Forecast PriceTime Oracle [CHE] Forecast PriceTime Oracle — Prioritizes quality over quantity by using Power Pivots via RSI %B metric to forecast future pivot highs/lows in price and time
Summary
This indicator identifies potential pivot highs and lows based on out-of-bounds conditions in a modified RSI %B metric, then projects future occurrences by estimating time intervals and price changes from historical medians. It provides visual forecasts via diagonal and horizontal lines, tracks achievement with color changes and symbols, and displays a dashboard for statistical overview including hit rates. Signals are robust due to median-based aggregation, which reduces outlier influence, and optional tolerance settings for near-misses, making it suitable for anticipating reversals in ranging or trending markets.
Motivation: Why this design?
Standard pivot detection often lags or generates false signals in volatile conditions, missing the timing of true extrema. This design leverages out-of-bounds excursions in RSI %B to capture "Power Pivots" early—focusing on quality over quantity by prioritizing significant extrema rather than every minor swing—then uses historical deltas in time and price to forecast the next ones, addressing the need for proactive rather than reactive analysis. It assumes that pivot spacing follows statistical patterns, allowing users to prepare entries or exits ahead of confirmation.
What’s different vs. standard approaches?
- Reference baseline: Diverges from traditional ta.pivothigh/low, which require fixed left/right lengths and confirm only after bars close, often too late for dynamic markets.
- Architecture differences:
- Detects extrema during OOB runs rather than post-bar symmetry.
- Aggregates deltas via medians (or alternatives) over a user-defined history, capping arrays to manage resources.
- Applies tolerance thresholds for hit detection, with options for percentage, absolute, or volatility-adjusted (ATR) flexibility.
- Freezes achieved forecasts with visual states to avoid clutter.
- Practical effect: Charts show proactive dashed projections instead of retrospective dots; the dashboard reveals evolving hit rates, helping users gauge reliability over time without manual calculation.
How it works (technical)
The indicator first computes a smoothed RSI over a specified length, then applies Bollinger Bands to derive %B, flagging out-of-bounds below zero or above one hundred as potential run starts. During these runs, it tracks the extreme high or low price and bar index. Upon exit from the OOB state, it confirms the Power Pivot at that extreme and records the time delta (bars since prior) and price change percentage to rolling arrays.
For forecasts, it calculates the median (or selected statistic) of recent deltas, subtracts the confirmation delay (bars from apex to exit), and projects ahead by that adjusted amount. Price targets use the median change applied to the origin pivot value. Lines are drawn from the apex to the target bar and price, with a short horizontal at the endpoint. Arrays store up to five active forecasts, pruning oldest on overflow.
Tolerance adjusts hit checks: for highs, if the high reaches or exceeds the target (adjusted by tolerance); for lows, if the low drops to or below. Once hit, the forecast freezes, changing colors and symbols, and extends the horizontal to the hit bar. Persistent variables maintain last pivot states across bars; arrays initialize empty and grow until capped at history length.
Parameter Guide
Source: Specifies the data input for the RSI computation, influencing how price action is captured. Default is close. For conservative signals in noisy environments, switch to high; using low boosts responsiveness but may increase false positives.
RSI Length: Sets the smoothing period for the RSI calculation, with longer values helping to filter out whipsaws. Default is 32. Opt for shorter lengths like 14 to 21 on faster timeframes for quicker reactions, or extend to 50 or more in strong trends to enhance stability at the cost of some lag.
BB Length: Defines the period for the Bollinger Bands applied to %B, directly affecting how often out-of-bounds conditions are triggered. Default is 20. Align it with the RSI length: shorter periods detect more potential runs but risk added noise, while longer ones provide better filtering yet might overlook emerging extrema.
BB StdDev: Controls the multiplier for the standard deviation in the bands, where wider settings reduce false out-of-bounds alerts. Default is 2.0. Narrow it to 1.5 for highly volatile assets to catch more signals, or broaden to 2.5 or higher to emphasize only major movements.
Show Price Forecast: Enables or disables the display of diagonal and target lines along with their updates. Default is true. Turn it off for simpler chart views, or keep it on to aid in trade planning.
History Length: Determines the number of recent pivot samples used for median-based statistics, where more history leads to smoother but potentially less current estimates. Default is 50. Start with a minimum of 5 to build data; limit to 100 to 200 to prevent outdated regimes from skewing results.
Max Lookahead: Limits the number of bars projected forward to avoid overly extended lines. Default is 500. Reduce to 100 to 200 for intraday focus, or increase for longer swing horizons.
Stat Method: Selects the aggregation technique for time and price deltas: Median for robustness against outliers, Trimmed Mean (20%) for a balanced trim of extremes, or 75th Percentile for a conservative upward tilt. Default is Median. Use Median for even distributions; switch to Percentile when emphasizing potential upside in trending conditions.
Tolerance Type: Chooses the approach for flexible hit detection: None for exact matches, Percentage for relative adjustments, Absolute for fixed point offsets, or ATR for scaling with volatility. Default is None. Begin with Percentage at 0.5 percent for currency pairs, or ATR for adapting to cryptocurrency swings.
Tolerance %: Provides the relative buffer when using Percentage mode, forgiving small deviations. Default is 0.5. Set between 0.2 and 1.0 percent; higher values accommodate gaps but can overstate hit counts.
Tolerance Points: Establishes a fixed offset in price units for Absolute mode. Default is 0.0010. Tailor to the asset, such as 0.0001 for forex pairs, and validate against past wick behavior.
ATR Length: Specifies the period for the Average True Range in dynamic tolerance calculations. Default is 14. This is the standard setting; shorten to 10 to reflect more recent volatility.
ATR Multiplier: Adjusts the ATR scale for tolerance width in ATR mode. Default is 0.5. Range from 0.3 for tighter precision to 0.8 for greater leniency.
Dashboard Location: Positions the summary table on the chart. Default is Bottom Right. Consider Top Left for better visibility on mobile devices.
Dashboard Size: Controls the text scaling for dashboard readability. Default is Normal. Choose Tiny for dense overlays or Large for detailed review sessions.
Text/Frame Color: Sets the color scheme for dashboard text and borders. Default is gray. Align with your chart theme, opting for lighter shades on dark backgrounds.
Reading & Interpretation
Forecast lines appear as dashed diagonals from confirmed pivots to projected targets, with solid horizontals at endpoints marking price levels. Open targets show a target symbol (🎯); achieved ones switch to a trophy symbol (🏆) in gray, with lines fading to gray. The dashboard summarizes median time/price deltas, sample counts, and hit rates—rising rates indicate improving forecast alignment. Colors differentiate highs (red) from lows (lime); frozen states signal validated projections.
Practical Workflows & Combinations
- Trend following: Enter long on low forecast hits during uptrends (higher highs/lower lows structure); filter with EMA crossovers to ignore counter-trend signals.
- Reversal setups: Short above high projections in overextended rallies; use volume spikes as confirmation to reduce false breaks.
- Exits/Stops: Trail stops to prior pivot lows; conservative on low hit rates (below 50%), aggressive above 70% with tight tolerance.
- Multi-TF: Apply on 1H for entries, 4H for time projections; combine with Ichimoku clouds for confluence on targets.
- Risk management: Position size inversely to delta uncertainty (wider history = smaller bets); avoid low-liquidity sessions.
Behavior, Constraints & Performance
Confirmation occurs on OOB exit, so live-bar pivots may adjust until close, but projections update only on events to minimize repaint. No security or HTF calls, so no external lookahead issues. Arrays cap at history length with shifts; forecasts limited to five active, pruning FIFO. Loops iterate over small fixed sizes (e.g., up to 50 for stats), efficient on most hardware. Max lines/labels at 500 prevent overflow.
Known limits: Sensitive to OOB parameter tuning—too tight misses runs; assumes stationary pivot stats, which may shift in regime changes like low vol. Gaps or holidays distort time deltas.
Sensible Defaults & Quick Tuning
Defaults suit forex/crypto on 1H–4H: RSI 32/BB 20 for balanced detection, Median stats over 50 samples, None tolerance for exactness.
- Too many false runs: Increase BB StdDev to 2.5 or RSI Length to 50 for filtering.
- Lagging forecasts: Shorten History Length to 20; switch to 75th Percentile for forward bias.
- Missed near-hits: Enable Percentage tolerance at 0.3% to capture wicks without overcounting.
- Cluttered charts: Reduce Max Lookahead to 200; disable dashboard on lower TFs.
What this indicator is—and isn’t
This is a forecasting visualization layer for pivot-based analysis, highlighting statistical projections from historical patterns. It is not a standalone system—pair with price action, volume, and risk rules. Not predictive of all turns; focuses on OOB-derived extrema, ignoring volume or news impacts.
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
在脚本中搜索"one一季度财报"
Herd Flow Oscillator — Volume Distribution Herd Flow Oscillator — Scientific Volume Distribution (herd-accurate rev)
A composite order-flow oscillator designed to surface true herding behavior — not just random bursts of buying or selling.
It’s built to detect when market participants start acting together, showing persistent, one-sided activity that statistically breaks away from normal market randomness.
Unlike traditional volume or momentum indicators, this tool doesn’t just look for “who’s buying” or “who’s selling.”
It tries to quantify crowd behavior by blending multiple statistical tests that describe how collective sentiment and coordination unfold in price and volume dynamics.
What it shows
The Herd Flow Oscillator works as a multi-layer detector of crowd-driven flow in the market. It examines how signed volume (buy vs. sell pressure) evolves, how persistent it is, and whether those actions are unusually coordinated compared to random expectations.
HerdFlow Composite (z) — the main signal line, showing how statistically extreme the current herding pressure is.
When this crosses above or below your set thresholds, it suggests a high probability of collective buying or selling.
You can optionally reveal component panels for deeper insight into why herding is detected:
DVI (Directional Volume Imbalance): Measures the ratio of bullish vs. bearish volume.
If it’s strongly positive, more volume is hitting the ask (buying); if negative, more is hitting the bid (selling).
LSV-style Herd Index : Inspired by academic finance measures of “herding.”
It compares how often volume is buying vs. selling versus what would happen by random chance.
If the result is significantly above chance, it means traders are collectively biased in one direction.
O rder-Flow Persistence (ρ 1..K): Averages autocorrelation of signed volume over several lags.
In simpler terms: checks if buying/selling pressure tends to continue in the same direction across bars.
Positive persistence = ongoing coordination, not just isolated trades.
Runs-Test Herding (−Z) : Statistical test that checks how often trade direction flips.
When there are fewer direction changes than expected, it means trades are clustering — a hallmark of herd behavior.
Skew (signed volume): Measures whether signed volume is heavily tilted to one side.
A positive skew means more aggressive buying bursts; a negative skew means more intense selling bursts.
CVD Slope (z): Looks at the slope of the Cumulative Volume Delta — essentially how quickly buy/sell pressure is accelerating.
It’s a short-term flow acceleration measure.
Shapes & background
▲ “BH” at the bottom = Bull Herding; ▼ “BH-” at the top = Bear Herding.
These markers appear when all conditions align to confirm a herding regime.
Persistence and clustering both confirm coordinated downside flow.
Core Windows
Primary Window (N) — the main sample length for herding calculations.
It’s like the "memory span" for detecting coordinated behavior. A longer N means smoother, more reliable signals.
Short Window (Nshort) — used for short-term measurements like imbalance and slope.
Smaller values react faster but can be noisy; larger values are steadier but slower.
Long Window (Nlong) — used for z-score normalization (statistical scaling).
This helps the indicator understand what’s “normal” behavior over a longer horizon, so it can spot when things deviate too far.
Autocorr lags (acLags) — how many steps to check when measuring persistence.
Higher values (e.g., 3–5) look further back to see if trends are truly continuing.
Calculation Options
Price Proxy for Tick Rule — defines how to decide if a trade is “buy” or “sell.”
hlc3 (average of high, low, and close) works as a neutral, smooth price proxy.
Use ATR for scaling — keeps signals comparable across assets and timeframes by dividing by volatility (ATR).
Prevents high-volatility periods from dominating the signal.
Median Filter (bars) — smooths out erratic data spikes without heavily lagging the response.
Odd values like 3 or 5 work best.
Signal Thresholds
Composite z-threshold — determines how extreme behavior must be before it counts as “herding.”
Higher values = fewer, more confident signals.
Imbalance threshold — the minimum directional volume imbalance to trigger interest.
Plotting
Show component panels — useful for analysts and developers who want to inspect the math behind signals.
Fill strong herding zones — purely visual aid to highlight key periods of coordinated trading.
How to use it (practical tips)
Understand the purpose: This is not just a “buy/sell” tool.
It’s a behavioral detector that identifies when traders or algorithms start acting in the same direction.
Timeframe flexibility:
15m–1h: reveals short-term crowd shifts.
4h–1D: better for swing-trade context and institutional positioning.
Combine with structure or trend:
When HerdFlow confirms a bullish regime during a breakout or retest, it adds confidence.
Conversely, a bearish cluster at resistance may hint at a crowd-driven rejection.
Threshold tuning:
To make it more selective, increase zThr and imbThr.
To make it more sensitive, lower those thresholds but expand your primary window N for smoother results.
Cross-market consistency:
Keep “Use ATR for scaling” enabled to maintain consistency across different instruments or timeframes.
Denoising:
A small median filter (3–5 bars) removes flicker from volume spikes but still preserves the essential crowd patterns.
Reading the components (why signals fire)
Each sub-metric describes a unique “dimension” of crowd behavior:
DVI: how imbalanced buying vs selling is.
Herd Index: how biased that imbalance is compared to random expectation.
Persistence (ρ): how continuous those flows are.
Runs-Test: how clumped together trades are — clustering means the crowd’s acting in sync.
Skew: how lopsided the volume distribution is — sudden surges of one-sided aggression.
CVD Slope: how strongly accelerating the current directional flow is.
When all of these line up, you’re seeing evidence that market participants are collectively moving in the same direction — i.e., true herding.
TradeScope: MA Reversion • RVOL • Trendlines • GAPs • TableTradeScope is an all-in-one technical analysis suite that brings together price action, momentum, volume dynamics, and trend structure into one cohesive and fully customizable indicator.
An advanced, modular trading suite that combines moving averages, reversion signals, RSI/CCI momentum, relative volume, gap detection, trendline analysis, and dynamic tables — all within one powerful dashboard.
Perfect for swing traders, intraday traders, and analysts who want to read price strength, volume context, and market structure in real time.
⚙️ Core Components & Inputs
🧮 Moving Average Settings
Moving Average Type & Length:
Choose between SMA or EMA and set your preferred period for smoother or more reactive trend tracking.
Multi-MA Plotting:
Up to 8 customizable moving averages (each with independent type, color, and length).
Includes a “window filter” to show only the last X bars, reducing chart clutter.
MA Reversion Engine:
Detects when price has extended too far from its moving average.
Reversion Lookback: Number of bars analyzed to determine historical extremes.
Reversion Threshold: Sensitivity multiplier—lower = more frequent signals, higher = stricter triggers.
🔄 Trend Settings
Short-Term & Long-Term Trend Lookbacks:
Uses linear regression to detect the slope and direction of the short- and long-term trend.
Results are displayed in the live table with color-coded bias:
🟩 Bullish | 🟥 Bearish
📈 Momentum Indicators
RSI (Relative Strength Index):
Adjustable period; displays the current RSI value, overbought (>70) / oversold (<30) zones, and trending direction.
CCI (Commodity Channel Index):
Customizable length with color-coded bias:
🟩 Oversold (< -100), 🟥 Overbought (> 100).
Tooltip shows whether the CCI is trending up or down.
📊 Volume Analysis
Relative Volume (RVOL):
Estimates end-of-day projected volume using intraday progress and compares it against the 20-day average.
Displays whether today’s volume is expected to exceed yesterday’s, and highlights color by strength.
Volume Trend (Short & Long Lookbacks):
Visual cues for whether current volume is above or below short-term and long-term averages.
