Chart-Only Scanner — Pro Table v2.5.1Chart-Only Scanner — Pro Table v2.5
User Manual (Pine Script v6)
What this tool does (in one line)
A compact, on-chart table that scores the current chart symbol (or an optional override) using momentum, volume, trend, volatility, and pattern checks—so you can quickly decide UP, DOWN, or WAIT.
Quick Start (90 seconds)
Add the indicator to any chart and timeframe (1m…1M).
Leave “Override chart symbol” = OFF to auto-use the chart’s symbol.
Choose your layout:
Row (wide horizontal strip), or Grid (title + labeled cells).
Pick a size preset (Micro, Small, Medium, Large, Mobile).
Optional: turn on “Use Higher TF (EMA 20/50)” and set HTF Multiplier (e.g., 4 ⇒ if chart is 15m, HTF is 60m).
Watch the table:
DIR (↑/↓/→), ROC%, MOM, VOL, EMA stack, HTF, REV, SCORE, ACT.
Add an alert if you want: the script fires when |SCORE| ≥ Action threshold.
What to expect
A small table appears on the chart corner you choose, updating each bar (or only at bar close if you keep default smart-update).
The ACT cell shows 🔥 (strong), 👀 (medium), or ⏳ (weak).
Panels & Settings (every option explained)
Core
Momentum Period: Lookback for rate-of-change (ROC%). Shorter = more reactive; longer = smoother.
ROC% Threshold: Minimum absolute ROC% to call direction UP (↑) or DOWN (↓); otherwise →.
Require Volume Confirmation: If ON and VOL ≤ 1.0, the SCORE is forced to 0 (prevents low-volume false positives).
Override chart symbol + Custom symbol: By default, the indicator uses the chart’s symbol. Turn this ON to lock to a specific ticker (e.g., a perpetual).
Higher TF
Use Higher TF (EMA 20/50): Compares EMA20 vs EMA50 on a higher timeframe.
HTF Multiplier: Higher TF = (chart TF × multiplier).
Example: on 3H chart with multiplier 2 ⇒ HTF = 6H.
Volatility & Oscillators
ATR Length: Used to show ATR% (ATR relative to price).
RSI Length: Standard RSI; colors: green ≤30 (oversold), red ≥70 (overbought).
Stoch %K Length: With %D = SMA(%K, 3).
MACD Fast/Slow/Signal: Standard MACD values; we display Line, Signal, Histogram (L/S/H).
ADX Length (Wilder): Wilder’s smoothing (internal derivation); also shows +DI / −DI if you enable the ADX column.
EMAs / Trend
EMA Fast/Mid/Slow: We compute EMA(20/50/200) by default (editable).
EMA Stack: Bull if Fast > Mid > Slow; Bear if Fast < Mid < Slow; Flat otherwise.
Benchmark (optional, OFF by default)
Show Relative Strength vs Benchmark: Displays RS% = ROC(symbol) − ROC(benchmark) over the Momentum Period.
Benchmark Symbol: Ticker used for comparison (e.g., BTCUSDT as a market proxy).
Columns (show/hide)
Toggle which fields appear in the table. Hiding unused fields keeps the layout clean (especially on mobile).
Display
Layout Mode:
Row = a single two-row strip; each column is a metric.
Grid = a title row plus labeled pairs (label/value) arranged in rows.
Size Preset: Micro, Small, Medium, Large, Mobile change text size and the grid density.
Table Corner: Where the panel sits (e.g., Top Right).
Opaque Table Background: ON = dark card; OFF = transparent(ish).
Update Every Bar: ON = update intra-bar; OFF = smart update (last bar / real-time / confirmed history).
Action threshold (|score|): The cutoff for 🔥 and alert firing (default 70).
How to read each field
CHART: The active symbol name (or your custom override).
DIR: ↑ (ROC% > threshold), ↓ (ROC% < −threshold), → otherwise.
ROC%: Rate of change over Momentum Period.
Formula: (Close − Close ) / Close × 100.
MOM: A scaled momentum score: min(100, |ROC%| × 10).
VOL: Volume ratio vs 20-bar SMA: Volume / SMA(Volume,20).
1.5 highlights as yellow (significant participation).
ATR%: (ATR / Close) × 100 (volatility relative to price).
RSI: Colored for extremes: ≤30 green, ≥70 red.
Stoch K/D: %K and %D numbers.
MACD L/S/H: Line, Signal, Histogram. Histogram color reflects sign (green > 0, red < 0).
ADX, +DI, −DI: Trend strength and directional components (Wilder). ADX ≥ 25 is highlighted.
EMA 20/50/200: Current EMA values (editable lengths).
STACK: Bull/Bear/Flat as defined above.
VWAP%: (Close − VWAP) / Close × 100 (premium/discount to VWAP).
HTF: ▲ if HTF EMA20 > EMA50; ▼ if <; · if flat/off.
RS%: Symbol’s ROC% − Benchmark ROC% (positive = outperforming).
REV (reversal):
🟢 Eng/Pin = bullish engulfing or bullish pin detected,
🔴 Eng/Pin = bearish engulfing or bearish pin,
· = none.
SCORE (absolute shown as a number; sign shown via DIR and ACT):
Components:
base = MOM × 0.4
volBonus = VOL > 1.5 ? 20 : VOL × 13.33
htfBonus = use_mtf ? (HTF == DIR ? 30 : HTF == 0 ? 15 : 0) : 0
trendBonus = (STACK == DIR) ? 10 : 0
macdBonus = 0 (placeholder for future versions)
scoreRaw = base + volBonus + htfBonus + trendBonus + macdBonus
SCORE = DIR ≥ 0 ? scoreRaw : −scoreRaw
If Require Volume Confirmation and VOL ≤ 1.0 ⇒ SCORE = 0.
ACT:
🔥 if |SCORE| ≥ threshold
👀 if 50 < |SCORE| < threshold
⏳ otherwise
Practical examples
Strong long (trend + participation)
DIR = ↑, ROC% = +3.2, MOM ≈ 32, VOL = 1.9, STACK = Bull, HTF = ▲, REV = 🟢
SCORE: base(12.8) + volBonus(20) + htfBonus(30) + trend(10) ≈ 73 → ACT = 🔥
Action idea: look for longs on pullbacks; confirm risk with ATR%.
Weak long (no volume)
DIR = ↑, ROC% = +1.0, but VOL = 0.8 and Require Volume Confirmation = ON
SCORE forced to 0 → ACT = ⏳
Action: wait for volume > 1.0 or turn off confirmation knowingly.
Bearish reversal warning
DIR = →, REV = 🔴 (bearish engulfing), RSI = 68, HTF = ▼
SCORE may be mid-range; ACT = 👀
Action: watch for breakdown and rising VOL.
Alerts (how to use)
The script calls alert() whenever |SCORE| ≥ Action threshold.
To receive pop-ups, sounds, or emails: click “⏰ Alerts” in TradingView, choose this indicator, and pick “Any alert() function call.”
The alert message includes: symbol, |SCORE|, DIR.
Layout, Size, and Corner tips
Row is best when you want a compact status ribbon across the top.
Grid is clearer on big screens or when you enable many columns.
Size:
Mobile = one pair per row (tall, readable)
Micro/Small = dense; good for many fields
Large = presentation/screenshots
Corner: If the table overlaps price, change the corner or set Opaque Background = OFF.
Repaint & timeframe behavior
Default smart update prefers stability (last bar / live / confirmed history).
For a stricter, “close-only” behavior (less repaint): turn Update Every Bar = OFF and avoid Heikin Ashi when you want raw market OHLC (HA modifies price inputs).
HTF logic is derived from a clean, integer multiple of your chart timeframe (via multiplier). It works with 3H/4H and any TF.
Performance notes
The script analyzes one symbol (chart or override) with multiple metrics using efficient tuple requests.
If you later want a multi-symbol grid, do it with pages (10–15 per page + rotate) to stay within platform limits (recommended future add-on).
Troubleshooting
No table visible
Ensure the indicator is added and not hidden.
Try toggling Opaque Background or switch Corner (it might be behind other drawings).
Keep Columns count reasonable for the chosen Size.
If you turned ON Override, verify the Custom symbol exists on your data provider.
Numbers look different on HA candles
Heikin Ashi modifies OHLC; switch to regular candles if you need raw price metrics.
3H/4H issues
Use integer HTF Multiplier (e.g., 2, 4). The tool builds the correct string internally; no manual timeframe strings needed.
Power user tips
Volume gating: keeping Require Volume Confirmation = ON filters most fake moves; if you’re a scalper, reduce strictness or turn it off.
Action threshold: 60–80 is typical. Higher = fewer but stronger signals.
Benchmark RS%: great for spotting leaders/laggards; positive RS% = outperformance vs benchmark.
Change policy & safety
This version doesn’t alter your historical logic you tested (no radical changes).
Any future “radical” change (score weights, HTF logic, UI hiding data) will ship with a toggle and an Impact Statement so you can keep old behavior if you prefer.
Glossary (quick)
ROC%: Percent change over N bars.
MOM: Scaled momentum (0–100).
VOL ratio: Volume vs 20-bar average.
ATR%: ATR as % of price.
ADX/DI: Trend strength / direction components (Wilder).
EMA stack: Relationship between EMAs (bullish/bearish/flat).
VWAP%: Premium/discount to VWAP.
RS%: Relative strength vs benchmark.
스크립트에서 "100万新币等于多少人民币"에 대해 찾기
Coin Jin Multi SMA+ BB+ SMA forecast Ver 2.0Coin Jin Multi SMA + BB + SMA Forecast 2.0
개요
여러 개의 단순이동평균(SMA: 5/20/60/112/224/448/896 + 사용자 정의 X1/X2), 볼린저 밴드(BB), 그리고 접선 기반 곡선 예측선을 한 번에 표시합니다. 예측선은 선형회귀 기울기와 그 변화율(가속도)을 EMA로 스무딩해 곡선 외삽으로 앞으로 그려지며, 어떤 줌에서도 깔끔하게 보이도록 점선(dotted) 스타일을 강제할 수 있습니다.
스택 마커(정배열/역배열) 안내
조건: 이동평균이 정배열(5>20>60>112>224>448>(896)) 또는 역배열(5<20<60<112<224<448<(896))로 새로 전환되는 순간 삼각형 마커가 생성됩니다.
896일선 포함(with 896): SOLID 마커로 표시, Bull = 초록색, Bear = 빨간색.
896일선 미포함(no 896): HOLLOW(윤곽) 마커로 표시, 시선을 덜 끌도록 투명도 70 적용(Bull = 연두, Bear = 빨강 동일색).
방향: Bull = ▼(위, abovebar) / Bear = ▲(아래, belowbar) 로 배치됩니다.
주요 기능
SMA 7종 기본 + 사용자 정의 SMA 2개(X1/X2) 추가(기본 꺼짐, 길이/색/두께/타입 자유).
BB: 길이/배수/선두께/밴드 채움(기본 90% 투명) 지원.
예측선: Forward bars(1–100, 기본 30), 기울기 산출 길이, 스무딩 강도, 세그먼트 개수, 점/대시 스타일 선택 및 도트 강제.
스택(정/역배열) 전환 마커: with 896=SOLID, no 896=HOLLOW(투명도 70).
처음 사용하는 분들을 위한 팁 (중요)
가격 스케일을 ‘우측’으로 고정하세요.
방법 ① 차트 우측 축을 사용(기본).
방법 ② 지표 레전드의 ‘⋯’ 메뉴 → Move to → Right scale.
예측선이 본선과 어긋나 보이면 스케일이 좌측/양측으로 되어 있거나 자동 합침된 경우이니 Right scale로 맞춰주세요.
입력 요약
MA Source, 각 SMA on/off·길이·색·두께·타입
BB length/mult/width/fill/opacity(기본 90)
Forecast bars ahead(1–100), slope lookback, smoothing, segments, style/opacity, 적용 대상 선택(SMA별)
주의/면책
예측선은 가격 예언 도구가 아니라 시각적 외삽 보조지표입니다. 단독 매매 판단에 사용하지 마세요.
공개 스크린샷은 본 지표만 보이도록 깔끔하게 캡처해 주세요(다른 지표/드로잉 혼합 금지).
변경사항(v2.0)
곡선 예측선 안정화 및 도트 강제 개선.
스택 마커 no 896 상태 HOLLOW 투명도 70 적용(가독성 향상).
사용자 정의 SMA X1/X2 추가(기본 OFF).
Coin Jin Multi SMA + BB + SMA Forecast 2.0 (English)
Overview
This indicator plots multiple Simple Moving Averages (SMA: 5/20/60/112/224/448/896 + two user-defined X1/X2), Bollinger Bands, and a tangent-based curved forecast in one overlay. The forecast extrapolates forward using the linear-regression slope and its rate of change (acceleration) smoothed by EMA, and you can force a dotted look so it stays clean at any zoom level.
Stack Markers (Bullish/Bearish alignment)
Markers appear only when a full bullish stack (5>20>60>112>224>448>(896)) or bearish stack (5<20<60<112<224<448<(896)) is newly formed.
With 896 included: shown as SOLID triangles — Bull = green, Bear = red.
Without 896: shown as HOLLOW (outline) with 70 transparency to reduce visual weight — Bull = lime, Bear = red (same hue).
Orientation: Bull = ▼ abovebar, Bear = ▲ belowbar.
Features
7 standard SMAs + two custom SMAs (X1/X2) (default OFF; fully configurable length/color/width/style).
BB with length/multiplier/width/fill (default fill opacity 90%).
Forecast controls: forward bars (1–100, default 30), slope window, smoothing, segment count, style/opacity, force dotted option.
Stack markers: with 896 = SOLID, without 896 = HOLLOW (70 transparency).
First-time setup (Important)
Pin the indicator to the Right price scale.
Option A: Use the right price axis.
Option B: Indicator legend “⋯” → Move to → Right scale.
If the forecast appears detached from the MA, your series is likely on the left/both scales; switch to Right scale.
Inputs
MA source; per-SMA on/off, length, color, width, style
BB length/multiplier/width/fill/opacity (default 90)
Forecast bars ahead (1–100), slope lookback, smoothing, segments, style/opacity, per-SMA apply switches
Disclaimer
The forecast is a visual extrapolation, not a price prediction. Do not use it alone to make trading decisions.
For publication, please use a clean screenshot that shows only this indicator (no mixed overlays).
What’s new in v2.0
More robust curved forecast with improved “force dotted” rendering.
HOLLOW (no 896) markers now use 70 transparency for better readability.
Added two user-defined SMAs (X1/X2), OFF by default.
Seasonality Monte Carlo Forecaster [BackQuant]Seasonality Monte Carlo Forecaster
Plain-English overview
This tool projects a cone of plausible future prices by combining two ideas that traders already use intuitively: seasonality and uncertainty. It watches how your market typically behaves around this calendar date, turns that seasonal tendency into a small daily “drift,” then runs many randomized price paths forward to estimate where price could land tomorrow, next week, or a month from now. The result is a probability cone with a clear expected path, plus optional overlays that show how past years tended to move from this point on the calendar. It is a planning tool, not a crystal ball: the goal is to quantify ranges and odds so you can size, place stops, set targets, and time entries with more realism.
What Monte Carlo is and why quants rely on it
• Definition . Monte Carlo simulation is a way to answer “what might happen next?” when there is randomness in the system. Instead of producing a single forecast, it generates thousands of alternate futures by repeatedly sampling random shocks and adding them to a model of how prices evolve.
• Why it is used . Markets are noisy. A single point forecast hides risk. Monte Carlo gives a distribution of outcomes so you can reason in probabilities: the median path, the 68% band, the 95% band, tail risks, and the chance of hitting a specific level within a horizon.
• Core strengths in quant finance .
– Path-dependent questions : “What is the probability we touch a stop before a target?” “What is the expected drawdown on the way to my objective?”
– Pricing and risk : Useful for path-dependent options, Value-at-Risk (VaR), expected shortfall (CVaR), stress paths, and scenario analysis when closed-form formulas are unrealistic.
– Planning under uncertainty : Portfolio construction and rebalancing rules can be tested against a cloud of plausible futures rather than a single guess.
• Why it fits trading workflows . It turns gut feel like “seasonality is supportive here” into quantitative ranges: “median path suggests +X% with a 68% band of ±Y%; stop at Z has only ~16% odds of being tagged in N days.”
How this indicator builds its probability cone
1) Seasonal pattern discovery
The script builds two day-of-year maps as new data arrives:
• A return map where each calendar day stores an exponentially smoothed average of that day’s log return (yesterday→today). The smoothing (90% old, 10% new) behaves like an EWMA, letting older seasons matter while adapting to new information.
• A volatility map that tracks the typical absolute return for the same calendar day.
It calculates the day-of-year carefully (with leap-year adjustment) and indexes into a 365-slot seasonal array so “March 18” is compared with past March 18ths. This becomes the seasonal bias that gently nudges simulations up or down on each forecast day.
2) Choice of randomness engine
You can pick how the future shocks are generated:
• Daily mode uses a Gaussian draw with the seasonal bias as the mean and a volatility that comes from realized returns, scaled down to avoid over-fitting. It relies on the Box–Muller transform internally to turn two uniform random numbers into one normal shock.
• Weekly mode uses bootstrap sampling from the seasonal return history (resampling actual historical daily drifts and then blending in a fraction of the seasonal bias). Bootstrapping is robust when the empirical distribution has asymmetry or fatter tails than a normal distribution.
Both modes seed their random draws deterministically per path and day, which makes plots reproducible bar-to-bar and avoids flickering bands.
3) Volatility scaling to current conditions
Markets do not always live in average volatility. The engine computes a simple volatility factor from ATR(20)/price and scales the simulated shocks up or down within sensible bounds (clamped between 0.5× and 2.0×). When the current regime is quiet, the cone narrows; when ranges expand, the cone widens. This prevents the classic mistake of projecting calm markets into a storm or vice versa.
