MTF Trend + Crossover AlertsMulti-Timeframe Trend Dashboard + Crossover Signals
This indicator provides a clean, real-time visual dashboard of trend directions across multiple timeframes (1m, 5m, 15m, 30m, 1H, 4H, 1D), based on moving average crossovers.
🔹 Trend Detection Logic:
When the fast MA is above the slow MA → Bullish trend 🚀
When the fast MA is below the slow MA → Bearish trend 🐻
When both are equal or ranging → Neutral
📊 Dashboard Features:
Displays trend status for each selected timeframe
Color-coded cells (green = bullish, red = bearish, gray = neutral)
Includes emojis for visual clarity
🔔 Buy/Sell Alerts:
On the active chart timeframe, buy and sell signals are plotted when the fast MA crosses above or below the slow MA. You can also enable TradingView alerts based on these events.
🧩 Customizable Inputs:
Fast MA period
Slow MA period
MA type (EMA or SMA)
🔧 Ideal for traders who want a quick snapshot of market structure across multiple timeframes and receive real-time crossover signals on their current chart.
Hareketli Ortalamalar
muraThis indicator is based on moving averages over a certain period using Fibonacci numbers. Pivot points are provided for active trading, which help to trade from the levels.
The indicator takes into account the trend direction, identifies key levels and possible targets.
You can also easily set up notifications for trend changes and receive them in the tradingview app or as an audio signal on your computer. This will help you not to miss anything in the market.
The moving averages with a uniquely recognized trend. 6 moving averages create 3 important trends. The first trend is local from your time frame, the second is global from your time frame and the third trend is predicated for tracking the global trend on an older time frame.
I have identified ways to trade my indicator:
- On a 4-hour time frame you can open trades in one direction with a change in the global trend. That is, if the red trend changes to green, it is time to open longs and vice versa.
- The global trend works best when paired with the local trend. That is, if the global trend is green, we wait until the local trend becomes green and then open longs, as well as in the opposite direction.
- Opening positions when the local trend changes towards the global trend also works well on a 12 hour time frame.
This indicator can be used on any timeframes from 1 minute to 1 year. You can also select levels for trading:
- intraday
- intra-week
- within a month
Note:
- Before you start trading, I strongly recommend you to do your own analysis on your own time frame or pairs
- Recommended for use with additional technical analysis tools
GOYD📊 GOYD (Daily Average Percentage Change) Indicator
Created by: Emre Yavuz - @emreyavuz84
This indicator calculates and displays the average daily percentage change for each day of the week. It helps traders identify which days tend to be more volatile, offering valuable insights for timing strategies and market behavior analysis.
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🔧 How It Works
Daily Percentage Change Calculation:
For each candle, the indicator calculates the percentage change using the formula:
Percentage Change = (High - Low) / Low * 100
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Day-Based Data Collection:
The script stores the daily percentage changes in separate arrays for each day of the week:
Monday → mondayChanges
Tuesday → tuesdayChanges
...
Sunday → sundayChanges
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Average Calculation:
For each day, the script calculates the average of all recorded percentage changes. This gives a historical view of how volatile each weekday tends to be.
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Visual Table Display:
A table is displayed in the top-right corner of the chart, showing:
Column 1: Day of the week
Column 2: Average percentage change for that day
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🎯 Use Cases
This indicator is useful for:
Weekly Volatility Analysis: Identify which days are historically more volatile.
Timing Strategies: Optimize entry/exit points based on day-specific behavior.
Data-Driven Decisions: Make informed choices using historical volatility trends.
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🎨 Customization
The table color can be customized via the _tc input parameter.
The indicator is set to display directly on the chart (overlay=true).
If you find this indicator helpful, feel free to like, comment, or add it to your favorites. Your feedback is always appreciated! 📈
Moving Average Price Deviation Spread
**Moving Average Price Deviation Spread (MA Dev)**
This indicator visualizes the deviation of price from its exponential moving average (EMA) and scales it within dynamic upper and lower bounds. The core logic measures the smoothed spread between price and EMA, then calculates standard deviation over a rolling window to define statistical thresholds.
* **Spread**: EMA of (Close - EMA), highlighting directional bias.
* **Upper/Lower Bounds**: EMA of ±1.96 \* standard deviation of the spread, framing high/low deviation zones.
* **Use Case**: Spot overextended conditions, mean reversion setups, or volatility-driven breakouts. Ideal for timing entries and exits around price extremes.
Uptrick: Asset Rotation SystemOverview
The Uptrick: Asset Rotation System is a high-level performance-based crypto rotation tool. It evaluates the normalized strength of selected assets and dynamically simulates capital rotation into the strongest asset while optionally sidestepping into cash when performance drops. Built to deliver an intelligent, low-noise view of where capital should move, this system is ideal for traders focused on strength-driven allocation without relying on standard technical indicators.
Purpose
The purpose of this tool is to identify outperforming assets based strictly on relative price behavior and automatically simulate how a portfolio would evolve if it consistently moved into the strongest performer. By doing so, it gives users a realistic and dynamic model for capital optimization, making it especially suitable during trending markets and major crypto cycles. Additionally, it includes an optional safety fallback mechanism into cash to preserve capital during risk-off conditions.
