PPP – Info Table (Anchor + Corr/Alpha/Beta) v3PPP – Info Table (Anchor + Corr/Alpha/Beta)
- By P3 Analytics, run by Puranam Pradeep Picasso Sharma
🔎 Overview
This indicator creates a clean, dynamic information table on your chart that lets you quickly analyze how your chosen asset is performing relative to BTC, ETH, or any other benchmarks.
With a single glance, you can see:
% change from today’s open (for the anchor asset, BTC, and ETH)
Previous day % change (self + benchmarks)
Correlation, Beta, and Alpha statistics for the selected window (1W, 1M, 1Y)
Anchor values at any bar you choose (via Bars Back or Anchor Time)
Perfect for traders who want to measure coin strength vs benchmarks and make better rotation, risk, or hedging decisions.
📊 Key Metrics
Correlation (Corr): How closely the asset moves with the benchmark.
+1 = moves together, 0 = no relation, -1 = moves opposite.
Beta (β): Sensitivity of returns vs the benchmark.
β = 1 → moves 1:1 with BTC.
β > 1 → more volatile (amplifies BTC moves).
β < 1 → less volatile (defensive).
Alpha (α): Excess return beyond what Beta predicts.
Positive α = outperforming benchmark-adjusted expectation.
Negative α = underperforming.
⚙️ Features
Flexible Anchor Mode:
Bars Back → quickly step through bars.
Time → pin analysis to a specific historical candle.
Customizable Benchmarks: Default BTC & ETH (futures), but replaceable with any ticker.
Adjustable Stats Window:
1 Week, 1 Month, 1 Year (auto-scales if using chart timeframe).
Compact Mode for a smaller table layout.
Dark/Light Theme, font size, corner placement, transparency, and decimal control.
Runs efficiently with minimal chart clutter.
🧑💻 About P3 Analytics
This indicator is developed under P3 Analytics, a research & trading technology initiative led by Puranam Pradeep Picasso Sharma.
P3 Analytics builds tools that merge machine learning, statistics, and trading strategy into accessible products for traders across crypto, equities, forex, and commodities.
✅ How to Use
Add indicator to your chart.
In settings:
Pick your benchmarks (default = BTCUSDT.P, ETHUSDT.P).
Choose your anchor (Bars Back or Time).
Set window length for correlation/alpha/beta.
Read the table:
Left side = your asset.
Right side = benchmarks.
Colors: Green = positive % change, Red = negative.
🚀 Why Use This?
Quickly compare your asset vs BTC/ETH without juggling multiple charts.
Spot whether a coin is truly leading or just following BTC.
Identify outperformance (alpha) coins for rotation or trend plays.
Manage risk by knowing which assets are high beta (high leverage-like moves).
✦ Indicator by P3 Analytics
✦ Created & published by Puranam Pradeep Picasso Sharma
Statistics
Rolling Performance Toolkit (Returns, Correlation and Sharpe)This script provides a flexible toolkit for evaluating rolling performance metrics between any asset and a benchmark.
Features:
Library-based: Built on a custom utilities library for consistent return and statistics calculations.
Rolling Window Control: Choose the lookback period (in days) to calculate metrics.
Multiple Modes: Toggle between Rolling Returns, Rolling Correlation, and Rolling Sharpe Ratio.
Benchmark Comparison: Compare your selected ticker against a benchmark (default: S&P 500 / SPX), but you can easily switch to any symbol.
Risk-Free Rate Options: Choose from zero, a constant annual % rate, or a proxy symbol (default: US03M – 3-Month Treasury Yield).
Annualized Sharpe: Sharpe ratios are annualized by default (×√252) for intuitive interpretation.
This tool is useful for traders and investors who want to monitor relative performance, diversification benefits, or risk-adjusted returns over time.
utilitiesLibrary for commonly used utilities, for visualizing rolling returns, correlations and sharpe
Machine Learning : Neural Network Prediction -EasyNeuro-Machine Learning: Neural Network Prediction
— An indicator that learns and predicts price movements using a neural network —
Overview
The indicator “Machine Learning: Neural Network Prediction” uses price data from the chart and applies a three-layer Feedforward Neural Network (FNN) to estimate future price movements.
Key Features
Normally, training and inference with neural networks require advanced programming languages that support machine learning frameworks (such as TensorFlow or PyTorch) as well as high-performance hardware with GPUs. However, this indicator independently implements the neural network mechanism within TradingView’s Pine Script environment, enabling real-time training and prediction directly on the chart.
Since Pine Script does not support matrix operations, the backpropagation algorithm—necessary for neural network training—has been implemented entirely through scalar operations. This unique approach makes the creation of such a groundbreaking indicator possible.
Significance of Neural Networks
Neural networks are a core machine learning method, forming the foundation of today’s widely used generative AI systems, such as OpenAI’s GPT and Google’s Gemini. The feedforward neural network adopted in this indicator is the most classical architecture among neural networks. One key advantage of neural networks is their ability to perform nonlinear predictions.
