Strategy Forecast EngineThe Strategy Forecast Engine is a regime-based Monte Carlo forecasting tool that estimates the future return distribution of trend-following strategies across different market environments. The model identifies the current market regime, conditions forecasts on historical returns observed during comparable regimes, and generates thousands of potential future price paths using Monte Carlo simulation. The resulting return distribution is presented through percentile projections and a structured, color-coded table that provides a comprehensive assessment of the forecast.
First, the model identifies the current market regime using the selected trend-following strategy. Users can choose between a moving-average crossover strategy, a volatility-based trailing stop strategy, or a combined strategy that incorporates both approaches. Supported moving-average types include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), and Weighted Moving Average (WMA). Supported volatility types include the Average True Range (ATR), Standard Deviation (SD), and Mean Absolute Deviation (MAD). By default, the model applies an asymmetric design in which conflicting signals default to bullish unless neutral regimes are enabled in the menu. Market regimes are determined as follows:
Bullish Trend Regime = (Fast MA – Slow MA) > (ATR × Trend Margin)
Bearish Trend Regime = (Fast MA – Slow MA) < –(ATR × Trend Margin)
Bullish Volatility Regime = Price > (Highest Price – (Volatility × Stop Factor))
Bearish Volatility Regime = Price < (Lowest Price + (Volatility × Stop Factor))
Bullish Combined Regime = Bullish Trend Regime and Bullish Volatility Regime
Bearish Combined Regime = Bearish Trend Regime and Bearish Volatility Regime
Once the current regime has been identified, the model collects all historical logarithmic returns that occurred during the same regime beginning from the selected start date. Only returns from the matching regime are used to generate the forecast, allowing projections to be conditioned on historically comparable market environments rather than treating all historical observations as equally relevant. If duration-adjusted forecast is enabled in the menu, the model further restricts the sample pool to returns from regimes that were at least as mature as the current regime.
The Monte Carlo simulation engine then generates thousands of possible future price paths over the selected forecast horizon. Each simulation randomly samples historical returns from the sample pool associated with the current regime and compounds them forward to generate a potential future price path. This process is repeated for the specified number of simulations to produce a broad range of possible future outcomes. The random seed controls reproducibility, ensuring that identical settings produce identical forecasts. Once all individual simulations have been completed, the resulting return distribution is summarized using percentile projections:
95% = 5% of simulations ended above this level and 95% ended below it.
75% = 25% of simulations ended above this level and 75% ended below it.
Median = 50% of simulations ended above this level and 50% ended below it.
25% = 25% of simulations ended below this level and 75% ended above it.
5% = 5% of simulations ended below this level and 95% ended above it.
The upper quartile (75%) and lower quartile (25%) define the Interquartile Range (IQR), which contains the middle 50% of all simulated outcomes and represents the central range of the projected outcome distribution. The upper and lower tail percentiles can be set to 10% (90% / 10%), 5% (95% / 5%), or 1% (99% / 1%). The default setting is 5%, which captures the middle 90% of simulated outcomes. At 10%, the range captures 80% of simulated outcomes, while at 1%, the range captures 98% of simulated outcomes. To further evaluate the risk/reward characteristics of the forecast, the model includes a built-in table with the following metrics:
Regime = Current market regime based on the selected strategy configuration.
Duration = Percentile rank of current regime duration relative to past regimes.
Forecast = Percentile rank of current duration including the forecast horizon.
Win Rate = Percentage of profitable simulations relative to total simulations.
Profit Factor = Ratio of total simulated profits to total simulated losses.
Expectancy = Average expected percentage return across all simulations.
Reward/Risk = Ratio of upper quartile return to lower quartile return.
Asymmetry = Ratio of selected upper tail return to selected lower tail return.
Skewness = Ratio of upside potential to downside risk relative to the median.
Sample Size = Number of historical returns available for the current regime.
Frequency = Percentage of historical returns belonging to the current regime.
In summary, the Strategy Forecast Engine is a comprehensive forecasting tool designed to help investors evaluate the return distribution of trend-following strategies based on the current market regime. By combining regime detection with Monte Carlo simulation, the model conditions forecasts on historical returns observed during comparable market regimes to estimate the distribution of potential outcomes and their associated risk/reward characteristics. While the model provides valuable insight into historical return patterns, investors should remain mindful that historical market behavior may not necessarily persist under future market conditions. Indicator
Market Memory Average (Zeiierman)█ Overview
Market Memory Average (Zeiierman) is a similarity-based market regime tool that scans historical price behavior to identify past conditions that closely resemble the current market state.
The script compares momentum, RSI, volatility, and relative volume to build a “market memory” model. It then extracts the internal momentum of the most similar historical states and blends them into a dynamic projection line.
The result is an adaptive average that reflects how the market has historically behaved when conditions looked like this, rather than relying on fixed formulas or traditional lagging averages.
█ How It Works
⚪ Market State Encoding
The script defines the current market using momentum (ROC), RSI, volatility (ATR%), and relative volume. These features describe how the market is behaving, not just price position.
⚪ Historical Similarity Scan
Each past bar is compared to the current state using a multi-feature distance model.
Closer matches receive higher weights through exponential decay:
similarity = 100 * exp(-distance * sensitivity)
⚪ Top Match Selection
The script ranks all historical states and keeps only the most similar ones. These represent past environments that closely resemble current conditions.
⚪ Memory Momentum
From each match, the script extracts its internal momentum (ROC).
A similarity-weighted average is then calculated:
avgMomentum = weightedMomentum / totalWeight
⚪ Market Memory Average
This averaged momentum is applied to the current price to form the line:
memoryLine = close * (1 + avgMomentum / 100)
The result reflects how similar market states have historically behaved.
⚪ Historical Match Zones
Optional boxes highlight where similar conditions occurred in the past, along with their similarity strength.
█ How to Use
⚪ Market Memory Average
Bullish color → market conditions align with historically positive momentum.
Bearish color → market conditions align with historically negative momentum.
Unlike traditional averages, this line is built from the similarity-weighted momentum of past market matches. The cloud and structure dynamically adapt based on how those historical conditions behaved.
This gives the line a context-driven, memory-based approach, rather than relying on fixed calculations. The result is a dynamic reference for directional bias and regime context, grounded in how the market has behaved under similar conditions before.
⚪ Study Historical Match Zones (Example 1)
The match boxes show where similar market conditions occurred in the past, based on momentum, volatility, RSI, and volume alignment.
Each box represents a moment where the market behaved as it does now.
These zones can help:
Visualize recurring structures: See how similar conditions previously formed, such as pullbacks, bottoms, or consolidation phases within a trend.
Identify behavioral clustering: When multiple matches appear around similar types of price action, it suggests the market frequently revisits this behavior.
Understand the current environment: By comparing where those matches occurred (trend, range, recovery), you can interpret what kind of phase the market is currently in.
Build a contextual bias: If most matches come from pullbacks and recoveries (as in the example), the current state aligns more with pause → stabilize → continue behavior, rather than reversal or breakdown conditions.
These zones provide context, not prediction, helping you understand how the market is behaving relative to its own history.
⚪ Study Historical Match Zones (Example 2)
In this example, the current market state most closely aligns with these two highlighted zones.
Both matches formed during bearish conditions:
The left match shows a rejection after a strong move up, with momentum quickly flipping into a sharp drop.
The right match shows a weak bounce inside a downtrend, where the price attempted to recover but continued lower.
Looking at the current state (circled area), the price is:
Breaking down aggressively
Moving similarly to those past rejection phases
This suggests the current behavior aligns more with rejection → continuation, rather than stabilization or reversal.
█ Settings
Historical Scan Depth: Controls how far back the script searches for similar market states.
Top Similar Matches: Determines how many historical matches influence the average.
Historical Pattern Length: Sets the width of the displayed historical match zones.
Similarity Sensitivity: Controls how strict the similarity comparison is.
RSI Length: Defines the oscillator component of the market state.
ATR Length: Controls volatility measurement used in both similarity and cloud calculations.
Volume MA Length: Defines how relative volume is calculated.
Average Smoothing: Controls the responsiveness of the Market Memory Average.
Slope Detection Length: Determines how trend direction is evaluated.
Cloud Spread: Controls how far the cloud extends from the line.
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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.
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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. Indicator
Smart Trader, Episode 03, by Ata Sabanci, Candles and TradelinesA volume-based multi-block analysis system designed for educational purposes. This indicator helps traders understand their current market situation through aggregated block analysis, volumetric calculations, trend detection, and an AI-style narrative engine.
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DESIGN PHILOSOPHY: CLEAN CHART, RICH DASHBOARD
Traditional indicators often clutter charts with dozens of support/resistance lines, making it difficult to see price action clearly. This indicator takes a different approach:
The Chart:
Displays only the most meaningful, nearest levels (1 up, 1 down) that have not been consumed by price. This keeps your chart clean and focused on what matters right now.
The Dashboard:
Contains all detailed metrics, calculations, and analysis. Instead of drawing 20 lines on your chart, you get comprehensive data in an organized table format.
Why this approach?
• A clean chart allows you to see price action without visual noise
• Fewer but more meaningful levels help focus attention on immediate reference points
• The dashboard provides depth without sacrificing chart clarity
• Beginners can learn chart reading with an uncluttered view while accessing detailed analysis when needed
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1. BLOCK SEGMENTATION
What it does:
Divides the analysis window into fixed-size blocks. Each block contains multiple bars that are analyzed as a single unit.
Why:
Individual bars contain noise. A single red candle in an uptrend might cause unnecessary concern, but when you view 5-10 bars as one block, the overall direction becomes clear. Block segmentation filters out bar-to-bar noise and reveals the underlying structure.
Benefit:
• Clearer view of market structure at a higher aggregation level
• Enables comparison between time periods (Block 1 vs Block 2 vs Block 3)
• Creates the foundation for composite candles and trend detection
• Reduces emotional reaction to single-bar movements
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2. COMPOSITE CANDLES (FRACTAL CONCEPT)
What it does:
Each block generates a "ghost candle" representing aggregated OHLC:
• Open: First bar's open in the block
• High: Highest high across all bars in the block
• Low: Lowest low across all bars in the block
• Close: Last bar's close in the block
Why:
This is essentially a FRACTAL view of the market. The same candlestick patterns that appear on a daily chart also appear on hourly charts, and on 5-minute charts. By aggregating bars into composite candles, you create a synthetic higher timeframe view without changing your actual timeframe.
Benefit:
• See higher timeframe patterns while staying on your preferred timeframe
• Identify block-level candlestick patterns (Doji, Hammer, Marubozu, Engulfing, etc.)
• Compare composite candle relationships: Does Block 1 engulf Block 2? Is Block 1 an inside bar relative to Block 2?
• Recognize patterns that individual bars obscure due to noise
Fractal Nature:
A hammer pattern means the same thing whether it appears on a 1-minute chart or a weekly chart: price tested lower levels and was rejected. Composite candles let you see these patterns at your chosen aggregation level, providing a multi-scale view of market behavior.