Originality
This system stands out due to its strict use of normalized performance as the only basis for decision-making. No RSI, no MACD, no trend oscillators. It does not rely on any traditional indicator logic. The rotation logic depends purely on how each asset is performing over a user-defined lookback period. There is a single optional moving average filter, but this is used internally for refinement, not for entry or exit logic. The system’s intelligence lies in its minimalism and precision — using normalized asset scores to continuously rotate capital with clarity and consistency.
Inputs
General
Normalization Length: Defines how many bars are used to calculate each asset’s normalized score. This score is used to compare asset performance.
Visuals: Selects between Equity Curve (show strategy growth over time) or Asset Performance (compare asset strength visually).
Detect after bar close: Ensures changes only happen after a candle closes (for safety), or allows bar-by-bar updates for quicker reactions.
Moving Average
Used internally for optional signal filtering.
MA Type: Lets you choose which moving average type to use (EMA, SMA, WMA, RMA, SMMA, TEMA, DEMA, LSMA, EWMA, SWMA).
MA Length: Sets how many bars the moving average should calculate over.
Use MA Filter: Turns the filter on or off. It doesn’t affect the signal directly — just adds a layer of control.
Backtest
Used to simulate equity tracking from a chosen starting point. All calculations begin from the selected start date. Prior data is ignored for equity tracking, allowing users to isolate specific market cycles or testing periods.
Starting Day / Month / Year: The exact day the strategy starts tracking equity.
Initial Capital $: The amount of simulated starting capital used for performance calculation.
Rotation Assets
Each asset has 3 controls:
Enable: Include or exclude this asset from the rotation engine.
Symbol: The ticker for the asset (e.g., BINANCE:BTCUSDT).
Color: The color for visualization (labels, plots, tables).
Assets supported by default:
BTC, ETH, SOL, XRP, BNB, NEAR, PEPE, ADA, BRETT, SUI
Cash Rotation
Normalization Threshold USDC: If all assets fall below this threshold, the system rotates into cash.
Symbol & Color: Sets the cash color for plots and tables.
Customization
Dynamic Label Colors: Makes labels change color to match the current asset.
Enable Asset Label: Plots asset name labels on the chart.
Asset Table Position: Choose where the key asset usage table appears.
Performance Table Position: Choose where the backtest performance table appears.
Enable Realism: Enables slippage and fee simulation for realistic equity tracking. Adjusted profit is shown in the performance table.
Equity Styling
Show Equity Curve (STYLING): Toggles an extra-thick visual equity curve.
Background Color: Adds a soft background color that matches the current asset.
Features
Dual Visualization Modes
The script offers two powerful modes for real-time visual insights:
Equity Curve Mode: Simulates the growth of a portfolio over time using dynamic asset rotation. It visually tracks capital as it moves between outperforming assets, showing compounded returns and the current allocation through both line plots and background color.
Asset Performance Mode: Displays the normalized performance of all selected assets over the chosen lookback period. This mode is ideal for comparing relative strength and seeing how different coins perform in real-time against one another, regardless of price level.
Multi-Asset Rotation Logic
You can choose up to 10 unique assets, each fully customizable by symbol and color. This allows full flexibility for different strategies — whether you're rotating across majors like BTC, ETH, and SOL, or including meme tokens and stablecoins. You decide the rotation universe. If none of the selected assets meet the strength threshold, the system automatically moves to cash as a protective fallback.
Key Asset Selection Table
This on-screen table displays how frequently each enabled asset was selected as the top performer. It updates in real time and can help traders understand which assets the system has historically favored.
Asset Name: Shortened for readability
Color Box: Visual color representing the asset
% Used: How often the asset was selected (as a percentage of strategy runtime)
This table gives clear insight into historical rotation behavior and asset dominance over time.
Performance Comparison Table
This second table shows a full backtest vs. chart comparison, broken down into key performance metrics:
Backtest Start Date
Chart Asset Return (%) – The performance of the asset you’re currently viewing
System Return (%) – The equity growth of the rotation strategy
Outperformed By – Shows how many times the system beat the chart (e.g., 2.1x)
Slippage – Estimated total slippage costs over the strategy
Fees – Estimated trading fees based on rotation activity
Total Switches – Number of times the system changed assets
Adjusted Profit (%) – Final net return after subtracting fees and slippage
Equity Curve Styling
To enhance visual clarity and aesthetics, the equity curve includes styling options:
Custom Thickness Curve: A second stylized line plots a shadow or highlight of the main equity curve for stronger visual feedback
Dynamic Background Coloring: The chart background changes color to match the currently held asset, giving instant visual context
Realism Mode
By enabling Realism, the system calculates estimated:
Trading Fees (default 0.1%)
Slippage (default 0.05%)
These costs are subtracted from the equity curve in real time, and shown in the table to produce an Adjusted Return metric — giving users a more honest and execution-aware picture of system performance.
Adaptive Labeling System
Each time the asset changes, an on-chart label updates to show:
Current Asset
Live Equity Value
These labels dynamically adjust in color and visibility depending on the asset being held and your styling preferences.
Full Customization
From visual position settings to table placements and custom asset color coding, the entire system is fully modular. You can move tables around the screen, toggle background visuals, and control whether labels are colored dynamically or uniformly.
