Breakouts & Pullbacks [Trendoscope®]🎲 Breakouts & Pullbacks - All-Time High Breakout Analyzer
Probability-Based Post-Breakout Behavior Statistics | Real-Time Pullback & Runup Tracker
A professional-grade Pine Script v6 indicator designed specifically for analyzing the historical and real-time behavior of price after strong All-Time High (ATH) breakouts. It automatically detects significant ATH breakouts (with configurable minimum gap), measures the depth and duration of pullbacks, the speed of recovery, and the subsequent run-up strength — then turns all this data into easy-to-read statistical probabilities and percentile ranks.
Perfect for swing traders, breakout traders, and anyone who wants objective, data-driven insight into questions like:
“How deep do pullbacks usually get after a strong ATH breakout?”
“How many bars does it typically take to recover the breakout level?”
“What is the median run-up after recovery?”
“Where is the current pullback or run-up relative to historical ones?”
🎲 Core Concept & Methodology
Indicator is more suitable for indices or index ETFs that generally trade in all-time highs however subjected to regular pullbacks, recovery and runups.
For every qualified ATH breakout, the script identifies 4 distinct phases:
Breakout Point – The exact bar where price closes above the previous ATH after at least Minimum Gap bars.
Pullback Phase – From breakout candle high → lowest low before price recovers back above the breakout level.
Recovery Phase – From the pullback low → the bar where price first trades back above the original breakout price.
Post-Recovery Run-up Phase – From the recovery point → current price (or highest high achieved so far).
Each completed cycle is stored permanently and used to build a growing statistical database unique to the loaded chart and timeframe.
🎲 Visual Elements
Yellow polyline triangle connecting Previous ATH / Pullback point(start), New ATH Breakout point (end), Recovery point (lowest pullback price), and extends to recent ATH price.
Small green label at the pullback low showing detailed tooltip on hover with all measured values
Clean, color-coded statistics table in the top-right corner (visible only on the last bar)
Powerful Statistics Table – The Heart of the Indicator
The table constantly compares the current situation against all past qualified breakouts and shows details about pullbacks, and runups that help us calculate the probability of next pullback, recovery or runup.
🎲 Settings & Inputs
Minimum Gap
The minimum number of bars that must pass between breaking a new ATH and the previous one.
Higher values = stricter filter → only the strongest, cleanest breakouts are counted.
Lower values = more data points (useful on lower timeframes or very trending instruments).
Recommendation:
Daily charts: 30–50
4H charts: 40–80
1H charts: 100–200
🎲 How to Use It in Practice
This indicator helps investors to understand when to be bullish, bearish or cautious and anticipate regular pullbacks, recovery of markets using quantitative methods.
The indicator does not generate buy/sell signals. However, helps traders set expectations and anticipate market movements based on past behavior.
Statistics
Forward Returns – (Next Month Start)This indicator calculates 1-month, 3-month, 6-month, and 12-month forward returns starting from the first trading day of the month following a defined price event.
A price event occurs when the selected asset drops below a user-defined threshold over a chosen timeframe (Day, Week, or Month).
For monthly conditions, the script evaluates the entire performance of the previous calendar month and triggers the event only at the first trading session of the next month, ensuring accurate forward-return alignment with historical monthly cycles.
The forward returns for each detected event are displayed in a paginated performance table, allowing users to navigate through large datasets using a page selector. Each page includes:
Entry Date
Forward returns (1M, 3M, 6M, 12M)
Average forward return
Win rate (percentage of positive outcomes)
This tool is useful for studying historical performance after major drawdowns, identifying seasonal patterns, and building evidence-based risk-management or timing models.
Weekday Close vs Open — Last N (per weekday)# Weekday Close vs Open - Last N Occurrences
This indicator distills every weekday's historical open-to-close behavior into a compact table so you can see how "typical" the current session is before the day even closes. It runs independently of your chart timeframe by pulling daily OHLCV data under the hood, tracking the last **N** completed occurrences for each weekday, and refreshing only when a daily bar closes. On daily charts you can also shade every past bar that matches today's weekday (excluding the in-progress session) to reinforce the pattern visually while the table remains non-repainting.
## What It Shows
- **Win/Loss/Tie counts** - how many of the last `N` occurrences closed above the open (wins), below (losses), or inside the tie threshold you define as "flat".
- **Win % heatmap** - the win column is color-coded (deep green > deep red) so you immediately recognize strong or weak weekdays.
- **Advanced metrics (optional)** - average daily volume plus the average percentage excursion above/below the open (`AvgUp%`, `AvgDn%`) for that weekday.
- **Totals row** - aggregates every weekday into one row to estimate overall hit rate and average stats across the entire data set.
- **Weekday shading (optional)** - on daily charts you can tint every bar that matches today's weekday (all Mondays, all Fridays, etc.) for instant pattern recognition.
## How It Works
1. The script requests daily OHLCV data (non-repainting) regardless of the chart timeframe.
2. When a new daily bar confirms, it packs that day's data into one of seven arrays (one per weekday). Each day contributes five floats (O/H/L/C/V) so trimming and statistics stay in lockstep.
3. A helper function (`f_dayMetrics`) scans daily history to compute average volume, average excursion above/below the open, and win/loss/tie counts for the requested weekday.
4. The table populates on the last bar of the chart session, respecting your advanced/totals toggles and keeping text at `size.normal`.
## Reading the Table
- **Win/Loss/Tie columns**: raw counts taken from your chosen `N`.
- **Win %***: excludes ties from the denominator so it reflects only decisive closes.
- **AvgUp% / AvgDn%**: typical intraday extension (high vs open, open vs low) in percent.
- **Avg Vol**: arithmetic mean of daily volume for that weekday.
- **TOTAL row**: provides a global win rate plus volume/up/down averages weighted by how many samples each weekday contributed.
## Practical Uses
- Spot weekdays that historically trend higher or lower before entering a trade.
- Compare current price action against the typical intraday range (`AvgUp%` vs today's move).
- Filter mean-reversion vs breakout setups based on the most reliable weekday patterns.
- Quickly gauge whether today is behaving "in character" by referencing the highlighted row or the optional whole-chart weekday shading.
> **Tip:** Use smaller `N` values (e.g., 10-20) for adaptive, recent behavior and larger values (50+) to capture longer-term seasonality. Tighten the tie threshold if you want almost every candle to register as win/loss, or widen it to focus only on meaningful moves.
SVE Daily ATR + SDTR Context BandsSVE Daily ATR + SDTR Context Bands is a free companion overlay from The Volatility Engine™ ecosystem.