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3. VOLUME ENGINE
What it does:
This indicator is 100% VOLUME-BASED. It separates total volume into buying volume and selling volume using two methods:
Method 1 - Geometric (Approximation):
• Buy Volume = Total Volume × ((Close - Low) / Range)
• Sell Volume = Total Volume × ((High - Close) / Range)
Method 2 - Intrabar LTF (Precise):
Uses actual tick-level or lower timeframe data to determine real buy/sell distribution.
Why:
Raw volume tells you HOW MUCH was traded, but not WHO was aggressive. A large volume bar could mean heavy buying, heavy selling, or both. By separating buy and sell volume, you can identify which side is driving the market.
Benefit:
• Identify whether buyers or sellers are more aggressive
• Detect when volume contradicts price direction (divergence)
• Measure accumulation (buying into weakness) vs distribution (selling into strength)
• Quantify the delta (buy minus sell) to see net pressure
Why Delta Matters:
If price is rising but delta is negative, sellers are actually more aggressive despite the price increase. This divergence often precedes reversals because the price movement lacks volume confirmation.
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4. PIN ANALYSIS (WICK MEASUREMENT)
What it does:
Calculates average upper pin (wick) and lower pin sizes for each block, then tracks how these change across consecutive blocks.
Why:
Upper pins represent price levels that were tested but rejected by sellers. Lower pins represent price levels that were tested but rejected by buyers. The size and direction of pins reveal rejection strength at specific price zones.
Benefit:
• Large upper pins = strong selling pressure at higher levels
• Large lower pins = strong buying support at lower levels
• Increasing upper pins across blocks = intensifying selling pressure
• Decreasing lower pins across blocks = weakening buying support
Why Track Pin Changes:
Pin behavior often changes before price direction changes. If lower pins are shrinking while price is still rising, the buying support that was defending dips is weakening. This is observable data, not prediction.
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5. TREND CHANNEL DETECTION
What it does:
Identifies trend direction using block-level price structure:
• UPTREND: Block highs are higher than previous block highs, AND block lows are higher than previous block lows (HH/HL pattern)
• DOWNTREND: Block highs are lower than previous block highs, AND block lows are lower than previous block lows (LH/LL pattern)
• RANGE: No consistent directional pattern
Once detected, the system draws upper and lower channel boundaries by connecting extreme points within each trend segment.
Why:
HH/HL and LH/LL are the classical definitions of trend. By applying this logic to composite candles (blocks) rather than individual bars, the trend detection becomes more stable and less prone to whipsaws from single-bar noise.
Benefit:
• Clear visual boundaries showing the current trend channel
• Upper channel line = dynamic resistance based on actual price structure
• Lower channel line = dynamic support based on actual price structure
• Channel angle indicates trend strength (steeper = stronger)
• Channel width indicates volatility
Why Lock Trend States:
Once a block's trend classification is determined, it locks and does not change on subsequent recalculations. Without locking, the same block could flip between UP and DOWN repeatedly, creating inconsistent analysis. Locking ensures stability.
Why Project Lines Forward:
Channel lines can be projected into the future to show where support/resistance would be if the current trend continues at the same angle. This is not a prediction; it is a visual reference showing the trend's trajectory.
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6. CORE LEVELS: POC, MAX BUY, MAX SELL
What it does:
Identifies key price levels within each block based on volume data:
POC (Point of Control):
The price level where the highest total volume occurred within the block.
MAX BUY Level:
The bar with the highest buying volume. The HIGH of this bar marks the level.
MAX SELL Level:
The bar with the highest selling volume. The LOW of this bar marks the level.
MIN BUY/SELL Levels:
Optional levels showing where minimum buy/sell volume occurred.
Why:
High volume at a specific price means many participants entered positions there. These participants have a vested interest in that price level. If price returns to that area, those same participants may act to defend their positions.
Benefit:
• POC acts as a volume-based magnet; price tends to revisit high-volume areas
• MAX BUY level shows where buyers committed most aggressively
• MAX SELL level shows where sellers committed most aggressively
• These levels are based on actual transaction data, not arbitrary calculations
Why Consumed Levels Disappear:
When price crosses through a level, that level has been "tested." Keeping consumed levels on the chart creates visual clutter and suggests they are still relevant when they may no longer be. Removing them keeps focus on levels that have not yet been tested.
Why Show Only Nearest Levels:
If you have 20 blocks, you could have 60+ potential levels (POC, MAX BUY, MAX SELL for each). Displaying all of them makes the chart unreadable. Showing only the nearest untested level above and below current price keeps the chart clean while providing immediate reference points.
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7. QUALITY SCORE AND TREND INTELLIGENCE
What it does:
Calculates a quality score (0-100) for the current trend based on multiple factors:
• Angle steepness (stronger trends have steeper angles)
• Delta consistency (does volume support the trend direction?)
• Volume momentum (is participation increasing or decreasing?)
• Body expansion (are candle bodies growing or shrinking?)
• Pin alignment (do pins support the trend direction?)
• Contradiction count (how many factors disagree?)
Why:
Not all trends are equal. A trend with consistent volume support, expanding bodies, and aligned pins is healthier than a trend with contradicting signals. The quality score quantifies this.
Benefit:
• HIGH quality (80+): Multiple factors confirm the trend
• MEDIUM quality (60-79): Some factors confirm, some neutral
• LOW quality (below 60): Multiple contradictions exist
• Strength rating based on channel angle: VERY STRONG, STRONG, MODERATE, WEAK
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8. NARRATIVE ENGINE
What it does:
Generates a text-based market analysis by synthesizing all calculated data into readable sentences.
How it works:
1. Analyzes current candle: pattern type (Doji, Hammer, Marubozu, etc.), body/wick ratios, range vs ATR
2. Analyzes composite candle: Block 1 pattern and relationship to Block 2 (Engulfing, Inside, Outside)
3. Evaluates trend context: direction, duration, quality, transitions
4. Examines volume data: delta, dominance, momentum direction
5. Checks proximity to key levels: channel boundaries, POC, core levels
6. Identifies divergences: when price and volume directions contradict
7. Produces a coherent narrative describing the current situation
Why:
Numbers and charts require interpretation. The narrative engine translates calculated data into plain language, helping traders understand what the data means in context. This is especially valuable for beginners learning to read charts.
Benefit:
• Synthesizes multiple data points into a coherent story
• Explicitly flags divergences and contradictions
• Describes the current situation without making predictions
• Educational: shows how different factors relate to each other
What the Narrative Does NOT Do:
The narrative describes what IS, not what WILL BE. It does not predict future price movement. It reports the current candle pattern, the current trend state, the current volume situation, and the current proximity to levels.
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9. SMART DASHBOARD
What it does:
Displays all metrics in an organized table with multiple sections.
Sections:
• Volume Engine: Calculation method, data availability, current candle buy/sell/delta
• Trend Volumetrics: Aggregated buy/sell/delta across the current trend, trend type
• Pressure and Momentum: Average pins, pin change percentages, body expansion status
• Trend Channel Boundaries: Upper/lower levels with exact prices, distances, percentages
• Trend Intelligence: Quality score, confidence level, strength rating, volume momentum
Why:
All the detailed calculations need to live somewhere without cluttering the chart. The dashboard provides comprehensive data in a structured format.
Benefit:
• All metrics in one place
• Organized by category for easy reference
• Hover over any label to see a tooltip explaining that metric
• No need to draw dozens of lines on the chart
TIP: Hover over dashboard headers and labels to see tooltips explaining each metric.
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10. LANGUAGE SUPPORT
The indicator supports three languages:
• English
• Türkçe (Turkish)
• हिन्दी (Hindi)
Why only three languages?
Each additional language requires duplicate strings throughout the code, increasing memory usage and compilation time. To keep the script optimized and responsive, language options are limited to these three.
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11. DATA ACCURACY AND LIMITATIONS
This indicator is 100% VOLUME-BASED and requires Lower Timeframe (LTF) intrabar data for accurate calculations.
DATA ACCURACY LEVELS:
• 1T (Tick): Most accurate, real volume distribution per tick
• 1S (1 Second): Reasonably accurate approximation
• 15S (15 Seconds): Good approximation, longer historical data available
• 1M (1 Minute): Rough approximation, maximum historical data range
BACKTEST AND REPLAY LIMITATIONS:
• Replay mode results may differ from live trading due to data availability
• For longer backtest periods, use higher LTF settings (15S or 1M)
• Not all symbols/exchanges support tick-level data
• Crypto and Forex typically have better LTF data availability than stocks
A NOTE ON DATA ACCESS:
Higher PulseWire plans provide access to more historical intrabar data, which directly impacts the accuracy of volume-based calculations. More precise volume data leads to more reliable calculations.
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12. SETTINGS OVERVIEW
Main Settings:
• Window Bars: Total bars to analyze
• Group Count: Number of blocks to create
• Calculation Basis: Current bar (live updates) or Closed bar (stable, no repaint)
Block Analytics:
• Show Composite Candle: Toggle ghost candles on/off
• Composite Candle Transparency: Adjust visibility
• Dim Original Candles: Fade original candles when composites are shown
Volume Engine:
• Calculation Method: Geometric (approx) or Intrabar (precise)
• Lower Timeframe: Select LTF for intrabar calculations
Multi-Segment Trend:
• Enable Trend Detection: Toggle trend channels on/off
• Range Angle Threshold: Angle below which trend is classified as RANGE
• Line colors, width, and style
• Project to Future: Extend trend lines forward
Core Calculation:
• Enable Core Calculation: Toggle POC and core levels
• Show POC Nearest Up/Down: Display nearest untested POC levels
• Include MAX/MIN Buy/Sell Levels: Toggle extremes display
• Nearest Only: Show only the closest level above and below price
Market Narrative:
• Enable Market Narrative: Toggle narrative text
• Language selection
• Show Educational Disclaimer: Toggle disclaimer in dashboard
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EDUCATIONAL PURPOSE
This indicator is designed to help traders:
1. Understand their current market situation at a glance
2. Learn chart reading through block analysis and composite candles
3. See how volume relates to price movement
4. Recognize when technical factors align or contradict
5. Focus on meaningful levels without chart clutter
Whether you are a beginner learning to read charts or an experienced trader seeking a cleaner analytical view, this tool provides structured data to support your analysis.
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IMPORTANT DISCLAIMER
This indicator is for EDUCATIONAL PURPOSES ONLY and does not constitute investment advice. Always conduct your own research and consult with a qualified financial advisor before making investment decisions.
