Mulai

Cari skrip untuk "Table"

CanvasLibrary "Canvas" A library implementing a kind of "canvas" using a table where each pixel is represented by a table cell and the pixel color by the background color of each cell. To use the library, you need to create a color matrix (represented as an array) and a canvas table. The canvas table is the container of the canvas, and the color matrix determines what color each pixel in the canvas should have. max_canvas_size() Function that returns the maximum size of the canvas (100). The canvas is always square, so the size is equal to rows (as opposed to not rows multiplied by columns).   Returns: The maximum size of the canvas (100). get_bg_color(color_matrix) Get the current background color of the color matrix. This is the default color used when erasing pixels or clearing a canvas.   Parameters:      color_matrix : The color matrix.   Returns: The current background color. get_fg_color(color_matrix) Get the current foreground color of the color matrix. This is the default color used when drawing pixels.   Parameters:      color_matrix : The color matrix.   Returns: The current foreground color. set_bg_color(color_matrix, bg_color) Set the background color of the color matrix. This is the default color used when erasing pixels or clearing a canvas.   Parameters:      color_matrix : The color matrix.      bg_color : The new background color. set_fg_color(color_matrix, fg_color) Set the foreground color of the color matrix. This is the default color used when drawing pixels.   Parameters:      color_matrix : The color matrix.      fg_color : The new foreground color. color_matrix_rows(color_matrix, rows) Function that returns how many rows a color matrix consists of.   Parameters:      color_matrix : The color matrix.      rows : (Optional) The number of rows of the color matrix. This can be omitted, but if used, can speed up execution.   Returns: The number of rows a color matrix consists of. pixel_color(color_matrix, x, y, rows) Get the color of the pixel at the specified coordinates.   Parameters:      color_matrix : The color matrix.      x : The X coordinate for the pixel. Must be between 0 and "color_matrix_rows() - 1".      y : The Y coordinate for the pixel. Must be between 0 and "color_matrix_rows() - 1".      rows : (Optional) The number of rows of the color matrix. This can be omitted, but if used, can speed up execution.   Returns: The color of the pixel at the specified coordinates. draw_pixel(color_matrix, x, y, pixel_color, rows) Draw a pixel at the specified X and Y coordinates. Uses the specified color.   Parameters:      color_matrix : The color matrix.      x : The X coordinate for the pixel. Must be between 0 and "color_matrix_rows() - 1".      y : The Y coordinate for the pixel. Must be between 0 and "color_matrix_rows() - 1".      pixel_color : The color of the pixel.      rows : (Optional) The number of rows of the color matrix. This can be omitted, but if used, can speed up execution. draw_pixel(color_matrix, x, y, rows) Draw a pixel at the specified X and Y coordinates. Uses the current foreground color.   Parameters:      color_matrix : The color matrix.      x : The X coordinate for the pixel. Must be between 0 and "color_matrix_rows() - 1".      y : The Y coordinate for the pixel. Must be between 0 and "color_matrix_rows() - 1".      rows : (Optional) The number of rows of the color matrix. This can be omitted, but if used, can speed up execution. erase_pixel(color_matrix, x, y, rows) Erase a pixel at the specified X and Y coordinates, replacing it with the background color.   Parameters:      color_matrix : The color matrix.      x : The X coordinate for the pixel. Must be between 0 and "color_matrix_rows() - 1".      y : The Y coordinate for the pixel. Must be between 0 and "color_matrix_rows() - 1".      rows : (Optional) The number of rows of the color matrix. This can be omitted, but if used, can speed up execution. init_color_matrix(rows, bg_color, fg_color) Create and initialize a color matrix with the specified number of rows. The number of columns will be equal to the number of rows.   Parameters:      rows : The number of rows the color matrix should consist of. This can be omitted, but if used, can speed up execution. It can never be greater than "max_canvas_size()".      bg_color : (Optional) The initial background color. The default is black.      fg_color : (Optional) The initial foreground color. The default is white.   Returns: The array representing the color matrix. init_canvas(color_matrix, pixel_width, pixel_height, position) Create and initialize a canvas table.   Parameters:      color_matrix : The color matrix.      pixel_width : (Optional) The pixel width (in % of the pane width). The default width is 0.35%.      pixel_height : (Optional) The pixel width (in % of the pane height). The default width is 0.60%.      position : (Optional) The position for the table representing the canvas. The default is "position.middle_center".   Returns: The canvas table. clear(color_matrix, rows) Clear a color matrix, replacing all pixels with the current background color.   Parameters:      color_matrix : The color matrix.      rows : The number of rows of the color matrix. This can be omitted, but if used, can speed up execution. update(canvas, color_matrix, rows) This updates the canvas with the colors from the color matrix. No changes to the canvas gets plotted until this function is called.   Parameters:      canvas : The canvas table.      color_matrix : The color matrix.      rows : The number of rows of the color matrix. This can be omitted, but if used, can speed up execution.
Perpustakaan Pine Script®
oleh lejmer
1
Fib RSI++ by [JohnnySnow]Fib RSI++ by is an RSI Inspired by my absolutely favorite RSI on tradingview: RSI & EMA with Reverse Calculator Panel by balipour. Built for quicker and easily identify prices at current RSI /possibly reversals/ RSI direction and RSI landings. From balipour, I reuse /adapt to pinescriptV5 3 lines of code ( ) - the balipour implementation for reversing RSI formula in order to calculate price estimation based on the Given RSI level. Credits to the author. Inspired by it, I also combine RSI with a MA but tuned to reads better the support/resistance levels (my humble opinion). For quicker price target identification 2 features were added: - Gridlines based on Fib levels, standard overbought/oversold levels and other levels I personally use. All of the grid lines can be configured according to user preferences. - 2 information tables: --First with a collection of 'close' numbers and Fib RSI levels price estimations at given RSI --The second table allows the user to add up to 3 custom RSI levels to further target the price estimation. Author UI Preferences to be used with this indicator: dark theme, hidden vertical and horizontal chart gridlines.
Indikator Pine Script®
oleh jmgneves
4
logLibrary "log" A Library to log and display messages in a table, with different colours. The log consists of 3 columns: Bar Index / Message / Log Credits QuantNomad - for his idea on logging messages as Error/Warnings and displaying the color based on the type of the message setHeader(_t, _location, _header1, _header2, _header3, _halign, _valign, _size) Sets the header for the table to be used for displaying the logs.   Parameters:      _t : table, table to be used for printing      _location : string, Location of the table.      _header1 : string, the name to put into the Index Queue Header. Default is 'Bar #'      _header2 : string, the name to put into the Message Queue Header. Default is 'Message'      _header3 : string, the name to put into the Log Queue Header. Default is 'Log'      _halign : string, the horizontal alignment of header. Options - Left/Right/Center      _valign : string, the vertical alignment of header. Options - Top/Bottom/Center      _size : string, the size of text of header. Options - Tiny/Small/Normal/Large/Huge/Auto   Returns: Void initHeader(_location, _rows, _header1, _header2, _header3, _halign, _valign, _size, _frameBorder, _cellBorder) Creates the table for logging. 3 columns will be displayed. Bar Index Q / Message Q / Log Q   Parameters:      _location : string, Location of the table.      _rows : int, table size, excluding the header. Default value is 40.      _header1 : string, the name to put into the Index Queue Header. Default is 'Bar #'      _header2 : string, the name to put into the Message Queue Header. Default is 'Message'      _header3 : string, the name to put into the Log Queue Header. Default is 'Log'      _halign : string, the horizontal alignment of header. Options - Left/Right/Center      _valign : string, the vertical alignment of header. Options - Top/Bottom/Center      _size : string, the size of text of header. Options - Tiny/Small/Normal/Large/Huge/Auto      _frameBorder : int, table Frame BorderWidth. Default value is 1.      _cellBorder : int, table Cell Borders Width, Default value is 2.   Returns: table init(_rows) Initiate array variables for logging.   Parameters:      _rows : int, table size, excluding the header. Default value is 40.   Returns: tuple, arrays - > error code Q, bar_index Q, Message Q, Log Q log(_ec, _idx, _1, _2, _m1, _m2, _code, _prefix, _suffix) logs a message to logging queue.   Parameters:      _ec : int , Error/Codes (1-7) for colouring. Default Colour Code is 1 - Gray, 2 - Orange, 3 - Red, 4 - Blue, 5 - Green, 6 - Cream, 7 - Offwhite      _idx : int , bar index Q. The index of current bar is logged automatically you can add before and after this index value, whatever you choose to, via the _prefix and _suffix variables.      _1 : string , Message Q.      _2 : string , Log Q      _m1 : string, message needed to be logged to Message Q      _m2 : string, detailed log needed to be logged to Log Q      _code : int, Error/Code to be assigned. Default code is 1.      _prefix : string, prefix to Bar State Q message      _suffix : string, suffix to Bar State Q message Order of logging would be Bar Index Q / Message Q / Log Q   Returns: void resize(_ec, _idx, _1, _2, _rows) Resizes the all messaging queues. a resize will delete the existing table, so a new header/table has to be initiated after the resize. This is because pine doesnt allow changing the table dimensions once they have been recreated. If size is decreased then removes the oldest messages   Parameters:      _ec : int , Error/Codes (1-7) for colouring.      _idx : int , bar index Q.      _1 : string , Message Q.      _2 : string , Log Q      _rows : int, the new size needed for the queue. Default value is 40.   Returns: void print(_t, _ec, _idx, _1, _2, halign, halign, _size) Prints Bar Index Q / Message Q / Log Q   Parameters:      _t : table, table to be used for printing      _ec : int , Error/Codes (1-7) for colouring. Default Colour Code is 1 - Gray, 2 - Orange, 3 - Red, 4 - Blue, 5 - Green, 6 - Cream, 7 - Offwhite      _idx : int , for bar index Q.      _1 : string , Message Q.      _2 : string , Log Q      halign : string, the horizontal alignment of all message column. Options - Left/Right/Center      halign : string, the vertical alignment of all message column. Options - Top/Bottom/Center      _size : string, the size of text across the table, excepr the headers. Options - Tiny/Small/Normal/Large/Huge/Auto   Returns: void printx(_t, _idx, _1, _2, _ec, _fg, _bg, _halign, _valign, _size) Prints Bar Index Q / Message Q / Log Q, but with custom options to format the table and colours   Parameters:      _t : table, table to be used for printing      _idx : int , for bar index Q.      _1 : string , Message Q.      _2 : string , Log Q      _ec : int , Error/Codes (1-7) for colouring.      _fg : color , Color array specifying colours for foreground. Maximum length is seven. Need not provide all seven, but atleast one. If not enough provided then last colour in the array is used for missing codes      _bg : color , Same as fg.      _halign : string, the horizontal alignment of all message column. Options - Left/Right/Center      _valign : string, the vertical alignment of all message column. Options - Top/Bottom/Center      _size : string, the size of text across the table, excepr the headers. Options - Tiny/Small/Normal/Large/Huge/Auto   Returns: void flush(_t, _idx, _1, _2, _ec) Clears queues of existing messages, filling with blanks and 0   Parameters:      _t : table, table to be flushed      _idx : int , for bar index Q.      _1 : string , Message Q.      _2 : string , Log Q      _ec : int , Error/Codes (1-7) for colouring.   Returns: void. erase(_idx, _1, _2, _ec) Deletes message queue and the table used for displaying the queue   Parameters:      _idx : int , for bar index Q.      _1 : string , Message Q.      _2 : string , Log Q      _ec : int , Error/Codes (1-7) for colouring.   Returns: void
Perpustakaan Pine Script®
oleh GETpacman
trend_vol_forecastNote: The following description is copied from the script's comments. Since TradingView does not allow me to edit this description, please refer to the comments and release notes for the most up-to-date information. ----------- USAGE This script compares trend trading with a volatility stop to "buy and hold". Trades are taken with the trend, except when price exceeds a volatility forecast. The trend is defined by a moving average crossover. The forecast is based on projecting future volatility from historical volatility. The trend is defined by two parameters: - long: the length of a long ("slow") moving average. - short: the length of a short ("fast") moving average. The trend is up when the short moving average is above the long. Otherwise it is down. The volatility stop is defined by three parameters: - volatility window: determines the number of periods in the historical volatility calculation. More periods means a slower (smoother) estimate of historical volatility. - stop forecast periods: the number of periods in the volatility forecast. For example, "7" on a daily chart means that the volatility will be forecasted with a one week lag. - stop forecast stdev: the number of standard deviations in the stop forecast. For example, "2" means two standard deviations. EXAMPLE The default parameters are: - long: 50 - short: 20 - volatility window: 30 - stop forecast periods: 7 - stop forecast standard deviations: 1 The trend will be up when the 20 period moving average is above the 50 period moving average. On each bar, the historical volatility will be calculated from the previous 30 bars. If the historical volatility is 0.65 (65%), then a forecast will be drawn as a fuchsia line, subtracting 0.65 * sqrt(7 / 365) from the closing price. If price at any point falls below the forecast, the volatility stop is in place, and the trend is negated. OUTPUTS Plots: - The trend is shown by painting the slow moving average green (up), red (down), or black (none; volatility stop). - The fast moving average is shown in faint blue - The previous volatility forecasts are shown in faint fuchsia - The current volatility forecast is shown as a fuchsia line, projecting into the future as far as it is valid. Tables: - The current historical volatility is given in the top right corner, as a whole number percentage. - The performance table shows the mean, standard deviation, and sharpe ratio of the volatility stop trend strategy, as well as buy and hold. If the trend is up, each period's return is added to the sample (the strategy is long). If the trend is down, the inverse of each period's return is added to the sample (the strategy is short). If there is no trend (the volatility stop is active), the period's return is excluded from the sample. Every period is added to the buy-and-hold strategy's sample. The total number of periods in each sample is also shown.