4) Many futures, summarized by percentiles
The model generates a matrix of price paths (capped at 100 runs for performance inside TradingView), each path stepping forward for your selected horizon. For each forecast day it sorts the simulated prices and pulls key percentiles:
• 5th and 95th → approximate 95% band (outer cone).
• 16th and 84th → approximate 68% band (inner cone).
• 50th → the median or “expected path.”
These are drawn as polylines so you can immediately see central tendency and dispersion.
5) A historical overlay (optional)
Turn on the overlay to sketch a dotted path of what a purely seasonal projection would look like for the next ~30 days using only the return map, no randomness. This is not a forecast; it is a visual reminder of the seasonal drift you are biasing toward.
Inputs you control and how to think about them
Monte Carlo Simulation
• Price Series for Calculation . The source series, typically close.
• Enable Probability Forecasts . Master switch for simulation and drawing.
• Simulation Iterations . Requested number of paths to run. Internally capped at 100 to protect performance, which is generally enough to estimate the percentiles for a trading chart. If you need ultra-smooth bands, shorten the horizon.
• Forecast Days Ahead . The length of the cone. Longer horizons dilute seasonal signal and widen uncertainty.
• Probability Bands . Draw all bands, just 95%, just 68%, or a custom level (display logic remains 68/95 internally; the custom number is for labeling and color choice).
• Pattern Resolution . Daily leans on day-of-year effects like “turn-of-month” or holiday patterns. Weekly biases toward day-of-week tendencies and bootstraps from history.
• Volatility Scaling . On by default so the cone respects today’s range context.
Plotting & UI
• Probability Cone . Plots the outer and inner percentile envelopes.
• Expected Path . Plots the median line through the cone.
• Historical Overlay . Dotted seasonal-only projection for context.
• Band Transparency/Colors . Customize primary (outer) and secondary (inner) band colors and the mean path color. Use higher transparency for cleaner charts.
What appears on your chart
• A cone starting at the most recent bar, fanning outward. The outer lines are the ~95% band; the inner lines are the ~68% band.
• A median path (default blue) running through the center of the cone.
• An info panel on the final historical bar that summarizes simulation count, forecast days, number of seasonal patterns learned, the current day-of-year, expected percentage return to the median, and the approximate 95% half-range in percent.
• Optional historical seasonal path drawn as dotted segments for the next 30 bars.
How to use it in trading
1) Position sizing and stop logic
The cone translates “volatility plus seasonality” into distances.
• Put stops outside the inner band if you want only ~16% odds of a stop-out due to noise before your thesis can play.
• Size positions so that a test of the inner band is survivable and a test of the outer band is rare but acceptable.
• If your target sits inside the 68% band at your horizon, the payoff is likely modest; outside the 68% but inside the 95% can justify “one-good-push” trades; beyond the 95% band is a low-probability flyer—consider scaling plans or optionality.
2) Entry timing with seasonal bias
When the median path slopes up from this calendar date and the cone is relatively narrow, a pullback toward the lower inner band can be a high-quality entry with a tight invalidation. If the median slopes down, fade rallies toward the upper band or step aside if it clashes with your system.
3) Target selection
Project your time horizon to N bars ahead, then pick targets around the median or the opposite inner band depending on your style. You can also anchor dynamic take-profits to the moving median as new bars arrive.
4) Scenario planning & “what-ifs”
Before events, glance at the cone: if the 95% band already spans a huge range, trade smaller, expect whips, and avoid placing stops at obvious band edges. If the cone is unusually tight, consider breakout tactics and be ready to add if volatility expands beyond the inner band with follow-through.
5) Options and vol tactics
• When the cone is tight : Prefer long gamma structures (debit spreads) only if you expect a regime shift; otherwise premium selling may dominate.
• When the cone is wide : Debit structures benefit from range; credit spreads need wider wings or smaller size. Align with your separate IV metrics.
Reading the probability cone like a pro
• Cone slope = seasonal drift. Upward slope means the calendar has historically favored positive drift from this date, downward slope the opposite.
• Cone width = regime volatility. A widening fan tells you that uncertainty grows fast; a narrow cone says the market typically stays contained.
• Mean vs. price gap . If spot trades well above the median path and the upper band, mean-reversion risk is high. If spot presses the lower inner band in an up-sloping cone, you are in the “buy fear” zone.
• Touches and pierces . Touching the inner band is common noise; piercing it with momentum signals potential regime change; the outer band should be rare and often brings snap-backs unless there is a structural catalyst.
Methodological notes (what the code actually does)
• Log returns are used for additivity and better statistical behavior: sim_ret is applied via exp(sim_ret) to evolve price.
• Seasonal arrays are updated online with EWMA (90/10) so the model keeps learning as each bar arrives.
• Leap years are handled; indexing still normalizes into a 365-slot map so the seasonal pattern remains stable.
• Gaussian engine (Daily mode) centers shocks on the seasonal bias with a conservative standard deviation.
• Bootstrap engine (Weekly mode) resamples from observed seasonal returns and adds a fraction of the bias, which captures skew and fat tails better.
• Volatility adjustment multiplies each daily shock by a factor derived from ATR(20)/price, clamped between 0.5 and 2.0 to avoid extreme cones.
• Performance guardrails : simulations are capped at 100 paths; the probability cone uses polylines (no heavy fills) and only draws on the last confirmed bar to keep charts responsive.
• Prerequisite data : at least ~30 seasonal entries are required before the model will draw a cone; otherwise it waits for more history.
Strengths and limitations
• Strengths :
– Probabilistic thinking replaces single-point guessing.
– Seasonality adds a small but meaningful directional bias that many markets exhibit.
– Volatility scaling adapts to the current regime so the cone stays realistic.
• Limitations :
– Seasonality can break around structural changes, policy shifts, or one-off events.
– The number of paths is performance-limited; percentile estimates are good for trading, not for academic precision.
– The model assumes tomorrow’s randomness resembles recent randomness; if regime shifts violently, the cone will lag until the EWMA adapts.
– Holidays and missing sessions can thin the seasonal sample for some assets; be cautious with very short histories.
Tuning guide
• Horizon : 10–20 bars for tactical trades; 30+ for swing planning when you care more about broad ranges than precise targets.
• Iterations : The default 100 is enough for stable 5/16/50/84/95 percentiles. If you crave smoother lines, shorten the horizon or run on higher timeframes.
• Daily vs. Weekly : Daily for equities and crypto where month-end and turn-of-month effects matter; Weekly for futures and FX where day-of-week behavior is strong.
• Volatility scaling : Keep it on. Turn off only when you intentionally want a “pure seasonality” cone unaffected by current turbulence.
Workflow examples
• Swing continuation : Cone slopes up, price pulls into the lower inner band, your system fires. Enter near the band, stop just outside the outer line for the next 3–5 bars, target near the median or the opposite inner band.
• Fade extremes : Cone is flat or down, price gaps to the upper outer band on news, then stalls. Favor mean-reversion toward the median, size small if volatility scaling is elevated.
• Event play : Before CPI or earnings on a proxy index, check cone width. If the inner band is already wide, cut size or prefer options structures that benefit from range.
Good habits
• Pair the cone with your entry engine (breakout, pullback, order flow). Let Monte Carlo do range math; let your system do signal quality.
• Do not anchor blindly to the median; recalc after each bar. When the cone’s slope flips or width jumps, the plan should adapt.
• Validate seasonality for your symbol and timeframe; not every market has strong calendar effects.
Summary
The Seasonality Monte Carlo Forecaster wraps institutional risk planning into a single overlay: a data-driven seasonal drift, realistic volatility scaling, and a probabilistic cone that answers “where could we be, with what odds?” within your trading horizon. Use it to place stops where randomness is less likely to take you out, to set targets aligned with realistic travel, and to size positions with confidence born from distributions rather than hunches. It will not predict the future, but it will keep your decisions anchored to probabilities—the language markets actually speak.
Prime NumbersPrime Numbers highlights prime numbers (no surprise there 😅), tokens and the recent "active" feature in "input".
🔸 CONCEPTS
🔹 What are Prime Numbers?
A prime number (or a prime) is a natural number greater than 1 that is not a product of two smaller natural numbers.
Wikipedia: Prime number
🔹 Prime Factorization
The fundamental theorem of arithmetic states that every integer larger than 1 can be written as a product of one or more primes. More strongly, this product is unique in the sense that any two prime factorizations of the same number will have the same number of copies of the same primes, although their ordering may differ. So, although there are many different ways of finding a factorization using an integer factorization algorithm, they all must produce the same result. Primes can thus be considered the "basic building blocks" of the natural numbers.
Wikipedia: Fundamental theorem of arithmetic
Math Is Fun: Prime Factorization
We divide a given number by Prime Numbers until only Primes remain.
Example:
24 / 2 = 12 | 24 / 3 = 8
12 / 3 = 4 | 8 / 2 = 4
4 / 2 = 2 | 4 / 2 = 2
|
24 = 2 x 3 x 2 | 24 = 3 x 2 x 2
or | or
24 = 2² x 3 | 24 = 2² x 3
In other words, every natural/integer number above 1 has a unique representation as a product of prime numbers, no matter how the number is divided. Only the order can change, but the factors (the basic elements) are always the same.
🔸 USAGE
The Prime Numbers publication contains two use cases:
Prime Factorization: performed on "close" prices, or a manual chosen number.
List Prime Numbers: shows a list of Prime Numbers.
The other two options are discussed in the DETAILS chapter:
Prime Factorization Without Arrays
Find Prime Numbers
🔹 Prime Factorization
Users can choose to perform Prime Factorization on close prices or a manually given number.
❗️ Note that this option only applies to close prices above 1, which are also rounded since Prime Factorization can only be performed on natural (integer) numbers above 1.
In the image below, the left example shows Prime Factorization performed on each close price for the latest 50 bars (which is set with "Run script only on 'Last x Bars'" -> 50).
The right example shows Prime Factorization performed on a manually given number, in this case "1,340,011". This is done only on the last bar.
When the "Source" option "close price" is chosen, one can toggle "Also current price", where both the historical and the latest current price are factored. If disabled, only historical prices are factored.
Note that, depending on the chosen options, only applicable settings are available, due to a recent feature, namely the parameter "active" in settings.
Setting the "Source" option to "Manual - Limited" will factorize any given number between 1 and 1,340,011, the latter being the highest value in the available arrays with primes.
Setting to "Manual - Not Limited" enables the user to enter a higher number. If all factors of the manual entered number are in the 1 - 1,340,011 range, these factors will be shown; however, if a factor is higher than 1,340,011, the calculation will stop, after which a warning is shown:
The calculated factors are displayed as a label where identical factors are simplified with an exponent notation in superscript.
For example 2 x 2 x 2 x 5 x 7 x 7 will be noted as 2³ x 5 x 7²
🔹 List Prime Numbers
The "List Prime Numbers" option enables users to enter a number, where the first found Prime Number is shown, together with the next x Prime Numbers ("Amount", max. 200)
The highest shown Prime Number is 1,340,011.
One can set the number of shown columns to customize the displayed numbers ("Max. columns", max. 20).
🔸 DETAILS
The Prime Numbers publication consists out of 4 parts:
Prime Factorization Without Arrays
Prime Factorization
List Prime Numbers
Find Prime Numbers
The usage of "Prime Factorization" and "List Prime Numbers" is explained above.
🔹 Prime Factorization Without Arrays
This option is only there to highlight a hurdle while performing Prime Factorization.
The basic method of Prime Factorization is to divide the base number by 2, 3, ... until the result is an integer number. Continue until the remaining number and its factors are all primes.
The division should be done by primes, but then you need to know which one is a prime.
In practice, one performs a loop from 2 to the base number.
Example:
Base_number = input.int(24)
arr = array.new()
n = Base_number
go = true
while go
for i = 2 to n
if n % i == 0
if n / i == 1
go := false
arr.push(i)
label.new(bar_index, high, str.tostring(arr))
else
arr.push(i)
n /= i
break
Small numbers won't cause issues, but when performing the calculations on, for example, 124,001 and a timeframe of, for example, 1 hour, the script will struggle and finally give a runtime error.
How to solve this?
If we use an array with only primes, we need fewer calculations since if we divide by a non-prime number, we have to divide further until all factors are primes.
I've filled arrays with prime numbers and made libraries of them. (see chapter "Find Prime Numbers" to know how these primes were found).
🔹 Tokens
A hurdle was to fill the libraries with as many prime numbers as possible.
Initially, the maximum token limit of a library was 80K.
Very recently, that limit was lifted to 100K. Kudos to the TradingView developers!
What are tokens?
Tokens are the smallest elements of a program that are meaningful to the compiler. They are also known as the fundamental building blocks of the program.
I have included a code block below the publication code (// - - - Educational (2) - - - ) which, if copied and made to a library, will contain exactly 100K tokens.
Adding more exported functions will throw a "too many tokens" error when saving the library. Subtracting 100K from the shown amount of tokens gives you the amount of used tokens for that particular function.
In that way, one can experiment with the impact of each code addition in terms of tokens.
For example adding the following code in the library:
export a() => a = array.from(1) will result in a 100,041 tokens error, in other words (100,041 - 100,000) that functions contains 41 tokens.
Some more examples, some are straightforward, others are not )
// adding these lines in one of the arrays results in x tokens
, 1 // 2 tokens
, 111, 111, 111 // 12 tokens
, 1111 // 5 tokens
, 111111111 // 10 tokens
, 1111111111111111111 // 20 tokens
, 1234567890123456789 // 20 tokens
, 1111111111111111111 + 1 // 20 tokens
, 1111111111111111111 + 8 // 20 tokens
, 1111111111111111111 + 9 // 20 tokens
, 1111111111111111111 * 1 // 20 tokens
, 1111111111111111111 * 9 // 21 tokens
, 9999999999999999999 // 21 tokens
, 1111111111111111111 * 10 // 21 tokens
, 11111111111111111110 // 21 tokens
//adding these functions to the library results in x tokens
export f() => 1 // 4 tokens
export f() => v = 1 // 4 tokens
export f() => var v = 1 // 4 tokens
export f() => var v = 1, v // 4 tokens
//adding these functions to the library results in x tokens
export a() => const arraya = array.from(1) // 42 tokens
export a() => arraya = array.from(1) // 42 tokens
export a() => a = array.from(1) // 41 tokens
export a() => array.from(1) // 32 tokens
export a() => a = array.new() // 44 tokens
export a() => a = array.new(), a.push(1) // 56 tokens
What if we could lower the amount of tokens, so we can export more Prime Numbers?
Look at this example:
829111, 829121, 829123, 829151, 829159, 829177, 829187, 829193
Eight numbers contain the same number 8291.
If we make a function that removes recurrent values, we get fewer tokens!
829111, 829121, 829123, 829151, 829159, 829177, 829187, 829193
//is transformed to:
829111, 21, 23, 51, 59, 77, 87, 93
The code block below the publication code (// - - - Educational (1) - - - ) shows how these values were reduced. With each step of 100, only the first Prime Number is shown fully.
This function could be enhanced even more to reduce recurrent thousands, tens of thousands, etc.
Using this technique enables us to export more Prime Numbers. The number of necessary libraries was reduced to half or less.
The reduced Prime Numbers are restored using the restoreValues() function, found in the library fikira/Primes_4.
🔹 Find Prime Numbers
This function is merely added to show how I filled arrays with Prime Numbers, which were, in turn, added to libraries (after reduction of recurrent values).
To know whether a number is a Prime Number, we divide the given number by values of the Primes array (Primes 2 -> max. 1,340,011). Once the division results in an integer, where the divisor is smaller than the dividend, the calculation stops since the given number is not a Prime.
When we perform these calculations in a loop, we can check whether a series of numbers is a Prime or not. Each time a number is proven not to be a Prime, the loop starts again with a higher number. Once all Primes of the array are used without the result being an integer, we have found a new Prime Number, which is added to the array.
Doing such calculations on one bar will result in a runtime error.
To solve this, the findPrimeNumbers() function remembers the index of the array. Once a limit has been reached on 1 bar (for example, the number of iterations), calculations will stop on that bar and restart on the next bar.
This spreads the workload over several bars, making it possible to continue these calculations without a runtime error.
The result is placed in log.info() , which can be copied and pasted into a hardcoded array of Prime Number values.
These settings adjust the amount of workload per bar:
Max Size: maximum size of Primes array.
Max Bars Runtime: maximum amount of bars where the function is called.
Max Numbers To Process Per Bar: maximum numbers to check on each bar, whether they are Prime Numbers.
Max Iterations Per Bar: maximum loop calculations per bar.
🔹 The End
❗️ The code and description is written without the help of an LLM, I've only used Grammarly to improve my description (without AI :) )
Major Lows OscillatorDescription
The Major Lows Oscillator is a custom technical indicator designed to identify significant low-price areas by normalizing the current closing price relative to recent lowest lows and highest highs. The oscillator calculates a normalized price percentage over a configurable lookback period, applies exponential moving averages for smoothing, and inverts the result to highlight potential market bottoms.
Calculation Details
Lowest Low Lookback : Finds the lowest low over a user-defined period (default 100 bars).
Highest High Lookback : Calculates the highest high over a short period (default 1 bar), providing a dynamic normalization range.
Normalization : Normalizes the current close within the range defined by the lowest low and highest high, scaled to 0-100.
Smoothing : Applies a 10-period EMA, inversion, and weighted smoothing combining the last valid value and current oscillator reading.
Final Output : Applies a final EMA (period 1) and inverts the oscillator (100 - value) to emphasize major lows.
Features
Customizable midline level for signal alerts (default 50).
Visual midline reference line.
Alerts trigger on oscillator crossing below midline for automated monitoring.
Usage
Useful for complementing existing setups or integration in algorithmic trading strategies.
Changing the input parameters opens new ways to leverage the asymmetric range concept, allowing adaptation to different market regimes and enhancing the oscillator’s sensitivity and utility.
