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Quant Market Predictor v3 (No Signals)Quant Market Predictor – Lite is a streamlined version of the full Quant Market Predictor model. This version focuses purely on market structure analysis and statistical forecasting, without generating trading signals. The indicator combines multiple quantitative techniques commonly used in financial modeling to provide a clearer view of trend dynamics, volatility regimes, and short-term price projections. It is designed for traders and analysts who want model-driven market context while making their own discretionary decisions. Core Models 1. Kalman Filter Trend A recursive Kalman filter smooths price data to estimate the underlying trend. This approach reduces noise while remaining more responsive than traditional moving averages. 2. Adaptive AR(2) Price Model An autoregressive model estimates short-term price momentum using recent price changes. The coefficients adapt to volatility conditions to prevent overreaction during unstable markets. 3. GARCH-Inspired Volatility Model A simplified volatility estimator tracks conditional variance similar to GARCH models. This allows the indicator to identify high-volatility trend regimes and low-volatility consolidation regimes. 4. Multi-Bar Forecast The AR model generates a short-term projection path for the next few bars. The forecast gradually decays to reflect increasing uncertainty further into the future. What the Lite Version Shows • Kalman trend line (dynamic trend estimate) • Volatility bands derived from conditional variance • Regime background highlighting trend vs. consolidation environments • Short-term statistical forecast path • Information table displaying model diagnostics Important This Lite version does not produce buy or sell signals. It is intended as an analytical tool to help interpret market conditions rather than a complete trading system. Typical Use Cases • Understanding whether the market is in a trend or mean-reversion regime • Visualizing volatility expansion and contraction • Viewing a probabilistic short-term forecast path • Supporting discretionary trading decisions
Индикатор Pine Script®
от liebmann_
Adaptive Spectral Forecast [WillyAlgoTrader]📡 Adaptive Spectral Forecast is an overlay indicator that applies Goertzel spectral analysis to decompose price into its dominant cyclical components, reconstructs them as a harmonic sum, and then extrapolates the resulting waveform forward in time to generate a visual forecast with confidence bands. Signals fire when the forecast direction changes with sufficient signal-to-noise ratio and trend alignment — projecting where price is likely to oscillate next based on the cycles detected in recent history. This is a fundamentally different approach from trend-following or momentum-based indicators. Instead of asking "where is price going based on its direction and speed?", spectral analysis asks "what recurring cycles exist in this price data, and where do they project to next?" The Goertzel algorithm is a targeted frequency detector — it scans a range of cycle periods, measures the power (amplitude²) at each frequency, identifies the dominant peaks, computes their exact phase and amplitude via DFT projection, and recombines them into a multi-harmonic forecast that decays toward the adaptive trend as it extends into the future. 🧩 WHY THESE COMPONENTS WORK TOGETHER Raw price is a mix of trend, cycles, and noise. Attempting to forecast raw price directly fails because trend and cycles require different extrapolation methods: trend continues linearly, cycles repeat sinusoidally, and noise should not be extrapolated at all. This indicator solves the problem through decomposition and reassembly: EMA trend extraction → Hann-windowed detrending → Goertzel spectral scan → SNR peak detection → DFT coefficient extraction → Harmonic recombination with decay → Trend re-addition → Confidence bands Each stage addresses a specific challenge: — Trend extraction separates the slow directional component so it can be extrapolated linearly (via slope), not sinusoidally — Hann windowing reduces spectral leakage — without it, the finite data window creates false frequency peaks that contaminate the analysis — Goertzel scanning efficiently measures power at each candidate frequency without computing a full FFT — enabling targeted, adaptive-resolution frequency detection — SNR filtering ensures only cycles with meaningful signal strength are included — weak noise-level frequencies are discarded — DFT coefficient extraction computes the exact amplitude and phase of each selected cycle on un-windowed data (the Hann window is only for spectral scanning, not for coefficient calculation — this preserves correct amplitudes) — Harmonic decay causes the cyclic component to gradually fade toward the trend as the forecast extends forward — reflecting the reality that detected cycles have limited persistence — Confidence bands widen with √(bars ahead) × ATR, showing that forecast uncertainty grows with distance Removing any component breaks the pipeline: without detrending, the Goertzel scan detects the trend as a low-frequency "cycle". Without Hann windowing, spectral leakage creates phantom peaks. Without SNR filtering, the forecast includes noise-level harmonics that produce random oscillations. Without decay, the harmonic projection repeats forever at full amplitude (unrealistic). The full pipeline is required. 🔍 WHAT MAKES IT ORIGINAL 1️⃣ Goertzel spectral analysis for cycle detection. The Goertzel algorithm is a single-frequency DFT that computes the power at one specific period using a recursive formula: s0 = data + 2×cos(2π/period) × s1 − s2 After iterating through all N data points, the power is: (s1 − s2×cos(ω))² + (s2×sin(ω))², normalized by N² for cross-period comparability. The indicator scans every integer period from Min Cycle Period (default 8) up to N/2, with an adaptive step size: every period for fast cycles (≤30 bars), step of 2 for longer cycles (reducing computation without losing resolution where it matters most). For each period, the Goertzel power is computed, producing a power spectrum — a map of which cycle lengths carry the most energy in the current price data. This is fundamentally more targeted than an FFT. An FFT computes power at all frequencies simultaneously but at fixed resolution (determined by window size). The Goertzel approach allows scanning exactly the frequency range of interest with customizable resolution. 2️⃣ SNR-based peak detection with fallback. From the power spectrum, the indicator identifies local peaks (frequencies where power is higher than both neighbors) and computes the Signal-to-Noise Ratio for each: SNR = peak_power / mean_power_across_all_frequencies. Only peaks with SNR ≥ Min Cycle SNR (default 2.0) are accepted as genuine cycles — the rest are considered noise-level fluctuations. If no peaks pass the SNR filter (possible in highly random or trend-dominated price action), the algorithm falls back to the single strongest frequency. In this case, the dashboard displays "Weak*" strength and buy/sell signals are suppressed — the forecast is shown for visual reference only, but the indicator acknowledges that no reliable cyclical structure was found. The top N peaks (sorted by power, N = Harmonics Count, default 5) are selected as the dominant cycles. 3️⃣ DFT coefficient extraction on un-windowed data. For each selected cycle period, the indicator computes exact sine and cosine coefficients using standard DFT projection: a = (2/N) × Σ data × sin(2π × i / period) b = (2/N) × Σ data × cos(2π × i / period) Critically, this computation uses the raw detrended data (without Hann windowing). The Hann window was only applied for the spectral scan (to identify which frequencies are dominant). Using windowed data for coefficient extraction would distort the amplitude of the harmonics. This two-pass approach — windowed scan for detection, raw data for coefficients — is a key design choice that preserves forecast accuracy. 4️⃣ Harmonic extrapolation with configurable decay. The forecast is constructed by evaluating the harmonic sum at each future bar: forecast = trend_projection + Σ (a_k × sin(ω_k × t) + b_k × cos(ω_k × t)) × decay^(t − t_base) Where trend_projection = trend_last + trend_slope × bars_ahead (linear extrapolation of the EMA trend). The decay factor (default 0.97) causes harmonic amplitude to reduce by 3% per bar, so the cyclic component gradually fades and the forecast converges toward the trend line. At decay = 1.0, harmonics repeat at full amplitude forever (pure cycle projection). At decay = 0.95, they fade rapidly (forecast becomes trend-only within ~20 bars). Default 0.97 provides meaningful oscillation for the first 20–30 bars before fading. The forecast line is colored by segment: green segments where the forecast is rising, red where falling. Reversal dots mark predicted peaks and troughs. 5️⃣ ATR-based confidence bands with √t scaling. Uncertainty in the forecast grows with distance. The confidence band width is calculated as: band_width = ATR(14) × confidence_multiplier × √(bars_ahead) The √t scaling follows the mathematical principle that forecast variance grows linearly with time horizon (standard deviation grows with square root). The ATR provides the natural volatility scale of the instrument. At bar +1, the band is approximately ATR × multiplier. At bar +25, it's 5× wider. This gives a realistic visual envelope of where price might actually be, not just the central harmonic forecast. 6️⃣ 3-bar consensus forecast direction. Instead of using a simple "is the next bar higher or lower?" check (which is noisy), the forecast direction is determined by majority vote over 3 bars: The indicator evaluates the forecast at t+0, t+1, t+2, t+3 and counts upward moves: upVotes = (y1>y0 ? 1:0) + (y2>y1 ? 1:0) + (y3>y2 ? 1:0). If ≥2 of 3 transitions are upward → forecast direction is bullish. If ≤1 → bearish. This consensus approach prevents a single-bar oscillation from flipping the forecast direction. 7️⃣ Trend alignment filter for signal quality. When enabled (default on), buy signals require the EMA trend slope to be positive, and sell signals require negative slope. This prevents the indicator from generating counter-trend signals when a cycle oscillation temporarily points against the broader trend — which is the most common source of false signals in cycle-based systems. If the trend filter blocks a direction change, the forecast visualization still updates (you can see the cycle projection) but no signal label is emitted. Additionally, signals are suppressed when the SNR is below the minimum threshold or when the spectral scan fell back to a single non-significant frequency. 8️⃣ Adaptive trend extraction with linear extrapolation. The trend component is extracted using an EMA with configurable smoothing (default 30 bars), applied forward using the buildTrendArray function. The trend's slope is computed via weighted linear regression over the last 5 points of the trend array — providing a stable slope estimate that isn't dominated by a single bar. The trend is extrapolated linearly into the forecast: trend_forecast = trend_last + slope × i. This linear projection is appropriate for the short-term forecast horizon (15–55 bars) where trend curvature is typically negligible. 9️⃣ Historical fit visualization. The reconstructed harmonic sum + trend is plotted over historical data as a polyline, showing how well the detected cycles explain the actual price movement. This serves as an immediate visual validation: if the fit tracks price well, the detected cycles are meaningful. If the fit diverges significantly, the current market regime may not have strong cyclical structure (reflected in low SNR scores in the dashboard). The fit line is colored by trend direction (green for bullish slope, red for bearish) and decimated for performance (every 1–3 bars depending on lookback length). 🔟 Four presets with coordinated parameter scaling. — Conservative : lookback ≥ 150, harmonics ≤ 3, forecast ≥ 40 bars — stable, long-term cycles, less overfitting risk — Default : user settings unchanged — Aggressive : lookback ≤ 80, harmonics +1, forecast ≤ 25 — faster adaptation, more cycles included — Scalping : lookback ≤ 50, harmonics +2, forecast ≤ 15 — shortest window, most harmonics, very short projection Each preset adjusts lookback (spectral window), harmonics count (complexity), and forecast length (projection horizon) as a coordinated unit. Longer lookback needs fewer harmonics (the cycles are more stable). Shorter lookback needs more harmonics (to capture the faster fluctuations within the compressed window). ⚙️ HOW IT WORKS — CALCULATION FLOW Step 1 — Data collection: The last N bars (Analysis Lookback, default 144) of the selected price source are collected into an array, oldest first. Step 2 — Trend extraction: An EMA with the Trend Smoothing period (default 30) is applied across the array using a forward-pass recursive formula: trend = α × price + (1−α) × trend . This produces a smooth trend array. Step 3 — Detrending + Hann window: Each bar's trend value is subtracted from its price. The residual is multiplied by a Hann window: w = 0.5 × (1 − cos(2π×i/(N−1))). This isolates the cyclical component while minimizing spectral leakage at the data boundaries. Step 4 — Goertzel spectral scan: For each candidate period from minPeriod to N/2 (adaptive step: 1 for periods ≤30, 2 for longer), the Goertzel algorithm computes power. The result is a power spectrum across all scanned frequencies. Step 5 — Peak detection: Local maxima in the power spectrum are identified (power > both neighbors). Each peak's SNR is computed against the mean power. Peaks with SNR ≥ threshold are accepted. If none pass, the strongest single frequency is used as fallback (with "Weak*" marking). Step 6 — Coefficient extraction: For the top N peaks (by power), sine and cosine coefficients are computed via DFT projection on raw (un-windowed) detrended data. This gives exact amplitude and phase for each cycle. Step 7 — Forecast generation: The trend is extrapolated linearly using its 5-bar regression slope. Each harmonic is evaluated at future time steps with decay applied. The sum of trend + decayed harmonics produces the central forecast line. Confidence bands = ATR × multiplier × √(bars_ahead). Step 8 — Direction and signals: 3-bar consensus determines forecast direction. Trend filter and SNR check gate signal emission. Buy/sell labels appear on bar-close confirmation. 