Estimated Full-Day Volume & Multiplier:
Converts raw volume into “X” multiples (e.g., 2.3X average) for quick interpretation.
🕳️ Gap Detection
Automatically identifies and plots bullish and bearish price gaps within a defined lookback period.
Gap Lookback: Defines how far back to search for gaps.
Gap Line Width / Visibility: Controls the thickness and display of gap lines on chart.
Displays the closest open gap in the live table, including its distance from current price (%).
🔍 ATR & Volatility
14-day ATR (% of price):
Automatically converts the Average True Range into a percent, providing quick volatility context:
🟩 Low (<3%) | 🟨 Moderate (3–5%) | 🟥 High (>5%)
💬 Candlestick Pattern Recognition
Auto-detects popular reversal and continuation patterns such as:
Bullish/Bearish Engulfing
Hammer / Hanging Man
Shooting Star / Inverted Hammer
Doji / Harami / Kicking / Marubozu / Morning Star
Each pattern is shown with contextual color coding in the table.
🧱 Pivot Points & Support/Resistance
Optional Pivot High / Pivot Low Labels
Adjustable left/right bar lengths for pivot detection
Theme-aware text and label color options
Automatically drawn diagonal trendlines for both support and resistance
Adjustable line style, color, and thickness
Detects and tracks touches for reliability
Includes breakout alerts (with optional volume confirmation)
🚨 Alerts
MA Cross Alerts:
Triggers when price crosses the fast or slow moving average within a tolerance band (default ±0.3%).
Diagonal Breakout Alerts:
Detects and alerts when price breaks diagonal trendlines.
Volume-Confirmed Alerts:
Filters breakouts where volume exceeds 1.5× the 20-bar average.
🧾 Live Market Table
A fully dynamic table displayed on-chart, customizable via input toggles:
Choose which rows to show (e.g., RSI, ATR, RVOL, Gaps, CCI, Trend, MA info, Diff, Low→Close%).
Choose table position (top-right, bottom-left, etc.) and text size.
Theme selection: Light or Dark
Conditional background colors for instant visual interpretation:
🟩 Bullish or Oversold
🟥 Bearish or Overbought
🟨 Neutral / Moderate
🎯 Practical Uses
✅ Identify confluence setups combining MA reversion, volume expansion, and RSI/CCI extremes.
✅ Track trend bias and gap proximity directly in your dashboard.
✅ Monitor relative volume behavior for intraday strength confirmation.
✅ Automate MA cross or breakout alerts to stay ahead of key price action.
🧠 Ideal For
Swing traders seeking confluence-based setups
Intraday traders monitoring multi-factor bias
Analysts looking for compact market health dashboards
💡 Summary
TradeScope is designed as a single-pane-of-glass market view — combining momentum, trend, volume, structure, and reversion into one clear visual system.
Fully customizable. Fully dynamic.
Use it to see what others miss — clarity, confluence, and confidence in every trade.
Session Gap Fill [LuxAlgo]The Session Gap Fill tool detects and highlights filled and unfilled price gaps between regular sessions. It features a dashboard with key statistics about the detected gaps.
The tool is highly customizable, allowing users to filter by different types of gaps and customize how they are displayed on the chart.
🔶 USAGE
By default, the tool detects all price gaps between sessions. A price gap is defined as a difference between the opening price of one session and the closing price of the previous session. In this case, the tool uses the opening price of the first bar of the session against the closing price of the previous bar.
A bullish gap is detected when the session open price is higher than the last close, and a bearish gap is detected when the session open price is lower than the last close.
Gaps represent a change in market sentiment, a difference in what market participants think between the close of one trading session and the open of the next.
What is useful to traders is not the gap itself, but how the market reacts to it.
Unfilled gaps occur when prices do not return to the previous session's closing price.
Filled gaps occur when prices come back to the previous session's close price.
By analyzing how markets react to gaps, traders can understand market sentiment, whether different prices are accepted or rejected, and take advantage of this information to position themselves in favor of bullish or bearish market sentiment.
Next, we will cover the Gap Type Filter and Statistics Dashboard.
🔹 Gap Type Filter
Traders can choose from three options: display all gaps, display only overlapping gaps, or display only non-overlapping gaps. All gaps are displayed by default.
An overlapping gap is defined when the first bar of the session has any price in common with the previous bar. No overlapping gap is defined when the two bars do not share any price levels.
As we will see in the next section, there are clear differences in market behavior around these types of gaps.
🔹 Statistics Dashboard
The Statistics Dashboard displays key metrics that help traders understand market behavior around each type of gap.
Gaps: The percentage of bullish and bearish gaps.
Filled: The percentage of filled bullish and bearish gaps.
Reversed: The percentage of filled gaps that move in favor of the gap
Bars Avg.: The average number of bars for a gap to be filled.
Now, let's analyze the chart on the left of the image to understand those stats. These are the stats for all gaps, both overlapping and non-overlapping.
Of the total, bullish gaps represent 55%, and bearish ones represent 44%. The gap bias is pretty balanced in this market.
The second statistic, Filled, shows that 63% of gaps are filled, both bullish and bearish. Therefore, there is a higher probability that a gap will be filled than not.
The third statistic is reversed. This is the percentage of filled gaps where prices move in favor of the gap. This applies to filled bullish gaps when the close of the session is above the open, and to filled bearish gaps when the close of the session is below the open. In other words, first there is a gap, then it fills, and finally it reverses. As we can see in the chart, this only happens 35% of the time for bullish gaps and 29% of the time for bearish gaps.
The last statistic is Bars Avg., which is the average number of bars for a gap to be filled. On average, it takes between one and two bars for both bullish and bearish gaps. On average, gaps fill quickly.
As we can see on the chart, selecting different types of gaps yields different statistics and market behavior. For example, overlapping gaps have a greater than 90% chance of being filled, whereas non-overlapping gaps have a less than 40% chance.
🔶 SETTINGS
Gap Type: Select the type of gap to display.
🔹 Dashboard
Dashboard: Enable or disable the dashboard.
Position: Select the location of the dashboard.
Size: Select the dashboard size.
🔹 Style
Filled Bullish Gap: Enable or disable this gap and choose the color.
Filled Bearish Gap: Enable or disable this gap and choose the color.
Unfilled Gap: Enable or disable this gap and choose the color.
Max Deviation Level: Enable or disable this level and choose the color.
Open Price Level: Enable or disable this level and choose the color.
Dominant DATR [CHE] Dominant DATR — Directional ATR stream with dominant-side EMA, bands, labels, and alerts
Summary
Dominant DATR builds two directional volatility streams from the true range, assigns each bar’s range to the up or down side based on the sign of the close-to-close move, and then tracks the dominant side through an exponential average. A rolling band around the dominant stream defines recent extremes, while optional gradient coloring reflects relative magnitude. Swing-based labels mark new higher highs or lower lows on the dominant stream, and alerts can be enabled for swings, zero-line crossings, and band breakouts. The result is a compact pane that highlights regime bias and intensity without relying on price overlays.
Motivation: Why this design?
Conventional ATR treats all range as symmetric, which can mask directional pressure, cause late regime shifts, and produce frequent false flips during noisy phases. This design separates the range into up and down contributions, then emphasizes whichever side is stronger. A single smoothed dominant stream clarifies bias, while the band and swing markers help distinguish continuation from exhaustion. Optional normalization by close makes the metric comparable across instruments with different price scales.
What’s different vs. standard approaches?
Reference baseline: Classic ATR or a basic EMA of price.
Architecture differences:
Directional weighting of range using positive and negative close-to-close moves.
Separate moving averages for up and down contributions combined into one dominant stream.
Rolling highest and lowest of the dominant stream to form a band.
Optional normalization by close, window-based scaling for color intensity, and gamma adjustment for visual contrast.
Event logic for swing highs and lows on the dominant stream, with label buffering and pruning.
Configurable alerts for swings, zero-line crossings, and band breakouts.
Practical effect: You see when volatility is concentrated on one side, how strong that bias currently is, and when the dominant stream pushes through or fails at its recent envelope.
How it works (technical)
Each bar’s move is split into an up component and a down component based on whether the close increased or decreased relative to the prior close. The bar’s true range is proportionally assigned to up or down using those components as weights.
Each side is smoothed with a Wilder-style moving average. The dominant stream is the side with the larger value, recorded as positive for up dominance and negative for down dominance.
The dominant stream is then smoothed with an exponential moving average to reduce noise and provide a responsive yet stable signal line.
A rolling window tracks the highest and lowest values of the dominant EMA to form an envelope. Crossings of these bounds indicate unusual strength or weakness relative to recent history.
For visualization, the absolute value of the dominant EMA is scaled over a lookback window and passed through a gamma curve to modulate gradient intensity. Colors are chosen separately for up and down regimes.
Swing events are detected by comparing the dominant EMA to its recent extremes over a short lookback. Labels are placed when a prior bar set an extreme and the current bar confirms it. A managed array prunes older labels when the user-defined maximum is exceeded.
Alerts mirror these events and also include zero-line crossings and band breakouts. The script does not force closed-bar confirmation; users should configure alert execution timing to suit their workflow.
There are no higher-timeframe requests and no security calls. State is limited to simple arrays for labels and persistent color parameters.
Parameter Guide
Parameter — Effect — Default — Trade-offs/Tips
ATR Length — Smoothing of directional true range streams — fourteen — Longer reduces noise and may delay regime shifts; shorter increases responsiveness.
EMA Length — Smoothing of the dominant stream — twenty-five — Lower values react faster; higher values reduce whipsaw.
Band Length — Window for recent highs and lows of the dominant stream — ten — Short windows flag frequent breakouts; long windows emphasize only exceptional moves.
Normalize by Close — Divide by close price to produce a percent-like scale — false — Useful across assets with very different price levels.
Enable gradient color — Turn on magnitude-based coloring — true — Visual aid only; can be disabled for simplicity.
Gradient window — Lookback used to scale color intensity — one hundred — Larger windows stabilize the color scale.
Gamma (lines) — Adjust gradient intensity curve — zero point eight — Lower values compress variation; higher values expand it.
Gradient transparency — Transparency for gradient plots — zero, between zero and ninety — Higher values mute colors.
Up dark / Up neon — Base and peak colors for up dominance — green tones — Styling only.
Down dark / Down neon — Base and peak colors for down dominance — red tones — Styling only.
Show zero line / Background tint — Visual references for regime — true and false — Background tint can help quick scanning.
Swing length — Bars used to detect swing highs or lows — two — Larger values demand more structure.
Show labels / Max labels / Label offset — Label visibility, cap, and vertical offset — true, two hundred, zero — Increase cap with care to avoid clutter.
Alerts: HH/LL, Zero Cross, Band Break — Toggle alert rules — true, false, false — Enable only what you need.
Reading & Interpretation
The dominant EMA above zero indicates up-side dominance; below zero indicates down-side dominance.
Band lines show recent extremes of the dominant EMA; pushes through the band suggest unusual momentum on the dominant side.
Gradient intensity reflects local magnitude of dominance relative to the chosen window.
HH/LL labels appear when the dominant stream prints a new local extreme in the current regime and that extreme is confirmed on the next bar.
Zero-line crosses suggest regime flips; combine with structure or filters to reduce noise.
Practical Workflows & Combinations
Trend following: Consider entries when the dominant EMA is on the regime side and expands away from zero. Band breakouts add confirmation; structure such as higher highs or lower lows in price can filter signals.
Exits and stops: Tighten exits when the dominant stream stalls near the band or fades toward zero. Opposite swing labels can serve as early caution.
Multi-asset and multi-timeframe: Works across liquid assets and common timeframes. For lower noise instruments, reduce smoothing slightly; for high noise, increase lengths and swing length.
Behavior, Constraints & Performance
Repaint and confirmation: No security calls and no future-looking references. Swing labels confirm one bar later by design. Real-time crosses can change intra-bar; use bar-close alerts if needed.
Resources: `max_bars_back` is two thousand. The script uses an array for labels with pruning, gradient color computations, and a simple while loop that runs only when the label cap is exceeded.
Known limits: The EMA can lag at sharp turns. Normalization by close changes scale and may affect thresholds. Extremely gappy data can produce abrupt shifts in the dominant side.
Sensible Defaults & Quick Tuning
Starting point: ATR Length fourteen, EMA Length twenty-five, Band Length ten, Swing Length two, gradient enabled.
Too many flips: Increase EMA Length and swing length, or enable only swing alerts.
Too sluggish: Decrease EMA Length and Band Length.
Inconsistent scales across symbols: Enable Normalize by Close.
Visual clutter: Disable gradient or reduce label cap.
What this indicator is—and isn’t
This is a volatility-bias visualization and signal layer that highlights directional pressure and intensity. It is not a complete trading system and does not produce position sizing or risk management. Use it with market structure, context, and independent risk controls.
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
Multi-Timeframe MACD with Color Mix (Nikko)Multi-Timeframe MACD with Color Mix (Nikko) Indicator
This documentation explains the benefits of the "Multi-Timeframe MACD with Color Mix (Nikko)" indicator for traders and provides easy-to-follow steps on how to use it. Written as of 05:06 AM +07 on Saturday, October 04, 2025, this guide focuses on helping you, as a trader, get the most out of this tool with clear, practical advice before diving into the technical details.
Benefits for Traders
1. Multi-Timeframe Insight
This indicator lets you see momentum trends across 15-minute, 1-hour, 1-day, and 1-week timeframes all on one chart. This big-picture view helps you catch both quick market moves and long-term trends without flipping between charts, saving you time and giving you a fuller understanding of the market.
2. Visual Momentum Representation
The background changes from red to green based on short-term (15m) momentum, giving you a quick, easy-to-see signal—red means bearish (prices might drop), and green means bullish (prices might rise). The histogram uses a mix of red, green, and blue colors to show the combined strength of the 1-hour, 1-day, and 1-week timeframes, helping you spot strong trends at a glance (e.g., a bright mix for strong momentum, darker for weaker).
3. Enhanced Decision-Making
The background and histogram colors work together to confirm trends across different timeframes, making it less likely you’ll act on a false signal. This helps you feel more confident when deciding when to buy, sell, or hold.
4. Proactive Alert System
You can set alerts to notify you when the percentage of bullish timeframes hits your chosen levels (e.g., below 10% for bearish, above 90% for bullish). This keeps you in the loop on big momentum shifts without needing to watch the chart all day—perfect for when you’re busy.
5. Flexibility and Efficiency
You can turn timeframes on or off, adjust settings like speed of the moving averages, and tweak transparency to fit your trading style—whether you’re a fast scalper or a patient swing trader. Everything is shown on one chart, saving you effort, and the colors make it simple to read, even if you’re new to trading.
How to Use It
Getting Started
Add the Indicator: Load the "Multi-Timeframe MACD with Color Mix (Nikko)" onto your TradingView chart using the Pine Script editor or indicator library.
Pick Your Timeframes: Turn on the timeframes that match your trading—use 15m and 1h for quick trades, or 1d and 1w for longer holds—using the enable_15m, enable_1h, enable_1d, enable_1w, and enable_background options.
Reading the Colors
Background Gradient: Watch for red to signal bearish 15m momentum and green for bullish momentum. Adjust the Background_transparency (default 75%, or 25% opacity) if the chart feels too busy—try lowering it to 50 for clearer candlesticks in fast markets.
Histogram and EMA Colors:
The histogram and its Exponential Moving Average (EMA) line show a mix of red (1-week), green (1-day), and blue (1-hour) based on how strong the momentum is in each timeframe.
Brighter colors mean stronger momentum—white (all bright) shows all timeframes are pushing up hard, while darker shades (like gray or black) mean weaker or mixed momentum.
Turn off a timeframe (e.g., enable_1h = false) to see how it changes the color mix and focus on what matters to you.
Setting Alerts
Set Your Levels: Choose a threshold_low (default 10%) and threshold_high (default 90%) based on your comfort zone or past market patterns to catch big turns.
Get Notifications: Use TradingView alerts to get pings when the market hits your set levels, so you can act without staring at the screen.
