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.
Medie mobili
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.
ADX EMA's DistanceIt is well known to technical analysts that the price of the most volatile and traded assets do not tend to stay in the same place for long. A notable observation is the recurring pattern of moving averages that tend to move closer together prior to a strong move in some direction to initiate the trend, it is precisely that distance that is measured by the blue ADX EMA's Distance lines on the chart, normalized and each line being the distance between 2, 3 or all 4 moving averages, with the zero line being the point where the distance between them is zero, but it is also necessary to know the direction of the movement, and that is where the modified ADX will be useful.
This is the well known Directional Movement Indicator (DMI), where the +DI and -DI lines of the ADX will serve to determine the direction of the trend.
MTF RSI Fibonacci Levels & MTF Moving Avreages (EMA-SMA-WMA)Thanks for Kadir Türok Özdamar. @kadirturokozdmr
Formula Purpose of Use
This formula combines the traditional RSI indicator with Fibonacci levels to create a special technical indicator that aims to identify potential support and resistance points:
Thanks for Kadir Türok Özdamar. @kadirturokozdmr
Formula Purpose of Use
This formula combines the traditional RSI indicator with Fibonacci levels to create a special technical indicator that aims to identify potential support and resistance points:
Determines the historical RSI range of 144 periods (PEAK and DIP)
Calculates Fibonacci retracement levels within this range, and shows the direction of momentum by calculating the moving average of the RSI
This indicator can be used to identify potential reversal points, especially when the RSI is not in overbought (70+) or oversold (30-) areas.
Practical Use
Investors can use this indicator as follows:
1⃣When the RSI approaches one of the determined Fibonacci levels, it is considered a potential support/resistance area.
2⃣When the RSI approaches the DIP level, it can be interpreted as oversold, and when it approaches the PEAK level, it can be interpreted as overbought.
3⃣When the RSI crosses the SM (moving average) line upwards or downwards, it can be evaluated as a momentum change signal.
4⃣Fibonacci levels (especially M386, M500 and M618) can be monitored as important transition zones for the RSI.
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In this version, some features and a multi-timeframe averages (SMA-EMA-WMA) were added to the script. It was made possible for the user to enter multi-timeframe RSI and multi-timeframe Fibo lengths.
MK Multi-Timeframe MA RibbonThe MK Ribbon MTF is an overlay indicator that lets you plot up to eight independently configurable moving averages on any chart.
For each MA you can choose its length, type (EMA, SMA, WMA, or HMA), timeframe (current chart, Daily, Weekly, or Monthly), and easily toggle it on or off. Each MA line colors itself above, below, or equal to price—green, red, or gray by default—and you can freely customize those colors to suit your theme.
You also get optional semi-transparent “Golden Cross” and “Death Cross” labels for daily 50/200 SMA crossovers, which you can enable or disable separately, so you can tailor the ribbon and signals exactly to your workflow.
A.K Dynamic EMA/SMA / MTF S&R Zones Toolkit with AlertsThe A.K Dynamic EMA/SMA / MTF Support & Resistance Zones Toolkit is a powerful all-in-one technical analysis tool designed for traders who want a clean yet comprehensive market view. Whether you're scalping lower timeframes or swing trading higher timeframes, this indicator gives you both the structure and signals to take action with confidence.
Key Features:
✅ Customizable EMA/SMA Suite
Display key Exponential and Simple Moving Averages including 5, 9, 20, 50, 100, and 200 EMAs, plus optional 50 SMA for trend filtering. Each line can be toggled individually and color-customized.
✅ Multi-Timeframe Support & Resistance Zones
Automatically detects dynamic S/R zones on key timeframes (5min, 15min, 30min, 1H, 4H, 1D) using swing highs/lows. Zones are color-coded by strength and whether they're broken or active, providing a clear visual roadmap for price reaction levels.
✅ Zone Strength & Break Detection
Distinguishes between strong and weak zones based on price proximity and reaction depth, with visual shading and automatic label updates when a level is broken.
✅ Price Action-Based Buy/Sell Signals
Generates BUY signals when bullish candles react to strong support (supply) zones, and SELL signals when bearish candles react to strong resistance (demand) zones. All logic is adjustable — including candle body vs wick detection, tolerance range, and strength thresholds.
