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MA 50 / 200 Cross Dashboard [Matrix Trading Lab]Simple and powerful Moving Average crossover dashboard built for traders who want clarity. This indicator tracks the classic 50 MA and 200 MA relationship and displays: ✔ Golden Cross signals ✔ Death Cross signals ✔ Last crossover date & time ✔ Current trend alignment ✔ Bars since last signal ✔ Clean dashboard interface Perfect for swing traders, trend followers and investors watching long-term market direction. Use it on stocks, crypto, forex and indices. Interested in this system or looking for more advanced professional indicators? For more details, please contact me directly. Email: goktugozi@hotmail.com
Wskaźnik Pine Script®
od goktugozi26
Iterative Periodic EnvelopeThe Iterative Periodic Envelope is a phase-conditioned kernel estimator with endogenous dispersion modeling, implemented as a Nadaraya–Watson estimator under a canonical periodic kernel. The periodic kernel defines similarity through cyclical phase alignment rather than temporal proximity or multi-scale distance decay. Observations contribute to the estimator based on their position within a repeating cycle structure, emphasizing structural recurrence over linear time dependence. The indicator computes a latent equilibrium using a kernel-weighted mean and a dispersion measure using kernel-weighted variance under the same weighting structure. The resulting envelope reflects cycle-consistent deviation, rather than a conventional volatility band. All values are computed exclusively on closed historical bars using a bounded lookback window, ensuring non-repainting behavior. This indicator belongs to a broader class of iterative kernel-based envelopes that includes Gaussian and Rational Quadratic variants. All share a common Nadaraya–Watson estimation framework, differentiated by their kernel. TRADING USES The Iterative Periodic Envelope is best interpreted as a cycle-aware structural estimator rather than a volatility-based band. Equilibrium Tracking The latent equilibrium represents the phase-conditioned central tendency of price under periodic similarity weighting. Oscillations around this level reflect movement within a repeating structural cycle rather than directional drift. Cycle Regime Structure The envelope emphasizes repeating structural behavior through phase recurrence weighting. Changes in symmetry, amplitude, or persistence of oscillation around the latent equilibrium may indicate transitions between cyclical regimes. Mean Reversion Within Cycles When a stable periodic structure is present, deviations from the latent equilibrium may revert toward phase-consistent levels. This supports mean-reversion behavior that is conditioned on cycle structure rather than purely statistical dispersion. Structural Extremes Extreme deviations relative to the envelope correspond to phase-inconsistent states where cyclical structure becomes stretched or destabilized. These conditions often precede transitions such as cycle inversion, expansion, or compression. State Estimation The system defines a latent equilibrium as the inferred central cyclical state, with dispersion derived from kernel-weighted variance under identical periodic similarity constraints. This produces a structurally consistent representation of market state. PERIODIC ENVELOPE CONSTRUCTION The envelope is constructed using kernel-weighted variance under the same periodic similarity measure used to estimate the latent equilibrium. The latent equilibrium defines the central state estimate and kernel-weighted variance defines dispersion under identical weighting, producing an endogenously determined envelope. The band width is fixed at ±1 kernel standard deviation with no multiplier, ensuring dispersion remains an intrinsic property of the periodic similarity structure rather than an externally imposed scaling parameter. THEORY The periodic kernel defines similarity in terms of cyclical phase recurrence rather than linear temporal distance. Observations contribute to the estimator based on alignment within a repeating cycle structure. The estimator is formulated as a Nadaraya–Watson kernel regression under a canonical periodic kernel, where weights are defined as: k(i) = exp( -2 · sin²(πi / p) / L² ) Where: p = period (cycle length) L = lookback window (bandwidth parameter; effective smoothing scales with L²) In this MacKay consistent formulation, the lookback window acts as a bandwidth control parameter, governing phase selectivity and structural smoothing. As L increases, the kernel becomes broader, producing stronger smoothing and reduced phase sensitivity. As L decreases, phase selectivity increases and the estimator becomes more locally sensitive to cyclical alignment. This induces a cyclical similarity structure in which influence concentrates at recurring phase intervals. The resulting estimator defines a latent equilibrium governed by phase alignment rather than temporal proximity. This formulation can be interpreted as a periodic extension of kernel regression on a circular phase manifold. CALIBRATION Length (Lookback / Bandwidth) Controls structural depth of the estimator and acts as the primary kernel bandwidth parameter. - 50–100: high responsiveness, short-cycle sensitivity - 150–250: balanced regime stability - 300+: strong structural smoothing, reduced sensitivity to phase noise Period (Cycle Length) Defines the recurrence interval of the kernel and governs phase alignment and cyclical structure. Commonly aligns with dominant market rhythms such as intraday or macro-cycle structure. - Lower values: faster cycle sensitivity - Higher values: slower, broader structural cycles Start At Bar Offsets the kernel window backward from the most recent bars and excludes newer observations from the estimator. This ensures all calculations are based strictly on closed historical data and preserves non-repainting behavior. MARKET USAGE Stock, Forex, Crypto, Commodities, and Indices. Performance is dependent on the presence of stable cyclical structure; in regimes lacking periodic coherence, the estimator converges toward a smoother, low-information state.
Wskaźnik Pine Script®
od SensitiveSuit
Trend filter [Jamallo](2025) This is a trend qualification and filtering tool. Breakdown: Signal Line — Vervoort ATR Trailing Stop The signal line is an implementation of the ATR Trailing Stop developed by Sylvain Vervoort, first published in Technical Analysis of Stocks & Commodities, Volume 27, Issue 6 (June 2009), in his article "Average True Range Trailing Stops" . The stop uses a modified ATR calculated on the average price (O+H+L+C)/4 rather than the plain close. Baseline — Kalman Filter The baseline is derived from the Kalman Filter, a recursive algorithm developed in 1960 by Rudolf E. Kálmán, a Hungarian-American engineer and mathematician. Originally developed for aerospace applications in the early 1960s — including guidance of the Apollo spacecraft. In this indicator, the Kalman Filter acts as a smooth, low-lag trend estimate. Rather than applying fixed weights to historical data like conventional moving averages, it dynamically adjusts its trust between the predicted trend and observed prices. Bands — Volatility Channels Two pairs of bands (inner and outer) are built around the Kalman baseline using a 200-bar WMA.
