อินดิเคเตอร์
อินดิเคเตอร์
อินดิเคเตอร์
BTC vs Net USD Liquidity 13W ROC LeadBTC vs Net USD Liquidity — ROC, Lead & Correlation
This indicator compares Bitcoin’s medium-term price momentum with changes in U.S. net dollar liquidity.
Net USD liquidity is calculated as:
Federal Reserve Total Assets
− Overnight Reverse Repo
− U.S. Treasury General Account
Data sources:
• ECONOMICS:USCBBS — Federal Reserve total assets
• FRED:RRPONTTLD — Overnight Reverse Repurchase Agreements
• FRED:WTREGEN — U.S. Treasury General Account balance
• User-selected BTC/USD symbol
The indicator displays:
• Bitcoin’s percentage change over a selected number of weeks
• Net USD liquidity’s percentage change over the same period
• Liquidity data shifted by a configurable lead period
• Rolling correlation between BTC momentum and the lag-adjusted liquidity impulse
Settings:
Change Period
Defines the number of weeks used to calculate the rate of change. For example, 13 measures the percentage change over the previous 13 weeks.
Liquidity Lead
Tests whether changes in net USD liquidity tend to lead Bitcoin. For example, a setting of 8 compares Bitcoin’s current momentum with the liquidity impulse observed eight weeks earlier.
Correlation Window
Defines the number of weeks used to calculate the rolling correlation.
Correlation interpretation:
• Values closer to +1 indicate a stronger positive relationship
• Values near 0 indicate weak or inconsistent linear correlation
• Values closer to −1 indicate a stronger inverse relationship
Rising net USD liquidity may create a more supportive macro environment for Bitcoin and other risk assets. Falling liquidity may create a more restrictive environment.
However, this relationship is not stable across all market cycles. Bitcoin may react with a changing time lag, and other factors such as ETF flows, leverage, interest rates, market positioning and global liquidity may dominate price action.
The indicator is intended for macroeconomic, liquidity-cycle and market-cycle analysis. It is not a standalone buy or sell signal.
For best results, use it on the weekly timeframe and compare several lead settings, such as 0, 4, 8 and 12 weeks.
Disclaimer:
This indicator is provided for informational and educational purposes only. It does not constitute financial or investment advice. Past correlation does not guarantee future performance. อินดิเคเตอร์
อินดิเคเตอร์
VWAP Mean Reversion Strategy with Session and Volume FilterDescription:
Volume Weighted Average Price, is one of the most referenced levels on any intraday chart. It appears on almost every institutional trading desk as a benchmark for execution quality: did you buy below VWAP or above it? Did you sell above it or below it? That institutional significance is what makes it useful as a trading level, not because it is a magical support and resistance line, but because enough participants are watching it and acting around it that it creates real, observable price behavior.
This strategy is built around one of the most consistent behaviors VWAP produces: mean reversion. In sessions with no strong directional trend, price tends to oscillate around VWAP rather than trending away from it indefinitely. When price moves significantly above VWAP in a non-trending session, institutional sellers often step in, bringing price back toward the average. When price moves significantly below VWAP, buyers who missed the open use VWAP as a reference level for value. The result is a gravitational pull back toward VWAP that is observable, repeatable, and, with the right filters, tradable.
What VWAP Actually Measures
VWAP is calculated by summing the product of price and volume for every transaction during a session, then dividing by total volume. The result is the average price at which the instrument has traded during the day, weighted by how much traded at each price. A stock trading at $102 when VWAP is $100 means that, on average, every share transacted during the session changed hands at $100, and the current price is 2% above that average. Whether that premium is justified depends on whether volume is expanding in the direction of the move or shrinking, which is exactly what this strategy checks.
VWAP resets every session. This is important: VWAP is an intraday concept. Using it on daily charts or holding positions across sessions removes the institutional context that makes it meaningful. This strategy trades only within the active session for that reason.
The Mean Reversion Logic
Entries fire when two conditions are met simultaneously. First, price must have moved a defined distance away from VWAP, measured in ATR multiples to scale the threshold to the instrument's actual volatility rather than a fixed percentage. Second, volume on the move away from VWAP must be declining relative to its recent average. This second condition is the critical filter. A price move away from VWAP accompanied by expanding volume suggests a real directional move with genuine participation, shorting into that is dangerous. A move away from VWAP on declining volume suggests the move is losing conviction and the pull back to VWAP is more likely.
When price is above VWAP by more than the ATR threshold and volume is declining, a short entry fires. When price is below VWAP by more than the ATR threshold and volume is declining, a long entry fires. The target for both is VWAP itself — not a fixed ATR level, but the actual VWAP value at the time the target would be hit. Stop-loss is placed at an ATR multiple beyond the entry in the opposite direction from VWAP.
