Zarattini Intra-day Threshold Bands (ZITB)This indicator implements the intraday threshold band methodology described in the research paper by Carlo Zarattini et al.
Overview:
Plots intraday threshold bands based on daily open/close levels.
Supports visualization of BaseUp/BaseDown levels and Threshold Upper/Lower bands.
Optional shading between threshold bands for easier interpretation.
Usage Notes / Limitations:
Originally studied on SPY (US equities), this implementation is adapted for NSE intraday market timing, specifically the NIFTY50 index.
Internally, 2-minute candles are used if the chart timeframe is less than 2 minutes.
Values may be inaccurate if the chart timeframe is more than 1 day.
Lookback days are auto-capped to avoid exceeding TradingView’s 5000-bar limit.
The indicator automatically aligns intraday bars across multiple days to compute average deltas.
For better returns, it is recommended to use this indicator in conjunction with VWAP and a volatility-based position sizing mechanism.
Can be used as a reference for Open Range Breakout (ORB) strategies.
Customizations:
Toggle plotting of base levels and thresholds.
Toggle shading between thresholds.
Line colors and styles can be adjusted in the Style tab.
Intended for educational and research purposes only.
This indicator implements the approach described in the research paper by Zarattini et al.
Note: This implementation is designed for the NSE NIFTY50 index. While Zarattini’s original study was conducted on SPY, this version adapts the methodology for the Indian market.
Methodology Explanation
This indicator is primarily designed for Open Range Breakout (ORB) strategies.
Base Levels
BaseUp = Maximum of today’s open and previous day’s close
BaseDown = Minimum of today’s open and previous day’s close
Delta Calculation
For the past 14 trading days (lookbackDays), the delta for each intraday candle is calculated as the ab
solute difference from the close of the first candle of that day.
Average Delta
For a given intraday time/candle today, deltaAvg is computed as the average of the deltas at the same time across the previous 14 days.
Threshold Bands
ThresholdUp = BaseUp + deltaAvg
ThresholdDown = BaseDown − deltaAvg
Signals
Spot price moving above ThresholdUp → Long signal
Spot price moving below ThresholdDown → Short signal
Tip: For better returns, combine this indicator with VWAP and a volatility-based position sizing mechanism.
Индикаторы и стратегии
NFCI National Financial Conditions IndexChicago Fed National Financial Conditions Index (NFCI)
This indicator plots the Chicago Fed’s National Financial Conditions Index (NFCI).
The NFCI updates weekly, and its latest value is displayed across all chart intervals.
The NFCI measures how tight or loose overall U.S. financial conditions are. It combines over 100 weekly indicators from the money, bond, and equity markets—along with credit and leverage data—into a single composite index.
The NFCI has three key subcomponents, each of which can be independently selected within the indicator:
Risk: Captures volatility, credit spreads, and overall market stress.
Credit: Tracks how easy or difficult it is to borrow across households and businesses.
Leverage: Reflects the level of debt and balance-sheet strength in the financial system.
When the NFCI rises, financial conditions are tightening — liquidity is contracting, borrowing costs are climbing, and investors tend to reduce risk.
When the NFCI falls, conditions are loosening — liquidity expands, credit flows more freely, and markets generally become more risk-seeking.
Traders often use the NFCI as a macro backdrop for risk appetite: rising values signal growing stress and defensive positioning, while falling values indicate improving liquidity and a more supportive market environment.
(FTD) Follow-Through Day SignalFollow-Through Day (FTD) Signal
This indicator detects potential Follow-Through Days (FTDs) — a concept popularized by William O’Neil — to help identify possible market trend confirmations.
A Follow-Through Day occurs when an index shows strong upside action on higher volume several days after a market low, suggesting institutional buying rather than short covering.
How it works:
The indicator checks for a session where the price gains a defined minimum percentage from the prior close (default: 1.2% or more).
Volume must be greater than the previous day’s volume.
The rally must occur at least three days after a recent low, determined by the lookback period (default: 20 days).
Additional safeguards require that recent bars are not making new lows and that the bar three days prior either closed positive or was not at a new low — filtering out false signals from oversold bounces.
When all conditions are met, a blue up arrow is plotted beneath the bar, and an optional “FTD” label appears if enabled.
Inputs:
Min % Gain from Previous Close (%): Sets the minimum daily percentage gain to qualify as a Follow-Through Day.
Lookback Period for Lowest Low Checks: Defines how many bars back to search for a recent market low (default: 20).
Show Signal Label: Toggles the on-chart “FTD” label display.
Usage:
This indicator is intended for use on daily charts of major market indexes — such as the Nasdaq Composite (symbol: IXIC) or broad index ETFs including QQQ, SPY, and DIA — where Follow-Through Day signals are most relevant for confirming potential trend reversals.
Session Streaks [LuxAlgo]The Session Streaks tool allows traders to identify whether a session is bullish or bearish on the chart. It also shows the current session streak, or the number of consecutive bullish or bearish sessions.
The tool features a dashboard with information about the session streaks of the underlying product on the chart.
🔶 USAGE
Analyzing session streaks is commonly used for market timing by studying the number of consecutive sessions over time and how long they last before the market changes direction.
We identify a bullish session as one in which the closing price is equal to or greater than the opening price, and a bearish session as one in which the closing price is below the opening price.
Each session is labeled according to its bias (bullish or bearish) and the number of consecutive sessions of the same type that conform the current streak.
🔹 Dashboard
The dashboard at the top shows information about the current session.
Under the "Streaks" header, historical information about session streaks is displayed, divided into bullish and bearish categories.
Number: Total number of streaks.
Median: The average duration of those streaks. We chose the median over the mean to avoid misrepresentation due to outliers.
Mode: The most common streak duration.
As the image shows, for this particular market, there are more bullish streaks than bearish ones. Bullish streaks have an average duration that is longer than that of bearish streaks, and both have the same most common streak duration.
If the current session is bullish and the median streak duration for bullish sessions is three, then we could consider scenarios in which the next two sessions are bullish.
🔶 DETAILS
🔹 Streaks On Larger Timeframes
On timeframes lower than or equal to Daily, the tool identifies each consecutive session, but this behavior changes on larger timeframes.
On timeframes larger than daily, the tool identifies the last session of each bar. Let's use the chart in the image as a reference.
At the top of the image, there is a daily chart where each session corresponds to each candle. One candle equals one day.
In the middle, we have a weekly chart where each session is the last session of each week, which is usually Friday for the Nasdaq 100 futures contract. The levels and labels displayed correspond to the last session within each candle, which is the last day of each week.
The levels and labels on the monthly chart correspond to the last session of each month, which is the last day of each month.
🔹 Gradient Style
Traders can choose between two different color gradients for the session background. Each gradient provides different information about price behavior within each session.
Horizontal: Green indicates prices at the top of the session range and red indicates prices at the bottom.
Vertical: Green indicates prices that are equal to or greater than the open price and red indicates prices that are below the open price of the session.
🔶 SETTINGS
🔹 Dashboard
Dashboard: Enable or disable the dashboard.
Position: Select the location of the dashboard.
Size: Select the dashboard size.
🔹 Style
Bullish: Select a color for bullish sessions.
Bearish: Select a color for bearish sessions.
Transparency: Select a transparency level from 100 to 0.
Gradient: Select a horizontal or vertical gradient.
Auto Fibonacci LevelsAuto Fibonacci Momentum Zones with Visible Range Table
Overview and Originality
The Auto Fibonacci Momentum Zones indicator offers a streamlined, static overlay of Fibonacci retracement levels inspired by extreme RSI momentum thresholds, enhanced with a dynamic table displaying the high and low of the currently visible chart range. This isn't a repackaged RSI oscillator or basic Fib drawer—common in TradingView's library—but a purposeful fusion of geometric harmony (Fibonacci ratios) with momentum psychology (RSI extremes at 35/85), projected as fixed horizontal reference lines on the price chart. The addition of the visible range table, powered by PineCoders' VisibleChart library, provides real-time context for the chart's current view, enabling traders to quickly assess range compression or expansion relative to these zones.
This script's originality stems from its "static momentum mapping": by hardcoding Fib levels on a dynamic chart, it creates universal psychological support/resistance lines that transcend specific assets or timeframes.
Unlike dynamic Fib tools that auto-adjust to price swings (risking noise in ranging markets) or standalone RSI plots (confined to panes), this delivers clean, bias-adjustable overlays for confluence analysis. The visible range table justifies the library integration—it's not a gratuitous add-on but a complementary tool that quantifies the "screen real estate" of price action, helping users correlate Fib touches with actual volatility. Drawn from original code (no auto-generation or public templates), it builds TradingView's body of knowledge by simplifying multi-tool workflows into one indicator, ideal for discretionary traders who value visual efficiency over algorithmic complexity.
How It Works: Underlying Concepts
Fibonacci retracements, derived from the Fibonacci sequence and the golden ratio (≈0.618), identify potential reversal points based on the idea that markets retrace prior moves in predictable proportions: shallow (23.6%, 38.2%), mid (50%), and deep (61.8%, 78.6%).
Adjustable Outputs
1. The "Invert Fibs" toggle (default: true) for bearish/topping bias, can be flipped aligning with trend context.
2. Fibonacci Levels: Seven semi-transparent horizontal lines are drawn using `hline()`:
- 0.0 at high (gray).
- 0.236: high - (range × 0.236) (light cyan, shallow pullback).
- 0.382: high - (range × 0.382) (teal, common retracement).
- 0.5: midpoint average (green, equilibrium).
- 0.618: high - (range × 0.618) (amber, golden pocket for reversals).
- 0.786: high - (range × 0.786) (orange, deep support).
- 1.0 at low (gray).
Colors progress from cool (shallow) to warm (deep) for intuitive scanning.
3. Optional Fib Labels: Right-edge text labels (e.g., "0.618") appear only if enabled, positioned at the last bar + offset for non-cluttering visibility.
4. Visible Range Table: Leveraging the VisibleChart library's `visible.high()` and `visible.low()` functions, a compact 2x2 table (top-right corner) updates on the last bar to show the extrema of bars currently in view. This mashup enhances utility: Fib zones provide fixed anchors, while the table's dynamic values reveal if price is "pinned" to a zone (e.g., visible high hugging 0.382 signals resistance). The library is invoked sparingly for performance, adding value by bridging static geometry with viewport-aware data—unavailable in built-ins without custom code.
How to Use It
1. Setup:
Add to any chart (e.g., 15M for scalps, Daily for swings). As an overlay, lines appear directly on price candles—adjust chart scaling if needed.
2. Input Tweaks:
Invert Fibs: Enable for downtrends (85 top), disable for uptrends (35 bottom).
Show Fibs: Toggle labels for ratio callouts (off for clean charts).
Show Table: Display/hide the visible high/low summary (red for high, green for low, formatted to 2 decimals).
3. Trading Application:
Zone Confluence: Seek price reactions at each fibonacci level—e.g., a doji at 0.618 + rising volume suggests entry; use 0.0/1.0 as invalidation.