📖 HOW TO USE 🎯 Quick start: 1. Add the indicator — the historical fit and forecast line appear on the last bar 2. The colored dotted line extending right is the forecast (green = rising, red = falling) 3. Colored dots (●) on the forecast mark predicted peaks and troughs 4. Dotted lines above and below = confidence bands (forecast uncertainty zone) 5. ▲/▼ labels = buy/sell signals when the forecast direction changes 👁️ Reading the chart: — 🟢 Green solid line on history = harmonic fit (uptrend slope) — 🔴 Red solid line on history = harmonic fit (downtrend slope) — 🟢🔴 Dotted line extending right = forecast (colored by direction: green rising, red falling) — 🔵 Upper/lower dotted lines = confidence bands (uncertainty grows with distance) — 🟢 ● dots = predicted troughs (potential support) — 🔴 ● dots = predicted peaks (potential resistance) — 🟢 ▲ below bar = buy signal (forecast changed to bullish) — 🔴 ▼ above bar = sell signal (forecast changed to bearish) 📊 Dashboard fields: — Trend: current EMA trend direction (Bullish / Bearish / Neutral) — Forecast: predicted direction (▲ Up / ▼ Down / — Flat) — Signal: current state (BUY / SELL / Bullish Bias / Bearish Bias / Wait) — Strength: cycle quality based on average SNR (Strong > 5.0 / Medium > 2.5 / Weak / Weak* = fallback) — Dom. Cycle: dominant cycle period in bars (e.g., "34 bars") — Cycles: how many cycles passed SNR filter vs. requested (e.g., "3 / 5") — Timeframe, preset, version 🔧 Tuning guide: — Forecast too noisy: reduce Harmonics Count (3), increase Min SNR (3.0+), increase Trend Smoothing — Forecast too smooth: increase Harmonics Count (5–7), decrease Min SNR (1.5), decrease Trend Smoothing — Cycles don't match price: increase Analysis Lookback (200+) for more stable cycle detection, or decrease for faster adaptation — Forecast fades too fast: increase Decay Rate toward 0.99–1.0 — Forecast unrealistic long-term: decrease Decay Rate toward 0.95, reduce Forecast Bars — Too many false signals: enable Trend Alignment Filter, increase Min SNR — Scalping 1–5M: use Scalping preset (lookback ≤50, 7 harmonics, 15-bar forecast) — Swing 4H–1D: use Conservative preset (lookback ≥150, 3 harmonics, 40-bar forecast) ⚙️ KEY SETTINGS REFERENCE ⚙️ Main: — Analysis Lookback (default 144): spectral analysis window — the last N bars analyzed — Harmonics Count (default 5): how many dominant cycles to include in the forecast — Min Cycle Period (default 8): shortest cycle to scan for (bars) — Preset (default Default): Conservative / Default / Aggressive / Scalping — Trend Alignment Filter (default On): require trend-forecast agreement for signals 🔮 Forecast: — Forecast Bars (default 55): projection length into the future — Confidence Band Width (default 1.5× ATR): band multiplier — Trend Smoothing (default 30): EMA period for trend component — Harmonic Decay Rate (default 0.97): amplitude reduction per bar (1.0 = no decay) — Min Cycle SNR (default 2.0): signal-to-noise threshold for cycle acceptance 🎨 Visual: — Historical fit, trend line, reversal dots, confidence bands (all toggleable) — Auto / Dark / Light theme 🔔 Alerts — 🟢 BUY — ticker, price, timeframe, time — 🔴 SELL — same fields Both support plain text and JSON webhook format. Signals are bar-close confirmed, direction-locked, trend-filtered, and SNR-gated. ⚠️ IMPORTANT NOTES — 📐 This is spectral analysis, not trend following. The indicator detects and projects recurring cycles. In markets with strong cyclical structure (commodities, forex majors, crypto with regular oscillations), it performs well. In news-driven or momentum-dominated markets with no cyclical structure, the SNR will be low and the forecast unreliable — the dashboard reflects this via the Strength reading. — 🚫 No repainting of signals. The spectral analysis runs on barstate.islast (updating the forecast in real time on the current forming bar). Signals only fire on the next barstate.isconfirmed bar, after the forecast direction has been set. This means the forecast line itself updates in real time (by design — it's a live projection), but buy/sell signals are confirmed and do not change retroactively. — 📊 The forecast is a projection, not a prediction. It shows where price would go if the detected cycles continue with their current amplitude and phase. Real markets introduce new information that disrupts cycles. The confidence bands reflect this growing uncertainty. Treat the forecast as a probabilistic zone, not a target. — 🔄 "Weak*" strength means no cycles passed the SNR filter and the indicator fell back to the single strongest frequency. In this state, signals are suppressed. The forecast is still shown for visual reference but should not be trusted for trading decisions. — ⚖️ The Hann window is applied only for spectral scanning , not for coefficient extraction. This is deliberate: the window prevents spectral leakage during frequency detection, but the un-windowed data preserves correct harmonic amplitudes for the forecast. — 📏 The forecast extends a fixed number of bars into the future. Accuracy degrades with distance — the first 10–15 bars are typically the most reliable. The confidence bands quantify this degradation visually. — 🛠️ This is a spectral analysis and forecasting tool , not an automated trading bot. It detects cycles, projects them forward, and generates directional signals — trade decisions remain yours. — 🌐 Works on all markets and timeframes. Cycle periods adapt automatically to whatever timeframe you apply it on.
Индикатор Pine Script®
от WillyAlgoTrader
KNN Trend Forecaster [UAlgo]KNN Trend Forecaster is a chart overlay forecasting tool that uses a K Nearest Neighbors style similarity engine to estimate the next directional bias and project a probabilistic price path. It converts the current market state into a compact feature vector, compares it to a rolling memory of historical states, and computes an expected forward change as a weighted consensus of the most similar past observations. The indicator is built for decision support rather than signal chasing. It provides a projected path, a volatility aware tunnel around that path, and optional ghost structures that visualize how the forecast could evolve bar by bar. A minimal UI panel summarizes the current projection and sentiment, while the plot color adapts to bullish, bearish, or neutral expectation. This script is most effective when treated as a contextual layer. It can help align trade selection with the dominant statistical bias implied by recent conditions, while still leaving execution to your own confirmation rules. 🔹 Features 1) KNN Similarity Forecasting Engine The core model is a K Nearest Neighbors approach. For each bar, the script builds a three dimensional feature set from momentum, volatility, and relative strength. It then measures the distance between the current feature set and each stored historical feature set. The closest K neighbors are selected, and their realized forward returns are combined into a single prediction. Model Sensitivity controls K. Lower values behave more reactive and can change bias quickly. Higher values behave more stable and tend to smooth the projection. 2) Feature Design Focused On Trading Context The feature set is intentionally practical: RSI captures directional pressure and mean reversion tendencies ROC captures normalized momentum ATR captures normalized volatility regime Normalization ensures that ROC and ATR values are scaled into comparable ranges so the distance metric remains balanced and does not get dominated by raw magnitude differences. 3) Rolling Memory With Outcome Labels The script builds a training memory in real time. On each confirmed bar, it stores the features from ten bars ago and labels them with the percentage change over the next ten bars. This creates a consistent supervised learning target: Feature snapshot at time t Outcome equals return from time t to t plus ten bars Memory is capped to a fixed size to keep performance stable. 4) Weighted Neighbor Voting For Robust Predictions Rather than using a simple average of neighbor outcomes, the script assigns higher weight to closer neighbors. Weight is the inverse of distance, which prioritizes highly similar historical states and reduces the influence of weaker matches. This helps stabilize results when the market is transitioning and the feature landscape becomes noisier. 5) Forecast Path With Adaptive Step Decay Once a prediction is produced, the script generates a forward path for a user selected Forecast Horizon. The step applied to the path decays with the square root of the forecast index, which makes the projection more confident near the present and more conservative further out. The result is a smooth curve rather than an aggressive linear extrapolation. 6) Multi Layer Volatility Tunnel A tunnel can be drawn around the projected path. Its width scales with ATR and expands over the forecast horizon using a square root growth profile. Tunnel Volatility controls how wide the envelope becomes. This provides a practical view of expected dispersion around the forecast rather than a single deterministic line. 7) Ghost Structures For Bar To Bar Projection Framing Optional ghost boxes are printed for each forward step. Each box visualizes the projected candle body from the current projected close to the next projected close. The ghost color adapts to whether the step is rising or falling, making momentum and path rhythm easier to read. 8) Neon Glow Path And Target Tag When enabled, the path is rendered twice using polylines: A wider glow stroke for visual depth A thinner main stroke for precision A target label is placed at the end of the horizon, showing the model predicted change in percent. 9) Projection Basis And Dashboard A 21 period EMA is plotted as a reference basis, colored by the current prediction bias. A compact table displays the projection value and a sentiment label that classifies the forecast into bullish, bearish, or neutral ranges. 🔹 Calculations 1) Feature Construction The model uses three features built from common market analytics. RSI uses standard 14 period RSI: float f_rsi = ta.rsi(close, 14) ROC and ATR are normalized into a 0 to 100 style range using rolling min max scaling: normalize(float src, int len) => float mn = ta.lowest(src, len) float mx = ta.highest(src, len) (src - mn) / (math.max(mx - mn, 0.000001)) * 100 float f_roc = normalize(ta.roc(close, 10), 100) float f_atr = normalize(ta.atr(14), 100) The current feature vector: FeatureSet current_f = FeatureSet.new(f_rsi, f_roc, f_atr) 2) Training Memory And Outcome Labeling On each confirmed bar, the script stores the feature snapshot from ten bars earlier and labels it with the forward ten bar return. Outcome in percent: float outcome = (close - close ) / close * 100 Training point created from the past feature snapshot: memory.push(TrainingPoint.new(FeatureSet.new(f_rsi , f_roc , f_atr ), outcome)) Memory is limited for stability: if memory.size() > 1000 memory.shift() 3) Distance Metric Between Feature Vectors Similarity is computed using Euclidean distance in three dimensions: method distance(FeatureSet v1, FeatureSet v2) => math.sqrt(math.pow(v1.f1 - v2.f1, 2) + math.pow(v1.f2 - v2.f2, 2) + math.pow(v1.f3 - v2.f3, 2)) Smaller distance means greater similarity. 4) Neighbor Selection And Weighted Prediction The script computes distances from the current state to each stored training point and gathers the outcomes. It then selects the closest K entries by repeatedly taking the minimum distance. Each neighbor is weighted by inverse distance: float w = 1.0 / math.max(td.get(idx), 0.0001) twc += tc.get(idx) * w ws += w pred := twc / ws This produces pred, a percentage change estimate inferred from the most similar historical contexts. 5) Signal Color Classification The display color adapts to the sign and magnitude of the prediction. Small values map to a neutral tone, stronger positive values map to bullish tone, and stronger negative values map to bearish tone. color sig_col = pred > 0.005 ? THEME_UP : pred < -0.005 ? THEME_DN : THEME_MID 6) Forecast Path Generation Path construction begins from the current close. A base step is derived from the prediction and then decayed across the horizon. Base step: float step = (pred / 10.0) * 0.01 Forward projection with square root decay: float next_c = cur_c * (1 + step * (1.0 / math.sqrt(i))) This produces a smooth forecast curve where early steps carry more weight than later steps. 7) Volatility Tunnel Width Model The tunnel uses ATR as the volatility anchor and expands across the horizon: Outer width: float v_outer = (atr * expansion * 0.3 * math.sqrt(i)) Inner width is half of the outer width: float v_inner = v_outer * 0.5 Upper and lower bounds are then computed around the projected close: float h_out = next_c + v_outer float l_out = next_c - v_outer float h_in = next_c + v_inner float l_in = next_c - v_inner 8) Ghost Structures For each forecast step, a box is drawn between the current projected close and the next projected close. Its color reflects whether the path step is rising or falling. color g_col = next_c >= cur_c ? color.new(THEME_UP, 40) : color.new(THEME_DN, 40) box b = box.new(x1, math.max(cur_c, next_c), x2, math.min(cur_c, next_c), border_color=color.new(g_col, 20), bgcolor=g_col, border_width=1) 9) Path Rendering And Target Tag When glow is enabled, the script renders a thick glow polyline and a thinner main polyline over the same projected points. A label is placed at the final horizon index showing the predicted percent change. path_glow := polyline.new(pts, curved=true, line_color=color.new(sig_col, 70), line_width=8) path_main := polyline.new(pts, curved=true, line_color=sig_col, line_width=2) target_tag := label.new(bar_index + forecast_len, cur_c, "TARGET: " + str.tostring(pred, "#.##") + "%") 10) UI Summary And Basis Plot A 21 period EMA is plotted and colored by the current bias. A table panel prints the projection value and a sentiment classification: Bullish when pred is above 0.01 Bearish when pred is below minus 0.01 Neutral otherwise This gives an at a glance readout that matches the on chart color theme.