All conventional indicators—such as moving averages and oscillators like RSI—are essentially linear predictors. Linear prediction inherently lags behind past price fluctuations. In contrast, nonlinear prediction makes it theoretically possible to dynamically anticipate future price movements based on past patterns. This offers a significant benefit for using neural networks as prediction tools among the multitude of available indicators.
Moreover, neural networks excel at pattern recognition. Since technical analysis is largely based on recognizing market patterns, this makes neural networks a highly compatible approach.
Structure of the Indicator
This indicator is based on a three-layer feedforward neural network (FNN). Every time a new candlestick forms, the model samples random past data and performs online learning using stochastic gradient descent (SGD).
SGD is known as a more versatile learning method compared to standard gradient descent, particularly effective for uncertain datasets like financial market price data. Considering Pine Script’s computational constraints, SGD is a practical choice since it can learn effectively from small amounts of data. Because online learning is performed with each new candlestick, the indicator becomes a little “smarter” over time.
Adjustable Parameters
Learning Rate
Specifies how much the network’s parameters are updated per training step. Values between 0.0001 and 0.001 are recommended. Too high causes divergence and unstable predictions, while too low prevents sufficient learning.
Iterations per Online Learning Step
Specifies how many training iterations occur with each new candlestick. More iterations improve accuracy but may cause timeouts if excessive.
Seed
Random seed for initializing parameters. Changing the seed may alter performance.
Architecture Settings
Number of nodes in input and hidden layers:
Increasing input layer nodes allows predictions based on longer historical periods. Increasing hidden layer nodes increases the network’s interpretive capacity, enabling more flexible nonlinear predictions. However, more nodes increase computational cost exponentially, risking timeouts and overfitting.
Hidden layer activation function (ReLU / Sigmoid / Tanh):
Sigmoid:
Classical function, outputs between 0–1, approximates a normal distribution.
Tanh:
Similar to Sigmoid but outputs between -1 and 1, centered around 0, often more accurate.
ReLU:
Simple function (outputs input if ≥ 0, else 0), efficient and widely effective.
Input Features (selectable and combinable)
RoC (Rate of Change):
Measures relative price change over a period. Useful for predicting movement direction.
RSI (Relative Strength Index):
Oscillator showing how much price has risen/fallen within a period. Widely used to anticipate direction and momentum.
Stdev (Standard Deviation, volatility):
Measures price variability. Useful for volatility prediction, though not directional.
Optionally, input data can be smoothed to stabilize predictions.
Other Parameters
Data Sampling Window:
Period from which random samples are drawn for SGD.
Prediction Smoothing Period:
Smooths predictions to reduce spikes, especially when RoC is used.
Prediction MA Period:
Moving average applied to smoothed predictions.
Visualization Features
The internal state of the neural network is displayed in a table at the upper-right of the chart:
Network architecture:
Displays the structure of input, hidden, and output layers.
Node activations:
Shows how input, hidden, and output node values dynamically change with market conditions.
This design allows traders to intuitively understand the inner workings of the neural network, which is often treated as a black box.
Glossary of Terms
Feature:
Input variables fed to the model (RoC/RSI/Stdev).
Node/Unit:
Smallest computational element in a layer.
Activation Function:
Nonlinear function applied to node outputs (ReLU/Sigmoid/Tanh).
MSE (Mean Squared Error):
Loss function using average squared errors.
Gradient Descent (GD/SGD):
Optimization method that gradually adjusts weights in the direction that reduces loss.
Online Learning:
Training method where the model updates sequentially with each new data point.
Deadband Hysteresis Filter [BackQuant]Deadband Hysteresis Filter
What this is
This tool builds a “debounced” price baseline that ignores small fluctuations and only reacts when price meaningfully departs from its recent path. It uses a deadband to define how much deviation matters and a hysteresis scheme to avoid rapid flip-flops around the decision boundary. The baseline’s slope provides a simple trend cue, used to color candles and to trigger up and down alerts.
Why deadband and hysteresis help
They filter micro noise so the baseline does not react to every tiny tick.
They stabilize state changes. Hysteresis means the rule to start moving is stricter than the rule to keep holding, which reduces whipsaw.
They produce a stepped, readable path that advances during sustained moves and stays flat during chop.
How it works (conceptual)
At each bar the script maintains a running baseline dbhf and compares it to the input price p .
Compute a base threshold baseTau using the selected mode (ATR, Percent, Ticks, or Points).
Build an enter band tauEnter = baseTau × Enter Mult and an exit band tauExit = baseTau × Exit Mult where typically Exit Mult < Enter Mult .
Let diff = p − dbhf .
If diff > +tauEnter , raise the baseline by response × (diff − tauEnter) .
If diff < −tauEnter , lower the baseline by response × (diff + tauEnter) .
Otherwise, hold the prior value.
Trend state is derived from slope: dbhf > dbhf → up trend, dbhf < dbhf → down trend.
Inputs and what they control
Threshold mode
ATR — baseTau = ATR(atrLen) × atrMult . Adapts to volatility. Useful when regimes change.
