Monte-carlo — อินดิเคเตอร์และกลยุทธ์

Monte Carlo Simulation Bands Monte Carlo Simulation v2.4.2 Plots a one-bar-ahead price distribution band built from many simulated paths. The green band shows empirical percentiles of simulated final prices—these are distribution bounds, not a confidence interval of the mean. What It Does Simulates many one-bar price paths using a directional random walk with volatility scaling (uniform shocks, not Gaussian GBM). Plots Mean Forecast, Median Forecast, and configurable percentile bounds (default 5th/95th). Optional rolling HTF-days mean line (yellow) for trend context. Optional labels and forward projection lines. Alerts when the confirmed close breaks above or below the percentile band. Non-Repainting & HTF Behavior (Fail-Closed) All calculations are gated to confirmed bars only via explicit no_repaint_ok gate (barstate.isconfirmed). If you select an HTF Resolution, the script uses a strict request.security(..., lookahead_off, gaps_off) pipeline. If HTF data is unavailable, outputs are na—no silent fallback to chart timeframe. A separate "HTF Alignment (lagged)" plot shows the prior HTF close (htf_price ) as visual proof of no look-ahead. Volatility Source & Scaling If "Use Historical Volatility" is enabled, volatility is estimated from log returns on the selected resolution (HTF if set, otherwise chart). Annualization adapts to session type: Equities: 6.5 hours/day, 252 trading days/year Crypto: 24 hours/day, 365 days/year Substeps increase path smoothness within the same one-bar horizon—they do not extend the forecast to multiple bars. Key Inputs • Prob Up / Prob Down — Must satisfy Prob Up + Prob Down ≤ 1.0. If violated, simulation is skipped and table shows "✗ PROB>1". • # Simulations / # Substeps — Higher = smoother/more stable, but slower. Default 100×100 is a good balance. • Lower/Upper Percentile — Define the band width (e.g., 5 and 95 for a 90% distribution band). • Run On Last Bar Only — Performance mode (recommended). Skips historical computation; updates on each new confirmed bar. • Resolution (HTF) — Leave blank for chart timeframe, or set to Weekly/Monthly for HTF-aligned simulation. • Crypto 24/7 Session? — Enable for crypto markets to use correct annualization (365d, 24h). How to Use (Quickstart) Start with defaults and keep Run On Last Bar Only = true for speed. Set Prob Up and Prob Down so their sum ≤ 1.0 (e.g., 0.5 + 0.5 = 1.0 for neutral). Enable "Use Historical Volatility" and set a Volatility Lookback (e.g., 20 bars) for data-driven vol. Set Resolution (HTF) if you want the model to run on higher timeframe data (e.g., 1W). Expect updates only when a new HTF interval starts. Choose percentiles (e.g., 5 and 95) to define your distribution band width. Enable alerts for "Price Above Upper Percentile" or "Price Below Lower Percentile" to get notified of breakouts. Limitations & Disclosures Forecast horizon is one bar only. Substeps do not create a multi-bar forecast. Model uses uniform shocks with direction chosen from Prob Up/Down. This is not Geometric Brownian Motion (GBM) and is not calibrated to any option-implied distribution. Bounds are percentiles of final simulated prices, not a statistical confidence interval of the mean. HTF mode updates at the start of a new HTF interval (first chart bar where the HTF timestamp changes), so the band appears "step-like" in realtime. Historical volatility requires enough bars for the selected lookback; until then, values may be na. Performance depends on Sims × Substeps; extreme settings (e.g., 500×500) can be slow. This indicator does not predict direction—it shows a probabilistic range based on your inputs.

Multi Brownian Forecast 📊 Multi Brownian Forecast (Time-Adaptive, Probabilistic) This indicator uses a sophisticated Geometric Brownian Motion (GBM) Monte Carlo simulation to project future price paths. It adapts to any chart timeframe and provides quantitative, multi-period probability signals. --- 🧠 Core Mathematical Methodology The model relies on GBM, which is a continuous-time stochastic process that models asset prices. 1. Historical Analysis (Drift & Volatility): * The script first calculates Logarithmic Returns over a user-defined Historical Lookback (Hours) . * Drift ($\mu$): Computed as the average of the log returns. * Volatility ($\sigma$): Computed as the standard deviation of the log returns. * These values are then time-adapted to an hourly step, compensating for the chart's current timeframe (e.g., 5-minute, 1-hour). 2. Monte Carlo Simulation: * It runs a specified Number of Simulations (e.g., 1000). * For each simulation, the price is stepped forward hourly using the GBM formula, which incorporates the calculated drift and a random shock drawn from a normal distribution (generated via the Box-Muller transform ). --- ✨ Key Features Probabilistic Quartile Forecast: Plots a dynamic "cone" of probability on the chart. It shows key price percentiles (Q1, Q2/Median, Q3, and Q4/Outer Bound) at the forecast's expiration, visualizing the expected range of price outcomes based on the simulations. Multi-Period Probability Signals: This is the core signal feature. Users can define multiple, independent forecast periods (e.g., 4h, 16h, 48h) in a comma-separated list. * For each period, a Probability Up and Probability Down is calculated based on hitting a custom Target Price Change (%) (e.g., 2%) at a certain confidence level given a simulation over the historical backlook. * The probabilities are displayed in a chart table. The cell text turns white if the calculated probability exceeds the user-defined Signal Confidence (%) . Conditional Fibonacci Retracement: Optionally displays a Fibonacci Retracement on the chart. This feature is only activated when one of the multi-period signals reaches its minimum confidence threshold, providing a contextual technical level when a probabilistic edge is found.