Индикатор Pine Script®
от UAlgo
1
Variable Sine Wave Fit [LuxAlgo]The Variable Sine Wave Fit indicator uses Ordinary Least Squares (OLS) to fit a dynamic, damped, or expanding sine wave with an underlying linear trend to recent price action. This tool aims to identify cyclical patterns and project their potential continuation into the future, providing a mathematical framework for understanding market regimes and turning points. This indicator is subject to repainting and is displayed retrospectively. 🔶 USAGE The indicator fits a complex trigonometric model to the price data within a user-defined window. The resulting fit is displayed as a solid line over historical bars and transitions into a dashed extrapolation for the forecasted period. To use the indicator effectively, traders should observe the relationship between the price and the RMSE bands. If the price remains within these bands, the current cyclical model is considered to be tracking the price action effectively. If the price breaks significantly outside, the cycle may be shifting or breaking down. 🔹 Extrema Markers Small dot markers are placed at the local maxima and minima of the dashed forecast line. These serve as visual guides for the timing of potential future turning points based on the current mathematical fit. 🔹 Market Regime Dashboard The dashboard provides a real-time summary of the fitted model's characteristics: State: Classified based on the amplitude behavior (Damped, Expanding, or Constant) and the linear component (Trending or Ranging). Best Period: The cycle length (in bars) that currently provides the best fit to the data. RMSE: The Root Mean Square Error, representing the average deviation of price from the fit. 🔶 DETAILS The script solves for the best parameters of the following equation: y = e^(λ * t) * (a * sin(ω * t) + b * cos(ω * t)) + m * t + c Where: e^(λ * t): The damping/expansion factor. If λ > 0, the cycle is expanding; if λ < 0, it is damping. a, b: Coefficients determining the phase and initial amplitude of the sine wave. m * t + c: A linear regression component that accounts for the underlying price trend. The "Best Period" is determined through a grid search that minimizes the Sum of Squared Errors (SSE), ensuring the frequency (ω) matches the most dominant local cycle within the search range. 🔶 SETTINGS 🔹 Settings Window Size (N): The number of historical bars used to calculate the fit. Auto Period: When enabled, the script searches for the best period within the specified min/max range. Fixed Period (P): The period used if Auto Period is disabled. Min/Max Search Period: Defines the boundaries for the automatic cycle search. Forecast Length: The number of bars to project the fit into the future. RMSE Band Multiplier: Determines the width of the bands surrounding the fit based on the fit error. 🔹 Visuals Bullish/Bearish Color: Colors used for the fit line and extrema markers based on the final slope. Band Color: The color of the RMSE-based envelope. 🔹 Dashboard Dashboard: Toggles the visibility of the data table. Position: Moves the dashboard to different corners of the chart. Size: Adjusts the text and table scale.
Индикатор Pine Script®
от LuxAlgo
Hidden Markov Model: Regime Probability [AlgoPoint]Hidden Markov Model: Regime Probability Traditional technical indicators are deterministic and lagging; they tell you what the price has already done. The Hidden Markov Model (HMM) Regime Probability system takes a completely different, quantitative approach. It uses probabilistic mathematics to estimate the unobservable "Hidden State" (Market Regime) the price is currently operating in. Inspired by the mathematical models used by institutional quantitative hedge funds, this script doesn't just look at price direction—it calculates the probability of the market being in a specific regime based on real-time observations of Momentum and Volatility. 1. The Three Hidden States (Regimes) The market is modeled as existing in one of three hidden states: ↗ Bullish Regime: High positive momentum with low or stable volatility. (Steady, grinding uptrends). ↘ Bearish Regime: High negative momentum with high volatility. (Aggressive sell-offs and panic). ↕ Chop / Chaos Regime: Zero/low momentum with high volatility. (Whipsaw, ranging, and unpredictable noise). 2. How It Works (The Quant Engine) Since Pine Script does not natively support complex matrix optimization, this script builds a robust Pseudo-HMM using a predefined Transition Matrix and Bayesian Updates. Observables (Emissions): The script calculates the Z-Scores of Smoothed Momentum (Rate of Change) and Volatility (ATR). Emission Probabilities (Gaussian PDF): It feeds these Z-Scores into a Gaussian Probability Density Function to see how well the current market matches the expected profile of a Bull, Bear, or Chop regime. Bayesian Update: Using a predefined Markov Transition Matrix (the statistical inertia of a trend), it updates the prior probabilities to give you a real-time percentage (0-100%) for each regime. 3. Advanced Visual Features & UI We built a custom UI/UX engine to make digesting complex probabilities instantaneous: Exponential Color Smoothing (Bar Colors): As the probability of a regime increases, the bar colors smoothly transition. We implemented an exponential color blending algorithm to prevent abrupt, distracting color changes and eliminate "muddy" colors during transitions. Pro Quant Dashboard: A built-in HUD (Heads-Up Display) provides a quick summary. It features a dominant state readout, an overall "Confidence Score", and ASCII-style mini progress bars (████░░░) for rapid visual processing of probabilities without needing to read the numbers. Stacked Area Oscillator: The bottom panel displays a 0-100 stacked area chart, showing the exact distribution of probabilities across Bull (Green), Chop (Purple), and Bear (Red) states. 4. How to Use This Tool This is not a standalone Buy/Sell signal indicator. It is a Strategy Filter and a Risk Manager. When Bull/Bear Probability is Dominant (>50%): The market is trending. Turn ON your trend-following indicators (like Moving Averages or Breakout systems) and ignore overbought/oversold signals. When Chop Probability is Dominant (>50%): The market is noisy. Turn OFF your trend-following systems. Either switch to Mean Reversion strategies (like RSI or Bollinger Bands) or stay in cash until a clear regime emerges. Watch the Confidence Score: If the Dashboard shows "LOW" confidence, it means the probabilities are split (e.g., 34% Bull, 33% Chop, 33% Bear). Wait for the model to gain confidence before committing capital. 5. Alerts The script includes non-repainting alerts that trigger only when the dominant regime changes: HMM Regime: BULLISH 🚀 * HMM Regime: BEARISH 🩸 * HMM Regime: CHOP ⚖️ 6. Settings Lookback Period: The window used to calculate the Z-scores for momentum and volatility. Transition Matrix: Allows advanced users to tweak the statistical likelihood of the market staying in its current state versus transitioning to a new one. Color Transition Speed: Adjusts the smoothness of the bar coloring. A lower value creates a buttery-smooth fade between regimes, while a value of 1.0 makes it instant.
Индикатор Pine Script®
от Iamalala
2
Singular Spectrum Decomposition [LuxAlgo]The Singular Spectrum Decomposition indicator is a powerful analytical tool that decomposes price action into distinct, interpretable components—Trend, Periodic cycles, and Noise—using the Singular Spectrum Analysis (SSA) methodology. It provides traders with a clear view of underlying market structures and offers a jump-free, extrapolated trend forecast based on Linear Recurrence Relations (LRR). Warning: This decomposition is displayed retrospectively ; historical values observed are subject to repainting . 🔶 USAGE The indicator operates by analyzing a specific window of recent price data to extract its most significant internal dynamics. It splits the "messy" raw price into four visual layers: Trend (Overlay): The primary low-frequency component, plotted directly on the price chart. This represents the core directional bias of the asset. Long Term Periodic (P1): The most dominant cyclical component, typically representing major swings or seasonalities. Short Term Periodic (P2): The second most dominant cycle, capturing faster oscillations and intermediate pullbacks. Noise: The high-frequency residual data that lacks a consistent pattern, useful for identifying market volatility or "washout" periods. 🔹 Cycle Exhaustion (P1/P2 Extremes) Traders can monitor the separate indicator pane to identify when cyclical components (P1 and P2) reach historical extremes. When the Long Term Periodic (P1) line begins to curve back toward the zero line after a prolonged extension, it often signals "cycle exhaustion," suggesting that the current swing is losing momentum and a reversal or consolidation may be imminent. 🔹 Trend-Forecast Confluence & Mean Reversion The dashed Trend extrapolation acts as a projected path for the market's core bias. If the current market price is significantly far from the solid Trend line while the forecast indicates a flattening or reversal, traders can look for mean-reversion opportunities. A price returning to a rising Trend forecast confirms the trend's strength, while a price crossing through a flat Trend forecast suggests a structural shift. 🔹 Timing Entries with Dashboard Metrics The "Average Period" displayed on the dashboard provides a mathematical blueprint for entry timing. For example, if the Short Term (P2) Average Period is 20 bars, a trader might look for long entries approximately 10 bars after a peak (the expected trough). By aligning these peak-to-trough measurements with the Trend's direction, users can improve the precision of their entries within a trending market. 🔹 Filtering Fakeouts with the Noise Component The Noise component helps distinguish between high-conviction moves and market "static." A sharp price breakout accompanied by a relatively flat Noise component suggests a sustainable, structurally supported move. Conversely, if a breakout occurs while the Noise component is spiking aggressively, it may indicate a "washout" or a liquidity-driven fakeout that lacks a fundamental trend shift. 🔶 DETAILS The script implements a full SSA pipeline: Embedding (creating a trajectory matrix), Singular Value Decomposition (via eigendecomposition of the covariance matrix), and Diagonal Averaging (reconstructed the time series). 🔹 Jump-Free Extrapolation A common issue with LRR-based forecasts is a vertical "jump" at the connection point between historical data and the forecast. This tool solves this by calculating the relative deltas of the LRR projection and anchoring them to the final value of the smoothed SSA reconstruction. This ensures a seamless visual transition while maintaining the mathematical integrity of the projected trajectory. 🔹 Dashboard Metrics The indicator includes a real-time dashboard that calculates the "Average Period" of the periodic components using zero-crossing detection. This allows traders to quantify the frequency of cycles (e.g., a 40-bar cycle vs. a 15-bar cycle) without manual measurement. 🔶 SETTINGS Window Length (L): The embedding window. Larger values capture longer cycles and provide a smoother trend, but may increase lag in the decomposition. Buffer Length (N): The number of recent bars used for the static decomposition. Forecast Length: The number of bars to extrapolate the Trend component into the future. Show Trend on Price: Toggles the visibility of the reconstructed trend line on the main chart. Show Periodic/Noise: Toggles the visibility of the individual sub-components in the indicator pane. Show Extrapolation: Enables or disables the dashed forecast line for the trend. Dashboard Settings: Controls the visibility, position, and size of the metrics table.
