Power Balance ForecasterHey trader buddy! Remember the old IBM 5150 on Wall Street back in the 80s? :) Well, I wanted to pay tribute to it with this retro-style code when MS DOS and CRT screens were the cutting edge of technology...
Analysis of the balance of power between buyers and sellers with price predictions
What This Indicator Does
The Power Balance Forecaster indicator analyzes the relationship between buyer and seller strength to predict future price movements. Here's what it does in detail:
Main Features:
Power Balance Analysis: Calculates real-time percentage of buyer power vs seller power
Price Predictions: Estimates next closing level based on current momentum
Market State Detection: Identifies 5 different market conditions
Visual Signals: Shows directional arrows and price targets
How the Trading Logic Works
Power Balance Calculation:
Analyzes Consecutive Bars - Counts consecutive bullish and bearish bars
Calculates Momentum - Uses ATR-normalized momentum to measure trend strength
Determines Market State - Assigns one of 5 market states based on conditions
Market States:
Bull Control: Strong uptrend (75% buyer power)
Bear Control: Strong downtrend (75% seller power)
Buying Pressure: Bullish pressure (65% buyer power)
Selling Pressure: Bearish pressure (65% seller power)
Balance Area: Market in equilibrium (50/50)
Prediction System:
Bullish Condition: Buyer power > 55% + Positive momentum = Bullish prediction
Bearish Condition: Seller power > 55% + Negative momentum = Bearish prediction
Price Target: Based on ATR multiplied by timeframe factor
Configurable Parameters:
Analysis Sensitivity (5-50): Controls how responsive the indicator is
Low values (5-15): More sensitive, ideal for scalping
High values (30-50): More stable, ideal for swing trading
Table Position: Choose from 9 positions to display the data table
Trading Signals:
Green Triangle ▲: Bullish signal, price expected to increase
Green Triangle ▼: Bearish signal, price expected to decrease
Dashed Line: Shows the price target projection
Label: Displays the exact target value
Recommended Timeframes:
Lower Timeframes (1-15 minutes):
Sensitivity: 10-20
Automatic Low TF mode
Higher Timeframes (1 hour - 1 day):
Sensitivity: 25-40
Automatic High TF mode
Important Notes:
Always use this indicator in combination with:
Market context analysis
Proper risk management
Confirmation from other indicators
Mandatory stop losses
The indicator works best in trending markets and may be less effective during extreme consolidation periods.
Forecast
AI MEDEA FORECASTAI MEDEA searches for similar historical patterns and uses them to generate predictions. The longer it runs, the more data it gathers and the better the predictions become.
Important:
The indicator must remain enabled to:
- Collect predictions and check their accuracy
- Have as much data as possible for comparison
- Provide more accurate results
Recommendation:
Let the indicator run for several days on different timeframes (15m, 30m, 1H, 4H). The accuracy table will show the actual accuracy only after gathering enough predictions.
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
Volume Surprise [LuxAlgo]The Volume Surprise tool displays the trading volume alongside the expected volume at that time, allowing users to spot unexpected trading activity on the chart easily.
The tool includes an extrapolation of the estimated volume for future periods, allowing forecasting future trading activity.
🔶 USAGE
We define Volume Surprise as a situation where the actual trading volume deviates significantly from its expected value at a given time.
Being able to determine if trading activity is higher or lower than expected allows us to precisely gauge the interest of market participants in specific trends.
A histogram constructed from the difference between the volume and expected volume is provided to easily highlight the difference between the two and may be used as a standalone.
The tool can also help quantify the impact of specific market events, such as news about an instrument. For example, an important announcement leading to volume below expectations might be a sign of market participants underestimating the impact of the announcement.
Like in the example above, it is possible to observe cases where the volume significantly differs from the expected one, which might be interpreted as an anomaly leading to a correction.
🔹 Detecting Rare Trading Activity
Expected volume is defined as the mean (or median if we want to limit the impact of outliers) of the volume grouped at a specific point in time. This value depends on grouping volume based on periods, which can be user-defined.
However, it is possible to adjust the indicator to overestimate/underestimate expected volume, allowing for highlighting excessively high or low volume at specific times.
In order to do this, select "Percentiles" as the summary method, and change the percentiles value to a value that is close to 100 (overestimate expected volume) or to 0 (underestimate expected volume).
In the example above, we are only interested in detecting volume that is excessively high, we use the 95th percentile to do so, effectively highlighting when volume is higher than 95% of the volumes recorded at that time.
🔶 DETAILS
🔹 Choosing the Right Periods
Our expected volume value depends on grouping volume based on periods, which can be user-defined.
For example, if only the hourly period is selected, volumes are grouped by their respective hours. As such, to get the expected volume for the hour 7 PM, we collect and group the historical volumes that occurred at 7 PM and average them to get our expected value at that time.
Users are not limited to selecting a single period, and can group volume using a combination of all the available periods.
Do note that when on lower timeframes, only having higher periods will lead to less precise expected values. Enabling periods that are too low might prevent grouping. Finally, enabling a lot of periods will, on the other hand, lead to a lot of groups, preventing the ability to get effective expected values.
In order to avoid changing periods by navigating across multiple timeframes, an "Auto Selection" setting is provided.
🔹 Group Length
The length setting allows controlling the maximum size of a volume group. Using higher lengths will provide an expected value on more historical data, further highlighting recurring patterns.
🔹 Recommended Assets
Obtaining the expected volume for a specific period (time of the day, day of the week, quarter, etc) is most effective when on assets showing higher signs of periodicity in their trading activity.
This is visible on stocks, futures, and forex pairs, which tend to have a defined, recognizable interval with usually higher trading activity.
Assets such as cryptocurrencies will usually not have a clearly defined periodic trading activity, which lowers the validity of forecasts produced by the tool, as well as any conclusions originating from the volume to expected volume comparisons.
🔶 SETTINGS
Length: Maximum number of records in a volume group for a specific period. Older values are discarded.
Smooth: Period of a SMA used to smooth volume. The smoothing affects the expected value.
🔹 Periods
Auto Selection: Automatically choose a practical combination of periods based on the chart timeframe.
Custom periods can be used if disabling "Auto Selection". Available periods include:
- Minutes
- Hours
- Days (can be: Day of Week, Day of Month, Day of Year)
- Months
- Quarters
🔹 Summary
Method: Method used to obtain the expected value. Options include Mean (default) or Percentile.
Percentile: Percentile number used if "Method" is set to "Percentile". A value of 50 will effectively use a median for the expected value.
🔹 Forecast
Forecast Window: Number of bars ahead for which the expected volume is predicted.
Style: Style settings of the forecast.
Cyclical Phases of the Market🧭 Overview
“Cyclical Phases of the Market” automatically detects major market cycles by connecting swing lows and measuring the average number of bars between them.
Once it learns the rhythm of past cycles, it projects the next expected cycle (in time and price) using a dashed orange line and a forecast label.
In simple terms:
The indicator shows where the next potential low is statistically expected to occur, based on the timing and depth of previous cycles.
⚙️ Core Logic – Step by Step
1️⃣ Pivot Detection
The script uses the built-in ta.pivotlow() and ta.pivothigh() functions to find local turning points:
pivotLow marks a local swing low, defined by pivotLeft and pivotRight bars on each side.
Only confirmed lows are used to define the major cycle points.
Each new pivot low is stored in two arrays:
cycleLows → price level of the low
cycleBars → bar index where the low occurred
2️⃣ Cycle Identification and Drawing
Every time two consecutive swing lows are found, the indicator:
Calculates the number of bars between them (cycle length).
If that distance is greater than or equal to minCycleBars, it draws a teal line connecting the two lows — visually representing one complete cycle.
These teal lines form the historical cycle structure of the market.
3️⃣ Average Cycle Length
Once there are at least three completed cycles, the script calculates the average duration (mean number of bars between lows).
This value — avgCycleLength — represents the dominant periodicity or cycle rhythm of the market.
4️⃣ Forecasting the Next Cycle
When a valid average cycle length exists, the model projects the next expected cycle:
Time projection:
Adds avgCycleLength to the last cycle’s ending bar index to find where the next low should occur.
Price projection:
Estimates the vertical amplitude by taking the difference between the last two cycle lows (priceDiff).
Adds this same difference to the last low price to forecast the next probable low level.
The result is drawn as an orange dashed line extending into the future, representing the Next Expected Cycle.
5️⃣ Forecast Label
An orange label 🔮 appears at the projected future point showing:
Text:
🔮 Upcoming Cycle Forecast
Price:
The label marks the probable area and timing of the next cyclical low.
(Note: the date/time calculation currently multiplies bar count by 7 days, so it’s designed mainly for daily charts. On other timeframes, that conversion can be adapted.)
📊 How to Read It on the Chart
Visual Element Meaning Interpretation
Teal lines Completed historical cycles (low to low) Show actual periodic rhythm of the market
Orange dashed line Projection of the next expected cycle Anticipated path toward the next cyclical low
Orange label 🔮 Upcoming Cycle Forecast Displays expected price and bar location
Average cycle length Internal variable (bars between lows) Represents the dominant cycle period
📈 Interpretation
When teal segments show consistent spacing, the market is following a stable rhythm → cycles are predictable.
When cycle spacing shortens, the market is accelerating (volatility rising).
When it widens, the market is slowing down or entering accumulation.
The orange dashed line represents the next expected low zone:
If the market drops near this line → cyclical pattern confirmed.
If the market breaks well below → cycle amplitude has increased (trend weakening).
If the market rises above and delays → a new longer cycle may be forming.
🧠 Practical Use
Combine with oscillators (e.g., RSI or TSI) to confirm momentum alignment near projected lows.
Use in conjunction with volume to identify accumulation or exhaustion near the expected turning point.
Compare across timeframes: weekly cycles confirm long-term rhythm; daily cycles refine short-term entries.
⚡ Summary
Aspect Description
Purpose Detect and forecast recurring market cycles
Cycle basis Low-to-Low pivot analysis
Visuals Teal historical cycles + Orange forecast line
Forecast Next expected low (price and time)
Ideal timeframe Daily
Main outputs Average cycle length, next projected cycle, visual cycle map
Statistical Projection over N Days (drift + σ) – v1.2 [EN]🧭 Overview
“Statistical Projection over N Days (drift + σ)” is a quantitative forecasting model that estimates the expected future price range of any asset over a chosen horizon (default = 10 days).
It combines average drift (trend direction) and historical volatility (σ) to produce a probabilistic cone of future price movement.
The indicator displays:
a blue dashed line (expected price path),
1σ / 2σ deviation bands (volatility envelopes),
and a summary table with the key forecast values and expected return.
⚙️ Core Logic (Explained Simply)
The indicator analyses recent price behavior to estimate two key elements:
the average daily tendency of the market (called drift), and
the average daily variability (called volatility).
Here’s how it works, step by step:
Measures daily percentage changes (using logarithmic returns) to understand how much the price typically moves from one bar to the next.
It then calculates the average of those returns over a chosen historical window (for example, 70 bars).
If the average is positive → the market has a rising tendency (upward drift).
If the average is negative → the market tends to decline (downward drift).
At the same time, it computes the standard deviation of those returns — this shows how “wide” the movements are, i.e. how volatile the asset is.
Using these two measures — drift and volatility — it estimates where the price is statistically expected to move over the next N bars:
The mean projection (blue dashed line) represents the most likely price path.
The 1σ and 2σ lines (teal and gray) define confidence zones, where price is expected to remain about 68% and 95% of the time, respectively.
The model updates continuously with every new bar, recalculating both drift and volatility, so the projection cone expands, contracts, or changes direction depending on the latest market behavior.
📉 Interpretation of the Blue Line
The blue dashed line (pMean) is the statistical forecast path of price over the next N bars.
🔹 When the blue line is below the current price
The recent drift (average log return) is negative → the model expects a gradual decline.
Interpretation:
The prevailing statistical bias is bearish — the market is expected to move lower toward equilibrium.
🔹 When the blue line is above the current price
The recent drift is positive → the model expects a continued rise.
Interpretation:
The price is statistically likely to trend upward, maintaining momentum in the direction of the current drift.
🔹 When the blue line is sloping upward
The mean projection pMean is rising with each new bar.
Indicates positive drift → the average daily return is positive.
Interpretation:
The asset is in a growth phase; volatility bands act as potential expansion corridors.
🔹 When the blue line is sloping downward
The mean projection pMean decreases bar after bar.
Indicates negative drift → average daily return is negative.
Interpretation:
The asset is in a corrective or declining phase, with volatility determining potential drawdown limits.
🔹 When the blue line is flat
The drift (μ) is approximately zero.
Interpretation:
The model sees no directional bias; price equilibrium dominates.
Expect a sideways range unless new volatility (σ) expansion occurs.