Range Context: Check the table: If visible high/low spans <20% of the Fib arc (e.g., both near 0.5), anticipate breakout; wider spans signal consolidation.
Multi-Timeframe: Overlay on higher TF for bias, lower for precision—e.g., Daily Fibs guide 1H entries.
Enhancements: Pair with volume or candlesticks; set alerts on line crosses via TradingView's built-in tools. Backtest on your symbols to validate (e.g., equities favor 0.382, forex the 0.786).
This indicator automates advanced Fibonacci synthesis dynamically, eliminating manual measurement and calculations.
published by ozzy_livin
Pullback Levels from ATH# ATH Pullback Levels
**Assess correction depth with precision – 5%, 10%, 15%, 20% below All-Time High**
---
### Overview
This indicator draws **horizontal support lines** at **5%, 10%, 15%, and 20%** below the **All-Time High (ATH)** of any asset. Perfect for **swing traders**, **long-term investors**, and **bull market participants** who want to:
- Measure **pullback depth** in real-time
- Identify **potential support zones**
- Set **alerts** when price enters key retracement levels
---
### Features
| Feature | Description |
|--------|-------------|
| **Dynamic ATH Tracking** | Automatically updates with every new high |
| **4 Pullback Levels** | 5%, 10%, 15%, 20% below ATH |
| **Live Pullback % Label** | Shows current % drop from ATH (top-right) |
| **Customizable Lines** | Toggle visibility, change colors & styles |
| **Built-in Alerts** | Trigger on entry into each zone |
| **No Errors** | Works on 50k+ bar charts (BTC, SPX, etc.) |
| **Time-Based Lines** | Uses `xloc.bar_time` – no 500-bar future limit |
---
### How to Use
1. Apply to any chart (stocks, crypto, forex, indices)
2. Watch the **info box** for current pullback %
3. Use lines as **potential buy zones** during corrections
4. Set **alerts** to be notified when price enters a level
> Example: If ATH = $100 →
> - 5% = $95
> - 10% = $90
> - 15% = $85
> - 20% = $80
---
### Inputs
- **Show 5% / 10% / 15% / 20% Level** → Toggle on/off
- **Line Colors** → Fully customizable
- **Line Style** → Solid, Dashed, or Dotted
---
### Alerts
Create alerts directly from the indicator:
- `"Entered 5% Pullback"`
- `"Entered 10% Pullback"`
- etc.
---
### Best For
- Bull market corrections
- Long-term position sizing
- Risk management in uptrends
- Swing entries on dips
---
### Notes
- Works on **all timeframes**
- **Log scale compatible** (lines adjust correctly)
- No repainting – ATH only updates on confirmed highs
---
**Built with Pine Script v6 – Clean, fast, reliable.**
*Happy trading!*
FVG MagicFVG Magic — Fair Value Gaps with Smart Mitigation, Inversion & Auto-Clean-up
FVG Magic finds every tradable Fair Value Gap (FVG), shows who powered it, and then manages each gap intelligently as price interacts with it—so your chart stays actionable and clean.
Attribution
This tool is inspired by the idea popularized in “Volumatic Fair Value Gaps ” by BigBeluga (licensed CC BY-NC-SA 4.0). Credit to BigBeluga for advancing FVG visualization in the community.
Important: This is a from-scratch implementation—no code was copied from the original. I expanded the concept substantially with a different detection stack, a gap state machine (ACTIVE → 50% SQ → MITIGATED → INVERSED), auto-clean up rules, lookback/nearest-per-side pruning, zoom-proof volume meters, and timeframe auto-tuning for 15m/H1/H4.
What makes this version more accurate
Full-coverage detection (no “missed” gaps)
Default ICT-minimal rule (Bullish: low > high , Bearish: high < low ) catches all valid 3-candle FVGs.
Optional Strict filter (stricter structure checks) for traders who prefer only “clean” gaps.
Optional size percentile filter—off by default so nothing is hidden unless you choose to filter.
Correct handling of confirmations (wick vs close)
Mitigation Source is user-selectable: high/low (wick-based) or close (strict).
This avoids false “misses” when you expect wick confirmations (50% or full fill) but your logic required closes.
State-aware labelling to prevent misleading data
The Bull%/Bear% meter is shown only while a gap is ACTIVE.
As soon as a gap is 50% SQ, MITIGATED, or INVERSED, the meter is hidden and replaced with a clear tag—so you never read stale participation stats.
Robust zoom behaviour
The meter uses a fixed bar-width (not pixels), so it stays proportional and readable at any zoom level.
Deterministic lifecycle (no stale boxes)
Remove on 50% SQ (instant or delayed).
Inversion window after first entry: if price enters but doesn’t invert within N bars, the box auto-removes once fully filled.
Inversion clean up: after a confirmed flip, keep for N bars (context) then delete (or 0 = immediate).
Result: charts auto-maintain themselves and never “lie” about relevance.
Clarity near current price
Nearest-per-side (keep N closest bullish & bearish gaps by distance to the midpoint) focuses attention where it matters without altering detection accuracy.
Lookback (bars) ensures reproducible behaviour across accounts with different data history.
Timeframe-aware defaults
Sensible auto-tuning for 15m / H1 / H4 (right-extension length, meter width, inversion windows, clean up bars) to reduce setup friction and improve consistency.
What it does (under the hood)
Detects FVGs using ICT-minimal (default) or a stricter rule.
Samples volume from a 10× lower timeframe to split participation into Bull % / Bear % (sum = 100%).
Manages each gap through a state machine:
ACTIVE → 50% SQ (midline) → MITIGATED (full) → INVERSED (SR flip after fill).
Auto-clean up keeps only relevant levels, per your rules.
Dashboard (top-right) displays counts by side and the active state tags.
How to use it
First run (show everything)
Use Strict FVG Filter: OFF
Enable Size Filter (percentile): OFF
Mitigation Source: high/low (wick-based) or close (stricter), as you prefer.
Remove on 50% SQ: ON, Delay: 0
Read the context
While ACTIVE, use the Bull%/Bear% meter to gauge demand/supply behind the impulse that created the gap.
Confluence with your HTF structure, sessions, VWAP, OB/FVG, RSI/MACD, etc.
Trade interactions
50% SQ: often the highest-quality interaction; if removal is ON, the box clears = “job done.”
Full mitigation then rejection through the other side → tag changes to INVERSED (acts like SR). Keep for N bars, then auto-remove.
Keep the chart tidy (optional)
If too busy, enable Size Filter or set Nearest per side to 2–4.
Use Lookback (bars) to make behaviour consistent across symbols and histories.
Inputs (key ones)
Use Strict FVG Filter: OFF(default)/ON
Enable Size Filter (percentile): OFF(default)/ON + threshold
Mitigation Source: high/low or close
Remove on 50% SQ + Delay
Inversion window after entry (bars)
Remove inversed after (bars)
Lookback (bars), Nearest per side (N)
Right Extension Bars, Max FVGs, Meter width (bars)
Colours: Bullish, Bearish, Inversed fill
Suggested defaults (per TF)
15m: Extension 50, Max 12, Inversion window 8, Clean up 8, Meter width 20
H1: Extension 25, Max 10, Inversion window 6, Clean up 6, Meter width 15
H4: Extension 15, Max 8, Inversion window 5, Clean up 5, Meter width 10
Notes & edge cases
If a wick hits 50% or the far edge but state doesn’t change, you’re likely on close mode—switch to high/low for wick-based behaviour.
If a gap disappears, it likely met a clean up condition (50% removal, inversion window, inversion clean up, nearest-per-side, lookback, or max-cap).
Meters are hidden after ACTIVE to avoid stale percentages.
SuperTrend Cyan — Split ST & Triple Bands (A/B/C)SuperTrend Cyan — Split ST & Triple Bands (A/B/C)
✨ Concept:
The SuperTrend Cyan indicator expands the classical SuperTrend logic into a split-line + triple-band visualization for clearer structure and volatility mapping.
Instead of a single ATR-based line, this tool separates SuperTrend direction from volatility envelopes (A/B/C), providing a layered view of both regime and range compression.
✨ The design goal:
Preserve the simplicity of SuperTrend
Add volatility context via multi-band envelopes
Provide a compact MTF (Multi-Timeframe) summary for broader trend alignment
✨ How It Works
1. SuperTrend Core (Active & Opposite Lines)
Uses ATR-based bands (Factor × ATR-Length).
Active SuperTrend is plotted according to current regime.
Opposite SuperTrend (optional) shows potential reversal threshold.
2. Triple Band System (A/B/C)
Each band (A, B, C) scales from the median price (hl2) by different ATR multipliers.
A: Outer band (wider, long-range context)
B: Inner band (mid-range activity)
C: Core band (closest to price, short-term compression)
Smoothness can be controlled with EMA.
Uptrend fills are lime-toned, downtrend fills are red-toned, with adjustable opacity (gap intensity).
3. Automatic Directional Switch
When the regime flips from up → down (or vice versa), the overlay automatically switches between lower and upper bands for a clean transition.
4. Multi-Timeframe SuperTrend Table
Displays SuperTrend direction across 5m, 15m, 1h, 4h, and 1D frames.
Green ▲ = Uptrend, Red ▼ = Downtrend.
Useful for checking cross-timeframe trend alignment.
✨ How to Read It
Green SuperTrend + Lime Bands
- Uptrend regime; volatility expanding upward
Red SuperTrend + Red Bands
- Downtrend regime; volatility expanding downward
Narrow gaps (A–C)
- Low volatility / compression (potential squeeze)
Wide gaps
- High volatility / active trend phase
Opposite ST line close to price
- Early warning for regime transition
✨ Practical Use
Identify trend direction (SuperTrend color & line position).
Assess volatility conditions (band width and gap transparency).
Watch for MTF alignment: consistent up/down signals across 1h–4h–1D = strong structural trend.
Combine with momentum indicators (e.g., RSI, DFI, PCI) for confirmation of trend maturity or exhaustion.
✨ Customization Tips
ST Factor / ATR Length
- Adjust sensitivity of SuperTrend direction changes
Band ATR Length
- Controls overall smoothness of volatility envelopes
Band Multipliers (A/B/C)
- Define how wide each volatility band extends
Gap Opacity
- Affects visual contrast between layers
MTF Table
- Enable/disable multi-timeframe display
✨ Educational Value
This script visualizes the interaction between trend direction (SuperTrend) and volatility envelopes, helping traders understand how price reacts within layered ATR zones.
It also introduces a clean MTF (multi-timeframe) perspective — ideal for discretionary and system traders alike.
✨ Disclaimer
This indicator is provided for educational and research purposes only.
It does not constitute financial advice or a trading signal.
Use at your own discretion and always confirm with additional tools.
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📘 한국어 설명 (Korean translation below)
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✨개념
SuperTrend Cyan 지표는 기존의 SuperTrend를 확장하여,
추세선 분리(Split Line) + 3중 밴드 시스템(Triple Bands) 으로
시장의 구조적 흐름과 변동성 범위를 동시에 시각화합니다.