It plots daily ATR-based expansion levels and a Standardized Deviation Threshold Range (SDTR) to give traders a clean, quantitative view of where intraday price sits relative to typical daily movement and volatility extremes.
This module is designed as an SVE-compatible context layer—using discrete, RTH-aligned daily zones, expected-move bands, and a standardized volatility shell—so traders can build situational awareness even without the full SPX Volatility Engine™ (SVE).
It does not generate trade signals.
Its sole purpose is to provide a clear volatility framework you can combine with your own structure, Fibonacci, or signal logic (including SVE, if you use it).
🔍 What It Shows
* Daily ATR Bands (expHigh / expLow)
- Expected high/low based on smoothed daily ATR
- Updates at the RTH open
* Daily SDTR Bands (expHighSDTR / expLowSDTR)
- Standard deviation threshold range for volatility extremes
- Helps identify overextended conditions
Discrete RTH-aligned Zones
- Bands reset cleanly at each RTH session
No continuous carry-over from prior days
Daily ATR & SDTR stats label
Quick-reference box showing current ATR and SDTR values
🎯 Purpose
This tool helps traders:
- Gauge intraday context relative to expected daily movement
- Assess volatility state (quiet, normal, expanded, extreme)
- Identify likely exhaustion or expansion zones
- Frame intraday price action inside daily volatility rails
- Support decision-making with objective context rather than emotion
It complements any strategy and works on any intraday timeframe.
⚙️ Inputs
- ATR Lookback (default: 20 days)
- RTH Session Times
- SDTR Lookback
- Show/Hide Daily Stats Label
🧩 Part of the SVE Ecosystem
This module is part of the broader SPX Volatility Engine™ framework.
The full SVE system includes:
- Composite signal scoring
- Volatility compression logic
- Histogram slope and momentum analysis
- Internals (VIX / VVIX / TICK)
- Structural zone awareness
- Real-time bias selection
- High-clarity decision support
⚠️ Disclaimer
This tool is provided for educational and informational purposes only.
No performance claims are made or implied.
Not investment advice.
BTC Macro Heatmap (Fed Cuts & Hikes)🔴 1. Red line – Fed Funds Rate (policy trend)
This line tells you what stage of the monetary cycle we’re in.
Rising red line = the Fed is hiking → liquidity is tightening → money leaves risk assets like BTC.
Flat = pause → markets start pricing in the next move (often sideways BTC).
Falling = easing / cutting → liquidity returns → bullish environment builds.
The rate of change matters more than the level. When the slope turns down, capital starts seeking yield again — BTC benefits first because it’s the most volatile asset.
💚 2. Dim green zones – detected cuts
These are data-based easing events pulled directly from FRED.
They show when the actual effective rate began moving down, not necessarily the exact meeting day.
Think of them as the Fed’s “foot off the brake” — that’s when risk markets begin responding.
🟩 3. Bright green lines – official FOMC cuts
These are the real policy shifts — the Fed formally changed direction.
After these appear, BTC historically transitions from accumulation → markup phase.
Look at 2020: the bright green lines came right before BTC’s full reversal.
You’re seeing the same thing now with the 2025 lines — early-stage liquidity return.
🟠 4. Orange line – DXY (US Dollar Index)
DXY is your “risk-off” gauge.
When DXY rises, global investors flock to dollars → BTC usually weakens.
When DXY peaks and starts dropping, it means risk appetite is coming back → BTC rallies.
BTC and DXY are inversely correlated about 70–80% of the time.
Watch for DXY lower highs after rate cuts — that’s your macro confirmation of a BTC-friendly environment.
🟦 5. Aqua line – BTC (normalized)
You’re not looking for the price itself here, but its shape relative to DXY and the Fed line.
When BTC curls up as the red line flattens and DXY rolls over → that’s historically the start of a major bull phase.
BTC tends to bottom before the first cut and explode once DXY decisively breaks down.
🧠 Putting it together
Here’s the rhythm this chart shows over and over:
Fed hikes (red line rising) → BTC weakens, DXY climbs.
Fed pauses (red line flat) → BTC stops falling, DXY tops.
Fed cuts (dim + bright green) → DXY turns down → BTC begins long recovery → bull cycle starts.
Static Beta for Pair and Quant Trading A beta coefficient shows the volatility of an individual stock compared to the systematic risk of the entire market. Beta represents the slope of the line through a regression of data points. In finance, each point represents an individual stock's returns against the market.
Beta effectively describes the activity of a security's returns as it responds to swings in the market. It is used in the capital asset pricing model (CAPM), which describes the relationship between systematic risk and expected return for assets. CAPM is used to price risky securities and to estimate the expected returns of assets, considering the risk of those assets and the cost of capital.
Calculating Beta
A security's beta is calculated by dividing the product of the covariance of the security's returns and the market's returns by the variance of the market's returns over a specified period. The calculation helps investors understand whether a stock moves in the same direction as the rest of the market. It also provides insights into how volatile—or how risky—a stock is relative to the rest of the market.
For beta to provide useful insight, the market used as a benchmark should be related to the stock. For example, a bond ETF's beta with the S&P 500 as the benchmark would not be helpful to an investor because bonds and stocks are too dissimilar.
Beta Values
Beta equal to 1: A stock with a beta of 1.0 means its price activity correlates with the market. Adding a stock to a portfolio with a beta of 1.0 doesn’t add any risk to the portfolio, but it doesn’t increase the likelihood that the portfolio will provide an excess return.
Beta less than 1: A beta value less than 1.0 means the security is less volatile than the market. Including this stock in a portfolio makes it less risky than the same portfolio without the stock. Utility stocks often have low betas because they move more slowly than market averages.
Beta greater than 1: A beta greater than 1.0 indicates that the security's price is theoretically more volatile than the market. If a stock's beta is 1.2, it is assumed to be 20% more volatile than the market. Technology stocks tend to have higher betas than the market benchmark. Adding the stock to a portfolio will increase the portfolio’s risk, but may also increase its return.
Negative beta: A beta of -1.0 means that the stock is inversely correlated to the market benchmark on a 1:1 basis. Put options and inverse ETFs are designed to have negative betas. There are also a few industry groups, like gold miners, where a negative beta is common.
LET'S START
Now I'll give my own definition.
Beta:
If we assume market caps are equal ,
it is an indicator that shows how much of the second instrument we should buy if we buy one of the first, taking into account the price volatility of two instruments.
But if the market caps are not equal:
For example, the ETF for A is $300.
The ETF for B is $600.
