Volatility Targeting: Single Asset [BackQuant]Volatility Targeting: Single Asset
An educational example that demonstrates how volatility targeting can scale exposure up or down on one symbol, then applies a simple EMA cross for long or short direction and a higher timeframe style regime filter to gate risk. It builds a synthetic equity curve and compares it to buy and hold and a benchmark.
Important disclaimer
This script is a concept and education example only . It is not a complete trading system and it is not meant for live execution. It does not model many real world constraints, and its equity curve is only a simplified simulation. If you want to trade any idea like this, you need a proper strategy() implementation, realistic execution assumptions, and robust backtesting with out of sample validation.
Single asset vs the full portfolio concept
This indicator is the single asset, long short version of the broader volatility targeted momentum portfolio concept. The original multi asset concept and full portfolio implementation is here:
That portfolio script is about allocating across multiple assets with a portfolio view. This script is intentionally simpler and focuses on one symbol so you can clearly see how volatility targeting behaves, how the scaling interacts with trend direction, and what an equity curve comparison looks like.
What this indicator is trying to demonstrate
Volatility targeting is a risk scaling framework. The core idea is simple:
If realized volatility is low relative to a target, you can scale position size up so the strategy behaves like it has a stable risk budget.
If realized volatility is high relative to a target, you scale down to avoid getting blown around by the market.
Instead of always being 1x long or 1x short, exposure becomes dynamic. This is often used in risk parity style systems, trend following overlays, and volatility controlled products.
This script combines that risk scaling with a simple trend direction model:
Fast and slow EMA cross determines whether the strategy is long or short.
A second, longer EMA cross acts as a regime filter that decides whether the system is ACTIVE or effectively in CASH.
An equity curve is built from the scaled returns so you can visualize how the framework behaves across regimes.
How the logic works step by step
1) Returns and simple momentum
The script uses log returns for the base return stream:
ret = log(price / price )
It also computes a simple momentum value:
mom = price / price - 1
In this version, momentum is mainly informational since the directional signal is the EMA cross. The lookback input is shared with volatility estimation to keep the concept compact.
2) Realized volatility estimation
Realized volatility is estimated as the standard deviation of returns over the lookback window, then annualized:
vol = stdev(ret, lookback) * sqrt(tradingdays)
The Trading Days/Year input controls annualization:
252 is typical for traditional markets.
365 is typical for crypto since it trades daily.
3) Volatility targeting multiplier
Once realized vol is estimated, the script computes a scaling factor that tries to push realized volatility toward the target:
volMult = targetVol / vol
This is then clamped into a reasonable range:
Minimum 0.1 so exposure never goes to zero just because vol spikes.
Maximum 5.0 so exposure is not allowed to lever infinitely during ultra low volatility periods.
This clamp is one of the most important “sanity rails” in any volatility targeted system. Without it, very low volatility regimes can create unrealistic leverage.
4) Scaled return stream
The per bar return used for the equity curve is the raw return multiplied by the volatility multiplier:
sr = ret * volMult
Think of this as the return you would have earned if you scaled exposure to match the volatility budget.
5) Long short direction via EMA cross
Direction is determined by a fast and slow EMA cross on price:
If fast EMA is above slow EMA, direction is long.
If fast EMA is below slow EMA, direction is short.
This produces dir as either +1 or -1. The scaled return stream is then signed by direction:
avgRet = dir * sr
So the strategy return is volatility targeted and directionally flipped depending on trend.
6) Regime filter: ACTIVE vs CASH
A second EMA pair acts as a top level regime filter:
If fast regime EMA is above slow regime EMA, the system is ACTIVE.
If fast regime EMA is below slow regime EMA, the system is considered CASH, meaning it does not compound equity.
This is designed to reduce participation in long bear phases or low quality environments, depending on how you set the regime lengths. By default it is a classic 50 and 200 EMA cross structure.
Important detail, the script applies regime_filter when compounding equity, meaning it uses the prior bar regime state to avoid ambiguous same bar updates.
7) Equity curve construction
The script builds a synthetic equity curve starting from Initial Capital after Start Date . Each bar:
If regime was ACTIVE on the previous bar, equity compounds by (1 + netRet).
If regime was CASH, equity stays flat.
Fees are modeled very simply as a per bar penalty on returns:
netRet = avgRet - (fee_rate * avgRet)
This is not realistic execution modeling, it is just a simple turnover penalty knob to show how friction can reduce compounded performance. Real backtesting should model trade based costs, spreads, funding, and slippage.