Wskaźnik Pine Script®
od Jamallo22
TRSI Div+ combines RSI overbought/oversold context, regular + hidden divergence signals, and a trend “ribbon” filter into one clean overlay. Divergences are drawn directly on price: Bullish Divergence (price makes a lower low while RSI makes a higher low) Bearish Divergence (price makes a higher high while RSI makes a lower high) Hidden Bullish / Hidden Bearish divergences (trend-continuation variants) Pivot dots help visualize the swing points used to confirm divergence. Optional OB/OS bar tint highlights potential exhaustion zones. The Ribbon Trend provides a quick visual read on market direction (up / down / neutral) and can be used as a confirmation filter. Multiple themes let you quickly change the look of all plotted elements. Note: This indicator is for educational and informational purposes only and does not constitute financial advice. Always manage risk. 4 Ways to Use It for Trading Bullish #1 | Bullish Divergence + Ribbon Flip (Reversal Entry) Setup Price prints a lower low RSI prints a higher low → Bull Div prints Ribbon transitions to Up (or at least neutral) Trade idea Entry: On the next bullish candle close after the divergence prints, ideally as ribbon turns up. Stop: Below the divergence swing low. Target: Prior swing high / resistance or trail as ribbon stays up. Bullish #2 | Hidden Bullish Divergence (Trend Continuation) Setup Ribbon is Up Price makes a higher low RSI makes a lower low → Hid Bull prints Trade idea Entry: On a break above the candle that forms after the hidden divergence. Stop: Below the higher-low pivot. Target: New high extension, or scale out at prior highs while ribbon remains up. Bearish #1 | Bearish Divergence + Ribbon Down (Reversal Entry) Setup Price prints a higher high RSI prints a lower high → Bear Div prints Ribbon transitions to Down (or at least neutral → down) Trade idea Entry: On the next bearish candle close after divergence, ideally with ribbon turning down. Stop: Above the divergence swing high. Target: Prior swing low / support or trail as ribbon stays down. Bearish #2 | Hidden Bearish Divergence (Trend Continuation) Setup Ribbon is Down Price makes a lower high RSI makes a higher high → Hid Bear prints Trade idea
Wskaźnik Pine Script®
od sudhirfull
CryptoRSICryptoRSI – Presets This strategy is inspired by the work of Pavel Kýček (Robuxio). CryptoRSI is a rule-based approach built around RSI extremes with a simple market regime filter . This is a systematic framework. It does not guarantee results. Crypto markets are volatile and can produce long streaks of false signals. Core idea The strategy seeks to participate in strong upside phases after momentum becomes extreme. It does so by combining: An RSI-based entry rule A regime filter based on BTC vs its EMA A mechanical exit rule (WMA or RSI threshold, depending on preset) Presets Only one input is exposed: the Preset . Original – smoother and more selective. Fewer signals, typically higher quality. Agressif – faster RSI and higher thresholds. More signals, more noise. Entry rule RSI is computed on the traded symbol. In Original , RSI is smoothed (SMA smoothing) to reduce noise. A long entry triggers when RSI exceeds the Buy Threshold . Market filter (BTC regime) A market filter is enabled by default. The filter uses BITSTAMP:BTCUSD on the Daily timeframe. Trades are allowed only when BTC is above its EMA(50) . When the filter is not satisfied, the background turns red. Exit rule Exit depends on the preset: Original : exit when price crosses below a WMA(50) . Agressif : exit when RSI drops below the Sell Threshold . Chart elements RSI plot (smoothed or raw depending on preset) Horizontal lines showing Buy / Sell thresholds Background regime highlighting the market filter Important notes This strategy is long-only. Backtests assume frictionless execution (no fees, no slippage). Real-world results may differ significantly. The objective is to provide a consistent decision framework, not to predict tops or bottoms.
Strategia Pine Script®
od Konscio
Moving Average Weighted PlusWMA+ is a weighted moving average with an optional smoothing layer, using the same smoothing options found in TradingView’s built-in Moving Average scripts, such as their provided Simple Moving Average (SMA). I have often wondered why this smoothing option is not included in all of the TradingView provided moving averages. WMA+ now brings that same smoothing menu and flexible Bollinger Band option to a WMA. The main plot is a standard Weighted Moving Average (WMA) of the selected source (Close by default). When Smoothing is enabled, a secondary moving average is applied to the WMA output (not to price), which can reduce noise and make the baseline easier to interpret. An optional Bollinger Bands overlay can also be applied to the smoothing line, providing a volatility envelope around the smoothed WMA. As displayed on the chart above. How it works WMA: Calculates a weighted moving average of the selected Source using the chosen Length. Smoothing (optional): Applies a second MA to the WMA output. Available types: - SMA - EMA - SMMA (RMA) - WMA - VWMA - SMA + Bollinger Bands (bands built from the smoothing length and standard deviation) Bollinger Bands (optional): When "SMA + Bollinger Bands" is selected, upper/lower bands are plotted around the smoothing line using BB StdDev. Inputs: Length: WMA length. Source: Price source used for the WMA. Offset: Shifts the WMA plot left/right. Smoothing → Type: Enables/disables smoothing and selects the smoothing MA type. Smoothing → Length: Length for the smoothing MA. Smoothing → BB StdDev: Standard deviation multiplier for Bollinger Bands (only when SMA + Bollinger Bands is selected). Notes: Smoothing is computed from the WMA result (WMA → smoothing MA), which preserves the character of the WMA while allowing an additional layer of filtering. The smoothing UI/logic is adapted from TradingView’s built-in Moving Average scripts for consistency and familiarity. Attribution: Smoothing input structure and smoothing/Bollinger code are adapted from TradingView’s built-in Moving Average scripts and applied to the WMA. Disclaimer: This script is provided for educational and informational purposes only and is not financial advice. If you would like to read TradingView's information on the WMA you can find that here: www.tradingview.com
Wskaźnik Pine Script®
od DigitalLRS
