EMA 20The EMA 20 (Exponential Moving Average 20) is a simple trend-following indicator designed to smooth price fluctuations and highlight short-term market direction.
This script plots a 20-period exponential moving average in red, allowing traders to quickly assess whether price is trading above or below the short-term trend.
When price remains above the EMA 20, it often suggests bullish strength; when price falls below it, it may indicate short-term weakness.
This indicator is minimal, clear, and useful as a foundational trend reference in any trading system.
Indicadores y estrategias
Visible RangeOverview This is a precision tool designed for quantitative traders and engineers who need exact control over their chart's visual scope. Unlike standard time calculations that fail in markets with trading breaks (like A-Shares, Futures, or Stocks), this indicator uses a loop-back mechanism to count the actual number of visible bars, ensuring your indicators (e.g., MA60, MA200) have sufficient sample data.
Why use this? If you use multi-timeframe layouts (e.g., Daily/Hourly/15s), it is critical to know exactly how much data is visible.
The Problem: In markets like the Chinese A-Share market (T+1, 4-hour trading day), calculating Time Range / Timeframe results in massive errors because it includes closed market hours (lunch breaks, nights, weekends).
The Solution: This script iterates through the visible range to count the true bar_index, providing 100% accurate data density metrics.
Key Features
True Bar Counting: Uses a for loop to count actual candles, ignoring market breaks. perfect for non-24/7 markets.
Integer Precision: Displays time ranges (Days, Hours, Mins, Secs) in clean integers. No messy decimals.
Compact UI: Displays information in a single line (e.g., View: 30 Days (120 Bars)), default to the Top Right corner to save screen space.
Fully Customizable: Adjustable position, text size, and colors to fit any dark/light theme.
Performance Optimized: Includes max_bars_back limits to prevent browser lag on deep history lookups.
Settings
Position: Default Top Right (can be moved to any corner).
Max Bar Count: Default 5000 (Safety limit for loop calculation).
ICT Fair Value Gap (FVG) Detector │ Auto-Mitigated │ 2025Accurate ICT / Smart Money Concepts Fair Value Gap (FVG) detector
Features:
• Detects both Bullish (-FVG) and Bearish (+FVG) using strict 3-candle rule
• Boxes automatically extend right until price mitigates them
• Boxes auto-delete when price closes inside the gap (true mitigation)
• No repainting – 100% reliable
• Clean, lightweight, and works on all markets & timeframes
• Fully customizable colors and transparency
How to use:
– Bullish FVG (green) = potential support / buy zone in uptrend
– Bearish FVG (red) = potential resistance / sell zone in downtrend
Exactly matches The Inner Circle Trader (ICT) methodology used by thousands of SMC traders in 2024–2025.
Enjoy and trade safe!
[CT] Donchian Histogram w/Candle ColorsDonchian Histogram, originally created by RafaelZioni and enhanced with optional price bar coloring, is a momentum-style oscillator that shows where the current close sits inside a dynamic Donchian channel and how that position is evolving over time. The script calculates a rolling high and low over a multi-session lookback period based on your chosen Donchian timeframe, then normalizes the close within that range to create a percentage position between the recent high and low. This normalized value is smoothed with a signal length and plotted as a histogram around a zero line, making it easy to see whether price is pressing toward the upper side of its recent range, the lower side, or oscillating near the middle. Positive values indicate that price is trading closer to the Donchian high, negative values indicate price is closer to the Donchian low, and the magnitude of the histogram reflects how strongly price is favoring one side of the range. The color logic highlights this state visually: stronger positive conditions can be shown in teal, moderate positive conditions in lime, stronger negative conditions in red, and neutral or transitional states in orange. The script also includes an option to color the actual chart candles with the same colors as the histogram, so traders can see Donchian-based pressure directly on the main price chart without constantly looking down at the lower pane. The indicator works on completed bars using standard highest/lowest and moving average functions, so it behaves like a normal oscillator and does not use any lookahead tricks. It is best used as a contextual tool to gauge whether price is pushing to the edges of its recent range or reverting toward balance, and to visually synchronize that information with candle colors when desired.
Relative Volume EMA (RVOL)Relative Volume EMA (RVOL) measures the current bar’s volume relative to its typical volume over a selected lookback period.
It helps traders identify whether a price move is supported by real participation or if it’s occurring on weak, low-quality volume.
This version uses:
RVOL = Current Volume ÷ Volume EMA
Volume EMA Length: adjustable
Signal Threshold: a customizable horizontal line (default = 1.2)
How to Use
1. RVOL > 1.2 → High-Quality Momentum
A value above 1.2 indicates that the current bar has at least 20% more volume than normal, suggesting:
Strong conviction
Algorithmic activity
Momentum-backed breakout or breakdown
Higher probability trend continuation
These bars are ideal for confirming entries after a technical setup (e.g., pullback, engulfing pattern, Ichimoku trend confirmation, etc.).
2. RVOL < 1.0 → Weak or Low-Quality Move
When RVOL is below 1.0:
Volume is below average
Moves are more likely to fail or reverse
Breakouts are unreliable
Triggers lack institutional participation
These bars are best avoided for trade entries.
Why This Indicator Is Useful
In many strategies, price alone is not enough.
RVOL acts as a filter to ensure that your signals occur during times when the market is actually active and committed.
Typical use cases:
Confirm trend-following entries
Validate pullbacks and breakout candles
Filter out low-volume chop
Identify session-based volume surges
Improve risk-to-reward quality by entering only during true momentum
Recommended Settings
EMA Length: 20
Threshold Line: 1.2
Works well on Forex, Crypto, and Indices
Best used on 15m, 30m, 1H, and 4H charts
ADX Forecast Colorful [DiFlip]ADX Forecast Colorful
Introducing one of the most advanced ADX indicators available — a fully customizable analytical tool that integrates forward-looking forecasting capabilities. ADX Forecast Colorful is a scientific evolution of the classic ADX, designed to anticipate future trend strength using linear regression. Instead of merely reacting to historical data, this indicator projects the future behavior of the ADX, giving traders a strategic edge in trend analysis.
⯁ Real-Time ADX Forecasting
For the first time, a public ADX indicator incorporates linear regression (least squares method) to forecast the future behavior of ADX. This breakthrough approach enables traders to anticipate trend strength changes based on historical momentum. By applying linear regression to the ADX, the indicator plots a projected trendline n periods ahead — helping users make more accurate and timely trading decisions.
