0.5).  If bullish divergence (price lower low, RSI higher low) and raw score currently negative, add a penalty. o This reduces score magnitude to reflect weakening momentum, without flipping the trend outright. 5. Normalize and Smooth o Normalized score = (raw_score / number_of_enabled_components) * 100. This yields a roughly range. o Optional EMA smoothing of this normalized score to reduce noise. 6. Interpretation o Sign: >0 = net bullish bias; <0 = net bearish bias; near zero = neutral. o Magnitude Zones: Compare |score| to thresholds (Weak, Medium, Strong) to label trend strength (e.g., “Weak Bullish Trend”, “Medium Bearish Trend”, “Strong Bullish Trend”). o Δ Score Histogram: The histogram bars from zero show change from previous bar’s score; positive bars indicate acceleration, negative bars indicate deceleration. o Confidence: Percentage of sub-indicators aligned with the score’s sign. o Regime: Indicates whether trend-based signals are fully weighted or down-weighted. ________________________________________ ## 7. Oscillator Plot & Visualization: How to Read It Main Score Line & Area The oscillator plots the aggregated score as a line, with colored fill: green above zero for bullish area, red below zero for bearish area. Horizontal reference lines at ±Weak, ±Medium, and ±Strong thresholds mark zones: crossing above +Weak suggests beginning of bullish bias, above +Medium for moderate strength, above +Strong for strong trend; similarly for bearish below negative thresholds. Δ Score Histogram If enabled, a histogram shows score - score . When positive, bars appear in green above zero, indicating accelerating bullish momentum; when negative, bars appear in red below zero, indicating decelerating or reversing momentum. The height of each bar reflects the magnitude of change in the aggregated score from the prior bar. Divergence Highlight Fill If enabled, when a pivot-based divergence is confirmed: • Bullish Divergence : fill the area below zero down to –Weak threshold in green, signaling potential reversal from bearish to bullish. • Bearish Divergence : fill the area above zero up to +Weak threshold in red, signaling potential reversal from bullish to bearish. These fills appear with a lag equal to pivot lookback (the number of bars needed to confirm the pivot). They do not repaint after confirmation, but users must understand this lag. Trend Direction Label When score crosses above or below the Weak threshold, a small label appears near the score line reading “Bullish” or “Bearish.” If the score returns within ±Weak, the label “Neutral” appears. This helps quickly identify shifts at the moment they occur. Dashboard Panel In the indicator pane’s top-right, a table shows: 1. EMA Cross status: “Bull”, “Bear”, “Flat”, or “Disabled” 2. VWMA Momentum status: similarly 3. Volume Spike status: “Bull”, “Bear”, “No”, or “Disabled” 4. ATR Breakout status: “Bull”, “Bear”, “No”, or “Disabled” 5. Higher-Timeframe Trend status: “Bull”, “Bear”, “Flat”, or “Disabled” 6. Score: numeric value (rounded) 7. Confidence: e.g., “80%” (colored: green for high, amber for medium, red for low) 8. Regime: “Trending” or “Ranging” (colored accordingly) 9. Trend Strength: textual label based on magnitude (e.g., “Medium Bullish Trend”) 10. Gauge: a bar of blocks representing |score|/100 All rows remain visible at all times; changing Dashboard Size only scales text size (Normal, Small, Tiny). ________________________________________ ## 8. Example Usage (Illustrative Scenario) Example: BTCUSD 5 Min 1. Setup: Add “Trend Gauge ” to your BTCUSD 5 Min chart. Defaults: EMAs (8/21), VWMA 14 with lookback 3, volume spike settings, ATR breakout 14/5, HTF = 5m (or adjust to 4H if preferred), ADX threshold 25, ranging weight 0.5, divergence RSI length 14 pivot lookback 5, penalty 0.5, smoothing length 3, thresholds Weak=20, Medium=50, Strong=80. Dashboard Size = Small. 