Percent — baseTau = |price| × pctThresh% . Scale-free across symbols of different prices.
Ticks — baseTau = syminfo.mintick × tickThresh . Good for futures where tick size matters.
Points — baseTau = ptsThresh . Fixed distance in price units.
Band multipliers and response
Enter Mult — outer band. Price must travel at least this far from the baseline before an update occurs. Larger values reject more noise but increase lag.
Exit Mult — inner band for hysteresis. Keep this smaller than Enter Mult to create a hold zone that resists small re-entries.
Response — step size when outside the enter band. Higher response tracks faster; lower response is smoother.
UI settings
Show Filtered Price — plots the baseline on price.
Paint candles — colors bars by the filtered slope using your long/short colors.
How it can be used
Trend qualifier — take entries only in the direction of the baseline slope and skip trades against it.
Debounced crossovers — use the baseline as a stabilized surrogate for price in moving-average or channel crossover rules.
Trailing logic — trail stops a small distance beyond the baseline so small pullbacks do not eject the trade.
Session aware filtering — widen Enter Mult or switch to ATR mode for volatile sessions; tighten in quiet sessions.
Parameter interactions and tuning
Enter Mult vs Response — both govern sensitivity. If you see too many flips, increase Enter Mult or reduce Response. If turns feel late, do the opposite.
Exit Mult — widening the gap between Enter and Exit expands the hold zone and reduces oscillation around the threshold.
Mode choice — ATR adapts automatically; Percent keeps behavior consistent across instruments; Ticks or Points are useful when you think in fixed increments.
Timeframe coupling — on higher timeframes you can often lower Enter Mult or raise Response because raw noise is already reduced.
Concrete starter recipes
General purpose — ATR mode, atrLen=14 , atrMult=1.0–1.5 , Enter=1.0 , Exit=0.5 , Response=0.20 . Balanced noise rejection and lag.
Choppy range filter — ATR mode, increase atrMult to 2.0, keep Response≈0.15 . Stronger suppression of micro-moves.
Fast intraday — Percent mode, pctThresh=0.1–0.3 , Enter=1.0 , Exit=0.4–0.6 , Response=0.30–0.40 . Quicker turns for scalping.
Futures ticks — Ticks mode, set tickThresh to a few spreads beyond typical noise; start with Enter=1.0 , Exit=0.5 , Response=0.25 .
Strengths
Clear, explainable logic with an explicit noise budget.
Multiple threshold modes so the same tool fits equities, futures, and crypto.
Built-in hysteresis that reduces flip-flop near the boundary.
Slope-based coloring and alerts that make state changes obvious in real time.
Limitations and notes
All filters add lag. Larger thresholds and smaller response trade faster reaction for fewer false turns.
Fixed Points or Ticks can under- or over-filter when volatility regime shifts. ATR adapts, but will also expand bands during spikes.
On extremely choppy symbols, even a well tuned band will step frequently. Widen Enter Mult or reduce Response if needed.
This is a chart study. It does not include commissions, slippage, funding, or gap risks.
Alerts
DBHF Up Slope — baseline turns from down to up on the latest bar.
DBHF Down Slope — baseline turns from up to down on the latest bar.
Implementation details worth knowing
Initialization sets the baseline to the first observed price to avoid a cold-start jump.
Slope is evaluated bar-to-bar. The up and down alerts check for a change of slope rather than raw price crossings.
Candle colors and the baseline plot share the same long/short palette with transparency applied to the line.
Practical workflow
Pick a mode that matches how you think about distance. ATR for volatility aware, Percent for scale-free, Ticks or Points for fixed increments.
Tune Enter Mult until the number of flips feels appropriate for your timeframe.
Set Exit Mult clearly below Enter Mult to create a real hold zone.
Adjust Response last to control “how fast” the baseline chases price once it decides to move.
Final thoughts
Deadband plus hysteresis gives you a principled way to “only care when it matters.” With a sensible threshold and response, the filter yields a stable, low-chop trend cue you can use directly for bias or plug into your own entries, exits, and risk rules.
Valid H/LsOverview
Advanced breakout indicator that identifies valid high and low levels with statistical performance analytics. Features confirmation systems, visual customization, and historical MFE/MAE analysis to help traders identify high-probability setups.