Indikator Pine Script®
oleh taylor_o
DeeptestDeeptest: Quantitative Backtesting Library for Pine Script ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ OVERVIEW Deeptest is a Pine Script library that provides quantitative analysis tools for strategy backtesting. It calculates over 100 statistical metrics including risk-adjusted return ratios (Sharpe, Sortino, Calmar), drawdown analysis, Value at Risk (VaR), Conditional VaR, and performs Monte Carlo simulation and Walk-Forward Analysis. █ WHY THIS LIBRARY MATTERS Pine Script is a simple yet effective coding language for algorithmic and quantitative trading. Its accessibility enables traders to quickly prototype and test ideas directly within TradingView. However, the built-in strategy tester provides only basic metrics (net profit, win rate, drawdown), which is often insufficient for serious strategy evaluation. Due to this limitation, many traders migrate to alternative backtesting platforms that offer comprehensive analytics. These platforms require other language programming knowledge, environment setup, and significant time investment—often just to test a simple trading idea. Deeptest bridges this gap by bringing institutional-level quantitative analytics directly to Pine Script. Traders can now perform sophisticated analysis without leaving TradingView or learning complex external platforms. All calculations are derived from strategy.closedtrades.* , ensuring compatibility with any existing Pine Script strategy. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ ORIGINALITY AND USEFULNESS This library is original work that adds value to the TradingView community in the following ways: 1. Comprehensive Metric Suite: Implements 112+ statistical calculations in a single library, including advanced metrics not available in TradingView's built-in tester (p-value, Z-score, Skewness, Kurtosis, Risk of Ruin). 2. Monte Carlo Simulation: Implements trade-sequence randomization to stress-test strategy robustness by simulating 1000+ alternative equity curves. 3. Walk-Forward Analysis: Divides historical data into rolling in-sample and out-of-sample windows to detect overfitting by comparing training vs. testing performance. 4. Rolling Window Statistics: Calculates time-varying Sharpe, Sortino, and Expectancy to analyze metric consistency throughout the backtest period. 5. Interactive Table Display: Renders professional-grade tables with color-coded thresholds, tooltips explaining each metric, and period analysis cards for drawdowns/trades. 6. Benchmark Comparison: Automatically fetches S&P 500 data to calculate Alpha, Beta, and R-squared, enabling objective assessment of strategy skill vs. passive investing. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ KEY FEATURES Performance Metrics Net Profit, CAGR, Monthly Return, Expectancy Profit Factor, Payoff Ratio, Sample Size Compounding Effect Analysis Risk Metrics Sharpe Ratio, Sortino Ratio, Calmar Ratio (MAR) Martin Ratio, Ulcer Index Max Drawdown, Average Drawdown, Drawdown Duration Risk of Ruin, R-squared (equity curve linearity) Statistical Distribution Value at Risk (VaR 95%), Conditional VaR Skewness (return asymmetry) Kurtosis (tail fatness) Z-Score, p-value (statistical significance testing) Trade Analysis Win Rate, Breakeven Rate, Loss Rate Average Trade Duration, Time in Market Consecutive Win/Loss Streaks with Expected values Top/Worst Trades with R-multiple tracking Advanced Analytics Monte Carlo Simulation (1000+ iterations) Walk-Forward Analysis (rolling windows) Rolling Statistics (time-varying metrics) Out-of-Sample Testing Benchmark Comparison Alpha (excess return vs. benchmark) Beta (systematic risk correlation) Buy & Hold comparison R-squared vs. benchmark ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ QUICK START Basic Usage //@version=6 strategy("My Strategy", overlay=true) // Import the library import Fractalyst/Deeptest/1 as * // Your strategy logic fastMA = ta.sma(close, 10) slowMA = ta.sma(close, 30) if ta.crossover(fastMA, slowMA) strategy.entry("Long", strategy.long) if ta.crossunder(fastMA, slowMA) strategy.close("Long") // Run the analysis DT.runDeeptest() ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ METRIC EXPLANATIONS The Deeptest table displays 23 metrics across the main row, with 23 additional metrics in the complementary row. Each metric includes detailed tooltips accessible by hovering over the value. Main Row — Performance Metrics (Columns 0-6) Net Profit — (Final Equity - Initial Capital) / Initial Capital × 100 — >20%: Excellent, >0%: Profitable, <0%: Loss — Total return percentage over entire backtest period Payoff Ratio — Average Win / Average Loss — >1.5: Excellent, >1.0: Good, <1.0: Losses exceed wins — Average winning trade size relative to average losing trade. Breakeven win rate = 100% / (1 + Payoff) Sample Size — Count of closed trades — >=30: Statistically valid, <30: Insufficient data — Number of completed trades. Includes 95% confidence interval for win rate in tooltip Profit Factor — Gross Profit / Gross Loss — >=1.5: Excellent, >1.0: Profitable, <1.0: Losing — Ratio of total winnings to total losses. Uses absolute values unlike payoff ratio CAGR — (Final / Initial)^(365.25 / Days) - 1 — >=10%: Excellent, >0%: Positive growth — Compound Annual Growth Rate - annualized return accounting for compounding Expectancy — Sum of all returns / Trade count — >0.20%: Excellent, >0%: Positive edge — Average return per trade as percentage. Positive expectancy indicates profitable edge Monthly Return — Net Profit / (Months in test) — >0%: Profitable month average — Average monthly return. Geometric monthly also shown in tooltip Main Row — Trade Statistics (Columns 7-14) Avg Duration — Average time in position per trade — Mean holding period from entry to exit. Influenced by timeframe and trading style Max CW — Longest consecutive winning streak — Maximum consecutive wins. Expected value = ln(trades) / ln(1/winRate) Max CL — Longest consecutive losing streak — Maximum consecutive losses. Important for psychological risk tolerance Win Rate — Wins / Total Trades — Higher is better — Percentage of profitable trades. Breakeven win rate shown in tooltip BE Rate — Breakeven Trades / Total Trades — Lower is better — Percentage of trades that broke even (neither profit nor loss) Loss Rate — Losses / Total Trades — Lower is better — Percentage of unprofitable trades. Together with win rate and BE rate, sums to 100% Frequency — Trades per month — Trading activity level. Displays intelligently (e.g., "12/mo", "1.5/wk", "3/day") Exposure — Time in market / Total time × 100 — Lower = less risk — Percentage of time the strategy had open positions Main Row — Risk Metrics (Columns 15-22) Sharpe Ratio — (Return - Rf) / StdDev × sqrt(Periods) — >=3: Excellent, >=2: Good, >=1: Fair, <1: Poor — Measures risk-adjusted return using total volatility. Annualized using sqrt(252) for daily Sortino Ratio — (Return - Rf) / DownsideDev × sqrt(Periods) — >=2: Excellent, >=1: Good, <1: Needs improvement — Similar to Sharpe but only penalizes downside volatility. Can be higher than Sharpe Max DD — (Peak - Trough) / Peak × 100 — <5%: Excellent, 5-15%: Moderate, 15-30%: High, >30%: Severe — Largest peak-to-trough decline in equity. Critical for risk tolerance and position sizing RoR — Risk of Ruin probability — <1%: Excellent, 1-5%: Acceptable, 5-10%: Elevated, >10%: Dangerous — Probability of losing entire trading account based on win rate and payoff ratio R² — R-squared of equity curve vs. time — >=0.95: Excellent, 0.90-0.95: Good, 0.80-0.90: Moderate, <0.80: Erratic — Coefficient of determination measuring linearity of equity growth MAR — CAGR / |Max Drawdown| — Higher is better, negative = bad — Calmar Ratio. Reward relative to worst-case loss. Negative if max DD exceeds CAGR CVaR — Average of returns below VaR threshold — Lower absolute is better — Conditional Value at Risk (Expected Shortfall). Average loss in worst 5% of outcomes p-value — Binomial test probability — <0.05: Significant, 0.05-0.10: Marginal, >0.10: Likely random — Probability that observed results are due to chance. Low p-value means statistically significant edge Complementary Row — Extended Metrics Compounding — (Compounded Return / Total Return) × 100 — Percentage of total profit attributable to compounding (position sizing) Avg Win — Sum of wins / Win count — Average profitable trade return in percentage Avg Trade — Sum of all returns / Total trades — Same as Expectancy (Column 5). Displayed here for convenience Avg Loss — Sum of losses / Loss count — Average unprofitable trade return in percentage (negative value) Martin Ratio — CAGR / Ulcer Index — Similar to Calmar but uses Ulcer Index instead of Max DD Rolling Expectancy — Mean of rolling window expectancies — Average expectancy calculated across rolling windows. Shows consistency of edge Avg W Dur — Avg duration of winning trades — Average time from entry to exit for winning trades only Max Eq — Highest equity value reached — Peak equity achieved during backtest Min Eq — Lowest equity value reached — Trough equity point. Important for understanding worst-case absolute loss Buy & Hold — (Close_last / Close_first - 1) × 100 — >0%: Passive profit — Return of simply buying and holding the asset from backtest start to end Alpha — Strategy CAGR - Benchmark CAGR — >0: Has skill (beats benchmark) — Excess return above passive benchmark. Positive alpha indicates genuine value-added skill Beta — Covariance(Strategy, Benchmark) / Variance(Benchmark) — <1: Less volatile than market, >1: More volatile — Systematic risk correlation with benchmark Avg L Dur — Avg duration of losing trades — Average time from entry to exit for losing trades only Rolling Sharpe/Sortino — Dynamic based on win rate — >2: Good consistency — Rolling metric across sliding windows. Shows Sharpe if win rate >50%, Sortino if <=50% Curr DD — Current drawdown from peak — Lower is better — Present drawdown percentage. Zero means at new equity high DAR — CAGR adjusted for target DD — Higher is better — Drawdown-Adjusted Return. DAR^5 = CAGR if max DD = 5% Kurtosis — Fourth moment / StdDev^4 - 3 — ~0: Normal, >0: Fat tails, <0: Thin tails — Measures "tailedness" of return distribution (excess kurtosis) Skewness — Third moment / StdDev^3 — >0: Positive skew (big wins), <0: Negative skew (big losses) — Return distribution asymmetry VaR — 5th percentile of returns — Lower absolute is better — Value at Risk at 95% confidence. Maximum expected loss in worst 5% of outcomes Ulcer — sqrt(mean(drawdown^2)) — Lower is better — Ulcer Index - root mean square of drawdowns. Penalizes both depth AND duration ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ MONTE CARLO SIMULATION Purpose Monte Carlo simulation tests strategy robustness by randomizing the order of trades while keeping trade returns unchanged. This simulates alternative equity curves to assess outcome variability. Method Extract all historical trade returns Randomly shuffle the sequence (1000+ iterations) Calculate cumulative equity for each shuffle Build distribution of final outcomes Output The stress test table shows: Median Outcome: 50th percentile result 5th Percentile: Worst 5% of outcomes 95th Percentile: Best 95% of outcomes Success Rate: Percentage of simulations that were profitable Interpretation If 95% of simulations are profitable: Strategy is robust If median is far from actual result: High variance/unreliability If 5th percentile shows large loss: High tail risk ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ WALK-FORWARD ANALYSIS Purpose Walk-Forward Analysis (WFA) is the gold standard for detecting strategy overfitting. It simulates real-world trading by dividing historical data into rolling "training" (in-sample) and "validation" (out-of-sample) periods. A strategy that performs well on unseen data is more likely to succeed in live trading. Method The implementation uses a non-overlapping window approach following AmiBroker's gold standard methodology: Segment Calculation: Total trades divided into N windows (default: 12), IS = ~75%, OOS = ~25%, Step = OOS length Window Structure: Each window has IS (training) followed by OOS (validation). Each OOS becomes the next window's IS (rolling forward) Metrics Calculated: CAGR, Sharpe, Sortino, MaxDD, Win Rate, Expectancy, Profit Factor, Payoff Aggregation: IS metrics averaged across all IS periods, OOS metrics averaged across all OOS periods Output IS CAGR: In-sample annualized return OOS CAGR: Out-of-sample annualized return ( THE key metric ) IS/OOS Sharpe: In/out-of-sample risk-adjusted return Success Rate: % of OOS windows that were profitable Interpretation Robust: IS/OOS CAGR gap <20%, OOS Success Rate >80% Some Overfitting: CAGR gap 20-50%, Success Rate 50-80% Severe Overfitting: CAGR gap >50%, Success Rate <50% Key Principles: OOS is what matters — Only OOS predicts live performance Consistency > Magnitude — 10% IS / 9% OOS beats 30% IS / 5% OOS Window count — More windows = more reliable validation Non-overlapping OOS — Prevents data leakage ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ TABLE DISPLAY Main Table — Organized into three sections: Performance Metrics (Cols 0-6): Net Profit, Payoff, Sample Size, Profit Factor, CAGR, Expectancy, Monthly Trade Statistics (Cols 7-14): Avg Duration, Max CW, Max CL, Win, BE, Loss, Frequency, Exposure Risk Metrics (Cols 15-22): Sharpe, Sortino, Max DD, RoR, R², MAR, CVaR, p-value Color Coding 🟢 Green: Excellent performance 🟠 Orange: Acceptable performance ⚪ Gray: Neutral / Fair 🔴 Red: Poor performance ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ IMPLEMENTATION NOTES Data Source: All metrics calculated from strategy.closedtrades , ensuring compatibility with any Pine Script strategy Calculation Timing: All calculations occur on barstate.islastconfirmedhistory to optimize performance Limitations: Requires at least 1 closed trade for basic metrics, 30+ trades for reliable statistical analysis ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ QUICK NOTES ➙ This library has been developed and refined over two years of real-world strategy testing. Every calculation has been validated against industry-standard quantitative finance references. ➙ The entire codebase is thoroughly documented inline. If you are curious about how a metric is calculated or want to understand the implementation details, dive into the source code -- it is written to be read and learned from. ➙ This description focuses on usage and concepts rather than exhaustively listing every exported type and function. The library source code is thoroughly documented inline -- explore it to understand implementation details and internal logic. ➙ All calculations execute on barstate.islastconfirmedhistory to minimize runtime overhead. The library is designed for efficiency without sacrificing accuracy. ➙ Beyond analysis, this library serves as a learning resource. Study the source code to understand quantitative finance concepts, Pine Script advanced techniques, and proper statistical methodology. ➙ Metrics are their own not binary good/bad indicators. A high Sharpe ratio with low sample size is misleading. A deep drawdown during a market crash may be acceptable. Study each function and metric individually -- evaluate your strategy contextually, not by threshold alone. ➙ All strategies face alpha decay over time. Instead of over-optimizing a single strategy on one timeframe and market, build a diversified portfolio across multiple markets and timeframes. Deeptest helps you validate each component so you can combine robust strategies into a trading portfolio. ➙ Screenshots shown in the documentation are solely for visual representation to demonstrate how the tables and metrics will be displayed. Please do not compare your strategy's performance with the metrics shown in these screenshots -- they are illustrative examples only, not performance targets or benchmarks. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ HOW-TO Using Deeptest is intentionally straightforward. Just import the library and call DT.runDeeptest() at the end of your strategy code in main scope. . //@version=6 strategy("My Strategy", overlay=true) // Import the library import Fractalyst/Deeptest/1 as DT // Your strategy logic fastMA = ta.sma(close, 10) slowMA = ta.sma(close, 30) if ta.crossover(fastMA, slowMA) strategy.entry("Long", strategy.long) if ta.crossunder(fastMA, slowMA) strategy.close("Long") // Run the analysis DT.runDeeptest() And yes... it's compatible with any TradingView Strategy! 🪄 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ CREDITS Author: @Fractalyst Font Library: by @fikira - @kaigouthro - @Duyck Community: Inspired by the @PineCoders community initiative, encouraging developers to contribute open-source libraries and continuously enhance the Pine Script ecosystem for all traders. if you find Deeptest valuable in your trading journey, feel free to use it in your strategies and give a shoutout to @Fractalyst -- Your recognition directly supports ongoing development and open-source contributions to Pine Script. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ █ DISCLAIMER This library is provided for educational and research purposes. Past performance does not guarantee future results. Always test thoroughly and use proper risk management. The author is not responsible for any trading losses incurred through the use of this code.
Perpustakaan Pine Script®
oleh Fractalyst
8
Volatility-Targeted Momentum Portfolio [BackQuant]Volatility-Targeted Momentum Portfolio A complete momentum portfolio engine that ranks assets, targets a user-defined volatility, builds long, short, or delta-neutral books, and reports performance with metrics, attribution, Monte Carlo scenarios, allocation pie, and efficiency scatter plots. This description explains the theory and the mechanics so you can configure, validate, and deploy it with intent. Table of contents What the script does at a glance Momentum, what it is, how to know if it is present Volatility targeting, why and how it is done here Portfolio construction modes: Long Only, Short Only, Delta Neutral Regime filter and when the strategy goes to cash Transaction cost modelling in this script Backtest metrics and definitions Performance attribution chart Monte Carlo simulation Scatter plot analysis modes Asset allocation pie chart Inputs, presets, and deployment checklist Suggested workflow 1) What the script does at a glance Pulls a list of up to 15 tickers, computes a simple momentum score on each over a configurable lookback, then volatility-scales their bar-to-bar return stream to a target annualized volatility. Ranks assets by raw momentum, selects the top 3 and bottom 3, builds positions according to the chosen mode, and gates exposure with a fast regime filter. Accumulates a portfolio equity curve with risk and performance metrics, optional benchmark buy-and-hold for comparison, and a full alert suite. Adds visual diagnostics: performance attribution bars, Monte Carlo forward paths, an allocation pie, and scatter plots for risk-return and factor views. 2) Momentum: definition, detection, and validation Momentum is the tendency of assets that have performed well to continue to perform well, and of underperformers to continue underperforming, over a specific horizon. You operationalize it by selecting a horizon, defining a signal, ranking assets, and trading the leaders versus laggards subject to risk constraints. Signal choices . Common signals include cumulative return over a lookback window, regression slope on log-price, or normalized rate-of-change. This script uses cumulative return over lookback bars for ranking (variable cr = price/price - 1). It keeps the ranking simple and lets volatility targeting handle risk normalization. How to know momentum is present . Leaders and laggards persist across adjacent windows rather than flipping every bar. Spread between average momentum of leaders and laggards is materially positive in sample. Cross-sectional dispersion is non-trivial. If everything is flat or highly correlated with no separation, momentum selection will be weak. Your validation should include a diagnostic that measures whether returns are explained by a momentum regression on the timeseries. Recommended diagnostic tool . Before running any momentum portfolio, verify that a timeseries exhibits stable directional drift. Use this indicator as a pre-check: It fits a regression to price, exposes slope and goodness-of-fit style context, and helps confirm if there is usable momentum before you force a ranking into a flat regime. 3) Volatility targeting: purpose and implementation here Purpose . Volatility targeting seeks a more stable risk footprint. High-vol assets get sized down, low-vol assets get sized up, so each contributes more evenly to total risk. Computation in this script (per asset, rolling): Return series ret = log(price/price ). Annualized volatility estimate vol = stdev(ret, lookback) * sqrt(tradingdays). Leverage multiplier volMult = clamp(targetVol / vol, 0.1, 5.0). This caps sizing so extremely low-vol assets don’t explode weight and extremely high-vol assets don’t go to zero. Scaled return stream sr = ret * volMult. This is the per-bar, risk-adjusted building block used in the portfolio combinations. Interpretation . You are not levering your account on the exchange, you are rescaling the contribution each asset’s daily move has on the modeled equity. In live trading you would reflect this with position sizing or notional exposure. 4) Portfolio construction modes Cross-sectional ranking . Assets are sorted by cr over the chosen lookback. Top and bottom indices are extracted without ties. Long Only . Averages the volatility-scaled returns of the top 3 assets: avgRet = mean(sr_top1, sr_top2, sr_top3). Position table shows per-asset leverages and weights proportional to their current volMult. Short Only . Averages the negative of the volatility-scaled returns of the bottom 3: avgRet = mean(-sr_bot1, -sr_bot2, -sr_bot3). Position table shows short legs. Delta Neutral . Long the top 3 and short the bottom 3 in equal book sizes. Each side is sized to 50 percent notional internally, with weights within each side proportional to volMult. The return stream mixes the two sides: avgRet = mean(sr_top1,sr_top2,sr_top3, -sr_bot1,-sr_bot2,-sr_bot3). Notes . The selection metric is raw momentum, the execution stream is volatility-scaled returns. This separation is deliberate. It avoids letting volatility dominate ranking while still enforcing risk parity at the return contribution stage. If everything rallies together and dispersion collapses, Long Only may behave like a single beta. Delta Neutral is designed to extract cross-sectional momentum with low net beta. 5) Regime filter A fast EMA(12) vs EMA(21) filter gates exposure. Long Only active when EMA12 > EMA21. Otherwise the book is set to cash. Short Only active when EMA12 < EMA21. Otherwise cash. Delta Neutral is always active. This prevents taking long momentum entries during obvious local downtrends and vice versa for shorts. When the filter is false, equity is held flat for that bar. 6) Transaction cost modelling There are two cost touchpoints in the script. Per-bar drag . When the regime filter is active, the per-bar return is reduced by fee_rate * avgRet inside netRet = avgRet - (fee_rate * avgRet). This models proportional friction relative to traded impact on that bar. Turnover-linked fee . The script tracks changes in membership of the top and bottom baskets (top1..top3, bot1..bot3). The intent is to charge fees when composition changes. The template counts changes and scales a fee by change count divided by 6 for the six slots. Use case: increase fee_rate to reflect taker fees and slippage if you rebalance every bar or trade illiquid assets. Reduce it if you rebalance less often or use maker orders. Practical advice . If you rebalance daily, start with 5–20 bps round-trip per switch on liquid futures and adjust per venue. For crypto perp microcaps, stress higher cost assumptions and add slippage buffers. If you only rotate on lookback boundaries or at signals, use alert-driven rebalances and lower per-bar drag. 7) Backtest metrics and definitions The script computes a standard set of portfolio statistics once the start date is reached. Net Profit percent over the full test. Max Drawdown percent, tracked from running peaks. Annualized Mean and Stdev using the chosen trading day count. Variance is the square of annualized stdev. Sharpe uses daily mean adjusted by risk-free rate and annualized. Sortino uses downside stdev only. Omega ratio of sum of gains to sum of losses. Gain-to-Pain total gains divided by total losses absolute. CAGR compounded annual growth from start date to now. Alpha, Beta versus a user-selected benchmark. Beta from covariance of daily returns, Alpha from CAPM. Skewness of daily returns. VaR 95 linear-interpolated 5th percentile of daily returns. CVaR average of the worst 5 percent of daily returns. Benchmark Buy-and-Hold equity path for comparison. 8) Performance attribution Cumulative contribution per asset, adjusted for whether it was held long or short and for its volatility multiplier, aggregated across the backtest. You can filter to winners only or show both sides. The panel is sorted by contribution and includes percent labels. 9) Monte Carlo simulation The panel draws forward equity paths from either a Normal model parameterized by recent mean and stdev, or non-parametric bootstrap of recent daily returns. You control the sample length, number of simulations, forecast horizon, visibility of individual paths, confidence bands, and a reproducible seed. Normal uses Box-Muller with your seed. Good for quick, smooth envelopes. Bootstrap resamples realized returns, preserving fat tails and volatility clustering better than a Gaussian assumption. Bands show 10th, 25th, 75th, 90th percentiles and the path mean. 10) Scatter plot analysis Four point-cloud modes, each plotting all assets and a star for the current portfolio position, with quadrant guides and labels. Risk-Return Efficiency . X is risk proxy from leverage, Y is expected return from annualized momentum. The star shows the current book’s composite. Momentum vs Volatility . Visualizes whether leaders are also high vol, a cue for turnover and cost expectations. Beta vs Alpha . X is a beta proxy, Y is risk-adjusted excess return proxy. Useful to see if leaders are just beta. Leverage vs Momentum . X is volMult, Y is momentum. Shows how volatility targeting is redistributing risk. 11) Asset allocation pie chart Builds a wheel of current allocations. Long Only, weights are proportional to each long asset’s current volMult and sum to 100 percent. Short Only, weights show the short book as positive slices that sum to 100 percent. Delta Neutral, 50 percent long and 50 percent short books, each side leverage-proportional. Labels can show asset, percent, and current leverage. 12) Inputs and quick presets Core Portfolio Strategy . Long Only, Short Only, Delta Neutral. Initial Capital . For equity scaling in the panel. Trading Days/Year . 252 for stocks, 365 for crypto. Target Volatility . Annualized, drives volMult. Transaction Fees . Per-bar drag and composition change penalty, see the modelling notes above. Momentum Lookback . Ranking horizon. Shorter is more reactive, longer is steadier. Start Date . Ensure every symbol has data back to this date to avoid bias. Benchmark . Used for alpha, beta, and B&H line. Diagnostics Metrics, Equity, B&H, Curve labels, Daily return line, Rolling drawdown fill. Attribution panel. Toggle winners only to focus on what matters. Monte Carlo mode with Normal or Bootstrap and confidence bands. Scatter plot type and styling, labels, and portfolio star. Pie chart and labels for current allocation. Presets Crypto Daily, Long Only . Lookback 25, Target Vol 50 percent, Fees 10 bps, Regime filter on, Metrics and Drawdown on. Monte Carlo Bootstrap with Recent 200 bars for bands. Crypto Daily, Delta Neutral . Lookback 25, Target Vol 50 percent, Fees 15–25 bps, Regime filter always active for this mode. Use Scatter Risk-Return to monitor efficiency and keep the star near upper left quadrants without drifting rightward. Equities Daily, Long Only . Lookback 60–120, Target Vol 15–20 percent, Fees 5–10 bps, Regime filter on. Use Benchmark SPX and watch Alpha and Beta to keep the book from becoming index beta. 13) Suggested workflow Universe sanity check . Pick liquid tickers with stable data. Thin assets distort vol estimates and fees. Check momentum existence . Run on your timeframe. If slope and fit are weak, widen lookback or avoid that asset or timeframe. Set risk budget . Choose a target volatility that matches your drawdown tolerance. Higher target increases turnover and cost sensitivity. Pick mode . Long Only for bull regimes, Short Only for sustained downtrends, Delta Neutral for cross-sectional harvesting when index direction is unclear. Tune lookback . If leaders rotate too often, lengthen it. If entries lag, shorten it. Validate cost assumptions . Increase fee_rate and stress Monte Carlo. If the edge vanishes with modest friction, refine selection or lengthen rebalance cadence. Run attribution . Confirm the strategy’s winners align with intuition and not one unstable outlier. Use alerts . Enable position change, drawdown, volatility breach, regime, momentum shift, and crash alerts to supervise live runs. Important implementation details mapped to code Momentum measure . cr = price / price - 1 per symbol for ranking. Simplicity helps avoid overfitting. Volatility targeting . vol = stdev(log returns, lookback) * sqrt(tradingdays), volMult = clamp(targetVol / vol, 0.1, 5), sr = ret * volMult. Selection . Extract indices for top1..top3 and bot1..bot3. The arrays rets, scRets, lev_vals, and ticks_arr track momentum, scaled returns, leverage multipliers, and display tickers respectively. Regime filter . EMA12 vs EMA21 switch determines if the strategy takes risk for Long or Short modes. Delta Neutral ignores the gate. Equity update . Equity multiplies by 1 + netRet only when the regime was active in the prior bar. Buy-and-hold benchmark is computed separately for comparison. Tables . Position tables show current top or bottom assets with leverage and weights. Metric table prints all risk and performance figures. Visualization panels . Attribution, Monte Carlo, scatter, and pie use the last bars to draw overlays that update as the backtest proceeds. Final notes Momentum is a portfolio effect. The edge comes from cross-sectional dispersion, adequate risk normalization, and disciplined turnover control, not from a single best asset call. Volatility targeting stabilizes path but does not fix selection. Use the momentum regression link above to confirm structure exists before you size into it. Always test higher lag costs and slippage, then recheck metrics, attribution, and Monte Carlo envelopes. If the edge persists under stress, you have something robust.