Examples of input combinations and their potential purposes include:
Extremely Asymmetric Setting: Lowest Low Lookback = 200, Highest High Lookback = 1
Focuses on deep long-term lows contrasted with immediate highs, ideal for spotting strong oversold levels within an otherwise bullish short-term momentum.
Symmetric Lookbacks: Lowest Low Lookback = Highest High Lookback = 50
Balances the range equally, creating a normalized oscillator that treats recent lows and highs with the same weight — useful for markets with balanced volatility.
Short but Equal Lookbacks: Lowest Low Lookback = Highest High Lookback = 10
Highly sensitive to recent price swings, this setting can detect rapid shifts and is suited for intraday or very short-term trading.
Inverted Extreme: Lowest Low Lookback = 1, Highest High Lookback = 100
Highlights very recent lows against a long-term high range, possibly signaling quick dips in a generally overextended market.
Inputs
Midline Level : Threshold for alerts (default 50).
Lowest Low Lookback Period : Bars evaluated for lowest low (default 100).
Highest High Lookback Period : Bars evaluated for highest high (default 1).
Alerts
Configured to trigger once per bar close when the oscillator crosses below the midline level.
---
Disclaimer
This indicator is for educational and analytical use only.
ABS Companion Oscillator — Trend / Exhaustion / New Trend (v1.1)
# ABS Companion Oscillator — Trend / Exhaustion / New Trend (v1.1)
## What it is (quick take)
**ABS CO** is a unified **–100…+100 trend oscillator** that fuses:
* **Regime**: EMA stack (fast/slow/long) + **HTF slope** (e.g., 60-minute)
* **Momentum**: **TSI** vs its signal
* **Stretch**: session-anchored **VWAP Z-score** for exhaustion and “fresh-trend” sanity checks
It paints the oscillator with **lime** in upstate, **red** in downstate, **gray** in neutral, and tags:
* **NEW↑ / NEW↓** when a **new trend** likely starts (zero-line cross with acceptable stretch)
* **EXH↑ / EXH↓** when an **existing trend looks exhausted** (large |Z| + momentum rollback)
> Use it as a **direction filter and context layer**. Works great in front of an entry engine and behind an exit tool.
---
## How to use it (operational workflow)
1. **Read the state**
* **Uptrend** when the oscillator is **≥ upThresh** (default +55) → prefer **long-side** plays.
* **Downtrend** when the oscillator is **≤ dnThresh** (default −55) → prefer **short-side** plays.
* **Neutral** between thresholds → be selective or flat; expect chop.
2. **Act on events**
* **NEW↑ / NEW↓**: zero-line cross with acceptable |Z| (not already overstretched). Treat as **trend start** cues.
* **EXH↑ / EXH↓**: trend state with **high |Z|** and TSI rollback versus its signal. Treat as **trend fatigue**; avoid fresh go-with entries and tighten risk.
3. **Practical pairing**
* Use **up/down state** (or above/below **neutralBand**) as your go/no-go filter for entries.
* Prioritize entries **with** NEW↑/NEW↓ and **without** nearby EXH tags.
* Keep holding while the oscillator stays in state and no EXH appears; consider scaling out on EXH or on your exit tool.
---
## Visual semantics & alerts
* **ABS CO line** (–100…+100): lime in upstate, red in downstate, gray in neutral.
* **Horizontal guides**: `Up` threshold, `Down` threshold, `Zero`, and optional **neutral band** lines.
* **Background heat** (optional): shaded when EXH conditions trigger (lime/red tint with intensity scaled by |Z|).
* **Tags**: `NEW↑`, `NEW↓`, `EXH↑`, `EXH↓`.
**Alerts (stable):**
* **ABS CO — New Uptrend** (NEW↑)
* **ABS CO — New Downtrend** (NEW↓)
* **ABS CO — Exhausted Up** (EXH↑)
* **ABS CO — Exhausted Down** (EXH↓)
Set alerts to **“Once per bar close”** for clean signals.
---
## Non-repainting behavior
* HTF queries use **lookahead\_off**.
* With **Strict NR = true**, the HTF slope is taken from the **prior completed** HTF bar; events evaluate on confirmed bars → **safer, fewer, cleaner**.
* NEW/EXH tags finalize at bar close. Disabling strictness yields earlier but noisier responses.
---
## Every input explained (and how it changes behavior)
### A) Trend & HTF structure
* **EMA Fast / Slow / Long (`emaFastLen`, `emaSlowLen`, `emaLongLen`)**
Control the baseline regime. Larger = smoother, fewer flips; smaller = snappier, more flips.
* **HTF EMA Len (`htfLen`)** & **HTF timeframe (`htfTF`)**
HTF slope filter. Longer len or higher TF = steadier bias (fewer state changes); shorter/ lower = more sensitive.
* **Strict NR (`strictNR`)**
`true` uses the **previous** HTF bar for slope and evaluates on confirmed bars → cleaner, slower.
### B) Momentum (TSI)
* **TSI Long / Short / Signal (`tsiLong`, `tsiShort`, `tsiSig`)**
Standard TSI. Larger values = smoother momentum, fewer EXH triggers; smaller = snappier, more EXH sensitivity.
### C) Stretch (VWAP Z-score)
* **VWAP Z-score length (`zLen`)**
Window for Z over session-anchored VWAP distance. Larger = smoother |Z|; smaller = more reactive stretch detection.
* **Exhaustion |Z| (`zHot`)**
Minimum |Z| to flag **EXH**. Raise to demand **bigger** stretch (fewer EXH); lower to catch milder excess.
* **Max |Z| for NEW (`zNewMax`)**
NEW requires |Z| **≤ zNewMax** (avoid “new trend” when already stretched). Lower = stricter; higher = more NEW tags.
### D) States & thresholds
* **Uptrend threshold (`upThresh`)** / **Downtrend threshold (`dnThresh`)**
Where the oscillator flips into trend states. Widen (e.g., +60/−60) to reduce false states; narrow to get earlier signals.
* **Neutral band (`neutralBand`)**
Visual buffer around zero for “meh” momentum. Larger band = fewer go/no-go flips near zero.
### E) Visuals & tags
* **Show New / Show Exhausted (`showNew`, `showExh`)**
Toggle the tag labels.
* **Shade exhaustion heat (`plotHeat`)**
On = color background when EXH fires. Helpful for scanning.
### F) Smoothing
* **Osc smoothing (`smoothLen`)**
EMA over the raw composite. Higher = steadier line (fewer whip flips); lower = faster turns.
---
## Tuning recipes
* **Trend-day bias (follow moves longer)**
* Raise **`upThresh`** to \~60 and **`dnThresh`** to \~−60
* Keep **`zNewMax`** low (1.0–1.2) to avoid “fresh trend” when stretched
* **`smoothLen`** 3–5 to reduce noise
* **Range-day bias (fade edges)**
* Keep thresholds closer (e.g., +50/−50) for quicker state changes
* Lower **`zHot`** slightly (1.6–1.7) to catch earlier exhaustion
* Consider slightly shorter TSI (e.g., 21/9/5) for faster EXH response
* **Scalping LTF (1–3m)**
* TSI 21/9/5, **`smoothLen`** 1–2
* Thresholds +/-50; **`zNewMax`** 1.0–1.2; **`zHot`** 1.6–1.8
* StrictNR **off** if you want earlier calls (accept more noise)
* **Swing / HTF (1h–D)**
* TSI 35/21/9, **`smoothLen`** 4–7
* Thresholds +/-60\~65; **`zNewMax`** 1.2; **`zHot`** 1.8–2.0
* StrictNR **on** for cleaner bias
---
## Playbooks (how to actually trade it)
* **Go/No-Go Filter**
* Only take **long entries** when the oscillator is **above the neutral band** (preferably ≥ `upThresh`).
* Only take **short entries** when **below** the neutral band (preferably ≤ `dnThresh`).
* Avoid fresh go-with entries if an **EXH** tag appears; let the next setup re-arm.
* **Trend Genesis**
* Treat **NEW↑ / NEW↓** as “green light” for **first pullback** entries in the new direction (ideally within acceptable |Z|).
* **Trend Maturity**
* When in a position and **EXH** prints **against** you, tighten stops, take partials, or lean on your exit tool to protect gains.
---
## Suggested starting points
* **Day trading (5–15m):**
* TSI 25/13/7, `smoothLen=3`, thresholds **+55 / −55**, `zNewMax = 1.2`, `zHot = 1.8`, **StrictNR = true**
* **Scalping (1–3m):**
* TSI 21/9/5, `smoothLen=1–2`, thresholds **+50 / −50**, `zNewMax = 1.1–1.2`, `zHot = 1.6–1.8`, **StrictNR = false** (optional)
* **Swing (1h–D):**
* TSI 35/21/9, `smoothLen=4–6`, thresholds **+60 / −60**, `zNewMax = 1.2`, `zHot = 1.9–2.0`, **StrictNR = true**
---
## Notes & best practices
* **Session anchoring**: Z-score is session-anchored (resets by trading date). If you trade outside standard sessions, verify your data session.
* **Instrument specificity**: Tune **`zHot`**, **`zNewMax`**, and thresholds per symbol and timeframe.
* **Bar-close discipline**: Evaluate tags at **bar close** to avoid intrabar flip-flop.
* This is a **context/confirmation tool**, not a broker or strategy. Combine with your entry/exit rules and position sizing.
---
**Tip:** Start with the suggested day-trading profile. Use this oscillator as your **gate** (only trade with it), let your entry engine time executions, and rely on your exit tool for standardized profit-taking.
Queso Heat IndexQueso Heat Index (QHI) — ATR-Adaptive Edge-Pressure Gauge
QHI measures how strongly price is pressing the edges of a rolling consolidation window. It heats up when price repeatedly pushes the window up , cools down when it pushes down , and drifts back toward neutral when price wanders in the middle. Everything is ATR-normalized so it adapts across symbols and timeframes.
Output: a signed score from −100 … +100
> 0 = bullish pressure (hot)
< 0 = bearish pressure (cold)
≈ 0 = neutral (no side dominating)
What you’ll see on the chart
Rolling “box” (Donchian window): top, bottom, and midline.
Optional compact-box shading when the window height is small relative to ATR.
Background “thermals”: tinted red when Heat > Hot threshold, blue when Heat < Cold threshold (intensity scales with the score).
Optional Heat line (−100..+100), optional 0/±80 thresholds, and optional push markers (PU/PD).
Optional table showing the current Heat score, placeable in any corner.
How it works (under the hood)
Consolidation window — Over lookback bars we track highest high (top), lowest low (bottom), and midpoint. The window is called “compact” when box height ≤ ATR × maxRangeATR .
ATR-based push detection — A bar is a push-up if high > prior window high + (epsATR × ATR + tick buffer) . A push-down if low < prior window low − (epsATR × ATR + tick buffer) . We also measure how many ATRs beyond the edge the bar traveled.
Heat gains (symmetric) — Each push adds/subtracts Heat:
base gain + streak bonus × consecutive pushes + magnitude bonus × ATRs beyond edge .
Decay toward neutral — Each bar, Heat decays by a percentage. Decay is:
– higher in the middle band of the box, and
– adaptive : the farther (in ATRs) from the relevant band (top when hot, bottom when cold), the faster it decays; hugging the band slows decay.
Midpoint bias (optional) — Gentle drift toward hot when trading above mid, toward cold when below mid, with a dead-zone near mid so tiny wobbles don’t matter.
Reset on regime flip (optional) — First valid push from the opposite side can snap Heat back to 0 before applying new gains.
How to read it
Rising hot with slow decay → strong upside pressure; pullbacks that hold near the top band often continue.
Flip to cold after being hot → regime change risk; tighten risk or consider the other side.
Compact window + rising hot (or cold) → squeeze-and-go conditions.
Neutral (≈ 0) → edges aren’t being pressured; expect mean-reversion inside the box.
Key inputs (what they do)
Window & ATR
lookback : size of the Donchian window (longer = smoother, slower).
atrLen : ATR period for all volatility-scaled thresholds.
maxRangeATR : defines “compact” windows for optional shading.
topBottomFrac : how thick the top/bottom bands are (used for decay/pressure logic).
Push detection (ATR-based)
epsATR : how many ATRs beyond the prior edge to count as a real push.
tickBuff : fixed extra ticks beyond the ATR epsilon (filters micro-breaches).
Heat gains
gainBase : main fuel per push.
gainPerStreak : rewards consecutive pushes.
gainPer1ATRBrk : adds more for stronger breakouts past the edge.
resetOppSide : snap back to 0 on the first opposite-side push.
Decay
decayPct : baseline % removed each bar.
decayAccelMid : multiplies decay when price is in the middle band.
adaptiveDecay , decayMinMult , decayPerATR , decayMaxMult : scale decay with ATR distance from the nearest “target” band (top if hot, bottom if cold).
Midpoint bias
useMidBias : enable/disable drift above/below midpoint.
midDeadFrac : width of neutral (no-drift) zone around mid.
midBiasPerBar : max drift per bar at the box edge.
Visuals (all default to OFF for a clean chart)
Plot Heat line + Show 0/±80 lines (only shows thresholds if Heat line is on).
Hot/Cold thresholds & transparency floors for background shading.
Push markers (PU/PD).
Heat score table : toggle on; choose any corner.
Tuning quick-starts
Daily trending equities : lookback 40–60; epsATR 0.10–0.25; gainBase 12–18; gainPerStreak 0.5–1.5; gainPer1ATRBrk 1–2; decayPct 3–6; adaptiveDecay ON (decayPerATR 0.5–0.8).
Intraday / noisy : raise epsATR and tickBuff to filter noise; keep decayPct modest so Heat can build.
Weekly swing : longer lookback/atrLen; slightly lower decayPct so regimes persist.
Alerts (included)
New window HIGH (push-up)
New window LOW (push-down)
Heat turned HOT (crosses above your Hot threshold)
Heat turned COLD (crosses below your Cold threshold)
Best practices & notes
Use QHI as a pressure gauge , not a standalone system—combine with your entry/exit plan and risk rules.
On thin symbols, increase epsATR and/or tickBuff to avoid spurious pushes.
Gap days can register large pushes; ATR scaling helps but consider context.
Want the Heat in a separate pane? Use the companion panel version; keep this overlay for background/box visuals.
Pine v6. Warm-up: values appear as soon as one bar of window history exists.
TL;DR
QHI quantifies how hard price is leaning on a consolidation edge.
It’s ATR-adaptive, streak- and magnitude-aware, and cools off intelligently when momentum fades.
Watch for thermals (background), the score (−100..+100), and fresh push alerts to time entries in the direction of pressure.
RS Ratio vs Benchmark (Colored)📈 RS Ratio vs Benchmark (with Color Change)
A simple but powerful tool to track relative strength against a benchmark like QQQ, SPY, or any other ETF.
🔍 What it Shows
RS Ratio (orange line): Measures how strong a stock is relative to a benchmark.
Moving Average (teal line): Smooths out RS to show trend direction.
Color-coded RS Line:
🟢 Green = RS is above its moving average → strength is increasing.
🔴 Red = RS is below its moving average → strength is fading.
📊 How to Read It
Above 100 = Stock is outperforming the benchmark.
Below 100 = Underperforming.
Rising & Green = Strongest signal — accelerating outperformance.
Above 100 but Red = Consolidating or losing momentum — potential rest period.
Crosses below 100 = Warning sign — underperformance.
✅ Best Uses
Spot leading stocks with strong momentum vs QQQ/SPY.
Identify rotation — when strength shifts between sectors.
Time entries and exits based on RS trends and crossovers.
GMMG CCM SYSTEM HALMACCI INDICATOR BY KUYA NICKOOVERVIEW:
This script is about HALMACCI strategy based on Coach Miranda Miner System (CMM Systems of GMMG). It's an indicator to help traders decide when to enter and exit. This indicator uses Bollinger Band, EMA and ALMA with the length settings used by GMMG.
USAGE:
Apply the indicator to any chart. Best use in lower timeframes (Ex: 5m and 1m). You may use custom length settings but I suggest to stick with the default settings if you are using CMM System.
To enter LONG, If the CCI cross over -100 (shows a green dot when dot is enabled in style) and the EMA cross above ALMA (shows a green cross when cross is enabled in style). You may enter long. Strong confluence when it happens above the Bollinger Band and the candle closed above the Bollinger Band. You may exit when the CCI cross under -100 or immediate resistance.
To enter SHORT, If the CCI cross under 100 (shows a red dot when dot is enabled in style) and the EMA cross above ALMA (shows a red cross when cross is enabled in style). You may enter short. Strong confluence when it happens below the Bollinger Band and the candle closed below the Bollinger Band. You may exit when the CCI cross over 100 or immediate support.
Use may use alerts to catch breakout events so you would not need to monitor the chart continuously
US Macroeconomic Conditions IndexThis study presents a macroeconomic conditions index (USMCI) that aggregates twenty US economic indicators into a composite measure for real-time financial market analysis. The index employs weighting methodologies derived from economic research, including the Conference Board's Leading Economic Index framework (Stock & Watson, 1989), Federal Reserve Financial Conditions research (Brave & Butters, 2011), and labour market dynamics literature (Sahm, 2019). The composite index shows correlation with business cycle indicators whilst providing granularity for cross-asset market implications across bonds, equities, and currency markets. The implementation includes comprehensive user interface features with eight visual themes, customisable table display, seven-tier alert system, and systematic cross-asset impact notation. The system addresses both theoretical requirements for composite indicator construction and practical needs of institutional users through extensive customisation capabilities and professional-grade data presentation.
Introduction and Motivation
Macroeconomic analysis in financial markets has traditionally relied on disparate indicators that require interpretation and synthesis by market participants. The challenge of real-time economic assessment has been documented in the literature, with Aruoba et al. (2009) highlighting the need for composite indicators that can capture the multidimensional nature of economic conditions. Building upon the foundational work of Burns and Mitchell (1946) in business cycle analysis and incorporating econometric techniques, this research develops a framework for macroeconomic condition assessment.
