AIO Entry Size CalculatorThis indicator is designed to help traders dynamically adjust their position size and drawdown expectations when their trading capital changes.
By entering your original backtest parameters — including base capital, base drawdown percentage, and base position size — along with your current capital, the script calculates two key scenarios:
Lock Drawdown %: Keeps the original drawdown percentage unchanged and calculates the new position size required.
Lock Position Size: Keeps the original position size unchanged and shows how your drawdown percentage will change.
The results are displayed in a clear, color-coded table, allowing you to instantly see how capital fluctuations affect your risk profile.
With customizable themes (dark/light) and highlight options, it’s an essential tool for risk management, portfolio rebalancing, and position sizing decisions based on real backtest data.
Additionally, this indicator is highly useful when combined with TradingView strategies — helping you interpret backtest results more accurately and translate them into real-world position sizing adjustments as your account balance evolves.
Statistics
Time Range by exp3rtsTime Window highlights a custom time range directly on your chart, helping you focus on specific market sessions or trading hours.
Key Features:
Highlights a custom time range with a shaded background
Fully adjustable start and end time (hour & minute)
Supports multiple time zones (e.g., GMT, UTC, Europe/Berlin)
Optional market color shading inside the window (bull/bear neutral tone)
Use Cases:
Mark London Open, New York Session, or any session overlap
Focus on high-probability trading hours
Visualize your backtesting timeframe or algo activity window
Track premarket or after-hours activity for futures or indices
Customization:
Set the beginning and end time in your local or exchange time zone
Choose your timezone string (e.g., "GMT", "Etc/UTC", "America/New_York")
Automatically colors candles in the time window for easy visibility
Total Points Moved by exp3rtsThis lightweight utility tracks the total intraday range of price movement, giving you real-time insight into market activity.
It calculates:
🟩 Bullish Points – Total range from bullish candles (close > open)
🟥 Bearish Points – Total range from bearish candles (close < open)
🔁 Total Points Moved (TPM) – Sum of all high–low ranges for the day
Values are pulled from the 1-second chart for high precision and displayed in a compact tag in the top-right corner.
Quantile-Based Adaptive Detection🙏🏻 Dedicated to John Tukey. He invented the boxplot, and I finalized it.
QBAD (Quantile-Based Adaptive Detection) is ‘the’ adaptive (also optionally weighted = ready for timeseries) boxplot with more senseful fences. Instead of hardcoded multipliers for outer fences, I base em on a set of quantile-based asymmetry metrics (you can view it as an ‘algorithmic’ counter part of central & standardized moments). So outer bands are Not hardcoded, not optimized, not cross-validated etc, simply calculated at O(nlogn).
You can use it literally everywhere in any context with any continuous data, in any task that requires statistical control, novelty || outlier detection, without worrying and doubting the sense in arbitrary chosen thresholds. Obviously, given the robust nature of quantiles, it would fit best the cases where data has problems.
The thresholds are:
Basis: the model of the data (median in our case);
Deviations: represent typical spread around basis, together form “value” in general sense;
Extensions: estimate data’s extremums via combination of quantile-based asymmetry metrics without relying on actual blunt min and max, together form “range” / ”frame”. Datapoints outside the frame/range are novelties or outliers;
Limits: based also on quantile asymmetry metrics, estimate the bounds within which values can ‘ever’ emerge given the current data generating process stays the same, together form “field”. Datapoints outside the field are very rare, happen when a significant change/structural break happens in current data-generating process, or when a corrupt datapoint emerges.
…
The first part of the post is for locals xd, the second is for the wanderers/wizards/creators/:
First part:
In terms of markets, mostly u gotta worry about dem instruments that represent crypto & FX assets: it’s either activity hence data sources there are decentralized, or data is fishy.
For a higher algocomplexity cost O(nlong), unlike MBAD that is 0(n), this thing (a control system in fact) works better with ishy data (contaminated with wrong values, incomplete, missing values etc). Read about the “ breakdown point of an estimator ” if you wanna understand it.
Even with good data, in cases when you have multiple instruments that represent the same asset, e.g. CL and BRN futures, and for some reason you wanna skip constructing a proper index of em (while you should), QBAD should be better put on each instrument individually.
Another reason to use this algo-based rather than math-based tool, might be in cases when data quality is all good, but the actual causal processes that generate the data are a bit inconsistent and/or possess ‘increased’ activity in a way. SO in high volatility periods, this tool should provide better.
In terms of built-ins you got 2 weightings: by sequence and by inferred volume delta. The former should be ‘On’ all the time when you work with timeseries, unless for a reason you want to consciously turn it off for a reason. The latter, you gotta keep it ‘On’ unless you apply the tool on another dataset that ain’t got that particular additional dimension.
Ain’t matter the way you gonna use it, moving windows, cumulative windows with or without anchors, that’s your freedom of will, but some stuff stays the same:
Basis and deviations are “value” levels. From process control perspective, if you pls, it makes sense to Not only fade or push based on these levels, but to also do nothing when things are ambiguous and/or don’t require your intervention
Extensions and limits are extreme levels. Here you either push or fade, doing nothing is not an option, these are decisive points in all the meanings
Another important thing, lately I started to see one kind of trend here on tradingview as well and in general in near quant sources, of applying averages, percentiles etc ‘on’ other stationary metrics, so called “indicators”. And I mean not for diagnostic or development reasons, for decision making xd
This is not the evil crime ofc, but hillbilly af, cuz the metrics are stationary it means that you can model em, fit a distribution, like do smth sharper. Worst case you have Bayesian statistics armed with high density intervals and equal tail intervals, and even some others. All this stuff is not hard to do, if u aint’t doing it, it’s on you.
So what I’m saying is it makes sense to apply QBAD on returns ‘of your strategy’, on volume delta, but Not on other metrics that already do calculations over their own moving windows.
...
Second part:
Looks like some finna start to have lil suspicions, that ‘maybe’ after all math entities in reality are more like blueprints, while actual representations are physical/mechanical/algorithmic. Std & centralized moments is a math entity that represents location, scale & asymmetry info, and we can use it no problem, when things are legit and consistent especially. Real world stuff tho sometimes deviates from that ideal, so we need smth more handy and real. Add to the mix the algo counter part of means: quantiles.
Unlike the legacy quantile-based asymmetry metrics from the previous century (check quantile skewness & kurtosis), I don’t use arbitrary sets of quantiles, instead we get a binary pattern that is totally geometric & natural (check the code if interested, I made it very damn explicit). In spirit with math based central & standardized moments, each consequent pair is wider empathizing tail info more and more for each higher order metric.