Indikator Pine Script®
oleh BackQuant
Advanced Psychological Levels with Dynamic Spacing═══════════════════════════════════════ ADVANCED PSYCHOLOGICAL LEVELS WITH DYNAMIC SPACING ═══════════════════════════════════════ A comprehensive psychological price level indicator that automatically identifies and displays round number levels across multiple timeframes. Features dynamic ATR-based spacing, smart crypto detection, distance tracking, and customizable alert system. ─────────────────────────────────────── WHAT THIS INDICATOR DOES ─────────────────────────────────────── This indicator automatically draws psychological price levels (round numbers) that often act as support and resistance: - Dynamic ATR-Based Spacing - Adapts level spacing to market volatility - Multiple Level Types - Major (250 pip), Standard (100 pip), Mid, and Intraday levels - Smart Asset Detection - Automatically adjusts for Forex, Crypto, Indices, and CFDs - Crypto Price Adaptation - Intelligent level spacing based on cryptocurrency price magnitude - Distance Information Table - Real-time percentage distance to nearest levels - Combined Level Labels - Clear identification when multiple level types coincide - Performance Optimized - Configurable visible range and label limits - Comprehensive Alerts - Notifications when price crosses any level type ─────────────────────────────────────── HOW IT WORKS ─────────────────────────────────────── PSYCHOLOGICAL LEVELS CONCEPT: Psychological levels are round numbers where traders tend to place orders, creating natural support and resistance zones. These include: - Forex: 1.0000, 1.0100, 1.0050 (pips) - Crypto: $100, $1,000, $10,000 (whole numbers) - Indices: 10,000, 10,500, 11,000 (points) Why They Matter: - Traders naturally gravitate to round numbers - Stop losses cluster at these levels - Take profit orders concentrate here - Institutional algorithmic trading often targets these levels DYNAMIC ATR-BASED SPACING: Traditional Method: - Fixed spacing regardless of volatility - May be too tight in volatile markets - May be too wide in quiet markets Dynamic Method (Recommended): - Uses ATR (Average True Range) to measure volatility - Automatically adjusts level spacing - Tighter levels in low volatility - Wider levels in high volatility Calculation: 1. Calculate ATR over specified period (default: 14) 2. Multiply by ATR multiplier (default: 2.0) 3. Round to nearest psychological level 4. Generate levels at dynamic intervals Benefits: - Adapts to market conditions - More relevant levels in all volatility regimes - Reduces clutter in trending markets - Provides more detail in ranging markets LEVEL TYPES: Major Levels (250 pip/point): - Highest significance - Primary support/resistance zones - Color: Red (default) - Style: Solid lines - Spacing: 2.5x standard step Standard Levels (100 pip/point): - Secondary importance - Common psychological barriers - Color: Blue (default) - Style: Dashed lines - Spacing: Standard step Mid Levels (50% between major): - Optional intermediate levels - Halfway between major levels - Color: Gray (default) - Style: Dotted lines - Usage: Additional confluence points Intraday Levels (sub-100 pip): - For intraday traders - Fine-grained precision - Color: Yellow (default) - Style: Dotted lines - Only shown on intraday timeframes SMART ASSET DETECTION: Forex Pairs: - Detects major currency pairs automatically - Uses pip-based calculations - Standard: 100 pips (0.0100) - Major: 250 pips (0.0250) - Intraday: 20, 50, 80 pip subdivisions Cryptocurrencies: - Automatic price magnitude detection - Adaptive spacing based on price: * Under $0.10: Levels at $0.01, $0.05 * $0.10-$1: Levels at $0.10, $0.50 * $1-$10: Levels at $1, $5 * $10-$100: Levels at $10, $50 * $100-$1,000: Levels at $100, $500 * $1,000-$10,000: Levels at $1,000, $5,000 * Over $10,000: Levels at $5,000, $10,000 Indices & CFDs: - Fixed point-based system - Major: 500 point intervals (with 250 sub-levels) - Standard: 100 point intervals - Suitable for stock indices like SPX, NASDAQ COMBINED LEVEL LABELS: When multiple level types coincide at the same price: - Single line drawn (highest priority color) - Combined label shows all types - Priority: Major > Standard > Mid > Intraday Example Label Formats: - "1.1000 Major" - Major level only - "1.1000 Std + Major" - Both standard and major - "50000 Intra + Mid + Std" - Three levels coincide Benefits: - Cleaner chart appearance - Clear identification of confluence - Reduced visual clutter - Easy to spot high-importance levels DISTANCE INFORMATION TABLE: Real-time tracking of nearest levels: Table Contents: - Nearest major level above (price and % distance) - Nearest standard level above (price and % distance) - Nearest standard level below (price and % distance) Display: - Top right corner (configurable) - Color-coded by level type - Real-time percentage calculations - Helpful for position management Usage: - Identify proximity to key levels - Set realistic profit targets - Gauge potential move magnitude - Monitor approaching resistance/support ALERT SYSTEM: Comprehensive crossing alerts: Alert Types: - Major Level Crosses - Standard Level Crosses - Intraday Level Crosses Alert Modes: - First Cross Only: Alert once when level is crossed - All Crosses: Alert every time level is crossed Alert Information: - Level type crossed - Specific price level - Direction (above/below) - One alert per bar to prevent spam Configuration: - Enable/disable by level type - Choose alert frequency - Customize for your trading style ─────────────────────────────────────── HOW TO USE ─────────────────────────────────────── INITIAL SETUP: General Settings: 1. Enable "Use Dynamic ATR-Based Spacing" (recommended) 2. Set ATR Period (14 is standard) 3. Adjust ATR Multiplier (2.0 is balanced) Visibility Settings: 1. Set Visible Range % (10% recommended for clarity) 2. Adjust Label Offset for readability 3. Configure performance limits if needed Level Selection: 1. Enable/disable level types based on trading style 2. Adjust line counts for each type 3. Choose line styles and colors for visibility TRADING STRATEGIES: Breakout Trading: 1. Wait for price to approach major or standard level 2. Monitor for consolidation near level 3. Enter on confirmed break above/beyond level 4. Stop loss just beyond the broken level 5. Target: Next major or standard level Rejection Trading: 1. Identify major psychological level 2. Wait for price to test the level 3. Look for rejection signals (wicks, bearish/bullish candles) 4. Enter in direction of rejection 5. Stop beyond the level 6. Target: Previous level or mid-level Range Trading: 1. Identify range between two major levels 2. Buy at lower psychological level 3. Sell at upper psychological level 4. Use standard and mid-levels for position management 5. Exit if major level breaks with volume Confluence Trading: 1. Look for combined levels (Std + Major) 2. These represent high-probability zones 3. Use as primary support/resistance 4. Increase position size at confluence 5. Expect stronger reactions at these levels Session-Based Trading: 1. Note opening level at session start (Asian/London/NY) 2. Trade breakouts of major levels during high-volume sessions 3. London/NY sessions: More likely to break levels 4. Asian session: More likely to respect levels (range trading) RISK MANAGEMENT WITH PSYCHOLOGICAL LEVELS: Stop Loss Placement: - Place stops just beyond psychological levels - Add buffer (5-10 pips for forex) - Avoid exact round numbers (stop hunting risk) - Use previous major level as maximum stop Take Profit Strategy: - First target: Next standard level (partial profit) - Second target: Next major level (remaining position) - Trail stops to breakeven at first target - Use distance table to calculate risk/reward Position Sizing: - Larger positions at major levels (higher probability) - Smaller positions at intraday levels (lower probability) - Scale in at standard levels between major levels - Reduce size when multiple levels are close together TIMEFRAME CONSIDERATIONS: Higher Timeframes (4H, Daily, Weekly): - Focus on Major and Standard levels only - Disable Intraday and Mid levels - Wider level spacing expected - Use for swing trading and position trading Lower Timeframes (5m, 15m, 1H): - Enable all level types - Use Intraday levels for precision - Tighter level spacing acceptable - Good for day trading and scalping Multi-Timeframe Approach: - Identify major levels on Daily/4H charts - Refine entries using 15m/1H intraday levels - Trade in direction of higher timeframe bias - Use lower timeframe levels for position management ─────────────────────────────────────── CONFIGURATION GUIDE ─────────────────────────────────────── GENERAL SETTINGS: Dynamic ATR-Based Spacing: - Enabled: Recommended for most markets - Disabled: Fixed psychological levels - ATR Period: 14 (standard), 10 (responsive), 20 (smooth) - ATR Multiplier: 1.0-5.0 (2.0 is balanced) VISIBILITY SETTINGS: Visible Range %: - 5%: Very tight range, minimal clutter - 10%: Balanced view (recommended) - 20%: Wide range, more context - 50%: Maximum range, all levels visible Label Offset: - 10-20 bars: Close to current price - 30-50 bars: Moderate distance - 50-100 bars: Far from price action Performance Limits: - Max Historical Bars: Reduce if indicator loads slowly - Max Labels: Reduce for cleaner chart (20-30 recommended) LEVEL CUSTOMIZATION: Line Count: - Lower (1-3): Cleaner chart, fewer levels - Medium (4-6): Balanced view - Higher (7-10): More context, busier chart Line Styles: - Solid: High importance, easy to see - Dashed: Medium importance, clear but subtle - Dotted: Low importance, minimal visual weight Colors: - Use contrasting colors for different level types - Red/Blue/Yellow default works well - Adjust based on chart background and personal preference DISTANCE TABLE: Position: - Top Right: Doesn't interfere with price action - Top Left: Good for right-side price scale - Bottom positions: Less common but available Colors: - Default (white text, dark background) works for most charts - Match your chart theme for consistency - Ensure text is readable against background ALERT CONFIGURATION: Alert by Level Type: - Major: Most important, fewer false signals - Standard: Balance of frequency and importance - Intraday: Many signals, best for active traders Alert Frequency: - First Cross Only: Cleaner, less noise (recommended for swing trading) - All Crosses: Every touch, good for scalping Alert Setup in TradingView: 1. Configure desired alert types in indicator settings 2. Right-click chart → Add Alert 3. Select this indicator 4. Choose "Any alert() function call" 5. Set delivery method (mobile, email, webhook) ─────────────────────────────────────── ASSET-SPECIFIC TIPS ─────────────────────────────────────── FOREX (EUR/USD, GBP/USD, etc.): - Major levels at x.x000, x.x500 - Standard levels at x.xx00 - Intraday levels at 20/50/80 pips - Most effective during London/NY sessions - Watch for "figure" levels (1.0000, 1.1000) CRYPTOCURRENCIES (BTC, ETH, etc.): - Enable dynamic spacing for volatile markets - Levels adjust automatically based on price - Watch major $1,000 increments for BTC - $100 levels important for ETH - Smaller caps: Use standard levels - High volatility: Increase ATR multiplier to 3.0 STOCK INDICES (SPX, NASDAQ, etc.): - 100-point levels most important - 500-point levels for major S/R - 50-point mid-levels for refinement - Watch end-of-day for level reactions - Futures often lead spot on level breaks GOLD/COMMODITIES: - Major levels at $50 increments ($1,900, $1,950) - Standard levels at $10 increments - Very reactive to psychological levels - Watch for false breaks during low volume - Best reactions during active trading hours ─────────────────────────────────────── BEST PRACTICES ─────────────────────────────────────── Chart Setup: - Use clean price action charts - Avoid too many indicators - Ensure psychological levels are clearly visible - Match colors to your chart theme Level Selection: - Start with Major and Standard levels only - Add Mid and Intraday as needed - Less is more - avoid chart clutter - Adjust based on timeframe Combining with Other Tools: - Volume profile for confluence - Trendlines intersecting psychological levels - Moving averages near round numbers - Fibonacci levels coinciding with psychological levels Common Mistakes to Avoid: - Trading every level touch (be selective) - Ignoring volume confirmation - Setting stops exactly at levels (stop hunting) - Forgetting to adjust for different assets - Over-relying on levels without price action confirmation Performance Optimization: - Reduce visible range for faster loading - Lower max historical bars on lower timeframes - Limit labels to 30-50 for clarity - Disable unused level types ─────────────────────────────────────── EDUCATIONAL DISCLAIMER ─────────────────────────────────────── This indicator identifies psychological price levels based on round numbers that tend to act as support and resistance. The methodology includes: - Round number detection algorithms - ATR-based dynamic spacing calculations - Asset-specific level determination - Distance percentage calculations Psychological levels are a recognized concept in technical analysis, studied by traders and institutions. However, they do not guarantee price reactions and should be used as part of a comprehensive trading strategy including proper risk management, volume analysis, and price action confirmation. ─────────────────────────────────────── USAGE DISCLAIMER ─────────────────────────────────────── This tool is for educational and analytical purposes. Psychological levels can act as support or resistance but price reactions are not guaranteed. Dynamic spacing may generate different levels in different market conditions. Always conduct independent analysis, use proper risk management, and never risk capital you cannot afford to lose. Past performance does not indicate future results. ─────────────────────────────────────── CREDITS & ATTRIBUTION ─────────────────────────────────────── Original Concept: Sonar Lab
Indikator Pine Script®
oleh Fx-Jithin
ORDER BLCOK custom strategy# OB Matrix Strategy - Documentation **Version:** 1.0 **Author:** HPotter **Date:** 31/07/2017 The **OB Matrix Strategy** is based on the identification of **bullish and bearish Order Blocks** and the management of conditional orders with multiple Take Profit (TP) and Stop Loss (SL) levels. It uses trend filters, ATR, and percentage-based risk management. --- ## 1. Main Parameters ### Strategy - `initial_capital`: 50 - `default_qty_type`: percentage of capital - `default_qty_value`: 10 ### Money Management - `rr_threshold`: minimum Risk/Reward threshold to open a trade - `risk_percent`: percentage of capital to risk per trade (default 2%) - `maxPendingBars`: maximum number of bars for a pending order - `maxBarsOpen`: maximum number of bars for an open position - `qty_tp1`, `qty_tp2`, `qty_tp3`: quantity percentages for multiple TPs --- ## 2. Order Block Identification ### Order Block Parameters - `obLookback`: number of bars to identify an Order Block - `obmode`: method to calculate the block (`Full` or `Breadth`) - `obmiti`: method to determine block mitigation (`Close`, `Wick`, `Avg`) - `obMaxBlocks`: maximum number of Order Blocks displayed ### Main Variables - `bullBlocks`: array of bullish blocks - `bearBlocks`: array of bearish blocks - `last_bull_volume`, `last_bear_volume`: volume of the last block - `dom_block`: dominant block type (Bullish/Bearish/None) - `block_strength`: block strength (normalized volume) - `price_distance`: distance between current price and nearest block --- ## 3. Visual Parameters - `Width`: line thickness for swing high/low - `amountOfBoxes`: block grid segments - `showBorder`: show block borders - `borderWidth`: width of block borders - `showVolume`: display volume inside blocks - `volumePosition`: vertical position of volume text Customizable colors: - `obHighVolumeColor`, `obLowVolumeColor`, `obBearHighVolumeColor`, `obBearLowVolumeColor` - `obBullBorderColor`, `obBearBorderColor` - `obBullFillColor`, `obBearFillColor` - `volumeTextColor` --- ## 4. Screener Table - `showScreener`: display the screener table - `tablePosition`: table position (`Top Left`, `Top Right`, `Bottom Left`, `Bottom Right`) - `tableSize`: table size (`Small`, `Normal`, `Large`) The table shows: - Symbol, Timeframe - Type and status of Order Block - Number of retests - Bullish and bearish volumes --- ## 5. Trend Filters - EMA as a trend filter (`emaPeriod`, default 223) - `bullishTrend` if close > EMA - `bearishTrend` if close < EMA --- ## 6. ATR and Swing Points - ATR calculated with a customizable period (`atrLength`) - Swing High/Low for SL/TP calculation - `f_getSwingTargets` function to calculate SL and TP based on direction --- ## 7. Trade Logic ### Buy Limit on Bullish OB - Conditions: - New bullish block - Uptrend - RR > threshold (`rr_threshold`) - SL: `bullishOBPrice * (1 - atr * atrMultiplier)` - Multiple TPs: TP1 (50%), TP2 (80%), TP3 (100% max) - Quantity calculation based on percentage risk ### Sell Limit on Bearish OB - Conditions: - New bearish block - Downtrend - RR > threshold (`rr_threshold`) - SL: `bearishOBPrice * (1 + atr * atrMultiplier)` - Multiple TPs: TP1 (50%), TP2 (80%), TP3 (100% max) - Quantity calculation based on percentage risk --- ## 8. Order Management and Timeout - Close pending orders after `maxPendingBars` bars - Close open positions after `maxBarsOpen` bars - Label management for open orders --- ## 9. Alert Conditions - `bull_touch`: price inside maximum bullish volume zone - `bear_touch`: price inside maximum bearish volume zone - `bull_reject`: confirmation of bullish zone rejection - `bear_reject`: confirmation of bearish zone rejection - `new_bull`: new bullish block - `new_bear`: new bearish block --- ## 10. Level Calculation - Swing levels based on selected timeframe (`SelectPeriod`) - `xHigh` and `xLow` for S1 and R1 calculation - Levels plotted on chart --- ## 11. Take Profit / Stop Loss - Extended horizontal lines (`extendBars`) to visualize TP and SL - Customizable colors (`tpColor`, `slColor`) --- ## 12. Notes - Complete script based on Pine Script v5 - Advanced graphical management with boxes, lines, labels - Dynamically displays volumes and Order Blocks - Integrated internal screener --- ### End of Documentation
Strategi Pine Script®
oleh CRYPTO_EWT
