StoRsi# StoRSI Indicator: Combining RSI and Stochastic with multiTF
## Overview
The StoRSI indicator combines Relative Strength Index (RSI) and Stochastic oscillators in a single view to provide powerful momentum and trend analysis. By displaying both indicators together with multi-timeframe analysis, it helps traders identify stronger signals when both indicators align.
## Key Components
### 1. RSI (Relative Strength Index)
### 2. Stochastic Oscillator
### 3. EMA (Exponential Moving Average)
### 4. Multi-Timeframe Analysis
## Visual Features
- **Color-coded zones**: Highlights overbought/oversold areas
- **Signal backgrounds**: Shows when both indicators align
- **Multi-timeframe table**: Displays RSI, Stochastic, and trend across timeframes
- **Customizable colors**: Allows full visual customization
## Signal Generation (some need to uncomment in code)
The indicator generates several types of signals:
1. **RSI crosses**: When RSI crosses above/below overbought/oversold levels
2. **Stochastic crosses**: When Stochastic %K crosses above/below overbought/oversold levels
3. **Combined signals**: When both indicators show the same condition
4. **Trend alignment**: When multiple timeframes show the same trend direction
## Conclusion
The StoRSI indicator provides a comprehensive view of market momentum by combining two powerful oscillators with multi-timeframe analysis. By looking for alignment between RSI and Stochastic across different timeframes, traders can identify stronger signals and filter out potential false moves. The visual design makes it easy to spot opportunities at a glance, while the customizable parameters allow adaptation to different markets and trading styles.
For best results, use this indicator as part of a complete trading system that includes proper risk management, trend analysis, and confirmation from price action patterns.
Indicators and strategies
Smart Fib StrategySmart Fibonacci Strategy
This advanced trading strategy combines the power of adaptive SMA entries with Fibonacci-based exit levels to create a comprehensive trend-following system that self-optimizes based on historical market conditions. Credit goes to Julien_Eche who created the "Best SMA Finder" which received an Editors Pick award.
Strategy Overview
The Smart Fibonacci Strategy employs a two-pronged approach to trading:
1. Intelligent Entries: Uses a self-optimizing SMA (Simple Moving Average) to identify optimal entry points. The system automatically tests multiple SMA lengths against historical data to determine which period provides the most robust trading signals.
2. Fibonacci-Based Exits: Implements ATR-adjusted Fibonacci bands to establish precise exit targets, with risk-management options ranging from conservative to aggressive.
This dual methodology creates a balanced system that adapts to changing market conditions while providing clear visual reference points for trade management.
Key Features
- **Self-Optimizing Entries**: Automatically calculates the most profitable SMA length based on historical performance
- **Adjustable Risk Parameters**: Choose between low-risk and high-risk exit targets
- **Directional Flexibility**: Trade long-only, short-only, or both directions
- **Visualization Tools**: Customizable display of entry lines and exit bands
- **Performance Statistics**: Comprehensive stats table showing key metrics
- **Smoothing Option**: Reduces noise in the Fibonacci bands for cleaner signals
Trading Rules
Entry Signals
- **Long Entry**: When price crosses above the blue center line (optimal SMA)
- **Short Entry**: When price crosses below the blue center line (optimal SMA)
### Exit Levels
- **Low Risk Option**: Exit at the first Fibonacci band (1.618 * ATR)
- **High Risk Option**: Exit at the second Fibonacci band (2.618 * ATR)
Strategy Parameters
Display Settings
- Toggle visibility of the stats table and indicator components
Strategy Settings
- Select trading direction (long, short, or both)
- Choose exit method (low risk or high risk)
- Set minimum trades threshold for SMA optimization
SMA Settings
- Option to use auto-optimized or fixed-length SMA
- Customize SMA length when using fixed option
Fibonacci Settings
- Adjust ATR period and SMA basis for Fibonacci bands
- Enable/disable smoothing function
- Customize Fibonacci ratio multipliers
Appearance Settings
- Modify colors, line widths, and transparency
Optimization Methodology
The strategy employs a sophisticated optimization algorithm that:
1. Tests multiple SMA lengths against historical data
2. Evaluates performance based on trade count, profit factor, and win rate
3. Calculates a "robustness score" that balances profitability with statistical significance
4. Selects the SMA length with the highest robustness score
This ensures that the strategy's entry signals are continuously adapting to the most effective parameters for current market conditions.
Risk Management
Position sizing is fixed at $2,000 per trade, allowing for consistent exposure across all trading setups. The Fibonacci-based exit system provides two distinct risk management approaches:
- **Conservative Approach**: Using the first Fibonacci band for exits produces more frequent but smaller wins
- **Aggressive Approach**: Using the second Fibonacci band allows for larger potential gains at the cost of increased volatility
Ideal Usage
This strategy is best suited for:
- Trending markets with clear directional moves
- Timeframes from 4H to Daily for most balanced results
- Instruments with moderate volatility (stocks, forex, commodities)
Traders can further enhance performance by combining this strategy with broader market analysis to confirm the prevailing trend direction.
HiLo EMA Custom bandsHILo Ema custom bands
This advanced technical indicator is a powerful variation of "HiLo Ema squeeze bands" that combines the best elements of Donchian channels and EMAs. It's specially designed to identify price squeezes before significant market moves while providing dynamic support/resistance levels and predictive price targets.
Indicator Concept:
The indicator initializes EMAs at each new high or low - the upper EMA tracks highs while the lower EMA tracks lows. It draws maximum of 6 custom bands based on percentage, fixed value or Atr
Upper EM bands are drawn below uper ema, Lower EMA bands are drawn above lower ema
Customizable Options:
Ema length: 200 default
Calculation type: Ema (Default), HILO
Calculation type: Percent,Fixed Value, ATR
Band Value: Percent/Value/ATR multiple This is value to use for calculation type
Band Selection: Both,Upper,Lower
Key Features:
You can choose to draw either of one or both, the latter can be overwhelming initially but as you get used to it, it becomes a powerful tool.
When both bands are selected, upper and lower bands provide provides dual references and intersections
This creates a more trend-responsive alternative to traditional Donchian channels with clearly defined zones for trade planning.
If you select percaentage, note that the calulation is based FROM the respective EMA bands. So bands from lower EMA band will appear narrower compared to the those drawn from upper EMA band
Price targets or reversals:
Look of alignment of lines and price. The current level of one order could align with that of previous level of a different order because often markets move in steps
Settings Guide:
Recommended Settings:
Ema length: 200
Use one of the bands (not both) if using large length of say 1000
Calculation type: EMA
HILO will draw donchian like bands, this is useful if you only want flat price levels. In a rising market use upper and vise versa
Calculation type:
percentage for indices : 5, for symbols 10 or higher based on symbol volatility
Fixed value: about 10% of symbol value converted to value
Atr: 2 ideally
Perfect for swing traders and position traders looking for a more sophisticated volatility-based overlay that adapts to changing market conditions and provides predictive reversal levels.
Note: This indicator works well across multiple timeframes but is especially effective on H4, Daily and Weekly charts for trend trading.
(OFPI) Order Flow Polarity Index - Momentum Gauge (DAFE) (OFPI) Order Flow Polarity Index - Momentum Gauge: Decode Market Aggression
The (OFPI) Gauge Bar is your front-row seat to the battle between buyers and sellers. This isn’t just another indicator—it’s a momentum tracker that reveals market aggression through a sleek, centered gauge bar and a smart dashboard. Built for traders who want clarity without clutter, it’s your edge for spotting who’s driving price, bar by bar.
What Makes It Unique?