Key Concepts
Normalized values represent how much an asset has changed relative to its past price over a fixed period, allowing performance comparisons across different assets. Outperforming refers to the asset with the highest normalized value at a given time. Cash fallback means the system moves into a stable asset like USDC when no strong performers are available. The equity curve is a running total of simulated capital over time. Slippage is the small price difference between expected and actual trade execution due to market movement.
Use Case Flexibility
You don’t need to use all 10 assets. The system works just as effectively with only 1 asset — such as rotating between CASH and SOL — for a simple, minimal strategy. This is ideal for more focused portfolios or thematic rotation systems.
How to Use the Indicator
To use the Uptrick: Asset Rotation System, start by selecting which assets to include and entering their symbols (e.g., BINANCE:BTCUSDT). Choose between Equity Curve mode to see simulated portfolio growth, or Asset Performance mode to compare asset strength. Set your lookback period, backtest start date, and optionally enable the moving average filter or realism settings for slippage and fees. The system will then automatically rotate into the strongest asset, or into cash if no asset meets the strength threshold. Use alerts to be notified when a rotation occurs.
Asset Switch Alerts
The script includes built-in alert conditions for when the system rotates into a new asset. You can enable these to be notified when the system reallocates to a different coin or to cash. Each alert message is labeled by target asset and can be used for automation or monitoring purposes.
Conclusion
The Uptrick: Asset Rotation System is a next-generation rotation engine designed to cut through noise and overcomplication. It gives users direct insight into capital strength, without relying on generic indicators. Whether used to track a broad basket or focus on just two assets, it is built for accuracy, adaptability, and transparency — all in real-time.
Disclaimer
This script is for research and educational purposes only. It is not intended as financial advice. Past performance is not a guarantee of future results. Always consult with a financial professional and evaluate risks before trading or investing.
WaveTrend Filtered Signals (LazyBear Style)WaveTrend Filtered Signals (LazyBear Style)
This indicator is based on the popular WaveTrend oscillator (LazyBear) and adds several optional filters to improve signal quality:
✅ Available filters:
WT oversold/overbought zones – enabled by default. Signals are shown only if WT was previously in the specified zone (e.g., < -60 for longs, > 60 for shorts).
SMA trend filter – allows filtering signals in the direction of the moving average trend.
SMA position filter – signals appear only when price is above (for long) or below (for short) the moving average.
Consolidation filter – ignores signals during low-volatility sideways price movement.
💡 All filters are optional and can be enabled or disabled in the settings.
The default setup focuses on a clean approach: WaveTrend + oversold/overbought zones, with other filters left for customization.
Livermore-Seykota Breakout StrategyStrategy Name: Livermore-Seykota Breakout Strategy
Objective: Execute breakout trades inspired by Jesse Livermore, filtered by trend confirmation (Ed Seykota) and risk-managed with ATR (Paul Tudor Jones style).
Entry Conditions:
Long Entry:
Close price breaks above recent pivot high.
Price is above main EMA (EMA50).
EMA20 > EMA200 (uptrend confirmation).
Current volume > 20-period SMA (volume confirmation).
Short Entry:
Close price breaks below recent pivot low.
Price is below main EMA (EMA50).
EMA20 < EMA200 (downtrend confirmation).
Current volume > 20-period SMA.
Exit Conditions:
Stop-loss: ATR × 3 from entry price.
Trailing stop: activated with offset of ATR × 2.
Strengths:
Trend-aligned entries with volume breakout confirmation.
Dynamic ATR-based risk management.
Inspired by principles of three legendary traders.
Intraday Trading Hit and Run# Strategy Overview
This is a short-term trading system designed for quick entries/exits (intraday). It uses multiple technical indicators to identify momentum trades in the direction of the trend, with built-in risk management through trailing stops.
# Main Components
1. Trend Filter
Uses two EMAs (10-period "fast" blue line and 100-period "slow" red line)
Only trades when:
Long: Price AND fast EMA are above slow EMA
Short: Price AND fast EMA are below slow EMA
2. Main Signal
////Stochastic Oscillator (14-period):
Buy when %K line crosses above %D line
Sell when %K crosses below %D
////Trend Strength Check
Smoothed ADX indicator (5-period EMA of ADX):
Requires ADX value ≥ 25 to confirm strong trend
3. Confirmation using Volume Filter (Optional)
Checks if current volume is 1.5× greater than 20-period average volume
# Entry Rules
A trade is only taken when:
All 3 indicators agree (EMA trend, Stochastic momentum, ADX strength)
Volume filter is satisfied (if enabled)
# Exit Rules
1. Emergency Exit:
Close long if price drops below fast EMA
Close short if price rises above fast EMA
2. Trailing Stop:
Actively protects profits by moving stop-loss:
Maintains 0.1% distance from highest price (longs) or lowest price (shorts)
# Risk Management
Only use 10% of account per trade
Includes 0.04% commission cost in calculations
All trades monitored with trailing stops
# How It Operates
The strategy looks for strong, high-volume momentum moves in the direction of the established trend (as determined by EMAs). It jumps in quickly ("hit") when conditions align, then protects gains with an automatic trailing stop ("run"). Designed for fast markets where trends develop rapidly.