⯁ Highly Customizable
The indicator adapts seamlessly to any trading style. It offers a total of 26 long entry conditions and 26 short entry conditions, making it one of the most configurable ADX tools on TradingView. Each condition is fully adjustable, enabling the creation of statistical, quantitative, and automated strategies. You maintain full control over the signals to align perfectly with your system.
⯁ Innovative and Science-Based
This is the first public ADX indicator to apply least-squares predictive modeling to ADX dynamics. Technically, it embeds machine learning logic into a traditional trend-strength indicator. Using linear regression as a predictive engine adds powerful statistical rigor to the ADX, turning it into an intelligent, forward-looking signal generator.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental method in statistics and machine learning used to model the relationship between a dependent variable y and one or more independent variables x. The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted value (e.g., future ADX)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept
β₁ = slope (rate of change)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the ADX projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error, the regression coefficients are calculated as:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ = summation
x̄ and ȳ = means of x and y
i ranges from 1 to n (number of data points)
These formulas provide the best linear unbiased estimator under Gauss-Markov conditions — assuming constant variance and linearity.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational algorithm in supervised learning. Its power in producing quantitative predictions makes it essential in AI systems, predictive analytics, time-series forecasting, and automated trading. Applying it to the ADX essentially places an intelligent forecasting engine inside a classic trend tool.
⯁ Visual Interpretation
Imagine an ADX time series like this:
Time →
ADX →
The regression line smooths these values and projects them n periods forward, creating a predictive trajectory. This forecasted ADX line can intersect with the actual ADX, offering smarter buy and sell signals.
⯁ Summary of Scientific Concepts
Linear Regression: Models variable relationships with a straight line.
Least Squares: Minimizes prediction errors for best fit.
Time-Series Forecasting: Predicts future values using historical data.
Supervised Learning: Trains models to predict outcomes from inputs.
Statistical Smoothing: Reduces noise and highlights underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Based on rigorous statistical theory.
Unprecedented: First public ADX using least-squares forecast modeling.
Smart: Uses machine learning logic.
Forward-Looking: Generates predictive, not just reactive, signals.
Customizable: Flexible for any strategy or timeframe.
⯁ Conclusion
By merging ADX and linear regression, this indicator enables traders to predict market momentum rather than merely follow it. ADX Forecast Colorful is not just another indicator — it’s a scientific leap forward in technical analysis. With 26 fully configurable entry conditions and smart forecasting, this open-source tool is built for creating cutting-edge quantitative strategies.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the ADX?
The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down.
The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength.
⯁ How to use the ADX?
The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones:
Strong Trend: When the ADX is above 25, indicating a strong trend.
Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend.
Neutral Zone: Between 20 and 25, where the trend strength is unclear.
⯁ Entry Conditions
Each condition below is fully configurable and can be combined to build precise trading logic.
📈 BUY
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
📉 SELL
🅰️ Signal Validity: The signal will remain valid for X bars .
🅰️ Signal Sequence: Configurable as AND or OR .
🅰️ +DI > -DI
🅰️ +DI < -DI
🅰️ +DI > ADX
🅰️ +DI < ADX
🅰️ -DI > ADX
🅰️ -DI < ADX
🅰️ ADX > Threshold
🅰️ ADX < Threshold
🅰️ +DI > Threshold
🅰️ +DI < Threshold
🅰️ -DI > Threshold
🅰️ -DI < Threshold
🅰️ +DI (Crossover) -DI
🅰️ +DI (Crossunder) -DI
🅰️ +DI (Crossover) ADX
🅰️ +DI (Crossunder) ADX
🅰️ +DI (Crossover) Threshold
🅰️ +DI (Crossunder) Threshold
🅰️ -DI (Crossover) ADX
🅰️ -DI (Crossunder) ADX
🅰️ -DI (Crossover) Threshold
🅰️ -DI (Crossunder) Threshold
🔮 +DI (Crossover) -DI Forecast
🔮 +DI (Crossunder) -DI Forecast
🔮 ADX (Crossover) +DI Forecast
🔮 ADX (Crossunder) +DI Forecast
🤖 Automation
All BUY and SELL conditions are compatible with TradingView alerts, making them ideal for fully or semi-automated systems.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Plot Labels in the Graph Above: BUY/SELL
Automate and Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Gold Signal System + Alerts // GOLD SIGNAL SYSTEM + ALERTS
//@version=5
indicator("Gold Signal System + Alerts", overlay=true)
// EMAs
ema50 = ta.ema(close, 50)
ema200 = ta.ema(close, 200)
// Conditions
buySignal = ta.crossover(ema50, ema200)
sellSignal = ta.crossunder(ema50, ema200)
// Plot
plot(ema50, color=color.yellow)
plot(ema200, color=color.blue)
// Signals
plotshape(buySignal, title="BUY", style=shape.labelup, color=color.new(color.green,0), text="BUY", size=size.small)
plotshape(sellSignal, title="SELL", style=shape.labeldown, color=color.new(color.red,0), text="SELL", size=size.small)
// Alerts
alertcondition(buySignal, title="Buy Signal", message="BUY signal on GOLD")
alertcondition(sellSignal, title="Sell Signal", message="SELL signal on GOLD")
Regime MapRegime Map — Volatility State Detector
This indicator is a PineScript friendly approximation of a more advanced Python regime-analysis engine.
The original backed identifies market regimes using structural break detection, Hidden-Markov Models, wavelet decomposition, and long-horizon volatility clustering. Since Pine Script cannot execute these statistical models directly, this version implements a lightweight, real-time proxy using realised volatility and statistical thresholds.
The purpose is to provide a clear visual map of evolving volatility conditions without requiring any heavy offline computation.
________________________________________
Mathematical Basis: Python vs Pine
1. Volatility Estimation
Python (Realised Volatility):
RVₜ = √N × stdev( log(Pₜ) − log(Pₜ₋₁) )
Pine Approximation:
RVₜ = stdev( log(Pₜ) − log(Pₜ₋₁), lookback )
Rationale:
Realised volatility captures volatility clustering — a key characteristic of regime transitions.
________________________________________
2. Regime Classification
Python (HMM Volatility States):
Volatility is modelled as belonging to hidden states with different means and variances:
State μ₁, σ₁
State μ₂, σ₂
State μ₃, σ₃
with state transitions determined by a probability matrix.
