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CVD Pressure Aura [Weather HUD]Order flow rendered as living weather around the price — an immersive perception tool, not a signal generator. CVD Pressure Aura turns cumulative volume delta into a living atmospheric heads-up display that surrounds your candles like weather around a glass pane. Instead of staring at abstract bars in a separate pane, you now feel the pressure regime directly on the chart: buying pressure pushing up from below as a glowing green aura, selling pressure pouring down from above as a red flood. The market’s breathing, compression, and exhaustion become visible at a glance. How to Read It A smooth envelope (default: Bollinger 20 SMA ± 2σ, now with optional smoothed Keltner channel) acts as the “glass.” All order flow lives outside it — the candles stay clean and readable at all times. 🌱 Green aura below — buying pressure rising from beneath. Taller and brighter = stronger, more persistent demand. 🌧 Red flood above — selling pressure pouring down. Same logic, mirrored. The Aura Breathes A slow SMA (200 default) drives the regime engine: Compression — When volatility squeezes, cyan clamps close in from above and below, tightening the coil. You can literally watch the market rally straight into a pressure zone and feel the crowd standing at that level. Exhaustion — When price stretches far from the slow SMA, the opposing aura swells (gravity above an over-extended rally, spring loading beneath an over-extended sell-off). ⚡ marks full stretch. Symbols (full weather legend available on-chart) 🡑 / 🡓 — strong one-sided flow this bar (sparks / rain) ☀ / ☂ — extreme flow, top 10% of lookback (thermal / storm) ⚡ — full exhaustion (maximally stretched from slow SMA) ▼ cyan — full coil (tightest squeeze of the lookback) ✦ orange (above) — bearish divergence: new price high, CVD refuses to follow ✦ cyan (below) — bullish divergence: new price low, CVD refuses to follow Key Features New: Smoothed Keltner Channel option for a cleaner, more adaptive glass pane Cyan compression clamps that visually tighten during squeezes Overdrive slider (0–100) for full immersion Rolling regime engine with breathing mechanics Works on any timeframe and symbol with volume This is a perception instrument, engineered to make order flow feelable rather than merely measurable. Nothing here is financial advice — it exists to help you develop an intuitive sense of market pressure. Engineered with Claude (Anthropic). Feedback and ideas for future iterations are very welcome. Release notes (v1.2) NEW: Keltner Channel envelope mode (SMA or EMA basis ± ATR) — the smoothest glass NEW: Cyan compression clamps replace the coil glyph — squeeze pressure now presses visibly on the envelope from both sides, readable at any zoom level NEW: State-colored moving averages — slow SMA by slope, basis by regime (cyan coil / green bull / red bear / gray indecision) On-chart weather legend, default bottom-left (clear of the TradingView symbol overlay) Plot budget optimized to fit the 64-plot engine limit
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của FJW_Trading
3D Isometric MFI (Christmas Edition) [Kodexius]3D Isometric MFI (Christmas Edition) is a visual-first interpretation of the classic Money Flow Index, rendered as a projected 3D-style ribbon using an isometric mapping. Instead of plotting a standard oscillator line, the script reconstructs recent MFI history as a depth-aware ribbon that moves from back to front, producing a layered perspective effect that helps you read momentum shifts, regime transitions, and relative strength changes as a continuous structure. This Christmas Edition was also built for fun and as a creative seasonal experiment. The goal is to keep the underlying indicator logic familiar, while presenting it in a playful, “3D showroom” style that looks great in a separate oscillator panel. The indicator is designed for presentation quality and chart readability. It uses controlled object management (lines, polylines, labels) and renders only the most recent portion of the MFI history (user-defined depth). A decorative snow background effect adds atmosphere. 🔹 Features 🎄 🔸 Isometric 3D Projection Engine The ribbon is produced by projecting 3D points (time offset, MFI value, depth) into 2D chart coordinates. - X represents bar offset into history - Y represents the MFI value - Z introduces depth and perspective Angle controls the projection direction, and Vertical Zoom scales the perceived amplitude. 🔸 Depth-Limited Ribbon Rendering (Back to Front) Only the most recent History Depth values are drawn to keep performance and readability stable. - Each segment connects two consecutive MFI values - A top edge, bottom edge, and filled face are drawn to simulate thickness - Older segments fade into the background 🔸 Dynamic Gradient Coloring + Depth Fade Ribbon color follows a value-based gradient: - Lower values lean red (risk-off pressure) - Higher values lean green (risk-on pressure) - Mid values blend naturally Transparency increases with depth so older history is less dominant but still readable. 🔸 Tip Label (Value + Candy Marker) 🍭🍬 The most recent ribbon tip displays current MFI value. A candy symbol that switches based on the 50 midpoint The label is offset so it does not cover the ribbon tip. 🔸 Projected Reference Grid (80, 50, 20) A projected grid is drawn at classic MFI reference levels to improve orientation: - 80 Overbought reference - 50 Midpoint reference - 20 Oversold reference These grid lines use the same projection math, so they stay aligned at any angle or zoom. 🔸 Seasonal Snow Background Effect ❄️ Randomized snow is rendered behind the ribbon using lightweight labels. This is purely decorative and does not alter MFI values or logic. 🔸 Object Lifecycle Management Because 3D-style drawing uses many objects, the script manages them explicitly by storing references in arrays, deleting old objects, and redrawing on the last bar. This helps prevent visual stacking artifacts and keeps the panel clean. 🔹 Calculations 1) Money Flow Index Computation The script separates “positive” and “negative” money flow based on the direction of change in the selected source, then converts their ratio into the standard 0 to 100 oscillator. Classic MFI Calculations. calc_mfi(int length, float source) => float upper = math.sum(volume * (ta.change(source) <= 0 ? 0 : source), length) float lower = math.sum(volume * (ta.change(source) >= 0 ? 0 : source), length) float mfi = 100.0 if lower != 0 float r = upper / lower mfi := 100 - (100 / (1 + r)) mfi Interpretation: upper accumulates volume-weighted source values on up moves lower accumulates volume-weighted source values on down moves if lower is zero, MFI defaults to 100 to avoid division errors otherwise, MFI is computed from the ratio transform 2) History Buffer Management The current MFI value is pushed into the front of an array every bar. The array is trimmed to History Depth so rendering stays bounded. array.unshift(ctx.history_val, mfi_curr) if ctx.history_val.size() > depth ctx.history_val.pop() 3) 3D Point Model and Ribbon Thickness Each segment is built from four projected points to form a filled face (a simple quad). A small thickness is applied to create the “ribbon” look, and depth is used to simulate perspective. 4) Isometric Projection to Chart Coordinates 3D points are mapped into chart coordinates with an angle rotation and scaling for zoom and depth. method project(Point3 p, int anchor_bar, float angle_rad, float zoom, float z_scale) => float x_world = -float(p.x) * 2.0 float z_val = p.z * z_scale float screen_x_offset = (x_world * math.cos(angle_rad)) - (z_val * 1.0) float screen_y_offset = (p.y * zoom) + (x_world * math.sin(angle_rad)) * 0.5 int final_x = anchor_bar + int(math.round(screen_x_offset)) float final_y = screen_y_offset chart.point.from_index(final_x, final_y) 5) Gradient and Depth Transparency Color is derived from MFI value via a gradient, and transparency increases with segment depth so recent data remains dominant while older context stays visible. 6) Projected Reference Grid Construction The 80, 50, 20 levels are drawn as dotted segments across the same historical span, using the same projection and depth fade logic for consistent alignment. 🎆 Wishing you a great year ahead 🎄✨ May your charts be clear, your risk be controlled, and your next year be filled with health, peace, and good trades. Happy Holidays and Happy New Year.