단순한 SuperTrend의 강점을 유지하면서도,
ATR 기반의 A/B/C 밴드를 통해 변동성 압축·확장 구간을 직관적으로 파악할 수 있습니다.
✨ 작동 방식
1. SuperTrend 코어 (활성/반대 라인)
ATR×Factor를 기반으로 추세선을 계산합니다.
현재 추세 방향에 따라 활성 라인이 표시되고, “Show Opposite” 옵션을 켜면 반대편 경계선도 함께 보입니다.
2. 트리플 밴드 시스템 (A/B/C)
hl2(중간값)를 기준으로 ATR 배수에 따라 세 개의 밴드를 계산합니다.
A: 외곽 밴드 (가장 넓고 장기 구조 반영)
B: 중간 밴드 (중기적 움직임)
C: 코어 밴드 (가격에 가장 근접, 단기 변동성 반영)
EMA 스무딩으로 부드럽게 조정 가능.
업트렌드 구간은 라임색, 다운트렌드는 빨간색 음영으로 표시됩니다.
3. 자동 전환 시스템
추세가 전환될 때(Up ↔ Down), 밴드 오버레이도 자동으로 교체되어 깔끔한 시각적 구조를 유지합니다.
4. MTF SuperTrend 테이블
5m / 15m / 1h / 4h / 1D 프레임별 SuperTrend 방향을 표시합니다.
초록 ▲ = 상승, 빨강 ▼ = 하락.
복수 타임프레임 정렬 확인용으로 유용합니다.
✨ 해석 방법
초록 SuperTrend + 라임 밴드
- 상승 추세 및 확장 구간
빨강 SuperTrend + 레드 밴드
- 하락 추세 및 확장 구간
밴드 폭이 좁음
- 변동성 축소 (스퀴즈)
밴드 폭이 넓음
- 변동성 확장, 추세 강화
반대선이 근접
- 추세 전환 가능성 높음
✨ 활용 방법
SuperTrend 색상으로 추세 방향을 확인
A/B/C 밴드 폭으로 변동성 수준을 판단
MTF 테이블을 통해 복수 타임프레임 정렬 여부 확인
RSI, DFI, PCI 등 다른 지표와 함께 활용 시, 추세 피로·모멘텀 변화를 조기에 파악 가능
✨ 교육적 가치
이 스크립트는 추세 구조(SuperTrend) 와 변동성 레이어(ATR Bands) 의 상호작용을
시각적으로 학습하기 위한 교육용 지표입니다.
또한, MTF 구조를 통해 시장의 “위계적 정렬(hierarchical alignment)”을 쉽게 인식할 수 있습니다.
✨ 면책
이 지표는 교육 및 연구 목적으로만 제공됩니다.
투자 판단의 책임은 사용자 본인에게 있으며, 본 지표는 매매 신호를 보장하지 않습니다.
Rolling Correlation vs Another Symbol (SPY Default)This indicator visualizes the rolling correlation between the current chart symbol and another selected asset, helping traders understand how closely the two move together over time.
It calculates the Pearson correlation coefficient over a user-defined period (default 22 bars) and plots it as a color-coded line:
• Green line → positive correlation (move in the same direction)
• Red line → negative correlation (move in opposite directions)
• A gray dashed line marks the zero level (no correlation).
The background highlights periods of strong relationship:
• Light green when correlation > +0.7 (strong positive)
• Light red when correlation < –0.7 (strong negative)
Use this tool to quickly spot diversification opportunities, confirm hedges, or understand how assets interact during different market regimes.
Cumulative Delta Volume MTFCumulative Delta Volume MTF (CDV_MTF)
Within volume analytics, “delta (buy − sell)” often acts as a leading indicator for price.
This indicator is a cumulative delta tailored for day trading.
It differs from conventional cumulative delta in two key ways:
Daily Reset
If heavy buying hits into the prior day’s close, a standard cumulative delta “carries” that momentum into the next day’s open. You can then misread direction—selling may actually be dominant, but yesterday’s residue still pushes the delta positive. With Daily Reset, accumulation uses only the current day’s delta, giving you a more reliable, open-to-close read for intraday decision-making.
Timeframe Selection (MTF)
You might chart 30s/15s candles to capture micro structure, while wanting the cumulative delta on 5-minute to judge the broader flow. With Timeframe (MTF), you can view a lower-timeframe chart and a higher-timeframe delta in one pane.
Overview
MTF aggregation: choose the delta’s computation timeframe via Timeframe (empty = chart) (empty = chart timeframe).
Daily Reset: toggle on/off to accumulate strictly within the current session/day.
Display: Candle or Line (Candle supports Heikin Ashi), with Bull/Bear background shading.
Overlays: up to two SMA and two EMA lines.
Panel: plotted in a sub-window (overlay=false).
Example Use Cases
At the open: turn Daily Reset = ON to see the pure, same-day buy/sell build-up.
Entry on lower TF, bias from higher TF: chart at 30s, set Timeframe = 5 to reduce noise and false signals.
Quick read of momentum: Candle + HA + background shading for intuitive direction; confirm with SMA/EMA slope or crosses.
Key Parameters
Timeframe (empty = chart): timeframe used to compute cumulative delta.
Enable Daily Reset: resets accumulation when the trading day changes.
Style: Candle / Line; Heikin Ashi toggle for Candle mode.
SMA/EMA 1 & 2: individual length and color settings.
Background: customize Bull and Bear background colors.
How to Read
Distance from zero: positive build = buy-side dominance; negative = sell-side dominance.
Slope × MAs: use CDV slope and MA direction/crossovers for momentum and potential turns.
Reset vs. non-reset:
ON → isolates intraday net flow.
OFF → tracks multi-day accumulation/dispersion.
Notes & Caveats
The delta here is a heuristic derived from candle body/wick proportions—it is not true bid/ask tape.
MTF updates are based on the selected timeframe’s bar closes; values can fluctuate intrabar.
Date logic follows the symbol’s exchange timezone.
Renders in a separate pane.
Suggested Defaults
Timeframe = 5 (or 15) / Daily Reset = ON
Style = Candle + Heikin Ashi = ON
EMA(50/200) to frame trend context
For the first decisions after the open—and for scalps/day trades throughout the session—MTF × Daily Reset helps you lock onto the flow that actually matters, right now.
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Cumulative Delta Volume MTF（CDV_MTF）
出来高の中でも“デルタ（買い−売り）”は株価の先行指標になりやすい。
本インジケーターはデイトレードに特化した累積デルタです。
通常の累積デルタと異なるポイントは2つ。
デイリーリセット機能
前日の大引けで大きな買いが入ると、通常の累積デルタはその勢いを翌日の寄りにも“持ち越し”ます。実際は売り圧が強いのに、前日の残渣に引っ張られて方向を誤ることがある。デイリーリセットを使えば当日分だけで累積するため、寄り直後からの判断基準として信頼度が上がります。
タイムフレーム指定（MTF）機能
たとえばチャートは30秒足/15秒足で細部の動きを追い、累積デルタは5分足で“大きな流れ”を確認したい──そんなニーズに対応。**一画面で“下位足の値動き × 上位足のフロー”**を同時に把握できます。
概要
MTF対応：Timeframe で集計足を指定（空欄＝チャート足）
デイリーリセット：当日分のみで累積（オン/オフ切替）
表示：Candle/Line（CandleはHA切替可）、背景をBull/Bearで自動塗り分け
補助線：SMA/EMA（各2本）を重ね描き
表示先：サブウィンドウ（overlay=false）
使い方の例
寄りのフロー判定：デイリーリセット＝オンで、寄り直後の純粋な買い/売りの積み上がりを確認
下位足のエントリー × 上位足のバイアス：チャート＝30秒、Timeframe=5分で騙しを減らす
勢いの視認：Candle＋HA＋背景色で直感的に上げ下げを把握、SMA/EMAの傾きで補強
主なパラメータ
Timeframe (empty = chart)：累積に使う時間足
デイリーリセットを有効にする：日付切替で累積をリセット
Style：Candle / Line、Heikin Ashi切替
SMA/EMA 1・2：期間・色を個別設定
背景色：Bull背景 / Bear背景 を任意のトーンに
読み取りのコツ
ゼロからの乖離：＋側へ積み上がるほど買い優位、−側は売り優位
傾き×MA：CDVの傾きと移動平均の方向/クロスで転換やモメンタムを推測
日内/日跨ぎの切替：デイリーリセット＝オンで日内の純流入出、オフで期間全体の偏り
仕様・注意
本デルタはローソクのボディ/ヒゲ比率から近似したヒューリスティックで、実際のBid/Ask集計とは異なります。
MTFは指定足の確定ベースで更新されます。
日付判定はシンボルの取引所タイムゾーン準拠。
推奨初期セット
Timeframe=5（または15）／デイリーリセット=有効
Style=Candle＋HA=有効
EMA(50/200)で流れの比較
寄りの一手、そしてスキャル/デイの判断材料に。MTF×デイリーリセットで、“効いているフロー”を最短距離で捉えます。
MTF 20 SMA Table - DXY**MTF 20 SMA Table - Multi-Timeframe Trend Analysis Dashboard**
**Overview:**
This indicator provides a comprehensive multi-timeframe analysis dashboard that displays the relationship between price and the 20-period Simple Moving Average (SMA) across four key timeframes: 15-minute, 1-hour, 4-hour, and Daily. It's designed to help traders quickly identify trend alignment and potential trading opportunities across multiple timeframes at a glance. It's definitely not perfect but has helped me speed up my backtesting efforts as it's worked well for me eliminating flipping back and forth between timeframes excpet when I have confluence on the table, then I check the HTF.
