Custom MTF VWAP 5x This is a combination of all VWAPs I use to find high probability trade setups and targets by only taking trades when different VWAPs align
Indicadores y estrategias
SMC & ICTSMC & ICT Concepts
Key Features:
• Real-time Market Structure: MSS (Market Structure Shift), BOS, CHOCH with labels
• Order Blocks (Bullish & Bearish) – auto-mitigation & breaker detection
• Fair Value Gaps (FVG), Implied FVG, Balance Price Range (BPR)
• Liquidity Grabs (Buyside/Sellside pools from equal highs/lows)
• Volume Imbalance (VI) detection
• Displacement candles
• Killzones: New York, London Open/Close, Asian session background highlight
• NWOG (New Week Opening Gap) & NDOG (New Day Opening Gap)
• Automatic Fibonacci Retracement & Extension between latest FVG, OB, Liquidity, or VI
• Two display modes:
→ Present Mode: Shows only recent & relevant structures (clean chart – recommended for live trading)
→ Historical Mode: Shows full structure history
Perfect confluence tool for scalping, day trading, and swing trading.
Hierarchical Hidden Markov ModelHierarchical Hidden Markov Models (HHMMs) are an advanced version of standard Hidden Markov Models (HMMs). While HMMs model systems with a single layer of hidden states, each transitioning to other states based on fixed probabilities, HHMMs introduce multiple layers of hidden states. This hierarchical structure allows for more complex and nuanced modeling of systems, making HHMMs particularly useful in representing systems with nested states or regimes. In HHMMs, the hidden states are organized into levels, where each state at a higher level is defined by a set of states at a lower level. This nesting of states enables the model to capture longer-term dependencies in the time series, as each state at a higher level can represent a broader regime, and the states within it can represent finer sub-regimes. For example, in financial markets, a high-level state might represent a general market condition like high volatility, while the nested lower-level states could represent more specific conditions such as trending or oscillating within the high volatility regime.
The hierarchical nature of HHMMs is facilitated through the concept of termination probabilities. A termination probability is the probability that a given state will stop emitting observations and transition control back to its parent state. This mechanism allows the model to dynamically switch between different levels of the hierarchy, thereby modeling the nested structure effectively. Beside the transition, emission and initial probabilities that generally define a HMM, termination probabilities distinguish HHMMs from HMMs because they define when the process in a sub-state concludes, allowing the model to transition back to the higher-level state and potentially move to a different branch of the hierarchy.
In financial markets, HHMMs can be applied similiarly to HMMs to model latent market regimes such as high volatility, low volatility, or neutral, along with their respective sub-regimes. By identifying the most likely market regime and sub-regime, traders and analysts can make informed decisions based on a more granular probabilistic assessment of market conditions. For instance, during a high volatility regime, the model might detect sub-regimes that indicate different types of price movements, helping traders to adapt their strategies accordingly.
MODEL FIT:
By default, the indicator displays the posterior probabilities, which represent the likelihood that the market is in a specific hidden state at any given time, based on the observed data and the model fit. These posterior probabilities strictly represent the model fit, reflecting how well the model explains the historical data it was trained on. This model fit is inherently different from out-of-sample predictions, which are generated using data that was not included in the training process. The posterior probabilities from the model fit provide a probabilistic assessment of the state the market was in at a particular time based on the data that came before and after it in the training sequence. Out-of-sample predictions, on the other hand, offer a forward-looking evaluation to test the model's predictive capability.
MODEL TESTING:
When the "Test Out of Sample" option is enabled, the indicator plots the selected display settings based on models' out-of-sample predictions. The display settings for out-of-sample testing include several options:
State Probability option displays the probability of each state at a given time for segments of data points not included in the training process. This is particularly useful for real-time identification of market regimes, ensuring that the model's predictive capability is tested on unseen data. These probabilities are calculated using the forward algorithm, which efficiently computes the likelihood of the observed sequence given the model parameters. Higher probabilities for a particular state suggest that the market is currently in that state. Traders can use this information to adjust their strategies according to the identified market regime and their statistical features.
Confidence Interval Bands option plots the upper, lower, and median confidence interval bands for predicted values. These bands provide a range within which future values are expected to lie with a certain confidence level. The width of the interval is determined by the current probability of different states in the model and the distribution of data within these states. The confidence level can be specified in the Confidence Interval setting.
Omega Ratio option displays a risk-adjusted performance measure that offers a more comprehensive view of potential returns compared to traditional metrics like the Sharpe ratio. It takes into account all moments of the returns distribution, providing a nuanced perspective on the risk-return tradeoff in the context of the HHMM's identified market regimes. The minimum acceptable return (MAR) used for the calculation of the omega can be specified in the settings of the indicator. The plot displays both the current Omega ratio and a forecasted "N day Omega" ratio. A higher Omega ratio suggests better risk-adjusted performance, essentially comparing the probability of gains versus the probability of losses relative to the minimum acceptable return. The Omega ratio plot is color-coded, green indicates that the long-term forecasted Omega is higher than the current Omega (suggesting improving risk-adjusted returns over time), while red indicates the opposite. Traders can use omega ratio to assess the risk-adjusted forecast of the model, under current market conditions with a specific target return requirement (MAR). By leveraging the HHMM's ability to identify different market states, the Omega ratio provides a forward-looking risk assessment tool, helping traders make more informed decisions about position sizing, risk management, and strategy selection.
Model Complexity option shows the complexity of the model, as well as complexity of individual states if the “complexity components” option is enabled. Model complexity is measured in terms of the entropy expressed through transition probabilities. The used complexity metric can be related to the models entropy rate and is calculated as the sum of the p*log(p) for every transition probability of a given state. Complexity in this context informs us on how complex the models transitions are. A model that might transition between states more often would be characterised by higher complexity, while a model that tends to transition less often would have lower complexity. High complexity can also suggest the model captures noise rather than the underlying market structure also known as overfitting, whereas lower complexity might indicate underfitting, where the model is too simplistic to capture important market dynamics. It is useful to assess the stability of the model complexity as well as understand where changes come from when a shift happens. A model with irregular complexity values can be strong sign of overfitting, as it suggests that the process that the model is capturing changes siginficantly over time.
Akaike/Bayesian Information Criterion option plots the AIC or BIC values for the model on both the training and out-of-sample data. These criteria are used for model selection, helping to balance model fit and complexity, as they take into account both the goodness of fit (likelihood) and the number of parameters in the model. The metric therefore provides a value we can use to compare different models with different number of parameters. Lower values generally indicate a better model. AIC is considered more liberal while BIC is considered a more conservative criterion which penalizes the likelihood more. Beside comparing different models, we can also assess how much the AIC and BIC differ between the training sets and test sets. A test set metric, which is consistently significantly higher than the training set metric can point to a drift in the models parameters, a strong drift of model parameters might again indicate overfitting or underfitting the sampled data.
Indicator settings:
- Source : Data source which is used to fit the model
- Training Period : Adjust based on the amount of historical data available. Longer periods can capture more trends but might be computationally intensive.