Rhokeo-VW-RSI Histogram for Cumulative Delta by ZeiirmanRhokeo-VW-RSI Histogram: Volume-Weighted Momentum (use with Cumulative Delta from Zeiierman) Note that Cumulative Delta is a paid indicator. Overview: The Rhokeo-VW-RSI Histogram is a momentum oscillator designed to filter out market noise by integrating volume directly into the RSI calculation. Unlike a standard RSI, which only considers price change, this indicator weights those changes by the volume occurring at the time. It creates a momentum profile in the form of a Histogram. If the price moves on high volume, the indicator reflects that strong market interest through its volume-weighted gain and loss calculations. It is particularly effective as a complementary filter for “Cumulative Delta” from Zeiierman to confirm the strength behind a move before you enter a trade. How It Works The indicator operates on a normalized scale of -1.0 to +1.0 for easier visual interpretation and compatibility with Cumulative Delta indicator: • The Volume-Weighted Core: Gains and losses are calculated by multiplying the price change by volume to ensure the "Relative Strength" reflects true capital flow. • Smoothing for Clarity: The raw Volume Weighted RSI (VW-RSI) is processed through a customizable Moving Average—such as SMA, EMA, SMMA, WMA, or VWMA—to produce the smooth histogram. • Four-Zone Coloring System: The histogram changes color dynamically based on momentum intensity: o Strong Bull: Price is trending up with high-volume conviction. o Weak Bull: Positive momentum, but not yet overextended. o Weak Bear: Negative momentum starting to build. o Strong Bear: Heavy selling pressure with high-volume conviction. Key Features • Shading: The background features optional red and green shading in the "Extreme" zones to warn traders of potential exhaustion areas. • Dynamic Zero Line: The center line flips color between Green and Red based on whether the VW-RSI is positive or negative. • Customization: Traders can adjust the smoothing length, source price, and the specific levels for overbought/oversold zones. Best Use Case for New Traders: New traders often get "faked out" by price spikes that have no volume behind them. This indicator helps confirm and time better entries: 1. Wait for your Cumulative Delta indicator to give a signal. 2. Check the VW-RSI Histogram and whether it confirms or not. 3. Long Entry: Only enter if the histogram is positive and rising (above 0). 4. Short Entry: Only enter if the histogram is negative and decreasing (below 0). ________________________________________ Disclaimer Financial Risk: • Trading involves significant risk, and most traders lose money. • This indicator is a tool for technical analysis and does not constitute financial, investment, or trading advice. • Past performance is not indicative of future results; never trade with money you cannot afford to lose. Usage & Reliability: • The Rhokeo-VW-RSI Histogram is provided "as-is" for educational and informational purposes only. • While volume-weighting aims to filter market noise, no indicator can guarantee 100% accuracy or predict future market movements with certainty. • This script is intended to be a complementary tool that works well with other indicators in this case the Cumulative Delta from Zeiirman; it should be used in conjunction with other forms of analysis, risk management, and your own due diligence. Commercial Notice: • If you are using this alongside a third-party paid indicator, please note that I am not responsible for the performance or support of external products. • Users are responsible for their own trade execution and account management.
Wskaźnik Pine Script®
od nabil007
WMA MAD Trend | RakoQuantWMA MAD Trend | RakoQuant is a robust volatility-regime trend system built on Weighted Moving Average structure and Median Absolute Deviation dispersion, engineered to produce clean directional states while suppressing wick-driven noise and unstable ATR distortions. This tool belongs to the RakoQuant protected research line, combining a smooth WMA baseline, statistically robust volatility envelopes (MAD bands), SuperTrend-style regime logic, and a strength-aware visualization layer designed for consistent performance across trending, mean-reverting, and mixed market environments. Core Concept This indicator answers one fundamental question: Is price holding a statistically meaningful deviation from its WMA baseline, or reverting back into range? Unlike classic SuperTrend variants that rely on ATR (highly sensitive to spikes and wicks), WMA MAD Trend uses Median Absolute Deviation as its volatility engine — a robust dispersion measure that remains stable in the presence of outliers. How It Works 1) WMA Baseline (Directional Structure) At its core, the indicator defines the market’s structural center using a Weighted Moving Average: * WMA Baseline tracks directional bias with smoother, trend-weighted responsiveness * The baseline can optionally be smoothed further in intraday mode to reduce micro-chop This provides a stable anchor for dispersion-based regime classification. 2) MAD Volatility Engine (Robust Dispersion Core) Instead of ATR, volatility is measured via Median Absolute Deviation (MAD) around the baseline: * Compute absolute deviation:
|Close − Baseline| * Take rolling median of deviation over madLen * Optional normalization scales MAD toward a stdev-like measure (via constant factor) This makes volatility estimation: * Outlier-resistant * Wick-resistant * Regime-stable during abnormal price spikes 3) MAD Bands + SuperTrend Trailing Logic (Regime State Model) Bands are built as: * Upper Band = Baseline + Factor × MAD * Lower Band = Baseline − Factor × MAD Then classic SuperTrend-style trailing constraints are applied so the active band persists until a true regime break occurs. That produces a state engine: * Bull regime when price breaks above the trailing upper logic (transition into trend-up state) * Bear regime when price breaks below the trailing lower logic (transition into trend-down state) This behaves like a structural market regime model, not a reactive oscillator. 4) Strength Engine (Deviation-Based Intensity) A defining layer of this tool is the MAD Z-score intensity system: * Compute Z-score:
z = |Close − Baseline| / MAD * Map into a 0 → 1 strength scale Interpretation: * Low deviation = weak regime confidence (likely chop / mean reversion) * High deviation = strong regime confidence (trend expansion) 5) Intensity Visual Engine (Signal Clarity Layer) WMA MAD Trend includes a protected visual engine that scales opacity with strength: * Strong expansion = solid trend band * Weak deviation = faded band This gives immediate clarity:
Not all flips are equal — strength is displayed structurally. 6) Optional Institutional Filters Two optional confirmation modules allow institutional-grade filtering: Baseline Confirmation * Bull flips only accepted if price is above baseline * Bear flips only accepted if price is below baseline EMA Stack Filter * Bull only when Fast EMA > Slow EMA * Bear only when Fast EMA < Slow EMA These modules make the tool suitable for: * Directional portfolio bias frameworks (RSPS) * Regime classification overlays * Trend confirmation filters for execution systems 7) Strong Flip Tier Alerts Signal quality is tiered: * Standard flip alerts * Strong flip alerts only when deviation strength exceeds a threshold This produces a higher-confidence regime transition model for swing positioning and exposure scaling. How To Use ✅ Trend regime overlay
✅ Wick-resistant volatility trend filter
✅ MAD-based deviation strength engine
✅ Directional bias tool for portfolio systems Best use cases: * 1H–1D trend frameworks * Regime filters for signal stacking * Chop suppression in volatile markets Suggested workflow: * Bull bias when the regime is bullish and strength is rising * Reduce risk / defensive when strength fades or a bearish flip occurs * Pair with execution tools (breakout/mean-reversion entries) for timing Screenshot Placement 📸 Example chart / screenshot: snapshot
