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EMA Crossover + SuperTrend Retest//@version=5 indicator("EMA Crossover + SuperTrend Retest", overlay=true) // Inputs for SuperTrend atrLength = input.int(10, "ATR Length", minval=1) Factor = input.float(3.0, "SuperTrend Multiplier", step=0.1) // Calculate EMAs ema13 = ta.ema(close, 13) ema50 = ta.ema(close, 50) ema200 = ta.ema(close, 200) // Calculate SuperTrend = ta.supertrend(Factor, atrLength) // Plot EMAs plot(ema13, "EMA 13", color=color.new(#FF6D00, 0)) plot(ema50, "EMA 50", color=color.new(#2962FF, 0)) plot(ema200, "EMA 200", color=color.new(#00BFA5, 0)) // Plot SuperTrend plot(supertrend, "SuperTrend", color=direction == 1 ? color.rgb(230, 13, 13) : color.rgb(17, 255, 0), linewidth=2) // Trend Conditions uptrendFilter = ema50 > ema200 and close > supertrend downtrendFilter = ema50 < ema200 and close < supertrend // Crossover Conditions with Trend Filter bullishCross = ta.crossover(ema13, ema50) and uptrendFilter bearishCross = ta.crossunder(ema13, ema50) and downtrendFilter // Retest Conditions (Both EMAs and SuperTrend) bullishRetest = bullishCross and (low <= ema50) and (low <= ema200) and (low <= supertrend) bearishRetest = bearishCross and (high >= ema50) and (high >= ema200) and (high >= supertrend) // Plot Signals plotshape(bullishRetest, "Bullish Signal", shape.triangleup, location.belowbar, color=color.green, size=size.small) plotshape(bearishRetest, "Bearish Signal", shape.triangledown, location.abovebar, color=color.red, size=size.small) // Alert Conditions alertcondition(bullishRetest, "Bullish Alert", "EMA13↑50 + Retest 50/200/SuperTrend") alertcondition(bearishRetest, "Bearish Alert", "EMA13↓50 + Retest 50/200/SuperTrend")
Pine Script® göstergesi
kumarmailkumar89 tarafından
Anti-SMT + FVG StrategieMade by Laila 4h gives 57% winrate! Instead of trading based on an expected SMT divergence, you assume that the divergence will not continue. You combine this with a Fair Value Gap (FVG) that is touched by price as additional confirmation. Anti-SMT Logic (False Divergence) Short: EURUSD makes a new high (candle 1) DXY does not make a new low Long: EURUSD makes a new low (candle 1) DXY does not make a new high This looks like SMT divergence, but your expectation is: "There will be no SMT." Fair Value Gap (FVG) Detection Detects an unfilled gap between candle 1 and 3. You only trade if the FVG is touched during: 🔹 Candle 1 (the false SMT candle) or 🔹 Candle 2 (the entry candle) Extra Filters Only go long if price is above the 50 EMA Only go short if price is below the 50 EMA Only trade between 08:00 and 18:00 UTC Wait 10 candles cooldown between trades Result: You only trade when: There is a possible SMT illusion An FVG is touched The setup aligns with trend, session, and timing This gives you a rational, rare, but strong edge.
Pine Script® stratejisi
Gerritnotsnailo tarafından
OBV PanelOBV Panel – Volume-Based Price Prediction & Signal Dashboard Powered by Pine Script v6 🔍 Overview This multi-functional indicator is designed around the On-Balance Volume (OBV) concept, enhancing it with prediction models, trend tracking, and actionable buy/sell signals. It uses a combination of real-time OBV movement, smoothed OBV with EMA, and linear regression-based OBV forecasts to deliver both intraday and weekly insights — all neatly displayed in a table panel and directly plotted on your chart. ⚙️ Core Components 📌 1. OBV Core OBV is calculated based on volume flowing into or out of a stock as price moves up or down. Tracks raw OBV and its EMA (Exponential Moving Average) for smoother trend reading. Computes a Predicted OBV using Linear Regression (ta.linreg) over a user-defined number of bars. 🔮 2. Predicted Price Forecast Uses OBV percentage changes combined with a user-set sensitivity factor to project next day’s expected price. Offers an AI-style price forecast based on OBV strength, not just price action. 💹 3. Buy/Sell Signal Logic Daily Signals: Triggered when OBV, OBV EMA, and Predicted OBV all move upward or downward from the previous day. Weekly Signals: Based on EMA changes over a 5-bar period (approx. 1 week). Signal markers are drawn on the chart for visual reference. 📊 Table Panel (Top-Right Overlay) A detailed visual panel shows: Metric Description OBV, OBV EMA, Predicted OBV From previous close to current value OBV - Predicted OBV Difference In lakhs (scaled for readability) % Change Stats Daily percentage change in OBV, EMA, and Predicted OBV Weekly OBV EMA Change Actual & % change over 5 bars Signal Summary BUY/SELL or HOLD based on logic OBV Dominance Whether OBV > EMA and Predicted OBV Predicted Price (Next Day) Based on OBV dynamics and sensitivity
