Metatrader CalculatorThe “ Metatrader Calculator ” indicator calculates the position size, risk, and potential gain of a trade, taking into account the account balance, risk percentage, entry price, stop loss price, and risk/reward ratio. It supports the XAUUSD, XAGUSD, and BTCUSD pairs, automatically calculating the position size (in lots) based on these parameters. The calculation is displayed in a table on the chart, showing the lot size, loss in dollars, and potential gain based on the defined risk.
Statistics
CME Price LimitCalculates the CME Price Limit
The reference price is obtained from the previous day's closing settlement price
(data pulled from the asset's daily chart with settlement enabled)
Percentage limit can be modified in settings
Buffer can be enabled (for example, 2% buffer on a 7% limit, so a line gets drawn at 5% too)
Alert can be enabled for price crossing a certain percentage from reference on the day
You can choose to plot the historical lines on every day, or the current day only
The reference price output can be found in the data window, or in the indicator status line if enabled in the settings.
Before placing real trades with this, you should compare the indicator's reference price to what's shown on CME's website, to double check that TradingView's data matches for your contract.
www.cmegroup.com
ML Deep Regression Pro (TechnoBlooms)ML Deep Regression Pro is a machine-learning-inspired trading indicator that integrates Polynomial Regression, Linear Regression and Statistical Deviation models to provide a powerful, data-driven approach to market trend analysis.
Designed for traders, quantitative analysts and developers, this tool transforms raw market data into predictive trend insights, allowing for better decision-making and trend validation.
By leveraging statistical regression techniques, ML Deep Regression Pro eliminates market noise and identifies key trend shifts, making it a valuable addition to both manual and algorithmic trading strategies.
REGRESSION ANALYSIS
Regression is a statistical modeling technique used in machine learning and data science to identify patterns and relationships between variables. In trading, it helps detect price trends, reversals and volatility changes by fitting price data into a predictive model.
1. Linear Regression -
The most widely used regression model in trading, providing a best-fit plotted line to track price trends.
2. Polynomial Regression -
A more advanced form of regression that fits curved price structures, capturing complex market cycles and improving trend forecasting accuracy.
3. Standard Deviation Bands -
Based on regression calculations, these bands measure price dispersion and identify overbought/ oversold conditions, similar to Bollinger Bands. By default, these lines are hidden and user can make it visible through Settings.
KEY FEATURES :-
✅ Hybrid Regression Engine – Combines Linear and Polynomial Regression to detect market trends with greater accuracy.
✅ Dynamic Trend Bias Analysis – Identifies bullish & bearish market conditions using real-time regression models.
✅ Standard Deviation Bands – Measures price volatility and potential reversals with an advanced deviation model.
✅ Adaptive EMA Crossover Signals – Generates buy/sell signals when price momentum shifts relative to the regression trend.
VNIndex Over 6.5% Downside Drop Indicator with TableOverview: The VNIndex 6.5% Downside Drop Indicator is a powerful tool designed to help traders and investors identify significant market drops on the VNIndex (or any other asset) based on a 6.5% downside threshold. This Pine Script® indicator automatically detects when the price of an asset drops by more than 6.5% within a single day, and visually marks those events on the chart.
Key Features:
6.5% Downside Drop Detection: Automatically calculates the daily percentage drop and identifies when the price falls by more than 6.5%.
Table Display: Displays the dates and corresponding percentage drops of all identified instances in a convenient table at the bottom right of the chart.
Markers: Red down-pointing markers are plotted above bars where the price drop exceeds the 6.5% threshold, making it easy to spot critical drop events at a glance.
Easy-to-Read Table: The table lists the date and drop percentage, updating dynamically as new drops are detected. This allows for easy tracking of significant downside moves over time.
How to Use:
Install the Script: Add this indicator to your TradingView chart.
Monitor Price Drops: The indicator will automatically detect when the price drops by over 6.5% from the previous close and display a marker on the chart and the table in the bottom right corner.
View the Table: The table displays the date and the percentage drop of each detected event, making it easy to track past significant moves.
Alerts: You can set an alert for 6.5% drops to receive notifications in real-time.
Customization Options:
The drop percentage threshold (6.5%) can be adjusted in the script to fit other market conditions or assets.
The table can be resized or styled based on user preference for better visibility.
Why Use This Indicator? This indicator is perfect for traders looking to spot large, significant price movements quickly. Large downside drops can signal potential market reversals or trading opportunities, and this tool helps you track such events effortlessly. Whether you're monitoring the VNIndex or any other asset, this indicator provides crucial insights into volatile price action, helping you make more informed decisions.
Open Source License: This indicator is open source and free to use under the Mozilla Public License 2.0. You are welcome to modify, distribute, and contribute to the project.
Contributions: Feel free to contribute improvements, fixes, or new features by creating a pull request. Let’s collaborate to make this indicator even better for the community!
IBAC Strategy - ZygoraIBAC - Intrinsic Binary Averaging based Contrarian
A contrarian scalping strategy in the futures market, designed to stabilize market efficiency by capitalizing on price reversals. The strategy has no stop loss, instead employing a cascading approach—adding to the position size each time the price moves in the wrong direction—and closes the full position when the target profit is reached. Without delving into intricate details, the strategy adheres to the following basic rules:
Position sizing is determined by a customized indicator based on cumulative reversal probability, which also contributes to identifying the signal’s direction.
Direction is determined by the Moving Average: price above the Moving Average signals a Short position, while price below it signals a Long position.
The threshold for entries and exits is adjusted based on the range between extremes (highest high minus lowest low) over the past 100 historical bars.
The next limit entry is placed at a distance equal to the threshold length below (for Long) or above (for Short) the current average price.
The next target profit is set at a distance equal to the threshold length above (for Long) or below (for Short) the current average price.
A signal is triggered when there is a sudden price movement detected by the RSI (Relative Strength Index).
When a signal is identified, the strategy starts with a risk-reward ratio (RR) of 1:1. However, the RR worsens as the cascading steps—referred to as inventory I—increase, because the average entry price shifts unfavorably with each new position added. To mitigate the risk of liquidation, the strategy aims to hold a smaller inventory amount over time. This is achieved by using a multiple threshold multiplier: when a specified inventory limit is reached, the threshold for the next entry increases, and the threshold for the next target profit decreases. As a result, with higher inventory levels, the strategy accepts a lower RR but increases the likelihood of hitting the target profit.
The target profit is always set above the average entry price (for Long) or below it (for Short), ensuring that the strategy eventually closes at a profit. This leads to a 100% win rate but comes with relatively high drawdowns due to the absence of a stop loss and the cascading nature of the positions. The strategy performs best in a consolidation market in 1 minute timeframe, where price tends to oscillate within a range, allowing the contrarian approach to capitalize on reversals. The strategy’s name is derived from its customized indicator for position sizing, which leverages cumulative reversal probability to optimize position sizes and assist in determining the signal’s direction.
Manual Trade Ledger# Manual Options Trade Journal – Pine Script
This project is a Pine Script implementation for TradingView that allows users to manually log options trades into a live table overlay on a chart.
## ✨ Features
- 📥 Manual entry of ticker, premium, contracts, strike, expiry, notes
- 📈 Auto-filled live data: timestamp, price, and % change since first log
- 🧾 Tabular logging for trade journaling and exporting to Google Sheets
- 🔧 Fully customizable and designed to support product experimentation
## 🎯 Use Case
This project was built to support a real-world trading workflow for options traders who:
- Prefer to manually log trades while watching charts
- Want a visual, copyable ledger that evolves in real-time
- Want to later analyze entries/exits in spreadsheets or dashboards
## 🛠 How It Works
1. Toggle the `Log Trade` switch inside TradingView’s indicator settings
2. Fill in your trade metadata (ticker, premium, etc.)
3. The script captures timestamp, price, and calculates % change
4. Each new trade adds a row to the table (up to 50 max)
Zig Zag Trend Metrics“ Zig Zag Trend Metrics ” is a highly versatile indicator, built on the classic Zig Zag concept and thoughtfully designed for technical traders seeking a deeper, more structured view of market dynamics. This tool identifies significant swing highs and lows, classifies them, and annotates each with key metrics, offering a precise snapshot of each movement. It enhances visual analysis by drawing connecting lines that outline the flow of market structure, making trend progression and reversals instantly recognizable. Beyond visual mapping, it features a compact, real-time statistics table that calculates the average price and time deltas for both bullish and bearish swings, giving traders deep insights into trend momentum and rhythm. With extensive customization options, this indicator adapts seamlessly to vast trading styles or chart setups, empowering traders to spot patterns, evaluate trend strength, and make more confident, data-backed decisions.