Order Flow Pressure Index (OFPI): Splits volume into buy vs. sell pressure based on candle body position. It’s not just volume—it’s intent, showing who’s got the upper hand.
T3 Smoothing Magic: Uses a Tilson T3 moving average to keep signals smooth yet responsive. No laggy SMA nonsense here.
Centered Gauge Bar: A 20-segment bar splits bullish (lime) and bearish (red) momentum around a neutral center. Empty segments scream indecision—it’s like a visual heartbeat of the market.
Momentum Shift Alerts: Catches reversals with “Momentum Shift” flags when the OFPI crests, so you’re not caught off guard.
Clean Dashboard: A compact, bottom-left table shows momentum status, the gauge bar, and the OFPI value. Color-coded, transparent, and no chart clutter.
Inputs & Customization
Lookback Length (default 10): Set the window for pressure calculations. Short for scalps, long for trends.
T3 Smoothing Length (default 5): Tune the smoothness. Tight for fast markets, relaxed for chill ones.
T3 Volume Factor (default 0.7): Crank it up for snappy signals or down for silky trends.
Toggle the dashboard for minimalist setups or mobile trading.
How to Use It
Bullish Momentum (Lime, Right-Filled): Buyers are flexing. Look for breakouts or trend continuations. Pair with support levels.
Bearish Momentum (Red, Left-Filled): Sellers are in charge. Scout for breakdowns or shorts. Check resistance zones.
Neutral (Orange, Near Center): Market’s chilling. Avoid big bets—wait for a breakout or play the range.
Momentum Shift: A reversal might be brewing. Confirm with price action before jumping in.
Not a Solo Act: Combine with your strategy—trendlines, RSI, whatever. It’s a momentum lens, not a buy/sell bot.
Why Use the OFPI Gauge?
See the Fight: Most tools just count volume. OFPI shows who’s winning with a visual that slaps.
Works Anywhere: Crypto, stocks, forex, any timeframe. Tune it to your style.
Clean & Pro: No chart spam, just a sharp gauge and a dashboard that delivers.
Unique Edge: No other indicator blends body-based pressure, T3 smoothing, and a centered gauge like this.
The OFPI Gauge catches the market’s pulse so you can trade with confidence. It’s not about predicting the future—it’s about knowing who’s in control right now.
For educational purposes only. Not financial advice. Always use proper risk management.
Use with discipline. Trade your edge.
— Dskyz , for DAFE Trading Systems
Cointegration Heatmap & Spread Table [EdgeTerminal]The Cointegration Heatmap is a powerful visual and quantitative tool designed to uncover deep, statistically meaningful relationships between assets.
Unlike traditional indicators that react to price movement, this tool analyzes the underlying statistical relationship between two time series and tracks when they diverge from their long-term equilibrium — offering actionable signals for mean-reversion trades .
What Is Cointegration?
Most traders are familiar with correlation, which measures how two assets move together in the short term. But correlation is shallow — it doesn’t imply a stable or predictable relationship over time.
Cointegration, however, is a deeper statistical concept: Two assets are cointegrated if a linear combination of their prices or returns is stationary , even if the individual series themselves are non-stationary.
Cointegration is a foundational concept in time series analysis, widely used by hedge funds, proprietary trading firms, and quantitative researchers. This indicator brings that institutional-grade concept into an easy-to-use and fully visual TradingView indicator.
This tool helps answer key questions like:
“Which stocks tend to move in sync over the long term?”
“When are two assets diverging beyond statistical norms?”
“Is now the right time to short one and long the other?”
Using a combination of regression analysis, residual modeling, and Z-score evaluation, this indicator surfaces opportunities where price relationships are stretched and likely to snap back — making it ideal for building low-risk, high-probability trade setups.
In simple terms:
Cointegrated assets drift apart temporarily, but always come back together over time. This behavior is the foundation of successful pairs trading.
How the Indicator Works
Cointegration Heatmap indicator works across any market supported on TradingView — from stocks and ETFs to cryptocurrencies and forex pairs.
You enter your list of symbols, choose a timeframe, and the indicator updates every bar with live cointegration scores, spread signals, and trade-ready insights.
Indicator Settings:
Symbol list: a customizable list of symbols separated by commas
Returns timeframe: time frame selection for return sampling (Weekly or Monthly)
Max periods: max periods to limit the data to a certain time and to control indicator performance
This indicator accomplishes three major goals in one streamlined package:
Identifies stable long-term relationships (cointegration) between assets, using a heatmap visualization.
Tracks the spread — the difference between actual prices and the predicted linear relationship — between each pair.
Generates trade signals based on Z-score deviations from the mean spread, helping traders know when a pair is statistically overextended and likely to mean revert.
The math:
Returns are calculated using spread tickers to ensure alignment in time and adjust for dividends, splits, and other inconsistencies.
For each unique pair of symbols, we perform a linear regression
Yt=α+βXt+ε
Then we compute the residuals (errors from the regression):
Spreadt=Yt−(α+βXt)
Calculate the standard deviation of the spread over a moving window (default: 100 samples) and finally, define the Cointegration Score:
S=1/Standard Deviation of Residuals
This means, the lower the deviation, the tighter the relationship, so higher scores indicate stronger cointegration.
Always remember that cointegration can break down so monitor the asset over time and over multiple different timeframes before making a decision.
How to use the indicator
The heatmap table:
The indicator displays 2 very important tables, one in the middle and one on the right side. After entering your symbols, the first table to pay attention to is the middle heatmap table.
Any assets with a cointegration value of 25% is something to pay attention to and have a strong and stable relationship. Anything below is weak and not tradable.
Additionally, the 40% level is another important line to cross. Assets that have a cointegration score of over 40% will most likely have an extremely strong relationship.
Think about it this way, the higher the percentage, the tighter and more statistically reliable the relationship is.
The spread table:
After finding a good asset pair using heatmap, locate the same pair in the spread table (right side).
Here’s what you’ll see on the table:
Spread: Current difference between the two symbols based on the regression fit
Mean: Historical average of that spread
Z-score: How far current spread is from the mean in standard deviations
Signal: Trade suggestion: Short, Long, or Neutral
Since you’re expecting mean reversion, the idea is that the spread will return to the average. You want to take a trade when the z-score is either over +2 or below -2 and exit when z-score returns to near 0.
You will usually see the trade suggestion on the spread chart but you can make your own decision based on your risk level.
Keep in mind that the Z-score for each pair refers to how off the first asset is from the mean compared to the second one, so for example if you see STOCKA vs STOCKB with a Z-score of -1.55, we are regressing STOCKB (Y) on STOCKA (X).
In this case, STOCKB is the quoted asset and STOCKA is the base asset.
In this case, this means that STOCKB is much lower than expected relative to STOCKA, so the trade would be a long position on stock B and short position on stock A.
Smooth Fibonacci BandsSmooth Fibonacci Bands
This indicator overlays adaptive Fibonacci bands on your chart, creating dynamic support and resistance zones based on price volatility. It combines a simple moving average with ATR-based Fibonacci levels to generate multiple bands that expand and contract with market conditions.
## Features
- Creates three pairs of upper and lower Fibonacci bands
- Smoothing option for cleaner, less noisy bands
- Fully customizable colors and line thickness
- Adapts automatically to changing market volatility
## Settings
Adjust the SMA and ATR lengths to match your trading timeframe. For short-term trading, try lower values; for longer-term analysis, use higher values. The Fibonacci factors determine how far each band extends from the center line - standard Fibonacci ratios (1.618, 2.618, and 4.236) are provided as defaults.