Pine Approximation (Z-Score Regimes):
Zₜ = ( RVₜ − mean(RV) ) / stdev(RV)
Regime assignment:
• Regime 0 (Low Vol): Zₜ < Zₗₒw
• Regime 1 (Normal): Zₗₒw ≤ Zₜ ≤ Zₕᵢgh
• Regime 2 (High Vol): Zₜ > Zₕᵢgh
Rationale:
Z-scores provide clean statistical boundaries that behave similarly to HMM state separation but are computable in real time.
________________________________________
3. Structural Break Detection vs Rolling Windows
Python (Bai–Perron Structural Breaks):
Segments the volatility series into periods with distinct statistical properties by minimising squared error over multiple regimes.
Pine Approximation:
Rolling mean and rolling standard deviation of volatility over a long window.
Rationale:
When structural breaks are not available, long-window smoothing approximates slow regime changes effectively.
________________________________________
4. Multi-Scale Cycles
Python (Wavelet Decomposition):
Volatility decomposed into long-cycle (A₄) and short-cycle components (D bands).
Pine Approximation:
Single-scale smoothing using long-horizon averages of RV.
Rationale:
Wavelets reveal multi-frequency behaviour; Pine captures the dominant low-frequency component.
________________________________________
Indicator Output
The background colour reflects the active volatility regime:
• Low Volatility (Green): trending behaviour, cleaner directional movement
• Normal Volatility (Yellow): balanced environment
• High Volatility (Red): sharp swings, traps, mean-reversion phases
Regime labels appear on the chart, with a status panel displaying the current regime.
________________________________________
Operational Logic
1. Compute log returns
2. Calculate short-horizon realised volatility
3. Compute long-horizon mean and standard deviation
4. Derive volatility Z-score
5. Assign regime classification
6. Update background colour and labels
This provides a stable, real-time map of market state transitions.
________________________________________
Practical Applications
Intraday Trading
• Low-volatility regimes favour trend and breakout continuation
• High-volatility regimes favour mean reversion and wide stop placement
Swing Trading
• Compression phases often precede multi-day trending moves
• Volatility expansions accompany distribution or panic events
Risk Management
• Enables volatility-adjusted position sizing
• Helps avoid leverage during expansion regimes
________________________________________
Notes
• Does not repaint
• Fully configurable thresholds and lookbacks
• Works across indices, stocks, FX, crypto
• Designed for real-time volatility regime identification
________________________________________
Disclaimer
This script is intended solely for educational and research purposes.
It does not constitute financial advice or a recommendation to buy or sell any instrument.
Trading involves risk, and past volatility patterns do not guarantee future outcomes.
Users are responsible for their own trading decisions, and the author assumes no liability for financial loss.
Superior-Range Bound Renko - Alerts - 11-29-25 - Signal LynxSuperior-Range Bound Renko – Alerts Edition with Advanced Risk Management Template
Signal Lynx | Free Scripts supporting Automation for the Night-Shift Nation 🌙
1. Overview
This is the Alerts & Indicator Edition of Superior-Range Bound Renko (RBR).
The Strategy version is built for backtesting inside TradingView.
This Alerts version is built for automation: it emits clean, discrete alert events that you can route into webhooks, bots, or relay engines (including your own Signal Lynx-style infrastructure).
Under the hood, this script contains the same core engine as the strategy:
Adaptive Range Bounding based on volatility
Renko Brick Emulation on standard candles
A stack of Laguerre Filters for impulse detection
K-Means-style Adaptive SuperTrend for trend confirmation
The full Signal Lynx Risk Management Engine (state machine, layered exits, AATS, RSIS, etc.)
The difference is in what we output:
Instead of placing historical trades, this version:
Plots the entry and RM signals in a separate pane (overlay = false)
Exposes alertconditions for:
Long Entry
Short Entry
Close Long
Close Short
TP1, TP2, TP3 hits (Staged Take Profit)
This makes it ideal as the signal source for automated execution via TradingView Alerts + Webhooks.
2. Quick Action Guide (TL;DR)
Best Timeframe:
4H and above. This is a swing-trading / position-trading style engine, not a micro-scalper.
Best Assets:
Volatile but structured markets, e.g.:
BTC, ETH, XAUUSD (Gold), GBPJPY, and similar high-volatility majors or indices.
Script Type:
indicator() – Alerts & Visualization Only
No built-in order placement
All “orders” are emitted as alerts for your external bot or manual handling
Strategy Type:
Volatility-Adaptive Trend Following + Impulse Detection
using Renko-like structure and multi-layer Laguerre filters.
Repainting:
Designed to be non-repainting on closed candles.
The underlying Risk Management engine is built around previous-bar data (close , high , low ) for execution-critical logic.
Intrabar values can move while the bar is forming (normal for any advanced signal), but once a bar closes, the alert logic is stable.
Recommended Alert Settings:
Condition: one of the built-in signals (see section 3.B)
Options: “Once Per Bar Close” is strongly recommended for automation
Message: JSON, CSV, or simple tokens – whatever your webhook / relay expects
3. Detailed Report: How the Alerts Edition Works
A. Relationship to the Strategy Version
The Alerts Edition shares the same internal logic as the strategy version:
Same Adaptive Lookback and volatility normalization
Same Range and Close Range construction
Same Renko Brick Emulator and directional memory (renkoDir)
Same Fib structures, Laguerre stack, K-Means SuperTrend, and Baseline signals (B1, B2)
Same Risk Management Engine and layered exits
In the strategy script, these signals are wired into strategy.entry, strategy.exit, and strategy.close.
In the alerts script:
We still compute the final entry/exit signals (Fin, CloseEmAll, TakeProfit1Plot, etc.)
Instead of placing trades, we:
Plot them for visual inspection
Expose them via alertcondition(...) so that TradingView can fire alerts.
This ensures that:
If you use the same settings on the same symbol/timeframe, the Alerts Edition and Strategy Edition agree on where entries and exits occur.
(Subject only to normal intrabar vs. bar-close differences.)
B. Signals & Alert Conditions
The alerts script focuses on discrete, automation-friendly events.