- EM Iterations : Balance between computational efficiency and model fit. More iterations can improve the model but at the cost of speed.
- Test Out of Sample : turn on predict the test data out of sample, based on the model that is retrained every N bars
- Out of Sample Display: A selection of metrics to evaluate out of sample. Pick among State probability, confidence interval, model complexity and AIC/BIC.
- Test Model on N Bars : set the number of bars we perform out of sample testing on.
- Retrain Model on N Bars: Set based on how often you want to retrain the model when testing out of sample segments
- Confidence Interval : When confidence interval is selected in the out of sample display you can adjust the percentage to reflect the desired confidence level for predictions.
- Omega forecast: Specifies the number of days ahead the omega ratio will be forecasted to get a long run measure.
- Minimum Acceptable Return : Specifies the target minimum acceptable return for the omega ratio calculation
- Complexity Components : When model complexity is selected in the out of sample display, this option will display the complexity of each individual state.
-Bayesian Information Criterion : When AIC/BIC is selected, turning this on this will ensure BIC is calculated instead of AIC.
Hidden Markov ModelHidden Markov Models (HMMs) are a class of statistical models used to represent systems that follow a Markov process with hidden states. In such models, the system being modeled transitions between a finite number of states, with the probability of each transition dependent only on the current state. The hidden states are not directly observable; instead, we observe a sequence of emissions or outputs generated by these states. HMMs are widely used in various fields, including speech recognition, bioinformatics, and financial market analysis. In the context of financial markets, HMMs can be utilized to model the latent market regimes (e.g., bullish, bearish, or neutral) that influence the observed market data such as asset prices or returns. By estimating the posterior probabilities of these hidden states, traders and analysts can identify the most likely market regime and make informed decisions based on the probabilistic assessment of market conditions.
The Hidden Markov Model (HMM) comprises several states that work together to model the hidden market dynamics. The states represent the unobservable market regimes such as bullish, bearish or neutral. The states are 'hidden' in nature because we need to infer them from the data and cannot directly observe them.
Model components:
Initial Probabilities: These denote the likelihood of starting in each hidden state. They can be related to long-run probabilities, reflecting the overall likelihood of each state across extended periods. In equilibrium, these initial probabilities may converge to the stationary distribution of the Markov chain.
Transition Probabilities: These capture the likelihood of moving between states, including the probability of remaining in the current state. They model how market regimes evolve over time, allowing the HMM to adapt to changing conditions.
Emission Probabilities: Also known as observation likelihoods, these represent the probability of observing specific market data (like returns) given each hidden state. Emission probabilities can be often represented by continuous probability distributions. In our case we are using a laplace distribution with its location parameter reflecting the central tendency of returns in each state and the scale reflecting the dispersion or the magnitude of the returns.
The power of HMMs in financial modeling lies in their ability to capture complex market dynamics probabilistically. By analyzing patterns in market, the model can estimate the likelihood of being in each state at any given time. This can reveal insights into market behavior and dynamics that might not be apparent from data alone.
MODEL FIT:
By default, the indicator displays the posterior probabilities, which represent the likelihood that the market is in a specific hidden state at any given time, based on the observed data and the model fit. It is crucial to understand that these posterior probabilities strictly represent the model fit, reflecting how well the model explains the historical data it was trained on. This model fit is inherently different from out-of-sample predictions, which are generated using data that was not included in the training process. The posterior probabilities from the model fit provide a probabilistic assessment of the state the market was in at a particular time based on the data that came before and after it in the training sequeence. Out-of-sample predictions on the other hand offer a forward-looking evaluation to test the model's predictive capability.
MODEL TEST:
When the "Test Out of Sample” option is enabled, the indicator plots the selected display settings based on models out-of-sample predictions. The display settings for out-of-sample testing include several options:
State Probability option displays the probability of each state at a given time for segments of datapoints that were not included in the traning process. This is particularly useful for real-time identification of market regimes, ensuring that the model's predictive capability is rigorously tested on unseen data. These probabilities are calculated using the forward algorithm, which efficiently computes the likelihood of the observed sequence given the model parameters. Higher probability for a particular state indicate a higher likelihood that the market is currently in that state. Traders can use this information to adjust their strategies according to the identified market regime and their statistical features.
Confidence Interval Bands option plots the upper, lower, and median confidence interval bands for predicted values. These bands provide a range within which future values are expected to lie with a certain confidence level. The width of the interval is determined by the current probability of different states in the model and the distribution of data within these states. The confidence level can be specified in the Confidence Interval setting.
Omega Ratio option displays a risk-adjusted performance measure that offers a more comprehensive view of potential returns compared to traditional metrics like the Sharpe ratio. It takes into account all moments of the returns distribution, providing a nuanced perspective on the risk-return tradeoff in the context of the HHMM's identified market regimes. The minimum acceptable return (MAR) used for the calculation of the omega can be specified in the settings of the indicator. The plot displays both the current Omega ratio and a forecasted "N day Omega" ratio. A higher Omega ratio suggests better risk-adjusted performance, essentially comparing the probability of gains versus the probability of losses relative to the minimum acceptable return. The Omega ratio plot is color-coded, green indicates that the long-term forecasted Omega is higher than the current Omega (suggesting improving risk-adjusted returns over time), while red indicates the opposite. Traders can use omega ratio to assess the risk-adjusted forecast of the model, under current market conditions with a specific target return requirement (MAR). By leveraging the HHMM's ability to identify different market states, the Omega ratio provides a forward-looking risk assessment tool, helping traders make more informed decisions about position sizing, risk management, and strategy selection.
Model Complexity option shows the complexity of the model, as well as complexity of individual states if the “complexity components” option is enabled. Model complexity is measured in terms of the entropy expressed through transition probabilities. The used complexity metric can be related to the models entropy rate and is calculated as the sum of the p*log(p) for every transition probability of a given state. Complexity in this context informs us on how complex the models transitions are. A model that might transition between states more often would be characterised by higher complexity, while a model that tends to transition less often would have lower complexity. High complexity can also suggest the model captures noise rather than the underlying market structure also known as overfitting, whereas too low complexity might indicate underfitting, where the model is too simplistic to capture important market dynamics. It is also useful to assess the stability of the model complexity. A model with irregular complexity values can be sign of overfitting, as it suggests that the process that the model is capturing changes significantly over time.
Akaike/Bayesian Information Criterion option plots the AIC or BIC values for the model on both the training and out-of-sample data. These criteria are used for model selection, helping to balance model fit and complexity, as they take into account both the goodness of fit (likelihood) and the number of parameters in the model. The metric therefore provides a value we can use to compare different models with different number of parameters. Lower values generally indicate a better model. AIC is considered more liberal while BIC is considered a more conservative criterion which penalizes the likelihood more. Beside comparing different models, we can also assess how much the AIC and BIC differ between the training sets and test sets. A test set metric, which is consistently significantly higher than the training set metric can point to a drift in the models parameters, a strong drift of model parameters might again indicate overfitting or underfitting the sampled data.