✅ Alerts Engine
Built-in TradingView alerts for price touching support/resistance or triggering buy/sell signals. Perfect for automation or hands-free monitoring.
✅ Optional Candle & Trend Filters
Highlight bullish/bearish candles visually for additional confirmation.
Optional RSI display and 50-period SMA trend filter to guide directional bias.
🧠 Use Case Scenarios:
Identify dynamic supply & demand zones across multiple timeframes.
Confirm trend direction with EMAs and SMA filters.
React quickly to clean BUY/SELL signals based on actual price interaction with strong zones.
Customize it fully to suit scalping, day trading, or swing trading strategies.
📌 Recommended Settings:
Use default zone transparency (65%) and offset (250 bars) for optimal visual clarity.
Enable alerts to get notified when price enters key S/R levels or when a trade signal occurs.
Combine this tool with your entry/exit plan for better decision-making under pressure.
💡 Pro Tip: Add this indicator to a clean chart and let the zones + EMAs guide your directional bias. Use alerts to avoid screen-watching and improve discipline.
Created by:
Version: Pine Script v6
Platform: TradingView
Moving Average Candles**Moving Average Candles — MA-Based Smoothed Candlestick Overlay**
This script replaces traditional price candles with smoothed versions calculated using various types of moving averages. Instead of plotting raw price data, each OHLC component (Open, High, Low, Close) is independently smoothed using your selected moving average method.
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### 📌 Features:
- Choose from 13 MA types: `SMA`, `EMA`, `RMA`, `WMA`, `VWMA`, `HMA`, `T3`, `DEMA`, `TEMA`, `KAMA`, `ZLEMA`, `McGinley`, `EPMA`
- Fully configurable moving average length (1–1000)
- Color-coded candles based on smoothed Open vs Close
- Works directly on price charts as an overlay
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### 🎯 Use Cases:
- Visualize smoothed market structure more clearly
- Reduce noise in price action for better trend analysis
- Combine with other indicators or strategies for confluence
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> ⚠️ **Note:** Since all OHLC values are based on moving averages, these candles do **not** represent actual market trades. Use them for trend and structure analysis, not trade entries based on precise levels.
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*Created to support traders seeking a cleaner visual representation of price dynamics.*
Delta Momentum ShiftThe "Delta Momentum Shift" strategy combines Bollinger Band breakouts with trend alignment and higher timeframe filtering to capture momentum moves.
#Entry Signals:
Long: Price crosses above upper Bollinger Band, Micro EMA above Macro EMA, and higher timeframe uptrend.
Short: Price crosses below lower Bollinger Band, Micro EMA below Macro EMA, and higher timeframe downtrend.
#Exit Logic:
Trailing Stop: Dynamic stop based on entry price percentage.
Opposite Band Cross: Close position if price crosses the opposite band.
Time Exit: Close trades after a specified number of bars.
#Indicators:
Bollinger Bands (SMA basis, standard deviation bands).
Dual EMA trend filter (Macro and Micro EMAs).
Higher timeframe SMA for trend confirmation.
#Parameter Optimization:
The strategy effectively leverages momentum and multi-timeframe trends but requires careful parameter tuning.
1. Test different combinations of bbPeriod, bbStretch, and EMA lengths across various assets to find optimal settings
2. Adjusting the trailing stop value.
The default settings work well for both BTCUSDT and ETHUSDT.
I recommend using it on a 1 hour timeframe with higher timeframe settings: daily.
Rube Goldberg Top/Bottom Finder [theUltimator5]This is what I call the Rube Goldberg Top and Bottom Finder. It is an overly complex method of plotting a simple buy or sell label on a chart.
I utilize several standard TA techniques along with several of my own to try and locate ideal Buy/Sell conditions. I came up with the name because there are way too many conditional variables to come up with a single buy or sell condition, when most standard indicators use simple crossovers or levels.
There are two unique triggers that are calculated using completely independent techniques. If both triggers turn true within a small timeframe between each other, the buy/sell trigger turns true and plots a "buy" or "sell" label on the chart.