This disclaimer is also displayed within the indicator itself. If you prefer a cleaner chart, you can disable it in Settings under Market Narrative by unchecking Show Educational Disclaimer. Indicator
Smart Trader,Episode 1, by Ata Sabanci | Unified Matrix⚠️ **CRITICAL: READ BEFORE USING** ⚠️
This strategy is **100% VOLUME-BASED** and requires **Lower Timeframe (LTF) intrabar data** for accurate calculations. Please understand the following limitations before using:
**📊 DATA ACCURACY LEVELS:**
• **1T (Tick)** — Most accurate, real volume distribution per tick
• **1S (1 Second)** — Reasonably accurate approximation
• **15S (15 Seconds)** — Good approximation, longer historical data available
• **1M (1 Minute)** — Rough approximation, maximum historical data range
**⚠️ BACKTEST & REPLAY LIMITATIONS:**
• PulseWire's Strategy Tester uses historical LTF data which may be limited depending on your subscription plan
• Replay mode results may differ from live trading due to data availability
• For longer backtest periods, use higher LTF settings (15S or 1M)
• Not all symbols/exchanges support tick-level data
• Crypto and Forex typically have better LTF data availability than stocks
**💡 A NOTE ON TOOLS:**
Successful trading requires proper tools. Higher PulseWire plans provide access to more historical intrabar data, which directly impacts the accuracy of volume-based calculations. More precise volume data leads to more reliable signals. Consider this when evaluating your trading infrastructure.
**WHY "EPISODE 1"?**
This strategy is titled "Episode 1" because it focuses exclusively on **Highest Buyers (HB)** — a single but powerful concept in volume analysis.
**The Philosophy:**
A single high-volume buying event can tell us a story about market psychology:
• Where did the biggest buyers enter?
• How much of their power remains?
• Are sellers consuming their advantage?
• At what rate is the balance shifting?
By focusing on just ONE aspect of volume analysis, traders can deeply understand how a buying surge affects future price action before moving to more complex multi-factor analysis.
**The Reality:**
This script alone is approximately **2000 lines of code** — and it only analyzes buyers. A comprehensive system covering all aspects (sellers, combined analysis, multi-timeframe correlation) would be significantly larger and computationally heavier. Breaking this into focused modules allows for:
• Deeper understanding of each component
• Lighter, more responsive scripts
• Educational progression from simple to complex
**OVERVIEW**
Smart Trader EP1 is a volume-based trading strategy that tracks the balance of power between buyers and sellers through the lens of the **Highest Buyers event**. Unlike traditional indicators that rely on price patterns or mathematical formulas, this strategy analyzes *actual volume flow* to identify who is in control of the market.
The core philosophy is simple: **markets move when one side (buyers or sellers) exhausts their power while the opposing side accumulates strength.** By measuring this power shift in real-time, the strategy identifies high-probability entry and exit points.
**HOW IT WORKS**
**1. Volume Engine**
The strategy splits each candle's volume into buying volume and selling volume using intrabar data. In *Intrabar (Precise)* mode, it uses actual tick-by-tick or second-by-second data to calculate the exact buy/sell distribution. In *Geometry* mode, it approximates based on candle structure (close position within the range).
**2. Event Detection**
Within the lookback window, the strategy identifies key events:
• **HB (Highest Buyers)** — The candle with maximum buying volume (potential resistance when exhausted)
• **HS (Highest Sellers)** — The candle with maximum selling volume (potential support when exhausted)
• **LB (Lowest Buyers)** — The candle with minimum buying volume (buyer absence)
• **LS (Lowest Sellers)** — The candle with minimum selling volume (seller absence)
These events create dynamic support and resistance levels based on actual volume, not arbitrary price levels.
**3. Power Tracking (Attrition Model)**
For the Highest Buyers event (HB), the strategy tracks:
• **Start Power (X)** — The initial buying volume at the HB event
• **Consumed Power (Y)** — How much selling volume has accumulated since the event
• **Remaining Power (Z)** — Start Power minus Consumed Power (X - Y)
• **Opponent Dominance** — When Remaining Power goes negative (Z < 0), sellers have overtaken buyers
Think of it like a battle: buyers establish a position (HB), and sellers gradually consume their power. When buyers' power is exhausted (Remaining Power ≤ 0), sellers have taken control.
**4. Depletion Markers**
Visual markers appear on the chart when power reaches critical thresholds:
• **🔋** — Buyers consumed 100% (Remaining = 0)
• **🚨** — Buyers consumed 200% (Opponent Dominance = 100%)
• **🪫** — Sellers consumed 100%
• **⚠️** — Sellers consumed 200%
**5. Cumulative Delta**
Beyond tracking power at specific events, the strategy calculates the cumulative buy volume minus sell volume since the HB event. This shows the *net flow* of money:
• **Positive Delta** — More buying than selling since HB (bullish pressure)
• **Negative Delta** — More selling than buying since HB (bearish pressure)
**6. Trend Channel**
A 5-point linear regression channel identifies the current trend:
• **UPTREND** — Both upper and lower channel lines slope upward
• **DOWNTREND** — Both lines slope downward
• **RANGING** — Mixed or flat slopes
The strategy also tracks where the HB event occurred within this channel (TOP, UPPER, MIDDLE, LOWER, BOTTOM) to contextualize the signal.
**7. Nearest Event Analysis**
The strategy identifies which event is closest to the current candle and analyzes the price action *after* that event:
• How many bullish vs bearish candles followed?
• Does post-event momentum confirm or contradict the event type?
This prevents false signals when, for example, a bearish event occurs but is immediately followed by strong bullish candles.
**SIGNAL LOGIC**
**🟢 LONG Signal Conditions:**
• Uptrend with positive cumulative delta and buyers accumulating
• At channel bottom/lower with strong buyer power remaining
• After a bearish event (HS) with bullish post-event momentum (reversal signal)
• Ranging market with positive delta and strong power
**🔴 SHORT Signal Conditions:**
• Downtrend with negative cumulative delta and sellers in control
• Opponent Dominance (buyer power exhausted) with bearish momentum
• Buyer Trap: HB at TOP in uptrend but power exhausted and delta negative
• After a bullish event (HB) with bearish post-event momentum (trap signal)
**⏳ NO_TRADE Conditions:**
• Conflicting signals (e.g., bearish event but bullish post-momentum)
• Ranging market without clear direction
• Mixed power readings
• Price position contradicts signal direction
**STRATEGY EXECUTION**
**Entry Rules:**
• Enter LONG when signal is "LONG" and conditions are valid
• Enter SHORT when signal is "SHORT" and conditions are valid
• **Pyramid**: Up to 2 entries allowed in the same direction (configurable)
• Each entry uses 10% of equity by default
• Only one entry per confirmed candle (prevents multiple fills)
**Stop Loss (Event Line Based):**
• **LONG positions**: Stop Loss placed below the HS line (seller support level)
• **SHORT positions**: Stop Loss placed above the HB line (buyer resistance level)
• A small buffer percentage is added to prevent premature stops
**Take Profit (Event Line Based):**
• **LONG positions**: Take Profit near the HB line (buyer resistance target)
• **SHORT positions**: Take Profit near the HS line (seller support target)
• A small buffer percentage ensures realistic fill expectations
**Exit Rules:**
• Exit LONG when signal changes to SHORT
• Exit SHORT when signal changes to LONG
• **NO_TRADE signal = HOLD** (do not exit, wait for clear direction)
• SL/TP orders remain active regardless of signal changes
**SETTINGS GUIDE**
**⚙️ General Settings:**
• *Calculation Method* — Choose between Intrabar (Precise) or Geometry (approximation)
• *Intrabar Resolution* — LTF for volume data (1T, 1S, 15S, 1M)
• *Lookback Length* — Window for scanning events (10-150 bars)
• *Timezone Offset* — Adjust clock display to your local time
**📊 Matrix Display Settings:**
• *Show Unified Matrix* — Toggle the information dashboard
• *Show Event Lines* — Toggle horizontal lines at event prices
• *Panel Size/Position* — Customize dashboard appearance
• *Projection Bars* — Extend event lines into the future
• *Depletion Threshold* — Percentage for depletion markers (default: 100%)
**🏷️ Rank Labels Settings:**
• *Show Rank Labels (HB/HS)* — Display labels on highest volume candles
• *Show Low Labels (LB/LS)* — Display labels on lowest volume candles
• *Ranks Count* — Number of rankings to display (1-5)
**📐 Trend Channel Settings:**
• *Show Trend Channel* — Toggle the 5-point regression channel
• *Line Color/Fill/Width/Style* — Customize channel appearance
**🎯 Trade Signal Settings:**
• *Long: Min Remaining Power %* — Minimum buyer power for LONG signal (default: 50%)
• *Short: Max Remaining Power %* — Maximum power for SHORT signal (default: 30%)
• *Opponent Dominance Threshold* — When to consider power "exhausted" (default: 0%)
• *Max Decay Angle* — Maximum consumption rate for valid entries (default: 60°)
**📈 Strategy Execution Settings:**
• *Enable Strategy* — Turn automatic trading on/off
• *Allow LONG/SHORT* — Enable or disable specific directions
• *Max Pyramid Entries* — Maximum entries in same direction (1-3)
• *SL Buffer %* — Distance below/above event line for stop loss (default: 0.15%)
• *TP Buffer %* — Distance from event line for take profit (default: 0.05%)
**VISUAL ELEMENTS**
**Chart Labels:**
• **#1 HB** — Highest Buyers (rank label on candle high)
• **#1 HS** — Highest Sellers (rank label on candle low)
• **#1 LB** — Lowest Buyers (rank label on candle high)
• **#1 LS** — Lowest Sellers (rank label on candle low)
• **🔋 / 🚨** — Buyer power depletion markers
• **🪫 / ⚠️** — Seller power depletion markers
**Event Lines:**
• **Blue horizontal lines** — HB price levels (buyer entry points)
• **Red horizontal lines** — HS price levels (seller entry points)
• **Cyan lines** — LB price levels
• **Orange lines** — LS price levels
• **Dashed extensions** — Projected levels into future bars
**Trend Channel:**
• **Orange lines** — Upper and lower channel boundaries (5-point regression)
• **Orange fill** — Channel area (90% transparency)
**Matrix Dashboard (6 rows):**
• Row 1: Header with symbol, LTF setting, and local clock
• Row 2: Volume snapshot (Total, Buy, Sell, Delta)
• Row 3: Column headers
• Row 4: Highest Buyers data (Age, Start Power, Consumed, Remaining, Decay, ETA)
• Row 5: Highest Sellers data
• Row 6: Signal Evaluation (Trend, Zone, Nearest Event, Signal, Reason)
**Strategy Markers:**
• **Green triangle up** — LONG entry
• **Red triangle down** — SHORT entry
• **Faded triangles** — Pyramid entries
• **Colored lines** — SL (red) and TP (green) levels when in position
**BEST PRACTICES**
**For Maximum Accuracy:**
1. Use **1T (tick)** or **1S** intrabar resolution when available
2. Trade liquid markets with good volume data (crypto majors, forex majors, high-volume stocks)
3. Use smaller lookback length (20-30) to ensure all bars have valid LTF data
4. Monitor the "Intrabar Valid Bars" counter in the matrix header
5. If you see data warnings, reduce lookback or increase LTF resolution
**For Longer Backtests:**
1. Use **15S or 1M** intrabar resolution for more historical data
2. Increase lookback length if needed
3. Understand that accuracy decreases with higher LTF settings
4. Consider using Geometry mode for very long backtests (approximation but always available)
**Understanding the Signals:**
• Pay attention to the signal *reasoning* shown in the matrix — it explains WHY
• **NO_TRADE** means the system sees conflicting factors — respect this caution
• Event lines act as dynamic S/R — they update as new volume events occur
• Cumulative Delta (Δ) often provides early warning of trend changes
**Risk Management:**
• The default 10% per entry with max 2 pyramids = 20% maximum exposure
• Event-line-based SL/TP provides logical levels based on actual volume events
• Always verify signals with your own analysis before trading
**INTERPRETING THE MATRIX**
**Power Status Examples:**
• *Remaining Power: 75%* — Buyers still have most of their strength
• *Remaining Power: 25%* — Buyers nearly exhausted, watch for reversal
• *Opponent Dominance: -50%* — Sellers have consumed 150% of buyer power (strong bearish)
**Decay Angle:**
• *Low angle (0-30°)* — Slow consumption, power lasting longer
• *High angle (60-90°)* — Rapid consumption, expect quick exhaustion
**ETA to Parity:**
• Shows estimated bars until Remaining Power reaches zero
• *"Overtaken"* with 🚨 means sellers have already dominated
**LIMITATIONS & DISCLAIMER**
**Technical Limitations:**
• Requires sufficient historical LTF data (varies by PulseWire plan and symbol)
• Intrabar (Precise) mode may show invalid data warnings on symbols with limited history
• Strategy tester may not have access to the same LTF data as live trading
• Maximum 500 lines and 500 labels (PulseWire platform limits)
**Important Notes:**
• This strategy focuses on **Highest Buyers only** — it does not analyze all market factors
• Past performance does not guarantee future results
• Volume data quality varies significantly between symbols and exchanges
• The strategy's signals are analytical tools, not trading recommendations
**Risk Disclaimer:**
This strategy is provided for **educational and informational purposes only**. Trading involves substantial risk of loss and is not suitable for all investors.