Unlike the classic box plot, where inner thresholds are quartiles and the rest are based on em, here the basis is median (minimises L1), I base inner thresholds on it, and we continue the pattern by basing the further set of levels on the previous set. So unlike the classic box plot, here we have coherency in construction, symmetry.
Another thing to pay attention to, tho for some reason ain’t many talk about it, it’s not conceptually right to think that “you got data and you apply std moments on it”. No, you apply it to ‘centered around smth’ data. That ‘smth’ should minimize L2 error in case of math, L1 error in case of algo, and L0 error in case of learning/MLish/optimizational/whatever-you-cal-it stuff. So in the case of L0, that’s actually the ‘mode’ of KDE, but that’s for another time. Anyways, in case of L2 it’s mean, so we center data around mean, and apply std moments on residuals. That’s the precise way of framing it. If you understand this, suddenly very interesting details like 0th and 1st central moments start to make sense. In case of quantiles, we center data around the median, and do further processing on residuals, same.
Oth moment (I call it init) is always 1, tho it’s interesting to extrapolate backwards the sequence for higher order moments construction, to understand how we actually end up with this zero.
1st moment (I call it bias) of residuals would be zero if you match centering and residuals analysis methods. But for some reason you didn’t do that (e.g centered data around midhinge or mean and applied QBAD on the centered data), you have to account for that bias.
Realizing stuff > understanding stuff
Learning 2981234 human invented fields < realizing the same unified principles how the Universe works
∞
Seasonal Pattern DecoderSeasonal Pattern Decoder
The Seasonal Pattern Decoder is a powerful tool designed for traders and analysts who want to uncover and leverage seasonal tendencies in financial markets. Instead of cluttering your chart with complex visuals, this indicator presents a clean, intuitive table that summarizes historical monthly performance, allowing you to spot recurring patterns at a glance.
How It Works
The indicator fetches historical monthly data for any symbol and calculates the percentage return for each month over a specified number of years. It then organizes this data into a comprehensive table, providing a clear, year-by-year and month-by-month breakdown of performance.
Key Features
Historical Performance Table: Displays monthly returns for up to a user-defined number of years, making it easy to compare performance across different periods.
Color-Coded Heatmap: Each cell is colored based on the performance of the month. Strong positive returns are shaded in green, while strong negative returns are shaded in red, allowing for immediate visual analysis of monthly strength or weakness.
Annual Summary: A "Σ" column shows the total percentage return for each full calendar year.
AVG Row: Calculates and displays the average return for each month across all the years shown in the table.
WR Row: Shows the "Win Rate" for each month, which is the percentage of time that month had a positive return. This is crucial for identifying high-probability seasonal trends.
How to Use
Add the "Seasonal Pattern Decoder" indicator to your chart. Note that it works best on Daily, Weekly, or Monthly timeframes. A warning message will be displayed on intraday charts.
In the indicator settings, adjust the "Lookback Period" to control how many years of historical data you want to analyze.
Use the "Show Years Descending" option to sort the table from the most recent year to the oldest.
The "Heat Range" setting allows you to adjust the sensitivity of the color-coding to fit the volatility of the asset you are analyzing.
This tool is ideal for confirming trading biases, developing seasonal strategies, or simply gaining a deeper understanding of an asset's typical behavior throughout the year.
## Disclaimer
This indicator is designed as a technical analysis tool and should be used in conjunction with other forms of analysis and proper risk management.
Past performance does not guarantee future results, and traders should thoroughly test any strategy before implementing it with real capital.
Sector RSI (Auto-Select)This indicator measures the relative strength momentum of any stock against its most closely correlated sector ETF, using the Relative Strength Index (RSI).
Auto sector selection: The script computes correlations between your symbol’s short-term returns and all major SPDR sector ETFs (XLB, XLE, XLF, XLI, XLK, XLP, XLU, XLV, XLY, XTN). The sector with the highest correlation is automatically chosen as the benchmark.
Sector vs Symbol RSI: It calculates RSI (default 14-period) for both the chosen sector and the current chart’s symbol.
Display modes:
Line mode: Plots both RSIs with colored fill (red if the sector RSI is stronger, green if the symbol RSI is stronger).
Histogram mode: Shows the difference between Sector RSI and Symbol RSI as a column chart.
RSI bands: Standard 70/50/30 reference lines are available in line mode.
Status line: The selected sector’s ticker is shown on the TradingView status line so you always know which sector is being used.
Use Cases:
Identify whether a stock’s momentum is driven by its sector or if it’s showing independent relative strength.
Detect sector rotations: when the stock begins to outperform or underperform its sector on momentum basis.
Combine with absolute RSI levels (overbought/oversold) to filter signals.
Notes:
This tool infers sector membership via rolling correlation, not from static classification metadata. This means in some cases (e.g. diversified companies or news shocks) the “best” sector may not be the official one, but the one most correlated in the current market regime.
Use min positive correlation input to filter out weak matches and enforce a fallback (defaults to Technology XLK).
Advanced Market Structure [OmegaTools]📌 Market Structure
Advanced Market Structure is a next–generation indicator designed to decode price structure in real time by combining classical swing–based analysis with modern quantitative confirmation techniques. Built for traders who demand both precision and adaptability, it provides a robust multi–layered framework to identify structural shifts, trend continuations, and potential reversals across any asset class or timeframe.
Unlike traditional structure indicators that rely solely on visual swing identification, Market Structure introduces an integrated methodology: pivot detection, Donchian trend modeling, statistical confirmation via Z–Score, and volume–based validation. Each element contributes to a comprehensive, systematic representation of the underlying market dynamics.
🔑 Core Features
1. Five Distinct Market Structure Modes
Standard Mode:
Captures structural breaks through classical swing high/low pivots. Ideal for discretionary traders looking for clarity in directional bias.
Confirmed Breakout Mode:
Requires validation beyond the initial pivot break, filtering out noise and reducing false positives.
Donchian Trend HL (High/Low):
Establishes structure based on absolute highs and lows over rolling lookback windows. This approach highlights broader momentum shifts and trend–defining extremes.
Donchian Trend CC (Close/Close):
Similar to HL mode, but calculated using closing prices, enabling more precise bias identification where close–to–close structure carries stronger statistical weight.
Average Mode:
A composite methodology that synthesizes the four models into a weighted signal, producing a balanced structural bias designed to minimize model–specific weaknesses.
2. Dynamic Pivot Recognition with Auto–Updating Levels
Swing highs and lows are automatically detected and plotted with adaptive horizontal levels. These dynamic support/resistance markers continuously extend into the future, ensuring that historically significant levels remain visible and actionable.
3. Color–Adaptive Candlesticks
Price bars are dynamically recolored to reflect the prevailing structural regime: bullish (default blue), bearish (default red), or neutral (gray). This enables instant visual recognition of regime changes without requiring external confirmation.