Key Features
📈 Smart Breakout Detection
Automatically identifies confirmed valid high/low breakout levels
Shows potential setups before confirmation with different styling
Filters setups using institutional order flow (I/O) signals
🎯 Visual Display
Dynamic Lines: Full extension or shrinking previous lines for cleaner charts
Color Coding: Customizable colors for highs (red) and lows (green)
Confirmation Candles: Highlights breakout confirmation with custom colors
Background Boxes: Optional zone highlighting around valid levels
📊 MFE/MAE Analytics
Multi-Timeframe: 1-hour and 3-hour performance analysis
Historical Data: Analyzes 10-500 past setups for profit/risk statistics
Profit Targets: MFE lines show typical profit potential
Risk Levels: MAE lines indicate common drawdown zones
How to Use
Green = Bullish: Valid low breakout levels
Red = Bearish: Valid high breakout levels
Solid Lines: Confirmed setups ready for trading
Dashed Lines: Potential setups awaiting confirmation
Use Analytics: MFE for profit targets, MAE for stop loss placement
Key Settings
Show Valid Highs/Lows: Toggle breakout level display
Only Show with I/O Prints: Filter for institutional signals only
Line Display Mode: Choose full extension or shrinking lines
Show MFE/MAE Analytics: Enable statistical overlays
Analytics Lookback: Set number of historical setups to analyze (10-500)
Timeframe Options: Toggle 1-hour and 3-hour analytics
Best Practices
Wait for solid line confirmation before entries
Use MFE analytics for realistic profit targets
Use MAE analytics for appropriate stop losses
Combine multiple timeframe analytics for better context
Always apply proper risk management
Technical Notes
Works on all timeframes and markets
No repainting once confirmed
Analytics update dynamically with new data
Optimized for breakout trading strategies
Logit Transform -EasyNeuro-Logit Transform
This script implements a novel indicator inspired by the Fisher Transform, replacing its core arctanh-based mapping with the logit transform. It is designed to highlight extreme values in bounded inputs from a probabilistic and statistical perspective.
Background: Fisher Transform
The Fisher Transform, introduced by John Ehlers , is a statistical technique that maps a bounded variable x (between a and b) to a variable approximately following a Gaussian distribution. The standard form for a normalized input y (between -1 and 1) is F(y) = 0.5 * ln((1 + y)/(1 - y)) = arctanh(y).
This transformation has the following properties:
Linearization of extremes:
Small deviations around the mean are smooth, while movements near the boundaries are sharply amplified.
Gaussian approximation:
After transformation, the variable approximates a normal distribution, enabling analytical techniques that assume normality.
Probabilistic interpretation:
The Fisher Transform can be linked to likelihood ratio tests, where the transform emphasizes deviations from median or expected values in a statistically meaningful way.
In technical analysis, this allows traders to detect turning points or extreme market conditions more clearly than raw oscillators alone.
Logit Transform as a Generalization
The logit function is defined for p between 0 and 1 as logit(p) = ln(p / (1 - p)).
Key properties of the logit transform:
Maps probabilities in (0, 1) to the entire real line, similar to the Fisher Transform.
Emphasizes values near 0 and 1, providing sharp differentiation of extreme states.
Directly interpretable in terms of odds and likelihood ratios: logit(p) = ln(odds).
From a statistical viewpoint, the logit transform corresponds to the canonical link function in binomial generalized linear models (GLMs). This provides a natural interpretation of the transformed variable as the logarithm of the likelihood ratio between success and failure states, giving a rigorous probabilistic framework for extreme value detection.
Theoretical Advantages
Distributional linearization:
For inputs that can be interpreted as probabilities, the logit transform creates a variable approximately linear in log-odds, similar to Fisher’s goal of Gaussianization but with a probabilistic foundation.
Extreme sensitivity:
By amplifying small differences near 0 or 1, it allows for sharper detection of market extremes or overbought/oversold conditions.
Statistical interpretability:
Provides a link to statistical hypothesis testing via likelihood ratios, enabling integration with probabilistic models or risk metrics.
Applications in Technical Analysis
Oscillator enhancement:
Apply to RSI, Stochastic Oscillators, or other bounded indicators to accentuate extreme values with a well-defined probabilistic interpretation.
Comparative study:
Use alongside the Fisher Transform to analyze the effect of different nonlinear mappings on market signals, helping to uncover subtle nonlinearity in price behavior.
Probabilistic risk assessment:
Transforming input series into log-odds allows incorporation into statistical risk models or volatility estimation frameworks.
Practical Considerations
The logit diverges near 0 and 1, requiring careful scaling or smoothing to avoid numerical instability. As with the Fisher Transform, this indicator is not a standalone trading signal and should be combined with complementary technical or statistical indicators.
In summary, the Logit Transform builds upon the Fisher Transform’s theoretical foundation while introducing a probabilistically rigorous mapping. By connecting extreme-value detection to odds ratios and likelihood principles, it provides traders and analysts with a mathematically grounded tool for examining market dynamics.
Artharjan NSE Sectors Relative Strength DashboardArtharjan NSE Sectors Relative Strength Dashboard
This script provides a comprehensive dashboard for analyzing the relative strength of NSE sectors compared to a benchmark index (default: NIFTY). It is designed to give traders and investors a consolidated snapshot of sector performance, momentum, and short-term trend strength — all in one visual table.
Core Purpose
The goal is to simplify sector rotation analysis by combining relative strength, rate of change, momentum, and trend classification into a sortable, color-coded dashboard. Instead of scanning multiple charts, users can rely on this single panel for quick decision-making.
Key Features
Benchmark Comparison
Every sector is measured against the benchmark index (default: NIFTY). This allows users to spot outperforming or underperforming sectors relative to the market.
Multiple Performance Metrics
LTP % Change: Last traded price percentage change from the prior close.
RS Score: Relative strength score over a user-defined lookback.