If static beta predicted by this script is 0.5:
300 * 1 * a = 600 * 0.5 * b
Then we should use 1 b for 1 a.
(Long a and short b or vice versa )
So, we can try pair trading for a/b or a-b.
However, these values are generally close to each other, such as 0.8 and 0.93. However, the closer we can adjust our lot purchases to bring the double beta to a value closer to 1, the higher the hedge ratio will be.
Large commercials use dynamic betas, which are updated periodically, in addition to static betas
However, scaling this is very difficult for individual investors with limited investment tools.
But a static beta of 5,000 bars is still much better than not considering any beta at all.
Note: The presence of a beta value for two instruments does not necessarily mean they can be included in pair trading.
It is also important (%99) to consider historically very high correlations and cointegration relationships, as well as the compatibility of security structures.
Note 2 : This script is designed for low timeframes.
Do not use betas from different timeframes.
Beta dynamics are different for each timeframe.
Note 3 : I created this script with the help of ChatGPT.
Source for beta definition ( ) :
www.investopedia.com
Regards.
FVG – (auto close + age) GR V1.0FVG – Fair Value Gaps (auto close + age counter)
Short Description
Automatically detects Fair Value Gaps (FVGs) on the current timeframe, keeps them open until price fully fills the gap or a maximum bar age is reached, and shows how many candles have passed since each FVG was created.
Full Description
This indicator automatically finds and visualizes Fair Value Gaps (FVGs) using the classic 3-candle ICT logic on any timeframe.
It works on whatever timeframe you apply it to (M1, M5, H1, H4, etc.) and adapts to the current chart.
FVG detection logic
The script uses a 3-candle pattern:
Bullish FVG
Condition:
low > high
Gap zone:
Lower boundary: high
Upper boundary: low
Bearish FVG
Condition:
high < low
Gap zone:
Lower boundary: high
Upper boundary: low
Each detected FVG is drawn as a colored box (green for bullish, red for bearish in this version, but you can adjust colors in the inputs).
Auto-close rules
An FVG remains on the chart until one of the following happens:
Full fill / mitigation
A bullish FVG closes when any candle’s low goes down to or below the lower boundary of the gap.
A bearish FVG closes when any candle’s high goes up to or above the upper boundary of the gap.
Maximum bar age reached
Each FVG has a maximum lifetime measured in candles.
When the number of candles since its creation reaches the configured maximum (default: 200 bars), the FVG is automatically removed even if it has not been fully filled.
This keeps the chart cleaner and prevents very old gaps from cluttering the view.
Age counter (labels inside the boxes)
Inside every FVG box there is a small label that:
Shows how many bars have passed since the FVG was created.
Moves together with the right edge of the box and stays vertically centered in the gap.
This makes it easy to distinguish fresh gaps from older ones and prioritize which zones you want to pay attention to.
Inputs
FVG color – Main fill color for all FVG boxes.
Show bullish FVGs – Turn bullish gaps on/off.
Show bearish FVGs – Turn bearish gaps on/off.
Max bar age – Maximum number of candles an FVG is allowed to stay on the chart before it is removed.
Usage
Works on any symbol and any timeframe.
Can be combined with your own ICT / SMC concepts, order blocks, session ranges, market structure, etc.
You can also choose to only display bullish or only bearish FVGs depending on your directional bias.
Disclaimer
This script is for educational and informational purposes only and is not financial advice. Always do your own research and use proper risk management when trading.
HPAS mean reversion strategy testerTakes Krown HPAS values hardcoded and simulates longs and short with configurable standard deviation multiplier TP/SL. Best used on lower timeframes
Intraday Close Price VariationShows in the graph the intraday variation, being useful when using the replay feature.
RLSR logreg_support_libLibrary "logreg_support_lib"
sigmoid(z)
Parameters:
z (float)
prng01(seed1, seed2)
Parameters:
seed1 (float)
seed2 (float)
normalize(value, minval, maxval)
Parameters:
value (float)
minval (float)
maxval (float)
calcpercentilefast(arr, percentile)
Parameters:
arr (array)
percentile (float)
calcpercentile_series_sampled(s, length, percentile, stride)
Parameters:
s (float)
length (int)
percentile (float)
stride (int)
calcRangeWithLog(value, minval, maxval, uselog)
Parameters:
value (float)
minval (float)
maxval (float)
uselog (bool)
calcMomentumAdvanced(src, length, momType)
Parameters:
src (float)
length (simple int)
momType (string)
normalizeMomentumByType(rawMom, momType, momMin, momMax, momNorm)
Parameters:
rawMom (float)
momType (string)
momMin (float)
momMax (float)
momNorm (float)
normalizeMomentumByTypeExt(rawMom, momType, momMin, momMax, momNorm, bouncingdecay)
Parameters:
rawMom (float)
momType (string)
momMin (float)
momMax (float)
momNorm (float)
bouncingdecay (float)
calcrollingstddev(src, length)
Parameters:
src (float)
length (int)
addlog(buffer, level, msg)
Parameters:
buffer (string)
level (string)
msg (string)
calcfeaturecorrelation(x1, x2)
Parameters:
x1 (array)
x2 (array)
calcnoiseratio(src, lookback)
Parameters:
src (float)
lookback (int)
calccompatibilityscore(x1, x2)
Parameters:
x1 (array)
x2 (array)
getfuturereturn(offset, returnlookback)
Parameters:
offset (int)
returnlookback (int)
calculatema(source, length, matype)
Parameters:
source (float)
length (simple int)
matype (string)
adaptive_trigger_for_source(src, enabled, freeze, lookback, threshold, volahistory)
Parameters:
src (float)
enabled (bool)
freeze (bool)
lookback (int)
threshold (float)
volahistory (array)