❖ FEATURES
✦ Automatic Swing Detection
At its core, this indicator automatically identifies swing highs and lows based on a customizable lookback period (default: 10 bars).
✦ Labeling Swing Points
Each swing is visualized with a label that includes:
Swing Classification : “HH” (Higher High), “LH” (Lower High), “LL” (Lower Low), or “HL” (Higher Low).
Price Difference : Displayed in percentage or absolute value from the previous opposite swing.
Time Difference : The number of bars since the previous swing of the opposite type.
These labels offer traders clear, immediate insight into price movements and structural changes.
✦ Visual Lines
The indicator draws three types of lines:
Bullish Lines: Connect recent swing lows to new swing highs, indicating uptrends.
Bearish Lines: Connect recent swing highs to new swing lows, indicating downtrends.
Range Lines: Connect consecutive highs or lows to outline price channels.
Each line type can be color-coded and customized for visibility.
✦ Statistics Table
An on-screen metrics table provides a live summary of trends. Script uses Relative Averaging to smooth price and time changes. This prevents outliers from distorting the data and provides a more reliable sense of typical swing behavior.
Uptrend Metrics: Shows average price and time differences from recent bullish swings.
Downtrend Metrics: Shows the same for bearish swings.
🛠️ Customization Options
Ability to tailor the indicator to suit their strategy and aesthetic preferences:
Swing Period: Adjust sensitivity to short- or long-term swings.
Color Settings: Customize line and label colors.
Label Display: Choose between absolute or percentage price differences.
Table Settings: Modify size, location, or visibility.
This makes the indicator highly flexible and useful across various timeframes and assets.
Gioteen-NormThe "Gioteen-Norm" indicator is a versatile and powerful technical analysis tool designed to help traders identify key market conditions such as divergences, overbought/oversold levels, and trend strength. By normalizing price data relative to a moving average and standard deviation, this indicator provides a unique perspective on price behavior, making it easier to spot potential reversals or continuations in the market.
The indicator calculates a normalized value based on the difference between the selected price and its moving average, scaled by the standard deviation over a user-defined period. Additionally, an optional moving average of this normalized value (Green line) can be plotted to smooth the output and enhance signal clarity. This dual-line approach makes it an excellent tool for both short-term and long-term traders.
***Key Features
Divergence Detection: The Gioteen-Norm excels at identifying divergences between price action and the normalized indicator value. For example, if the price makes a higher high while Red line forms a lower high, it may signal a bearish divergence, hinting at a potential reversal.
Overbought/Oversold Conditions: Extreme values of Red line (e.g., significantly above or below zero) can indicate overbought or oversold conditions, helping traders anticipate pullbacks or bounces.
Trend Strength Insight: The normalized output reflects how far the price deviates from its average, providing a measure of momentum and trend strength.
**Customizable Parameters
Traders can adjust the period, moving average type, applied price, and shift to suit their trading style and timeframe.
**How It Works
Label1 (Red Line): Represents the normalized price deviation from a user-selected moving average (SMA, EMA, SMMA, or LWMA) divided by the standard deviation over the specified period. This line highlights the relative position of the price compared to its historical range.
Label2 (Green Line, Optional): A moving average of Label1, which smooths the normalized data to reduce noise and provide clearer signals. This can be toggled on or off via the "Draw MA" option.
**Inputs
Period: Length of the lookback period for normalization (default: 100).
MA Method: Type of moving average for normalization (SMA, EMA, SMMA, LWMA; default: EMA).
Applied Price: Price type used for calculation (Close, Open, High, Low, HL2, HLC3, HLCC4; default: Close).
Shift: Shifts the indicator forward or backward (default: 0).
Draw MA: Toggle the display of the Label2 moving average (default: true).
MA Period: Length of the moving average for Label2 (default: 50).
MA Method (Label2): Type of moving average for Label2 (SMA, EMA, SMMA, LWMA; default: SMA).
**How to Use
Divergence Trading: Look for discrepancies between price action and Label1. A bullish divergence (higher low in Label1 vs. lower low in price) may suggest a buying opportunity, while a bearish divergence could indicate a selling opportunity.
Overbought/Oversold Levels: Monitor extreme Label1 values. For instance, values significantly above +2 or below -2 could indicate overextension, though traders should define thresholds based on the asset and timeframe.
Trend Confirmation: Use Label2 to confirm trend direction. A rising Label2 suggests increasing bullish momentum, while a declining Label2 may indicate bearish pressure.
Combine with Other Tools: Pair Gioteen-Norm with support/resistance levels, RSI, or volume indicators for a more robust trading strategy.
**Notes
The indicator is non-overlay, meaning it plots below the price chart in a separate panel.
Avoid using a Period value of 1, as it may lead to unstable results due to insufficient data for standard deviation calculation.
This tool is best used as part of a broader trading system rather than in isolation.
**Why Use Gioteen-Norm?
The Gioteen-Norm indicator offers a fresh take on price normalization, blending statistical analysis with moving average techniques. Its flexibility and clarity make it suitable for traders of all levels—whether you're scalping on short timeframes or analyzing long-term trends. By publishing this for free, I hope to contribute to the TradingView community and help traders uncover hidden opportunities in the markets.
**Disclaimer
This indicator is provided for educational and informational purposes only. It does not constitute financial advice. Always backtest and validate any strategy before trading with real capital, and use proper risk management.
Uptrick: Z-Score FlowOverview
Uptrick: Z-Score Flow is a technical indicator that integrates trend-sensitive momentum analysi s with mean-reversion logic derived from Z-Score calculations. Its primary objective is to identify market conditions where price has either stretched too far from its mean (overbought or oversold) or sits at a statistically “normal” range, and then cross-reference this observation with trend direction and RSI-based momentum signals. The result is a more contextual approach to trade entry and exit, emphasizing precision, clarity, and adaptability across varying market regimes.
Introduction
Financial instruments frequently transition between trending modes, where price extends strongly in one direction, and ranging modes, where price oscillates around a central value. A simple statistical measure like Z-Score can highlight price extremes by comparing the current price against its historical mean and standard deviation. However, such extremes alone can be misleading if the broader market structure is trending forcefully. Uptrick: Z-Score Flow aims to solve this gap by combining Z-Score with an exponential moving average (EMA) trend filter and a smoothed RSI momentum check, thus filtering out signals that contradict the prevailing market environment.
Purpose
The purpose of this script is to help traders pinpoint both mean-reversion opportunities and trend-based pullbacks in a way that is statistically grounded yet still mindful of overarching price action. By pairing Z-Score thresholds with supportive conditions, the script reduces the likelihood of acting on random price spikes or dips and instead focuses on movements that are significant within both historical and current contextual frameworks.
Originality and Uniquness
Layered Signal Verification: Signals require the fulfillment of multiple layers (Z-Score extreme, EMA trend bias, and RSI momentum posture) rather than merely breaching a statistical threshold.
RSI Zone Lockout: Once RSI enters an overbought/oversold zone and triggers a signal, the script locks out subsequent signals until RSI recovers above or below those zones, limiting back-to-back triggers.
Controlled Cooldown: A dedicated cooldown mechanic ensures that the script waits a specified number of bars before issuing a new signal in the opposite direction.
Gradient-Based Visualization: Distinct gradient fills between price and the Z-Mean line enhance readability, showing at a glance whether price is trading above or below its statistical average.