2. Trend Onset: At some point, price breaks above recent high by ATR multiple, volume spikes upward, faster EMA crosses above slower EMA, HTF EMA also bullish, and ADX (manual) ≥ threshold → aggregated score rises above +20 (Weak threshold) into +Medium zone. Dashboard shows “Bull” for EMA, VWMA, Vol Spike, ATR, HTF; Score ~+60–+70; Confidence ~100%; Regime “Trending”; Trend Strength “Medium Bullish Trend”; Gauge ~6–7 blocks. Δ Score histogram bars are green and rising, indicating accelerating bullish momentum. Trader notes the alignment. 3. Divergence Warning: Later, price makes a slightly higher high but RSI fails to confirm (lower RSI high). Pivot lookback completes; the indicator highlights a bearish divergence fill above zero and subtracts a small penalty from the score, causing score to stall or retrace slightly. Dashboard still bullish but score dips toward +Weak. This warns the trader to tighten stops or take partial profits. 4. Trend Weakens: Score eventually crosses below +Weak back into neutral; a “Neutral” label appears, and a “Neutral Trend” alert fires if enabled. Trader exits or avoids new long entries. If score subsequently crosses below –Weak, a “Bearish” label and alert occur. 5. Customization: If the trader finds VWMA noise too frequent on this instrument, they may disable VWMA or increase lookback. If ATR breakouts are too rare, adjust ATR length or multiplier. If ADX threshold seems off, tune threshold. All these adjustments are explained in Inputs section. 6. Visualization: The screenshot shows the main score oscillator with colored areas, reference lines at ±20/50/80, Δ Score histogram bars below/above zero, divergence fill highlighting potential reversal, and the dashboard table in the top-right. ________________________________________ ## 9. Inputs Explanation A concise yet clear summary of inputs helps users understand and adjust: 1. General Settings • Theme (Dark/Light): Choose background-appropriate colors for the indicator pane. • Dashboard Size (Normal/Small/Tiny): Scales text size only; all dashboard elements remain visible. 2. Indicator Settings • Enable EMA Cross: Toggle on/off basic EMA alignment check. o Fast EMA Length and Slow EMA Length: Periods for EMAs. • Enable VWMA Momentum: Toggle VWMA momentum check. o VWMA Length: Period for VWMA. o VWMA Momentum Lookback: Bars to compare VWMA to measure momentum. • Enable Volume Spike: Toggle volume spike detection. o Volume SMA Length: Period to compute average volume. o Volume Spike Multiplier: How many times above average volume qualifies as spike. o Min Price Move (%): Minimum percent change in price during spike to qualify as bullish or bearish. • Enable ATR Breakout: Toggle ATR breakout detection. o ATR Length: Period for ATR. o Breakout Lookback: Bars to look back for recent highs/lows. o ATR Multiplier: Multiplier for breakout threshold. • Enable Higher Timeframe Trend: Toggle HTF EMA alignment. o Higher Timeframe: E.g., “5” for 5-minute when on 1-minute chart, or “60” for 5 Min when on 15m, etc. Uses lookahead_off. • Enable ADX Regime Filter: Toggles regime-based weighting. o ADX Length: Period for manual ADX calculation. o ADX Threshold: Value above which market considered trending. o Ranging Weight Multiplier: Weight applied to trend components when ADX < threshold (e.g., 0.5). • Scale VWMA Momentum: Toggle normalization of VWMA momentum magnitude. o VWMA Mom Scale Lookback: Period for average absolute VWMA momentum. • Scale ATR Breakout Strength: Toggle normalization of breakout distance by ATR. o ATR Scale Cap: Maximum multiple of ATR used for breakout strength. • Enable Price-RSI Divergence: Toggle divergence detection. o RSI Length for Divergence: Period for RSI. o Pivot Lookback for Divergence: Bars on each side to identify pivot high/low. o Divergence Penalty: Amount to subtract/add to score when divergence detected (e.g., 0.5). 3. Score Settings • Smooth Score: Toggle EMA smoothing of normalized score. • Score Smoothing Length: Period for smoothing EMA. • Weak Threshold: Absolute score value under which trend is considered weak or neutral. • Medium Threshold: Score above Weak but below Medium is moderate. • Strong Threshold: Score above this indicates strong trend. 