Monte Carlo (Polyline Traceback) [Kioseff Trading]Hello! This script "Monte Carlo (Polyline Traceback) " performs a Monte Carlo simulation using polylines! By using polylines, and tracing back the initial simulation to its origin point, we can better replicate the ideal output of a Monte Carlo simulation! Such as: The image above shows the output of a simulation (image sourced outside TV). With this script, and polyline capabilities, we can come quite close on TradingView. The image above shows the indicator in action! Not bad considering the ideal output. Of course, the script is quite heavy and tries its best to circumvent limitations :D You might run into load time errors, in which case you might try applying the built-in setting "Force Script Load". This setting will cut-off the visuals for some simulations, but has a higher chance of passing load-time limitations! As shown in the image above, you can select to only show worst-case and best-case simulations. Using this option will reduce chart lag and improve load times. Features Monte Carlo Simulation: Performs Monte Carlo simulation to generate multiple future paths. Asset Price: Can simulate future asset prices based on historical log returns. Statistical Methods: Offers two simulation methods—Gaussian (Normal) distribution and Bootstrapping. Adjustable Parameters: Offers numerous user-adjustable settings like number of simulations, forecast length, and more. Historical Data Points: Option to specify the amount of historical data to be used in the simulation (price). Best/Worst Case: Allows you to show only the best case / worst case outcome (range) for all simulations! Thank you!

Monte Carlo Simulation - Your Strategy [Kioseff Trading]Hello! This script “Monte Carlo Simulation - Your Strategy” uses Monte Carlo simulations for your inputted strategy returns or the asset on your chart! Features Monte Carlo Simulation: Performs Monte Carlo simulation to generate multiple future paths. Asset Price or Strategy: Can simulate either future asset prices based on historical log returns or a specific trading strategy's future performance. User-Defined Input: Allows you to input your own historical returns for simulation. Statistical Methods: Offers two simulation methods—Gaussian (Normal) distribution and Bootstrapping. Graphical Display: Provides options for graphical representation, including line plots and histograms. Cumulative Probability Target: Enables setting a user-defined cumulative probability target to quantify simulation results. Adjustable Parameters: Offers numerous user-adjustable settings like number of simulations, forecast length, and more. Historical Data Points: Option to specify the amount of historical data to be used in the simulation (price). Custom Binning: Allows you to select the binning method for histograms, with options like Sturges, Rice, and Square Root. Best/Worst Case: Allows you to show only the best case / worst case outcome (range) for all simulations! Scatterplot: allows you to show up to 1000 potential outcomes for a specified trade number (or bars forward price endpoint) using a scatter plot. The image above shows the primary components of the indicator! The image above shows the best/worst case outcome feature in action! The image above shows a "fun feature" where 1000 simulated end points for a 15-bar price trajectory are shown as a scatter plot! How To Perform a Monte Carlo Simulation On Your Strategy Really, you can input any data into the indicator it will perform a Monte Carlo Simulation on it :D The following instructions show how to export your strategy results from TradingView to an Excel File, copy the data, and input it into the indicator. However , you are not limited to following this method! Wherever your strategy results are stored, simply copy and paste them into the indicator text area in the settings and simulations will begin. Returns Should Follow This Format 1 3 -3 2 -5 The numbers are presented as a single column. No commas or separators used. The numbers above are in sequential order. A return of "1" for the first trade and a return of "-5" for the last trade. Your strategy returns will likely be in sequential order already so don't worry too much about this (: How To Perform a Monte Carlo Simulation On Your TradingView Strategy With Excel Data Export your strategy returns to an excel file using TradingView Navigate to your downloads folder to column G "Profit" Click the column and press CTRL + SPACE to highlight the entire column Press CTRL + C to copy the entire column Open this indicator's settings and paste the returns into the text area The image above illustrates the process! Notes on Inputting Returns *Must input your returns without a separate as a vertical list *The initial text area can only hold so many return values. If your list of trades is large you can input additional returns into two additional text areas at the bottom of the indicator settings. That should be it; thank you for checking this out!

Monte Carlo Simulation - Random Walk Hello All, Monte Carlo Simulation is a model used to predict the probability of different outcomes when the intervention of random variables is present. it is used by professionals in such widely disparate fields as finance, project management etc. You can find many articles about Monte Carlo Simulation on the net. In this script I tried to make Monte Carlo Simulation and "Random Walk". it calculates results over and over, each time using a different set of random values that is created using historical data (500 times by default) and show min-max and some random paths. number of "random walks" is calculated by using number of bars to predict, so if you change "Number of Bars to Predict" then number of random walks may change. Total number of the lines must be less than 500. "Number of Simulations " is 500 by default, more simulation better results. but if you increase it a lot then you may get "loop takes too long error" "Number of Bars to Predict" can be between 10-100 "Number of Bars to use as Data Source" is the number of historical bars to use in simulations Thanks to Ricardo Santos (@RicardoSantos) for letting me use his Random Number Generator Function. P.S. I am not mathematician and I tried to make it as far as I understood the method. so if you see any issue let me know please. Some examples: Number of Bars to Predict = 100: Number of Bars to Predict = 10: if you enable "Keep Past Min-Max Levels" option then min-max levels will stay on the chart Enjoy!