Session Filter
The strategy only trades between 9:45 AM and 3:15 PM ET. The first 15 minutes after the NYSE open are excluded deliberately. The opening session is when the largest institutional orders are being executed, VWAP has barely formed, and the spread between price and VWAP frequently reflects genuine price discovery rather than mean reversion opportunity. Trading into the first 15 minutes with a mean reversion approach is trading against the most aggressive order flow of the day. The final 45 minutes are excluded because end-of-day institutional rebalancing often moves price away from VWAP and keeps it there through the close, a mean reversion entry in that window frequently doesn't have time to play out before the session ends and the position needs to be closed.
What This Strategy Works Best With
VWAP mean reversion is most effective on highly liquid instruments where institutional participation is consistently high, major equity indices, large-cap individual stocks, and equity index futures. On thinly traded instruments, VWAP is less meaningful as a reference level because the institutional volume that creates the gravitational pull isn't present. On crypto markets, VWAP mean reversion can work but requires adjusting the session definition since crypto trades continuously, not in defined daily sessions.
What to Watch in Backtesting
Performance will vary significantly by market regime. In strongly trending sessions, where a catalyst like an earnings surprise, a Fed announcement, or a macro data release drives sustained directional movement, mean reversion against the trend produces losing trades. This is expected and not a flaw. Check the strategy's performance separately on trending days versus range-bound days if you can identify them. The most useful insight from backtesting this strategy is often not the aggregate win rate but the distribution of trade outcomes across different session types.
Shared for educational purposes and community discussion. This is not investment advice. Always backtest on your own instruments and timeframes using realistic commission assumptions before drawing any conclusions. กลยุทธ์
Post-Earnings Return DashboardPost-Earnings Return Dashboard
Post-Earnings Return Dashboard is designed to show how a stock has historically performed after earnings reports.
The script automatically detects earnings events using TradingView’s built-in earnings data, calculates post-earnings returns over several periods, and displays the results in an on-chart dashboard.
What the indicator measures
For each detected earnings report, the indicator calculates the stock’s return after:
1 trading session
5 trading sessions
10 trading sessions
20 trading sessions
The return is calculated using:
Return = Future closing price divided by the selected reference price, minus 1, multiplied by 100.
Dashboard statistics
For each return period, the dashboard displays:
Reports — the number of completed earnings observations included in the calculation
Average — the average return across the selected earnings history
Median — the middle result when all returns are arranged from lowest to highest
Win rate — the percentage of earnings events that produced a positive return
A return of exactly 0% is not counted as a win.
The number of observations may differ between periods because the latest earnings report may not yet have completed the 10-session or 20-session measurement window.
Latest row
The Latest row shows the completed returns following the most recently detected earnings report.
1D — return after 1 trading session
5D — return after 5 trading sessions
10D — return after 10 trading sessions
20D — return after 20 trading sessions
Once a measurement period has completed, its value is stored and does not continue changing.
If the latest earnings event occurred fewer than 20 sessions ago, some longer-period values may remain blank.
Current row
The Current row shows the live return from the latest earnings reference price to the current closing price.
It also displays:
Sessions — the number of trading sessions since the latest detected earnings report
Ticker — the symbol currently displayed on the chart
For example, Current -4.42%, Sessions 17, TSM means the stock is currently trading 4.42% below its selected earnings reference price, 17 trading sessions after the latest report.
EPS row
The bottom row displays information about the latest detected earnings event.
EPS — the reported earnings per share
Estimate — the analyst EPS estimate
Last X — the maximum number of recent earnings reports included in the historical statistics
For example, EPS 4.221, Estimate 3.81, Last 12 means the company reported EPS of 4.221 against an estimate of 3.81, while the dashboard is configured to use up to the latest 12 earnings reports.
Return starting price
The indicator provides two reference-price options.
Previous close
Uses the closing price immediately before the earnings bar.
This is generally the most consistent option for measuring the full market reaction when the exact earnings-release timing is unknown.
Earnings-bar close
Uses the closing price of the bar on which TradingView registers the earnings event.
This may be more appropriate when the report is known to have been released after that trading session closed.
Historical sample size
Users can choose how many recent earnings reports are included in the statistics.
For a company reporting quarterly:
4 reports is approximately 1 year
8 reports is approximately 2 years
12 reports is approximately 3 years
20 reports is approximately 5 years
A smaller sample may better reflect recent behaviour, while a larger sample may provide a broader long-term view.