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của UnknownUnicorn113699460
The Abramelin Protocol [MPL]"Any sufficiently advanced technology is indistinguishable from magic." — Arthur C. Clarke 🌑 SYSTEM OVERVIEW The Abramelin Protocol is not a standard technical indicator; it is a "Technomantic" trading algorithm engineered to bridge the gap between 15th-century esoteric mathematics and modern high-frequency markets. This script is the flagship implementation of the MPL (Magic Programming Language) project—an open-source experimental framework designed to compile metaphysical intent into executable Python and Pine Script algorithms. Unlike traditional indicators that rely on arbitrary constants (like the 14-period RSI or 200 SMA), this protocol calculates its parameters using "Dynamic Entity Gematria." We utilize a custom Python backend to analyze the ASCII vibrational frequencies of specific metaphysical archetypes, reducing them via Tesla's 3-6-9 harmonic principles to derive market-responsive periods. 🧬 WHAT IS ? MPL (Magic Programming Language) is a domain-specific language and research initiative created to explore Technomancy—the art of treating code as a spellbook and the market as a chaotic entity to be tamed. By integrating the logic of ancient Grimoires (such as The Book of Abramelin) with modern Data Science, MPL aims to discover hidden correlations in price action that standard tools overlook. 🔗 CONNECT WITH THE PROJECT: If you are a developer, a trader, or a seeker of hidden knowledge, examine the source code and join the order: • 📂 Official Project Site: hakanovski.github.io • 🐍 MPL Source Code (GitHub): github.com • 👨‍💻 Developer Profile (LinkedIn): www.linkedin.com 🔢 THE ALGORITHM: 452 - 204 - 50 The inputs for this script are mathematically derived signatures of the intelligence governing the system: 1. THE PAIMON TREND (Gravity) • Origin: Derived from the ASCII summation of the archetype PAIMON (King of Secret Knowledge). • Function: This 452-period Baseline acts as the market's "Event Horizon." It represents the deep, structural direction of the asset. • Price > Line: Bullish Domain. • Price < Line: Bearish Void. 2. THE ASTAROTH SIGNAL (Trigger) • Origin: Derived from the ASCII summation of ASTAROTH (Knower of Past & Future), reduced by Tesla’s 3rd Harmonic. • Function: This is the active trigger line. It replaces standard moving averages with a precise, gematria-aligned trajectory. 3. THE VOLATILITY MATRIX (Scalp) • Origin: Based on the 9th Harmonic reduction. • Function: Creates a "Cloud" around the signal line to visualize market noise. 🛡️ THE MILON GATE (Matrix Filter) Unique to this script is the "MILON Gate" toggle found in the settings. • ☑️ Active (Default): The algorithm applies the logic of the MILON Magic Square. Signals are ONLY generated if Volume and Volatility align with the geometric structure of the move. This filters out ~80% of false signals (noise). • ⬜ Inactive: The algorithm operates in "Raw Mode," showing every mathematical crossover without the volume filter. ⚠️ OPERATIONAL USAGE • Timeframe: Optimized for 4H (The Builder) and Daily (The Architect) charts. • Strategy: Use the Black/Grey Line (452) as your directional bias. Take entries only when the "EXECUTE" (Long) or "PURGE" (Short) sigils appear. Use this tool wisely. Risk responsibly. Let the harmonics guide your entries. — Hakan Yorganci Technomancer & Full Stack Developer
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của hknyrgnc
Volume Based Sampling [BackQuant]Volume Based Sampling What this does This indicator converts the usual time-based stream of candles into an event-based stream of “synthetic” bars that are created only when enough trading activity has occurred . You choose the activity definition: Volume bars : create a new synthetic bar whenever the cumulative number of shares/contracts traded reaches a threshold. Dollar bars : create a new synthetic bar whenever the cumulative traded dollar value (price × volume) reaches a threshold. The script then keeps an internal ledger of these synthetic opens, highs, lows, closes, and volumes, and can display them as candles, plot a moving average calculated over the synthetic closes, mark each time a new sample is formed, and optionally overlay the native time-bars for comparison. Why event-based sampling matters Markets do not release information on a clock: activity clusters during news, opens/closes, and liquidity shocks. Event-based bars normalize for that heteroskedastic arrival of information: during active periods you get more bars (finer resolution); during quiet periods you get fewer bars (coarser resolution). Research shows this can reduce microstructure pathologies and produce series that are closer to i.i.d. and more suitable for statistical modeling and ML. In particular: Volume and dollar bars are a common event-time alternative to time bars in quantitative research and are discussed extensively in Advances in Financial Machine Learning (AFML). These bars aim to homogenize information flow by sampling on traded size or value rather than elapsed seconds. The Volume Clock perspective models market activity in “volume time,” showing that many intraday phenomena (volatility, liquidity shocks) are better explained when time is measured by traded volume instead of seconds. Related market microstructure work on flow toxicity and liquidity highlights that the risk dealers face is tied to information intensity of order flow, again arguing for activity-based clocks. How the indicator works (plain English) Choose your bucket type Volume : accumulate volume until it meets a threshold. Dollar Bars : accumulate close × volume until it meets a dollar threshold. Pick the threshold rule Dynamic threshold : by default, the script computes a rolling statistic (mean or median) of recent activity to set the next bucket size. This adapts bar size to changing conditions (e.g., busier sessions produce more frequent synthetic bars). Fixed threshold : optionally override with a constant target (e.g., exactly 100,000 contracts per synthetic bar, or $5,000,000 per dollar bar). Build the synthetic bar While a bucket fills, the script tracks: o_s: first price of the bucket (synthetic open) h_s: running maximum price (synthetic high) l_s: running minimum price (synthetic low) c_s: last price seen (synthetic close) v_s: cumulative native volume inside the bucket d_samples: number of native bars consumed to complete the bucket (a proxy for “how fast” the threshold filled) Emit a new sample Once the bucket meets/exceeds the threshold, a new synthetic bar is finalized and stored. If overflow occurs (e.g., a single native bar pushes you past the threshold by a lot), the code will emit multiple synthetic samples to account for the extra activity. Maintain a rolling history efficiently A ring buffer can overwrite the oldest samples when you hit your Max Stored Samples cap, keeping memory usage stable. Compute synthetic-space statistics The script computes an SMA over the last N synthetic closes and basic descriptors like average bars per synthetic sample, mean and standard deviation of synthetic returns, and more. These are all in event time , not clock time. Inputs and options you will actually use Data Settings Sampling Method : Volume or Dollar Bars. Rolling Lookback : window used to estimate the dynamic threshold from recent activity. Filter : Mean or Median for the dynamic threshold. Median is more robust to spikes. Use Fixed? / Fixed Threshold : override dynamic sizing with a constant target. Max Stored Samples : cap on synthetic history to keep performance snappy. Use Ring Buffer : turn on to recycle storage when at capacity. Indicator Settings SMA over last N samples : moving average in synthetic space . Because its index is sample count, not minutes, it adapts naturally: more updates in busy regimes, fewer in quiet regimes. Visuals Show Synthetic Bars : plot the synthetic OHLC candles. Candle Color Mode : Green/Red: directional close vs open Volume Intensity: opacity scales with synthetic size Neutral: single color Adaptive: graded by how large the bucket was relative to threshold Mark new samples : drop a small marker whenever a new synthetic bar prints. Comparison & Research Show Time Bars : overlay the native time-based candles to visually compare how the two sampling schemes differ. How to read it, step by step Turn on “Synthetic Bars” and optionally overlay “Time Bars.” You will see that during high-activity bursts, synthetic bars print much faster than time bars. Watch the synthetic SMA . Crosses in synthetic space can be more meaningful because each update represents a roughly comparable amount of traded information. Use the “Avg Bars per Sample” in the info table as a regime signal. Falling average bars per sample means activity is clustering, often coincident with higher realized volatility. Try Dollar Bars when price varies a lot but share count does not; they normalize by dollar risk taken in each sample. Volume Bars are ideal when share count is a better proxy for information flow in your instrument. Quant finance background and citations Event time vs. clock time : Easley, López de Prado, and O’Hara advocate measuring intraday phenomena on a volume clock to better align sampling with information arrival. This framing helps explain volatility bursts and liquidity droughts and motivates volume-based bars. Flow toxicity and dealer risk : The same authors show how adverse selection risk changes with the intensity and informativeness of order flow, further supporting activity-based clocks for modeling and risk management. AFML framework : In Advances in Financial Machine Learning , event-driven bars such as volume, dollar, and imbalance bars are presented as superior sampling units for many ML tasks, yielding more stationary features and fewer microstructure distortions than fixed time bars. ( Alpaca ) Practical use cases 1) Regime-aware moving averages The synthetic SMA in event time is not fooled by quiet periods: if nothing of consequence trades, it barely updates. This can make trend filters less sensitive to calendar drift and more sensitive to true participation. 2) Breakout logic on “equal-information” samples The script exposes simple alerts such as breakout above/below the synthetic SMA . Because each bar approximates a constant amount of activity, breakouts are conditioned on comparable informational mass, not arbitrary time buckets. 3) Volatility-adaptive backtests If you use synthetic bars as your base data stream, most signal rules become self-paced : entry and exit opportunities accelerate in fast markets and slow down in quiet regimes, which often improves the realism of slippage and fill modeling in research pipelines (pair this indicator with strategy code downstream). 4) Regime diagnostics Avg Bars per Sample trending down: activity is dense; expect larger realized ranges. Return StdDev (synthetic) rising: noise or trend acceleration in event time; re-tune risk. Interpreting the info panel Method : your sampling choice and current threshold. Total Samples : how many synthetic bars have been formed. Current Vol/Dollar : how much of the next bucket is already filled. Bars in Bucket : native bars consumed so far in the current bucket. Avg Bars/Sample : lower means higher trading intensity. Avg Return / Return StdDev : return stats computed over synthetic closes . Research directions you can build from here Imbalance and run bars Extend beyond pure volume or dollar thresholds to imbalance bars that trigger on directional order flow imbalance (e.g., buy volume minus sell volume), as discussed in the AFML ecosystem. These often further homogenize distributional properties used in ML. alpaca.markets Volume-time indicators Re-compute classical indicators (RSI, MACD, Bollinger) on the synthetic stream. The premise is that signals are updated by traded information , not seconds, which may stabilize indicator behavior in heteroskedastic regimes. Liquidity and toxicity overlays Combine synthetic bars with proxies of flow toxicity to anticipate spread widening or volatility clustering. For instance, tag synthetic bars that surpass multiples of the threshold and test whether subsequent realized volatility is elevated. Dollar-risk parity sampling for portfolios Use dollar bars to align samples across assets by notional risk, enabling cleaner cross-asset features and comparability in multi-asset models (e.g., correlation studies, regime clustering). AFML discusses the benefits of event-driven sampling for cross-sectional ML feature engineering. Microstructure feature set Compute duration in native bars per synthetic sample , range per sample , and volume multiple of threshold as inputs to state classifiers or regime HMMs . These features are inherently activity-aware and often predictive of short-horizon volatility and trend persistence per the event-time literature. ( Alpaca ) Tips for clean usage Start with dynamic thresholds using Median over a sensible lookback to avoid outlier distortion, then move to Fixed thresholds when you know your instrument’s typical activity scale. Compare time bars vs synthetic bars side by side to develop intuition for how your market “breathes” in activity time. Keep Max Stored Samples reasonable for performance; the ring buffer avoids memory creep while preserving a rolling window of research-grade data.