## Trading Applications
- Use band crossovers as potential entry and exit signals
- Look for price bouncing off bands as reversal opportunities
- Watch for price breaking through multiple bands as strong trend confirmation
- Identify potential support/resistance zones for placing stop losses or take profits
Fibonacci Bands combines the reliability of moving averages with the adaptability of ATR and the natural market harmony of Fibonacci ratios, offering a robust framework for both trend and range analysis.
Square of Nine Price & Time Forecasting Grid📐 Square of Nine Price and Time Forecasting Grid
Adapted and Interpreted by Javonnii | Inspired by Patrick Mikula
The So9 Price and Time Forecasting Grid is a self-contained forecasting grid that allows traders to visually forecast levels in both price and time, using only Square of Nine calculations.
This indicator dynamically generates an expandable grid of angles, levels, and timeline based on the placement of a single anchor point, typically from a significant high or low. It requires no manual drawing or external tools.
🔍 How It Works:
The So9 Price and Time Forecasting Grid uses Square of Nine calculation and rotational logic to project price levels, time intervals, and internal angular structure from a single anchor point.
Once applied to the chart, the grid self-generates:
• Price levels using 360° degree Square of Nine intervals
• Timeline projections using Square of Nine progression intervals
• Diagonal and cardinal cross angles that dynamically propagate from the anchor
• Granular diagonal angle control, letting you refine internal grid resolution for tighter price structural analysis in relation to the forecasting grid.
⚙️ Setup Instructions
1. Select a Significant Pivot:
After loading the indicator, you’ll be prompted to select a significant high or low on your chart. This serves as the anchor point for the entire grid.
2. Price Grid Auto-Population:
Once anchored, Square of Nine price levels will automatically populate on the chart. These levels reflect Dynamic Square of Nine intervals from the anchor using rotational math.
3. Scale to Fit Your Instrument:
Use the provided UI settings to scale the grid to fit your instrument’s price structure. This ensures the levels align with actual historical price.
4. Engage Timelines:
Activate the timeline progression feature to generate forward-projected time intervals using Square of Nine-based timing logic. The entire grid can be extended up to 500 bars ahead on any timeframe.
5. Add Diagonal Angles:
Select which Square of Nine angle resolution you’d like to overlay from either the cardinal cross or diagonal cross based on Square of Nine geometry. This will populate diagonal levels within the grid, creating a full structural grid.
6. Customize Visuals:
You can toggle or hide price levels, timelines, or diagonal angles independently.
The entire grid can also be color-coded and customized to match your charting preferences.
All elements are plotted automatically. There is no manual drawing or calculation required. You can toggle the components on or off based on your workflow:
• Hide price levels if you just want time lines.
• Focus on angles without price levels.
• Use timeline progression independently.
📘 Attribution and Permission
This tool is inspired by concepts from "The Definitive Guide to Forecasting Using W.D. Gann’s Square of Nine" by Patrick Mikula.
The indicator reflects my personal adaptation and implementation of these forecasting principles within TradingView.
I have asked for and received permission from Patrick Mikula to share and publish tools derived from his work.
This applies to this script and to any other indicators I’ve developed that incorporate or build upon his material.
Documentation of this permission is available upon request.
Credit and respect to Patrick Mikula for his contributions to Gann-based research and for granting me the opportunity to share these tools with others.
Professor Up-Down Prediction V2Professor Up-Down Prediction V2 is designed for easier use and only displays the dominant directional probability estimates.
This indicator analyzes various variables to estimate the potential upward or downward movement that may occur in the future. It does not predict whether the next candle will go up or down.
Since it forecasts based on the timeframe, it predicts short-term movements in lower timeframes and long-term movements in higher timeframes.
It never provides certainty and should be used alongside other indicators for better directional analysis.
The probabilistic estimate on the most recent candle should always be prioritized.
The continuation of an upward or downward movement is directly proportional to the dominance of the corresponding probability. In other words, if the upward probability is dominant, the likelihood and continuation of an upward movement increases. The increase or decrease in the upward or downward probability also carries meaningful implications about the related movement.
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Professor Up-Down Prediction V2, daha kolay kullanım için tasarlanmıştır. Sadece baskın olan yöndeki oransal tahminlemeleri gösterir.
Bu gösterge farklı değişkenleri ölçümleyerek gelecekte oluşabilecek düşüş ya da yükseliş hareketini oransal olarak tahminler. Bir sonraki mumun düşüş ya da yükseliş olduğunu TAHMİNLEMEZ.
Zaman dilimine özgü gelecekte oluşabilecek hareketi tahminlediği için kısa vade zaman dilimlerinde kısa vadeyi tahminler uzun zaman dilimlerinde uzun vadeyi tahmin eder.
Asla ve asla kesinlik içermez ve farklı göstergelerle kullanılarak yön tespiti daha iyi sonuç verir.
Her zaman son bardaki oransal tahminleme dikkate alınmalıdır.
Yükseliş ya da düşüş hareketinin devamlılığı, düşüş ya da yükseliş oranının baskınlığı ile doğru orantılıdır. Yani, yükseliş ihtimali baskınlığı hakimse yükseliş hareketinin ihtimali ve devamlılığı artar.Düşüş ya da yükseliş ihtimalinin oransal olarak artıp azalması da düşüş ve yükselişle alakalı anlam taşımaktadır.
Breakout Core | by Solid#SignalsBreakout Core | by SolidSignals
General Overview
Breakout Core is an advanced breakout trading strategy designed for Bitcoin (BTC). Optimized for the unique market dynamics following the launch of BlackRock’s Spot ETFs in January 2024, it adapts to Bitcoin’s post-ETF volatility patterns. The strategy’s core strength lies in its low drawdown, achieved through a proprietary time-based signal-filtering algorithm that sets it apart from traditional breakout strategies. Breakout Core offers traders a reliable tool for navigating Bitcoin’s evolving market with reduced risk and enhanced precision.
Mechanisms
Breakout Core combines well-known indicators BB, EMAs, MAs with custom-tuned parameters to improve signal accuracy. Its unique feature is a proprietary time-filter algorithm that prioritizes high-probability breakout signals during specific high-volatility trading hours, derived from market analysis post-ETF launch. This algorithm minimizes false positives, particularly in volatile conditions, by integrating time-based volatility patterns with price action. The result is a robust strategy that optimizes entry and exit points for Bitcoin trading.
Objectives
Breakout Core aims to provide steady returns with controlled risk by targeting Bitcoin’s breakout patterns in the post-ETF market. Its low drawdown, achieved through extensive optimization and proprietary logic, makes it suitable for leverage trading (e.g., 3–5x leverage), balancing growth with capital protection. Tailored for BTC, the strategy equips traders with a precise tool to navigate Bitcoin’s transformed market dynamics.
Backtesting and Parameter Notes
Backtesting was performed using a $10,000 USDT account, risking up to 10% of equity per trade, including 0.06% commission fees and 2-tick slippage, aligned with standard exchange conditions. The strategy report details backtesting results from the launch of BlackRock’s Spot ETFs. These settings are the script’s defaults, ensuring transparency. Traders are encouraged to verify results using TradingView’s Deep Backtest feature to adapt to current market conditions.
Please note: Past performance does not guarantee future results.
Chart and Usage
The chart is clean and intuitive, displaying only Breakout Core’s buy and sell signals for easy interpretation. Parameters are pre-optimized for immediate use, with adjustable Take Profit (TP) and Stop Loss (SL) levels. Traders should validate custom settings via TradingView’s backtesting tools to ensure market compatibility. An integrated Alarm Panel supports API connectivity, providing clear Entry/Exit commands for Long and Short positions, enabling seamless automated trading workflows.