4. Visualization Settings • Show Δ Score Histogram: Toggle display of the bar-to-bar change in score as a histogram. Default true. • Show Divergence Fill: Toggle background fill highlighting confirmed divergences. Default true. Each input has a tooltip in the code. ________________________________________ ## 10. Limitations, Repaint Notes, and Disclaimers 10.1. Repaint & Lag Considerations • Pivot-Based Divergence Lag: The divergence detection uses ta.pivothigh / ta.pivotlow with a specified lookback. By design, a pivot is only confirmed after the lookback number of bars. As a result: o Divergence labels or fills appear with a delay equal to the pivot lookback. o Once the pivot is confirmed and the divergence is detected, the fill/label does not repaint thereafter, but you must understand and accept this lag. o Users should not treat divergence highlights as predictive signals without additional confirmation, because they appear after the pivot has fully formed. • Higher-Timeframe EMA Alignment: Uses request.security(..., lookahead=barmerge.lookahead_off), so no future data from the higher timeframe is used. This avoids lookahead bias and ensures signals are based only on completed higher-timeframe bars. • No Future Data: All calculations are designed to avoid using future information. For example, manual ADX uses RMA on past data; security calls use lookahead_off. 10.2. Market & Noise Considerations • In very choppy or low-liquidity markets, some components (e.g., volume spikes or VWMA momentum) may be noisy. Users can disable or adjust those components’ parameters. • On extremely low timeframes, noise may dominate; consider smoothing lengths or disabling certain features. • On very high timeframes, pivots and breakouts occur less frequently; adjust lookbacks accordingly to avoid sparse signals. 10.3. Not a Standalone Trading System • This is an indicator, not a complete trading strategy. It provides signals and context but does not manage entries, exits, position sizing, or risk management. • Users must combine it with their own analysis, money management, and confirmations (e.g., price patterns, support/resistance, fundamental context). • No guarantees: past behavior does not guarantee future performance. 10.4. Disclaimers • Educational Purposes Only: The script is provided as-is for educational and informational purposes. It does not constitute financial, investment, or trading advice. • Use at Your Own Risk: Trading involves risk of loss. Users should thoroughly test and use proper risk management. • No Guarantees: The author is not responsible for trading outcomes based on this indicator. • License: Published under Mozilla Public License 2.0; code is open for viewing and modification under MPL terms. ________________________________________ ## 11. Alerts • The indicator defines three alert conditions: 1. Bullish Trend: when the aggregated score crosses above the Weak threshold. 2. Bearish Trend: when the score crosses below the negative Weak threshold. 3. Neutral Trend: when the score returns within ±Weak after being outside. Good luck – BullByte
Chỉ báo Pine Script®
của BullByte
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CDP - Counter-Directional-Pivot🎯 CDP - Counter-Directional-Pivot 📊 Overview The Counter-Directional-Pivot (CDP) indicator calculates five critical price levels based on the previous day's OHLC data, specifically designed for multi-timeframe analysis. Unlike standard pivot points, CDP levels are calculated using a unique formula that identifies potential reversal zones where price action often changes direction. ⚡ What Makes This Script Original This implementation solves several technical challenges that existing pivot indicators face: 🔄 Multi-Timeframe Consistency: Values remain identical across all timeframes (1m, 5m, 1h, daily) - a common problem with many pivot implementations 🔒 Intraday Stability: Uses advanced value-locking technology to prevent the "stepping" effect that occurs when pivot lines shift during the trading session 💪 Robust Data Handling: Optimized for both liquid and illiquid stocks with enhanced data synchronization 🧮 CDP Calculation Formula The indicator calculates five key levels using the previous day's High (H), Low (L), and Close (C): CDP = (H + L + C) ÷ 3 (Central