4. Statistical Reversal Triggers
The script integrates a 21–period Z–Score calculation applied to closing prices, combined with multi–layered volume confirmation (SMA and EMA convergence).
Bullish trigger: Z–Score < –2 with structural confirmation and volume support.
Bearish trigger: Z–Score > +2 with structural confirmation and volume support.
Signals are plotted as diamond markers above or below the bars, identifying potential high–probability reversal setups in real time.
5. Integrated Alpha Backtesting Engine
Each market structure mode is evaluated through a built–in backtesting routine, tracking hit ratios and consistency across the most recent ~2000 structural events.
Performance metrics (“Alpha”) are displayed directly on–chart via a dedicated Performance Dashboard Table, allowing side–by–side comparison of Standard, Confirmed Breakout, Donchian HL, Donchian CC, and Average models.
Traders can instantly evaluate which structural methodology best adapts to the current market conditions.
🎯 Practical Advantages
Systematic Clarity: Eliminates subjectivity in defining structural bias, offering a rules–based framework.
Statistical Transparency: Built–in performance metrics validate each mode in real time, allowing informed decision–making.
Noise Reduction: Confirmed Breakouts and Donchian modes filter out common traps in structural trading.
Multi–Asset Adaptability: Optimized for scalping, intraday, swing, and multi–day strategies across FX, equities, futures, commodities, and crypto.
Complementary Usage: Works as a stand–alone structure identifier or as a quantitative filter in larger algorithmic/trading frameworks.
⚙️ Ideal Users
Discretionary traders seeking an objective reference for structural bias.
Quantitative/systematic traders requiring on–chart statistical validation of structural regimes.
Technical analysts leveraging pivots, Donchian channels, and price action as part of broader frameworks.
Portfolio traders integrating structure into multi–factor models.
💡 Why This Tool?
Market Structure is not a static indicator — it is an adaptive framework. By merging classical pivot theory with Donchian–style momentum analysis, and reinforcing both with statistical backtesting and volume confirmation, it provides traders with a unique ability:
To see the structure,
To measure its reliability,
And to act with confidence on quantifiably validated signals.
Z-Score Trend Channels [BackQuant]Z-Score Trend Channels
A self-contained price-statistics framework that turns a rolling z-score into price channels, bias states, and trade markers. Run either trend-following or mean-reversion from the same tool with clear, on-chart context.
What it is
A rolling statistical map that measures how far price is from its recent average in standard-deviation units (z-score).
Adaptive channels drawn in price space from fixed z thresholds, so the rails breathe with volatility.
A simple trend proxy from z-score momentum to separate trending from ranging conditions.
On-chart signals for pullback entries, stretched extremes, and practical exits.
Core idea (plain English math)
Rolling mean and volatility - Over a lookback you get the average price and its standard deviation.
Z-score - How many standard deviations the current price is above or below its average: z = (price - mean) / stdev. z near 0 means near average; positive is above; negative is below.
Noise control - An EMA smooths the raw z to reduce jitter and false flickers.
Channels back in price - Fixed z levels are converted back to price to form the upper, lower, and extreme rails.
Trend proxy - A smoothed change in z is used as a lightweight trend-strength line. Positive strength with positive z favors uptrend; negative strength with negative z favors downtrend.
What you see on the chart
Channels and fills - Mean, upper, lower, and optional extreme lines. The area mean->upper tints with the bearish color, mean->lower tints with the bullish color.
Background tint (optional) - Soft green, red, or neutral based on detected trend state.
Signals - Bullish Entry (triangle up) when z exits the oversold zone upward; Bearish Entry (triangle down) when z exits the overbought zone downward; Extreme markers (diamonds) at the extreme bands with a one-bar turn.
Table - Current z, trend state, trend strength, distance to bands, market state tag, and a quick volatility regime label.
Edge labels - MEAN, OB, and OS labels slightly projected forward with level values.
Inputs you will actually use
Z-Score Period - Lookback for mean and stdev. Larger = slower and steadier rails, smaller = more reactive.
Smoothing Period - EMA on z. Lower = earlier but choppier flips; higher = later but cleaner.
Price Source - Default hlc3. Choose close if you prefer session-close logic.
Upper and Lower Thresholds - Default around +2.0 and -2.0. Tighten for more signals, widen for fewer and stronger.
Extreme Upper and Lower - Deeper stretch guards, e.g., +/- 2.5.
Strength Period - EMA on z momentum. Sets how fast the trend proxy flips.
Trend Threshold - Minimum absolute z to accept a directional bias.
Visual toggles - Channels, signals, background tint, stats table, colors, and optional last-bar trend label.
How to use it: trend-following playbook
Read the state - Uptrend when z > Trend Threshold and trend strength > 0. Downtrend when z < -Trend Threshold and trend strength < 0. Neutral otherwise.
Entries - In an uptrend, prefer Bullish Entry signals that fire near the lower channel. In a downtrend, prefer Bearish Entry signals that fire near the upper channel.
Stops - Conservative: beyond the extreme channel on your side. Tighter: just outside the standard band that framed the signal.
Exits - For longs, exit or trim on a cross back through z = 0 or a clean tag of the upper threshold. For shorts, mirror with z = 0 up-cross or tag of the lower threshold. You can also reduce if trend strength flips against you.
Adds - In strong trends, additional signals near your side’s band can be add points. Avoid adding once z hovers near the opposite band for several bars.
How to use it: mean-reversion playbook
Find stretch - Standard reversions: Bullish Entry when z leaves the oversold zone upward; Bearish Entry when z leaves the overbought zone downward. Aggressive reversions: Extreme markers at extreme bands with a one-bar turn.
Entries - Take the signal as price exits the zone. Prefer setups where trend strength is near zero or tilting against the prior push.
Targets - First target is the mean line. A runner can aim for the opposite standard channel if momentum keeps flipping.
Stops - Outside the extreme band beyond your entry. If fading without extremes, place risk just beyond the opposite standard band.
Filters - Optional: skip counter-trend fades against a very strong trend state unless your risk is tight and predefined.
Reading the stats table
Current Z-Score - Magnitude and sign of displacement now.
Trend State - Uptrend, Downtrend, or Ranging.
Trend Strength - Smoothed z momentum. Higher absolute values imply stronger directional conviction.
Distance to Upper/Lower - Percent distance from price to each band, useful for sizing targets or judging room left.
Market State - Overbought, Oversold, Extreme OB, Extreme OS, or Normal.
Volatility Regime - High, Normal, or Low relative to recent distribution. Expect bands to widen in High and tighten in Low.
Parameter guidance (conceptual)
Z-Score Period - Choose longer for a structural mean, shorter for a reactive mean.