Originality Statement
Breakout Core is an original strategy developed by SolidSignals, leveraging standard indicators (Bollinger Bands, EMAs, MAs) combined with a proprietary time-filter algorithm. No third-party or open-source code is used, ensuring full compliance with TradingView’s originality requirements. The time-filter mechanism, based on post-ETF volatility analysis, distinguishes this strategy from conventional breakout approaches.
Important Disclaimer
Market conditions evolve continuously, and past performance is not indicative of future results. Traders are responsible for validating the strategy’s settings and performance under current market conditions before use.
MACD of RSI [TORYS]MACD of RSI — Momentum & Divergence Scanner
Description:
This enhanced oscillator applies MACD logic directly to the Relative Strength Index (RSI) rather than price, giving traders a clearer look at internal momentum and early shifts in trend strength. Now featuring a custom histogram, dual MA types, and RSI-based divergence detection — it’s a complete toolkit for identifying exhaustion, acceleration, and hidden reversal points in real time.
How It Works:
Calculates the MACD line as the difference between a fast and slow moving average of RSI. Adds a Signal Line (MA of the MACD) and plots a Histogram to show momentum acceleration/deceleration. Both RSI MAs and the Signal Line can be toggled between EMA and SMA for custom tuning.
Divergence Detection:
Bullish Divergence : Price makes a lower low while RSI makes a higher low → labeled with a green “D” below the curve.
Bearish Divergence : Price makes a higher high while RSI makes a lower high → labeled with a red “D” above the curve.
Configurable lookback window for tuning sensitivity to pivots, with 4 as the sweet spot.
RSI Pivot Dot Signals:
Plots green dots at RSI oversold pivot lows below 30,
Plots red dots at overbought pivot highs above 70.
Helps detect short-term exhaustion or bounce zones, plotted right on the MACD-RSI curve.
RSI 50 Crosses (Optional):
Optional ▲ and ▼ labels when RSI crosses its 50 midline — useful for momentum trend shifts or pullback confirmation, or to detect consolidation.
Histogram:
Plotted as a column chart showing the distance between MACD and Signal Line.
Colored dynamically:
Bright green : Momentum rising above zero
Light green : Weakening above zero
Bright red : Momentum falling below zero
Light red : Weakening below zero
The zero line serves as the mid-point:
Above = Bullish Bias
Below = Bearish Bias
How to Interpret:
Momentum Confirmation:
Use MACD cross above Signal Line with a rising histogram to confirm breakouts or trend entries.
Histogram shrinking near zero = momentum weakening → caution or reversal.
Exhaustion & Reversals:
Dot signals near RSI extremes + histogram peak can suggest overbought/oversold pressure.
Use divergence labels ("D") to spot early reversal signals before price breaks structure.
Inputs & Settings:
RSI Length
Fast/Slow MA Lengths for MACD (applied to RSI)
Signal Line Length
MA Type: Choose between EMA and SMA for MACD and Signal Line
Pivot Sensitivity for dot markers
Divergence Logic Toggle
Show/hide RSI 50 Crosses
Best For:
Traders who want momentum insight from inside RSI, not price
Scalpers using divergence or exhaustion entries
Swing traders seeking entry confirmation from signal crossovers
Anyone using multi-timeframe confluence with RSI and trend filters
Pro Tips:
Combine this with:
Bollinger Bands breakouts and reversals
VWAP or EMAs to filter entries by trend
Volume spikes or BBW squeezes for volatility confirmation
TTM Scalper Alert to sync structure and momentum
Why EMA Isn't What You Think It IsMany new traders adopt the Exponential Moving Average (EMA) believing it's simply a "better Simple Moving Average (SMA)". This common misconception leads to fundamental misunderstandings about how EMA works and when to use it.
EMA and SMA differ at their core. SMA use a window of finite number of data points, giving equal weight to each data point in the calculation period. This makes SMA a Finite Impulse Response (FIR) filter in signal processing terms. Remember that FIR means that "all that we need is the 'period' number of data points" to calculate the filter value. Anything beyond the given period is not relevant to FIR filters – much like how a security camera with 14-day storage automatically overwrites older footage, making last month's activity completely invisible regardless of how important it might have been.
EMA, however, is an Infinite Impulse Response (IIR) filter. It uses ALL historical data, with each past price having a diminishing - but never zero - influence on the calculated value. This creates an EMA response that extends infinitely into the past—not just for the last N periods. IIR filters cannot be precise if we give them only a 'period' number of data to work on - they will be off-target significantly due to lack of context, like trying to understand Game of Thrones by watching only the final season and wondering why everyone's so upset about that dragon lady going full pyromaniac.
If we only consider a number of data points equal to the EMA's period, we are capturing no more than 86.5% of the total weight of the EMA calculation. Relying on he period window alone (the warm-up period) will provide only 1 - (1 / e^2) weights, which is approximately 1−0.1353 = 0.8647 = 86.5%. That's like claiming you've read a book when you've skipped the first few chapters – technically, you got most of it, but you probably miss some crucial early context.
▶️ What is period in EMA used for?
What does a period parameter really mean for EMA? When we select a 15-period EMA, we're not selecting a window of 15 data points as with an SMA. Instead, we are using that number to calculate a decay factor (α) that determines how quickly older data loses influence in EMA result. Every trader knows EMA calculation: α = 1 / (1+period) – or at least every trader claims to know this while secretly checking the formula when they need it.
Thinking in terms of "period" seriously restricts EMA. The α parameter can be - should be! - any value between 0.0 and 1.0, offering infinite tuning possibilities of the indicator. When we limit ourselves to whole-number periods that we use in FIR indicators, we can only access a small subset of possible IIR calculations – it's like having access to the entire RGB color spectrum with 16.7 million possible colors but stubbornly sticking to the 8 basic crayons in a child's first art set because the coloring book only mentioned those by name.
For example:
Period 10 → alpha = 0.1818
Period 11 → alpha = 0.1667
What about wanting an alpha of 0.17, which might yield superior returns in your strategy that uses EMA? No whole-number period can provide this! Direct α parameterization offers more precision, much like how an analog tuner lets you find the perfect radio frequency while digital presets force you to choose only from predetermined stations, potentially missing the clearest signal sitting right between channels.
Sidenote: the choice of α = 1 / (1+period) is just a convention from 1970s, probably started by J. Welles Wilder, who popularized the use of the 14-day EMA. It was designed to create an approximate equivalence between EMA and SMA over the same number of periods, even thought SMA needs a period window (as it is FIR filter) and EMA doesn't. In reality, the decay factor α in EMA should be allowed any valye between 0.0 and 1.0, not just some discrete values derived from an integer-based period! Algorithmic systems should find the best α decay for EMA directly, allowing the system to fine-tune at will and not through conversion of integer period to float α decay – though this might put a few traditionalist traders into early retirement. Well, to prevent that, most traditionalist implementations of EMA only use period and no alpha at all. Heaven forbid we disturb people who print their charts on paper, draw trendlines with rulers, and insist the market "feels different" since computers do algotrading!
▶️ Calculating EMAs Efficiently
The standard textbook formula for EMA is:
EMA = CurrentPrice × alpha + PreviousEMA × (1 - alpha)
But did you know that a more efficient version exists, once you apply a tiny bit of high school algebra:
EMA = alpha × (CurrentPrice - PreviousEMA) + PreviousEMA
The first one requires three operations: 2 multiplications + 1 addition. The second one also requires three ops: 1 multiplication + 1 addition + 1 subtraction.