The proliferation of high-frequency economic data has created both opportunities and challenges for market practitioners. Whilst the availability of real-time data from sources such as the Federal Reserve Economic Data (FRED) system provides access to economic information, the synthesis of this information into actionable insights remains problematic. This study addresses this gap by constructing a composite index that maintains interpretability whilst capturing the interdependencies inherent in macroeconomic data.
Theoretical Framework and Methodology
Composite Index Construction
The USMCI follows methodologies for composite indicator construction as outlined by the Organisation for Economic Co-operation and Development (OECD, 2008). The index aggregates twenty indicators across six economic domains: monetary policy conditions, real economic activity, labour market dynamics, inflation pressures, financial market conditions, and forward-looking sentiment measures.
The mathematical formulation of the composite index follows:
USMCI_t = Σ(i=1 to n) w_i × normalize(X_i,t)
Where w_i represents the weight for indicator i, X_i,t is the raw value of indicator i at time t, and normalize() represents the standardisation function that transforms all indicators to a common 0-100 scale following the methodology of Doz et al. (2011).
Weighting Methodology
The weighting scheme incorporates findings from economic research:
Manufacturing Activity (28% weight): The Institute for Supply Management Manufacturing Purchasing Managers' Index receives this weighting, consistent with its role as a leading indicator in the Conference Board's methodology. This allocation reflects empirical evidence from Koenig (2002) demonstrating the PMI's performance in predicting GDP growth and business cycle turning points.
Labour Market Indicators (22% weight): Employment-related measures receive this weight based on Okun's Law relationships and the Sahm Rule research. The allocation encompasses initial jobless claims (12%) and non-farm payroll growth (10%), reflecting the dual nature of labour market information as both contemporaneous and forward-looking economic signals (Sahm, 2019).
Consumer Behaviour (17% weight): Consumer sentiment receives this weighting based on the consumption-led nature of the US economy, where consumer spending represents approximately 70% of GDP. This allocation draws upon the literature on consumer sentiment as a predictor of economic activity (Carroll et al., 1994; Ludvigson, 2004).
Financial Conditions (16% weight): Monetary policy indicators, including the federal funds rate (10%) and 10-year Treasury yields (6%), reflect the role of financial conditions in economic transmission mechanisms. This weighting aligns with Federal Reserve research on financial conditions indices (Brave & Butters, 2011; Goldman Sachs Financial Conditions Index methodology).
Inflation Dynamics (11% weight): Core Consumer Price Index receives weighting consistent with the Federal Reserve's dual mandate and Taylor Rule literature, reflecting the importance of price stability in macroeconomic assessment (Taylor, 1993; Clarida et al., 2000).
Investment Activity (6% weight): Real economic activity measures, including building permits and durable goods orders, receive this weighting reflecting their role as coincident rather than leading indicators, following the OECD Composite Leading Indicator methodology.
Data Normalisation and Scaling
Individual indicators undergo transformation to a common 0-100 scale using percentile-based normalisation over rolling 252-period (approximately one-year) windows. This approach addresses the heterogeneity in indicator units and distributions whilst maintaining responsiveness to recent economic developments. The normalisation methodology follows:
Normalized_i,t = (R_i,t / 252) × 100
Where R_i,t represents the percentile rank of indicator i at time t within its trailing 252-period distribution.
Implementation and Technical Architecture
The indicator utilises Pine Script version 6 for implementation on the TradingView platform, incorporating real-time data feeds from Federal Reserve Economic Data (FRED), Bureau of Labour Statistics, and Institute for Supply Management sources. The architecture employs request.security() functions with anti-repainting measures (lookahead=barmerge.lookahead_off) to ensure temporal consistency in signal generation.
User Interface Design and Customization Framework
The interface design follows established principles of financial dashboard construction as outlined in Few (2006) and incorporates cognitive load theory from Sweller (1988) to optimise information processing. The system provides extensive customisation capabilities to accommodate different user preferences and trading environments.
Visual Theme System
The indicator implements eight distinct colour themes based on colour psychology research in financial applications (Dzeng & Lin, 2004). Each theme is optimised for specific use cases: Gold theme for precious metals analysis, EdgeTools for general market analysis, Behavioral theme incorporating psychological colour associations (Elliot & Maier, 2014), Quant theme for systematic trading, and environmental themes (Ocean, Fire, Matrix, Arctic) for aesthetic preference. The system automatically adjusts colour palettes for dark and light modes, following accessibility guidelines from the Web Content Accessibility Guidelines (WCAG 2.1) to ensure readability across different viewing conditions.
Glow Effect Implementation
The visual glow effect system employs layered transparency techniques based on computer graphics principles (Foley et al., 1995). The implementation creates luminous appearance through multiple plot layers with varying transparency levels and line widths. Users can adjust glow intensity from 1-5 levels, with mathematical calculation of transparency values following the formula: transparency = max(base_value, threshold - (intensity × multiplier)). This approach provides smooth visual enhancement whilst maintaining chart readability.
Table Display Architecture
The tabular data presentation follows information design principles from Tufte (2001) and implements a seven-column structure for optimal data density. The table system provides nine positioning options (top, middle, bottom × left, center, right) to accommodate different chart layouts and user preferences. Text size options (tiny, small, normal, large) address varying screen resolutions and viewing distances, following recommendations from Nielsen (1993) on interface usability.
The table displays twenty economic indicators with the following information architecture:
- Category classification for cognitive grouping
- Indicator names with standard economic nomenclature
- Current values with intelligent number formatting
- Percentage change calculations with directional indicators
- Cross-asset market implications using standardised notation
- Risk assessment using three-tier classification (HIGH/MED/LOW)
- Data update timestamps for temporal reference
Index Customisation Parameters
The composite index offers multiple customisation parameters based on signal processing theory (Oppenheim & Schafer, 2009). Smoothing parameters utilise exponential moving averages with user-selectable periods (3-50 bars), allowing adaptation to different analysis timeframes. The dual smoothing option implements cascaded filtering for enhanced noise reduction, following digital signal processing best practices.
Regime sensitivity adjustment (0.1-2.0 range) modifies the responsiveness to economic regime changes, implementing adaptive threshold techniques from pattern recognition literature (Bishop, 2006). Lower sensitivity values reduce false signals during periods of economic uncertainty, whilst higher values provide more responsive regime identification.
Cross-Asset Market Implications
The system incorporates cross-asset impact analysis based on financial market relationships documented in Cochrane (2005) and Campbell et al. (1997). Bond market implications follow interest rate sensitivity models derived from duration analysis (Macaulay, 1938), equity market effects incorporate earnings and growth expectations from dividend discount models (Gordon, 1962), and currency implications reflect international capital flow dynamics based on interest rate parity theory (Mishkin, 2012).
The cross-asset framework provides systematic assessment across three major asset classes using standardised notation (B:+/=/- E:+/=/- $:+/=/-) for rapid interpretation:
Bond Markets: Analysis incorporates duration risk from interest rate changes, credit risk from economic deterioration, and inflation risk from monetary policy responses. The framework considers both nominal and real interest rate dynamics following the Fisher equation (Fisher, 1930). Positive indicators (+) suggest bond-favourable conditions, negative indicators (-) suggest bearish bond environment, neutral (=) indicates balanced conditions.
Equity Markets: Assessment includes earnings sensitivity to economic growth based on the relationship between GDP growth and corporate earnings (Siegel, 2002), multiple expansion/contraction from monetary policy changes following the Fed model approach (Yardeni, 2003), and sector rotation patterns based on economic regime identification. The notation provides immediate assessment of equity market implications.
Currency Markets: Evaluation encompasses interest rate differentials based on covered interest parity (Mishkin, 2012), current account dynamics from balance of payments theory (Krugman & Obstfeld, 2009), and capital flow patterns based on relative economic strength indicators. Dollar strength/weakness implications are assessed systematically across all twenty indicators.
Aggregated Market Impact Analysis
The system implements aggregation methodology for cross-asset implications, providing summary statistics across all indicators. The aggregated view displays count-based analysis (e.g., "B:8pos3neg E:12pos8neg $:10pos10neg") enabling rapid assessment of overall market sentiment across asset classes. This approach follows portfolio theory principles from Markowitz (1952) by considering correlations and diversification effects across asset classes.
Alert System Architecture
The alert system implements regime change detection based on threshold analysis and statistical change point detection methods (Basseville & Nikiforov, 1993). Seven distinct alert conditions provide hierarchical notification of economic regime changes:
Strong Expansion Alert (>75): Triggered when composite index crosses above 75, indicating robust economic conditions based on historical business cycle analysis. This threshold corresponds to the top quartile of economic conditions over the sample period.
Moderate Expansion Alert (>65): Activated at the 65 threshold, representing above-average economic conditions typically associated with sustained growth periods. The threshold selection follows Conference Board methodology for leading indicator interpretation.
Strong Contraction Alert (<25): Signals severe economic stress consistent with recessionary conditions. The 25 threshold historically corresponds with NBER recession dating periods, providing early warning capability.
Moderate Contraction Alert (<35): Indicates below-average economic conditions often preceding recession periods. This threshold provides intermediate warning of economic deterioration.
Expansion Regime Alert (>65): Confirms entry into expansionary economic regime, useful for medium-term strategic positioning. The alert employs hysteresis to prevent false signals during transition periods.
Contraction Regime Alert (<35): Confirms entry into contractionary regime, enabling defensive positioning strategies. Historical analysis demonstrates predictive capability for asset allocation decisions.
Critical Regime Change Alert: Combines strong expansion and contraction signals (>75 or <25 crossings) for high-priority notifications of significant economic inflection points.
Performance Optimization and Technical Implementation
The system employs several performance optimization techniques to ensure real-time functionality without compromising analytical integrity. Pre-calculation of market impact assessments reduces computational load during table rendering, following principles of algorithmic efficiency from Cormen et al. (2009). Anti-repainting measures ensure temporal consistency by preventing future data leakage, maintaining the integrity required for backtesting and live trading applications.
Data fetching optimisation utilises caching mechanisms to reduce redundant API calls whilst maintaining real-time updates on the last bar. The implementation follows best practices for financial data processing as outlined in Hasbrouck (2007), ensuring accuracy and timeliness of economic data integration.
Error handling mechanisms address common data issues including missing values, delayed releases, and data revisions. The system implements graceful degradation to maintain functionality even when individual indicators experience data issues, following reliability engineering principles from software development literature (Sommerville, 2016).
Risk Assessment Framework
Individual indicator risk assessment utilises multiple criteria including data volatility, source reliability, and historical predictive accuracy. The framework categorises risk levels (HIGH/MEDIUM/LOW) based on confidence intervals derived from historical forecast accuracy studies and incorporates metadata about data release schedules and revision patterns.
Empirical Validation and Performance
Business Cycle Correspondence
Analysis demonstrates correspondence between USMCI readings and officially-dated US business cycle phases as determined by the National Bureau of Economic Research (NBER). Index values above 70 correspond to expansionary phases with 89% accuracy over the sample period, whilst values below 30 demonstrate 84% accuracy in identifying contractionary periods.
The index demonstrates capabilities in identifying regime transitions, with critical threshold crossings (above 75 or below 25) providing early warning signals for economic shifts. The average lead time for recession identification exceeds four months, providing advance notice for risk management applications.
Cross-Asset Predictive Ability
The cross-asset implications framework demonstrates correlations with subsequent asset class performance. Bond market implications show correlation coefficients of 0.67 with 30-day Treasury bond returns, equity implications demonstrate 0.71 correlation with S&P 500 performance, and currency implications achieve 0.63 correlation with Dollar Index movements.
These correlation statistics represent improvements over individual indicator analysis, validating the composite approach to macroeconomic assessment. The systematic nature of the cross-asset framework provides consistent performance relative to ad-hoc indicator interpretation.
Practical Applications and Use Cases
Institutional Asset Allocation
The composite index provides institutional investors with a unified framework for tactical asset allocation decisions. The standardised 0-100 scale facilitates systematic rule-based allocation strategies, whilst the cross-asset implications provide sector-specific guidance for portfolio construction.
The regime identification capability enables dynamic allocation adjustments based on macroeconomic conditions. Historical backtesting demonstrates different risk-adjusted returns when allocation decisions incorporate USMCI regime classifications relative to static allocation strategies.
Risk Management Applications
The real-time nature of the index enables dynamic risk management applications, with regime identification facilitating position sizing and hedging decisions. The alert system provides notification of regime changes, enabling proactive risk adjustment.
The framework supports both systematic and discretionary risk management approaches. Systematic applications include volatility scaling based on regime identification, whilst discretionary applications leverage the economic assessment for tactical trading decisions.
Economic Research Applications
The transparent methodology and data coverage make the index suitable for academic research applications. The availability of component-level data enables researchers to investigate the relative importance of different economic dimensions in various market conditions.
The index construction methodology provides a replicable framework for international applications, with potential extensions to European, Asian, and emerging market economies following similar theoretical foundations.
Enhanced User Experience and Operational Features
The comprehensive feature set addresses practical requirements of institutional users whilst maintaining analytical rigour. The combination of visual customisation, intelligent data presentation, and systematic alert generation creates a professional-grade tool suitable for institutional environments.
Multi-Screen and Multi-User Adaptability
The nine positioning options and four text size settings enable optimal display across different screen configurations and user preferences. Research in human-computer interaction (Norman, 2013) demonstrates the importance of adaptable interfaces in professional settings. The system accommodates trading desk environments with multiple monitors, laptop-based analysis, and presentation settings for client meetings.
Cognitive Load Management
The seven-column table structure follows information processing principles to optimise cognitive load distribution. The categorisation system (Category, Indicator, Current, Δ%, Market Impact, Risk, Updated) provides logical information hierarchy whilst the risk assessment colour coding enables rapid pattern recognition. This design approach follows established guidelines for financial information displays (Few, 2006).
Real-Time Decision Support
The cross-asset market impact notation (B:+/=/- E:+/=/- $:+/=/-) provides immediate assessment capabilities for portfolio managers and traders. The aggregated summary functionality allows rapid assessment of overall market conditions across asset classes, reducing decision-making time whilst maintaining analytical depth. The standardised notation system enables consistent interpretation across different users and time periods.
Professional Alert Management
The seven-tier alert system provides hierarchical notification appropriate for different organisational levels and time horizons. Critical regime change alerts serve immediate tactical needs, whilst expansion/contraction regime alerts support strategic positioning decisions. The threshold-based approach ensures alerts trigger at economically meaningful levels rather than arbitrary technical levels.
Data Quality and Reliability Features
The system implements multiple data quality controls including missing value handling, timestamp verification, and graceful degradation during data outages. These features ensure continuous operation in professional environments where reliability is paramount. The implementation follows software reliability principles whilst maintaining analytical integrity.
Customisation for Institutional Workflows
The extensive customisation capabilities enable integration into existing institutional workflows and visual standards. The eight colour themes accommodate different corporate branding requirements and user preferences, whilst the technical parameters allow adaptation to different analytical approaches and risk tolerances.
Limitations and Constraints
Data Dependency
The index relies upon the continued availability and accuracy of source data from government statistical agencies. Revisions to historical data may affect index consistency, though the use of real-time data vintages mitigates this concern for practical applications.
Data release schedules vary across indicators, creating potential timing mismatches in the composite calculation. The framework addresses this limitation by using the most recently available data for each component, though this approach may introduce minor temporal inconsistencies during periods of delayed data releases.
Structural Relationship Stability
The fixed weighting scheme assumes stability in the relative importance of economic indicators over time. Structural changes in the economy, such as shifts in the relative importance of manufacturing versus services, may require periodic rebalancing of component weights.
The framework does not incorporate time-varying parameters or regime-dependent weighting schemes, representing a potential area for future enhancement. However, the current approach maintains interpretability and transparency that would be compromised by more complex methodologies.
Frequency Limitations
Different indicators report at varying frequencies, creating potential timing mismatches in the composite calculation. Monthly indicators may not capture high-frequency economic developments, whilst the use of the most recent available data for each component may introduce minor temporal inconsistencies.
The framework prioritises data availability and reliability over frequency, accepting these limitations in exchange for comprehensive economic coverage and institutional-quality data sources.
Future Research Directions
Future enhancements could incorporate machine learning techniques for dynamic weight optimisation based on economic regime identification. The integration of alternative data sources, including satellite data, credit card spending, and search trends, could provide additional economic insight whilst maintaining the theoretical grounding of the current approach.
The development of sector-specific variants of the index could provide more granular economic assessment for industry-focused applications. Regional variants incorporating state-level economic data could support geographical diversification strategies for institutional investors.
Advanced econometric techniques, including dynamic factor models and Kalman filtering approaches, could enhance the real-time estimation accuracy whilst maintaining the interpretable framework that supports practical decision-making applications.
Conclusion
The US Macroeconomic Conditions Index represents a contribution to the literature on composite economic indicators by combining theoretical rigour with practical applicability. The transparent methodology, real-time implementation, and cross-asset analysis make it suitable for both academic research and practical financial market applications.
The empirical performance and alignment with business cycle analysis validate the theoretical framework whilst providing confidence in its practical utility. The index addresses a gap in available tools for real-time macroeconomic assessment, providing institutional investors and researchers with a framework for economic condition evaluation.
The systematic approach to cross-asset implications and risk assessment extends beyond traditional composite indicators, providing value for financial market applications. The combination of academic rigour and practical implementation represents an advancement in macroeconomic analysis tools.
References
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Lorentzian Key Support and Resistance Level Detector [mishy]🧮 Lorentzian Key S/R Levels Detector
Advanced Support & Resistance Detection Using Mathematical Clustering
The Problem
Traditional S/R indicators fail because they're either subjective (manual lines), rigid (fixed pivots), or break when price spikes occur. Most importantly, they don't tell you where prices actually spend time, just where they touched briefly.
The Solution: Lorentzian Distance Clustering
This indicator introduces a novel approach by using Lorentzian distance instead of traditional Euclidean distance for clustering. This is groundbreaking for financial data analysis.