Chart markers and labels
The indicator can optionally:
Mark earnings dates with an E symbol
Display historical post-earnings return labels
Show the selected 1, 5, 10, or 20-session return directly on the chart
Display reported and estimated EPS where available
These options can be disabled to keep the chart clean.
Recommended timeframe
This indicator is designed for use on the daily chart.
The script counts chart bars as trading sessions.
On a daily chart:
1 bar equals 1 trading session
5 bars is approximately 1 trading week
20 bars is approximately 1 trading month
Using the indicator on an intraday chart will cause it to count intraday candles rather than trading days, which will produce misleading results.
How to interpret the statistics
The statistics should be considered together rather than individually.
A positive average, positive median, and high win rate may indicate historically consistent post-earnings strength.
A positive average with a negative median may mean that a small number of large gains are distorting the average.
A high win rate with a low average may indicate frequent small gains but occasional large losses.
A low win rate with a positive average may indicate less frequent but much larger winning reactions.
A negative average and negative median may indicate persistent post-earnings weakness over the selected sample.
Potential uses
This indicator may be useful for:
Reviewing a stock before an upcoming earnings report
Studying post-earnings announcement drift
Comparing the latest earnings reaction with previous reports
Assessing whether earnings gaps tend to continue or reverse
Identifying stocks with historically consistent earnings reactions
Supporting swing-trade research
Comparing post-earnings behaviour across different stocks
Evaluating whether holding after earnings has historically been favourable
Data source
The script retrieves earnings information through TradingView’s built-in request.earnings function.
It uses:
Reported EPS
Estimated EPS
The earnings event bar supplied by TradingView
The indicator does not scrape company filings or external websites.
Results depend on the earnings and price history available for the selected symbol on TradingView.
Limitations
TradingView does not consistently expose the precise release time for every earnings report.
The script may therefore be unable to determine whether an individual report was released before market open, during the trading session, or after market close.
This can affect which closing price most accurately represents the price immediately before the market received the information.
The indicator measures raw stock returns and does not currently adjust for overall market performance, sector performance, index movement, dividends, currency changes, volatility, earnings gap size, revenue surprises, or forward guidance.
A positive return does not necessarily mean the stock outperformed the wider market.
อินดิเคเตอร์
อินดิเคเตอร์
อินดิเคเตอร์
SVKO InfoSVKO Info displays key market information for a chart symbol and its mapped native counterpart in one compact table. It lets users follow an underlying TradingView listing from a broker CFD chart without leaving the chart.
█ OVERVIEW
The table can show company and symbol identity, multi-period performance, extended-hours movement, a configurable history high, daily ATR, earnings, analyst target, and P/E data.
Each optional feature controls its related data request. Native TradingView charts can display their own metrics without a mapping. A broker chart remains silent when no native counterpart can be resolved.
On supported broker charts, the optional mapped price marker places the mapped symbol's latest available extended-session price beside the CFD candles for direct comparison.
█ SYMBOL RESOLUTION
SVKO Info first checks its own Mappings input. Local mappings have priority and work without a companion indicator.
When the local list has no match, the indicator can use SVKO CFD Symbol Mapper as a fallback. Connect 1st code to Map: 1st code and 2nd code to Map: 2nd code from the same Mapper instance.
Broker charts use the resolved native symbol for calculations. Native TradingView charts use their own standard ticker and can show an available inverse-mapped CFD counterpart.
█ HOW TO USE
• Add SVKO Info to the chart.
• Add complete source and target ticker pairs to Mappings for symbols that need a local mapping.
• Add SVKO CFD Symbol Mapper when the shared fallback table is required, then connect both code inputs to the same Mapper instance.
• Enable only the table rows and mapped-price drawing that you need.
• Adjust the history window before requesting long performance periods.
• Use the table and mapped-price styling controls to fit the chart layout.
█ PERFORMANCE AND MARKET DATA
Performance uses regular-session daily prices and supports rolling periods, calendar-based periods, and custom start dates. Periods outside the selected history window are omitted.
Extended-hours data compares the active premarket or postmarket price with the most recent regular-session close. Daily ATR always uses daily data, regardless of the chart timeframe.
Earnings, analyst targets, valuation data, company metadata, and mapped quotes depend on TradingView coverage and the user's data permissions.
█ ALERTS
Days to Earnings is a hidden numeric plot for manually configured Crossing alerts. The script does not create an alert by itself.
█ LIMITATIONS
Performance excludes dividends, currency conversion, position sizing, fees, and extended-hours movement. Live values refresh only when the chart receives an update, and current-bar values can change before the bar closes.