SuperTrend Optimizer Remastered[CHE] SuperTrend Optimizer Remastered — Grid-ranked SuperTrend with additive or multiplicative scoring Summary This indicator evaluates a fixed grid of one hundred and two SuperTrend parameter pairs and ranks them by a simple flip-to-flip return model. It auto-selects the currently best-scoring combination and renders its SuperTrend in real time, with optional gradient coloring for faster visual parsing. The original concept is by KioseffTrading Thanks a lot for it. For years I wanted to shorten the roughly two thousand three hundred seventy-one lines; I have now reduced the core to about three hundred eighty lines without triggering script errors. The simplification is generalizable to other indicators. A multiplicative return mode was added alongside the existing additive aggregation, enabling different rankings and often more realistic compounding behavior. Motivation: Why this design? SuperTrend is sensitive to its factor and period. Picking a single pair statically can underperform across regimes. This design sweeps a compact parameter grid around user-defined lower bounds, measures flip-to-flip outcomes, and promotes the combination with the strongest cumulative return. The approach keeps the visual footprint familiar while removing manual trial-and-error. The multiplicative mode captures compounding effects; the additive mode remains available for linear aggregation. Originally (by KioseffTrading) Very long script (~2,371 lines), monolithic structure. SuperTrend optimization with additive (cumulative percentage-sum) scoring only. Heavier use of repetitive code; limited modularity and fewer UI conveniences. No explicit multiplicative compounding option; rankings did not reflect sequence-sensitive equity growth. Now (remastered by CHE) Compact core (~380 lines) with the same functional intent, no compile errors. Adds multiplicative (compounding) scoring alongside additive, changing rankings to reflect real equity paths and penalize drawdown sequences. Fixed 34×3 grid sweep, live ranking, gradient-based bar/wick/line visuals, top-table display, and an optional override plot. Cleaner arrays/state handling, last-bar table updates, and reusable simplification pattern that can be applied to other indicators. What’s different vs. standard approaches? Baseline: A single SuperTrend with hand-picked inputs. Architecture differences: Fixed grid of thirty-four factor offsets across three ATR offsets. Per-combination flip-to-flip backtest with additive or multiplicative aggregation. Live ranking with optional “Best” or “Worst” table output. Gradient bar, wick, and line coloring driven by consecutive trend counts. Optional override plot to force a specific SuperTrend independent of ranking. Practical effect: Charts show the currently best-scoring SuperTrend, not a static choice, plus an on-chart table of top performers for transparency. How it works (technical) For each parameter pair, the script computes SuperTrend value and direction. It monitors direction transitions and treats a change from up to down as a long entry and the reverse as an exit, measuring the move between entry and exit using close prices. Results are aggregated per pair either by summing percentage changes or by compounding return factors and then converting to percent for comparison. On the last bar, open trades are included as unrealized contributions to ranking. The best combination’s line is plotted, with separate styling for up and down regimes. Consecutive regime counts are normalized within a rolling window and mapped to gradients for bars, wicks, and lines. A two-column table reports the best or worst performers, with an optional row describing the parameter sweep. Parameter Guide Factor (Lower Bound) — Starting SuperTrend factor; the grid adds offsets between zero and three point three. Default three point zero. Higher raises distance to price and reduces flips. ATR Period (Lower Bound) — Starting ATR length; the grid adds zero, one, and two. Default ten. Longer reduces noise at the cost of responsiveness. Best vs Worst — Ranks by top or bottom cumulative return. Default Best. Use Worst for stress tests. Calculation Mode — Additive sums percents; Multiplicative compounds returns. Multiplicative is closer to equity growth and can change the leaderboard. Show in Table — “Top Three” or “All”. Fewer rows keep charts clean. Show “Parameters Tested” Label — Displays the effective sweep ranges for auditability. Plot Override SuperTrend — If enabled, the override factor and ATR are plotted instead of the ranked winner. Override Factor / ATR Period — Values used when override is on. Light Mode (for Table) — Adjusts table colors for bright charts. Gradient/Coloring controls — Toggles for gradient bars and wick coloring, window length for normalization, gamma for contrast, and transparency settings. Use these to emphasize or tone down visual intensity. Table Position and Text Size — Places the table and sets typography. Reading & Interpretation The auto SuperTrend plots one line for up regimes and one for down regimes. Color intensity reflects consecutive trend persistence within the chosen window. A small square at the bottom encodes the same gradient as a compact status channel. Optional wick coloring uses the same gradient for maximum contrast. The performance table lists parameter pairs and their cumulative return under the chosen aggregation; positive values are tinted with the up color, negative with the down color. “Long” labels mark flips that open a long in the simplified model. Practical Workflows & Combinations Trend following: Use the auto line as your primary bias. Enter on flips aligned with structure such as higher highs and higher lows. Filter with higher-timeframe trend or volatility contraction. Exits/Stops: Consider conservative exits when color intensity fades or when the opposite line is approached. Aggressive traders can trail near the plotted line. Override mode: When you want stability across instruments, enable override and standardize factor and ATR; keep the table visible for sanity checks. Multi-asset/Multi-TF: Defaults travel well on liquid instruments and intraday to daily timeframes. Heavier assets may prefer larger lower bounds or multiplicative mode. Behavior, Constraints & Performance Repaint/confirmation: Signals are based on SuperTrend direction; confirmation is best assessed on closed bars to avoid mid-bar oscillation. No higher-timeframe requests are used. Resources: One hundred and two SuperTrend evaluations per bar, arrays for state, and a last-bar table render. This is efficient for the grid size but avoid stacking many instances. Known limits: The flip model ignores costs, slippage, and short exposure. Rapid whipsaws can degrade both aggregation modes. Gradients are cosmetic and do not change logic. Sensible Defaults & Quick Tuning Start with the provided lower bounds and “Top Three” table. Too many flips → raise the lower bound factor or period. Too sluggish → lower the bounds or switch to additive mode. Rankings feel unstable → prefer multiplicative mode and extend the normalization window. Visuals too strong → increase gradient transparency or disable wick coloring. What this indicator is—and isn’t This is a parameter-sweep and visualization layer for SuperTrend selection. It is not a complete trading system, not predictive, and does not include position sizing, transaction costs, or risk management. Combine with market structure, higher-timeframe context, and explicit risk controls. Attribution and refactor note: The original work is by KioseffTrading. The script has been refactored from approximately two thousand three hundred seventy-one lines to about three hundred eighty core lines, retaining behavior without compiler errors. The general simplification pattern is reusable for other indicators. Metadata Name/Tag: SuperTrend Optimizer Remastered Pine version: v6 Overlay or separate pane: true (overlay) Core idea/principle: Grid-based SuperTrend selection by cumulative flip returns with additive or multiplicative aggregation. Primary outputs/signals: Auto-selected SuperTrend up and down lines, optional override lines, gradient bar and wick colors, “Long” labels, performance table. Inputs with defaults: See Parameter Guide above. Metrics/functions used: SuperTrend, ATR, arrays, barstate checks, windowed normalization, gamma-based contrast adjustment, table API, gradient utilities. Special techniques: Fixed grid sweep, compounding vs linear aggregation, last-bar UI updates, gradient encoding of persistence. Performance/constraints: One hundred and two SuperTrend calls, arrays of length one hundred and two, label budget, last-bar table updates, no higher-timeframe requests. Recommended use-cases/workflows: Trend bias selection, quick parameter audits, override standardization across assets. Compatibility/assets/timeframes: Standard OHLC charts across intraday to daily; liquid instruments recommended. Limitations/risks: Costs and slippage omitted; mid-bar instability possible; not suitable for synthetic chart types. Debug/diagnostics: Ranking table, optional tested-range label; internal counters for consecutive trends. 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
Indikator Pine Script®
oleh chervolino
Crypto Perp Calc v1Advanced Perpetual Position Calculator for TradingView Description A comprehensive position sizing and risk management tool designed specifically for perpetual futures trading. This indicator eliminates the confusion of calculating leveraged positions by providing real-time position metrics directly on your chart. Key Features: Interactive Price Selection: Click directly on chart to set entry, stop loss, and take profit levels Accurate Lot Size Calculation: Instantly calculates the exact position size needed for your margin and leverage Multiple Entry Support: DCA into positions with up to 3 entry points with customizable allocation Multiple Take Profit Levels: Scale out of positions with up to 3 TP targets Comprehensive Risk Metrics: Shows dollar P&L, account risk percentage, and liquidation price Visual Risk/Reward: Color-coded boxes and lines display your trade setup clearly Real-time Info Table: All critical position data in one organized panel Perfect for traders using perpetual futures who need precise position sizing with leverage. --------- How to Use Quick Start (3 Clicks) 1. Add the indicator to your chart 2. Click three times when prompted: First click: Set your entry price Second click: Set your stop loss Third click: Set your take profit 3. Read the TOTAL LOTS value from the info table (highlighted in yellow) 4. Use this lot size in your exchange when placing the trade Detailed Setup Step 1: Configure Your Account Enter your account balance (total USDT in account) Set your margin amount (how much USDT to risk on this trade) Choose your leverage (1x to 125x) Select Long or Short position Step 2: Set Price Levels Main levels use interactive clicking (Entry, SL, TP) For multiple entries or TPs, use the settings panel to manually input prices and percentages Step 3: Read the Results The info table shows: TOTAL LOTS - The position size to enter on your exchange Margin Used - Your actual capital at risk Notional - Total position value (margin × leverage) Max Risk - Dollar amount you'll lose at stop loss Total Profit - Dollar amount you'll gain at take profit R:R Ratio - Risk to reward ratio Account Risk - Percentage of account at risk Liquidation - Price where position gets liquidated Step 4: Advanced Features (Optional) Multiple Entries (DCA): Enable "Use Multiple Entries" Set up to 3 entry prices Allocate percentage for each (must total 100%) See individual lot sizes for each entry Multiple Take Profits: Enable "Use Multiple TPs" Set up to 3 TP levels Allocate percentage to close at each level (must total 100%) View profit at each target Visual Elements Blue lines/labels: Entry points Red lines/labels: Stop loss Green lines/labels: Take profit targets Colored boxes: Visual risk (red) and reward (green) zones Info table: Can be positioned anywhere on screen Alerts Set price alerts for: Entry zones reached Stop loss approached Take profit levels hit Works with TradingView's alert system Tips for Best Results Always verify the lot size matches your intended risk Check the liquidation price stays far from your stop loss Monitor the account risk percentage (recommended: keep under 2-3%) Use the warning indicators if risk exceeds margin For quick trades, use single entry/TP; for complex strategies, use multiple levels Example Workflow Find your trade setup using your analysis Add this indicator and click to set levels Check risk metrics in the table Copy the TOTAL LOTS value Enter this exact position size on your exchange Set alerts for key levels if desired This tool bridges the gap between TradingView charting and exchange execution, ensuring your position sizing is always accurate when trading with leverage. Disclaimer, this was coded with help of AI, double check calculations if they are off.
Indikator Pine Script®
oleh bujayjay
cd_SMT_Sweep_CISD_CxGeneral This indicator is designed to show trading opportunities after sweeps of higher timeframe (HTF) highs/lows and, if available, Smart Money Technique (SMT) divergence with a correlated asset, followed by confirmation from a lower timeframe change in state delivery (CISD). Users can track SMT, Sweep, and CISD levels across nine different timeframes. ________________________________________ Usage and Details Commonly correlated timeframes are available in the menu by default. Users can also enter other compatible timeframes manually if necessary. The indicator output is presented as: • A summary table • Display on HTF candles • CISD levels shown as lines Users can disable any of these from the menu. Presentations of selected timeframes are displayed only if they are greater than or equal to the active chart timeframe. From the Show/Hide section, you can control the display of: • SMT table • Sweep table • HTF candles • CISD levels • HTF boxes aligned with the active timeframe ________________________________________ SMT Analysis To receive analysis, users must enter correlated assets in the menu (or adjust them as needed). If asset X is paired with correlated asset Y, then a separate entry for Y correlated with X is not required. Four correlation pairs are included by default. Users should check them according to their broker/exchange or define new ones. Checkboxes at the beginning of each row allow activation/deactivation of pairs. SMT analysis is performed on the last three candles of each selected HTF. If one asset makes a new high while the correlated one does not (or one makes a new low while the other does not), this is considered SMT and will be displayed both in the table and on the chart. Charts without defined correlated assets will not display an SMT table. ________________________________________ Sweep Analysis For the selected timeframes, the current candle is compared with the previous one. If price violates the previous level and then pulls back behind it, this is considered a sweep. It is displayed in both the table and on the chart. Within correlated pairs, the analysis is done separately and shown only in the table. Example with correlated and non-correlated pairs: • In the table, X = false, ✓ = true. • The Sweep Table has two columns for Bullish and Bearish results. • For correlated pairs, both values appear side by side. • For undefined pairs, only the active asset is shown. Example 1: EURUSD and GBPUSD pair • If both sweep → ✓ ✓ • If one sweeps, the other does not → ✓ X • If neither sweeps → X X Example 2: AUDUSD with no correlated pair defined • If sweep → ✓ • If no sweep → X ________________________________________ HTF Candles For every HTF enabled by the user, the last three candles (including the current one) are shown on the chart. SMT and sweep signals are marked where applicable. ________________________________________ CISD Levels For the selected timeframes, bullish and bearish CISD levels are plotted on the chart. ________________________________________ HTF Boxes HTF boxes aligned with the active timeframe are displayed on the chart. Box border colors change according to whether the active HTF candle is bullish or bearish. ________________________________________ How to Read the Chart? Let’s break down the example below: • Active asset: Nasdaq • Correlated asset: US500 (defined in the menu, confirmed in the table bottom row) • Active timeframe: H1 → therefore, the HTF box is shown for Daily • Since a correlated pair is defined, the indicator runs both SMT and Sweep analysis for the selected timeframes. Without correlation, only Sweep analysis would be shown. Table is prepared for H1 and higher timeframes (as per user selection and active TF). Observations: • SMT side → H1 timeframe shows a bearish warning • Sweep side → Bearish column shows X and ✓ o X → no sweep on Nasdaq o ✓ → sweep on US500 Meaning: US500 made a new high (+ sweep) while Nasdaq did not → SMT formed. The last column of the table shows the compatible LTF for confirmation. For H1, it suggests checking the 5m timeframe. On the chart: • CISD levels for selected timeframes are drawn • SMT line is marked on H1 candles • Next step: move to 5m chart for CISD confirmation before trading (with other confluences). Similarly, the Daily row in the table shows a Bullish Sweep on US500. ________________________________________ Alerts Two alert options are available: 1. Activate Alert (SMT + Sweep): Triggers if both SMT and Sweep occur in the selected timeframes. (Classic option) 2. Activate Alert (Sweep + Sweep): Triggers if sweeps occur in both assets of a correlated pair at the same timeframe. Interpretation: If SMT + Sweep are already present on higher timeframes, and simultaneous sweeps appear on lower timeframes, this may indicate a strong directional move. Of course, this must be validated with CISD and other confluences. ________________________________________ HTF CISD Levels Although CISD levels act as confirmation levels in their own timeframe, observing how price reacts to HTF CISD levels can provide valuable insights for intraday analysis. POIs overlapping with these levels may be higher priority. ________________________________________ What’s Next in Future Versions? • Completed CISD confirmations • Additional alert options • Plus your feedback and suggestions ________________________________________ Final Note I’ll be happy to hear your opinions and feedback. Happy trading!