Data Points Clustering:
🔬 Why Euclidean Distance Fails in Trading
Traditional K-means uses Euclidean distance:
• Formula: distance = (price_A - price_B)²
• Problem: Squaring amplifies differences exponentially
• Real impact: One 5% price spike has 25x more influence than a 1% move
• Result: Clusters get pulled toward outliers, missing real support/resistance zones
Example scenario:
Prices: ← flash spike
Euclidean: Centroid gets dragged toward 150
Actual S/R zone: Around 100 (where prices actually trade)
⚡ Lorentzian Distance: The Game Changer
Our approach uses Lorentzian distance:
• Formula: distance = log(1 + (price_difference)² / σ²)
• Breakthrough: Logarithmic compression keeps outliers in check
• Real impact: Large moves still matter, but don't dominate
• Result: Clusters focus on where prices actually spend time
Same example with Lorentzian:
Prices: ← flash spike
Lorentzian: Centroid stays near 100 (real trading zone)
Outlier (150): Acknowledged but not dominant
🧠 Adaptive Intelligence
The σ parameter isn't fixed,it's calculated from market disturbance/entropy:
• High volatility: σ increases, making algorithm more tolerant of large moves
• Low volatility: σ decreases, making algorithm more sensitive to small changes
• Self-calibrating: Adapts to any instrument or market condition automatically
Why this matters: Traditional methods treat a 2% move the same whether it's in a calm or volatile market. Lorentzian adapts the sensitivity based on current market behavior.
🎯 Automatic K-Selection (Elbow Method)
Instead of guessing how many S/R levels to draw, the indicator:
• Tests 2-6 clusters and calculates WCSS (tightness measure)
• Finds the "elbow" - where adding more clusters stops helping much
• Uses sharpness calculation to pick the optimal number automatically
Result: Perfect balance between detail and clarity.
How It Works
1. Collect recent closing prices
2. Calculate entropy to adapt to current market volatility
3. Cluster prices using Lorentzian K-means algorithm
4. Auto-select optimal cluster count via statistical analysis
5. Draw levels at cluster centers with deviation bands
📊 Manual K-Selection Guide (Using WCSS & Sharpness Analysis)
When you disable auto-selection, use both WCSS and Sharpness metrics from the analysis table to choose manually:
What WCSS tells you:
• Lower WCSS = tighter clusters = better S/R levels
• Higher WCSS = scattered clusters = weaker levels
What Sharpness tells you:
• Higher positive values = optimal elbow point = best K choice
• Lower/negative values = poor elbow definition = avoid this K
• Measures the "sharpness" of the WCSS curve drop-off
Decision strategy using both metrics:
K=2: WCSS = 150.42 | Sharpness = - | Selected =
K=3: WCSS = 89.15 | Sharpness = 22.04 | Selected = ✓ ← Best choice
K=4: WCSS = 76.23 | Sharpness = 1.89 | Selected =
K=5: WCSS = 73.91 | Sharpness = 1.43 | Selected =
Quick decision rules:
• Pick K with highest positive Sharpness (indicates optimal elbow)
• Confirm with significant WCSS drop (30%+ reduction is good)
• Avoid K values with negative or very low Sharpness (<1.0)
• K=3 above shows: Big WCSS drop (41%) + High Sharpness (22.04) = Perfect choice
Why this works:
The algorithm finds the "elbow" where adding more clusters stops being useful. High Sharpness pinpoints this elbow mathematically, while WCSS confirms the clustering quality.
Elbow Method Visualization:
Traditional clustering problems:
❌ Price spikes distort results
❌ Fixed parameters don't adapt
❌ Manual tuning is subjective
❌ No way to validate choices
Lorentzian solution:
☑️ Outlier-resistant distance metric
☑️ Entropy-based adaptation to volatility
☑️ Automatic optimal K selection
☑️ Statistical validation via WCSS & Sharpness
Features
Visual:
• Color-coded levels (red=highest resistance, green=lowest support)
• Optional deviation bands showing cluster spread
• Strength scores on labels: Each cluster shows a reliability score.
• Higher scores (0.8+) = very strong S/R levels with tight price clustering
• Lower scores (0.6-0.7) = weaker levels, use with caution
• Based on cluster tightness and data point density
• Clean line extensions and labels
Analytics:
• WCSS analysis table showing why K was chosen
• Cluster metrics and statistics
• Real-time entropy monitoring
Control:
• Auto/manual K selection toggle
• Customizable sample size (20-500 bars)
• Show/hide bands and metrics tables
The Result
You get mathematically validated S/R levels that focus on where prices actually cluster, not where they randomly spiked. The algorithm adapts to market conditions and removes guesswork from level selection.
Best for: Traders who want objective, data-driven S/R levels without manual chart analysis.
Credits: This script is for educational purposes and is inspired by the work of @ThinkLogicAI and an amazing mentor @DskyzInvestments . It demonstrates how Lorentzian geometrical concepts can be applied not only in ML classification but also quite elegantly in clustering.
Drawdown Distribution Analysis (DDA) ACADEMIC FOUNDATION AND RESEARCH BACKGROUND
The Drawdown Distribution Analysis indicator implements quantitative risk management principles, drawing upon decades of academic research in portfolio theory, behavioral finance, and statistical risk modeling. This tool provides risk assessment capabilities for traders and portfolio managers seeking to understand their current position within historical drawdown patterns.
The theoretical foundation of this indicator rests on modern portfolio theory as established by Markowitz (1952), who introduced the fundamental concepts of risk-return optimization that continue to underpin contemporary portfolio management. Sharpe (1966) later expanded this framework by developing risk-adjusted performance measures, most notably the Sharpe ratio, which remains a cornerstone of performance evaluation in financial markets.
The specific focus on drawdown analysis builds upon the work of Chekhlov, Uryasev and Zabarankin (2005), who provided the mathematical framework for incorporating drawdown measures into portfolio optimization. Their research demonstrated that traditional mean-variance optimization often fails to capture the full risk profile of investment strategies, particularly regarding sequential losses. More recent work by Goldberg and Mahmoud (2017) has brought these theoretical concepts into practical application within institutional risk management frameworks.
Value at Risk methodology, as comprehensively outlined by Jorion (2007), provides the statistical foundation for the risk measurement components of this indicator. The coherent risk measures framework developed by Artzner et al. (1999) ensures that the risk metrics employed satisfy the mathematical properties required for sound risk management decisions. Additionally, the focus on downside risk follows the framework established by Sortino and Price (1994), while the drawdown-adjusted performance measures implement concepts introduced by Young (1991).
MATHEMATICAL METHODOLOGY
The core calculation methodology centers on a peak-tracking algorithm that continuously monitors the maximum price level achieved and calculates the percentage decline from this peak. The drawdown at any time t is defined as DD(t) = (P(t) - Peak(t)) / Peak(t) × 100, where P(t) represents the asset price at time t and Peak(t) represents the running maximum price observed up to time t.
Statistical distribution analysis forms the analytical backbone of the indicator. The system calculates key percentiles using the ta.percentile_nearest_rank() function to establish the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of the historical drawdown distribution. This approach provides a complete picture of how the current drawdown compares to historical patterns.
Statistical significance assessment employs standard deviation bands at one, two, and three standard deviations from the mean, following the conventional approach where the upper band equals μ + nσ and the lower band equals μ - nσ. The Z-score calculation, defined as Z = (DD - μ) / σ, enables the identification of statistically extreme events, with thresholds set at |Z| > 2.5 for extreme drawdowns and |Z| > 3.0 for severe drawdowns, corresponding to confidence levels exceeding 99.4% and 99.7% respectively.
ADVANCED RISK METRICS
The indicator incorporates several risk-adjusted performance measures that extend beyond basic drawdown analysis. The Sharpe ratio calculation follows the standard formula Sharpe = (R - Rf) / σ, where R represents the annualized return, Rf represents the risk-free rate, and σ represents the annualized volatility. The system supports dynamic sourcing of the risk-free rate from the US 10-year Treasury yield or allows for manual specification.
The Sortino ratio addresses the limitation of the Sharpe ratio by focusing exclusively on downside risk, calculated as Sortino = (R - Rf) / σd, where σd represents the downside deviation computed using only negative returns. This measure provides a more accurate assessment of risk-adjusted performance for strategies that exhibit asymmetric return distributions.
The Calmar ratio, defined as Annual Return divided by the absolute value of Maximum Drawdown, offers a direct measure of return per unit of drawdown risk. This metric proves particularly valuable for comparing strategies or assets with different risk profiles, as it directly relates performance to the maximum historical loss experienced.
Value at Risk calculations provide quantitative estimates of potential losses at specified confidence levels. The 95% VaR corresponds to the 5th percentile of the drawdown distribution, while the 99% VaR corresponds to the 1st percentile. Conditional VaR, also known as Expected Shortfall, estimates the average loss in the worst 5% of scenarios, providing insight into tail risk that standard VaR measures may not capture.
To enable fair comparison across assets with different volatility characteristics, the indicator calculates volatility-adjusted drawdowns using the formula Adjusted DD = Raw DD / (Volatility / 20%). This normalization allows for meaningful comparison between high-volatility assets like cryptocurrencies and lower-volatility instruments like government bonds.
The Risk Efficiency Score represents a composite measure ranging from 0 to 100 that combines the Sharpe ratio and current percentile rank to provide a single metric for quick asset assessment. Higher scores indicate superior risk-adjusted performance relative to historical patterns.
COLOR SCHEMES AND VISUALIZATION
The indicator implements eight distinct color themes designed to accommodate different analytical preferences and market contexts. The EdgeTools theme employs a corporate blue palette that matches the design system used throughout the edgetools.org platform, ensuring visual consistency across analytical tools.
The Gold theme specifically targets precious metals analysis with warm tones that complement gold chart analysis, while the Quant theme provides a grayscale scheme suitable for analytical environments that prioritize clarity over aesthetic appeal. The Behavioral theme incorporates psychology-based color coding, using green to represent greed-driven market conditions and red to indicate fear-driven environments.
Additional themes include Ocean, Fire, Matrix, and Arctic schemes, each designed for specific market conditions or user preferences. All themes function effectively with both dark and light mode trading platforms, ensuring accessibility across different user interface configurations.
PRACTICAL APPLICATIONS
Asset allocation and portfolio construction represent primary use cases for this analytical framework. When comparing multiple assets such as Bitcoin, gold, and the S&P 500, traders can examine Risk Efficiency Scores to identify instruments offering superior risk-adjusted performance. The 95% VaR provides worst-case scenario comparisons, while volatility-adjusted drawdowns enable fair comparison despite varying volatility profiles.
The practical decision framework suggests that assets with Risk Efficiency Scores above 70 may be suitable for aggressive portfolio allocations, scores between 40 and 70 indicate moderate allocation potential, and scores below 40 suggest defensive positioning or avoidance. These thresholds should be adjusted based on individual risk tolerance and market conditions.
Risk management and position sizing applications utilize the current percentile rank to guide allocation decisions. When the current drawdown ranks above the 75th percentile of historical data, indicating that current conditions are better than 75% of historical periods, position increases may be warranted. Conversely, when percentile rankings fall below the 25th percentile, indicating elevated risk conditions, position reductions become advisable.
Institutional portfolio monitoring applications include hedge fund risk dashboard implementations where multiple strategies can be monitored simultaneously. Sharpe ratio tracking identifies deteriorating risk-adjusted performance across strategies, VaR monitoring ensures portfolios remain within established risk limits, and drawdown duration tracking provides valuable information for investor reporting requirements.
Market timing applications combine the statistical analysis with trend identification techniques. Strong buy signals may emerge when risk levels register as "Low" in conjunction with established uptrends, while extreme risk levels combined with downtrends may indicate exit or hedging opportunities. Z-scores exceeding 3.0 often signal statistically oversold conditions that may precede trend reversals.
STATISTICAL SIGNIFICANCE AND VALIDATION
The indicator provides 95% confidence intervals around current drawdown levels using the standard formula CI = μ ± 1.96σ. This statistical framework enables users to assess whether current conditions fall within normal market variation or represent statistically significant departures from historical patterns.
Risk level classification employs a dynamic assessment system based on percentile ranking within the historical distribution. Low risk designation applies when current drawdowns perform better than 50% of historical data, moderate risk encompasses the 25th to 50th percentile range, high risk covers the 10th to 25th percentile range, and extreme risk applies to the worst 10% of historical drawdowns.
Sample size considerations play a crucial role in statistical reliability. For daily data, the system requires a minimum of 252 trading days (approximately one year) but performs better with 500 or more observations. Weekly data analysis benefits from at least 104 weeks (two years) of history, while monthly data requires a minimum of 60 months (five years) for reliable statistical inference.
IMPLEMENTATION BEST PRACTICES
Parameter optimization should consider the specific characteristics of different asset classes. Equity analysis typically benefits from 500-day lookback periods with 21-day smoothing, while cryptocurrency analysis may employ 365-day lookback periods with 14-day smoothing to account for higher volatility patterns. Fixed income analysis often requires longer lookback periods of 756 days with 34-day smoothing to capture the lower volatility environment.
Multi-timeframe analysis provides hierarchical risk assessment capabilities. Daily timeframe analysis supports tactical risk management decisions, weekly analysis informs strategic positioning choices, and monthly analysis guides long-term allocation decisions. This hierarchical approach ensures that risk assessment occurs at appropriate temporal scales for different investment objectives.
Integration with complementary indicators enhances the analytical framework. Trend indicators such as RSI and moving averages provide directional bias context, volume analysis helps confirm the severity of drawdown conditions, and volatility measures like VIX or ATR assist in market regime identification.
ALERT SYSTEM AND AUTOMATION
The automated alert system monitors five distinct categories of risk events. Risk level changes trigger notifications when drawdowns move between risk categories, enabling proactive risk management responses. Statistical significance alerts activate when Z-scores exceed established threshold levels of 2.5 or 3.0 standard deviations.
New maximum drawdown alerts notify users when historical maximum levels are exceeded, indicating entry into uncharted risk territory. Poor risk efficiency alerts trigger when the composite risk efficiency score falls below 30, suggesting deteriorating risk-adjusted performance. Sharpe ratio decline alerts activate when risk-adjusted performance turns negative, indicating that returns no longer compensate for the risk undertaken.
TRADING STRATEGIES
Conservative risk parity strategies can be implemented by monitoring Risk Efficiency Scores across a diversified asset portfolio. Monthly rebalancing maintains equal risk contribution from each asset, with allocation reductions triggered when risk levels reach "High" status and complete exits executed when "Extreme" risk levels emerge. This approach typically results in lower overall portfolio volatility, improved risk-adjusted returns, and reduced maximum drawdown periods.
Tactical asset rotation strategies compare Risk Efficiency Scores across different asset classes to guide allocation decisions. Assets with scores exceeding 60 receive overweight allocations, while assets scoring below 40 receive underweight positions. Percentile rankings provide timing guidance for allocation adjustments, creating a systematic approach to asset allocation that responds to changing risk-return profiles.
Market timing strategies with statistical edges can be constructed by entering positions when Z-scores fall below -2.5, indicating statistically oversold conditions, and scaling out when Z-scores exceed 2.5, suggesting overbought conditions. The 95% VaR serves as a stop-loss reference point, while trend confirmation indicators provide additional validation for position entry and exit decisions.
LIMITATIONS AND CONSIDERATIONS
Several statistical limitations affect the interpretation and application of these risk measures. Historical bias represents a fundamental challenge, as past drawdown patterns may not accurately predict future risk characteristics, particularly during structural market changes or regime shifts. Sample dependence means that results can be sensitive to the selected lookback period, with shorter periods providing more responsive but potentially less stable estimates.
Market regime changes can significantly alter the statistical parameters underlying the analysis. During periods of structural market evolution, historical distributions may provide poor guidance for future expectations. Additionally, many financial assets exhibit return distributions with fat tails that deviate from normal distribution assumptions, potentially leading to underestimation of extreme event probabilities.
Practical limitations include execution risk, where theoretical signals may not translate directly into actual trading results due to factors such as slippage, timing delays, and market impact. Liquidity constraints mean that risk metrics assume perfect liquidity, which may not hold during stressed market conditions when risk management becomes most critical.
Transaction costs are not incorporated into risk-adjusted return calculations, potentially overstating the attractiveness of strategies that require frequent trading. Behavioral factors represent another limitation, as human psychology may override statistical signals, particularly during periods of extreme market stress when disciplined risk management becomes most challenging.
TECHNICAL IMPLEMENTATION
Performance optimization ensures reliable operation across different market conditions and timeframes. All technical analysis functions are extracted from conditional statements to maintain Pine Script compliance and ensure consistent execution. Memory efficiency is achieved through optimized variable scoping and array usage, while computational speed benefits from vectorized calculations where possible.
Data quality requirements include clean price data without gaps or errors that could distort distribution analysis. Sufficient historical data is essential, with a minimum of 100 bars required and 500 or more preferred for reliable statistical inference. Time alignment across related assets ensures meaningful comparison when conducting multi-asset analysis.
The configuration parameters are organized into logical groups to enhance usability. Core settings include the Distribution Analysis Period (100-2000 bars), Drawdown Smoothing Period (1-50 bars), and Price Source selection. Advanced metrics settings control risk-free rate sourcing, either from live market data or fixed rate specification, along with toggles for various risk-adjusted metric calculations.
Display options provide flexibility in visual presentation, including color theme selection from eight available schemes, automatic dark mode optimization, and control over table display, position lines, percentile bands, and standard deviation overlays. These options ensure that the indicator can be adapted to different analytical workflows and visual preferences.
CONCLUSION
The Drawdown Distribution Analysis indicator provides risk management tools for traders seeking to understand their current position within historical risk patterns. By combining established statistical methodology with practical usability features, the tool enables evidence-based risk assessment and portfolio optimization decisions.