Decision Point) AH = 2×CDP + H – 2×L (Anchor High - Strong Resistance) NH = 2×CDP – L (Near High - Moderate Resistance) AL = 2×CDP – 2×H + L (Anchor Low - Strong Support) NL = 2×CDP – H (Near Low - Moderate Support) ✨ Key Features 🎨 Visual Elements 📈 Five Distinct Price Levels: Each with customizable colors and line styles 🏷️ Smart Label System: Shows exact price values for each level 📋 Optional Value Table: Displays all levels in an organized table format 🎯 Clean Chart Display: Minimal visual clutter while maximizing information ⚙️ Technical Advantages 🔐 Session-Locked Values: Prices are locked at market open, preventing intraday shifts 🔄 Multi-Timeframe Sync: Perfect consistency between daily and intraday charts ✅ Data Validation: Built-in checks ensure reliable calculations 🚀 Performance Optimized: Efficient code structure for fast loading 💼 Trading Applications 🔄 Reversal Zones: AH and AL often act as strong turning points 💥 Breakout Confirmation: Price movement beyond these levels signals trend continuation 🛡️ Risk Management: Use levels for stop-loss and take-profit placement 🏗️ Market Structure: Understand daily ranges and potential price targets 📚 How to Use 🚀 Basic Setup Add the indicator to your chart (works on any timeframe) Customize colors for easy identification of support/resistance zones Enable the value table for quick reference of exact price levels 📈 Trading Strategy Examples 🟢 Long Bias: Look for bounces at NL or AL levels 🔴 Short Bias: Watch for rejections at NH or AH levels 💥 Breakout Trading: Enter positions when price decisively breaks through anchor levels ↔️ Range Trading: Use CDP as the central reference point for range-bound markets 🎯 Advanced Strategy Combinations RSI Integration for Enhanced Signals: 📊 📉 Oversold Bounces: Combine RSI below 30 with price touching AL/NL levels for high-probability long entries 📈 Overbought Rejections: Look for RSI above 70 with price rejecting AH/NH levels for short opportunities 🔍 Divergence Confirmation: When RSI shows bullish divergence at support levels (AL/NL) or bearish divergence at resistance levels (AH/NH), it often signals stronger reversal potential ⚡ Momentum Confluence: RSI crossing 50 while price breaks through CDP can confirm trend direction changes ⚙️ Configuration Options 🎨 Line Customization: Adjust width, style (solid/dashed/dotted), and colors 👁️ Display Preferences: Toggle individual levels, labels, and value table 📍 Table Position: Place the value table anywhere on your chart 🔔 Alert System: Get notifications when price crosses key levels 🔧 Technical Implementation Details 🎯 Data Reliability The script uses request.security() with lookahead settings to ensure historical accuracy while maintaining real-time functionality. The value-locking mechanism prevents the common issue where pivot levels shift during the trading day. 🔄 Multi-Timeframe Logic ⏰ Intraday Charts: Display previous day's calculated levels as stable horizontal lines 📅 Daily Charts: Show current day's levels based on yesterday's OHLC 🔍 Consistency Check: All timeframes reference the same source data 🤔 Why CDP vs Standard Pivots? Counter-Directional Pivots often provide more accurate reversal points than traditional pivot calculations because they incorporate the relationship between high/low ranges and closing prices more effectively. The formula creates levels that better reflect market psychology and institutional trading behaviors. 💡 Best Practices 💧 Use on liquid markets for most reliable results 📊 RSI Combination: Add RSI indicator for overbought/oversold confirmation and divergence analysis 📊 Combine with volume analysis for confirmation 🔍 Consider multiple timeframe analysis (daily levels on hourly charts) 📝 Test thoroughly in paper trading before live implementation 💪 Example Market Applications NASDAQ:AAPL AAPL - Tech stock breakouts through AH levels $NYSE:SPY SPY - Index trading with CDP range analysis NASDAQ:TSLA TSLA - Volatile stock reversals at AL/NL levels ⚠️ This indicator is designed for educational and analytical purposes. Always combine with proper risk management and additional technical analysis tools.
