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Medie mobili

StoRsi# StoRSI Indicator: Combining RSI and Stochastic with multiTF ## Overview The StoRSI indicator combines Relative Strength Index (RSI) and Stochastic oscillators in a single view to provide powerful momentum and trend analysis. By displaying both indicators together with multi-timeframe analysis, it helps traders identify stronger signals when both indicators align. ## Key Components ### 1. RSI (Relative Strength Index) ### 2. Stochastic Oscillator ### 3. EMA (Exponential Moving Average) ### 4. Multi-Timeframe Analysis ## Visual Features - **Color-coded zones**: Highlights overbought/oversold areas - **Signal backgrounds**: Shows when both indicators align - **Multi-timeframe table**: Displays RSI, Stochastic, and trend across timeframes - **Customizable colors**: Allows full visual customization ## Signal Generation (some need to uncomment in code) The indicator generates several types of signals: 1. **RSI crosses**: When RSI crosses above/below overbought/oversold levels 2. **Stochastic crosses**: When Stochastic %K crosses above/below overbought/oversold levels 3. **Combined signals**: When both indicators show the same condition 4. **Trend alignment**: When multiple timeframes show the same trend direction ## Conclusion The StoRSI indicator provides a comprehensive view of market momentum by combining two powerful oscillators with multi-timeframe analysis. By looking for alignment between RSI and Stochastic across different timeframes, traders can identify stronger signals and filter out potential false moves. The visual design makes it easy to spot opportunities at a glance, while the customizable parameters allow adaptation to different markets and trading styles. For best results, use this indicator as part of a complete trading system that includes proper risk management, trend analysis, and confirmation from price action patterns.
Indicatore Pine Script®
di kasapop56394
2
Elder Envelope V2Based on the public script by idu. (Elder Impulse System with AutoEnvelope combined by idu) I enhanced it by adding: - second set of envelope bands -alerts for Short and Center EMAs cross - fill between EMAs I find this strategy good for swing trading on daily timeframes to determine overbought/oversold conditions. When stock is near 2nd upper band - take profits/short, when near lower -2nd band cover short/buy longs. Bull bear trend is determined by the short and long(center) emas cross. In rangebound flat trends gives false signals. Best edge is when near extremes of the bands.
Indicatore Pine Script®
di PvloFF
1
Candle % High/Low Bar + HL Order + MA by Barty&PitPapcioWhat does the indicator show? The "Candle % High/Low Bar + HL Order + MA by Barty&PitPapcio" indicator displays the percentage deviation of each candle’s high and low relative to its open price. The zero line represents the candle’s open — bars above zero show upward movement from the open (to high), bars below zero show downward movement (to low). Additionally, the indicator plots a dot above or below each bar indicating which came first during the candle — the high or the low — based on data from a lower timeframe two steps below the current chart (for example, on a 1-hour chart it uses 15-minute data). Finally, the indicator calculates and plots a user-selectable moving average (EMA, SMA, or WMA) of these "first high or low" signals, helping identify trends whether the first move is more often upwards or downwards. Where do the data come from? Percentage values are calculated directly from the current chart’s candles: highPerc=(High−Open)/Open×100%, lowPerc=(Low−Open)/Open×100% The timing of the first high or low for each candle is retrieved from a lower timeframe, stepping down two levels from the current timeframe (e.g. from 1H to 15 min), providing better precision in detecting the order of highs and lows that may be blurred on higher timeframes. Additional features: Full customization of colors for bars, dots, zero line, grid, and thicknesses. Background grid with adjustable scale and style. Safety checks for missing lower timeframe data. A moving average smoothing the sequence of first high/low signals to reveal directional tendencies. Suggested strategy for technical analysis support Identify dominant candle direction: If the dot often appears above the bar (first high), it indicates buying pressure; if below (first low), selling pressure dominates. Use percentage deviations: Large percent bars indicate heightened volatility and potential reversal points. Moving average on order signals: The EMA of high/low first signals smooths the noise, showing the dominant trend in the sequence of price moves, useful for filtering other signals. Combine with other tools: This indicator can act as a directional filter on multiple timeframes, synergizing well with momentum indicators, RSI, or support/resistance levels to confirm move strength. Lots of love, Bartosz
Indicatore Pine Script®
di B4RTY
4 EMA Modified [Ryu_xp] - Enhanced4 EMA Modified – Enhanced (Pine v6) A highly configurable, four-line exponential moving average (EMA) overlay built in Pine Script v6. This indicator empowers traders to monitor short- and long-term trends simultaneously, with the ability to toggle each EMA on or off and adjust its period and data source—all from a single, inline control panel. Key Features: Pine Script v6: updated to leverage the latest performance improvements and language features. Four EMAs: EMA 3 for ultra-short momentum (white, medium line) EMA 10 for short-term trend (light blue, thin line) EMA 55 for intermediate trend (orange, thicker line) EMA 200 for long-term trend (dynamic green/red, thickest line) Inline Controls: Each EMA has its own checkbox, length input, and source selector arranged on a single line for fast configuration. Dynamic Coloring: EMA 200 switches to green when price is above it (bullish) and red when price is below it (bearish). Toggle Visibility: Enable or disable any EMA instantly without removing it from your chart. Clean Overlay: All EMAs plotted in one pane; ideal for multi-timeframe trend confluence and crossover strategies. Inputs: Show/Hide each EMA EMA Length and Source for periods 3, 10, 55, and 200 Usage: Add the script to any price chart. Use the inline checkboxes to show only the EMAs you need. Adjust lengths and sources to fit your instrument and timeframe. Watch for crossovers between EMAs or price interactions with EMA 200 to confirm trend shifts. This open-source script offers maximum flexibility for traders seeking a customizable EMA toolkit in one simple overlay.