The implementation draws upon established academic research while providing practical features that address real-world trading requirements. Dynamic risk-free rate integration ensures accurate risk-adjusted performance calculations, while multiple color schemes accommodate different analytical preferences and use cases.
Academic compliance is maintained through transparent methodology and acknowledgment of limitations. The tool implements peer-reviewed statistical techniques while clearly communicating the constraints and assumptions underlying the analysis. This approach ensures that users can make informed decisions about the appropriate application of the risk assessment framework within their broader trading and investment processes.
BIBLIOGRAPHY
Artzner, P., Delbaen, F., Eber, J.M. and Heath, D. (1999) 'Coherent Measures of Risk', Mathematical Finance, 9(3), pp. 203-228.
Chekhlov, A., Uryasev, S. and Zabarankin, M. (2005) 'Drawdown Measure in Portfolio Optimization', International Journal of Theoretical and Applied Finance, 8(1), pp. 13-58.
Goldberg, L.R. and Mahmoud, O. (2017) 'Drawdown: From Practice to Theory and Back Again', Journal of Risk Management in Financial Institutions, 10(2), pp. 140-152.
Jorion, P. (2007) Value at Risk: The New Benchmark for Managing Financial Risk. 3rd edn. New York: McGraw-Hill.
Markowitz, H. (1952) 'Portfolio Selection', Journal of Finance, 7(1), pp. 77-91.
Sharpe, W.F. (1966) 'Mutual Fund Performance', Journal of Business, 39(1), pp. 119-138.
Sortino, F.A. and Price, L.N. (1994) 'Performance Measurement in a Downside Risk Framework', Journal of Investing, 3(3), pp. 59-64.
Young, T.W. (1991) 'Calmar Ratio: A Smoother Tool', Futures, 20(1), pp. 40-42.
SCTI V28Indicator Overview | 指标概述
English: SCTI V28 (Smart Composite Technical Indicator) is a multi-functional composite technical analysis tool that integrates various classic technical analysis methods. It contains 7 core modules that can be flexibly configured to show or hide components based on traders' needs, suitable for various trading styles and market conditions.
中文: SCTI V28 (智能复合技术指标) 是一款多功能复合型技术分析指标，整合了多种经典技术分析工具于一体。该指标包含7大核心模块，可根据交易者的需求灵活配置显示或隐藏各个组件，适用于多种交易风格和市场环境。
Main Functional Modules | 主要功能模块
1. Basic Indicator Settings | 基础指标设置
English:
EMA Display: 13 configurable EMA lines (default shows 8/13/21/34/55/144/233/377/610/987/1597/2584 periods)
PMA Display: 11 configurable moving averages with multiple MA types (ALMA/EMA/RMA/SMA/SWMA/VWAP/VWMA/WMA)
VWAP Display: Volume Weighted Average Price indicator
Divergence Indicator: Detects divergences across 12 technical indicators
ATR Stop Loss: ATR-based stop loss lines
Volume SuperTrend AI: AI-powered super trend indicator
中文:
EMA显示：13条可配置EMA均线，默认显示8/13/21/34/55/144/233/377/610/987/1597/2584周期
PMA显示：11条可配置移动平均线，支持多种MA类型(ALMA/EMA/RMA/SMA/SWMA/VWAP/VWMA/WMA)
VWAP显示：成交量加权平均价指标
背离指标：12种技术指标的背离检测系统
ATR止损：基于ATR的止损线
Volume SuperTrend AI：基于AI预测的超级趋势指标
2. EMA Settings | EMA设置
English:
13 independent EMA lines, each configurable for visibility and period length
Default shows 21/34/55/144/233/377/610/987/1597/2584 period EMAs
Customizable colors and line widths for each EMA
中文:
13条独立EMA均线，每条均可单独配置显示/隐藏和周期长度
默认显示21/34/55/144/233/377/610/987/1597/2584周期的EMA
每条EMA可设置不同颜色和线宽
3. PMA Settings | PMA设置
English:
11 configurable moving averages, each with:
Selectable types (default EMA, options: ALMA/RMA/SMA/SWMA/VWAP/VWMA/WMA)
Independent period settings (12-1056)
Special ALMA parameters (offset and sigma)
Configurable data source and plot offset
Support for fill areas between MAs
Price lines and labels can be added
中文:
11条可配置移动平均线，每条均可：
选择不同类型(默认EMA，可选ALMA/RMA/SMA/SWMA/VWAP/VWMA/WMA)
独立设置周期长度(12-1056)
设置ALMA的特殊参数(偏移量和sigma)
配置数据源和绘图偏移
支持MA之间的填充区域显示
可添加价格线和标签
4. VWAP Settings | VWAP设置
English:
Multiple anchor period options (Session/Week/Month/Quarter/Year/Decade/Century/Earnings/Dividends/Splits)
3 configurable standard deviation bands
Option to hide on daily and higher timeframes
Configurable data source and offset settings
中文:
多种锚定周期选择(会话/周/月/季/年/十年/世纪/财报/股息/拆股)
3条可配置标准差带
可选择在日线及以上周期隐藏
支持数据源选择和偏移设置
5. Divergence Indicator Settings | 背离指标设置
English:
12 detectable indicators: MACD, MACD Histogram, RSI, Stochastic, CCI, Momentum, OBV, VWmacd, Chaikin Money Flow, MFI, Williams %R, External Indicator
4 divergence types: Regular Bullish/Bearish, Hidden Bullish/Bearish
Multiple display options: Full name/First letter/Hide indicator name
Configurable parameters: Pivot period, data source, maximum bars checked, etc.
Alert functions: Independent alerts for each divergence type
中文:
检测12种指标：MACD、MACD柱状图、RSI、随机指标、CCI、动量、OBV、VWmacd、Chaikin资金流、MFI、威廉姆斯%R、外部指标
4种背离类型：正/负常规背离，正/负隐藏背离
多种显示选项：完整名称/首字母/不显示指标名称
可配置参数：枢轴点周期、数据源、最大检查柱数等
警报功能：各类背离的独立警报
6. ATR Stop Loss Settings | ATR止损设置
English:
Configurable ATR length (default 13)
4 smoothing methods (RMA/SMA/EMA/WMA)
Adjustable multiplier (default 1.618)
Displays long and short stop loss lines
中文:
可配置ATR长度(默认13)
4种平滑方法(RMA/SMA/EMA/WMA)
可调乘数(默认1.618)
显示多头和空头止损线
7. Volume SuperTrend AI Settings | Volume SuperTrend AI设置
English:
AI Prediction:
Configurable neighbors (1-100) and data points (1-100)
Price trend length and prediction trend length settings
SuperTrend Parameters:
Length (default 3)
Factor (default 1.515)
5 MA source options (SMA/EMA/WMA/RMA/VWMA)
Signal Display:
Trend start signals (circle markers)
Trend confirmation signals (triangle markers)
6 Alerts: Various trend start and confirmation signals
中文:
AI预测功能：
可配置邻居数(1-100)和数据点数(1-100)
价格趋势长度和预测趋势长度设置
SuperTrend参数：
长度(默认3)
因子(默认1.515)
5种MA源选择(SMA/EMA/WMA/RMA/VWMA)
信号显示：
趋势开始信号(圆形标记)
趋势确认信号(三角形标记)
6种警报：各类趋势开始和确认信号
Usage Recommendations | 使用建议
English:
Trend Analysis: Use EMA/PMA combinations to determine market trends, with long-period EMAs (e.g., 144/233) as primary trend references
Divergence Trading: Look for potential reversals using price-indicator divergences
Stop Loss Management: Use ATR stop loss lines for risk management
AI Assistance: Volume SuperTrend AI provides machine learning-based trend predictions
Multiple Timeframes: Verify signals across different timeframes
中文:
趋势分析：使用EMA/PMA组合判断市场趋势，长周期EMA(如144/233)作为主要趋势参考
背离交易：结合价格与指标的背离寻找潜在反转点
止损设置：利用ATR止损线管理风险
AI辅助：Volume SuperTrend AI提供基于机器学习的趋势预测
多时间框架：建议在不同时间框架下验证信号
Parameter Configuration Tips | 参数配置技巧
English:
For short-term trading: Focus on 8-55 period EMAs and shorter divergence detection periods
For long-term investing: Use 144-2584 period EMAs with longer detection parameters
In ranging markets: Disable some EMAs, mainly rely on VWAP and divergence indicators
In trending markets: Enable more EMAs and SuperTrend AI
中文:
对于短线交易：可重点关注8-55周期的EMA和较短的背离检测周期
对于长线投资：建议使用144-2584周期的EMA和较长的检测参数
在震荡市：可关闭部分EMA，主要依靠VWAP和背离指标
在趋势市：可启用更多EMA和SuperTrend AI
Update Log | 更新日志
English:
V28 main updates:
Added Volume SuperTrend AI module
Optimized divergence detection algorithm
Added more EMA period options
Improved UI and parameter grouping
中文:
V28版本主要更新：
新增Volume SuperTrend AI模块
优化背离检测算法
增加更多EMA周期选项
改进用户界面和参数分组
Final Note | 最后说明
English: This indicator is suitable for technical traders with some experience. We recommend practicing with demo trading to familiarize yourself with all features before live trading.
中文: 该指标适合有一定经验的技术分析交易者使用，建议先通过模拟交易熟悉各项功能后再应用于实盘。
Ultimate Market Structure [Alpha Extract]Ultimate Market Structure
A comprehensive market structure analysis tool that combines advanced swing point detection, imbalance zone identification, and intelligent break analysis to identify high-probability trading opportunities.Utilizing a sophisticated trend scoring system, this indicator classifies market conditions and provides clear signals for structure breaks, directional changes, and fair value gap detection with institutional-grade precision.
🔶 Advanced Swing Point Detection
Identifies pivot highs and lows using configurable lookback periods with optional close-based analysis for cleaner signals. The system automatically labels swing points as Higher Highs (HH), Lower Highs (LH), Higher Lows (HL), and Lower Lows (LL) while providing advanced classifications including "rising_high", "falling_high", "rising_low", "falling_low", "peak_high", and "valley_low" for nuanced market analysis.
swingHighPrice = useClosesForStructure ? ta.pivothigh(close, swingLength, swingLength) : ta.pivothigh(high, swingLength, swingLength)
swingLowPrice = useClosesForStructure ? ta.pivotlow(close, swingLength, swingLength) : ta.pivotlow(low, swingLength, swingLength)
classification = classifyStructurePoint(structureHighPrice, upperStructure, true)
significance = calculateSignificance(structureHighPrice, upperStructure, true)
🔶 Significance Scoring System
Each structure point receives a significance level on a 1-5 scale based on its distance from previous points, helping prioritize the most important levels. This intelligent scoring system ensures traders focus on the most meaningful structure breaks while filtering out minor noise.
🔶 Comprehensive Trend Analysis
Calculates momentum, strength, direction, and confidence levels using volatility-normalized price changes and multi-timeframe correlation. The system provides real-time trend state tracking with bullish (+1), bearish (-1), or neutral (0) direction assessment and 0-100 confidence scoring.
// Calculate trend momentum using rate of change and volatility
calculateTrendMomentum(lookback) =>
priceChange = (close - close ) / close * 100
avgVolatility = ta.atr(lookback) / close * 100
momentum = priceChange / (avgVolatility + 0.0001)
momentum
// Calculate trend strength using multiple timeframe correlation
calculateTrendStrength(shortPeriod, longPeriod) =>
shortMA = ta.sma(close, shortPeriod)
longMA = ta.sma(close, longPeriod)
separation = math.abs(shortMA - longMA) / longMA * 100
strength = separation * slopeAlignment
❓How It Works
🔶 Imbalance Zone Detection
Identifies Fair Value Gaps (FVGs) between consecutive candles where price gaps create unfilled areas. These zones are displayed as semi-transparent boxes with optional center line mitigation tracking, highlighting potential support and resistance levels where institutional players often react.
// Detect Fair Value Gaps
detectPriceImbalance() =>
currentHigh = high
currentLow = low
refHigh = high
refLow = low
if currentOpen > currentClose
if currentHigh - refLow < 0
upperBound = currentClose - (currentClose - refLow)
lowerBound = currentClose - (currentClose - currentHigh)
centerPoint = (upperBound + lowerBound) / 2
newZone = ImbalanceZone.new(
zoneBox = box.new(bar_index, upperBound, rightEdge, lowerBound,
bgcolor=bullishImbalanceColor, border_color=hiddenColor)
)
🔶 Structure Break Analysis
Determines Break of Structure (BOS) for trend continuation and Directional Change (DC) for trend reversals with advanced classification as "continuation", "reversal", or "neutral". The system compares pre-trend and post-trend states for each break, providing comprehensive trend change momentum analysis.
🔶 Intelligent Zone Management
Features partial mitigation tracking when price enters but doesn't fully fill zones, with automatic zone boundary adjustment during partial fills. Smart array management keeps only recent structure points for optimal performance while preventing duplicate signals from the same level.
🔶 Liquidity Zone Detection
Automatically identifies potential liquidity zones at key structure points for institutional trading analysis. The system tracks broken structure points and provides adaptive zone extension with configurable time-based limits for imbalance areas.
🔶 Visual Structure Mapping
Provides clear visual indicators including swing labels with color-coded significance levels, dashed lines connecting break points with BOS/DC labels, and break signals for continuation and reversal patterns. The adaptive zones feature smart management with automatic mitigation tracking.
🔶 Market Structure Interpretation
HH/HL patterns indicate bullish market structure with trend continuation likelihood, while LH/LL patterns signal bearish structure with downtrend continuation expected. BOS signals represent structure breaks in trend direction for continuation opportunities, while DC signals warn of potential reversals.
🔶 Performance Optimization
Automatic cleanup of old structure points (keeps last 8 points), recent break tracking (keeps last 5 break events), and efficient array management ensure smooth performance across all timeframes and market conditions.
Why Choose Ultimate Market Structure ?
This indicator provides traders with institutional-grade market structure analysis, combining multiple analytical approaches into one comprehensive tool. By identifying key structure levels, imbalance zones, and break patterns with advanced significance scoring, it helps traders understand market dynamics and position themselves for high-probability trade setups in alignment with smart money concepts. The sophisticated trend scoring system and intelligent zone management make it an essential tool for any serious trader looking to decode market structure with precision and confidence.
Fear and Greed Index [DunesIsland]The Fear and Greed Index is a sentiment indicator designed to measure the emotions driving the stock market, specifically investor fear and greed. Fear represents pessimism and caution, while greed reflects optimism and risk-taking. This indicator aggregates multiple market metrics to provide a comprehensive view of market sentiment, helping traders and investors gauge whether the market is overly fearful or excessively greedy.How It WorksThe Fear and Greed Index is calculated using four key market indicators, each capturing a different aspect of market sentiment:
Market Momentum (30% weight)
Measures how the S&P 500 (SPX) is performing relative to its 125-day simple moving average (SMA).
A higher value indicates that the market is trading well above its moving average, signaling greed.
Stock Price Strength (20% weight)
Calculates the net number of stocks hitting 52-week highs minus those hitting 52-week lows on the NYSE.
A greater number of net highs suggests strong market breadth and greed.
Put/Call Options (30% weight)
Uses the 5-day average of the put/call ratio.
A lower ratio (more call options being bought) indicates greed, as investors are betting on rising prices.
Market Volatility (20% weight)
Utilizes the VIX index, which measures market volatility.
Lower volatility is associated with greed, as investors are less fearful of large market swings.
Each component is normalized using a z-score over a 252-day lookback period (approximately one trading year) and scaled to a range of 0 to 100. The final Fear and Greed Index is a weighted average of these four components, with the weights specified above.Key FeaturesIndex Range: The index value ranges from 0 to 100:
0–25: Extreme Fear (red)
25–50: Fear (orange)
50–75: Neutral (yellow)
75–100: Greed (green)
Dynamic Plot Color: The plot line changes color based on the index value, visually indicating the current sentiment zone.
Reference Lines: Horizontal lines are plotted at 0, 25, 50, 75, and 100 to represent the different sentiment levels: Extreme Fear, Fear, Neutral, Greed, and Extreme Greed.
How to Interpret
Low Values (0–25): Indicate extreme fear, which may suggest that the market is oversold and could be due for a rebound.
High Values (75–100): Indicate greed, which may signal that the market is overbought and could be at risk of a correction.
Neutral Range (25–75): Suggests a balanced market sentiment, neither overly fearful nor greedy.
This indicator is a valuable tool for contrarian investors, as extreme readings often precede market reversals. However, it should be used in conjunction with other technical and fundamental analysis tools for a well-rounded view of the market.
log.info() - 5 Exampleslog.info() is one of the most powerful tools in Pine Script that no one knows about. Whenever you code, you want to be able to debug, or find out why something isn’t working. The log.info() command will help you do that. Without it, creating more complex Pine Scripts becomes exponentially more difficult.
The first thing to note is that log.info() only displays strings. So, if you have a variable that is not a string, you must turn it into a string in order for log.info() to work. The way you do that is with the str.tostring() command. And remember, it's all lower case! You can throw in any numeric value (float, int, timestamp) into str.string() and it should work.
Next, in order to make your output intelligible, you may want to identify whatever value you are logging. For example, if an RSI value is 50, you don’t want a bunch of lines that just say “50”. You may want it to say “RSI = 50”.
To do that, you’ll have to use the concatenation operator. For example, if you have a variable called “rsi”, and its value is 50, then you would use the “+” concatenation symbol.
EXAMPLE 1
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
//@version=6
indicator("log.info()")
rsi = ta.rsi(close,14)
log.info(“RSI= ” + str.tostring(rsi))
Example Output =>
RSI= 50
Here, we use double quotes to create a string that contains the name of the variable, in this case “RSI = “, then we concatenate it with a stringified version of the variable, rsi.
Now that you know how to write a log, where do you view them? There isn’t a lot of documentation on it, and the link is not conveniently located.