Indicator settings:
- Source : Data source which is used to fit the model
- Training Period : Adjust based on the amount of historical data available. Longer periods can capture more trends but might be computationally intensive.
- EM Iterations : Balance between computational efficiency and model fit. More iterations can improve the model but at the cost of speed.
- Test Out of Sample : turn on predict the test data out of sample, based on the model that is retrained every N bars
- Out of Sample Display: A selection of metrics to evaluate out of sample. Pick among State probability, confidence interval, model complexity and AIC/BIC.
- Test Model on N Bars : set the number of bars we perform out of sample testing on.
- Retrain Model on N Bars: Set based on how often you want to retrain the model when testing out of sample segments
- Confidence Interval : When confidence interval is selected in the out of sample display you can adjust the percentage to reflect the desired confidence level for predictions.
- Omega forecast: Specifies the number of days ahead the omega ratio will be forecasted to get a long run measure.
- Minimum Acceptable Return : Specifies the target minimum acceptable return for the omega ratio calculation
- Complexity Components : When model complexity is selected in the out of sample display, this option will display the complexity of each individual state.
-Bayesian Information Criterion : When AIC/BIC is selected, turning this on this will ensure BIC is calculated instead of AIC.
元宝均线趋势指标Yuanbao Moving Average Trend Indicator (元宝均线趋势指标)
A powerful, trend-following indicator designed to simplify market dynamics while capturing reliable trend signals—named for its "gold ingot" (Yuanbao) inspiration, symbolizing stability, precision, and wealth accumulation in trading. Built on optimized moving average (MA) logic, this tool filters noise, identifies trend direction, and highlights potential entry/exit zones, making it suitable for forex, stocks, cryptocurrencies, and commodities across all timeframes (from 1-minute scalping to daily swing trading).
Core Logic & Features
1. Multi-Layered MA Architecture
Combines short-term, medium-term, and long-term moving averages (customizable lengths) to balance responsiveness and reliability:
Short MA (e.g., 20-period): Tracks recent price momentum for timely signals.
Medium MA (e.g., 50-period): Confirms trend strength and filters false breakouts.
Long MA (e.g., 200-period): Acts as a dynamic support/resistance level and identifies major trend direction.
All MA types (SMA, EMA, WMA) are selectable—tailor to your trading style (EMA for faster reactions, SMA for smoother trends).
2. Trend Direction Visualization
Intuitive color-coding and line styling eliminate guesswork:
Bullish Trend: Short MA above Medium MA, and Medium MA above Long MA—lines turn green (customizable) to signal upward momentum.
Bearish Trend: Short MA below Medium MA, and Medium MA below Long MA—lines turn red (customizable) to indicate downward pressure.
Sideways/Consolidation: MAs cluster closely (with a built-in "range filter" to reduce noise)—lines turn blue (customizable) to alert neutral market conditions.
3. Dynamic Support/Resistance Zones
The indicator automatically highlights key levels based on MA crossovers and price interactions:
When price pulls back to the Medium/Long MA in a bullish trend: The MA line thickens to mark a potential "support zone" for long entries.
When price rallies to the Medium/Long MA in a bearish trend: The MA line thickens to mark a potential "resistance zone" for short entries.
Breaks above/below clustered MAs trigger "trend reversal alerts" (optional pop-up/alert conditions).
4. Customization for All Traders
Flexible parameters to adapt to any asset or strategy:
Adjust MA periods (short/medium/long) for different volatility levels (e.g., shorter periods for crypto, longer for blue-chip stocks).
Toggle MA type (SMA/EMA/WMA) to match your analysis style.
Customize color schemes, line thickness, and alert conditions (crossovers, trend shifts, price touches).
Enable/disable "noise reduction mode" (smoothes price data to filter choppy markets).
How to Use
Entry Signals
Long Entry:
Bullish trend confirmed (green MA stack: Short > Medium > Long).
Price pulls back to Medium MA (or Long MA for stronger trends) and bounces.
Optional: Confirm with volume or a candlestick pattern (e.g., hammer, bullish engulfing).
Short Entry:
Bearish trend confirmed (red MA stack: Short < Medium < Long).
Price rallies to Medium MA (or Long MA for stronger trends) and rejects.
Optional: Confirm with volume or a candlestick pattern (e.g., shooting star, bearish engulfing).
Exit Signals
Take Profit: Target next resistance/support level, or trail stop using the Short MA (exit if price crosses below Short MA in a bullish trend).
Stop Loss: Place below the Long MA (bullish trades) or above the Long MA (bearish trades) to limit downside.
Trend Reversal: Exit if the MA stack flips color (e.g., green → red for long trades).
Why Choose Yuanbao MA Trend Indicator?
Simplicity: No complex calculations—clear visual cues for trend direction and key levels.
Versatility: Works on all assets (forex, BTC, stocks, oil) and timeframes (1min, 15min, 4h, daily).
Reliability: Multi-MA confirmation reduces false signals, ideal for both beginners and experienced traders.
Customization: Adapt to your trading style, whether you’re a scalper, day trader, or swing trader.
Tips for Optimal Performance
For high-volatility assets (e.g., crypto), use shorter MA periods (e.g., 15/30/100) to stay responsive.
For low-volatility assets (e.g., bonds, blue-chip stocks), use longer MA periods (e.g., 50/100/200) for smoother trends.
Combine with oscillators (e.g., RSI, MACD) to avoid trading against overbought/oversold conditions.
Always test parameters on historical data before live trading—adjust based on asset-specific volatility.
BHA BUY SELLit will generate BUY SELL Signals and Support and Resistance levels and works on all instruments and all timeframes
MA Groups with ShiftsThis indicator plots up to five groups of moving averages of user-selectable types. Each group consists of a base moving average and its 5- and 10-period shifted values.
The purpose of this indicator is to gauge the strength of the trend.
Impulse Trend Suite (LITE) — v1.2🚀 Impulse Trend Suite (LITE) — v1.2
Smart trend visualization with precise flip arrows. A lightweight, momentum-filtered trend tool designed to stay clean, avoid repeated signals, and keep you focused only on real market direction.
✨ What’s New in v1.2
Minor upgrades mostly visual
Higher-contrast trend zones (green/red/neutral)
Larger BUY/SELL arrows + clearer labels
Clean, smoother ATR channel lines (improved)
Minor UI polishing to reduce chart noise
📌 Core Features
Trend flip arrows (no spam, 1 signal per turn)
Continuous background zones (gap-free trend shading)
Adaptive Baseline + ATR structure channel
RSI + MACD momentum filter (suppresses weak signals)
Trend Status Panel (UP, DOWN, NEUTRAL)
🔍 Quick Guide
BUY setup = green arrow + green background
SELL setup = red arrow + red background
Stay in the move while color doesn’t change
ATR channel helps avoid chasing overextended candles
🆚 LITE vs PRO
Feature LITE PRO
--------------------- -------- ------------------------------
Trend shading + arrows ✔ ✔ + confirmations
Neutral trend state ✔ ✔ enhanced
Alerts ✖ ✔ full suite
Reversal Zones ✖ ✔ predictive boxes
HTF Filter ✖ ✔ smarter trend bias
Included strategies ✖ ✔ + PDF training
========================================================
🔓 Upgrade to PRO
Reversal Zones • Alerts • HTF Filter • Trend Continuation Strategy
👉 fxsharerobots.com/impulse-trend-pro/
📈 Works on Forex, Stocks, Crypto, Indices, Metals
⌚ Scalping • Intraday • Swing • Long-term
==========================================================
⚠️ LITE - Educational tool. Backtest before trading live.