📈 How to Read the Entire Projection
Blue dashed line → expected mean path (most probable price trajectory).
Teal lines (±1σ) → statistically normal range (≈68% of future outcomes).
Gray lines (±2σ) → extreme bounds (≈95% of outcomes).
Labels on the right show exact forecast prices for each band.
If the actual price moves outside the gray 2σ range →
→ it signals volatility breakout or regime shift, meaning the past volatility no longer explains the present movement.
🧮 Summary Table
Located at the top-right corner, it provides:
Field Description
Projection (days) Number of bars used for projection (h).
Anchor price Starting close used for forecast.
Mean target (h) Expected price after h bars (blue line endpoint).
1σ Band (↓ / ↑) 68% confidence interval.
2σ Band (↓ / ↑) 95% confidence interval.
Expected return Projected % change from current close to mean target.
Colors can be customized — for example:
white headers,
aqua for anchor price,
lime for target,
orange/red for σ bands,
yellow for expected return.
🧠 Practical Meaning
Blue Line State Interpretation Bias
Above price, rising Ongoing positive drift Bullish
Below price, falling Negative drift Bearish
Flat, near price Neutral drift Sideways
Steep slope Strong directional momentum Trend confirmation
Price > +2σ band Excess volatility / overextension Possible correction
Price < −2σ band Undervaluation or panic Reversion likely
⚡ Summary
Aspect Description
Purpose Statistical forecast of expected price range
Method Drift (μ) + Volatility (σ) from log returns
Outputs Mean projection (blue), 1σ & 2σ bands, expected return
Interpretation Directional bias from blue line and its slope
Recommended timeframe Daily
Best use Trend confirmation, probabilistic target estimation, volatility analysis.
Markov Chain Regime & Next‑Bar Probability Forecast✨ What it is
A regime-aware, math-driven panel that forecasts the odds for the very next candle. It shows:
• P(next r > 0)
• P(next r > +θ)
• P(next r < −θ)
• A 4-bucket split of next-bar outcomes (>+θ | 0..+θ | −θ..0 | <−θ)
• Next-regime probabilities: Calm | Neutral | Volatile
🧠 Why the math is strong
• Markov regimes: Markets cluster in volatility “moods.” We learn a 3-state regime S∈{Calm, Neutral, Volatile} with a transition matrix A, where A = P(Sₜ₊₁=j | Sₜ=i).
• Condition on the future state: We estimate event odds given the next regime j—
q_pos(j)=P(rₜ₊₁>0 | Sₜ₊₁=j), q_gt(j)=P(rₜ₊₁>+θ | Sₜ₊₁=j), q_lt(j)=P(rₜ₊₁<−θ | Sₜ₊₁=j)—
and mix them with transitions from the current (or frozen) state sNow:
P(event) = Σⱼ A · q(event | j).
This mixture-of-regimes view (HMM-style one-step prediction) ties next-bar outcomes to where volatility is likely headed.
• Statistical hygiene: Laplace/Beta smoothing, minimum-sample gating, and unconditional fallbacks keep estimates stable. Heavy computations run on confirmed bars; “Freeze at close” avoids intrabar flicker.
📊 What each value means
• Regime label & background: 🟩 Calm, 🟧 Neutral, 🟥 Volatile — quick read of market context.
• P(next r > 0): Directional tilt for the very next bar.
• P(next r > +θ): Odds of an outsized positive move beyond θ.
• P(next r < −θ): Odds of an outsized negative move beyond −θ.
• Partition row: Distributes next-bar probability across four intuitive buckets; they ≈ sum to 100%.
• Next Regime Probs: Likelihood of switching to Calm/Neutral/Volatile on the next bar (row of A for the current/frozen state).
• Samples row: How many next-bar samples support each next-state estimate (a confidence cue).
• Smoothing α: The Laplace prior used to stabilize binary event rates.
⚙️ Inputs you control
• Returns: Log (default) or %
• Include Volume (z-score) + lookback
• Include Range (HL/PrevClose)
• Rolling window N (transitions & estimates)
• θ as percent (e.g., 0.5%)
• Freeze forecast at last close (recommended)
• Display toggles (plots, partition, samples)
🎯 How to use it
• Volatility awareness & sizing: Rising P(next regime = Volatile) → consider smaller size, wider stops, or skipping marginal entries.
• Breakout preparation: Elevated P(next r > +θ) highlights environments where range expansion is more likely; pair with your setup/trigger.
• Defense for mean-reversion: If P(next r < −θ) lifts while you’re late long (or P(next r > +θ) lifts while late short), tighten risk or wait for better context.
• Calibration tip: Start θ near your market’s typical bar size; adjust until “>+θ” flags truly meaningful moves for your timeframe.
📝 Method notes & limits
Activity features (|r|, volume z, range) are standardized; only positive z’s feed the composite activity score. Estimates adapt to instrument/timeframe; rare regimes or small windows increase variance (hence smoothing, sample gating, fallbacks). This is a context/forecast tool, not a standalone signal—combine with your entry/exit rules and risk management.
🧩 Strategies too
We also develop full strategy versions that use these probabilities for entries, filters, and position sizing. Like this publication if you’d like us to release the strategy edition next.
⚠️ Disclaimer
Educational use only. Not financial advice. Markets involve risk. Past performance does not guarantee future results.
Machine Learning Price Predictor: Ridge AR [Bitwardex]🔹Machine Learning Price Predictor: Ridge AR is a research-oriented indicator demonstrating the use of Regularized AutoRegression (Ridge AR) for short-term price forecasting.
The model combines autoregressive structure with Ridge regularization , providing stability under noisy or volatile market conditions.
The latest version introduces Bull and Bear signals , visually representing the current momentum phase and model direction directly on the chart.
Unlike traditional linear regression, Ridge AR minimizes overfitting, stabilizes coefficient dynamics, and enhances predictive consistency in correlated datasets.
The script plots:
Fit Line — in-sample fitted data;
Forecast Line — out-of-sample projection;
Trend Segments — color-coded bullish/bearish sections;
Bull/Bear Labels 🐂🐻 — dynamic visual signals showing directional bias.
Designed for researchers, students, and developers, this tool helps explore regularized time-series forecasting in Pine Script™.
🧩 Ridge AR Settings
Training Window — number of bars used for model training;
Forecast Horizon — forecast length (bars ahead);
AR Order — number of lags used as features;
Ridge Strength (λ) — regularization coefficient;
Damping Factor — exponential trend decay rate;
Trend Length — period for trend/volatility estimation;
Momentum Weight — strength of the recent move;
Mean Reversion — pullback intensity toward the mean.
🧮 Data Processing
Prefilter:
None — raw close price;
EMA — exponential smoothing;
SuperSmoother — Ehlers filter for noise reduction.
EMA Length, SuperSmoother Length — smoothing parameters.
🖥️ Display Settings
Update Mode:
Lock — static model;
Update Once Reached — rebuild after forecast horizon;
Continuous — update every bar.
Forecast Color — projection line color;
Bullish/Bearish Colors — colors for trend segments.
🐂🐻 Bull/Bear Signal System
The Bull/Bear Signal System adds directional visual cues to highlight local momentum shifts and model-based trend confirmation.
Bull (🐂) — appears when upward momentum is confirmed (momentum > 0) .
Displayed below the bar, colored with Bullish Color.
Bear (🐻) — appears when downward momentum is dominant (momentum < 0) .
Displayed above the bar, colored with Bearish Color.
Signals are generated during model recalculations or when the directional bias changes in Continuous mode.
These visual markers are analytical aids , not trading triggers.
🧠 Core Algorithmic Components
Regularized AutoRegression (Ridge AR):
Solves: (X′X+λI)−1X′y
to derive stable regression coefficients.
Matrix and Pseudoinverse Operations — implemented natively in Pine Script™.
Prefiltering (EMA / Ehlers SuperSmoother) — stabilizes noisy data.
Forecast Dynamics — integrates damping, momentum, and mean reversion.
Trend Visualization — color-coded bullish/bearish line segments.
Bull/Bear Signal Engine — visualizes real-time impulse direction.
📊 Applications
Academic and educational purposes;
Demonstration of Ridge Regression and AR models;
Analysis of bull/bear market phase transitions;
Visualization of time-series dependencies.
⚠️ Disclaimer
This script is provided for educational and research purposes only.
It does not provide trading or investment advice.
The author assumes no liability for financial losses resulting from its use.
Use responsibly and at your own risk.
KAMENICZKI PROSCAPLERPROSCAPLER is an advanced trading indicator that combines a dynamic channel with a prediction line for maximum accuracy and trading success. The indicator is designed for professional traders who need reliable signals with high success rates.
Adaptive Intelligence
Automatic optimal period detection - the indicator adapts to various market conditions
Intelligent timeframe settings - automatically optimizes periods based on TF
Dynamic adaptation - the channel changes according to volatility and trend.
High Signal Accuracy
Pearson R correlation - filters only strong trends with high reliability
Multi-timeframe confirmation - confirms signals on higher timeframe
Volatility and volume filters - eliminates false signals
RSI extreme values - captures only the best entry points
Prediction Line
Future price direction - shows where the price will move
Adaptive length - adapts to timeframe
Strong signals - when the entire prediction line is in the center of the channel
Quality Filters
Minimum Pearson R 0.5+ - only strong trends
Volume filter 1.2x - only signals with sufficient volume
ATR volatility filter - eliminates low volatility
RSI extreme levels - only at oversold/overbought values
Anomalies
Anomaly detection - captures exceptional opportunities
Bright yellow/pink color - immediately visible
Fast Reaction
Minimum trend bars = 1 - fast turning
Adaptive detection - immediate reaction to changes
Automatic optimizations - without manual settings
News & Volatility Filters
News filter - disables channel during high impact news
Volatility filter - protects against high volatility
Gap detection - filters dangerous gaps
Combined Filters
All filters must be met - maximum reliability
Multi-timeframe confirmation - double check
Pearson R validation - mathematical accuracy
Volume confirmation - institutional interest
Reaction Speed
Instant signals - without delay
Adaptive settings - automatic optimization
Fast turning - minimum 1 bar trend
Signal Accuracy
Quality filters increase success rate to 70-80%
Anomalies have 80-90% success rate
STRONG signals (prediction line in center) 85-95%
HAVE FUN :)
Foresight Cone (HoltxF1xVWAP) [KedArc Quant]Description:
This is a time-series forecasting indicator that estimates the next bar (F1) and projects a path a few bars ahead. It also draws a confidence cone based on how accurate the recent forecasts have been. You can optionally color the projection only when price agrees with VWAP.
Why it’s different
* One clear model: Everything comes from Holt’s trend-aware forecasting method—no mix of unrelated indicators.
* Transparent visuals: You see the next-bar estimate (F1), the forward projection, and a cone that widens or narrows based on recent forecast error.
* Context, not signals: The VWAP option only changes colors. It doesn’t add trade rules.
* No look-ahead: Accuracy is measured using the forecast made on the previous bar versus the current bar.
Inputs (what they mean)
* Source: Price series to forecast (default: Close).
* Preset: Quick profiles for fast, smooth, or momentum markets (see below).
* Alpha (Level): How fast the model reacts to new prices. Higher = faster, twitchier.
* Beta (Trend): How fast the model updates the slope. Higher = faster pivots, more flips in chop.
* Horizon: How many bars ahead to project. Bigger = wider cone.
* Residual Window: How many bars to judge recent accuracy. Bigger = steadier cone.
* Confidence Z: How wide the cone should be (typical setting ≈ “95% style” width).
* Show Bands / Draw Forward Path: Turn the cone and forward lines on/off.
* Color only when aligned with VWAP: Highlights projections only when price agrees with the trend side of VWAP.
* Colors / Show Panel: Styling plus a small panel with RMSE, MAPE, and trend slope.
Presets (when to pick which)
* Scalp / Fast (1-min): Very responsive; best for quick moves. More twitch in chop.
* Smooth Intraday (1–5 min): Calmer and steadier; a good default most days.
* Momentum / Breakout: Quicker slope tracking during strong pushes; may over-react in ranges.
* Custom: Set your own values if you know exactly what you want.
What is F1 here?
F1 is the model’s next-bar fair value. Crosses of price versus F1 can hint at short-term momentum shifts or mean-reversion, especially when viewed with VWAP or the cone.
How this helps
* Gives a baseline path of where price may drift and a cone that shows normal wiggle room.
* Helps you tell routine noise (inside cone) from information (edges or breaks outside the cone).
* Keeps you aware of short-term bias via the trend slope and F1.
How to use (step by step)
1. Add to chart → choose a Preset (start with Smooth Intraday).
2. Set Horizon around 8–15 bars for intraday.
3. (Optional) Turn on VWAP alignment to color only when price agrees with the trend side of VWAP.
4. Watch where price sits relative to the cone and F1:
* Inside = normal noise.