Internally, the main signals are:
Fin – Final entry decision from the RM engine
CloseEmAll – RM-driven “hard close” signal (for full-position exits)
TakeProfit1Plot / 2Plot / 3Plot – One-time event markers when each TP stage is hit
On the chart (in the separate indicator pane), you get:
plot(Fin) – where:
+2 = Long Entry event
-2 = Short Entry event
plot(CloseEmAll) – where:
+1 = “Close Long” event
-1 = “Close Short” event
plot(TP1/TP2/TP3) (if Staged TP is enabled) – integer tags for TP hits:
+1 / +2 / +3 = TP1 / TP2 / TP3 for Longs
-1 / -2 / -3 = TP1 / TP2 / TP3 for Shorts
The corresponding alertconditions are:
Long Entry
alertcondition(Fin == 2, title="Long Entry", message="Long Entry Triggered")
Fire this to open/scale a long position in your bot.
Short Entry
alertcondition(Fin == -2, title="Short Entry", message="Short Entry Triggered")
Fire this to open/scale a short position.
Close Long
alertcondition(CloseEmAll == 1, title="Close Long", message="Close Long Triggered")
Fire this to fully exit a long position.
Close Short
alertcondition(CloseEmAll == -1, title="Close Short", message="Close Short Triggered")
Fire this to fully exit a short position.
TP 1 Hit
alertcondition(TakeProfit1Plot != 0, title="TP 1 Hit", message="TP 1 Level Reached")
First staged take profit hit (either long or short). Your bot can interpret the direction based on position state or message tags.
TP 2 Hit
alertcondition(TakeProfit2Plot != 0, title="TP 2 Hit", message="TP 2 Level Reached")
TP 3 Hit
alertcondition(TakeProfit3Plot != 0, title="TP 3 Hit", message="TP 3 Level Reached")
Together, these give you a complete trade lifecycle:
Open Long / Short
Optionally scale out via TP1/TP2/TP3
Close remaining via Close Long / Close Short
All while the Risk Management Engine enforces the same logic as the strategy version.
C. Using This Script for Automation
This Alerts Edition is designed for:
Webhook-based bots
Execution relays (e.g., your own Lynx-Relay-style engine)
Dedicated external trade managers
Typical setup flow:
Add the script to your chart
Same symbol, timeframe, and settings you use in the Strategy Edition backtests.
Configure Inputs:
Longs / Shorts enabled
Risk Management toggles (SL, TS, Staged TP, AATS, RSIS)
Weekend filter (if you do not want weekend trades)
RBR-specific knobs (Adaptive Lookback, Brick type, ATR vs Standard Brick, etc.)
Create Alerts for Each Event Type You Need:
Long Entry
Short Entry
Close Long
Close Short
TP1 / TP2 / TP3 (optional, if your bot handles partial closes)
For each:
Condition: the corresponding alertcondition
Option: “Once Per Bar Close” is strongly recommended
Message:
You can use structured JSON or a simple token set like:
{"side":"long","event":"entry","symbol":"{{ticker}}","time":"{{timenow}}"}
or a simpler text for manual trading like:
LONG ENTRY | {{ticker}} | {{interval}}
Wire Up Your Bot / Relay:
Point TradingView’s webhook URL to your execution engine
Parse the messages and map them into:
Exchange
Symbol
Side (long/short)
Action (open/close/partial)
Size and risk model (this script does not position-size for you; it only signals when, not how much.)
Because the alerts come from a non-repainting, RM-backed engine that you’ve already validated via the Strategy Edition, you get a much cleaner automation pipeline.
D. Repainting Protection (Alerts Edition)
The same protections as the Strategy Edition apply here:
Execution-critical logic (trailing stop, TP triggers, SL, RM state changes) uses previous bar OHLC:
open , high , low , close
No security() with lookahead or future-bar dependencies.
This means:
Alerts are designed to fire on states that would have been visible at bar close, not on hypothetical “future history.”
Important practical note:
Intrabar: While a bar is forming, internal conditions can oscillate.
Bar Close: With “Once Per Bar Close” alerts, the fired signal corresponds to the final state of the engine for that candle, matching your Strategy Edition expectations.
4. For Developers & Modders
You can treat this Alerts script as an ”RM + Alert Framework” and inject any signal logic you want.
Where to plug in:
Find the section:
// BASELINE & SIGNAL GENERATION
You’ll see how B1 and B2 are built from the RBR stack and then combined:
baseSig = B2
altSig = B1
finalSig = sigSwap ? baseSig : altSig
To use your own logic:
Replace or wrap the code that sets baseSig / altSig with your own conditions:
e.g., RSI, MACD, Heikin Ashi filters, candle patterns, volume filters, etc.
Make sure your final decision is still:
2 → Long / Buy signal
-2 → Short / Sell signal
0 → No trade
finalSig is then passed into the RM engine and eventually becomes Fin, which:
Drives the Long/Short Entry alerts
Interacts with the RM state machine to integrate properly with AATS, SL, TS, TP, etc.
Because this script already exposes alertconditions for key lifecycle events, you don’t need to re-wire alerts each time — just ensure your logic feeds into finalSig correctly.
This lets you use the Signal Lynx Risk Management Engine + Alerts wrapper as a drop-in chassis for your own strategies.
5. About Signal Lynx
Automation for the Night-Shift Nation 🌙
Signal Lynx builds tools and templates that help traders move from:
“I have an indicator” → “I have a structured, automatable strategy with real risk management.”
This Superior-Range Bound Renko – Alerts Edition is the automation-focused companion to the Strategy Edition. It’s designed for:
Traders who backtest with the Strategy version
Then deploy live signals with this Alerts version via webhooks or bots
While relying on the same non-repainting, RM-driven logic
We release this code under the Mozilla Public License 2.0 (MPL-2.0) to support the Pine community with:
Transparent, inspectable logic
A reusable Risk Management template
A reference implementation of advanced adaptive logic + alerts
If you are exploring full-stack automation (TradingView → Webhooks → Exchange / VPS), keep Signal Lynx in your search.
License: Mozilla Public License 2.0 (Open Source).
If you build improvements or helpful variants, please consider sharing them back with the community.