Visit us for Full Trading Tools Collection here:
www.fxsharerobots.com
Happy trading! — FxShareRobots Team
John NQ levels. v2NQ levels lines critical points
so you can take longs or shorts from levels that fail or become resistance.
enjoy
MTF Stoch RSI + RSI Signalsthis script will provide Buy and sell signals considering RSI and price action
GOD MODE HUNT v2.0 — SCREENER ULTIME 2025test screener pour détecter les crypto basée sur des règles strict
Gold Multi-Timeframe Strength Analyzer (XAU Basket Table)Gold Multi-Timeframe Strength Analyzer (XAU Basket Table)
This indicator is designed to measure real Gold strength across multiple XAU pairs and timeframes, all displayed inside a clean, structured performance table.
It reads the percentage change from open → close for each timeframe:
Weekly (W)
Daily (D)
4H (H4)
1H (H1)
By collecting these percent-change values from all XAU-based currency pairs, this indicator gives a full multi-dimensional view of Gold’s real momentum in the global market.
SWhat This Indicator Does
1. Multi-Timeframe Gold Strength
The indicator calculates and compares percent change for every selected XAU pair:
XAUUSD, XAUJPY, XAUEUR, XAUGBP, XAUAUD, XAUCHF, XAUCAD, XAUNZD
(Users can add or edit pairs freely.)
Positive % = bullish movement
Negative % = bearish movement
This creates a real strength map of Gold across the world.
2. Buy & Sell Simulation (True Market Pressure)
All positive percentages are combined into Buy Simulation Strength,
and all negative percentages combined into Sell Simulation Strength.
This reveals:
Which direction has more pressure
How strong buyers vs sellers truly are
How consistent Gold movement is across multiple pairs
This is NOT a technical signal — this is raw data-driven pressure analysis.
3. Automated Trend Bias
The indicator determines Buy Bias or Sell Bias based on the total aggregated strength.
No repainting.
No lag.
No crossover logic.
Just pure open-to-close price movement data.
4. Safe vs Unsafe Trade Detection
The indicator measures the difference between total buy pressure and total sell pressure.
Large difference → Safe Trade
Small difference → Unsafe Trade (choppy or transition zone)
This helps avoid false entries during sideways markets.
Why This Indicator Is Powerful
Uses real percent-change data from multiple Gold pairs
Reads global XAU movement, not just one chart
Confirms bias with objective mathematical logic
Works for trend, swing, and intraday Gold traders
Reduces noise and clarifies direction
This is essentially a Global Gold Strength Dashboard.
How To Use It
Check the Trend Bias (Buy or Sell).
Confirm Safe Trade status.
Use your main chart for entry timing.
Treat this table as your “Gold market confidence layer.”
My Multiple MA-BandsRelease Notes
MY-BAND – Adaptive Moving Average Channel Indicator
MY-BAND is a customizable Moving Average Band / Channel indicator designed to help traders clearly visualize trend direction, dynamic support & resistance, and market structure on any timeframe.
This indicator builds adaptive price bands around Moving Averages, making it easier to identify:
Trend continuation
Trend reversal
Volatility expansion and contraction
Key breakout and pullback zones
It works perfectly for crypto, forex, and stock markets.
🔧 Key Features
Multi-Timeframe MA Bands
HiLo & LLMA Moving Average Types
Dynamic Channel Width
ZigZag Structure Detection
Average Center Line
Trend Bending Option
Support & Resistance Layer
Fully Adjustable Inputs
Works on All Timeframes
📊 How to Use
Trend Trading
Price above upper band → Strong bullish trend
Price below lower band → Strong bearish trend
Pullback Entries
Enter on pullback to middle MA in trend direction
Breakout Trading
Strong breakout outside the band signals continuation
Market Structure
ZigZag feature helps identify swing highs & lows
⚙️ Inputs Explanation
MA Timeframe (MA TF) – Select the timeframe for MA calculation
Length 1 & Length 2 – Fine-tune band sensitivity
MA Type – Choose between HiLo or LLMA
Width – Controls band distance
AVG Line – Show central average line
Zigzag – Display market structure swings
Extend – Extend channel into the future
Bending – Smooth adaptive band behavior
✅ Best For
Trend Followers
Scalpers
Swing Traders
Crypto Futures Traders
Breakout & Pullback Strategies
⚠️ Disclaimer
This indicator is for educational and analytical purposes only. It does not provide financial advice. Always use proper risk management and confirm signals with other indicators.
Smoothed Heiken Ashi - Thrust Body HighlightSmoothed Heiken Ashi – Thrust Body Highlight is a price–action visualization tool designed to make strong directional “thrust” candles stand out and filter out noisy, wick-heavy bars.
Instead of using raw OHLC data, this script first applies an EMA smoothing (user-defined length) to open, high, low, and close, then builds a smoothed Heiken Ashi candle from those values. It then measures the total range of each HA candle and compares the wick size to that range. When the lower wick is small and the candle closes above its open, the bar is highlighted as a bull thrust (green). When the upper wick is small and the candle closes below its open, the bar is highlighted as a bear thrust (red). All other candles are shown as neutral (gray), helping you visually focus only on strong, decisive moves.
Use this indicator to:
Quickly spot momentum thrusts in the current trend
Filter out choppy, indecisive price action
Refine entries/exits when combined with your existing strategy (structure, EMAs, volume, etc.)
Inputs
Smoothing Length: EMA length used to smooth price before building Heiken Ashi candles.
Max Lower Wick % for Bull Thrust: Maximum lower wick as a percentage of total range for a candle to qualify as a bullish thrust.
Max Upper Wick % for Bear Thrust: Maximum upper wick as a percentage of total range for a candle to qualify as a bearish thrust.
This tool is intended as an aid to visual analysis, not as a standalone buy/sell signal.
SwiftEdge – Professional Levels Dashboard 2025SwiftEdge – Professional Levels Dashboard 2025
by SwiftEdge
A clean, powerful, all-in-one indicator combining the most important daily, weekly, and intraday reference levels used by serious traders.