The extended-hours row and mapped price marker are current-display tools, not historical signals or backtest series. A mapped quote cannot refresh independently when the chart symbol stops producing updates.
Unavailable data appears as N/A where applicable. If a broker symbol cannot be resolved through either mapping method, the indicator draws nothing and skips its mapped data requests.
█ CREDITS AND LICENCE
Original work by SVKO. Published open source under the Mozilla Public License 2.0. อินดิเคเตอร์
Moving Average Convergence Divergence TIMEOverview
This is a customized MACD (Moving Average Convergence Divergence) indicator for TradingView, labeled "MACDH" on the chart, with a few enhancements beyond the stock MACD.
Core Components
MACD Calculation
Uses a fast moving average (default 12) and slow moving average (default 26), both configurable as EMA or SMA
MACD line = Fast MA − Slow MA
Signal line = moving average of the MACD line (default 9-period, EMA or SMA)
Histogram = MACD line − Signal line
Multi-Timeframe Support
You can calculate the MACD on a different timeframe than the chart itself (e.g., view 5-minute chart but calculate MACD on 1-hour data) via request.security
A "Wait for timeframe closes" toggle lets you choose between using the confirmed (closed) value of the higher timeframe or the live, repainting value
Color-Coded Histogram
Dark teal: histogram is positive and rising
Light teal: histogram is positive but falling
Light red: histogram is negative but rising
Dark red: histogram is negative and falling
This gives a quick visual read on momentum direction, not just above/below zero
Plots
Histogram as columns
MACD line
Signal line (orange)
Zero line for reference
Alerts
Fires when the histogram crosses from positive to negative ("rising to falling")
Fires when the histogram crosses from negative to positive ("falling to rising")
Timeframe Watermark
Displays a small label on the chart (e.g., "1H", "4H", "1D") showing which timeframe the MACD is actually being calculated on — useful since it can differ from the chart's own timeframe
Position (corner) and font size are both configurable
In short: it's a standard MACD with configurable multi-timeframe calculation, momentum-based histogram coloring, crossover alerts, and an on-chart label confirming which timeframe is driving the calculation. อินดิเคเตอร์
ORB + Key Levels (PDH/PDL, PM H/L, PDC, Open)Restructured for backtesting. The key change: instead of drawing only the current day's rays at the last bar, the script now creates real line objects day by day as it processes history. So:
Every past day on the chart keeps its own ORB 15/30, PDH/PDL, PDC, PM H/L, and opening print lines — each one starting at its forming candle and ending at that day's close
When you enter bar replay mode and jump to any date, the script recalculates up to the replay point, so that date's levels are drawn correctly and the "active" day at the replay head extends to the right edge — exactly like live trading
As you step forward through replay, ORB lines pop in the moment the 15/30-min window completes (9:45/10:00 ET), PM levels update tick-by-tick during premarket, and everything freezes when the next day's premarket begins — so you're seeing exactly what you'd have seen in real time, with no lookahead
Two practical notes for your replay testing:
Line history depth: max_lines_count=500 keeps roughly the last 50 sessions of lines visible (10 lines per day). TradingView's hard cap is 500, so older days silently drop their lines — but in replay this doesn't matter, since everything recalculates from the replay point anyway.
On the very first day of loaded history there are no PDH/PDL/PDC lines (there's no tracked previous day yet) — start your replay at least one session in. อินดิเคเตอร์
Fund + Pullback Screener v5Fund + Pullback Screener — fundamental filter + pullback entry signal
WHAT IT DOES
This indicator combines fundamental screening and technical entry timing in a single tool. The logic is two-stage: a stock is first checked against fundamental criteria, then against a pullback setup (a retracement within an uptrend). All checks are displayed in an on-chart table: value, threshold, pass/fail.
PRESETS
5 built-in fundamental filter modes (switchable in settings):
- GARP — quality companies at a reasonable price: P/E 8–35, ROE > 15%, operating margin > 15%, EPS growth > 10%, Debt/Equity < 1, PEG < 2
- Growth — profitable companies with aggressive growth: P/E up to 120, EPS growth > 25%, revenue growth > 20%, PEG < 3
- Deep Value — cheap and profitable: P/E 5–18, PEG < 1.2, Debt/Equity < 0.7
- Emerging Growth — pre-profit hypergrowth (P/E, PEG, EPS, ROE are not used): revenue growth > 30%, revenue acceleration, gross margin > 40%, improving operating margin, Rule of 40, share dilution < 8%
- Custom — manual thresholds
SIGNAL LOGIC
Status in the table: NO / WATCH / ENTRY.