SCALPING PRO V2 - INTERMÉDIANT (Dashboard + TP/SL + Alerts)//@version=5
indicator("SCALPING PRO V2 - INTERMÉDIANT (Dashboard + TP/SL + Alerts)", overlay=true, max_labels_count=500)
// ---------------- INPUTS ----------------
emaFastLen = input.int(9, "EMA Fast")
emaSlowLen = input.int(21, "EMA Slow")
atrLen = input.int(14, "ATR Length")
atrMultSL = input.float(1.2, "SL = ATR *")
tp1mult = input.float(1.0, "TP1 = ATR *")
tp2mult = input.float(1.5, "TP2 = ATR *")
tp3mult = input.float(2.0, "TP3 = ATR *")
minBars = input.int(3, "Min bars between signals")
showDashboard = input.bool(true, "Show Dashboard")
// ---------------- INDICATORS ----------------
emaFast = ta.ema(close, emaFastLen)
emaSlow = ta.ema(close, emaSlowLen)
atr = ta.atr(atrLen)
bullTrend = emaFast > emaSlow
bearTrend = emaFast < emaSlow
crossUp = ta.crossover(emaFast, emaSlow) and bullTrend
crossDown = ta.crossunder(emaFast, emaSlow) and bearTrend
var int lastSignal = na
okSignal = na(lastSignal) or (bar_index - lastSignal > minBars)
buySignal = crossUp and okSignal
sellSignal = crossDown and okSignal
if buySignal or sellSignal
lastSignal := bar_index
// ---------------- TP & SL ----------------
var float sl = na
var float tp1 = na
var float tp2 = na
var float tp3 = na
if buySignal
sl := close - atr * atrMultSL
tp1 := close + atr * tp1mult
tp2 := close + atr * tp2mult
tp3 := close + atr * tp3mult
if sellSignal
sl := close + atr * atrMultSL
tp1 := close - atr * tp1mult
tp2 := close - atr * tp2mult
tp3 := close - atr * tp3mult
// ---------------- ALERTS ----------------
alertcondition(buySignal, title="BUY", message="BUY Signal")
alertcondition(sellSignal, title="SELL", message="SELL Signal")
alertcondition(ta.cross(close, tp1), title="TP1", message="TP1 Hit")
alertcondition(ta.cross(close, tp2), title="TP2", message="TP2 Hit")
alertcondition(ta.cross(close, tp3), title="TP3", message="TP3 Hit")
alertcondition(ta.cross(close, sl), title="SL", message="Stop Loss Hit")
// ---------------- DASHBOARD ----------------
if showDashboard
var table dash = table.new(position.top_right, 1, 5)
if barstate.islast
table.cell(dash, 0, 0, "SCALPING PRO V2", bgcolor=color.new(color.black, 0), text_color=color.white)
table.cell(dash, 0, 1, "Trend: " + (bullTrend ? "Bull" : bearTrend ? "Bear" : "Neutral"))
table.cell(dash, 0, 2, "ATR: " + str.tostring(atr, format.mintick))
table.cell(dash, 0, 3, "Last Signal: " + (buySignal ? "BUY" : sellSignal ? "SELL" : "NONE"))
table.cell(dash, 0, 4, "EMA Fast/Slow OK")
MA200 Deviation Percentile200-Day MA Deviation with Dynamic Thresholds
OVERVIEW
This indicator measures price deviation from the 200-day moving average as a percentage, with dynamically calculated overbought/oversold thresholds based on historical percentiles.
Best suited for broad market indices (SPY, QQQ, IWM, etc.) where the 200-day MA serves as a reliable long-term trend indicator. Individual stocks may exhibit more erratic behavior around this level.
CALCULATION
Deviation (%) = (Close - 200MA) / 200MA x 100
Dynamic thresholds are derived from actual historical distribution rather than assuming normal distribution:
- Overbought threshold = 97.5th percentile of historical deviations
- Oversold threshold = 2.5th percentile of historical deviations
SETTINGS
MA Length (default: 200)
Moving average period.
Lookback Period (default: 1260)
Historical window for threshold calculation. 1260 bars approximates 5 years of daily data.
Threshold Percentile (default: 5%)
Two-tailed threshold. 5% places overbought/oversold boundaries at the 97.5th and 2.5th percentiles respectively.
INTERPRETATION
Deviation Value
- Positive: Price trading above 200MA
- Negative: Price trading below 200MA
- Magnitude indicates extent of deviation
Percentile Ranking (0-100%)
- Shows where current deviation ranks historically
- Above 90%: Historically elevated
- Below 10%: Historically depressed
Dynamic Threshold Lines
- Red line: Upper boundary based on historical distribution
- Green line: Lower boundary based on historical distribution
- These adapt automatically to each asset's volatility characteristics
APPLICATION
Mean Reversion
Extreme deviations tend to normalize over time. When deviation exceeds dynamic thresholds, probability of mean reversion increases.
Trend Assessment
Sustained positive/negative deviation confirms trend direction. Zero-line crossovers may signal trend changes.
NOTES
- Optimized for daily timeframe on market indices
- Requires sufficient historical data (minimum equal to lookback period)
- Extreme readings do not guarantee immediate reversals
- Use in conjunction with other analysis methods
Single AHR DCA (HM) — AHR Pane (customized quantile)Customized note
The log-regression window LR length controls how long a long-term fair value path is estimated from historical data.
The AHR window AHR window length controls over which historical regime you measure whether the coin is “cheap / expensive”.
When you choose a log-regression window of length L (years) and an AHR window of length A (years), you can intuitively read the indicator as:
“Within the last A years of this regime, relative to the long-term trend estimated over the same A years, the current price is cheap / neutral / expensive.”
Guidelines:
In general, set the AHR window equal to or slightly longer than the LR window:
If the AHR window is much longer than LR, you mix different baselines (different LR regimes) into one distribution.
If the AHR window is much shorter than LR, quantiles mostly reflect a very local slice of history.
For BTC / ETH and other BTC-like assets, you can use relatively long horizons (e.g. LR ≈ 3–5 years, AHR window ≈ 3–8 years).
For major altcoins (BNB / SOL / XRP and similar high-beta assets), it is recommended to use equal or slightly shorter horizons, e.g. LR ≈ 2–3 years, AHR window ≈ 2–3 years.
1. Price series & windows
Working timeframe: daily (1D).
Let the daily close of the current symbol on day t be P_t .
Main length parameters:
HM window: L_HM = maLen (default 200 days)
Log-regression window: L_LR = lrLen (default 1095 days ≈ 3 years)
AHR window (regime window): W = windowLen (default 1095 days ≈ 3 years)
2. Harmonic moving average (HM)
On a window of length L_HM, define the harmonic mean:
HM_t = ^(-1)
Here eps = 1e-10 is used to avoid division by zero.
Intuition: HM is more sensitive to low prices – an extremely low price inside the window will drag HM down significantly.
3. Log-regression baseline (LR)
On a window of length L_LR, perform a linear regression on log price:
Over the last L_LR bars, build the series
x_k = log( max(P_k, eps) ), for k = t-L_LR+1 ... t, and fit
x_k ≈ a + b * k.