Indikator Pine Script®
oleh cdikici71
25
Relative Strength Heat [InvestorUnknown]The Relative Strength Heat (RSH) indicator is a relative strength of an asset across multiple RSI periods through a dynamic heatmap and provides smoothed signals for overbought and oversold conditions. The indicator is highly customizable, allowing traders to adjust RSI periods, smoothing methods, and visual settings to suit their trading strategies. The RSH indicator is particularly useful for identifying momentum shifts and potential reversal points by aggregating RSI data across a range of periods. It presents this data in a visually intuitive heatmap, with color-coded bands indicating overbought (red), oversold (green), or neutral (gray) conditions. Additionally, it includes signal lines for overbought and oversold indices, which can be smoothed using RAW, SMA, or EMA methods, and a table displaying the current index values. Features Dynamic RSI Periods: Calculates RSI across 31 periods, starting from a user-defined base period and incrementing by a specified step. Heatmap Visualization: Displays RSI strength as a color-coded heatmap, with red for overbought, green for oversold, and gray for neutral zones. Customizable Smoothing: Offers RAW, SMA, or EMA smoothing for overbought and oversold signals. Signal Lines: Plots scaled overbought (purple) and oversold (yellow) signal lines with a midline for reference. Information Table: Displays real-time overbought and oversold index values in a table at the top-right of the chart. User-Friendly Inputs: Allows customization of RSI source, period ranges, smoothing length, and colors. How It Works The RSH indicator aggregates RSI calculations across 31 periods, starting from the user-defined Starting Period and incrementing by the Period Increment. For each period, it computes the RSI and determines whether the asset is overbought (RSI > threshold_ob) or oversold (RSI < threshold_os). These states are stored in arrays (ob_array for overbought, os_array for oversold) and used to generate the following outputs: Heatmap: The indicator plots 31 horizontal bands, each representing an RSI period. The color of each band is determined by the f_col function: Red if the RSI for that period is overbought (>threshold_ob). Green if the RSI is oversold (
Indikator Pine Script®
oleh InvestorUnknown
Money Risk Management with Trade Tracking Overview The Money Risk Management with Trade Tracking indicator is a powerful tool designed for traders on TradingView to simplify trade simulation and risk management. Unlike the TradingView Strategy Tester, which can be complex for beginners, this indicator provides an intuitive, beginner-friendly interface to evaluate trading strategies in a realistic manner, mirroring real-world trading conditions. Built on the foundation of open-source contributions from LuxAlgo and TCP, this indicator integrates external indicator signals, overlays take-profit (TP) and stop-loss (SL) levels, and provides detailed money management analytics. It empowers traders to visualize potential profits, losses, and risk-reward ratios, making it easier to understand the financial outcomes of their strategies. Key Features Signal Integration: Seamlessly integrates with external long and short signals from other indicators, allowing traders to overlay TP/SL levels based on their preferred strategies. Realistic Trade Simulation: Simulates trades as they would occur in real-world scenarios, accounting for initial capital, risk percentage, leverage, and compounding effects. Money Management Dashboard: Displays critical metrics such as current capital, unrealized P&L, risk amount, potential profit, risk-reward ratio, and trade status in a customizable, beginner-friendly table. TP/SL Visualization: Plots TP and SL levels on the chart with customizable styles (solid, dashed, dotted) and colors, along with optional labels for clarity. Performance Tracking: Tracks total trades, win/loss counts, win rate, and profit factor, providing a clear overview of strategy performance. Liquidation Risk Alerts: Warns traders if stop-loss levels risk liquidation based on leverage settings, enhancing risk awareness. Benefits for Traders Beginner-Friendly: Simplifies the complexities of the TradingView Strategy Tester, offering an intuitive interface for new traders to simulate and evaluate trades without confusion. Real-World Insights: Helps traders understand the actual profit or loss potential of their strategies by factoring in capital, risk, and leverage, bridging the gap between theoretical backtesting and real-world execution. Enhanced Decision-Making: Provides clear, real-time analytics on risk-reward ratios, unrealized P&L, and trade performance, enabling informed trading decisions. Customizable and Flexible: Allows customization of TP/SL settings, table positions, colors, and sizes, catering to individual trader preferences. Risk Management Focus: Encourages disciplined trading by highlighting risk amounts, potential profits, and liquidation risks, fostering better financial planning. Why This Indicator Stands Out Many traders struggle to translate backtested strategy results into real-world outcomes due to the abstract nature of percentage-based profitability metrics. This indicator addresses that challenge by providing a practical, user-friendly tool that simulates trades with real-world parameters like capital, leverage, and compounding. Its open-source nature ensures accessibility, while its integration with other indicators makes it versatile for various trading styles. How to Use Add to TradingView: Copy the Pine Script code into TradingView’s Pine Editor and add it to your chart. Configure Inputs: Set your initial capital, risk percentage, leverage, and TP/SL values in the indicator settings. Select external long/short signal sources if integrating with other indicators. Monitor Dashboards: Use the Money Management and Target Dashboard tables to track trade performance and risk metrics in real time. Analyze Results: Review win rates, profit factors, and P&L to refine your trading strategy. Credits This indicator builds upon the open-source contributions of LuxAlgo and TCP , whose efforts in sharing their code have made this tool possible. Their dedication to the trading community is deeply appreciated.
Indikator Pine Script®
oleh mishy_Champ
1
Canuck Trading IndicatorOverview The Canuck Trading Indicator is a versatile, overlay-based technical analysis tool designed to assist traders in identifying potential trading opportunities across various timeframes and market conditions. By combining multiple technical indicators—such as RSI, Bollinger Bands, EMAs, VWAP, MACD, Stochastic RSI, ADX, HMA, and candlestick patterns—the indicator provides clear visual signals for bullish and bearish entries, breakouts, long-term trends, and options strategies like cash-secured puts, straddles/strangles, iron condors, and short squeezes. It also incorporates 20-day and 200-day SMAs to detect Golden/Death Crosses and price positioning relative to these moving averages. A dynamic table displays key metrics, and customizable alerts help traders stay informed of market conditions. Key Features Multi-Timeframe Adaptability: Automatically adjusts parameters (e.g., ATR multiplier, ADX period, HMA length) based on the chart's timeframe (minute, hourly, daily, weekly, monthly) for optimal performance. Comprehensive Signal Generation: Identifies short-term entries, breakouts, long-term bullish trends, and options strategies using a combination of momentum, trend, volatility, and candlestick patterns. Candlestick Pattern Detection: Recognizes bullish/bearish engulfing, hammer, shooting star, doji, and strong candles for precise entry/exit signals. Moving Average Analysis: Plots 20-day and 200-day SMAs, detects Golden/Death Crosses, and evaluates price position relative to these averages. Dynamic Table: Displays real-time metrics, including zone status (bullish, bearish, neutral), RSI, MACD, Stochastic RSI, short/long-term trends, candlestick patterns, ADX, ROC, VWAP slope, and MA positioning. Customizable Alerts: Over 20 alert conditions for entries, exits, overbought/oversold warnings, and MA crosses, with actionable messages including ticker, price, and suggested strategies. Visual Clarity: Uses distinct shapes, colors, and sizes to plot signals (e.g., green triangles for bullish entries, red triangles for bearish entries) and overlays key levels like EMA, VWAP, Bollinger Bands, support/resistance, and HMA. Options Strategy Signals: Suggests opportunities for selling cash-secured puts, straddles/strangles, iron condors, and capitalizing on short squeezes. How to Use Add to Chart: Apply the indicator to any TradingView chart by selecting "Canuck Trading Indicator" from the Pine Script library. Interpret Signals: Bullish Signals: Green triangles (short-term entry), lime diamonds (breakout), blue circles (long-term entry). Bearish Signals: Red triangles (short-term entry), maroon diamonds (breakout). Options Strategies: Purple squares (cash-secured puts), yellow circles (straddles/strangles), orange crosses (iron condors), white arrows (short squeezes). Exits: X-cross shapes in corresponding colors indicate exit signals. Monitor: Gray circles suggest holding cash or monitoring for setups. Review Table: Check the top-right table for real-time metrics, including zone status, RSI, MACD, trends, and MA positioning. Set Alerts: Configure alerts for specific signals (e.g., "Short-Term Bullish Entry" or "Golden Cross") to receive notifications via TradingView. Adjust Inputs: Customize input parameters (e.g., RSI period, EMA length, ATR period) to suit your trading style or market conditions. Input Parameters The indicator offers a wide range of customizable inputs to fine-tune its behavior: RSI Period (default: 14): Length for RSI calculation. RSI Bullish Low/High (default: 35/70): RSI thresholds for bullish signals. RSI Bearish High (default: 65): RSI threshold for bearish signals. EMA Period (default: 15): Main EMA length (15 for day trading, 50 for swing). Short/Long EMA Length (default: 3/20): For momentum oscillator. T3 Smoothing Length (default: 5): Smooths momentum signals. Long-Term EMA/RSI Length (default: 20/15): For long-term trend analysis. Support/Resistance Lookback (default: 5): Periods for support/resistance levels. MACD Fast/Slow/Signal (default: 12/26/9): MACD parameters. Bollinger Bands Period/StdDev (default: 15/2): BB settings. Stochastic RSI Period/Smoothing (default: 14/3/3): Stochastic RSI settings. Uptrend/Short-Term/Long-Term Lookback (default: 2/2/5): Candles for trend detection. ATR Period (default: 14): For volatility and price targets. VWAP Sensitivity (default: 0.1%): Threshold for VWAP-based signals. Volume Oscillator Period (default: 14): For volume surge detection. Pattern Detection Threshold (default: 0.3%): Sensitivity for candlestick patterns. ROC Period (default: 3): Rate of change for momentum. VWAP Slope Period (default: 5): For VWAP trend analysis. TradingView Publishing Compliance Originality: The Canuck Trading Indicator is an original script, combining multiple technical indicators and custom logic to provide unique trading signals. It does not replicate existing public scripts. No Guaranteed Profits: This indicator is a tool for technical analysis and does not guarantee profits. Trading involves risks, and users should conduct their own research and risk management. Clear Instructions: The description and usage guide are detailed and accessible, ensuring users understand how to apply the indicator effectively. No External Dependencies: The script uses only built-in Pine Script functions (e.g., ta.rsi, ta.ema, ta.vwap) and requires no external libraries or data sources. Performance: The script is optimized for performance, using efficient calculations and adaptive parameters to minimize lag on various timeframes. Visual Clarity: Signals are plotted with distinct shapes and colors, and the table provides a concise summary of market conditions, enhancing usability. Limitations and Risks Market Conditions: The indicator may generate false signals in choppy or low-liquidity markets. Always confirm signals with additional analysis. Timeframe Sensitivity: Performance varies by timeframe; test settings on your preferred chart (e.g., 5-minute for day trading, daily for swing trading). Risk Management: Use stop-losses and position sizing to manage risk, as suggested in alert messages (e.g., "Stop -20%"). Options Trading: Options strategies (e.g., straddles, iron condors) carry unique risks; consult a financial advisor before trading. Feedback and Support For questions, suggestions, or bug reports, please leave a comment on the TradingView script page or contact the author via TradingView. Your feedback helps improve the indicator for the community. Disclaimer The Canuck Trading Indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves significant risks, and past performance is not indicative of future results. Always perform your own due diligence and consult a qualified financial advisor before making trading decisions.
Indikator Pine Script®
oleh ctoincanada
NFP High/Low Levels PlusNFP High/Low Levels Plus Description: This indicator stores the 12 most recent NFP (Non-Farm-Payroll) days and their values. Values are captured from 0830 (NFP Release) until close of market The High and Low values for each NFP month are drawn on the chart with horizontal lines. - Labels indicating the month's high or low line are placed after the line - Optionally the high/low price can be displayed additionally Support and Resistance boxes can be drawn at the closest NFP level above and below the current price. - Boxes will automatically update as prices cross the NFP value Macro Indicator - This option displays a small table in the top right corner that says "Up" or " Down" - The Macro Indicator can be used to judge the potential direction for the current month - Macro direction is calculated by the following: - UP: If two consecutive days both open and close above the most recent NFP High level - DOWN: If two consecutive days both open and close below the most recent NFP Low level Micro Indicator - This option displays a small table in the top right corner that says "Up" or " Down" - The Micro Indicator can be used to judge the potential direction for low timeframes 1H or lower - Micro direction is calculated by the following: - UP: If two consecutive 10m candles close above the 20EMA - DOWN: If two consecutive 10m candles close below the 20EMA NFP Session Bars - This feature draws an arrow at the bottom of the chart for each candle that falls within the NFP session day - This is useful for identifying NFP Days Support / Resistance Table - This displays a table bottom center showing the nearest high and low NFP line level What is an NFP Day and why is it useful to add to my chart? - NFP Days are one of the most important data releases monthly - NFP (Non-Farm-Payroll) is the official release of 80% of the US workforce employed in manufacturing, construction, and goods - It does not include those who work on farms, private households, non-profit and government workers - Historically these high/low levels for the day create strong support and resistance levels - Having them displayed on the chart can help identify potential strong levels and pivot points Full Indicator with all options enabled and identified Easily update NFP Release Days in the indicator settings Modify various options: Show/Hide lines, labels, directional indicator tables, values tables Adjust line width, offsets, colors, font sizes, box widths Enable individual Directional Indicators and modify colors Example of full indicator enabled You can find a list of the NFP Release Schedule on the official US Bureau of Labor Statistics website. This is useful for updating the indicator settings with the correct dates
Indikator Pine Script®
oleh rogman
13
Overbought / Oversold Screener## Introduction **The Versatile RSI and Stochastic Multi-Symbol Screener** **Unlock a wealth of trading opportunities with this customizable screener, designed to pinpoint potential overbought and oversold conditions across 17 symbols, with alert support!** ## Description This screener is suitable for tracking multiple instruments continuously. With the screener, you can see the instant RSI or Stochastic values of the instruments you are tracking, and easily catch the moments when they are overbought / oversold according to your settings. The purpose of the screener is to facilitate the continuous tracking of multiple instruments. The user can track up to 17 different instruments in different time intervals. If they wish, they can set an alarm and learn overbought oversold according to the values they set for the time interval of the instruments they are tracking.** Key Features: Comprehensive Analysis: Monitors RSI and Stochastic values for 17 symbols simultaneously. Automatically includes the current chart's symbol for seamless integration. Supports multiple timeframes to uncover trends across different time horizons. Personalized Insights: Adjust overbought and oversold thresholds to align with your trading strategy. Sort results by symbol, RSI, or Stochastic values to prioritize your analysis. Choose between Automatic, Dark, or Light mode for optimal viewing comfort. Dynamic Visual Cues: Instantly highlights oversold and overbought symbols based on threshold levels. Timely Alerts: Stay informed of potential trading opportunities with alerts for multiple oversold or overbought symbols. ## Settings ### Display **Timeframe** The screener displays the values according to the selected timeframe. The default timeframe is "Chart". For example, if the timeframe is set to "15m" here, the screener will show the RSI and stochastic values for the 15-minute chart. ** Theme ** This setting is for changing the theme of the screener. You can set the theme to "Automatic", "Dark", or "Light", with "Automatic" being the default value. When the "Automatic" theme is selected, the screener appearance will also be automatically updated when you enable or disable dark mode from the TradingView settings. ** Position ** This option is for setting the position of the table on the chart. The default setting is "middle right". The available options are (top, middle, bottom)-(left, center, right). ** Sort By ** This option is for changing the sorting order of the table. The default setting is "RSI Descending". The available options are (Symbol, RSI, Stoch)-(Ascending, Descending). It is important to note that the overbought and oversold coloring of the symbols may also change when the sorting order is changed. If RSI is selected as the sorting order, the symbols will be colored according to the overbought and oversold threshold values specified for RSI. Similarly, if Stoch is selected as the sorting order, the symbols will be colored according to the overbought and oversold threshold values specified for Stoch. From this perspective, you can also think of the sorting order as a change in the main indicator. ### RSI / Stochastic This area is for selecting the parameters of the RSI and stochastic indicators. You can adjust the values for "length", "overbought", and "oversold" for both indicators according to your needs. The screener will perform all RSI and stochastic calculations according to these settings. All coloring in the table will also be according to the overbought and oversold values in these settings. ### Symbols The symbols to be tracked in the table are selected from here. Up to 16 symbols can be selected from here. Since the symbol in the chart is automatically added to the table, there will always be at least 1 symbol in the table. Note that the symbol in the chart is shown in the table with "(C)". For example, if SPX is open in the chart, it is shown as SPX(C) in the table. ## Alerts The screener is capable of notifying you with an alarm if multiple symbols are overbought or oversold according to the values you specify along with the desired timeframe. This way, you can instantly learn if multiple symbols are overbought or oversold with one alarm, saving you time.
Indikator Pine Script®
oleh eraydiler
2
Machine Learning: Optimal RSI [YinYangAlgorithms]This Indicator, will rate multiple different lengths of RSIs to determine which RSI to RSI MA cross produced the highest profit within the lookback span. This ‘Optimal RSI’ is then passed back, and if toggled will then be thrown into a Machine Learning calculation. You have the option to Filter RSI and RSI MA’s within the Machine Learning calculation. What this does is, only other Optimal RSI’s which are in the same bullish or bearish direction (is the RSI above or below the RSI MA) will be added to the calculation. You can either (by default) use a Simple Average; which is essentially just a Mean of all the Optimal RSI’s with a length of Machine Learning. Or, you can opt to use a k-Nearest Neighbour (KNN) calculation which takes a Fast and Slow Speed. We essentially turn the Optimal RSI into a MA with different lengths and then compare the distance between the two within our KNN Function. RSI may very well be one of the most used Indicators for identifying crucial Overbought and Oversold locations. Not only that but when it crosses its Moving Average (MA) line it may also indicate good locations to Buy and Sell. Many traders simply use the RSI with the standard length (14), however, does that mean this is the best length? By using the length of the top performing RSI and then applying some Machine Learning logic to it, we hope to create what may be a more accurate, smooth, optimal, RSI. Tutorial: This is a pretty zoomed out Perspective of what the Indicator looks like with its default settings (except with Bollinger Bands and Signals disabled). If you look at the Tables above, you’ll notice, currently the Top Performing RSI Length is 13 with an Optimal Profit % of: 1.00054973. On its default settings, what it does is Scan X amount of RSI Lengths and checks for when the RSI and RSI MA cross each other. It then records the profitability of each cross to identify which length produced the overall highest crossing profitability. Whichever length produces the highest profit is then the RSI length that is used in the plots, until another length takes its place. This may result in what we deem to be the ‘Optimal RSI’ as it is an adaptive RSI which changes based on performance. In our next example, we changed the ‘Optimal RSI Type’ from ‘All Crossings’ to ‘Extremity Crossings’. If you compare the last two examples to each other, you’ll notice some similarities, but overall they’re quite different. The reason why is, the Optimal RSI is calculated differently. When using ‘All Crossings’ everytime the RSI and RSI MA cross, we evaluate it for profit (short and long). However, with ‘Extremity Crossings’, we only evaluate it when the RSI crosses over the RSI MA and RSI <= 40 or RSI crosses under the RSI MA and RSI >= 60. We conclude the crossing when it crosses back on its opposite of the extremity, and that is how it finds its Optimal RSI. The way we determine the Optimal RSI is crucial to calculating which length is currently optimal. In this next example we have zoomed in a bit, and have the full default settings on. Now we have signals (which you can set alerts for), for when the RSI and RSI MA cross (green is bullish and red is bearish). We also have our Optimal RSI Bollinger Bands enabled here too. These bands allow you to see where there may be Support and Resistance within the RSI at levels that aren’t static; such as 30 and 70. The length the RSI Bollinger Bands use is the Optimal RSI Length, allowing it to likewise change in correlation to the Optimal RSI. In the example above, we’ve zoomed out as far as the Optimal RSI Bollinger Bands go. You’ll notice, the Bollinger Bands may act as Support and Resistance locations within and outside of the RSI Mid zone (30-70). In the next example we will highlight these areas so they may be easier to see. Circled above, you may see how many times the Optimal RSI faced Support and Resistance locations on the Bollinger Bands. These Bollinger Bands may give a second location for Support and Resistance. The key Support and Resistance may still be the 30/50/70, however the Bollinger Bands allows us to have a more adaptive, moving form of Support and Resistance. This helps to show where it may ‘bounce’ if it surpasses any of the static levels (30/50/70). Due to the fact that this Indicator may take a long time to execute and it can throw errors for such, we have added a Setting called: Adjust Optimal RSI Lookback and RSI Count. This settings will automatically modify the Optimal RSI Lookback Length and the RSI Count based on the Time Frame you are on and the Bar Indexes that are within. For instance, if we switch to the 1 Hour Time Frame, it will adjust the length from 200->90 and RSI Count from 30->20. If this wasn’t adjusted, the Indicator would Timeout. You may however, change the Setting ‘Adjust Optimal RSI Lookback and RSI Count’ to ‘Manual’ from ‘Auto’. This will give you control over the ‘Optimal RSI Lookback Length’ and ‘RSI Count’ within the Settings. Please note, it will likely take some “fine tuning” to find working settings without the Indicator timing out, but there are definitely times you can find better settings than our ‘Auto’ will create; especially on higher Time Frames. The Minimum our ‘Auto’ will create is: Optimal RSI Lookback Length: 90 RSI Count: 20 The Maximum it will create is: Optimal RSI Lookback Length: 200 RSI Count: 30 If there isn’t much bar index history, for instance, if you’re on the 1 Day and the pair is BTC/USDT you’ll get < 4000 Bar Indexes worth of data. For this reason it is possible to manually increase the settings to say: Optimal RSI Lookback Length: 500 RSI Count: 50 But, please note, if you make it too high, it may also lead to inaccuracies. We will conclude our Tutorial here, hopefully this has given you some insight as to how calculating our Optimal RSI and then using it within Machine Learning may create a more adaptive RSI. Settings: Optimal RSI: Show Crossing Signals: Display signals where the RSI and RSI Cross. Show Tables: Display Information Tables to show information like, Optimal RSI Length, Best Profit, New Optimal RSI Lookback Length and New RSI Count. Show Bollinger Bands: Show RSI Bollinger Bands. These bands work like the TDI Indicator, except its length changes as it uses the current RSI Optimal Length. Optimal RSI Type: This is how we calculate our Optimal RSI. Do we use all RSI and RSI MA Crossings or just when it crosses within the Extremities. Adjust Optimal RSI Lookback and RSI Count: Auto means the script will automatically adjust the Optimal RSI Lookback Length and RSI Count based on the current Time Frame and Bar Index's on chart. This will attempt to stop the script from 'Taking too long to Execute'. Manual means you have full control of the Optimal RSI Lookback Length and RSI Count. Optimal RSI Lookback Length: How far back are we looking to see which RSI length is optimal? Please note the more bars the lower this needs to be. For instance with BTC/USDT you can use 500 here on 1D but only 200 for 15 Minutes; otherwise it will timeout. RSI Count: How many lengths are we checking? For instance, if our 'RSI Minimum Length' is 4 and this is 30, the valid RSI lengths we check is 4-34. RSI Minimum Length: What is the RSI length we start our scans at? We are capped with RSI Count otherwise it will cause the Indicator to timeout, so we don't want to waste any processing power on irrelevant lengths. RSI MA Length: What length are we using to calculate the optimal RSI cross' and likewise plot our RSI MA with? Extremity Crossings RSI Backup Length: When there is no Optimal RSI (if using Extremity Crossings), which RSI should we use instead? Machine Learning: Use Rational Quadratics: Rationalizing our Close may be beneficial for usage within ML calculations. Filter RSI and RSI MA: Should we filter the RSI's before usage in ML calculations? Essentially should we only use RSI data that are of the same type as our Optimal RSI? For instance if our Optimal RSI is Bullish (RSI > RSI MA), should we only use ML RSI's that are likewise bullish? Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None? KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using. Machine Learning Length: How far back is our Machine Learning going to keep data for. k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for? Fast ML Data Length: What is our Fast ML Length? This is used with our Slow Length to create our KNN Distance. Slow ML Data Length: What is our Slow ML Length? This is used with our Fast Length to create our KNN Distance. If you have any questions, comments, ideas or concerns please don't hesitate to contact us. HAPPY TRADING!