**How It Works:**
The indicator creates a table overlay on your chart showing three critical metrics for each timeframe:
1. **Price vs SMA (Row 1):** Shows whether price is currently above (bullish) or below (bearish) the 20 SMA
- Green = Price Above SMA
- Red = Price Below SMA
2. **SMA Direction (Row 2):** Indicates the trend direction of the SMA itself over a lookback period
- Green (↗ Rising) = Uptrend
- Red (↘ Falling) = Downtrend
- Gray (→ Flat) = Ranging/Consolidation
3. **Strength (Row 3):** Displays the distance between current price and the SMA in pips
- Purple background = Strong move (>50 pips away)
- Orange background = Moderate move (20-50 pips)
- Gray background = Weak/consolidating (<20 pips)
- Text color: Green for positive distance, Red for negative
**Key Features:**
- **Customizable Table Position:** Place the table anywhere on your chart (9 position options)
- **Adjustable SMA Lengths:** Modify the SMA period for each timeframe independently (default: 20)
- **Direction Lookback Settings:** Fine-tune how far back the indicator looks to determine SMA direction for each timeframe
- **Flat Threshold:** Set the pip threshold for determining when an SMA is "flat" vs trending (default: 5 pips)
- **DXY Optimized:** Calculations are calibrated for the US Dollar Index (1 pip = 0.01)
**Best Use Cases:**
1. **Trend Alignment:** Identify when multiple timeframes align in the same direction for higher probability trades
2. **Divergence Spotting:** Detect when lower timeframes diverge from higher timeframes (potential reversals)
3. **Entry Timing:** Use lower timeframe signals while higher timeframes confirm overall trend
4. **Strength Assessment:** Gauge how extended price is from the mean (SMA) to avoid overextended entries
**Settings Guide:**
- **SMA Settings Group:** Adjust the SMA period for each timeframe (15M, 1H, 4H, Daily)
- **SMA Direction Group:** Control lookback periods to determine trend direction
- 15M: Default 5 candles
- 1H: Default 10 candles
- 4H: Default 15 candles
- Daily: Default 20 candles
- **Flat Threshold:** Set sensitivity for "flat" detection (lower = more sensitive to ranging markets)
**Trading Strategy Examples:**
1. **Trend Following:** Look for all timeframes showing the same direction (all green or all red)
2. **Pullback Trading:** When Daily/4H are green but 15M/1H show red, wait for lower timeframes to flip green for entry
3. **Ranging Markets:** When multiple SMAs show "flat", consider range-bound strategies
**Important Notes:**
- This is a reference tool only, not a standalone trading system
- Always use proper risk management and combine with other analysis methods
- Best suited for trending instruments like indices and major forex pairs
- Calculations are optimized for DXY but can be used on other instruments (pip calculations may need adjustment)
**Credits:**
Feel free to modify and improve this code! Suggestions for enhancements are welcome in the comments.
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**Installation Instructions:**
1. Add the indicator to your TradingView chart
2. Adjust the table position via settings to avoid overlap with price action
3. Customize SMA lengths and lookback periods to match your trading style
4. Monitor the table for timeframe alignment and trend confirmation
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This indicator is published as open source for the community to learn from and improve upon. Happy trading! 📈
Z-Score Bands + SignalsZ-Score Statistical Market Analyzer
A multi-dimensional market structure indicator based on standardized deviation & regime logic
English Description
Concept
This indicator builds a statistical model of price behaviour by converting every candle’s movement into a Z-score — how many standard deviations each close is away from its moving average.
It visualizes the normal distribution structure of returns and provides adaptive entry signals for both Mean Reversion and Breakout regimes.
Rather than predicting price direction, it measures statistical displacement from equilibrium and dynamically adjusts the decision logic according to the market’s volatility regime.
⚙️ Main Components
Z-Score Bands (±1σ, ±2σ, ±3σ)
– The core structure visualizes volatility boundaries based on rolling mean and standard deviation.
– Price outside ±2σ often indicates statistical extremes.
Dual Signal Systems
Mean Reversion (MRL / MRS): when price (or return z-score) crosses back inside ±2σ bands.
Breakout (BOL / BOS): when price continues to expand beyond ±2σ.
Volatility Regime Classification
The indicator detects whether the market is currently in a low-vol or high-vol regime using percentile statistics of σ.
Low vol → Mean Reversion preferred
High vol → Breakout preferred
🧠 Adaptive Switches
A. Freeze MA/σ - Use previous-bar stats to avoid repainting and lag.
B. Confirm on Close - Only generate signals once the base-timeframe bar closes (eliminates look-ahead bias).
C. Return-based Signal - Use log-return Z-score instead of price deviation — normalizes volatility across assets.
D. Outlier Filter - Exclude bars with abnormal single-bar returns (e.g., >20%). Reduces false spikes.
E. Regime Gating - Automatically switch between Mean Reversion and Breakout logic depending on volatility percentile.
Each module can be toggled individually to test different statistical behaviours or tailor to a specific market condition.
📊 Interpretation
When the histogram of returns approximates a normal distribution, mean-reversion logic is often more effective.
When price persistently drifts beyond ±2σ or ±3σ, the distribution becomes leptokurtic (fat-tailed) — a breakout structure dominates.
Hence, this tool can help you:
Identify whether an asset behaves more “Gaussian” or “fat-tailed”;
Select the correct trading regime (MR or BO);
Quantitatively measure market tension and volatility clusters.
🧩 Recommended Use
Works on any timeframe and any asset.
Best used on liquid instruments (e.g., XAU/USD, indices, major FX pairs).
Combine with volume, sentiment or structural filters to confirm signals.
For strategy automation, pair with the companion script:
🧠 “Z-Score Strategy • Multi-Source Confirm (MRL/MRS/BOL/BOS)”.
⚠️ Disclaimer
This script is designed for educational and research purposes.
Statistical deviation ≠ directional prediction — use with sound risk management.
Past distribution patterns may shift under new volatility regimes.
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中文说明（简体）
概念简介
该指标基于价格的统计分布原理，将每根 K 线的波动转化为标准化的 Z-Score（标准差偏离值），用于刻画市场处于均衡或偏离状态。
它同时支持 均值回归（Mean Reversion） 与 突破延展（Breakout） 两种逻辑，并可根据市场波动结构自动切换策略模式。
⚙️ 主要功能模块
Z-Score 通道（±1σ / ±2σ / ±3σ）
用滚动均值与标准差动态绘制的统计波动带，价格超出 ±2σ 区域通常意味着极端偏离。
双信号系统
MRL / MRS（均值回归多空）：价格重新回到 ±2σ 以内时触发。
BOL / BOS（突破延展多空）：价格持续运行在 ±2σ 之外时触发。
波动率分层
自动识别市场处于高波动还是低波动区间：
低波动期 → 适合均值回归逻辑；
高波动期 → 适合突破趋势逻辑。
🧠 A–E 模块说明
A. 固定统计参数：使用上一根 K 线的均值和标准差，防止重绘。
B. 收盘确认信号：仅在当前时间框架收盘后生成信号，避免前视偏差。
C. 收益率信号模式：采用对数收益率的 Z-Score，更具普适性。
D. 异常波过滤：忽略单根极端波动（如 >20%）的噪声信号。
E. 波动率调节逻辑：根据市场处于高/低波动区间，自动切换 MRL/MRS 或 BOL/BOS。
📊 应用解读
如果收益率分布接近正态分布 → 市场倾向震荡，MRL/MRS 效果较佳；
若价格频繁偏离 ±2σ 或 ±3σ → 市场呈现“肥尾”分布，趋势延展占主导。
因此，该指标的核心目标是：
识别当前市场的统计结构类型；
根据波动特征自动切换交易逻辑；
提供结构化、可量化的市场状态刻画。
💡 使用建议
适用于所有时间框架与金融品种。
建议结合成交量或结构性指标过滤。
若用于策略回测，可搭配同名 “Z-Score Strategy • Multi-Source Confirm” 策略脚本。
⚠️ 免责声明
本指标仅用于研究与教学，不构成任何投资建议。
统计偏离 ≠ 趋势预测，实际市场行为可能在不同波动结构下改变。
True Average PriceTrue Average Price
Overview
The indicator plots a single line representing the cumulative average closing price of any symbol you choose. It lets you project a long-term mean onto your active chart, which is useful when your favourite symbol offers limited history but you still want context from an index or data-rich feed.
How It Works
The script retrieves all available historical bars from the selected symbol, sums their closes, counts the bars, and divides the totals to compute the lifetime average. That value is projected onto the chart you are viewing so you can compare current price action to the broader historical mean.
Inputs
Use Symbol : Toggle on to select an alternate symbol; leave off to default to the current chart.
Symbol : Pick the data source used for the average when the toggle is enabled.
Line Color : Choose the display color of the average line.
Line Width : Adjust the thickness of the plotted line.
Usage Tips
Apply the indicator to exchanges with shallow history while sourcing the average from a complete index (e.g., INDEX:BTCUSD for crypto pairs).
Experiment with different symbols to understand how alternative data feeds influence the baseline level.
Disclaimer
This indicator is designed as a technical analysis tool and should be used in conjunction with other forms of analysis and proper risk management.
Past performance does not guarantee future results, and traders should thoroughly test any strategy before implementing it with real capital.
Price Action Brooks ProPrice Action Brooks Pro (PABP) - Professional Trading Indicator
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📊 OVERVIEW
Price Action Brooks Pro (PABP) is a professional-grade TradingView indicator developed based on Al Brooks' Price Action trading methodology. It integrates decades of Al Brooks' trading experience and price action analysis techniques into a comprehensive technical analysis tool, helping traders accurately interpret market structure and identify trading opportunities.
• Applicable Markets: Stocks, Futures, Forex, Cryptocurrencies
• Timeframes: 1-minute to Daily (5-minute chart recommended)
• Theoretical Foundation: Al Brooks Price Action Trading Method
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🎯 CORE FEATURES
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1️⃣ INTELLIGENT GAP DETECTION SYSTEM
Automatically identifies and marks three critical types of gaps in the market.
TRADITIONAL GAP
• Detects complete price gaps between bars
• Upward gap: Current bar's low > Previous bar's high
• Downward gap: Current bar's high < Previous bar's low
• Hollow border design - doesn't obscure price action
• Color coding: Upward gaps (light green), Downward gaps (light pink)
• Adjustable border: 1-5 pixel width options
TAIL GAP
• Detects price gaps between bar wicks/shadows
• Analyzes across 3 bars for precision
• Identifies hidden market structure
BODY GAP
• Focuses only on gaps between bar bodies (open/close)
• Filters out wick noise
• Disabled by default, enable as needed
Trading Significance:
• Gaps signal strong momentum
• Gap fills provide trading opportunities
• Consecutive gaps indicate trend continuation
✓ Independent alert system for all gap types
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2️⃣ RTH BAR COUNT (Trading Session Counter)
Intelligent counting system designed for US stock intraday trading.
FEATURES
• RTH Only Display: Regular Trading Hours (09:30-15:00 EST)
• 5-Minute Chart Optimized: Displays every 3 bars (15-minute intervals)
• Daily Auto-Reset: Counting starts from 1 each trading day
SMART COLOR CODING
• 🔴 Red (Bars 18 & 48): Critical turning moments (1.5h & 4h)
• 🔵 Sky Blue (Multiples of 12): Hourly markers (12, 24, 36...)
• 🟢 Light Green (Bar 6): Half-hour marker (30 minutes)
• ⚫ Gray (Others): Regular 15-minute interval markers
Al Brooks Time Theory:
• Bar 18 (90 min): First 90 minutes determine daily trend
• Bar 48 (4 hours): Important afternoon turning point
• Hourly markers: Track institutional trading rhythm
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3️⃣ FOUR-LINE EMA SYSTEM
Professional-grade configurable moving average system.
DEFAULT CONFIGURATION
• EMA 20: Short-term trend (Al Brooks' most important MA)
• EMA 50: Medium-short term reference
• EMA 100: Medium-long term confirmation
• EMA 200: Long-term trend and bull/bear dividing line
FLEXIBLE CUSTOMIZATION
Each EMA can be independently configured:
• On/Off toggle
• Data source selection (close/high/low/open, etc.)