Индикатор Pine Script®
от LuxAlgo
Adaptive Harmonic Forecast [LuxAlgo]The Adaptive Harmonic Forecast indicator decomposes price action into multiple cyclical components and a linear trend to forecast future market movement. By extracting the most dominant frequencies from recent price data, the tool projects a multi-harmonic model into the future to identify potential reversal points and trend continuations. 🔶 USAGE The indicator provides a mathematical projection of price action based on the assumption that markets exhibit cyclical behavior. Users can utilize the forecast to anticipate upcoming shifts in momentum or to identify the underlying trend direction. It is important to note that the forecast is dynamic and recalculates on the most recent bar; therefore, it is best used to confirm momentum shifts when price action aligns with the projected harmonic direction. 🔹 Historical Fit & Forecast The script displays a solid line over the historical lookback period, representing how well the harmonic model fits the actual price data. Beyond the current bar, a dotted line extends the forecast. This forecast is color-coded: green represents projected upward movement, while red represents projected downward movement. The forecast should be viewed primarily as a timing tool rather than an exact price target, as it projects where the "rhythm" of the market is heading based on current harmonics. 🔹 Trend Line & Reversal Markers A linear trend line is calculated alongside the sinusoids to show the overall bias (slope) of the lookback period. Additionally, the indicator can plot reversal markers (dots) at the specific points where the forecasted cycles reach a peak or trough. These markers highlight potential future turning points where the composite cycles converge to create a local maximum or minimum. 🔹 Detected Cycles Table The "Detected Cycles" dashboard allows traders to identify if current price action is dominated by short-term "noise" cycles or larger "structural" cycles. By observing the period lengths (in bars), users can determine the frequency of market swings. If the detected periods are small relative to the lookback, the market is in a high-frequency state; if they are large, the market is exhibiting more stable, long-term cyclicality. 🔶 DETAILS The script operates through a two-step mathematical process involving spectral analysis and matrix-based regression: Periodogram Logic (Cycle Detection): The indicator first detrends the data within the lookback window using a linear fit. It then performs a spectral analysis by scanning a range of periods to calculate "spectral power" (the correlation between price and a specific frequency). It identifies "spectral peaks" where price variance is most concentrated, ensuring that only the most meaningful cycles are selected for modeling rather than random noise. Multi-Harmonic OLS Regression: Once the dominant periods are identified, the script uses Ordinary Least Squares (OLS) regression to solve for the coefficients of a linear combination of basis functions. Specifically, it constructs a model consisting of multiple sine and cosine waves (representing the cycles) and a first-order polynomial (representing the trend). By solving the normal equation using matrix math, the script finds the optimal amplitudes and phases that minimize the squared error against historical price. This composite model is then solved for future time coordinates to create the extrapolation. 🔶 SETTINGS 🔹 Settings Fit Lookback (N): Determines the number of historical bars used to analyze cycles and fit the model. Extrapolation Bars: Sets how many bars into the future the forecast should extend. Number of Sinusoids: The maximum number of individual cycles to include in the composite model (1-10). 🔹 Automatic Cycle Detection Min Period: The shortest cycle length (in bars) the algorithm is allowed to detect. 🔹 Visuals Show Reversal Dots: Toggles the markers at forecasted local highs and lows. Dot Size: Adjusts the visual scale of the reversal markers. Show Detected Periods: Toggles the data table showing the lengths of the dominant cycles. 🔹 Trend Line Show Trend Line: Toggles the display of the underlying linear regression line. Trend Line Color: Sets the color for the historical and projected trend line.
Индикатор Pine Script®
от LuxAlgo
11
Recursive Least Squares Forecast [LuxAlgo]The Recursive Least Squares Forecast indicator uses an adaptive linear regression algorithm to estimate price trends in real-time, projecting future movements via a "Ghost Line" and providing dynamic bands and mean reversion signals for identifying market extremes. By continuously updating its internal model with every new bar, the script provides a highly responsive framework for both trend forecasting and volatility-adjusted trading. 🔶 USAGE The indicator aims to identify trend direction and potential exhaustion points. The central RLS mean line represents the current equilibrium price based on the adaptive model, while the bands represent volatility-adjusted extremes. Users can utilize the tool for both trend following and mean reversion strategies: Trend Following: Observe the slope and direction of the RLS Mean and the "Ghost Line" projection to determine the prevailing market bias. Mean Reversion: Use the dynamic bands to identify when price has deviated significantly from its adaptive equilibrium. Responsiveness: Adjust the Forgetting Factor (λ) to control the model's memory. A lower value (e.g., 0.95) makes the model react quickly to new price pivots, while a higher value (e.g., 0.99) provides a smoother, more stable trend line. 🔹 Mean Reversion Signals The indicator identifies mean reversion opportunities using a two-step process: Overextension: A setup begins when the price crosses outside the Upper or Lower Band, indicating an overbought or oversold state. Entry Signal: A "BUY" or "SELL" signal is triggered when the price crosses back inside the band, suggesting a return to the RLS mean. Targets: The RLS mean line serves as the primary take-profit target for these mean reversion setups. 🔶 DETAILS The model assumes a linear relationship where the intercept and slope are updated recursively. The RLS algorithm is an adaptive filter that effectively "learns" the trend at every bar. It uses a state-space approach where the transition matrix is updated using a gain vector, ensuring the most efficient estimate of the current trend trajectory. Unlike standard Moving Averages, the Recursive Least Squares (RLS) algorithm minimizes the sum of squared prediction errors by giving more weight to recent data. This allows the mean line to pivot quickly when market conditions change without the lag associated with traditional smoothing techniques. The "Ghost Line" extends from the last bar into the future, providing a linear projection of where the current trend is headed. Surrounding this projection are "Standard Deviation Forecast Bands," which indicate the expected range of price movement based on the current model state. 🔶 SETTINGS Forgetting Factor (λ): Controls how quickly the model forgets old data. Values closer to 1.0 make the model stable, while lower values make it more adaptive to recent price changes. Band Multiplier: Standard deviation multiplier for the forecast bands, controlling the width of the mean reversion zones. Forecast Horizon: Number of bars to project the "Ghost Line" and uncertainty bands into the future. Show Ghost Line & Bands: Toggles the visibility of the future projection polylines. Show Mean Reversion Signals: Toggles the visibility of the BUY/SELL labels on the chart.
Индикатор Pine Script®
от LuxAlgo
6
Bitcoin Halving Cycles [DotGain]Halving Cycles A lightweight, time-anchored Bitcoin halving cycle visualizer built for clean charting, repeatable process planning, and simple profit/DCA timing references. This Code was heavily inspired by KevinSvenson_ who created Bitcoin Halving Cycle Profit . What this indicator does This script plots the key “cycle landmarks” relative to each halving date: Halving (⛏) – the cycle anchor Profit START – marks the beginning of the post-halving profit window (default: 40 weeks ) Profit END / Last Call – marks the final phase of the profit window (default: 77 weeks ) DCA START – marks the point where long-term accumulation becomes the focus again (default: 135 weeks ) How to read it Vertical lines = the exact cycle milestones Bottom labels = description of each milestone aligned to its line (keeps the chart clean) Green background (optional) = active Profit Zone on existing bars Red background (optional) = optional warning zone after Profit END HUD Panel (top-right) The HUD gives you a fast “where are we in the cycle?” view with two modes: Current Cycle Shows: Halving date, Weeks since, and time remaining to Profit START / Last Call / DCA START within the current cycle. Next Halving (Projection) Shows: Countdown to the next enabled future halving, plus the projected weeks from today to Profit START / Last Call / DCA START after that future halving. Future Halvings (manual) You can manually add up to 3 future halving dates (Halving #1–#3). This is useful for forward planning and cycle projection even before the event happens. Enable Halving #1 / #2 / #3 Set Year / Month / Day for each Optional: show/hide future markers & projections Note: background zones only shade existing bars . Future projections are shown via lines/labels. Settings overview Show all cycles – plots every enabled cycle (historical + optional future). If disabled, only the current cycle is drawn. Show Profit Zone background – green shading during the active profit window (current cycle only). Show vertical markers + labels – toggles all milestone lines + labels. Show HUD – toggles the HUD panel. HUD Mode – switch between Current Cycle and Next Halving (Projection). Cycle Logic – edit offsets in weeks (Profit START / Profit END / DCA START). Optional Warning Zone – show a post-profit warning shading for a chosen number of weeks. Have fun :) Disclaimer This Halving Cycles indicator is provided for informational and educational purposes only. It does not, and should not be construed as, financial, investment, or trading advice. This indicator is an independent implementation of a time-based Bitcoin halving cycle visualization tool and is not affiliated with, or endorsed by, any third-party trading systems, strategies, protocols, or trademarked methodologies. The cycle zones, milestone markers, and countdown values displayed by this indicator are generated by a predefined set of algorithmic rules based on historical halving dates and user-defined time offsets. They do not constitute a direct recommendation to buy, sell, or hold any financial instrument or digital asset. All trading and investing in financial markets involves a substantial risk of loss. You may lose part or all of your invested capital. Past performance does not guarantee future results. This indicator highlights historical and projected time-based market cycles and may produce false, lagging, incomplete, or misleading signals. Market behavior is influenced by many external factors and can deviate significantly from historical patterns or expectations. The creator DotGain assumes no responsibility or liability for any financial losses, damages, or decisions made based on the use of this indicator or the information it provides. You are solely responsible for your own trading and investment decisions. Always conduct your own research (DYOR), use proper risk management, validate insights with additional tools or analysis, and consider your personal financial situation and risk tolerance before making any financial decision.