This indicator was designed to be fully functioning out of the box and can be customized only if the user wishes to. It is effective on all timeframes, but longer timeframes (daily +) may require signal length adjustment for best results.
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The signals used in the leading trigger are as follows:
(1)RSI
The user can select among any of the following moving averages (base is EMA) (#3) , and have an RSI generated at a user defined length (base is 14). (#4)
SMA, EMA, DEMA, TEMA, WMA, VWMA, SMMA, HMA, LSMA, ALMA
The user can select whether or not the RSI is filtered with the following options:
None, Kalman, Double EMA, ALMA
The filter conditions are hard coded to minimize the amount of selections that the user is required to make to reduce the user interface complexity.
The user can define overbought (base 70) and oversold (base 30) conditions. (#2)
When the RSI crosses above or below the threshold values, the plot will turn red. This creates condition 1 of the leading trigger.
(2) ADX and DI
This portion of the indicator is a derivative of my ADX Divergence and Gap Monitor indicator.
This technique looks at the ADX value as well as for spikes in either +DI or -DI for large divergences. When the ADX reaches a certain threshold and also outpaces a preset ADX moving average, this creates condition 2 of the leading trigger.
There is an additional built-in functionality in this portion of the indicator that looks for gaps. It triggers when the ADX is below a certain threshold value and either the +DI or -DI spike above a certain threshold value, indicating a sudden gap in price after a period of low volatility.
The user can set whether or nor to show when a gap appears on the chart or as a label on the plot below the chart (disabled by default) . If the user chooses to overlay gaps on the chart, it creates a horizontal fill showing the starting point of the gap. The theory here is that the price will return at some point in the near future to the starting point of the gap.
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(3) DI based Multi-Symbol reference and divergence
Part of the script computes both the +DI (positive directional index) and -DI (negative directional index) for the currently selected chart symbol and three reference symbols.
The averaged directional move of the reference symbols are compared to the current ticker on your chart and if the divergence exceeds a certain threshold, then the third condition of the trigger is met.
The components that are referenced are based on what stock/chart you are looking at. The script automatically detects if you are looking at a crypto, and uses a user selectable toggle between Large Cap or Small Cap. (#1) The threshold levels are determined by the asset type and market cap.
The leading trigger highlights under several conditions:
1) All (3) portions of the trigger result in true simultaneously
OR
2) Any of triggers 2 or 3 reach a certain threshold that indicates extreme market/price divergence as well as trigger 1 being overbought or oversold.
AND
3) If the trigger didn't highlight
For the lagging part of the trigger:
The lagging trigger is used as a confirmation after the leading trigger to indicate a possible optimized entry/exit point. It can also be used by itself, as well as the leading indicator.
The lagging indicator utilizes the parabolic Stop And Reverse (SAR). It utilizes the RSI length that is defined in portion 1 of the leading trigger as well as the overbought and oversold thresholds. I have found excellent results in catching reversals because it catches rate-of-change events rather than price reversals alone.
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When both the leading triggers FOLLOWED BY the lagging trigger result in true within a user defined timeframe, then the buy or sell trigger results in true, plotting a label on the chart.
All portions of the leading and lagging indicators can be toggled on or off, but most of them are toggled off by default in order to reduce noise on the plot.
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The leading, lagging, and buy/sell triggers each have built-in alerts that can be toggled on or off in the alert menu.
I have an optional built-in toggle to show green or red dots on the RSI line using two separate RSI lengths that are amplified and plot based on RSI divergence and strength. This can be used as a visual confirmation (or rejection) against the chart overlay plots.
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This indicator is not a strategy, so there are no built-in exits or stop losses.
Simple Volatility ConeThe Simple Volatility Cone indicator projects the potential future price range of a stock based on recent volatility. It calculates rolling standard deviation from log returns over a defined window, then uses a confidence interval to estimate the upper and lower bounds the price could reach over a future time horizon. These bounds are plotted directly on the chart, offset into the future, allowing traders to visualize expected price dispersion under a geometric Brownian motion assumption. This tool is useful for risk management, trade planning, and visualizing the potential impact of volatility.