Practical Tips
Pair with Other Tools: Use it with support/resistance lines or the RSI to double-check your moves and build a solid plan.
Tweak Settings: Adjust fast_length, slow_length, and signal_smoothing to match your asset’s speed, and bump up the lookback (default 50) for steadier trends in wild markets.
Practice First: Test different timeframe combos on a demo account to find what works best for you.
Understanding the Colors (Simple Explanation)
How Colors Work
The histogram and its EMA line use a color mix based on a simple idea from color theory, like mixing paints with red, green, and blue (RGB):
Red comes from the 1-week timeframe, green from 1-day, and blue from 1-hour.
When all three timeframes show strong upward momentum, they blend into bright white—the brightest color, like a super-bright light telling you the market’s roaring up.
If some timeframes are weak or pulling down, the mix gets darker (like gray or black), warning you the momentum might not be solid.
Brighter is Better
Bright Colors = Strong Opportunity: The brighter the histogram and EMA (closer to white), the more all your chosen timeframes are in agreement that prices are rising. This is your signal to think about buying or holding, as it points to a powerful trend you can ride.
Dark Colors = Caution: A darker mix (toward black) means some timeframes are lagging or bearish, suggesting you might wait or consider selling. It’s like a dim light saying, “Hold on, check again.”
Benefit in Practice: Watching the brightness helps you jump on the best trades fast. For example, a bright white histogram on a green background is like a green traffic light—go for it! A dark gray on red is like a red light—pause and rethink. This quick color check can save you from bad moves and boost your profits when the trend is strong.
Why It Helps
These colors are your fast friend in trading. A bright histogram means all your timeframes are cheering for an uptrend, giving you the confidence to act. A dull one tells you to be careful, helping you avoid traps. It’s like having a color-coded guide to pick the hottest market moments!
Technical Details
Input Parameters
Fast Length (default: 12): Short-term moving average speed.
Slow Length (default: 26): Long-term moving average speed.
Source (default: close): Price data used.
Signal Smoothing (default: 9): Smooths the signal line.
MA Type (default: EMA): Choose EMA or SMA.
Timeframe and Scaling
Timeframes: 15m, 1h, 1d, 1w, with on/off switches.
Lookback Period (default: 50): Sets the data window for trends.
Background Transparency (default: 75%): Controls background see-through level.
MACD Calculation
Per Timeframe: Uses request.security():
MACD Line: ta.ema(src, fast_length) - ta.ema(src, slow_length).
Signal Line: ta.ema(MACD, signal_length).
Histogram: (macd - signal) / 3.0.
Background Gradient
15m Normalization: norm_value = (hist_15m - hist_15m_min) / max(hist_15m_range, 1e-10), limited to 0-1.
RGB Mix: Red drops from 255 to 0, green rises from 0 to 255, blue stays 0.
Apply: color.new(color.rgb(r_val, g_val, b_val), Background_transparency).
Histogram and EMA Colors
Color Assignment:
1h: Blue (#0000FF) if hist_1h >= 0, else black.
1d: Green (#00FF00) if hist_1d >= 0, else black.
1w: Red (#FF0000) if hist_1w >= 0, else black.
Final Color: final_color = color.rgb(min(r, 255), min(g, 255), min(b, 255)).
Plotting: Histogram and EMA use final_color; MACD (#2962FF), signal (#FF6D00).
Alerts
Bullish Percentage: bullish_pct = (bullish_count / bullish_total) * 100, counting hist >= 0.
Triggers: Below threshold_low or above threshold_high.
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Conclusion
The "Multi-Timeframe MACD with Color Mix (Nikko)" is your all-in-one tool to spot trends, confirm moves, and trade smarter with its bright, easy-to-read colors. By using it wisely, you can sharpen your market edge and trade with more confidence.
This README is tailored for traders and reflects the indicator's practical value as of 05:06 AM +07 on October 04, 2025.
Extreme Pressure Zones Indicator (EPZ) [BullByte]Extreme Pressure Zones Indicator(EPZ)
The Extreme Pressure Zones (EPZ) Indicator is a proprietary market analysis tool designed to highlight potential overbought and oversold "pressure zones" in any financial chart. It does this by combining several unique measurements of price action and volume into a single, bounded oscillator (0–100). Unlike simple momentum or volatility indicators, EPZ captures multiple facets of market pressure: price rejection, trend momentum, supply/demand imbalance, and institutional (smart money) flow. This is not a random mashup of generic indicators; each component was chosen and weighted to reveal extreme market conditions that often precede reversals or strong continuations.
What it is?
EPZ estimates buying/selling pressure and highlights potential extreme zones with a single, bounded 0–100 oscillator built from four normalized components. Context-aware weighting adapts to volatility, trendiness, and relative volume. Visual tools include adaptive thresholds, confirmed-on-close extremes, divergence, an MTF dashboard, and optional gradient candles.
Purpose and originality (not a mashup)
Purpose: Identify when pressure is building or reaching potential extremes while filtering noise across regimes and symbols.
Originality: EPZ integrates price rejection, momentum cascade, pressure distribution, and smart money flow into one bounded scale with context-aware weighting. It is not a cosmetic mashup of public indicators.
Why a trader might use EPZ
EPZ provides a multi-dimensional gauge of market extremes that standalone indicators may miss. Traders might use it to:
Spot Reversals: When EPZ enters an "Extreme High" zone (high red), it implies selling pressure might soon dominate. This can hint at a topside reversal or at least a pause in rallies. Conversely, "Extreme Low" (green) can highlight bottom-fish opportunities. The indicator's divergence module (optional) also finds hidden bullish/bearish divergences between price and EPZ, a clue that price momentum is weakening.
Measure Momentum Shifts: Because EPZ blends momentum and volume, it reacts faster than many single metrics. A rising MPO indicates building bullish pressure, while a falling MPO shows increasing bearish pressure. Traders can use this like a refined RSI: above 50 means bullish bias, below 50 means bearish bias, but with context provided by the thresholds.
Filter Trades: In trend-following systems, one could require EPZ to be in the bullish (green) zone before taking longs, or avoid new trades when EPZ is extreme. In mean-reversion systems, one might specifically look to fade extremes flagged by EPZ.
Multi-Timeframe Confirmation: The dashboard can fetch a higher timeframe EPZ value. For example, you might trade a 15-minute chart only when the 60-minute EPZ agrees on pressure direction.
Components and how they're combined
Rejection (PRV) – Captures price rejection based on candle wicks and volume (see Price Rejection Volume).
Momentum Cascade (MCD) – Blends multiple momentum periods (3,5,8,13) into a normalized momentum score.
Pressure Distribution (PDI) – Measures net buy/sell pressure by comparing volume on up vs down candles.
Smart Money Flow (SMF) – An adaptation of money flow index that emphasizes unusual volume spikes.
Each of these components produces a 0–100 value (higher means more bullish pressure). They are then weighted and averaged into the final Market Pressure Oscillator (MPO), which is smoothed and scaled. By combining these four views, EPZ stands out as a comprehensive pressure gauge – the whole is greater than the sum of parts
Context-aware weighting:
Higher volatility → more PRV weight
Trendiness up (RSI of ATR > 25) → more MCD weight
Relative volume > 1.2x → more PDI weight
SMF holds a stable weight
The weighted average is smoothed and scaled into MPO ∈ with 50 as the neutral midline.
What makes EPZ stand out
Four orthogonal inputs (price action, momentum, pressure, flow) unified in a single bounded oscillator with consistent thresholds.
Adaptive thresholds (optional) plus robust extreme detection that also triggers on crossovers, so static thresholds work reliably too.
Confirm Extremes on Bar Close (default ON): dots/arrows/labels/alerts print on closed bars to avoid repaint confusion.
Clean dashboard, divergence tools, pre-alerts, and optional on-price gradients. Visual 3D layering uses offsets for depth only,no lookahead.
Recommended markets and timeframes
Best: liquid symbols (index futures, large-cap equities, major FX, BTC/ETH).
Timeframes: 5–15m (more signals; consider higher thresholds), 1H–4H (balanced), 1D (clear regimes).
Use caution on illiquid or very low TFs where wick/volume geometry is erratic.
Logic and thresholds
MPO ∈ ; 50 = neutral. Above 50 = bullish pressure; below 50 = bearish.
Static thresholds (defaults): thrHigh = 70, thrLow = 30; warning bands 5 pts inside extremes (65/35).
Adaptive thresholds (optional):
thrHigh = min(BaseHigh + 5, mean(MPO,100) + stdev(MPO,100) × ExtremeSensitivity)
thrLow = max(BaseLow − 5, mean(MPO,100) − stdev(MPO,100) × ExtremeSensitivity)
Extreme detection
High: MPO ≥ thrHigh with peak/slope or crossover filter.
Low: MPO ≤ thrLow with trough/slope or crossover filter.
Cooldown: 5 bars (default). A new extreme will not print until the cooldown elapses, even if MPO re-enters the zone.
Confirmation
"Confirm Extremes on Bar Close" (default ON) gates extreme markers, pre-alerts, and alerts to closed bars (non-repainting).
Divergences
Pivot-based bullish/bearish divergence; tags appear only after left/right bars elapse (lookbackPivot).
MTF
HTF MPO retrieved with lookahead_off; values can update intrabar and finalize at HTF close. This is disclosed and expected.
Inputs and defaults (key ones)
Core: Sensitivity=1.0; Analysis Period=14; Smoothing=3; Adaptive Thresholds=OFF.
Extremes: Base High=70, Base Low=30; Extreme Sensitivity=1.5; Confirm Extremes on Bar Close=ON; Cooldown=5; Dot size Small/Tiny.
Visuals: Heatmap ON; 3D depth optional; Strength bars ON; Pre-alerts OFF; Divergences ON with tags ON; Gradient candles OFF; Glow ON.
Dashboard: ON; Position=Top Right; Size=Normal; MTF ON; HTF=60m; compact overlay table on price chart.
Advanced caps: Max Oscillator Labels=80; Max Extreme Guide Lines=80; Divergence objects=60.
Dashboard: what each element means
Header: EPZ ANALYSIS.
Large readout: Current MPO; color reflects state (extreme, approaching, or neutral).
Status badge: "Extreme High/Low", "Approaching High/Low", "Bullish/Neutral/Bearish".
HTF cell (when MTF ON): Higher-timeframe MPO, color-coded vs extremes; updates intrabar, settles at HTF close.
Predicted (when MTF OFF): Simple MPO extrapolation using momentum/acceleration—illustrative only.
Thresholds: Current thrHigh/thrLow (static or adaptive).
Components: ASCII bars + values for PRV, MCD, PDI, SMF.
Market metrics: Volume Ratio (x) and ATR% of price.
Strength: Bar indicator of |MPO − 50| × 2.
Confidence: Heuristic gauge (100 in extremes, 70 in warnings, 50 with divergence, else |MPO − 50|). Convenience only, not probability.
How to read the oscillator
MPO Value (0–100): A reading of 50 is neutral. Values above ~55 are increasingly bullish (green), while below ~45 are increasingly bearish (red). Think of these as "market pressure".
Extreme Zones: When MPO climbs into the bright orange/red area (above the base-high line, default 70), the chart will display a dot and downward arrow marking that extreme. Traders often treat this as a sign to tighten stops or look for shorts. Similarly, a bright green dot/up-arrow appears when MPO falls below the base-low (30), hinting at a bullish setup.
Heatmap/Candles: If "Pressure Heatmap" is enabled, the background of the oscillator pane will fade green or red depending on MPO. Users can optionally color the price candles by MPO value (gradient candles) to see these extremes on the main chart.
Prediction Zone(optional): A dashed projection line extends the MPO forward by a small number of bars (prediction_bars) using current MPO momentum and acceleration. This is a heuristic extrapolation best used for short horizons (1–5 bars) to anticipate whether MPO may touch a warning or extreme zone. It is provisional and becomes less reliable with longer projection lengths — always confirm predicted moves with bar-close MPO and HTF context before acting.
Divergences: When price makes a higher high but EPZ makes a lower high (bearish divergence), the indicator can draw dotted lines and a "Bear Div" tag. The opposite (lower low price, higher EPZ) gives "Bull Div". These signals confirm waning momentum at extremes.
Zones: Warning bands near extremes; Extreme zones beyond thresholds.
Crossovers: MPO rising through 35 suggests easing downside pressure; falling through 65 suggests waning upside pressure.
Dots/arrows: Extreme markers appear on closed bars when confirmation is ON and respect the 5-bar cooldown.
Pre-alert dots (optional): Proximity cues in warning zones; also gated to bar close when confirmation is ON.
Histogram: Distance from neutral (50); highlights strengthening or weakening pressure.
Divergence tags: "Bear Div" = higher price high with lower MPO high; "Bull Div" = lower price low with higher MPO low.
Pressure Heatmap : Layered gradient background that visually highlights pressure strength across the MPO scale; adjustable intensity and optional zone overlays (warning / extreme) for quick visual scanning.
A typical reading: If the oscillator is rising from neutral towards the high zone (green→orange→red), the chart may see strong buying culminating in a stall. If it then turns down from the extreme, that peak EPZ dot signals sell pressure.
Alerts
EPZ: Extreme Context — fires on confirmed extremes (respects cooldown).
EPZ: Approaching Threshold — fires in warning zones if no extreme.
EPZ: Divergence — fires on confirmed pivot divergences.
Tip: Set alerts to "Once per bar close" to align with confirmation and avoid intrabar repaint.
Practical usage ideas
Trend continuation: In positive regimes (MPO > 50 and rising), pullbacks holding above 50 often precede continuation; mirror for bearish regimes.
Exhaustion caution: E High/E Low can mark exhaustion risk; many wait for MPO rollover or divergence to time fades or partial exits.
Adaptive thresholds: Useful on assets with shifting volatility regimes to maintain meaningful "extreme" levels.
MTF alignment: Prefer setups that agree with the HTF MPO to reduce countertrend noise.
Examples
Screenshots captured in TradingView Replay to freeze the bar at close so values don't fluctuate intrabar. These examples use default settings and are reproducible on the same bars; they are for illustration, not cherry-picking or performance claims.
Example 1 — BTCUSDT, 1h — E Low
MPO closed at 26.6 (below the 30 extreme), printing a confirmed E Low. HTF MPO is 26.6, so higher-timeframe pressure remains bearish. Components are subdued (Momentum/Pressure/Smart$ ≈ 29–37), with Vol Ratio ≈ 1.19x and ATR% ≈ 0.37%. A prior Bear Div flagged weakening impulse into the drop. With cooldown set to 5 bars, new extremes are rate-limited. Many traders wait for MPO to curl up and reclaim 35 or for a fresh Bull Div before considering countertrend ideas; if MPO cannot reclaim 35 and HTF stays weak, treat bounces cautiously. Educational illustration only.
Example 2 — ETHUSD, 30m — E High
A strong impulse pushed MPO into the extreme zone (≥ 70), printing a confirmed E High on close. Shortly after, MPO cooled to ~61.5 while a Bear Div appeared, showing momentum lag as price pushed a higher high. Volume and volatility were elevated (≈ 1.79x / 1.25%). With a 5-bar cooldown, additional extremes won't print immediately. Some treat E High as exhaustion risk—either waiting for MPO rollover under 65/50 to fade, or for a pullback that holds above 50 to re-join the trend if higher-timeframe pressure remains constructive. Educational illustration only.
Known limitations and caveats
The MPO line itself can change intrabar; extreme markers/alerts do not repaint when "Confirm Extremes on Bar Close" is ON.
HTF values settle at the close of the HTF bar.
Illiquid symbols or very low TFs can be noisy; consider higher thresholds or longer smoothing.
Prediction line (when enabled) is a visual extrapolation only.
For coders
Pine v6. MTF via request.security with lookahead_off.
Extremes include crossover triggers so static thresholds also yield E High/E Low.
Extreme markers and pre-alerts are gated by barstate.isconfirmed when confirmation is ON.