* At edges = stretched.
* Outside = possible regime change.
5. Check the panel: if RMSE/MAPE spike, expect a wider cone; consider a smoother preset or a higher timeframe.
6. Tweak Alpha/Beta only if needed: faster for momentum, slower for chop.
7. Combine with your own plan for entries, exits, and risk.
Accuracy Panel — what it tells you
Preset & Horizon: Shows which preset you’re using and how many bars ahead the projection goes. Longer horizons mean more uncertainty.
RMSE (error in price units): A “typical miss” measured in the chart’s currency (e.g., ₹).
Lower = tighter fit and a usually narrower cone. Rising = conditions getting noisier; the cone will widen.
MAPE (error in %): The same idea as RMSE but in percent.
Good for comparing different symbols or timeframes. Sudden spikes often hint at a regime change.
Slope T: The model’s short-term trend reading.
Positive = gentle up-bias; negative = gentle down-bias; near zero = mostly flat/drifty.
How to read it at a glance
Calm & directional: RMSE/MAPE steady or falling + Slope T positive (or negative) → trends tend to respect the cone’s mid/upper (or mid/lower) area.
Choppy/uncertain: RMSE/MAPE climbing or jumping → expect more whipsaw; rely more on the cone edges and higher-TF context.
Flat tape: Slope T near zero → mean-revert behavior is common; treat cone edges as stretch zones rather than breakout zones.
Warm-up & tweaks
Warm-up: Right after adding the indicator, the panel may be blank for a short time while it gathers enough bars.
Too twitchy? Switch to Smooth Intraday or increase the Residual Window.
Too slow? Use Scalp/Fast or Momentum/Breakout to react quicker.
Timeframe tips
* 1–3 min: Scalp/Fast or Momentum/Breakout; horizon \~8–12.
* 5–15 min: Smooth Intraday; horizon \~12–15.
* 30–60 min+: Consider a larger residual window for a steadier cone.
FAQ
Q: Is this a strategy or an indicator?
A: It’s an indicator only. It does not place orders, TP/SL, or run backtests.
Q: Does it repaint?
A: The next-bar estimate (F1) and the cone are calculated using only information available at that time. The forward path is a projection drawn on the last bar and will naturally update as new bars arrive. Historical bars aren’t revised with future data.
Q: What is F1?
A: F1 is the indicator’s best guess for the next bar.
Price crossing above/below F1 can hint at short-term momentum shifts or mean-reversion.
Q: What do “Alpha” and “Beta” do?
A: Alpha controls how fast the indicator reacts to new prices
(higher = faster, twitchier). Beta controls how fast the slope updates (higher = quicker pivots, more flips in chop).
Q: Why does the cone width change?
A: It reflects recent forecast accuracy. When the market gets noisy, the cone widens. When the tape is calm, it narrows.
Q: What does the Accuracy Panel tell me?
A:
* Preset & Horizon you’re using.
* RMSE: typical forecast miss in price units.
* MAPE: typical forecast miss in percent.
* Slope T: short-term trend reading (up, down, or flat).
If RMSE/MAPE rise, expect a wider cone and more whipsaw.
Q: The panel shows “…” or looks empty. Why?
A: It needs a short warm-up to gather enough bars. This is normal after you add the indicator or change settings/timeframes.
Q: Which timeframe is best?
A:
* 1–3 min: Scalp/Fast or Momentum/Breakout, horizon \~8–12.
* 5–15 min: Smooth Intraday, horizon \~12–15.
Higher timeframes work too; consider a larger residual window for steadier cones.
Q: Which preset should I start with?
A: Start with Smooth Intraday. If the market is trending hard, try Momentum/Breakout.
For very quick tapes, use Scalp/Fast. Switch back if things get choppy.
Q: What does the VWAP option do?
A: It only changes colors (highlights when price agrees with the trend side of VWAP).
It does not add or remove signals.
Q: Are there alerts?
A: Yes—alerts for price crossing F1 (up/down). Use “Once per bar close” to reduce noise on fast charts.
Q: Can I use this on stocks, futures, crypto, or FX?
A: Yes. It works on any symbol/timeframe. You may want to adjust Horizon and the Residual Window based on volatility.
Q: Can I use it with Heikin Ashi or other non-standard bars?
A: You can, but remember you’re forecasting the synthetic series of those bars. For pure price behavior, use regular candles.
Q: The cone feels too wide/too narrow. What do I change?
A:
* Too wide: lower Alpha/Beta a bit or increase the Residual Window.
* Too narrow (misses moves): raise Alpha/Beta slightly or try Momentum/Breakout.
Q: Why do results change when I switch timeframe or symbol?
A: Different noise levels and trends. The accuracy stats reset per chart, so the cone adapts to each context.
Q: Any limits or gotchas?
A: Extremely large Horizon may hit TradingView’s line-object limits; reduce Horizon or turn
off extra visuals if needed. Big gaps or news spikes will widen errors—expect the cone to react.
Q: Can this predict exact future prices?
A: No. It provides a baseline path and context. Always combine with your own rules and risk management.
Glossary
* TS (Time Series): Data over time (prices).
* Holt’s Method: A forecasting approach that tracks a current level and a trend to predict the next bars.
* F1: The indicator’s best guess for the next bar.
* F(h): The projected value h bars ahead.
* VWAP: Volume-Weighted Average Price—used here for optional color alignment.
* RMSE: Typical forecast miss in price units (how far off, on average).
* MAPE: Typical forecast miss in percent (scale-free, easy to compare).
Notes & limitations
* The panel needs a short warm-up; stats may be blank at first.
* The cone reflects recent conditions; sudden volatility changes will widen it.
* This is a tool for context. It does not place trades and does not promise results.
⚠️ Disclaimer
This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
Initial Balance Breakout Signals [LuxAlgo]The Initial Balance Breakout Signals help traders identify breakouts of the Initial Balance (IB) range.
The indicator includes automatic detection of IB or can use custom sessions, highlights top and bottom IB extensions, custom Fibonacci levels, and goes further with an IB forecast with two different modes.
🔶 USAGE
The initial balance is the price range made within the first hour of the trading session. It is an intraday concept based on the idea that high volume and volatility enter the market through institutional trading at the start of the session, setting the tone for the rest of the day.
The initial balance is useful for gauging market sentiment, or, in other words, the relationship between buyers and sellers.
Bullish sentiment: Price trades above the IB range.
Mixed sentiment: Price trades within the IB range.
Bearish sentiment: Price trades below the IB range.
The initial balance high and low are important levels that many traders use to gauge sentiment. There are two main ideas behind trading around the IB range.
IB Extreme Breakout: When the price breaks and holds the IB high or low, there is a high probability that the price will continue in that direction.
IB Extreme Rejection: When the price tries to break those levels but fails, there is a high probability that it will reach the opposite IB extreme.
This indicator is a complete Initial Balance toolset with custom sessions, breakout signals, IB extensions, Fibonacci retracements, and an IB forecast. All of these features will be explained in the following sections.
🔹 Custom Sessions and Signals
By default, sessions for Initial Balance and breakout signals are in Auto mode. This means that Initial Balance takes the first hour of the trading session and shows breakout signals for the rest of the session.
With this option, traders can use the tool for open range trading, making it highly versatile. The concept behind open range (OR) is the same as that of initial balance (IB), but in OR, the range is determined by the first minute, three or five minutes, or up to the first 30 minutes of the trading session.
As shown in the image above, the top chart uses the Auto feature for the IB and Breakouts sessions. The bottom chart has the Auto feature disabled to use custom sessions for both parameters. In this case, the first three minutes of the trading session are used, turning the tool into an Open Range trading indicator.
This chart shows another example of using custom sessions to display overnight NASDAQ futures sessions.
The left chart shows a custom session from the Tokyo open to the London open, and the right chart shows a custom session from the London open to the New York open.
The chart shows both the Asian and European sessions, their top and bottom extremes, and the breakout signals from those extremes.
🔹 Initial Balance Extensions
Traders can easily extend both extremes of the Initial Balance to display their preferred targets for breakouts. Enable or disable any of them and set the IB percentage to use for the extension.
As the chart shows, the percentage selected on the settings panel directly affects the displayed levels.
Setting 25 means the tool will use a quarter of the detected initial balance range for extensions beyond the IB extremes. Setting 100 means the full IB range will be used.
Traders can use these extensions as targets for breakout signals.
🔹 Fibonacci Levels
Traders can display default or custom Fibonacci levels on the IB range to trade retracements and assess the strength of market movements. Each level can be enabled or disabled and customized by level, color, and line style.
As we can see on the chart, after the IB was completed, prices were unable to fall below the 0.236 Fibonacci level. This indicates significant bullish pressure, so it is expected that prices will rise.
Traders can use these levels as guidelines to assess the strength of the side trying to penetrate the IB. In this case, the sellers were unable to move the market beyond the first level.
🔹 Initial Balance Forecast
The tool features two different forecasting methods for the current IB. By default, it takes the average of the last ten values and applies a multiplier of one.
IB Against Previous Open: averages the difference between IB extremes and the open of the previous session.
Filter by current day of the week: averages the difference between IB extremes and the open of the current session for the same day of the week.
This feature allows traders to see the difference between the current IB and the average of the last IBs. It makes it very easy to interpret: if the current IB is higher than the average, buyers are in control; if it is lower than the average, sellers are in control.
For example, on the left side of the chart, we can see that the last day was very bullish because the IB was completely above the forecasted value. This is the IB mean of the last ten trading days.
On the right, we can see that on Monday, September 15, the IB traded slightly higher but within the forecasted value of the IB mean of the last ten Mondays. In this case, it is within expectations.
🔶 SETTINGS
Display Last X IBs: Select how many IBs to display.
Initial Balance: Choose a custom session or enable the Auto feature.
Breakouts: Enable or disable breakouts. Choose custom session or enable the Auto feature.
🔹 Extensions
Top Extension: Enable or disable the top extension and choose the percentage of IB to use.
Bottom extension: Enable or disable the bottom extension and choose the percentage of IB to use.
🔹 Fibonacci Levels
Display Fibonacci: Enable or disable Fibonacci levels.
Reverse: Reverse Fibonacci levels.
Levels, Colors & Style
Display Labels: Enable or disable labels and choose text size.
🔹 Forecast
Display Forecast: Select the forecast method.
- IB Against Previous Open: Calculates the average difference between the IB high and low and the previous day's IB open price.
- Filter by Current Day of Week: Calculates the average difference between the IB high and low and the IB open price for the same day of the week.
Forecast Memory: The number of data points used to calculate the average.
Forecast Multiplier: This multiplier will be applied to the average. Bigger numbers will result in wider predicted ranges.
Forecast Colors: Choose from a variety of colors.
Forecast Style: Choose a line style.
🔹 Style
Initial Balance Colors
Extension Transparency: Choose the extension's transparency. 0 is solid, and 100 is fully transparent.
Implied Volatility RangeThe Implied Volatility Range is a forward-looking tool that transforms option market data into probability ranges for future prices. Based on the lognormal distribution of asset prices assumed in modern option pricing models, it converts the implied volatility curve into a volatility cone with dynamic labels that show the market’s expectations for the price distribution at a specific point in time. At the selected future date, it displays projected price levels and their percentage change from today’s close across 1, 2, and 3 standard deviation (σ) ranges:
1σ range = ~68.2% probability the price will remain within this range.
2σ range = ~95.4% probability the price will remain within this range.
3σ range = ~99.7% probability the price will remain within this range.
What makes this indicator especially useful is its ability to incorporate implied volatility skew. When only ATM IV (%) is entered, the indicator displays the standard Black–Scholes lognormal distribution. By adding High IV (%) and Low IV (%) values tied to strikes above and below the current price, the indicator interpolates between these inputs to approximate the implied volatility skew. This adjustment produces a market-implied probability distribution that indicates whether the option market is leaning bullish or bearish, based on the data entered in the menu:
ATM IV (%) = Implied volatility at the current spot price (at-the-money).
High IV (%) = Implied volatility at a strike above the current spot price.
High Strike = Strike price corresponding to the High IV input (OTM call).
Low IV (%) = Implied volatility at a strike below the current spot price.
Low Strike = Strike price corresponding to the Low IV input (OTM put).
Expiration (Day, Month, Year) = Option expiration date for the projection.
Once these inputs are entered, the indicator calculates implied probability ranges and, if both High IV and Low IV values are provided, adjusts for skew to approximate the option market’s distribution. If no implied volatility data is supplied, the indicator defaults to a lognormal distribution based on historical volatility, using past realized volatility over the same forward horizon. This keeps the tool functional even without implied volatility inputs, though in that case the output represents only an approximation of ATM IV, not the actual market view.