• Custom period length
• Offset adjustment
• Color and transparency
COLOR SCHEME
• EMA 20: Dark brown, opaque (most important)
• EMA 50/100/200: Blue-purple gradient, 70% transparent
TRADING APPLICATIONS
• Bullish Alignment: Price > 20 > 50 > 100 > 200
• Bearish Alignment: 200 > 100 > 50 > 20 > Price
• EMA Confluence: All within <1% = major move precursor
Al Brooks Quote:
"The EMA 20 is the most important moving average. Almost all trading decisions should reference it."
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4️⃣ PREVIOUS VALUES (Key Prior Price Levels)
Automatically marks important price levels that often act as support/resistance.
THREE INDEPENDENT CONFIGURATIONS
Each group configurable for:
• Timeframe (1D/60min/15min, etc.)
• Price source (close/high/low/open/CurrentOpen, etc.)
• Line style and color
• Display duration (Today/TimeFrame/All)
SMART OPEN PRICE LABELS ⭐
• Auto-displays "Open" label when CurrentOpen selected
• Label color matches line color
• Customizable label size
TYPICAL SETUP
• 1st Line: Previous close (Support/Resistance)
• 2nd Line: Previous high (Breakout target)
• 3rd Line: Previous low (Support level)
Al Brooks Magnet Price Theory:
• Previous open: Price frequently tests opening price
• Previous high/low: Strongest support/resistance
• Breakout confirmation: Breaking prior levels = trend continuation
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5️⃣ INSIDE & OUTSIDE BAR PATTERN RECOGNITION
Automatically detects core candlestick patterns from Al Brooks' theory.
ii PATTERN (Consecutive Inside Bars)
• Current bar contained within previous bar
• Two or more consecutive
• Labels: ii, iii, iiii (auto-accumulates)
• High-probability breakout setup
• Stop loss: Outside both bars
Trading Significance:
"Inside bars are one of the most reliable breakout setups, especially three or more consecutive inside bars." - Al Brooks
OO PATTERN (Consecutive Outside Bars)
• Current bar engulfs previous bar
• Two or more consecutive
• Labels: oo, ooo (auto-accumulates)
• Indicates indecision or volatility increase
ioi PATTERN (Inside-Outside-Inside)
• Three-bar combination: Inside → Outside → Inside
• Auto-detected and labeled
• Tug-of-war pattern
• Breakout direction often very strong
SMART LABEL SYSTEM
• Auto-accumulation counting
• Dynamic label updates
• Customizable size and color
• Positioned above bars
✓ Independent alerts for all patterns
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💡 USE CASES
INTRADAY TRADING
✓ Bar Count (timing rhythm)
✓ Traditional Gap (strong signals)
✓ EMA 20 + 50 (quick trend)
✓ ii/ioi Patterns (breakout points)
SWING TRADING
✓ Previous Values (key levels)
✓ EMA 20 + 50 + 100 (trend analysis)
✓ Gaps (trend confirmation)
✓ iii Patterns (entry timing)
TREND FOLLOWING
✓ All four EMAs (alignment analysis)
✓ Gaps (continuation signals)
✓ Previous Values (targets)
BREAKOUT TRADING
✓ iii Pattern (high-reliability setup)
✓ Previous Values (targets)
✓ EMA 20 (trend direction)
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🎨 DESIGN FEATURES
PROFESSIONAL COLOR SCHEME
• Gaps: Hollow borders + light colors
• Bar Count: Smart multi-color coding
• EMAs: Gradient colors + transparency hierarchy
• Previous Values: Customizable + smart labels
CLEAR VISUAL HIERARCHY
• Important elements: Opaque (EMA 20, bar count)
• Reference elements: Semi-transparent (other EMAs, gaps)
• Hollow design: Doesn't obscure price action
USER-FRIENDLY INTERFACE
• Clear functional grouping
• Inline layout saves space
• All colors and sizes customizable
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📚 AL BROOKS THEORY CORE
READING PRICE ACTION
"Don't try to predict the market, read what the market is telling you."
PABP converts core concepts into visual tools:
• Trend Assessment: EMA system
• Time Rhythm: Bar Count
• Market Structure: Gap analysis
• Trade Setups: Inside/Outside Bars
• Support/Resistance: Previous Values
PROBABILITY THINKING
• ii pattern: Medium probability
• iii pattern: High probability
• iii + EMA 20 support: Very high probability
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⚙️ TECHNICAL SPECIFICATIONS
• Pine Script Version: v6
• Maximum Objects: 500 lines, 500 labels, 500 boxes
• Alert Functions: 8 independent alerts
• Supported Timeframes: All (5-min recommended for Bar Count)
• Compatibility: All TradingView plans, Mobile & Desktop
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🚀 RECOMMENDED INITIAL SETTINGS
GAPS
• Traditional Gap: ✓
• Tail Gap: ✓
• Border Width: 2
BAR COUNT
• Use Bar Count: ✓
• Label Size: Normal
EMA
• EMA 20: ✓
• EMA 50: ✓
• EMA 100: ✓
• EMA 200: ✓
PREVIOUS VALUES
• 1st: close (Previous close)
• 2nd: high (Previous high)
• 3rd: low (Previous low)
INSIDE & OUTSIDE BAR
• All patterns: ✓
• Label Size: Large
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🌟 WHY CHOOSE PABP?
✅ Solid Theoretical Foundation
Based on Al Brooks' decades of trading experience
✅ Complete Professional Features
Systematizes complex price action analysis
✅ Highly Customizable
Every feature adjustable to personal style
✅ Excellent Performance
Optimized code ensures smooth experience
✅ Continuous Updates
Constantly improving based on feedback
✅ Suitable for All Levels
Benefits beginners to professionals
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📖 RECOMMENDED LEARNING
Al Brooks Books:
• "Trading Price Action Trends"
• "Trading Price Action Trading Ranges"
• "Trading Price Action Reversals"
Learning Path:
1. Understand basic candlestick patterns
2. Learn EMA applications
3. Master market structure analysis
4. Develop trading system
5. Continuous practice and optimization
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⚠️ RISK DISCLOSURE
IMPORTANT NOTICE:
• For educational and informational purposes only
• Does not constitute investment advice
• Past performance doesn't guarantee future results
• Trading involves risk and may result in capital loss
• Trade according to your risk tolerance
• Test thoroughly in demo account first
RESPONSIBLE TRADING:
• Always use stop losses
• Control position sizes reasonably
• Don't overtrade
• Continuous learning and improvement
• Keep trading journal
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📜 COPYRIGHT
Price Action Brooks Pro (PABP)
Author: © JimmC98
License: Mozilla Public License 2.0
Pine Script Version: v6
Acknowledgments:
Thanks to Dr. Al Brooks for his contributions to price action trading. This indicator is developed based on his theories.
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Experience professional-grade price action analysis now!
"The best traders read price action, not indicators. But when indicators help you read price action better, use them." - Al Brooks
Previous D/W/M HLOCHey traders,
Here's a simple Multi-Timeframe indicator that essentially turns time and price into a box. It'll take the previous high, low, opening price, or closing price from one of the three timeframes of your choice (day, week, or month). For whatever reason I can't get the opening price to function consistently so if you find improvements feel free to let me know, this will help traders who prefer to use opening price over closing price.
Naturally this form of charting is classical and nature and some key figures you could use to study its usage are
- Richard W. Schabacker (1930s)
- Edwards & Magee (1948)
- Peter Brandt
- Stacey Burke (more on the intraday side - typically our preference)
It's usage put plainly:
- Quantifying Accumulation or Distribution
- Revealing Energy Build-Up (Compression)
- Framing Breakouts and False Breakouts
- Structuring Time
- Identifying opportunities to trade a daily, weekly, or monthly range.
cd_correlation_analys_Cxcd_correlation_analys_Cx
General:
This indicator is designed for correlation analysis by classifying stocks (487 in total) and indices (14 in total) traded on Borsa İstanbul (BIST) on a sectoral basis.
Tradingview's sector classifications (20) have been strictly adhered to for sector grouping.
Depending on user preference, the analysis can be performed within sectors, between sectors, or manually (single asset).
Let me express my gratitude to the code author, @fikira, beforehand; you will find the reason for my thanks in the context.
Details:
First, let's briefly mention how this indicator could have been prepared using the classic method before going into details.
Classically, assets could be divided into groups of forty (40), and the analysis could be performed using the built-in function:
ta.correlation(source1, source2, length) → series float.
I chose sectoral classification because I believe there would be a higher probability of assets moving together, rather than using fixed-number classes.
In this case, 21 arrays were formed with the following number of elements:
(3, 11, 21, 60, 29, 20, 12, 3, 31, 5, 10, 11, 6, 48, 73, 62, 16, 19, 13, 34 and indices (14)).
However, you might have noticed that some arrays have more than 40 elements. This is exactly where @Fikira's indicator came to the rescue. When I examined their excellent indicator, I saw that it could process 120 assets in a single operation. (I believe this was the first limit overrun; thanks again.)
It was amazing to see that data for 3 pairs could be called in a single request using a special method.
You can find the details here:
When I adapted it for BIST, I found it sufficient to call data for 2 pairs instead of 3 in a single go. Since asset prices are regular and have 2 decimal places, I used a fixed multiplier of $10^8$ and a fixed decimal count of 2 in Fikira's formulas.
With this method, the (high, low, open, close) values became accessible for each asset.
The summary up to this point is that instead of the ready-made formula + groups of 40, I used variable-sized groups and the method I will detail now.
Correlation/harmony/co-movement between assets provides advantages to market participants. Coherent assets are expected to rise or fall simultaneously.
Therefore, to convert co-movement into a mathematical value, I defined the possible movements of the current candle relative to the previous candle bar over a certain period (user-defined). These are:
Up := high > high and low > low
Down := high < high and low < low
Inside := high <= high and low >= low
Outside := high >= high and low <= low and NOT Inside.
Ignore := high = low = open = close
If both assets performed the same movement, 1 was added to the tracking counter.
If (Up-Up), (Down-Down), (Inside-Inside), or (Outside-Outside), then counter := counter + 1.
If the period length is 100 and the counter is 75, it means there is 75% co-movement.
Corr = counter / period ($75/100$)
Average = ta.sma(Corr, 100) is obtained.
The highest coefficients recorded in the array are presented to the user in a table.
From the user menu options, the user can choose to compare:
• With assets in its own sector
• With assets in the selected sector
• By activating the confirmation box and manually entering a single asset for comparison.
Table display options can be adjusted from the Settings tab.
In the attached examples:
Results for AKBNK stock from the Finance sector compared with GARAN stock from the same sector:
Timeframe: Daily, Period: 50 => Harmony 76% (They performed the same movement in 38 out of 50 bars)
Comment: Opposite movements at swing high and low levels may indicate a change in the direction of the price flow (SMT).