Comprehensive Metrics Panel: An optional on-chart table summarizes the Z-Score’s key metrics, streamlining the process of verifying current statistical extremes, mean levels, and momentum directions.
Why these indicators were merged
Z-Score measurements excel at identifying when price deviates from its mean, but they do not intrinsically reveal whether the market’s trajectory supports a reversion or if price might continue along its trend. The EMA, commonly used for spotting trend directions, offers valuable insight into whether price is predominantly ascending or descending. However, relying solely on a trend filter overlooks the intensity of price moves. RSI then adds a dedicated measure of momentum, helping confirm if the market’s energy aligns with a potential reversal (for example, price is statistically low but RSI suggests looming upward momentum). By uniting these three lenses—Z-Score for statistical context, EMA for trend direction, and RSI for momentum force—the script offers a more comprehensive and adaptable system, aiming to avoid false positives caused by focusing on just one aspect of price behavior.
Calculations
The core calculation begins with a simple moving average (SMA) of price over zLen bars, referred to as the basis. Next, the script computes the standard deviation of price over the same window. Dividing the difference between the current price and the basis by this standard deviation produces the Z-Score, indicating how many standard deviations the price is from its mean. A positive Z-Score reveals price is above its average; a negative reading indicates the opposite.
To detect overall market direction, the script calculates an exponential moving average (emaTrend) over emaTrendLen bars. If price is above this EMA, the script deems the market bullish; if below, it’s considered bearish. For momentum confirmation, the script computes a standard RSI over rsiLen bars, then applies a smoothing EMA over rsiEmaLen bars. This smoothed RSI (rsiEma) is monitored for both its absolute level (oversold or overbought) and its slope (the difference between the current and previous value). Finally, slopeIndex determines how many bars back the script compares the basis to check whether the Z-Mean line is generally rising, falling, or flat, which then informs the coloring scheme on the chart.
Calculations and Rational
Simple Moving Average for Baseline: An SMA is used for the core mean because it places equal weight on each bar in the lookback period. This helps maintain a straightforward interpretation of overbought or oversold conditions in the context of a uniform historical average.
Standard Deviation for Volatility: Standard deviation measures the variability of the data around the mean. By dividing price’s difference from the mean by this value, the Z-Score can highlight whether price is unusually stretched given typical volatility.
Exponential Moving Average for Trend: Unlike an SMA, an EMA places more emphasis on recent data, reacting quicker to new price developments. This quicker response helps the script promptly identify trend shifts, which can be crucial for filtering out signals that go against a strong directional move.
RSI for Momentum Confirmation: RSI is an oscillator that gauges price movement strength by comparing average gains to average losses over a set period. By further smoothing this RSI with another EMA, short-lived oscillations become less influential, making signals more robust.
SlopeIndex for Slope-Based Coloring: To clarify whether the market’s central tendency is rising or falling, the script compares the basis now to its level slopeIndex bars ago. A higher current reading indicates an upward slope; a lower reading, a downward slope; and similar readings, a flat slope. This is visually represented on the chart, providing an immediate sense of the directionality.
Inputs
zLen (Z-Score Period)
Specifies how many bars to include for computing the SMA and standard deviation that form the basis of the Z-Score calculation. Larger values produce smoother but slower signals; smaller values catch quick changes but may generate noise.
emaTrendLen (EMA Trend Filter)
Sets the length of the EMA used to detect the market’s primary direction. This is pivotal for distinguishing whether signals should be considered (price aligning with an uptrend or downtrend) or filtered out.
rsiLen (RSI Length)
Defines the window for the initial RSI calculation. This RSI, when combined with the subsequent smoothing EMA, forms the foundation for momentum-based signal confirmations.
rsiEmaLen (EMA of RSI Period)
Applies an exponential moving average over the RSI readings for additional smoothing. This step helps mitigate rapid RSI fluctuations that might otherwise produce whipsaw signals.
zBuyLevel (Z-Score Buy Threshold)
Determines how negative the Z-Score must be for the script to consider a potential oversold signal. If the Z-Score dives below this threshold (and other criteria are met), a buy signal is generated.
zSellLevel (Z-Score Sell Threshold)
Determines how positive the Z-Score must be for a potential overbought signal. If the Z-Score surpasses this threshold (and other checks are satisfied), a sell signal is generated.
cooldownBars (Cooldown (Bars))
Enforces a bar-based delay between opposite signals. Once a buy signal has fired, the script must wait the specified number of bars before registering a new sell signal, and vice versa.
slopeIndex (Slope Sensitivity (Bars))
Specifies how many bars back the script compares the current basis for slope coloration. A bigger slopeIndex highlights larger directional trends, while a smaller number emphasizes shorter-term shifts.
showMeanLine (Show Z-Score Mean Line)
Enables or disables the plotting of the Z-Mean and its slope-based coloring. Traders who prefer minimal chart clutter may turn this off while still retaining signals.
Features
Statistical Core (Z-Score Detection):
This feature computes the Z-Score by taking the difference between the current price and the basis (SMA) and dividing by the standard deviation. In effect, it translates price fluctuations into a standardized measure that reveals how significant a move is relative to the typical variation seen over the lookback. When the Z-Score crosses predefined thresholds (zBuyLevel for oversold and zSellLevel for overbought), it signals that price could be at an extreme.
How It Works: On each bar, the script updates the SMA and standard deviation. The Z-Score is then refreshed accordingly. Traders can interpret particularly large negative or positive Z-Score values as scenarios where price is abnormally low or high.
EMA Trend Filter:
An EMA over emaTrendLen bars is used to classify the market as bullish if the price is above it and bearish if the price is below it. This classification is applied to the Z-Score signals, accepting them only when they align with the broader price direction.
How It Works: If the script detects a Z-Score below zBuyLevel, it further checks if price is actually in a downtrend (below EMA) before issuing a buy signal. This might seem counterintuitive, but a “downtrend” environment plus an oversold reading often signals a potential bounce or a mean-reversion play. Conversely, for sell signals, the script checks if the market is in an uptrend first. If it is, an overbought reading aligns with potential profit-taking.
RSI Momentum Confirmation with Oversold/Overbought Lockout:
RSI is calculated over rsiLen, then smoothed by an EMA over rsiEmaLen. If this smoothed RSI dips below a certain threshold (for example, 30) and then begins to slope upward, the indicator treats it as a potential sign of recovering momentum. Similarly, if RSI climbs above a certain threshold (for instance, 70) and starts to slope downward, that suggests dwindling momentum. Additionally, once RSI is in these zones, the indicator locks out repetitive signals until RSI fully exits and re-enters those extreme territories.
How It Works: Each bar, the script measures whether RSI has dropped below the oversold threshold (like 30) and has a positive slope. If it does, the buy side is considered “unlocked.” For sell signals, RSI must exceed an overbought threshold (70) and slope downward. The combination of threshold and slope helps confirm that a reversal is genuinely in progress instead of issuing signals while momentum remains weak or stuck in extremes.
Cooldown Mechanism:
The script features a custom bar-based cooldown that prevents issuing new signals in the opposite direction immediately after one is triggered. This helps avoid whipsaw situations where the market quickly flips from oversold to overbought or vice versa.
How It Works: When a buy signal fires, the indicator notes the bar index. If the Z-Score and RSI conditions later suggest a sell, the script compares the current bar index to the last buy signal’s bar index. If the difference is within cooldownBars, the signal is disallowed. This ensures a predefined “quiet period” before switching signals.
Slope-Based Coloring (Z-Mean Line and Shadow):
The script compares the current basis value to its value slopeIndex bars ago. A higher reading now indicates a generally upward slope, while a lower reading indicates a downward slope. The script then shades the Z-Mean line in a corresponding bullish or bearish color, or remains neutral if little change is detected.
How It Works: This slope calculation is refreshingly straightforward: basis – basis . If the result is positive, the line is colored bullish; if negative, it is colored bearish; if approximately zero, it remains neutral. This provides a quick visual cue of the medium-term directional bias.