Momentum (ROC Difference): Convergence/divergence between two ROC values for added confirmation.
ROC1 / ROC2: Short- and medium-term rate-of-change measures.
Trend Classification Engine
Each sector is tagged as Ultra Bullish, Bullish Breakout, Strong/Moderate Bullish, Neutral, Moderate/Strong Bearish, Bearish Breakdown, or Ultra Bearish. This classification is based on sectoral price behavior and candlestick relationships.
Sorting & Customization
Users can sort the dashboard by any metric (e.g., RS Score, % Change, Momentum), in ascending or descending order, to highlight what matters most for their strategy.
Table Presentation
Adjustable text size, thickness, and positioning on the chart.
Optional color-coded cells for visual cues — green shades for strength, red shades for weakness, neutral shades for sideways trends.
“Last Updated” timestamp for clarity on when the snapshot was generated.
How It Helps
This tool reduces the noise of flipping through individual sector charts. Traders can identify sector leadership, monitor momentum shifts, and catch early signs of rotation without leaving a single chart window. It acts as both a macro lens (sector overview) and a micro tool (spotting exact strength/weakness transitions).
Closing Note
This dashboard was built with a simple goal: to bring clarity to complex sectoral movements. Use it as a guiding compass while respecting your broader trading or investing framework.
With Thanks,
Rrahul Desai
@Artharjan
Mean-Reversion Indicator_V2_SamleeOverview
This is the second version of my mean reversion indicator. It combines a moving average with adaptive standard deviation bands to detect when the price deviates significantly from its mean. The script provides automatic entry/exit signals, real-time PnL tracking, and shaded trade zones to make mean reversion trading more intuitive.
Core Logic
Mean benchmark: Simple Moving Average (MA).
Volatility bands: Standard deviation of the spread (close − MA) defines upper and lower bands.
Trading rules:
Price breaks below the lower band → Enter Long
Price breaks above the upper band → Enter Short
Price reverts to MA → Exit position
What’s different vs. classic Bollinger/Keltner
Bandwidth is based on the standard deviation of the price–MA spread, not raw closing prices.
Entry signals use previous-bar confirmation to reduce intrabar noise.
Exit rule is a mean-touch condition, rather than fixed profit/loss targets.
Enhanced visualization:
A shaded box dynamically shows the distance between entry and current/exit price, making it easy to see profit/loss zones over the holding period.
Instant PnL labels display current position side (Long/Short/Flat) and live profit/loss in both pips and %.
Entry and exit points are clearly marked on the chart with labels and exact prices.
These visualization tools go beyond what most indicators provide, giving traders a clearer, more practical view of trade evolution.
Key Features
Automatic detection of position status (Long / Short / Flat).
Chart labels for entries (“Entry”) and exits (“Exit”).
Real-time floating PnL calculation in both pips and %.
Info panel (top-right) showing entry price, current price, position side, and PnL.
Dynamic shading between entry and current/exit price to visualize profit/loss zones.
Usage Notes & Risk
Mean reversion may underperform in strong trending markets; parameters (len_ma, len_std, mult) should be validated per instrument and timeframe.
Works best on relatively stable, mean-reverting pairs (e.g., AUDNZD).
Risk management is essential: use independent stop-loss rules (e.g., limit risk to 1–2% of equity per trade).
This script is provided for educational purposes only and is not financial advice.
Volatility % Bands (O→C)Volatility % Bands (O→C) is an indicator designed to visualize the percentage change from Open to Close of each candle, providing a clear view of short-term momentum and volatility.
**Histogram**: Displays bar-by-bar % change (Close vs Open). Green bars indicate positive changes, while red bars indicate negative ones, making momentum shifts easy to identify.
**Moving Average Line**: Plots the Simple Moving Average (SMA) of the absolute % change, helping traders track the average volatility over a chosen period.
**Background Bands**: Based on the user-defined Level Step, ±1 to ±5 zones are highlighted as shaded bands, allowing quick recognition of whether volatility is low, moderate, or extreme.
**Label**: Shows the latest candle’s % change and the current SMA value as a floating label on the right, making it convenient for real-time monitoring.
This tool can be useful for volatility breakout strategies, day trading, and short-term momentum analysis.
Machine Learning Z-Score Buy and Sell [SS]Hey everyone,
Releasing this Z-Score based buy and sell indicator.
What it does
This indicator:
Uses Z-score and trend to identify potential buy and sell areas.
Signals those buy and sell areas and provides a target price based on the mean.
Plots the target price for buy and sell signals as a red line (for sell signals) or green line (for buy signals).
Has some "machine learning" aspects, namely, it is able to auto select its lookback length based on its analysis of the trend using Pienscript's trend correlation function iterated over multiple lengths, in order for the indicator to identify:
a) The strongest trend; and
b) The correct target price
What is Z-Score
Z-Score is a measure of the mean. Thus, this is a mean reverting type strategy, as it uses z-score to determine price's distance from the mean (or a Z-Score of 0) and then it looks at historic deviations from the mean to signal the buy and sell signals (i.e. how far has price traditionally drifted from the mean before reverting).