- Trend filter: weekly timeframe, price above SMA200 and SMA50 above SMA200. For young companies without 200 weeks of history, it automatically falls back to SMA50.
- WATCH: fundamentals passed, uptrend intact, RSI below 50, price within 4 ATR of support (pivot low or SMA50). A candidate to monitor.
- ENTRY: RSI in the 30–50 zone turning up, price within 1.5 ATR above support (an undercut down to 0.5 ATR is allowed). Shown as a triangle below the bar.
Distances are measured in ATR units, so the logic automatically adapts to each instrument's volatility.
HANDLING UNAVAILABLE DATA (N/A POLICY)
Some sectors structurally lack certain metrics (e.g., gross margin for insurers). Lenient mode (default) skips up to 2 unavailable metrics, marking them with a gray "–" and showing a skip counter. Core metrics (market cap, revenue growth) are never skipped. Strict mode: any unavailable metric = fail.
HOW TO USE
1. Recommended timeframe — daily.
2. Pick the preset matching the company type: mature names — GARP/Deep Value, expensive growers — Growth, pre-profit — Emerging Growth.
3. Shortlist candidates with the native TradingView screener, add them to a watchlist and flip through the charts — the table instantly shows the full picture for each ticker.
4. All thresholds are configurable in the settings.
LIMITATIONS
- Works on stocks only — crypto, forex and futures have no fundamental data.
- EPS and revenue growth are computed from the history of quarterly reports accumulated on the chart: full history is required (at least 8–9 quarters), otherwise growth metrics show n/a.
- Fundamental data updates quarterly as reports are released.
- The indicator is informational only and is not investment advice. อินดิเคเตอร์
อินดิเคเตอร์
Gold Dual-Model Fair Value [Regime Adaptive]█ OVERVIEW
Gold Dual-Model Fair Value is a chart overlay that plots two competing fair value estimates for gold, one regressed on the 10 year real yield and one regressed on a global M2 money supply composite, then combines them into a single fair value line by continuously measuring which model currently fits better. The thesis: gold's dominant macro driver is not constant, so a useful fair value model must detect the driver in force rather than assume it.
█ HISTORY / BACKGROUND
The inverse relationship between gold and real interest rates is one of the most widely documented regularities in the asset's modern history and is treated in academic work such as Erb and Harvey's "The Golden Dilemma" (2013). That relationship visibly weakened after 2022, when gold rose while real yields climbed to multi year highs, a divergence commonly attributed to price insensitive official sector buying. Macro strategists, notably Jurrien Timmer, have illustrated this as a regime change in which a real yield model stops explaining gold and a global liquidity model takes over.
The weakness of that illustration is that the regime break is declared after the fact, by inspection. This script's contribution is to make the regime decision endogenous: both models are estimated continuously, and the model in force is chosen by trailing out of sample fit, with no hardcoded break date. The regime change around 2022, if present in the data, emerges from the computation rather than being asserted.
█ HOW IT WORKS
All computation runs on the chart timeframe. The script requests the following series with request.security at the chart resolution, with no lookahead: FRED:DFII10 (10 year TIPS real yield), five M2 series (ECONOMICS:USM2, CNM2, EUM2, JPM2, GBM2) and four conversion rates (FX_IDC:CNYUSD, EURUSD, JPYUSD, GBPUSD).
Step 1. Global M2 composite. Each enabled non US component is converted to US dollars and the enabled components are summed. Two aggregation modes exist. Spot FX converts at the current exchange rate, replicating the standard global liquidity composite. Constant FX converts every bar at the rate captured on the first bar at or after a user defined anchor date, which freezes the currency translation effect and isolates changes in underlying money stocks. The composite is na until every enabled component has data, so its membership never changes mid history and no artificial level jumps are introduced.
Step 2. Two rolling regressions. Over a rolling fit window the script estimates ordinary least squares coefficients from running moments (beta equals covariance over variance, alpha equals mean of y minus beta times mean of x):
• Model A regresses the natural log of the chart close on the level of the 10 year real yield.
• Model B regresses the natural log of the chart close on the natural log of the M2 composite.
Each model produces a log fair value each bar from its current alpha, beta and regressor value.
Step 3. Fit measurement. Each model's residual (log price minus log fair value) is squared and averaged over a shorter trailing evaluation window; the square root is that model's rolling RMSE.
Step 4. Regime and combination. Two output modes:
• Blend (default): the combined log fair value is a weighted average of the two model fair values with weights proportional to inverse RMSE, so the better fitting model dominates smoothly.
• Hard switch: the combined fair value is the fair value of the incumbent model, and the incumbent only changes when the challenger's RMSE beats it by a user set hysteresis margin, which prevents rapid flip flopping when the models fit similarly.