Индикатор Pine Script®
от DotGain
Monte Carlo Simulation BandsMonte Carlo Simulation v2.4.2 Plots a one-bar-ahead price distribution band built from many simulated paths. The green band shows empirical percentiles of simulated final prices—these are distribution bounds, not a confidence interval of the mean. What It Does Simulates many one-bar price paths using a directional random walk with volatility scaling (uniform shocks, not Gaussian GBM). Plots Mean Forecast, Median Forecast, and configurable percentile bounds (default 5th/95th). Optional rolling HTF-days mean line (yellow) for trend context. Optional labels and forward projection lines. Alerts when the confirmed close breaks above or below the percentile band. Non-Repainting & HTF Behavior (Fail-Closed) All calculations are gated to confirmed bars only via explicit no_repaint_ok gate (barstate.isconfirmed). If you select an HTF Resolution, the script uses a strict request.security(..., lookahead_off, gaps_off) pipeline. If HTF data is unavailable, outputs are na—no silent fallback to chart timeframe. A separate "HTF Alignment (lagged)" plot shows the prior HTF close (htf_price ) as visual proof of no look-ahead. Volatility Source & Scaling If "Use Historical Volatility" is enabled, volatility is estimated from log returns on the selected resolution (HTF if set, otherwise chart). Annualization adapts to session type: Equities: 6.5 hours/day, 252 trading days/year Crypto: 24 hours/day, 365 days/year Substeps increase path smoothness within the same one-bar horizon—they do not extend the forecast to multiple bars. Key Inputs • Prob Up / Prob Down — Must satisfy Prob Up + Prob Down ≤ 1.0. If violated, simulation is skipped and table shows "✗ PROB>1". • # Simulations / # Substeps — Higher = smoother/more stable, but slower. Default 100×100 is a good balance. • Lower/Upper Percentile — Define the band width (e.g., 5 and 95 for a 90% distribution band). • Run On Last Bar Only — Performance mode (recommended). Skips historical computation; updates on each new confirmed bar. • Resolution (HTF) — Leave blank for chart timeframe, or set to Weekly/Monthly for HTF-aligned simulation. • Crypto 24/7 Session? — Enable for crypto markets to use correct annualization (365d, 24h). How to Use (Quickstart) Start with defaults and keep Run On Last Bar Only = true for speed. Set Prob Up and Prob Down so their sum ≤ 1.0 (e.g., 0.5 + 0.5 = 1.0 for neutral). Enable "Use Historical Volatility" and set a Volatility Lookback (e.g., 20 bars) for data-driven vol. Set Resolution (HTF) if you want the model to run on higher timeframe data (e.g., 1W). Expect updates only when a new HTF interval starts. Choose percentiles (e.g., 5 and 95) to define your distribution band width. Enable alerts for "Price Above Upper Percentile" or "Price Below Lower Percentile" to get notified of breakouts. Limitations & Disclosures Forecast horizon is one bar only. Substeps do not create a multi-bar forecast. Model uses uniform shocks with direction chosen from Prob Up/Down. This is not Geometric Brownian Motion (GBM) and is not calibrated to any option-implied distribution. Bounds are percentiles of final simulated prices, not a statistical confidence interval of the mean. HTF mode updates at the start of a new HTF interval (first chart bar where the HTF timestamp changes), so the band appears "step-like" in realtime. Historical volatility requires enough bars for the selected lookback; until then, values may be na. Performance depends on Sims × Substeps; extreme settings (e.g., 500×500) can be slow. This indicator does not predict direction—it shows a probabilistic range based on your inputs.
Индикатор Pine Script®
от actemplet
Yearly Projection ExplorerThis indicator helps you understand how the current market period has behaved historically by overlaying the same date window from previous years and projecting it forward from today’s price. The script works the following way: Aligns past years to today’s calendar date Normalizes all paths to the last close at the start Projects historical performance X bars forward Displays each year as a separate performance path Calculates and plots the mean (average) projection for quick reference 🔧 How It Works Number of Years: choose how many past years to include (e.g. last 10, 20, or 25 years) Projection Length: choose how many bars (days) ahead to project Each line shows how the market moved during the same period in a specific year Labels show the year and total return at the projection end The mean line highlights the average historical outcome 🧠 Why This Is Useful Identify seasonal tendencies Compare current price action to historical analogs Visualize best / worst historical outcomes Set realistic expectations for short-term moves Add context to discretionary or systematic decisions This tool does not predict the future, but it provides a powerful historical framework to assess what has been typical, rare, or extreme for the current market window. ⚠️ Notes Script works on timenow variable for now, and you might see unexpected periods if today is a day off. Results depend on the selected timeframe and instrument Past performance is not a guarantee of future results Designed for analysis and context, not standalone signals
Индикатор Pine Script®
от QuantNomad
Bollinger Bands Forecast with Signals (Zeiierman)█ Overview Bollinger Bands Forecast with Signals (Zeiierman) extends classic Bollinger Bands into a forward-looking framework. Instead of only showing where volatility has been, it projects where the basis (midline) and band width are likely to drift next, based on recent trend and volatility behavior. The projection is built from the measured slopes of the Bollinger basis, the standard deviation (or ATR, depending on the mode), and a volatility “breathing” component. On top of that, the script includes an optional projected price path that can be blended with a deterministic random walk, plus rejection signals to highlight failed band breaks. █ How It Works ⚪ Bollinger Core The script first computes standard Bollinger Bands using the selected Source, Length, and Multiplier: Basis = SMA(Source, Length) Band width = Multiplier × StDev(Source, Length) Upper/Lower = Basis ± Width This remains the “live” (non-forecast) structure on the chart. ⚪ Trend & Volatility Slope Estimation To project forward, the indicator measures directional drift and volatility drift using linear regression differences: Basis slope from the Bollinger basis StDev slope from the Bollinger deviation ATR slope for ATR-based projection mode These slopes drive the forecast bands forward, reflecting the market’s recent directional and volatility regime. ⚪ Projection Engine (Forecast Bands) At the last bar, the indicator draws projected basis, upper, and lower lines out to Forecast Bars. The projected basis can be: Trend (straight linear projection) Curved (ease-in/out transition toward projected endpoints) Smoothed (extra smoothing on projected basis/width) ⚪ Price Path Projection + Optional Random Walk In addition to projecting the bands, the script can draw a price forecast path made of a small number of zigzag swings. Each swing targets a point offset from the projected basis by a multiple of the projected half-width (“width units”). Decay gradually reduces swing size as the forecast deepens. The Optional Random Walk Blend adds a deterministic drift component to the zigzag path. It’s not true randomness; it’s a stable pseudo-random sequence, so the drawing doesn’t jump around on refresh, while still adding “natural” variation. ⚪ Rejection Signals Signals are based on failed attempts to break a band: Bear Signal (Down): price tries to push above the upper band, then falls back inside, while still closing above the basis. Bull Signal (Up): price tries to push below the lower band, then returns back inside, while still closing below the basis. █ How to Use ⚪ Forward Support/Resistance Corridors Treat the projected upper/lower bands as a future volatility envelope, not a guarantee: The upper projection ≈ is likely a resistance level if the regime persists The lower projection ≈ is likely a support level if the regime persists Best used for trade planning, targets, and “where price could travel” under similar conditions. ⚪ Regime Read: Trend + Volatility The projection shape is informative: Rising basis + expanding width → trend with increasing volatility (needs wider stops / more caution) Flat basis + compressing width → contraction regime (often precedes expansion) ⚪ Signals for Mean-Reversion / Failed Breakouts The rejection markers are useful for fade-style setups: A Down signal near/after upper-band failure can imply rotation back toward the basis. An Up signal near/after lower-band failure can imply snap-back toward the basis. With MA filtering enabled, signals are constrained to align with the broader bias, helping reduce chop-driven noise. █ Related Publications Donchian Predictive Channel (Zeiierman) █ Settings ⚪ Bollinger Band Controls the live Bollinger Bands on the chart. Source – Price used for calculations. Length – Lookback period; higher = smoother, lower = more reactive. Multiplier – Bandwidth; higher = wider bands, lower = tighter bands. ⚪ Forecast Controls the forward projection of the Bollinger Bands. Forecast Bars – How far into the future the bands are projected. Trend Length – Lookback used to estimate trend and volatility slopes. Forecast Band Mode – Defines projection behavior (linear, curved, breathing, ATR-based, or smoothed). ⚪ Price Forecast Controls the projected price path inside the bands. ZigZag Swings – Number of projected oscillations. Amplitude – Distance from basis, measured in bandwidth units. Decay – Shrinks swings further into the forecast. ⚪ Random-Walk Adds controlled randomness to the price path. Enable – Toggle random-walk influence. Blend – Strength of randomness vs. zigzag. Step Size – Size of random steps (band-width units). Decay – Reduces randomness as the forecast deepens. Seed – Changes the (stable) random sequence. ⚪ Signals Controls rejection/mean-reversion signals. Show Signals – Enable/disable signal markers. MA Filter (Type/Length) – Filters signals by trend direction. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Индикатор Pine Script®
от Zeiierman
10
Future Ichimoku Cloud - HorizonIchimoku Horizon is an advanced Ichimoku indicator that projects future cloud formations and component lines, giving traders unprecedented visibility into potential support/resistance zones before they form. 1. Future Ichimoku Projections Project Ichimoku components forward in time using simulated price evolution based on rolling Tenkan/Kijun windows Manual forecast periods up to 125 bars (all 4 components) or 500 bars (cloud only) Smart limit management automatically adjusts to TradingView's drawing object limits while maximizing visible projections 2. Preset & Custom Ichimoku Configurations Choose from multiple common Ichimoku presets or fully customize your own 3. Multi-Timeframe Display & Projections Display Ichimoku from higher/lower timeframes directly on your current timeframe chart Automatic scaling adjusts Ichimoku periods correctly across timeframes Intelligent handling of 24/7 markets (crypto/forex) vs traditional session-based markets Built-in detection of problematic timeframe combinations with optional MTF cloud fetching for accuracy Automatic notifications when future projections are unavailable due to MTF constraints 4. Tenkan & Kijun Range Windows Visual range windows that display the exact high/low range used for Tenkan and Kijun calculations Optional High/Low markers placed at the exact bars they occur Optional countdown labels show how many bars remain until the current High/Low expires from the rolling window Range windows scale up and down dynamically to match display timeframe 5. Comprehensive Alert Suite Built-in alerts for all major Ichimoku events: TK crosses, E2E entires, Kumo breakouts, etc. All alerts are cloud-aware and displacement-correct. How It Works The indicator uses the traditional Donchian channel method to calculate Ichimoku components, then extends this logic forward by simulating future price action within the calculation windows (no new highs or lows). This creates a forward-looking projection of where support and resistance zones will form. The range display feature helps traders understand why the lines are where they are by showing the exact high/low points and countdown timers for when these points will expire from the calculation. Who This Indicator Is For: Ichimoku traders who want future-aware context Multi-timeframe analysts seeking correctly aligned clouds Traders who want to understand Tenkan/Kijun mechanics Users who need precision without manual recalculation Notes: Maximum 500 drawing objects limit managed automatically Due to Pinescript/TradingView limitations, future Tenkan/Kijun line width is only modifiable in the source code.
Индикатор Pine Script®
от CryptoPl3be
NeuroSwarm ETH — Crowd vs Experts Forecast TrackerEnglish: NeuroSwarm — Crowd vs Experts Forecast Tracker (ETH) This indicator visualizes monthly forecast data collected from two independent groups: Crowd – a large sample of retail participants Experts – a curated group of analysts and experienced market participants For each month, the indicator plots the following values as horizontal levels on the price chart: Median forecast (Crowd) Average forecast (Crowd) Median forecast (Experts) Average forecast (Experts) Shaded zones highlighting the difference between median and mean All values are fixed for each month and stay unchanged historically. This allows traders to analyze sentiment dynamics and compare how expectations from both groups align or diverge from actual price action. Purpose: This tool is intended for sentiment visualization and analytical insight — it does not generate trading signals. Its main goal is to compare collective expectations of retail traders vs experts across time. Data source: All forecasts come from monthly surveys conducted within the NeuroSwarm project between the 1st and 5th day of each month. Interface notice: The script's UI may contain non-English labels for convenience, but a full English documentation is provided here in compliance with TradingView rules. Русская версия: NeuroSwarm — Мудрость Толпы vs Эксперты (ETH) Индикатор отображает ежемесячные прогнозы двух групп: Толпа: медиана и средняя прогнозов Эксперты: медиана и средняя прогнозов Значения фиксируются для каждого месяца и показываются горизонтальными уровнями. Заливка отображает диапазон между медианой и средней, что упрощает визуальное сравнение настроений. Это аналитический инструмент для визуализации настроений — не торговая стратегия. Все данные берутся из ежемесячных опросов проекта NeuroSwarm.