checkadaptivetrigger5(s1, enabled1, freeze1, hist1, s2, enabled2, freeze2, hist2, s3, enabled3, freeze3, hist3, s4, enabled4, freeze4, hist4, s5, enabled5, freeze5, hist5, lookback, threshold)
Parameters:
s1 (float)
enabled1 (bool)
freeze1 (bool)
hist1 (array)
s2 (float)
enabled2 (bool)
freeze2 (bool)
hist2 (array)
s3 (float)
enabled3 (bool)
freeze3 (bool)
hist3 (array)
s4 (float)
enabled4 (bool)
freeze4 (bool)
hist4 (array)
s5 (float)
enabled5 (bool)
freeze5 (bool)
hist5 (array)
lookback (int)
threshold (float)
ring_start_index(rb_write_idx, rb_count, rb_cap)
Parameters:
rb_write_idx (int)
rb_count (int)
rb_cap (int)
reversalLibrary "reversals"
psar(af_start, af_increment, af_max)
Calculates Parabolic Stop And Reverse (SAR)
Parameters:
af_start (simple float) : Initial acceleration factor (Wilder's original: 0.02)
af_increment (simple float) : Acceleration factor increment per new extreme (Wilder's original: 0.02)
af_max (simple float) : Maximum acceleration factor (Wilder's original: 0.20)
Returns: SAR value (stop level for current trend)
fractals()
Detects Williams Fractal patterns (5-bar pattern)
Returns: Tuple with fractal values (na if no fractal)
swings(lookback, source_high, source_low)
Detects swing highs and swing lows using lookback period
Parameters:
lookback (simple int) : Number of bars on each side to confirm swing point
source_high (float) : Price series for swing high detection (typically high)
source_low (float) : Price series for swing low detection (typically low)
Returns: Tuple with swing point values (na if no swing)
pivot(tf)
Calculates classic/standard/floor pivot points
Parameters:
tf (simple string) : Timeframe for pivot calculation ("D", "W", "M")
Returns: Tuple with pivot levels
pivotcam(tf)
Calculates Camarilla pivot points with 8 levels for short-term trading
Parameters:
tf (simple string) : Timeframe for pivot calculation ("D", "W", "M")
Returns: Tuple with pivot levels
pivotdem(tf)
Calculates d-mark pivot points with conditional open/close logic
Parameters:
tf (simple string) : Timeframe for pivot calculation ("D", "W", "M")
Returns: Tuple with pivot levels (only 3 levels)
pivotext(tf)
Calculates extended traditional pivot points with R4-R5 and S4-S5 levels
Parameters:
tf (simple string) : Timeframe for pivot calculation ("D", "W", "M")
Returns: Tuple with pivot levels
pivotfib(tf)
Calculates Fibonacci pivot points using Fibonacci ratios
Parameters:
tf (simple string) : Timeframe for pivot calculation ("D", "W", "M")
Returns: Tuple with pivot levels
pivotwood(tf)
Calculates Woodie's pivot points with weighted closing price
Parameters:
tf (simple string) : Timeframe for pivot calculation ("D", "W", "M")
Returns: Tuple with pivot levels
Pivot Fib 4H — EAStrategy uses the pivot standard to open position, it has well define entry and exit point with SL, it also has a proper money management plan, maximum 4 trades a day, each trade risk 0.5% of the account, I have it EA version of it also.
Last CLOSED Bar OHLCThis TradingView Pine Script (@version=6) creates a label that displays the previous fully closed candlestick’s OHLC data on the chart.
Bitcoin vs M2 Global Liquidity (Lead 3M) - Table Ticker═══════════════════════════════════════════════════════════════
Bitcoin vs M2 Global Liquidity - Regression Indicator
═══════════════════════════════════════════════════════════════
TECHNICAL SPECS
• Pine Script v6
• Overlay: false (separate pane)
• Data sources: 5 M2 series + 4 FX pairs (request.security)
• Calculation: Rolling OLS linear regression with configurable lead
• Output: Regression line + ±1σ/±2σ confidence bands + R² ticker
CORE FUNCTIONALITY
Aggregates M2 money supply from 5 central banks (CN, US, EU, JP, GB),
converts to USD, applies time-lead, runs rolling linear regression
vs Bitcoin price, plots predicted value with confidence intervals.
CONFIGURABLE PARAMETERS
Input Controls:
• Lead Period: 0-365 days (default: 90)
• Lookback Window: 50-2000 bars (default: 750)
• Bands: Toggle ±1σ and ±2σ visibility
• Colors: BTC, M2, regression line, confidence zones
• Ticker: Position, size, colors, transparency
Advanced Settings:
• Table display: R², lead, M2 total, country breakdown (%)
• Ticker customization: 9 position options, 6 text sizes
• Border: Width 0-10px, color, outline-only mode
DATA AGGREGATION
Sources (via request.security):
• ECONOMICS:CNM2, USM2, EUM2, JPM2, GBM2
• FX_IDC:CNYUSD, JPYUSD (others: FX:EURUSD, GBPUSD)
• Conversion: All M2 → USD → Sum / 1e12 (trillions)
REGRESSION ENGINE
• Arrays: m2Array, btcArray (dynamic sizing, auto-trim)
• Window: Rolling (lookbackPeriod bars)
• Lead: Time-shift via array indexing (i + leadPeriodDays)
• Calc: Manual OLS (covariance/variance), no built-in ta functions
• Outputs: slope, intercept, r2, stdResiduals
CONFIDENCE BANDS
±1σ and ±2σ calculated from standard deviation of residuals.
Fill zones between upper/lower bounds with configurable transparency.
ALERTS
5 pre-configured alertcondition():
• Divergence > 15%
• Price crosses ±1σ bands (up/down)
• Price crosses ±2σ bands (up/down)
TICKER TABLE
Dynamic table.new() with 9 rows:
• R² value (4 decimals)
• Lead period (days + months)
• M2 Global total (trillions USD)
• Country breakdown: CN, US, EU, JP, GB (absolute + %)
• Optional: Hide/show M2 details
VISUAL CUSTOMIZATION
All plot() elements support:
• Color picker inputs (group="Couleurs")
• Line width: 1-3px
• Transparency: 0-100% for zones
• Offset: M2 plot has +leadPeriodDays offset option
PERFORMANCE
• Max arrays size: lookbackPeriod + leadPeriodDays + 200
• Calculations: Only when array.size >= lookbackPeriod + leadPeriodDays
• Table update: barstate.islast (once per bar)
• Request.security: gaps_off mode
CODE STRUCTURE
1. Inputs (lines 7-54)
2. Data fetch (lines 56-76)
3. M2 aggregation (line 78)
4. Array management (lines 84-95)
5. Regression calc (lines 97-172)
6. Prediction + bands (lines 174-183)
7. Plots (lines 185-199)
8. Ticker table (lines 201-236)
9. Alerts (lines 238-246)
DEPENDENCIES
None. Pure Pine Script v6. No external libraries.