Benchmark and buy and hold comparison
The script pulls a benchmark symbol via request.security and builds a buy and hold equity curve starting from the same date and initial capital. The buy and hold curve is based on benchmark price appreciation, not the strategy’s asset price, so you can compare:
Strategy equity on the chart symbol.
Buy and hold equity for the selected benchmark instrument.
By default the benchmark is TVC:SPX, but you can set it to anything, for crypto you might set it to BTC, or a sector index, or a dominance proxy depending on your study.
What it plots
If enabled, the indicator plots:
Strategy Equity as a line, colored by recent direction of equity change, using Positive Equity Color and Negative Equity Color .
Buy and Hold Equity for the chosen benchmark as a line.
Optional labels that tag each curve on the right side of the chart.
This makes it easy to visually see when volatility targeting and regime gating change the shape of the equity curve relative to a simple passive hold.
Metrics table explained
If Show Metrics Table is enabled, a table is built and populated with common performance statistics based on the simulated daily returns of the strategy equity curve after the start date. These include:
Net Profit (%) total return relative to initial capital.
Max DD (%) maximum drawdown computed from equity peaks, stored over time.
Win Rate percent of positive return bars.
Annual Mean Returns (% p/y) mean daily return annualized.
Annual Stdev Returns (% p/y) volatility of daily returns annualized.
Variance of annualized returns.
Sortino Ratio annualized return divided by downside deviation, using negative return stdev.
Sharpe Ratio risk adjusted return using the risk free rate input.
Omega Ratio positive return sum divided by negative return sum.
Gain to Pain total return sum divided by absolute loss sum.
CAGR (% p/y) compounded annual growth rate based on time since start date.
Portfolio Alpha (% p/y) alpha versus benchmark using beta and the benchmark mean.
Portfolio Beta covariance of strategy returns with benchmark returns divided by benchmark variance.
Skewness of Returns actually the script computes a conditional value based on the lower 5 percent tail of returns, so it behaves more like a simple CVaR style tail loss estimate than classic skewness.
Important note, these are calculated from the synthetic equity stream in an indicator context. They are useful for concept exploration, but they are not a substitute for professional backtesting where trade timing, fills, funding, and leverage constraints are accurately represented.
How to interpret the system conceptually
Vol targeting effect
When volatility rises, volMult falls, so the strategy de risks and the equity curve typically becomes smoother. When volatility compresses, volMult rises, so the system takes more exposure and tries to maintain a stable risk budget.
This is why volatility targeting is often used as a “risk equalizer”, it can reduce the “biggest drawdowns happen only because vol expanded” problem, at the cost of potentially under participating in explosive upside if volatility rises during a trend.
Long short directional effect
Because direction is an EMA cross:
In strong trends, the direction stays stable and the scaled return stream compounds in that trend direction.
In choppy ranges, the EMA cross can flip and create whipsaws, which is where fees and regime filtering matter most.
Regime filter effect
The 50 and 200 style filter tries to:
Keep the system active in sustained up regimes.
Reduce exposure during long down regimes or extended weakness.
It will always be late at turning points, by design. It is a slow filter meant to reduce deep participation, not to catch bottoms.
Common applications
This script is mainly for understanding and research, but conceptually, volatility targeting overlays are used for:
Risk budgeting normalize risk so your exposure is not accidentally huge in high vol regimes.
System comparison see how a simple trend model behaves with and without vol scaling.
Parameter exploration test how target volatility, lookback length, and regime lengths change the shape of equity and drawdowns.
Framework building as a reference blueprint before implementing a proper strategy() version with trade based execution logic.
Tuning guidance
Lookback lower values react faster to vol shifts but can create unstable scaling, higher values smooth scaling but react slower to regime changes.
Target volatility higher targets increase exposure and drawdown potential, lower targets reduce exposure and usually lower drawdowns, but can under perform in strong trends.
Signal EMAs tighter EMAs increase trade frequency, wider EMAs reduce churn but react slower.
Regime EMAs slower regime filters reduce false toggles but will miss early trend transitions.
Fees if you crank this up you will see how sensitive higher turnover parameter sets are to friction.