You can use it on 15m, 1h or 4h
Buy/Sell Ei - Premium Edition (Fixed Momentum)**📈 Buy/Sell Ei Indicator - Smart Trading System with Price Pattern Detection 📉**
**🔍 What is it?**
The **Buy/Sell Ei** indicator is a professional tool designed to identify **buy and sell signals** based on a combination of **candlestick patterns** and **moving averages**. With high accuracy, it pinpoints optimal entry and exit points in **both bullish and bearish trends**, making it suitable for forex pairs, stocks, and cryptocurrencies.
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### **🌟 Key Features:**
✅ **Advanced Candlestick Pattern Detection**
✅ **Momentum Filter (Customizable consecutive candle count)**
✅ **Live Trade Mode (Instant signals for active trading)**
✅ **Dual MA Support (Fast & Slow MA with multiple types: SMA, EMA, WMA, VWMA)**
✅ **Date Filter (Focus on specific trading periods)**
✅ **Win/Loss Tracking (Performance analytics with success rate)**
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### **🚀 Why Choose Buy/Sell Ei?**
✔ **Precision:** Reduces false signals with strict pattern rules.
✔ **Flexibility:** Works in both live trading and backtesting modes.
✔ **User-Friendly:** Clear labels and alerts for easy decision-making.
✔ **Adaptive:** Compatible with all timeframes (M1 to Monthly).
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### **🛠 How It Works:**
1. **Trend Confirmation:** Uses MAs to filter trades in the trend’s direction.
2. **Pattern Recognition:** Detects "Ready to Buy/Sell" and confirmed signals.
3. **Momentum Check:** Optional filter for consecutive bullish/bearish candles.
4. **Live Alerts:** Labels appear instantly in Live Trade Mode.
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### **📊 Ideal For:**
- **Day Traders** (Scalping & Intraday)
- **Swing Traders** (Medium-term setups)
- **Technical Analysts** (Backtesting strategies)
**🔧 Designed by Sahar Chadri | Optimized for TradingView**
**🎯 Trade Smarter, Not Harder!**
EMA Trend with MACD-Based Bar Coloring (Customized)This indicator blends trend-following EMAs with MACD-based momentum signals to provide a visually intuitive view of market conditions. It's designed for traders who value clean, color-coded charts and want to quickly assess both trend direction and overbought/oversold momentum.
🔍 Key Features:
Multi-EMA Trend Visualization:
Includes four Exponential Moving Averages (EMAs):
Fast (9)
Medium (21)
Slow (50)
Long (89)
Each EMA is dynamically color-coded based on its slope—green for bullish, red for bearish, and gray for neutral—to help identify the trend strength and alignment at a glance.
MACD-Based Bar Coloring:
Candlesticks are colored based on MACD's relationship to its Bollinger Bands:
Green bars signal strong bullish momentum (MACD > Upper Band)
Red bars signal strong bearish momentum (MACD < Lower Band)
Gray bars reflect neutral conditions
Compact Visual Dashboard:
A clean, top-right table displays your current EMA and MACD settings, helping you track parameter configurations without opening the settings menu.
✅ Best Used For:
Identifying trend alignment across short- to medium-term timeframes
Filtering entries based on trend strength and MACD overextension
Enhancing discretion-based or rule-based strategies with visual confirmation
TradersFriendCandles v2
TradersFriendCandles
A fully customizable candle‑color and banding indicator built on percentile + ATR, with optional EMA vs. ALMA trend filtering and higher‑timeframe support.
Key Features
Dynamic Percentile Center Line
Compute any Nth percentile over M bars (default 20th over 15) to serve as a reference “mid‑price” level.
ATR‑Based Bands
Envelope that percentile line with upper/lower bands at X × ATR (default 1×), plus an extended upper band at 3.5× ATR.
Higher‑Timeframe Mode
Plot bands based on a higher timeframe (e.g. daily bands on a 15m chart) so you can gauge macro support/resistance in micro timeframes.
Custom‑Color Candles
5 user‑editable colors for:
Strong bullish
Light bullish
Neutral
Light bearish
Strong bearish
Optional EMA vs. ALMA Trend Filter
When enabled, candles simply turn “bull” or “bear” based on fast EMA crossing above/below slow ALMA.
Border‑Only Coloring
Keep candle bodies transparent and color only the border & wick.
Live Plot Labels & Track Price
All lines carry titles and can display current values directly on the price scale.
Alerts
Strong Bull Breakout (price stays above upper band)
Strong Bear Breakdown (price closes below lower band)
EMA/ALMA crossovers
Inputs & Customization
Percentile level & lookback length
ATR length, multiplier, opacity
Fast EMA length, ALMA parameters (offset, length, sigma)
Toggle bands, lines, custom candles, higher‑timeframe mode
Pick your own colors via color‑picker inputs
Use TradersFriendCandles to visualize momentum shifts, dynamic support/resistance, and trend strength all in one overlay. Perfect for pinpointing breakouts, breakdowns, and filtering noise with adjustable sensitivity.
Candle % High/Low Bar + HL Order + MA by Barty&PitPapcioWhat does the indicator show?
The "Candle % High/Low Bar + HL Order + MA by Barty&PitPapcio" indicator displays the percentage deviation of each candle’s high and low relative to its open price. The zero line represents the candle’s open — bars above zero show upward movement from the open (to high), bars below zero show downward movement (to low).