Independently of the mode, the hard switch state machine always runs and its current state is reported in the table and as the background tint, so blend users can still see the discrete regime call.
Step 5. Deviation statistics. The deviation is log price minus combined log fair value. Its rolling standard deviation over the evaluation window defines a z score, and bands are drawn at the fair value times e to the plus and minus (band multiple times sigma). The z score's percent rank over a user defined lookback gives a deviation percentile.
If one model's inputs are unavailable (for example the M2 composite before all enabled components exist), the combined fair value falls back to the available model alone.
█ HOW TO USE
Apply the indicator to a gold chart (spot, futures or a fund proxy) on the weekly timeframe. Weekly is the design resolution for a structural reason: the M2 inputs are monthly series, so on lower resolutions the liquidity regressor is a long staircase while the real yield updates daily, which biases the fit comparison toward the real yield model for reasons unrelated to explanatory power. The default windows (156 and 52 bars) are calibrated as roughly three years and one year of weekly bars. An on chart warning label appears on intraday charts.
Visual elements:
• Orange line: the combined fair value, the primary output.
• Blue thin line: Model A fair value (real yields). Red thin line: Model B fair value (global liquidity). Comparing their paths shows where each model succeeded or failed.
• Gray bands and fill: the plus and minus sigma envelope around the combined fair value. Price above the upper band is statistically rich against the currently fitting model mix, below the lower band statistically cheap, between the bands unremarkable.
• Background tint: blue when the hard switch regime is the real yield model, red when it is the liquidity model.
• Status table: hard switch regime, active mode, each model's RMSE, the real yield model's blend weight, the deviation z score (colored when beyond the band multiple), its percentile, and the M2 aggregation mode.
Interpretation cautions. The fair value is a rolling fit, so a deviation can close either by price moving toward the line or by the line re estimating toward price; a band touch is a valuation observation, not a mechanical entry signal. The regime readout tells you which catalyst matters: in the liquidity regime, a cheap reading resolves with money supply reacceleration rather than falling yields. Comparing Spot FX and Constant FX modes shows how much of the liquidity signal is currency translation rather than money creation; if a stretched reading shrinks materially under Constant FX, part of it was the US dollar itself.
█ SETTINGS
Model group:
• Regression fit window (bars), default 156: the rolling OLS estimation window for both models.
• Fit evaluation window (bars), default 52: the trailing window for RMSE, regime detection and deviation sigma.
• Regime mode, default Blend (inverse RMSE weights): selects between the blended fair value and the hard switch fair value.
• Switch hysteresis (%), default 10: hard switch mode only, the margin by which the challenger RMSE must beat the incumbent before the regime flips.
• Deviation band (sigma), default 2.0: the band multiple and the z score threshold for table coloring.
• Deviation percentile lookback (bars), default 260: the window for the z score percent rank.
Global M2 composite group:
• FX aggregation, default Spot FX: Spot FX or Constant FX (anchor date), as described above.
• Constant FX anchor date, default 1 January 2018: the date whose exchange rates are frozen in Constant FX mode.
• US M2, China M2, Eurozone M2, Japan M2, UK M2, all enabled by default: component toggles. Disabling a short history component lets the composite, and therefore Model B, begin earlier.
Display group:
• Show individual model lines, default on.
• Show deviation bands, default on.
• Show status table, default on.
• Regime background tint, default on.
• Table position, default top right.
█ WHAT MAKES IT ORIGINAL
Published global M2 composites plot the liquidity series itself, usually with a fixed time offset against an asset, and published regime indicators classify price behavior such as trending versus ranging. This script occupies a different intersection and does three things no script in either group does:
• It converts both macro drivers into explicit fair value estimates via rolling least squares regression rather than displaying the raw series, so the drivers and the asset live on the same axis and disagreement between them is measurable in price terms.
• It selects or weights the two models by trailing out of sample RMSE, so the widely discussed post 2022 handoff from real yields to liquidity is detected by the data instead of hardcoded, and any future handoff back requires no code change.
• Its liquidity composite offers a Constant FX aggregation mode alongside the standard Spot FX mode. Spot converted composites embed the US dollar's own fluctuations, which are correlated with gold, into the liquidity measure. The Constant FX mode removes that translation effect, giving users a built in test of how much of the liquidity signal is monetary and how much is currency denomination. To this author's knowledge no published composite exposes this distinction.
█ NOTES / LIMITATIONS
• The M2 inputs are monthly economic series requested at the chart resolution. They hold their value between releases, so the liquidity fair value moves in steps between prints, and releases arrive with publication lag.