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của BackQuant
Time-Decaying Percentile Oscillator [BackQuant]Time-Decaying Percentile Oscillator 1. Big-picture idea Traditional percentile or stochastic oscillators treat every bar in the look-back window as equally important. That is fine when markets are slow, but if volatility regime changes quickly yesterday’s print should matter more than last month’s. The Time-Decaying Percentile Oscillator attempts to fix that blind spot by assigning an adjustable weight to every past price before it is ranked. The result is a percentile score that “breathes” with market tempo much faster to flag new extremes yet still smooth enough to ignore random noise. 2. What the script actually does Build a weight curve • You pick a look-back length (default 28 bars). • You decide whether weights fall Linearly , Exponentially , by Power-law or Logarithmically . • A decay factor (lower = faster fade) shapes how quickly the oldest price loses influence. • The array is normalised so all weights still sum to 1. Rank prices by weighted mass • Every close in the window is paired with its weight. • The pairs are sorted from low to high. • The cumulative weight is walked until it equals your chosen percentile level (default 50 = median). • That price becomes the Time-Decayed Percentile . Find dispersion with robust statistics • Instead of a fragile standard deviation the script measures weighted Median-Absolute-Deviation about the new percentile. • You multiply that deviation by the Deviation Multiplier slider (default 1.0) to get a non-parametric volatility band. Build an adaptive channel • Upper band = percentile + (multiplier × deviation) • Lower band = percentile – (multiplier × deviation) Normalise into a 0-100 oscillator • The current close is mapped inside that band: 0 = lower band, 50 = centre, 100 = upper band. • If the channel squeezes, tiny moves still travel the full scale; if volatility explodes, it automatically widens. Optional smoothing • A second-stage moving average (EMA, SMA, DEMA, TEMA, etc.) tames the jitter. • Length 22 EMA by default—change it to tune reaction speed. Threshold logic • Upper Threshold 70 and Lower Threshold 30 separate standard overbought/oversold states. • Extreme bands 85 and 15 paint background heat when aggressive fade or breakout trades might trigger. Divergence engine • Looks back twenty bars. • Flags Bullish divergence when price makes a lower low but oscillator refuses to confirm (value < 40). • Flags Bearish divergence when price prints a higher high but oscillator stalls (value > 60). 3. Component walk-through • Source – Any price series. Close by default, switch to typical price or custom OHLC4 for futures spreads. • Look-back Period – How many bars to rank. Short = faster, long = slower. • Base Percentile Level – 50 shows relative position around the median; set to 25 / 75 for quartile tracking or 90 / 10 for extreme tails. • Deviation Multiplier – Higher values widen the dynamic channel, lowering whipsaw but delaying signals. • Decay Settings – Type decides the curve shape. Exponential (default 1.16) mimics EMA logic. – Factor < 1 shrinks influence faster; > 1 spreads influence flatter. – Toggle Enable Time Decay off to compare with classic equal-weight stochastic. • Smoothing Block – Choose one of seven MA flavours plus length. • Thresholds – Overbought / Oversold / Extreme levels. Push them out when working on very mean-reverting assets like FX; pull them in for trend monsters like crypto. • Display toggles – Show or hide threshold lines, extreme filler zones, bar colouring, divergence labels. • Colours – Bullish green, bearish red, neutral grey. Every gradient step is automatically blended to generate a heat map across the 0-100 range. 4. How to read the chart • Oscillator creeping above 70 = market auctioning near the top of its adaptive range. • Fast poke above 85 with no follow-through = exhaustion fade candidate. • Slow grind that lives above 70 for many bars = valid bullish trend, not a fade. • Cross back through 50 shows balance has shifted; treat it like a micro trend change. • Divergence arrows add extra confidence when you already see two-bar reversal candles at range extremes. • Background shading (semi-transparent red / green) warns of extreme states and throttles your position size. 5. Practical trading playbook Mean-reversion scalps  1. Wait for oscillator to reach your desired OB/ OS levels  2. Check the slope of the smoothing MA—if it is flattening the squeeze is mature.  3. Look for a one- or two-bar reversal pattern.  4. Enter against the move; first target = midline 50, second target = opposite threshold.  5. Stop loss just beyond the extreme band. Trend continuation pullbacks  1. Identify a clean directional trend on the price chart.  2. During the trend, TDP will oscillate between midline and extreme of that side.  3. Buy dips when oscillator hits OS levels, and the same for OB levels & shorting  4. Exit when oscillator re-tags the same-side extreme or prints divergence. Volatility regime filter  • Use the Enable Time Decay switch as a regime test.  • If equal-weight oscillator and decayed oscillator diverge widely, market is entering a new volatility regime—tighten stops and trade smaller. Divergence confirmation for other indicators  • Pair TDP divergence arrows with MACD histogram or RSI to filter false positives.  • The weighted nature means TDP often spots divergence a bar or two earlier than standard RSI. Swing breakout strategy  1. During consolidation, band width compresses and oscillator oscillates around 50.  2. Watch for sudden expansion where oscillator blasts through extreme bands and stays pinned.  3. Enter with momentum in breakout direction; trail stop behind upper or lower band as it re-expands. 6. Customising decay mathematics Linear – Each older bar loses the same fixed amount of influence. Intuitive and stable; good for slow swing charts. Exponential – Influence halves every “decay factor” steps. Mirrors EMA thinking and is fastest to react. Power-law – Mid-history bars keep more authority than exponential but oldest data still fades. Handy for commodities where seasonality matters. Logarithmic – The gentlest curve; weight drops sharply at first then levels off. Mimics how traders remember dramatic moves for weeks but forget ordinary noise quickly. Turn decay off to verify the tool’s added value; most users never switch back. 7. Alert catalogue • TD Overbought / TD Oversold – Cross of regular thresholds. • TD Extreme OB / OS – Breach of danger zones. • TD Bullish / Bearish Divergence – High-probability reversal watch. • TD Midline Cross – Momentum shift that often precedes a window where trend-following systems perform. 8. Visual hygiene tips • If you already plot price on a dark background pick Bullish Color and Bearish Color default; change to pastel tones for light themes. • Hide threshold lines after you memorise the zones to declutter scalping layouts. • Overlay mode set to false so the oscillator lives in its own panel; keep height about 30 % of screen for best resolution. 9. Final notes Time-Decaying Percentile Oscillator marries robust statistical ranking, adaptive dispersion and decay-aware weighting into a simple oscillator. It respects both recent order-flow shocks and historical context, offers granular control over responsiveness and ships with divergence and alert plumbing out of the box. Bolt it onto your price action framework, trend-following system or volatility mean-reversion playbook and see how much sooner it recognises genuine extremes compared to legacy oscillators. Backtest thoroughly, experiment with decay curves on each asset class and remember: in trading, timing beats timidity but patience beats impulse. May this tool help you find that edge.