Additionally, the indicator plots a dot above or below each bar indicating which came first during the candle — the high or the low — based on data from a lower timeframe two steps below the current chart (for example, on a 1-hour chart it uses 15-minute data).
Finally, the indicator calculates and plots a user-selectable moving average (EMA, SMA, or WMA) of these "first high or low" signals, helping identify trends whether the first move is more often upwards or downwards.
Where do the data come from?
Percentage values are calculated directly from the current chart’s candles:
highPerc=(High−Open)/Open×100%,
lowPerc=(Low−Open)/Open×100%
The timing of the first high or low for each candle is retrieved from a lower timeframe, stepping down two levels from the current timeframe (e.g. from 1H to 15 min), providing better precision in detecting the order of highs and lows that may be blurred on higher timeframes.
Additional features:
Full customization of colors for bars, dots, zero line, grid, and thicknesses.
Background grid with adjustable scale and style.
Safety checks for missing lower timeframe data.
A moving average smoothing the sequence of first high/low signals to reveal directional tendencies.
Suggested strategy for technical analysis support
Identify dominant candle direction: If the dot often appears above the bar (first high), it indicates buying pressure; if below (first low), selling pressure dominates.
Use percentage deviations: Large percent bars indicate heightened volatility and potential reversal points.
Moving average on order signals: The EMA of high/low first signals smooths the noise, showing the dominant trend in the sequence of price moves, useful for filtering other signals.
Combine with other tools: This indicator can act as a directional filter on multiple timeframes, synergizing well with momentum indicators, RSI, or support/resistance levels to confirm move strength.
Lots of love, Bartosz
CANX MA Crossover© CanxStixTrader
Moving average crossover systems measure drift in the market. They are great strategies for time-limited traders. KEEP IT SIMPLE
This strategy works both for buys and sells using the reaction line to guide your position against the reactions.
HOW TO USE THE INDICATOR
1) Choose your market and timeframe.
2) Choose the length.
3) Choose the multiplier.
4) Choose if the strategy is long-only or bidirectional (longs & shorts).
TIPS
The strategy works best in bullish markets as that is the primary direction that market such as stocks, indexes and metals like to move.
- Increase the multiplier to reduce whipsaws
- Increase the length to take fewer trades
- Decrease the length to take more trades
- Try a Long-Only strategy to see if that performs better.
The base set up when you load the indicator is for the 1 minute chart on gold. We found that it also works well on the US Indexes. For other markets you may need to change the length and multiplier to suit the market and back test its results.
Multi Moving Average with CustomizationCore Functionality
The indicator allows you to display up to 5 different moving averages on your chart simultaneously.
Each moving average can be fully customized with its own settings.
You can choose between
1. Simple Moving Average (SMA),
2. Exponential Moving Average (EMA)
3. Weighted Moving Average (WMA) types
Multi-Timeframe Support
One standout feature is the ability to display higher timeframe moving averages on lower timeframe charts.
For example, you can show a 200 EMA from the daily chart while viewing a 15-minute chart.
Advanced Visualization Features
The indicator includes several visualization enhancements:
1. MA Cloud - Creates a filled area between any two selected moving averages. The cloud automatically changes color based on which MA is on top - typically green when the faster MA is above (bullish) and red when below (bearish).
2. Golden/Death Cross Detection - Automatically detects and marks important MA crossover events:
* Golden Cross: When a shorter-term MA crosses above a longer-term MA (bullish signal)
* Death Cross: When a shorter-term MA crosses below a longer-term MA (bearish signal)
3. Trend Background - Colors the entire chart background based on whether price is above or below a specified MA, giving a clear visual indicator of the overall trend.
Alert System
The indicator can generate alerts when price crosses above or below any selected moving average. This feature is useful for automated trading signals or notifications, and can be configured to trigger once per bar.
Flexible Architecture
The code uses several programming techniques to maximize flexibility:
* Switch statements for selecting MA types and cloud values
* Conditional logic throughout the code
* Function abstraction for calculating MAs and handling multi-timeframe display
* String identifiers to select which MAs to use for cloud visualization
Unique Technical Aspects
1. The multi-timeframe plotting function solves the common problem of higher timeframe MAs looking distorted on lower timeframe charts.
2. The cloud feature uses string identifiers to select which MAs to use, allowing for any combination.
3. The indicator employs smart conditional logic to handle complex decision trees efficiently.
4. Every visual aspect (colors, line widths, display conditions) is customizable through the settings.
This indicator combines multiple technical analysis tools into a single, highly configurable package that can adapt to different trading styles and timeframes.
Its ability to correctly display higher timeframe MAs on lower timeframe charts makes it particularly valuable for traders who analyze multiple timeframes simultaneously.
UT Bot + Hull MA Confirmed Signal DelayOverview
This indicator is designed to detect high-probability reversal entry signals by combining "UT Bot Alerts" (UT Bot Alerts script adapted from QuantNomad - Originally developed by Yo_adriiiiaan and idea of original code for "UT Bot Alerts" from HPotter ) with confirmation from a Hull Moving Average (HMA) Developed by Alan Hull . It focuses on capturing momentum shifts that often precede trend reversals, helping traders identify potential entry points while filtering out false signals.