Indikator Pine Script®
oleh YinYangAlgorithms
24
arraysLibrary "arrays" Supplementary array methods. method delete(arr, index)   remove int object from array of integers at specific index   Namespace types: array   Parameters:      arr (array) : int array      index (int) : index at which int object need to be removed   Returns: void method delete(arr, index)   remove float object from array of float at specific index   Namespace types: array   Parameters:      arr (array) : float array      index (int) : index at which float object need to be removed   Returns: float method delete(arr, index)   remove bool object from array of bool at specific index   Namespace types: array   Parameters:      arr (array) : bool array      index (int) : index at which bool object need to be removed   Returns: bool method delete(arr, index)   remove string object from array of string at specific index   Namespace types: array   Parameters:      arr (array) : string array      index (int) : index at which string object need to be removed   Returns: string method delete(arr, index)   remove color object from array of color at specific index   Namespace types: array   Parameters:      arr (array) : color array      index (int) : index at which color object need to be removed   Returns: color method delete(arr, index)   remove chart.point object from array of chart.point at specific index   Namespace types: array   Parameters:      arr (array) : chart.point array      index (int) : index at which chart.point object need to be removed   Returns: void method delete(arr, index)   remove line object from array of lines at specific index and deletes the line   Namespace types: array   Parameters:      arr (array) : line array      index (int) : index at which line object need to be removed and deleted   Returns: void method delete(arr, index)   remove label object from array of labels at specific index and deletes the label   Namespace types: array   Parameters:      arr (array) : label array      index (int) : index at which label object need to be removed and deleted   Returns: void method delete(arr, index)   remove box object from array of boxes at specific index and deletes the box   Namespace types: array   Parameters:      arr (array) : box array      index (int) : index at which box object need to be removed and deleted   Returns: void method delete(arr, index)   remove table object from array of tables at specific index and deletes the table   Namespace types: array   Parameters:      arr (array) : table array      index (int) : index at which table object need to be removed and deleted   Returns: void method delete(arr, index)   remove linefill object from array of linefills at specific index and deletes the linefill   Namespace types: array   Parameters:      arr (array) : linefill array      index (int) : index at which linefill object need to be removed and deleted   Returns: void method delete(arr, index)   remove polyline object from array of polylines at specific index and deletes the polyline   Namespace types: array   Parameters:      arr (array) : polyline array      index (int) : index at which polyline object need to be removed and deleted   Returns: void method popr(arr)   remove last int object from array   Namespace types: array   Parameters:      arr (array) : int array   Returns: int method popr(arr)   remove last float object from array   Namespace types: array   Parameters:      arr (array) : float array   Returns: float method popr(arr)   remove last bool object from array   Namespace types: array   Parameters:      arr (array) : bool array   Returns: bool method popr(arr)   remove last string object from array   Namespace types: array   Parameters:      arr (array) : string array   Returns: string method popr(arr)   remove last color object from array   Namespace types: array   Parameters:      arr (array) : color array   Returns: color method popr(arr)   remove last chart.point object from array   Namespace types: array   Parameters:      arr (array) : chart.point array   Returns: void method popr(arr)   remove and delete last line object from array   Namespace types: array   Parameters:      arr (array) : line array   Returns: void method popr(arr)   remove and delete last label object from array   Namespace types: array   Parameters:      arr (array) : label array   Returns: void method popr(arr)   remove and delete last box object from array   Namespace types: array   Parameters:      arr (array) : box array   Returns: void method popr(arr)   remove and delete last table object from array   Namespace types: array   Parameters:      arr (array) : table array   Returns: void method popr(arr)   remove and delete last linefill object from array   Namespace types: array   Parameters:      arr (array) : linefill array   Returns: void method popr(arr)   remove and delete last polyline object from array   Namespace types: array   Parameters:      arr (array) : polyline array   Returns: void method shiftr(arr)   remove first int object from array   Namespace types: array   Parameters:      arr (array) : int array   Returns: int method shiftr(arr)   remove first float object from array   Namespace types: array   Parameters:      arr (array) : float array   Returns: float method shiftr(arr)   remove first bool object from array   Namespace types: array   Parameters:      arr (array) : bool array   Returns: bool method shiftr(arr)   remove first string object from array   Namespace types: array   Parameters:      arr (array) : string array   Returns: string method shiftr(arr)   remove first color object from array   Namespace types: array   Parameters:      arr (array) : color array   Returns: color method shiftr(arr)   remove first chart.point object from array   Namespace types: array   Parameters:      arr (array) : chart.point array   Returns: void method shiftr(arr)   remove and delete first line object from array   Namespace types: array   Parameters:      arr (array) : line array   Returns: void method shiftr(arr)   remove and delete first label object from array   Namespace types: array   Parameters:      arr (array) : label array   Returns: void method shiftr(arr)   remove and delete first box object from array   Namespace types: array   Parameters:      arr (array) : box array   Returns: void method shiftr(arr)   remove and delete first table object from array   Namespace types: array   Parameters:      arr (array) : table array   Returns: void method shiftr(arr)   remove and delete first linefill object from array   Namespace types: array   Parameters:      arr (array) : linefill array   Returns: void method shiftr(arr)   remove and delete first polyline object from array   Namespace types: array   Parameters:      arr (array) : polyline array   Returns: void method push(arr, val, maxItems)   add int to the end of an array with max items cap. Objects are removed from start to maintain max items cap   Namespace types: array   Parameters:      arr (array) : int array      val (int) : int object to be pushed      maxItems (int) : max number of items array can hold   Returns: int method push(arr, val, maxItems)   add float to the end of an array with max items cap. Objects are removed from start to maintain max items cap   Namespace types: array   Parameters:      arr (array) : float array      val (float) : float object to be pushed      maxItems (int) : max number of items array can hold   Returns: float method push(arr, val, maxItems)   add bool to the end of an array with max items cap. Objects are removed from start to maintain max items cap   Namespace types: array   Parameters:      arr (array) : bool array      val (bool) : bool object to be pushed      maxItems (int) : max number of items array can hold   Returns: bool method push(arr, val, maxItems)   add string to the end of an array with max items cap. Objects are removed from start to maintain max items cap   Namespace types: array   Parameters:      arr (array) : string array      val (string) : string object to be pushed      maxItems (int) : max number of items array can hold   Returns: string method push(arr, val, maxItems)   add color to the end of an array with max items cap. Objects are removed from start to maintain max items cap   Namespace types: array   Parameters:      arr (array) : color array      val (color) : color object to be pushed      maxItems (int) : max number of items array can hold   Returns: color method push(arr, val, maxItems)   add chart.point to the end of an array with max items cap. Objects are removed and deleted from start to maintain max items cap   Namespace types: array   Parameters:      arr (array) : chart.point array      val (chart.point) : chart.point object to be pushed      maxItems (int) : max number of items array can hold   Returns: chart.point method push(arr, val, maxItems)   add line to the end of an array with max items cap. Objects are removed and deleted from start to maintain max items cap   Namespace types: array   Parameters:      arr (array) : line array      val (line) : line object to be pushed      maxItems (int) : max number of items array can hold   Returns: line method push(arr, val, maxItems)   add label to the end of an array with max items cap. Objects are removed and deleted from start to maintain max items cap   Namespace types: array   Parameters:      arr (array) : label array      val (label) : label object to be pushed      maxItems (int) : max number of items array can hold   Returns: label method push(arr, val, maxItems)   add box to the end of an array with max items cap. Objects are removed and deleted from start to maintain max items cap   Namespace types: array   Parameters:      arr (array) : box array      val (box) : box object to be pushed      maxItems (int) : max number of items array can hold   Returns: box method push(arr, val, maxItems)   add table to the end of an array with max items cap. Objects are removed and deleted from start to maintain max items cap   Namespace types: array   Parameters:      arr (array) : table array      val (table) : table object to be pushed      maxItems (int) : max number of items array can hold   Returns: table method push(arr, val, maxItems)   add linefill to the end of an array with max items cap. Objects are removed and deleted from start to maintain max items cap   Namespace types: array   Parameters:      arr (array) : linefill array      val (linefill) : linefill object to be pushed      maxItems (int) : max number of items array can hold   Returns: linefill method push(arr, val, maxItems)   add polyline to the end of an array with max items cap. Objects are removed and deleted from start to maintain max items cap   Namespace types: array   Parameters:      arr (array) : polyline array      val (polyline) : polyline object to be pushed      maxItems (int) : max number of items array can hold   Returns: polyline method unshift(arr, val, maxItems)   add int to the beginning of an array with max items cap. Objects are removed from end to maintain max items cap   Namespace types: array   Parameters:      arr (array) : int array      val (int) : int object to be unshift      maxItems (int) : max number of items array can hold   Returns: int method unshift(arr, val, maxItems)   add float to the beginning of an array with max items cap. Objects are removed from end to maintain max items cap   Namespace types: array   Parameters:      arr (array) : float array      val (float) : float object to be unshift      maxItems (int) : max number of items array can hold   Returns: float method unshift(arr, val, maxItems)   add bool to the beginning of an array with max items cap. Objects are removed from end to maintain max items cap   Namespace types: array   Parameters:      arr (array) : bool array      val (bool) : bool object to be unshift      maxItems (int) : max number of items array can hold   Returns: bool method unshift(arr, val, maxItems)   add string to the beginning of an array with max items cap. Objects are removed from end to maintain max items cap   Namespace types: array   Parameters:      arr (array) : string array      val (string) : string object to be unshift      maxItems (int) : max number of items array can hold   Returns: string method unshift(arr, val, maxItems)   add color to the beginning of an array with max items cap. Objects are removed from end to maintain max items cap   Namespace types: array   Parameters:      arr (array) : color array      val (color) : color object to be unshift      maxItems (int) : max number of items array can hold   Returns: color method unshift(arr, val, maxItems)   add chart.point to the beginning of an array with max items cap. Objects are removed and deleted from end to maintain max items cap   Namespace types: array   Parameters:      arr (array) : chart.point array      val (chart.point) : chart.point object to be unshift      maxItems (int) : max number of items array can hold   Returns: chart.point method unshift(arr, val, maxItems)   add line to the beginning of an array with max items cap. Objects are removed and deleted from end to maintain max items cap   Namespace types: array   Parameters:      arr (array) : line array      val (line) : line object to be unshift      maxItems (int) : max number of items array can hold   Returns: line method unshift(arr, val, maxItems)   add label to the beginning of an array with max items cap. Objects are removed and deleted from end to maintain max items cap   Namespace types: array   Parameters:      arr (array) : label array      val (label) : label object to be unshift      maxItems (int) : max number of items array can hold   Returns: label method unshift(arr, val, maxItems)   add box to the beginning of an array with max items cap. Objects are removed and deleted from end to maintain max items cap   Namespace types: array   Parameters:      arr (array) : box array      val (box) : box object to be unshift      maxItems (int) : max number of items array can hold   Returns: box method unshift(arr, val, maxItems)   add table to the beginning of an array with max items cap. Objects are removed and deleted from end to maintain max items cap   Namespace types: array   Parameters:      arr (array) : table array      val (table) : table object to be unshift      maxItems (int) : max number of items array can hold   Returns: table method unshift(arr, val, maxItems)   add linefill to the beginning of an array with max items cap. Objects are removed and deleted from end to maintain max items cap   Namespace types: array   Parameters:      arr (array) : linefill array      val (linefill) : linefill object to be unshift      maxItems (int) : max number of items array can hold   Returns: linefill method unshift(arr, val, maxItems)   add polyline to the beginning of an array with max items cap. Objects are removed and deleted from end to maintain max items cap   Namespace types: array   Parameters:      arr (array) : polyline array      val (polyline) : polyline object to be unshift      maxItems (int) : max number of items array can hold   Returns: polyline method isEmpty(arr)   checks if an int array is either null or empty   Namespace types: array   Parameters:      arr (array) : int array   Returns: bool method isEmpty(arr)   checks if a float array is either null or empty   Namespace types: array   Parameters:      arr (array) : float array   Returns: bool method isEmpty(arr)   checks if a string array is either null or empty   Namespace types: array   Parameters:      arr (array) : string array   Returns: bool method isEmpty(arr)   checks if a bool array is either null or empty   Namespace types: array   Parameters:      arr (array) : bool array   Returns: bool method isEmpty(arr)   checks if a color array is either null or empty   Namespace types: array   Parameters:      arr (array) : color array   Returns: bool method isEmpty(arr)   checks if a chart.point array is either null or empty   Namespace types: array   Parameters:      arr (array) : chart.point array   Returns: bool method isEmpty(arr)   checks if a line array is either null or empty   Namespace types: array   Parameters:      arr (array) : line array   Returns: bool method isEmpty(arr)   checks if a label array is either null or empty   Namespace types: array   Parameters:      arr (array) : label array   Returns: bool method isEmpty(arr)   checks if a box array is either null or empty   Namespace types: array   Parameters:      arr (array) : box array   Returns: bool method isEmpty(arr)   checks if a linefill array is either null or empty   Namespace types: array   Parameters:      arr (array) : linefill array   Returns: bool method isEmpty(arr)   checks if a polyline array is either null or empty   Namespace types: array   Parameters:      arr (array) : polyline array   Returns: bool method isEmpty(arr)   checks if a table array is either null or empty   Namespace types: array   Parameters:      arr (array) : table array   Returns: bool method isNotEmpty(arr)   checks if an int array is not null and has at least one item   Namespace types: array   Parameters:      arr (array) : int array   Returns: bool method isNotEmpty(arr)   checks if a float array is not null and has at least one item   Namespace types: array   Parameters:      arr (array) : float array   Returns: bool method isNotEmpty(arr)   checks if a string array is not null and has at least one item   Namespace types: array   Parameters:      arr (array) : string array   Returns: bool method isNotEmpty(arr)   checks if a bool array is not null and has at least one item   Namespace types: array   Parameters:      arr (array) : bool array   Returns: bool method isNotEmpty(arr)   checks if a color array is not null and has at least one item   Namespace types: array   Parameters:      arr (array) : color array   Returns: bool method isNotEmpty(arr)   checks if a chart.point array is not null and has at least one item   Namespace types: array   Parameters:      arr (array) : chart.point array   Returns: bool method isNotEmpty(arr)   checks if a line array is not null and has at least one item   Namespace types: array   Parameters:      arr (array) : line array   Returns: bool method isNotEmpty(arr)   checks if a label array is not null and has at least one item   Namespace types: array   Parameters:      arr (array) : label array   Returns: bool method isNotEmpty(arr)   checks if a box array is not null and has at least one item   Namespace types: array   Parameters:      arr (array) : box array   Returns: bool method isNotEmpty(arr)   checks if a linefill array is not null and has at least one item   Namespace types: array   Parameters:      arr (array) : linefill array   Returns: bool method isNotEmpty(arr)   checks if a polyline array is not null and has at least one item   Namespace types: array   Parameters:      arr (array) : polyline array   Returns: bool method isNotEmpty(arr)   checks if a table array is not null and has at least one item   Namespace types: array   Parameters:      arr (array) : table array   Returns: bool method flush(arr)   remove all int objects in an array   Namespace types: array   Parameters:      arr (array) : int array   Returns: int method flush(arr)   remove all float objects in an array   Namespace types: array   Parameters:      arr (array) : float array   Returns: float method flush(arr)   remove all bool objects in an array   Namespace types: array   Parameters:      arr (array) : bool array   Returns: bool method flush(arr)   remove all string objects in an array   Namespace types: array   Parameters:      arr (array) : string array   Returns: string method flush(arr)   remove all color objects in an array   Namespace types: array   Parameters:      arr (array) : color array   Returns: color method flush(arr)   remove all chart.point objects in an array   Namespace types: array   Parameters:      arr (array) : chart.point array   Returns: chart.point method flush(arr)   remove and delete all line objects in an array   Namespace types: array   Parameters:      arr (array) : line array   Returns: line method flush(arr)   remove and delete all label objects in an array   Namespace types: array   Parameters:      arr (array) : label array   Returns: label method flush(arr)   remove and delete all box objects in an array   Namespace types: array   Parameters:      arr (array) : box array   Returns: box method flush(arr)   remove and delete all table objects in an array   Namespace types: array   Parameters:      arr (array) : table array   Returns: table method flush(arr)   remove and delete all linefill objects in an array   Namespace types: array   Parameters:      arr (array) : linefill array   Returns: linefill method flush(arr)   remove and delete all polyline objects in an array   Namespace types: array   Parameters:      arr (array) : polyline array   Returns: polyline