Final note
This is a compact educational demonstration of a volatility targeted, long short single asset framework with a regime gate and a synthetic equity curve. If you want a production ready implementation, the correct next step is to convert this concept into a strategy() script, add realistic execution and cost modeling, test across multiple timeframes and market regimes, and validate out of sample before making any decision based on the results.
스크립트에서 "spx"에 대해 찾기
Daily Levels ImporterUser Guide: Daily Levels Importer
What This Indicator Does
This tool allows you to instantly draw multiple support and resistance lines on your TradingView chart by pasting a list of data. It avoids the need to manually draw lines one by one. It also features a dashboard to identify the ticker and filters to toggle specific line colors on or off.
1. The Data Format
The indicator reads text in a specific 3-column format (Comma Separated).
Format: \, \, \
* Ticker: The symbol name (used for the dashboard display).
* Price: The price level where the line will be drawn.
* Color Code:
r = Red
g = Green
y = Yellow
Example:
ES, 4150.25, r
ES, 4200.00, g
ES, 4175.50, y
2. How to Use It
3. Copy Your Data: Select your list of levels (from Excel, a text file, or a website) and copy them to your clipboard.
4. Open Settings: On your TradingView chart, hover over the indicator name and click the Settings (Gear Icon).
5. Paste Data:
* Find the "Paste Data Here" text box in the Inputs tab.
* Delete any existing text.
* Paste your new list.
6. Save: Click OK. The lines will instantly render on your chart.
7. Controls & Filters
You can customize the view without deleting data by using the checkboxes in the Settings menu:
* Line Filters:
* Show Red Levels: Uncheck to hide all red lines.
* Show Green Levels: Uncheck to hide all green lines.
* Show Yellow Levels: Uncheck to hide all yellow lines.
* Dashboard Location:
* Use the dropdowns to move the Ticker ID box to any corner of the screen (e.g., Top Right, Bottom Left) or change its size.
8. Troubleshooting
Lines aren't showing up?
* Ensure the prices match the asset you are viewing (e.g., don't paste SPX prices on an AAPL chart).
* Check if you accidentally unchecked the "Show " box in the settings.
"No Data" in Dashboard?
* The script reads the ticker name from the first row of your pasted data. Ensure the first row is not blank.
Is there a limit?
* Yes. TradingView allows approximately 4,000 characters in the text box. This is roughly 250 lines of price levels. If you need more, add a second instance of the indicator to the chart.
CRS (2 symbols: Ratio or Normalized) + InverseMade for Crosrate comparison By Leo Hanhart
This script is made to do a comparison between two assets under your current chart.
For example if you want to compare SPX over Growth ETF's Below a current asset to find momentum in your stock trading above it
Smart Money Alpha Signals (Performance Dashboard) Smart Money Alpha Signals: Identifying Market Leaders & Generating Alpha
GMP Alpha Signals (Global Market Performance Alpha) is a specialized analysis tool designed not merely to find stocks that are rising, but to identify "Alpha" assets—Market Leaders that defend their price or rise even under adverse conditions where the market index falls or consolidates.
This indicator visualizes the concept of Comparative Relative Strength (RS) and Smart Money accumulation patterns, helping traders capture profit opportunities even during bearish market phases.
Key Objectives (Purpose)
Alpha Capture: Identifying assets generating 'excess returns' that outperform the market Beta.
Smart Money Tracking: Detecting traces of 'institutional buying' and 'accumulation' that defend prices during index plunges.
Decoupling Identification: Spotting assets moving on independent catalysts or momentum, regardless of the broader market direction.
Stop Hunt Filtering: Distinguishing 'fake drops' where price dips temporarily, but Relative Strength remains intact.
Dashboard Guide
Interpretation of the information panel (Table) displayed on the chart.
Rel. Performance: Shows the excess return compared to the index over the set period. (Positive/Green = Stronger than the market).
Decoupling Strength: The correlation coefficient with the index. Lower values (0 or negative) indicate movement independent of market risk.
Bullish: The count/rate of rising or limiting losses when the index drops sharply (e.g., < -0.5%). (Gold = Market Crash Leader).
Defended: The count/rate of holding support levels when the index shows mild weakness (e.g., < -0.05%). (Gold = Strong Accumulation).
Bench. Defense: The defense rate of the comparison benchmark (e.g., TSLA, ETH). Your target asset must be higher to be considered the sector leader.
Input Options & Settings Guide
You can optimize settings according to your trading style and asset class (Stocks/Crypto).