Pine Script® göstergesi
raghavprakash05 tarafından
ALMA Trend-boxThis indicator uses the ALMA (Arnaud Legoux Moving Average) – a special type of moving average that provides a smoother and more responsive trend line. Based on the slope (angle) of the ALMA line and the price position relative to it, the indicator: Colors candles in three different ways (to reflect market structure), Plots the ALMA line on the chart, Detects consolidation and highlights it with blue candles, background shading, and horizontal "box" lines. 📘 Candle Colors – How to Interpret Them Candle Color Meaning Interpretation 🟩 Green Uptrend ALMA is sloping upward and price is above ALMA – look for buying opportunities. 🟥 Red Downtrend ALMA is sloping downward and price is below ALMA – look for selling opportunities. 🔵 Blue Sideways (Consolidation) Weak or neutral trend – market is moving sideways or accumulating. 🔵 What Do Blue Candles and the “Trend-box” Mean? Blue candles represent consolidation periods, which occur when: The slope of the ALMA line is less than ±40°, indicating a lack of strong trend, The price behavior is not consistent with the direction of the slope (e.g., price is below ALMA even though ALMA is pointing upward). During this time: Blue candles and a blue background appear to visually highlight the consolidation, Two dashed horizontal lines (a “box”) are drawn at the high and low of the consolidation range. 📌 The Trend-box helps you visually spot ranging markets, which often precede strong breakouts. 📈 How to Use This Indicator in Practice Trend Following Strategy: When candles are green → consider long trades. When candles are red → consider short trades. Use additional indicators (like RSI, MACD, or volume) to confirm entries. Breakout Trading: When blue candles and the box appear, wait for the price to: Break above the box → potential long breakout. Break below the box → potential short breakout. You can set pending orders (buy stop/sell stop) just outside the box range. Avoiding Choppy Entries: Blue candles signal uncertainty – avoid entering impulsively during this time. Wait for trend confirmation. ⚙️ Adjustable Settings ALMA Length – controls how quickly the moving average reacts. Slope Threshold – determines the minimum angle required to define a trend. Candle Colors – fully customizable (green/red/blue by default). ✅ Conclusion ALMA Trend-box is a powerful visual tool for identifying: Trending conditions (bullish or bearish), Sideways markets (consolidation), Breakout setups with clearly marked zones. It works well on its own or as part of a larger trading system. Blue candles tell you to be patient, while transitions into green/red candles indicate developing trends.
Pine Script® göstergesi
Aleksin_Aleksandar tarafından
Color Change EMA 200 (4H)200 Color Change EMA (4H Locked) Overview This indicator displays a 200-period Exponential Moving Average (EMA) that is locked to the 4-hour timeframe, regardless of what chart timeframe you're currently viewing. The EMA line changes color dynamically based on price action to provide clear visual trend signals. Key Features • Multi-Timeframe Capability : Always shows the 4H 200 EMA on any chart timeframe • Dynamic Color Coding : - Green: Price is above the 200 EMA (bullish condition) - Red: Price is below the 200 EMA (bearish condition) • Clean Visual Design : Bold 2-pixel line width for clear visibility • Real-time Updates : Colors change instantly as price crosses above or below the EMA How to Use 1. Add the indicator to any timeframe chart 2. The 4H 200 EMA will appear as a smooth line 3. Watch for color changes: - When the line turns green, it indicates price strength above the key moving average - When the line turns red, it suggests price weakness below the moving average 4. Use for trend identification, support/resistance levels, and entry/exit timing Best Practices • Combine with other technical analysis tools for confirmation • Use the color changes as alerts for potential trend shifts • Consider the 200 EMA as a major support/resistance level • Works well for swing trading and position sizing decisions Settings • Length : Default 200 periods (customizable) • Source : Default closing price (customizable) Perfect for traders who want to keep the important 4H 200 EMA visible across all timeframes with instant visual trend feedback.