• Always use proper risk management
• Never risk more than you can afford to lose
• Backtest results may differ significantly from live trading
• You are solely responsible for your trading decisions
**TECHNICAL SPECIFICATIONS**
• Pine Script Version: 6
• Calculation: calc_on_every_tick=true, use_bar_magnifier=true
• Default Capital: 10,000
• Default Position Size: 10% of equity
• Maximum Lines: 500
• Maximum Labels: 500
• External Library: PulseWire/ta/10 (for requestUpAndDownVolume)
*Smart Trader EP1 — Understanding Volume, One Event at a Time* Strategy
Smart Money Concept, Modern ViewSmart Money Concept, Modern View (SMCMV)
Institutional Volume Flow Analysis with VWMA Matrix
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📌 OVERVIEW
SMCMV is an advanced institutional-grade indicator that combines Volume-Weighted Moving Average (VWMA) matrix analysis with sophisticated volume decomposition to detect buyer and seller entry points. The indicator provides a comprehensive real-time dashboard displaying market structure, volume dynamics, and validated trading signals.
Key Features:
• Dual Volume Model: Geometry-based (candle range split) and Intrabar (precise LTF data)
• 10-Period VWMA Spectrum: Multi-timeframe support/resistance matrix (7, 13, 19, 23, 31, 41, 47, 67, 83, 97)
• 5-Layer Scoring System: 100-point institutional-grade signal quality assessment
• State Machine Signal Engine: Validated entry/exit signals with timer and range confirmation
• Real-time Prediction Engine: Candle-by-candle buyer/seller probability estimation
• High Volume Node Detection: Automatic identification of significant volume zones
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📊 DASHBOARD REFERENCE
1) NOW VECTOR (Current Market State)
This section captures the immediate market conditions:
• FLOW ANGLE: Directional angle of price movement in degrees (from VWMA-5). Positive = bullish, Negative = bearish.
• LTP: Last Traded Price - current close price.
• NET FLOW (Δ): Volume Delta - net difference between buying and selling volume. Shows ⚡+ or ⚡-.
• LIQUIDITY: Total volume on the current bar (K/M format).
• BUY VOL: Estimated buying volume based on selected model.
• SELL VOL: Estimated selling volume.
• BID PRES.: Buying volume as percentage of total volume.
• ASK PRES.: Selling volume as percentage of total volume.
• DIRECTION: Current state with hysteresis: BULL (🐂), BEAR (🐻), or NEUT (⚪).
2) DATA QUALITY / CONFIG
Configuration status and data integrity monitoring:
• VOL MODEL: INTRABAR (uses LTF data) or GEOMETRY (estimates from candle structure).
• IB LTF: Intrabar Lower Timeframe for precise volume decomposition.
• MODE: Micro (7 periods: 7-47) or Macro (10 periods: 7-97).
• IB OK: Intrabar data validity - OK or NO.
• IB STREAK: Consecutive bars with valid intrabar data.
• LATENCY: Data freshness indicator. ✓ = current, ↺ = using historical reference.
3) STRUCTURE RADAR
Market structure analysis showing price position relative to VWMA matrix:
• WIRES ▲/▼: Count of VWMAs above (resistance) and below (support).
• RES: Nearest Resistance - shows MA period, "ZN RES", or "BLUE SKY".
• SUPP: Nearest Support - shows MA period, "ZN SUPP", or "FREE FALL".
4) ACTIVE INTERACTION
Real-time analysis of price interaction with key levels:
• Header Status: "⚠ TESTING SUPPLY (ASK SIDE)" / "⚠ TESTING DEMAND (BID SIDE)" / "--- NO KEY INTERACTION ---"
• TARGET: Active level being tested (MA period or zone type).
• TEST LEVEL: Exact price level being tested.
• SCORE: Total score (0-100%) with letter grade .
• VOLUME POWER: Volume ratio vs historical average (e.g., "2.5x").
• BREAKOUT: "CONFIRMED" if attacking volume exceeds defending, "REJECTED" otherwise.
• DELTA DIR: "ALIGNED" if delta matches accumulation trend, "CONFLICT" if opposing.
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🎯 5-LAYER SCORING SYSTEM (100 Points Total)
Layer 1: Volume Quality (Max 25 pts)
• Mass (0-10): Volume ratio vs average. 0.5x=0, 1.0x=5, 2.0x=8, 3.0x+=10
• Spike (0-8): Volume Z-Score intensity
• Trend (0-7): Volume trend alignment with price direction
Layer 2: Battle Structure (Max 25 pts)
• Break (0-10): Breakout intensity ratio (attacker vs defender)
• Dom (0-8): Internal dominance ratio
• Pres (0-7): Pressure imbalance percentage
Layer 3: Flow & Energy (Max 20 pts)
• Delta (0-8): Delta alignment with accumulation trend
• Accel (0-6): Delta acceleration
• Mom (0-6): Flow momentum
Layer 4: Geometry (Max 15 pts)
• Impact (0-7): Impact angle directness
• Vec (0-5): Vector alignment
• PriceZ (0-3): Price Z-Score position
Layer 5: Army Structure (Max 15 pts)
• Stack (0-5): MA stack depth
• Conf (0-5): Confluence percentage
• Trend (0-5): Trend alignment count (7>13, 13>23, 23>97)
Grade Scale:
• A+ = 90-100 pts (Exceptional)
• A = 80-89 pts (Strong)
• B+ = 70-79 pts (Good)
• B = 60-69 pts (Moderate)
• C+ = 50-59 pts (Below average)
• C/D/F = Below 50 pts (Weak)
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5) SIGNAL STATUS PANEL
Real-time signal state machine status:
• Header: "🐂 BUYERS ACTIVE" / "🐻 SELLERS ACTIVE" / "⏳ VALIDATING..." / "⏸ RANGE / FLAT"
• LOCK PRICE: Price at which signal was locked/confirmed.
• RANGE ±: Validation range percentage.
• POSITION: Price vs lock: "▲ ABOVE" / "▼ BELOW" / "● AT LOCK"
• DISTANCE: Percentage distance from lock price.
• vs RANGE: Position vs validation range: "IN_RANGE" / "ABOVE" / "BELOW"
• VAL TICKS: Validation progress (current/required ticks).
6) REALTIME PREDICTION PANEL
Candle prediction engine:
• WINNER: Predicted dominant side: "BUYERS" / "SELLERS" / "NEUTRAL"
• CONFIDENCE: Prediction confidence percentage.
• ACCURACY: Historical prediction accuracy (session-specific).
• BUY/SELL PROB: Individual probabilities for each side.
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🏷️ SIGNAL LABELS REFERENCE
• 🐂 BUYER ENTRY (Green): Confirmed buyer entry signal. Validation complete.
• 🐻 SELLER ENTRY (Red): Confirmed seller entry signal. Validation complete.
• 🔻 REVERSAL BUY→SELL (Magenta): Reversal from buyer to seller position.
• 🔺 REVERSAL SELL→BUY (Cyan): Reversal from seller to buyer position.
• ⏹ EXIT → FLAT (Gray): Position exit to flat/neutral state.
• ⬆ BUYER STRONGER (Small Green): Lock price updated higher during buyer state.
• ⬇ SELLER STRONGER (Small Red): Lock price updated lower during seller state.
Display Modes:
• Minimal: Icon only (hover for tooltip details)
• Normal: Icon + Price level
• Detailed: Full information (price, score, grade)
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📈 CHART ELEMENTS
VWMA Spectrum Lines
Colored gradient lines representing the 10-period VWMA matrix. Color progresses from light blue (fast: 7-period) through purple to orange (slow: 97-period). These act as dynamic support/resistance levels weighted by volume.
High Volume Node Lines
• Blue Lines: High Buy Volume zones - potential demand areas
• Red Lines: High Sell Volume zones - potential supply areas
• Yellow Lines: Overlapping zones (buy + sell extremes) - high conflict areas
Lock Price Line & Range Band
• Dashed Line: Locked price level (green for buyers, red for sellers)
• Dotted Lines: Upper/lower bounds of validation range
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⚙️ INPUT SETTINGS GUIDE
Volume Model
• Calculation Method: "Geometry (Candle-Range Split)" for universal compatibility or "Intrabar (Precise)" for accurate buy/sell separation.
• Intrabar LTF: Lower timeframe for Intrabar mode (e.g., "1" for 1-minute).
Direction Filter
• Direction Trigger Angle: Threshold for directional state change (default: 1.5°)
• Neutral Reset Angle: Threshold for returning to neutral (default: 0.7°)
Testing Filter
• Level Proximity (%): How close price must be to "test" a level (default: 0.25%)
• Require Wick Touch: If enabled, requires high/low to touch proximity band.
Signal Validation
• Lock Range (%): Price range for validation (default: 0.5%)
• Validation Ticks: Consecutive bars required (default: 3)
• Validation Time: Minimum seconds for real-time confirmation (default: 5)
• Minimum Hold Bars: Stay in position for at least this many bars (default: 5)
• Exit Mode: "Reversal Only" / "Signal Loss" / "Price Stop"
• Stop Loss (%): Exit threshold (default: 1.0%)
Signal Score Filter
• Score Range Minimum: Minimum score for signal generation (default: 10%)
• Score Range Maximum: Maximum score threshold (default: 100%)
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💡 USAGE RECOMMENDATIONS
1. Start with Macro mode to see the complete VWMA spectrum, then switch to Micro for cleaner charts.
2. Use Intrabar mode when your broker provides lower timeframe data.
3. Focus on high-grade signals (B+ or better) for higher probability setups.
4. Wait for validation to complete before acting on signals.
5. Use the Lock Price line as your reference for position management.
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⚠️ IMPORTANT NOTES
• This indicator is designed for educational and analytical purposes.