Smoothing Period - Lower for earlier but noisier reads; higher for slower but steadier reads.
Thresholds - Start around +/- 2.0. Tighten for scalping or quiet ranges. Widen for noisy or fast markets.
Trend Threshold and Strength Period - Raise to avoid weak, transient bias. Lower to capture earlier regime shifts.
Practical examples
Trend pullback long - State shows Uptrend. Price tests the lower channel; z dips near or below the lower threshold; a Bullish Entry prints. Stop just below extreme lower; first target mean; keep a runner if trend strength stays positive.
Mean-revert short - State is Ranging. z tags the extreme upper, an Extreme Bearish marker prints, then a Bearish Entry prints on the leave. Stop above extreme upper; target the mean; consider a runner toward the lower channel if strength turns negative.
Potential Questions you might have
Why z-score instead of fixed offsets - Because the bands adapt with volatility. When the tape gets quiet the rails tighten, when it runs hot the rails expand. Your entries stay normalized.
Do I need both modes - No. Many users run only trend pullbacks or only mean-reversions. The tool lets you toggle what you need and keep the chart readable.
Multi-timeframe workflow - A common approach is to set bias from a higher timeframe’s trend state and execute on a lower timeframe’s signals that align with it.
Summary
Z-Score Trend Channels gives you an adaptive mean, volatility-aware rails, a simple trend lens, and clear signals. Trade the trend by buying pullbacks in green and selling pullbacks in red, or fade stretched extremes back to the mean with defined risk. One framework, two strategies, consistent logic.
Week Start/End Bar CoordinatesGive the Bar Index for the First and Last of the Week,
input the coordinates directly into the Fixed Range Volume Profile
NOK Basket (Equal-Weighted)Measures the Norwegian crown's relative value to a basket of other currencies: EUR, USD, GBP, SEK AND DKK.
Global M2 Money SupplyThis indicator calculates and plots an aggregated estimate of the Global M2 money supply, expressed in U.S. dollar terms. It combines M2 data from major economies and regions—including the U.S., Eurozone, Canada, the U.K., Switzerland, China, Japan, India, Brazil, and others—and adjusts each by its respective FX rate to USD. By summing these series, the script provides a broad view of worldwide liquidity conditions in one line.
A user-defined offset in days allows you to shift the global M2 line forward or backward, making it easier to visually compare liquidity trends against asset prices such as Bitcoin, gold, or equities. This tool is designed for traders and macro observers who want to study how global money supply growth or contraction correlates with financial markets over time.
이 지표는 전 세계 주요 국가와 지역의 M2 통화량을 달러 기준으로 합산하여 글로벌 유동성 지표로 보여줍니다. 미국, 유로존, 캐나다, 영국, 스위스, 중국, 일본, 인도, 브라질 등 여러 지역의 M2 데이터를 각 통화의 환율(USD 환산)로 조정한 뒤 합산해 하나의 흐름으로 표현합니다. 이를 통해 글로벌 차원의 통화 공급 변화를 한눈에 파악할 수 있습니다.
또한 사용자가 지정한 일 단위 오프셋 기능을 통해 글로벌 M2 라인을 앞뒤로 이동시켜, 비트코인·금·주식 등 다양한 자산 가격과의 시차적 관계를 직관적으로 비교할 수 있습니다. 거시경제 환경과 자산시장 간의 상관성을 연구하거나 시장 유동성 추이를 모니터링하려는 투자자에게 유용한 도구입니다.
Gold Lagging (N days)This indicator overlays the price of gold (XAUUSD) on any chart with a customizable lag in days. You can choose the price source (open, high, low, close, hlc3, ohlc4), shift the series by a set number of daily bars, and optionally normalize the values so that the first visible bar equals 100. The original gold line can also be displayed alongside the lagged series for direct comparison.
It is especially useful for analyzing delayed correlations between gold and other assets, observing shifts in safe-haven demand, or testing hypotheses about lagging market reactions. Since the lag is calculated on daily data, it remains consistent even if applied on intraday charts, while the indicator itself can be plotted on a separate price scale for clarity.
이 지표는 금(XAUUSD) 가격을 원하는 차트 위에 N일 지연된 형태로 표시합니다. 가격 소스(시가, 고가, 저가, 종가, hlc3, ohlc4)를 선택할 수 있으며, 지정한 일 수만큼 시리즈를 뒤로 이동시킬 수 있습니다. 또한 첫 값 기준으로 100에 맞춰 정규화하거나, 원래 금 가격선을 함께 표시해 비교할 수도 있습니다.
금과 다른 자산 간의 지연 상관관계를 분석하거나 안전자산 수요 변화를 관찰할 때 유용하며, 시장 반응의 시차 효과를 검증하는 데에도 활용할 수 있습니다. 지연은 일봉 데이터 기준으로 계산되므로 단기 차트에 적용해도 일 단위 기준이 유지되며, 별도의 가격 스케일에 표시되어 가독성을 높일 수 있습니다.
Morning Peak FadeMorning Peak Fade is an intraday analysis tool that identifies and measures the probability of early session rallies turning into sharp pullbacks.
📊 Core Idea
• Many stocks surge after the open, reaching an intraday peak before fading lower.
• This script anchors at the first significant morning high and tracks the drawdowns that follow within a customizable time window.
• It provides:
• Probability of a fade after the peak
• Average and maximum drawdown statistics
• Event-day hit rate (how often such setups occur)
🎯 Use Cases
• Spot potential “fade setups” where early enthusiasm exhausts quickly.
• Quantify how often chasing the morning high turns into a losing trade.
• Backtest opening range failure or fade strategies with hard data.
⚙️ Features
• Customizable thresholds for the initial surge (relative to prior close).
• Marks the peak (max) and subsequent low (min) used in calculations.
• Draws a reference line at the surge threshold to visualize when the fade triggers.
• Outputs summary stats directly on the chart.
Premarket Power MovePremarket Power Move is an intraday research tool that tracks what happens after strong premarket or opening gaps.
📊 Core Idea
• When a stock opens +X% above the prior close, it often attracts momentum traders.
• This script measures whether the stock continues to follow through higher or instead fades back down within the first trading hour.
• It calculates:
• The probability of a post-gap rally vs. a drawdown
• Average and maximum retracements after the surge
• Event-day hit rate (how many days actually triggered the condition)
🎯 Use Cases
• Identify “gap-and-go” opportunities where strong premarket strength leads to further gains.
• Spot potential fade setups where early enthusiasm quickly reverses.
• Backtest your intraday strategies with objective statistics instead of gut feeling.
⚙️ Features
• Customizable thresholds for premarket/open surge (%) and follow-through window (minutes).