Features:
• Yesterday’s Close (Anchor), High & Low
• Previous Week High & Low
• Live Today’s High & Low (updates in real time)
• Adaptive Volatility Forecast – projects today’s expected range based on recent true range and regime detection
• Yesterday’s POC + simulated Value Area (VAH/VAL)
• Built-in Risk Calculator – shows position size based on account % risk and today’s forecast range
• Fully customizable colors, line thickness, and visibility
No repainting · Works on all timeframes · Perfect for GER40, NQ, ES, BTC, and major indices
Clean chart. Clear levels. Better decisions.
Created with precision by SwiftEdge.
Daily High/Low/50%Daily High/Low/50% Levels Indicator
This Pine Script v6 indicator displays three horizontal lines from the previous daily candle:
High: The highest price of the last daily candle
Low: The lowest price of the last daily candle
50%: The midpoint between high and low
Key Features:
Lines extend from one daily candle to the next (Monday to Tuesday, Tuesday to Wednesday, etc.)
Fully customizable styling for each line independently:
Color selection
Line style (Solid, Dashed, Dotted)
Line width/thickness
Small labels ("H", "L", "50%") mark the start of each new day
Works on any timeframe (intraday charts show daily levels as reference)
Use Case:
Perfect for intraday traders who want to see the previous day's key levels as support/resistance zones. The 50% level often acts as a pivot point for price action.
EMAs Cloud by LuigiTradezWhat you get now:
Beautiful EMA cloud with dynamic coloring
Regular Bullish/Bearish Divergence (big green/red triangles + "DIV")
Hidden Bullish/Bearish Divergence (smaller aqua/orange triangles + "H")
Fully customizable RSI length and lookback
Built-in alert conditions (you can create alerts in TradingView)
SmartFlow Trend Engine SmartFlow Trend Engine (STE) is a premium trend-strength model designed for intraday & positional traders who want a cleaner, faster way to identify market direction without relying on lagging indicators.
Instead of using moving averages, oscillators, or traditional momentum tools, STE uses a proprietary flow-based algorithm that tracks how efficiently price is moving through the session.
The result is a real-time trend score that instantly tells you whether the market is dominated by buyers or sellers.
What SmartFlow helps you do
✔ Identify trend continuation early
✔ Spot weak or fading trends before price reverses
✔ Stay aligned with market direction (great for option sellers)
✔ Avoid chop zones by confirming whether the day has real strength
✔ Gain confidence during volatile intraday movement
How to use (Simple Rules)
✔ Green background → Strong positive flow (bullish pressure)
✔ Red background → Negative flow (bearish pressure)
✔ TrendScore line gives additional clarity on momentum strength
✔ Works beautifully on index options, futures, and stocks
Best for
✔ BankNifty / Nifty option sellers
✔ Positional traders
✔ Intraday scalpers
✔ Index futures traders
✔ Anyone who needs a simple, reliable trend confirmation tool
Protected Algorithm (Invite-Only Script)
SmartFlow Trend Engine uses a protected calculation model designed exclusively for invite-only users.
The underlying logic is not based on common indicators, making it extremely difficult to reverse-engineer and ensuring premium value for subscribers.
Volume essential parameters overlayVolume EPO – Essential Volume Parameters Overlay
1. Motivation and design philosophy
Volume EPO is designed as a conceptual overlay rather than a self contained trading system. The main idea behind this script is to take complex, foundational market concepts out of heavy, menu driven strategies and express them as lightweight, independent layers that sit on top of any chart or indicator.
In many TradingView scripts, a single strategy tries to handle everything at once: signal logic, risk settings, visual cues, multi timeframe controls, and conceptual explanations. This usually leads to long input menus, performance issues, and difficult maintenance. The architectural approach behind Volume EPO is the opposite: keep the core strategy lean, and move the explanation and measurement of key concepts into dedicated overlays.
In this framework, Volume EPO is the base layer for the concept of volume. It does not decide anything about entries or exits. Instead, it exposes and clarifies how different definitions of volume behave candle by candle. Other layers or strategies can then build on top of this understanding.
2. What Volume EPO does
Volume EPO focuses on four essential volume parameters for each bar:
- Buy volume - Sell volume - Total volume - Delta volume (the difference between buy and sell volume)
The script presents these parameters in a compact heads up display (HUD) table that can be positioned anywhere on the chart. It is designed to be visually minimal, language aware, and usable on top of any other indicator or price action without cluttering the view.
The indicator does not output signals, alerts, arrows, or strategy entries. It is a descriptive and educational tool that shows how volume is distributed, not a prescriptive tool that tells the trader what to do.
3. Two definitions of volume
A central theme of this script is that there is more than one way to define and interpret “volume” inside a single candle. Volume EPO implements and clearly separates two different approaches:
- A geometric, candle based approximation that uses only OHLC and volume of the current bar. - An intrabar, data driven definition that uses lower timeframe up and down volume when it is available.
The user can switch between these modes via the calculation method input. The mode is prominently shown inside the on chart table so that the context is always explicit.
3.1 Geometry mode (Source File, approximate)
In Geometry mode, Volume EPO works only with the current bar’s OHLC values and total volume. No lower timeframe data is required.
The candle’s range is defined as high minus low. If the range is positive, the position of the close inside that range is used as a simple model for how volume might have been distributed between buyers and sellers:
- The closer the close is to the high, the more of the total volume is attributed to the buying side. - The closer the close is to the low, the more of the total volume is attributed to the selling side. - In a rare case where the bar has no price range (for example a flat or doji bar), total volume is split evenly between buy and sell volume.
From this model, the script derives:
- Buy volume (approximated) - Sell volume (approximated) - Total volume (as reported by the bar) - Delta volume as the difference between buy and sell volume
This approach is intentionally labeled as “Geometry (Approx)” in the HUD. It is a theoretical reconstruction based solely on the candle’s geometry and total volume, and it is always available on any market or timeframe that provides OHLCV data.
3.2 Intrabar mode (Precise)
In Intrabar mode, Volume EPO uses the TradingView built in library for up and down volume on a user selected lower timeframe. Instead of inferring volume from the shape of the candle, it reads the underlying lower timeframe data when that data is accessible.
The script requests up and down volume from a lower timeframe such as 15 seconds, using the official TA library functions. The results are then interpreted as follows:
- Buy volume is taken as the absolute value of the up volume. - Sell volume is taken as the absolute value of the down volume. - Total volume is the sum of buy and sell volume. - Delta volume is provided directly by the library as the difference between up and down volume.
If valid lower timeframe data exists for a bar, the bar is counted as covered by Intrabar data. If not, that bar is marked as invalid for this precise calculation and is excluded from the covered count.
This mode is labeled “Precise” in the HUD, together with the selected lower timeframe, because it is anchored in actual intrabar data rather than in a geometric model. It provides a closer view of how buying and selling pressure unfolded inside the bar, at the cost of requiring more data and being dependent on the availability of that data.
4. Coverage, lookback, and what the numbers mean
The top part of the HUD reports not only which volume definition is active, but also an additional line that describes the effective coverage of the data.