Gradient Overlays:
With gradient fills, the script highlights where price stands in relation to the Z-Mean. When price is above the basis, a purple-shaded region is painted, visually indicating a “bearish zone” for potential overbought conditions. When price is below, a teal-like overlay is used, suggesting a “bullish zone” for potential oversold conditions.
How It Works: Each bar, the script checks if price is above or below the basis. It then applies a fill between close and basis, using distinct colors to show whether the market is trading above or below its mean. This creates an immediate sense of how extended the market might be.
Buy and Sell Labels (with Alerts):
When a legitimate buy or sell condition passes every check (Z-Score threshold, EMA trend alignment, RSI gating, and cooldown clearance), the script plots a corresponding label directly on the chart. It also fires an alert (if alerts are set up), making it convenient for traders who want timely notifications.
How It Works: If rawBuy or rawSell conditions are met (refined by RSI, EMA trend, and cooldown constraints), the script calls the respective plot function to paint an arrow label on the chart. Alerts are triggered simultaneously, carrying easily recognizable messages.
Metrics Table:
The optional on-chart table (activated by showMetrics) presents real-time Z-Score data, including the current Z-Score, its rolling mean, the maximum and minimum Z-Score values observed over the last zLen bars, a percentile position, and a short-term directional note (rising, falling, or flat).
Current – The present Z-Score reading
Mean – Average Z-Score over the zLen period
Min/Max – Lowest and highest Z-Score values within zLen
Position – Where the current Z-Score sits between the min and max (as a percentile)
Trend – Whether the Z-Score is increasing, decreasing, or flat
Conclusion
Uptrick: Z-Score Flow offers a versatile solution for traders who need a statistically informed perspective on price extremes combined with practical checks for overall trend and momentum. By leveraging a well-defined combination of Z-Score, EMA trend classification, RSI-based momentum gating, slope-based visualization, and a cooldown mechanic, the script reduces the occurrence of false or premature signals. Its gradient fills and optional metrics table contribute further clarity, ensuring that users can quickly assess market posture and make more confident trading decisions in real time.
Disclaimer
This script is intended solely for informational and educational purposes. Trading in any financial market comes with substantial risk, and there is no guarantee of success or the avoidance of loss. Historical performance does not ensure future results. Always conduct thorough research and consider professional guidance prior to making any investment or trading decisions.
Session Range (Pips/Points) Marcos Trader## English Description
Title: Session Range Indicator (Pips/Points)
Summary:
This indicator calculates and displays the price range (high - low) for the Asian, London, and New York trading sessions directly on your chart. It helps you quickly visualize the volatility of each recent session, showing the result in whole Pips for Forex or in Points for other instruments.
Key Features:
Calculates the High-Low range for the Asia, London, & NY sessions.
Displays the range in whole Pips for Forex (automatically detects JPY pairs for correct calculation).
Displays the range in Points (based on syminfo.mintick) for Indices, Crypto, Commodities, Stocks, etc.
100% Configurable Session Times: Define the exact start time, end time, and most importantly, the Time Zone for each session (Asia, London, NY) in the indicator settings. This ensures accuracy regardless of Daylight Saving Time or your chart's timezone!
Shows clear labels with the range near the end of each calculated session.
Options to individually show or hide the labels for each session.
Allows configuration of label transparency.
Allows defining how many past session labels to display on the chart (default is 5).
Developed in Pine Script v6.
How to Use:
Add the indicator to your chart.
Open the indicator Settings (gear icon).
Go to the "Session Times" section.
For each session (Asia, London, NY), enter the schedule in HHMM-HHMM format and ensure you add the correct Time Zone using a colon followed by the standard name (e.g., :Europe/London, :America/New_York, :Asia/Tokyo, :UTC+2, :UTC-5). This step is crucial.
Adjust the display options under "Show Sessions" and "Appearance" according to your preferences.
Click "OK".
Notes:
The accuracy of the indicator critically depends on the correct configuration of the times and time zones in the settings. The range label appears near the last bar belonging to the defined session.
Magnetic Trend filterMagnetic Trend Filter – A Smarter Way to Trade Trends 🚀
I’m excited to introduce a powerful trend filtering method that I’ve been working on—Magnetic Trend Filter (MTF). If you’ve ever struggled with noisy price action, false signals, or unclear trends, this indicator might be just what you need!
🔍 What is the Magnetic Trend Filter?
MTF is designed to smooth out market noise and help traders focus on clean, high-probability trend signals. It works by applying an intelligent filtering mechanism to Close price data, reducing whipsaws while maintaining trend sensitivity.
Instead of relying solely on conventional moving averages or lagging indicators, MTF adapts dynamically to market conditions, providing a more refined view of trend direction.
🎯 How it Works
• MTF processes filtered Close price data, making trends more visible.
• It reduces unnecessary price fluctuations, helping you stay in trades longer.
• The filtering mechanism ensures better accuracy in defining trend direction.
📈 How to Use It
• Buy Signals: When the trend filter turns bullish (uptrend confirmation).
• Sell Signals: When the trend filter turns bearish (downtrend confirmation).
• Combine with Other Indicators: MTF works great alongside VWAP, Bollinger Bands, and Ichimoku Cloud for added confluence.
Personally, I use it with my price range filter to catch good exits. Have added that to the Magnetic trend filter and will also publish advanced version independently.
🛠 Customization & Optimization
I’ve optimized the script to reduce computation load, making it efficient and responsive even on lower timeframes. You can tweak smoothing parameters to adjust the sensitivity of the filter based on your trading style.
📌 Final Thoughts
Magnetic Trend Filter is an efficient way to identify trends while avoiding unnecessary noise in price movements. Whether you’re a day trader or swing trader, this tool can help improve decision-making and increase trading accuracy.
💡 Try it out and let me know your thoughts! I’d love to hear feedback and explore potential improvements together. 🚀
Disclaimer:
This is for educational purpose only, no matter how promising things look on chart, they are past performances and reality may vary in real-time.
So use at your own risk.
IQ Liquidation Heatmap [TradingIQ]Introducing "IQ Liquidation Heatmap".
IQ Liquidation Heatmap is a proprietary indicator designed to identify and display price zones where large numbers of crypto position liquidations are likely to occur. It presents both current liquidation zones—areas where a cascade of liquidations would be triggered if the price is reached—and historical liquidation zones, where such events have taken place before.
Why Liquidations and Liquidation Cascades Are Important
Liquidation cascades are important because they can lead to rapid and significant price moves in the market. When many traders have set stop-loss orders or are highly leveraged at similar price levels, a move that hits these zones can force a large number of positions to close at once. This mass closing of positions not only accelerates the price movement but can also trigger further liquidations in a self-reinforcing loop.
Understanding where these cascades occur helps traders recognize potential support and resistance levels. It also provides insights into where market participants are most vulnerable, allowing for better risk management and more informed trading decisions. In short, liquidation cascades highlight key areas of market stress that can lead to increased volatility and opportunities for those prepared to act.
In short, if a lot of short positions are liquidated simultaneously, an upside liquidation cascade can occur. During an upside liquidation cascade, price will increase intensely to the upside with high volatility.
If a lot of long positions are liquidated simultaneously, a downside liquidation cascade can occur. During a downside liquidation cascade, price will decrease intensely to the downside with high volatility.
Knowing where these liquidation cascades can occur is invaluable information for crypto traders.
What IQ Liquidation Heatmap Does
IQ Liquidation Heatmap visually maps price levels that have seen or may see liquidation cascades. In plain terms, it shows you where many stop-losses or leveraged positions have been triggered in the past and where similar events can occur in the future. By highlighting these zones, the indicator helps you understand areas of market stress that could lead to rapid price movements.
The image above shows a historical liquidation cascade occurring. Clustered bubbles show large amounts of liquidations occurring - the more bubbles and the brighter they are, the stronger the liquidation cascade. During a liquidation cascade, there is a higher chance that a strong downtrend or uptrend will continue.
Current Liquidation Levels
The image above explains current liquidation levels.
Current liquidations levels are price areas where a large number of positions will be liquidated. If a liquidation level is above the current price, then it is considered a price zone where shorts will be liquidated. If a liquidation level is below the current price, then it is considered a price zone where longs will be liquidated.