Arrays prune oldest objects to respect resource limits; defaults (80/80/60) are conservative for low TFs.
3D layering uses negative offsets purely for drawing depth (no lookahead).
Screenshot methodology:
To make labels legible and to demonstrate non-repainting behavior, the examples were captured in TradingView Replay with "Confirm Extremes on Bar Close" enabled. Replay is used only to freeze the bar at close so plots don't change intrabar. The examples use default settings, include both Extreme Low and Extreme High cases, and can be reproduced by scrolling to the same bars outside Replay. This is an educational illustration, not a performance claim.
Disclaimer
This script is for educational purposes only and does not constitute financial advice. Markets involve risk; past behavior does not guarantee future results. You are responsible for your own testing, risk management, and decisions.
Mystic Pulse V2.0 [CHE] Mystic Pulse V2.0 — Adaptive DI streaks with gradient intensity for clearer trend persistence
Summary
Mystic Pulse V2.0 measures directional persistence by counting how often the positive or negative directional index strengthens and dominates. These counts drive gradient colors for bars, wicks, and helper plots, so intensity reflects local momentum rather than absolute values. A windowed normalization and gamma control adapt the visuals to recent conditions, preventing one regime from overpowering the next. The result is an immediate, at-a-glance read of trend direction and stamina without relying on crossovers alone.
Motivation: Why this design?
Classical DI and ADX signals can flip during choppy phases or feel sluggish in calm regimes. This script focuses on persistence: it increments a positive or negative streak only when the corresponding directional pressure both strengthens compared with the prior bar and dominates the other side. Simple OHLC pre-smoothing reduces micro-noise, and local normalization keeps the scale relevant to the last segment of data, not a distant past.
What’s different vs. standard approaches?
Reference baseline: Traditional DI and ADX lines with crossovers and fixed-scale thresholds.
Architecture differences:
Wilder-style recursive smoothing on true range and directional movement.
Streak counters for positive and negative pressure that advance only on strengthening and dominance.
Windowed normalization and gamma shaping for visual intensity.
Wick coloring via `plotcandle` with forced overlay from a pane indicator.
Practical effect: Bars and wicks grow more vivid during sustained pressure and fade during indecision. The column plots show streak depth directly, which helps filter one-bar flips.
How it works (technical)
1. Pre-smoothing: Open, high, low, and close are averaged over a short simple moving window to dampen micro-ticks.
2. Directional inputs: True range and directional movement are formed from the smoothed prices, then recursively smoothed using a Wilder-style update that carries prior state forward.
3. DI comparison: The script derives positive and negative directional ratios relative to smoothed range. A side advances its streak when it increases compared with the previous bar and exceeds the opposite side. The other streak resets.
4. Trend score and color base: The difference between positive and negative streaks defines the active side.
5. Normalization and gamma: The absolute streak magnitude and each side’s streak are normalized within a rolling window. Gamma parameters reshape intensity so mid-range values are either compressed or emphasized.
6. Rendering:
Two column plots show positive and negative streak counts in the pane with gradient colors.
A square marker at the bottom uses the global gradient as a compact heat cue.
Bar colors on the main chart use either the gradient, neutral trend colors, or no paint depending on toggles.
Wick, border, and candle overlays are colored via `plotcandle` with forced overlay.
7. State handling: Smoothed values and counters persist across bars; initialization uses first available values without lookahead. No higher-timeframe requests are used, so repaint risk is limited to normal live-bar evolution.
Parameter Guide
Show neutral candles (fallback) — Paints main-chart bars in plain up or down colors when gradients are disabled — Default false — Use when you prefer simple up/down coloring.
Show last N shapes — Limits bottom square markers — Default 333 — Reduce if your chart gets cluttered.
ADX smoothing length — Controls the Wilder smoothing window for range and directional movement — Default 9 — Larger values increase stability but respond later.
OHLC SMA length — Pre-smoothing for inputs — Default 1 — Increase slightly on noisy assets to reduce flip risk.
Gradient barcolor — Enables gradient bar paint on the main chart — Default true — Turn off to use wicks only or neutral bars.
Wick coloring — Colors wicks, borders, and bodies via overlay — Default true — Disable if it conflicts with other overlays.
Gradient window — Lookback for local normalization — Default 100 — Shorter windows adapt faster; longer windows provide steadier intensity.
Gradient transparency — Overall transparency for gradient paints — Default 0 — Increase to make gradients subtler.
Gamma bars/shapes — Contrast for bar and shape intensity — Default 0.70 — Lower values brighten mid-tones; higher values compress them.
Gamma plots — Contrast for the column plots — Default 0.80 — Tune separately from bar intensity.
Wick transparency — Transparency for wick coloring — Default 0 — Raise to let price action show through.
Up/Down colors (dark and neon) — Base and accent colors for both directions — Defaults as provided — Adjust to match your chart theme.
Reading & Interpretation
Pane columns: The green column represents the positive streak count; the red column represents the negative streak count. Taller columns signal stronger persistence.
Gradient marker: The bottom square indicates the active side and persistence strength at a glance.
Main-chart bars and wicks: Color direction shows the dominant side; intensity reflects the normalized and gamma-shaped streak magnitude. Faded tones suggest weak or fading pressure.
Practical Workflows & Combinations
Trend following: Enter in the direction of the active side when the corresponding column expands over several bars. Confirm with structure such as higher highs and higher lows or lower highs and lower lows.
Exits and stops: Consider scaling out when intensity fades toward mid-range while structure stalls. Tighten stops after extended streaks or when wicks lose intensity.
Multi-asset/Multi-TF: Use defaults for liquid assets on intraday to swing timeframes. For highly volatile instruments, raise smoothing and the normalization window. For calm markets, lower them to regain sensitivity.
Behavior, Constraints & Performance
Repaint/confirmation: Values update during the live bar and stabilize after bar close. No historical repaint beyond normal live-bar updates.
security()/HTF: Not used; cross-timeframe repaint paths do not apply.
Resources: Declared `max_bars_back` two thousand; no explicit loops or arrays; plot and label limits are generous.
Known limits: Streak counters can remain elevated during slow reversals. Very short normalization windows can cause rapid intensity swings. Gaps or extreme spikes may temporarily distort intensity until the window adapts.
Sensible Defaults & Quick Tuning
Start with: ADX smoothing nine, OHLC SMA one, normalization window one hundred, gradient and wick coloring enabled, gamma around zero point seven to zero point eight.
Too many flips: Increase ADX smoothing and the normalization window; consider a small bump in OHLC SMA.
Too sluggish: Decrease ADX smoothing and the normalization window.
Colors overpower chart: Increase gradient and wick transparency or raise gamma to compress mid-tones.
What this indicator is—and isn’t
This is a visualization and signal layer that represents directional persistence and intensity. It does not issue trade entries or exits on its own and is not predictive. Use it alongside market structure, volume, and risk controls.
Disclaimer
The content, including any code, is for educational and informational purposes only and does not constitute financial advice or a recommendation to buy or sell any instrument. Trading involves substantial risk, including the possible loss of principal. Past performance is not indicative of future results. Always do your own research and consider consulting a qualified professional.
Momentum Shift Oscillator (MSO) [SharpStrat]Momentum Shift Oscillator (MSO)
The Momentum Shift Oscillator (MSO) is a custom-built oscillator that combines the best parts of RSI, ROC, and MACD into one clean, powerful indicator. Its goal is to identify when momentum shifts are happening in the market, filtering out noise that a single momentum tool might miss.
Why MSO?
Most traders rely on just one momentum indicator like RSI, MACD, or ROC. Each has strengths, but also weaknesses:
RSI → great for overbought/oversold, but often lags in strong trends.
ROC (Rate of Change) → captures price velocity, but can be too noisy.
MACD Histogram → shows trend strength shifts, but reacts slowly at times.
By blending all three (with adjustable weights), MSO gives a balanced view of momentum. It captures trend strength, velocity, and exhaustion in one oscillator.
How MSO Works
Inputs:
RSI, ROC, and MACD Histogram are calculated with user-defined lengths.
Each is normalized (so they share the same scale of -100 to +100).
You can set weights for RSI, ROC, and MACD to emphasize different components.
The components are blended into a single oscillator value.
Smoothing (SMA, EMA, or WMA) is applied.
MSO plots as a smooth line, color-coded by slope (green rising, red falling).
Overbought and oversold levels are plotted (default: +60 / -60).
A zero line helps identify bullish vs bearish momentum shifts.
How to trade with MSO
Zero line crossovers → crossing above zero suggests bullish momentum; crossing below zero suggests bearish momentum.
Overbought and oversold zones → values above +60 may indicate exhaustion in bullish moves; values below -60 may signal exhaustion in bearish moves.
Slope of the line → a rising line shows strengthening momentum, while a falling line signals fading momentum.
Divergences → if price makes new highs or lows but MSO does not, it can point to a possible reversal.
Why MSO is Unique
Combines trend + momentum + velocity into one view.
Filters noise better than standalone RSI/MACD.
Adapts to both trend-following and mean-reversion styles.
Can be used across any timeframe for confirmation.
Dynamic Stop Loss Optimizer [BackQuant]Dynamic Stop Loss Optimizer
Overview
Stop placement decides expectancy. This tool gives you three professional-grade, adaptive stop engines, ATR, Volatility, and Hybrid. So your exits scale with current conditions instead of guessing fixed ticks. It trails intelligently, redraws as the market evolves, and annotates the chart with clean labels/lines and a compact stats table. Pick the engine that fits the trade, or switch on the fly.
What it does
Calculates three adaptive stops in real time (ATR-based, Volatility-based, and Hybrid) and keeps them trailed as price makes progress.
Shows exactly where your risk lives with on-chart levels, color-coded markers (long/short), and precise “Risk %” labels at the current bar.
Surfaces context you actually use - current ATR, daily volatility, selected method, and the live stop level—in a tidy, movable table.
Fires alerts on stop hits so you can automate exits or journal outcomes without staring at the screen.
Why it matters
Adaptive risk control: Stops expand in fast tape and tighten in quiet tape. You’re not punished for volatility; you’re aligned with it.
Consistency across assets: The same playbook works whether you’re trading indexes, FX, crypto, or equities, because the engine normalizes to each symbol’s behavior.
Cleaner decision-making: One chart shows your entry idea and its invalidation in the same breath. If price trespasses, you know it instantly.
The three methods (choose your engine)
1) ATR Based “Structure-aware” distance
This classic approach keys off Average True Range to set a stop just beyond typical bar-to-bar excursion. It adapts smoothly to changing ranges and respects swing structure.
Use when: you want a steady, intuitive buffer that tracks trend legs without hugging price.
See it in action:
2) Volatility Based “Behavior-aware” distance
This engine derives stop distance from current return volatility (annualized, then scaled back down to the session). It reacts to regime shifts quickly and normalizes risk across symbols with very different prices.
Use when: you want the stop to breathe with realized volatility and respond faster to heat-ups/cool-downs.
See it in action:
3) Hybrid “Best of both worlds”
The Hybrid blends the ATR and Volatility distances into one consensus level, then trails it intelligently. You get the structural common sense of ATR and the regime sensitivity of Vol.
Use when: you want robust, all-weather behavior without micromanaging inputs.
See it in action:
How it trails
Longs: The stop ratchets up with favorable movement and holds its ground on shallow pullbacks. If price closes back into the risk zone, the level refreshes to the newest valid distance.
Shorts: Mirror logic ratchets down with trend, resists noise, and refreshes if price reclaims the zone.
Hybrid trailing: Uses the blended distance and the same “no give-backs” principle to keep gains protected as structure builds.
Reading the chart
Markers: Circles = ATR stops, Crosses = Vol stops, Diamonds = Hybrid. Colors indicate long (red level under price) vs short (green level above price).
Lines: The latest active stop is extended with a dashed line so you can see it at a glance.
Labels: “Long SL / Short SL” shows the exact price and current risk % from the last close no math required.
Table: ATR value, Daily Vol %, your chosen Method, the Current SL, and Risk %—all in one compact block that you can pin top-left/right/center.
Quick workflow
Define the idea: Long or Short, and which engine fits the tape (ATR, Vol, or Hybrid).
Place and trail: Let the optimizer print the level; trail automatically as the move develops.
Manage outcomes: If the line is tagged, you’re out clean. If it holds, you’ve contained heat while giving the trade room to work.
Inputs you’ll actually touch
Calculation Settings
ATR Length / Multiplier: Controls the “structural” cushion.
Volatility Length / Multiplier: Controls the “behavioral” cushion.
Trading Days: 252 or 365 to keep the volatility math aligned with the asset’s trading calendar.
Stop Loss Method
ATR Based | Volatility Based | Hybrid : Switch engines instantly to fit the trade.
Position Type
Long | Short | Both : Show only what you need for the current strategy.
Visual Settings
Show ATR / Vol / Hybrid Stops: Toggle families on/off.
Show Labels: Print price + Risk % at the live stop.
Table Position: Park the metrics where you like.
Coloring
Long/Short/Hybrid colors: Set a palette that matches your theme and stands out on your background.
Practical patterns to watch
Trend-pullback continuation: The stop ratchets behind higher lows (long) or lower highs (short). If price tests the level and rejects, that’s your risk-defined continuation cue.
Break-and-run: After a clean break, the Hybrid will usually sit slightly wider than pure Vol, use it to avoid getting shaken on the first retest.
Range compression: When the ATR and Vol distances converge, the table will show small Risk %. That’s your green light to size up with the same dollar risk, or keep it conservative if you expect expansion.
Alerts
Long Stop Loss Hit : Notifies when price crosses below the live long stop.
Short Stop Loss Hit : Notifies when price crosses above the live short stop.
Why this feels “set-and-serious”
You get a single look that answers three questions in real time: “Where’s my line in the sand?”, “How much heat am I taking right now?”, and “Is this distance appropriate for current conditions?” With ATR, Vol, and Hybrid in one tool, you can run the exact same playbook across symbols and regimes while keeping your chart clean and your risk explicit.
FU + SMI Validator (Proper FU, 30m)Overview
The FU + SMI Validator is a sophisticated technical analysis indicator designed to detect Proper FU (Fakeouts or Liquidity Sweeps) on the 30-minute timeframe. This tool aims to help traders identify high-probability reversal setups that occur when price briefly breaks key levels (sweeping liquidity), then reverses with momentum confirmation.
Fakeouts are common market events where price action “hunts stops” before reversing direction. Correctly identifying these events can offer excellent entry points with defined risk. This indicator combines price action logic with momentum and volatility filters to provide reliable signals.
Core Concepts
Proper FU (Fakeout) Detection
At its core, the script identifies proper fakeouts by checking if the current bar’s price:
For bullish fakeouts: dips below the previous bar’s low (sweeping stops) and then closes above the previous bar’s high
For bearish fakeouts: spikes above the previous bar’s high and then closes below the previous bar’s low
This ensures that the breakout is a true sweep rather than just a one-sided close.
Optionally, the script can require one additional confirmation bar after the FU, ensuring that the momentum is sustained and reducing false signals.
SMI-style Momentum Validation
To improve the quality of signals, the indicator uses a proxy for the Stochastic Momentum Index (SMI) by calculating the difference between current and past linear regression slopes of price. This momentum check helps ensure that fakeouts occur alongside actual directional strength.
Key points:
Momentum must be increasing in the direction of the FU signal.
Momentum filters can be enabled or disabled based on user preference.
Squeeze Condition to Avoid Low-Volatility Traps
The script includes a volatility filter based on a squeeze-like condition:
It compares Bollinger Bands (BB) and Keltner Channels (KC).
When BB bands contract inside KC bands, the market is in a squeeze state, signaling low volatility.
Fakeouts during squeeze conditions are often unreliable; the script can filter these out to reduce false alarms.
Killzone Session Timing Filter
Recognizing that liquidity and volatility vary by session, this tool supports optional filtering for:
London Killzone: 09:00 to 10:30 (UK time)
New York Killzone: 13:00 to 14:30 (UK time)
Signals only trigger during these high-activity windows if enabled, helping traders focus on periods with the best liquidity and market participation.