In summary, the Implied Volatility Range is a powerful tool that translates implied volatility inputs into a clear and practical estimate of the market’s expectations for future prices. It allows traders to visualize the probability of price ranges while also highlighting directional bias, a dimension often difficult to interpret from traditional implied volatility charts. It should be emphasized, however, that this tool reflects only the market’s expectations at a specific point in time, which may change as new information and trading activity reshape implied volatility.
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.
Daily Seasonality Strength + Prediction TableDaily Seasonality Strength + Prediction Table
Return Estimates:
This indicator uses historical price data to calculate average returns for each day (of the week or month) and uses these to predict the next day’s return.
Seasonality Strength:
It measures seasonality strength by comparing predicted returns with actual returns, using the inverse of MSE (higher values mean stronger seasonality).
supports up to 10 assets
This script is for informational and educational purposes only. It does not constitute financial, investment, or trading advice. I am not a financial advisor. Any decisions you make based on this indicator are your own responsibility. Always do your own research and consult with a qualified financial professional before making any investment decisions.
Past performance is no guarantee of future results. The value of the instruments may fluctuate and is not guaranteed
Seasonality Monte Carlo Forecaster [BackQuant]Seasonality Monte Carlo Forecaster
Plain-English overview
This tool projects a cone of plausible future prices by combining two ideas that traders already use intuitively: seasonality and uncertainty. It watches how your market typically behaves around this calendar date, turns that seasonal tendency into a small daily “drift,” then runs many randomized price paths forward to estimate where price could land tomorrow, next week, or a month from now. The result is a probability cone with a clear expected path, plus optional overlays that show how past years tended to move from this point on the calendar. It is a planning tool, not a crystal ball: the goal is to quantify ranges and odds so you can size, place stops, set targets, and time entries with more realism.
What Monte Carlo is and why quants rely on it
• Definition . Monte Carlo simulation is a way to answer “what might happen next?” when there is randomness in the system. Instead of producing a single forecast, it generates thousands of alternate futures by repeatedly sampling random shocks and adding them to a model of how prices evolve.
• Why it is used . Markets are noisy. A single point forecast hides risk. Monte Carlo gives a distribution of outcomes so you can reason in probabilities: the median path, the 68% band, the 95% band, tail risks, and the chance of hitting a specific level within a horizon.
• Core strengths in quant finance .
– Path-dependent questions : “What is the probability we touch a stop before a target?” “What is the expected drawdown on the way to my objective?”
– Pricing and risk : Useful for path-dependent options, Value-at-Risk (VaR), expected shortfall (CVaR), stress paths, and scenario analysis when closed-form formulas are unrealistic.
– Planning under uncertainty : Portfolio construction and rebalancing rules can be tested against a cloud of plausible futures rather than a single guess.
• Why it fits trading workflows . It turns gut feel like “seasonality is supportive here” into quantitative ranges: “median path suggests +X% with a 68% band of ±Y%; stop at Z has only ~16% odds of being tagged in N days.”
How this indicator builds its probability cone
1) Seasonal pattern discovery
The script builds two day-of-year maps as new data arrives:
• A return map where each calendar day stores an exponentially smoothed average of that day’s log return (yesterday→today). The smoothing (90% old, 10% new) behaves like an EWMA, letting older seasons matter while adapting to new information.
• A volatility map that tracks the typical absolute return for the same calendar day.
It calculates the day-of-year carefully (with leap-year adjustment) and indexes into a 365-slot seasonal array so “March 18” is compared with past March 18ths. This becomes the seasonal bias that gently nudges simulations up or down on each forecast day.
2) Choice of randomness engine
You can pick how the future shocks are generated:
• Daily mode uses a Gaussian draw with the seasonal bias as the mean and a volatility that comes from realized returns, scaled down to avoid over-fitting. It relies on the Box–Muller transform internally to turn two uniform random numbers into one normal shock.
• Weekly mode uses bootstrap sampling from the seasonal return history (resampling actual historical daily drifts and then blending in a fraction of the seasonal bias). Bootstrapping is robust when the empirical distribution has asymmetry or fatter tails than a normal distribution.
Both modes seed their random draws deterministically per path and day, which makes plots reproducible bar-to-bar and avoids flickering bands.
3) Volatility scaling to current conditions
Markets do not always live in average volatility. The engine computes a simple volatility factor from ATR(20)/price and scales the simulated shocks up or down within sensible bounds (clamped between 0.5× and 2.0×). When the current regime is quiet, the cone narrows; when ranges expand, the cone widens. This prevents the classic mistake of projecting calm markets into a storm or vice versa.
4) Many futures, summarized by percentiles
The model generates a matrix of price paths (capped at 100 runs for performance inside TradingView), each path stepping forward for your selected horizon. For each forecast day it sorts the simulated prices and pulls key percentiles:
• 5th and 95th → approximate 95% band (outer cone).
• 16th and 84th → approximate 68% band (inner cone).
• 50th → the median or “expected path.”
These are drawn as polylines so you can immediately see central tendency and dispersion.
5) A historical overlay (optional)
Turn on the overlay to sketch a dotted path of what a purely seasonal projection would look like for the next ~30 days using only the return map, no randomness. This is not a forecast; it is a visual reminder of the seasonal drift you are biasing toward.
Inputs you control and how to think about them
Monte Carlo Simulation
• Price Series for Calculation . The source series, typically close.
• Enable Probability Forecasts . Master switch for simulation and drawing.
• Simulation Iterations . Requested number of paths to run. Internally capped at 100 to protect performance, which is generally enough to estimate the percentiles for a trading chart. If you need ultra-smooth bands, shorten the horizon.
• Forecast Days Ahead . The length of the cone. Longer horizons dilute seasonal signal and widen uncertainty.
• Probability Bands . Draw all bands, just 95%, just 68%, or a custom level (display logic remains 68/95 internally; the custom number is for labeling and color choice).
• Pattern Resolution . Daily leans on day-of-year effects like “turn-of-month” or holiday patterns. Weekly biases toward day-of-week tendencies and bootstraps from history.
• Volatility Scaling . On by default so the cone respects today’s range context.
Plotting & UI
• Probability Cone . Plots the outer and inner percentile envelopes.
• Expected Path . Plots the median line through the cone.
• Historical Overlay . Dotted seasonal-only projection for context.
• Band Transparency/Colors . Customize primary (outer) and secondary (inner) band colors and the mean path color. Use higher transparency for cleaner charts.
What appears on your chart
• A cone starting at the most recent bar, fanning outward. The outer lines are the ~95% band; the inner lines are the ~68% band.
• A median path (default blue) running through the center of the cone.
• An info panel on the final historical bar that summarizes simulation count, forecast days, number of seasonal patterns learned, the current day-of-year, expected percentage return to the median, and the approximate 95% half-range in percent.
• Optional historical seasonal path drawn as dotted segments for the next 30 bars.
How to use it in trading
1) Position sizing and stop logic
The cone translates “volatility plus seasonality” into distances.
• Put stops outside the inner band if you want only ~16% odds of a stop-out due to noise before your thesis can play.
• Size positions so that a test of the inner band is survivable and a test of the outer band is rare but acceptable.
• If your target sits inside the 68% band at your horizon, the payoff is likely modest; outside the 68% but inside the 95% can justify “one-good-push” trades; beyond the 95% band is a low-probability flyer—consider scaling plans or optionality.
2) Entry timing with seasonal bias
When the median path slopes up from this calendar date and the cone is relatively narrow, a pullback toward the lower inner band can be a high-quality entry with a tight invalidation. If the median slopes down, fade rallies toward the upper band or step aside if it clashes with your system.
3) Target selection
Project your time horizon to N bars ahead, then pick targets around the median or the opposite inner band depending on your style. You can also anchor dynamic take-profits to the moving median as new bars arrive.
4) Scenario planning & “what-ifs”
Before events, glance at the cone: if the 95% band already spans a huge range, trade smaller, expect whips, and avoid placing stops at obvious band edges. If the cone is unusually tight, consider breakout tactics and be ready to add if volatility expands beyond the inner band with follow-through.
5) Options and vol tactics
• When the cone is tight : Prefer long gamma structures (debit spreads) only if you expect a regime shift; otherwise premium selling may dominate.
• When the cone is wide : Debit structures benefit from range; credit spreads need wider wings or smaller size. Align with your separate IV metrics.
Reading the probability cone like a pro
• Cone slope = seasonal drift. Upward slope means the calendar has historically favored positive drift from this date, downward slope the opposite.
• Cone width = regime volatility. A widening fan tells you that uncertainty grows fast; a narrow cone says the market typically stays contained.
• Mean vs. price gap . If spot trades well above the median path and the upper band, mean-reversion risk is high. If spot presses the lower inner band in an up-sloping cone, you are in the “buy fear” zone.
• Touches and pierces . Touching the inner band is common noise; piercing it with momentum signals potential regime change; the outer band should be rare and often brings snap-backs unless there is a structural catalyst.
Methodological notes (what the code actually does)
• Log returns are used for additivity and better statistical behavior: sim_ret is applied via exp(sim_ret) to evolve price.
• Seasonal arrays are updated online with EWMA (90/10) so the model keeps learning as each bar arrives.
• Leap years are handled; indexing still normalizes into a 365-slot map so the seasonal pattern remains stable.
• Gaussian engine (Daily mode) centers shocks on the seasonal bias with a conservative standard deviation.
• Bootstrap engine (Weekly mode) resamples from observed seasonal returns and adds a fraction of the bias, which captures skew and fat tails better.
• Volatility adjustment multiplies each daily shock by a factor derived from ATR(20)/price, clamped between 0.5 and 2.0 to avoid extreme cones.
• Performance guardrails : simulations are capped at 100 paths; the probability cone uses polylines (no heavy fills) and only draws on the last confirmed bar to keep charts responsive.
• Prerequisite data : at least ~30 seasonal entries are required before the model will draw a cone; otherwise it waits for more history.
Strengths and limitations
• Strengths :
– Probabilistic thinking replaces single-point guessing.
– Seasonality adds a small but meaningful directional bias that many markets exhibit.
– Volatility scaling adapts to the current regime so the cone stays realistic.
• Limitations :
– Seasonality can break around structural changes, policy shifts, or one-off events.
– The number of paths is performance-limited; percentile estimates are good for trading, not for academic precision.
– The model assumes tomorrow’s randomness resembles recent randomness; if regime shifts violently, the cone will lag until the EWMA adapts.
– Holidays and missing sessions can thin the seasonal sample for some assets; be cautious with very short histories.
Tuning guide
• Horizon : 10–20 bars for tactical trades; 30+ for swing planning when you care more about broad ranges than precise targets.
• Iterations : The default 100 is enough for stable 5/16/50/84/95 percentiles. If you crave smoother lines, shorten the horizon or run on higher timeframes.
• Daily vs. Weekly : Daily for equities and crypto where month-end and turn-of-month effects matter; Weekly for futures and FX where day-of-week behavior is strong.
• Volatility scaling : Keep it on. Turn off only when you intentionally want a “pure seasonality” cone unaffected by current turbulence.
Workflow examples
• Swing continuation : Cone slopes up, price pulls into the lower inner band, your system fires. Enter near the band, stop just outside the outer line for the next 3–5 bars, target near the median or the opposite inner band.
• Fade extremes : Cone is flat or down, price gaps to the upper outer band on news, then stalls. Favor mean-reversion toward the median, size small if volatility scaling is elevated.
• Event play : Before CPI or earnings on a proxy index, check cone width. If the inner band is already wide, cut size or prefer options structures that benefit from range.
Good habits
• Pair the cone with your entry engine (breakout, pullback, order flow). Let Monte Carlo do range math; let your system do signal quality.
• Do not anchor blindly to the median; recalc after each bar. When the cone’s slope flips or width jumps, the plan should adapt.
• Validate seasonality for your symbol and timeframe; not every market has strong calendar effects.
Summary
The Seasonality Monte Carlo Forecaster wraps institutional risk planning into a single overlay: a data-driven seasonal drift, realistic volatility scaling, and a probabilistic cone that answers “where could we be, with what odds?” within your trading horizon. Use it to place stops where randomness is less likely to take you out, to set targets aligned with realistic travel, and to size positions with confidence born from distributions rather than hunches. It will not predict the future, but it will keep your decisions anchored to probabilities—the language markets actually speak.
TradersAID / Adaptive Smoothing Channel (use on 1W chart)TradersAID – Adaptive Smoothing Channel (use on 1-Week chart)
Overview
TradersAID – Adaptive Smoothing Channel is a two-line price overlay designed to help traders interpret trend structure and shifting momentum zones on the 1-week chart only.
Unlike traditional moving averages or fixed smoothing methods, this tool uses an adaptive approach inspired by Kalman filtering — a concept widely used in robotics and control systems to track signals in noisy environments. Applied to price, this allows the band to adapt to directional flow and volatility while filtering out distracting short-term fluctuations.