In this image, bright green levels represent price areas where the highest amount of positions will be liquidated, while dark purple levels represent price areas where the lowest amount of positions will be liquidated.
An active (current) liquidation level will extend to the right beyond the current price because they have not yet been hit.
When strong liquidation levels (green - bright green) are hit and are above price, it is expected that an upside liquidation cascade will occur. When strong liquidations are hit and are below price, it is expected that a downside liquidation cascade will occur.
Historical Liquidation Levels
The image above explains historical liquidation levels.
Historical liquidation levels stop at the bar where they are hit, so you can see how price responded to hitting a key liquidation level.
In this image, bright green levels represent price areas where the highest amount of positions will be liquidated, while dark purple levels represent price areas where the lowest amount of positions will be liquidated.
If price moves up into a liquidation level, then shorts are being liquidated. If price moves down into a liquidation level, then longs are being liquidated. In the image, we can see that when bright green liquidation levels were hit - a liquidation cascade occurred. During this cascade, price continued to move strongly to the downside with high volatility.
During the uptrend after the downtrend, we can see some bright green liquidation levels were also hit - causing an upside liquidation cascade that resulted in strong, volatile upside price moves.
Gradient Bar
The image above explains the liquidations gradient bar.
The bar located on the right of your chart shows what colors correspond to low, medium, and high liquidation levels.
In this image, bright green means the liquidation level is strong, while dark purple means the liquidation level is weak. By extension, we would expect liquidation cascades or strong price moves to more likely occur when a cluster of bright green liquidation zones are hit. Additionally, we would expect a small reaction (or no reaction at all) when dark purple liquidation zones are hit.
Colors are customizable.
Liquidation Cluster Bar
The image above explains the liquidation cluster bar.
The liquidation cluster bar aggregates liquidation zones and shows the approximate price areas where the highest number of liquidation points are located.
In this image, the green portion of the bar represents where the largest number of traders will be liquidated in aggregate. While the purple portions of the bar shows where the smallest number of traders will be liquidated in aggregate.
This bar is useful for clustering liquidations zones across larger price areas to see where the highest number of traders are likely to be liquidated.
Concept Behind IQ Liquidation Heatmap
The basic idea is simple: in crypto markets, when price reaches certain levels, many traders’ positions can be liquidated at once, causing sharp moves in price. These zones are not random. They are built on historical price data and statistical analysis of past liquidation events. IQ Liquidation Heatmap captures this information and presents it in an easy-to-read format.
Key points include:
Current Liquidation Zones: These are the areas where, if the price moves into them, a high number of liquidations could occur.
Historical Liquidation Zones: These show where liquidation cascades have happened in the past, offering context on how the market has behaved under stress.
Key Features of IQ Liquidation Heatmap
Real-Time and Historical Data:
The indicator combines current market conditions with historical liquidation events. It updates dynamically to reflect real-time data while also showing past liquidation zones.
Visual Heatmap:
The display uses color gradients to represent the intensity of liquidation activity. Brighter or more intense colors indicate zones with a higher likelihood of triggering liquidations, while darker colors represent areas with lower activity.
User-Friendly Interface:
IQ Liquidation Heatmap is designed to be simple and straightforward. The visual output clearly marks the price levels of interest, making it easy for traders to see where liquidations might occur.
Proprietary Calculation:
The data behind the indicator is calculated using proprietary methods that consider historical price action, statistical ranges, and liquidity distribution. This means the indicator adapts to the specific characteristics of different crypto assets and timeframes.
Dynamic Updates:
The indicator recalculates its output in real time as new price data comes in. This ensures that the displayed liquidation zones are always current and reflect the latest market conditions.
How IQ Liquidation Heatmap Works
Data Collection:
IQ Liquidation Heatmap gathers historical price data as well as data on liquidation events. This data is used to identify key price ranges and levels where liquidations have previously occurred.
Statistical Analysis:
The indicator applies statistical methods—such as calculating medians and percentiles—to determine the significance of each price range. This analysis helps to rank the importance of various liquidation zones.
Liquidity Clustering:
Areas with a high concentration of liquidations are identified by examining how many positions or stop orders are clustered at specific price levels. These clusters are then represented on the chart using a heatmap style.
Visual Mapping:
The calculated data is overlaid onto the trading chart. Graphical elements like lines, boxes, or filled regions mark the identified liquidation zones. Color gradients help to differentiate between zones with high versus low liquidation risk.
Real-Time Recalculation:
As new price data becomes available, IQ Liquidation Heatmap continuously updates its analysis. This ensures that the indicator remains relevant throughout the trading session and can quickly adjust if market conditions change.
Using IQ Liquidation Heatmap
Traders can use IQ Liquidation Heatmap as an additional tool to support their trading decisions. Here are some practical applications:
Trade Entry And Exit Planning:
The visual cues provided by the indicator can serve as reference points for planning entries and exits. When the price nears a zone known for triggering liquidations, traders can adjust their strategies accordingly.
Risk Management:
By identifying key liquidation zones, traders can better manage risk. Knowing where a liquidation cascade is likely to occur helps in setting more effective stop-loss orders and managing overall exposure.
Market Structure Analysis:
The historical data offered by IQ Liquidation Heatmap gives insight into how the market has reacted in the past during periods of stress. This historical perspective can help in understanding broader market trends and potential future movements.
Summary
IQ Liquidation Heatmap is a straightforward indicator that provides clear visual information about price levels where liquidation cascades have occurred or are likely to occur. By merging historical data with real-time updates and proprietary liquidity analysis, it offers traders a neutral and data-driven way to understand areas of potential market stress for entries and exits. The indicator is simple to use and does not require complex adjustments, making it suitable for traders looking for clear visual cues in the crypto market.
By incorporating IQ Liquidation Heatmap into your analysis toolkit, you can gain a better understanding of key price zones, support effective risk management, and identify liquidation cascades before they occur and potentially identify breakouts before they occur.
15% Below Daily LowESPP discount pricing (15%) - Line chart that follows the daily low of the chart to show what price you could buy a company stock with the typical discount of 15%.
Ratio S/RRatio S/R - Intraday Support & Resistance Levels
Introduction
This script identifies key intraday support and resistance (S/R) levels where price tends to reverse frequently. It is designed specifically for intraday trading and aims to help traders find high-probability reversal zones.
The logic behind the script revolves around logarithmic returns, historical volatility, and ratio-based price levels. The script dynamically calculates price ranges using standard deviation-based volatility and applies preset ratio levels to determine potential support and resistance zones.
How It Works
Dynamic Range Calculation
The script calculates the price range based on the previous day’s logarithmic return volatility.
The range is then used to project different levels of price movement.
Reference Price
You can choose whether the reference price is from today’s open or yesterday’s close (oporcl setting).
This helps adapt the levels based on market behavior.
Ratio-Based Levels
The script applies specific ratios to the calculated range:
0.0833 (Minor Reversal Zone)
0.25 & 0.38 (Primary Reversal Zones)
0.62 & 0.75 (Significant Reversal Zones)
1.0 & 1.25 (Extreme Reversal Zones)
These levels act as potential support and resistance points.
Disclaimer: This is just for educational purpose . Trading is risky activity and how you use this tool is your own responsibility. The publisher of this tool does not make any claims.
Market Conditions with RSI v6Market Conditions with RSI Indicator
This indicator combines price action, volume, and RSI (Relative Strength Index) to identify market conditions and generate trading signals.
What It Does
The indicator classifies market conditions into four categories:
1.Strong Bullish: When price is rising, volume is up, and the volume-based "open interest" is increasing
2.Weak Bullish: When price is rising, but volume is down, and the volume-based "open interest" is decreasing
3.Weak Bearish: When price is declining, volume is up, and the volume-based "open interest" is increasing
4.Strong Bearish: When price is declining, volume is down, and the volume-based "open interest" is decreasing
These market conditions are then combined with RSI readings to generate buy and sell signals.