Note: For Killzone filtering to work accurately, your TradingView chart must be set to the UK timezone.
Features & Benefits
Robust FU detection ensures the breakout price action is meaningful, reducing noise.
Momentum filter via linear regression slope captures trend strength in a smooth, mathematically sound way.
Low-volatility squeeze avoidance helps reduce false signals in choppy or range-bound markets.
Killzone timing filter focuses your attention on the most liquid and active market hours.
Optional confirmation bar increases signal reliability.
Raw FU markers allow visualization of all detected fakeouts for pattern recognition and manual analysis.
Alerts built-in for both valid buy and sell FU setups, enabling real-time notification and quicker decision-making.
Customization Options
Killzone usage: Enable or disable the session timing filter.
Sessions: Configure London and New York killzone time ranges.
Momentum alignment: Enable or disable momentum filter based on SMI proxy.
Volatility filter: Avoid signals during squeeze or low-volatility conditions.
FU confirmation: Option to require one additional confirming candle after the initial FU.
Squeeze and momentum parameters: Adjust Bollinger Bands length and multiplier, Keltner Channel length and ATR multiplier.
Raw FU markers: Show or hide all detected fakeouts regardless of filters.
How to Use This Indicator
Apply to 30-minute charts for forex pairs, indices, cryptocurrencies, or other instruments.
Set your chart timezone to UK time if using Killzone filters.
Adjust input parameters based on your preferred sessions and risk tolerance.
Look for green “VALID BUY FU” labels below bars for bullish fakeout entries.
Look for red “VALID SELL FU” labels above bars for bearish fakeout entries.
Use the alert system to receive notifications on setups.
Combine with your existing analysis or risk management strategy for entries, stops, and profit targets.
Why Use FU + SMI Validator?
Fakeouts are some of the most lucrative but tricky setups for many traders. Without proper filters, they can lead to false entries and losses. This script integrates price action, momentum, volatility, and session timing into one package, providing a robust tool to spot high-quality fakeout opportunities and improve trading confidence.
Limitations
Requires chart to be set to UK timezone for session filters.
Designed specifically for 30-minute timeframe — performance on other timeframes may vary.
Momentum is a proxy, not a direct SMI calculation.
Like all indicators, best used in conjunction with sound risk management and other analysis tools.
Potential Enhancements
Conversion into a full strategy script for backtesting entries and exits.
Addition of other momentum indicators (RSI, MACD) or volume filters.
Customizable time zones or auto time zone detection.
Multi-timeframe analysis capabilities.
Visual dashboard for summary of signal stats.
LSMAsThis indicator calculates and plots two Least Squares Moving Averages (LSMA) based on different lengths and a Smoothed Moving Average (SMMA) of the longer LSMA.
Inputs
lengthA : Period length for the first, longer LSMA.
lengthB : Period length for the second, shorter LSMA.
signAl : Signal period used in SMMA smoothing.
Calculations
LSMA-A and LSMA-B : Calculates the linear regression (least squares) of source over lengthA and lengthB respectively, with no offset. These represent two LSMAs, one slow and one fast.
SMMA : This is a smoothed moving average of the longer LSMA (LSMA-A).
Purpose
This indicator helps traders identify trend directions and momentum by using two least squares regression lines of different lengths to capture short- and long-term trends in price. The SMMA smoothing of the longer LSMA may be used as a signal or confirmation line to reduce noise and produce smoother signals.
It generates buy and sell signals based on the intersection of the LSMA-A and SMMA. If the LSMA-A crosses the SMMA upwards, a BUY signal is generated; if it crosses the SMMA downwards, a SELL signal is generated.
The LSMA-B, which is short-term, can be used for wave analysis. When a peak forms, a high is observed on the chart, and when a valley forms, a low is observed. This allows us to determine whether the wave is rising or falling.
Summary
Two LSMAs are calculated: one slow (lengthA), one fast (lengthB).
A smoothed moving average (SMMA) of the slow LSMA is computed using the signal length (signAl).
All three curves are overlaid on the price chart for visual trend and momentum analysis.
FiniteStateMachine🟩 OVERVIEW
A flexible framework for creating, testing and implementing a Finite State Machine (FSM) in your script. FSMs use rules to control how states change in response to events.
This is the first Finite State Machine library on TradingView and it's quite a different way to think about your script's logic. Advantages of using this vs hardcoding all your logic include:
• Explicit logic : You can see all rules easily side-by-side.
• Validation : Tables show your rules and validation results right on the chart.
• Dual approach : Simple matrix for straightforward transitions; map implementation for concurrent scenarios. You can combine them for complex needs.
• Type safety : Shows how to use enums for robustness while maintaining string compatibility.
• Real-world examples : Includes both conceptual (traffic lights) and practical (trading strategy) demonstrations.
• Priority control : Explicit control over which rules take precedence when multiple conditions are met.
• Wildcard system : Flexible pattern matching for states and events.
The library seems complex, but it's not really. Your conditions, events, and their potential interactions are complex. The FSM makes them all explicit, which is some work. However, like all "good" pain in life, this is front-loaded, and *saves* pain later, in the form of unintended interactions and bugs that are very hard to find and fix.
🟩 SIMPLE FSM (MATRIX-BASED)
The simple FSM uses a matrix to define transition rules with the structure: state > event > state. We look up the current state, check if the event in that row matches, and if it does, output the resulting state.
Each row in the matrix defines one rule, and the first matching row, counting from the top down, is applied.
A limitation of this method is that you can supply only ONE event.
You can design layered rules using widlcards. Use an empty string "" or the special string "ANY" for any state or event wildcard.
The matrix FSM is foruse where you have clear, sequential state transitions triggered by single events. Think traffic lights, or any logic where only one thing can happen at a time.
The demo for this FSM is of traffic lights.
🟩 CONCURRENT FSM (MAP-BASED)
The map FSM uses a more complex structure where each state is a key in the map, and its value is an array of event rules. Each rule maps a named condition to an output (event or next state).
This FSM can handle multiple conditions simultaneously. Rules added first have higher priority.
Adding more rules to existing states combines the entries in the map (if you use the supplied helper function) rather than overwriting them.
This FSM is for more complex scenarios where multiple conditions can be true simultaneously, and you need to control which takes precedence. Like trading strategies, or any system with concurrent conditions.
The demo for this FSM is a trading strategy.
🟩 HOW TO USE
Pine Script libraries contain reusable code for importing into indicators. You do not need to copy any code out of here. Just import the library and call the function you want.
For example, for version 1 of this library, import it like this:
import SimpleCryptoLife/FiniteStateMachine/1
See the EXAMPLE USAGE sections within the library for examples of calling the functions.
For more information on libraries and incorporating them into your scripts, see the Libraries section of the Pine Script User Manual.
🟩 TECHNICAL IMPLEMENTATION
Both FSM implementations support wildcards using blank strings "" or the special string "ANY". Wildcards match in this priority order:
• Exact state + exact event match
• Exact state + empty event (event wildcard)
• Empty state + exact event (state wildcard)
• Empty state + empty event (full wildcard)
When multiple rules match the same state + event combination, the FIRST rule encountered takes priority. In the matrix FSM, this means row order determines priority. In the map FSM, it's the order you add rules to each state.
The library uses user-defined types for the map FSM:
• o_eventRule : Maps a condition name to an output
• o_eventRuleWrapper : Wraps an array of rules (since maps can't contain arrays directly)
Everything uses strings for maximum library compatibility, though the examples show how to use enums for type safety by converting them to strings.
Unlike normal maps where adding a duplicate key overwrites the value, this library's `m_addRuleToEventMap()` method *combines* rules, making it intuitive to build rule sets without breaking them.
🟩 VALIDATION & ERROR HANDLING
The library includes comprehensive validation functions that catch common FSM design errors:
Error detection:
• Empty next states
• Invalid states not in the states array
• Duplicate rules
• Conflicting transitions
• Unreachable states (no entry/exit rules)
Warning detection:
• Redundant wildcards
• Empty states/events (potential unintended wildcards)
• Duplicate conditions within states
You can display validation results in tables on the chart, with tooltips providing detailed explanations. The helper functions to display the tables are exported so you can call them from your own script.
🟩 PRACTICAL EXAMPLES
The library includes four comprehensive demos:
Traffic Light Demo (Simple FSM) : Uses the matrix FSM to cycle through traffic light states (red → red+amber → green → amber → red) with timer events. Includes pseudo-random "break" events and repair logic to demonstrate wildcards and priority handling.
Trading Strategy Demo (Concurrent FSM) : Implements a realistic long-only trading strategy using BOTH FSM types:
• Map FSM converts multiple technical conditions (EMA crosses, gaps, fractals, RSI) into prioritised events
• Matrix FSM handles state transitions (idle → setup → entry → position → exit → re-entry)
• Includes position management, stop losses, and re-entry logic
Error Demonstrations : Both FSM types include error demos with intentionally malformed rules to showcase the validation system's capabilities.
🟩 BRING ON THE FUNCTIONS
f_printFSMMatrix(_mat_rules, _a_states, _tablePosition)
Prints a table of states and rules to the specified position on the chart. Works only with the matrix-based FSM.
Parameters:
_mat_rules (matrix)
_a_states (array)
_tablePosition (simple string)
Returns: The table of states and rules.
method m_loadMatrixRulesFromText(_mat_rules, _rulesText)
Loads rules into a rules matrix from a multiline string where each line is of the form "current state | event | next state" (ignores empty lines and trims whitespace).
This is the most human-readable way to define rules because it's a visually aligned, table-like format.
Namespace types: matrix
Parameters:
_mat_rules (matrix)
_rulesText (string)
Returns: No explicit return. The matrix is modified as a side-effect.
method m_addRuleToMatrix(_mat_rules, _currentState, _event, _nextState)
Adds a single rule to the rules matrix. This can also be quite readble if you use short variable names and careful spacing.
Namespace types: matrix
Parameters:
_mat_rules (matrix)
_currentState (string)
_event (string)
_nextState (string)
Returns: No explicit return. The matrix is modified as a side-effect.
method m_validateRulesMatrix(_mat_rules, _a_states, _showTable, _tablePosition)
Validates a rules matrix and a states array to check that they are well formed. Works only with the matrix-based FSM.
Checks: matrix has exactly 3 columns; no empty next states; all states defined in array; no duplicate states; no duplicate rules; all states have entry/exit rules; no conflicting transitions; no redundant wildcards. To avoid slowing down the script unnecessarily, call this method once (perhaps using `barstate.isfirst`), when the rules and states are ready.
Namespace types: matrix
Parameters:
_mat_rules (matrix)
_a_states (array)
_showTable (bool)
_tablePosition (simple string)
Returns: `true` if the rules and states are valid; `false` if errors or warnings exist.
method m_getStateFromMatrix(_mat_rules, _currentState, _event, _strictInput, _strictTransitions)
Returns the next state based on the current state and event, or `na` if no matching transition is found. Empty (not na) entries are treated as wildcards if `strictInput` is false.
Priority: exact match > event wildcard > state wildcard > full wildcard.
Namespace types: matrix
Parameters:
_mat_rules (matrix)
_currentState (string)
_event (string)
_strictInput (bool)
_strictTransitions (bool)
Returns: The next state or `na`.
method m_addRuleToEventMap(_map_eventRules, _state, _condName, _output)
Adds a single event rule to the event rules map. If the state key already exists, appends the new rule to the existing array (if different). If the state key doesn't exist, creates a new entry.
Namespace types: map
Parameters:
_map_eventRules (map)
_state (string)
_condName (string)
_output (string)
Returns: No explicit return. The map is modified as a side-effect.
method m_addEventRulesToMapFromText(_map_eventRules, _configText)
Loads event rules from a multiline text string into a map structure.
Format: "state | condName > output | condName > output | ..." . Pairs are ordered by priority. You can have multiple rules on the same line for one state.
Supports wildcards: Use an empty string ("") or the special string "ANY" for state or condName to create wildcard rules.
Examples: " | condName > output" (state wildcard), "state | > output" (condition wildcard), " | > output" (full wildcard).
Splits lines by \n, extracts state as key, creates/appends to array with new o_eventRule(condName, output).
Call once, e.g., on barstate.isfirst for best performance.
Namespace types: map
Parameters:
_map_eventRules (map)
_configText (string)
Returns: No explicit return. The map is modified as a side-effect.
f_printFSMMap(_map_eventRules, _a_states, _tablePosition)
Prints a table of map-based event rules to the specified position on the chart.
Parameters:
_map_eventRules (map)
_a_states (array)
_tablePosition (simple string)
Returns: The table of map-based event rules.
method m_validateEventRulesMap(_map_eventRules, _a_states, _a_validEvents, _showTable, _tablePosition)
Validates an event rules map to check that it's well formed.
Checks: map is not empty; wrappers contain non-empty arrays; no duplicate condition names per state; no empty fields in o_eventRule objects; optionally validates outputs against matrix events.
NOTE: Both "" and "ANY" are treated identically as wildcards for both states and conditions.
To avoid slowing down the script unnecessarily, call this method once (perhaps using `barstate.isfirst`), when the map is ready.
Namespace types: map
Parameters:
_map_eventRules (map)
_a_states (array)
_a_validEvents (array)
_showTable (bool)
_tablePosition (simple string)
Returns: `true` if the event rules map is valid; `false` if errors or warnings exist.
method m_getEventFromConditionsMap(_currentState, _a_activeConditions, _map_eventRules)
Returns a single event or state string based on the current state and active conditions.
Uses a map of event rules where rules are pre-sorted by implicit priority via load order.
Supports wildcards using empty string ("") or "ANY" for flexible rule matching.
Priority: exact match > condition wildcard > state wildcard > full wildcard.
Namespace types: series string, simple string, input string, const string
Parameters:
_currentState (string)
_a_activeConditions (array)
_map_eventRules (map)
Returns: The output string (event or state) for the first matching condition, or na if no match found.
o_eventRule
o_eventRule defines a condition-to-output mapping for the concurrent FSM.
Fields:
condName (series string) : The name of the condition to check.
output (series string) : The output (event or state) when the condition is true.
o_eventRuleWrapper
o_eventRuleWrapper wraps an array of o_eventRule for use as map values (maps cannot contain collections directly).
Fields:
a_rules (array) : Array of o_eventRule objects for a specific state.
Triple-EMA Cloud (3× configurable EMAs + timeframe + fill)About This Script
Name: Triple-EMA Cloud (3× configurable EMAs + timeframe + fill)
What it does:
The script plots three Exponential Moving Averages (EMAs) on your chart.
You can set each EMA’s length (how many bars or days it averages over), source (for example, closing price, opening price, or the midpoint of high + low), and timeframe (you can have one EMA use daily data, another hourly data, etc.).
The indicator draws a “cloud” or channel by shading the area between the outermost two EMAs of the three. This lets you see a band or zone that the price is moving in, defined by those EMAs.
You also get full control over how each of the three EMA‐lines looks: color, thickness, transparency, and plot style (solid line, steps, circles, etc.).
How to Use It (for Beginners)
Here’s how a trader who’s new to charts can use this tool, especially when looking for pullbacks or undercut price action.
Key Concepts
Trend: Imagine the market price is generally going up or down. EMAs are a way to smooth out price movements so you can see the trend more clearly.
Pullback: When a price has been going up (an uptrend), sometimes it dips down a little before going up again. That dip is the pullback. It’s a chance to enter or add to a position at a “better price.”
Undercut: This is when price drops below an important level (for example an EMA) and then comes back up. It looks like it broke below, but then it recovers. That may show reverse pressure or strength building.
How the Script Helps With Pullbacks & Undercuts
Marking Trend Zones with the Cloud
The cloud between the outer EMA lines gives you a zone of expected support/resistance. If the price is above the cloud, that zone can act like a “floor” in uptrends; if it is below, the cloud might act like a “ceiling” in downtrends.