1.What It Does
This tool builds a dynamic corridor around price using:
• A faster line that follows near-term directional movement
• A slower line that anchors broader market structure
Together, they form a responsive band that:
• Tilts with trend direction (via slope)
• Expands or contracts with volatility
• Fills the space between to show directional rhythm
It’s especially useful for observing how price moves within sustained trends or compression zones, helping traders visually interpret market structure with more clarity.
2. How to Use It
• Trend Structure:
Follow the slope of the band to understand overall direction. A narrowing band may indicate consolidation; a widening band may reflect strong follow-through.
• Momentum Compression Zones:
Watch for tightening distance between the lines — this may signal the market is preparing for a structural transition or breakout.
• Clarity Layer:
Overlay this tool with others (e.g. TradersAID Warning Dots) to reduce noise and improve decision context.
3. Key Features
• Dual Adaptive Lines: One fast, one slow — capturing different time dynamics
• Shaded Fill Zone: Highlights directional bias and rhythm
• 3 Reaction Modes: Slow / Regular / Fast for different sensitivities
• Overlay Style: Plots directly on price
• Minimalist Layout: Clean visual language
4. Technical Basis (Why It’s Closed Source)
This tool is based on a custom smoothing logic inspired by Kalman filtering, adapted specifically for charting market structure.
While it does not replicate a full Kalman system, it borrows key principles: dynamically adjusting to noisy input while maintaining structural clarity.
The algorithm was developed internally to provide a visual layer that integrates into the broader TradersAID analysis system — offering something distinct from public indicators. Its behavior, flexibility, and integration were designed to serve advanced structural analysis, and as such, the script is closed to protect proprietary logic and intellectual property.
5. Settings
• Mode Selector: Fast / Regular / Slow
• Color Fill Toggle & Styling
• Frame Lock:
✅ This script is built to work exclusively on the 1-week timeframe.
6. Disclaimer
This tool is for educational and informational purposes only. It does not offer financial advice or generate trading signals. Always use with your own strategy and discretion.
BeeQuant - Hive Bars🔶 OVERVIEW
The "Hive Bars" indicator is a truly revolutionary analytical instrument, meticulously engineered to transcend the limitations of conventional price charting and unveil the profound, underlying essence of market dynamics. Imagine possessing a sophisticated visual engine that intelligently reconstructs raw price data into unique, dynamically consolidated "Hive Bars." These specialized constructs intuitively reveal the dominant market momentum and highlight high-conviction signals often obscured by the ubiquitous noise of traditional candlesticks. This indicator acts as a precision filter, illuminating exactly when pivotal shifts are occurring by coloring these reconstructed units with an adaptive, unparalleled accuracy. It is expertly crafted for the discerning trader seeking an undeniable analytical advantage, offering a fresh, meticulously refined perspective that enables the discernment of concealed patterns, fostering more decisive and confident trading actions. Crucially, "Hive Bars" now feature proactive, real-time alert capabilities, ensuring no critical market inflection point ever goes unnoticed.
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🧠 CONCEPTS
At its intellectual core, the "Hive Bars" indicator operates upon an advanced, proprietary framework that fundamentally reinterprets market data. It presents this refined information through its unique "Hive Bars"—specialized visual constructs that dynamically encapsulate the consolidated spirit and true directional bias of price action, delivering unparalleled clarity.
⬜ Smart Bar Reconstruction: Hive Bars don’t follow time, they follow the market. They are derived through a sophisticated, multi-faceted internal process that precisely captures the dominant price influence and momentum over variable periods. This structure adapts dynamically to changing conditions, letting you see the real pressure behind price moves with consistency that time-based candles can’t match. This proprietary reconstruction creates a new, inherently consistent, and highly focused visual narrative of underlying market flow, effectively stripping away extraneous "noise" and revealing the market's authentic directional intent.
⬜ Multi-Layered Internal Analysis: A dynamic and live, adaptive line powers the core of Hive Bars. It recalibrates constantly, tracking market structure in real time. Every bar is formed in relation to this internal baseline, giving immediate context to price behavior. You choose the data that drives this line—open, close, high, low, or custom blends—to match your style.
⬜ Intelligent Bar Formation Sequences: Bars are created when the market speaks, not when the clock ticks. A built-in pattern engine reads the flow and waits for real structure to form. This allows the indicator to autonomously consolidate price action, presenting a cleaner, more coherent visualization of trend development as it truly unfolds, rather than fragmented snapshots based on time.
⬜ Visual Signal Precision: "Hive Bars" spring to life with an intuitively powerful coloring system. While primary colors (Green for upward bias, Red for downward bias) denote the prevailing market direction, the "Hive Bars" indicator introduces distinctively colored "Signal Hive Bars". These specialized bars emerge when the market price exhibits a particularly robust, high-conviction interaction with the adaptive internal baseline, standing out instantly and often mark key turning points or breakouts you want to act on.
⬜ Daily Reset Option: For intraday traders, there’s a reset feature that clears the internal build-up at the start of each new trading day. This ensures fresh, unbiased perspectives that are meticulously tailored to the distinct market dynamics and cyclic rhythms of the current trading day.
⬜ Adjustable Sensitivity: With Hive Smoothing, you’re in full control. This setting lets you fine-tune how sensitive the bars are to price movement. Want tighter, faster signals? Dial it down. Prefer broader, more filtered setups? Turn it up. You decide when a new Hive Bar forms—and when a Signal Bar confirms. It’s all based on how you trade and how your asset moves. No guesswork, no one-size-fits-all defaults. Hive Bars adapts to your strategy and trading style, not the other way around.
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✨ FEATURES
The "Hive Bars" indicator is equipped with a comprehensive suite of cutting-edge features, designed for unparalleled clarity, adaptive responsiveness, augmented analytical depth, seamless interoperability with your broader analytical toolkit, and proactive real-time notifications:
🔹Proprietary Hive Bar Reconstruction
Experience a uniquely advanced visual representation of price action that dynamically consolidates market data, leading to enhanced trend and momentum clarity that goes beyond standard charting and candlestick data.
🔹Customizable Internal Analysis Line
Gain precise control over the underlying adaptive baseline's calculation by selecting various internal price source options, ensuring its alignment with your specific analytical focus.
🔹 Smart Alerts for Key Events 🔔
Get notified in real time when:
◦ A new Hive Bar completes – signaling a fresh structural range reset
◦ A new Signal Hive Bar closes – identifying a potential overbought or oversold condition
Built-in alert conditions make it easy to stay ahead of shifts without watching every candle manually.
🔹Intelligent Bar Formation Sequencing
Diamond-shaped markers clearly indicate the start of the indicator's internal combination logic for enhanced visual understanding.
🔹High-Conviction "Signal Hive Bars" (Distinct Colors)
Receive specialized, uniquely colored visual alerts when Hive Bars exhibit strong, decisive movements relative to the adaptive baseline, indicating moments of heightened market conviction and potential opportunity.
🔹Session-Based Reconstruction
Opt for the "Daily New Start" to intelligently reset the indicator's perspective with each new trading day, providing fresh, session-aligned insights tailored for intraday precision.
🔹Unrivaled External Indicator Collaboration
A truly unique and powerful advantage of "Hive Bars" is its capability to seamlessly integrate and profoundly enhance the performance of other external indicators. By outputting clean, smoothed price data, it lets you feed a higher-quality source into tools like RSI, MACD, moving averages etc. Use close for indicators like RSI, and close for moving averages. The result is better clarity, fewer false signals, and a stronger edge across your setup. Hive Bars isn’t just an indicator, it’s an upgrade for everything you use.
🔹Non-Repainting Historical Integrity
Hive Bars never repaints. Each bar is locked in only after all internal conditions are fully met. This means you can trust every historical signal—it won’t shift or vanish after the fact. What you see in hindsight is exactly what was shown in real time.
🔹Universal Timeframe Compatibility
Whether you're scalping on the 1-minute chart or analyzing multi-month trends, Hive Bars delivers consistent, clean insights. Its architecture adapts to any timeframe without losing fidelity, making it a reliable tool for any strategy or style.
🔹Cross-Market Versatility
Hive Bars is engineered to perform with precision across all major markets—whether you're trading forex, commodities, stocks, or indices. Its adaptive logic automatically aligns with the unique volatility and structure of each asset class, delivering consistently reliable insights no matter where you trade.
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⚙️ USAGE
Integrating the "Hive Bars" indicator into your daily analytical regimen is an intuitive process that will profoundly enhance your ability to discern crucial market dynamics and spot high-conviction opportunities with unprecedented clarity:
💁 Effortless Application
Simply add the "Hive Bars" indicator to any chart within your TradingView platform. Note that it plots on a separate panel below your main price chart to provide its unique visual output without obscuring the primary price action.
📊 Strategic Calibration
Access the indicator's comprehensive settings panel to meticulously calibrate its powerful engines and unlock its full potential:
⚙ "Internal EMA Config"
Configure the internal adaptive baseline by choosing its source (e.g., CLOSE, HL/2) and its specific EMA length. This shapes the core reference point for the dynamic formation of the "Hive Bars."
🤖 "CONFIG Group"
Here, you decide if you want "Daily New Start" for session-based analytical resets (particularly beneficial for intraday strategies). The "Hive Smoothing" input allows you to control a further layer of consolidation for the "Hive Bars."
🟩🟥 "Color": Customize the appearance of both standard "Hive Bars" and "Signal Hive Bars" to suit your visual preferences, enhancing their immediate interpretability.
🧭 Empirical Exploration
Experimentation with these parameters is paramount. Dedicate time to exploring different combinations across various assets and timeframes to discover the optimal configuration that resonates with your unique trading methodology and the inherent volatility of the market being analyzed.
👀 Interpreting the Unveiled Market Reality: Once calibrated, the "Hive Bars" will present a strikingly clear and actionable picture of market dynamics:
+ Green/Red Hive Bars: These visually denote the consolidated directional bias of the market over the reconstructed period. A sustained sequence of Green "Hive Bars" suggests pervasive bullish pressure and an upward path of least resistance, while a series of Red "Hive Bars" indicates dominant bearish control and a clear downward momentum.
+ "Signal Hive Bars" (Distinct Colors): Pay close attention to these specially colored "Hive Bars." They signify critical moments where the reconstructed price action exhibits a particularly strong, high-conviction interaction with its adaptive internal baseline. These often precede or confirm significant market movements and serve as your clearest, most reliable visual triggers for potential shifts in market control.
⛓️ Intermittent Appearance: Observe that "Hive Bars" do not necessarily appear for every single native time unit of your chart. They are intelligently reconstructed and consolidated representations of price action, appearing only when specific internal conditions are met to present a coherent, high-impact view of distinct market phases.
🔗 Harnessing Advanced External Synergy: To unlock a new dimension of analytical power, profoundly enhance your existing indicator suite by integrating the output of "Hive Bars" as the data source for other external indicators. When adding or configuring indicators such as RSI, Stochastic Oscillators, various Moving Averages (EMA, SMA), or any other indicator that prompts for a 'source' input, you can now select the purified output of the "Hive Bars" as your desired data stream.
For oscillators (e.g., RSI, MACD), select the close or a similar relevant output from "Hive Bars" as your source. This allows the oscillator to react to the purified, consolidated momentum of the "Hive Bars" rather than the potentially noisy raw price data, leading to smoother and more meaningful oscillator signals.
For moving averages (e.g., EMA, SMA), utilize the close or other pertinent "Hive Bar" output as your source. This provides an exceptionally smooth, highly responsive, and less choppy average that precisely tracks the true underlying trend as identified by "Hive Bars." This unique capability allows for the construction of powerfully layered and synergistic trading strategies.
📢 Setting Up Proactive Alerts for Critical Events: Leverage the newly incorporated alert capabilities to maintain real-time awareness of pivotal market developments, even when not actively monitoring your charts.
You can now choose to be alerted specifically when a "New Hive Bar Closed" (signifying the definitive completion of a major market phase as identified by the indicator) or when a "New Signal Hive Bar Closed" (highlighting a high-conviction market event that warrants immediate attention due to its pronounced significance).
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⚠️ LIMITATIONS
While the "Hive Bars" indicator is an incredibly powerful and advanced tool for dissecting market dynamics, it is vital to understand its inherent design parameters and the prevailing platform-specific constraints for optimal and informed utilization:
👉 Visual Gaps in Plotting: Due to current platform limitations pertaining to custom candle plotting functionality, you may occasionally observe visual gaps or intermittent non-contiguous plotting between "Hive Bars" on the chart. They’re not missing data, but a result of strict plotting rules. A bar is only drawn when all internal conditions are met. This ensures accuracy, even if the chart shows some spacing.