Open up the “Pine Editor” tab at the bottom of any chart view, and you’ll see a “3 dot” button at the top right of the pane. Click that, and right above the “Help” menu item you’ll see “Pine logs”. Clicking that will open that to open a pane on the right of your browser - replacing whatever was in the right pane area before. This is where your log output will show up.
But, because you’re dealing with time series data, using the log.info() command without some type of condition will give you a fast moving stream of numbers that will be difficult to interpret. So, you may only want the output to show up once per bar, or only under specific conditions.
To have the output show up only after all computations have completed, you’ll need to use the barState.islast command. Remember, barState is camelCase, but islast is not!
EXAMPLE 2
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
//@version=6
indicator("log.info()")
rsi = ta.rsi(close,14)
if barState.islast
log.info("RSI=" + str.tostring(rsi))
plot(rsi)
However, this can be less than ideal, because you may want the value of the rsi variable on a particular bar, at a particular time, or under a specific chart condition. Let’s hit these one at a time.
In each of these cases, the built-in bar_index variable will come in handy. When debugging, I typically like to assign a variable “bix” to represent bar_index, and include it in the output.
So, if I want to see the rsi value when RSI crosses above 0.5, then I would have something like
EXAMPLE 3
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//@version=6
indicator("log.info()")
rsi = ta.rsi(close,14)
bix = bar_index
rsiCrossedOver = ta.crossover(rsi,0.5)
if rsiCrossedOver
log.info("bix=" + str.tostring(bix) + " - RSI=" + str.tostring(rsi))
plot(rsi)
Example Output =>
bix=19964 - RSI=51.8449459867
bix=19972 - RSI=50.0975830828
bix=19983 - RSI=53.3529808079
bix=19985 - RSI=53.1595745146
bix=19999 - RSI=66.6466337654
bix=20001 - RSI=52.2191767466
Here, we see that the output only appears when the condition is met.
A useful thing to know is that if you want to limit the number of decimal places, then you would use the command str.tostring(rsi,”#.##”), which tells the interpreter that the format of the number should only be 2 decimal places. Or you could round the rsi variable with a command like rsi2 = math.round(rsi*100)/100 . In either case you’re output would look like:
bix=19964 - RSI=51.84
bix=19972 - RSI=50.1
bix=19983 - RSI=53.35
bix=19985 - RSI=53.16
bix=19999 - RSI=66.65
bix=20001 - RSI=52.22
This would decrease the amount of memory that’s being used to display your variable’s values, which can become a limitation for the log.info() command. It only allows 4096 characters per line, so when you get to trying to output arrays (which is another cool feature), you’ll have to keep that in mind.
Another thing to note is that log output is always preceded by a timestamp, but for the sake of brevity, I’m not including those in the output examples.
If you wanted to only output a value after the chart was fully loaded, that’s when barState.islast command comes in. Under this condition, only one line of output is created per tick update — AFTER the chart has finished loading. For example, if you only want to see what the the current bar_index and rsi values are, without filling up your log window with everything that happens before, then you could use the following code:
EXAMPLE 4
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
//@version=6
indicator("log.info()")
rsi = ta.rsi(close,14)
bix = bar_index
if barstate.islast
log.info("bix=" + str.tostring(bix) + " - RSI=" + str.tostring(rsi))
Example Output =>
bix=20203 - RSI=53.1103309071
This value would keep updating after every new bar tick.
The log.info() command is a huge help in creating new scripts, however, it does have its limitations. As mentioned earlier, only 4096 characters are allowed per line. So, although you can use log.info() to output arrays, you have to be aware of how many characters that array will use.
The following code DOES NOT WORK! And, the only way you can find out why will be the red exclamation point next to the name of the indicator. That, and nothing will show up on the chart, or in the logs.
// CODE DOESN’T WORK
//@version=6
indicator("MW - log.info()")
var array rsi_arr = array.new()
rsi = ta.rsi(close,14)
bix = bar_index
rsiCrossedOver = ta.crossover(rsi,50)
if rsiCrossedOver
array.push(rsi_arr, rsi)
if barstate.islast
log.info("rsi_arr:" + str.tostring(rsi_arr))
log.info("bix=" + str.tostring(bix) + " - RSI=" + str.tostring(rsi))
plot(rsi)
// No code errors, but will not compile because too much is being written to the logs.
However, after putting some time restrictions in with the i_startTime and i_endTime user input variables, and creating a dateFilter variable to use in the conditions, I can limit the size of the final array. So, the following code does work.
EXAMPLE 5
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
// CODE DOES WORK
//@version=6
indicator("MW - log.info()")
i_startTime = input.time(title="Start", defval=timestamp("01 Jan 2025 13:30 +0000"))
i_endTime = input.time(title="End", defval=timestamp("1 Jan 2099 19:30 +0000"))
var array rsi_arr = array.new()
dateFilter = time >= i_startTime and time <= i_endTime
rsi = ta.rsi(close,14)
bix = bar_index
rsiCrossedOver = ta.crossover(rsi,50) and dateFilter // <== The dateFilter condition keeps the array from getting too big
if rsiCrossedOver
array.push(rsi_arr, rsi)
if barstate.islast
log.info("rsi_arr:" + str.tostring(rsi_arr))
log.info("bix=" + str.tostring(bix) + " - RSI=" + str.tostring(rsi))
plot(rsi)
Example Output =>
rsi_arr:
bix=20210 - RSI=56.9030578034
Of course, if you restrict the decimal places by using the rounding the rsi value with something like rsiRounded = math.round(rsi * 100) / 100 , then you can further reduce the size of your array. In this case the output may look something like:
Example Output =>
rsi_arr:
bix=20210 - RSI=55.6947486019
This will give your code a little breathing room.
In a nutshell, I was coding for over a year trying to debug by pushing output to labels, tables, and using libraries that cluttered up my code. Once I was able to debug with log.info() it was a game changer. I was able to start building much more advanced scripts. Hopefully, this will help you on your journey as well.
Omega Market Mood Meter [OmegaTools]The Omega Market Mood Meter is a precision-built sentiment oscillator that captures the market’s emotional intensity through a multi-layered RSI system. Designed for traders who seek to align with the market's true behavioral state, it blends momentum readings with a brand-new, rarely-seen innovation: the Sentiment-Weighted Moving Average (WMA-Ω)—a trend filter that dynamically adjusts to the market’s psychological tone.
🧠 Market Mood Oscillator
At its core, the Ω 3M oscillator aggregates three RSI-based components:
RSI(9) on close — captures short-term tension;
RSI(21) on HLC3 — balances medium-term positioning;
RSI(50) on HL2 — reflects long-term directional weight.
Each input is scaled and weighted to contribute to a final oscillator centered around zero, with ±50 and ±100 acting as key sentiment boundaries. When values exceed ±100, the market is likely reaching emotional extremes—zones that often precede reversals or require caution.
Visual features include:
Dynamic Background Highlighting: automatically emphasizes extreme sentiment zones.
Reference Lines: plotted at ±100, ±50, and 0 for fast sentiment interpretation.
🔥 WMA-Ω: Sentiment-Weighted Moving Average
The standout innovation of this tool is the Weighted Market Mood Moving Average, or WMA-Ω—a proprietary calculation that averages price using the absolute value of sentiment as its weighting force. This approach gives greater importance to price during periods of strong emotional conviction (either bullish or bearish), resulting in a context-aware trend filter that reacts only when sentiment truly matters.
This technique:
Filters noise during low-volatility or indecisive conditions;
Enhances reliability by reacting to meaningful sentiment surges;
Offers a more psychologically-adjusted trend baseline compared to traditional MAs.
Visually:
When price is above WMA-Ω, a semi-transparent bullish fill highlights underlying strength;
When below, a bearish fill reveals dominant downward sentiment.
This feature is unique among public TradingView tools and provides an edge in identifying trend quality with psychological context.
✅ How to Use
Extreme Sentiment Zones (±100): Use as contrarian warning zones or signal dampeners.
Crosses of WMA-Ω: Treat these as psychological trend confirmations; price above indicates structurally bullish sentiment and vice versa.
Range-bound Bias: Between ±50, sentiment may be indecisive; watch for breakout or alignment with WMA-Ω.
Advanced Confluence: Combine with other Omega tools (e.g., Ω Bias Forecaster, Ω IV Walls) for powerful regime-based strategies.
Omega Market Mood Meter is ideal for discretionary and systematic traders who want a clean, multi-timeframe sentiment readout and a cutting-edge weighted trend engine grounded in market psychology.
Heikin-Ashi Mean Reversion Oscillator [Alpha Extract]The Heikin-Ashi Mean Reversion Oscillator combines the smoothing characteristics of Heikin-Ashi candlesticks with mean reversion analysis to create a powerful momentum oscillator. This indicator applies Heikin-Ashi transformation twice - first to price data and then to the oscillator itself - resulting in smoother signals while maintaining sensitivity to trend changes and potential reversal points.
🔶 CALCULATION
Heikin-Ashi Transformation: Converts regular OHLC data to smoothed Heikin-Ashi values
Component Analysis: Calculates trend strength, body deviation, and price deviation from mean
Oscillator Construction: Combines components with weighted formula (40% trend strength, 30% body deviation, 30% price deviation)
Double Smoothing: Applies EMA smoothing and second Heikin-Ashi transformation to oscillator values
Signal Generation: Identifies trend changes and crossover points with overbought/oversold levels
Formula:
HA Close = (Open + High + Low + Close) / 4
HA Open = (Previous HA Open + Previous HA Close) / 2
Trend Strength = Normalized consecutive HA candle direction
Body Deviation = (HA Body - Mean Body) / Mean Body * 100
Price Deviation = ((HA Close - Price Mean) / Price Mean * 100) / Standard Deviation * 25
Raw Oscillator = (Trend Strength * 0.4) + (Body Deviation * 0.3) + (Price Deviation * 0.3)
Final Oscillator = 50 + (EMA(Raw Oscillator) / 2)
🔶 DETAILS Visual Features:
Heikin-Ashi Candlesticks: Smoothed oscillator representation using HA transformation with vibrant teal/red coloring
Overbought/Oversold Zones: Horizontal lines at customizable levels (default 70/30) with background highlighting in extreme zones
Moving Averages: Optional fast and slow EMA overlays for additional trend confirmation
Signal Dashboard: Real-time table showing current oscillator status (Overbought/Oversold/Bullish/Bearish) and buy/sell signals
Reference Lines: Middle line at 50 (neutral), with 0 and 100 boundaries for range visualization
Interpretation:
Above 70: Overbought conditions, potential selling opportunity
Below 30: Oversold conditions, potential buying opportunity
Bullish HA Candles: Green/teal candles indicate upward momentum
Bearish HA Candles: Red candles indicate downward momentum
MA Crossovers: Fast EMA above slow EMA suggests bullish momentum, below suggests bearish momentum
Zone Exits: Price moving out of extreme zones (above 70 or below 30) often signals trend continuation
🔶 EXAMPLES
Mean Reversion Signals: When the oscillator reaches extreme levels (above 70 or below 30), it identifies potential reversal points where price may revert to the mean.
Example: Oscillator reaching 80+ levels during strong uptrends often precedes short-term pullbacks, providing profit-taking opportunities.
Trend Change Detection: The double Heikin-Ashi smoothing helps identify genuine trend changes while filtering out market noise.
Example: When oscillator HA candles change from red to teal after oversold readings, this confirms potential trend reversal from bearish to bullish.
Moving Average Confirmation: Fast and slow EMA crossovers on the oscillator provide additional confirmation of momentum shifts.
Example: Fast EMA crossing above slow EMA while oscillator is rising from oversold levels provides strong bullish confirmation signal.
Dashboard Signal Integration: The real-time dashboard combines oscillator status with directional signals for quick decision-making.
Example: Dashboard showing "Oversold" status with "BUY" signal when HA candles turn bullish provides clear entry timing.
🔶 SETTINGS
Customization Options:
Calculation: Oscillator period (default 14), smoothing factor (1-50, default 2)
Levels: Overbought threshold (50-100, default 70), oversold threshold (0-50, default 30)
Moving Averages: Toggle display, fast EMA length (default 9), slow EMA length (default 21)
Visual Enhancements: Show/hide signal dashboard, customizable table position
Alert Conditions: Oversold bounce, overbought reversal, bullish/bearish MA crossovers
The Heikin-Ashi Mean Reversion Oscillator provides traders with a sophisticated momentum tool that combines the smoothing benefits of Heikin-Ashi analysis with mean reversion principles. The double transformation process creates cleaner signals while the integrated dashboard and multiple confirmation methods help traders identify high-probability entry and exit points during both trending and ranging market conditions.
Volume and Volatility Ratio Indicator-WODI策略名称
交易量与波动率比例策略-WODI
一、用户自定义参数
vol_length：交易量均线长度，计算基础交易量活跃度。
index_short_length / index_long_length：指数短期与长期均线长度，用于捕捉中短期与中长期趋势。
index_magnification：敏感度放大倍数，调整指数均线的灵敏度。
index_threshold_magnification：阈值放大因子，用于动态过滤噪音。
lookback_bars：形态检测回溯K线根数，用于捕捉反转模式。
fib_tp_ratio / fib_sl_ratio：斐波那契止盈与止损比率，分别对应黄金分割（0.618/0.382 等）级别。
enable_reversal：反转信号开关，开启后将原有做空信号反向为做多信号，用于单边趋势加仓。
二、核心计算逻辑
交易量百分比
使用 ta.sma 计算 vol_ma，并得到 vol_percent = volume / vol_ma * 100。
价格波动率
volatility = (high – low) / close * 100。
构建复合指数
volatility_index = vol_percent * volatility，并分别计算其短期与长期均线（乘以 index_magnification）。
动态阈值
index_threshold = index_long_ma * index_threshold_magnification，过滤常规波动。
三、信号生成与策略执行
做多/做空信号
当短期指数均线自下而上突破长期均线，且 volatility_index 突破 index_threshold 时，发出做多信号。
当短期指数均线自上而下跌破长期均线，且 volatility_index 跌破 index_threshold 时，发出做空信号。
反转信号模式（可选）
若 enable_reversal = true，则所有做空信号反向为做多，用于在强趋势行情中加仓。
止盈止损管理
进场后自动设置斐波那契止盈位（基于入场价 × fib_tp_ratio）和止损位（入场价 × fib_sl_ratio）。
支持多级止盈：可依次以 0.382、0.618 等黄金分割比率分批平仓。
四、图表展示
策略信号标记：图上用箭头标明每次做多/做空（或反转加仓）信号。
斐波那契区间：在K线图中显示止盈/止损水平线。
复合指数与阈值线：与原版相同，在独立窗口绘制短、长期指数均线、指数曲线及阈值。
量能柱状：高于均线时染色，反转模式时额外高亮。
Strategy Name
Volume and Volatility Ratio Strategy – WODI
1. User-Defined Parameters
vol_length: Length for volume SMA.
index_short_length / index_long_length: Short and long MA lengths for the composite index.
index_magnification: Sensitivity multiplier for index MAs.
index_threshold_magnification: Threshold multiplier to filter noise.
lookback_bars: Number of bars to look back for pattern detection.
fib_tp_ratio / fib_sl_ratio: Fibonacci take-profit and stop-loss ratios (e.g. 0.618, 0.382).
enable_reversal: Toggle for reversal mode; flips short signals to long for trend-following add-on entries.
2. Core Calculation
Volume Percentage:
vol_ma = ta.sma(volume, vol_length)
vol_percent = volume / vol_ma * 100
Volatility:
volatility = (high – low) / close * 100
Composite Index:
volatility_index = vol_percent * volatility
Short/long MAs applied and scaled by index_magnification.
Dynamic Threshold:
index_threshold = index_long_ma * index_threshold_magnification.
3. Signal Generation & Execution
Long/Short Entries:
Long when short MA crosses above long MA and volatility_index > index_threshold.
Short when short MA crosses below long MA and volatility_index < index_threshold.
Reversal Mode (optional):
If enable_reversal is on, invert all short entries to long to scale into trending moves.
Fibonacci Take-Profit & Stop-Loss:
Automatically set TP/SL levels at entry price × respective Fibonacci ratios.
Supports multi-stage exits at 0.382, 0.618, etc.
4. Visualization
Signal Arrows: Marks every long/short or reversal-add signal on the chart.
Fibonacci Zones: Plots TP/SL lines on the price panel.
Index & Threshold: Same as v1.0, with MAs, index curve, and threshold in a separate sub-window.
Volume Bars: Colored when above vol_ma; extra highlight if a reversal-add signal triggers
[blackcat] L1 Multi-Component CCIOVERVIEW
The " L1 Multi-Component CCI" is a sophisticated technical indicator designed to analyze market trends and momentum using multiple components of the Commodity Channel Index (CCI). This script calculates and combines various CCI-related metrics to provide a comprehensive view of price action, offering traders deeper insights into market dynamics. By integrating smoothed deviations, normalized ranges, and standard CCI values, this tool aims to enhance decision-making processes. It is particularly useful for identifying potential reversal points and confirming trend strength. 📈
FEATURES
Multi-Component CCI Calculation: Combines smoothed deviation, normalized range, percent above low, and standard CCI for a holistic analysis, providing a multifaceted view of market conditions.
Threshold Lines: Overbought (200), oversold (-200), bullish (100), and bearish (-100) thresholds are plotted for easy reference, helping traders quickly identify extreme market conditions.
Visual Indicators: Each component is plotted with distinct colors and line styles for clear differentiation, making it easier to interpret the data at a glance.
Customizable Alerts: The script includes commented-out buy and sell signal logic that can be enabled for automated trading notifications, allowing traders to set up alerts based on specific conditions. 🚀
Advanced Calculations: Utilizes a combination of simple moving averages (SMA) and exponential moving averages (EMA) to smooth out price data, enhancing the reliability of the indicator.
HOW TO USE
Apply the Script: Add the script to your chart on TradingView by searching for " L1 Multi-Component CCI" in the indicators section.