LIMITATIONS
• Daily timeframe recommended (1D)
• Requires 750+ bars history for optimal calculation
• M2 data availability: TradingView ECONOMICS feed
• Max lines: 500 (declared in indicator())
CUSTOMIZATION EXAMPLES
• Shorter lookback (200d): More reactive, lower R²
• Longer lookback (1500d): More stable, regime mixing
• No bands: Set showBands=false for clean view
• Different lead: Test 60d, 120d for sensitivity analysis
TECHNICAL NOTES
• Manual OLS implementation (no ta.linreg)
• Array-based lead application (not plot offset)
• M2 values stored in trillions (/ 1e12) for readability
• Residuals array cleared/rebuilt each calculation
OPEN SOURCE
Code fully visible. Modify, fork, analyze freely.
No hidden calculations. No proprietary data.
VERSION
1.0 | November 2025 | Pine Script v6
═══════════════════════════════════════════════════════════════
High Volume Bars (Advanced)High Volume Bars (Advanced)
High Volume Bars (Advanced) is a Pine Script v6 indicator for TradingView that highlights bars with unusually high volume, with several ways to define “unusual”:
Classic: volume > moving average + N × standard deviation
Change-based: large change in volume vs previous bar
Z-score: statistically extreme volume values
Robust mode (optional): median + MAD, less sensitive to outliers
It can:
Recolor candles when volume is high
Optionally highlight the background
Optionally plot volume bands (center ± spread × multiplier)
⸻
1. How it works
At each bar the script:
Picks the volume source:
If Use Volume Change vs Previous Bar? is off → uses raw volume
If on → uses abs(volume - volume )
Computes baseline statistics over the chosen source:
Lookback bars
Moving average (SMA or EMA)
Standard deviation
Optionally replaces mean/std with robust stats:
Center = median (50th percentile)
Spread = MAD (median absolute deviation, scaled to approx σ)
Builds bands:
upper = center + spread * multiplier
lower = max(center - spread * multiplier, 0)
Flags a bar as “high volume” if:
It passes the mode logic:
Classic abs: volume > upper
Change mode: abs(volume - volume ) > upper
Z-score mode: z-score ≥ multiplier
AND the relative filter (optional): volume > average_volume * Min Volume vs Avg
AND it is past the first Skip First N Bars from the start of the chart
Colors the bar and (optionally) the background accordingly.
⸻
2. Inputs
2.1. Statistics
Lookback (len)
Number of bars used to compute the baseline stats (mean / median, std / MAD).
Typical values: 50–200.
StdDev / Z-Score Multiplier (mult)
How far from the baseline a bar must be to count as “high volume”.
In classic mode: volume > mean + mult × std
In z-score mode: z ≥ mult
Typical values: 1.0–2.5.
Use EMA Instead of SMA? (smooth_with_ema)
Off → uses SMA (slower but smoother).
On → uses EMA (reacts faster to recent changes).
Use Robust Stats (Median & MAD)? (use_robust)
Off → mean + standard deviation
On → median + MAD (less sensitive to a few insane spikes)
Useful for assets with occasional volume blow-ups.
⸻
2.2. Detection Mode
These inputs control how “unusual” is defined.
• Use Volume Change vs Previous Bar? (mode_change)
• Off (default) → uses absolute volume.
• On → uses abs(volume - volume ).
You then detect jumps in volume rather than absolute size.
Note: This is ignored if Z-Score mode is switched on (see below).
• Use Z-Score on Volume? (Overrides change) (mode_zscore)
• Off → high volume when raw value exceeds the upper band.
• On → computes z-score = (value − center) / spread and flags a bar as high when z ≥ multiplier.
Z-score mode can be combined with robust stats for more stable thresholds.
• Min Volume vs Avg (Filter) (min_rel_mult)
An extra filter to ignore tiny-volume bars that are statistically “weird” but not meaningful.
• 0.0 → no filter (all stats-based candidates allowed).
• 1.0 → high-volume bar must also be at least equal to average volume.
• 1.5 → bar must be ≥ 1.5 × average volume.
• Skip First N Bars (from start of chart) (skip_open_bars)
Skips the first N bars of the chart when evaluating high-volume conditions.
This is mostly a safety / cosmetic option to avoid weird behavior on very early bars or backfill.
⸻
2.3. Visuals
• Show Volume Bands? (show_bands)
• If on, plots:
• Upper band (upper)
• Lower band (lower)
• Center line (vol_center)
These are plotted on the same pane as the script (usually the price chart).
• Also Highlight Background? (use_bg)
• If on, fills the background on high-volume bars with High-Vol Background.
• High-Vol Bar Transparency (0–100) (bar_transp)
Controls the opacity of the high-volume bar colors (up / down).
• 0 → fully opaque
• 100 → fully transparent (no visible effect)
• Up Color (upColor) / Down Color (dnColor)
• Regular bar colors (non high-volume) for up and down bars.
• Up High-Vol Base Color (upHighVolBase) / Down High-Vol Base Color (dnHighVolBase)
Base colors used for high-volume up/down bars. Transparency is applied on top of these via bar_transp.
• High-Vol Background (bgHighVolColor)
Background color used when Also Highlight Background? is enabled.
⸻
3. What gets colored and how
• Bar color (barcolor)
• Up bar:
• High volume → Up High-Vol Color
• Normal volume → Up Color
• Down bar:
• High volume → Down High-Vol Color
• Normal volume → Down Color
• Flat bar → neutral gray
• Background color (bgcolor)
• If Also Highlight Background? is on, high-volume bars get High-Vol Background.
• Otherwise, background is unchanged.
⸻
4. Alerts
The indicator exposes three alert conditions:
• High Volume Bar
Triggers whenever is_high is true (up or down).
• High Volume Up Bar
Triggers only when is_high is true and the bar closed up (close > open).
• High Volume Down Bar
Triggers only when is_high is true and the bar closed down (close < open).
You can use these in TradingView’s “Create Alert” dialog to:
• Get notified of potential breakout / exhaustion bars.
• Trigger webhook events for bots / custom infra.
⸻
5. Recommended presets
5.1. “Classic” high-volume detector (closest to original)
• Lookback: 150–200
• StdDev / Z-Score Multiplier: 1.0–1.5
• Use EMA Instead of SMA?: off
• Use Robust Stats?: off
• Use Volume Change vs Previous Bar?: off
• Use Z-Score on Volume?: off
• Min Volume vs Avg (Filter): 0.0–1.0
Behavior: Flags bars whose volume is notably above the recent average (plus a bit of noise filtering), same spirit as your initial implementation.
⸻
5.2. Volatility-aware (Z-score) mode
• Lookback: 100–200
• StdDev / Z-Score Multiplier: 1.5–2.0
• Use EMA Instead of SMA?: on
• Use Robust Stats?: on (if asset has huge spikes)
• Use Volume Change vs Previous Bar?: off (ignored anyway in z-score mode)
• Use Z-Score on Volume?: on
• Min Volume vs Avg (Filter): 0.5–1.0
Behavior: Flags bars that are “statistically extreme” relative to recent volume behavior, not just absolutely large. Good for assets where baseline volume drifts over time.