That's pathetic, you say? Not worth implementing? In most computational models, multiplications cost much more than additions/subtractions – much like how ordering dessert costs more than asking for a water refill at restaurants.
Relative CPU cost of float operations :
Addition/Subtraction: ~1 cycle
Multiplication: ~5 cycles (depending on precision and architecture)
Now you see the difference? 2 * 5 + 1 = 11 against 5 + 1 + 1 = 7. That is ≈ 36.36% efficiency gain just by swapping formulas around! And making your high school math teacher proud enough to finally put your test on the refrigerator.
▶️ The Warmup Problem: how to start the EMA sequence right
How do we calculate the first EMA value when there's no previous EMA available? Let's see some possible options used throughout the history:
Start with zero : EMA(0) = 0. This creates stupidly large distortion until enough bars pass for the horrible effect to diminish – like starting a trading account with zero balance but backdating a year of missed trades, then watching your balance struggle to climb out of a phantom debt for months.
Start with first price : EMA(0) = first price. This is better than starting with zero, but still causes initial distortion that will be extra-bad if the first price is an outlier – like forming your entire opinion of a stock based solely on its IPO day price, then wondering why your model is tanking for weeks afterward.
Use SMA for warmup : This is the tradition from the pencil-and-paper era of technical analysis – when calculators were luxury items and "algorithmic trading" meant your broker had neat handwriting. We first calculate an SMA over the initial period, then kickstart the EMA with this average value. It's widely used due to tradition, not merit, creating a mathematical Frankenstein that uses an FIR filter (SMA) during the initial period before abruptly switching to an IIR filter (EMA). This methodology is so aesthetically offensive (abrupt kink on the transition from SMA to EMA) that charting platforms hide these early values entirely, pretending EMA simply doesn't exist until the warmup period passes – the technical analysis equivalent of sweeping dust under the rug.
Use WMA for warmup : This one was never popular because it is harder to calculate with a pencil - compared to using simple SMA for warmup. Weighted Moving Average provides a much better approximation of a starting value as its linear descending profile is much closer to the EMA's decay profile.
These methods all share one problem: they produce inaccurate initial values that traders often hide or discard, much like how hedge funds conveniently report awesome performance "since strategy inception" only after their disastrous first quarter has been surgically removed from the track record.
▶️ A Better Way to start EMA: Decaying compensation
Think of it this way: An ideal EMA uses an infinite history of prices, but we only have data starting from a specific point. This creates a problem - our EMA starts with an incorrect assumption that all previous prices were all zero, all close, or all average – like trying to write someone's biography but only having information about their life since last Tuesday.
But there is a better way. It requires more than high school math comprehension and is more computationally intensive, but is mathematically correct and numerically stable. This approach involves compensating calculated EMA values for the "phantom data" that would have existed before our first price point.
Here's how phantom data compensation works:
We start our normal EMA calculation:
EMA_today = EMA_yesterday + α × (Price_today - EMA_yesterday)
But we add a correction factor that adjusts for the missing history:
Correction = 1 at the start
Correction = Correction × (1-α) after each calculation
We then apply this correction:
True_EMA = Raw_EMA / (1-Correction)
This correction factor starts at 1 (full compensation effect) and gets exponentially smaller with each new price bar. After enough data points, the correction becomes so small (i.e., below 0.0000000001) that we can stop applying it as it is no longer relevant.
Let's see how this works in practice:
For the first price bar:
Raw_EMA = 0
Correction = 1
True_EMA = Price (since 0 ÷ (1-1) is undefined, we use the first price)
For the second price bar:
Raw_EMA = α × (Price_2 - 0) + 0 = α × Price_2
Correction = 1 × (1-α) = (1-α)
True_EMA = α × Price_2 ÷ (1-(1-α)) = Price_2
For the third price bar:
Raw_EMA updates using the standard formula
Correction = (1-α) × (1-α) = (1-α)²
True_EMA = Raw_EMA ÷ (1-(1-α)²)
With each new price, the correction factor shrinks exponentially. After about -log₁₀(1e-10)/log₁₀(1-α) bars, the correction becomes negligible, and our EMA calculation matches what we would get if we had infinite historical data.
This approach provides accurate EMA values from the very first calculation. There's no need to use SMA for warmup or discard early values before output converges - EMA is mathematically correct from first value, ready to party without the awkward warmup phase.
Here is Pine Script 6 implementation of EMA that can take alpha parameter directly (or period if desired), returns valid values from the start, is resilient to dirty input values, uses decaying compensator instead of SMA, and uses the least amount of computational cycles possible.
// Enhanced EMA function with proper initialization and efficient calculation
ema(series float source, simple int period=0, simple float alpha=0)=>
// Input validation - one of alpha or period must be provided
if alpha<=0 and period<=0
runtime.error("Alpha or period must be provided")
// Calculate alpha from period if alpha not directly specified
float a = alpha > 0 ? alpha : 2.0 / math.max(period, 1)
// Initialize variables for EMA calculation
var float ema = na // Stores raw EMA value
var float result = na // Stores final corrected EMA
var float e = 1.0 // Decay compensation factor
var bool warmup = true // Flag for warmup phase
if not na(source)
if na(ema)
// First value case - initialize EMA to zero
// (we'll correct this immediately with the compensation)
ema := 0
result := source
else
// Standard EMA calculation (optimized formula)
ema := a * (source - ema) + ema
if warmup
// During warmup phase, apply decay compensation
e *= (1-a) // Update decay factor
float c = 1.0 / (1.0 - e) // Calculate correction multiplier
result := c * ema // Apply correction
// Stop warmup phase when correction becomes negligible
if e <= 1e-10
warmup := false
else
// After warmup, EMA operates without correction
result := ema
result // Return the properly compensated EMA value
▶️ CONCLUSION
EMA isn't just a "better SMA"—it is a fundamentally different tool, like how a submarine differs from a sailboat – both float, but the similarities end there. EMA responds to inputs differently, weighs historical data differently, and requires different initialization techniques.
By understanding these differences, traders can make more informed decisions about when and how to use EMA in trading strategies. And as EMA is embedded in so many other complex and compound indicators and strategies, if system uses tainted and inferior EMA calculatiomn, it is doing a disservice to all derivative indicators too – like building a skyscraper on a foundation of Jell-O.
The next time you add an EMA to your chart, remember: you're not just looking at a "faster moving average." You're using an INFINITE IMPULSE RESPONSE filter that carries the echo of all previous price actions, properly weighted to help make better trading decisions.
EMA done right might significantly improve the quality of all signals, strategies, and trades that rely on EMA somewhere deep in its algorithmic bowels – proving once again that math skills are indeed useful after high school, no matter what your guidance counselor told you.
Plyo Tap'n'Slap (TnS) by OutOfOptionsThe Model
This Strategy/Model takes advantage of the strongest trend signature in the market, which is also the most basic move in the market. This basic move is what most traders consider to be a staircase, or trendline. ICT traders call this setup a “unicorn” which is just another word for when an Order block overlaps with an FVG. The beauty of this model is that you don't need to know what ANY of these things are.