## How to Use It
1. Add the indicator to your TradingView chart
2. The indicator will display below your price chart (since it's not an overlay)
3. Look for buy signals (green triangles at the bottom) and sell signals (red triangles at the top)
4. Use the color-coded background to quickly identify the current market condition
5. Check the information table in the top-right corner for detailed metrics
What It Shows
1. RSI Line: The blue line showing the Relative Strength Index value
2. Background Color:
- Green = Strong Bullish
- Light Green = Weak Bullish
- Orange = Weak Bearish
- Red = Strong Bearish
3. Buy Signals (green triangles) appear when:
- Strong Bullish condition with RSI below 50 (catching momentum early)
- Weak Bearish condition with RSI below 30 (oversold opportunity)
4. Sell Signals (red triangles) appear when:
- Strong Bearish condition with RSI above 50 (catching downward momentum)
- Weak Bullish condition with RSI above 70 (overbought opportunity)
5. Information Table showing:
- Current market condition
- RSI value
- Price direction (rising/declining)
- Volume status (up/down)
- Volume-based "open interest" proxy (up/down)
Customization Options
You can adjust:
- RSI Length (default: 14)
- RSI Overbought Level (default: 70)
- RSI Oversold Level (default: 30)
- Volume Moving Average Length (default: 20)
- "Open Interest" Moving Average Length (default: 20)
Open Price on Selected TimeframeIndicator Name: Open Price on Selected Timeframe
Short Title: Open Price mtf
Type: Technical Indicator
Description:
Open Price on Selected Timeframe is an indicator that displays the Open price of a specific timeframe on your chart, with the ability to dynamically change the color of the open price line based on the change between the current candle's open and the previous candle's open.
Selectable Timeframes: You can choose the timeframe you wish to monitor the Open price of candles, ranging from M1, M5, M15, H1, H4 to D1, and more.
Dynamic Color Change: The Open price line changes to green when the open price of the current candle is higher than the open price of the previous candle, and to red when the open price of the current candle is lower than the open price of the previous candle. This helps users quickly identify trends and market changes.
Features:
Easy Timeframe Selection: Instead of editing the code, users can select the desired timeframe from the TradingView interface via a dropdown.
Dynamic Color Change: The color of the Open price line changes automatically based on whether the open price of the current candle is higher or lower than the previous candle.
Easily Track Open Price Levels: The indicator plots a horizontal line at the Open price of the selected timeframe, making it easy for users to track this important price level.
How to Use:
Select the Timeframe: Users can choose the timeframe they want to track the Open price of the candles.
Interpret the Color Signal: When the open price of the current candle is higher than the open price of the previous candle, the Open price line is colored green, signaling an uptrend. When the open price of the current candle is lower than the open price of the previous candle, the Open price line turns red, signaling a downtrend.
Observe the Open Price Levels: The indicator will draw a horizontal line at the Open price level of the selected timeframe, allowing users to easily monitor this important price.
Benefits:
Enhanced Technical Analysis: The indicator allows you to quickly identify trends and market changes, making it easier to make trading decisions.
User-Friendly: No need to modify the code; simply select your preferred timeframe to start using the indicator.
Disclaimer:
This indicator is not a complete trading signal. It only provides information about the Open price and related trends. Users should combine it with other technical analysis tools to make more informed trading decisions.
Summary:
Open Price on Selected Timeframe is a simple yet powerful indicator that helps you track the Open price on various timeframes with the ability to change colors dynamically, providing a visual representation of the market's trend.
Black–Scholes model - Options premium calculatorBlack-Scholes Options Pricing Calculator in Pine Script Introduction
The Black-Scholes model is one of the most widely used mathematical models for pricing options. It provides a theoretical estimate of the price of European-style options based on factors such as the underlying asset price, strike price, time to expiration, volatility, risk-free rate, and option type.
This Pine Script implementation of the Black-Scholes options pricing model enables traders to calculate call and put option prices directly within TradingView, helping them assess potential trades more efficiently.
What Does This Script Do?
This script allows traders to input essential option parameters and instantly calculate both call and put option prices using the Black-Scholes formula. It provides:
• A user-friendly interface for inputting option parameters.
• Automatic computation of option prices.
• Real-time updates as market data changes.
Key Features:
• Uses the Black-Scholes formula to compute European call and put option prices.
• User-defined inputs for stock price, strike price, time to expiration, volatility, and risk-free rate.
• Displays calculated option prices on the TradingView chart.
Understanding the Black-Scholes Formula:
The Black-Scholes model is given by the following equations:
C=S0N(d1)−Xe−rtN(d2)C = S_0 N(d_1) - Xe^{-rt} N(d_2) P=Xe−rtN(−d2)−S0N(−d1)P = Xe^{-rt} N(-d_2) - S_0 N(-d_1)
Where:
• CC = Call option price
• PP = Put option price
• S0S_0 = Current stock price
• XX = Strike price
• rr = Risk-free interest rate
• tt = Time to expiration (in years)
• σ\sigma = Volatility of the stock (annualized)
• N(x)N(x) = Cumulative standard normal distribution
• d1d_1 and d2d_2 are given by:
d1=ln(S0/X)+(r+σ2/2)tσtd_1 = \frac{ \ln(S_0/X) + (r + \sigma^2/2)t }{ \sigma \sqrt{t} } d2=d1−σtd_2 = d_1 - \sigma \sqrt{t}
This script implements these calculations efficiently in Pine Script to help traders quickly determine fair values for options based on current market conditions.
Example Calculation:
(The following example values were true at the time of publishing this script. Option prices fluctuate constantly, so actual values may vary.)
• Underlying asset price (NIFTY): 23,519.35
• ATM Call Strike Price: 23,500
• ATM Put Strike Price: 23,550
• IV (Implied Volatility) for Call Option: 8.1%
• IV (Implied Volatility) for Put Option: 10.1%
• Expiry Date: April 3, 2025
Using the Black-Scholes model, the calculated theoretical prices are:
• Theoretical ATM CE price: ₹129
• Theoretical ATM PE price: ₹118
For comparison, the actual option prices from the option chain table at the time of writing were:
• Actual ATM CE price: ₹139.70
• Actual ATM PE price: ₹120.30
As we can see, there is a larger difference between the theoretical price and actual market price for the ATM Call option compared to the ATM Put option.
If you're an experienced trader, you likely know how to use this kind of information to identify potential market inefficiencies or trading opportunities.
How to Use This Script:
1. Add the script to your TradingView chart.
2. Input the necessary parameters such as stock price, strike price, volatility, risk-free rate, and time to expiration.
3. View the calculated call and put option prices directly on the chart.
This Black-Scholes options pricing calculator provides a convenient way to compute theoretical option prices within TradingView. It helps traders analyse whether an option is fairly priced based on market conditions.
While the Black-Scholes model has its limitations (e.g., it does not account for early exercise of American options or dividend payments), it remains a powerful tool for European-style options pricing and a foundational concept in financial markets.
A handy little tool! Unfortunately, this script requires manual data entry since automatic data capture is currently not possible. If this ever becomes feasible in the future, an updated version will be released.
Try it out and let me know your feedback!
Disclaimer:
Please note that this is only for study/educational purpose and is just one of the many tools a trader may use.
Use it at your own risk.
Regards!
Candle Height & Trend Probability DashboardDescription and Guide
Description:
This Pine Script for TradingView displays a dashboard that calculates the probability of price increases or decreases based on past price movements. It analyzes the last 30 candles (by default) and shows the probabilities for different timeframes (from 1 minute to 1 week). Additionally, it checks volatility using the ATR indicator.
Script Features:
Calculates probabilities of an upward (Up %) or downward (Down %) price move based on past candles.
Displays a dashboard showing probabilities for multiple timeframes.
Color-coded probability display:
Green if the upward probability exceeds a set threshold.
Red if the downward probability exceeds the threshold.
Yellow if neither threshold is exceeded.
Considers volatility using the ATR indicator.
Triggers alerts when probabilities exceed specific values.