⸻
5.3. “Wake-up bar” (volume acceleration)
• Lookback: 50–100
• StdDev / Z-Score Multiplier: 1.0–1.5
• Use EMA Instead of SMA?: on
• Use Robust Stats?: optional
• Use Volume Change vs Previous Bar?: on
• Use Z-Score on Volume?: off
• Min Volume vs Avg (Filter): 0.5–1.0
Behavior: Emphasis on sudden increases in volume rather than absolute size – useful to catch “first active bar” after a quiet period.
⸻
6. Limitations / notes
• Time-of-day effects
The script currently treats the entire chart as one continuous “session”. On 24/7 markets (crypto) this is fine. For regular-session assets (equities, futures), volume naturally spikes at open/close; you may want to:
• Use a shorter Lookback, or
• Add a session-aware filter in a future iteration.
• Illiquid symbols
On very low-liquidity symbols, robust stats (Use Robust Stats) and a non-zero Min Volume vs Avg can help avoid “everything looks extreme” problems.
• Overlay behavior
overlay = true means:
• Bars are recolored on the price pane.
• Volume bands are also drawn on the price pane if enabled.
If you want a dedicated panel for the bands, duplicate the logic in a separate script with overlay = false.
Price Distance from SMA (% with StdDev)Measures the % distance of current price from given sma and how it compares to historic.
Reversal Correlation Pressure [OmegaTools]Reversal Correlation Pressure is a quantitative regime-detection and signal-filtering framework designed to enhance both reversal timing and breakout validation across intraday and multi-session markets.
It is built for discretionary and systematic traders who require a statistically grounded filter capable of adapting to changing market conditions in real time.
1. Purpose and Overview
Market conditions constantly rotate through phases of expansion, contraction, trend persistence, and noise-driven mean reversion. Many strategies break down not because the signal is wrong, but because the regime is unsuitable.
This indicator solves that structural problem.
The tool measures the evolving correlation relationship between highs and lows — a robust proxy for how “organized” or “fragmented” price discovery currently is — and transforms it into a regime pressure reading. This reading is then used as the core variable to validate or filter reversal and breakout opportunities.
Combined with an internal performance-based filter that learns from its past signals, the indicator becomes a dynamic decision engine: it highlights only the signals that statistically perform best under the current market regime.
2. Core Components
2.1 Correlation-Based Regime Mapping
The relationship between highs and lows contains valuable information about market structure:
High correlation generally corresponds to coherent, directional markets where momentum and breakouts tend to prevail.
Low or unstable correlation often appears in overlapping, rotational phases where price oscillates and mean-reversion behavior dominates.
The indicator continuously evaluates this correlation, normalizes it statistically, and displays it as a pressure histogram:
Higher values indicate regimes favorable to trend continuation or momentum breakouts.
Lower values indicate regimes where reversals, pullbacks, and fade setups historically perform better.
This regime mapping is the foundation upon which the adaptive filter operates.
2.2 Reversal Stress & Breakout Stress Signaling
Raw directional opportunities are identified using statistically significant deviations from short-term equilibrium (overbought/oversold dynamics).
However, unlike traditional mean-reversion or breakout tools, signals here are not automatically taken. They must first be validated by the regime framework and then compared against the performance of similar past setups.
This dual evaluation sharply reduces the noise associated with reversal attempts during strong trends, while also preventing breakout attempts during choppy, anti-directional conditions.
2.3 Adaptive Regime-Selection Backtester
A key innovation of this indicator is its embedded micro-backtester, which continuously tracks how reversal or breakout signals have performed under each correlation regime.
The system evaluates two competing hypotheses:
Signals perform better during high-correlation regimes.
Signals perform better during low-correlation or neutral regimes.
For each new trigger, the indicator looks back at a rolling sample of past setups and measures short-term performance under both regimes. It then automatically selects the regime that currently demonstrates the superior historical edge.
In other words, the indicator:
Learns from recent market behavior
Determines which regime supports reversals
Determines which regime supports breakouts
Applies the optimal filter in real time
Highlights only the signals that historically outperformed under similar conditions
This creates a dynamic, statistically supervised approach to signal filtering — a substantial improvement over static or fixed-threshold systems.
2.4 Visual Components
To support rapid decision-making:
Correlation Pressure Histogram:
Encodes regime strength through a gradient-based color system, transitioning from neutral contexts into strong structural phases.
Directional Markers:
Visual arrows appear when a signal passes all filters and conditions.
Bar Coloring:
Bars can optionally be recolored to reflect active bullish or bearish bias after the adaptive filter approves a signal.
These components integrate seamlessly to give the trader a concise but complete view of the underlying conditions.
3. How to Use This Indicator
3.1 Identifying Regimes
The histogram is the anchor:
High, brightly colored columns suggest trend-friendly behavior where breakout alignment and directional follow-through have historically been stronger.
Low or muted columns suggest mean-reversion contexts where counter-trend opportunities and reversal setups gain reliability.
3.2 Filtering Signals
The indicator automatically decides whether a reversal or breakout trigger should be respected based on:
the current correlation regime,
the learned performance of recent signals under similar conditions, and
the directional stress detected in price.
The user does not need to adjust anything manually.
3.3 Integration with Other Tools
This indicator works best when combined with:
VWAP or session levels
Market internals and breadth metrics
Volume, order flow, or delta-based tools
Local structural frameworks (support/resistance, liquidity highs and lows)
Its strength is in telling you when your other signals matter and when they should be ignored.
4. Strengths of the Framework
Automatically adapts to changing micro-regimes
Reduces false reversals during strong trends
Avoids false breakouts in overlapping, rotational markets
Learns from recent historical performance
Provides a statistically driven confirmation layer
Works on all liquid assets and timeframes
Suitable for both discretionary and automated environments
5. Disclaimer
This indicator is provided strictly for educational and analytical purposes.
It does not constitute trading advice, investment guidance, or a recommendation to buy or sell any financial instrument.
Past performance of any statistical filter or adaptive method does not guarantee future results.
All trading involves significant risk, and users are responsible for their own decisions and risk management.
By using this indicator, you acknowledge that you are fully responsible for your trading activity.
Spot-Futures SpreadSpot-Futures Spread Indicator
A comprehensive indicator that automatically calculates and visualizes the percentage spread between spot and perpetual futures prices across multiple exchanges.