Chỉ báo Pine Script®
của joericke
EMA 200 Monitor - Bybit CoinsEMA 200 Monitor - Bybit Coins 📊 OVERVIEW The EMA 200 Monitor - Bybit Coins is an advanced indicator that automatically monitors 30 of the top cryptocurrencies traded on Bybit, alerting you when they are close to the 200-period Exponential Moving Average on the 4-hour timeframe. This indicator was developed especially for traders who use the EMA 200 as a key support/resistance level in their swing trading and position trading strategies. 🎯 WHAT IT'S FOR Multi-Asset Monitoring: Simultaneous monitoring of 30 cryptocurrencies without having to switch between charts Opportunity Identification: Detects when coins are approaching the 200 EMA, a crucial technical level Automated Alerts: Real-time notifications when a coin reaches the configured proximity Time Efficiency: Eliminates the need to manually check chart collections ⚙️ HOW IT WORKS Main Functionality The indicator uses the request.security() function to fetch price data and calculate the 200 EMA of each monitored asset. With each new bar, the script: Calculates the distance between the current price and the 200 EMA for each coin Identifies proximity based on the configured percentage (default: 2%) Displays results in a table organized on the chart Generates automatic alerts when proximity is detected Monitored Coins Major : BTC, ETH, BNB, ADA, XRP, SOL, DOT, DOGE, AVAX DeFi : UNI, LINK, ATOM, ICP, NEAR, OP, ARB, INJ Memecoins : SHIB, PEPE, WIF, BONK, FLOKI Emerging : SUI, TON, APT, POL (ex-MATIC) 📋 AVAILABLE SETTINGS Adjustable Parameters EMA Length (Default: 200): Exponential Moving Average Period Proximity Percentage (Default: 2%): Distance in percentage to consider "close" Show Table (Default: Active): Show/hide results table Table Position: Position of the table on the chart (9 options available) Color System 🔴 Red: Distance ≤ 1% (very close) 🟠 Orange: Distance ≤ 1.5% (close) 🟡 Yellow: Distance ≤ 2% (approaching) 🚀 HOW TO USE Initial Configuration Add the indicator to the 4-hour timeframe chart Set the parameters according to your strategy Position the table where there is no graphic preference Setting Alerts Click "Create Alert" in TradingView Select the "EMA 200 Monitor" indicator Set the notification frequency and method Activate the alert to receive automatic notifications Results Interpretation The table shows: Coin: Asset name (e.g. BTC, ETH) Price: Current currency quote EMA 200: Current value of the moving average Distance: Percentage of proximity to the core code 💡 STRATEGIES TO USE Reversal Trading Entry: When price touches or approaches the EMA 200 Stop: Below/above the EMA with a safety margin Target: Previous resistance/support levels Breakout Trading Monitoring: Watch for currencies consolidating near the EMA 200 Entry: When the media is finally broken Confirmation: Volume and close above/below the EMA Swing Trading Identification: Use the monitor to detect setups in formation Timing: Wait for the EMA 200 to approach for detailed analysis Management: Use the EMA as a reference for stops dynamics ⚠️ IMPORTANT CONSIDERATIONS Technical Limitations Request Bybit data: Access to exchange symbols required Specific timeframe: Optimized for 4-hour analysis Minimum delay: Data updated with each new bar Usage Recommendations Combine with technical analysis: Use together with other indicators Confirm the configuration: Check the graphic patterns before trading Manage risk: Always use stop loss and adequate position sizing Backtesting: Test your strategy before applying with real capital Disclaimer This indicator is a technical analysis tool and does not constitute investment advice. Always do your own analysis and manage detailed information about the risks of your operations. 🔧 TECHNICAL INFORMATION Pine Script version: v6 Type: Indicator (overlay=true) Compatibility: All TradingView plans Resources used: request.security(), arrays, tables Performance: Optimized for multiple simultaneous queries 📈 COMPETITIVE ADVANTAGES ✅ Simultaneous monitoring of 30 major assets ✅ Clear visual interface with intuitive core system ✅ Customizable alerts for different details ✅ Optimized code for maximum performance ✅ Flexible configuration adaptable to different strategies ✅ Real-time update without the need for manual refresh Developed for traders who value efficiency and accuracy in identifying market opportunities based on the EMA 20
Chỉ báo Pine Script®
của MestreDoFOMO