Индикатор Pine Script®
от Nikki_23
NeuroSwarm BTC — Crowd vs Experts Forecast TrackerEnglish: NeuroSwarm — Crowd vs Experts Forecast Tracker (BTC) This indicator visualizes monthly forecasts collected from two independent groups: Crowd – a large sample of retail traders Experts – a smaller, curated group of analysts and experienced market participants For each month, the following values are displayed as horizontal levels on the chart: Median forecast of the Crowd Average forecast of the Crowd Median forecast of Experts Average forecast of Experts Shaded zones showing the range between median and mean The values remain fixed throughout each month. This allows traders to compare sentiment dynamics between groups and see how expectations evolve relative to actual market movement. Purpose: This indicator is designed for sentiment analysis — NOT for generating trading signals. It helps identify divergences between retail expectations and expert forecasts, which can be informative during trend transitions. Data source: All values come from monthly surveys conducted within the NeuroSwarm project (1–5 of every month). Crowd and Expert groups are collected separately to avoid bias and to preserve independent aggregation. Interface language note: The indicator’s interface may contain non-English labels for ease of use, but full English documentation is provided here in compliance with TradingView House Rules. Русская версия (optional, allowed only AFTER English): NeuroSwarm — Мудрость Толпы vs Эксперты (BTC) Индикатор показывает ежемесячные прогнозы двух групп: Толпа: медиана и средняя прогнозов Эксперты: медиана и средняя прогнозов Значения фиксируются на весь месяц и отображаются на графике горизонтальными уровнями. Заливка показывает диапазон между медианой и средней. Цель индикатора — визуализировать настроение толпы и экспертов и сравнить его с реальным движением цены. Это аналитический инструмент, а не торговая стратегия. Данные берутся из ежемесячных опросов (1–5 числа), проводимых в рамках проекта NeuroSwarm.
Индикатор Pine Script®
от Nikki_23
RSI Forecast Colorful [DiFlip]RSI Forecast Colorful Introducing one of the most complete RSI indicators available — a highly customizable analytical tool that integrates advanced prediction capabilities. RSI Forecast Colorful is an evolution of the classic RSI, designed to anticipate potential future RSI movements using linear regression. Instead of simply reacting to historical data, this indicator provides a statistical projection of the RSI’s future behavior, offering a forward-looking view of market conditions. ⯁ Real-Time RSI Forecasting For the first time, a public RSI indicator integrates linear regression (least squares method) to forecast the RSI’s future behavior. This innovative approach allows traders to anticipate market movements based on historical trends. By applying Linear Regression to the RSI, the indicator displays a projected trendline n periods ahead, helping traders make more informed buy or sell decisions. ⯁ Highly Customizable The indicator is fully adaptable to any trading style. Dozens of parameters can be optimized to match your system. All 28 long and short entry conditions are selectable and configurable, allowing the construction of quantitative, statistical, and automated trading models. Full control over signals ensures precise alignment with your strategy. ⯁ Innovative and Science-Based This is the first public RSI indicator to apply least-squares predictive modeling to RSI calculations. Technically, it incorporates machine-learning logic into a classic indicator. Using Linear Regression embeds strong statistical foundations into RSI forecasting, making this tool especially valuable for traders seeking quantitative and analytical advantages. ⯁ Scientific Foundation: Linear Regression Linear regression is a fundamental statistical method that models the relationship between a dependent variable y and one or more independent variables x. The general formula for simple linear regression is: y = β₀ + β₁x + ε where: y  = predicted variable (e.g., future RSI value) x  = explanatory variable (e.g., bar index or time) β₀ = intercept (value of y when x = 0) β₁ = slope (rate of change of y relative to x) ε   = random error term The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the RSI projection based on recent trends. ⯁ Least Squares Estimation To minimize the error between predicted and observed values, we use the formulas: β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) β₀ = ȳ - β₁x̄ Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables. ⯁ Linear Regression in Machine Learning Linear regression is a foundational component of supervised learning. Its simplicity and precision in numerical prediction make it essential in AI, predictive algorithms, and time-series forecasting. Applying regression to RSI is akin to embedding artificial intelligence inside a classic indicator, adding a new analytical dimension. ⯁ Visual Interpretation Imagine a time series of RSI values like this: Time → RSI    → The regression line smooths these historical values and projects itself n periods forward, creating a predictive trajectory. This projected RSI line can cross the actual RSI, generating sophisticated entry and exit signals. In summary, the RSI Forecast Colorful indicator provides both the current RSI and the forecasted RSI, allowing comparison between past and future trend behavior. ⯁ Summary of Scientific Concepts Used Linear Regression: Models relationships between variables using a straight line. Least Squares: Minimizes squared prediction errors for optimal fit. Time-Series Forecasting: Predicts future values from historical patterns. Supervised Learning: Predictive modeling based on known output values. Statistical Smoothing: Reduces noise to highlight underlying trends. ⯁ Why This Indicator Is Revolutionary Scientifically grounded: Built on statistical and mathematical theory. First of its kind: The first public RSI with least-squares predictive modeling. Intelligent: Incorporates machine-learning logic into RSI interpretation. Forward-looking: Generates predictive, not just reactive, signals. Customizable: Exceptionally flexible for any strategic framework. ⯁ Conclusion By combining RSI and linear regression, the RSI Forecast Colorful allows traders to predict market momentum rather than simply follow it. It's not just another indicator: it's a scientific advancement in technical analysis technology. Offering 28 configurable entry conditions and advanced signals, this open-source indicator paves the way for innovative quantitative systems. ⯁ Example of simple linear regression with one independent variable This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables. ⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x) This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component. ⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models. ⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model. ⯁ Result of fitting a set of data points with a quadratic function This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line. ⯁ What Is RSI? The RSI (Relative Strength Index) is a technical indicator developed by J. Welles Wilder. It measures the velocity and magnitude of recent price movements to identify overbought and oversold conditions. The RSI ranges from 0 to 100 and is commonly used to identify potential reversals and evaluate trend strength. ⯁ How RSI Works RSI is calculated from average gains and losses over a set period (commonly 14 bars) and plotted on a 0–100 scale. It consists of three key zones: Overbought: RSI above 70 may signal an overbought market. Oversold: RSI below 30 may signal an oversold market. Neutral Zone: RSI between 30 and 70, indicating no extreme condition. These zones help identify potential price reversals and confirm trend strength. ⯁ Entry Conditions All conditions below are fully customizable and allow detailed control over entry signal creation. 📈 BUY 🧲 Signal Validity: Signal remains valid for X bars. 🧲 Signal Logic: Configurable using AND or OR. 🧲 RSI > Upper 🧲 RSI < Upper 🧲 RSI > Lower 🧲 RSI < Lower 🧲 RSI > Middle 🧲 RSI < Middle 🧲 RSI > MA 🧲 RSI < MA 🧲 MA > Upper 🧲 MA < Upper 🧲 MA > Lower 🧲 MA < Lower 🧲 RSI (Crossover) Upper 🧲 RSI (Crossunder) Upper 🧲 RSI (Crossover) Lower 🧲 RSI (Crossunder) Lower 🧲 RSI (Crossover) Middle 🧲 RSI (Crossunder) Middle 🧲 RSI (Crossover) MA 🧲 RSI (Crossunder) MA 🧲 MA (Crossover)Upper 🧲 MA (Crossunder)Upper 🧲 MA (Crossover) Lower 🧲 MA (Crossunder) Lower 🧲 RSI Bullish Divergence 🧲 RSI Bearish Divergence 🔮 RSI (Crossover) Forecast MA 🔮 RSI (Crossunder) Forecast MA 📉 SELL 🧲 Signal Validity: Signal remains valid for X bars. 🧲 Signal Logic: Configurable using AND or OR. 🧲 RSI > Upper 🧲 RSI < Upper 🧲 RSI > Lower 🧲 RSI < Lower 🧲 RSI > Middle 🧲 RSI < Middle 🧲 RSI > MA 🧲 RSI < MA 🧲 MA > Upper 🧲 MA < Upper 🧲 MA > Lower 🧲 MA < Lower 🧲 RSI (Crossover) Upper 🧲 RSI (Crossunder) Upper 🧲 RSI (Crossover) Lower 🧲 RSI (Crossunder) Lower 🧲 RSI (Crossover) Middle 🧲 RSI (Crossunder) Middle 🧲 RSI (Crossover) MA 🧲 RSI (Crossunder) MA 🧲 MA (Crossover)Upper 🧲 MA (Crossunder)Upper 🧲 MA (Crossover) Lower 🧲 MA (Crossunder) Lower 🧲 RSI Bullish Divergence 🧲 RSI Bearish Divergence 🔮 RSI (Crossover) Forecast MA 🔮 RSI (Crossunder) Forecast MA 🤖 Automation All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies. ⯁ Unique Features Linear Regression Forecast Signal Validity: Keep signals active for X bars Signal Logic: AND/OR configuration Condition Table: BUY/SELL Condition Labels: BUY/SELL Chart Labels: BUY/SELL markers above price Automation & Alerts: BUY/SELL Background Colors: bgcolor Fill Colors: fill Linear Regression Forecast Signal Validity: Keep signals active for X bars Signal Logic: AND/OR configuration Condition Table: BUY/SELL Condition Labels: BUY/SELL Chart Labels: BUY/SELL markers above price Automation & Alerts: BUY/SELL Background Colors: bgcolor Fill Colors: fill
Индикатор Pine Script®
от DiFlip
2
MarketSurge EPS Line [tradeviZion]MarketSurge EPS Line EPS trend line overlay for TradingView charts, inspired by the IBD MarketSurge (formerly MarketSmith) EPS line style. Comparison: Left side shows IBD MarketSurge EPS line as reference. Right side shows this TradingView script producing similar output with interactive tooltips. The left side image is for reference only to demonstrate similarity - it is not part of the TradingView script. Features: Displays EPS trend line on price charts Uses 4-quarter earnings moving average Shows earnings momentum over time Works with actual, estimated, or standardized earnings data Customizable line color and width Interactive tooltips with detailed earnings information Custom symbol analysis support How to Use: Add script to chart EPS line appears automatically Adjust color and width in settings if needed Hover over line for earnings details Settings Explained: Display Settings: Show EPS Line: Toggle to show or hide the EPS trend line EPS Line Color: Choose the color for the EPS trend line and labels EPS Line Width: Adjust the thickness of the EPS trend line (1-5 pixels) Symbol Settings: By default, the indicator analyzes the EPS data for the symbol currently displayed on your chart. The Custom Symbol feature allows you to: Analyze EPS data for a different symbol without changing your chart Compare earnings trends of related stocks or competitors View EPS data for one symbol while analyzing price action of another To use Custom Symbol: Enable "Use Custom Symbol" checkbox Click on "Custom Symbol" field to open TradingView's symbol picker Search and select the symbol you want to analyze The indicator will fetch and display EPS data for the selected symbol Note: The chart will still show price action for your current symbol, but the EPS line will reflect the custom symbol's earnings data. Data Settings: EPS Field: Choose which earnings data source to use: Actual Earnings: Reported earnings from company financial statements (default). Use this to analyze historical performance based on what companies actually reported. Estimated Earnings: Analyst consensus forecasts for future quarters. Use this to see what analysts expect and compare expectations with actual results. Standardized Earnings: Earnings adjusted for comparability across companies. Use this when comparing multiple stocks as it normalizes accounting differences. Display Scale: For the indicator to display correctly on the existing chart, it uses its own axis (right scale) by default. However, you can change this, but the view will not look the same. The right scale is recommended for optimal visibility as it allows the EPS line to be clearly visible alongside price action without compression. Example: EPS line on separate right scale (recommended) - hover over labels to view detailed earnings tooltips Example: EPS line pinned to Scale A (not recommended - appears as straight line due to small EPS range compared to price) Example: EPS line displayed in separate pane below price chart Methodology Credits: This indicator implements the EPS line visualization methodology developed by Investor's Business Daily (IBD) for their MarketSurge platform (formerly known as MarketSmith). The EPS line concept helps visualize earnings momentum alongside price action, providing a fundamental overlay for technical analysis. Technical Details: Designed for daily, weekly, and monthly timeframes Minimum 4 quarters of earnings data required Uses TradingView's built-in earnings data Automatically handles missing or invalid data This indicator helps you visualize earnings trends alongside price action, providing a fundamental overlay for your technical analysis.