Indicatore Pine Script®
di ProfitXbtc
Smooth Fibonacci BandsSmooth Fibonacci Bands This indicator overlays adaptive Fibonacci bands on your chart, creating dynamic support and resistance zones based on price volatility. It combines a simple moving average with ATR-based Fibonacci levels to generate multiple bands that expand and contract with market conditions. ## Features - Creates three pairs of upper and lower Fibonacci bands - Smoothing option for cleaner, less noisy bands - Fully customizable colors and line thickness - Adapts automatically to changing market volatility ## Settings Adjust the SMA and ATR lengths to match your trading timeframe. For short-term trading, try lower values; for longer-term analysis, use higher values. The Fibonacci factors determine how far each band extends from the center line - standard Fibonacci ratios (1.618, 2.618, and 4.236) are provided as defaults. ## Trading Applications - Use band crossovers as potential entry and exit signals - Look for price bouncing off bands as reversal opportunities - Watch for price breaking through multiple bands as strong trend confirmation - Identify potential support/resistance zones for placing stop losses or take profits Fibonacci Bands combines the reliability of moving averages with the adaptability of ATR and the natural market harmony of Fibonacci ratios, offering a robust framework for both trend and range analysis.
Indicatore Pine Script®
di ShadowTraderBali
1
Buy/Sell Signals (Dynamic v2)//@version=5 indicator(title="Buy/Sell Signals (Dynamic v2)", shorttitle="Buy/Sell Dyn v2", overlay=true) // Input for moving average lengths lengthMA = input.int(20, title="Moving Average Length") lengthEMA = input.int(5, title="Exponential Moving Average Length") // Calculate Moving Averages ma = ta.sma(close, lengthMA) ema = ta.ema(close, lengthEMA) // --- Buy Signal Conditions --- buyMarketBelowMA = close < ma buyMarketBelowEMA = close < ema buyEMABelowMA = ema < ma buyMarketCondition = buyMarketBelowMA and buyMarketBelowEMA and buyEMABelowMA buyFollowingHighNotTouchedEMA = high < ema buyCurrentCrossCloseAboveFollowingHigh = high > high and close > high buySignalCondition = buyMarketCondition and buyFollowingHighNotTouchedEMA and buyCurrentCrossCloseAboveFollowingHigh // Plot Buy Signal plotshape(buySignalCondition, title="Buy Signal", style=shape.triangleup, location=location.belowbar, color=color.green, size=size.small) // --- Sell Signal Conditions (Occurring After a Buy Signal Sequence) --- sellMarketAboveMA = close > ma sellMarketAboveEMA = close > ema sellEMAAboveMA = ema > ma sellMarketConditionSell = sellMarketAboveMA and sellMarketAboveEMA and sellEMAAboveMA var bool buySignalOccurredRecently = false if buySignalCondition buySignalOccurredRecently := true sellSignalCondition = buySignalOccurredRecently and sellMarketConditionSell and close < close // Plot Sell Signal plotshape(sellSignalCondition, title="Sell Signal", style=shape.triangledown, location=location.abovebar, color=color.red, size=size.small) // Reset the buySignalOccurredRecently only after a sell signal if sellSignalCondition buySignalOccurredRecently := false // Plot the Moving Averages for visual reference plot(ma, color=color.blue, title="MA") plot(ema, color=color.red, title="EMA")
Indicatore Pine Script®
di T33608
Moving Average Price Deviation Spread **Moving Average Price Deviation Spread (MA Dev)** This indicator visualizes the deviation of price from its exponential moving average (EMA) and scales it within dynamic upper and lower bounds. The core logic measures the smoothed spread between price and EMA, then calculates standard deviation over a rolling window to define statistical thresholds. * **Spread**: EMA of (Close - EMA), highlighting directional bias. * **Upper/Lower Bounds**: EMA of ±1.96 \* standard deviation of the spread, framing high/low deviation zones. * **Use Case**: Spot overextended conditions, mean reversion setups, or volatility-driven breakouts. Ideal for timing entries and exits around price extremes.
Indicatore Pine Script®
di gunnjac
Multi EMADescription – Multi-EMA Indicator (10/21/66/110/200 EMA) This indicator combines five essential Exponential Moving Averages (EMAs) into one tool: the 10, 21, 66, 110, and 200 EMA. It's designed for traders who want a clear view of short-, medium-, and long-term trends all in one place. With this indicator, you can: Quickly assess trend direction across multiple timeframes Identify dynamic support and resistance levels Spot EMA crossovers that may signal potential trade entries or exits Each EMA is color-coded and can be toggled on or off individually, giving you full control over your chart setup.
Indicatore Pine Script®
di schwarzman
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.
Indicatore Pine Script®
di mihakralj