In Intrabar (Precise) mode, the script displays:
- “Scanned: N Bars”
Here, N counts how many bars since the indicator was loaded have successfully received valid lower timeframe delta data. It is a measure of how much of the visible history has been truly covered by intrabar information, not a lookback window in the sense of a rolling calculation.
In Geometry mode, the script displays:
- “Lookback: L Bars”
In this extracted layer, the lookback value L is purely descriptive. It does not change how the current bar’s volume is computed, and it is not used in any iterative or statistical calculation inside this script. It is meant as a conceptual label, for example to keep the volume layer consistent with a broader framework where lookback length is a structural parameter.
Summarizing these two fields:
- Scanned tells you how many bars have been processed using real intrabar data. - Lookback is a descriptive parameter in Geometry mode in this specific overlay, not a direct driver of the computations.
5. The HUD layout on the chart
The on chart table is intentionally compact and structured to be read quickly:
- Header: a title identifying the overlay as Volume EPO. - Mode line: explicitly states whether the script is in Precise or Geometry mode, and for Precise mode also shows the lower timeframe used. - Coverage line: - In Precise mode, it shows “Scanned: N Bars”. - In Geometry mode, it shows “Lookback: L Bars”. - Volume block: - A line for buy and sell volume, marked with clear directional symbols. - A line for total volume and the absolute delta, accompanied by the sign of the delta. - Numeric formatting uses human friendly suffixes (for example K, M, B) to keep the display readable. - Footer: the current symbol and a time stamp, adjusted by a user selectable timezone offset so that the HUD can be aligned with the trader’s local time reference.
The table can be positioned anywhere on the chart and resized via inputs, and it supports multiple color themes and languages in order to integrate cleanly into different chart layouts.
6. How to use Volume EPO in practice
Volume EPO is meant to be read together with price action and other tools, not in isolation. Typical uses include:
- Studying how often a strong directional candle is actually supported by dominant buy or sell volume. - Comparing the behavior of delta volume between Geometry and Intrabar definitions. - Building a personal intuition for how intrabar data refines or contradicts the simple candle based approximation. - Feeding these insights into separate, lean strategy scripts that do not need to carry the full explanatory logic of volume inside them.
Because it is an overlay layer, Volume EPO can be stacked with other custom indicators without adding new signals or complexity to their logic. It simply adds a clear and consistent view of volume behavior on top of whatever the trader is already watching.
7. Educational and non signalling nature
Finally, it is important to stress that Volume EPO is not a trading system, not a signal generator, and not financial advice. The script does not tell the user when to enter or exit. It only reports how different definitions of volume describe the current bar.
Deciding whether to trade, how to trade, and which risk parameters to use remains entirely with the user and with their own strategy. Volume EPO provides context and clarity around the concept of volume so that those decisions can be informed by a better understanding of how buying and selling pressure is structured inside each candle.
Note: Even on lower timeframes, every reconstruction of volume remains an approximation, except at the true single tick level. However, the closer the chosen lower timeframe is to a one tick stream, the more accurately it can reflect the underlying order flow and balance between buying and selling pressure.
BTC Spot vs Perpetual CVD Divergence + Delta Confirm + Band FillThis indicator detects real market turning points by comparing Spot vs Perpetual CVD flows to identify forced positioning changes, leverage clean-ups, and true spot absorption.
It tracks normalized CVD for both Spot and Perps, calculates the divergence between them, and applies a dynamic volatility-based threshold to filter noise. Signals only trigger at confirmed pivot points, ensuring accuracy over early false reversals. An optional Delta confirmation layer further validates setups by requiring aggressive market flow in the direction of the pivot reversal.
This tool is not designed for blind entries — it highlights high-probability reversal zones. Best used in combination with VWAP, HTF structure, OI, and funding rate analysis to time optimal entries via pullbacks and momentum confirmation.
✅ Ideal for:
• Identifying local tops & bottoms
• Tracking spot vs leverage dominance
• Trading mean reversion and squeeze setups
• Flow-based scalping
❌ Not intended for:
• Chasing breakouts
• Standalone entry signals without price structure
MA Strength Indicator EnhancedThe "MA Strength" is an indicator that measures market trend strength or (in the case of forex pairs) the relative strength of individual currencies based on up to five different moving averages (MA). It offers multiple calculation methods, such as simple summation, normalized value, or measuring ATR/percentage distance from the price. The results are summarized in a clear table, and it provides customizable alerts for trend changes or shifts in currency strength. The high level of configurability (e.g., MA weighting, "all MA alignment" requirement) allows for fine-tuning the strategy.
💬 Interpreting the Table (Top Rows)
The top row of the table shows the final output of the indicator. This changes according to the set "Table Mode".
Trend Mode: The top row shows the final, aggregated trend status (e.g., "BULLISH", "NEUTRAL") and the corresponding "Trend Value". This is the value the indicator compares to its thresholds.
Forex Mode: (Only on 6-character pairs): The top two rows show the strength of the Base currency and the Quote currency separately.
Calculation of the top rows:
The indicator calculates the individual score of all active MAs (according to the chosen method).
Trend Value: This is the final value calculated from the scores.
If "Enable Averaging" is ON, this will be the average of the scores (e.g., MA1 score is 5.0, MA2 score is 7.0 -> Trend Value is 6.0).
If averaging is OFF, this will be the sum of the scores (e.g., 5.0 + 7.0 = 12.0).
Forex Calculation: "Forex Mode" uses this "Trend Value". If the Trend Value is +6.0 (on an EURUSD pair):
The Base currency (EUR) value will be +6.0.
The Quote currency (USD) value will be -6.0.
The indicator compares these values to the thresholds to determine the "STRONG" status for EUR and "WEAK" status for USD.
📊 Calculation Methods
The indicator can calculate trend strength using 5 methods. The final "Trend Value" is derived from the results of these calculations.
Sum:
Description: Simply adds up the individual scores of all enabled moving averages (MA).
Formula: If the price is above an MA, it gets the "Score Above" value (e.g., +2.0); if below, it gets the "Score Below" value (e.g., -2.0).
Example: Result = (MA1 score) + (MA2 score) + ...
Normalized:
Description: Takes the sum obtained by the "Sum" method and converts it to a scale between -100% (maximally bearish) and +100% (maximally bullish). It takes into account the maximum possible positive and negative scores.
Formula: Result = (Total Score / Max Possible Score) * 100
Percentage Distance:
Description: This method also considers distance. The further the price is from the MA in percentage terms, the higher the score.
Formula: MA Score = (|Close Price - MA| / MA * 100) * Weight (The "Weight" is the "Score Above/Below" value set in settings).
ATR Distance:
Description: Similar to percentage distance, but normalizes the distance using volatility via ATR (Average True Range).
Formula: MA Score = (|Close Price - MA| / ATR) * Weight
Candle Count:
Description: Counts how many consecutive candles have been above or below the MA. It multiplies this number by the set weight.
Formula: MA Score = (Number of consecutive candles) * Weight
⚙️ Settings Options
Moving Averages (MA 1-5)
For each moving average, you can set:
Enable MA: Turn the specific MA on or off.