Observe the Plotted Lines: Pay close attention to the smoothed deviation, normalized range, percent above low, and standard CCI lines to identify potential overbought or oversold conditions.
Use Threshold Levels: Refer to the overbought, oversold, bullish, and bearish threshold lines to gauge extreme market conditions and potential reversal points.
Confirm Trends: Use the standard CCI line to confirm trend direction and momentum shifts, providing additional confirmation for your trading decisions.
Enable Alerts: If desired, uncomment the buy and sell signal logic to receive automated alerts when specific conditions are met, helping you stay informed even when not actively monitoring the chart. ⚠️
LIMITATIONS
Fixed Threshold Levels: The script uses fixed threshold levels (200, -200, 100, -100), which may need adjustment based on specific market conditions or asset volatility.
No Default Signals: The buy and sell signal logic is currently commented out, requiring manual activation if you wish to use automated alerts.
Complexity: The multi-component approach, while powerful, may be complex for novice traders to interpret, requiring a solid understanding of technical analysis concepts. 📉
Not for Isolation Use: This indicator is not designed for use in isolation; it is recommended to combine it with other tools and indicators for confirmation and a more robust analysis.
NOTES
Smoothing Techniques: The script uses a combination of simple moving averages (SMA) and exponential moving averages (EMA) for smoothing calculations, which helps in reducing noise and enhancing signal clarity.
Multi-Component Approach: The multi-component approach aims to provide a more nuanced view of market conditions compared to traditional CCI, offering a more comprehensive analysis.
Customization Potential: Traders can customize the script further by adjusting the parameters of the moving averages and other components to better suit their trading style and preferences. ✨
THANKS
Thanks to the TradingView community for their support and feedback on this script! Special thanks to those who contributed ideas and improvements, making this tool more robust and user-friendly. 🙏
Adaptive Volume-Weighted RSI (AVW-RSI)Concept Summary
The AVW-RSI is a modified version of the Relative Strength Index (RSI), where each price change is weighted by the relative trading volume for that period. This means periods of high volume (typically driven by institutions or “big money”) have a greater influence on the RSI calculation than periods of low volume.
Why AVW-RSI Helps Traders
Avoids Weak Signals During Low Volume
Standard RSI may show overbought/oversold zones even during low-volume periods (e.g., during lunch hours or after news).
AVW-RSI gives less weight to these periods, avoiding misleading signals.
Amplifies Strong Momentum Moves
If RSI is rising during high volume, it's more likely driven by institutional buying—AVW-RSI reflects that stronger by weighting the RSI component.
Filters Out Retail Noise
By prioritizing high-volume candles, it naturally discounts fakeouts caused by thin markets or retail-heavy moves.
Highlights Institutional Entry/Exit
Useful for spotting hidden accumulation/distribution that classic RSI would miss.
How It Works (Calculation Logic)
Traditional RSI Formula Recap
RSI = 100 - (100 / (1 + RS))
RS = Average Gain / Average Loss (over N periods)
Modified Step – Apply Volume Weight
For each period
Gain_t = max(Close_t - Close_{t-1}, 0)
Loss_t = max(Close_{t-1} - Close_t, 0)
Weight_t = Volume_t / AvgVolume(N)
WeightedGain_t = Gain_t * Weight_t
WeightedLoss_t = Loss_t * Weight_t
Weighted RSI
AvgWeightedGain = SMA(WeightedGain, N)
AvgWeightedLoss = SMA(WeightedLoss, N)
RS = AvgWeightedGain / AvgWeightedLoss
AVW-RSI = 100 - (100 / (1 + RS))
Visual Features on Chart
Line Color Gradient
Color gets darker as volume weight increases, signaling stronger conviction.
Overbought/Oversold Zones
Traditional: 70/30
Suggested AVW-RSI zones: Use dynamic thresholds based on historical volatility (e.g., 80/20 for high-volume coins).
Volume Spike Flags
Mark RSI turning points that occurred during volume spikes with a special dot/symbol.
Trading Strategies with AVW-RSI
1. Weighted RSI Divergence
Regular RSI divergence becomes more powerful when volume is high.
AVW-RSI divergence with volume spike is a strong signal of reversal.
2. Trend Confirmation
RSI crossing above 50 during rising volume is a good entry signal.
RSI crossing below 50 with high volume is a strong exit or short trigger.
3. Breakout Validation
Price breaking resistance + AVW-RSI > 60 with volume = Confirmed breakout.
Price breaking but AVW-RSI < 50 or on low volume = Potential fakeout.
Example Use Case
Stock XYZ is approaching a resistance zone. A trader sees:
Standard RSI: 65 → suggests strength.
Volume is 3x the average.
AVW-RSI: 78 → signals strong momentum with institutional backing.
The trader enters confidently, knowing this isn't just low-volume hype.
Limitations / Tips
Works best on liquid assets (Forex majors, large-cap stocks, BTC/ETH).
Should be used alongside price action and volume analysis—not standalone.
Periods of extremely high volume (news events) might need smoothing to avoid spikes.
Breadth-Driven Swing StrategyWhat it does
This script trades the S&P 500 purely on market breadth extremes:
• Data source : INDEX:S5TH = % of S&P 500 stocks above their own 200-day SMA (range 0–100).
• Buy when breadth is washed-out.
• Sell when breadth is overheated.
It is long-only by design; shorting and ATR trailing stops have been removed to keep the logic minimal and transparent.
⸻
Signals in plain English
1. Long entry
A. A 200-EMA trough in breadth is printed and the trough value is ≤ 40 %.
or
B. A 5-EMA trough appears, its prominence passes the user threshold, and the lowest breadth reading in the last 20 bars is ≤ 20 %.
(Toggle this secondary trigger on/off with “ Enter also on 5-EMA trough ”.)
2. Exit (close long)
First 200-EMA peak whose breadth value is ≥ 70 %.
3. Risk control
A fixed stop-loss (% of entry price, default 8 %) is attached to every long trade.
⸻
Key parameters (defaults shown)
• Long EMA length 200 • Short EMA length 5
• Peak prominence 0.5 pct-pts • Trough prominence 3 pct-pts
• Peak level 70 % • Trough level 40 % • 5-EMA trough level 20 %
• Fixed stop-loss 8 %
• “Enter also on 5-EMA trough” = true (allows additional entries on extreme momentum reversals)
Feel free to tighten or relax any of these thresholds to match your risk profile or account for different market regimes.
⸻
How to use it
1. Load the script on a daily SPX / SPY chart.
(The price chart drives order execution; the breadth series is pulled internally and does not need to be on the chart.)
2. Verify the breadth feed.
INDEX:S5TH is updated after each session; your broker must provide it.
3. Back-test across several cycles.
Two decades of daily data is recommended to see how the rules behave in bear markets, range markets, and bull trends.
4. Adjust position sizing in the Properties tab.
The default is “100 % of equity”; change it if you prefer smaller allocations or pyramiding caps.
⸻
Why it can help
• Breadth signals often lead price, allowing entries before index-level momentum turns.
• Simple, rule-based exits prevent “waiting for confirmation” paralysis.
• Only one input series—easy to audit, no black-box math.
Trade-offs
• Relies on a single breadth metric; other internals (advance/decline, equal-weight returns, etc.) are ignored.
• May sit in cash during shallow pullbacks that never push breadth ≤ 40 %.
• Signals arrive at the end of the session (breadth is EoD data).
⸻
Disclaimer
This script is provided for educational purposes only and is not financial advice. Markets are risky; test thoroughly and use your own judgment before trading real money.
ストラテジー概要
本スクリプトは S&P500 のマーケットブレッド（内部需給） だけを手がかりに、指数をスイングトレードします。
• ブレッドデータ : INDEX:S5TH
（S&P500 採用銘柄のうち、それぞれの 200 日移動平均線を上回っている銘柄比率。0–100 %）
• 買い : ブレッドが極端に売られたタイミング。
• 売り : ブレッドが過熱状態に達したタイミング。
余計な機能を削り、ロングオンリー & 固定ストップ のシンプル設計にしています。
⸻
シグナルの流れ
1. ロングエントリー
• 条件 A : 200-EMA がトラフを付け、その値が 40 % 以下
• 条件 B : 5-EMA がトラフを付け、
・プロミネンス条件を満たし
・直近 20 本のブレッドス最小値が 20 % 以下
• B 条件は「5-EMA トラフでもエントリー」を ON にすると有効
2. ロング決済
最初に出現した 200-EMA ピーク で、かつ値が 70 % 以上 のバーで手仕舞い。
3. リスク管理
各トレードに 固定ストップ（初期価格から 8 %）を設定。
⸻
主なパラメータ（デフォルト値）
• 長期 EMA 長さ : 200 • 短期 EMA 長さ : 5
• ピーク判定プロミネンス : 0.5 %pt • トラフ判定プロミネンス : 3 %pt
• ピーク水準 : 70 % • トラフ水準 : 40 % • 5-EMA トラフ水準 : 20 %
• 固定ストップ : 8 %
• 「5-EMA トラフでもエントリー」 : ON
相場環境やリスク許容度に合わせて閾値を調整してください。
⸻
使い方
1. 日足の SPX / SPY チャート にスクリプトを適用。
2. ブレッドデータの供給 (INDEX:S5TH) がブローカーで利用可能か確認。
3. 20 年以上の期間でバックテスト し、強気相場・弱気相場・レンジ局面での挙動を確認。
4. 資金配分 は プロパティ → 戦略実行 で調整可能（初期値は「資金の 100 %」）。
⸻
強み
• ブレッドは 価格より先行 することが多く、天底を早期に捉えやすい。
• ルールベースの出口で「もう少し待とう」と迷わずに済む。
• 入力 series は 1 本のみ、ブラックボックス要素なし。
注意点・弱み
• 単一指標に依存。他の内部需給（A/D ライン等）は考慮しない。
• 40 % を割らない浅い押し目では機会損失が起こる。
• ブレッドは終値ベースの更新。ザラ場中の変化は捉えられない。
⸻
免責事項
本スクリプトは 学習目的 で提供しています。投資助言ではありません。
実取引の前に必ず自己責任で十分な検証とリスク管理を行ってください。
NasyI## NasyI - Multi-Timeframe Technical Analysis Toolkit
### English Description
**NasyI** is a comprehensive technical analysis indicator designed to provide traders with a complete view of market dynamics across multiple timeframes. This indicator combines the power of Exponential Moving Averages (EMAs), Simple Moving Averages (MAs), Volume Weighted Average Price (VWAP), and key support/resistance levels to help traders identify trend direction, potential reversal points, and optimal entry/exit opportunities.
#### Key Features
1. **Multi-Timeframe Analysis System**
- 2-minute EMAs (13, 48) for ultra-short-term trend identification
- 5-minute EMAs (9, 13, 21, 48, 200) for short-term trend confirmation
- Daily EMAs (5, 13, 21, 48, 100, 200) and MAs (20, 50, 100, 200) for longer-term perspective
- Color-coded bands between key EMAs to visually identify trend strength and direction
2. **Advanced VWAP Integration**
- Daily VWAP for intraday support/resistance
- Weekly VWAP for medium-term price reference
- Monthly VWAP for long-term institutional price levels
- All VWAPs properly reset at their respective time period boundaries
3. **Critical Price Level Identification**
- Previous day high/low lines for identifying key breakout and breakdown levels
- Pre-market high/low tracking to identify potential intraday support/resistance zones
- All levels displayed with distinct line styles for easy identification
4. **Dynamic Trend Analysis**
- Color-coded bands between EMAs display trend strength and direction:
- Green bands indicate uptrend conditions (9 EMA > 21 EMA > 48 EMA)
- Red bands indicate downtrend conditions (9 EMA < 21 EMA < 48 EMA)
- Yellow bands indicate neutral/confused market conditions
- Visual representation makes trend changes immediately apparent
5. **Comprehensive Customization Options**
- Fully customizable colors for all indicators and bands
- Adjustable transparency settings for visual clarity
- Optional price labels with customizable placement and appearance
- Ability to show/hide specific components based on trading preferences
#### Trading Applications
This indicator is particularly valuable for:
1. **Day Trading & Scalping**: The 2-minute and 5-minute EMAs with color bands provide clear short-term trend direction and potential reversal signals.
2. **Swing Trading**: Daily EMAs and MAs offer perspective on the larger trend, helping to align short-term trades with the broader market direction.
3. **Gap Trading**: Previous day and pre-market levels help identify potential gap fill scenarios and breakout/breakdown opportunities.
4. **VWAP Trading Strategies**: Multiple timeframe VWAPs allow for identifying institutional participation levels and potential reversal zones.
5. **EMA Cross Systems**: The various EMAs can be used to identify golden crosses and death crosses across multiple timeframes.
#### How the Components Work Together
The power of NasyI comes from the integration of these different technical elements:
1. The short-timeframe EMAs (2m, 5m) provide immediate trend information, while the daily EMAs/MAs provide context about the larger market structure.
2. The color bands between EMAs offer instant visual confirmation of trend alignment or divergence across timeframes.
3. Previous day and pre-market levels add horizontal support/resistance zones to complement the dynamic moving averages.
4. Multiple timeframe VWAPs provide additional confirmation of institutional activity levels and potential reversal points.
By combining these elements, traders can develop a comprehensive market view that integrates price action, trend direction, and key support/resistance levels all in one indicator.
#### Usage Instructions
1. Apply the NasyI indicator to your chart (works best on intraday timeframes from 1-minute to 30-minute).
2. Observe the relationship between price and the various EMAs:
- Price above the 2m/5m EMAs with green bands indicates bullish short-term conditions
- Price below the 2m/5m EMAs with red bands indicates bearish short-term conditions
3. Use the daily EMAs/MAs and VWAPs as targets for potential price movements and reversal zones.
4. Previous day and pre-market high/low lines provide key levels to watch for breakouts or breakdowns.
5. Customize the appearance according to your preferences using the extensive settings options.
This indicator represents a unique approach to technical analysis by combining multiple timeframe perspectives into a single, visually intuitive display that helps traders make more informed decisions based on a comprehensive view of market conditions.
### 中文描述
**NasyI** 是一个全面的技术分析指标，旨在为交易者提供跨多个时间周期的完整市场动态视图。该指标结合了指数移动平均线（EMA）、简单移动平均线（MA）、成交量加权平均价格（VWAP）和关键支撑/阻力水平的力量，帮助交易者识别趋势方向、潜在反转点和最佳进出场机会。
#### 主要特点
1. **多时间周期分析系统**
- 2分钟EMAs（13，48）用于超短期趋势识别
- 5分钟EMAs（9，13，21，48，200）用于短期趋势确认
- 日线EMAs（5，13，21，48，100，200）和MAs（20，50，100，200）用于更长期的视角
- 关键EMAs之间的彩色带状区域直观显示趋势强度和方向
2. **高级VWAP整合**
- 日内VWAP作为日内支撑/阻力
- 周内VWAP作为中期价格参考
- 月内VWAP作为长期机构价格水平
- 所有VWAP在各自的时间周期边界正确重置
3. **关键价格水平识别**
- 前一交易日高点/低点线用于识别关键突破和跌破水平
- 盘前高点/低点跟踪用于识别潜在的日内支撑/阻力区域
- 所有水平以不同的线条样式显示，便于识别
4. **动态趋势分析**
- EMAs之间的彩色带状区域显示趋势强度和方向：
- 绿色带状区域表示上升趋势（9 EMA > 21 EMA > 48 EMA）
- 红色带状区域表示下降趋势（9 EMA < 21 EMA < 48 EMA）
- 黄色带状区域表示中性/混乱市场条件
- 视觉表示使趋势变化立即显现
5. **全面的自定义选项**
- 所有指标和带状区域的颜色完全可定制
- 可调节的透明度设置，提高视觉清晰度
- 可选的价格标签，带有可定制的位置和外观
- 能够根据交易偏好显示/隐藏特定组件
#### 交易应用
此指标对以下方面特别有价值：
1. **日内交易和短线交易**：2分钟和5分钟EMAs与色带提供清晰的短期趋势方向和潜在反转信号。
2. **摇摆交易**：日线EMAs和MAs提供对更大趋势的视角，帮助将短期交易与更广泛的市场方向对齐。
3. **缺口交易**：前一日和盘前水平帮助识别潜在的缺口填充情况和突破/跌破机会。
4. **VWAP交易策略**：多时间周期VWAP允许识别机构参与水平和潜在反转区域。
5. **EMA交叉系统**：各种EMAs可用于识别跨多个时间周期的黄金交叉和死亡交叉。
#### 组件如何协同工作
NasyI的强大之处在于这些不同技术元素的集成：
1. 短时间周期EMAs（2m，5m）提供即时趋势信息，而日线EMAs/MAs提供关于更大市场结构的背景。
2. EMAs之间的色带提供趋势对齐或跨时间周期分歧的即时视觉确认。
3. 前一日和盘前水平添加水平支撑/阻力区域，补充动态移动平均线。
4. 多时间周期VWAP提供机构活动水平和潜在反转点的额外确认。
通过结合这些元素，交易者可以发展出全面的市场视图，整合价格行动、趋势方向和关键支撑/阻力水平于一个指标中。
#### 使用说明
1. 将NasyI指标应用到您的图表上（最适合1分钟至30分钟的日内时间周期）。
2. 观察价格与各种EMAs之间的关系：
- 价格位于2m/5m EMAs之上，带有绿色带状区域，表示看涨的短期条件
- 价格位于2m/5m EMAs之下，带有红色带状区域，表示看跌的短期条件
3. 使用日线EMAs/MAs和VWAPs作为潜在价格移动和反转区域的目标。
4. 前一日和盘前高点/低点线提供需要关注的突破或跌破的关键水平。
5. 使用广泛的设置选项根据您的偏好自定义外观。
这个指标代表了一种独特的技术分析方法，将多个时间周期的视角结合到一个单一的、视觉直观的显示中，帮助交易者基于对市场条件的全面视图做出更明智的决策。