MVRV | Lyro RS📊 MVRV | Lyro RS is a powerful on-chain valuation tool designed to assess the relative market positioning of Bitcoin (BTC) or Ethereum (ETH) based on the Market Value to Realized Value (MVRV) ratio. It highlights potential undervaluation or overvaluation zones, helping traders and investors anticipate cyclical tops and bottoms.
✨ Key Features :
🔁 Dual Asset Support: Analyze either BTC or ETH with a single toggle.
📐 Dynamic MVRV Thresholds: Automatically calculates median-based bands at 50%, 64%, 125%, and 170%.
📊 Median Calculation: Period-based median MVRV for long-term trend context.
💡 Optional Smoothing: Use SMA to smooth MVRV for cleaner analysis.
🎯 Visual Threshold Alerts: Background and bar colors change based on MVRV position relative to thresholds.
⚠️ Built-in Alerts: Get notified when MVRV enters under- or overvalued territory.
📈 How It Works :
💰 MVRV Calculation: Uses data from IntoTheBlock and CoinMetrics to obtain real-time MVRV values.
🧠 Threshold Bands: Median MVRV is used as a baseline. Ratios like 50%, 64%, 125%, and 170% signal various levels of market extremes.
🎨 Visual Zones: Green zones for undervaluation and red zones for overvaluation, providing intuitive visual cues.
🛠️ Custom Highlights: Toggle individual threshold zones on/off for a cleaner view.
⚙️ Customization Options :
🔄 Switch between BTC or ETH for analysis.
📏 Adjust period length for median MVRV calculation.
🔧 Enable/disable threshold visibility (50%, 64%, 125%, 170%).
📉 Toggle smoothing to reduce noise in volatile markets.
📌 Use Cases :
🟢 Identify undervalued zones for long-term entry opportunities.
🔴 Spot potential overvaluation zones that may precede corrections.
🧭 Use in confluence with price action or macro indicators for better timing.
⚠️ Disclaimer :
This indicator is for educational purposes only. It should not be used in isolation for making trading or investment decisions. Always combine with price action, fundamentals, and proper risk management.
Bollinger Bands Z-ScoreBollinger Bands Z-Score Indicator
This indicator transforms the classic Bollinger Bands into a Z-Score oscillator displayed in a separate pane. It standardizes the Bollinger Bands’ basis line by calculating the Z-Score over a user-defined period, allowing you to see how many standard deviations the price deviates from the mean.
Upper and Lower Fixed Lines: These are set at +2 and -2 Z-Score levels, representing common thresholds for overbought and oversold conditions.
Z-Score Oscillator: The normalized Bollinger Bands oscillate smoothly between these fixed boundaries, providing a clearer perspective on volatility extremes.
Z-Score Table: Displayed on the right side, this table shows the current Z-Score value, along with fixed maximum (+2) and minimum (-2) limits, making it easy to track current momentum and volatility in real-time.
Use Cases:
Identify overextended price moves with standardized volatility measures.
Spot potential reversals or continuation setups by observing the Z-Score crossing key levels.
Complement traditional Bollinger Bands analysis with a statistically normalized perspective.
Input Parameters:
Length: The period used for Bollinger Bands and Z-Score calculation.
MA Type: Choose the moving average type for the basis line (SMA, EMA, SMMA, WMA, VWMA).
StdDev: Multiplier for the standard deviation bands.
Z-Score Length: The lookback period used to compute the mean and standard deviation for Z-Score normalization.
This indicator is perfect for traders seeking a statistically sound and visually clear representation of Bollinger Bands volatility and extremes.
Dskyz (DAFE) GENESIS Dskyz (DAFE) GENESIS: Adaptive Quant, Real Regime Power
Let’s be honest: Most published strategies on TradingView look nearly identical—copy-paste “open-source quant,” generic “adaptive” buzzwords, the same shallow explanations. I’ve even fallen into this trap with my own previously posted strategies. Not this time.
What Makes This Unique
GENESIS is not a black-box mashup or a pre-built template. It’s the culmination of DAFE’s own adaptive, multi-factor, regime-aware quant engine—built to outperform, survive, and visualize live edge in anything from NQ/MNQ to stocks and crypto.
True multi-factor core: Volume/price imbalances, trend shifts, volatility compression/expansion, and RSI all interlock for signal creation.