Looking at ASELS from the Electronic Technology sector over the last 30 daily candles, they performed the same movements by 40% with XU100, 73.3% (22/30) with XUTEK (Technology Index), and 86.9% according to the averages.
Comment: It is more appropriate to follow ASELS stock with XUTEK (Technology index) instead of the general index (XU100). Opposite movements at swing high and low levels may indicate a change in the direction of the price flow (SMT).
Again, when ASELS stock is taken on H1 instead of daily, and the length is 100 instead of 30, the harmony rate is seen to be 87%.
Please share your thoughts and criticisms regarding the indicator, which I prepared with a bit of an educational purpose specifically for BIST.
Happy trading.
Ultimate Oscillator (ULTOSC)The Ultimate Oscillator (ULTOSC) is a technical momentum indicator developed by Larry Williams that combines three different time periods to reduce the volatility and false signals common in single-period oscillators. By using a weighted average of three Stochastic-like calculations across short, medium, and long-term periods, the Ultimate Oscillator provides a more comprehensive view of market momentum while maintaining sensitivity to price changes.
The indicator addresses the common problem of oscillators being either too sensitive (generating many false signals) or too slow (missing opportunities). By incorporating multiple timeframes with decreasing weights for longer periods, ULTOSC attempts to capture both short-term momentum shifts and longer-term trend strength, making it particularly valuable for identifying divergences and potential reversal points.
## Core Concepts
* **Multi-timeframe analysis:** Combines three different periods (typically 7, 14, 28) to capture various momentum cycles
* **Weighted averaging:** Assigns higher weights to shorter periods for responsiveness while including longer periods for stability
* **Buying pressure focus:** Measures the relationship between closing price and the true range rather than just high-low range
* **Divergence detection:** Particularly effective at identifying momentum divergences that precede price reversals
* **Normalized scale:** Oscillates between 0 and 100, with clear overbought/oversold levels
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|---------------|
| Fast Period | 7 | Short-term momentum calculation | Lower (5-6) for more sensitivity, higher (9-12) for smoother signals |
| Medium Period | 14 | Medium-term momentum calculation | Adjust based on typical swing duration in the market |
| Slow Period | 28 | Long-term momentum calculation | Higher values (35-42) for longer-term position trading |
| Fast Weight | 4.0 | Weight applied to fast period | Higher weight increases short-term sensitivity |
| Medium Weight | 2.0 | Weight applied to medium period | Adjust to balance medium-term influence |
| Slow Weight | 1.0 | Weight applied to slow period | Usually kept at 1.0 as the baseline weight |
**Pro Tip:** The classic 7/14/28 periods with 4/2/1 weights work well for most markets, but consider using 5/10/20 with adjusted weights for faster markets or 14/28/56 for longer-term analysis.
## Calculation and Mathematical Foundation
**Simplified explanation:**
The Ultimate Oscillator calculates three separate "buying pressure" ratios using different time periods, then combines them using weighted averaging. Buying pressure is defined as the close minus the true low, divided by the true range.
**Technical formula:**
```
BP = Close - Min(Low, Previous Close)
TR = Max(High, Previous Close) - Min(Low, Previous Close)
BP_Sum_Fast = Sum(BP, Fast Period)
TR_Sum_Fast = Sum(TR, Fast Period)
Raw_Fast = 100 × (BP_Sum_Fast / TR_Sum_Fast)
BP_Sum_Medium = Sum(BP, Medium Period)
TR_Sum_Medium = Sum(TR, Medium Period)
Raw_Medium = 100 × (BP_Sum_Medium / TR_Sum_Medium)
BP_Sum_Slow = Sum(BP, Slow Period)
TR_Sum_Slow = Sum(TR, Slow Period)
Raw_Slow = 100 × (BP_Sum_Slow / TR_Sum_Slow)
ULTOSC = 100 × / (Fast_Weight + Medium_Weight + Slow_Weight)
```
Where:
- BP = Buying Pressure
- TR = True Range
- Fast Period = 7, Medium Period = 14, Slow Period = 28 (defaults)
- Fast Weight = 4, Medium Weight = 2, Slow Weight = 1 (defaults)
> 🔍 **Technical Note:** The implementation uses efficient circular buffers for all three period calculations, maintaining O(1) time complexity per bar. The algorithm properly handles true range calculations including gaps and ensures accurate buying pressure measurements across all timeframes.
## Interpretation Details
ULTOSC provides several analytical perspectives:
* **Overbought/Oversold conditions:** Values above 70 suggest overbought conditions, below 30 suggest oversold conditions
* **Momentum direction:** Rising ULTOSC indicates increasing buying pressure, falling indicates increasing selling pressure
* **Divergence analysis:** Divergences between ULTOSC and price often precede significant reversals
* **Trend confirmation:** ULTOSC direction can confirm or question the prevailing price trend
* **Signal quality:** Extreme readings (>80 or <20) indicate strong momentum that may be unsustainable
* **Multiple timeframe consensus:** When all three underlying periods agree, signals are typically more reliable
## Trading Applications
**Primary Uses:**
- **Divergence trading:** Identify when momentum diverges from price for reversal signals
- **Overbought/oversold identification:** Find potential entry/exit points at extreme levels
- **Trend confirmation:** Validate breakouts and trend continuations
- **Momentum analysis:** Assess the strength of current price movements
**Advanced Strategies:**
- **Multi-divergence confirmation:** Look for divergences across multiple timeframes
- **Momentum breakouts:** Trade when ULTOSC breaks above/below key levels with volume
- **Swing trading entries:** Use oversold/overbought levels for swing position entries
- **Trend strength assessment:** Evaluate trend quality using momentum consistency
## Signal Combinations
**Strong Bullish Signals:**
- ULTOSC rises from oversold territory (<30) with positive price divergence
- ULTOSC breaks above 50 after forming a base near 30
- All three underlying periods show increasing buying pressure
**Strong Bearish Signals:**
- ULTOSC falls from overbought territory (>70) with negative price divergence
- ULTOSC breaks below 50 after forming a top near 70
- All three underlying periods show decreasing buying pressure
**Divergence Signals:**
- **Bullish divergence:** Price makes lower lows while ULTOSC makes higher lows
- **Bearish divergence:** Price makes higher highs while ULTOSC makes lower highs
- **Hidden bullish divergence:** Price makes higher lows while ULTOSC makes lower lows (trend continuation)
- **Hidden bearish divergence:** Price makes lower highs while ULTOSC makes higher highs (trend continuation)
## Comparison with Related Oscillators
| Indicator | Periods | Focus | Best Use Case |
|-----------|---------|-------|---------------|
| **Ultimate Oscillator** | 3 periods | Buying pressure | Divergence detection |
| **Stochastic** | 1-2 periods | Price position | Overbought/oversold |
| **RSI** | 1 period | Price momentum | Momentum analysis |
| **Williams %R** | 1 period | Price position | Short-term signals |
## Advanced Configurations
**Fast Trading Setup:**
- Fast: 5, Medium: 10, Slow: 20
- Weights: 4/2/1, Thresholds: 75/25
**Standard Setup:**
- Fast: 7, Medium: 14, Slow: 28
- Weights: 4/2/1, Thresholds: 70/30
**Conservative Setup:**
- Fast: 14, Medium: 28, Slow: 56
- Weights: 3/2/1, Thresholds: 65/35
**Divergence Focused:**
- Fast: 7, Medium: 14, Slow: 28
- Weights: 2/2/2, Thresholds: 70/30
## Market-Specific Adjustments
**Volatile Markets:**
- Use longer periods (10/20/40) to reduce noise
- Consider higher threshold levels (75/25)
- Focus on extreme readings for signal quality
**Trending Markets:**
- Emphasize divergence analysis over absolute levels
- Look for momentum confirmation rather than reversal signals
- Use hidden divergences for trend continuation
**Range-Bound Markets:**
- Standard overbought/oversold levels work well
- Trade reversals from extreme levels
- Combine with support/resistance analysis
## Limitations and Considerations
* **Lagging component:** Contains inherent lag due to multiple moving average calculations
* **Complex calculation:** More computationally intensive than single-period oscillators
* **Parameter sensitivity:** Performance varies significantly with different period/weight combinations
* **Market dependency:** Most effective in trending markets with clear momentum patterns
* **False divergences:** Not all divergences lead to significant price reversals
* **Whipsaw potential:** Can generate conflicting signals in choppy markets
## Best Practices
**Effective Usage:**
- Focus on divergences rather than absolute overbought/oversold levels
- Combine with trend analysis for context
- Use multiple timeframe analysis for confirmation
- Pay attention to the speed of momentum changes
**Common Mistakes:**
- Over-relying on overbought/oversold levels in strong trends
- Ignoring the underlying trend direction
- Using inappropriate period settings for the market being analyzed
- Trading every divergence without additional confirmation
**Signal Enhancement:**
- Combine with volume analysis for confirmation
- Use price action context (support/resistance levels)
- Consider market volatility when setting thresholds
- Look for convergence across multiple momentum indicators
## Historical Context and Development
The Ultimate Oscillator was developed by Larry Williams and introduced in his 1985 article "The Ultimate Oscillator" in Technical Analysis of Stocks and Commodities magazine. Williams designed it to address the limitations of single-period oscillators by:
- Reducing false signals through multi-timeframe analysis
- Maintaining sensitivity to short-term momentum changes
- Providing more reliable divergence signals
- Creating a more robust momentum measurement tool
The indicator has become a standard tool in technical analysis, particularly valued for its divergence detection capabilities and its balanced approach to momentum measurement.
## References
* Williams, L. R. (1985). The Ultimate Oscillator. Technical Analysis of Stocks and Commodities, 3(4).
* Williams, L. R. (1999). Long-Term Secrets to Short-Term Trading. Wiley Trading.
Standardization (Z-score)Standardization, often referred to as Z-score normalization, is a data preprocessing technique that rescales data to have a mean of 0 and a standard deviation of 1. The resulting values, known as Z-scores, indicate how many standard deviations an individual data point is from the mean of the dataset (or a rolling sample of it).
This indicator calculates and plots the Z-score for a given input series over a specified lookback period. It is a fundamental tool for statistical analysis, outlier detection, and preparing data for certain machine learning algorithms.
## Core Concepts
* **Standardization:** The process of transforming data to fit a standard normal distribution (or more generally, to have a mean of 0 and standard deviation of 1).
* **Z-score (Standard Score):** A dimensionless quantity that represents the number of standard deviations by which a data point deviates from the mean of its sample.
The formula for a Z-score is:
`Z = (x - μ) / σ`
Where:
* `x` is the individual data point (e.g., current value of the source series).
* `μ` (mu) is the mean of the sample (calculated over the lookback period).
* `σ` (sigma) is the standard deviation of the sample (calculated over the lookback period).
* **Mean (μ):** The average value of the data points in the sample.
* **Standard Deviation (σ):** A measure of the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean, while a high standard deviation indicates that the values are spread out over a wider range.