Wskaźnik Pine Script®
od RakoQuant
1
VIOP Scalping - OriginalVIOP Scalping – Original is a rule-based scalping strategy ported from an original C# logic set. It aims to trade only when trend direction, momentum, and trend strength align, then manages the position using fixed take-profit/stop-loss percentages with an optional trailing mechanism to protect gains during favorable moves. This strategy is provided for educational and backtesting purposes only. It is not financial advice and does not guarantee profitability. Always test thoroughly before any live usage. Core Concept Follow the dominant WMA trend, confirm momentum with EMA separation, filter conditions with RSI + ADX, then exit using fixed TP/SL with trailing behavior after a defined profit threshold. How the Strategy Works Trend Direction is defined by WMA: above WMA = long bias, below WMA = short bias. Momentum is measured via Fast EMA vs Slow EMA and the EMA difference. Trend Strength is confirmed using ADX (must exceed a threshold). RSI filters trades to avoid entering when momentum is likely overextended or weak. A no-trade session blocks entries during a predefined time window (default 09:30–10:05). Exit logic uses fixed percent TP/SL, with an optional trailing mechanism that activates after a profit threshold. Inputs and Settings Trend and Indicator Settings Main Trend WMA: Determines directional bias (price above = long, price below = short). Fast EMA / Slow EMA: Used to measure momentum and directional separation. RSI Period: Filters entries based on RSI range constraints. ADX Period: Measures trend strength (must exceed threshold to allow entries). Threshold Settings EMA Difference Threshold: Minimum EMA separation required to validate momentum. ADX Threshold: Minimum ADX required to confirm trend strength. RSI Long Ceiling: RSI must remain below this value for long entries. RSI Short Floor: RSI must remain above this value for short entries. Risk Management Settings Take Profit %: Default TP distance in percent. Strong Trend Take Profit %: Higher TP used when a “strong trend” condition is detected. Stop Loss %: Fixed SL distance in percent. Trailing Activation %: Profit threshold at which trailing starts. Trailing Distance %: Trailing offset distance used once trailing is active. Time Filter No-Trade Hours: Default session is 09:30–10:05. During this window, the strategy does not open new trades. Entry Logic No-Trade Time Filter If the current bar falls inside the no-trade session, entries are blocked. Long Entry Conditions Price is above the WMA trend line. EMA difference is positive and greater than the EMA Difference Threshold. EMA momentum is increasing (current EMA diff > previous EMA diff). RSI is within the defined range (RSI > 48 and RSI < RSI Long Ceiling). Close is higher than the previous close. ADX is above the ADX Threshold. Short Entry Conditions Price is below the WMA trend line. EMA difference is negative and lower than -EMA Difference Threshold. Bearish momentum is increasing (current EMA diff < previous EMA diff). RSI is within the defined range (RSI < 52 and RSI > RSI Short Floor). Close is lower than the previous close. ADX is above the ADX Threshold. Strong Trend Logic (Dynamic TP Selection) If price is far from the WMA (absolute distance > 20 points) AND EMA separation is strong (absolute EMA diff > 1.5 points), the strategy treats the environment as a strong trend. In strong trend mode, the strategy uses “Strong Trend Take Profit %” instead of the default “Take Profit %”. Exit Management (TP/SL + Trailing) The strategy uses fixed percentage-based TP and SL levels. Trailing logic is enabled via strategy.exit and activates only after price moves in profit by the defined Trailing Activation %. Once activated, trailing follows price using the defined Trailing Distance % offset. This is designed to secure partial gains during extended moves while still allowing room for continuation. What You See on the Chart WMA Trend Line (Main Trend Filter). Fast EMA and Slow EMA (Momentum Confirmation). Strategy entry/exit markers generated by TradingView. Recommended Use Scalping systems that rely on trend-following and momentum confirmation. Markets where ADX filtering helps avoid choppy conditions. Traders who want a simple, parameter-driven TP/SL system with trailing after confirmation. Important Notes The no-trade session depends on your chart/session settings. Ensure your symbol/session configuration matches your intended market hours. Percent-based exits scale with price; results will vary across instruments and volatility regimes. Always validate behavior using bar replay, forward testing, and realistic commission/slippage assumptions.
Strategia Pine Script®
od mehmettopbas_
VIOP Scalping - ATR SNIPERVIOP Scalping – ATR SNIPER is a momentum-based scalping strategy designed to capture short, high-probability moves while keeping risk strictly defined using ATR-based stop-loss and fixed risk/reward targets. The strategy trades only when trend direction, momentum, and strength are aligned. This script is provided for educational and testing purposes only. It does not guarantee profitability and must be used with proper risk management. Core Idea Trade in the direction of the dominant trend, confirm momentum acceleration, and manage risk using ATR-based dynamic stops and targets. How the Strategy Works The main trend is defined using a Weighted Moving Average (WMA). Momentum is measured by the distance and direction between a fast EMA and a slow EMA. Trend strength is confirmed using ADX. RSI is used as a filter to avoid weak or overextended market conditions. Entries are blocked during a predefined no-trade time window to avoid high-noise periods. Long Entry Conditions Fast EMA is above Slow EMA and the EMA difference is greater than the minimum threshold. EMA momentum is increasing compared to the previous bar. RSI is within the user-defined long range. Current close is higher than the previous close. ADX is above the minimum strength threshold. Price is above the WMA trend line. The current bar is not inside the no-trade session. Short Entry Conditions Fast EMA is below Slow EMA and the EMA difference is below the negative threshold. Bearish EMA momentum is increasing. RSI is within the user-defined short range. Current close is lower than the previous close. ADX is above the minimum strength threshold. Price is below the WMA trend line. The current bar is not inside the no-trade session. Risk Management – ATR Sniper Logic Stop-loss distance is calculated as ATR multiplied by the ATR Multiplier. Take-profit distance is calculated using the defined Risk/Reward ratio. Stop-loss and take-profit levels are dynamically calculated per trade. Only one position can be open at any given time. What You See on the Chart Weighted Moving Average (WMA) trend line. Fast EMA and Slow EMA lines. Dynamic stop-loss line during active trades. Dynamic take-profit line during active trades. Recommended Use Intraday scalping on VİOP instruments. Momentum-based short-term trading. Traders who prefer rule-based systems with strict risk control. Always backtest and forward-test on your own instruments and timeframes before using this strategy in live markets.