Adaptive regime logic: Trades only in healthy, actionable conditions—no “one-size-fits-all” signals.
Momentum normalization: Uses rolling, percentile-based fast/slow EMA differentials, ALWAYS normalized, ALWAYS relevant—no “is it working?” ambiguity.
Position sizing that adapts: Not fixed-lot, not naive—not a loophole for revenge trading.
No hidden DCA or pyramiding—what you see is what you trade.
Dashboard and visual system: Directly connected to internal logic. If it’s shown, it’s used—and nothing cosmetic is presented on your chart that isn’t quantifiable.
📊 Inputs and What They Mean (Read Carefully)
Maximum Raw Score: How many distinct factors can contribute to regime/trade confidence (default 4). If you extend the quant logic, increase this.
RSI Length / Min RSI for Shorts / Max RSI for Longs: Fine-tunes how “overbought/oversold” matters; increase the length for smoother swings, tighten floors/ceilings for more extreme signals.
⚡ Regime & Momentum Gates
Min Normed Momentum/Score (Conf): Raise to demand only the strongest trends—your filter to avoid algorithmic chop.
🕒 Volatility & Session
ATR Lookback, ATR Low/High Percentile: These control your system’s awareness of when the market is dead or ultra-volatile. All sizing and filter logic adapts in real time.
Trading Session (hours): Easy filter for when entries are allowed; default is regular trading hours—no surprise overnight fills.
📊 Sizing & Risk
Max Dollar Risk / Base-Max Contracts: All sizing is adaptive, based on live regime and volatility state—never static or “just 1 contract.” Control your max exposures and real $ risk. ATR will effect losses in high volatility times.
🔄 Exits & Scaling
Stop/Trail/Scale multipliers: You choose how dynamic/flexible risk controls and profit-taking need to be. ATR-based, so everything auto-adjusts to the current market mode.
Visuals That Actually Matter
Dashboard (Top Right): Shows only live, relevant stats: scoring, status, position size, win %, win streak, total wins—all from actual trade engine state (not “simulated”).
Watermark (Bottom Right): Momentum bar visual is always-on, regime-aware, reflecting live regime confidence and momentum normalization. If the bar is empty, you’re truly in no-momentum. If it glows lime, you’re riding the strongest possible edge.
*No cosmetics, no hidden code distractions.
Backtest Settings
Initial capital: $10,000
Commission: Conservative, realistic roundtrip cost:
15–20 per contract (including slippage per side) I set this to $25
Slippage: 3 ticks per trade
Symbol: CME_MINI:NQ1!
Timeframe: 1 min (but works on all timeframes)
Order size: Adaptive, 1–3 contracts
No pyramiding, no hidden DCA
Why these settings?
These settings are intentionally strict and realistic, reflecting the true costs and risks of live trading. The 10,000 account size is accessible for most retail traders. 25/contract including 3 ticks of slippage are on the high side for NQ, ensuring the strategy is not curve-fit to perfect fills. If it works here, it will work in real conditions.
Why It Wins
While others put out “AI-powered” strategies with little logic or soul, GENESIS is ruthlessly practical. It is built around what keeps traders alive:
- Context-aware signals, not just patterns
- Tight, transparent risk
- Inputs that adapt, not confuse
- Visuals that clarify, not distract
- Code that runs clean, efficient, and with minimal overfitting risk (try it on QQQ, AMD, SOL, etc. out of the box)
Disclaimer (for TradingView compliance):
Trading is risky. Futures, stocks, and crypto can result in significant losses. Do not trade with funds you cannot afford to lose. This is for educational and informational purposes only. Use in simulation/backtest mode before live trading. No past performance is indicative of future results. Always understand your risk and ownership of your trades.
This will not be my last—my goal is to keep raising the bar until DAFE is a brand or I’m forced to take this private.
Use with discipline, use with clarity, and always trade smarter.
— Dskyz , powered by DAFE Trading Systems.
(DAFE) DEVMA - Crossover (Deviation Moving Average) (DAFE) DEVMA - Crossover (Deviation Moving Average)
Let’s keep pushing the edge. After the breakthrough of Deviation over Deviation (DoD)—which gave traders a true lens into volatility’s hidden regime shifts—many asked: “What’s next?” The answer is DEVMA: a crossover engine built not on price, but on the heartbeat of the market itself.