Z-Score is a powerful tool in this sense, and if you folow my other indicators, you will know how much I love Z-score!
How to use the indicator
If you want to use the full Machine Learning capabilities of the indicator, its best to just leave all default settings. These default settings will automatically adjust the mean target price and buy and sell signals to align with the current price action.
If you want to be more aggressive in your
Target Price; and
Signals
Then you can opt to manually input a lookback length and mean reversion standard deviation. However, I generally suggest to avoid this as you are then making your own determination of trend by qualitative assessment. It can work, but its just not suggested.
In the input menu, you will see the option to "Manually select lookback" thus over-riding the auto-determination of trend and targets.
You will also see "manual pullback" enabler and "Pullback Standard Deviation". You can set your pullback standard deviation if you want to be more aggressive. The indicator will naturally shift to conservative target prices based on a neutral mean. However, if you want to increase the aggressiveness of the target price, you can increase or decrease the pullback standard deviation.
General Tips about Manually Adjusting Pullback Target
Here are some tips if you want to manually adjust the pullback targets:
The pullback target needs to be in a standard deviation value, this can be anywhere from 0 to 4 or 0 to -4 (you can theoretically go higher but its not really realistic). You can also do decimals, so 1.5 or 1.25 etc.
To determine whether you should be doing negative or positive standard deviation, you should determine the trend. If it is a downtrend and you are looking to short the rips, you will want to select a negative number, like -1.
If it is an uptrend and you want to buy the dips, you should be selecting a positive number, like 1 or 1.5.
Again, I do suggest leaving the indicator to decide for itself, but the options are there for those who wish.
Overall strategy
This is a mean reverting strategy. So if you are a mean reversion trader, this may be of particular interest to you.
Optional
Optionally, you can have the indicator plot the target prices or not, simply toggle this functionality off or on in the settings menu.
Concluding remarks
That is the indicator in a nutshell!
I hope you enjoy it and find it helpful.
Feel free to check out my other Z-Score based indicators if you find this interesting or want to learn more about the power of Z-Score in trading!
Thanks all and safe trades!
AI KNN-Dual SuperTrend MTF - by Trading Pine Lab🇬🇧
The AI KNN-Dual SuperTrend MTF is a next-generation trading strategy that merges two higher-timeframe SuperTrends with dual KNN (K-Nearest Neighbors) classifiers, an ADX/DMI filter, and a Pivot Percentile bias module. This layered architecture ensures stronger signal confirmation by requiring consensus across AI models, multi-timeframe SuperTrends, and statistical filters.
Entries occur only when both SuperTrends align with bullish or bearish KNN labels, while the ADX/DMI filter validates momentum. Exits are managed dynamically with adaptive trailing stops (ST ± ATR × factor) or when market conditions flip according to percentile bias.
All parameters are fully configurable:
-Trading direction filter: Long / Short / Both.
-KNN classifiers: neighbors (K), dataset size (N), smoothing lengths.
-Dual SuperTrend: higher timeframes, ATR length, ATR factor, baseline type.
-ADX/DMI filter: customizable length and timeframe.
-Pivot Percentile module: multi-scale statistical bias.
-Visualization: AI markers, ribbons, aura lines, and per-trend coloring.
Hourly Range Dashboard (2.0)This dashboard displays each hourly candles range, High minus Low based on an adjustable Look Back in Days. This clearly shows the most active times/hour of day and range of an instrument and the specific hour(s) that its volatility is low during a 24-hour trading session and the hours that the volatility is high. This can help to focus your trading hours based on the most active/volatility.
Price-Volume RelationshipVolume is the relationship between price and performance. Set the candlestick quantity in the settings. It analyzes price and volume based on the number of candlesticks you specify to determine price expectations.
Bull-Bear Power ZScore - by Trading Pine Lab🇬🇧
The Bull-Bear Power ZScore Strategy is an advanced trading framework that integrates Bull-Bear Power (BBP) with a statistical Z-Score model.
BBP measures the relative strength of buyers vs. sellers against an EMA baseline, while the Z-Score standardizes this relationship to detect statistically significant breakouts.
This dual-layer approach provides early trend detection while reducing noise from raw momentum signals.
Entries are triggered when the Z-Score crosses above or below its threshold (long above +T, short below –T). Exits occur when the Z-Score crosses back to zero, ensuring trades close when momentum fades.
A dynamic multi-level take-profit system is integrated, using ATR-based targets (TP1, TP2, TP3) that automatically adapt to **volume context** (high/medium/low) and **percentile analysis** (distribution of price and volume).
This ensures profit targets stretch in strong environments and tighten in weaker conditions, optimizing both risk and reward.
All parameters are fully configurable:
-Bull-Bear Power Settings: EMA length, Z-Score length, Z-Score threshold.
-Take Profit Settings: enable/disable TP system, ATR period, TP1–TP3 multipliers, TP1–TP3 position sizes.
-Volume Analysis: volume MA period, high/medium/low multipliers, adjustment factors.