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của BackQuant
Fuzzy SMA Trend Analyzer (experimental)[FibonacciFlux]Fuzzy SMA Trend Analyzer (Normalized): Advanced Market Trend Detection Using Fuzzy Logic Theory Elevate your technical analysis with institutional-grade fuzzy logic implementation Research Genesis & Conceptual Framework This indicator represents the culmination of extensive research into applying fuzzy logic theory to financial markets. While traditional technical indicators often produce binary outcomes, market conditions exist on a continuous spectrum. The Fuzzy SMA Trend Analyzer addresses this limitation by implementing a sophisticated fuzzy logic system that captures the nuanced, multi-dimensional nature of market trends. Core Fuzzy Logic Principles At the heart of this indicator lies fuzzy logic theory - a mathematical framework designed to handle imprecision and uncertainty: // Improved fuzzy_triangle function with guard clauses for NA and invalid parameters. fuzzy_triangle(val, left, center, right) => if na(val) or na(left) or na(center) or na(right) or left > center or center > right // Guard checks 0.0 else if left == center and center == right // Crisp set (single point) val == center ? 1.0 : 0.0 else if left == center // Left-shoulder shape (ramp down from 1 at center to 0 at right) val >= right ? 0.0 : val <= center ? 1.0 : (right - val) / (right - center) else if center == right // Right-shoulder shape (ramp up from 0 at left to 1 at center) val <= left ? 0.0 : val >= center ? 1.0 : (val - left) / (center - left) else // Standard triangle math.max(0.0, math.min((val - left) / (center - left), (right - val) / (right - center))) This implementation of triangular membership functions enables the indicator to transform crisp numerical values into degrees of membership in linguistic variables like "Large Positive" or "Small Negative," creating a more nuanced representation of market conditions. Dynamic Percentile Normalization A critical innovation in this indicator is the implementation of percentile-based normalization for SMA deviation: // ----- Deviation Scale Estimation using Percentile ----- // Calculate the percentile rank of the *absolute* deviation over the lookback period. // This gives an estimate of the 'typical maximum' deviation magnitude recently. diff_abs_percentile = ta.percentile_linear_interpolation(math.abs(raw_diff), normLookback, percRank) + 1e-10 // ----- Normalize the Raw Deviation ----- // Divide the raw deviation by the estimated 'typical max' magnitude. normalized_diff = raw_diff / diff_abs_percentile // ----- Clamp the Normalized Deviation ----- normalized_diff_clamped = math.max(-3.0, math.min(3.0, normalized_diff)) This percentile normalization approach creates a self-adapting system that automatically calibrates to different assets and market regimes. Rather than using fixed thresholds, the indicator dynamically adjusts based on recent volatility patterns, significantly enhancing signal quality across diverse market environments. Multi-Factor Fuzzy Rule System The indicator implements a comprehensive fuzzy rule system that evaluates multiple technical factors: SMA Deviation (Normalized): Measures price displacement from the Simple Moving Average Rate of Change (ROC): Captures price momentum over a specified period Relative Strength Index (RSI): Assesses overbought/oversold conditions These factors are processed through a sophisticated fuzzy inference system with linguistic variables: // ----- 3.1 Fuzzy Sets for Normalized Deviation ----- diffN_LP := fuzzy_triangle(normalized_diff_clamped, 0.7, 1.5, 3.0) // Large Positive (around/above percentile) diffN_SP := fuzzy_triangle(normalized_diff_clamped, 0.1, 0.5, 0.9) // Small Positive diffN_NZ := fuzzy_triangle(normalized_diff_clamped, -0.2, 0.0, 0.2) // Near Zero diffN_SN := fuzzy_triangle(normalized_diff_clamped, -0.9, -0.5, -0.1) // Small Negative diffN_LN := fuzzy_triangle(normalized_diff_clamped, -3.0, -1.5, -0.7) // Large Negative (around/below percentile) // ----- 3.2 Fuzzy Sets for ROC ----- roc_HN := fuzzy_triangle(roc_val, -8.0, -5.0, -2.0) roc_WN := fuzzy_triangle(roc_val, -3.0, -1.0, -0.1) roc_NZ := fuzzy_triangle(roc_val, -0.3, 0.0, 0.3) roc_WP := fuzzy_triangle(roc_val, 0.1, 1.0, 3.0) roc_HP := fuzzy_triangle(roc_val, 2.0, 5.0, 8.0) // ----- 3.3 Fuzzy Sets for RSI ----- rsi_L := fuzzy_triangle(rsi_val, 0.0, 25.0, 40.0) rsi_M := fuzzy_triangle(rsi_val, 35.0, 50.0, 65.0) rsi_H := fuzzy_triangle(rsi_val, 60.0, 75.0, 100.0) Advanced Fuzzy Inference Rules The indicator employs a comprehensive set of fuzzy rules that encode expert knowledge about market behavior: // --- Fuzzy Rules using Normalized Deviation (diffN_*) --- cond1 = math.min(diffN_LP, roc_HP, math.max(rsi_M, rsi_H)) // Strong Bullish: Large pos dev, strong pos roc, rsi ok strength_SB := math.max(strength_SB, cond1) cond2 = math.min(diffN_SP, roc_WP, rsi_M) // Weak Bullish: Small pos dev, weak pos roc, rsi mid strength_WB := math.max(strength_WB, cond2) cond3 = math.min(diffN_SP, roc_NZ, rsi_H) // Weakening Bullish: Small pos dev, flat roc, rsi high strength_N := math.max(strength_N, cond3 * 0.6) // More neutral strength_WB := math.max(strength_WB, cond3 * 0.2) // Less weak bullish This rule system evaluates multiple conditions simultaneously, weighting them by their degree of membership to produce a comprehensive trend assessment. The rules are designed to identify various market conditions including strong trends, weakening trends, potential reversals, and neutral consolidations. Defuzzification Process The final step transforms the fuzzy result back into a crisp numerical value representing the overall trend strength: // --- Step 6: Defuzzification --- denominator = strength_SB + strength_WB + strength_N + strength_WBe + strength_SBe if denominator > 1e-10 // Use small epsilon instead of != 0.0 for float comparison fuzzyTrendScore := (strength_SB * STRONG_BULL + strength_WB * WEAK_BULL + strength_N * NEUTRAL + strength_WBe * WEAK_BEAR + strength_SBe * STRONG_BEAR) / denominator The resulting FuzzyTrendScore ranges from -1 (strong bearish) to +1 (strong bullish), providing a smooth, continuous evaluation of market conditions that avoids the abrupt signal changes common in traditional indicators. Advanced Visualization with Rainbow Gradient The indicator incorporates sophisticated visualization using a rainbow gradient coloring system: // Normalize score to for gradient function normalizedScore = na(fuzzyTrendScore) ? 0.5 : math.max(0.0, math.min(1.0, (fuzzyTrendScore + 1) / 2)) // Get the color based on gradient setting and normalized score final_color = get_gradient(normalizedScore, gradient_type) This color-coding system provides intuitive visual feedback, with color intensity reflecting trend strength and direction. The gradient can be customized between Red-to-Green or Red-to-Blue configurations based on user preference. Practical Applications The Fuzzy SMA Trend Analyzer excels in several key applications: Trend Identification: Precisely identifies market trend direction and strength with nuanced gradation Market Regime Detection: Distinguishes between trending markets and consolidation phases Divergence Analysis: Highlights potential reversals when price action and fuzzy trend score diverge Filter for Trading Systems: Provides high-quality trend filtering for other trading strategies Risk Management: Offers early warning of potential trend weakening or reversal Parameter Customization The indicator offers extensive customization options: SMA Length: Adjusts the baseline moving average period ROC Length: Controls momentum sensitivity RSI Length: Configures overbought/oversold sensitivity Normalization Lookback: Determines the adaptive calculation window for percentile normalization Percentile Rank: Sets the statistical threshold for deviation normalization Gradient Type: Selects the preferred color scheme for visualization These parameters enable fine-tuning to specific market conditions, trading styles, and timeframes. Acknowledgments The rainbow gradient visualization component draws inspiration from LuxAlgo's "Rainbow Adaptive RSI" (used under CC BY-NC-SA 4.0 license). This implementation of fuzzy logic in technical analysis builds upon Fermi estimation principles to overcome the inherent limitations of crisp binary indicators. This indicator is shared under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. Remember that past performance does not guarantee future results. Always conduct thorough testing before implementing any technical indicator in live trading.