Watching Price vs the EMAs
If the price pulls back toward the cloud (or toward one of the EMAs) and then bounces back up, that’s a signal that the uptrend might continue.
If the price undercuts (goes a bit below) one of the EMAs or the cloud and then returns above it, that can also be a signal. It suggests that even though there was a temporary drop, buyers stepped in.
Using the Three EMAs for Confirmation
Because the script uses three EMAs, you can see how tightly or loosely they are spaced.
If all three EMAs are broadly aligned (for example, in an uptrend: shorter length above longer length, each pulling from reliable price source), that gives more confidence in trend strength.
If the middle EMA (or different source/timeframe) is holding up as support while others are above, it strengthens signal.
Entry & Exit Points
Entry: For example, after a pullback toward the cloud or “mid‐EMA”, wait for price to show a bounce up. That could be a better entry than buying at the top.
Stop Loss / Risk: You might place a stop loss just below the cloud or the lowest of your selected EMAs so that if price breaks through, the idea is invalidated.
Profit Target: Could be a recent high, resistance level, or a fixed reward-risk multiple (for example aiming to make twice what you risked).
Practical Steps for New Traders
Set up the EMAs
Choose simple lengths like 10, 21, 50.
For example, EMA #1 = length 10, source Close, timeframe “current chart”; EMA #2 = length 21, source (H+L)/2; EMA #3 = length 50, maybe timeframe daily.
Observe the Price Action
When price moves up, then dips, see if it comes back near the shaded cloud or one of the EMAs.
See if the dip touches the EMAs lightly (not a big drop) and then price starts climbing again.
Look for undercuts
If price briefly goes below a line (or below cloud) and then closes back above, that’s undercut + recovery. That bounce back is often meaningful.
Manage risk
Only put in money you can afford to lose.
Use small position size until you get comfortable.
Use stop-loss (as mentioned) in case the price doesn’t bounce as expected.
Practice
Put this indicator on charts (stocks you follow) in past time periods. See how price behaved with pullbacks / undercuts relative to the EMAs & cloud. This helps you learn to see signals.
What It Doesn’t Do (and What to Be Careful Of)
It doesn’t predict the future — it simply shows zones and trends. Price can still break down through the cloud.
In a “choppy” market (i.e. when price is going up and down without a clear trend), signals from EMAs / clouds are less reliable. You’ll get more “false bounces.”
Under / overshoots & big news events can break through clean levels, so always watch for confirmation (volume, price behavior) before putting big money in.
Greer Gap# Greer Gap Indicator (No mitigation: i.e. removing false signals)
## Summary
The **Greer Gap Indicator** identifies **Fair Value Gaps (FVGs)** and introduces specialized **Greer Bull Gaps (Blue)** and **Greer Bear Gaps (Orange)** to highlight high-probability trading opportunities. Unlike traditional FVG indicators, it avoids hindsight bias by not removing historical gaps based on future price action, ensuring transparency in signal accuracy. Built upon LuxAlgo’s FVG logic, it adds unique filtering: only the first Greer Gap after an opposite gap is plotted if its level (min for Bull, max for Bear) is not higher/lower than the previous Greer Gap of the same type, while all valid gaps are recorded for comparison. Traders can use these gaps as support/resistance or entry signals, customizable via timeframe, look back, and display options.
## Description
This indicator detects and displays **Fair Value Gaps (FVGs)** on the chart, with a focus on specialized **Greer Gaps**:
- **Bullish Gaps (Green)**: Areas where the low of the current candle is above the high of a previous candle (look back period), indicating potential upward momentum.
- **Bearish Gaps (Red)**: Areas where the high of the current candle is below the low of a previous candle, indicating potential downward momentum.
- **Greer Bull Gaps (Blue)**: A bullish gap that is above the latest bearish gap's max. Only the first such gap after a bearish gap is plotted if it meets criteria (not higher than the previous Greer Bull Gap's min), but all valid ones are recorded for comparison.
- **Greer Bear Gaps (Orange)**: A bearish gap that is below the latest bullish gap's min. Only the first such gap after a bullish gap is plotted if it meets criteria (not lower than the previous Greer Bear Gap's max), but all valid ones are recorded.
## How It Works
The script uses a dynamic look back period to detect FVGs. It maintains a record of all detected gaps and applies additional logic for Greer Gaps:
- **Greer Bull Gaps**: Checks if the new bullish gap's min is above the latest bearish gap's max. Plots only if it's the first since the last bearish gap and its min is <= previous Greer Bull min (or first one).
- **Greer Bear Gaps**: Checks if the new bearish gap's max is below the latest bullish gap's min. Plots only if it's the first since the last bullish gap and its max is >= previous Greer Bear max (or first one).
- **Resets**: A new bearish gap resets the Greer Bull Gap flag, and a new bullish gap resets the Greer Bear Gap flag.
## How to Use
- **Timeframe**: Set a higher timeframe (e.g., 'D' for daily) to detect gaps from that timeframe on the current chart.
- **Look back Period**: Adjust to change gap detection sensitivity (default: 34). Use 2 if you want to compare to LuxAlgo
- **Extend**: Controls how far right the gap boxes extend.
- **Show Options**: Toggle visibility of all bullish/bearish gaps or Greer Gaps.
- **Colors**: Customize colors for each gap type.
- **Application**: Use Greer Gaps as potential support/resistance levels or entry signals, but combine with other analysis for confirmation.
## Originality and Credits
This script is inspired by and builds upon the **"Fair Value Gap "** indicator by LuxAlgo (available on TradingView: ()).
**Credits**: Thanks to LuxAlgo for the core FVG detection logic.
**Significant Changes**:
- Added **Greer Bull and Bear Gap** logic for filtered, directional gaps with reset mechanisms.
- Introduced recording of all valid Greer Gaps without plotting all, to compare levels without hindsight bias.
- **No mitigation/removal of gaps**: Unlike LuxAlgo's approach, which mitigates (removes or alters) gaps based on future price action (e.g., when filled), this can create a hindsight bias where incorrect signals disappear over time. If a signal is used for a trade and later removed due to new data, it doesn't reflect real-time performance accurately. The Greer Gap avoids this by using gap comparisons to validate signals without altering historical boxes, ensuring transparency in when signals were right or wrong.
NY Anchored VWAP and Auto SMANY Anchored VWAP and Auto SMA
This script is a versatile trading indicator for the TradingView platform that combines two powerful components: a New York-anchored Volume-Weighted Average Price (VWAP) and a dynamic Simple Moving Average (SMA). Designed for traders who utilize VWAP for intraday trend analysis, this tool provides a clear visual representation of average price and volatility-adjusted moving averages, generating automated alerts for key crossover signals.
Indicator Components
1. NY Anchored VWAP
The VWAP is a crucial tool that represents the average price of a security adjusted for volume. This version is "anchored" to the start of the New York trading session, resetting at the beginning of each new session. This provides a clean, session-specific anchor point to gauge market sentiment and trend. The VWAP line changes color to reflect its slope:
Green: When the VWAP is trending upwards, indicating a bullish bias.
Red: When the VWAP is trending downwards, indicating a bearish bias.
2. Auto SMA
The Auto SMA is a moving average with a unique twist: its lookback period is not fixed. Instead, it dynamically adjusts based on market volatility. The script measures volatility using the Average True Range (ATR) and a Z-Score calculation.
When volatility is expanding, the SMA's length shortens, making it more sensitive to recent price changes.
When volatility is contracting, the SMA's length lengthens, smoothing out the price action to filter out noise.
This adaptive approach allows the SMA to react appropriately to different market conditions.
Suggested Trading Strategy
This indicator is particularly effective when used on a one-minute chart for identifying high-probability trade entries. The core of the strategy is to trade the crossover between the VWAP and the Auto SMA, with confirmation from a candle close.
The strategy works best when the entry signal aligns with the overall bias of the higher timeframe market structure. For example, if the daily or 4-hour chart is in an uptrend, you would look for bullish signals on the one-minute chart.
Bullish Entry Signal: A potential entry is signaled when the VWAP crosses above the Auto SMA, and is confirmed when the one-minute candle closes above both the VWAP and the SMA. This indicates a potential continuation of the bullish momentum.
Bearish Entry Signal: A potential entry is signaled when the VWAP crosses below the Auto SMA, and is confirmed when the one-minute candle closes below both the VWAP and the SMA. This indicates a potential continuation of the bearish momentum.
The built-in alerts for these crossovers allow you to receive notifications without having to constantly monitor the charts, ensuring you don't miss a potential setup.
Algo + Trendlines :: Medium PeriodThis indicator helps me to avoid overlooking Trendlines / Algolines. So far it doesn't search explicitly for Algolines (I don't consider volume at all), but it's definitely now already not horribly bad.
These are meant to be used on logarithmic charts btw! The lines would be displayed wrong on linear charts.
The biggest challenge is that there are some technical restrictions in TradingView, f. e. a script stops executing if a for-loop would take longer than 0.5 sec.
So in order to circumvent this and still be able to consider as many candles from the past as possible, I've created multiple versions for different purposes that I use like this:
Algo + Trendlines :: Medium Period : This script looks for "temporary highs / lows" (meaning the bar before and after has lower highs / lows) on the daily chart, connects them and shows the 5 ones that are the closest to the current price (=most relevant). This one is good to find trendlines more thoroughly, but only up to 4 years ago.
Algo + Trendlines :: Long Period : This version looks instead at the weekly charts for "temporary highs / lows" and finds out which days caused these highs / lows and connects them, Taking data from the weekly chart means fewer data points to check whether a trendline is broken, which allows to detect trendlines from up to 12 years ago! Therefore it misses some trendlines. Personally I prefer this one with "Only Confirmed" set to true to really show only the most relevant lines. This means at least 3 candle highs / lows touched the line. These are more likely stronger resistance / support lines compared to those that have been touched only twice.
Very important: sometimes you might see dotted lines that suddenly stop after a few months (after 100 bars to be precise). This indicates you need to zoom further out for TradingView to be able to load the full line. Unfortunately TradingView doesn't render lines if the starting point was too long ago, so this is my workaround. This is also the script's biggest advantage: showing you lines that you might have missed otherwise since the starting bars were outside of the screen, and required you to scroll f. e back to 2015..
One more thing to know:
Weak colored line = only 2 "collision" points with candle highs/lows (= not confirmed)
Usual colored line = 3+ "collision" points (= confirmed)
Make sure to move this indicator above the ticker in the Object Tree, so that it is drawn on top of the ticker's candles!
More infos: www.reddit.com
Algo + Trendlines :: Long PeriodThis indicator helps me to avoid overlooking Trendlines / Algolines. So far it doesn't search explicitly for Algolines (I don't consider volume at all), but it's definitely now already not horribly bad.
These are meant to be used on logarithmic charts btw! The lines would be displayed wrong on linear charts.
The biggest challenge is that there are some technical restrictions in TradingView, f. e. a script stops executing if a for-loop would take longer than 0.5 sec.
So in order to circumvent this and still be able to consider as many candles from the past as possible, I've created multiple versions for different purposes that I use like this:
Algo + Trendlines :: Medium Period : This script looks for "temporary highs / lows" (meaning the bar before and after has lower highs / lows) on the daily chart, connects them and shows the 5 ones that are the closest to the current price (=most relevant). This one is good to find trendlines more thoroughly, but only up to 4 years ago.
Algo + Trendlines :: Long Period : This version looks instead at the weekly charts for "temporary highs / lows" and finds out which days caused these highs / lows and connects them, Taking data from the weekly chart means fewer data points to check whether a trendline is broken, which allows to detect trendlines from up to 12 years ago! Therefore it misses some trendlines. Personally I prefer this one with "Only Confirmed" set to true to really show only the most relevant lines. This means at least 3 candle highs / lows touched the line. These are more likely stronger resistance / support lines compared to those that have been touched only twice.
Very important: sometimes you might see dotted lines that suddenly stop after a few months (after 100 bars to be precise). This indicates you need to zoom further out for TradingView to be able to load the full line. Unfortunately TradingView doesn't render lines if the starting point was too long ago, so this is my workaround. This is also the script's biggest advantage: showing you lines that you might have missed otherwise since the starting bars were outside of the screen, and required you to scroll f. e back to 2015..
One more thing to know:
Weak colored line = only 2 "collision" points with candle highs/lows (= not confirmed)
Usual colored line = 3+ "collision" points (= confirmed)
Make sure to move this indicator above the ticker in the Object Tree, so that it is drawn on top of the ticker's candles!
More infos: www.reddit.com
Cnagda Liquidit Trading SystemCnagda Liquidit Trading System helps spot where price is likely to trap traders and reverse, then gives simple, actionable Level to entry, place SL, and take profits with confidence. It blends imbalance zones, trend bias, order blocks, liquidity pools, high-probability fake Signal, and context-aware candle patterns into one clean workflow.
🟩🟥 Imbalance boxes: “Crowd rushed, gaps left”
What it is: Green/red boxes mark fast, one-sided moves where price “skipped” orders—think FVG-like zones that often get revisited.
Why it helps: Price frequently pulls back to “fill” these zones, creating clean retest entries with logical stops.
⏩How to use:
Green box = potential demand retest; Red box = potential supply retest. Enter on pullback into box, not on first impulse. Put stop on far side of box and aim first targets at recent swing points.
↕️ Swing bias (HH/HL vs LH/LL): “Which way is the road?”
What it is: Higher-highs/higher-lows = up-bias; Lower-highs/lower-lows = down-bias. system plots Buy/Sell OB levels aligned with that bias.
Why it helps: Trading with the broader flow reduces “hero trades” against institutions. Bias gives clearer entries and cleaner drawdowns.
⏩How to use:
Up-bias: look for long on Buy OB retests. Down-bias: look for short on Sell OB retests. Wait for a small rejection/engulfing to confirm before triggering.
🧱Order blocks: “Where big players remember”
What it is: last opposite-colored candle before an impulsive move—these zones often hold memory and reaction. system plots these as Buy/Sell OB lines.
Why it helps: Many breakouts pull back to the origin. Good entries often happen on retest, not on the breakout chase.
⏩ How to use:
Let price return into the OB, show wick rejection, and decent volume. Enter with stop beyond OB; define risk-reward before entry.
📊Volume coloring: “How Volume is move?”
What it is: Bar color reflects relative volume; inside bars are black. The dashboard also shows Volume and “Volume vs Prev.”
Why it helps: Patterns without volume often fade; volume validates strength and intent of moves.
⏩ How to use:
Favor entries where imbalance/OB/liquidity-grab coincide with higher volume. If volume is weak, reduce size or skip.
🧲 BSL/SSL liquidity pools: “Fishing for stops”
What it is: Equal highs cluster stops above (BSL); equal lows cluster stops below (SSL). system plots these and highlights the nearest one (“magnet”).
Why it helps: Price often sweeps these pools to trigger stops before reversing. This is a prime trap-reversal location.
⏩ How to use:
Watch nearest BSL/SSL. If price wicks through and closes back inside, anticipate a reversal. Trade reaction, not first poke. When price closes beyond, consider that pool mitigated and move on.
🟢🔴 Advanced liquidity grab: “Catch fakeout”
What it is: Bullish grab = makes a new low beyond a prior low but closes back above it, with a long lower wick, small body, and higher volume. Bearish is mirror. Labeled automatically.
Why it helps: It exposes trap moves (stop hunts) and often precedes true direction.
⏩ How to use:
Best when it aligns with a nearby imbalance/OB and supportive volume. Enter on reversal candle break or on retest. Stop goes beyond sweep wick.
🧠 Smart candlestick patterns (only in right place)
What it is: Engulfing, Hammer, Shooting Star, Hanging Man, Doji (with high volume), Morning/Evening Star, Piercing—but marked “effective” only if context (swing/trend/location) agrees.
Why it helps: same pattern in the wrong place is noise; in the right place, it’s signal.
⏩ How to use:
Location first (BSL/SSL/OB/imbalance), then pattern. Treat pattern as trigger/confirmation—one fresh label shows to keep chart clean.