👉 Complementary Tool: This indicator excels at providing high-conviction directional insights and identifying significant market phases. However, it is fundamentally designed as a sophisticated complementary tool to a broader trading strategy, not as a standalone, all-encompassing system. Its true power is unlocked when integrated with other analytical methods.
👉 Input Calibration Essential: The efficacy and depth of insights derived from the "Hive Bars" are highly dependent on the careful and thoughtful calibration of its input parameters, including the "Internal EMA Config," "Hive Smoothing" setting. Optimal results necessitate empirical user experimentation and fine-tuning to discover the configurations best suited for specific assets, analytical objectives, and market conditions.
👉 Exclusion of Auxiliary Data: The "Hive Bars" indicator's primary focus is exclusively on transforming and presenting price data. It does not natively incorporate other vital market information such as fundamental economic data, or news events. Integrating these additional analytical layers remains an essential aspect of constructing a truly comprehensive and robust trading strategy.
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🎯 CONCLUSION
The "Hive Bars" indicator offers an unparalleled, intuitively accessible, and highly adaptable framework for instantly grasping true price momentum and direction through its intelligent, non-repainting reconstruction of market data. By transforming chaotic raw data into strikingly clear, high-conviction "Hive Bars" and dynamic signals, and now with proactive alerts to highlight critical moments, it empowers you to cut through distractions and identify market currents with unprecedented ease. Think of it as a custom lens for the market. It filters out the clutter and shows you the real structure—bars formed not by time, but by intent. It's about seeing the unseen, with enhanced clarity and a deeper understanding of market forces, now with the power to supercharge all your other tools and keep you informed. No fluff. No hype. Just an edge you can actually see—and use.
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🚨 RISK DISCLAIMER
Engagement in financial market speculation inherently carries a substantial degree of inherent risk, and the potential for capital diminution, potentially exceeding initial deposits, is a pervasive and non-trivial consideration. All content, algorithmic tools, scripts, articles, and educational materials disseminated by "Hive Bars" are exclusively purposed for informational and pedagogical objectives, strictly for reference. Historical performance data, whether explicitly demonstrated or implicitly suggested, offers no infallible assurance or guarantee of future outcomes. Users bear sole and ultimate accountability for their individual trading decisions and are emphatically urged to meticulously assess their financial disposition, risk tolerance parameters, and conduct independent due diligence prior to engaging in any speculative activity.
BeeQuant - Hive Factra🔶 OVERVIEW
The "Hive Factra" is a groundbreaking analytical instrument designed to unveil the true essence of market movement, transforming complex price action into powerfully consolidated insights. Imagine having a specialized lens that intelligently reconstructs market periods into unique "Hive Factra Bars," revealing underlying momentum and high-conviction signals often obscured in traditional charts. This indicator cuts through the noise, showing you precisely when significant shifts are occurring by coloring these reconstructed bars with an adaptive precision. It's built for traders who seek unfiltered perspective that helps see hidden patterns and make more decisive moves.
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🧠 CONCEPTS
Markets move in impulses and compressions. Most trend indicators rely on single-frame slope logic, which often flips during minor pullbacks. Hive Factra takes a different route. At its core, the "Hive Factra" operates on a sophisticated framework that reinterprets market data, presenting it through its proprietary "Hive Factra Bars", unique visualizations that capture the consolidated spirit of price action.
⬜ The "Hive Factra" Reconstruction: Unlike standard candles, "Hive Factra Bars" are intelligently re-engineered representations of market activity. They are derived through a proprietary process that captures the dominant price influence over specific periods, presenting a clearer, more focused view of underlying momentum. These unique bars visually consolidate information, making the core directional bias immediately apparent.
⬜ The Adaptive Baseline: An internal, dynamic analysis line constantly adjusts to market flow, serving as a crucial reference point for the "Hive Factra Bars." This adaptive baseline provides real-time context, helping the indicator precisely determine the significance of each reconstructed bar's movement.
⬜ High-Conviction Coloring & Signal Bars: The "Factra Bars" come to life with a discerning coloring system. While they reflect the primary market direction (Green for upward bias, Red for downward bias), the "Hive Factra" introduces specialized "Signal Hive Bars" with distinct colors. These unique bars appear when the consolidated price action exhibits a particularly strong, high-conviction interaction with the adaptive baseline, acting as powerful visual alerts for moments of heightened significance.
⬜ Session-Aligned Insights: For intraday traders, the "Daily New Start" option provides a unique advantage. When enabled, the indicator can reset its internal reconstruction process with each new trading session, offering fresh, unbiased perspectives tailored to the day's distinct market dynamics.
⬜ Dynamic Sensitivity: A configurable "Offset" allows you to fine-tune the indicator's responsiveness and the thresholds for initiating these "Hive Factra Bars" and "Signal Hive Bars." This ensures the indicator aligns perfectly with your individual trading style and the volatility of the asset you're analyzing.
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✨ FEATURES
The "Hive Factra" is equipped with a suite of cutting-edge features, all meticulously designed for unparalleled clarity, adaptive responsiveness, and augmented analytical depth:
🔹 Proprietary Hive Factra Bars
Experience a unique visual representation of price action that consolidates market data for enhanced trend and momentum clarity.
🔹 Customizable Internal Analysis Line
Control the underlying adaptive baseline's calculation for precise alignment with market flow, utilizing various price source options.
🔹 High-Conviction "Signal Hive Bars" (Distinct Colors)
Receive specialized visual alerts when Factra Bars exhibit strong, decisive movements relative to the adaptive baseline, indicating moments of heightened market conviction.
🔹 Overbought/Oversold Visuals
Signal Hive Bars highlight areas of potential exhaustion, providing intuitive insight into stretched conditions
🔹 Session-Based Reconstruction
Opt for the "Daily New Start" to reset the indicator's perspective with each new trading day, providing fresh, session-aligned insights.
🔹 Dynamic Offset Control
Adjust the "Offset" parameter to fine-tune the sensitivity of the Factra Bar reconstruction and signal generation thresholds, tailoring the indicator to specific market conditions.
🔹 Non-Repainting Logic for Historical Reliability
Each "Hive Factra Bar" is plotted only when its internal reconstruction conditions are fully met and confirmed. This ensures that the historical display of Factra Bars does not repaint, providing a high degree of reliability and trust in past signals and visualizations.
🔹 Cross-Market Versatility
This indicator is engineered to perform with precision across all major markets—whether you're trading forex, commodities, stocks, or indices. Its adaptive logic automatically aligns with the unique volatility and structure of each asset class, delivering consistently reliable insights no matter where you trade.
🔹 Custom Range Start Marker
A subtle diamond-shaped symbol is plotted to indicate the start of the Hive Factra logic cycle. This marks the bar from which the internal price range begins accumulating until a new Hive Factra Bar is confirmed and displayed. Helps visualize the dynamic evaluation period used in Factra’s structural detection.
🔹 Smart Alerts for Key Events
Get notified in real time when:
◦ A new Hive Factra Bar completes – signaling a fresh structural range reset
◦ A new Signal Hive Bar closes – identifying a potential overbought or oversold condition
Built-in alert conditions make it easy to stay ahead of shifts without watching every candle manually.
🔹 Universal Timeframe Compatibility: The "Hive Factra" is meticulously engineered to perform flawlessly across all timeframes, from rapid intraday charts to long-term weekly and monthly views. This universal compatibility ensures you receive consistent, high-quality insights regardless of your analytical horizon.
🔹 Unrivaled External Indicator Collaboration: A truly unique advantage of the "Hive Factra" is its capability to seamlessly integrate and enhance the performance of other external indicators. Its meticulously processed output, can serve as a highly purified and consolidated 'source' for indicators that accept such inputs (e.g., RSI, StochRSI, moving averages), which allows for more insightful data stream into your favorite indicators, potentially unlocking new levels of responsiveness and signal accuracy for your entire analytical setup.
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⚙️ USAGE
Integrating the "Hive Factra" into your daily analytical regimen is intuitive and will profoundly enhance your ability to discern crucial market dynamics and spot high-conviction opportunities:
💁 Effortless Application
Simply add the "Hive Factra" indicator to any chart within your TradingView platform. Note that it plots on a separate panel below your main price chart to provide its unique visual output without obscuring price.
📊 Tailored Calibration: Access the indicator's settings to unlock its full potential:
⚙ "Internal EMA Config"
Configure the internal adaptive baseline by choosing its source (e.g., Close, HL/2) and length. This shapes the core reference point for the Factra Bars.
⚙ "Hive Factra"
Decide if you want "Daily New Start" for session-based analysis and choose the "Source" type for how the Factra Bars are built.
🤖 "Offset"
Experiment with the "Offset" percentage to adjust the sensitivity of the Factra Bar's reconstruction. A smaller offset will make the Factra Bars appear more frequently, while a larger one will highlight only more significant movements.
🟩🟥 Green/Red Hive Factra Bars
These indicate the consolidated directional bias of the market over the reconstructed period. A sequence of Green bars suggests sustained bullish pressure, while Red bars point to dominant bearish control.
🚀 "Signal Hive Bars" (Unique Colors)
Pay close attention to these specially colored Hive Factra Bars. They signify moments where the reconstructed price action exhibits a high-conviction interaction with its adaptive baseline, often preceding or confirming significant market moves. These are your clearest signals for potential shifts.
✨ Appearance of Hive Factra Bars
Notice that these Bars do not necessarily appear for every single time unit. They intelligently reconstruct and consolidate price action, appearing only when conditions align to present a coherent, high-impact view of market phases.
🪢 Harnessing External Synergy
To unlock a new dimension of analysis, consider integrating "Hive Factra" as the data source for other indicators:
1. When adding indicators like RSI, StochRSI, or others that prompt for a 'source' input, you can select the "Hive Factra" as the input.
2. For oscillators (e.g., RSI, Stochastic), choose the close or similar output from "Hive Factra" as your source. This allows the oscillator to react to the purified, consolidated momentum of the Factra Bars rather than raw price.
For moving averages (e.g., EMA, SMA), use the close or other relevant Factra Bar output as your source. This provides an exceptionally smooth and responsive average that tracks the true underlying trend.
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⚠️ LIMITATIONS
While the "Hive Factra" is an incredibly powerful tool for dissecting market dynamics, it's vital to understand its design parameters for optimal use. It does not attempt to front-run reversals or predict market turns. Instead, it focuses on framing price behavior so traders can react with context.
👉 Visual Gaps in Plotting: Due to Tradingview platform limitations with custom candle plotting functionality, you may observe visual gaps between "Hive Factra Bars" on the chart. This occurs because the indicator only plots a Hive Factra Bar when its internal conditions for reconstruction are fully met, and there isn't an 'offset' parameter for custom candles to bridge these visual discontinuities. Importantly, this behavior ensures that each plotted Factra Bar is confirmed and does not repaint, providing reliable historical analysis.
👉 Reconstructed Data, Not Raw Price: It's crucial to remember that "Hive Factra Bars" are not traditional candles. They are a derived visualization that intelligently consolidates price data.
👉 Complementary Tool: This indicator excels at providing high-conviction directional insights and identifying significant market phases. However, it is designed as a sophisticated complement to a broader trading strategy, not a standalone system.
👉 Input Calibration Essential: The effectiveness of the "Hive Factra" is highly dependent on careful calibration of its input parameters, especially the "Offset" and internal EMA settings. Optimal results require user experimentation to find settings best suited for specific assets and timeframes.
👉 Exclusion of Auxiliary Data: The "Hive Factra" focuses solely on transforming price data. It does not incorporate other vital market information such as trading volume, market breadth, or fundamental news. Integrating these additional analytical layers remains essential for a comprehensive trading strategy.
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🎯 CONCLUSION
The "Hive Factra" offers an unparalleled, intuitive, and highly adaptable framework for instantly grasping true price momentum and direction through its intelligent reconstruction of market data. By transforming chaotic raw data into strikingly clear, high-conviction "Factra Bars" and dynamic signals, it empowers you to cut through distractions and identify critical market currents with ease. Its revolutionary capability for seamless collaboration with external indicators (like RSI, EMA, etc., by using its purified output as their source) means you can elevate the performance of your entire analytical suite to new levels of precision and clarity. Seamlessly integrate this advanced visual tool within your analytical framework to gain a sharper, more confident perspective, and elevate your strategic decision-making in the markets. It's about seeing the unseen, with enhanced clarity and a deeper understanding of market forces, now with the power to supercharge all your other tools.
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🚨 RISK DISCLAIMER
Engagement in financial market speculation inherently carries a substantial degree of inherent risk, and the potential for capital diminution, potentially exceeding initial deposits, is a pervasive and non-trivial consideration. All content, algorithmic tools, scripts, articles, and educational materials disseminated by "Hive Factra" are exclusively purposed for informational and pedagogical objectives, strictly for reference. Historical performance data, whether explicitly demonstrated or implicitly suggested, offers no infallible assurance or guarantee of future outcomes. Users bear sole and ultimate accountability for their individual trading decisions and are emphatically urged to meticulously assess their financial disposition, risk tolerance parameters, and conduct independent due diligence prior to engaging in any speculative market activity.