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của FibonacciFlux
Fibonacci Cycle Finder🟩 Fibonacci Cycle Finder is an indicator designed to explore Fibonacci-based waves and cycles through visualization and experimentation, introducing a trigonometric approach to market structure analysis. Unlike traditional Fibonacci tools that rely on static horizontal levels, this indicator incorporates the dynamic nature of market cycles, using adjustable wavelength, phase, and amplitude settings to visualize the rhythm of price movements. By applying a sine function, it provides a structured way to examine Fibonacci relationships in a non-linear context. Fibonacci Cycle Finder unifies Fibonacci principles with a wave-based method by employing adjustable parameters to align each wave with real-time price action. By default, the wave begins with minimal curvature, preserving the structural familiarity of horizontal Fibonacci retracements. By adjusting the input parameters, the wave can subtly transition from a horizontal line to a more pronounced cycle,visualizing cyclical structures within price movement. This projective structure extends potential cyclical outlines on the chart, opening deeper exploration of how Fibonacci relationships may emerge over time. Fibonacci Cycle Finder further underscores a non-linear representation of price by illustrating how wave-based logic can uncover shifts that are missed by static retracement tools. Rather than imposing immediate oscillatory behavior, the indicator encourages a progressive approach, where the parameters may be incrementally modified to align wave structures with observed price action. This refinement process deepens the exploration of Fibonacci relationships, offering a systematic way to experiment with non-linear price dynamics. In doing so, it revisits fundamental Fibonacci concepts, demonstrating their broader adaptability beyond fixed horizontal retracements. 🌀 THEORY & CONCEPT 🌀 What if Fibonacci relationships could be visualized as dynamic waves rather than confined to fixed horizontal levels? Fibonacci Cycle Finder introduces a trigonometric approach to market structure analysis, offering a different perspective on Fibonacci-based cycles. This tool provides a way to visualize market fluctuations through cyclical wave motion, opening the door to further exploration of Fibonacci’s role in non-linear price behavior. Traditional Fibonacci tools, such as retracements and extensions, have long been used to identify potential support and resistance levels. While valuable for analyzing price trends, these tools assume linear price movement and rely on static horizontal levels. However, market fluctuations often exhibit cyclical tendencies , where price follows natural wave-like structures rather than strictly adhering to fixed retracement points. Although Fibonacci-based tools such as arcs, fans, and time zones attempt to address these patterns, they primarily apply geometric projections. The Fibonacci Cycle Finder takes a different approach by mapping Fibonacci ratios along structured wave cycles, aligning these relationships with the natural curvature of market movement rather than forcing them onto rigid price levels. Rather than replacing traditional Fibonacci methods, the Fibonacci Cycle Finder supplements existing Fibonacci theory by introducing an exploratory approach to price structure analysis. It encourages traders to experiment with how Fibonacci ratios interact with cyclical price structures, offering an additional layer of insight beyond static retracements and extensions. This approach allows Fibonacci levels to be examined beyond their traditional static form, providing deeper insights into market fluctuations. 📊 FIBONACCI WAVE IMPLEMENTATION 📊 The Fibonacci Cycle Finder uses two user-defined swing points, A and B, as the foundation for projecting these Fibonacci waves. It first establishes standard horizontal levels that correspond to traditional Fibonacci retracements, ensuring a baseline reference before wave adjustments are applied. By default, the wave is intentionally subtle— Wavelength is set to 1 , Amplitude is set to 1 , and Phase is set to 0 . In other words, the wave starts as “stretched out.” This allows a slow, measured start, encouraging users to refine parameters incrementally rather than producing abrupt oscillations. As these parameters are increased, the wave takes on more distinct sine and cosine characteristics, offering a flexible approach to exploring Fibonacci-based cyclicity within price action. Three parameters control the shape of the Fibonacci wave: 1️⃣ Wavelength Controls the horizontal spacing of the wave along the time axis, determining the length of one full cycle from peak to peak (or trough to trough). In this indicator, Wavelength acts as a scaling input that adjusts how far the wave extends across time, rather than a strict mathematical “wavelength.” Lower values further stretch the wave, increasing the spacing between oscillations, while higher values compress it into a more frequent cycle. Each full cycle is divided into four quarter-cycle segments, a deliberate design choice to minimize curvature by default. This allows for subtle oscillations and smoother transitions, preventing excessive distortion while maintaining flexibility in wave projections. The wavelength is calculated relative to the A-B swing, ensuring that its scale adapts dynamically to the selected price range. 2️⃣ Amplitude Defines the vertical displacement of the wave relative to the baseline Fibonacci level. Higher values increase the height of oscillations, while lower values reduce the height, Negative values will invert the wave’s initial direction. The amplitude is dynamically applied in relation to the A-B swing direction, ensuring that an upward swing results in upward oscillations and a downward swing results in downward oscillations. 3️⃣ Phase Shifts the wave’s starting position along its cycle, adjusting alignment relative to the swing points. A phase of 0 aligns with a sine wave, where the cycle starts at zero and rises. A phase of 25 aligns with a cosine wave, starting at a peak and descending. A phase of 50 inverts the sine wave, beginning at zero but falling first, while a phase of 75 aligns with an inverted cosine , starting at a trough and rising. Intermediate values between these phases create gradual shifts in wave positioning, allowing for finer alignment with observed market structures. By fine-tuning these parameters, users can adapt Fibonacci waves to better reflect observed market behaviors. The wave structure integrates with price movements rather than simply overlaying static levels, allowing for a more dynamic representation of cyclical price tendencies. This indicator serves as an exploratory tool for understanding potential market rhythms, encouraging traders to test and visualize how Fibonacci principles extend beyond their traditional applications. 🖼️ CHART EXAMPLES 🖼️ Following this downtrend, price interacts with curved Fibonacci levels, highlighting resistance at the 0.236 and 0.382 levels, where price stalls before pulling back. Support emerges at the 0.5, 0.618, and 0.786 levels, where price finds stability and rebounds In this Fibonacci retracement, price initially finds support at the 1.0 level, following the natural curvature of the cycle. Resistance forms at 0.786, leading to a pullback before price breaks through and tests 0.618 as resistance. Once 0.618 is breached, price moves upward to test 0.5, illustrating how Fibonacci-based cycles may align with evolving market structure beyond static, horizontal retracements. Following this uptrend, price retraces downward and interacts with the Fibonacci levels, demonstrating both support and resistance at key levels such as 0.236, 0.382, 0.5, and 0.618. With only the 0.5 and 1.0 levels enabled, this chart remains uncluttered while still highlighting key price interactions. The short cycle length results in a mild curvature, aligning smoothly with market movement. Price finds resistance at the 0.5 level while showing strong support at 1.0, which follows the natural flow of the market. Keeping the focus on fewer levels helps maintain clarity while still capturing how price reacts within the cycle. 🛠️ CONFIGURATION AND SETTINGS 🛠️ Wave Parameters Wavelength : Stretches or compresses the wave along the time axis, determining the length of one full cycle. Higher values extend the wave across more bars, while lower values compress it into a shorter time frame. Amplitude : Expands or contracts the wave along the price axis, determining the height of oscillations relative to Fibonacci levels. Higher values increase the vertical range, while negative values invert the wave’s initial direction. Phase : Offsets the wave along the time axis, adjusting where the cycle begins. Higher values shift the starting position forward within the wave pattern. Fibonacci Levels Levels : Enable or disable specific Fibonacci levels (0.0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.0) to focus on relevant price zones. Color : Modify level colors for enhanced visual clarity. Visibility Trend Line/Color : Toggle and customize the trend line connecting swing points A and B. Setup Lines : Show or hide lines linking Fibonacci levels to projected waves. A/B Labels Visibility : Control the visibility of swing point labels. Left/Right Labels : Manage the display of Fibonacci level labels on both sides of the chart. Fill % : Adjust shading intensity between Fibonacci levels (0% = no fill, 100% = maximum fill). A and B Points (Time/Price): These user-defined anchor points serve as the basis for Fibonacci wave calculations and can be manually set. A and B points can also be adjusted directly on the chart, with automatic synchronization to the settings panel, allowing for seamless modifications without needing to manually input values. ⚠️ DISCLAIMER ⚠️ The Fibonacci Cycle Finder is a visual analysis tool designed to illustrate Fibonacci relationships and serve as a supplement to traditional Fibonacci tools. While the indicator employs mathematical and geometric principles, no guarantee is made that its calculations will align with other Fibonacci tools or proprietary methods. Like all technical and visual indicators, the Fibonacci levels generated by this tool may appear to visually align with key price zones in hindsight. However, these levels are not intended as standalone signals for trading decisions. This indicator is intended for educational and analytical purposes, complementing other tools and methods of market analysis. 🧠 BEYOND THE CODE 🧠 Fibonacci Cycle Finder is the latest indicator in the Fibonacci Geometry Series. Building on the concepts of the Fibonacci Time-Price Zones and the Fibonacci 3-D indicators, this tool introduces a trigonometric approach to market structure analysis. The Fibonacci Cycle Finder indicator, like other xxattaxx indicators , is designed to encourage both education and community engagement. Your feedback and insights are invaluable to refining and enhancing the Fibonacci Cycle Finder indicator. We look forward to the creative applications, observations, and discussions this tool inspires within the trading community.