Pine Script® göstergesi
hazedoptions tarafından
David_FairPriceCandlestick_calculatedDescription: This indicator displays the "Typical Price" for each candle as a visual marker (cross) directly on the chart. The Typical Price is calculated as the average of the High, Low, and Close values of each bar: (High + Low + Close) / 3 The marker provides a quick visual reference to the fair or average price level within every single candle. Unlike a Point of Control (POC) or volume-based indicators, this script works purely with price data and is independent of volume or order flow. Use cases: Identify where most trading activity may have been concentrated within the candle (for price-based strategies) Support as a reference line for mean-reversion or fair value concepts Works on all timeframes and instruments Customization: You can easily change the marker style (cross, dot, triangle, etc.) and color within the script.
Pine Script® göstergesi
GolfTrade tarafından
JonnyBtc Daily Pullback Strategy (Volume + ADX)📈 JonnyBtc Daily Optimized Pullback Strategy (With Volume + ADX) This strategy is designed for Bitcoin swing trading on the daily timeframe and uses a combination of price action, moving averages, volume, RSI, and ADX strength filtering to time high-probability entries during strong trending conditions. 🔍 Strategy Logic: Trend Filter: Requires price to be aligned with both 50 EMA and 200 EMA. Pullback Entry: Looks for a pullback to a fast EMA (default 21) and a crossover signal back above it. RSI Confirmation: RSI must be above a minimum threshold for long entries (default 55), or below for short entries. Volume Filter: Entry is confirmed only when volume is above a 20-day average. ADX Filter: Only enters trades when ADX is above a strength threshold (default 20), filtering out sideways markets. Trailing Stop (optional): Uses ATR-based trailing stop-loss and take-profit system, fully configurable. ⚙️ Default Settings: Timeframe: Daily Trade Direction: Long-only by default (can be toggled) Trailing Stop: Enabled (can disable) Session Filter: Off by default for daily timeframe 📊 Best Use: Optimized for Bitcoin (BTCUSD) on the 1D chart Can be adapted to other trending assets with proper tuning Works best in strong trending markets — not ideal for choppy/ranging conditions 🛠️ Customizable Parameters: EMA lengths (Fast, Mid, Long) RSI and ADX thresholds ATR-based TP/SL multipliers Trailing stop toggle Volume confirmation toggle Time/session filter ⚠️ Disclaimer: This script is for educational and research purposes only. Past performance does not guarantee future results. Always backtest and verify before trading with real funds.
Pine Script® stratejisi
mulysport35 tarafından
Performance Metrics With Bracketed Rebalacing [BackQuant]Performance Metrics With Bracketed Rebalancing The Performance Metrics With Bracketed Rebalancing script offers a robust method for assessing portfolio performance, integrating advanced portfolio metrics with different rebalancing strategies. With a focus on adaptability, the script allows traders to monitor and adjust portfolio weights, equity, and other key financial metrics dynamically. This script provides a versatile approach for evaluating different trading strategies, considering factors like risk-adjusted returns, volatility, and the impact of portfolio rebalancing. Please take the time to read the following: Key Features and Benefits of Portfolio Methods Bracketed Rebalancing: Bracketed Rebalancing is an advanced strategy designed to trigger portfolio adjustments when an asset's weight surpasses a predefined threshold. This approach minimizes overexposure to any single asset while maintaining flexibility in response to market changes. The strategy is particularly beneficial for mitigating risks that arise from significant asset weight fluctuations. The following image illustrates how this method reacts when asset weights cross the threshold: Daily Rebalancing: Unlike the bracketed method, Daily Rebalancing adjusts portfolio weights every trading day, ensuring consistent asset allocation. This method aims for a more even distribution of portfolio weights, making it a suitable option for traders who prefer less sensitivity to individual asset volatility. Here's an example of Daily Rebalancing in action: No Rebalancing: For traders who prefer a passive approach, the "No Rebalancing" option allows the portfolio to remain static, without any adjustments to asset weights. This method may appeal to long-term investors or those who believe in the inherent stability of their selected assets. Here’s how the portfolio looks when no rebalancing is applied: Portfolio Weights Visualization: One of the standout features of this script is the visual representation of portfolio weights. With adjustable settings, users can track the current allocation of assets in real-time, making it easier to analyze shifts and trends. The following image shows the real-time weight distribution across three assets: Rolling Drawdown Plot: Managing drawdown risk is a critical aspect of portfolio management. The Rolling Drawdown Plot visually tracks the drawdown over time, helping traders monitor the risk exposure and performance relative to the peak equity levels. This feature is essential for assessing the portfolio's resilience during market downturns: Daily Portfolio Returns: Tracking daily returns is crucial for evaluating the short-term performance of the portfolio. The script allows users to plot daily portfolio returns to gain insights into daily profit or loss, helping traders stay updated on their portfolio’s progress: Performance Metrics Net Profit (%): This metric represents the total return on investment as a percentage of the initial capital. A positive net profit