Type: The type of moving average (SMA, EMA, WMA, etc.).
Period: The period of the MA (e.g., 50, 200).
Score Above / Below: The most important setting. This defines the "weight" of the MA in the calculation. In "Sum" mode, this is a fixed score; in distance-based modes, this is a multiplier (weight). It is advisable to write a positive number for "Score Above" and a negative number for "Score Below".
Calculation Settings
Enable Averaging: If this is on, the indicator shows the average of the active MA scores, not the total score.
Exception: This function is not available in "Normalized" mode.
Require All MA Alignment: This is a strict filter. If enabled, the indicator only gives a "BULLISH" (or "STRONG") signal if the price is above all enabled moving averages. Similarly, a "BEARISH" signal only occurs if the price is below all moving averages. If the price is on the opposite side of even just one MA (e.g., above 4, below 1), the status becomes "NEUTRAL", regardless of the scores.
Strength / Trend Thresholds
Enable Extra Levels: If active, statuses are expanded: "EXT. BULLISH" / "EXT. BEARISH" (Trend mode) or "EXT. STRONG" / "EXT. WEAK" (Forex mode). This indicates stronger, overbought/oversold conditions.
Threshold setting: The thresholds (e.g., "Strong Above - ATR") determine when the calculated value counts as a "STRONG" or "WEAK" status.
🔢 Setting Thresholds via Calculation
If "Enable Averaging" is OFF, the "Trend Value" shown in the table will be the sum of the individual MA scores. Therefore, we must define the threshold by adding up the minimum expected performance from each moving average. This allows us to set different expectations for short, medium, and long-term averages.
Step 1: Determine MA weights
In our example, we use 3 active MAs with the following weights (Score Above values):
MA1 (Short): Weight = +2
MA2 (Medium): Weight = +3
MA3 (Long): Weight = +4
Step 2: Determine the minimum expected distance
Define a minimum distance expected from each MA to trigger a "Strong" signal.
Step 3: Calculate target scores and the final threshold
Note: If "Enable Averaging" is ON, the resulting value (sum of target scores) must be
averaged to get the final threshold.
Example 1: ATR Distance
-Goal: I want a "Strong" signal if the price is...
...at least 1.0 ATR above MA1 (Short),
...at least 1.5 ATR above MA2 (Medium),
...and at least 2.0 ATR above MA3 (Long).
-Calculation (Expected Distance * Weight):
MA1 Target Score: 1.0 * 2 = 2.0
MA2 Target Score: 1.5 * 3 = 4.5
MA3 Target Score: 2.0 * 4 = 8.0
-Final Threshold (Sum of Target Scores): 2.0 + 4.5 + 8.0 = 14.5
-Setting: Set "Strong Above - ATR" threshold to 14.5.
If "Enable Averaging" is ON, the obtained value must be averaged, and the result will be the
threshold: 4.8 (14.5 / 3 = 4.83).
Example 2: Percentage Distance
-Goal: I want a "Strong" signal if the price is...
...at least 0.5% above MA1,
...at least 1.0% above MA2,
...and at least 1.5% above MA3.
-Calculation (Expected Distance * Weight):
MA1 Target Score: 0.5 * 2.0 = 1.0
MA2 Target Score: 1.0 * 3.0 = 3.0
MA3 Target Score: 1.5 * 4.0 = 6.0
-Final Threshold (Sum): 1.0 + 3.0 + 6.0 = 10.0
-Setting: Set "Strong Above - Percentage" threshold to 10.0.
If "Enable Averaging" is ON, the obtained value must be averaged, and the result will be the
threshold.
Example 3: Candle Count
-Goal: I want a "Strong" signal if...
...at least 3 consecutive candles are above MA1,
...at least 5 consecutive candles are above MA2,
...and at least 10 consecutive candles are above MA3.
-Calculation (Expected Candle Count * Weight):
MA1 Target Score: 3 * 2.0 = 6.0
MA2 Target Score: 5 * 3.0 = 15.0
MA3 Target Score: 10 * 4.0 = 40.0
-Final Threshold (Sum): 6.0 + 15.0 + 40.0 = 61.0
-Setting: Set "Strong Above - Candle" threshold to 61.0.
If "Enable Averaging" is ON, the obtained value must be averaged, and the result will be the
threshold.
Example 4: Sum
In this mode, distance does not matter, only whether the price is above or below the MA.
-Goal: "Strong" signal if the price is above the long-term averages, but can be below the short-term (MA1).
MA1 (Short): Can be below (Weight: -2.0)
MA2 (Medium): Must be above (Weight: +3.0)
MA3 (Long): Must be above (Weight: +4.0)
-Calculation: -2.0 + 3.0 + 4.0 = 5.0
-Setting: Set "Strong Above - Sum" threshold to 5.0.
If it must be above all three moving averages, the threshold would be 2.0 + 3.0 + 4.0 = 9.0.
If "Enable Averaging" is ON, the obtained value must be averaged, and the result will be the
threshold.
Example 5: Normalized
The basic logic is similar to the "Sum" method.
-Goal: "Strong" signal if price is above MA2 and MA3, but potentially below MA1.
-Calculation: Target Sum: 5.0. Max Possible Score (above all): 9.0.
-Threshold: (5.0 / 9.0) * 100 = 55.5
In this calculation method, averaging cannot be set.
The Usage of the "ATR %" Row
The "ATR %" row shows the percentage movement of an average candle.
How to use this with "Percentage Distance" mode:
This number gives a baseline. It helps decide if the "Percentage Distance" threshold is realistic.
Example: You see the "ATR %" value is hovering around 1.2%. This means a "normal" candle moves about 1.2%.
If you set the Percentage threshold to 0.5%, it is too low. The indicator will constantly give a "Strong" signal because even average movement (noise) exceeds the threshold.
Correct Usage: If "normal" movement is 1.2%, then a "strong" movement (trend) needs to be significantly larger. For example, set the threshold to double the ATR %: 2.4 (2 * 1.2). Thus, you only get a "Strong" signal if the movement is twice the average volatility.
Supplementary Information
Rounding Differences:
The numbers displayed in the table and the precision of calculations in the background differ.
Table Display: The indicator rounds numbers to two decimal places in the table. So, if the value is 0.996, the table shows 1.00 (rounded up).
Internal Calculation: The background calculation uses much higher precision. When determining status (STRONG vs NEUTRAL), the program compares the precise, unrounded value to the threshold.
Result: Due to rounding, it may happen that if the threshold is 1.00 and the table shows 1.00, the status flickers between Strong and Neutral. If this is bothersome, it is advisable to set a slightly lower threshold (e.g., 0.98).
🔔 Alert Settings
The indicator can send alerts when the status changes.
Alert Method:
Trend: Alerts when the main trend status changes (e.g., from "NEUTRAL" to "BULLISH"). You can specify which direction to alert for (e.g., only "BULLISH").
Forex: Works only on 6-character forex pairs. You can set separate alerts for the Base or Quote currency.