Strategia Pine Script®
od mehmettopbas_
llama fixed-length moving averages [SMA, WMA]Llama Moving Averages I needed moving averages on my charts, but I wanted them hardcoded based on the following values: timframe --- MA length Daily --- 20D (==1 trading month) Weekly --- 30W (30W moving averages for assessing long term trends) 15m --- 1W (or 5 days) Two averages: EMA and WMA: fast moving and slow moving based on closes. In addition to this, I needed the following things: 1. MAs ONLY on 15m, 1D and 1W timeframes. 2. Consistent colors. 3. for 15m chart, we want a 5D SMA, so 1D = 15m * 25(±1); times 5; 25*5 = 125 4. Option to configure different values for the daily chart, since I trade the daily chart. Defaults: 20. If you were looking for something similar, enjoy!
Wskaźnik Pine Script®
od wildhorse3
Hyper Insight MA Strategy [Universal]Hyper Insight MA Strategy ** is a comprehensive trend-following engine designed for traders who require precision and flexibility. Unlike standard indicators that lock you into a single calculation method, this strategy serves as a "Universal Adapter," allowing you to **Mix & Match 13 different Moving Average types** for both the Fast and Slow trend lines independently. Whether you need the smoothness of T3, the responsiveness of HMA, or the classic reliability of SMA, this script enables you to backtest thousands of combinations to find the perfect edge for your specific asset class. --- 🔬 Deep Dive: Calculation Logic of Included MAs This strategy includes 13 distinct calculation methods. Understanding the math behind them will help you choose the right tool for your specific market conditions. #### 1. Standard Averages * **SMA (Simple Moving Average):** The unweighted mean of the previous $n$ data points. * *Logic:* Treats every price point in the period with equal importance. Good for identifying long-term macro trends but reacts slowly to recent volatility. * **WMA (Weighted Moving Average):** A linear weighted average. * *Logic:* Assigns heavier weight to current data linearly (e.g., $1, 2, 3... n$). It reacts faster than SMA but is still relatively smooth. * **SWMA (Symmetrically Weighted Moving Average):** * *Logic:* Uses a fixed-length window (usually 4 bars) with symmetrical weights $ $. It prioritizes the center of the recent data window. #### 2. Exponential & Lag-Reducing Averages * **EMA (Exponential Moving Average):** * *Logic:* Applies an exponential decay weighting factor. Recent prices have significantly more impact on the average than older prices, reducing lag compared to SMA. * **RMA (Running Moving Average):** Also known as Wilder's Smoothing (used in RSI). * *Logic:* It is essentially an EMA but with a slower alpha weight of $1/length$. It provides a very smooth, stable line that filters out noise effectively. * **DEMA (Double Exponential Moving Average):** * *Logic:* Calculated as $2 \times EMA - EMA(EMA)$. By subtracting the "lag" (the smoothed EMA) from the original EMA, DEMA provides a much faster reaction to price changes with less noise than a standard EMA. * **TEMA (Triple Exponential Moving Average):** * *Logic:* Calculated as $3 \times EMA - 3 \times EMA(EMA) + EMA(EMA(EMA))$. This effectively eliminates the lag inherent in single and double EMAs, making it an extremely fast-tracking indicator for scalping. #### 3. Advanced & Adaptive Averages * **HMA (Hull Moving Average):** * *Logic:* A composite formula involving Weighted Moving Averages: ASX:WMA (2 \times Integer(n/2)) - WMA(n)$. The result is then smoothed by a $\sqrt{n}$ WMA. * *Effect:* It eliminates lag almost entirely while managing to improve curve smoothness, solving the traditional trade-off between speed and noise. * **ZLEMA (Zero Lag Exponential Moving Average):** * *Logic:* This calculation attempts to remove lag by modifying the data source before smoothing. It calculates a "lag" value $(length-1)/2$ and applies an EMA to the data: $Source + (Source - Source )$. This creates a projection effect that tracks price tightly. * **T3 (Tillson T3 Moving Average):** * *Logic:* A complex smoothing technique that runs an EMA through a filter multiple times using a "Volume Factor" (set to 0.7 in this script). * *Effect:* It produces a curve that is incredibly smooth and free of "overshoot," making it excellent for filtering out market chop. * **ALMA (Arnaud Legoux Moving Average):** * *Logic:* Uses a Gaussian distribution (bell curve) to assign weights. It allows the user to offset the moving average (moving the peak of the weight) to align it perfectly with the price, balancing smoothness and responsiveness. * **LSMA (Least Squares Moving Average):** * *Logic:* Calculates the endpoint of a Linear Regression line for the lookback period. It essentially guesses where the price "should" be based on the best-fit line of the recent trend. * **VWMA (Volume Weighted Moving Average):** * *Logic:* Weights the closing price by the volume of that bar. * *Effect:* Prices on high volume days pull the MA harder than prices on low volume days. This is excellent for validating true trend strength (i.e., a breakout on high volume will move the VWMA significantly). --- ### 🛠 Features & Settings * **Universal Switching:** Change the `Fast MA` and `Slow MA` types instantly via the settings menu. * **Trend Cloud:** A dynamic background fill (Green/Red) highlights the crossover zone for immediate visual trend identification. * **Strategy Mode:** Built-in Backtesting logic triggers `LONG` entries when Fast MA crosses over Slow MA, and `EXIT` when Fast MA crosses under. ### ⚠️ Disclaimer This script is intended for educational and research purposes. The wide variety of MA combinations can produce vastly different results. Past performance is not indicative of future results. Please use proper risk management.
Strategia Pine Script®
od waranyutrkm
MACD Above Signal & Price Above VWAP IndicatorThis strategy provides a buy signal with a green arrow pointing up when three conditions are met. The MACD has to be above the signal line. The settings for MACD can be adjusted, but the default is the standard settings for MACD. The second condition is the price has to be above the VWAP line. The third condition is that the price of the current candle needs to be higher than the HIGH price of the previous candle.