Key Features:
Automatic Exchange Detection - Automatically detects your current exchange and finds the corresponding spot/futures pair
Smart Fallback System - If the counterpart isn't available on your exchange, it automatically searches across 7+ major exchanges (Binance, Bybit, OKX, Gate.io, MEXC, KuCoin, HTX) and uses the first valid match
Multi-Exchange Support - Works with 14 exchanges including Binance, Bybit, OKX, MEXC, BitGet, Gate.io, KuCoin, and more
Clear Exchange Attribution - Shows exactly which exchanges are providing spot and futures data in the statistics table
Configurable Moving Average - Track the average spread with customizable period
Standard Deviation Bands - Identify unusual spread conditions with Bollinger-style bands
Built-in Alerts - Get notified when spread crosses bands or zero (parity)
Statistics Table - Real-time stats showing current spread, MA, std dev, and bands
Manual Override Options - Advanced users can manually specify exchanges and symbols
How It Works:
The indicator calculates the spread as: (Futures Price - Spot Price) / Spot Price × 100
Positive spread = Futures trading at a premium (contango)
Negative spread = Futures trading at a discount (backwardation)
Zero = Parity between spot and futures
Use Cases:
Funding Rate Analysis - Correlates with perpetual funding rates
Arbitrage Opportunities - Identify significant spot-futures divergences
Market Sentiment - Premium/discount indicates bullish/bearish positioning
Cross-Exchange Analysis - Compare spreads when spot and futures are on different exchanges
Smart Features:
Works whether you're viewing a spot or futures chart
Automatically handles exchange-specific perpetual contract naming (.P, PERP, SWAP, etc.)
Color-coded visualization (green for premium, red for discount)
Customizable colors and display options
Background shading based on spread direction
Perfect For:
Crypto traders monitoring funding rates, arbitrage traders, market makers, and anyone interested in spot-futures dynamics across multiple exchanges.
Getting Started:
Simply add the indicator to any spot or perpetual futures chart. It will automatically detect the exchange and find the corresponding pair. The statistics table shows which exchanges are being used for maximum transparency.
Note: The indicator automatically ignores invalid symbols, so you'll never see errors even if a specific pair doesn't exist on a particular exchange.
Kudos to @AlekMel that made the "Spot - Fut Spread v2" indicator that I enhance the Automatic detection feature which was not working in some case.
Stochastic Ensembling of OutputsStochastic Ensembling of Outputs
🙏🏻 This is a simple tool/method that would solve naturally many well known problems:
“Price reversed 1 tick before the actual level, not executing my limit order”
“I consider intraday trend change by checking whether price is above/below VWAP, but is 1 tick enough? What to do, price is now whipsawing around vwap...”.
“I want to gradually accumulate a position around a chosen anchor. But where exactly should I put my orders? And I want to automate it ofc.“
“All these DSP adepts are telling you about some kind of noise in the markets… But how can I actually see it?”
The easy fix is to make things more analog less digital, by synthesizing numerous noise instances & adding it to any price-applied metric of yours. The ones who fw techno & psytrance, and other music, probably don’t need any more explanations. Then by checking not just 2 lines or 1 process against another one, you will be checking cloud vs cloud of lines, even allowing you to introduce proxies of probabilities. More crosses -> more confirmation to act.
How-to use:
The tool has 2 inputs: source and target:
Sources should always be the underlying process. If you apply the tool to price based metric, leave it hlcc4 unless you have a better one point estimate for each bar;
Target is your target, e.g if you want to apply it to VWAP, pick VWAP as target. You can thee on the chart above how trading activity recently never exactly touched VWAP, however noised instances of VWAP 'were' touched
The code is clean and written in modular form, you can simply copy paste it to any script of yours if you don't want to have multiple study-on-study script pairs.
^^ applied to prev days highs and lows
^^ applied to MBAD extensions and basis
^^ applied to input series itself
Here’s how it works, no ML, no “AI”, no 1k lines of code, just stats:
The problem with metrics, even if they are time aware like WMA, is that they still do not directly gain information about “changes” between datapoints. If we pick noise characteristics to match these changes, we’d effectively introduce this info into our ops.
^^ this screenshot represents 2 very different processes: a sine wave and white noise, see how the noise instances learned from each process differ significantly.
Changes can be represented as AR1 process . It’s dead simple, no PHD needed, it’s just how the current datapoint is related (or not) to the previous datapoint, no more than 1, and how this relationship holds/evolves over time. Unlike the mainstream approach like MLE, I estimate this relationship (phi parameter) via MoM but giving more weights to more recent datapoints via exponential smoothing over all the data available on your charts (so I encode temporal information), algocomplexity is O(1), lighting fast, just one pass. <- that gives phi , we’d use it as color for our noise generator
Then we just need to estimate noise amplitude ( gamma ) via checking what AR1 model actually thought vs the reality, variance of these innovations. Same via exponential smoothing, time aware, O(1), one pass, it’s all it does.
Then we generate white gaussian noise, and apply 2 estimated parameters (phi and gamma), and that’s all.
Omg, I think I just made my first real DSP script xd
Just like Monte Carlo for risk management, this is so simple and natural I can’t believe so many “pros” hide it and never talk about it in open access. Sharing it here on TradingView would’ve not done anything critical for em, but many would’ve benefited.
∞
Indicator for Confirming AccumulationStrong Buying Pressure Confirmation Indicator
This indicator helps identify stocks showing strong buying pressure, highlighting moments when significant money is flowing into a symbol. By analyzing active buying volume, price strength, momentum, and institutional-style accumulation, it automatically marks stocks with powerful upward behavior.
It is useful for spotting early accumulation, potential breakouts, and high-probability bullish setups.
Global M2 Money Supply Growth (GDP-Weighted)📊 Global M2 Money Supply Growth (GDP-Weighted)
This indicator tracks the weighted aggregate M2 money supply growth across the world's four largest economies: United States, China, Eurozone, and Japan. These economies represent approximately 69.3 trillion USD in combined GDP and account for the majority of global liquidity, making this a comprehensive macro indicator for analyzing worldwide monetary conditions.
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🔧 KEY FEATURES:
📈 GDP-Weighted Aggregation
Each economy is weighted proportionally by its nominal GDP using 2025 IMF World Economic Outlook data:
• United States: 44.2% (30.62 trillion USD)
• China: 28.0% (19.40 trillion USD)
• Eurozone: 21.6% (15.0 trillion USD)
• Japan: 6.2% (4.28 trillion USD)
The weights are fully adjustable through the indicator settings, allowing you to update them annually as new IMF forecasts are released (typically April and October).