• Always combine with proper risk management and additional confirmation.
• Past performance and signal quality do not guarantee future results.
• The prediction accuracy is session-specific and resets on chart reload.
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Volume-Based Indicator — Data Granularity & Table Guide
1) Critical warning about data granularity (read first)
Important: This indicator is built entirely on volume-derived calculations (volume, volume delta, and related flow metrics). Because of that, its precision is only as good as the granularity and history of the data you feed it.
The most granular view is a tick-based interval (e.g., 1T = one trade/tick). If tick-based intervals are not available for your symbol or your plan, the closest time-based approximation is a 1-second chart (1S).
If you enable any "high-precision / intrabar" options (anything that relies on the smallest updates), make sure you understand which PulseWire plan you are using, because intrabar historical depth (how many bars you can load) varies by plan. More history generally means more stable baselines for volume statistics, regime detection, and long lookback features.
Plan-related notes (PulseWire)
PulseWire limits how many intrabar historical bars can be loaded, depending on your plan. The exact limits are defined by PulseWire and can change over time, but as of the current documentation, the intrabar limits are:
• Basic: 5,000 bars
• Essential: 10,000 bars
• Plus: 10,000 bars
• Premium: 20,000 bars
• Expert: 25,000 bars
• Ultimate: 40,000 bars
Tick charts / tick-based intervals are currently positioned as a feature of professional-tier plans (e.g., Expert/Elite/Ultimate). Availability may also vary by symbol and data feed.
Indicator
Indicator
Gann Octave Pro - Angles & Time Cycles 🎯 Gann Octave Pro - Angles & Time Cycles
## Complete Gann Trading System - Price, Angles & Time in One Indicator
A professional-grade Gann analysis tool combining **Octave Price Levels**, **Gann Angles (1x1, 2x1, 1x2)**, and **Advanced Time Cycle Projections**. Perfect for traders seeking precision market timing through geometric confluence.
---
## 🌟 Key Features
### 📐 Octave Price Levels
- **5 Key Levels**: 0%, 25%, 50%, 75%, 100%
- **Color-Coded**: Green (support) → Blue (50% pivot) → Red (resistance) → Black (boundaries)
- **Dynamic Updates**: Auto-adjusts to swing structure
- **Trading Edge**: 50% level is the most powerful reversal zone
### 📏 Gann Angles
- **1x1 Angle** (Black) - Natural 45° trend line
- **2x1 Angle** (Red) - Steep acceleration zone
- **1x2 Angle** (Red) - Gradual support/resistance
- **Customizable Extension**: Fixed bars or % of swing length
### ⏰ Advanced Time Cycles
**Three Calculation Methods:**
1. **Angle-Level Confluence** ⭐ (Recommended)
- Calculates intersections of Gann angles with octave levels
- Most sophisticated timing system
- Based on price-time geometry
2. **Swing Duration** - Uses actual swing bar length
3. **Harmonic (Swing/8)** - Classic Gann harmonic division
**Cycle Visualization:**
- **Full Cycles** (Purple, solid) - Major turning points, labeled "◆ FC1 (176 bars) "
- **Sub-Cycles** (Blue, dotted) - Minor pivots, labeled "S1 "
- **Mid-Cycles** (Orange, dashed) - Half-cycle inflection points
- **Past Display**: Shows 4 complete past cycles for validation
- **Future Projection**: Projects 8 future cycles for anticipation
---
## 🎯 How to Use
### Quick Start
1. Apply to chart (works all timeframes/instruments)
2. Select period: Default 44 bars (adjust based on timeframe)
3. Choose cycle method: "Angle-Level Confluence" for best results
4. Observe past cycles to validate timing accuracy
### Trading Strategies
**Triple Confluence Setup** (Highest Probability)
- Price at octave level (especially 50%)
- Price touches Gann angle (1x1 most reliable)
- Time cycle arrives (full cycle preferred)
- **Entry**: On confluence | **Stop**: Below/above octave level | **Target**: Next level
**Cycle Anticipation**
- Enter 1-2 bars before cycle line if price at octave level
- Exit at next cycle or target octave level
- **Edge**: Anticipate cycles instead of reacting
**Angle Breakout + Cycle**
- Price breaks 1x1 angle + next cycle within 20 bars
- Hold through cycle, exit at 2x1 angle or next major level
---
## ⚙️ Customization
### Period Selection (88-Based)
11 harmonic options: 3, 6, 11, 22, **44**, 88, 176, 352, 704, 1408, 2816 bars
- **Intraday** (15m-1h): Period 3-4
- **Swing Trading** (4h-Daily): Period 4-5
- **Position Trading** (Daily-Weekly): Period 5-6
### Visual Controls
- **Colors**: Independent for all elements
- **Line Widths**: Separate controls (1-5) for levels, angles, cycles
- **Label Size**: Tiny/Small/Normal/Large (unified)
- **Label Position**: Top/Middle/Bottom
- **Show/Hide**: Toggle any component
### Alerts
- 50% octave level breakouts
- Customizable messages
---
## 💡 Pro Tips
1. **Validate First**: Observe 2-3 past cycles before trading
2. **Adjust to Volatility**: High volatility = lower period (22-44), Low = higher (88-176)
3. **Multiple Timeframes**: Apply on different timeframes for confirmation
4. **Respect 50% Level**: Most powerful reversal zone in Gann theory
5. **Focus on Full Cycles**: Highest probability setups (◆ FC markers)
6. **Combine with Price Action**: Indicator shows WHERE/WHEN, price action shows HOW
---
## 🚀 What Makes It Unique
✅ **Intelligent Confluence Cycles** - Unique angle-level intersection calculation
✅ **Historical Validation** - See past cycles to trust future projections
✅ **Professional Design** - Color-coded hierarchy, clean labels, no clutter
✅ **Complete Automation** - Everything updates in real-time
✅ **Three-Dimensional Analysis** - Price + Angles + Time = complete picture
---
## 📊 Best Markets
- Stock indices (S&P 500, NASDAQ, Dow)
- Forex majors (EUR/USD, GBP/USD, USD/JPY)
- Commodities (Gold, Silver, Oil)
- Crypto (BTC, ETH)
- Liquid stocks
✅ Complete Gann system (price + angles + time)
✅ 3 time cycle methods
✅ Auto swing detection
✅ 4 past + 8 future cycle projections
✅ Professional visualization
✅ Extensive customization
✅ Real-time alerts
✅ Works all markets/timeframes
---
## ⚠️ Disclaimer
This indicator is for educational purposes and applies W.D. Gann methodology principles. Not financial advice. Always use proper risk management, position sizing, and stop losses. Practice on paper before live trading. Past performance doesn't guarantee future results.
---
**The market moves in patterns of price and time. This indicator helps you see them.**
Trade with geometry. Trade with time. Trade with confidence.
Indicator
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
Indicator
Cyclic Reversal Engine [AlgoPoint]Overview
Most indicators focus on price and momentum, but they often ignore a critical third dimension: time. Markets move in rhythmic cycles of expansion and contraction, but these cycles are not fixed; they speed up in trending markets and slow down in choppy conditions.
The Cyclic Reversal Engine is an advanced analytical tool designed to decode this rhythm. Instead of relying on static, lagging formulas, this indicator learns from past market behavior to anticipate when the current trend is statistically likely to reach its exhaustion point, providing high-probability reversal signals.
It achieves this by combining a sophisticated time analysis with a robust price-action confirmation.
How It Works: The Core Logic
The indicator operates on a multi-stage process to identify potential turning points in the market.
1. Market Regime Analysis (The Brain): Before analyzing any cycles, the indicator first diagnoses the current "personality" of the market. Using a combination of the ADX, Choppiness Index, and RSI, it classifies the market into one of three primary regimes:
- Trending: Strong, directional movement.
- Ranging: Sideways, non-directional chop.
- Reversal: An over-extended state (overbought/oversold) where a turn is imminent.
2. Adaptive Cycle Learning (The "Machine Learning" Aspect): This is the indicator's smartest feature. It constantly analyzes past cycles by measuring the bar-count between significant swing highs and swing lows. Crucially, it learns the average cycle duration for each specific market regime. For example, it learns that "in a strong trending market, a new swing low tends to occur every 35 bars," while "in a ranging market, this extends to 60 bars."
3. The Countdown & Timing Signal: The indicator identifies the last major swing high or low and starts a bar-by-bar countdown. Based on the current market regime, it selects the appropriate learned cycle length from its memory. When the bar count approaches this adaptive target, the indicator determines that a reversal is "due" from a timing perspective.
4. Price Confirmation (The Trigger): A signal is never generated based on timing alone. Once the timing condition is met (the cycle is "due"), the indicator waits for a final price-action confirmation. The default confirmation is the RSI entering an extreme overbought or oversold zone, signaling momentum exhaustion. The signal is only triggered when Time + Price Confirmation align.
How to Use This Indicator
- The Dashboard: The panel in the bottom-right corner is your command center.
- Market Regime: Shows the current market personality analyzed by the engine.
- Adaptive Cycle / Bar Count: This is the core of the indicator. It shows the target cycle length for the current regime (e.g., 50) and the current bar count since the last swing point (e.g., 45). The background turns orange when the bar count enters the "due zone," indicating that you should be on high alert for a reversal.
- BUY/SELL Signals: A label appears on the chart only when the two primary conditions are met:
The timing is right (Bar Count has reached the Adaptive Cycle target).
The price confirms exhaustion (RSI is in an extreme zone).
A BUY signal suggests a downtrend cycle is likely complete, and a SELL signal suggests an uptrend cycle is likely complete.
Key Settings
- Pivot Lookback: Controls the sensitivity of the swing point detection. Higher values will identify more significant, longer-term cycles.
- Market Regime Engine: The ADX, Choppiness, and RSI settings can be fine-tuned to adjust how the indicator classifies the market's personality.
- Require Price Confirmation: You can toggle the RSI confirmation on or off. It is highly recommended to keep it enabled for higher-quality signals. Indicator
Markov 3D Trend AnalyzerMarkov 3D Trend Analyzer
🔹 What Is a Markov State?
A Markov chain models systems as states with probabilities of transitioning from one state to another. The key property is memorylessness: the next state depends only on the current state, not the full past history. In financial markets, this allows us to study how conditions tend to persist or flip — for example, whether a green candle is more likely to be followed by another green or by a red.
🔹 How This Indicator Uses It
The Markov 3D Trend Analyzer tracks three independent Markov chains:
Direction Chain (short-term): Probability that a green/red candle continues or reverses.
Volatility Chain (mid-term): Probability of volatility staying Low/Medium/High or transitioning between them.
Momentum Chain (structural): Probability of momentum (Bullish, Neutral, Bearish) persisting or flipping.