Wskaźnik Pine Script®
od R39813
SwRp - Multi Trapezoidal WMASwRp - Multi TWMA (TF-aware) shows up to five configurable Trapezoidal Weighted Moving Averages (TWMA) across a single pane. Each line supports an independent length, weighted taper percent, source (Close/Open/High/Low/H&L/H&L+HL2), optional band (High & Low) with fill, and a post-calculation smoothing option. The indicator is timeframe-aware: calculations can run on the chart timeframe or on a selected higher/lower timeframe. Designed to visualize dynamic support/resistance and the market’s weighted center across multiple horizons.
Wskaźnik Pine Script®
od swaropz
Double Weighted Moving Average (DWMA)# DWMA: Double Weighted Moving Average ## Overview and Purpose The Double Weighted Moving Average (DWMA) is a technical indicator that applies weighted averaging twice in sequence to create a smoother signal with enhanced noise reduction. Developed in the late 1990s as an evolution of traditional weighted moving averages, the DWMA was created by quantitative analysts seeking enhanced smoothing without the excessive lag typically associated with longer period averages. By applying a weighted moving average calculation to the results of an initial weighted moving average, DWMA achieves more effective filtering while preserving important trend characteristics. ## Core Concepts * **Cascaded filtering:** DWMA applies weighted averaging twice in sequence for enhanced smoothing and superior noise reduction * **Linear weighting:** Uses progressively increasing weights for more recent data in both calculation passes * **Market application:** Particularly effective for trend following strategies where noise reduction is prioritized over rapid signal response * **Timeframe flexibility:** Works across multiple timeframes but particularly valuable on daily and weekly charts for identifying significant trends The core innovation of DWMA is its two-stage approach that creates more effective noise filtering while minimizing the additional lag typically associated with longer-period or higher-order filters. This sequential processing creates a more refined output that balances noise reduction and signal preservation better than simply increasing the length of a standard weighted moving average. ## Common Settings and Parameters | Parameter | Default | Function | When to Adjust | |-----------|---------|----------|---------------| | Length | 14 | Controls the lookback period for both WMA calculations | Increase for smoother signals in volatile markets, decrease for more responsiveness | | Source | close | Price data used for calculation | Consider using hlc3 for a more balanced price representation | **Pro Tip:** For trend following, use a length of 10-14 with DWMA instead of a single WMA with double the period - this provides better smoothing with less lag than simply increasing the period of a standard WMA. ## Calculation and Mathematical Foundation **Simplified explanation:** DWMA first calculates a weighted moving average where recent prices have more importance than older prices. Then, it applies the same weighted calculation again to the results of the first calculation, creating a smoother line that reduces market noise more effectively. **Technical formula:** ``` DWMA is calculated by applying WMA twice: 1. First WMA calculation: WMA₁ = (P₁ × w₁ + P₂ × w₂ + ... + Pₙ × wₙ) / (w₁ + w₂ + ... + wₙ) 2. Second WMA calculation applied to WMA₁: DWMA = (WMA₁₁ × w₁ + WMA₁₂ × w₂ + ... + WMA₁ₙ × wₙ) / (w₁ + w₂ + ... + wₙ) ``` Where: - Linear weights: most recent value has weight = n, second most recent has weight = n-1, etc. - n is the period length - Sum of weights = n(n+1)/2 **O(1) Optimization - Inline Dual WMA Architecture:** This implementation uses an advanced O(1) algorithm with two complete inline WMA calculations. Each WMA uses the dual running sums technique: 1. **First WMA (source → wma1)**: - Maintains buffer1, sum1, weighted_sum1 - Recurrence: `W₁_new = W₁_old - S₁_old + (n × P_new)` - Cached denominator norm1 after warmup 2. **Second WMA (wma1 → dwma)**: - Maintains buffer2, sum2, weighted_sum2 - Recurrence: `W₂_new = W₂_old - S₂_old + (n × WMA₁_new)` - Cached denominator norm2 after warmup **Implementation details:** - Both WMAs fully integrated inline (no helper functions) - Each maintains independent state: buffers, sums, counters, norms - Both warm up independently from bar 1 - Performance: ~16 operations per bar regardless of period (vs ~10,000 for naive O(n²) implementation) **Why inline architecture:** Unlike helper functions, the inline approach makes all state variables and calculations visible in a single scope, eliminating function call overhead and making the dual-pass nature explicit. This is ideal for educational purposes and when debugging complex cascaded filters. > 🔍 **Technical Note:** The dual-pass O(1) approach creates a filter that effectively increases smoothing without the quadratic increase in computational cost. Original O(n²) implementations required ~10,000 operations for period=100; this optimized version requires only ~16 operations, achieving a 625x speedup while maintaining exact mathematical equivalence. ## Interpretation Details DWMA can be used in various trading strategies: * **Trend identification:** The direction of DWMA indicates the prevailing trend * **Signal generation:** Crossovers between price and DWMA generate trade signals, though they occur later than with single WMA * **Support/resistance levels:** DWMA can act as dynamic support during uptrends and resistance during downtrends * **Trend strength assessment:** Distance between price and DWMA can indicate trend strength * **Noise filtering:** Using DWMA to filter noisy price data before applying other indicators ## Limitations and Considerations * **Market conditions:** Less effective in choppy, sideways markets where its lag becomes a disadvantage * **Lag factor:** More lag than single WMA due to double calculation process * **Initialization requirement:** Requires more data points for full calculation, showing more NA values at chart start * **Short-term trading:** May miss short-term trading opportunities due to increased smoothing * **Complementary tools:** Best used with momentum oscillators or volume indicators for confirmation ## References * Jurik, M. "Double Weighted Moving Averages: Theory and Applications in Algorithmic Trading Systems", Jurik Research Papers, 2004 * Ehlers, J.F. "Cycle Analytics for Traders," Wiley, 2013
Wskaźnik Pine Script®
od mihakralj