Why is this different?
DEVMA isn’t just a moving average crossover. It’s a regime detector that tracks the expansion and contraction of deviation—giving you a real-time readout of when the market’s energy is about to shift. This is the next step for anyone who wants to anticipate volatility, not just react to it.
What sets DEVMA apart:
Volatility-First Logic:Both fast and slow lines are moving averages of deviation, not price. You’re tracking the market’s “energy,” not just its direction. This is the quant edge that most scripts miss.
Regime-Colored Lines:
The fast and slow DEVMA lines change color in real time—green/aqua for expansion, maroon/orange for contraction—so you can see regime shifts at a glance.
Quant-Pro Visuals:
Subtle glow, clean cross markers, and a minimalist dashboard keep your focus on what matters: the regime, not the noise.
Static Regime Thresholds:
Reference lines at 1.5 and 0.5 (custom colors) give you instant context for “normal” vs. “extreme” volatility states.
No Price Chasing:
This isn’t about following price. It’s about anticipating the next volatility regime—before the crowd even knows what’s coming.
How this builds on DoD:
DoD showed you when volatility itself was about to change. DEVMA takes that insight and turns it into a crossover engine—so you can see, filter, and act on regime shifts in real time. If DoD was the radar, DEVMA is the navigation system.
Inputs/Signals—explained for clarity:
Deviation Lookback:
Controls the sensitivity of the regime detector. Shorter = more signals, longer = only the rarest events.
Fast/Slow DEVMA Lengths:
Fine-tune how quickly the regime lines react. Fast for scalping, slow for swing trading.
Source Selection:
Choose from price, volume, volatility, or VoVix. Each source gives you a different lens on market stress. VoVix is for those who want to see the “regime quake” before the aftershocks.
VoVix Parameters:
Fine-tune the volatility-of-volatility engine for your market. Lower ATR Fast = more responsive; higher ATR Slow = more selective.
Bottom line:
DEVMA is for those who want to see the market’s heartbeat, not just its shadow. Use it to filter your trades, time your entries, or simply understand the market’s true rhythm. Every input is there for a reason. Every plot is a direct readout of the quant logic. Use with discipline, and make it your own.
Disclaimer:
Trading is risky. This script is for research and informational purposes only, not financial advice. Backtest, paper trade, and know your risk before going live. Past performance is not a guarantee of future results.
*Updated the Dashboard/Metrics Display for better visibility
Use with discipline. Trade your edge.
— Dskyz, for DAFE Trading Systems
ZMVZMV-STRATEGY
Z – Zero-Based Thinking
At the core of the ZMV-STRATEGY lies zero-based thinking: the practice of assessing actions, projects, or goals as if starting from scratch. This principle encourages:
Eliminating outdated assumptions
Prioritizing current relevance over historical momentum
Making decisions based on present and future potential, not sunk costs
M – Momentum Mapping
Momentum is essential for sustained progress. The "M" emphasizes:
Identifying key areas where traction exists
Mapping energy flows within a team, project, or market
Leveraging small wins to catalyze exponential growth
V – Value Alignment
Finally, the “V” represents value alignment, which ensures that:
Every move aligns with core values and purpose
Stakeholders are engaged through shared vision
Ethical, meaningful impact is prioritized alongside metrics
EMA Validation4 EMA with Support/Resistance Validation (EMA-V)
This indicator displays four Exponential Moving Averages (EMAs) with customizable periods (default: 21, 50, 100, 200) and visually validates their roles as support or resistance.
Each EMA changes color based on its behavior: green for respected support , red for respected resistance, or default colors when unvalidated.
Ideal for traders seeking to identify reliable support and resistance levels across multiple timeframes.
Adaptive Dual MA Trend FilterAdaptive Dual MA Trend Filter is a versatile Pine Script™ indicator that delivers clear, reliable trend signals using customizable moving averages:
Dual‑Stage Filtering – Apply any traditional MA (SMA, EMA, VWMA, HMA, RMA, TEMA, DEMA, FRAMA, TRIMA) or advanced smoothing (ALMA, T3) as your “main” and “filter” MAs. The filter MA is double‑smoothed for noise suppression, then converted into a robust “double‑filtered” baseline.