• Marks the chart with reference lines:
• Prior close
• Surge threshold (e.g. +6%)
• Intraday high/low used for probability calculations.
• Outputs summary statistics (probabilities, averages, counts) directly on the chart.
🔔 Note
This is not a buy/sell signal generator. It is a probability and behavior analysis tool that helps traders understand how often strong premarket gaps continue vs. fade.
Yasser Multiple Inside Bar Breakout SignalsDescription
Yasser Multiple Inside Bar Breakout Signals (Yasser_MIB) is a powerful TradingView indicator designed to detect high-probability breakout setups based on multiple inside bar (MIB) formations. Inside bar breakouts often precede strong market moves, making this tool ideal for traders who rely on price action, volatility compression, and breakout trading strategies.
🔑 Key Features:
✅ Automatic MIB Detection – Identifies and counts consecutive inside bars.
✅ Breakout Signals – Generates BUY/SELL signals upon valid breakout of the mother bar.
✅ Custom Risk:Reward Settings – Adjustable risk-to-reward ratio with built-in Stop Loss (SL) and Take Profit (TP) levels.
✅ ATR-based Stop Loss (Optional) – Dynamic volatility-based risk management.
✅ Trend Filter – Optional EMA filter to trade only in the trend direction.
✅ Visual Clarity – Mother bar levels, inside bar marks, entry/SL/TP lines, and breakout highlights.
✅ Alerts Ready – Receive instant alerts for MIB setups and breakouts.
This indicator is suitable for Forex, Stocks, Indices, Commodities, and Crypto markets across multiple timeframes. Whether you are a trend trader or a breakout trader, Yasser_MIB provides a structured approach to capture explosive market moves with disciplined risk management.
📂 Categories
Indicators
Technical Analysis
Price Action
Breakout Strategies
Risk Management
🏷 Tags
inside bar
multiple inside bar
MIB breakout
price action
mother bar
breakout strategy
trend filter
EMA filter
ATR stop loss
risk reward
forex trading
crypto trading
stocks
commodities
indices
Yasser indicators
PongExperience PONG! The classic arcade game, now on your charts!
With this indicator, you can finally achieve your lifelong dream of beating the Markets. . . at PONG!
Pong is jam-packed with features! Such as:
2 Paddles
A moving dot
Floating numbers
The idea of a net
This indicator is solely a visualization, it serves simply as an exercise to depict what is capable through PineScript. It can be used to re-skin other indicators or data, but on its own, is not intended as a market indicator.
With that out of the way...
> PONG
The Pong indicator is a recreation of the classic arcade game Pong developed to pit the markets against the cold hard logic of a CPU player.
Given the lack of interaction that is capable, the game is not played in the typical sense, by a player and computer or 2 players.
This version of Pong uses the chart price movements to control the "Market" Paddle, and it is contrasted by a (not AI) "CPU" Paddle, which is controlled by its own set of logic.
> Market Paddle
The Market Paddle is controlled by a data source which can be input by the user.
By default (Auto Mode), the Market Paddle is controlled through a fixed length Donchian channel range, pinning the range high to 100 and range low to 0. As seen below.
This can be altered to use data from different symbols or indicators, and can optionally be smoothed using multiple types of Moving Averages.
In the chart below, you can see how the RSI indicator is imported and smoothed to control the Market Paddle.
Note: The Market Paddle follows the moving average. If not desired, simply set the "Smoothing" input to "NONE".
> CPU Paddle
In simple terms, the CPU Paddle is a handicapped Aimbot.
Its logic is, more or less, "move directly towards the ball's vertical location".
If it were allowed to have full range of the screen, it would be impossible for it to lose a point. Due to this, we must slow it down to "play fair"... as fair as that may be.
The CPU Paddle is allowed to move at a rate specified by a certain Percent of its vertical width. By default, this is set to 2%.
Each update, the CPU Paddle can advance up or down 2% of its vertical width. The directional movement is determined based on the angle of the ball, and it's current position relative to the CPU Paddle's position. Given that it is not a direct follow, it may at times seem more... "human".
When a point is scored, the CPU paddle maintains its position, similar to the original Pong game, the paddles were controlled solely by the raw output of the controllers and did not reset.
> Ball
At the start of each point, the ball begins at the center of the screen and moves in a randomly determined angle at its base speed.
The direction is determined by the player who scored the last point. The loser of the last point "serves" the ball.
Given the circumstances, serving is a gigantic advantage. So the loser serving is just another place where the Market is given an advantage.
The ball's base speed is 1, it will move 1 (horizontal) bar on each update of the script. This speed can "technically" increase to infinity over time, if given the perfect rally. This is due to the hit logic as described below.
Note: The minimum ball speed is also 1.
> Bonk Math
When the ball hits a paddle, essentially 3 outcomes can occur, each resulting in the ball's direction being changed from positive to negative.
Action A: Its angle is doubled, and its speed is doubled.
Action B: Its angle is reversed, and its speed is decreased if it is going faster than base speed.
Action C: Its angle is preserved, and its speed is preserved. "Basic Bounce"
Each paddle is segmented into 3 zones, with the higher and lower tips (20%) of the paddles producing special actions.
The central 60% of each paddle produces a basic bounce. The special actions are determined by the trajectory of the ball and location on the paddle.
> Custom Mode
As stated above, the script loads in "Auto Mode" by default. While this works fine to simply watch the gameplay, the Custom Mode unlocks the ability to visualize countless possibilities of indicators and analyses playing Pong!
In the chart below, we have set up the game to use the NYSE TICK Index as our Market Player. The NYSE TICK Index shows the number of NYSE stocks trading on an uptick minus those on a downtick. Its values fluctuate throughout the day, typically ranging between +1000 and -1000.
Therefore, we have set up Pong to use Ticker USI:TICK and set the Upper Boundary to 1000 and Lower Boundary to -1000. With this method, the paddle is directly controlled by the overall (NYSE) market behaviors.
As seen in a chart earlier, you can also take advantage of the Custom Mode to overlay Pong onto traditional oscillators for use anywhere!
> Styles
This version of Pong comes stocked with 5 colorways to suit your chart vibes!
> Pro Tips & Additional Information
- This game has sound! For the full experience, set alerts for this indicator and a notification sound will play on each hit!*
*Due to server processing, the notification sounds are not precisely played at each hit. :(
- In auto mode, decreasing the length used will give an advantage to the market, as its actions become more sporadic over this window.
- The CPU logic system actually allows the market to have a "technical" edge, since the Market Paddle is not bound to any speed, and is solely controlled by the raw market movements/data input.
- This type of visualization only works on live charts, charts without updates will not see any movement.
- Indicator sources can only be imported from other indicators on the same chart.