Bitcoin Power Law Clock [LuxAlgo]The Bitcoin Power Law Clock is a unique representation of Bitcoin prices proposed by famous Bitcoin analyst and modeler Giovanni Santostasi.
It displays a clock-like figure with the Bitcoin price and average lines as spirals, as well as the 12, 3, 6, and 9 hour marks as key points in the cycle.
🔶 USAGE
Giovanni Santostasi, Ph.D., is the creator and discoverer of the Bitcoin Power Law Theory. He is passionate about Bitcoin and has 12 years of experience analyzing it and creating price models.
As we can see in the above chart, the tool is super intuitive. It displays a clock-like figure with the current Bitcoin price at 10:20 on a 12-hour scale.
This tool only works on the 1D INDEX:BTCUSD chart. The ticker and timeframe must be exact to ensure proper functionality.
According to the Bitcoin Power Law Theory, the key cycle points are marked at the extremes of the clock: 12, 3, 6, and 9 hours. According to the theory, the current Bitcoin prices are in a frenzied bull market on their way to the top of the cycle.
🔹 Enable/Disable Elements
All of the elements on the clock can be disabled. If you disable them all, only an empty space will remain.
The different charts above show various combinations. Traders can customize the tool to their needs.
🔹 Auto scale
The clock has an auto-scale feature that is enabled by default. Traders can adjust the size of the clock by disabling this feature and setting the size in the settings panel.
The image above shows different configurations of this feature.
🔶 SETTINGS
🔹 Price
Price: Enable/disable price spiral, select color, and enable/disable curved mode
Average: Enable/disable average spiral, select color, and enable/disable curved mode
🔹 Style
Auto scale: Enable/disable automatic scaling or set manual fixed scaling for the spirals
Lines width: Width of each spiral line
Text Size: Select text size for date tags and price scales
Prices: Enable/disable price scales on the x-axis
Handle: Enable/disable clock handle
Halvings: Enable/disable Halvings
Hours: Enable/disable hours and key cycle points
🔹 Time & Price Dashboard
Show Time & Price: Enable/disable time & price dashboard
Location: Dashboard location
Size: Dashboard size
Bitcoin Power Law [LuxAlgo]The Bitcoin Power Law tool is a representation of Bitcoin prices first proposed by Giovanni Santostasi, Ph.D. It plots BTCUSD daily closes on a log10-log10 scale, and fits a linear regression channel to the data.
This channel helps traders visualise when the price is historically in a zone prone to tops or located within a discounted zone subject to future growth.
🔶 USAGE
Giovanni Santostasi, Ph.D. originated the Bitcoin Power-Law Theory; this implementation places it directly on a TradingView chart. The white line shows the daily closing price, while the cyan line is the best-fit regression.
A channel is constructed from the linear fit root mean squared error (RMSE), we can observe how price has repeatedly oscillated between each channel areas through every bull-bear cycle.
Excursions into the upper channel area can be followed by price surges and finishing on a top, whereas price touching the lower channel area coincides with a cycle low.
Users can change the channel areas multipliers, helping capture moves more precisely depending on the intended usage.
This tool only works on the daily BTCUSD chart. Ticker and timeframe must match exactly for the calculations to remain valid.
🔹 Linear Scale
Users can toggle on a linear scale for the time axis, in order to obtain a higher resolution of the price, (this will affect the linear regression channel fit, making it look poorer).
🔶 DETAILS
One of the advantages of the Power Law Theory proposed by Giovanni Santostasi is its ability to explain multiple behaviors of Bitcoin. We describe some key points below.
🔹 Power-Law Overview
A power law has the form y = A·xⁿ , and Bitcoin’s key variables follow this pattern across many orders of magnitude. Empirically, price rises roughly with t⁶, hash-rate with t¹² and the number of active addresses with t³.
When we plot these on log-log axes they appear as straight lines, revealing a scale-invariant system whose behaviour repeats proportionally as it grows.
🔹 Feedback-Loop Dynamics
Growth begins with new users, whose presence pushes the price higher via a Metcalfe-style square-law. A richer price pool funds more mining hardware; the Difficulty Adjustment immediately raises the hash-rate requirement, keeping profit margins razor-thin.
A higher hash rate secures the network, which in turn attracts the next wave of users. Because risk and Difficulty act as braking forces, user adoption advances as a power of three in time rather than an unchecked S-curve. This circular causality repeats without end, producing the familiar boom-and-bust cadence around the long-term power-law channel.
🔹 Scale Invariance & Predictions
Scale invariance means that enlarging the timeline in log-log space leaves the trajectory unchanged.
The same geometric proportions that described the first dollar of value can therefore extend to a projected million-dollar bitcoin, provided no catastrophic break occurs. Institutional ETF inflows supply fresh capital but do not bend the underlying slope; only a persistent deviation from the line would falsify the current model.
🔹 Implications
The theory assigns scarcity no direct role; iterative feedback and the Difficulty Adjustment are sufficient to govern Bitcoin’s expansion. Long-term valuation should focus on position within the power-law channel, while bubbles—sharp departures above trend that later revert—are expected punctuations of an otherwise steady climb.
Beyond about 2040, disruptive technological shifts could alter the parameters, but for the next order of magnitude the present slope remains the simplest, most robust guide.
Bitcoin behaves less like a traditional asset and more like a self-organising digital organism whose value, security, and adoption co-evolve according to immutable power-law rules.
🔶 SETTINGS
🔹 General
Start Calculation: Determine the start date used by the calculation, with any prior prices being ignored. (default - 15 Jul 2010)
Use Linear Scale for X-Axis: Convert the horizontal axis from log(time) to linear calendar time
🔹 Linear Regression
Show Regression Line: Enable/disable the central power-law trend line
Regression Line Color: Choose the colour of the regression line
Mult 1: Toggle line & fill, set multiplier (default +1), pick line colour and area fill colour
Mult 2: Toggle line & fill, set multiplier (default +0.5), pick line colour and area fill colour
Mult 3: Toggle line & fill, set multiplier (default -0.5), pick line colour and area fill colour
Mult 4: Toggle line & fill, set multiplier (default -1), pick line colour and area fill colour
🔹 Style
Price Line Color: Select the colour of the BTC price plot
Auto Color: Automatically choose the best contrast colour for the price line
Price Line Width: Set the thickness of the price line (1 – 5 px)
Show Halvings: Enable/disable dotted vertical lines at each Bitcoin halving
Halvings Color: Choose the colour of the halving lines
Fair Value Trend Model [SiDec]ABSTRACT
This pine script introduces the Fair Value Trend Model, an on-chart indicator for TradingView that constructs a continuously updating "fair-value" estimate of an asset's price via a logarithmic regression on historical data. Specifically, this model has been applied to Bitcoin (BTC) to fully grasp its fair value in the cryptocurrency market. Symmetric channel bands, defined by fixed percentage offsets around this central fair-value curve, provide a visual band within which normal price fluctuations may occur. Additionally, a short-term projection extends both the fair-value trend and its channel bands forward by a user-specified number of bars.
INTRODUCTION
Technical analysts frequently seek to identify an underlying equilibrium or "fair value" about which prices oscillate. Traditional approaches-moving averages, linear regressions in price-time space, or midlines-capture linear trends but often misrepresent the exponential or power-law growth patterns observable in many financial markets. The Fair Value Trend Model addresses this by performing an ordinary least squares (OLS) regression in log-space, fitting ln(Price) against ln(Days since inception). In practice, the primary application has been to Bitcoin, aiming to fully capture Bitcoin's underlying value dynamics.
The result is a curved trend line in regular (price-time) coordinates, reflecting Bitcoin's long-term compounding characteristics. Surrounding this fair-value curve, symmetric bands at user-specified percentage deviations serve as dynamic support and resistance levels. A simple linear projection extends both the central fair-value and its bands into the immediate future, providing traders with a heuristic for short-term trend continuation.
This exposition details:
Data transformation: converting bar timestamps into days since first bar, then applying natural logarithms to both time and price.
Regression mechanics: incremental (or rolling-window) accumulation of sums to compute the log-space fit parameters.
Fair-value reconstruction: exponentiation of the regression output to yield a price-space estimate.
Channel-band definition: establishing ±X% offsets around the fair-value curve and rendering them visually.
Forecasting methodology: projecting both the fair-value trend and channel bands by extrapolating the most recent incremental change in price-space.
Interpretation: how traders can leverage this model for trend identification, mean-reversion setups, and breakout analysis, particularly in Bitcoin trading.
Analysing the macro cycle on Bitcoin's monthly timeframe illustrates how the fair-value curve aligns with multi-year structural turning points.
DATA TRANSFORMATION AND NOTATION
1. Timestamp Baseline (t0)
Let t0 = timestamp of the very first bar on the chart (in milliseconds). Each subsequent bar has a timestamp ti, where ti ≥ t0.
2. Days Since Inception (d(t))
Define the “days since first bar” as
d(t) = max(1, (t − t0) / 86400000.0)
Here, 86400000.0 represents the number of milliseconds in one day (1,000 ms × 60 seconds × 60 minutes × 24 hours). The lower bound of 1 ensures that we never compute ln(0).
3. Logarithmic Coordinates:
Given the bar’s closing price P(t), define:
xi = ln( d(ti) )
yi = ln( P(ti) )
Thus, each data point is transformed to (xi, yi) in log‐space.
REGRESSION FORMULATION
We assume a log‐linear relationship:
yi = a + b·xi + εi
where εi is the residual error at bar i. Ordinary least squares (OLS) fitting minimizes the sum of squared residuals over N data points. Define the following accumulated sums:
Sx = Σ for i = 1 to N
Sy = Σ for i = 1 to N
Sxy = Σ for i = 1 to N
Sx2 = Σ for i = 1 to N
N = number of data points
The OLS estimates for b (slope) and a (intercept) are:
b = ( N·Sxy − Sx·Sy ) / ( N·Sx2 − (Sx)^2 )
a = ( Sy − b·Sx ) / N
All‐Time Versus Rolling‐Window Mode:
All-Time Mode:
Each new bar increments N by 1.
Update Sx ← Sx + xN, Sy ← Sy + yN, Sxy ← Sxy + xN·yN, Sx2 ← Sx2 + xN^2.
Recompute a and b using the formulas above on the entire dataset.
Rolling-Window Mode:
Fix a window length W. Maintain two arrays holding the most recent W values of {xi} and {yi}.
On each new bar N:
Append (xN, yN) to the arrays; add xN, yN, xN·yN, xN^2 to the sums Sx, Sy, Sxy, Sx2.
If the arrays’ length exceeds W, remove the oldest point (xN−W, yN−W) and subtract its contributions from the sums.
Update N_roll = min(N, W).
Compute b and a using N_roll, Sx, Sy, Sxy, Sx2 as above.
This incremental approach requires only O(1) operations per bar instead of recomputing sums from scratch, making it computationally efficient for long time series.
FAIR‐VALUE RECONSTRUCTION
Once coefficients (a, b) are obtained, the regressed log‐price at time t is:
ŷ(t) = a + b·ln( d(t) )
Mapping back to price space yields the “fair‐value”:
F(t) = exp( ŷ(t) )
= exp( a + b·ln( d(t) ) )
= exp(a) · ^b
In other words, F(t) is a power‐law function of “days since inception,” with exponent b and scale factor C = exp(a). Special cases:
If b = 1, F(t) = C · d(t), which is an exponential function in original time.
If b > 1, the fair‐value grows super‐linearly (accelerating compounding).
If 0 < b < 1, it grows sub‐linearly.
If b < 0, the fair‐value declines over time.
CHANNEL‐BAND DEFINITION
To visualise a “normal” range around the fair‐value curve F(t), we define two channel bands at fixed percentage offsets:
1. Upper Channel Band
U(t) = F(t) · (1 + α_upper)
where α_upper = (Channel Band Upper %) / 100.
2. Lower Channel Band
L(t) = F(t) · (1 − α_lower)
where α_lower = (Channel Band Lower %) / 100.
For example, default values of 50% imply α_upper = α_lower = 0.50, so:
U(t) = 1.50 · F(t)
L(t) = 0.50 · F(t)
When “Show FV Channel Bands” is enabled, both U(t) and L(t) are plotted in a neutral grey, and a semi‐transparent fill is drawn between them to emphasise the channel region.