How to Use:
Insert the script into TradingView: Copy and paste the script into the Pine Script editor.
Adjust parameters:
lookback: Number of past candles used for calculation (default: 30).
alertThresholdUp & alertThresholdDown: Thresholds for probabilities (default: 51%).
volatilityLength & volatilityThreshold: ATR volatility settings.
dashboardPosition: Choose where the dashboard appears on the chart.
Enable visualization: The dashboard will be displayed over the chart.
Set alerts: The script triggers notifications when probabilities exceed set thresholds.
Strategy Stats [presentTrading]Hello! it's another weekend. This tool is a strategy performance analysis tool. Looking at the TradingView community, it seems few creators focus on this aspect. I've intentionally created a shared version. Welcome to share your idea or question on this.
█ Introduction and How it is Different
Strategy Stats is a comprehensive performance analytics framework designed specifically for trading strategies. Unlike standard strategy backtesting tools that simply show cumulative profits, this analytics suite provides real-time, multi-timeframe statistical analysis of your trading performance.
Multi-timeframe analysis: Automatically tracks performance metrics across the most recent time periods (last 7 days, 30 days, 90 days, 1 year, and 4 years)
Advanced statistical measures: Goes beyond basic metrics to include Information Coefficient (IC) and Sortino Ratio
Real-time feedback: Updates performance statistics with each new trade
Visual analytics: Color-coded performance table provides instant visual feedback on strategy health
Integrated risk management: Implements sophisticated take profit mechanisms with 3-step ATR and percentage-based exits
BTCUSD Performance
The table in the upper right corner is a comprehensive performance dashboard showing trading strategy statistics.
Note: While this presentation uses Vegas SuperTrend as the underlying strategy, this is merely an example. The Stats framework can be applied to any trading strategy. The Vegas SuperTrend implementation is included solely to demonstrate how the analytics module integrates with a trading strategy.
⚠️ Timeframe Limitations
Important: TradingView's backtesting engine has a maximum storage limit of 10,000 bars. When using this strategy stats framework on smaller timeframes such as 1-hour or 2-hour charts, you may encounter errors if your backtesting period is too long.
Recommended Timeframe Usage:
Ideal for: 4H, 6H, 8H, Daily charts and above
May cause errors on: 1H, 2H charts spanning multiple years
Not recommended for: Timeframes below 1H with long history
█ Strategy, How it Works: Detailed Explanation
The Strategy Stats framework consists of three primary components: statistical data collection, performance analysis, and visualization.
🔶 Statistical Data Collection
The system maintains several critical data arrays:
equityHistory: Tracks equity curve over time
tradeHistory: Records profit/loss of each trade
predictionSignals: Stores trade direction signals (1 for long, -1 for short)
actualReturns: Records corresponding actual returns from each trade
For each closed trade, the system captures:
float tradePnL = strategy.closedtrades.profit(tradeIndex)
float tradeReturn = strategy.closedtrades.profit_percent(tradeIndex)
int tradeType = entryPrice < exitPrice ? 1 : -1 // Direction
🔶 Performance Metrics Calculation
The framework calculates several key performance metrics:
Information Coefficient (IC):
The correlation between prediction signals and actual returns, measuring forecast skill.
IC = Correlation(predictionSignals, actualReturns)
Where Correlation is the Pearson correlation coefficient:
Correlation(X,Y) = (nΣXY - ΣXY) / √
Sortino Ratio:
Measures risk-adjusted return focusing only on downside risk:
Sortino = (Avg_Return - Risk_Free_Rate) / Downside_Deviation
Where Downside Deviation is:
Downside_Deviation = √
R_i represents individual returns, T is the target return (typically the risk-free rate), and n is the number of observations.
Maximum Drawdown:
Tracks the largest percentage drop from peak to trough:
DD = (Peak_Equity - Trough_Equity) / Peak_Equity * 100
🔶 Time Period Calculation
The system automatically determines the appropriate number of bars to analyze for each timeframe based on the current chart timeframe:
bars_7d = math.max(1, math.round(7 * barsPerDay))
bars_30d = math.max(1, math.round(30 * barsPerDay))
bars_90d = math.max(1, math.round(90 * barsPerDay))
bars_365d = math.max(1, math.round(365 * barsPerDay))
bars_4y = math.max(1, math.round(365 * 4 * barsPerDay))
Where barsPerDay is calculated based on the chart timeframe:
barsPerDay = timeframe.isintraday ?
24 * 60 / math.max(1, (timeframe.in_seconds() / 60)) :
timeframe.isdaily ? 1 :
timeframe.isweekly ? 1/7 :
timeframe.ismonthly ? 1/30 : 0.01
🔶 Visual Representation
The system presents performance data in a color-coded table with intuitive visual indicators:
Green: Excellent performance
Lime: Good performance
Gray: Neutral performance
Orange: Mediocre performance
Red: Poor performance
█ Trade Direction
The Strategy Stats framework supports three trading directions:
Long Only: Only takes long positions when entry conditions are met
Short Only: Only takes short positions when entry conditions are met
Both: Takes both long and short positions depending on market conditions
█ Usage
To effectively use the Strategy Stats framework:
Apply to existing strategies: Add the performance tracking code to any strategy to gain advanced analytics
Monitor multiple timeframes: Use the multi-timeframe analysis to identify performance trends
Evaluate strategy health: Review IC and Sortino ratios to assess predictive power and risk-adjusted returns
Optimize parameters: Use performance data to refine strategy parameters
Compare strategies: Apply the framework to multiple strategies to identify the most effective approach
For best results, allow the strategy to generate sufficient trade history for meaningful statistical analysis (at least 20-30 trades).
█ Default Settings
The default settings have been carefully calibrated for cryptocurrency markets:
Performance Tracking:
Time periods: 7D, 30D, 90D, 1Y, 4Y
Statistical measures: Return, Win%, MaxDD, IC, Sortino Ratio
IC color thresholds: >0.3 (green), >0.1 (lime), <-0.1 (orange), <-0.3 (red)
Sortino color thresholds: >1.0 (green), >0.5 (lime), <0 (red)
Multi-Step Take Profit:
ATR multipliers: 2.618, 5.0, 10.0
Percentage levels: 3%, 8%, 17%
Short multiplier: 1.5x (makes short take profits more aggressive)
Stop loss: 20%
Spent Output Profit Ratio (SOPR) Z-Score | [DeV]SOPR Z-Score
The Spent Output Profit Ratio (SOPR) is an advanced on-chain metric designed to provide deep insights into Bitcoin market dynamics by measuring the ratio between the combined USD value of all Bitcoin outputs spent on a given day and their combined USD value at the time of creation (typically, their purchase price). As a member of the Realized Profit/Loss family of metrics, SOPR offers a window into aggregate seller behavior, effectively representing the USD amount received by sellers divided by the USD amount they originally paid. This indicator enhances this metric by normalizing it into a Z-Score, enabling a statistically robust analysis of market sentiment relative to historical trends, augmented by a suite of customizable features for precision and visualization.
SOPR Settings -
Lookback Length (Default: 150 days): Determines the historical window for calculating the Z-Score’s mean and standard deviation. A longer lookback captures broader market cycles, providing a stable baseline for identifying extreme deviations, which is particularly valuable for long-term strategic analysis.
Smoothing Period (Default: 100 days): Applies an EMA to the raw SOPR, balancing responsiveness to recent changes with noise reduction. This extended smoothing period ensures the indicator focuses on sustained shifts in seller behavior, ideal for institutional-grade trend analysis.
Moving Average Settings -
MA Lookback Length (Default: 90 days): Sets the period for the Z-Score’s moving average, offering a shorter-term trend signal relative to the 150-day Z-Score lookback. This contrast enhances the ability to detect momentum shifts within the broader context.
MA Type (Default: EMA): Provides six moving average types, from the simple SMA to the volume-weighted VWMA. The default EMA strikes an optimal balance between smoothness and responsiveness, while alternatives like HMA (Hull) or VWMA (volume-weighted) allow for specialized applications, such as emphasizing recent price action or incorporating volume dynamics.