- The base screen resolution is 159 bars wide, with the height determined by the boundaries.
- When using a symbol and an outside source, be mindful that the script is attempting to pull the source from the input symbol. Data can appear wonky when not considering the interactions of these inputs.
There are many small interesting details that can't be seen through the description. For example, the mid-line is made from a box. This is because a line object would not appear on top of the box used for the screen. For those keen eye'd coders, feel free to poke around in the source code to make the game truly custom.
Just remember:
The market may never be fair, but now at least it can play Pong!
Enjoy!
Hosoda’s CloudsMany investors aim to develop trading systems with a high win rate, mistakenly associating it with substantial profits. In reality, high returns are typically achieved through greater exposure to market trends, which inevitably lowers the win rate due to increased risk and more volatile conditions.
The system I present, called “Hosoda’s Clouds” in honor of Goichi Hosoda , the creator of the Ichimoku Kinko Hyo indicator, is likely one of the first profitable systems many traders will encounter. Designed to capture trends, it performs best in markets with clear directional movements and is less suitable for range-bound markets like Forex, which often exhibit lateral price action.
This system is not recommended for low timeframes, such as minute charts, due to the random and emotionally driven nature of price movements in those periods. For a deeper exploration of this topic, I recommend reading my article “Timeframe is Everything”, which discusses the critical importance of selecting the appropriate timeframe.
I suggest testing and applying the “Hosoda’s Clouds” strategy on assets with a strong trending nature and a proven track record of performance. Ideal markets include Tesla (1-hour, 4-hour, and daily), BTC/USDT (daily), SPY (daily), and XAU/USD (daily), as these have consistently shown clear directional trends over time.
Commissions and Configuration
Commissions can be adjusted in the system’s settings to suit individual needs. For evaluating the effectiveness of “Hosoda’s Clouds,” I’ve used a standard commission of $1 per order as a baseline, though this can be modified in the code to accommodate different brokers or preferences.
The margin per trade is set to $1,000 by default, but users are encouraged to experiment with different margin settings in the configuration to match their trading style.
Rules of the “Hosoda’s Clouds” System (Bullish Strategy)
This strategy is designed to capture trending movements in bullish markets using the Ichimoku Kinko Hyo indicator. The rules are as follows:
Long Entry: A long position is triggered when the Tenkan-sen crosses above the Kijun-sen below the Ichimoku cloud, identifying potential reversals or bounces in a bearish context.
Stop Loss (SL): Placed at the low of the candle 12 bars prior to the entry candle. This setting has proven optimal in my tests, but it can be adjusted in the code based on risk tolerance.
Take Profit (TP): The position is closed when the Tenkan-sen crosses below the bottom of the Ichimoku cloud (the minimum of Senkou Span A and Senkou Span B).
Notes on the Code
margin_long=0: Ideal for strategies requiring a fixed position size, particularly useful for manual entries or testing with a constant capital allocation.
margin_long=100: Recommended for high-frequency systems where positions are closed quickly, simulating gradual growth based on realized profits and reflecting real-world broker constraints.
System Performance
The following performance metrics account for $1 per order commissions and were tested on the specified assets and timeframes:
Tesla (H1)
Trades: 148
Win Rate: 29.05%
Period: Jan 2, 2014 – Jan 6, 2020 (+172%)
Simple Annual Growth Rate: +34.3%
Trades: 130
Win Rate: 30.77%
Period: Jan 2, 2020 – Sep 24, 2025 (+858.90%)
Simple Annual Growth Rate: +150.7%
Tesla (H4)
Trades: 102
Win Rate: 32.35%
Period: Jun 29, 2010 – Sep 24, 2025 (+11,356.36%)
Simple Annual Growth Rate: +758.5%
Tesla (Daily)
Trades: 56
Win Rate: 35.71%
Period: Jun 29, 2010 – Sep 24, 2025 (+3,166.64%)
Simple Annual Growth Rate: +211.5%
BTC/USDT (Daily)
Trades: 44
Win Rate: 31.82%
Period: Sep 30, 2017 – Sep 24, 2025 (+2,592.23%)
Simple Annual Growth Rate: +324.8%
SPY (Daily)
Trades: 81
Win Rate: 37.04%
Period: Jan 23, 1993 – Sep 24, 2025 (+476.90%)
Simple Annual Growth Rate: +14.3%
XAU/USD (Daily)
Trades: 216
Win Rate: 32.87%
Period: Jan 6, 1833 – Sep 24, 2025 (+5,241.73%)
Simple Annual Growth Rate: +27.1%
SPX (Daily)
Trades: 217
Win Rate: 38.25%
Period: Feb 1, 1871 – Sep 24, 2025 (+16,791.02%)
Simple Annual Growth Rate: +108.1%
Conclusion
With the “ Hosoda’s Clouds ” strategy, I aim to showcase the potential of technical analysis to generate consistent profits in trending markets, challenging recent doubts about its effectiveness. My goal is for this system to serve as both a practical tool for traders and a source of inspiration for the trading community I deeply respect. I hope it encourages the creation of new strategies, fosters creativity in technical analysis, and empowers traders to approach the markets with confidence and discipline.
Irrationality Index by CRYPTO_ADA_BTC"The market can be irrational longer than you can stay solvent" ~ John Maynard Keynes
This indicator, the Irrationality Index, measures how far the current market price has deviated from a smoothed estimate of its "fair value," normalized for recent volatility. It provides traders with a visual sense of when the market may be behaving irrationally, without giving direct buy or sell signals.
How it works:
1. Fair Value Calculation
The indicator estimates a "fair value" for the asset using a combination of a long-term EMA (exponential moving average) and a linear regression trend over a configurable period. This fair value serves as a smoothed baseline for price, balancing trend-following and mean-reversion.
2. Volatility-Adjusted Z-Score
The deviation between price and fair value is measured in standard deviations of recent log returns:
Z = (log(price) - log(fairValue)) / volatility
This standardization accounts for different volatility environments, allowing comparison across assets.
3. Irrationality Score (0–100)
The Z-score is transformed using a logistic mapping into a 0–100 scale:
- 50 → price near fair value (rational zone)
- >75 → high irrationality, price stretched above fair value
- >90 → extreme irrationality, unsustainable extremes
- <25 → high irrationality, price stretched below fair value
- <10 → extreme bearish irrationality
4. Price vs Fair Value (% deviation)
The indicator plots the percentage difference between price and fair value:
pctDiff = (price - fairValue) / fairValue * 100
- Positive values → Percentage above fair value (optimistic / overvalued)
- Negative values → Percentage below fair value (pessimistic / undervalued)
Visuals:
- Irrationality (%) Line (0–100) shows irrationality level.