Perpustakaan Pine Script®
oleh FunkyMax
Market Regime# MARKET REGIME IDENTIFICATION & TRADING SYSTEM ## Complete User Guide --- ## 📋 TABLE OF CONTENTS 1. (#overview) 2. (#regimes) 3. (#indicator-usage) 4. (#entry-signals) 5. (#exit-signals) 6. (#regime-strategies) 7. (#confluence) 8. (#backtesting) 9. (#optimization) 10. (#examples) --- ## OVERVIEW ### What This System Does This is a **complete market regime identification and trading system** that: 1. **Identifies 6 distinct market regimes** automatically 2. **Adapts trading tactics** to each regime 3. **Provides high-probability entry signals** with confluence scoring 4. **Shows optimal exit points** for each trade 5. **Can be backtested** to validate performance ### Two Components Provided 1. **Indicator** (`market_regime_indicator.pine`) - Visual regime identification - Entry/exit signals on chart - Dynamic support/resistance - Info tables with live data - Use for manual trading 2. **Strategy** (`market_regime_strategy.pine`) - Fully automated backtestable version - Same logic as indicator - Position sizing and risk management - Performance metrics - Use for backtesting and automation --- ## THE 6 MARKET REGIMES ### 1. 🟢 BULL TRENDING **Characteristics:** - Strong uptrend - Price above SMA50 and SMA200 - ADX > 25 (strong trend) - Higher highs and higher lows - DI+ > DI- (bullish momentum) **What It Means:** - Market has clear upward direction - Buyers in control - Pullbacks are buying opportunities - Strongest regime for long positions **How to Trade:** - ✅ **BUY dips to EMA20 or SMA20** - ✅ Enter when RSI < 60 on pullback - ✅ Hold through minor corrections - ❌ Don't short against the trend - ❌ Don't sell too early **Expected Behavior:** - Pullbacks are shallow (5-10%) - Bounces are strong - Support at moving averages holds - Volume increases on rallies --- ### 2. 🔴 BEAR TRENDING **Characteristics:** - Strong downtrend - Price below SMA50 and SMA200 - ADX > 25 (strong trend) - Lower highs and lower lows - DI- > DI+ (bearish momentum) **What It Means:** - Market has clear downward direction - Sellers in control - Rallies are selling opportunities - Strongest regime for short positions **How to Trade:** - ✅ **SELL rallies to EMA20 or SMA20** - ✅ Enter when RSI > 40 on bounce - ✅ Hold through minor bounces - ❌ Don't buy against the trend - ❌ Don't cover shorts too early **Expected Behavior:** - Rallies are weak (5-10%) - Selloffs are strong - Resistance at moving averages holds - Volume increases on declines --- ### 3. 🔵 BULL RANGING **Characteristics:** - Bullish bias but consolidating - Price near or above SMA50 - ADX < 20 (weak trend) - Trading in range - Choppy price action **What It Means:** - Uptrend is pausing - Accumulation phase - Support and resistance zones clear - Lower volatility **How to Trade:** - ✅ **BUY at support zone** - ✅ Enter when RSI < 40 - ✅ Take profits at resistance - ⚠️ Smaller position sizes - ⚠️ Tighter stops **Expected Behavior:** - Range-bound oscillations - Support bounces repeatedly - Resistance rejections common - Eventually breaks higher (usually) --- ### 4. 🟠 BEAR RANGING **Characteristics:** - Bearish bias but consolidating - Price near or below SMA50 - ADX < 20 (weak trend) - Trading in range - Choppy price action **What It Means:** - Downtrend is pausing - Distribution phase - Support and resistance zones clear - Lower volatility **How to Trade:** - ✅ **SELL at resistance zone** - ✅ Enter when RSI > 60 - ✅ Take profits at support - ⚠️ Smaller position sizes - ⚠️ Tighter stops **Expected Behavior:** - Range-bound oscillations - Resistance holds repeatedly - Support bounces are weak - Eventually breaks lower (usually) --- ### 5. ⚪ CONSOLIDATION **Characteristics:** - No clear direction - Range compression - Very low ADX (< 15 often) - Price inside tight range - Neutral sentiment **What It Means:** - Market is coiling - Building energy for next move - Indecision between buyers/sellers - Calm before the storm **How to Trade:** - ✅ **WAIT for breakout direction** - ✅ Enter on high-volume breakout - ✅ Direction becomes clear - ❌ Don't trade inside the range - ❌ Avoid choppy scalping **Expected Behavior:** - Narrow range - Low volume - False breakouts possible - Explosive move when it breaks --- ### 6. 🟣 CHAOS (High Volatility) **Characteristics:** - Extreme volatility - No clear direction - Erratic price swings - ATR > 2x average - Unpredictable **What It Means:** - Market panic or euphoria - News-driven moves - Emotion dominates logic - Highest risk environment **How to Trade:** - ❌ **STAY OUT!** - ❌ No positions - ❌ Wait for stability - ✅ Protect existing positions - ✅ Reduce risk **Expected Behavior:** - Large intraday swings - Gaps up/down - Stop hunts - Whipsaws - Eventually calms down --- ## INDICATOR USAGE ### Visual Elements #### 1. Background Colors - **Light Green** = Bull Trending (go long) - **Light Red** = Bear Trending (go short) - **Light Teal** = Bull Ranging (buy dips) - **Light Orange** = Bear Ranging (sell rallies) - **Light Gray** = Consolidation (wait) - **Purple** = Chaos (stay out!) #### 2. Regime Labels - Appear when regime changes - Show new regime name - Positioned at highs (bullish) or lows (bearish) #### 3. Entry Signals - **Green "LONG"** labels = Buy here - **Red "SHORT"** labels = Sell here - Number shows confluence score (X/5 signals) - Hover for details (stop, target, RSI, etc.) #### 4. Exit Signals - **Orange "EXIT LONG"** = Close long position - **Orange "EXIT SHORT"** = Close short position - Shows exit reason in tooltip #### 5. Support/Resistance Lines - **Green line** = Dynamic support (buy zone) - **Red line** = Dynamic resistance (sell zone) - Adapts to regime automatically #### 6. Moving Averages - **Blue** = SMA 20 (short-term trend) - **Orange** = SMA 50 (medium-term trend) - **Purple** = SMA 200 (long-term trend) ### Information Tables #### Top Right Table (Main Info) Shows real-time market conditions: - **Current Regime** - What regime we're in - **Bias** - Long, Short, Breakout, or Stay Out - **ADX** - Trend strength (>25 = strong) - **Trend** - Strong, Moderate, or Weak - **Volatility** - High or Normal - **Vol Ratio** - Current vs average volatility - **RSI** - Momentum (>70 overbought, <30 oversold) - **vs SMA50/200** - Price position relative to MAs - **Support/Resistance** - Exact price levels - **Long/Short Signals** - Confluence scores (X/5) #### Bottom Right Table (Regime Guide) Quick reference for each regime: - What action to take - What strategy to use - Color-coded for quick identification --- ## ENTRY SIGNALS EXPLAINED ### Confluence Scoring System (5 Factors) Each entry signal is scored 0-5 based on how many factors align: #### For LONG Entries: 1. ✅ **Regime Alignment** - In Bull Trending or Bull Ranging 2. ✅ **RSI Pullback** - RSI between 35-50 (not overbought) 3. ✅ **Near Support** - Price within 2% of dynamic support 4. ✅ **MACD Turning Up** - Momentum shifting bullish 5. ✅ **Volume Confirmation** - Above average volume #### For SHORT Entries: 1. ✅ **Regime Alignment** - In Bear Trending or Bear Ranging 2. ✅ **RSI Rejection** - RSI between 50-65 (not oversold) 3. ✅ **Near Resistance** - Price within 2% of dynamic resistance 4. ✅ **MACD Turning Down** - Momentum shifting bearish 5. ✅ **Volume Confirmation** - Above average volume ### Confluence Requirements **Minimum Confluence** (default = 2): - 2/5 = Entry signal triggered - 3/5 = Good signal - 4/5 = Strong signal - 5/5 = Excellent signal (rare) **Higher confluence = Higher probability = Better trades** ### Specific Entry Patterns #### 1. Bull Trending Entry ``` Requirements: - Regime = Bull Trending - Price pulls back to EMA20 - Close above EMA20 (bounce) - Up candle (close > open) - RSI < 60 - Confluence ≥ 2 ``` #### 2. Bear Trending Entry ``` Requirements: - Regime = Bear Trending - Price rallies to EMA20 - Close below EMA20 (rejection) - Down candle (close < open) - RSI > 40 - Confluence ≥ 2 ``` #### 3. Bull Ranging Entry ``` Requirements: - Regime = Bull Ranging - RSI < 40 (oversold) - Price at or below support - Up candle (reversal) - Confluence ≥ 1 (more lenient) ``` #### 4. Bear Ranging Entry ``` Requirements: - Regime = Bear Ranging - RSI > 60 (overbought) - Price at or above resistance - Down candle (rejection) - Confluence ≥ 1 (more lenient) ``` #### 5. Consolidation Breakout ``` Requirements: - Regime = Consolidation - Price breaks above/below range - Volume > 1.5x average (explosive) - Strong directional candle ``` --- ## EXIT SIGNALS EXPLAINED ### Three Types of Exits #### 1. Regime Change Exits (Automatic) - **Long Exit**: Regime changes to Bear Trending or Chaos - **Short Exit**: Regime changes to Bull Trending or Chaos - **Reason**: Market character changed, strategy no longer valid #### 2. Support/Resistance Break Exits - **Long Exit**: Price breaks below support by 2% - **Short Exit**: Price breaks above resistance by 2% - **Reason**: Key level violated, trend may be reversing #### 3. Momentum Exits - **Long Exit**: RSI > 70 (overbought) AND down candle - **Short Exit**: RSI < 30 (oversold) AND up candle - **Reason**: Overextension, take profits ### Stop Loss & Take Profit **Stop Loss** (Automatic in strategy): - Placed at Entry - (ATR × 2) - Adapts to volatility - Protected from whipsaws - Typically 2-4% for stocks, 5-10% for crypto **Take Profit** (Automatic in strategy): - Placed at Entry + (Stop Distance × R:R Ratio) - Default 2.5:1 reward:risk - Example: $2 risk = $5 reward target - Allows winners to run --- ## TRADING EACH REGIME ### BULL TRENDING - Most Profitable Long Environment **Strategy: Buy Every Dip** **Entry Rules:** 1. Wait for pullback to EMA20 or SMA20 2. Look for RSI < 60 3. Enter when candle closes above MA 4. Confluence should be 2+ **Stop Loss:** - Below the recent swing low - Or 2 × ATR below entry **Take Profit:** - At previous high - Or 2.5:1 R:R minimum **Position Size:** - Can use full size (2% risk) - High win rate regime **Example Trade:** ``` Price: $100, pulls back to $98 (EMA20) Entry: $98.50 (close above EMA) Stop: $96.50 (2 ATR) Target: $103.50 (2.5:1) Risk: $2, Reward: $5 ``` --- ### BEAR TRENDING - Most Profitable Short Environment **Strategy: Sell Every Rally** **Entry Rules:** 1. Wait for bounce to EMA20 or SMA20 2. Look for RSI > 40 3. Enter when candle closes below MA 4. Confluence should be 2+ **Stop Loss:** - Above the recent swing high - Or 2 × ATR above entry **Take Profit:** - At previous low - Or 2.5:1 R:R minimum **Position Size:** - Can use full size (2% risk) - High win rate regime **Example Trade:** ``` Price: $100, rallies to $102 (EMA20) Entry: $101.50 (close below EMA) Stop: $103.50 (2 ATR) Target: $96.50 (2.5:1) Risk: $2, Reward: $5 ``` --- ### BULL RANGING - Buy Low, Sell High **Strategy: Range Trading (Long Bias)** **Entry Rules:** 1. Wait for price at support zone 2. Look for RSI < 40 3. Enter on reversal candle 4. Confluence should be 1-2+ **Stop Loss:** - Below support zone - Tighter than trending (1.5 ATR) **Take Profit:** - At resistance zone - Don't hold through resistance **Position Size:** - Reduce to 1-1.5% risk - Lower win rate than trending **Example Trade:** ``` Range: $95-$105 Entry: $96 (at support, RSI 35) Stop: $94 (below support) Target: $104 (at resistance) Risk: $2, Reward: $8 (4:1) ``` --- ### BEAR RANGING - Sell High, Buy Low **Strategy: Range Trading (Short Bias)** **Entry Rules:** 1. Wait for price at resistance zone 2. Look for RSI > 60 3. Enter on rejection candle 4. Confluence should be 1-2+ **Stop Loss:** - Above resistance zone - Tighter than trending (1.5 ATR) **Take Profit:** - At support zone - Don't hold through support **Position Size:** - Reduce to 1-1.5% risk - Lower win rate than trending **Example Trade:** ``` Range: $95-$105 Entry: $104 (at resistance, RSI 65) Stop: $106 (above resistance) Target: $96 (at support) Risk: $2, Reward: $8 (4:1) ``` --- ### CONSOLIDATION - Wait for Breakout **Strategy: Breakout Trading** **Entry Rules:** 1. Identify consolidation range 2. Wait for VOLUME SURGE (1.5x+ avg) 3. Enter on close outside range 4. Direction must be clear **Stop Loss:** - Opposite side of range - Or 2 ATR **Take Profit:** - Measure range height, project it - Example: $10 range = $10 move expected **Position Size:** - Reduce to 1% risk - 50% false breakout rate **Example Trade:** ``` Consolidation: $98-$102 (4-point range) Breakout: $102.50 (high volume) Entry: $103 Stop: $100 (back in range) Target: $107 (4-point range projected) Risk: $3, Reward: $4 ``` --- ### CHAOS - STAY OUT! **Strategy: Preservation** **What to Do:** - ❌ NO new positions - ✅ Close existing positions if near entry - ✅ Tighten stops on profitable trades - ✅ Reduce position sizes dramatically - ✅ Wait for regime to stabilize **Why It's Dangerous:** - Stop hunts are common - Whipsaws everywhere - News-driven volatility - No technical reliability - Even "perfect" setups fail **When Does It End:** - Volatility ratio drops < 1.5 - ADX starts rising (direction appears) - Price respects support/resistance again - Usually 1-5 days --- ## CONFLUENCE SYSTEM ### How It Works The system scores each potential entry on 5 factors. More factors aligning = higher probability. ### Confluence Requirements by Regime **Trending Regimes** (strictest): - Minimum 2/5 required - 3/5 = Good - 4-5/5 = Excellent **Ranging Regimes** (moderate): - Minimum 1-2/5 required - 2/5 = Good - 3+/5 = Excellent **Consolidation** (breakout only): - Volume is most critical - Direction confirmation - Less confluence needed ### Adjusting Minimum Confluence **If too few signals:** - Lower from 2 to 1 - More trades, lower quality **If too many false signals:** - Raise from 2 to 3 - Fewer trades, higher quality **Recommendation:** - Start at 2 - Adjust based on win rate - Aim for 55-65% win rate --- ## STRATEGY BACKTESTING ### Loading the Strategy 1. Copy `market_regime_strategy.pine` 2. Open Pine Editor in TradingView 3. Paste and "Add to Chart" 4. Strategy Tester tab opens at bottom ### Initial Settings ``` Risk Per Trade: 2% ATR Stop Multiplier: 2.0 Reward:Risk Ratio: 2.5 Trade Longs: ✓ Trade Shorts: ✓ Trade Trending Only: ✗ (test both) Avoid Chaos: ✓ Minimum Confluence: 2 ``` ### What to Look For **Good Results:** - Win Rate: 50-60% - Profit Factor: 1.8-2.5 - Net Profit: Positive - Max Drawdown: <20% - Consistent equity curve **Warning Signs:** - Win Rate: <45% (too many losses) - Profit Factor: <1.5 (barely profitable) - Max Drawdown: >30% (too risky) - Erratic equity curve (unstable) ### Testing Different Regimes **Test 1: Trending Only** ``` Trade Trending Only: ✓ Result: Higher win rate, fewer trades ``` **Test 2: All Regimes** ``` Trade Trending Only: ✗ Result: More trades, potentially lower win rate ``` **Test 3: Long Only** ``` Trade Longs: ✓ Trade Shorts: ✗ Result: Works in bull markets ``` **Test 4: Short Only** ``` Trade Longs: ✗ Trade Shorts: ✓ Result: Works in bear markets ``` --- ## SETTINGS OPTIMIZATION ### Key Parameters to Adjust #### 1. Risk Per Trade (Most Important) - **0.5%** = Very conservative - **1.0%** = Conservative (recommended for beginners) - **2.0%** = Moderate (recommended) - **3.0%** = Aggressive - **5.0%** = Very aggressive (not recommended) **Impact:** Higher risk = higher returns BUT bigger drawdowns #### 2. Reward:Risk Ratio - **2:1** = More wins needed, hit target faster - **2.5:1** = Balanced (recommended) - **3:1** = Fewer wins needed, hold longer - **4:1** = Very patient, best in trending **Impact:** Higher R:R = can have lower win rate #### 3. Minimum Confluence - **1** = More signals, lower quality - **2** = Balanced (recommended) - **3** = Fewer signals, higher quality - **4** = Very selective - **5** = Almost never triggers **Impact:** Higher = fewer but better trades #### 4. ADX Thresholds - **Trending: 20-30** (default 25) - Lower = detect trends earlier - Higher = only strong trends - **Ranging: 15-25** (default 20) - Lower = identify ranging earlier - Higher = only weak trends #### 5. Trend Period (SMA) - **20-50** = Short-term trends - **50** = Medium-term (default, recommended) - **100-200** = Long-term trends **Impact:** Longer period = slower regime changes, more stable ### Optimization Workflow **Step 1: Baseline** - Use all default settings - Test on 3+ years - Record: Win Rate, PF, Drawdown **Step 2: Risk Optimization** - Test 1%, 1.5%, 2%, 2.5% - Find best risk-adjusted return - Balance profit vs drawdown **Step 3: R:R Optimization** - Test 2:1, 2.5:1, 3:1 - Check which maximizes profit factor - Consider holding time **Step 4: Confluence Optimization** - Test 1, 2, 3 - Find sweet spot for win rate - Aim for 55-65% win rate **Step 5: Regime Filter** - Test with/without trend filter - Test with/without chaos filter - Find what works for your asset --- ## REAL TRADING EXAMPLES ### Example 1: Bull Trending - SPY **Setup:** - Regime: BULL TRENDING - Price pulls back from $450 to $445 - EMA20 at $444 - RSI drops to 45 - Confluence: 4/5 **Entry:** - Price closes at $445.50 (above EMA20) - LONG signal appears - Enter at $445.50 **Risk Management:** - Stop: $443 (2 ATR = $2.50) - Target: $451.75 (2.5:1 = $6.25) - Risk: $2.50 per share - Position: 80 shares (2% of $10k = $200 risk) **Outcome:** - Price rallies to $452 in 3 days - Target hit - Profit: $6.50 × 80 = $520 - Return: 2.6 × risk (excellent) --- ### Example 2: Bear Ranging - AAPL **Setup:** - Regime: BEAR RANGING - Range: $165-$175 - Price rallies to $174 - Resistance at $175 - RSI at 68 - Confluence: 3/5 **Entry:** - Rejection candle at $174 - SHORT signal appears - Enter at $173.50 **Risk Management:** - Stop: $176 (above resistance) - Target: $166 (support) - Risk: $2.50 - Position: 80 shares **Outcome:** - Price drops to $167 in 2 days - Target hit - Profit: $6.50 × 80 = $520 - Return: 2.6 × risk --- ### Example 3: Consolidation Breakout - BTC **Setup:** - Regime: CONSOLIDATION - Range: $28,000 - $30,000 - Compressed for 2 weeks - Volume declining **Breakout:** - Price breaks $30,000 - Volume surges 200% - Close at $30,500 - LONG signal **Entry:** - Enter at $30,500 **Risk Management:** - Stop: $29,500 (back in range) - Target: $32,000 (range height = $2k) - Risk: $1,000 - Position: 0.2 BTC ($200 risk on $10k) **Outcome:** - Price runs to $33,000 - Target exceeded - Profit: $2,500 × 0.2 = $500 - Return: 2.5 × risk --- ### Example 4: Avoiding Chaos - Tesla **Setup:** - Regime: BULL TRENDING - LONG position from $240 - Elon tweets something crazy - Regime changes to CHAOS **Action:** - EXIT signal appears - Close position immediately - Current price: $242 (small profit) **Outcome:** - Next 3 days: wild swings - High $255, Low $230 - By staying out, avoided: - Potential stop out - Whipsaw losses - Stress **Result:** - Small profit preserved - Capital protected - Re-enter when regime stabilizes --- ## ALERTS SETUP ### Available Alerts 1. **Bull Trending Regime** - Market goes bullish 2. **Bear Trending Regime** - Market goes bearish 3. **Chaos Regime** - High volatility, stay out 4. **Long Entry Signal** - Buy opportunity 5. **Short Entry Signal** - Sell opportunity 6. **Long Exit Signal** - Close long 7. **Short Exit Signal** - Close short ### How to Set Up 1. Click **⏰ (Alert)** icon in TradingView 2. Select **Condition**: Choose indicator + alert type 3. **Options**: Popup, Email, Webhook, etc. 4. **Message**: Customize notification 5. Click **Create** ### Recommended Alert Strategy **For Active Traders:** - Long Entry Signal - Short Entry Signal - Long Exit Signal - Short Exit Signal **For Position Traders:** - Bull Trending Regime (enter longs) - Bear Trending Regime (enter shorts) - Chaos Regime (exit all) **For Conservative:** - Only regime change alerts - Manually review entries - More selective --- ## TIPS FOR SUCCESS ### 1. Start Small - Paper trade first - Then 0.5% risk - Build to 1-2% over time ### 2. Follow the Regime - Don't fight it - Adapt your style - Different tactics for each ### 3. Trust the Confluence - 4-5/5 = Best trades - 2-3/5 = Good trades - 1/5 = Skip unless desperate ### 4. Respect Exits - Don't hope and hold - Cut losses quickly - Take profits at targets ### 5. Avoid Chaos - Seriously, just stay out - Protect your capital - Wait for clarity ### 6. Keep a Journal - Record every trade - Note regime and confluence - Review weekly - Learn patterns ### 7. Backtest Thoroughly - 3+ years minimum - Multiple market conditions - Different assets - Walk-forward test ### 8. Be Patient - Best setups are rare - 1-3 trades per week is normal - Quality over quantity - Compound over time --- ## COMMON QUESTIONS **Q: How many trades per month should I expect?