Each chain is updated dynamically using exponentially weighted probabilities (EMA), which balance the law of large numbers (stability) with adaptivity to new market conditions.
The indicator then classifies each chain’s dominant state and combines them into an actionable summary at the bottom of the table (e.g. “📈 Bullish breakout,” “⚠️ Choppy bearish fakeouts,” “⏳ Trend squeeze / possible reversal”).
🔹 Settings
Direction Lookback / Volatility Lookback / Momentum Lookback
Control the rolling window length (sample size) for each chain. Larger = smoother but slower to adapt.
EMA Weight
Adjusts how much weight is given to recent transitions vs. older history. Lower values adapt faster, higher values stabilize.
Table Position
Choose where the table is displayed on your chart.
Table Size
Adjust the font size for readability.
🔹 How To Consider Using
Contextual tool: Use the summary row to understand the current market condition (trending, mean-reverting, expanding, compressing, continuation, fakeout risk).
Complementary filter: Combine with your existing strategies to confirm or filter signals. For example:
📈 If your breakout strategy fires and the summary says Bullish breakout, that’s confirmation.
⚠️ If it says Choppy fakeouts, be cautious of traps.
Visualization aid: The table lets you see how probabilities shift across direction, volatility, and momentum simultaneously.
⚠️ This indicator is not a signal generator. It is designed to help interpret market states probabilistically. Always use in conjunction with broader analysis and risk management.
🔹 Disclaimer
This script is for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any security, cryptocurrency, or instrument. Trading involves risk, and past probabilities or behaviors do not guarantee future outcomes. Always conduct your own research and use proper risk management. Indicator
ATAI Triangles — Volume-Based & Price Pattern Analysis (v1.01)ATAI Triangles — Volume-Based & Price Pattern Analysis (v1.01)
Overview
ATAI Triangles identifies two synchronized triangle structures — Hi-Lo-Hi (HLH) and Lo-Hi-Lo (LHL) — and analyzes them both geometrically and volumetrically. For each triangle, volume is split between its two legs (segments), providing interpretable insights into buyer vs seller activity along each path.
The idea is that certain geometric shapes, when paired with volume distribution on each leg, can reveal patterns worth exploring. Users are encouraged to share their observations and interpretations in the PulseWire comments section so that more aspects of these triangle combinations can be discovered collectively.
Extra (for fun)
For a bit of entertainment, we’ve included a symbolic “hexagram” glyph that appears when both triangle types align in a particular way — it’s just a visual nod to geometry and has no predictive or trading value.
Interface & data clarity
- Inputs and parameters are organized by function (pattern geometry, volume analysis, visuals, HUD, labels).
- Each input includes tooltips explaining its purpose, units, and possible effects on calculations.
- All on-chart objects (polylines, labels, connectors) are named and colored to reflect their role, with volume values formatted in engineering notation (K, M, B).
- HUD columns and label texts use concise terms and consistent units, so that every displayed value is directly traceable to a calculation in the code.
- Daily and lower-timeframe volume series are clearly separated, with update logic documented to indicate intrabar provisional values vs finalized bar-close values.
Usage notes
Designed to be used alongside other indicators and chart tools for context; it is not a standalone signal generator.
All Buy/Sell volumes are absolute (non-negative); Δ = Buy − Sell.
Intrabar values update live and finalize at bar close (no repaint after close).
Disclaimer
For research, discussion, and educational purposes only. This is not financial advice and does not guarantee any outcome. Trade at your own risk.
Indicator
Price Statistical Strategy-Z Score V 1.01
Price Statistical Strategy – Z Score V 1.01
Overview
A technical breakdown of the logic and components of the “Price Statistical Strategy – Z Score V 1.01”.
This script implements a smoothed Z-Score crossover mechanism applied to the closing price to detect potential statistical deviations from local price mean. The strategy operates solely on price data (close) and includes signal spacing control and momentum-based candle filters. No volume-based or trend-detection components are included.
Core Methodology
The strategy is built on the statistical concept of Z-Score, which quantifies how far a value (closing price) is from its recent average, normalized by standard deviation. Two moving averages of the raw Z-Score are calculated: a short-term and a long-term smoothed version. The crossover between them generates long entries and exits.
Signal Conditions
Entry Condition:
A long position is opened when the short-term smoothed Z-Score crosses above the long-term smoothed Z-Score, and additional entry conditions are met.
Exit Condition:
The position is closed when the short-term Z-Score crosses below the long-term Z-Score, provided the exit conditions allow.
Signal Gapping:
A minimum number of bars (Bars gap between identical signals) must pass between repeated entry or exit signals to reduce noise.
Momentum Filter:
Entries are prevented during sequences of three or more consecutively bullish candles, and exits are prevented during three or more consecutively bearish candles.
Z-Score Function
The Z-Score is calculated as:
Z = (Close - SMA(Close, N)) / STDEV(Close, N)
Where N is the base period selected by the user.
Input Parameters
Enable Smoothed Z-Score Strategy
Enables or disables the Z-Score strategy logic. When disabled, no trades are executed.
Z-Score Base Period
Defines the number of bars used to calculate the simple moving average and standard deviation for the Z-Score. This value affects how responsive the raw Z-Score is to price changes.
Short-Term Smoothing
Sets the smoothing window for the short-term Z-Score. Higher values produce smoother short-term signals, reducing sensitivity to short-term volatility.
Long-Term Smoothing
Sets the smoothing window for the long-term Z-Score, which acts as the reference line in the crossover logic.
Bars gap between identical signals
Minimum number of bars that must pass before another signal of the same type (entry or exit) is allowed. This helps reduce redundant or overly frequent signals.
Trade Visualization Table
A table positioned at the bottom-right displays live PnL for open trades:
Entry Price
Unrealized PnL %
Text colors adapt based on whether unrealized profit is positive, negative, or neutral.
Technical Notes
This strategy uses only close prices — no trend indicators or volume components are applied.
All calculations are based on simple moving averages and standard deviation over user-defined windows.
Designed as a minimal, isolated Z-Score engine without confirmation filters or multi-factor triggers.
Strategy
Indicator
Doubled Numbered Square of Nine Progression | RegressionThe Doubled Number Square of Nine Progression | Regression Indicator enables forecasting of support and resistance levels using the SQ9 progression or regression from a user-selected pivot price. The indicator also plots adjustable pivots on both the chart and the Square of Nine.
How to use to forecast support and resistance levels:
1. Select a Pivot Point: Choose a top or bottom pivot to use as the starting point. Select High or Low in the indicator to set the correct price to the pivot.
2. Set Price Increment or Decrement: Select a price increment to advance the starting price on the Square of Nine (for example 1 or -0.01).
3. Rotate to add or decrease the number of rungs on the Square on Nine: Rotate the Square of Nine and adjust the dimensions. The horizontal levels correlate to the rotation of the Square of Nine.
4. Plot Levels: Select a Square of Nine angle to plot the support and resistance levels based on the selected increments. These levels auto expand and decrease depending on the number of rotations in step 3. The price values from each of the selected diagonal or cardinal cross are applied to the chart with labels on both left and right. Left labels represent the cell the time variable and the right label is price.
5. Review Levels: Conduct a simple review of the plotted support and resistance levels in relation to recent market pivots.
6. Identify Correlations: Identify the Square of Nine cardinal cross or diagonal cross angle that correlates with the most market pivots.
7. The indicator plots user-adjustable pivots on both candles/bars and displays their placement on the Square of Nine in both Progression and Regression. The close price is plotted on the Square of Nine in real-time with a white background. This helps users identify which angles the highs and lows favor from the selected pivot. The pivot levels' colors can be adjusted.
Each market has its tendencies, favoring specific angles from the cardinal or diagonal cross. The basic idea is to choose between the diagonal cross and cardinal cross angles to forecast support and resistance levels.
Settings:
Overview:
Example on Forecasting Support and resistance level using this indicator:
In the image above, I'm progressing from the significant low at a rate of $200 per cell. Based on the plotted pivots, I've selected the 315-degree angle on the diagonal cross as it aligns with a few recent pivots. Now, I've drawn a vertical line to show that as we add rungs to the Square of Nine, we introduce support and resistance levels based on the identified angle that aligns with these early pivots from the low.
In the image below, I've moved forward in time to show how the initial angle selected based on the early pivot forecasted support and resistance levels around the high in BTC. Do not expect the price to turn sharply at the levels the indicator generates, but use it to help identify SQ9 levels that may form market turns. As P. Mikula mentions in his work, "The Square of Nine successfully defines market price swings but it does not make the market form pivots."
This indicator includes enhancements made to the Gann Square of Nine indicator originally created by @ThiagoSchmitz.
This indicator is inspired by the methodologies detailed by Patrick Mikula in his book " The Definitive Guide to Forecasting using W.D Gann's Square of Nine ." I recommend checking it out. He also mentions another use case as a Price and Time chart in his " Gann Scientific Method Unveiled, Volume 2 ." FYI, I am not affiliated with Patrick Mikula in any way.
Known Issues:
Close price plotted on the Square of Nine in Regression "from a high pivot" takes a current candle to close in order to be plotted. Progression works as expected. Indicator
AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend)The AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend) is a cutting-edge indicator that combines advanced mathematical modeling, AI-driven analytics, and segment-based pattern recognition to forecast price movements with precision. This tool is designed to provide traders with deep insights into market dynamics by leveraging multivariate pattern detection and sophisticated predictive algorithms.
👽 Core Features
Segment-Based Pattern Recognition
At its heart, the indicator divides price data into discrete segments, capturing key elements like candle bodies, high-low ranges, and wicks. These segments are normalized using ATR-based volatility adjustments to ensure robustness across varying market conditions.
AI-Powered k-Nearest Neighbors (kNN) Prediction
The predictive engine uses the kNN algorithm to identify the closest historical patterns in a multivariate dictionary. By calculating the distance between current and historical segments, the algorithm determines the most likely outcomes, weighting predictions based on either proximity (distance) or averages.
Dynamic Dictionary of Historical Patterns
The indicator maintains a rolling dictionary of historical patterns, storing multivariate data for:
Candle body ranges, High-low ranges, Wick highs and lows.
This dynamic approach ensures the model adapts continuously to evolving market conditions.
Volatility-Normalized Forecasting
Using ATR bands, the indicator normalizes patterns, reducing noise and enhancing the reliability of predictions in high-volatility environments.
AI-Driven Trend Detection
The indicator not only predicts price levels but also identifies market regimes by comparing current conditions to historically significant highs, lows, and midpoints. This allows for clear visualizations of trend shifts and momentum changes.
👽 Deep Dive into the Core Mathematics
👾 Segment-Based Multivariate Pattern Analysis
The indicator analyzes price data by dividing each bar into distinct segments, isolating key components such as:
Body Ranges: Differences between the open and close prices.
High-Low Ranges: Capturing the full volatility of a bar.
Wick Extremes: Quantifying deviations beyond the body, both above and below.