## Common Settings and Parameters
| Parameter | Type | Default | Function | When to Adjust |
| :-------------- | :----------- | :------ | :------------------------------------------------------------------------------------------------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Source | series float | close | The input data series (e.g., price, volume, indicator values). | Choose the series you want to standardize. |
| Lookback Period | int | 20 | The number of bars (sample size) used for calculating the mean (μ) and standard deviation (σ). Min 2. | A larger period provides more stable estimates of μ and σ but will be less responsive to recent changes. A shorter period is more reactive. `minval` is 2 because `ta.stdev` requires it. |
**Pro Tip:** Z-scores are excellent for identifying anomalies or extreme values. For instance, applying Standardization to trading volume can help quickly spot days with unusually high or low activity relative to the recent norm (e.g., Z-score > 2 or < -2).
## Calculation and Mathematical Foundation
The Z-score is calculated for each bar as follows, using a rolling window defined by the `Lookback Period`:
1. **Calculate Mean (μ):** The simple moving average (`ta.sma`) of the `Source` data over the specified `Lookback Period` is calculated. This serves as the sample mean `μ`.
`μ = ta.sma(Source, Lookback Period)`
2. **Calculate Standard Deviation (σ):** The standard deviation (`ta.stdev`) of the `Source` data over the same `Lookback Period` is calculated. This serves as the sample standard deviation `σ`.
`σ = ta.stdev(Source, Lookback Period)`
3. **Calculate Z-score:**
* If `σ > 0`: The Z-score is calculated using the formula:
`Z = (Current Source Value - μ) / σ`
* If `σ = 0`: This implies all values in the lookback window are identical (and equal to the mean). In this case, the Z-score is defined as 0, as the current source value is also equal to the mean.
* If `σ` is `na` (e.g., insufficient data in the lookback period), the Z-score is `na`.
> 🔍 **Technical Note:**
> * The `Lookback Period` must be at least 2 for `ta.stdev` to compute a valid standard deviation.
> * The Z-score calculation uses the sample mean and sample standard deviation from the rolling lookback window.
## Interpreting the Z-score
* **Magnitude and Sign:**
* A Z-score of **0** means the data point is identical to the sample mean.
* A **positive Z-score** indicates the data point is above the sample mean. For example, Z = 1 means the point is 1 standard deviation above the mean.
* A **negative Z-score** indicates the data point is below the sample mean. For example, Z = -1 means the point is 1 standard deviation below the mean.
* **Typical Range:** For data that is approximately normally distributed (bell-shaped curve):
* About 68% of Z-scores fall between -1 and +1.
* About 95% of Z-scores fall between -2 and +2.
* About 99.7% of Z-scores fall between -3 and +3.
* **Outlier Detection:** Z-scores significantly outside the -2 to +2 range, and especially outside -3 to +3, are often considered outliers or extreme values relative to the recent historical data in the lookback window.
* **Volatility Indication:** When applied to price, large absolute Z-scores can indicate moments of high volatility or significant deviation from the recent price trend.
The indicator plots horizontal lines at ±1, ±2, and ±3 standard deviations to help visualize these common thresholds.
## Common Applications
1. **Outlier Detection:** Identifying data points that are unusual or extreme compared to the rest of the sample. This is a primary use in financial markets for spotting abnormal price moves, volume spikes, etc.
2. **Comparative Analysis:** Allows for comparison of scores from different distributions that might have different means and standard deviations. For example, comparing the Z-score of returns for two different assets.
3. **Feature Scaling in Machine Learning:** Standardizing features to have a mean of 0 and standard deviation of 1 is a common preprocessing step for many machine learning algorithms (e.g., SVMs, logistic regression, neural networks) to improve performance and convergence.
4. **Creating Normalized Oscillators:** The Z-score itself can be used as a bounded (though not strictly between -1 and +1) oscillator, indicating how far the current price has deviated from its moving average in terms of standard deviations.
5. **Statistical Process Control:** Used in quality control charts to monitor if a process is within expected statistical limits.
## Limitations and Considerations
* **Assumption of Normality for Probabilistic Interpretation:** While Z-scores can always be calculated, the probabilistic interpretations (e.g., "68% of data within ±1σ") strictly apply to normally distributed data. Financial data is often not perfectly normal (e.g., it can have fat tails).
* **Sensitivity of Mean and Standard Deviation to Outliers:** The sample mean (μ) and standard deviation (σ) used in the Z-score calculation can themselves be influenced by extreme outliers within the lookback period. This can sometimes mask or exaggerate the Z-score of other points.
* **Choice of Lookback Period:** The Z-score is highly dependent on the `Lookback Period`. A short period makes it very sensitive to recent fluctuations, while a long period makes it smoother and less responsive. The appropriate period depends on the analytical goal.
* **Stationarity:** For time series data, Z-scores are calculated based on a rolling window. This implicitly assumes some level of local stationarity (i.e., the mean and standard deviation are relatively stable within the window).
Triangular Moving Average (TRIMA)The Triangular Moving Average (TRIMA) is a technical indicator that applies a triangular weighting scheme to price data, providing enhanced smoothing compared to simpler moving averages. Originating in the early 1970s as technical analysts sought more effective noise filtering methods, the TRIMA was first popularized through the work of market technician Arthur Merrill. Its formal mathematical properties were established in the 1980s, and the indicator gained widespread adoption in the 1990s as computerized charting became standard. TRIMA effectively filters out market noise while maintaining important trends through its unique center-weighted calculation method.
## Core Concepts
* **Double-smoothing process:** TRIMA can be viewed as applying a simple moving average twice, creating more effective noise filtering
* **Triangular weighting:** Uses a symmetrical weight distribution that emphasizes central data points and reduces emphasis toward both ends
* **Constant-time implementation:** Two $O(1)$ SMA passes with circular buffers preserve exact triangular weights while keeping update cost constant per bar
* **Market application:** Particularly effective for identifying the underlying trend in noisy market conditions where standard moving averages generate too many false signals
* **Timeframe flexibility:** Works across multiple timeframes, with longer periods providing cleaner trend signals in higher timeframes
The core innovation of TRIMA is its unique triangular weighting scheme, which can be viewed either as a specialized weight distribution or as a twice-applied simple moving average with adjusted period. This creates more effective noise filtering without the excessive lag penalty typically associated with longer-period averages. The symmetrical nature of the weight distribution ensures zero phase distortion, preserving the timing of important market turning points.
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|---------------|
| Length | 14 | Controls the lookback period | Increase for smoother signals in volatile markets, decrease for responsiveness |
| Source | close | Price data used for calculation | Consider using hlc3 for a more balanced price representation |
**Pro Tip:** For a good balance between smoothing and responsiveness, try using a TRIMA with period N instead of an SMA with period 2N - you'll get similar smoothing characteristics but with less lag.
## Calculation and Mathematical Foundation
**Simplified explanation:**
TRIMA calculates a weighted average of prices where the weights form a triangle shape. The middle prices get the most weight, and weights gradually decrease toward both the recent and older ends. This creates a smooth filter that effectively removes random price fluctuations while preserving the underlying trend.
**Technical formula:**
TRIMA = Σ(Price × Weight ) / Σ(Weight )
Where the triangular weights form a symmetric pattern:
- Weight = min(i, n-1-i) + 1
- Example for n=5: weights =
- Example for n=4: weights =
Alternatively, TRIMA can be calculated as:
TRIMA(source, p) = SMA(SMA(source, (p+1)/2), (p+1)/2)
> 🔍 **Technical Note:** The double application of SMA explains why TRIMA provides better smoothing than a single SMA or WMA. This approach effectively applies smoothing twice with optimal period adjustment, creating a -18dB/octave roll-off in the frequency domain compared to -6dB/octave for a simple moving average, and the current implementation achieves $O(1)$ complexity through circular buffers and NA-safe warmup compensation.
## Interpretation Details
TRIMA can be used in various trading strategies:
* **Trend identification:** The direction of TRIMA indicates the prevailing trend
* **Signal generation:** Crossovers between price and TRIMA generate trade signals with fewer false alarms than SMA
* **Support/resistance levels:** TRIMA can act as dynamic support during uptrends and resistance during downtrends
* **Trend strength assessment:** Distance between price and TRIMA can indicate trend strength
* **Multiple timeframe analysis:** Using TRIMAs with different periods can confirm trends across different timeframes
## Limitations and Considerations
* **Market conditions:** Like all moving averages, less effective in choppy, sideways markets
* **Lag factor:** More lag than WMA or EMA due to center-weighted emphasis
* **Limited adaptability:** Fixed weighting scheme cannot adapt to changing market volatility
* **Response time:** Takes longer to reflect sudden price changes than directionally-weighted averages
* **Complementary tools:** Best used with momentum oscillators or volume indicators for confirmation
## References
* Ehlers, John F. "Cycle Analytics for Traders." Wiley, 2013
* Kaufman, Perry J. "Trading Systems and Methods." Wiley, 2013
* Colby, Robert W. "The Encyclopedia of Technical Market Indicators." McGraw-Hill, 2002
Savitzky-Golay Filter (SGF)The Savitzky-Golay Filter (SGF) is a digital filter that performs local polynomial regression on a series of values to determine the smoothed value for each point. Developed by Abraham Savitzky and Marcel Golay in 1964, it is particularly effective at preserving higher moments of the data while reducing noise. This implementation provides a practical adaptation for financial time series, offering superior preservation of peaks, valleys, and other important market structures that might be distorted by simpler moving averages.
## Core Concepts
* **Local polynomial fitting:** Fits a polynomial of specified order to a sliding window of data points
* **Moment preservation:** Maintains higher statistical moments (peaks, valleys, inflection points)
* **Optimized coefficients:** Uses pre-computed coefficients for common polynomial orders
* **Adaptive weighting:** Weight distribution varies based on polynomial order and window size
* **Market application:** Particularly effective for preserving significant price movements while filtering noise
The core innovation of the Savitzky-Golay filter is its ability to smooth data while preserving important features that are often flattened by other filtering methods. This makes it especially valuable for technical analysis where maintaining the shape of price patterns is crucial.
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|---------------|
| Window Size | 11 | Number of points used in local fitting (must be odd) | Increase for smoother output, decrease for better feature preservation |
| Polynomial Order | 2 | Order of fitting polynomial (2 or 4) | Use 2 for general smoothing, 4 for better peak preservation |
| Source | close | Price data used for calculation | Consider using hlc3 for more stable fitting |
**Pro Tip:** A window size of 11 with polynomial order 2 provides a good balance between smoothing and feature preservation. For sharper peaks and valleys, use order 4 with a smaller window size.
## Calculation and Mathematical Foundation
**Simplified explanation:**
The filter fits a polynomial of specified order to a moving window of price data. The smoothed value at each point is computed from this local fit, effectively removing noise while preserving the underlying shape of the data.