(1) Main Settings
Major Index: The baseline market index for comparison.
(US Stocks: NASDAQ:NDX or TVC:SPX / Crypto: BINANCE:BTCUSDT)
Benchmark Symbol: A competitor within the sector.
(e.g., Set NVDA when analyzing Semiconductor stocks).
Correlation Lookback: The lookback period for judging decoupling. (Default: 30)
Performance Lookback: The number of bars to calculate cumulative returns and defense rates. (Default: 60)
(2) Dashboard Thresholds
These settings define the criteria for what qualifies as "Defended" or "Bullish".
Performance (Max %): Used to find assets that haven't pumped yet. Signals trigger only when Alpha is below this value.
Defended Logic:
Index Drop Condition: The index must drop by at least this amount to start checking. (e.g., -0.05%)
Asset Buffer: How much the asset must outperform the index drop.
(Example: If Index drops -1.0% and Buffer is 0.2%, the asset must be at least -0.8% to count as 'Defended').
Bullish Logic: Measures resilience during steeper market dumps (e.g., -0.5% drop) compared to the Defended Logic.
Volume Settings: Decides whether to count Defended/Bullish instances only when accompanied by volume above the SMA.
(3) Signal Logic Settings (Crucial)
Customize conditions to trigger alerts. The choice between AND / OR is crucial.
AND: Condition must be met SIMULTANEOUSLY with other active conditions (Conservative/High Certainty).
OR: Condition triggers the signal INDEPENDENTLY (Aggressive/Opportunity Capture).
Performance: Is the relative performance within the threshold? (Basic Filter).
Decoupling: Has the correlation dropped? (Start of independent move).
Bullish Rate: Is the Bullish rate high during market dumps?
Defended Rate (High): (Recommended) Is there continuous price defense occurring? (Accumulation detection).
Defended Rate (Low): (Warning) Has the defense rate broken down? (For Stop Loss).
Defended > Benchmark: Is it stronger than the Benchmark (2nd tier)?
Volume Spike: Has volume surged compared to the average? (Institutional involvement).
RSI Oversold: Is it in oversold territory? (Counter-trend trading).
Decoupling Move: Does the current bar show the "Index Down / Asset Up" pattern?
Min USD Volume: Transaction value filter (To exclude low liquidity assets).
RSI Median DeviationRSI Median Deviation – Adaptive Statistical RSI for High-Probability Extremes
The Relative Strength Index (RSI) is a momentum oscillator developed by J. Welles Wilder in 1978 to measure the magnitude of recent price changes and identify potential overbought or oversold conditions. It calculates the ratio of upward to downward price movements over a specified period, scaled to 0-100. However, standard RSI often relies on fixed thresholds like 70/30, which can produce unreliable signals in varying market regimes due to their lack of adaptability to the actual distribution of RSI values.
This indicator was developed because I needed a reliable tool for spotting intermediate high-probability bottoms and tops. Instead of arbitrary horizontal lines, it uses the RSI’s own historical median as a dynamic centerline and measures how far the current RSI deviates from that median over a chosen lookback period. The main signals are triggered only at 2 standard deviation (2σ) extremes — statistically rare events that occur roughly 5 % of the time under a normal distribution. I selected 2σ because it is extreme enough to be meaningful yet frequent enough for practical trading. For oversold signals I further require RSI to be below 42, a filter that significantly improved results in my mean-reversion tests (enter on oversold, exit on the first bar the condition is no longer true).
The combination of percentile median + standard deviation bands is deliberate: the median is far more robust to outliers than a simple average, while the SD bands automatically adjust to the current volatility of the RSI itself, producing adaptive envelopes that work equally well in ranging and trending markets.
Underlying Concepts and Calculations
Base RSI: RSI = 100 − (100 / (1 + RS)), RS = average gain / average loss (default length 10).
Percentile Median: 50th percentile of the last "N" RSI values (default 28 = 4 weeks)
→ dynamic, outlier-resistant centerline.
Standard Deviation Bands: rolling stdev of RSI (default length 27 = = 4 weeks (almost))
→ bands = median ± 1σ / 2σ.
Optional Dynamic MA Envelopes: user-selectable moving average (TEMA, WMA, etc., default WMA length 37) for additional momentum context.