🧭 Dashboard: “Context in a glance”
⏩ Reversal Level: current swing anchor—expect turns or reactions nearby; great for alerts and planning.
⏩ Volume vs Prev + Volume: Strength meter for signal candle—higher adds conviction.
⏩ Nearest Pool: next “magnet” area—look for sweeps/rejections there.
🧩Step-by-step trading flow (with mindset)
⏩ Set bias: HH/HL = long bias, LH/LL = short bias. Counter-trend only on clean sweeps with strong confirmation.
⏩ Find magnet: Check Nearest Pool (BSL/SSL). Focus attention there; it saves screen time.
⏩ Wait for event: Look for a sweep/grab label, or sharp rejection at pool/OB/imbalance. Avoid FOMO.
⏩ Add confluence: Stack 2–3 of these—imbalance box, OB, contextual pattern, supportive volume.
⏩Plan entry: Bullish: trigger above reversal candle high or take retest of FVG/OB. Stop below sweep wick/zone. Target at least 1:1.5–1:2.
Bearish: mirror above.
⏩Manage smartly: Take partials, move to breakeven or trail thoughtfully. Don’t drag stops inside zone out of emotion.
🎛️ Parameter tuning (to reduce human error)
⏩ swingLen: Smaller = faster but noisier; larger = cleaner but slower. Backtest first, then go live.
⏩ Tolerance (ATR or percent): ATR tolerance adapts to volatility (good for fast markets and lower TFs). Start around 0.15–0.30. In calm markets, try percent 0.05–0.15%.
⏩ minBarsGap: Start with 3–5 so equal highs/lows are truly equal—reduces false pools.
❌Common mistakes → ✅ Better habits
⏩Chasing every breakout → Wait for sweep/rejection, then confirm.
⏩Ignoring volume → Validate strength; cut size or skip on weak volume.
⏩Losing history of pools → If reviewing/backtesting, keep mitigated pools visible (dashed/faded).
⏩Over-tight tolerance/too small swingLen → Increases false signals; backtest to find balance.
📝 checklist (before entry)
⏩ Is there a nearby BSL/SSL and did a sweep/grab happen there?
⏩ Is there a close imbalance/OB that price can retest?
⏩ Do we have an effective pattern plus supportive volume?
⏩Is the stop beyond the wick/zone and RR ≥ 1:1.5?
•?((¯°·._.• 🎀 𝐻𝒶𝓅𝓅𝓎 𝒯𝓇𝒶𝒹𝒾𝓃𝑔 🎀 •._.·°¯((?•
HorizonSigma Pro [CHE]HorizonSigma Pro
Disclaimer
Not every timeframe will yield good results . Very short charts are dominated by microstructure noise, spreads, and slippage; signals can flip and the tradable edge shrinks after costs. Very high timeframes adapt more slowly, provide fewer samples, and can lag regime shifts. When you change timeframe, you also change the ratios between horizon, lookbacks, and correlation windows—what works on M5 won’t automatically hold on H1 or D1. Liquidity, session effects (overnight gaps, news bursts), and volatility do not scale linearly with time. Always validate per symbol and timeframe, then retune horizon, z-length, correlation window, and either the neutral band or the z-threshold. On fast charts, “components” mode adapts quicker; on slower charts, “super” reduces noise. Keep prior-shift and calibration enabled, monitor Hit Rate with its confidence interval and the Brier score, and execute only on confirmed (closed-bar) values.
For example, what do “UP 61%” and “DOWN 21%” mean?
“UP 61%” is the model’s estimated probability that the close will be higher after your selected horizon—directional probability, not a price target or profit guarantee. “DOWN 21%” still reports the probability of up; here it’s 21%, which implies 79% for down (a short bias). The label switches to “DOWN” because the probability falls below your short threshold. With a neutral-band policy, for example ±7%, signals are: Long above 57%, Short below 43%, Neutral in between. In z-score mode, fixed z-cutoffs drive the call instead of percentages. The arrow length on the chart is an ATR-scaled projection to visualize reach; treat it as guidance, not a promise.
Part 1 — Scientific description
Objective.
The indicator estimates the probability that price will be higher after a user-defined horizon (a chosen number of bars) and emits long, short, or neutral decisions under explicit thresholds. It combines multi‑feature, z‑normalized inputs, adaptive correlation‑based weighting, a prior‑shifted sigmoid mapping, optional rolling probability calibration, and repaint‑safe confirmation. It also visualizes an ATR‑scaled forward projection and prints a compact statistics panel.
Data and labeling.
For each bar, the target label is whether price increased over the past chosen horizon. Learning is deliberately backward‑looking to avoid look‑ahead: features are associated with outcomes that are only known after that horizon has elapsed.
Feature engineering.
The feature set includes momentum, RSI, stochastic %K, MACD histogram slope, a normalized EMA(20/50) trend spread, ATR as a share of price, Bollinger Band width, and volume normalized by its moving average. All features are standardized over rolling windows. A compressed “super‑feature” is available that aggregates core trend and momentum components while penalizing excessive width (volatility). Users can switch between a “components” mode (weighted sum of individual features) and a “super” mode (single compressed driver).
Weighting and learning.
Weights are the rolling correlations between features (evaluated one horizon ago) and realized directional outcomes, smoothed by an EMA and optionally clamped to a bounded range to stabilize outliers. This produces an adaptive, regime‑aware weighting without explicit machine‑learning libraries.
Scoring and probability mapping.
The raw score is either the weighted component sum or the weighted super‑feature. The score is standardized again and passed through a sigmoid whose steepness is user‑controlled. A “prior shift” moves the sigmoid’s midpoint to the current base rate of up moves, estimated over the evaluation window, so that probabilities remain well‑calibrated when markets drift bullish or bearish. Probabilities and standardized scores are EMA‑smoothed for stability.
Decision policy.
Two modes are supported:
- Neutral band: go long if the probability is above one half plus a user‑set band; go short if it is below one half minus that band; otherwise stay neutral.
- Z‑score thresholds: use symmetric positive/negative cutoffs on the standardized score to trigger long/short.
Repaint protection.
All values used for decisions can be locked to confirmed (closed) bars. Intrabar updates are available as a preview, but confirmed values drive evaluation and stats.
Calibration.
An optional rolling linear calibration maps past confirmed probabilities to realized outcomes over the evaluation window. The mapping is clipped to the unit interval and can be injected back into the decision logic if desired. This improves reliability (probabilities that “mean what they say”) without necessarily improving raw separability.
Evaluation metrics.
The table reports: hit rate on signaled bars; a Wilson confidence interval for that hit rate at a chosen confidence level; Brier score as a measure of probability accuracy; counts of long/short trades; average realized return by side; profit factor; net return; and exposure (signal density). All are computed on rolling windows consistent with the learning scheme.
Visualization.
On the chart, an arrowed projection shows the predicted direction from the current bar to the chosen horizon, with magnitude scaled by ATR (optionally scaled by the square‑root of the horizon). Labels display either the decision probability or the standardized score. Neutral states can display a configurable icon for immediate recognition.
Computational properties.
The design relies on rolling means, standard deviations, correlations, and EMAs. Per‑bar cost is constant with respect to history length, and memory is constant per tracked series. Graphical objects are updated in place to obey platform limits.
Assumptions and limitations.
The method is correlation‑based and will adapt after regime changes, not before them. Calibration improves probability reliability but not necessarily ranking power. Intrabar previews are non‑binding and should not be evaluated as historical performance.
Part 2 — Trader‑facing description
What it does.
This tool tells you how likely price is to be higher after your chosen number of bars and converts that into Long / Short / Neutral calls. It learns, in real time, which components—momentum, trend, volatility, breadth, and volume—matter now, adjusts their weights, and shows you a probability line plus a forward arrow scaled by volatility.
How to set it up.
1) Choose your horizon. Intraday scalps: 5–10 bars. Swings: 10–30 bars. The default of 14 bars is a balanced starting point.
2) Pick a feature mode.
- components: granular and fast to adapt when leadership rotates between signals.
- super: cleaner single driver; less noise, slightly slower to react.
3) Decide how signals are triggered.
- Neutral band (probability based): intuitive and easy to tune. Widen the band for fewer, higher‑quality trades; tighten to catch more moves.
- Z‑score thresholds: consistent numeric cutoffs that ignore base‑rate drift.
4) Keep reliability helpers on. Leave prior shift and calibration enabled to stabilize probabilities across bullish/bearish regimes.
5) Smoothing. A short EMA on the probability or score reduces whipsaws while preserving turns.
6) Overlay. The arrow shows the call and a volatility‑scaled reach for the next horizon. Treat it as guidance, not a promise.
Reading the stats table.
- Hit Rate with a confidence interval: your recent accuracy with an uncertainty range; trust the range, not only the point.
- Brier Score: lower is better; it checks whether a stated “70%” really behaves like 70% over time.
- Profit Factor, Net Return, Exposure: quick triage of tradability and signal density.
- Average Return by Side: sanity‑check that the long and short calls each pull their weight.
Typical adjustments.
- Too many trades? Increase the neutral band or raise the z‑threshold.
- Missing the move? Tighten the band, or switch to components mode to react faster.
- Choppy timeframe? Lengthen the z‑score and correlation windows; keep calibration on.
- Volatility regime change? Revisit the ATR multiplier and enable square‑root scaling of horizon.
Execution and risk.
- Size positions by volatility (ATR‑based sizing works well).
- Enter on confirmed values; use intrabar previews only as early signals.
- Combine with your market structure (levels, liquidity zones). This model is statistical, not clairvoyant.
What it is not.
Not a black‑box machine‑learning model. It is transparent, correlation‑weighted technical analysis with strong attention to probability reliability and repaint safety.
Suggested defaults (robust starting point).
- Horizon 14; components mode; weight EMA 10; correlation window 500; z‑length 200.
- Neutral band around seven percentage points, or z‑threshold around one‑third of a standard deviation.
- Prior shift ON, Calibration ON, Use calibrated for decisions OFF to start.
- ATR multiplier 1.0; square‑root horizon scaling ON; EMA smoothing 3.
- Confidence setting equivalent to about 95%.
Disclaimer
No indicator guarantees profits. HorizonSigma Pro is a decision aid; always combine with solid risk management and your own judgment. Backtest, forward test, and size responsibly.
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.
Enhance your trading precision and confidence 🚀
Best regards
Chervolino
Adaptive Valuation [BackQuant]Adaptive Valuation
What this is
A composite, zero-centered oscillator that standardizes several classic indicators and blends them into one “valuation” line. It computes RSI, CCI, Demarker, and the Price Zone Oscillator, converts each to a rolling z-score, then forms a weighted average. Optional smoothing, dynamic overbought and oversold bands, and an on-chart table make the inputs and the final score easy to inspect.
How it works
Components
• RSI with its own lookback.
• CCI with its own lookback.
• DM (Demarker) with its own lookback.
• PZO (Price Zone Oscillator) with its own lookback.
Standardization via z-score
Each component is transformed using a rolling z-score over lookback bars:
z = (value − mean) ÷ stdev , where the mean is an EMA and the stdev is rolling.
This puts all inputs on a comparable scale measured in standard deviations.
Weighted blend
The z-scores are combined with user weights w_rsi, w_cci, w_dm, w_pzo to produce a single valuation series. If desired, it is then smoothed with a selected moving average (SMA, EMA, WMA, HMA, RMA, DEMA, TEMA, LINREG, ALMA, T3). ALMA’s sigma input shapes its curve.
Dynamic thresholds (optional)
Two ways to set overbought and oversold:
• Static : fixed levels at ob_thres and os_thres .
• Dynamic : ±k·σ bands, where σ is the rolling standard deviation of the valuation over dynLen .
Bands can be centered at zero or around the valuation’s rolling mean ( centerZero ).
Visualization and UI
• Zero line at 0 with gradient fill that darkens as the valuation moves away from 0.
• Optional plotting of band lines and background highlights when OB or OS is active.
• Optional candle and background coloring driven by the valuation.
• Summary table showing each component’s current z-score, the final score, and a compact status.
How it can be used
• Bias filter : treat crosses above 0 as bullish bias and below 0 as bearish bias.
• Mean-reversion context : look for exhaustion when the valuation enters the OB or OS region, then watch for exits from those regions or a return toward 0.
• Signal confirmation : use the final score to confirm setups from structure or price action.
• Adaptive banding : with dynamic thresholds, OB and OS adjust to prevailing variability rather than relying on fixed lines.
• Component tuning : change weights to emphasize trend (raise DM, reduce RSI/CCI) or range behavior (raise RSI/CCI, reduce DM). PZO can help in swing environments.
Why z-score blending helps
Indicators often live on different scales. Z-scoring places them on a common, unitless axis, so a one-sigma move in RSI has comparable influence to a one-sigma move in CCI. This reduces scale bias and allows transparent weighting. It also facilitates regime-aware thresholds because the dynamic bands scale with recent dispersion.
Inputs to know
• Component lookbacks : rsilb, ccilb, dmlb, pzolb control each raw signal.
• Standardization window : lookback sets the z-score memory. Longer smooths, shorter reacts.
• Weights : w_rsi, w_cci, w_dm, w_pzo determine each component’s influence.
• Smoothing : maType, smoothP, sig govern optional post-blend smoothing.
• Dynamic bands : dyn_thres, dynLen, thres_k, centerZero configure the adaptive OB/OS logic.
• UI : toggle the plot, table, candle coloring, and threshold lines.
Reading the plot
• Above 0 : composite pressure is positive.
• Below 0 : composite pressure is negative.
• OB region : valuation above the chosen OB line. Risk of mean reversion rises and momentum continuation needs evidence.
• OS region : mirror logic on the downside.
• Band exits : leaving OB or OS can serve as a normalization cue.
Strengths
• Normalizes heterogeneous signals into one interpretable series.
• Adjustable component weights to match instrument behavior.
• Dynamic thresholds adapt to changing volatility and drift.
• Transparent diagnostics from the on-chart table.
• Flexible smoothing choices, including ALMA and T3.
Limitations and cautions
• Z-scores assume a reasonably stationary window. Sharp regime shifts can make recent bands unrepresentative.
• Highly correlated components can overweight the same effect. Consider adjusting weights to avoid double counting.
• More smoothing adds lag. Less smoothing adds noise.
• Dynamic bands recalibrate with dynLen ; if set too short, bands may swing excessively. If too long, bands can be slow to adapt.
Practical tuning tips
• Trending symbols: increase w_dm , use a modest smoother like EMA or T3, and use centerZero dynamic bands.
• Choppy symbols: increase w_rsi and w_cci , consider ALMA with a higher sigma , and widen bands with a larger thres_k .
• Multiday swing charts: lengthen lookback and dynLen to stabilize the scale.
• Lower timeframes: shorten component lookbacks slightly and reduce smoothing to keep signals timely.
Alerts
• Enter and exit of Overbought and Oversold, based on the active band choice.
• Bullish and bearish zero crosses.
Use alerts as prompts to review context rather than as stand-alone trade commands.
Final Remarks
We created this to show people a different way of making indicators & trading.
You can process normal indicators in multiple ways to enhance or change the signal, especially with this you can utilise machine learning to optimise the weights, then trade accordingly.
All of the different components were selected to give some sort of signal, its made out of simple components yet is effective. As long as the user calibrates it to their Trading/ investing style you can find good results. Do not use anything standalone, ensure you are backtesting and creating a proper system.
AltCoin & MemeCoin Index Correlation [Eddie_Bitcoin]🧠 Philosophy of the Strategy
The AltCoin & MemeCoin Index Correlation Strategy by Eddie_Bitcoin is a carefully engineered trend-following system built specifically for the highly volatile and sentiment-driven world of altcoins and memecoins.
This strategy recognizes that crypto markets—especially niche sectors like memecoins—are not only influenced by individual price action but also by the relative strength or weakness of their broader sector. Hence, it attempts to improve the reliability of trading signals by requiring alignment between a specific coin’s trend and its sector-wide index trend.