**Technical formula:**
For a window of size N and polynomial order M, the filtered value is:
y = Σ(c_i × x )
Where:
- c_i are the pre-computed filter coefficients
- x are the input values in the window
- Coefficients depend on window size N and polynomial order M
> 🔍 **Technical Note:** The implementation uses optimized coefficient calculations for orders 2 and 4, which cover most practical applications while maintaining computational efficiency.
## Interpretation Details
The Savitzky-Golay filter can be used in various trading strategies:
* **Pattern recognition:** Preserves chart patterns while removing noise
* **Peak detection:** Maintains amplitude and width of significant peaks
* **Trend analysis:** Smooths price movement without distorting important transitions
* **Divergence trading:** Better preservation of local maxima and minima
* **Volatility analysis:** Accurate representation of price movement dynamics
## Limitations and Considerations
* **Computational complexity:** More intensive than simple moving averages
* **Edge effects:** First and last few points may show end effects
* **Parameter sensitivity:** Performance depends on appropriate window size and order selection
* **Data requirements:** Needs sufficient points for polynomial fitting
* **Complementary tools:** Best used with volume analysis and momentum indicators
## References
* Savitzky, A., Golay, M.J.E. "Smoothing and Differentiation of Data by Simplified Least Squares Procedures," Analytical Chemistry, 1964
* Press, W.H. et al. "Numerical Recipes: The Art of Scientific Computing," Chapter 14
* Schafer, R.W. "What Is a Savitzky-Golay Filter?" IEEE Signal Processing Magazine, 2011
Bilateral Filter (BILATERAL)The Bilateral Filter is an edge-preserving smoothing technique that combines spatial filtering with intensity filtering to achieve noise reduction while maintaining significant price structure. Originally developed in computer vision for image processing, this adaptive filter has been adapted for financial time series analysis to provide superior smoothing that preserves important market transitions. The filter intelligently reduces noise in stable price regions while preserving sharp transitions like breakouts, reversals, and other significant market structures that would be blurred by conventional filters.
## Core Concepts
* **Dual-domain filtering:** Combines traditional time-based (spatial) filtering with value-based (range) filtering for adaptive smoothing
* **Edge preservation:** Maintains important price transitions while aggressively smoothing areas of minor fluctuation
* **Adaptive processing:** Automatically adjusts filtering strength based on local price characteristics
The core innovation of the Bilateral Filter is its ability to distinguish between random noise and significant price movements. Unlike conventional filters that smooth everything equally, Bilateral filtering preserves major price transitions by reducing the influence of price points that differ significantly from the current price, effectively preserving market structure while still eliminating noise.
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|---------------|
| Length | 14 | Controls the lookback window size | Increase for more context in filtering decisions, decrease for quicker response |
| Sigma_S_Ratio | 0.3 | Controls spatial (time) weighting | Lower values emphasize recent bars, higher values distribute influence more evenly |
| Sigma_R_Mult | 2.0 | Controls range (price) sensitivity | Lower values increase edge preservation, higher values increase smoothing |
| Source | close | Price data used for calculation | Consider using hlc3 for a more balanced price representation |
**Pro Tip:** For breakout trading strategies, try reducing Sigma_R_Mult to 1.0-1.5 to make the filter more sensitive to significant price moves, allowing it to preserve breakout signals while still filtering noise.
## Calculation and Mathematical Foundation
**Simplified explanation:**
The Bilateral Filter calculates a weighted average of nearby prices, where the weights depend on two factors: how far away in time the price point is (spatial weight) and how different the price value is (range weight). Points that are close in time AND similar in value get the highest weight. This means stable price regions get smoothed while significant changes are preserved.
**Technical formula:**
BF = (1 / Wp) × Σ_{q ∈ S} G_s(||p - q||) × G_r(|I - I |) × I
Where:
- G_s is the spatial Gaussian kernel: exp(-||p - q||² / (2 × σ_s²))
- G_r is the range Gaussian kernel: exp(-|I - I |² / (2 × σ_r²))
- Wp is the normalization factor (sum of all weights)
> 🔍 **Technical Note:** The sigma_r parameter is typically calculated dynamically based on local price volatility (standard deviation) to provide adaptive filtering - this automatically adjusts filtering strength based on market conditions.
## Interpretation Details
The Bilateral Filter can be applied in various trading contexts:
* **Trend identification:** Reveals cleaner underlying price direction by removing noise while preserving trend changes
* **Support/resistance identification:** Provides clearer price levels by preserving significant turning points
* **Pattern recognition:** Maintains critical chart patterns while eliminating distracting minor fluctuations
* **Breakout trading:** Preserves sharp price transitions for more reliable breakout signals
* **Pre-processing:** Can be used as an initial filter before applying other technical indicators to reduce false signals
## Limitations and Considerations
* **Computational complexity:** More intensive calculations than traditional linear filters
* **Parameter sensitivity:** Performance highly dependent on proper parameter selection
* **Non-linearity:** Non-linear behavior may produce unexpected results in certain market conditions
* **Interpretation adjustment:** Requires different interpretation than conventional moving averages
* **Complementary tools:** Best used alongside volume analysis and traditional indicators for confirmation
## References
* Tomasi, C. and Manduchi, R. "Bilateral Filtering for Gray and Color Images," Proceedings of IEEE ICCV, 1998
* Paris, S. et al. "A Gentle Introduction to Bilateral Filtering and its Applications," ACM SIGGRAPH, 2008
Multi-Session Viewer and AnalyzerFully customizable multi-session viewer that takes session analysis to the next level. It allows you to fully customize each session to your liking. Includes a feature that highlights certain periods of time on the chart and a Time Range Marker.
It helps you analyze the instrument that you trade and pinpoint which times are more volatile than others. It also helps you choose the best time to trade your instrument and align your life schedule with the market.
NZDUSD Example:
- 3 major sessions displayed.
- Although this is NZDUSD, Sydney is not the best time to trade this pair. Volatility picks up at Tokyo open.
- I have time to trade in the evening from 18:00 to 22:00 PST. I live in a different time zone, whereas market is based on EST. How does the pair behave during the time I am available to trade based on my time zone? Time Range Marker feature allows you to see this clearly on the chart (black lines).
- I have some time in the morning to trade during New York session, but there is no way I am waking up at 05:00 PST. 06:30 PST seems doable. Blue highlighted area is good time to trade during New York session based on what Bob said. It seem like this aligns with when I am available and when I am able to trade. Volatility is also at its peak.
- I am also available to trade between London close and Tokyo open on some days of the week, but... based on what I see, green highlighted area is clearly showing that I probably don't want to waste my time trading this pair from London close and until Tokyo open. I will use this time for something else rather than be stuck in a range.
RightFlow Universal Volume Profile - Any Market Any TimeframeSummary in one paragraph
RightFlow is a right anchored microstructure volume profile for stocks, futures, FX, and liquid crypto on intraday and daily timeframes. It acts only when several conditions align inside a session window and presents the result as a compact right side profile with value area, POC, a bull bear mix by price bin, and a HUD of profile VWAP and pressure shares. It is original because it distributes each bar’s weight into multiple mid price slices, blends bull bear pressure per bin with a CLV based split, and grows the profile to the right so price action stays readable. Add to a clean chart, read the table, and use the visuals. For conservative workflows read on bar close.
Scope and intent
• Markets. Major FX pairs, index futures, large cap equities and ETFs, liquid crypto.
• Timeframes. One minute to daily.
• Default demo used in the publication. SPY on 15 minute.
• Purpose. See where participation concentrates, which side dominated by price level, and how far price sits from VA and POC.
Originality and usefulness
• Unique fusion. Right anchored growth plus per bar slicing and CLV split, with weight modes Raw, Notional, and DeltaProxy.
• Failure mode addressed. False reads from single bar direction and coarse binning.
• Testability. All parts sit in Inputs and the HUD.
• Portable yardstick. Value Area percent and POC are universal across symbols.
• Protected scripts. Not applicable. Method and use are fully disclosed.
Method overview in plain language
Pick a scope Rolling or Today or This Week. Define a window and number of price bins. For each bar, split its range into small slices, assign each slice a weight from the selected mode, and split that weight by CLV or by bar direction. Accumulate totals per bin. Find the bin with the highest total as POC. Expand left and right until the chosen share of total volume is covered to form the value area. Compute profile VWAP for all, buyers, and sellers and show them with pressure shares.
Base measures
Range basis. High minus low and mid price samples across the bar window.
Return basis. Not used. VWAP trio is price weighted by weights.
Components
• RightFlow Bins. Price histogram that grows to the right.
• Bull Bear Split. CLV based 0 to 1 share or pure bar direction.
• Weight Mode. Raw volume, notional volume times close, or DeltaProxy focus.
• Value Area Engine. POC then outward expansion to target share.
• HUD. Profile VWAP, Buy and Sell percent, winner delta, split and weight mode.
• Session windows optional. Scope resets on day or week.
Fusion rule
Color of each bin is the convex blend of bull and bear shares. Value area shading is lighter inside and darker outside.
Signal rule
This is context, not a trade signal. A strong separation between buy and sell percent with price holding inside VA often confirms balance. Price outside VA with skewed pressure often marks initiative moves.
What you will see on the chart
• Right side bins with blended colors.
• A POC line across the profile width.
• Labels for POC, VAH, and VAL.
• A compact HUD table in the top right.
Table fields and quick reading guide
• VWAP. Profile VWAP.
• Buy and Sell. Pressure shares in percent.
• Delta Winner. Winner side and margin in percent.
• Split and Weight. The active modes.
Reading tip. When Session scope is Today or This Week and Buy minus Sell is clearly positive or negative, that side often controls the day’s narrative.
Inputs with guidance
Setup
• Profile scope. Rolling or session reset. Rolling uses window bars.
• Rolling window bars. Typical 100 to 300. Larger is smoother.
Binning
• Price bins. Typical 32 to 128. More bins increase detail.
• Slices per bar. Typical 3 to 7. Raising it smooths distribution.
Weighting
• Weight mode. Raw, Notional, DeltaProxy. Notional emphasizes expensive prints.
• Bull Bear split. CLV or BarDir. CLV is more nuanced.
• Value Area percent. Typical 68 to 75.
View
• Profile width in bars, color split toggle, value area shading, opacities, POC line, VA labels.
Usage recipes
Intraday trend focus
• Scope Today, bins 64, slices 5, Value Area 70.
• Split CLV, Weight Notional.
Intraday mean reversion
• Scope Today, bins 96, Value Area 75.
• Watch fades back to POC after initiative pushes.
Swing continuation
• Scope Rolling 200 bars, bins 48.
• Use Buy Sell skew with price relative to VA.
Realism and responsible publication
No performance claims. Shapes can move while a bar forms and settle on close. Education only.
Honest limitations and failure modes
Thin liquidity and data gaps can distort bin weights. Very quiet regimes reduce contrast. Session time is the chart venue time.
Open source reuse and credits
None.
Legal
Education and research only. Not investment advice. Test on history and simulation before live use.