The fitted value at the current index t is
log_P_hat_t = a + b * t.
Exponentiate to get the log-regression baseline:
LR_t = exp( log_P_hat_t ).
Interpretation: LR_t is the long-term trend / fair value path of the current regime over the past L_LR days.
4. HM-based AHR (valuation ratio)
At each time t, build an HM-based AHR (valuation multiple):
AHR_t = ( P_t / HM_t ) * ( P_t / LR_t )
Interpretation:
P_t / HM_t : deviation of price from the mid-term HM (e.g. 200-day harmonic mean).
P_t / LR_t : deviation of price from the long-term log-regression trend.
Multiplying them means:
if price is above both HM and LR, “expensiveness” is amplified;
if price is below both, “cheapness” is amplified.
Typical reading:
AHR_t < 1 : price is below both mid-term mean and long-term trend → statistically cheaper.
AHR_t > 1 : price is above both mid-term mean and long-term trend → statistically more expensive.
5. Empirical quantile thresholds (Opp / Risk)
On each new day, whenever AHR_t is valid, add it into a rolling array:
A_t_window = { AHR_{t-W+1}, ..., AHR_t } (at most W = windowLen elements)
On this empirical distribution, define two quantiles:
Opportunity quantile: q_opp (default 15%)
Risk quantile: q_risk (default 65%)
Using standard percentile computation (order statistics + linear interpolation), we get:
Opp threshold:
theta_opp = Percentile( A_t_window, q_opp )
Risk threshold:
theta_risk = Percentile( A_t_window, q_risk )
We also compute the percentile rank of the current AHR inside the same history:
q_now = PercentileRank( A_t_window, AHR_t ) ∈
This yields three valuation zones:
Opportunity zone: AHR_t <= theta_opp
(corresponds to roughly the cheapest ~q_opp% of historical states in the last W days.)
Neutral zone: theta_opp < AHR_t < theta_risk
Risk zone: AHR_t >= theta_risk
(corresponds to roughly the most expensive ~(100 - q_risk)% of historical states in the last W days.)
All quantiles are purely empirical and symbol-specific: they are computed only from the current asset’s own history, without reusing BTC thresholds or assuming cross-asset similarity.
6. DCA simulation (lightweight, rolling window)
Given:
a daily budget B (input: budgetPerDay), and
a DCA simulation window H (input: dcaWindowLen, default 900 days ≈ 2.5 years),
The script applies the following rule on each new day t:
If thresholds are unavailable or AHR_t > theta_risk
→ classify as Risk zone → buy = 0
If AHR_t <= theta_opp
→ classify as Opportunity zone → buy = 2B (double size)
Otherwise (Neutral zone)
→ buy = B (normal DCA)
Daily invested cash:
C_t ∈ {0, B, 2B}
Daily bought quantity:
DeltaQ_t = C_t / P_t
The script keeps rolling sums over the last H days:
Cumulative position:
Q_H = sum_{k=t-H+1..t} DeltaQ_k
Cumulative invested cash:
C_H = sum_{k=t-H+1..t} C_k
Current portfolio value:
PortVal_t = Q_H * P_t
Cumulative P&L:
PnL_t = PortVal_t - C_H
Active days:
number of days in the last H with C_k > 0.
These results are only used to visualize how this AHR-quantile-driven DCA rule would have behaved over the recent regime, and do not constitute financial advice.
DAILY - 3-Condition Arrows - Buy & SellVersion 1.
On the DAILY time frame, this indicator will add a green BUY arrow to a stock price when the following 3 conditions are ALL true:
BUY (all 3 conditions are true)
1. Stock price > 50 EMA
2. MACD line above moving average
3. Williams %R (Best_Solve version) is above moving average
Conversely, a red SELL arrow will point out when the following 3 conditions are ALL true:
SELL (all 3 conditions are true)
1. Stock price < 50 EMA
2. MACD line below moving average
3. Williams %R (Best_Solve version) is below the moving average
new_youtube_strategy//@version=5
strategy("Dow + Homma 1m Scalper (15m filter)", overlay=true, margin_long=100, margin_short=100, initial_capital=10000)
//===== INPUTS =====
maLen = input.int(50, "Trend SMA Length", minval=5)
htf_tf = input.timeframe("15", "Higher TF")
priceTolPct = input.float(0.05, "SR tolerance %", step=0.01)
wickFactor = input.float(2.0, "Hammer/ShootingStar wick factor", step=0.1)
dojiThresh = input.float(0.1, "Doji body % of range", step=0.01)
risk_RR = input.float(2.0, "Reward:Risk", step=0.1)
capitalRiskPct = input.float(1.0, "Risk % of equity per trade", step=0.1)
//===== 1m TREND (SMA) =====
sma1 = ta.sma(close, maLen)
sma1Up = sma1 > sma1
sma1Down = sma1 < sma1
uptrend1 = close > sma1 and sma1Up
downtrend1 = close < sma1 and sma1Down
//===== 15m TREND VIA request.security =====
sma15 = request.security(syminfo.tickerid, htf_tf, ta.sma(close, maLen), lookahead=barmerge.lookahead_off)
sma15Up = sma15 > sma15
sma15Down = sma15 < sma15
uptrend15 = close > sma15 and sma15Up
downtrend15 = close < sma15 and sma15Down
//===== SWING HIGHS/LOWS (LOCAL EXTREMA) =====
var int left = 3
var int right = 3
swHigh = ta.pivothigh(high, left, right)
swLow = ta.pivotlow(low, left, right)
//===== SR FLIP LEVELS =====
var float srSupport = na
var float srResistance = na
// when a swing high is broken -> new support
if not na(swHigh)
if close > swHigh
srSupport := swHigh
// when a swing low is broken -> new resistance
if not na(swLow)
if close < swLow
srResistance := swLow
//===== CANDLE METRICS =====
body = math.abs(close - open)
cRange = high - low
upperW = high - math.max(open, close)
lowerW = math.min(open, close) - low
isBull() => close > open
isBear() => close < open
bullHammer() =>
cRange > 0 and
isBull() and
lowerW >= wickFactor * body and
upperW <= body
bearShootingStar() =>
cRange > 0 and
isBear() and
upperW >= wickFactor * body and
lowerW <= body
isDoji() =>
cRange > 0 and body <= dojiThresh * cRange
bullEngulfing() =>
isBear() and isBull() and
open <= close and close >= open
bearEngulfing() =>
isBull() and isBear() and
open >= close and close <= open
//===== SR PROXIMITY =====
tol = priceTolPct * 0.01 * close
nearSupport = not na(srSupport) and math.abs(close - srSupport) <= tol
nearResistance = not na(srResistance) and math.abs(close - srResistance) <= tol
//===== SIGNAL CONDITIONS =====
bullCandle = bullHammer() or isDoji() or bullEngulfing()