- Background Colors: Yellow= high bullish irrationality, Green= extreme bullish irrationality, Orange= high bearish irrationality, Red= extreme bearish irrationality.
- Price - FairValue (%) plot: price deviation vs fair value (%), Colored green above 0 and red below 0.
- Label: display actual price, estimated fair value, and Z-score for the latest bar.
- Alerts: configurable thresholds for high and extreme irrationality.
How to read it:
- 50 → Market trading near fair value.
- >75 / >90 → Price may be irrationally high; risk of pullback increases.
- <25 / <10 → Price may be irrationally low; potential rebound zones, but trends can continue.
- Price - FairValue (%) plot → visual guide for % price stretch relative to fair value.
Notes / Warnings:
- Measures relative deviation, not fundamental value!
- High irrationality scores do not automatically indicate trades; markets can remain can be irrational longer than you can stay solvent .
- Best used with other tools: momentum, volume, divergence, and multi-timeframe analysis.
Volume ClusteringThis Volume Clustering script is a powerful tool for analyzing intraday trading dynamics by combining two key metrics: volume Z-Score and Cumulative Volume Delta (CVD). By categorizing market activity into distinct clusters, it helps you identify high-conviction trading opportunities and understand underlying market pressure.
How It Works
The script operates on a simple, yet effective, premise: it classifies each trading bar based on its statistical significance (volume Z-Score) and buying/selling pressure (CVD).
Volume Z-Score
The volume Z-Score measures how far the current bar's volume is from its average, helping to identify periods of unusually high or low volume. This metric is a powerful way to spot when institutional or large players might be entering the market. A high Z-Score suggests a significant event is taking place, regardless of direction.
Cumulative Volume Delta (CVD)
CVD tracks the net buying and selling pressure across different timeframes. The script uses a lower timeframe (e.g., 1-minute) and anchors it to a higher timeframe (e.g., 1-day) to capture intraday pressure. A positive CVD indicates more buying pressure, while a negative CVD suggests more selling pressure.
Cluster Categories
The script analyzes the confluence of these two metrics to assign a cluster to each bar, providing actionable insights. The clusters are color-coded and labeled to make them easy to interpret:
🟢 High Conviction Bullish: Unusually high volume (high Z-Score) combined with significant buying pressure (high CVD). This cluster suggests strong bullish momentum.
🔴 High Conviction Bearish: Unusually high volume (high Z-Score) coupled with significant selling pressure (low CVD). This cluster suggests strong bearish momentum.
🟡 Low Conviction/Noise: Low to moderate volume and mixed buying/selling pressure. This represents periods of indecision or consolidation, where market noise is more prevalent.
🟣 Other Clusters: The script also identifies other combinations, such as high volume with moderate CVD, or low volume with high CVD, which can provide additional context for understanding market dynamics.
Key Features & Customization
The script offers several customizable settings to tailor the analysis to your specific trading style:
Z-Score Lookback Length: Adjust the lookback period for calculating the average volume. A shorter period focuses on recent volume trends, while a longer period provides a broader context.
CVD Anchor & Lower Timeframe: Define the timeframes used for CVD calculation. You can anchor the analysis to a daily or weekly timeframe while using a lower timeframe (e.g., 1-minute) to capture granular intraday pressure.
High/Low Volume Mode: Toggle between "High Volume" mode (which uses 90th and 10th percentiles for clustering) and "Low Volume" mode (which uses 75th and 25th percentiles). This allows you to choose whether to focus on extreme events or more subtle shifts in market sentiment.
Combined Cluster & Market StructureI barrowed code from the Mxwll Price Action Suite script as appreciated the structure in which the script defined structure, however I renamed variables and reduced the original script to define only the outer structure. I added volume and CVD clustering to define ranges and initiation market structures and add the ADX to assist with determining trend strength prior to labeling market structure breaks.
Combined Cluster & Market Structure indicator, a powerful and comprehensive tool for technical analysis. This script integrates two core concepts to provide a holistic view of market dynamics:
Z-Score Clustering & Volume Analysis: The indicator calculates Z-scores for both volume and Cumulative Volume Delta (CVD) to categorize market activity into six distinct clusters:
High-Conviction Bullish/Bearish: Signals of strong directional momentum based on high volume and corresponding CVD.
Effort vs. Result: High volume with moderate CVD, suggesting potential indecision or absorption.
Quiet Accumulation/Distribution: Low-volume periods with strong CVD, often preceding major moves.
Low Conviction/Noise: Represents periods of low market participation and weak signals.
These clusters are visually marked on the chart to provide real-time insight into market sentiment.
Market Structure Mapping: The indicator automatically detects and labels significant structural points to help you navigate price action. It identifies:
Higher Highs (HH) and Lower Lows (LL) to show the primary trend direction.
Breaks of Structure (BoS), indicating trend continuation.
Changes of Character (CHoCH), signaling a potential trend reversal.
Additionally, the script features consolidation box detection, which automatically highlights periods of low-conviction market activity, helping you avoid choppy, sideways markets. An integrated ADX filter ensures that structural breaks are only labeled during periods of strong trend strength, reducing false signals.
I want to thank Mxwll Capital for their contribution to the Combined Cluster & Market Structure indicator.
Pairs Trading Scanner [BackQuant]Pairs Trading Scanner
What it is
This scanner analyzes the relationship between your chart symbol and a chosen pair symbol in real time. It builds a normalized “spread” between them, tracks how tightly they move together (correlation), converts the spread into a Z-Score (how far from typical it is), and then prints clear LONG / SHORT / EXIT prompts plus an at-a-glance dashboard with the numbers that matter.
Why pairs at all?
Markets co-move. When two assets are statistically related, their relationship (the spread) tends to oscillate around a mean.
Pairs trading doesn’t require calling overall market direction you trade the relative mispricing between two instruments.
This scanner gives you a robust, visual way to find those dislocations, size their significance, and structure the trade.
How it works (plain English)
Step 1 Pick a partner: Select the Pair Symbol to compare against your chart symbol. The tool fetches synchronized prices for both.
Step 2 Build a spread: Choose a Spread Method that defines “relative value” (e.g., Log Spread, Price Ratio, Return Difference, Price Difference). Each lens highlights a different flavor of divergence.
Step 3 Validate relationship: A rolling Correlation checks if the pair is moving together enough to be tradable. If correlation is weak, the scanner stands down.
Step 4 Standardize & score: The spread is normalized (mean & variability over a lookback) to form a Z-Score . Large absolute Z means “stretched,” small means “near fair.”
Step 5 Signals: When the Z-Score crosses user-defined thresholds with sufficient correlation , entries print:
LONG = long chart symbol / short pair symbol,
SHORT = short chart symbol / long pair symbol,
EXIT = mean reversion into the exit zone or correlation failure.