• Economic series are subject to vendor revisions. A revised M2 or real yield history changes the regression inputs, so the historical fair value shown today can differ from what the script displayed in real time. This is a property of the data, not lookahead: the script uses no lookahead and requests no timeframe above the chart's.
• Model B is na until every enabled M2 component and its conversion rate have history, and each regression additionally needs the full fit window plus evaluation window of bars before its output and RMSE are defined. On deep weekly gold history the real yield model also cannot begin before the real yield series itself starts in 2003. Expect a substantial warm up period at the left edge of the chart, during which the script falls back to whichever single model is available, or plots nothing.
• The logic is designed for the weekly timeframe. On daily and lower resolutions the mixed update frequencies of the regressors distort the fit comparison, the default windows lose their intended calendar meaning, and an on chart label warns on intraday charts.
• The script uses the chart symbol's close as the dependent variable. Its economic reasoning applies to gold denominated symbols; applied to unrelated symbols it will still compute, but the output has no stated meaning.
• The fair value lines are descriptive regression fits over past data. They quantify the historical relationship between gold and each driver and say nothing about future prices. อินดิเคเตอร์
อินดิเคเตอร์
อินดิเคเตอร์
Realized Price by Baal Hadad v2 (multi-source)Based on "Realized Price" by Baal Hadad (open-source, MPL 2.0).
The original relied on IntoTheBlock's MVRV feed, which stopped updating on August 15, 2025 and was marked as discontinued by TradingView — the indicator froze. This version fixes that with a multi-source data cascade and adds self-diagnostics.
WHAT IT SHOWS
Realized Price is the average price at which each coin last moved on-chain — the market's aggregate on-chain cost basis. When spot price falls below realized price, the average holder is underwater; historically this marks capitulation zones and potential cycle bottoms. Multiples above it mark overheated zones.
Realized Price = Realized Cap / Circulating Supply
DATA SOURCES (NEW)
Realized Cap, by priority: Custom symbol → CoinMetrics RealCap (direct) → Custom MVRV → IntoTheBlock MVRV (derived as Market Cap / MVRV).
Circulating Supply: Custom → Glassnode → IntoTheBlock → Market Cap / Price (unbreakable fallback).
A source is considered dead if it hasn't updated for N days (default 7, configurable) — the script automatically switches to the next live one. You can also force a specific source or plug in your own symbols.
DIAGNOSTICS (NEW)
An on-chart table shows every feed's status (live / lagging / dead), its last value, date and age in days; the source currently in use is marked with ►. If all Realized Cap sources go silent, the table shows FROZEN and a "data stale" alert fires — instead of silently plotting a frozen line.
DISPLAY
- Realized Price smoothed by MA (default EMA 30 as in the original; SMA / WMA / RMA / HMA / HEMA selectable), optional raw line
- Lower bands: −30% / −50% / −70% (green, accumulation zones)
- Upper bands: ×2 / ×3 / ×4 / ×5 (red, distribution zones)
- Background highlight when price crosses a band; band and signal logic unchanged from the original
- Synthetic MVRV = Market Cap / Realized Cap in the Data Window — replaces the dead ITB feed
ALERTS
Buy zone, Sell zone, Data stale.
NOTES
The coin is detected automatically from the chart ticker, so it works on any coin covered by CoinMetrics / Glassnode — not just BTC / ETH / LTC. On-chain metrics are daily; use on the D timeframe or higher.
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Основано на "Realized Price" от Baal Hadad (открытый код, MPL 2.0).
Оригинал опирался на фид MVRV от IntoTheBlock, который перестал обновляться 15 августа 2025 и помечен TradingView как discontinued — индикатор замер.
Эта версия решает проблему каскадом источников данных и добавляет самодиагностику.
ЧТО ПОКАЗЫВАЕТ
Realized Price (реализованная цена) — средняя цена, по которой каждая монета в последний раз двигалась в блокчейне, то есть совокупная ончейн-себестоимость рынка. Когда спотовая цена уходит ниже реализованной, средний держатель в убытке — исторически это зоны капитуляции и потенциального дна цикла. Кратные превышения — зоны перегрева.
Realized Price = Realized Cap / Circulating Supply
ИСТОЧНИКИ ДАННЫХ (НОВОЕ)
Realized Cap, по приоритету: Custom-символ → CoinMetrics RealCap (напрямую) → Custom MVRV → IntoTheBlock MVRV (расчёт как Market Cap / MVRV).
Circulating Supply: Custom → Glassnode → IntoTheBlock → Market Cap / Price (несгораемый фолбэк).