🔍 How It Works
This strategy operates in two stages:
1. UT Bot Momentum Trigger
The foundation of this script is the "UT Bot Alerts" , which uses an ATR-based trailing stop to detect momentum changes. Specifically:
The script calculates a dynamic stop level based on the Average True Range (ATR) multiplied by a user-defined sensitivity factor (Key Value).
When price closes above this trailing stop and the short-term EMA crosses above the stop, a potential buy setup is triggered.
Conversely, when price closes below the trailing stop and the short-term EMA crosses below, a potential sell setup is triggered.
These UT Bot alerts are designed to identify the initial shift in market direction, acting as the first filter in the signal process.
2. Hull MA Confirmation
To reduce noise and false triggers from the UT Bot alone, this script delays the entry signal until price confirms the move by crossing the Hull Moving Average (or its variants: HMA, THMA, EHMA) in the same direction as the UT Bot trigger:
A Buy Signal is generated only when:
A UT Bot Buy condition is active, and
The price closes above the Hull MA.
Or, if a UT Bot Buy condition was recently triggered but price hadn’t yet crossed above the Hull MA, a delayed buy is signaled when price finally breaks above it.
A Sell Signal is generated only when:
A UT Bot Sell condition is active, and
The price closes below the Hull MA.
Similarly, a delayed sell signal can occur if price breaks below the Hull MA shortly after a UT Bot Sell trigger.
This dual-confirmation process helps traders avoid premature entries and improves the reliability of reversal signals.
📈 Best Use Cases
Reversal Trading: This strategy is particularly well-suited for catching early trend reversals rather than trend continuations. It excels at identifying momentum pivots that occur after pullbacks or exhaustion moves.
Heikin Ashi Charts Recommended: The script offers a Heikin Ashi mode for smoothing out noise and enhancing visual clarity. Using Heikin Ashi candles can further reduce whipsaws and highlight cleaner shifts in trend direction.
MACD Alignment: For best results, trade in the direction of the MACD trend or use it as a filter to avoid counter-trend trades.
⚠️ Important Notes
Entry Signals Only: This indicator only plots entry points (Buy and Sell signals). It does not define exit strategies, so users should manage trades manually using trailing stops, profit targets, or other exit indicators.
No Signal = No Confirmation: You may see a UT Bot trigger without a corresponding Buy/Sell signal. This means the price did not confirm the move by crossing the Hull MA, and therefore the setup was considered too weak or incomplete.
⚙️ Customization
UT Bot Sensitivity: Adjust the “Key Value” and “ATR Period” to make the UT Bot more or less reactive to price action.
Use Heikin Ashi: Toggle between standard candles or Heikin Ashi in the indicator settings for a smoother trading experience.
The HMA length may also be modified in the indicator settings from its standard 55 length to increase or decrease the sensitivity of signal.
This strategy is best used by traders looking for a structured, logic-based way to enter early into reversals with added confirmation to reduce risk. By combining two independent systems—momentum detection (UT Bot) and trend confirmation (Hull MA)—it aims to provide high-confidence entries without overwhelming complexity.
Let the indicator guide your entries—you manage the exits.
Examples of use:
Futures:
Stock:
Crypto:
As shown in the snapshots this strategy, like most, works the best when price action has a sizeable ATR and works the least when price is choppy. Therefore it is always best to use this system when price is coming off known support or resistance levels and when it is seen to respect short term EMA's like the 9 or 15.
My personal preference to use this system is for day trading on a 3 or 5 minute chart. But it is valid for all timeframes and simply marks a high probability for a new trend to form.
Sources:
Quant Nomad - www.tradingview.com
Yo_adriiiiaan - www.tradingview.com
HPotter - www.tradingview.com
Hull Moving Average - alanhull.com
EMA 10/20/50 Alignment Strategy### 📘 **Strategy Name**
**EMA 10/20/50 Trend Alignment Strategy**
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### 📝 **Description (for Publishing)**
This strategy uses the alignment of Exponential Moving Averages (EMAs) to identify strong bullish trends. It enters a long position when the short-term EMA is above the mid-term EMA, which is above the long-term EMA — a classic sign of trend strength.
#### 🔹 Entry Criteria:
* **EMA10 > EMA20 > EMA50**: A bullish alignment that signals momentum in an upward direction.
* The strategy enters a **long position** when this alignment occurs.
#### 🔹 Exit Criteria:
* The long position is closed when the EMA alignment breaks (i.e., the trend weakens or reverses).
#### 🔹 Additional Features:
* Includes a **date range filter**, allowing you to backtest the strategy over a specific period.
* Uses **100% of available capital** for each trade (position size auto-scales with account balance).
* No short positions, stop loss, or take profit are applied — this is a trend-following strategy meant to ride bullish moves.
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### ✅ Best For:
* Traders looking for a **simple, trend-based entry system**
* Testing price momentum strategies during specific market regimes
* Visualizing EMA stacking patterns in historical data
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.
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.
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.
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.
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.
Consecutive Candles Above/Below EMADescription:
This indicator identifies and highlights periods where the price remains consistently above or below an Exponential Moving Average (EMA) for a user-defined number of consecutive candles. It visually marks these sustained trends with background colors and labels, helping traders spot strong bullish or bearish market conditions. Ideal for trend-following strategies or identifying potential trend exhaustion points, this tool provides clear visual cues for price behavior relative to the EMA.