SHORT‐TERM FORECAST PROJECTION
To extend both the fair‐value and its channel bands M bars into the future, the model uses a simple constant‐increment extrapolation in price space. The procedure is:
1. Compute Recent Increments
Let
F_prev = F( t_{N−1} )
F_curr = F( t_N )
Then define the per‐bar change in fair‐value:
ΔF = F_curr − F_prev
Similarly, for channel bands:
U_prev = U( t_{N−1} ), U_curr = U( t_N ), ΔU = U_curr − U_prev
L_prev = L( t_{N−1} ), L_curr = L( t_N ), ΔL = L_curr − L_prev
2. Forecasted Values After M Bars
Assuming the same per‐bar increments continue:
F_future = F_curr + M · ΔF
U_future = U_curr + M · ΔU
L_future = L_curr + M · ΔL
These forecasted values produce dashed lines on the chart:
A dashed segment from (bar_N, F_curr) to (bar_{N+M}, F_future).
Dashed segments from (bar_N, U_curr) to (bar_{N+M}, U_future), and from (bar_N, L_curr) to (bar_{N+M}, L_future).
Forecasted channel bands are rendered in a subdued grey to distinguish them from the current solid bands. Because this method does not re‐estimate regression coefficients for future t > t_N, it serves as a quick visual heuristic of trend continuation rather than a precise statistical forecast.
MATHEMATICAL SUMMARY
Summarising all key formulas:
1. Days Since Inception
d(t_i) = max( 1, ( t_i − t0 ) / 86400000.0 )
x_i = ln( d(t_i) )
y_i = ln( P(t_i) )
2. Regression Summations (for i = 1..N)
Sx = Σ
Sy = Σ
Sxy = Σ
Sx2 = Σ
N = number of data points (or N_roll if using rolling‐window)
3. OLS Estimator
b = ( N · Sxy − Sx · Sy ) / ( N · Sx2 − (Sx)^2 )
a = ( Sy − b · Sx ) / N
4. Fair‐Value Computation
ŷ(t) = a + b · ln( d(t) )
F(t) = exp( ŷ(t) ) = exp(a) · ^b
5. Channel Bands
U(t) = F(t) · (1 + α_upper)
L(t) = F(t) · (1 − α_lower)
with α_upper = (Channel Band Upper %) / 100, α_lower = (Channel Band Lower %) / 100.
6. Forecast Projection
ΔF = F_curr − F_prev
F_future = F_curr + M · ΔF
ΔU = U_curr − U_prev
U_future = U_curr + M · ΔU
ΔL = L_curr − L_prev
L_future = L_curr + M · ΔL
IMPLEMENTATION CONSIDERATIONS
1. Time Precision
Timestamps are recorded in milliseconds. Dividing by 86400000.0 yields days with fractional precision.
For the very first bar, d(t) = 1 ensures x = ln(1) = 0, avoiding an undefined logarithm.
2. Incremental Versus Sliding Summation
All‐Time Mode: Uses persistent scalar variables (Sx, Sy, Sxy, Sx2, N). On each new bar, add the latest x and y contributions to the sums.
Rolling‐Window Mode: Employs fixed‐length arrays for {x_i} and {y_i}. On each bar, append (x_N, y_N) and update sums; if array length exceeds W, remove the oldest element and subtract its contribution from the sums. This maintains exact sums over the most recent W data points without recomputing from scratch.
3. Numerical Robustness
If the denominator N·Sx2 − (Sx)^2 equals zero (e.g., all x_i identical, as when only one day has passed), then set b = 0 and a = Sy / N. This produces a constant fair‐value F(t) = exp(a).
Enforcing d(t) ≥ 1 avoids attempts to compute ln(0).
4. Plotting Strategy
The fair‐value line F(t) is plotted on each new bar. Its color depends on whether the current price P(t) is above or below F(t): a “bullish” color (e.g., green) when P(t) ≥ F(t), and a “bearish” color (e.g., red) when P(t) < F(t).
The channel bands U(t) and L(t) are plotted in a neutral grey when enabled; otherwise they are set to “not available” (no plot).
A semi‐transparent fill is drawn between U(t) and L(t). Because the fill function is executed at global scope, it is automatically suppressed if either U(t) or L(t) is not plotted (na).
5. Forecast Line Management
Each projection line (for F, U, and L) is created via a persistent line object. On successive bars, the code updates the endpoints of the same line rather than creating a new one each time, preserving chart clarity.
If forecasting is disabled, any existing projection lines are deleted to avoid cluttering the chart.
INTERPRETATION AND APPLICATIONS
1. Trend Identification
The fair‐value curve F(t) represents the best‐fit long‐term trend under the assumption that ln(Price) scales linearly with ln(Days since inception). By capturing power‐law or exponential patterns, it can more accurately reflect underlying compounding behavior than simple linear regressions.
When actual price P(t) lies above U(t), it may be considered “overextended” relative to its long‐term trend; when price falls below L(t), it may be deemed “oversold.” These conditions can signal potential mean‐reversion or breakout opportunities.
2. Mean‐Reversion and Breakout Signals
If price re‐enters the channel after touching or slightly breaching L(t), some traders interpret this as a mean‐reversion bounce and consider initiating a long position.
Conversely, a sustained move above U(t) can indicate strong upward momentum and a possible bullish breakout. Traders often seek confirmation (e.g., price remaining above U(t) for multiple bars, rising volume, or corroborating momentum indicators) before acting.
3. Rolling Versus All‐Time Usage
All‐Time Mode: Captures the entire dataset since inception, focusing on structural, long‐term trends. It is less sensitive to short‐term noise or volatility spikes.
Rolling‐Window Mode: Restricts the regression to the most recent W bars, making the fair‐value curve more responsive to changing market regimes, sudden volatility expansions, or fundamental shifts. Traders who wish to align the model with local behaviour often choose W so that it approximates a market cycle length (e.g., 100–200 bars on a daily chart).
4. Channel Percentage Selection
A wider band (e.g., ±50 %) accommodates larger price swings, reducing the frequency of breaches but potentially delaying actionable signals.
A narrower band (e.g., ±10 %) yields more frequent “overbought/oversold” alerts but may produce more false signals during normal volatility. It is advisable to calibrate the channel width to the asset’s historical volatility regime.
5. Forecast Cautions
The short‐term projection assumes that the last single‐bar increment ΔF remains constant for M bars. In reality, trend acceleration or deceleration can occur, rendering the linear forecast inaccurate.
As such, the forecast serves as a visual guide rather than a statistically rigorous prediction. It is best used in conjunction with other momentum, volume, or volatility indicators to confirm trend continuation or reversal.
LIMITATIONS AND CONSIDERATIONS
1. Power‐Law Assumption
By fitting ln(P) against ln(d), the model posits that P(t) ≈ C · ^b. Real markets may deviate from a pure power‐law, especially around significant news events or structural regime changes. Temporary misalignment can occur.
2. Fixed Channel Width
Markets exhibit heteroskedasticity: volatility can expand or contract unpredictably. A static ±X % band does not adapt to changing volatility. During high‐volatility periods, a fixed ±50 % may prove too narrow and be breached frequently; in unusually calm periods, it may be excessively broad, masking meaningful variations.
3. Endpoint Sensitivity
Regression‐based indicators often display greater curvature near the most recent data, especially under rolling‐window mode. This can create sudden “jumps” in F(t) when new bars arrive, potentially confusing users who expect smoother behaviour.
4. Forecast Simplification
The projection does not re‐estimate regression slope b for future times. It only extends the most recent single‐bar change. Consequently, it should be regarded as an indicative extension rather than a precise forecast.
PRACTICAL IMPLEMENTATION ON TRADINGVIEW
1 Adding the Indicator
In TradingView’s “Indicators” dialog, search for Fair Value Trend Model or visit my profile, under "scripts" add it to your chart.
Add it to any chart (e.g., BTCUSD, AAPL, EURUSD) to see real‐time computation.
2. Configuring Inputs
Show Forecast Line: Toggle on or off the dashed projection of the fair‐value.
Forecast Bars: Choose M, the number of bars to extend into the future (default is often 30).
Forecast Line Colour: Select a high‐contrast colour (e.g., yellow).
Bullish FV Colour / Bearish FV Colour: Define the colour of the fair‐value line when price is above (e.g., green) or below it (e.g., red).
Show FV Channel Bands: Enable to display the grey channel bands around the fair‐value.
Channel Band Upper % / Channel Band Lower %: Set α_upper and α_lower as desired (defaults of 50 % create a ±50 % envelope).
Use Rolling Window?: Choose whether to restrict the regression to recent data.
Window Bars: If rolling mode is enabled, designate W, the number of bars to include.
3. Visual Output
The central curve F(t) appears on the price chart, coloured green when P(t) ≥ F(t) and red when P(t) < F(t).
If channel bands are enabled, the chart shows two grey lines U(t) and L(t) and a subtle shading between them.
If forecasting is active, dashed extensions of F(t), U(t), and L(t) appear, projecting forward by M bars in neutral hues.
CONCLUSION
The Fair Value Trend Model furnishes traders with a mathematically principled estimate of an asset’s equilibrium price curve by fitting a log‐linear regression to historical data. Its channel bands delineate a normal corridor of fluctuation based on fixed percentage offsets, while an optional short‐term projection offers a visual approximation of trend continuation.
By operating in log‐space, the model effectively captures exponential or power‐law growth patterns that linear methods overlook. Rolling‐window capability enables responsiveness to regime shifts, whereas all‐time mode highlights broader structural trends. Nonetheless, users should remain mindful of the model’s assumptions—particularly the power‐law form and fixed band percentages—and employ the forecast projection as a supplemental guide rather than a standalone predictor.
When combined with complementary indicators (e.g., volatility measures, momentum oscillators, volume analysis) and robust risk management, the Fair Value Trend Model can enhance market timing, mean‐reversion identification, and breakout detection across diverse trading environments.
REFERENCES
Draper, N. R., & Smith, H. (1998). Applied Regression Analysis (3rd ed.). Wiley.
Tsay, R. S. (2014). Introductory Time Series with R (2nd ed.). Springer.
Hull, J. C. (2017). Options, Futures, and Other Derivatives (10th ed.). Pearson.
These references provide background on regression, time-series analysis, and financial modeling.
Anchored Probability Cone by TenozenFirst of all, credit to @nasu_is_gaji for the open source code of Log-Normal Price Forecast! He teaches me alot on how to use polylines and inverse normal distribution from his indicator, so check it out!
What is this indicator all about?
This indicator draws a probability cone that visualizes possible future price ranges with varying levels of statistical confidence using Inverse Normal Distribution , anchored to the start of a selected timeframe (4h, W, M, etc.)
Feutures:
Anchored Cone: Forecasts begin at the first bar of each chosen higher timeframe, offering a consistent point for analysis.
Drift & Volatility-Based Forecast: Uses log returns to estimate market volatility (smoothed using VWMA) and incorporates a trend angle that users can set manually.
Probabilistic Price Bands: Displays price ranges with 5 customizable confidence levels (e.g., 30%, 68%, 87%, 99%, 99,9%).
Dynamic Updating: Recalculates and redraws the cone at the start of each new anchor period.
How to use:
Choose the Anchored Timeframe (PineScript only be able to forecast 500 bars in the future, so if it doesn't plot, try adjusting to a lower anchored period).
You can set the Model Length, 100 sample is the default. The higher the sample size, the higher the bias towards the overall volatility. So better set the sample size in a balanced manner.
If the market is inside the 30% conifidence zone (gray color), most likely the market is sideways. If it's outside the 30% confidence zone, that means it would tend to trend and reach the other probability levels.
Always follow the trend, don't ever try to trade mean reversions if you don't know what you're doing, as mean reversion trades are riskier.
That's all guys! I hope this indicator helps! If there's any suggestions, I'm open for it! Thanks and goodluck on your trading journey!
Bitcoin Power LawThis is the main body version of the script. The Oscillator version can be found here.
Understanding the Bitcoin Power Law Model
Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship.
By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges.
In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term.
The Bitcoin Power Law has the following function:
Power Law = 10^(a + b * log10(d))
Consisting of the following parameters:
a: Power Law Intercept (default: -17.668).
b: Power Law Slope (default: 5.926).
d: Number of days since a reference point(calculated by counting bars from the reference point with an offset).
Explanation of the a and b parameters:
Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post.
Applying vertical shifts to generate the other lines:
Once the initial power-law is created, additional lines are generated by applying a vertical shift. This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept.
In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest):
Base Power Law Line (no shift)
1-year shifted line
2-year shifted line
3-year shifted line
4-year shifted line
5-year shifted line
The six power law lines:
Bitcoin Power Law Oscillator
This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula: log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula:
normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line)
Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines.
Interpretation of the Bitcoin Power Law Model:
The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.).
By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame.
On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines.
In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples:
Example 1:
In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support at point B (Note: Vertical yearly grid lines have been added for this purpose👍).
Example 2:
In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point D, now aligning with the Base Power Law Line.
Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe :
Example 3:
In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