Индикатор Pine Script®
от TradeVizion
Bollinger Bands Regression Forecast [BigBeluga]🔵 OVERVIEW The Bollinger Bands Regression Forecast combines volatility envelopes from Bollinger Bands with a linear regression-based projection model . It visualizes both current and future price zones by extrapolating the Bollinger channel forward in time, giving traders a statistical forecast of probable support and resistance behavior. 🔵 CONCEPTS Classic Bollinger Bands use a moving average (basis) and standard deviation (deviation) to form dynamic envelopes around price. This indicator enhances them with linear regression slope detection , allowing it to forecast how the band may expand or contract in the future. Regression is applied to both the band’s basis and deviation components to predict their trajectory for a user-defined number of Forecast Bars . The resulting forecast creates a smoothed, funnel-shaped projection that dynamically adapts to volatility. ▲ and ▼ markers highlight potential mean reversion points when price crosses the outer bounds of the bands. 🔵 FEATURES Forecast Engine : Uses linear regression to project Bollinger Band movement into the future. Dynamic Channel Width : Adapts standard deviation and slope for realistic volatility modeling. Auto-Labeled Levels : Displays live upper and lower forecast values for quick reference. Cross Signals : Marks potential overbought and oversold zones with ▲/▼ signals when price exits the band. Trend-Adaptive Basis Color : Basis line automatically switches color to represent short-term trend direction. Customizable Colors and Widths for complete visual control. 🔵 HOW TO USE Apply the indicator to visualize both current Bollinger structure and its forward projection. Use ▲/▼ breakout markers to identify short-term reversals or volatility shifts. When price consistently rides the upper band forecast, the trend is strong and likely continuing. When regression shows narrowing bands ahead, expect a volatility contraction or consolidation period. For range traders, outer projected bands can be used as potential mean reversion entry points . Combine with volume or momentum filters to confirm whether breakouts are genuine or fading. 🔵 CONCLUSION Bollinger Bands Regression Forecast transforms classic Bollinger analysis into a predictive forecasting model . By merging volatility dynamics with regression-based extrapolation, it provides traders with a forward-looking visualization of likely price boundaries — revealing not only where volatility is but also where it’s heading next.
Индикатор Pine Script®
от BigBeluga
14
Power Balance ForecasterHey trader buddy! Remember the old IBM 5150 on Wall Street back in the 80s? :) Well, I wanted to pay tribute to it with this retro-style code when MS DOS and CRT screens were the cutting edge of technology... Analysis of the balance of power between buyers and sellers with price predictions What This Indicator Does The Power Balance Forecaster indicator analyzes the relationship between buyer and seller strength to predict future price movements. Here's what it does in detail: Main Features: Power Balance Analysis: Calculates real-time percentage of buyer power vs seller power Price Predictions: Estimates next closing level based on current momentum Market State Detection: Identifies 5 different market conditions Visual Signals: Shows directional arrows and price targets How the Trading Logic Works Power Balance Calculation: Analyzes Consecutive Bars - Counts consecutive bullish and bearish bars Calculates Momentum - Uses ATR-normalized momentum to measure trend strength Determines Market State - Assigns one of 5 market states based on conditions Market States: Bull Control: Strong uptrend (75% buyer power) Bear Control: Strong downtrend (75% seller power) Buying Pressure: Bullish pressure (65% buyer power) Selling Pressure: Bearish pressure (65% seller power) Balance Area: Market in equilibrium (50/50) Prediction System: Bullish Condition: Buyer power > 55% + Positive momentum = Bullish prediction Bearish Condition: Seller power > 55% + Negative momentum = Bearish prediction Price Target: Based on ATR multiplied by timeframe factor Configurable Parameters: Analysis Sensitivity (5-50): Controls how responsive the indicator is Low values (5-15): More sensitive, ideal for scalping High values (30-50): More stable, ideal for swing trading Table Position: Choose from 9 positions to display the data table Trading Signals: Green Triangle ▲: Bullish signal, price expected to increase Green Triangle ▼: Bearish signal, price expected to decrease Dashed Line: Shows the price target projection Label: Displays the exact target value Recommended Timeframes: Lower Timeframes (1-15 minutes): Sensitivity: 10-20 Automatic Low TF mode Higher Timeframes (1 hour - 1 day): Sensitivity: 25-40 Automatic High TF mode Important Notes: Always use this indicator in combination with: Market context analysis Proper risk management Confirmation from other indicators Mandatory stop losses The indicator works best in trending markets and may be less effective during extreme consolidation periods.
Индикатор Pine Script®
от LanfryBil
Holt Damped Forecast [CHE]A Friendly Note on These Pine Script Scripts Hey there! Just wanted to share a quick, heartfelt heads-up: All these Pine Script examples come straight from my own self-study adventures as a total autodidact—think late nights tinkering and learning on my own. They're purely for educational vibes, helping me (and hopefully you!) get the hang of Pine Script basics, cool indicators, and building simple strategies. That said, please know this isn't any kind of financial advice, investment nudge, or pro-level trading blueprint. I'd love for you to dive in with your own research, run those backtests like a champ, and maybe bounce ideas off a qualified expert before trying anything in a real trading setup. No guarantees here on performance or spot-on accuracy—trading's got its risks, and those are totally on each of us. Let's keep it fun and educational—happy coding! 😊 Holt Damped Forecast — Damped trend forecasts with fan bands for uncertainty visualization and momentum integration Summary This indicator applies damped exponential smoothing to generate forward price forecasts, displaying them as probabilistic fan bands to highlight potential ranges rather than point estimates. It incorporates residual-based uncertainty to make projections more reliable in varying market conditions, reducing overconfidence in strong trends. Momentum from the trend component is shown in an optional label alongside signals, aiding quick assessment of direction and strength without relying on lagging oscillators. Motivation: Why this design? Standard exponential smoothing often extrapolates trends indefinitely, leading to unrealistic forecasts during mean reversion or weakening momentum. This design uses damping to gradually flatten long-term projections, better suiting real markets where trends fade. It addresses the need for visual uncertainty in forecasts, helping traders avoid entries based on overly optimistic point predictions. What’s different vs. standard approaches? - Reference baseline: Diverges from basic Holt's linear exponential smoothing, which assumes persistent trends without decay. - Architecture differences: - Adds damping to the trend extrapolation for finite-horizon realism. - Builds fan bands from historical residuals for probabilistic ranges at multiple confidence levels. - Integrates a dynamic label combining forecast details, scaled momentum, and directional signals. - Applies tail background coloring to recent bars based on forecast direction for immediate visual cues. - Practical effect: Charts show converging forecast bands over time, emphasizing shorter horizons where accuracy is higher. This visibly tempers aggressive projections in trends, making it easier to spot when uncertainty widens, which signals potential reversals or consolidation. How it works (technical) The indicator maintains two persistent components: a level tracking the current price baseline and a trend capturing directional slope. On each bar, the level updates by blending the current source price with a one-step-ahead expectation from the prior level and damped trend. The trend then adjusts by weighting the change in level against the prior damped trend. Forecasts extend this forward over a user-defined number of steps, with damping ensuring the trend influence diminishes over distance. Uncertainty derives from the standard deviation of historical residuals—the differences between actual prices and one-step expectations—scaled by the damping structure for the forecast horizon. Bands form around the median forecast at specified confidence intervals using these scaled errors. Initialization seeds the level to the first bar's price and trend to zero, with persistence handling subsequent updates. A security call fetches the last bar index for tail logic, using lookahead to align with realtime but introducing minor repaint on unconfirmed bars. Parameter Guide The Source parameter selects the price input for level and residual calculations, defaulting to close; consider using high or low for assets sensitive to volatility, as close works well for most trend-following setups. Forecast Steps (h) defines the number of bars ahead for projections, defaulting to 4—shorter values like 1 to 5 suit intraday trading, while longer ones may widen bands excessively in choppy conditions. The Color Scheme (2025 Trends) option sets the base, up, and down colors for bands, labels, and backgrounds, starting with Ruby Dawn; opt for serene schemes on clean charts or vibrant ones to stand out in dark themes. Level Smoothing α controls the responsiveness of the price baseline, defaulting to 0.3—values above 0.5 enhance tracking in fast markets but may amplify noise, whereas lower settings filter disturbances better. Trend Smoothing β adjusts sensitivity to slope changes, at 0.1 by default; increasing to 0.2 helps detect emerging shifts quicker, but keeping it low prevents whipsaws in sideways action. Damping φ (0..1) governs trend persistence, defaulting to 0.8—near 0.9 preserves carryover in sustained moves, while closer to 0.5 curbs overextensions more aggressively. Show Fan Bands (50/75/95) toggles the probabilistic range display, enabled by default; disable it in oscillator panes to reduce clutter, but it's key for overlay forecasts. Residual Window (Bars) sets the length for deviation estimates, at 400 bars initially—100 to 200 works for short timeframes, and 500 or more adds stability over extended histories. Line Width determines the thickness of band and median lines, defaulting to 2; go thicker at 3 to 5 for emphasis on higher timeframes or thinner for layered indicators. Show Median/Forecast Line reveals the central projection, on by default—hide if bands provide enough detail, or keep for pinpoint entry references. Show Integrated Label activates the combined view of forecast, momentum, and signal, defaulting to true; it's right-aligned for convenience, so turn it off on smaller screens to save space. Show Tail Background colors the last few bars by forecast direction, enabled initially; pair low transparency for subtle hints or higher for bolder emphasis. Tail Length (Bars) specifies bars to color backward from the current one, at 3 by default—1 to 2 fits scalping, while 5 or more underscores building momentum. Tail Transparency (%) fades the background intensity, starting at 80; 50 to 70 delivers strong signals, and 90 or above allows seamless blending. Include Momentum in Label adds the scaled trend value, defaulting to true—ATR% scaling here offers relative strength context across assets. Include Long/Short/Neutral Signal in Label displays direction from the trend sign, on by default; neutral helps in ranging markets, though it can be overlooked during strong trends. Scaling normalizes momentum output (raw, ATR-relative, or level-relative), set to ATR% initially—ATR% ensures cross-asset comparability, while %Level provides percentage perspectives. ATR Length defines the period for true range averaging in scaling, at 14; align it with your chart timeframe or shorten for quicker volatility responses. Decimals sets precision in the momentum label, defaulting to 2—0 to 1 yields clean integers, and 3 or more suits detailed forex views. Show Zero-Cross Markers places arrows at direction changes, enabled by default; keep size small to minimize clutter, with text labels for fast scanning. Reading & Interpretation Fan bands expand outward from the current bar, with the median line as the central forecast—narrower bands indicate lower uncertainty, wider suggest caution. Colors tint up (positive forecast vs. prior level) in the scheme's up hue and down otherwise. The optional label lists the horizon, median, and range brackets at 50%, 75%, and 95% levels, followed by momentum (scaled per mode) and signal (Long if positive trend, Short if negative, Neutral if zero). Zero-cross arrows mark trend flips: upward triangle below bar for bullish cross, downward above for bearish. Tail background reinforces the forecast direction on recent bars. Practical Workflows & Combinations - Trend following: Enter long on upward zero-cross if median forecast rises above price and bands contain it; confirm with higher highs/lows. Short on downward cross with falling median. - Exits/Stops: Trail stops below 50% lower band in longs; exit if momentum drifts negative or signal turns neutral. Use wider bands (75/95%) for conservative holds in volatile regimes. - Multi-asset/Multi-TF: Defaults work across stocks, forex, crypto on 5m-1D; scale steps by TF (e.g., 10+ on daily). Layer with volume or structure tools—avoid over-reliance on isolated crosses. Behavior, Constraints & Performance Closed-bar logic ensures stable historical plots, but realtime updates via security lookahead may shift forecasts until bar confirmation, introducing minor repaint on the last bar. No explicit HTF calls beyond bar index fetch, minimizing gaps but watch for low-liquidity assets. Resources include a 2000-bar lookback for residuals and up to 500 labels, with no loops—efficient for most charts. Known limits: Early bars show wide bands due to sparse residuals; assumes stationary errors, so gaps or regime shifts widen inaccuracies. Sensible Defaults & Quick Tuning Start with defaults for balanced smoothing on 15m-4H charts. For choppy conditions (too many crosses), lower β to 0.05 and raise residual window to 600 for stability. In trending markets (sluggish signals), increase α/β to 0.4/0.2 and shorten steps to 2. If bands overexpand, boost φ toward 0.95 to preserve trend carry. Tune colors for theme fit without altering logic. What this indicator is—and isn’t This is a visualization and signal layer for damped forecasts and momentum, complementing price action analysis. It isn’t a standalone system—pair with risk rules and broader context. Not predictive beyond the horizon; use for confirmation, not blind entries. Disclaimer The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context. Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved. By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk. Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts. Best regards and happy trading Chervolino