How It Works:
EMA Calculation: The indicator calculates an EMA based on the user-specified period (default: 100). The EMA is plotted as a blue line on the chart for reference.
Consecutive Candle Tracking: It counts how many consecutive candles close above or below the EMA:
If a candle closes below the EMA, the "below" counter increments; any candle closing above resets it to zero.
If a candle closes above the EMA, the "above" counter increments; any candle closing below resets it to zero.
Highlighting Trends: When the number of consecutive candles above or below the EMA meets or exceeds the user-defined threshold (default: 200 candles):
A translucent red background highlights periods where the price has been below the EMA.
A translucent green background highlights periods where the price has been above the EMA.
Labeling: When the required number of consecutive candles is first reached:
A red downward arrow label with the text "↓ Below" appears for below-EMA streaks.
A green upward arrow label with the text "↑ Above" appears for above-EMA streaks.
Usage:
Trend Confirmation: Use the highlights and labels to confirm strong trends. For example, 200 candles above the EMA may indicate a robust uptrend.
Reversal Signals: Prolonged streaks (e.g., 200+ candles) might suggest overextension, potentially signaling reversals.
Customization: Adjust the EMA period to make it faster or slower, and modify the candle count to make the indicator more or less sensitive to trends.
Settings:
EMA Length: Set the period for the EMA calculation (default: 100).
Candles Count: Define the minimum number of consecutive candles required to trigger highlights and labels (default: 200).
Visuals:
Blue EMA line for tracking the moving average.
Red background for sustained below-EMA periods.
Green background for sustained above-EMA periods.
Labeled arrows to mark when the streak threshold is met.
This indicator is a powerful tool for traders looking to visualize and capitalize on persistent price trends relative to the EMA, with clear, customizable signals for market analysis.
Explain EMA calculation
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EMA 12/26 With ATR Volatility StoplossThe EMA 12/26 With ATR Volatility Stoploss
The EMA 12/26 With ATR Volatility Stoploss strategy is a meticulously designed systematic trading approach tailored for navigating financial markets through technical analysis. By integrating the Exponential Moving Average (EMA) and Average True Range (ATR) indicators, the strategy aims to identify optimal entry and exit points for trades while prioritizing disciplined risk management. At its core, it is a trend-following system that seeks to capitalize on price momentum, employing volatility-adjusted stop-loss mechanisms and dynamic position sizing to align with predefined risk parameters. Additionally, it offers traders the flexibility to manage profits either by compounding returns or preserving initial capital, making it adaptable to diverse trading philosophies. This essay provides a comprehensive exploration of the strategy’s underlying concepts, key components, strengths, limitations, and practical applications, without delving into its technical code.
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Core Philosophy and Objectives
The EMA 12/26 With ATR Volatility Stoploss strategy is built on the premise of capturing short- to medium-term price trends with a high degree of automation and consistency. It leverages the crossover of two EMAs—a fast EMA (12-period) and a slow EMA (26-period)—to generate buy and sell signals, which indicate potential trend reversals or continuations. To mitigate the inherent risks of trading, the strategy incorporates the ATR indicator to set stop-loss levels that adapt to market volatility, ensuring that losses remain within acceptable bounds. Furthermore, it calculates position sizes based on a user-defined risk percentage, safeguarding capital while optimizing trade exposure.
A distinctive feature of the strategy is its dual profit management modes:
SnowBall (Compound Profit): Profits from successful trades are reinvested into the capital base, allowing for progressively larger position sizes and potential exponential portfolio growth.
ZeroRisk (Fixed Equity): Profits are withdrawn, and trades are executed using only the initial capital, prioritizing capital preservation and minimizing exposure to market downturns.
This duality caters to both aggressive traders seeking growth and conservative traders focused on stability, positioning the strategy as a versatile tool for various market environments.
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Key Components of the Strategy
1. EMA-Based Signal Generation
The strategy’s trend-following mechanism hinges on the interaction between the Fast EMA (12-period) and Slow EMA (26-period). EMAs are preferred over simple moving averages because they assign greater weight to recent price data, enabling quicker responses to market shifts. The key signals are:
Buy Signal: Triggered when the Fast EMA crosses above the Slow EMA, suggesting the onset of an uptrend or bullish momentum.
Sell Signal: Occurs when the Fast EMA crosses below the Slow EMA, indicating a potential downtrend or the end of a bullish phase.
To enhance signal reliability, the strategy employs an Anchor Point EMA (AP EMA), a short-period EMA (e.g., 2 days) that smooths the input price data before calculating the primary EMAs. This preprocessing reduces noise from short-term price fluctuations, improving the accuracy of trend detection. Additionally, users can opt for a Consolidated EMA (e.g., 18-period) to display a single trend line instead of both EMAs, simplifying chart analysis while retaining trend insights.
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2. Volatility-Adjusted Risk Management with ATR
Risk management is a cornerstone of the strategy, achieved through the use of the Average True Range (ATR), which quantifies market volatility by measuring the average price range over a specified period (e.g., 10 days). The ATR informs the placement of stop-loss levels, which are set at a multiple of the ATR (e.g., 2x ATR) below the entry price for long positions. This approach ensures that stop losses are proportionate to current market conditions—wider during high volatility to avoid premature exits, and narrower during low volatility to protect profits.