Flexible Inputs – Select lengths, sources (close, high, low, hl2), offsets, sigma, and volume factors to tailor the responsiveness and smoothness to your favorite timeframe or asset class.
Intuitive Signals – The script detects confirmed bullish (green) and bearish (red) trend shifts as:
Circle marker on the MA line
Triangle arrows below/above bars
Full candles and MA line colored by current trend
Clean Overlay – Works directly on your price chart, with optional semi‑transparent fills for extra visual clarity.
Theme Support – Choose from Vibrant, Pastel, Neon, Classic, Monochrome, Solarized, or Material palettes for seamless chart styling.
Ideal for swing traders and intraday scalpers alike, Multi‑Source Double‑Filter Trend offers both “set‑and‑forget” simplicity and deep customization for power users.
Usage
Add to chart → Inputs → tweak MA types/lengths
Watch for color changes and markers
Combine with volume or momentum filters for entry confirmation
Enjoy clearer trend identification and smoother trade signals!
Disclaimer
This script is for educational and informational purposes only. Not financial advice. Use at your own risk.
Alert TrendThis indicator is designed to function as a dynamic BIAS tool but can be adapted to various strategies depending on user needs.
Key Features and Integration:
Personally, I pair it with the "EMA Suite" indicator, as my strategy revolves around Fibonacci-based moving averages. The indicator uses EMA 55 and EMA 233 as trend references, triggering a trend shift when a candle closes fully above or below these levels. To maintain structural integrity, the EMA values are not user-configurable in the settings: adjustments require direct script modification (e.g., switching to EMA 50 and EMA 200, widely recognized reference levels), this ensures logical consistency for advanced users familiar with Pine Script.
Output Signals and Interpretation:
The indicator generates four distinct signals:
1. Uptrend: Candle closes above both EMA 55 and EMA 233.
2. Weak Uptrend: Candle closes above EMA 55 but below EMA 233.
3. Downtrend: Candle closes below both EMA 55 and EMA 233.
4. Weak Downtrend: Candle closes below EMA 55 but above EMA 233.
The area between the two EMAs represents a "complex zone" where price action contradicts higher timeframe trends. To resolve ambiguity, combine this indicator with a primary timeframe (e.g., H4) and a confirmation timeframe (e.g., H1). In smaller timeframes may also serve as entry signals, a feature currently under exploration for automation.
Alert System and Strategy Integration:
The indicator includes customizable alerts for all four signals collectively or individually, streamlining integration into Strategy scripts. This flexibility enhances adaptability for backtesting or live trading.
Critical Note:
Configure the indicator to display exclusively on the selected timeframe. Higher intervals fail to render all signals due to overlapping visualizations, distorting analysis. To resolve this, set the visibility parameter to "Visibility on intervals/Current interval and below" in the chart settings. This ensures clarity and preserves signal accuracy.
Development Status and Collaboration:
As part of an ongoing project, this tool is already integrated into my personal strategy. While functional and publicly shareable, further refinements are planned. Though not a professional developer, I utilize Deepseek for coding assistance and possess sufficient Pine Script literacy to oversee the logic. Feedback, suggestions, and collaborations are welcome to optimize its utility.
I hope this tool proves valuable to fellow traders navigating multi-timeframe analysis and trend confirmation.
SR Nube 1.1The SR Nube 1.1 indicator offers a comprehensive perspective on price action through the strategic combination of three key elements: a dynamic cloud based on two Volume Weighted Moving Averages (VWMA), a consistent reference Exponential Moving Average (EMA) across all timeframes, and an intuitive information table.
The Dynamic Cloud: This cloud is calculated using two VWMA with lengths that automatically adjust based on the chart's timeframe. This dynamic adaptation allows for the identification of relevant support and resistance zones across different timeframes, providing contextual insight into potential price movement. The cloud visualizes areas of volume confluence, helping traders pinpoint zones where buying or selling pressure may be significant.