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của xxattaxx
9
Xmaster Formula Indicator [TradingFinder] No Repaint Strategies🔵 Introduction The Xmaster Formula Indicator is a powerful tool for forex trading, combining multiple technical indicators to provide insights into market trends, support and resistance levels, and price reversals. Developed in the early 2010s, it is widely valued for generating reliable buy and sell signals. Key components include Exponential Moving Averages (EMA) for identifying trends and price momentum, and MACD (Moving Average Convergence Divergence) for analyzing trend strength and direction. The Stochastic Oscillator and RSI (Relative Strength Index) enhance accuracy by signaling potential price reversals. Additionally, the Parabolic SAR assists in identifying trend reversals and managing risk. By integrating these tools, the Xmaster Formula Indicator provides a comprehensive view of market conditions, empowering traders to make informed decisions. 🔵 How to Use The Xmaster Formula Indicator offers two distinct methods for generating signals: Standard Mode and Advance Mode. Each method caters to different trading styles and strategies. Standard Mode : In Standard Mode, the indicator uses normalized moving average data to generate buy and sell signals. The difference between the short-term (10-period) and long-term (38-period) EMAs is calculated and normalized to a 0-100 scale. Buy Signal : When the normalized value crosses above 55, accompanied by the trend line turning green, a buy signal is generated. Sell Signal : When the normalized value crosses below 45, and the trend line turns red, a sell signal is issued. This mode is simple, making it ideal for traders looking for straightforward signals without the need for additional confirmations. Advance Mode : Advance Mode combines multiple technical indicators to provide more detailed and robust signals. This method analyzes trends by incorporating : 🟣 MACD Buy Signal : When the MACD histogram bars are positive. Sell Signal : When the MACD histogram bars are negative. 🟣 RSI Buy Signal : When RSI is below 30, indicating oversold conditions. Sell Signal : When RSI is above 70, suggesting overbought conditions. 🟣 Stochastic Oscillator Buy Signal : When Stochastic is below 20. Sell Signal : When Stochastic is above 80. 🟣 Parabolic SAR Buy Signal : When SAR is below the price. Sell Signal : When SAR is above the price. A signal is generated in Advance Mode only when all these indicators align : Buy Signal : All conditions point to a bullish trend. Sell Signal : All conditions indicate a bearish trend. This mode is more comprehensive and suitable for traders who prefer deeper analysis and stronger confirmations before executing trades. 🔵 Settings Method : Choose between "Standard" and "Advance" modes to determine how signals are generated. In Standard Mode, signals are based on normalized moving average data, while in Advance Mode, signals rely on the combination of MACD, RSI, Stochastic Oscillator, and Parabolic SAR. Moving Average Settings : Short Length : The period for the short-term EMA (default is 10). Mid Length : The period for the medium-term EMA (default is 20). Long Length : The period for the long-term EMA (default is 38). MACD Settings : Fast Length : The period for the fast EMA in the MACD calculation (default is 12). Slow Length : The period for the slow EMA in the MACD calculation (default is 26). Signal Line : The signal line period for MACD (default is 9). Stochastic Settings : Length : The period for the Stochastic Oscillator (default is 14). RSI Settings : Length : The period for the Relative Strength Index (default is 14). 🔵 Conclusion The Xmaster Formula Indicator is a versatile and reliable tool for forex traders, offering both simplicity and advanced analysis through its Standard and Advance modes. In Standard Mode, traders benefit from straightforward signals based on normalized moving average data, making it ideal for quick decision-making. Advance Mode, on the other hand, provides a more detailed analysis by combining multiple indicators like MACD, RSI, Stochastic Oscillator, and Parabolic SAR, delivering stronger confirmations for critical market decisions. While the Xmaster Formula Indicator offers valuable insights and reliable signals, it is important to use it alongside proper risk management and other analytical methods. By leveraging its capabilities effectively, traders can enhance their trading strategies and achieve better outcomes in the dynamic forex market.
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của TradingFinder
7
DemaRSI StrategyThis is a repost to a old script that cant be updated anymore, the request was made on Feb, 27, 2016. Here's a engaging description for the tradingview script: **DemaRSI Strategy: A Proven Trading System** Join thousands of traders who have already experienced the power of this highly effective strategy. The DemaRSI system combines two powerful indicators - DEMA (Double Exponential Moving Average) and RSI (Relative Strength Index) - to generate profitable trades with minimal risk. **Key Features:** * **Trend-Following**: Our algorithm identifies strong trends using a combination of DEMA and RSI, allowing you to ride the waves of market momentum. * **Risk Management**: The system includes built-in stop-loss and take-profit levels, ensuring that your gains are protected and losses are minimized. * **Session-Based Trading**: Trade during specific sessions only (e.g., London or New York) for even more targeted results. * **Customizable Settings**: Adjust the length of moving averages, RSI periods, and other parameters to suit your trading style. **What You'll Get:** * A comprehensive strategy that can be used with any broker or platform * Easy-to-use interface with customizable settings * Real-time performance metrics and backtesting capabilities **Start Trading Like a Pro Today!** This script is designed for intermediate to advanced traders who want to take their trading game to the next level. With its robust risk management features, this strategy can help you achieve consistent profits in various market conditions. **Disclaimer:** This script is not intended as investment advice and should be used at your own discretion. Trading carries inherent risks, and losses are possible. ~Llama3
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của RicardoSantos
1
Papercuts Recency CandlesPapercuts Recency Candles V0.8 by Joel Eckert @PapercutsTrading ***This is currently an experimental visual exploratory concept.*** *** Experimental tools should only be explored by fellow coders and experienced traders.*** DESCRIPTION: As coders, how can we seamlessly transition between actual and smoothed price data sets as data ages? This is a visual experiment to see if and how data can be smoothly transitioned from one value to another over a set number of candles. If we visualize a chart in 3 zones, a head, a body, and a tail we can start to understand how this could work. The head zone would represent the first data set of actual asset prices. The body zone would represent the transition period from the first to the to the second data set. Last, the tail zone would represent the second data set made of a Hull Moving Average of the asset. CONCEPT: It is conceived that data and position precision constantly shift as they decay or age, therefore making older price levels act more like price regions or zones vs exact price points. This is what I am calling Recency. This indicator utilizes the concept of "Recency" to explore the possibility of a new style of candle. It aims to maintain accurately on recent prices action but loosen up accuracy on older price action. The very nature of this requires ALTERING HISTORICAL DATA within the body zone or transition candles to achieve the effect. It is similar to trying to merge a line chart type with a candle chart type. This experiment of using recency for candles was to create candles that stay more accurate near current price but fade away into a simple line as they age out, resulting in a simplified view of the big picture which consists of older price action. This experimental design theoretically will help you stay focused only on what is currently unfolding and to minimize distractions from older price nuances. USAGE: WHO: This is not recommended for new traders or novices that are unfamiliar with standard tools. Standardized tools should always be used to get grounded and build a foundation. Active traders who are familiar with trading comfortably should experiment with this to see if they find it interesting or usable. Pine coders may find this concept interesting enough, and may adapt the idea to other elements of their own scripts if they find it interesting… I just ask they give credit where credit is due. HOW: The best way to visualize how this works is to do the following: Load it on a chart. Turn off Standard candles in Chart Setting of the current window. I actually just turn off the bodies and borders, and dim the old wicks as I like the way the old wicks look when left alone with these new candles. Enable chart replay at a faster speed, like 3x, and play back the chart to watch the behavior of the candles. You’ll be able to see how the head of the candle type preserves OHLC, and indicates direction but as the candle starts to age it progressively flowers into the HMA While it plays back try adjusting settings to see how they affect behavior. You can see the data average in real-time which often reveals how unstable actual price noise really is. The head candle diagonals indicate the candle body direction. SETTINGS: Coloring: You can choose your own bullish or bearish colors to match your scheme. Price Line: The price line is colored according to the trend and Head Length: These candles are true to the source high and low. They remain slightly brighter than transition candles. We have a max of 50 to keep things responsive. Time Decay Length: This is the amount of candles it takes to transition to the tail. Max is 300 to keep things responsive. Decay Continuity: This forces transition candles to complete the HMA curve instead of creating gaps when conforming to it. The best way to visualize this feature is to run a 3x replay of an asset, and toggle the result on and off. On is preferred. Tail HMA Length: This is the smoothing amount for the resulting HMA stepline that calculates every close, but has a delayed draw until after the transition candles. You can optionally turn off the delayed visibility to help with comprehension. Tail HMA Weight: This is simply an option to make the tail thicker or thinner. This also adjusts the border on the head candles to help them stand out. Show Side Bias Dots: Default true: Draws a dot when bias to one side changes to help keep you on the right side of trade. Side bias is simply the alignment of 3 moving averages in one direction. IMPORTANT NOTES: You'll have to turn off or dim the standard candles in your view "Chart Settings" to see this properly. Be aware that since the candles are based on boxes and utilize the “recency concept”, which means their data decays and changes as it ages. This results in a cleaner chart overall, but exact highs and lows will be averaged out as the data decays, forming a Hull Moving Average stepline of your defined length once decay has finished. SUMMARY OF HOW IT WORKS: First it takes candle information and creates unique boxes that represent each candle based on the high and low. It utilizes boxes because standard candles once written, cannot be later altered or removed… which is a key element for this effect to work. Next it creates a second box and line from open to close for the body of the Head candles. This indicates direction at a glance. As candles age beyond the defined distance of the “Head” they enter the "Body" aka "Time Decay" zone. Here the accuracy of the high and low will be averaged down using an incremental factor of the HMA, defined by "Time Decay Length" amount of candles. The resulting tail is an HMA of Tail HMA Length. This tail is always calculate at close, but is not drawn instantly. The draw is delayed so that there is not overlapping data, and this makes the effect look more elegant. There are also two EMAs within the script that do nothing but help candle coloring and help provide a trade side bias. When both EMA's and the HMA align, a side bias is defined. Only when the side bias changes will a new dot is formed. Head candles have been simplified from previous versions to be easier to read at a a glance.