For example, if a stock’s ATR is $1 and the multiplier is 2, the stop loss for a buy at $100 would be set at $98. This dynamic adjustment enhances the strategy’s adaptability, preventing stop-outs from normal market noise while capping potential losses.
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3. Dynamic Position Sizing
The strategy calculates position sizes to align with a user-defined Risk Per Trade, typically expressed as a percentage of capital (e.g., 2%). The position size is determined by:
The available capital, which varies depending on whether SnowBall or ZeroRisk mode is selected.
The distance between the entry price and the ATR-based stop-loss level, which represents the per-unit risk.
The desired risk percentage, ensuring that the maximum loss per trade does not exceed the specified threshold.
For instance, with a $1,000 capital, a 2% risk per trade ($20), and a stop-loss distance equivalent to 5% of the entry price, the strategy computes the number of units (shares or contracts) to ensure the total loss, if the stop loss is hit, equals $20. To prevent over-leveraging, the strategy includes checks to ensure that the position’s dollar value does not exceed available capital. If it does, the position size is scaled down to fit within the capital constraints, maintaining financial discipline.
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4. Flexible Capital Management
The strategy’s dual profit management modes—SnowBall and ZeroRisk—offer traders strategic flexibility:
SnowBall Mode: By compounding profits, traders can increase their capital base, leading to larger position sizes over time. This is ideal for those with a long-term growth mindset, as it harnesses the power of exponential returns.
ZeroRisk Mode: By withdrawing profits and trading solely with the initial capital, traders protect their gains and limit exposure to market volatility. This conservative approach suits those prioritizing stability over aggressive growth.
These options allow traders to tailor the strategy to their risk tolerance, financial goals, and market outlook, enhancing its applicability across different trading styles.
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5. Time-Based Trade Filtering
To optimize performance and relevance, the strategy includes an option to restrict trading to a specific time range (e.g., from 2018 onward). This feature enables traders to focus on periods with favorable market conditions, avoid historically volatile or unreliable data, or align the strategy with their backtesting objectives. By confining trades to a defined timeframe, the strategy ensures that performance metrics reflect the intended market context.
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Strengths of the Strategy
The EMA 12/26 With ATR Volatility Stoploss strategy offers several compelling advantages:
Systematic and Objective: By adhering to predefined rules, the strategy eliminates emotional biases, ensuring consistent execution across market conditions.
Robust Risk Controls: The combination of ATR-based stop losses and risk-based position sizing caps losses at user-defined levels, fostering capital preservation.
Customizability: Traders can adjust parameters such as EMA periods, ATR multipliers, and risk percentages, tailoring the strategy to specific markets or preferences.
Volatility Adaptation: Stop losses that scale with market volatility enhance the strategy’s resilience, accommodating both calm and turbulent market phases.
Enhanced Visualization: The use of color-coded EMAs (green for bullish, red for bearish) and background shading provides intuitive visual cues, simplifying trend and trade status identification.
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Limitations and Considerations
Despite its strengths, the strategy has inherent limitations that traders must address:
False Signals in Range-Bound Markets: EMA crossovers may generate misleading signals in sideways or choppy markets, leading to whipsaws and unprofitable trades.
Signal Lag: As lagging indicators, EMAs may delay entry or exit signals, causing traders to miss rapid trend shifts or enter trades late.
Overfitting Risk: Excessive optimization of parameters to fit historical data can impair the strategy’s performance in live markets, as past patterns may not persist.
Impact of High Volatility: In extremely volatile markets, wider stop losses may result in larger losses than anticipated, challenging risk management assumptions.
Data Reliability: The strategy’s effectiveness depends on accurate, continuous price data, and discrepancies or gaps can undermine signal accuracy.
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Practical Applications
The EMA 12/26 With ATR Volatility Stoploss strategy is versatile, applicable to diverse markets such as stocks, forex, commodities, and cryptocurrencies, particularly in trending environments. To maximize its potential, traders should adopt a rigorous implementation process:
Backtesting: Evaluate the strategy’s historical performance across various market conditions to assess its robustness and identify optimal parameter settings.
Forward Testing: Deploy the strategy in a demo account to validate its real-time performance, ensuring it aligns with live market dynamics before risking capital.
Ongoing Monitoring: Continuously track trade outcomes, analyze performance metrics, and refine parameters to adapt to evolving market conditions.
Additionally, traders should consider market-specific factors, such as liquidity and volatility, when applying the strategy. For instance, highly liquid markets like forex may require tighter ATR multipliers, while less liquid markets like small-cap stocks may benefit from wider stop losses.
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Conclusion
The EMA 12/26 With ATR Volatility Stoploss strategy is a sophisticated, systematic trading framework that blends trend-following precision with disciplined risk management. By leveraging EMA crossovers for signal generation, ATR-based stop losses for volatility adjustment, and dynamic position sizing for risk control, it offers a balanced approach to capturing market trends while safeguarding capital. Its flexibility—evident in customizable parameters and dual profit management modes—makes it suitable for traders with varying risk appetites and objectives. However, its limitations, such as susceptibility to false signals and signal lag, necessitate thorough testing and prudent application. Through rigorous backtesting, forward testing, and continuous refinement, traders can harness this strategy to achieve consistent, risk-adjusted returns in trending markets, establishing it as a valuable tool in the arsenal of systematic trading.