Trend Bias Coloring
Independent of the statistical extremes, the RSI line itself is colored green when above the user-defined Long Threshold (default 60) and red when below the Short Threshold (default 47). This provides an instant bullish/bearish bias overlay similar to classic RSI usage, without interfering with the main 2σ extreme signals.
Extremes are highlighted with background color (green for oversold 2σ + RSI<42, magenta for overbought 2σ) and small diamond markers for ultra-extremes (RSI <25 or >85).
Originality and Development Rationale
The indicator was built and refined through extensive testing on dozens of assets including major cryptocurrencies:
(BTC, ETH, SOL, SUI, BNB, XRP, TRX, DOGE, LINK, PAXG, CVX, HYPE, VIRTUAL and many more),
the Magnificent 7 stocks,, QQQ, SPX, and gold.
Default parameters were chosen to deliver consistent profitability in simple mean-reversion setups while maximizing Sortino ratio and minimizing maximum drawdown across this broad universe — ensuring the settings are robust and not overfitted to any single instrument or timeframe.
How to Use It
Ideal for swing / position trading on the 1h to daily charts (the same defaults work).
Oversold (high-probability long): RSI crosses below lower 2σ band AND RSI < 42
→ green background
→ enter long, exit the first bar the condition disappears.
Overbought (high-probability short): RSI crosses above upper 2σ band
→ magenta background
→ enter short, exit on opposite signal or at median. (Shorts were not tested, it's only an idea)
Use the green/red RSI line coloring for quick trend context and to avoid fighting strong momentum.
Always confirm with price action and manage risk appropriately.
This indicator is not a standalone trading system.
Disclaimer: This is not financial advice. Backtests are based on past results and are not indicative of future performance.
Pivots + MAs ISRSPivots + MAs ISRS is a complete market-structure tool designed for traders who want clear institutional levels combined with trend confirmation from moving averages and Fibonacci zones.
This indicator helps you identify breakouts, pullbacks, and reversal points with much higher accuracy.
It combines the best of three worlds:
🔹 1. Advanced Pivot Points (Standard TV Engine)
Includes every major professional pivot type:
Traditional
Fibonacci
Woodie
Classic
DM
Camarilla
You can choose pivot anchors such as:
Daily, Weekly, Monthly, Quarterly, Yearly, and extended periods (2, 3, 5, and 10 years).
✔ Fully customizable colors
✔ Show/hide each level individually
✔ Dynamic labels (left or right)
✔ Works with intraday + extended sessions
🔹 2. Built-in Moving Averages
The indicator includes:
3 EMAs to measure trend direction and momentum
A 5-period SMA for micro-structure and scalping precision
Great for identifying confluences between trend direction + pivot levels.
🔹 3. FiboISRS Zones
Fibonacci-based zones designed to enhance price-reaction detection:
Retracement levels
Liquidity zones
Confluences with EMAs + Pivot Points
Perfect for spotting high-probability reversal areas.
🎯 What This Indicator Helps You Do
✔ See active institutional levels on any timeframe
✔ Detect real breakouts (not fakeouts) using Pivots + MAs
✔ Identify clean pullbacks into key zones
✔ Spot reactions at S1/S2/S3 or R1/R2/R3
✔ Keep your chart clean with minimal noise
Works extremely well on:
Crypto with solid liquidity
Major indices (SPX, NASDAQ, Dow)
Forex
Gold and commodities
🧠 Pro Tip
The highest-probability setups occur when price touches:
👉 A Pivot Level
👉 An EMA (20, 50, or 200)
👉 A FiboISRS zone
When these three overlap, the market often reacts strongly.
⚡ Creator
Indicator created by Ismael Robles (ISRS) to bring a clean, institutional-grade structure to everyday traders.
Reversal WaveThis is the type of quantitative system that can get you hated on investment forums, now that the Random Walk Theory is back in fashion. The strategy has simple price action rules, zero over-optimization, and is validated by a historical record of nearly a century on both Gold and the S&P 500 index.
Recommended Markets
SPX (Weekly, Monthly)
SPY (Monthly)
Tesla (Weekly)
XAUUSD (Weekly, Monthly)
NVDA (Weekly, Monthly)
Meta (Weekly, Monthly)
GOOG (Weekly, Monthly)
MSFT (Weekly, Monthly)
AAPL (Weekly, Monthly)
System Rules and Parameters
Total capital: $10,000
We will use 10% of the total capital per trade
Commissions will be 0.1% per trade
Condition 1: Previous Bearish Candle (isPrevBearish) (the closing price was lower than the opening price).