The entry comes when a candles High or Low overlaps with a FVG that is at least 3 points away from both edges of the FVG. If the candle is too close to the edge then the setups is invalid (see rules for more). TO find a candle that overlaps with the FVG it also can not cut through any other price action, for example, A potential entry cant cut through another wick to make it overlap with the FVG. (see rules for more)
TnS gets its TP by analyzing what is called the "OG TP" The OG TP is determined by looking for the first tapped into the FVG, then looking for an immediate High or Low to the left of the candle that first tapped the FVG. IF there is no immediate High or low next to the candle that first tapped the FVG, then target the candle itself (see rules for more). IF the "OG TP" has already been hit before TnS gets its entry, then look to the left of the TnS entry candle for the immediate High or Low next to it. If there is no immediate High or Low next to the TnS Entry candle, then target the Entry candles, High or Low (see rules for more)
Model Rules
Overlapping H/L MUST be at least 3 points away from both edges of the FVG,
Overlapping H/L cannot cut through PA to make it overlap with the FVG,
Entries can only be the highest overlapping high or the lowest overlapping low,
If TnS Has already played out within the FVG then it should no longer be used,
If the FVGs OG TP has already been hit then use the TnS entry to re-align for your target,
No using NWOGs/NDOGs for setups. A NWOG is NOT the same thing as an FVG so this example
V2 Rules
If its a Bullish FVG then you need a bearish candle H/L that overlaps for your entry
If its a Bearish FVG then you need a bullish candle H/L that overlaps for your entry
Indicator Functionality
The indicator uses specific logic to identify FVGs that match the requirements of the TnS model, ensuring at least one valid entry exists per the default V1 rules of the model, or the stricter V2 rules if configured via settings. If entries (up to 2 per model rules) are identified, the FVG is highlighted, and each entry and its stop loss is marked with a line. The line styles, colors, and FVG color, which can vary depending on whether the entry is bullish or bearish, are configurable via settings.
Once the FVG is tapped into, the indicator will highlight the take profit spot and list all applicable entries, stop losses, and take profits in a table, the position and presence of which can be controlled within the indicator settings. When price action hits either stop loss or take profit, all elements are removed from the chart to avoid clutter.
Additionally, the indicator allows filtering of entries based on Risk/Reward (R:R), filtering out entries where take profit is less than the model stop loss and entries for which the stop loss resides inside the FVG itself. To help visualize setups where the FVG is outside the current visual range, the indicator has options to extend the FVG box and lines by a configurable number of bars. Once the FVG is tapped, the indicator will automatically extend lines/FVG box to the bar that tapped the FVG plus the configured number of bars.
Sector Relative StrengthDescription
This script compares sector performance relative to the S&P 500. Sector price levels or charts alone can mislead, because they tend to move with the broader market. An increase in a sector’s price does not necessarily indicate strength, as it may simply be following the index.
For more a more reliable picture, the script calculates a ratio between each sector ETF and SPY. If the ratio has increased, the sector has outperformed the index. In case it has declined, the sector has underperformed. If the value is near zero, the sector has moved in line with the index. The sectors are presented in a table and sorted on relative performance.
Calculation Method
The performance is expressed as a percentage change in the ratio over a user-defined lookback period. The default lookback is set to 21 bars, which corresponds to one month on a daily chart. This value can be adopted in the settings to match preferred time period.
Z-Score
In addition to the percentage change, the script calculates a Z-score of the ratio, which measures how far the current value deviates from its recent mean. A high positive Z-score indicates that the ratio is significantly above its average, while a negative value indicates it is below. This normalization allows for comparison between sectors with different price levels or volatility profiles.
Table Columns
- Relative %: The sector's performance relative to SPY over the selected lookback period
- Z-Score: Standardized measure of current performance ratio is relative to its average
- Trend Arrow: Indicates the direction of relative performance up down or flat
Example Interpretation
For example, if XLK shows a 3.7% change, it has outperformed SPY over the selected period. Another sector might show a -2.1% change, which indicates underperformance. While both values shows relative strength or weakness, the Z-score is optional and can provide additional context based on how unusual that performance is compared to the sector's own recent behavior.
Use Case
This approach helps evaluate overall market conditions and supports a top-down method. By starting with sector performance, it becomes easier to identify where the market is showing leadership or weakness. This allows the stock selection process to be more deliberate and can help refine or customize screeners based on certain sectors.
Dynamic Square of Nine AVWAP: Blueprint_So9📐 Dynamic Square of Nine Anchor VWAP
“Study the volume of sales, the space in price movements, and last and most important—the time period.”
— W.D. Gann, How to Make Profits Trading in Commodities
This indicator is one my personal interpretations of that principle. A unique variation of the traditional anchored VWAP.
It combines Square of Nine geometry, customizable degree-based price bands, and a volume-reactive visual layer, resulting in a tool that gives dimensional structure to price movement through time, space, and volume.
🛠️ How to Use:
Upon loading, you'll be prompted to select a chart anchor (date & time).
Once selected, the Anchor VWAP will populate, projecting bands outward based on Square of Nine degree intervals.
Scale the Anchor VWAP manually to align with your instrument's price structure.
By default, the tool uses classic Square of Nine pressure points:
±45°, ±90°, ±180°, and ±360°
You can customize these levels to reflect any meaningful degree intervals (e.g., 72°, 144°, 216°, etc.).
Enable adjustable fill zones between bands to enhance spatial awareness.
🔍 Volume-Infused Visualization:
Each band includes a volume-based color fill gradient:
Brighter fill = higher volume activity
Dimmer fill = lower volume
This gives you a visual readout of how price, time, and volume converge within the Dynamic Square of Nine AVWAP.
Adaptive Strength MACD [UM]Indicator Description
Adaptive Strength MACD is an adaptive variant of the classic MACD that uses a customized Strength Momentum moving average for both its oscillator and signal lines. This makes the indicator more responsive in trending conditions and more stable in sideways markets.
Key Features
1. Adaptive Strength Momentum MA
Leverages the Adaptive Momentum Oscillator to scale smoothing coefficients dynamically.
2. Trend-Validity Filters
Optional ADX filter ensures signals only fire when trend strength (ADX) exceeds a user threshold.
3. Directional Filter (DI+) confirms bullish or bearish momentum.
4. Color-Coded Histogram
5. Bars turn bright when momentum accelerates, faded when slowing.
6. Grayed out when trend filters disqualify signals.
7. Alerts
Bullish crossover (histogram from negative to positive) and bearish crossover (positive to negative) only when filters validate trend.
Comparison with Regular MACD
1. Moving Averages
Classic MACD uses fixed exponential moving averages (EMAs) for its fast and slow lines, so the smoothing factor is constant regardless of how strong or weak price momentum is.
Adaptive Strength MACD replaces those EMAs with a dynamic “Strength Momentum” MA that speeds up when momentum is strong and slows down in quiet or choppy markets.
2. Signal Line Smoothing
In the classic MACD, the signal is simply an EMA of the MACD line, with one user-selected period.
In the Adaptive Strength MACD , the signal line also uses the Strength Momentum MA on the MACD series—so both oscillator and signal adapt together to the underlying momentum strength.
3. Responsiveness to Momentum
A static EMA reacts the same way whether momentum is surging or fading; you either get too-slow entries when momentum spikes or too-fast whipsaws in noise.
The adaptive MA in your indicator automatically gives you quicker crossovers when there’s a trending burst, while damping down during low-momentum chop.
4. Trend Validation Filters
The classic MACD has no built-in mechanism to know whether price is actually trending versus ranging—you’ll see crossovers in both regimes.
Adaptive Strength MACD includes optional ADX filtering (to require a minimum trend strength) and a DI filter (to confirm bullish vs. bearish directional pressure). When those filters aren’t met, the histogram grays out to warn you.
5. Histogram Coloring & Clarity
Typical MACD histograms often use two colors (above/below zero) or a simple ramp but don’t distinguish accelerating vs. decelerating moves.