Weighted Moving Average (WMA)This implementation uses O(1) algorithm that eliminates the need to loop through all period values on each bar. It also generates valid WMA values from the first bar and is not returning NA when number of bars is less than period. ## Overview and Purpose The Weighted Moving Average (WMA) is a technical indicator that applies progressively increasing weights to more recent price data. Emerging in the early 1950s during the formative years of technical analysis, WMA gained significant adoption among professional traders through the 1970s as computational methods became more accessible. The approach was formalized in Robert Colby's 1988 "Encyclopedia of Technical Market Indicators," establishing it as a staple in technical analysis software. Unlike the Simple Moving Average (SMA) which gives equal weight to all prices, WMA assigns greater importance to recent prices, creating a more responsive indicator that reacts faster to price changes while still providing effective noise filtering. ## Core Concepts * **Linear weighting:** WMA applies progressively increasing weights to more recent price data, creating a recency bias that improves responsiveness * **Market application:** Particularly effective for identifying trend changes earlier than SMA while maintaining better noise filtering than faster-responding averages like EMA * **Timeframe flexibility:** Works effectively across all timeframes, with appropriate period adjustments for different trading horizons The core innovation of WMA is its linear weighting scheme, which strikes a balance between the equal-weight approach of SMA and the exponential decay of EMA. This creates an intuitive and effective compromise that prioritizes recent data while maintaining a finite lookback period, making it particularly valuable for traders seeking to reduce lag without excessive sensitivity to price fluctuations. ## Common Settings and Parameters | Parameter | Default | Function | When to Adjust | |-----------|---------|----------|---------------| | Length | 14 | Controls the lookback period | Increase for smoother signals in volatile markets, decrease for responsiveness | | Source | close | Price data used for calculation | Consider using hlc3 for a more balanced price representation | **Pro Tip:** For most trading applications, using a WMA with period N provides better responsiveness than an SMA with the same period, while generating fewer whipsaws than an EMA with comparable responsiveness. ## Calculation and Mathematical Foundation **Simplified explanation:** WMA calculates a weighted average of prices where the most recent price receives the highest weight, and each progressively older price receives one unit less weight. For example, in a 5-period WMA, the most recent price gets a weight of 5, the next most recent a weight of 4, and so on, with the oldest price getting a weight of 1. **Technical formula:** ``` WMA = (P₁ × w₁ + P₂ × w₂ + ... + Pₙ × wₙ) / (w₁ + w₂ + ... + wₙ) ``` Where: - Linear weights: most recent value has weight = n, second most recent has weight = n-1, etc. - The sum of weights for a period n is calculated as: n(n+1)/2 - For example, for a 5-period WMA, the sum of weights is 5(5+1)/2 = 15 **O(1) Optimization - Dual Running Sums:** The key insight is maintaining two running sums: 1. **Unweighted sum (S)**: Simple sum of all values in the window 2. **Weighted sum (W)**: Sum of all weighted values The recurrence relation for a full window is: ``` W_new = W_old - S_old + (n × P_new) ``` This works because when all weights decrement by 1 (as the window slides), it's mathematically equivalent to subtracting the entire unweighted sum. The implementation: - **During warmup**: Accumulates both sums as the window fills, computing denominator each bar - **After warmup**: Uses cached denominator (constant at n(n+1)/2), updates both sums in constant time - **Performance**: ~8 operations per bar regardless of period, vs ~100+ for naive O(n) implementation > 🔍 **Technical Note:** Unlike EMA which theoretically considers all historical data (with diminishing influence), WMA has a finite memory, completely dropping prices that fall outside its lookback window. This creates a cleaner break from outdated market conditions. The O(1) optimization achieves 12-25x speedup over naive implementations while maintaining exact mathematical equivalence. ## Interpretation Details WMA can be used in various trading strategies: * **Trend identification:** The direction of WMA indicates the prevailing trend with greater responsiveness than SMA * **Signal generation:** Crossovers between price and WMA generate trade signals earlier than with SMA * **Support/resistance levels:** WMA can act as dynamic support during uptrends and resistance during downtrends * **Moving average crossovers:** When a shorter-period WMA crosses above a longer-period WMA, it signals a potential uptrend (and vice versa) * **Trend strength assessment:** Distance between price and WMA can indicate trend strength ## Limitations and Considerations * **Market conditions:** Still suboptimal in highly volatile or sideways markets where enhanced responsiveness may generate false signals * **Lag factor:** While less than SMA, still introduces some lag in signal generation * **Abrupt window exit:** The oldest price suddenly drops out of calculation when leaving the window, potentially causing small jumps * **Step changes:** Linear weighting creates discrete steps in influence rather than a smooth decay * **Complementary tools:** Best used with volume indicators and momentum oscillators for confirmation ## References * Colby, Robert W. "The Encyclopedia of Technical Market Indicators." McGraw-Hill, 2002 * Murphy, John J. "Technical Analysis of the Financial Markets." New York Institute of Finance, 1999 * Kaufman, Perry J. "Trading Systems and Methods." Wiley, 2013
Wskaźnik Pine Script®
od mihakralj
Kalman Exponentialy Weighted Moving Average | MisinkoMasterThe Kalman Exponentialy Weighted Moving Average is a technical analysis tool providing users with more responsive and smoother signals, providing crystal-clear signals and giving investors valuable insights on market trends, however it could be used in many cases. A deeper dive into the indicator: When going through my creation of strategies, I had stumbled on an indicator called "EWMA", which worked decently, but it was far too simple in my opinion so I decided to combine the EMA & WMA, but with a little more complexity, and it has worked . I began by learning how both MAs work, I already knew how WMA works, but EMA I did not. After learning both I found out they were quite simple in principle and that there was a way to combine them in such way that you would get really good signals, however it was way too noisy. While it could avoid major dumps that were not avoided by most indicators, it would lose that edge because of being too noisy. After testing out many conditions, combinations & more, the best working one was this one: WMA > KEWMA = long WMA < KEWMA = short I will explain this later, but this gave fast signals, and while it still was noisy it was better then before. To smooth it out, I started testing price filters => Gaussian Filter and many more were tested out, but they either slowed it down to the point it was no longer of much use, or did not smooth it at all. After testing the Kalman filter on this thing, I was shocked. It was just right and made the indicator a lot better, smoothed it and kept most of the responsivness it had. Now to the big question: "How is it calculated?" Now first it needs to calculate the Kalman source, which smooths the source which will be used. After that, we calculate the Weighted Moving Average for " n " period on the Kalman source. Now that we have our WMA values, we need to calculate " a ". a is calculated in the following formula: a = 2/(1+ n ) where n is the user defined length Now for the last part: KEWMA = WMAyesterday * (1-a) + WMAtoday * a This creates a very accurate and reactive indicator, that can prove useful in many uses, beyond those I will and did talk about. For the trend logic as mentioned before: Long = WMA > KEWMA Short = WMA < KEWMA This worked best, but you might find better ways of using it. I think that is all I have to say about it, I left it open source so you can all code it in your strategies and play around with it. Enjoy Gs!