Condition 2: Midpoint of the Body The script calculates the exact midpoint of the body of that previous bearish candle.
• Formula: (Previous Open + Previous Close) / 2.
Condition 3: 50% Recovery (longCondition) The current candle must be bullish (green) and, most importantly, its closing price must be above the midpoint calculated in the previous step.
Once these parameters are met, the system executes a long entry and calculates the exit parameters:
Stop Loss (SL): Placed at the low of the candle that generated the entry signal.
Take Profit (TP): Calculated by projecting the risk distance upward.
• Calculation: Entry Price + (Risk * 1).
Risk:Reward Ratio of 1:1.
About the Profit Factor
In my experience, TradingView calculates profits and losses based on the percentage of movement, which can cause returns to not match expectations. This doesn’t significantly affect trending systems, but it can impact systems with a high win rate and a well-defined risk-reward ratio. It only takes one large entry candle that triggers the SL to translate into a major drop in performance.
For example, you might see a system with a 60% win rate and a 1:1 risk-reward ratio generating losses, even though commissions are under control relative to the number of trades.
My recommendation is to manually calculate the performance of systems with a well-defined risk-reward ratio, assuming you will trade using a fixed amount per trade and limit losses to a fixed percentage.
Remember that, even if candles are larger or smaller in size, we can maintain a fixed loss percentage by using leverage (in cases of low volatility) or reducing the capital at risk (when volatility is high).
Implementing leverage or capital reduction based on volatility is something I haven’t been able to incorporate into the code, but it would undoubtedly improve the system’s performance dramatically, as it would fix a consistent loss percentage per trade, preventing losses from fluctuating with volatility swings.
For example, we can maintain a fixed loss percentage when volatility is low by using the following formula:
Leverage = % of SL you’re willing to risk / % volatility from entry point to exit or SL
And if volatility is high and exceeds the fixed percentage we want to expose per trade (if SL is hit), we could reduce the position size.
For example, imagine we only want to risk 15% per SL on Tesla, where volatility is high and would cause a 23.57% loss. In this case, we subtract 23.57% from 15% (the loss percentage we’re willing to accept per trade), then subtract the result from our usual position size.
23.57% - 15% = 8.57%
Suppose I use $200 per trade.
To calculate 8.57% of $200, simply multiply 200 by 8.57/100. This simple calculation shows that 8.57% equals about $17.14 of the $200. Then subtract that value from $200:
$200 - $17.14 = $182.86
In summary, if we reduced the position size to $182.86 (from the usual $200, where we’re willing to lose 15%), no matter whether Tesla moves up or down 23.57%, we would still only gain or lose 15% of the $200, thus respecting our risk management.
Final Notes
The code is extremely simple, and every step of its development is detailed within it.
If you liked this strategy, which complements very well with others I’ve already published, stay tuned. Best regards.
Greater Moving AverageThe purpose for this indicator is to function as a comprehensive market-state detector, with the primary goal of avoiding a market crash.
Mendoza Lines (V-pattern detection) identify early crash conditions and warn when market structure becomes unstable.
RSI/volume-shaded candles + Supertrend confirm momentum and trend, creating a unified system to avoid major drawdowns.
Enhanced Wyckoff ranges with ATR.
Mendoza lines identify abrupt V-shaped reversals which often precede high-risk crash structures. By tracking both the formation and resolution of these patterns across multiple timeframes, the indicator provides early warning signals when the market is entering unstable territory, allowing traders to step aside before liquidity collapses or structural breakdowns begin.
Ideal configurations use Heikin Ashi to smooth out candle structure. Observe SPX on a Weekly Chart, which correctly identifies exits and entries during the 2001 and 2009 crashes. On a 6 hour chart, the Tariff low is correctly identified. The improved VWAP uses a cumulative metric rather than the built in ta.vwap calculation, and functions as a macro low beacon when crossed with the 200 EMA. Historically, these crosses have aligned closely with macro cycle lows.
To round out the system, the indicator overlays RSI-based and volume-weighted candle shading to reflect internal momentum and real buying/selling pressure directly on the chart, making shifts in strength immediately visible. A Supertrend confirmation layer acts as the final filter, smoothing noise and verifying trend direction before decisions are made.