** A: Depends on timeframe and settings. Daily chart: 5-15 trades/month. 4H chart: 15-30 trades/month. **Q: What's a good win rate?** A: 55-65% is excellent. 50-55% is good. Below 50% needs adjustment. **Q: Should I trade all regimes?** A: Beginners: Only trending. Intermediate: Trending + ranging. Advanced: All except chaos. **Q: Can I use this on any timeframe?** A: Best on Daily and 4H. Works on 1H with more noise. Not recommended <1H. **Q: What if I'm in a trade and regime changes?** A: Exit immediately (if using indicator) or let strategy handle it automatically. **Q: How do I know if I'm over-optimizing?** A: If results are perfect on one period but fail on another. Use walk-forward testing. **Q: Should I always take 5/5 confluence trades?** A: Yes, but they're rare (1-2/month). Don't wait only for these. **Q: Can I combine this with other indicators?** A: Yes, but keep it simple. RSI, MACD already included. Maybe add volume profile. **Q: What assets work best?** A: Liquid stocks, major crypto, futures. Avoid forex spot (use futures), penny stocks. **Q: How long to hold positions?** A: Trending: Days to weeks. Ranging: Hours to days. Breakout: Days. Let the regime guide you. --- ## FINAL THOUGHTS This system gives you: - ✅ Clear market context (regime) - ✅ High-probability entries (confluence) - ✅ Defined exits (automatic signals) - ✅ Adaptable tactics (regime-specific) - ✅ Backtestable results (strategy version) **Success requires:** - 📚 Understanding each regime - 🎯 Following the signals - 💪 Discipline to wait - 🧠 Emotional control - 📊 Proper risk management **Start your journey:** 1. Load the indicator 2. Watch for 1 week (no trading) 3. Identify regime patterns 4. Paper trade for 1 month 5. Go live with small size 6. Scale up as you gain confidence **Remember:** The market will always be here. There's no rush. Master one regime at a time, and you'll be profitable in all conditions! Good luck! 🚀
Indikator Pine Script®
oleh frankieho_92
SMC N-Gram Probability Matrix [PhenLabs]📊 SMC N-Gram Probability Matrix Version: PineScript™ v6 📌 Description The SMC N-Gram Probability Matrix applies computational linguistics methodology to Smart Money Concepts trading. By treating SMC patterns as a discrete “alphabet” and analyzing their sequential relationships through N-gram modeling, this indicator calculates the statistical probability of which pattern will appear next based on historical transitions. Traditional SMC analysis is reactive—traders identify patterns after they form and then anticipate the next move. This indicator inverts that approach by building a transition probability matrix from up to 5,000 bars of pattern history, enabling traders to see which SMC formations most frequently follow their current market sequence. The indicator detects and classifies 11 distinct SMC patterns including Fair Value Gaps, Order Blocks, Liquidity Sweeps, Break of Structure, and Change of Character in both bullish and bearish variants, then tracks how these patterns transition from one to another over time. 🚀 Points of Innovation First indicator to apply N-gram sequence modeling from computational linguistics to SMC pattern analysis Dynamic transition matrix rebuilds every 50 bars for adaptive probability calculations Supports bigram (2), trigram (3), and quadgram (4) sequence lengths for varying analysis depth Priority-based pattern classification ensures higher-significance patterns (CHoCH, BOS) take precedence Configurable minimum occurrence threshold filters out statistically insignificant predictions Real-time probability visualization with graphical confidence bars 🔧 Core Components Pattern Alphabet System: 11 discrete SMC patterns encoded as integers for efficient matrix indexing and transition tracking Swing Point Detection: Uses ta.pivothigh/pivotlow with configurable sensitivity for non-repainting structure identification Transition Count Matrix: Flattened array storing occurrence counts for all possible pattern sequence transitions Context Encoder: Converts N-gram pattern sequences into unique integer IDs for matrix lookup Probability Calculator: Transforms raw transition counts into percentage probabilities for each possible next pattern 🔥 Key Features Multi-Pattern SMC Detection: Simultaneously identifies FVGs, Order Blocks, Liquidity Sweeps, BOS, and CHoCH formations Adjustable N-Gram Length: Choose between 2-4 pattern sequences to balance specificity against sample size Flexible Lookback Range: Analyze anywhere from 100 to 5,000 historical bars for matrix construction Pattern Toggle Controls: Enable or disable individual SMC pattern types to customize analysis focus Probability Threshold Filtering: Set minimum occurrence requirements to ensure prediction reliability Alert Integration: Built-in alert conditions trigger when high-probability predictions emerge 🎨 Visualization Probability Table: Displays current pattern, recent sequence, sample count, and top N predicted patterns with percentage probabilities Graphical Probability Bars: Visual bar representation (█░) showing relative probability strength at a glance Chart Pattern Markers: Color-coded labels placed directly on price bars identifying detected SMC formations Pattern Short Codes: Compact notation (F+, F-, O+, O-, L↑, L↓, B+, B-, C+, C-) for quick pattern identification Customizable Table Position: Place probability display in any corner of your chart 📖 Usage Guidelines N-Gram Configuration N-Gram Length: Default 2, Range 2-4. Lower values provide more samples but less specificity. Higher values capture complex sequences but require more historical data. Matrix Lookback Bars: Default 500, Range 100-5000. More bars increase statistical significance but may include outdated market behavior. Min Occurrences for Prediction: Default 2, Range 1-10. Higher values filter noise but may reduce prediction availability. SMC Detection Settings Swing Detection Length: Default 5, Range 2-20. Controls pivot sensitivity for structure analysis. FVG Minimum Size: Default 0.1%, Range 0.01-2.0%. Filters insignificant gaps. Order Block Lookback: Default 10, Range 3-30. Bars to search for OB formations. Liquidity Sweep Threshold: Default 0.3%, Range 0.05-1.0%. Minimum wick extension beyond swing points. Display Settings Show Probability Table: Toggle the probability matrix display on/off. Show Top N Probabilities: Default 5, Range 3-10. Number of predicted patterns to display. Show SMC Markers: Toggle on-chart pattern labels. ✅ Best Use Cases Anticipating continuation or reversal patterns after liquidity sweeps Identifying high-probability BOS/CHoCH sequences for trend trading Filtering FVG and Order Block signals based on historical follow-through rates Building confluence by comparing predicted patterns with other technical analysis Studying how SMC patterns typically sequence on specific instruments or timeframes ⚠️ Limitations Predictions are based solely on historical pattern frequency and do not account for fundamental factors Low sample counts produce unreliable probabilities—always check the Samples display Market regime changes can invalidate historical transition patterns The indicator requires sufficient historical data to build meaningful probability matrices Pattern detection uses standardized parameters that may not capture all institutional activity 💡 What Makes This Unique Linguistic Modeling Applied to Markets: Treats SMC patterns like words in a language, analyzing how they “flow” together Quantified Pattern Relationships: Transforms subjective SMC analysis into objective probability percentages Adaptive Learning: Matrix rebuilds periodically to incorporate recent pattern behavior Comprehensive SMC Coverage: Tracks all major Smart Money Concepts in a unified probability framework 🔬 How It Works 1. Pattern Detection Phase Each bar is analyzed for SMC formations using configurable detection parameters A priority hierarchy assigns the most significant pattern when multiple detections occur 2. Sequence Encoding Phase Detected patterns are stored in a rolling history buffer of recent classifications The current N-gram context is encoded into a unique integer identifier 3. Matrix Construction Phase Historical pattern sequences are iterated to count transition occurrences Each context-to-next-pattern transition increments the appropriate matrix cell 4. Probability Calculation Phase Current context ID retrieves corresponding transition counts from the matrix Raw counts are converted to percentages based on total context occurrences 5. Visualization Phase Probabilities are sorted and the top N predictions are displayed in the table Chart markers identify the current detected pattern for visual reference 💡 Note: This indicator performs best when used as a confluence tool alongside traditional SMC analysis. The probability predictions highlight statistically common pattern sequences but should not be used as standalone trading signals. Always verify predictions against price action context, higher timeframe structure, and your overall trading plan. Monitor the sample count to ensure predictions are based on adequate historical data.
Indikator Pine Script®
oleh PhenLabs
Technology Stocks RSPSTechnology Stocks RSPS Indicator - TradingView Description Overview The Technology Stocks RSPS (Relative Strength Portfolio System) indicator is a sophisticated portfolio allocation tool designed specifically for technology sector stocks. It calculates relative strength positions and provides dynamic allocation recommendations based on technical price momentum analysis. Key Features - Relative Strength Analysis: Compares 15 major technology stocks and the XLK sector ETF against each other and gold as a baseline - Dynamic Portfolio Allocation: Automatically calculates optimal position sizes based on relative performance - Visual Portfolio Performance: Tracks cumulative portfolio returns with color-coded performance indicators - Customizable Table Display: Shows real-time allocation percentages and optional cash values for each position - Technical Momentum Filtering: Uses normalized indicators to identify strength and filter out weak positions Included Assets Sector ETF: XLK Major Tech Stocks: AAPL, MSFT, NVDA, AVGO, CRM, ORCL, CSCO, ADBE, ACN, AMD, IBM, INTC, NOW, TXN Benchmark: Gold (TVC:GOLD) How It Works The indicator calculates a relative strength score for each asset by comparing it against: Gold (baseline commodity) All other technology stocks in the pool The XLK sector ETF Assets with positive relative strength receive portfolio allocations proportional to their strength scores. Weak or negative performers are automatically filtered out (allocated 0%). Visual Elements Red Area: Aggregate strength of major technology stocks Navy Blue Area: Overall technical positioning index (TPI) Performance Line: Cumulative portfolio return (blue = cash-heavy, red = equity-heavy) Allocation Table: Bottom-left display showing current recommended positions Important Limitations This indicator primarily uses technical data and has significant limitations: ❌ No fundamental economic data (ISM, CLI, etc.) ❌ Limited monetary data - missing critical components: comprehensive monetary data Funding rates Detailed bond spreads analysis Collateral data ❌ No sentiment indicators ❌ No options flow or derivatives data ❌ No earnings or valuation metrics The indicator focuses purely on price-based relative strength and technical momentum. Users should combine this tool with fundamental analysis, economic data, and proper risk management for complete investment decisions. Settings Plot Table: Toggle allocation table visibility Use Cash: Enable to display dollar amounts based on portfolio size Cash Amount: Set your total portfolio value for cash allocation calculations Use Cases Sector rotation within technology stocks Relative strength-based portfolio rebalancing Technical momentum screening for tech sector Dynamic position sizing based on price trends Technical Notes The script avoids for-loops to reduce calculation errors and noise Uses semi-individual calculations for each asset Requires the Unicorpus/NormalizedIndicators/1 library for normalized momentum calculations Maximum lookback: 100 bars Disclaimer: This indicator is a technical tool only and should not be used as the sole basis for investment decisions. It does not incorporate fundamental, economic, or comprehensive monetary data. Always conduct thorough research and consider your risk tolerance before making investment decisions.
Indikator Pine Script®
oleh Unicorpus
LapseBacktestingTableLibrary "LapseBacktestingMetrics" This library provides a robust set of quantitative backtesting and performance evaluation functions for Pine Script strategies. It’s designed to help traders, quants, and developers assess risk, return, and robustness through detailed statistical metrics — including Sharpe, Sortino, Omega, drawdowns, and trade efficiency. Built to enhance any trading strategy’s evaluation framework, this library allows you to visualize performance with the quantlapseTable() function, producing an interactive on-chart performance table. Credit to EliCobra and BikeLife76 for original concept inspiration. curve(disp_ind)   Retrieves a selected performance curve of your strategy.   Parameters:     disp_ind (simple string): Type of curve to plot. Options include "Equity", "Open Profit", "Net Profit", "Gross Profit".   Returns: (float) Corresponding performance curve value. cleaner(disp_ind, plot)   Filters and displays selected strategy plots for clean visualization.   Parameters:     disp_ind (simple string): Type of display.     plot (simple float): Strategy plot variable.   Returns: (float) Filtered plot value. maxEquityDrawDown()   Calculates the maximum equity drawdown during the strategy’s lifecycle.   Returns: (float) Maximum equity drawdown percentage. maxTradeDrawDown()   Computes the worst intra-trade drawdown among all closed trades.   Returns: (float) Maximum intra-trade drawdown percentage. consecutive_wins()   Finds the highest number of consecutive winning trades.   Returns: (int) Maximum consecutive wins. consecutive_losses()   Finds the highest number of consecutive losing trades.   Returns: (int) Maximum consecutive losses. no_position()   Counts the maximum consecutive bars where no position was held.   Returns: (int) Maximum flat days count. long_profit()   Calculates total profit generated by long positions as a percentage of initial capital.   Returns: (float) Total long profit %. short_profit()   Calculates total profit generated by short positions as a percentage of initial capital.   Returns: (float) Total short profit %. prev_month()   Measures the previous month’s profit or loss based on equity change.   Returns: (float) Monthly equity delta. w_months()   Counts the number of profitable months in the backtest.   Returns: (int) Total winning months. l_months()   Counts the number of losing months in the backtest.   Returns: (int) Total losing months. checktf()   Returns the time-adjusted scaling factor used in Sharpe and Sortino ratio calculations based on chart timeframe.   Returns: (float) Annualization multiplier. stat_calc()   Performs complete statistical computation including drawdowns, Sharpe, Sortino, Omega, trade stats, and profit ratios.   Returns: (array)      . f_colors(x, nv)   Generates a color gradient for performance values, supporting dynamic table visualization.   Parameters:     x (simple string): Metric label name.     nv (simple float): Metric numerical value.   Returns: (color) Gradient color value for table background. quantlapseTable(option, position)   Displays an interactive Performance Table summarizing all major backtesting metrics.   Includes Sharpe, Sortino, Omega, Profit Factor, drawdowns, profitability %, and trade statistics.   Parameters:     option (simple string): Table type — "Full", "Simple", or "None".     position (simple string): Table position — "Top Left", "Middle Right", "Bottom Left", etc.   Returns: (table) On-chart performance visualization table. This library empowers advanced quantitative evaluation directly within Pine Script®, ideal for strategy developers seeking deeper performance diagnostics and intuitive on-chart metrics.
Perpustakaan Pine Script®
oleh QuantLapse
ka66: Symbol InformationThis shows a table of all current (Pine v6) `syminfo.` values. Please note this is primarily of use to Pine Developers, or the curious trader. There are a few of these around on TradingView, but many seem to focus on the use case they have. This script just dumps all values, in alphabetical order of properties. You can use this to inspect the details of the symbol, which in turn, can be fed into various scripts covering tasks such as: Position Sizing calculation (which requires things like tick, pointvalue, and currency details) Recommendation engines (which use the recommendation_* properties) Fundamentals on stocks (which may use share count information, and possibly employee information) Note that not all table values are populated, as they depend on the instrument being introspected. For example, a share ticker will have some different details to a Forex pair. The `NaN` values (the "Not A Number" special value in programming parlance) are not a bug, they are simply Pine reporting that no value is set for it. I have opted to dump out values as-is as the focus is developers. My motivation to create it was to write a position sizing tool. Additionally, the output of this script is cleanly formatted, with monospace fonts and conventional alignment for tables/forms with key and values. It also allows customising the table position. Ideally this feature is made part of the default TradingView customisation, but at this time, it is not, and tables don't auto-adjust their positions.
Indikator Pine Script®
oleh ka66