⏱️ Multiple Time Period Options
Choose between three calculation methods to analyze different timeframes:
• YoY (Year-over-Year): 12-month growth rate for identifying long-term liquidity trends and cycles
• MoM (Month-over-Month): 1-month growth rate for detecting short-term monetary policy shifts
• QoQ (Quarter-over-Quarter): 3-month growth rate for medium-term trend analysis
🔄 Advanced Offset Function
Shift the entire indicator forward by 0-365 days to test lead/lag relationships between global liquidity and asset prices. Research suggests a 56-70 day lag between M2 changes and Bitcoin price movements, but you can experiment with different offsets for various assets (equities, gold, commodities, etc.).
🌍 Individual Country Breakdown
Real-time display of each economy's M2 growth rate with:
• Current percentage change (YoY/MoM/QoQ)
• GDP weight contribution
• Color-coded values (green = monetary expansion, red = contraction)
📊 Smart Overlay Capability
Displays directly on your main price chart with an independent left-side scale, allowing you to visually correlate global liquidity trends with any asset's price action without cluttering the chart.
🔧 Customizable GDP Weights
All GDP values can be adjusted through the indicator settings without editing code, making annual updates simple and accessible for all users.
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📡 DATA SOURCES:
All M2 money supply data is sourced from ECONOMICS (Trading Economics) for consistency and reliability:
• ECONOMICS:USM2 (United States)
• ECONOMICS:CNM2 (China)
• ECONOMICS:EUM2 (Eurozone)
• ECONOMICS:JPM2 (Japan)
All values are normalized to USD using current daily exchange rates (USDCNY, EURUSD, USDJPY) before GDP-weighted aggregation, ensuring accurate cross-country comparisons.
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💡 USE CASES & APPLICATIONS:
🔹 Liquidity Cycle Analysis
Track global monetary expansion/contraction cycles to identify when central banks are coordinating loose or tight monetary policies.
🔹 Market Timing & Risk Assessment
High M2 growth (>10%) historically correlates with risk-on environments and rising asset prices across crypto, equities, and commodities. Negative M2 growth signals monetary tightening and potential market corrections.
🔹 Bitcoin & Crypto Correlation
Compare with Bitcoin price using the offset feature to identify the optimal lag period. Many traders use 60-70 day offsets to predict crypto market movements based on liquidity changes.
🔹 Macro Portfolio Allocation
Use as a regime filter to adjust portfolio exposure: increase risk assets during liquidity expansion, reduce during contraction.
🔹 Central Bank Policy Divergence
Monitor individual country metrics to identify when major central banks are pursuing divergent policies (e.g., Fed tightening while China eases).
🔹 Inflation & Economic Forecasting
Rapid M2 growth often leads inflation by 12-18 months, making this a leading indicator for future inflation trends.
🔹 Recession Early Warning
Negative M2 growth is extremely rare and has preceded major recessions, making this a valuable risk management tool.
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📊 INTERPRETATION GUIDE:
🟢 +10% or Higher
Aggressive monetary expansion, typically during crises (2001, 2008, 2020). The COVID-19 period saw M2 growth reach 20-27%, which preceded significant inflation and asset price surges. Strong bullish signal for risk assets.
🟢 +6% to +10%
Above-average liquidity growth. Central banks are providing stimulus beyond normal levels. Generally favorable for equities, crypto, and commodities.
🟡 +3% to +6%
Normal/healthy growth rate, roughly in line with GDP growth plus 2% inflation targets. Neutral environment with moderate support for risk assets.
🟠 0% to +3%
Slowing liquidity, potential tightening phase beginning. Central banks may be raising rates or reducing balance sheets. Caution warranted for high-beta assets.
🔴 Negative Growth
Monetary contraction - extremely rare. Only occurred during aggressive Fed tightening in 2022-2023. Strong warning signal for risk assets, often precedes recessions or major market corrections.
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🎯 OPTIMAL USAGE:
📅 Recommended Timeframes:
• Daily or Weekly charts for macro analysis
• Monthly charts for very long-term trends
💹 Compatible Asset Classes:
• Cryptocurrencies (especially Bitcoin, Ethereum)
• Equity indices (S&P 500, NASDAQ, global markets)
• Commodities (Gold, Silver, Oil)
• Forex majors (DXY correlation analysis)
⚙️ Suggested Settings:
• Default: YoY calculation with 0 offset for current liquidity conditions
• Bitcoin traders: YoY with 60-70 day offset for predictive analysis
• Short-term traders: MoM with 0 offset for recent policy changes
• Quarterly rebalancers: QoQ with 0 offset for medium-term trends
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📋 VISUAL DISPLAY:
The indicator plots a blue line showing the selected growth metric (YoY/MoM/QoQ), with a dashed reference line at 0% to clearly identify expansion vs. contraction regimes.
A comprehensive table in the top-right corner displays:
• Current global M2 growth rate (large, prominent display)
• Individual country breakdowns with their GDP weights
• Color-coded growth rates (green for positive, red for negative)
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🔄 MAINTENANCE & UPDATES:
GDP weights should be updated annually (ideally in April or October) when the IMF releases new World Economic Outlook forecasts. Simply adjust the four GDP input parameters in the indicator settings - no code editing required.
The relative GDP proportions between the Big 4 economies change very gradually (typically <1-2% per year), so even if you update weights once every 1-2 years, the impact on the indicator's accuracy is minimal.
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💭 TRADING PHILOSOPHY:
This indicator embodies the principle that "liquidity drives markets." By tracking the combined M2 money supply of the world's largest economies, weighted by their economic size, you gain insight into the fundamental liquidity conditions that underpin all asset prices.
Unlike single-country M2 indicators, this GDP-weighted approach captures the true global picture, accounting for the fact that US monetary policy has 2x the impact of Japanese policy due to economic size differences.
Perfect for macro-focused traders, long-term investors, and anyone seeking to understand the "tide that lifts all boats" in financial markets.
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Created for traders and investors who incorporate global liquidity trends into their decision-making process. Best used alongside other technical and fundamental analysis tools for comprehensive market assessment.
⚠️ Disclaimer: M2 money supply is a lagging macroeconomic indicator. Past correlations do not guarantee future results. Always use proper risk management and combine with other analysis methods.
Mean Reversion Signals (v6.4) – VWAP ±SD use with "support and resistence levels with breaks {lux algo} " at 5m tf for better results