EMD Trend [InvestorUnknown]EMD Trend is a dynamic trend-following indicator that utilizes Exponential Moving Deviation (EMD) to build adaptive channels around a selected moving average. Designed for traders who value responsive trend signals with built-in volatility sensitivity, this tool highlights directional bias, market regime shifts, and potential breakout opportunities. How It Works Instead of using standard deviation, EMD Trend employs the exponential moving average of the absolute deviation from a moving average—producing smoother, faster-reacting upper and lower bounds: Bullish (Risk-ON Long): Price crosses above the upper EMD band Bearish (Risk-ON Short): Price crosses below the lower EMD band Neutral: Price stays within the channel, indicating potential mean reversion or low momentum Trend direction is defined by price interaction with these bands, and visual cues (color-coded bars and fills) help quickly identify market conditions. Features 7 Moving Average Types: SMA, EMA, HMA, DEMA, TEMA, RMA, FRAMA Custom Price Source: Choose close, hl2, ohlc4, or others EMD Multiplier: Controls the width of the deviation envelope Bar Coloring: Candles change color based on current trend Intra-bar Signal Option: Enables faster updates (with optional repainting) Speculative Zones: Fills highlight aggressive momentum moves beyond EMD bounds Backtest Mode Switch to Backtest Mode for performance evaluation over historical data: Equity Curve Plot: Compare EMD Trend strategy vs. Buy & Hold Trade Metrics Table: View number of trades, win/loss stats, profits Performance Metrics Table: Includes CAGR, Sharpe, max drawdown, and more Custom Start Date: Select from which date the backtest should begin Trade Sizing: Configure capital and trade percentage per entry Signal Filters: Choose from Long Only, Short Only, or Both Alerts Built-in alerts let you automate entries, exits, and trend transitions: LONG (EMD Trend) - Trend flips to Long SHORT (EMD Trend) - Trend flips to Short RISK-ON LONG - Price crosses above upper EMD band RISK-OFF LONG - Price crosses back below upper EMD band RISK-ON SHORT - Price crosses below lower EMD band RISK-OFF SHORT - Price crosses back above lower EMD band Use Cases Trend Confirmation with volatility-sensitive boundaries Momentum Entry Filtering via breakout zones Mean Reversion Avoidance in sideways markets Backtesting & Strategy Building with real-time metrics Disclaimer This indicator is intended for informational and educational purposes only. It does not constitute investment advice. Historical performance does not guarantee future results. Always backtest and use in simulation before live trading.
Chỉ báo Pine Script®
của InvestorUnknown
5
RMSD Trend [InvestorUnknown]RMSD Trend is a trend-following indicator that utilizes Root Mean Square Deviation (RMSD) to dynamically construct a volatility-weighted trend channel around a selected moving average. This indicator is designed to enhance signal clarity, minimize noise, and offer quantitative insights into market momentum, ideal for both discretionary and systematic traders. How It Works At its core, RMSD Trend calculates a deviation band around a selected moving average using the Root Mean Square Deviation (similar to standard deviation but with squared errors), capturing the magnitude of price dispersion over a user-defined period. The logic is simple: When price crosses above the upper deviation band, the market is considered bullish (Risk-ON Long). When price crosses below the lower deviation band, the market is considered bearish (Risk-ON Short). If price stays within the band, the market is interpreted as neutral or ranging, offering low-risk decision zones. The indicator also generates trend flips (Long/Short) based on crossovers and crossunders of the price and the RMSD bands, and colors candles accordingly for enhanced visual feedback. Features 7 Moving Average Types: Choose between SMA, EMA, HMA, DEMA, TEMA, RMA, and FRAMA for flexibility. Customizable Source Input: Use price types like close, hl2, ohlc4, etc. Volatility-Aware Channel: Adjustable RMSD multiplier determines band width based on volatility. Smart Coloring: Candles and bands adapt their colors to reflect trend direction (green for bullish, red for bearish). Intra-bar Repainting Toggle: Option to allow more responsive but repaintable signals. Speculation Fill Zones: When price exceeds the deviation channel, a semi-transparent fill highlights potential momentum surges. Backtest Mode Switching to Backtest Mode unlocks a robust suite of simulation features: Built-in Equity Curve: Visualizes both strategy equity and Buy & Hold performance. Trade Metrics Table: Displays the number of trades, win rates, gross profits/losses, and long/short breakdowns. Performance Metrics Table: Includes key stats like CAGR, drawdown, Sharpe ratio, and more. Custom Date Range: Set a custom start date for your backtest. Trade Sizing: Simulate results using position sizing and initial capital settings. Signal Filters: Choose between Long & Short, Long Only, or Short Only strategies. Alerts The RMSD Trend includes six built-in alert conditions: LONG (RMSD Trend) - Trend flips from Short to Long SHORT (RMSD Trend) - Trend flips from Long to Short RISK-ON LONG (RMSD Trend) - Price crosses above upper RMSD band RISK-OFF LONG (RMSD Trend) - Price falls back below upper RMSD band RISK-ON SHORT (RMSD Trend) - Price crosses below lower RMSD band RISK-OFF SHORT (RMSD Trend) - Price rises back above lower RMSD band Use Cases Trend Confirmation: Confirms directional bias with RMSD-weighted confidence zones. Breakout Detection: Highlights moments when price breaks free from historical volatility norms. Mean Reversion Filtering: Avoids false signals by incorporating RMSD’s volatility sensitivity. Strategy Development: Backtest your signals or integrate with a broader system for alpha generation. Settings Summary Display Mode: Overlay (default) or Backtest Mode Average Type: Choose from SMA, EMA, HMA, DEMA, etc. Average Length: Lookback window for moving average RMSD Multiplier: Band width control based on RMS deviation Source: Input price source (close, hl2, ohlc4, etc.) Intra-bar Updating: Real-time updates (may repaint) Color Bars: Toggle bar coloring by trend direction Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Past performance, including backtest results, is not indicative of future results. Use with caution and always test thoroughly before live deployment.
Chỉ báo Pine Script®
của InvestorUnknown
1
OrderFlow Sentiment SwiftEdgeOrderFlow Sentiment SwiftEdge Overview OrderFlow Sentiment SwiftEdge is a visual indicator designed to help traders analyze market dynamics through a simulated orderbook and market sentiment display. It breaks down the current candlestick into 10 price bins, estimating buy and sell volumes, and presents this data in an orderbook table alongside a sentiment row showing the buy vs. sell bias. This tool provides a quick and intuitive way to assess orderflow activity and market sentiment directly on your chart. How It Works The indicator consists of two main components: an Orderbook Table and a Market Sentiment Row. Orderbook Table: Simulates buy and sell volumes for the current candlestick by distributing total volume into 10 price bins based on price movement and proximity to open/close levels. Displays the price bins in a table with columns for Price, Buy Volume, and Sell Volume, sorted from highest to lowest price. Highlights the current price level in orange for easy identification, while buy and sell dominance is indicated with green (buy) or red (sell) backgrounds. Market Sentiment Row: Calculates the overall buy and sell sentiment (as a percentage) for the current candlestick based on the simulated orderflow data. Displays the sentiment above the orderbook table, with the background colored green if buyers dominate or red if sellers dominate. Features Customizable Colors: Choose colors for buy (default: green), sell (default: red), and current price (default: orange) levels. Lot Scaling Factor: Adjust the volume scaling factor (default: 0.1 lots per volume unit) to simulate realistic lot sizes. Table Position: Select the table position on the chart (Top, Middle, or Bottom; default: Middle). Default Properties Positive Color: Green Negative Color: Red Current Price Color: Orange Lot Scaling Factor: 0.1 Table Position: Middle Usage This indicator is ideal for traders who want to visualize orderflow dynamics and market sentiment in real-time. The orderbook table provides a snapshot of buy and sell activity at different price levels within the current candlestick, helping you identify areas of high buying or selling pressure. The sentiment row offers a quick overview of market bias, allowing you to gauge whether buyers or sellers are currently dominating. Use this information to complement your trading decisions, such as identifying potential breakout levels or confirming trend direction. Limitations This indicator simulates orderflow data based on candlestick price movement and volume, as TradingView does not provide tick-by-tick data. The volume distribution is an approximation and should be used as a visual aid rather than a definitive measure of market activity. The indicator operates on the chart's current timeframe and does not incorporate higher timeframe data. The simulated volumes are scaled using a user-defined lot scaling factor, which may not reflect actual market lot sizes. Disclaimer This indicator is for informational purposes only and does not guarantee trading results. Always conduct your own analysis and manage risk appropriately. The simulated orderflow data is an estimation and may not reflect real market conditions.
Chỉ báo Pine Script®
của SwiftEdge
4