Индикатор Pine Script®
от chervolino
Volume Surprise [LuxAlgo]The Volume Surprise tool displays the trading volume alongside the expected volume at that time, allowing users to spot unexpected trading activity on the chart easily. The tool includes an extrapolation of the estimated volume for future periods, allowing forecasting future trading activity. 🔶 USAGE We define Volume Surprise as a situation where the actual trading volume deviates significantly from its expected value at a given time. Being able to determine if trading activity is higher or lower than expected allows us to precisely gauge the interest of market participants in specific trends. A histogram constructed from the difference between the volume and expected volume is provided to easily highlight the difference between the two and may be used as a standalone. The tool can also help quantify the impact of specific market events, such as news about an instrument. For example, an important announcement leading to volume below expectations might be a sign of market participants underestimating the impact of the announcement. Like in the example above, it is possible to observe cases where the volume significantly differs from the expected one, which might be interpreted as an anomaly leading to a correction. 🔹 Detecting Rare Trading Activity Expected volume is defined as the mean (or median if we want to limit the impact of outliers) of the volume grouped at a specific point in time. This value depends on grouping volume based on periods, which can be user-defined. However, it is possible to adjust the indicator to overestimate/underestimate expected volume, allowing for highlighting excessively high or low volume at specific times. In order to do this, select "Percentiles" as the summary method, and change the percentiles value to a value that is close to 100 (overestimate expected volume) or to 0 (underestimate expected volume). In the example above, we are only interested in detecting volume that is excessively high, we use the 95th percentile to do so, effectively highlighting when volume is higher than 95% of the volumes recorded at that time. 🔶 DETAILS 🔹 Choosing the Right Periods Our expected volume value depends on grouping volume based on periods, which can be user-defined. For example, if only the hourly period is selected, volumes are grouped by their respective hours. As such, to get the expected volume for the hour 7 PM, we collect and group the historical volumes that occurred at 7 PM and average them to get our expected value at that time. Users are not limited to selecting a single period, and can group volume using a combination of all the available periods. Do note that when on lower timeframes, only having higher periods will lead to less precise expected values. Enabling periods that are too low might prevent grouping. Finally, enabling a lot of periods will, on the other hand, lead to a lot of groups, preventing the ability to get effective expected values. In order to avoid changing periods by navigating across multiple timeframes, an "Auto Selection" setting is provided. 🔹 Group Length The length setting allows controlling the maximum size of a volume group. Using higher lengths will provide an expected value on more historical data, further highlighting recurring patterns. 🔹 Recommended Assets Obtaining the expected volume for a specific period (time of the day, day of the week, quarter, etc) is most effective when on assets showing higher signs of periodicity in their trading activity. This is visible on stocks, futures, and forex pairs, which tend to have a defined, recognizable interval with usually higher trading activity. Assets such as cryptocurrencies will usually not have a clearly defined periodic trading activity, which lowers the validity of forecasts produced by the tool, as well as any conclusions originating from the volume to expected volume comparisons. 🔶 SETTINGS Length: Maximum number of records in a volume group for a specific period. Older values are discarded. Smooth: Period of a SMA used to smooth volume. The smoothing affects the expected value. 🔹 Periods Auto Selection: Automatically choose a practical combination of periods based on the chart timeframe. Custom periods can be used if disabling "Auto Selection". Available periods include: - Minutes - Hours - Days (can be: Day of Week, Day of Month, Day of Year) - Months - Quarters 🔹 Summary Method: Method used to obtain the expected value. Options include Mean (default) or Percentile. Percentile: Percentile number used if "Method" is set to "Percentile". A value of 50 will effectively use a median for the expected value. 🔹 Forecast Forecast Window: Number of bars ahead for which the expected volume is predicted. Style: Style settings of the forecast.
Индикатор Pine Script®
от LuxAlgo
10
Cyclical Phases of the Market🧭 Overview “Cyclical Phases of the Market” automatically detects major market cycles by connecting swing lows and measuring the average number of bars between them. Once it learns the rhythm of past cycles, it projects the next expected cycle (in time and price) using a dashed orange line and a forecast label. In simple terms: The indicator shows where the next potential low is statistically expected to occur, based on the timing and depth of previous cycles. ⚙️ Core Logic – Step by Step 1️⃣ Pivot Detection The script uses the built-in ta.pivotlow() and ta.pivothigh() functions to find local turning points: pivotLow marks a local swing low, defined by pivotLeft and pivotRight bars on each side. Only confirmed lows are used to define the major cycle points. Each new pivot low is stored in two arrays: cycleLows → price level of the low cycleBars → bar index where the low occurred 2️⃣ Cycle Identification and Drawing Every time two consecutive swing lows are found, the indicator: Calculates the number of bars between them (cycle length). If that distance is greater than or equal to minCycleBars, it draws a teal line connecting the two lows — visually representing one complete cycle. These teal lines form the historical cycle structure of the market. 3️⃣ Average Cycle Length Once there are at least three completed cycles, the script calculates the average duration (mean number of bars between lows). This value — avgCycleLength — represents the dominant periodicity or cycle rhythm of the market. 4️⃣ Forecasting the Next Cycle When a valid average cycle length exists, the model projects the next expected cycle: Time projection: Adds avgCycleLength to the last cycle’s ending bar index to find where the next low should occur. Price projection: Estimates the vertical amplitude by taking the difference between the last two cycle lows (priceDiff). Adds this same difference to the last low price to forecast the next probable low level. The result is drawn as an orange dashed line extending into the future, representing the Next Expected Cycle. 5️⃣ Forecast Label An orange label 🔮 appears at the projected future point showing: Text: 🔮 Upcoming Cycle Forecast Price: The label marks the probable area and timing of the next cyclical low. (Note: the date/time calculation currently multiplies bar count by 7 days, so it’s designed mainly for daily charts. On other timeframes, that conversion can be adapted.) 📊 How to Read It on the Chart Visual Element Meaning Interpretation Teal lines Completed historical cycles (low to low) Show actual periodic rhythm of the market Orange dashed line Projection of the next expected cycle Anticipated path toward the next cyclical low Orange label 🔮 Upcoming Cycle Forecast Displays expected price and bar location Average cycle length Internal variable (bars between lows) Represents the dominant cycle period 📈 Interpretation When teal segments show consistent spacing, the market is following a stable rhythm → cycles are predictable. When cycle spacing shortens, the market is accelerating (volatility rising). When it widens, the market is slowing down or entering accumulation. The orange dashed line represents the next expected low zone: If the market drops near this line → cyclical pattern confirmed. If the market breaks well below → cycle amplitude has increased (trend weakening). If the market rises above and delays → a new longer cycle may be forming. 🧠 Practical Use Combine with oscillators (e.g., RSI or TSI) to confirm momentum alignment near projected lows. Use in conjunction with volume to identify accumulation or exhaustion near the expected turning point. Compare across timeframes: weekly cycles confirm long-term rhythm; daily cycles refine short-term entries. ⚡ Summary Aspect Description Purpose Detect and forecast recurring market cycles Cycle basis Low-to-Low pivot analysis Visuals Teal historical cycles + Orange forecast line Forecast Next expected low (price and time) Ideal timeframe Daily Main outputs Average cycle length, next projected cycle, visual cycle map
Индикатор Pine Script®
от LanfryBil
1
Machine Learning Price Predictor: Ridge AR [Bitwardex]🔹Machine Learning Price Predictor: Ridge AR is a research-oriented indicator demonstrating the use of Regularized AutoRegression (Ridge AR) for short-term price forecasting. The model combines autoregressive structure with Ridge regularization , providing stability under noisy or volatile market conditions. The latest version introduces Bull and Bear signals , visually representing the current momentum phase and model direction directly on the chart. Unlike traditional linear regression, Ridge AR minimizes overfitting, stabilizes coefficient dynamics, and enhances predictive consistency in correlated datasets. The script plots: Fit Line — in-sample fitted data; Forecast Line — out-of-sample projection; Trend Segments — color-coded bullish/bearish sections; Bull/Bear Labels 🐂🐻 — dynamic visual signals showing directional bias. Designed for researchers, students, and developers, this tool helps explore regularized time-series forecasting in Pine Script™. 🧩 Ridge AR Settings Training Window — number of bars used for model training; Forecast Horizon — forecast length (bars ahead); AR Order — number of lags used as features; Ridge Strength (λ) — regularization coefficient; Damping Factor — exponential trend decay rate; Trend Length — period for trend/volatility estimation; Momentum Weight — strength of the recent move; Mean Reversion — pullback intensity toward the mean. 🧮 Data Processing Prefilter: None — raw close price; EMA — exponential smoothing; SuperSmoother — Ehlers filter for noise reduction. EMA Length, SuperSmoother Length — smoothing parameters. 🖥️ Display Settings Update Mode: Lock — static model; Update Once Reached — rebuild after forecast horizon; Continuous — update every bar. Forecast Color — projection line color; Bullish/Bearish Colors — colors for trend segments. 🐂🐻 Bull/Bear Signal System The Bull/Bear Signal System adds directional visual cues to highlight local momentum shifts and model-based trend confirmation. Bull (🐂) — appears when upward momentum is confirmed (momentum > 0) . Displayed below the bar, colored with Bullish Color. Bear (🐻) — appears when downward momentum is dominant (momentum < 0) . Displayed above the bar, colored with Bearish Color. Signals are generated during model recalculations or when the directional bias changes in Continuous mode. These visual markers are analytical aids , not trading triggers. 🧠 Core Algorithmic Components Regularized AutoRegression (Ridge AR): Solves: (X′X+λI)−1X′y to derive stable regression coefficients. Matrix and Pseudoinverse Operations — implemented natively in Pine Script™. Prefiltering (EMA / Ehlers SuperSmoother) — stabilizes noisy data. Forecast Dynamics — integrates damping, momentum, and mean reversion. Trend Visualization — color-coded bullish/bearish line segments. Bull/Bear Signal Engine — visualizes real-time impulse direction. 📊 Applications Academic and educational purposes; Demonstration of Ridge Regression and AR models; Analysis of bull/bear market phase transitions; Visualization of time-series dependencies. ⚠️ Disclaimer This script is provided for educational and research purposes only. It does not provide trading or investment advice. The author assumes no liability for financial losses resulting from its use. Use responsibly and at your own risk.
Индикатор Pine Script®
от bitwardex