Display Settings -
Show Moving Average (Default: True): Toggles the visibility of the Z-Score MA plot, enabling users to focus solely on the raw Z-Score when preferred.
Show Background Colors (Default: True): Activates dynamic background shading, enhancing visual interpretation of market regimes.
Background Color Source (Default: SOPR): Allows users to tie the background color to either the SOPR Z-Score’s midline (reflecting adjustedZScore > 0) or the MA’s trend direction (zScoreMA > zScoreMA ). This dual-source option provides flexibility to align the visual context with the primary analytical focus.
Analytical Applications -
Bear Market Resistance: When the Z-Score approaches or exceeds zero (raw SOPR near 1), it often signals resistance as sellers rush to exit at break-even, a pattern historically observed during downtrends. A rising Z-Score MA crossing zero can confirm this pressure.
Bull Market Support: Conversely, a Z-Score dropping below zero in uptrends indicates reluctance to sell at a loss, forming support as sell pressure diminishes. The MA’s bullish coloring reinforces confirmation of renewed buying interest.
Extreme Deviations: Values significantly above or below zero highlight overbought or oversold conditions, respectively, offering opportunities for contrarian positioning when paired with other on-chain or price-based metrics.
Econometrica by [SS]This is Econometrica, an indicator that aims to bridge a big gap between the resources available for analysis of fundamental data and its impact on tickers and price action.
I have noticed a general dearth of available indicators that offer insight into how fundamentals impact a ticker and provide guidance on how they these economic factors influence ticker behaviour.
Enter Econometrica. Econometrica is a math based indicator that aims to co-integrate and model indicator price action in relation to critical economic metrics.
Econometrica supports the following US based economic data:
CPI
Non-Farm Payroll
Core Inflation
US Money Supply
US Central Bank Balance Sheet
GDP
PCE
Let's go over the functions of Econometrica.
Creating a Regression Cointegrated Model
The first thing Econometrica does is creates a co-integrated regression, as you see in the main chart, predicting ticker value ranges from fundamental economic data.
You can visualize this in the main chart above, but here are some other examples:
SPY vs Core Inflation:
BA vs PCE:
QQQ vs US Balance Sheet:
The band represents the anticipated range the ticker should theoretically fall in based on the underlying economic value. The indicator will breakdown the relationship between the economic indicator and the ticker more precisely. In the images above, you can see how there are some metrics provided, including Stationairty, lagged correlation, Integrated Correlation and R2. Let's discuss these very briefly:
Stationarity: checks to ensure that the relationship between the economic indicator and ticker is stationary. Stationary data is important for making unbiased inferences and projections, so having data that is stationary is valuable.
Lagged Correlation: This is a very interesting metric. Lagged correlation means whether there is a delay in the economic indicator and the response of the ticker. Typically, you will observed a lagged correlation between an economic indicator and price of a ticker, as it can take some time for economic changes to reach the market. This lagged correlation will provide you with how long it takes for the economic indicator to catch up with the ticker in months.
Integrated Correlation: This metric tells you how good of a fit the regression bands are in relation to the ticker price. A higher correlation, means the model is better at consistent and accurate information about the anticipated range for the ticker in relation to the economic indicator.
R2: Provides information on the variance and degree of model fit. A high R2 value means that the model is capable of explaining a large amount of variance between the economic indicator and the ticker price action.
Explaining the Relationship
Owning to the fact that the indicator is a bit on the mathy side (it has to be to do this kind of task), I have included ability for the indicator to explain and make suggestions based on the underlying data. It can assess the model's fit and make suggestions for tweaking. It can also explain the implications of the data being presented in the model.
Here is an example with QQQ and the US Balance Sheet:
This helps to simplify and interpret the results you are looking at.
Forecasting the Economic Indicator
In addition to assessing the economic indicator's impact on the ticker, the indicator is also capable of forecasting out the economic indicator over the next 25 releases.
Here is an example of the CPI forecast:
Overall use of the indicator
The indicator is meant to bridge the gap between Technical Analysis and Fundamental Analysis.
Any trader who is attune to fundamentals would benefit from this, as this provides you with objective data on how and to what extent fundamental and economic data impacts tickers.
It can help affirm hypothesis and dispel myths objectively.
It also omits the need from having to perform these types of analyses outside of Tradingview (i.e. in excel, R or Python), as you can get the data in just a few licks of enabling the indicator.
Conclusion
I have tried to make this indicator as user friendly as possible. Though it uses a lot of math, it is fairly straight forward to interpret.
The band plotted can be considered the fair market value or FMV of the ticker based on the underlying economic data, provided the indicator tells you that the relationship is significant (and it will blatantly give you this information verbatim, you don't have to interpret the math stuff).
This is US economic data only. It does not pull economic data from other countries. You can absolutely see how US economic data impacts other markets like the TSX, BANKNIFTY, NIFTY, DAX etc. but the indicator is only pulling US economic data.
That is it!
I hope you enjoy it and find this helpful!
Thanks everyone and safe trades as always 🚀🚀🚀
Trade Ladder Pro: Compounding & Risk ManagerTrade Ladder Pro: Compounding & Risk Manager
Inspired by the popular $20 to $52,000 trading challenge, this tool is designed to help you scale your trading account using systematic compounding and enhanced risk management techniques. Whether you’re aiming for disciplined growth or fine-tuning your risk/reward, Trade Ladder Pro offers a flexible approach to visualizing your trade levels.
How to Use:
Inputs:
Compounding Mode:
Set your starting balance, final balance goal, number of trades, and current trade level. You can move to the next trade after a successful trade in settings. The entries are not signals. They are there to help manage risk.
The script calculates the necessary compounding factor to grow your balance across the defined trades.
Risk Management Mode:
In addition to the above, specify a risk percentage and risk/reward ratio.
Input an entry price (or leave it at 0 to use the current price) to automatically compute the stop loss and take profit levels.
Display Options:
Choose the table’s position on the chart (e.g., Top Right, Top Left, Bottom Right, Bottom Left).
Pick between a vertical or horizontal layout for a display that suits your workflow.
Results:
The table will display the trade level, starting balance, risk amount, entry price, take profit, and (if in Risk Management mode) stop loss along with the projected ending balance.
Community & Feedback:
Your feedback is invaluable! Please share any tips or report any errors you encounter so we can continue to improve this tool. Happy trading!
Smart % Levels📈 Smart % Levels – Visualize Significant Percentage Moves
What it does:
This indicator plots horizontal levels based on a percentage change from the previous day's close (or open, if selected). It allows traders to visualize price movements relative to meaningful thresholds like ±1%, ±2%, etc.
What makes it different:
Unlike other level indicators, Smart % Levels only displays the relevant levels based on current price action. This avoids clutter by showing only the levels that are being approached or crossed by the current price. It's a clean and dynamic way to visualize key price zones for intraday analysis.
How it works:
- Select between using the previous day's Close or Open as the reference
- Choose the percentage spacing between levels (e.g., 1%, 0.5%, etc.)
- Enable optional labels to see the exact percentage of each level
- Automatically filters levels to only show those between yesterday's price and today's current price
- Includes customization for colors, line styles, widths, and opacity
Best for:
Day traders and scalpers who want a quick, clean view of how far the current price has moved from yesterday’s reference, without being overwhelmed by unnecessary lines.
Extra notes:
- The levels are recalculated each day at the market open
- All graphics reset at the start of each session to maintain clarity
- This script avoids repainting by only plotting levels relative to available historical data (no lookahead)
This tool is for informational purposes only and should not be considered as financial advice. Always do your own research before making trading decisions.
ATR & PTR TableThe ATR & PTR Table Indicator displays a dynamic table that provides Average True Range (measures market volatility over 1D, 1W, and 1M timeframes), Price trading range (difference between the high and low prices over the same periods) & percentage of the typical range that has been traded. This indicator will help traders identify potential breakout zones and assess volatility across multiple timeframes.
This had been optimized to show ATR and PTR on every time frame. The (1D) represents ATR on whatever timeframe you are currently on.