-Percentile Analysis: percentile lookback period, high/medium/low thresholds, adjustment factors.
Derivative Dynamics Indicator [MarktQuant]The Derivative Dynamics Indicator is a versatile technical indicator that combines several critical metrics used in cryptocurrency and derivatives trading. It helps traders understand the relationship between spot prices, perpetual contract prices, trading volume pressure, and open interest across multiple exchanges. This indicator provides real-time visualizations of:
Funding Rate : The cost traders pay or receive to hold perpetual contracts, indicating market sentiment.
Open Interest (OI) : The total value of outstanding derivative contracts, showing market activity.
Cumulative Volume Delta (CVD) : A measure of buying vs. selling pressure over time.
Additional Data: Includes customizable options for volume analysis, smoothing, and reset mechanisms.
Key Features & How It Works
1. Metric Selection
You can choose which main metric to display:
Funding Rate: Shows the current funding fee, reflecting market sentiment (positive or negative).
CVD: Tracks buying vs. selling pressure, helping identify trend strength.
Open Interest: Displays total outstanding contracts, indicating market activity levels.
2. Volume Data Validation
The script checks if the selected chart includes volume data, which is essential for accurate calculations, especially for CVD. If volume data is missing or zero for multiple bars, it warns you to verify your chart setup.
3. CVD Calculation Methods
You can select how the CVD (Cumulative Volume Delta) is calculated:
Basic: Uses candle open and close to estimate whether buying or selling pressure dominates.
Advanced: Uses a money flow multiplier considering price position within high-low range, generally more accurate.
Tick Estimation: Uses percentage price change to estimate pressure.
You can also choose to display a smoothed version of CVD via a Simple Moving Average (SMA) to better visualize overall trends.
4. CVD Reset Option
To prevent the CVD value from becoming too large over long periods, you can set the indicator to reset periodically after a specified number of bars.
5. CVD Scaling
Adjust the scale of CVD values for better visibility:
Auto: Automatically adjusts based on magnitude.
Raw: Shows raw numbers.
Thousands/Millions: Divides the CVD values for easier reading.
Funding Rate Calculation
The indicator fetches data from multiple popular exchanges (e.g., Binance, Bybit, OKX, MEXC, Bitget, BitMEX). You can select which exchanges to include.
It calculates the funding rate by taking the mean of spot and perpetual prices across selected exchanges.
Open interest is fetched similarly and scaled according to user preferences (auto, millions, billions). It indicates the total amount of open contracts, providing insight into market activity intensity.
Visualizations & Data Presentation
Funding Rate: Shown as colored columns—green for positive (bullish sentiment), red for negative (bearish sentiment).
Open Interest: Displayed as a line, showing overall market activity.
CVD & SMA: Plotted as lines to visualize buying/selling pressure and its smoothed trend.
Information Table: Located at the top right, summarizes:
Current base currency
Number of active sources (exchanges)
Calculated funding rate
Total open interest
Current CVD and its SMA
Last delta (buy vs. sell pressure)
How to Use It
Select Metrics & Exchanges: Choose which data you want to see and from which exchanges.
Adjust Settings: Tweak CVD calculation method, SMA length, reset interval, and scaling options.
Interpret Visuals:
A positive funding rate suggests traders are paying long positions, often indicating bullish sentiment.
Negative funding rates can indicate bearish market sentiment.
Rising CVD indicates increasing buying pressure.
Open interest spikes typically mean increased market participation.
Important Notes
The indicator relies on the availability of volume data for accurate CVD calculation.
Always verify that the exchanges and symbols are correctly set and supported on your chart.
Use the combined insights from funding rates, CVD, and open interest for a comprehensive market view. This tool is designed for research purposes only.
Peak Traker by Futures.RobbyOverview
Peak Tracker is a specialized tool designed to assist traders in proprietary trading challenges. Its main purpose is to help you identify and track the maximum value (the "peak") within an active trade. This is crucial for keeping an eye on your trailing drawdown and avoiding rule violations. The indicator visualizes up to three separate trade windows and provides all necessary data in a clear table.
Key Features
Trailing Drawdown Tracking: The primary function of this indicator is to accurately track the peak value from your entry point to your exit. This helps you minimize the risk of violating drawdown rules in your funding challenge.
Visual Representation: It draws vertical lines for the entry (green) and exit (red) points directly on the chart. This clearly visualizes the exact time frames that are relevant for managing your drawdown.
Dynamic Real-Time Tracking: Within an active trade window, the indicator continuously tracks the highest price reached (Peak) while the entry price (Entry) remains fixed. This allows you to calculate your current drawdown at any moment.
Clear Data Table: A customizable table provides all relevant information at a glance: Trade ID, Entry/Peak prices, and exact timestamps for entry and exit. The numbers are formatted for easy reading using the German number style (e.g., 12.345,67).
Flexible Input: The indicator supports various date and time formats (17:47:00, 2025-08-30 17:14:00, 27.08.25 15:00). The time zone is automatically converted from your local time to the chart's time for precise line placement.