Each segment contributes uniquely to the predictive model, ensuring a rich, multidimensional understanding of price action. These segments are stored in a rolling dictionary of patterns, enabling the indicator to reference historical behavior dynamically.
👾 Volatility Normalization Using ATR
To ensure robustness across varying market conditions, the indicator normalizes patterns using Average True Range (ATR). This process scales each component to account for the prevailing market volatility, allowing the algorithm to compare patterns on a level playing field regardless of differing price scales or fluctuations.
👾 k-Nearest Neighbors (kNN) Algorithm
The AI core employs the kNN algorithm, a machine-learning technique that evaluates the similarity between the current pattern and a library of historical patterns.
Euclidean Distance Calculation:
The indicator computes the multivariate distance across four distinct dimensions: body range, high-low range, wick low, and wick high. This ensures a comprehensive and precise comparison between patterns.
Weighting Schemes: The contribution of each pattern to the forecast is either weighted by its proximity (distance) or averaged, based on user settings.
👾 Prediction Horizon and Refinement
The indicator forecasts future price movements (Y_hat) by predicting logarithmic changes in the price and projecting them forward using exponential scaling. This forecast is smoothed using a user-defined EMA filter to reduce noise and enhance actionable clarity.
👽 AI-Driven Pattern Recognition
Dynamic Dictionary of Patterns: The indicator maintains a rolling dictionary of N multivariate patterns, continuously updated to reflect the latest market data. This ensures it adapts seamlessly to changing market conditions.
Nearest Neighbor Matching: At each bar, the algorithm identifies the most similar historical pattern. The prediction is based on the aggregated outcomes of the closest neighbors, providing confidence levels and directional bias.
Multivariate Synthesis: By combining multiple dimensions of price action into a unified prediction, the indicator achieves a level of depth and accuracy unattainable by single-variable models.
Visual Outputs
Forecast Line (Y_hat_line):
A smoothed projection of the expected price trend, based on the weighted contribution of similar historical patterns.
Trend Regime Bands:
Dynamic high, low, and midlines highlight the current market regime, providing actionable insights into momentum and range.
Historical Pattern Matching:
The nearest historical pattern is displayed, allowing traders to visualize similarities
👽 Applications
Trend Identification:
Detect and follow emerging trends early using dynamic trend regime analysis.
Reversal Signals:
Anticipate market reversals with high-confidence predictions based on historically similar scenarios.
Range and Momentum Trading:
Leverage multivariate analysis to understand price ranges and momentum, making it suitable for both breakout and mean-reversion strategies.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions. Indicator
Kalman PredictorThe **Kalman Predictor** indicator is a powerful tool designed for traders looking to enhance their market analysis by smoothing price data and projecting future price movements. This script implements a Kalman filter, a statistical method for noise reduction, to dynamically estimate price trends and velocity. Combined with ATR-based confidence bands, it provides actionable insights into potential price movement, while offering clear trend and momentum visualization.
---
#### **Key Features**:
1. **Kalman Filter Smoothing**:
- Dynamically estimates the current price state and velocity to filter out market noise.
- Projects three future price levels (`Next Bar`, `Next +2`, `Next +3`) based on velocity.
2. **Dynamic Confidence Bands**:
- Confidence bands are calculated using ATR (Average True Range) to reflect market volatility.
- Visualizes potential price deviation from projected levels.
3. **Trend Visualization**:
- Color-coded prediction dots:
- **Green**: Indicates an upward trend (positive velocity).
- **Red**: Indicates a downward trend (negative velocity).
- Dynamically updated label displaying the current trend and velocity value.
4. **User Customization**:
- Inputs to adjust the process and measurement noise for the Kalman filter (`q` and `r`).
- Configurable ATR multiplier for confidence bands.
- Toggleable trend label with adjustable positioning.
---
#### **How It Works**:
1. **Kalman Filter Core**:
- The Kalman filter continuously updates the estimated price state and velocity based on real-time price changes.
- Projections are based on the current price trend (velocity) and extend into the future (Next Bar, +2, +3).
2. **Confidence Bands**:
- Calculated using ATR to provide a dynamic range around the projected future prices.
- Indicates potential volatility and helps traders assess risk-reward scenarios.
3. **Trend Label**:
- Updates dynamically on the last bar to show:
- Current trend direction (Up/Down).
- Velocity value, providing insight into the expected magnitude of the price movement.
---
#### **How to Use**:
- **Trend Analysis**:
- Observe the direction and spacing of the prediction dots relative to current candles.
- Larger spacing indicates a potential strong move, while clustering suggests consolidation.
- **Risk Management**:
- Use the confidence bands to gauge potential price volatility and set stop-loss or take-profit levels accordingly.
- **Pullback Detection**:
- Look for flattening or clustering of dots during trends as a signal of potential pullbacks or reversals.
---
#### **Customizable Inputs**:
- **Kalman Filter Parameters**:
- `lookback`: Adjusts the smoothing window.
- `q`: Process noise (higher values make the filter more reactive to changes).
- `r`: Measurement noise (controls sensitivity to price deviations).
- **Confidence Bands**:
- `band_multiplier`: Multiplies ATR to define the range of confidence bands.
- **Visualization**:
- `show_label`: Option to toggle the trend label.
- `label_offset`: Adjusts the label’s distance from the price for better visibility.
---
#### **Examples of Use**:
- **Scalping**: Use on lower timeframes (e.g., 1-minute, 5-minute) to detect short-term price trends and reversals.
- **Swing Trading**: Identify pullbacks or continuations on higher timeframes (e.g., 4-hour, daily) by observing the prediction dots and confidence bands.
- **Risk Assessment**: Confidence bands help visualize potential price volatility, aiding in the placement of stops and targets.
---
#### **Notes for Traders**:
- The **Kalman Predictor** does not predict the future with certainty but provides a statistically informed estimate of price movement.
- Confidence bands are based on historical volatility and should be used as guidelines, not guarantees.
- Always combine this tool with other analysis techniques for optimal results.
---
This script is open-source, and the Kalman filter logic has been implemented uniquely to integrate noise reduction with dynamic confidence band visualization. If you find this indicator useful, feel free to share your feedback and experiences!
---
#### **Credits**:
This script was developed leveraging the statistical principles of Kalman filtering and is entirely original. It incorporates ATR for dynamic confidence band calculations to enhance trader usability and market adaptability.
Indicator
Holt-Winters Forecast BandsDescription:
The Holt-Winters Adaptive Bands indicator combines seasonal trend forecasting with adaptive volatility bands. It uses the Holt-Winters triple exponential smoothing model to project future price trends, while Nadaraya-Watson smoothed bands highlight dynamic support and resistance zones.
This indicator is ideal for traders seeking to predict future price movements and visualize potential market turning points. By focusing on broader seasonal and trend data, it provides insight into both short- and long-term market directions. It’s particularly effective for swing trading and medium-to-long-term trend analysis on timeframes like daily and 4-hour charts, although it can be adjusted for other timeframes.
Key Features:
Holt-Winters Forecast Line: The core of this indicator is the Holt-Winters model, which uses three components — level, trend, and seasonality — to project future prices. This model is widely used for time-series forecasting, and in this script, it provides a dynamic forecast line that predicts where price might move based on historical patterns.
Adaptive Volatility Bands: The shaded areas around the forecast line are based on Nadaraya-Watson smoothing of historical price data. These bands provide a visual representation of potential support and resistance levels, adapting to recent volatility in the market. The bands' fill colors (red for upper and green for lower) allow traders to identify potential reversal zones without cluttering the chart.
Dynamic Confidence Levels: The indicator adapts its forecast based on market volatility, using inputs such as average true range (ATR) and price deviations. This means that in high-volatility conditions, the bands may widen to account for increased price movements, helping traders gauge the current market environment.
How to Use:
Forecasting: Use the forecast line to gain insight into potential future price direction. This line provides a directional bias, helping traders anticipate whether the price may continue along a trend or reverse.
Support and Resistance Zones: The shaded bands act as dynamic support and resistance zones. When price enters the upper (red) band, it may be in an overbought area, while the lower (green) band may indicate oversold conditions. These bands adjust with volatility, so they reflect the current market conditions rather than fixed levels.
Timeframe Recommendations:
This indicator performs best on daily and 4-hour charts due to its reliance on trend and seasonality. It can be used on lower timeframes, but accuracy may vary due to increased price noise.
For traders looking to capture swing trades, the daily and 4-hour timeframes provide a balance of trend stability and signal reliability.
Adjustable Settings:
Alpha, Beta, and Gamma: These settings control the level, trend, and seasonality components of the forecast. Alpha is generally the most sensitive setting for adjusting responsiveness to recent price movements, while Beta and Gamma help fine-tune the trend and seasonal adjustments.
Band Smoothing and Deviation: These settings control the lookback period and width of the volatility bands, allowing users to customize how closely the bands follow price action.
Parameters:
Prediction Length: Sets the length of the forecast, determining how far into the future the prediction line extends.
Season Length: Defines the seasonality cycle. A setting of 14 is typical for bi-weekly cycles, but this can be adjusted based on observed market cycles.
Alpha, Beta, Gamma: These parameters adjust the Holt-Winters model's sensitivity to recent prices, trends, and seasonal patterns.
Band Smoothing: Determines the smoothing applied to the bands, making them either more reactive or smoother.
Ideal Use Cases:
Swing Trading and Trend Following: The Holt-Winters model is particularly suited for capturing larger market trends. Use the forecast line to determine trend direction and the bands to gauge support/resistance levels for potential entries or exits.
Identifying Reversal Zones: The adaptive bands act as dynamic overbought and oversold zones, giving traders potential reversal areas when price reaches these levels.
Important Notes:
No Buy/Sell Signals: This indicator does not produce direct buy or sell signals. It’s intended for visual trend analysis and support/resistance identification, leaving trade decisions to the user.
Not for High-Frequency Trading: Due to the nature of the Holt-Winters model, this indicator is optimized for higher timeframes like the daily and 4-hour charts. It may not be suitable for high-frequency or scalping strategies on very short timeframes.
Adjust for Volatility: If using the indicator on lower timeframes or more volatile assets, consider adjusting the band smoothing and prediction length settings for better responsiveness. Indicator
Trend Forecasting - The Quant Science🌏 Trend Forecasting | ENG 🌏
This plug-in acts as a statistical filter, adding new information to your chart that will allow you to quickly verify the direction of a trend and the probability with which the price will be above or below the average in the future, helping you to uncover probable market inefficiencies.
🧠 Model calculation
The model calculates the arithmetic mean in relation to positive and negative events within the available sample for the selected time series. Where a positive event is defined as a closing price greater than the average, and a negative event as a closing price less than the average. Once all events have been calculated, the probabilities are extrapolated by relating each event.
Example
Positive event A: 70
Negative event B: 30
Total events: 100
Probabilities A: (100 / 70) x 100 = 70%
Probabilities B: (100 / 30) x 100 = 30%
Event A has a 70% probability of occurring compared to Event B which has a 30% probability.
🔍 Information Filter
The data on the graph show the future probabilities of prices being above average (default in green) and the probabilities of prices being below average (default in red).