Источник считается мёртвым, если не обновлялся N дней (по умолчанию 7, настраивается) — скрипт автоматически переключается на следующий живой. Можно принудительно выбрать конкретный источник или подключить свои символы.
ДИАГНОСТИКА (НОВОЕ)
Таблица на графике показывает статус каждого фида (live / lagging / dead), последнее значение, дату и возраст в днях; активный источник помечен ►. Если все источники Realized Cap замолчали, таблица показывает FROZEN и срабатывает алерт "data stale" — вместо того чтобы молча рисовать замороженную линию.
ОТОБРАЖЕНИЕ
- Realized Price, сглаженная скользящей средней (по умолчанию EMA 30, как в оригинале; на выбор SMA / WMA / RMA / HMA / HEMA), опционально сырая линия
- Нижние полосы: −30% / −50% / −70% (зелёные, зоны накопления)
- Верхние полосы: ×2 / ×3 / ×4 / ×5 (красные, зоны распределения)
- Подсветка фона при пересечении полос; уровни полос и логика сигналов не менялись
- Синтетический MVRV = Market Cap / Realized Cap в окне данных (Data Window) — замена умершему фиду ITB
АЛЕРТЫ
Зона покупки, зона продажи, протухание данных (data stale).
ПРИМЕЧАНИЯ
Монета определяется автоматически из тикера графика, поэтому индикатор работает на любой монете с покрытием CoinMetrics / Glassnode — не только BTC / ETH / LTC. Ончейн-метрики дневные; использовать на таймфрейме D и выше. อินดิเคเตอร์
อินดิเคเตอร์
อินดิเคเตอร์
SBP Price Response EngineSBP Price Response Engine is a rule-based analytical indicator that evaluates how price responds to confirmed market reference levels. Rather than attempting to predict future direction or continuously follow trends, the indicator studies completed price interaction around established reference areas and identifies qualified bullish or bearish response observations after objective confirmation.
The methodology is based on the concept that significant price movements are often preceded by measurable interactions with previously confirmed price references. Instead of treating every touch of a level as equally meaningful, the indicator evaluates the quality of the interaction before displaying a BUY or SALE observation.
Methodology
The analytical workflow begins by building dynamic price references from confirmed pivot recurrence. Nearby confirmed pivots are merged into evolving reference zones, allowing the indicator to develop market memory that adapts as additional confirmations occur. References mature over time as they receive additional confirmation while automatically discarding stale levels that no longer remain relevant.
Each completed bar is then evaluated against these active references using several independent response models. Rather than relying on a single condition, the indicator measures different categories of observable market behaviour, including:
Controlled rejection after limited penetration
Closing acceptance beyond a reference
Timely recovery following an accepted break
Decisive directional displacement
Repeated pressure release near active references
Delayed break confirmation using short-term reference memory
Confirmed pivot-response events
Every response family is evaluated independently before contributing to the final analytical decision. The qualification process considers multiple objective measurements, including:
Candle body structure
Upper and lower wick characteristics
Closing location within the candle
ATR-normalized penetration and displacement
Reference maturity
Local price expansion
Optional relative-volume participation
Each response receives an internally calculated quality score. Only observations satisfying their respective qualification requirements become eligible for the final signal-routing process.
Signal Routing
All qualified response families are processed through a unified decision engine rather than operating as independent indicators. The routing process combines the qualified analytical observations into a single BUY or SALE output while enforcing signal alternation and a minimum spacing between consecutive observations. This helps reduce repeated indications during persistent directional movement while maintaining continuous internal analysis of every completed bar.
Visual Output
The indicator can display:
Dynamic upper and lower price reference lines
Qualified BUY observations
Qualified SALE observations
Confirmed pivot-response observations
Alert conditions for all supported analytical event families
The visual output is intentionally designed to remain uncluttered while presenting only qualified analytical observations.
Inputs
Users can adjust several analytical parameters, including:
Reference confirmation depth
Reference clustering tolerance
Minimum reference confirmations
Maximum reference age
Penetration requirements
Recovery requirements
Acceptance confirmation
Recovery window
Minimum response quality
Optional relative-volume participation
Display of reference lines and observations
These settings allow users to adapt the analytical sensitivity without altering the underlying methodology.
Notes
SBP Price Response Engine is an analytical indicator, not a trading strategy. It does not place trades, calculate position sizing, manage portfolio risk, estimate profitability, or predict future price movement. All observations are generated from completed market data according to predefined analytical rules.
Like any technical analysis tool, the indicator should be used alongside independent market analysis, risk management, and appropriate trading discipline. อินดิเคเตอร์
อินดิเคเตอร์
กลยุทธ์