How to Use
Add the indicator to your chart.
Open the indicator's settings (⚙️).
Under "Datums- und Zeit-Eingaben," enter the desired time frames for your trades.
The indicator updates in real time, showing your trade's progress.
Conclusion
This indicator is an essential tool for any trader participating in prop firm challenges who needs a precise method to monitor their trailing drawdown. It provides clarity and visual support to help you avoid rule violations and maximize your chances of success.
Simple MADSimple MAD is a lightweight and customizable indicator that calculates the Median Absolute Deviation (MAD) over a configurable period to measure market volatility. It dynamically displays Stop-Loss (SL) and Take-Profit (TP) levels based on MAD multipliers, both in absolute price and percentage terms.
The indicator includes a clean, watermark-style table with full layout controls — allowing you to adjust position, text size, alignment, and colors. It supports both manual entry price and automatic use of the latest close, making it ideal for traders who want to manage risk with precision and clarity.
Perfect for swing traders, volatility-based strategies, and anyone looking to integrate MAD into their decision-making.
Date Range Performance
Calculates total change and percentage change between two dates.
Computes average change per bar and per day.
Offers arithmetic and geometric daily %.
Supports auto mode (last N trading days) and manual date range.
Displays results as a watermark on the chart.
EMA Percentile Rank [SS]Hello!
Excited to release my EMA percentile Rank indicator!
What this indicator does
Plots an EMA and colors it by short-term trend.
When price crosses the EMA (up or down) and remains on that side for three subsequent bars, the cross is “confirmed.”
At the moment of the most recent cross, it anchors a reference price to the crossover point to ensure static price targets.
It measures the historical distance between price and the EMA over a lookback window, separately for bars above and below the EMA.
It computes percentile distances (25%, 50%, 85%, 95%, 99%) and draws target bands above/below the anchor.
Essentially what this indicator does, is it converts the raw “distance from EMA” behavior into probabilistic bands and historical hit rates you can use for targets, stop placement, or mean-reversion/continuation decisions.
Indicator Inputs
EMA length: Default is 21 but you can use any EMA you prefer.
Lookback: Default window is 500, this is length that the percentiles are calculated. You can increase or decrease it according to your preference and performance.
Show Accumulation Table: This allows you to see the table that shows the hits/price accumulation of each of the percentile ranges. UCL means upper confidence and LCL means lower confidence (so upper and lower targets).
About Percentiles
A percentile is a way of expressing the position of a value within a dataset relative to all the other values.
It tells you what percentage of the data points fall at or below that value.
For example:
The 25th percentile means 25% of the values are less than or equal to it.
The 50th percentile (also called the median) means half the values are below it and half are above.
The 99th percentile means only 1% of the values are higher.
Percentiles are useful because they turn raw measurements into context — showing how “extreme” or “typical” a value is compared to historical behavior.
In the EMA Percentile Rank indicator, this concept is applied to the distance between price and the EMA. By calculating percentile distances, the script can mark levels that have historically been reached often (low percentiles) or rarely (high percentiles), helping traders gauge whether current price action is stretched or within normal bounds.
Use Cases
The EMA Percentile Rank indicator is best suited for traders who want to quantify how far price has historically moved away from its EMA and use that context to guide decision-making.
One strong use case is target setting after trend shifts: when a confirmed crossover occurs, the percentile bands (25%, 50%, 85%, 95%, 99%) provide statistically grounded levels for scaling out profits or placing stops, based on how often price has historically reached those distances. This makes it valuable for traders who prefer data-driven risk/reward planning instead of arbitrary point targets. Another use case is identifying stretched conditions — if price rapidly tags the 95% or 99% band after a cross, that’s an unusually large move relative to history, which could signal exhaustion and prompt mean-reversion trades or protective actions.
Conversely, if the accumulation table shows price frequently resides in upper bands after bullish crosses, traders may anticipate continuation and hold positions longer . The indicator is also effective as a trend filter when combined with its EMA color-coding : only taking trades in the trend’s direction and using the bands as dynamic profit zones.
Additionally, it can support multi-timeframe confluence (if you align your chart to the timeframes of interest), where higher-timeframe trend direction aligns with lower-timeframe percentile behavior for higher-probability setups. Swing traders can use it to frame pullbacks — entering near lower percentile bands during an uptrend — while intraday traders might use it to fade extremes or ride breakouts past the median band. Because the anchor price resets only on EMA crosses, the indicator preserves a consistent reference for ongoing trades, which is especially helpful for managing swing positions through noise .
Overall, its strength lies in transforming raw EMA distance data into actionable, probability-weighted levels that adapt to the instrument’s own volatility and tendencies .
Summary
This indicator transforms a simple EMA into a distribution-aware framework: it learns how far price tends to travel relative to the EMA on either side, and turns those excursions into percentile bands and historical hit rates anchored to the most recent cross. That makes it a flexible tool for targets, stops, and regime filtering, and a transparent way to reason about “how stretched is stretched?”—with context from your chosen market and timeframe.
I hope you all enjoy!
And as always, safe trades!