The Consistent Reference EMA: An EMA with a specific length (calculated to be representative of a higher timeframe, such as 1 hour, and displayed consistently across all timeframes) is overlaid on the chart. This EMA serves as a macro trend guide and a constant visual reference point, making it easier to identify the overall market direction regardless of the active trading timeframe. Its consistency across timeframes helps maintain perspective and align trades with the dominant trend.
The Information Table: Located in the top-left corner of the chart, a concise table summarizes the current price status relative to the cloud (on the 20-minute timeframe, as a reference for the main strategy) and the price's position concerning the reference EMA (based on the 1-hour timeframe). This table provides a quick, color-coded overview of trend alignment across multiple key timeframes, which can assist traders in making more informed decisions.
Utility and Underlying Concepts:
This indicator is designed for traders seeking a tool that combines volume analysis (through the VWMA in the cloud) with a higher timeframe trend reference (the consistent EMA). The dynamic cloud helps identify potential entry and exit zones within the trading timeframe, while the reference EMA provides a directional filter. The information table simplifies the evaluation of trend confluence across multiple timeframes, potentially increasing the probability of successful trades.
The underlying strategy is based on the idea of trading in the direction of volume and in alignment with a higher timeframe trend, using the cloud to identify value areas and the EMA as a key directional filter. The information table acts as a quick visual aid for assessing this alignment.
How to Use:
Add the "SR Nube 1.1" indicator to your TradingView chart.
Observe the dynamic cloud to identify potential support and resistance zones on your trading timeframe.
Use the blue EMA as a guide for the overall market trend.
Consult the information table in the top-left corner to see the price alignment with the 20-minute cloud and the 1-hour EMA. The colors will provide a quick indication of the potential direction.
Look for confluence between the cloud signals on your trading timeframe, the price's position relative to the EMA, and the information provided in the table to identify potential entry and exit opportunities.
EMA Break & Retest + Trend TableThis script is designed to identify potential buy and sell trading opportunities based on 21 EMA (Exponential Moving Average) break and retest patterns, with confirmation from multi-timeframe trend analysis. It combines actionable signal generation with a clean, real-time trend overview table.
✅ 1. EMA Break & Retest Logic
Detects when the price crosses above or below the 21 EMA and then closes in the direction of the breakout.
Generates buy signals on upward break/retest, and sell signals on downward break/retest.
✅ 2. Multi-Timeframe Confirmation
Filters signals using higher timeframe trends to avoid false entries.
Buy signals are shown only if the 1H or 4H trend is bullish.
Sell signals are shown only if the 1H or 4H trend is bearish.
✅ 3. Visual Signal Plotting
Displays green "BUY" labels below bars and red "SELL" labels above bars.
Users can toggle buy/sell signals on or off with checkboxes.
✅ 4. Alerts
Built-in alertcondition() functions allow traders to set real-time alerts when buy or sell signals are triggered.
✅ 5. Multi-Timeframe Trend Table
A dynamic table appears in the top-right corner showing trend status across:
Daily (D)
4 Hour (4H)
1 Hour (1H)
15 Minute (15M)
5 Minute (5M)
Each timeframe is marked as Bullish (green) or Bearish (red) depending on the current price vs. 21 EMA.
The latest signal (“BUY” / “SELL” / “—”) is displayed at the bottom of the table.
Triple EMA Bundle (50, 100, 200) - Osbrah CRG📈 Advanced EMA Indicator – 50/100/200
This custom-built indicator displays the 50, 100, and 200 Exponential Moving Averages (EMAs), giving traders a powerful visual tool to identify key trend directions, dynamic support/resistance levels, and potential market reversals.
Designed for both beginners and advanced users, this tool offers extensive customization options:
* Select which EMAs to display (50, 100, 200)
* Adjust colors, line styles, and thickness
* Choose between different price sources (close, open, hl2, etc.)
* Set custom EMA lengths to fit your strategy
Use Cases:
* Spot trend direction and strength at a glance
* Identify key zones of support and resistance
* Confirm entries/exits based on EMA crossovers or rejections
* Align your trades with higher timeframe trends
Whether you're a swing trader or a scalper, this indicator helps you stay in sync with the market by bringing clarity to long-term momentum zones.