Forex Strength Level: You can specify at which status level to alert (e.g., "WEAK" or "EXT. STRONG").
📈 Trading Tips
Trend Confirmation: Use the "BULLISH" / "BEARISH" status to confirm your existing strategy (e.g., breakouts, bounces off support).
Forex Pairing: In Forex mode, look for pairs where the Base currency is "STRONG" and the Quote currency is "WEAK" (or "EXT. STRONG" / "EXT. WEAK") for a long position.
Short Position: Reverse the above (Base: WEAK, Quote: STRONG).
EMA Market Structure [BOSWaves]EMA Market Structure - Trend-Driven Structural Mapping with Adaptive Swing Detection
Overview
The EMA Market Structure indicator provides an advanced framework for visualizing market structure through dynamically filtered trend and swing analysis.
Unlike conventional EMA overlays, which merely indicate average price direction, this model integrates trend acceleration, swing highs/lows, and break-of-structure (BOS) logic into a unified, visually intuitive display.
Each element adapts in real time to price movement, offering traders a living map of support, resistance, and trend bias that reacts fluidly to market momentum.
The result is a comprehensive, trend-aware representation of price structure.
EMA slope and acceleration guide trend perception, while swing points identify key inflection zones.
Breaks of prior highs or lows are highlighted with visual BOS labels and stop-loss projections, giving traders actionable context for continuation or reversal setups.
Unlike static lines or simple moving averages, the EMA Market Structure indicator fuses dynamic trend analysis with structural awareness to provide a clear picture of market bias and potential turning points.
Theoretical Foundation
The EMA Market Structure builds on principles of momentum filtering and structural analysis.
Standard moving averages track average price but ignore acceleration and context; this indicator captures both the directional slope of the EMA and its rate of change, providing a proxy for trend strength.
Simultaneously, swing detection identifies statistically significant highs and lows, while BOS logic flags decisive breaks in structure, aligned with trend direction.
At its core are three interacting components:
EMA Trend & Acceleration : Smooths price data while highlighting acceleration changes, producing gradient-driven color cues for trend momentum.
Swing Detection Engine : Identifies swing highs and lows over configurable bar lengths, ensuring key turning points are captured with minimal clutter.
Break-of-Structure Logic : Detects price breaches of previous swings and aligns them with EMA trend for actionable BOS signals, including projected stop-loss levels for tactical decision-making.
By integrating these elements, the system scales effectively across timeframes and assets, maintaining structural clarity while visualizing trend dynamics in real time. Traders receive both macro and micro perspectives of market movement, with clear cues for trend continuation or reversal.
How It Works
The EMA Market Structure indicator operates through layered processing stages:
EMA Slope & Acceleration : Calculates the EMA and its rate of change, normalizing via ATR and a smoothing function to produce gradient color coding. This allows instant visual identification of bullish or bearish momentum.
Swing Identification : Swing highs and lows are computed using configurable left/right bar lengths, filtered through a cool-off mechanism to prevent redundant signals and maintain chart clarity.
Structural Lines & Zones : Swing points are connected with lines, and shaded zones are drawn between successive highs/lows to highlight key support and resistance regions.
Break-of-Structure Detection : BOS events occur when price breaches a prior swing in alignment with the EMA trend. Bullish and bearish BOS signals include enhanced label effects and projected stop-loss lines and zones, providing immediate tactical reference.
Dynamic Background Mapping : The chart background adapts to EMA trend direction, reinforcing trend context with subtle visual cues.
Through these processes, the indicator creates a living, adaptive map of market structure that reflects both trend strength and swing-based inflection points.
Interpretation
The EMA Market Structure reframes market reading from simple trend following to structured awareness of price behavior:
Uptrend Phases : EMA is rising with positive acceleration, swings confirm higher lows, and BOS events occur above prior highs, signaling trend continuation.
Downtrend Phases : EMA slope is negative, swings form lower highs, and BOS events occur below prior lows, confirming bearish bias.
Trend Reversals : Flat or decelerating EMA with BOS failures may indicate impending structural change.
Critical Zones : Swing-based lines and shaded zones highlight areas where price may pause, reverse, or accelerate, providing high-probability decision points.
Visually, EMA color gradients, structural lines, and BOS labels combine to provide both statistical trend confirmation and actionable structural cues.
Strategy Integration
EMA Market Structure integrates seamlessly into trend-following and swing-based trading systems:
Trend Alignment : Confirm higher-timeframe EMA slope before entering continuation trades.
BOS Entry Triggers : Use BOS events aligned with EMA trend for tactical entries and stop placement.
Support/Resistance Mapping : Swing lines and zones help define areas for scaling, exits, or reversals.
Volatility Context : ATR-based smoothing and stop-loss buffers accommodate varying market volatility, ensuring robustness across conditions.
Multi-Timeframe Coordination : Combine higher-timeframe EMA trend and swings with lower-timeframe structural events for precision entries.
Technical Implementation Details
Core Engine : EMA slope and ATR-normalized acceleration for gradient-driven trend visualization.
Swing Framework : Pivot-based high/low detection with configurable bar lengths and cool-off intervals.
Structural Visualization : Lines, zones, and labels for high-fidelity mapping of support/resistance and BOS events.
BOS Engine : Detects structural breaks aligned with EMA trend, automatically plotting stop-loss lines and visual cues.
Performance Profile : Lightweight, optimized for real-time responsiveness across multiple timeframes.
Optimal Application Parameters
Timeframe Guidance:
1 - 5 min : Ideal for intraday swing spotting and microstructure trend tracking.
15 - 60 min : Medium-range structural analysis and BOS-driven entries.
4H - Daily : Macro trend mapping and key swing-based support/resistance identification.
Suggested Configuration:
EMA Length : 50
Swing Length : 5
Swing Cooloff : 10 bars
BOS Cooloff : 15 bars
SL Buffer : 0.1%
These suggested parameters should be used as a baseline; their effectiveness depends on the asset volatility, liquidity, and preferred entry frequency, so fine-tuning is expected for optimal performance.
Performance Characteristics
High Effectiveness:
Trending markets with defined swings and structural consistency.
Markets where EMA slope and acceleration reliably indicate momentum changes.
Reduced Effectiveness:
Choppy or sideways markets with minimal swing definition.
Random walk assets lacking clear structural anchors.
Integration Guidelines
Confluence Framework : Combine with volume, momentum, or BOSWaves structural indicators
to validate entries.
Directional Control: Follow EMA slope and BOS alignment for high-conviction trades.
Risk Calibration: Use SL projections for disciplined exposure management.
Multi-Timeframe Synergy: Confirm higher-timeframe trend before executing lower-timeframe structural trades.
Disclaimer
The EMA Market Structure is a professional-grade trend and structure visualization tool. It is not predictive or guaranteed profitable; performance depends on parameter tuning, market regime, and disciplined execution. BOSWaves recommends using it as part of a comprehensive analytical stack integrating trend, liquidity, and structural context.