indicates that the portfolio has gained value over the evaluation period, while a negative value suggests a loss. It's a fundamental indicator of overall portfolio performance. Maximum Drawdown (Max DD): Maximum Drawdown measures the largest peak-to-trough decline in portfolio value during a specified period. It quantifies the most significant loss an investor would have experienced if they had invested at the highest point and sold at the lowest point within the timeframe. A smaller Max DD indicates better risk management and less exposure to significant losses. Annual Mean Returns (% p/y): This metric calculates the average annual return of the portfolio over the evaluation period. It provides insight into the portfolio's ability to generate returns on an annual basis, aiding in performance comparison with other investment opportunities. Annual Standard Deviation of Returns (% p/y): This measure indicates the volatility of the portfolio's returns on an annual basis. A higher standard deviation signifies greater variability in returns, implying higher risk, while a lower value suggests more stable returns. Variance: Variance is the square of the standard deviation and provides a measure of the dispersion of returns. It helps in understanding the degree of risk associated with the portfolio's returns. Sortino Ratio: The Sortino Ratio is a variation of the Sharpe Ratio that only considers downside risk, focusing on negative volatility. It is calculated as the difference between the portfolio's return and the minimum acceptable return (MAR), divided by the downside deviation. A higher Sortino Ratio indicates better risk-adjusted performance, emphasizing the importance of avoiding negative returns. Sharpe Ratio: The Sharpe Ratio measures the portfolio's excess return per unit of total risk, as represented by standard deviation. It is calculated by subtracting the risk-free rate from the portfolio's return and dividing by the standard deviation of the portfolio's excess return. A higher Sharpe Ratio indicates more favorable risk-adjusted returns. Omega Ratio: The Omega Ratio evaluates the probability of achieving returns above a certain threshold relative to the probability of experiencing returns below that threshold. It is calculated by dividing the cumulative probability of positive returns by the cumulative probability of negative returns. An Omega Ratio greater than 1 indicates a higher likelihood of achieving favorable returns. Gain-to-Pain Ratio: The Gain-to-Pain Ratio measures the return per unit of risk, focusing on the magnitude of gains relative to the severity of losses. It is calculated by dividing the total gains by the total losses experienced during the evaluation period. A higher ratio suggests a more favorable balance between reward and risk. www.linkedin.com Compound Annual Growth Rate (CAGR) (% p/y): CAGR represents the mean annual growth rate of the portfolio over a specified period, assuming the investment has been compounding over that time. It provides a smoothed annual rate of growth, eliminating the effects of volatility and offering a clearer picture of long-term performance. Portfolio Alpha (% p/y): Portfolio Alpha measures the portfolio's performance relative to a benchmark index, adjusting for risk. It is calculated using the Capital Asset Pricing Model (CAPM) and represents the excess return of the portfolio over the expected return based on its beta and the benchmark's performance. A positive alpha indicates outperformance, while a negative alpha suggests underperformance. Portfolio Beta: Portfolio Beta assesses the portfolio's sensitivity to market movements, indicating its exposure to systematic risk. A beta greater than 1 suggests the portfolio is more volatile than the market, while a beta less than 1 indicates lower volatility. Beta is used to understand the portfolio's potential for gains or losses in relation to market fluctuations. Skewness of Returns: Skewness measures the asymmetry of the return distribution. A positive skew indicates a distribution with a long right tail, suggesting more frequent small losses and fewer large gains. A negative skew indicates a long left tail, implying more frequent small gains and fewer large losses. Understanding skewness helps in assessing the likelihood of extreme outcomes. Value at Risk (VaR) 95th Percentile: VaR at the 95th percentile estimates the maximum potential loss over a specified period, given a 95% confidence level. It provides a threshold value such that there is a 95% probability that the portfolio will not experience a loss greater than this amount. Conditional Value at Risk (CVaR): CVaR, also known as Expected Shortfall, measures the average loss exceeding the VaR threshold. It provides insight into the tail risk of the portfolio, indicating the expected loss in the worst-case scenarios beyond the VaR level. These metrics collectively offer a comprehensive view of the portfolio's performance, risk exposure, and efficiency. By analyzing these indicators, investors can make informed decisions, balancing potential returns with acceptable levels of risk. Conclusion The Performance Metrics With Bracketed Rebalancing script provides a comprehensive framework for evaluating and optimizing portfolio performance. By integrating advanced metrics, adaptive rebalancing strategies, and visual analytics, it empowers traders to make informed decisions in managing their investment portfolios. However, it's crucial to consider the implications of rebalancing strategies, as academic research indicates that predictable rebalancing can lead to market impact costs. Therefore, adopting flexible and less predictable rebalancing approaches may enhance portfolio performance and reduce associated costs.