bearCandle = bearShootingStar() or isDoji() or bearEngulfing()
longTrendOK = uptrend1 and uptrend15
shortTrendOK = downtrend1 and downtrend15
longSignal = longTrendOK and nearSupport and bullCandle
shortSignal = shortTrendOK and nearResistance and bearCandle
//===== POSITION SIZING (IN RISK UNITS) =====
var float lastEquity = strategy.equity
riskCapital = strategy.equity * (capitalRiskPct * 0.01)
//===== ENTRY / EXIT PRICES =====
longStop = math.min(low, nz(srSupport, low))
longRisk = close - longStop
longTP = close + risk_RR * longRisk
shortStop = math.max(high, nz(srResistance, high))
shortRisk = shortStop - close
shortTP = close - risk_RR * shortRisk
// qty in contracts (approx; assumes price * qty ≈ capital used)
longQty = longRisk > 0 ? riskCapital / longRisk : 0.0
shortQty = shortRisk > 0 ? riskCapital / shortRisk : 0.0
//===== EXECUTION =====
if longSignal and longRisk > 0 and longQty > 0
strategy.entry("Long", strategy.long, qty=longQty)
strategy.exit("Long TP/SL", from_entry="Long", stop=longStop, limit=longTP)
if shortSignal and shortRisk > 0 and shortQty > 0
strategy.entry("Short", strategy.short, qty=shortQty)
strategy.exit("Short TP/SL", from_entry="Short", stop=shortStop, limit=shortTP)
//===== PLOTS =====
plot(sma1, color=color.orange, title="SMA 1m")
plot(sma15, color=color.blue, title="HTF SMA (15m)")
plot(srSupport, "SR Support", color=color.new(color.green, 50), style=plot.style_linebr)
plot(srResistance,"SR Resistance",color=color.new(color.red, 50), style=plot.style_linebr)
// Visual debug for signals
plotshape(longSignal, title="Long Signal", style=shape.triangleup, location=location.belowbar, color=color.lime, size=size.tiny)
plotshape(shortSignal, title="Short Signal", style=shape.triangledown, location=location.abovebar, color=color.red, size=size.tiny)
WEEKLY - 3-Condition Arrows - Buy & SellVersion 1.
On the WEEKLY time frame, this indicator will add a green BUY arrow to a stock price when the following 3 conditions are ALL true:
BUY (all 3 conditions are true)
1. Stock price > 50 EMA
2. MACD line above moving average
3. Williams %R (Best_Solve version) is above moving average
Conversely, a red SELL arrow will point out when the following 3 conditions are ALL true:
SELL (all 3 conditions are true)
1. Stock price < 50 EMA
2. MACD line below moving average
3. Williams %R (Best_Solve version) is below the moving average
Super-AO Engine - Sentiment Ribbon - 11-29-25Super-AO Sentiment Ribbon by Signal Lynx
Overview:
The Super-AO Sentiment Ribbon is the visual companion to the Super-AO Strategy Suite.
While the main strategy handles the complex mathematics of entries and risk management, this tool provides a simple "Traffic Light" visual at the top of your chart to gauge the overall health of the market.
How It Works:
This indicator takes the core components of the Super-AO strategy (The SuperTrend and the Awesome Oscillator), calculates the spread between them and the current price, and generates a normalized "Sentiment Score."
Reading the Colors:
🟢 Lime / Green: Strong Upward Momentum. Ideally, you only want to take Longs here.
🟤 Olive / Yellow: Trend is weakening. Be careful with new entries, or consider taking profit.
⚪ Gray: The "Kill Zone." The market is chopping sideways. Automated strategies usually suffer here.
🟠 Orange / Red: Strong Downward Momentum. Ideally, you only want to take Shorts here.
Integration:
This script uses the same default inputs as our Super-AO Strategy Template and Alerts Template. Use them together to confirm your automated entries visually.
About Signal Lynx:
Free Scripts supporting Automation for the Night-Shift Nation 🌙
(www.signallynx.com)
均线变色K线系统 with 转折箭头//@version=6
indicator("均线变色K线系统 with 转折箭头", overlay=true, max_lines_count=500, max_labels_count=200)
// 输入参数
ma_length = input.int(20, title="均线周期", minval=1)
atr_filter = input.bool(true, title="启用ATR波动过滤")
atr_length = input.int(14, title="ATR周期", minval=1)
atr_multiplier = input.float(1.5, title="ATR波动阈值", minval=0.1, step=0.1)
show_arrows = input.bool(true, title="显示转折箭头")
candle_coloring = input.bool(true, title="启用K线变色")
// 计算均线和ATR
ma = ta.sma(close, ma_length)
atr_value = ta.atr(atr_length)
avg_atr = ta.sma(atr_value, atr_length)
// 判断均线方向和趋势转折点
ma_rising = ta.rising(ma, 1)
ma_falling = ta.falling(ma, 1)
// 使用更严格的趋势转折检测（避免repainting）
ma_rising_prev = ta.rising(ma, 2)
ma_falling_prev = ta.falling(ma, 2)
// 检测趋势转折点（确保只在K线收盘确认时检测）
trend_change_up = ma_rising and not ma_rising_prev and (not atr_filter or atr_value >= avg_atr * atr_multiplier)
trend_change_down = ma_falling and not ma_falling_prev and (not atr_filter or atr_value >= avg_atr * atr_multiplier)
// 设置颜色
ma_color = ma_rising ? color.rgb(255, 0, 0) : color.rgb(0, 0, 255) // 红/蓝
candle_color = ma_rising ? color.rgb(255, 0, 0) : color.rgb(0, 0, 255)
border_color = ma_rising ? color.rgb(255, 0, 0) : color.rgb(0, 0, 255)
wick_color = ma_rising ? color.rgb(255, 0, 0) : color.rgb(0, 0, 255)
// 绘制彩色均线
plot(ma, color=ma_color, linewidth=2, title="变色均线")
// 使用plotcandle绘制彩色K线
plotcandle(candle_coloring ? open : na,
candle_coloring ? high : na,
candle_coloring ? low : na,
candle_coloring ? close : na,
title="变色K线",
color = candle_color,
wickcolor = wick_color,
bordercolor = border_color,
editable = true)
// 绘制趋势转折箭头（只在K线确认时显示）
if show_arrows and barstate.isconfirmed
if trend_change_up
label.new(bar_index, low * 0.998, "▲",
color=color.rgb(0, 255, 0),
textcolor=color.white,
style=label.style_label_up,
yloc=yloc.price,
size=size.normal)
else if trend_change_down
label.new(bar_index, high * 1.002, "▼",
color=color.rgb(255, 0, 0),
textcolor=color.white,
style=label.style_label_down,
yloc=yloc.price,
size=size.normal)
// 背景色轻微提示（可选）
bgcolor(ma_rising ? color.new(color.red, 95) : color.new(color.blue, 95), title="趋势背景提示")
Bull/Bear/Consolidation Zones Hariss 369This indicator helps to identify bullish, bearish, and consolidation zones using EMA and ATR-based calculations. It visually highlights zones on the chart and provides buy and sell signals with ATR-based stop-loss (SL) and take-profit (TP) levels.