Rather than treating each crypto asset in isolation, this strategy dynamically incorporates real-time dominance metrics from custom indices (OTHERS.D and MEME.D) and combines them with local price action through dual exponential moving average (EMA) crossovers. Only when both the asset and its sector are moving in the same direction does it allow for trade entries—making it a confluence-based system rather than a single-signal strategy.
It supports risk-aware capital allocation, partial exits, configurable stop loss and take profit levels, and a scalable equity-compounding model.
✅ Why did I choose OTHERS.D and MEME.D as reference indices?
I selected OTHERS.D and MEME.D because they offer a sector-focused view of crypto market dynamics, especially relevant when trading altcoins and memecoins.
🔹 OTHERS.D tracks the market dominance of all cryptocurrencies outside the top 10 by market cap.
This excludes not only BTC and ETH, but also major stablecoins like USDT and USDC, making it a cleaner indicator of risk appetite across true altcoins.
🔹 This is particularly useful for detecting "Altcoin Season"—periods where capital rotates away from Bitcoin and flows into smaller-cap coins.
A rising OTHERS.D often signals the start of broader altcoin rallies.
🔹 MEME.D, on the other hand, captures the speculative behavior of memecoin segments, which are often driven by retail hype and social media activity.
It's perfect for timing momentum shifts in high-risk, high-reward tokens.
By using these indices, the strategy aligns entries with broader sector trends, filtering out noise and increasing the probability of catching true directional moves, especially in phases of capital rotation and altcoin risk-on behavior.
📐 How It Works — Core Logic and Execution Model
At its heart, this strategy employs dual EMA crossover detection—one pair for the asset being traded and one pair for the selected market index.
A trade is only executed when both EMA crossovers agree on the direction. For example:
Long Entry: Coin's fast EMA > slow EMA and Index's fast EMA > slow EMA
Short Entry: Coin's fast EMA < slow EMA and Index's fast EMA < slow EMA
You can disable the index filter and trade solely based on the asset’s trend just to make a comparison and see if improves a classic EMA crossover strategy.
Additionally, the strategy includes:
- Adaptive position sizing, based on fixed capital or current equity (compound mode)
- Take Profit and Stop Loss in percentage terms
- Smart partial exits when trend momentum fades
- Date filtering for precise backtesting over specific timeframes
- Real-time performance stats, equity tracking, and visual cues on chart
⚙️ Parameters & Customization
🔁 EMA Settings
Each EMA pair is customizable:
Coin Fast EMA: Default = 47
Coin Slow EMA: Default = 50
Index Fast EMA: Default = 47
Index Slow EMA: Default = 50
These control the sensitivity of the trend detection. A wider spread gives smoother, slower entries; a narrower spread makes it more responsive.
🧭 Index Reference
The correlation mechanism uses CryptoCap sector dominance indexes:
OTHERS.D: Dominance of all coins EXCLUDING Top 10 ones
MEME.D: Dominance of all Meme coins
These are dynamically calculated using:
OTHERS_D = OTHERS_cap / TOTAL_cap * 100
MEME_D = MEME_cap / TOTAL_cap * 100
You can select:
Reference Index: OTHERS.D or MEME.D
Or disable the index reference completely (Don't Use Index Reference)
💰 Position Sizing & Risk Management
Two capital allocation models are supported:
- Fixed % of initial capital (default)
- Compound profits, which scales positions as equity grows
Settings:
- Compound profits?: true/false
- % of equity: Between 1% and 200% (default = 10%)
This is critical for users who want to balance growth with risk.
🎯 Take Profit / Stop Loss
Customizable thresholds determine automatic exits:
- TakeProfit: Default = 99999 (disabled)
- StopLoss: Default = 5 (%)
These exits are percentage-based and operate off the entry price vs. current close.
📉 Trend Weakening Exit (Scale Out)
If the position is in profit but the trend weakens (e.g., EMA color signals trend loss), the strategy can partially close a configurable portion of the position:
- Scale Position on Weak Trend?: true/false
- Scaled Percentage: % to close (default = 65%)
This feature is useful for preserving profits without exiting completely.
📆 Date Filter
Useful for segmenting performance over specific timeframes (e.g., bull vs bear markets):
- Filter Date Range of Backtest: ON/OFF
- Start Date and End Date: Custom time range
OTHER PARAMETERS EXPLANATION (Strategy "Properties" Tab):
- Initial Capital is set to 100 USD
- Commission is set to 0.055% (The ones I have on Bybit)
- Slippage is set to 3 ticks
- Margin (short and long) are set to 0.001% to avoid "overspending" your initial capital allocation
📊 Visual Feedback and Debug Tools
📈 EMA Trend Visualization
The slow EMA line is dynamically color-coded to visually display the alignment between the asset trend and the index trend:
Lime: Coin and index both bullish
Teal: Only coin bullish
Maroon: Only index bullish
Red: Both bearish
This allows for immediate visual confirmation of current trend strength.
💬 Real-Time PnL Labels
When a trade closes, a label shows:
Previous trade return in % (first value is the effective PL)
Green background for profit, Red for losses.
📑 Summary Table Overlay
This table appears in a corner of the chart (user-defined) and shows live performance data including:
Trade direction (yellow long, purple short)
Emojis: 💚 for current profit, 😡 for current loss
Total number of trades
Win rate
Max drawdown
Duration in days
Current trade profit/loss (absolute and %)
Cumulative PnL (absolute and %)
APR (Annualized Percentage Return)
Each metric is color-coded:
Green for strong results
Yellow/orange for average
Red/maroon for poor performance
You can select where this appears:
Top Left
Top Right
Bottom Left
Bottom Right (default)
📚 Interpretation of Key Metrics
Equity Multiplier: How many times initial capital has grown (e.g., “1.75x”)
Net Profit: Total gains including open positions
Max Drawdown: Largest peak-to-valley drop in strategy equity
APR: Annualized return calculated based on equity growth and days elapsed
Win Rate: % of profitable trades
PnL %: Percentage profit on the most recent trade
🧠 Advanced Logic & Safety Features
🛑 “Don’t Re-Enter” Filter
If a trade is closed due to StopLoss without a confirmed reversal, the strategy avoids re-entering in that same direction until conditions improve. This prevents false reversals and repetitive losses in sideways markets.
🧷 Equity Protection
No new trades are initiated if equity falls below initial_capital / 30. This avoids overleveraging or continuing to trade when capital preservation is critical.
Keep in mind that past results in no way guarantee future performance.
Eddie Bitcoin
Multi-TF Trend Table (Configurable)1) What this tool does (in one minute)
A compact, multi‑timeframe dashboard that stacks eight timeframes and tells you:
Trend (fast MA vs slow MA)
Where price sits relative to those MAs
How far price is from the fast MA in ATR terms
MA slope (rising, falling, flat)
Stochastic %K (with overbought/oversold heat)
MACD momentum (up or down)
A single score (0%–100%) per timeframe
Alignment tick when trend, structure, slope and momentum all agree
Use it to:
Frame bias top‑down (M→W→D→…→15m)
Time entries on your execution timeframe when the higher‑TF stack is aligned
Avoid counter‑trend traps when the table is mixed
2) Table anatomy (each column explained)
The table renders 9 columns × 8 rows (one row per timeframe label you define).
TF — The label you chose for that row (e.g., Month, Week, 4H). Cosmetic; helps you read the stack.
Trend — Arrow from fast MA vs slow MA: ↑ if fastMA > slowMA (up‑trend), ↓ otherwise (down‑trend). Cell is green for up, red for down.
Price Pos — One‑character structure cue:
🔼 if price is above both fast and slow MAs (bullish structure)
🔽 if price is below both (bearish structure)
– otherwise (between MAs / mixed)
MA Dist — Distance of price from the fast MA measured in ATR multiples:
XS < S < M < L < XL according to your thresholds (see §3.3). Useful for judging stretch/mean‑reversion risk and stop sizing.
MA Slope — The fast MA one‑bar slope:
↑ if fastMA - fastMA > 0
↓ if < 0
→ if = 0
Stoch %K — Rounded %K value (default 14‑1‑3). Background highlights when it aligns with the trend:
Green heat when trend up and %K ≤ oversold
Red heat when trend down and %K ≥ overbought Tooltip shows K and D values precisely.
Trend % — Composite score (0–100%), the dashboard’s confidence for that timeframe:
+20 if trendUp (fast>slow)
+20 if fast MA slope > 0
+20 if MACD up (signal definition in §2.8)
+20 if price above fast MA
+20 if price above slow MA
Background colours:
≥80 lime (strong alignment)
≥60 green (good)
≥40 orange (mixed)
<40 grey (weak/contrary)
MACD — 🟢 if EMA(12)−EMA(26) > its EMA(9), else 🔴. It’s a simple “momentum up/down” proxy.
Align — ✔ when everything is in gear for that trend direction:
For up: trendUp and price above both MAs and slope>0 and MACD up
For down: trendDown and price below both MAs and slope<0 and MACD down Tooltip spells this out.
3) Settings & how to tune them
3.1 Timeframes (TF1–TF8)
Inputs: TF1..TF8 hold the resolution strings used by request.security().
Defaults: M, W, D, 720, 480, 240, 60, 15 with display labels Month, Week, Day, 12H, 8H, 4H, 1H, 15m.
Tips
Keep a top‑down funnel (e.g., Month→Week→Day→H4→H1→M15) so you can cascade bias into entries.
If you scalp, consider D, 240, 120, 60, 30, 15, 5, 1.
Crypto weekends: consider 2D in place of W to reflect continuous trading.
3.2 Moving Average (MA) group
Type: EMA, SMA, WMA, RMA, HMA. Changes both fast & slow MA computations everywhere.
Fast Length: default 20. Shorten for snappier trend/slope & tighter “price above fast” signals.
Slow Length: default 200. Controls the structural trend and part of the score.
When to change
Swing FX/equities: EMA 20/200 is a solid baseline.
Mean‑reversion style: consider SMA 20/100 so trend flips slower.
Crypto/indices momentum: HMA 21 / EMA 200 will read slope more responsively.
3.3 ATR / Distance group
ATR Length: default 14; longer makes distance less jumpy.
XS/S/M/L thresholds: define the labels in column MA Dist. They are compared to |close − fastMA| / ATR.
Defaults: XS 0.25×, S 0.75×, M 1.5×, L 2.5×; anything ≥L is XL.
Usage
Entries late in a move often occur at L/XL; consider waiting for a pullback unless you are trading breakouts.
For stops, an initial SL around 0.75–1.5 ATR from fast MA often sits behind nearby noise; use your plan.
3.4 Stochastic group
%K Length / Smoothing / %D Smoothing: defaults 14 / 1 / 3.
Overbought / Oversold: defaults 70 / 30 (adjust to 80/20 for trendier assets).
Heat logic (column Stoch %K): highlights when a pullback aligns with the dominant trend (oversold in an uptrend, overbought in a downtrend).
3.5 View
Full Screen Table Mode: centers and enlarges the table (position.middle_center). Great for clean screenshots or multi‑monitor setups.
4) Signal logic (how each datapoint is computed)
Per‑TF data (via a single request.security()):
fastMA, slowMA → based on your MA Type and lengths
%K, %D → Stoch(High,Low,Close,kLen) smoothed by kSmooth, then %D smoothed by dSmooth
close, ATR(atrLen) → for structure and distance
MACD up → (EMA12−EMA26) > EMA9(EMA12−EMA26)
fastMA_prev → yesterday/previous‑bar fast MA for slope
TrendUp → fastMA > slowMA
Price Position → compares close to both MAs
MA Distance Label → thresholds on abs(close − fastMA)/ATR
Slope → fastMA − fastMA
Score (0–100) → sum of the five 20‑point checks listed in §2.7
Align tick → conjunction of trend, price vs both MAs, slope and MACD (see §2.9)
Important behaviour
HTF values are sampled at the execution chart’s bar close using Pine v6 defaults (no lookahead). So the daily row updates only when a daily bar actually closes.
5) How to trade with it (playbooks)
The table is a framework. Entries/exits still follow your plan (e.g., S/D zones, price action, risk rules). Use the table to know when to be aggressive vs patient.
Playbook A — Trend continuation (pullback entry)
Look for Align ✔ on your anchor TFs (e.g., Week+Day both ≥80 and green, Trend ↑, MACD 🟢).
On your execution TF (e.g., H1/H4), wait for Stoch heat with the trend (oversold in uptrend or overbought in downtrend), and MA Dist not at XL.
Enter on your trigger (break of pullback high/low, engulfing, retest of fast MA, or S/D first touch per your plan).
Risk: consider ATR‑based SL beyond structure; size so 0.25–0.5% account risk fits your rules.
Trail or scale at M/L distances or when score deteriorates (<60).
Playbook B — Breakout with confirmation
Mixed stack turns into broad green: Trend % jumps to ≥80 on Day and H4; MACD flips 🟢.
Price Pos shows 🔼 across H4/H1 (above both MAs). Slope arrows ↑.
Enter on the first clean base‑break with volume/impulse; avoid if MA Dist already XL.
Playbook C — Mean‑reversion fade (advanced)
Use only when higher TFs are not aligned and the row you trade shows XL distance against the higher‑TF context. Take quick targets back to fast MA. Lower win‑rate, faster management.
Playbook D — Top‑down filter for Supply/Demand strategy
Trade first retests only in the direction where anchor TFs (Week/Day) have Align ✔ and Trend % ≥60. Skip counter‑trend zones when the stack is red/green against you.
6) Reading examples
Strong bullish stack
Week: ↑, 🔼, S/M, slope ↑, %K=32 (green heat), Trend 100%, MACD 🟢, Align ✔
Day: ↑, 🔼, XS/S, slope ↑, %K=45, Trend 80%, MACD 🟢, Align ✔
Action: Look for H4/H1 pullback into demand or fast MA; buy continuation.
Late‑stage thrust
H1: ↑, 🔼, XL, slope ↑, %K=88
Day/H4: only 60–80%
Action: Likely overextended on H1; wait for mean reversion or multi‑TF alignment before chasing.
Bearish transition
Day flips from 60%→40%, Trend ↓, MACD turns 🔴, Price Pos “–” (between MAs)
Action: Stand aside for longs; watch for lower‑high + Align ✔ on H4/H1 to join shorts.
7) Practical tips & pitfalls
HTF closure: Don’t assume a daily row changed mid‑day; it won’t settle until the daily bar closes. For intraday anticipation, watch H4/H1 rows.
MA Type consistency: Changing MA Type changes slope/structure everywhere. If you compare screenshots, keep the same type.
ATR thresholds: Calibrate per asset class. FX may suit defaults; indices/crypto might need wider S/M/L.
Score ≠ signal: 100% does not mean “must buy now.” It means the environment is favourable. Still execute your trigger.
Mixed stacks: When rows disagree, reduce size or skip. The tool is telling you the market lacks consensus.
8) Customisation ideas
Timeframe presets: Save layouts (e.g., Swing, Intraday, Scalper) as indicator templates in TradingView.
Alternative momentum: Replace the MACD condition with RSI(>50/<50) if desired (would require code edit).
Alerts: You can add alert conditions for (a) Align ✔ changes, (b) Trend % crossing 60/80, (c) Stoch heat events. (Not shipped in this script, but easy to add.)
9) FAQ
Q: Why do I sometimes see a dash in Price Pos? A: Price is between fast and slow MAs. Structure is mixed; seek clarity before acting.
Q: Does it repaint? A: No, higher‑TF values update on the close of their own bars (standard request.security behaviour without lookahead). Intra‑bar they can fluctuate; decisions should be made at your bar close per your plan.
Q: Which columns matter most? A: For trend‑following: Trend, Price Pos, Slope, MACD, then Stoch heat for entries. The Score summarises, and Align enforces discipline.
Q: How do I integrate with ATR‑based risk? A: Use the MA Dist label to avoid chasing at extremes and to size stops in ATR terms (e.g., SL behind structure at ~1–1.5 ATR).