Your version employs four distinct states—accelerating bulls, decelerating bulls, accelerating bears, decelerating bears—plus a gray “no-signal” state when filters fail. This makes it easy at a glance to see not just direction but the quality of the move.
6. False-Signal Reduction
Because the classic MACD fires on every crossover, it can generate whipsaws in ranging markets.
The adaptive MA smoothing combined with ADX/DI gating in your script helps suppress those false breaks and keeps you focused on higher-quality entries.
7. Ideal Use Cases
Use the classic MACD when you need a reliable, well-understood trend-following oscillator and you’re comfortable manually filtering choppy signals.
Choose Adaptive Strength MACD \ when you want an all-in-one, automated way to speed up in strong trends, filter out noise, and receive clearer visual cues and alerts only when conditions align.
How to Use
1. Setup
- Adjust Fast and Slow Length to tune sensitivity.
- Change Signal Smoothing to smooth the histogram reaction.
- Enable ADX/DI filters and set ADX Threshold to suit your preferred trend strength (default = 20).
2. Interpretation
- Histogram > 0: Short‐term momentum above long‐term → bullish.
- Histogram < 0: Short‐term below long‐term → bearish.
- Faded greyed bars indicate a weakening move; gray bars show filter invalidation.
How to Trade
Buy Setup:
- Histogram crosses from negative to positive.
- ADX ≥ threshold and DI+ > DI–.
- Look for confirmation (bullish candlestick patterns or support zone).
Sell Setup:
- Histogram crosses from positive to negative.
- ADX ≥ threshold and DI– > DI+.
- Confirm with bearish price action (resistance test or bearish pattern).
Stop & Target
- Place stop just below recent swing low (long) or above recent swing high (short).
- Target risk–reward of at least 1:2, or trail with a shorter‐period adaptive MA.
Support & Resistance ZonesAdvanced Support & Resistance Detection Algorithm
This indicator identifies meaningful price levels by analyzing market structure using a proprietary statistical approach. Unlike traditional methods that rely on simple swing highs/lows or moving averages, this system dynamically detects zones where price has shown consistent interaction, revealing true areas of supply and demand.
Core Methodology
Price Data Aggregation
Collects highs and lows over a configurable lookback period.
Normalizes price data to account for volatility, ensuring levels remain relevant across different market conditions.
Statistical Significance Filtering
Rejection of random noise: Eliminates insignificant price fluctuations using adaptive thresholds.
Volume-weighted analysis (implied): Stronger reactions at certain price levels are given higher priority, even if volume data is unavailable.
Dynamic Level Extraction
Density-based S/R Zones: Instead of fixed swing points, the algorithm identifies zones where price has repeatedly consolidated.
Time decay adjustment: Recent price action has more influence, ensuring levels adapt to evolving market structure.
Strength Quantification
Each level is assigned a confidence score based on:
Touch frequency: How often price revisited the zone.
Reaction intensity: The magnitude of bounces/rejections.
Time relevance: Whether the level remains active or has been broken decisively.
Adaptive Level Merging & Pruning
Proximity-based merging: If two levels are too close (within a volatility-adjusted threshold), they combine into one stronger zone.
Decay mechanism: Old, untested levels fade away if price no longer respects them.
Why This Approach Works Better Than Traditional Methods
✅ No subjective drawing required – Levels are generated mathematically, removing human bias.
✅ Self-adjusting sensitivity – Works equally well on slow and fast-moving markets.
✅ Focuses on statistically meaningful zones – Avoids false signals from random noise.
✅ Non-repainting & real-time – Levels only update when new data confirms their validity.
How Traders Can Use These Levels
Support/Resistance Trading: Fade bounces off strong levels or trade breakouts with confirmation.
Confluence with Other Indicators: Combine with RSI, MACD, or volume profiles for higher-probability entries.
Stop Placement: Place stops just beyond key levels to avoid premature exits.
Technical Notes (For Advanced Users)
The algorithm avoids overfitting by dynamically adjusting zones sensitivity based on market conditions.
Unlike fixed pivot points, these levels adapt to trends, making them useful in both ranging and trending markets.
The strength percentage helps filter out weak levels—only trade those with a high score for better accuracy.
Note: Script takes some time to load.
FibSync - DynamicFibSupportWhat is this indicator?
FibSync – DynamicFibSupport overlays your chart with both static and dynamic Fibonacci retracement levels, making it easy to spot potential areas of support and resistance.
Static Fibs: Calculated from the highest and lowest price over a user-defined lookback period.
Dynamic Fibs: Calculated from the most recent swing high and swing low, automatically adapting as new swings form.
How to use
Add the indicator to your chart.
Configure the settings:
Static Fib Period: Sets the lookback window for static fib levels.
Show Dynamic Fibonacci Levels: Toggle dynamic fibs on/off.
Dynamic Fib Swing Search Window: How far back to search for valid swing highs/lows.
Swing Strength (bars left/right): How many bars define a swing high/low (higher = stronger swing).
Interpret the levels:
Solid lines are static fibs.
Transparent lines are dynamic fibs (if enabled).
Colors match standard fib conventions (yellow = 0.236, red = 0.382, blue = 0.618, green = 0.786, gray = 0.5).
Tips
Static and dynamic fibs can overlap-this often highlights especially important support/resistance zones.
Adjust the swing strength for your trading style: lower values for short-term, higher for long-term swings.
Hide/show individual lines using the indicator’s style settings in TradingView.
Trading Ideas (for higher timeframes and static fibs)
Close above the blue line (0.618 static fib):
This can be interpreted as a potential long (buy) signal, suggesting the market is breaking above a key resistance level.
Close below the red line (0.382 static fib):
This can be interpreted as a potential short (sell) signal, indicating the market is breaking below a key support level.
Note: These signals are most meaningful on higher timeframes and when using the static fib lines. Always confirm with your own strategy and risk management.
Q Impulse EntryQ Impulse Entry
A directional entry system combining impulse breakouts, Elder's momentum confirmation, and ADX trend validation. Designed for clean trade setups with multi-step filtering, entry markers, and real-time alerts.
🔧 Core Logic
This is not a basic mashup — each filter plays a distinct technical role:
1. Impulse Breakout Engine
• Detects sharp directional price breaks using ATR-adjusted dynamic zones
• Impulse window controls sensitivity to local highs/lows
2. Elder Momentum Filter
• Confirms signal using MACD histogram and EMA alignment
• Blocks entries when internal momentum contradicts price move
3. ADX Trend Strength Filter
• Uses threshold-based ADX logic to validate trend power
• Filters out noise in flat or weak markets
The system requires all three filters to agree before confirming an entry.
📈 Visual Feedback
• ⇑ / ⇓ arrows mark confirmed entry signals
• Colored entry dots plotted at signal price help confirm timing and aid in multi-position layering
• Impulse breakout zones and EMA are displayed for directional context
• Clean layout, no repainting, designed for real-time use
⚙️ Configurable Inputs
• Impulse Window — controls breakout signal sensitivity
• ATR Multiplier — defines width of impulse breakout zones
(Elder and ADX filters are embedded and fine-tuned)
✨ Highlights
• Triple-filter signal logic = fewer false positives
• Entry dots + arrows for visual clarity and scaling in
• Lightweight, non-repainting, and alert-ready
• Best suited for Forex and all timeframes
• Ideal for breakout, trend-following, or hybrid systems
• Built-in alerts and customizable zones
• Always apply risk management suited to your capital and strategy
Trade with clarity — stay for quality.