Pine Script® göstergesi
BackQuant tarafından
Wavelet Filter with Adaptive Upsampling [BackQuant]Wavelet Filter with Adaptive Upsampling The Wavelet Filter with Adaptive Upsampling is an advanced filtering and signal reconstruction tool designed to enhance the analysis of financial time series data. It combines wavelet transforms with adaptive upsampling techniques to filter and reconstruct price data, making it ideal for capturing subtle market movements and enhancing trend detection. This system uses high-pass and low-pass filters to decompose the price series into different frequency components, applying adaptive thresholding to eliminate noise and preserve relevant signal information. Shout out to Loxx for the Least Squares fitting of trigonometric series and Quinn and Fernandes algorithm for finding frequency www.tradingview.com Key Features 1. Frequency Decomposition with High-Pass and Low-Pass Filters: The indicator decomposes the input time series using high-pass and low-pass filters to separate the high-frequency (detail) and low-frequency (trend) components of the data. This decomposition allows for a more accurate analysis of underlying trends, while mitigating the impact of noise. 2. Soft Thresholding for Noise Reduction: A soft thresholding function is applied to the high-frequency component, allowing for the reduction of noise while retaining significant market signals. This function adjusts the coefficients of the high-frequency data, removing small fluctuations and leaving only the essential price movements. 3. Adaptive Upsampling Process: The upsampling process in this script can be customized using different methods: sinusoidal upsampling, advanced upsampling, and simple upsampling. Each method serves a unique purpose: Sinusoidal Upsample uses a sine wave to interpolate between data points, providing a smooth transition. Advanced Upsample utilizes a Quinn-Fernandes algorithm to estimate frequency and apply more sophisticated interpolation techniques, adapting to the market’s cyclical behavior. Simple Upsample linearly interpolates between data points, providing a basic upsampling technique for less complex analysis. 4. Reconstruction of Filtered Signal: The indicator reconstructs the filtered signal by summing the high and low-frequency components after upsampling. This allows for a detailed yet smooth representation of the original time series, which can be used for analyzing underlying trends in the market. 5. Visualization of Reconstructed Data: The reconstructed series is plotted, showing how the upsampling and filtering process enhances the clarity of the price movements. Additionally, the script provides the option to visualize the log returns of the reconstructed series as a histogram, with positive returns shown in green and negative returns in red. 6. Cumulative Series and Trend Detection: A cumulative series is plotted to visualize the compounded effect of the filtered and reconstructed data. This feature helps traders track the overall performance of the asset over time, identifying whether the asset is following a sustained upward or downward trend. 7. Adaptive Thresholding and Noise Estimation: The system estimates the noise level in the high-frequency component and applies an adaptive thresholding process based on the standard deviation of the downsampled data. This ensures that only significant price movements are retained, further refining the trend analysis. 8. Customizable Parameters for Flexibility: Users can customize the following parameters to adjust the behavior of the indicator: Frequency and Phase Shift: Control the periodicity of the wavelet transformation and the phase of the upsampling function. Upsample Factor: Adjust the level of interpolation applied during the upsampling process. Smoothing Period: Determine the length of time used to smooth the signal, helping to filter out short-term fluctuations. References Enhancing Cross-Sectional Currency Strategies with Context-Aware Learning to Rank arxiv.org Daubechies Wavelet - Wikipedia en.wikipedia.org Quinn Fernandes Fourier Transform of Filtered Price by Loxx Note on Usage for Mean-Reversion Strategy This indicator is primarily designed for trend-following strategies. However, by taking the inverse of the signals, it can be adapted for mean-reversion strategies. This involves buying underperforming assets and selling outperforming ones. Caution: This method may not work effectively with highly correlated assets, as the price movements between correlated assets tend to mirror each other, limiting the effectiveness of mean-reversion strategies. Final Thoughts The Wavelet Filter with Adaptive Upsampling is a powerful tool for traders seeking to improve their understanding of market trends and noise. By using advanced wavelet decomposition and adaptive upsampling, this system offers a clearer, more refined picture of price movements, enhancing trend-following strategies. It’s particularly useful for detecting subtle shifts in market momentum and reconstructing price data in a way that removes noise, providing more accurate insights into market conditions.