Key Features:
EMA Trend Filter: Determines the direction of the market.
Bull / Bear / Consolidation Zones: Colored zones to easily spot market phases.
ATR-Based SL & TP: Automatic calculation for each trade signal.
Buy / Sell Signals: Based on price relative to EMA and consolidation zones.
Relative Volume (RVOL) Filter: Optional filter to trade only when volume is significant, helping reduce low-probability signals.
Extended Zones: Option to extend zones forward until a breakout occurs.
Customizable Inputs: EMA length, ATR length, multipliers, RVOL period & multiplier, and toggle RVOL filter.
How to Use:
Identify bull/bear/consolidation zones on your chart. (These are already there) You can change the line as well zone color according to your needs.
Look for buy signals above EMA and consolidation zone, or sell signals below EMA and consolidation zone. The buy and sell labels are already there.
Confirm with RVOL filter (optional) to ensure higher volume support.
Use the plotted SL and TP levels for trade management.
This tool is designed for trend-following and market structure traders who want a visual guide to high-probability trading zones combined with volume confirmation.
One can also trail with EMA in trending market.
Super-AO with Risk Management Alerts Template - 11-29-25Super-AO with Risk Management: ALERTS & AUTOMATION Edition
Signal Lynx | Free Scripts supporting Automation for the Night-Shift Nation 🌙
1. Overview
This is the Indicator / Alerts companion to the Super-AO Strategy.
While the Strategy version is built for backtesting (verifying profitability and checking historical performance), this Indicator version is built for Live Execution.
We understand the frustration of finding a great strategy, only to realize you can't easily hook it up to your trading bot. This script solves that. It contains the exact same "Super-AO" logic and "Risk Management Engine" as the strategy version, but it is optimized to send signals to automation platforms like Signal Lynx, 3Commas, or any Webhook listener.
2. Quick Action Guide (TL;DR)
Purpose: Live Signal Generation & Automation.
Workflow:
Use the Strategy Version to find profitable settings.
Copy those settings into this Indicator Version.
Set a TradingView Alert using the "Any Alert() function call" condition.
Best Timeframe: 4 Hours (H4) and above.
Compatibility: Works with any webhook-based automation service.
3. Why Two Scripts?
Pine Script operates in two distinct modes:
Strategy Mode: Calculates equity, drawdowns, and simulates orders. Great for research, but sometimes complex to automate.
Indicator Mode: Plots visual data on the chart. This is the preferred method for setting up robust alerts because it is lighter weight and plots specific values that automation services can read easily.
The Golden Rule: Always backtest on the Strategy, but trade on the Indicator. This ensures that what you see in your history matches what you execute in real-time.
4. How to Automate This Script
This script uses a "Visual Spike" method to trigger alerts. Instead of drawing equity curves, it plots numerical values at the bottom of your chart when a trade event occurs.
The Signal Map:
Blue Spike (2 / -2): Entry Signal (Long / Short).
Yellow Spike (1 / -1): Risk Management Close (Stop Loss / Trend Reversal).
Green Spikes (1, 2, 3): Take Profit Levels 1, 2, and 3.
Setup Instructions:
Add this indicator to your chart.
Open your TradingView "Alerts" tab.
Create a new Alert.
Condition: Select SAO - RM Alerts Template.
Trigger: Select Any Alert() function call.
Message: Paste your JSON webhook message (provided by your bot service).
5. The Logic Under the Hood
Just like the Strategy version, this indicator utilizes:
SuperTrend + Awesome Oscillator: High-probability swing trading logic.
Non-Repainting Engine: Calculates signals based on confirmed candle closes to ensure the alert you get matches the chart reality.
Advanced Adaptive Trailing Stop (AATS): Internally calculates volatility to determine when to send a "Close" signal.
6. About Signal Lynx
Automation for the Night-Shift Nation 🌙
We are providing this code open source to help traders bridge the gap between manual backtesting and live automation. This code has been in action since 2022.
If you are looking to automate your strategies, please take a look at Signal Lynx in your search.
License: Mozilla Public License 2.0 (Open Source). If you make beneficial modifications, please release them back to the community!
RSI + EMA Dynamic Zones + Volume + Divergence (with RSI 50 line)RSI + EMA Dynamic Zones + Volume + Divergence (with RSI 50 line)
Buy Sell Signal — Ema crossover [© gyanapravah_odisha]Professional EMA Crossover + ATR Risk Control
Trade with confidence using a complete system that gives you clear entries, smart exits, and full automation.
Includes:
Precision 5/13 EMA crossover signals
ATR-based adaptive stop-loss
Multiple take-profit levels (with intermediate targets)
Fully customizable R:R ratios
ATR + volume filters to avoid choppy markets
Real-time trade dashboard
All alerts included
Built for: Crypto, Forex, Stocks • Scalping & Swing Trading
Built for you: Free, open-source & made for real-world trading.
VWAP & EMA9 Cross AlertAlerts the user when VWAP and EMA 9 cross. It gives a general direction of the market to help make decisions.
VWAP & EMA9 Cross AlertAlerts when EMA9 and VWAP Cross. This provides an indicator of general market direction based on these 2 indicators.