Core concepts (the three pillars)
Spread Method Your definition of “distance” between the two series.
Guidance:
Log Spread: Focuses on proportional differences; robust when prices live on different scales.
Price Ratio: Classic relative value; good when you care about “X per Y.”
Return Difference: Emphasizes recent performance gaps; nimble for momentum-to-mean plays.
Price Difference: Straight subtraction; intuitive for similar-scale assets (e.g., two ETFs).
Correlation A rolling score of co-movement. The scanner requires it to be above your Min Correlation before acting, so you’re not trading random divergence.
Z-Score “How abnormal is today’s spread?” Positive = chart richer than pair; negative = cheaper. Thresholds define entries/exits with transparent, statistical context.
What you’ll see on the chart
Correlation plot (blue line) with a dashed Min Correlation guide. Above the line = green zone for signals; below = hands off.
Z-Score plot (white line) with colored, dashed Entry bands and dotted Exit bands. Zero line for mean.
Normalized spread (yellow) for a quick “shape read” of recent divergence swings.
Signal markers :
LONG (green label) when Z < –Entry and corr OK,
SHORT (red label) when Z > +Entry and corr OK,
EXIT (gray label) when Z returns inside the Exit band or correlation drops below the floor.
Background tint for active state (faint green for long-spread stance, faint red for short-spread stance).
The two built-in dashboards
Statistics Table (top-right)
Pair Symbol Your chosen partner.
Correlation Live value vs. your minimum.
Z-Score How stretched the spread is now.
Current / Pair Prices Real-time anchors.
Signal State NEUTRAL / LONG / SHORT.
Price Ratio Context for ratio-style setups.
Analysis Table (bottom-right)
Avg Correlation Typical co-movement level over your window.
Max |Z| The recent extremes of dislocation.
Spread Volatility How “lively” the spread has been.
Trade Signal A human-readable prompt (e.g., “LONG A / SHORT B” or “NO TRADE” / “LOW CORRELATION”).
Risk Level LOW / MEDIUM / HIGH based on current stretch (absolute Z).
Signals logic (plain English)
Entry (LONG): The spread is unusually negative (chart cheaper vs pair) and correlation is healthy. Expect mean reversion upward in the spread: long chart, short pair.
Entry (SHORT): The spread is unusually positive (chart richer vs pair) and correlation is healthy. Expect mean reversion downward in the spread: short chart, long pair.
Exit: The spread relaxes back toward normal (inside your exit band), or correlation deteriorates (relationship no longer trusted).
A quick, repeatable workflow
1) Choose your pair in context (same sector/theme or known macro link). Think: “Do these two plausibly co-move?”
2) Pick a spread lens that matches your narrative (ratio for relative value, returns for short-term performance gaps, etc.).
3) Confirm correlation is above your floor no corr, no trade.
4) Wait for a stretch (Z beyond Entry band) and a printed LONG / SHORT .
5) Manage to the mean (EXIT band) or correlation failure; let the scanners’ state/labels keep you honest.
Settings that matter (and why)
Spread Method Defines the “mispricing” you care about.
Correlation Period Longer = steadier regime read, shorter = snappier to regime change.
Z-Score Period The window that defines “normal” for the spread; it sets the yardstick.
Use Percentage Returns Normalizes series when using return-based logic; keep on for mixed-scale assets.
Entry / Exit Thresholds Set your stretch and your target reversion zone. Wider entries = rarer but stronger signals.
Minimum Correlation The gatekeeper. Raising it favors quality over quantity.
Choosing pairs (practical cheat sheet)
Same family: two index ETFs, two oil-linked names, two gold miners, two L1 tokens.
Hedge & proxy: stock vs. sector ETF, BTC vs. BTC index, WTI vs. energy ETF.
Cross-venue or cross-listing: instruments that are functionally the same exposure but price differently intraday.
Reading the cues like a pro
Divergence shape: The yellow normalized spread helps you see rhythm fast spike and snap-back versus slow grind.
Corr-first discipline: Don’t fight the “Min Correlation” line. Good pairs trading starts with a relationship you can trust.
Exit humility: When Z re-centers, let the EXIT do its job. The edge is the journey to the mean, not overstaying it.
Frequently asked (quick answers)
“Long/Short means what exactly?”
LONG = long the chart symbol and short the pair symbol.
SHORT = short the chart symbol and long the pair symbol.
“Do I need same price scales?” No. The spread methods normalize in different ways; choose the one that fits your use case (log/ratio are great for mixed scales).
“What if correlation falls mid-trade?” The scanner will neutralize the state and print EXIT . Relationship first; trade second.
Field notes & patterns
Snap-back days: After a one-sided session, return-difference spreads often flag cleaner intraday mean reversions.
Macro rotations: Ratio spreads shine during sector re-weights (e.g., value vs. growth ETFs); look for steady corr + elevated |Z|.
Event bleed-through: If one symbol reacts to news and its partner lags, Z often flags a high-quality, short-horizon re-centering.
Display controls at a glance
Show Statistics Table Live state & key numbers, top-right.
Show Analysis Table Context/risk read, bottom-right.
Show Correlation / Spread / Z-Score Toggle the sub-charts you want visible.
Show Entry/Exit Signals Turn markers on/off as needed.
Coloring Adjust Long/Short/Neutral and correlation line colors to match your theme.
Alerts (ready to route to your workflow)
Pairs Long Entry Z falls through the long threshold with correlation above minimum.
Pairs Short Entry Z rises through the short threshold with correlation above minimum.
Pairs Trade Exit Z returns to neutral or the relationship fails your correlation floor.
Correlation Breakdown Rolling correlation crosses your minimum; relationship caution.
Final notes
The scanner is designed to keep you systematic: require relationship (correlation), quantify dislocation (Z-Score), act when stretched, stand down when it normalizes or the relationship degrades. It’s a full, visual loop for relative-value trading that stays out of your way when it should and gets loud only when the numbers line up.
FRANJAS POR FECHAS - RSDescription:
This indicator allows you to highlight specific dates on your chart with vertical background stripes, similar to a session indicator.
Input your dates in the format DD.MM.YYYY (you can separate them with commas, spaces, line breaks, or semicolons).
The script automatically normalizes the format and applies a shaded vertical band for each matching day.
Works on daily and intraday charts: in intraday, the shading will cover the full trading day.
Options available to adjust the color and transparency of the stripes.
Optional dotted lines can be enabled at the start and end of each highlighted day.
This is useful for marking important events such as FOMC meetings, earnings releases, economic data announcements, or any custom list of key dates you want to track directly on your chart.