Pine Script® göstergesi
BackQuant tarafından
GER40 BIAS Forecast [ML-Based]🎯 Purpose: This indicator provides a daily directional bias (LONG / SHORT / FLAT) for the German DAX40 index (GER40) using a statistically optimized scoring model, developed with 6 years of historical data and verified through machine learning analysis. 🧠 How the Score Works (ML-derived): Each trading day receives a bias score (0–3) for both long and short setups, based on these 3 factors from the daily candle: Condition Long Score Logic Short Score Logic 1. Candle Direction Close > Open → +1 Close < Open → +1 2. VWAP Slope VWAP > VWAP → +1 VWAP < VWAP → +1 3. Volatility Strength Range > SMA(20) → +1 Close < Yesterday's Low → +1 (Rejection) ➡️ A score of 2 or more triggers a Long or Short Bias for the day. These scoring rules are derived from a machine learning model trained on 6 years of DAX data, identifying the most predictive features for directional follow-through. 📘 Bias Interpretation: Score Result Daily Bias Background Color Long Score ≥ 2 LONG Green Short Score ≥ 2 SHORT Red Both < 2 FLAT Gray 📍 Indicator Features: 🎨 Background coloring to visualize daily bias directly on intraday charts 🔢 Optional score labels (e.g. “Long: 2 | Short: 1”) per calendar day 📈 VWAP line plotted for additional intraday context ❌ Entry signals removed – this version focuses solely on forecasting directional bias 💡 Use Case: Morning planning aid Filtering for high-probability intraday setups Combining with session-based entry systems
Pine Script® göstergesi
Precious_Life tarafından
📈 Pro EMA/SMA Buy Sell (Clean & Glowing) 📈 Pro EMA/SMA Buy Sell This indicator plots a crossover-based buy/sell signal system using: - A fast Exponential Moving Average (EMA) - A slower Simple Moving Average (SMA) 🔹 BUY Signal: When EMA crosses above SMA 🔹 SELL Signal: When EMA crosses below SMA Features: ✅ Clean glowing lines for EMA and SMA ✅ Transparent glowing BUY (green) and SELL (red) labels ✅ Real-time alert conditions for automated strategy triggers Ideal for: - Intraday and Swing Traders - Beginners looking for trend-based signals - Chart setups requiring minimal noise but powerful visuals
Pine Script® göstergesi
poligrowvenkatpolisetty tarafından
NP Screener with Alerts For Nifty 50 [NITIN PADALE]//@version=6 indicator('NP Screener with Alerts For Nifty 50 ', overlay = true) //////////// // INPUTS // filter_enabled = input.bool(false, '', group = 'Filter', inline = 'Filter') filter_column = input.string('Price', title = 'Column', options = , group = 'Filter', inline = 'Filter') filter_from = input.float(-9999999, 'From', group = 'Filter', inline = 'Filter') filter_to = input.float(9999999, 'To', group = 'Filter', inline = 'Filter') // SMA rsi_len = input.int(14, title = 'RSI Length', group = 'Indicators') rsi_os = input.float(30, title = 'RSI Overbought', group = 'Indicators') rsi_ob = input.float(70, title = 'RSI Oversold', group = 'Indicators') // TSI tsi_long_len = input.int(25, title = 'TSI Long Length', group = 'Indicators') tsi_shrt_len = input.int(13, title = 'TSI Short Length', group = 'Indicators') tsi_ob = input.float(30, title = 'TSI Overbought', group = 'Indicators') tsi_os = input.float(-30, title = 'TSI Oversold', group = 'Indicators') // ADX Params adx_smooth = input.int(14, title = 'ADX Smoothing', group = 'Indicators') adx_dilen = input.int(14, title = 'ADX DI Length', group = 'Indicators') adx_level = input.float(40, title = 'ADX Level', group = 'Indicators') // SuperTrend sup_atr_len = input.int(10, 'Supertrend ATR Length', group = 'Indicators') sup_factor = input.float(3.0, 'Supertrend Factor', group = 'Indicators') ///////////// // SYMBOLS // u01 = input.bool(true, title = '', group = 'Symbols', inline = 's01') u02 = input.bool(true, title = '', group = 'Symbols', inline = 's02') u03 = input.bool(true, title = '', group = 'Symbols', inline = 's03') u04 = input.bool(true, title = '', group = 'Symbols', inline = 's04') u05 = input.bool(true, title = '', group = 'Symbols', inline = 's05') u06 = input.bool(true, title = '', group = 'Symbols', inline = 's06') u07 = input.bool(true, title = '', group = 'Symbols', inline = 's07') u08 = input.bool(true, title = '', group = 'Symbols', inline = 's08') u09 = input.bool(true, title = '', group = 'Symbols', inline = 's09') u10 = input.bool(true, title = '', group = 'Symbols', inline = 's10') s01 = input.symbol('HDFCBANK', group = 'Symbols', inline = 's01') s02 = input.symbol('ICICIBANK', group = 'Symbols', inline = 's02') s03 = input.symbol('RELIANCE', group = 'Symbols', inline = 's03') s04 = input.symbol('INFY', group = 'Symbols', inline = 's04') s05 = input.symbol('BHARTIARTL', group = 'Symbols', inline = 's05') s06 = input.symbol('LT', group = 'Symbols', inline = 's06') s07 = input.symbol('ITC', group = 'Symbols', inline = 's07') s08 = input.symbol('TCS', group = 'Symbols', inline = 's08') s09 = input.symbol('AXISBANK', group = 'Symbols', inline = 's09') s10 = input.symbol('SBIN', group = 'Symbols', inline = 's10') ////////////////// // CALCULATIONS // filt_col_id = switch filter_column 'Price' => 1 'RSI' => 2 'TSI' => 3 'ADX' => 4 'SuperTrend' => 5 => 0 // Get only symbol only_symbol(s) => array.get(str.split(s, ':'), 1) id_symbol(s) => switch s 1 => only_symbol(s01) 2 => only_symbol(s02) 3 => only_symbol(s03) 4 => only_symbol(s04) 5 => only_symbol(s05) 6 => only_symbol(s06) 7 => only_symbol(s07) 8 => only_symbol(s08) 9 => only_symbol(s09) 10 => only_symbol(s10) => na // for TSI double_smooth(src, long, short) => fist_smooth = ta.ema(src, long) ta.ema(fist_smooth, short) // ADX dirmov(len) => up = ta.change(high) down = -ta.change(low) plusDM = na(up) ? na : up > down and up > 0 ? up : 0 minusDM = na(down) ? na : down > up and down > 0 ? down : 0 truerange = ta.rma(ta.tr, len) plus = fixnan(100 * ta.rma(plusDM, len) / truerange) minus = fixnan(100 * ta.rma(minusDM, len) / truerange) adx_func(dilen, adxlen) => = dirmov(dilen) sum = plus + minus adx = 100 * ta.rma(math.abs(plus - minus) / (sum == 0 ? 1 : sum), adxlen) adx screener_func() => // RSI rsi = ta.rsi(close, rsi_len) // TSI pc = ta.change(close) double_smoothed_pc = double_smooth(pc, tsi_long_len, tsi_shrt_len) double_smoothed_abs_pc = double_smooth(math.abs(pc), tsi_long_len, tsi_shrt_len) tsi = 100 * (double_smoothed_pc / double_smoothed_abs_pc) // ADX adx = adx_func(adx_dilen, adx_smooth) // Supertrend = ta.supertrend(sup_factor, sup_atr_len) // Set Up Matrix screenerMtx = matrix.new(0, 6, na) screenerFun(numSym, sym, flg) => = request.security(sym, timeframe.period, screener_func()) arr = array.from(numSym, cl, rsi, tsi, adx, sup) if flg matrix.add_row(screenerMtx, matrix.rows(screenerMtx), arr) // Security call screenerFun(01, s01, u01) screenerFun(02, s02, u02) screenerFun(03, s03, u03) screenerFun(04, s04, u04) screenerFun(05, s05, u05) screenerFun(06, s06, u06) screenerFun(07, s07, u07) screenerFun(08, s08, u08) screenerFun(09, s09, u09) screenerFun(10, s10, u10) /////////// // PLOTS // var tbl = table.new(position.top_right, 6, 41, frame_color = #151715, frame_width = 1, border_width = 2, border_color = color.new(color.white, 100)) log.info(str.tostring(filt_col_id)) alert_msg = '' if barstate.islast table.clear(tbl, 0, 0, 5, 40) table.cell(tbl, 0, 0, 'Symbol', text_halign = text.align_center, bgcolor = color.gray, text_color = color.white, text_size = size.small) table.cell(tbl, 1, 0, 'Price', text_halign = text.align_center, bgcolor = color.gray, text_color = color.white, text_size = size.small) table.cell(tbl, 2, 0, 'RSI', text_halign = text.align_center, bgcolor = color.gray, text_color = color.white, text_size = size.small) table.cell(tbl, 3, 0, 'TSI', text_halign = text.align_center, bgcolor = color.gray, text_color = color.white, text_size = size.small) table.cell(tbl, 4, 0, 'ADX', text_halign = text.align_center, bgcolor = color.gray, text_color = color.white, text_size = size.small) table.cell(tbl, 5, 0, 'Supertrend', text_halign = text.align_center, bgcolor = color.gray, text_color = color.white, text_size = size.small) if matrix.rows(screenerMtx) > 0 for i = 0 to matrix.rows(screenerMtx) - 1 by 1 is_filt = not filter_enabled or matrix.get(screenerMtx, i, filt_col_id) >= filter_from and matrix.get(screenerMtx, i, filt_col_id) <= filter_to if is_filt if str.length(alert_msg) > 0 alert_msg := alert_msg + ',' alert_msg alert_msg := alert_msg + id_symbol(matrix.get(screenerMtx, i, 0)) rsi_col = matrix.get(screenerMtx, i, 2) > rsi_ob ? color.red : matrix.get(screenerMtx, i, 2) < rsi_os ? color.green : #aaaaaa tsi_col = matrix.get(screenerMtx, i, 3) > tsi_ob ? color.red : matrix.get(screenerMtx, i, 3) < tsi_os ? color.green : #aaaaaa adx_col = matrix.get(screenerMtx, i, 4) > adx_level ? color.green : #aaaaaa sup_text = matrix.get(screenerMtx, i, 5) > 0 ? 'Down' : 'Up' sup_col = matrix.get(screenerMtx, i, 5) < 0 ? color.green : color.red table.cell(tbl, 0, i + 1, id_symbol(matrix.get(screenerMtx, i, 0)), text_halign = text.align_left, bgcolor = color.gray, text_color = color.white, text_size = size.small) table.cell(tbl, 1, i + 1, str.tostring(matrix.get(screenerMtx, i, 1)), text_halign = text.align_center, bgcolor = #aaaaaa, text_color = color.white, text_size = size.small) table.cell(tbl, 2, i + 1, str.tostring(matrix.get(screenerMtx, i, 2), '#.##'), text_halign = text.align_center, bgcolor = rsi_col, text_color = color.white, text_size = size.small) table.cell(tbl, 3, i + 1, str.tostring(matrix.get(screenerMtx, i, 3), '#.##'), text_halign = text.align_center, bgcolor = tsi_col, text_color = color.white, text_size = size.small) table.cell(tbl, 4, i + 1, str.tostring(matrix.get(screenerMtx, i, 4), '#.##'), text_halign = text.align_center, bgcolor = adx_col, text_color = color.white, text_size = size.small) table.cell(tbl, 5, i + 1, sup_text, text_halign = text.align_center, bgcolor = sup_col, text_color = color.white, text_size = size.small) if str.length(alert_msg) > 0 alert(alert_msg, freq = alert.freq_once_per_bar_close)
Pine Script® göstergesi
shaileshGole tarafından
AD BackGrand//@version=5 indicator("AD BackGrand", overlay=true) // فقط برای XAUUSD اجرا بشه isGold = syminfo.ticker == "XAUUSD" // ⚙️ ورودی‌ها threshold = input.float(4.0, title="🟡 Volatility Threshold ($)", minval=0.1, step=0.1) candleCount = input.int(3, title="🕒 Number of Candles to Check", minval=1, maxval=50) // محاسبه مجموع نوسان کندل‌ها (داینامیک) totalVol = 0.0 for i = 0 to candleCount - 1 totalVol += high - low // شرط‌ها isVolatile = isGold and (totalVol > threshold) isCalm = isGold and (totalVol <= threshold) // رنگ بک‌گراند bgcolor(isVolatile ? color.new(color.green, 80) : na, title="High Volatility") bgcolor(isCalm ? color.new(color.red, 85) : na, title="Low Volatility") // نمایش مجموع نوسان (اختیاری) plot(isGold ? totalVol : na, title="🔢 Total Volatility", color=color.orange)
Pine Script® göstergesi
ysramylady tarafından
Double Inside Bar Alert (1-1 Only)Double Inside Bar Alert Indicator Stay ahead of breakout opportunities with our Double Inside Bar Alert Indicator. This tool automatically scans your watchlist and notifies you in real-time whenever a double inside bar pattern appears—an advanced price action setup known for signaling strong potential breakouts or reversals. Whether you’re a day trader or swing trader, this indicator helps you: Spot consolidation zones early before major price moves Receive instant alerts across your entire watchlist Filter noise and focus on high-probability chart setups Perfect for traders who rely on price action and want to catch momentum shifts as they form.
Pine Script® göstergesi
mrf2rev tarafından