Index Futures vs Cash ArbitrageThis indicator measures the statistical spread between major stock index futures and their corresponding cash indices (e.g., ES vs SPX, NQ vs NDX) using Z-score normalization. It automatically detects commonly traded index pairs (S&P 500, Nasdaq, Dow Jones, Russell 2000) and calculates a smoothed spread between futures and spot prices. A Z-score is then derived from this spread to highlight potential overpricing or underpricing conditions.
Traders can use customizable thresholds to identify mean-reversion opportunities where the futures contract may be temporarily overvalued or undervalued relative to the index. The histogram highlights the direction of the Z-score (green = futures > index, red = futures < index), while built-in alerts notify users of key threshold breaches or zero-line crosses.
This tool is designed for discretionary traders, pairs traders, or anyone exploring statistical arbitrage strategies between futures and spot markets. It is not a buy/sell signal by itself and should be used with additional confluence or risk management techniques.
Liquidity stop huntThis tool identifies key liquidity zones where stop hunts are likely to occur.
**How it works:**
- Detects swing highs/lows on your selected timeframe.
- Marks levels where "liquidity sweeps" (fakeouts) often happen.
- Plots these zones as dotted lines for visual reference.
**How to use:**
1. Look for price rejections near marked levels.
2. Avoid placing stops too close to obvious liquidity zones.
3. Combine with price action for confirmation.
**Settings:**
- Timeframe: Choose the historical period for analysis (e.g., 1D, 1W).
- Sweep Type: "Wick Only" for precise tails, "Regular" for all breaks.
- Colors/Style: Customize appearance.
Note: Works best in trending markets. Not a standalone strategy — always confirm with additional analysis.
Another EMA/RSI trend indicatorAnother EMA/RSI trend indicator is a trend-following trade signal and back-testing tool. It leverages EMA, RSI, ATR, volume, and price breakouts to generate and track buy/sell signals, manage trades, and display performance statistics.
EMA (Exponential Moving Average): Used for identifying trend direction.
RSI (Relative Strength Index): Used to confirm momentum.
ATR (Average True Range): Used to calculate Stop Loss (SL) and Take Profit (TP) dynamically.
Volume: Only trades when current volume > average volume.
Price breakout filters: Detects bullish/bearish breakout candlesticks for signals.
Entry Logic
Entry placed slightly above/below current price using an ATR-based buffer.
Configurable SL and TP using ATR multipliers.
Optional: Stop existing trade on a new opposite signal.
Entry filters include price structure checks using highs/lows.
Visual output
Plots Buy/Sell signals on chart
Draws entry, SL, and TP lines for ongoing trades
Displays trade statistics in a table (top-right):
Trade count
Wins/Losses/Stopped
Win rate
Cumulative and average profit/loss
Start date
This is a semi-automated trading signal generator and visual back-tester aimed at helping traders:
Identify trend-based entry opportunities
Automate entry/exit evaluation using standard risk management
Evaluate performance with live stats
Seasonality DOW CombinedOverall Purpose
This script analyzes historical daily returns based on two specific criteria:
Month of the year (January through December)
Day of the week (Sunday through Saturday)
It summarizes and visually displays the average historical performance of the selected asset by these criteria over multiple years.
Step-by-Step Breakdown
1. Initial Settings:
Defines minimum year (i_year_start) from which data analysis will start.
Ensures the user is using a daily timeframe, otherwise prompts an error.
Sets basic display preferences like text size and color schemes.
2. Data Collection and Variables:
Initializes matrices to store and aggregate returns data:
month_data_ and month_agg_: store monthly performance.
dow_data_ and dow_agg_: store day-of-week performance.
COUNT tracks total number of occurrences, and COUNT_POSITIVE tracks positive-return occurrences.
3. Return Calculation:
Calculates daily percentage change (chg_pct_) in price:
chg_pct_ = close / close - 1
Ensures it captures this data only for the specified years (year >= i_year_start).
4. Monthly Performance Calculation:
Each daily return is grouped by month:
matrix.set updates total returns per month.
The script tracks:
Monthly cumulative returns
Number of occurrences (how many days recorded per month)
Positive occurrences (days with positive returns)
5. Day-of-Week Performance Calculation:
Similarly, daily returns are also grouped by day-of-the-week (Sunday to Saturday):
Daily return values are summed per weekday.
The script tracks:
Cumulative returns per weekday
Number of occurrences per weekday
Positive occurrences per weekday
6. Visual Display (Tables):
The script creates two visual tables:
Left Table: Monthly Performance.
Right Table: Day-of-the-Week Performance.
For each table, it shows:
Yearly data for each month/day.
Summaries at the bottom:
SUM row: Shows total accumulated returns over all selected years for each month/day.
+ive row: Shows percentage (%) of times the month/day had positive returns, along with a tooltip displaying positive occurrences vs total occurrences.
Cells are color-coded:
Green for positive returns.
Red for negative returns.
Gray for neutral/no change.
7. Interpreting the Tables:
Monthly Table (left side):
Helps identify seasonal patterns (e.g., historically bullish/bearish months).
Day-of-Week Table (right side):
Helps detect recurring weekday patterns (e.g., historically bullish Mondays or bearish Fridays).
Practical Use:
Traders use this to:
Identify patterns based on historical data.
Inform trading strategies, e.g., avoiding historically bearish days/months or leveraging historically bullish periods.
Example Interpretation:
If the table shows consistently green (positive) for March and April, historically the asset tends to perform well during spring. Similarly, if the "Friday" column is often red, historically Fridays are bearish for this asset.
India VIX TableThis indicator gives you the India Vix value in real time on your chart. You can change the position on the chart as per your preference.
ABC Market stage judgmentABC Stage Judgment Indicators · Introduction
Core ideology
The market situation is divided into three stages:
Zone B (Low Volatility Accumulation): Extremely low volatility, no trend, institutions accumulate chips.
Zone A (oscillation zone): The volatility has rebounded but there is no unilateral trend, suitable for short-term high selling and low buying.
Zone C (Trend Explosion): The volatility has significantly expanded and the trend is strong, making it profitable to follow the position.
Core Indicators
Volatility measurement
Bollinger Bands Width (BBWidth): 20 cycle moving average ± 2 σ bandwidth, reflecting relative volatility compression/release;
ATR (Average True Volatility): measures the absolute intensity of price volatility.
Trend Strength
ADX (Average Trend Index): measures the strength of a trend (without distinguishing direction),
ADX<20 → No trend (Zone B/A)
ADX>25 → Significant trend (Zone C)
Stage division logic
Zone B: Both BWidth and ATR are less than the set multiple of their respective historical means, and ADX is less than the threshold → "quiet bottoming out";
Zone C: ADX>threshold, and BBWidth or ATR>set multiple of their respective historical means, trading volume amplification → "trend takeoff";
Zone A: Time periods that do not belong to B/C are all classified as oscillation zones.
Optional enhanced filtering
Direction confirmation (+DI/- DI): avoid going against the trend;
Multi cycle verification (4H): in line with the trend of large-scale;
Momentum filtering (ROC/MACD/RSI): ensuring kinetic energy support;
ATR slope: Confirm the release of fluctuations;
Breakthrough Confirmation: Enter only after the breakthrough is confirmed at the closing level.
These filters are turned off by default and can be selected with one click for different scenarios such as "high-level oscillation", "low-level bottoming", "planting trees in the middle", etc.
usage
Multi cycle switching: Built in "5-minute/1-hour" two main cycles for free switching;
Visualization: The background color and labels display the current Zone at a glance;
Alarm: Stage switching automatically triggers an Alert, which can be pushed through mobile phones/Telegram.