Wskaźnik Pine Script®
od MisinkoMaster
8
Normalized WMA Oscillator | OquantNormalized WMA Oscillator | Oquant The Normalized WMA Oscillator is a trend-momentum indicator designed to help traders visualize the relative position of a Weighted Moving Average (WMA) within its recent price range. What is a WMA and How It Works: A Weighted Moving Average (WMA) is a type of moving average that gives more weight to recent price data, making it more responsive to price changes compared to a simple moving average. Each price point in the lookback period is multiplied by a weighting factor, with the most recent prices having the highest weights. The WMA helps traders identify potential trends more quickly. This indicator applies min-max normalization to the standard WMA, scaling its values between 0 and 1 over a configurable lookback period. This allows traders to see whether the WMA is near its recent highs, lows, or midpoint, regardless of the absolute price level. Key Features: WMA Source Input: Choose price source for wma calculation. Customizable WMA Length: Adjust the sensitivity of the WMA. Min-Max Normalization Length: Smooth the scaling of WMA values between 0 and 1. Signal Thresholds: Configurable upper and lower thresholds to indicate potential entries. Visual Alerts: Color-coded oscillator and candles plot for bullish (green) and bearish (purple) signals. Alerts Ready: Built-in alert conditions for crossovers and crossunders of the oscillator. How It Works: Calculate the WMA on the selected source. Normalize its value using the minimum and maximum WMA values over the specified lookback period. Generate long signals when the normalized WMA moves above the upper threshold, and short signals when it moves below the lower threshold. Plot the oscillator and candles in green for bullish signals and purple for bearish signals. Inputs: Source: Data used for WMA calculation. WMA Length: Period for Weighted Moving Average. Min-Max Length: Lookback period for min-max scaling. Upper Threshold: Level above which a long signal is considered. Lower Threshold: Level below which a short signal is considered. ⚠️ Disclaimer: This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate indicators/strategies before applying them in live markets. Use at your own risk.
Wskaźnik Pine Script®
od oquant
2
TEWMA Supertrend - [JTCAPITAL]TEWMA Supertrend - is a modified way to use Triple Exponential Weighted Moving Average (TEWMA) combined with ATR-based Supertrend logic for Trend-Following. The idea behind this indicator is to merge the smoothness and responsiveness of TEWMA with the robustness of ATR-based Supertrend volatility filtering. This results in a tool that not only reacts quickly to price changes but also adapts to market volatility, providing reliable trend detection with reduced noise. The indicator works by calculating in the following steps: Source Selection The user can select the price source (default is Close). This price series is the foundation of all calculations, and changing the source allows the indicator to adapt to different analytical perspectives, such as Open, High, Low, or HL2. TEWMA Calculation The script calculates a Weighted Moving Average (WMA) of the selected source, and then applies a Triple Exponential Moving Average (TEMA) smoothing on top of it. The result is what we call TEWMA. This hybrid method achieves two goals simultaneously: -WMA adds sensitivity by giving more weight to recent data. -TEMA reduces lag by combining multiple EMA calculations while keeping smoothness. ATR Volatility Measurement In parallel, the Average True Range (ATR) is calculated over the user-defined Supertrend length . ATR measures volatility and dynamically scales the upper and lower bands to adjust to different market conditions. Upper and Lower Band Construction The indicator builds two envelopes around the TEWMA: - Upper Band = TEWMA + (Multiplier × ATR) - Lower Band = TEWMA – (Multiplier × ATR) These bands expand and contract depending on volatility, creating a dynamic channel. Band Adjustment Logic To prevent false flips, the current upper/lower band values are compared to their previous values. If price has not broken above or below the prior band, the bands “stick” to their previous values, thereby filtering noise and avoiding unnecessary trend changes. Trend Detection -If price closes above the adjusted upper band, the direction is bullish. -If price closes below the adjusted lower band, the direction is bearish. -Otherwise, the trend direction continues from its prior state. The Trend line is then set to either the upper band (bearish) or lower band (bullish). Visual Representation -The TEWMA line itself is plotted and color-coded (blue for bullish, purple for bearish). -The active Supertrend line is plotted depending on trend direction. -Shaded regions are added around the lines for enhanced clarity, visually separating bullish and bearish phases. Buy and Sell Conditions : - Buy Signal : Triggered when price closes above the Supertrend line, confirming a bullish shift. - Sell Signal : Triggered when price closes below the Supertrend line, confirming a bearish shift. Features and Parameters : - TEWMA Source – Select the input price (Close, Open, High, Low, etc.). - TEWMA Length – Defines the lookback for the Weighted MA and subsequent TEMA smoothing. - Supertrend Length – Defines the ATR period used for volatility measurement. - Multiplier – Determines how far the Supertrend bands are placed from the TEWMA. Higher values mean wider bands and fewer trend flips, while lower values mean tighter bands and more frequent signals. Specifications : Weighted Moving Average (WMA) The WMA gives more importance to recent price points while still considering past values. This makes it more responsive to recent moves than a Simple Moving Average (SMA). Triple Exponential Moving Average (TEMA) TEMA reduces lag by combining multiple layers of EMA calculations. Unlike a simple EMA, which can be slow to react, TEMA anticipates changes faster, while still maintaining smoothness to avoid false signals. TEWMA (TEMA of WMA) By applying TEMA on top of WMA, we create a hybrid smoothing technique. This retains the responsiveness of WMA but reduces its lag via TEMA’s structure. The result is a highly adaptive moving average, ideal for fast trend detection. Average True Range (ATR) ATR measures the degree of volatility by looking at the full trading range of each candle. It ensures that the Supertrend bands expand in volatile markets and contract in calm markets, keeping signals relevant to current conditions. Supertrend Bands The upper and lower envelopes built around TEWMA act as dynamic support and resistance. Their adaptive nature reduces false trend shifts during choppy sideways markets. Band Adjustment Logic Instead of recalculating bands every candle, the script uses a memory mechanism (previous values) to prevent unnecessary trend switches. This stabilizes the indicator and avoids excessive noise. Trend Line The final output is a line that follows price in trending phases while holding steady during consolidations. Its placement above or below price clearly signals bullish or bearish market structure. Color Coding and Visuals The use of shaded fills and line coloring enhances readability. Traders can quickly distinguish trend direction and momentum without deep numerical analysis. Enjoy!
Wskaźnik Pine Script®
od JTCapitalNL
2