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của PapercutsTrading
1
Advanced Dynamic Threshold RSI [Elysian_Mind]Advanced Dynamic Threshold RSI Indicator Overview The Advanced Dynamic Threshold RSI Indicator is a powerful tool designed for traders seeking a unique approach to RSI-based signals. This indicator combines traditional RSI analysis with dynamic threshold calculation and optional Bollinger Bands to generate weighted buy and sell signals. Features Dynamic Thresholds: The indicator calculates dynamic thresholds based on market volatility, providing more adaptive signal generation. Performance Analysis: Users can evaluate recent price performance to further refine signals. The script calculates the percentage change over a specified lookback period. Bollinger Bands Integration: Optional integration of Bollinger Bands for additional confirmation and visualization of potential overbought or oversold conditions. Customizable Settings: Traders can easily customize key parameters, including RSI length, SMA length, lookback bars, threshold multiplier, and Bollinger Bands parameters. Weighted Signals: The script introduces a unique weighting mechanism for signals, reducing false positives and improving overall reliability. Underlying Calculations and Methods 1. Dynamic Threshold Calculation: The heart of the Advanced Dynamic Threshold RSI Indicator lies in its ability to dynamically calculate thresholds based on multiple timeframes. Let's delve into the technical details: RSI Calculation: For each specified timeframe (1-hour, 4-hour, 1-day, 1-week), the Relative Strength Index (RSI) is calculated using the standard 14-period formula. SMA of RSI: The Simple Moving Average (SMA) is applied to each RSI, resulting in the smoothing of RSI values. This smoothed RSI becomes the basis for dynamic threshold calculations. Dynamic Adjustment: The dynamically adjusted threshold for each timeframe is computed by adding a constant value (5 in this case) to the respective SMA of RSI. This dynamic adjustment ensures that the threshold reflects changing market conditions. 2. Weighted Signal System: To enhance the precision of buy and sell signals, the script introduces a weighted signal system. Here's how it works technically: Signal Weighting: The script assigns weights to buy and sell signals based on the crossover and crossunder events between RSI and the dynamically adjusted thresholds. If a crossover event occurs, the weight is set to 2; otherwise, it remains at 1. Signal Combination: The weighted buy and sell signals from different timeframes are combined using logical operations. A buy signal is generated if the product of weights from all timeframes is equal to 2, indicating alignment across timeframe. 3. Experimental Enhancements: The Advanced Dynamic Threshold RSI Indicator incorporates experimental features for educational exploration. While not intended as proven strategies, these features aim to offer users a glimpse into unconventional analysis. Some of these features include Performance Calculation, Volatility Calculation, Dynamic Threshold Calculation Using Volatility, Bollinger Bands Module, Weighted Signal System Incorporating New Features. 3.1 Performance Calculation: The script calculates the percentage change in the price over a specified lookback period (variable lookbackBars). This provides a measure of recent performance. pctChange(src, length) => change = src - src pctChange = (change / src ) * 100 recentPerformance1H = pctChange(close, lookbackBars) recentPerformance4H = pctChange(request.security(syminfo.tickerid, "240", close), lookbackBars) recentPerformance1D = pctChange(request.security(syminfo.tickerid, "1D", close), lookbackBars) 3.2 Volatility Calculation: The script computes the standard deviation of the closing price to measure volatility. volatility1H = ta.stdev(close, 20) volatility4H = ta.stdev(request.security(syminfo.tickerid, "240", close), 20) volatility1D = ta.stdev(request.security(syminfo.tickerid, "1D", close), 20) 3.3 Dynamic Threshold Calculation Using Volatility: The dynamic thresholds for RSI are calculated by adding a multiplier of volatility to 50. dynamicThreshold1H = 50 + thresholdMultiplier * volatility1H dynamicThreshold4H = 50 + thresholdMultiplier * volatility4H dynamicThreshold1D = 50 + thresholdMultiplier * volatility1D 3.4 Bollinger Bands Module: An additional module for Bollinger Bands is introduced, providing an option to enable or disable it. // Additional Module: Bollinger Bands bbLength = input(20, title="Bollinger Bands Length") bbMultiplier = input(2.0, title="Bollinger Bands Multiplier") upperBand = ta.sma(close, bbLength) + bbMultiplier * ta.stdev(close, bbLength) lowerBand = ta.sma(close, bbLength) - bbMultiplier * ta.stdev(close, bbLength) 3.5 Weighted Signal System Incorporating New Features: Buy and sell signals are generated based on the dynamic threshold, recent performance, and Bollinger Bands. weightedBuySignal = rsi1H > dynamicThreshold1H and rsi4H > dynamicThreshold4H and rsi1D > dynamicThreshold1D and crossOver1H weightedSellSignal = rsi1H < dynamicThreshold1H and rsi4H < dynamicThreshold4H and rsi1D < dynamicThreshold1D and crossUnder1H These features collectively aim to provide users with a more comprehensive view of market dynamics by incorporating recent performance and volatility considerations into the RSI analysis. Users can experiment with these features to explore their impact on signal accuracy and overall indicator performance. Indicator Placement for Enhanced Visibility Overview The design choice to position the "Advanced Dynamic Threshold RSI" indicator both on the main chart and beneath it has been carefully considered to address specific challenges related to visibility and scaling, providing users with an improved analytical experience. Challenges Faced 1. Differing Scaling of RSI Results: RSI values for different timeframes (1-hour, 4-hour, and 1-day) often exhibit different scales, especially in markets like gold. Attempting to display these RSIs on the same chart can lead to visibility issues, as the scaling differences may cause certain RSI lines to appear compressed or nearly invisible. 2. Candlestick Visibility vs. RSI Scaling: Balancing the visibility of candlestick patterns with that of RSI values posed a unique challenge. A single pane for both candlesticks and RSIs may compromise the clarity of either, particularly when dealing with assets that exhibit distinct volatility patterns. Design Solution Placing the buy/sell signals above/below the candles helps to maintain a clear association between the signals and price movements. By allocating RSIs beneath the main chart, users can better distinguish and analyze the RSI values without interference from candlestick scaling. Doubling the scaling of the 1-hour RSI (displayed in blue) addresses visibility concerns and ensures that it remains discernible even when compared to the other two RSIs: 4-hour RSI (orange) and 1-day RSI (green). Bollinger Bands Module is optional, but is turned on as default. When the module is turned on, the users can see the upper Bollinger Band (green) and lower Bollinger Band (red) on the main chart to gain more insight into price actions of the candles. User Flexibility This dual-placement approach offers users the flexibility to choose their preferred visualization: The main chart provides a comprehensive view of buy/sell signals in relation to candlestick patterns. The area beneath the chart accommodates a detailed examination of RSI values, each in its own timeframe, without compromising visibility. The chosen design optimizes visibility and usability, addressing the unique challenges posed by differing RSI scales and ensuring users can make informed decisions based on both price action and RSI dynamics. Usage Installation To ensure you receive updates and enhancements seamlessly, follow these steps: Open the TradingView platform. Navigate to the "Indicators" tab in the top menu. Click on "Community Scripts" and search for "Advanced Dynamic Threshold RSI Indicator." Select the indicator from the search results and click on it to add to your chart. This ensures that any future updates to the indicator can be easily applied, keeping you up-to-date with the latest features and improvements. Review Code Open TradingView and navigate to the Pine Editor. Copy the provided script. Paste the script into the Pine Editor. Click "Add to Chart." Configuration The indicator offers several customizable settings: RSI Length: Defines the length of the RSI calculation. SMA Length: Sets the length of the SMA applied to the RSI. Lookback Bars: Determines the number of bars used for recent performance analysis. Threshold Multiplier: Adjusts the multiplier for dynamic threshold calculation. Enable Bollinger Bands: Allows users to enable or disable Bollinger Bands integration. Interpreting Signals Buy Signal: Generated when RSI values are above dynamic thresholds and a crossover occurs. Sell Signal: Generated when RSI values are below dynamic thresholds and a crossunder occurs. Additional Information The indicator plots scaled RSI lines for 1-hour, 4-hour, and 1-day timeframes. Users can experiment with additional modules, such as machine-learning simulation, dynamic real-life improvements, or experimental signal filtering, depending on personal preferences. Conclusion The Advanced Dynamic Threshold RSI Indicator provides traders with a sophisticated tool for RSI-based analysis, offering a unique combination of dynamic thresholds, performance analysis, and optional Bollinger Bands integration. Traders can customize settings and experiment with additional modules to tailor the indicator to their trading strategy. Disclaimer: Use of the Advanced Dynamic Threshold RSI Indicator The Advanced Dynamic Threshold RSI Indicator is provided for educational and experimental purposes only. The indicator is not intended to be used as financial or investment advice. Trading and investing in financial markets involve risk, and past performance is not indicative of future results. The creator of this indicator is not a financial advisor, and the use of this indicator does not guarantee profitability or specific trading outcomes. Users are encouraged to conduct their own research and analysis and, if necessary, consult with a qualified financial professional before making any investment decisions. It is important to recognize that all trading involves risk, and users should only trade with capital that they can afford to lose. The Advanced Dynamic Threshold RSI Indicator is an experimental tool that may not be suitable for all individuals, and its effectiveness may vary under different market conditions. By using this indicator, you acknowledge that you are doing so at your own risk and discretion. The creator of this indicator shall not be held responsible for any financial losses or damages incurred as a result of using the indicator. Kind regards, Ely
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của Elysian_Mind
2
Test - Symbiotic Exiton Measure Enthropic Nexus indicatorThe Symbiotic Exiton Measure Enthropic Nexus (SEMEN) Indicator is a technical analysis tool used in trading and investing. It's name might sound complex, but its function is quite simple - to help traders make informed decisions about buying or selling stocks by predicting market trends. The SEMEN indicator uses a combination of various factors such as volume, price action, moving averages, and other indicators to generate a single numerical value that represents the overall health of the market. A high reading indicates a strong uptrend, while a low one suggests a downtrend. Traders can use this information to enter or exit positions with confidence. In essence, the SEMEN indicator provides a comprehensive view of the market's sentiment and direction, making it an essential tool for any trader or investor looking to make profitable decisions in today's volatile stock markets. ~description generated with Airoboros7b - The indicator is experimental so use at your own discretion..
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của RicardoSantos
17
TrendBars This new bar system consists of candles connected to each other with trend lines. I have defined a falling trend if the current peak is lower than the previous peak, and an rising trend if the current low is higher than the previous low. When the series of lows or highs is broken, indicating a reversal in the trend, the candles change color. This provides a fresh perspective when monitoring the trend. Continue to stay tuned for original concepts and many more new systems. Happy trading!
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của Genesis-Trader
Zigzag Array ExperimentalThis is experimental script for zigzag which uses type, method and array. Not recommend for actual usage, for pine script study maybe useful. In this experiment, I use type as coded below. It seems have limitation as specially when push as array. As Trading View recommendation, pushing float and int into array especially for type not guarantee to work. I agree with that. Preferred to push array as line or label especially for types. // @type Used for point especially for array // @field x int value for bar_index // @field y float value for price // @field sty label style // @field col color for text label // @field str high or low string type point int x = na float y = na string sty = na color col = na string str = na I simulate the arrays as below. var dirLine = array.new() var dirLabel = array.new() var dirPoint = array.new() .... dirPoint.unshift(zigzag.createPoint(0)) dirLabel.unshift(zigzag.createLabel(fontSize, 0, true)) dirLine.unshift(zigzag.createLine(width, switchLine, 0, true)) Here are some results.
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của RozaniGhani-RG
13
switches [experimental / tools]This scripts shows a tool which enables switching between settings without opening the settings In this case you can switch between a RSI of 3 different tickers, 3 different higher timeframes, and 4 different lengths. How does it work? The position of a 'time line' is compared with the position of a box. Changing the position of the line changes the settings. The settings need to be set upfront though, if you want to switch between length 7, 10, 14, 21 they need to be set first. Example: One wants to switch between RSI of SOLUSDT, ADAUSDT and FILUSDT First set your tickers -> Settings -> Set 1 -> Ticker Then tap/click and move the line And there you go! The same with Timeframe and Length Important: It is not possible to automatically set the boxes/line at current time, so these (settings -> date at Box, Line) need to be set in the beginning Cheers!
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của fikira
12
Moving Averages 3DHello All, This is an experimental work to show 10 moving averages in 3D by using linefill.new() feature of the Pine language. You can set moving average type as Simple (SMA) or Exponential (EMA) You can set "Starting Length" and set the "Step" to calculate other lengths. For example; if "Starting Length" is 10 and "step" is 5 then the lengths are 10, 15, 20, 25... You can set the colors as Red, Green, Blue for each direction. And you can change the width. you can see the examples below In following example Green and Blue used: In following example Blue and Red is used and Width is set as 5: Enjoy!
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của LonesomeTheBlue
68
FunctionPatternDecompositionLibrary "FunctionPatternDecomposition" Methods for decomposing price into common grid/matrix patterns. series_to_array(source, length) Helper for converting series to array.   Parameters:      source : float, data series.      length : int, size.   Returns: float array. smooth_data_2d(data, rate) Smooth data sample into 2d points.   Parameters:      data : float array, source data.      rate : float, default=0.25, the rate of smoothness to apply.   Returns: tuple with 2 float arrays. thin_points(data_x, data_y, rate) Thin the number of points.   Parameters:      data_x : float array, points x value.      data_y : float array, points y value.      rate : float, default=2.0, minimum threshold rate of sample stdev to accept points.   Returns: tuple with 2 float arrays. extract_point_direction(data_x, data_y) Extract the direction each point faces.   Parameters:      data_x : float array, points x value.      data_y : float array, points y value.   Returns: float array. find_corners(data_x, data_y, rate) ...   Parameters:      data_x : float array, points x value.      data_y : float array, points y value.      rate : float, minimum threshold rate of data y stdev.   Returns: tuple with 2 float arrays. grid_coordinates(data_x, data_y, m_size) transforms points data to a constrained sized matrix format.   Parameters:      data_x : float array, points x value.      data_y : float array, points y value.      m_size : int, default=10, size of the matrix.   Returns: flat 2d pseudo matrix.
Thư viện
của RicardoSantos
3
Inverse Divergence [HeWhoMustNotBeNamed]Experimental. In regular scenario divergence calculation follows these procedure Pivots on price are considered as primary source They are compared with pivots on oscillators Trend bias of price is used This is an experimental version where Pivots on oscillators are considered as primary source They are compared with pivots on price Trend bias of oscillator is used. Using percentrank to define oscillator trend Caution: Not meant for trading :)
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của Trendoscope
9