The information that can be quickly retrieved from this indicator is:
1. Trend: Above-average prices together with a constant of data in green greater than 50% + 1 indicate that the observed historical series shows a bullish trend. The probability is correlated proportionally to the value of the data; the higher and increasing the expected value, the greater the observed bullish trend. On the other hand, a below-average price together with a red-coloured data constant show quantitative data regarding the presence of a bearish trend.
2. Future Probability: By analysing the data, it is possible to find the probability with which the price will be above or below the average in the future. In green are classified the probabilities that the price will be higher than the average, in red are classified the probabilities that the price will be lower than the average.
🔫 Operational Filter .
The indicator can be used operationally in the search for investment or trading opportunities given its ability to identify an inefficiency within the observed data sample.
⬆ Bullish forecast
For bullish trades, the inefficiency will appear as a historical series with a bullish trend, with high probability of a bullish trend in the future that is currently below the average.
⬇ Bearish forecast
For short trades, the inefficiency will appear as a historical series with a bearish trend, with a high probability of a bearish trend in the future that is currently above the average.
📚 Settings
Input: via the Input user interface, it is possible to adjust the periods (1 to 500) with which the average is to be calculated. By default the periods are set to 200, which means that the average is calculated by taking the last 200 periods.
Style: via the Style user interface it is possible to adjust the colour and switch a specific output on or off.
🇮🇹Previsione Della Tendenza Futura | ITA 🇮🇹
Questo plug-in funge da filtro statistico, aggiungendo nuove informazioni al tuo grafico che ti permetteranno di verificare rapidamente tendenza di un trend, probabilità con la quale il prezzo si troverà sopra o sotto la media in futuro aiutandoti a scovare probabili inefficienze di mercato.
🧠 Calcolo del modello
Il modello calcola la media aritmetica in relazione con gli eventi positivi e negativi all'intero del campione disponibile per la serie storica selezionata. Dove per evento positivo si intende un prezzo alla chiusura maggiore della media, mentre per evento negativo si intende un prezzo alla chiusura minore della media. Calcolata la totalità degli eventi le probabilità vengono estrapolate rapportando ciascun evento.
Esempio
Evento positivo A: 70
Evento negativo B: 30
Totale eventi : 100
Formula A: (100 / 70) x 100 = 70%
Formula B: (100 / 30) x 100 = 30%
Evento A ha una probabilità del 70% di realizzarsi rispetto all' Evento B che ha una probabilità pari al 30%.
🔍 Filtro informativo
I dati sul grafico mostrano le probabilità future che i prezzi siano sopra la media (di default in verde) e le probabilità che i prezzi siano sotto la media (di default in rosso).
Le informazioni che si possono rapidamente reperire da questo indicatore sono:
1. Trend: I prezzi sopra la media insieme ad una costante di dati in verde maggiori al 50% + 1 indicano che la serie storica osservata presenta un trend rialzista. La probabilità è correlata proporzionalmente al valore del dato; tanto più sarà alto e crescente il valore atteso e maggiore sarà la tendenza rialzista osservata. Viceversa, un prezzo sotto la media insieme ad una costante di dati classificati in colore rosso mostrano dati quantitativi riguardo la presenza di una tendenza ribassista.
2. Probabilità future: analizzando i dati è possibile reperire la probabilità con cui il prezzo si troverà sopra o sotto la media in futuro. In verde vengono classificate le probabilità che il prezzo sarà maggiore alla media, in rosso vengono classificate le probabilità che il prezzo sarà minore della media.
🔫 Filtro operativo
L' indicatore può essere utilizzato a livello operativo nella ricerca di opportunità di investimento o di trading vista la capacità di identificare un inefficienza all'interno del campione di dati osservato.
⬆ Previsione rialzista
Per operatività di tipo rialzista l'inefficienza apparirà come una serie storica a tendenza rialzista, con alte probabilità di tendenza rialzista in futuro che attualmente si trova al di sotto della media.
⬇ Previsione ribassista
Per operatività di tipo short l'inefficienza apparirà come una serie storica a tendenza ribassista, con alte probabilità di tendenza ribassista in futuro che si trova attualmente sopra la media.
📚 Impostazioni
Input: tramite l'interfaccia utente Input è possibile regolare i periodi (da 1 a 500) con cui calcolare la media. Di default i periodi sono impostati sul valore di 200, questo significa che la media viene calcolata prendendo gli ultimi 200 periodi.
Style: tramite l'interfaccia utente Style è possibile regolare il colore e attivare o disattivare un specifico output.
Indicator
Nasan Moving Average with ForecastThe "Nasan Moving Average with Forecast" indicator is a technical analysis forecasting tool that combines the principles of historical data analysis and random walk theory. It calculates a customized moving average (Nasan Moving Average) by integrating price data and statistical measures and projects future price points by generating forecast values within calculated volatility bounds, creating a dynamic and insightful visualization of potential market movements. This indicator to blend past market behavior with probabilistic future trends to enhance forecasting.
Input Parameters:
len: Differencing length (default 21, Use a minimum of 5 and for lower time frames less than 15 min use values between 300 -3000)
len1: Correction Factor Length 1 (default 21, this determines the length of the MA you want , eg. 10 MA, 50 MA, 100 MA, )
len2: Correction Factor Length 2 (default 9, this works best if it is ~ </=1/2 of len1 )
len3: Smoothing Length (default 5, I would not change this and only use if I want to introduce lag where you want to use it for cross over strategies).
forecast_points: Number of points to forecast (default 30).
m: Multiplier for standard deviation (default 2.5).
bl: Block length for calculating max/min values (default 100).
use_calculated_max_min: Boolean to decide whether to use calculated max/min values.
Nasan Moving Average Calculation:
Calculates the simple moving average (mean) and standard deviation (sd) of the typical price (hlc3).
Computes intermediate variables (a, b, c, etc.) based on log transformation and cumulative sum.
Applies weighted moving averages (wma) to these intermediate variables to smooth them and derive the final value c6.
Plots c6 as the Nasan Moving Average if the bar is confirmed. To learn more see Nasan Moving Average.
Forecast Points Calculation:
Calculates maximum (max_val) and minimum (min_val) values for the forecast, either using a fixed value or based on standard deviation and a multiplier.
Initializes an array to store forecast values and creates polyline objects for plotting.
If the current bar is one of the last three bars and confirmed:
Clears and reinitializes the polyline.
Initializes the first forecast value from the cumulative sum c.
Generates subsequent forecast values using a random value within the range .
Updates the forecast array and plots the forecast points as an orange curved polyline.
Plotting Max/Min Values:
Plots max_val and min_val as green and red lines, respectively, to indicate the bounds of the forecast range.
Components of the Forecasting Model
Historical Dependence:
Nasan Moving Average Calculation: The script calculates a custom moving average (c6) that incorporates historical price data (hlc3), standard deviations (sd), and weighted moving averages (wma). This part of the code processes historical data to create a smoothed representation of the price trend.
Max/Min Value Calculation: The maximum (max_val) and minimum (min_val) values for the forecast can be calculated based on the historical standard deviation of a transformed variable b over a block length (bl). This introduces historical volatility into the bounds for the forecast.
Random Walk Model:
Random Value Generation: Within the forecast points calculation, a random value (random_val) is generated for each forecast point within the range . This random value introduces stochasticity into the model, characteristic of a random walk process.
Cumulative Sum for Forecasting: The script uses a cumulative sum (prev_f + random_val) to generate the next forecast point (next_f). This is a typical approach in random walk models where each new point is based on the previous point plus some random noise.
Explanation of the Forecast Model
Random Walk Characteristics: Each new forecast point is generated by adding a random value to the previous point, making the model a random walk with drift, where the drift is influenced by historical correction factors (c1, c4).
Historical and Statistical Dependence: The bounds of the random values and the initial conditions are derived from historical data, ensuring that the forecast respects historical volatility and trends.
The forecasting model in the script is a hybrid approach: It uses a random walk to generate future points, characterized by adding random values to the previous forecasted value.
The historical and statistical dependence is incorporated through initial conditions, scaling factors, and bounds derived from historical price data and its statistical properties.
This combination ensures that the forecasts are not purely stochastic but are grounded in historical price behavior, making the model more robust and potentially more accurate in reflecting market conditions. Indicator
Indicator
Price Action Fractal Forecasts [AlgoAlpha]🔮 Price Action Fractal Forecasts - Unleash the Power of Historical Patterns! 🌌✨
Dive into the future with AlgoAlpha's Price Action Fractal Forecasts ! This innovative indicator utilizes the mesmerizing complexity of fractals to predict future price movements, offering traders a unique edge in the market. By analyzing historical price action and identifying repeating patterns, this tool forecasts future price trends, providing visually engaging and actionable insights.
Key Features:
🔄 Flexible Data Series Selection: Choose your preferred data series for precise analysis.
🕰 Flexible Training and Reference Data Windows: Customize the length of training data and reference periods to match your trading style.
📈 Custom Forecast Length: Adjust the forecast horizon to suit your strategic objectives.
🌈 Customizable Visual Elements: Tailor the colors of forecast deviation cones, data reference areas, and more for optimal chart readability.
🔄 Anticipatory and Repetitive Forecast Modes: Select between anticipating future trends or identifying repetitive patterns for forecasts.
🔎 Enhanced Similarity Search: Leverages correlation metrics to find the most similar historical data segments.
📊 Forecast Deviation Cone: Visualize potential price range deviations with adjustable multipliers.
🚀 Quick Guide to Maximizing Your Trading with Price Action Fractal Forecasts:
🛠 Add the Indicator: Search for "Price Action Fractal Forecasts" in PulseWire's Indicators & Strategies. Customize settings according to your trading strategy.
📊 Strategic Forecasting: Monitor the forecast deviation cone and forecast directional changes for insights into potential future price movements.
🔔 Alerts for Swift Action: Set up notifications based on forecast changes to stay ahead of market movements without constant monitoring.
Behind the Magic: How It Works
The core of the Price Action Fractal Forecasts lies in its ability to compare current market behavior with historical data to unearth similar patterns. It first establishes a training data window to analyze historical prices. Within this window, it then defines a reference length to identify the most recent price action that will serve as the basis for comparison. The indicator searches through the historical data within the training window to find segments that closely match the recent price action in the reference period.
Depending on whether you choose the anticipatory or repetitive forecast mode, the indicator either looks ahead to predict future prices based on past outcomes following similar patterns or focuses on the repeating patterns within the reference period itself for forecasts. The forecast's direction can be configured to reflect the mean average of forecasted prices or the end-point relative to the start-point of the forecast, offering flexibility in how forecasts are interpreted.
To enhance the comprehensiveness and visualization, the indicator features a forecast deviation cone. This cone represents the potential range of price movements, providing a visual cue for volatility and uncertainty in the forecasted prices. The intensity of this cone can be adjusted to suit individual preferences, offering a visual guide to the level of risk and uncertainty associated with the forecasted price path.
Embrace the fractal magic of markets with AlgoAlpha's Price Action Fractal Forecasts and transform your trading today! 🌟🚀 Indicator