US Market Long Horizon Momentum Summary in one paragraph
US Market Long Horizon Momentum is a trend following strategy for US index ETFs and futures built around a single eighteen month time series momentum measure. It helps you stay long during persistent bull regimes and step aside or flip short when long term momentum turns negative.
Scope and intent
• Markets. Large cap US equity indices, liquid US index ETFs, index futures
• Timeframes. 4h/ Daily charts
• Default demo used in the publication. SPY on 4h timeframe chart
• Purpose. Provide a minimal long bias index timing model that can reduce deep drawdowns and capture major cycles without parameter mining
• Limits. This is a strategy. Orders are simulated on standard candles only
Originality and usefulness
• Unique concept or fusion. One unscaled multiple month log return of an external benchmark symbol drives all entries and exits, with optional volatility targeting as a single risk control switch.
• Failure mode addressed. Fully passive buy and hold ignores the sign of long horizon momentum and can sit through multi year drawdowns. This script offers a way to step down risk in prolonged negative momentum without chasing short term noise.
• Testability. All parameters are visible in Inputs and the momentum series is plotted so users can verify every regime change in the Tester and on price history.
• Portable yardstick. The log return over a fixed window is a unit that can be applied to any liquid symbol with daily data.
Method overview in plain language
The method looks at how far the benchmark symbol has moved in log return terms over an eighteen month window in our example. If that long horizon return is positive the strategy allows a long stance on the traded symbol. If it is negative and shorts are enabled the strategy can flip short, otherwise it goes flat. There is an optional realised volatility estimate on the traded symbol that can scale position size toward a target annual volatility, but in the default configuration the model uses unit leverage and only the sign of momentum matters.
Base measures
Return basis. The core yardstick is the natural log of close divided by the close eighteen months ago on the benchmark symbol. Daily log returns of the traded symbol feed the realised volatility estimate when volatility targeting is enabled.
Components
• Component one Momentum eighteen months. Log of benchmark close divided by its close mom_lookback bars ago. Its sign defines the trend regime. No extra smoothing is applied beyond the long window itself.
• Component two Realised volatility optional. Standard deviation of daily log returns on the traded symbol over sixty three days. Annualised by the square root of 252. Used only when volatility targeting is enabled.
• Optional component Volatility targeting. Converts target annual volatility and realised volatility into a leverage factor clipped by a maximum leverage setting.
Fusion rule
The model uses a simple gate. First compute the sign of eighteen month log momentum on the benchmark symbol. Optionally compute leverage from volatility. The sign decides whether the strategy wants to be long, short, or flat. Leverage only rescales position size when enabled and does not change direction.
Signal rule
• Long suggestion. When eighteen month log momentum on the benchmark symbol is greater than zero, the strategy wants to be long.
• Short suggestion. When that log momentum is less than zero and shorts are allowed, the strategy wants to be short. If shorts are disabled it stays flat instead.
• Wait state. When the log momentum is exactly zero or history is not long enough the strategy stays flat.
• In position. In practice the strategy sits IN LONG while the sign stays positive and flips to IN SHORT or flat only when the sign changes.
Inputs with guidance
Setup
• Momentum Lookback (months). Controls the horizon of the log return on the benchmark symbol. Typical range 6 to 24 months. Raising it makes the model slower and more selective. Lowering it makes it more reactive and sensitive to medium term noise.
• Symbol. External symbol used for the momentum calculation, SPY by default. Changing it lets you time other indices or run signals from a benchmark while trading a correlated instrument.
Logic
• Allow Shorts. When true the strategy will open short positions during negative momentum regimes. When false it will stay flat whenever momentum is negative. Practical setting is tied to whether you use a margin account or an ETF that supports shorting.
Internal risk parameters (not exposed as inputs in this version) are:
• Target Vol (annual). Target annual volatility for volatility targeting, default 0.2.
• Vol Lookback (days). Window for realised volatility, default 63 trading days.
• Max Leverage. Cap on leverage when volatility targeting is enabled, default 2.
Usage recipes
Swing continuation
• Signal timeframe. Use the daily chart.
• Benchmark symbol. Leave at SPY for US equity index exposure.
• Momentum lookback. Eighteen months as a default, with twelve months as an alternative preset for a faster swing bias.
Properties visible in this publication
• Initial capital. 100000
• Base currency. USD
• Default order size method. 5% of the total capital in this example
• Pyramiding. 0
• Commission. 0.03 percent
• Slippage. 3 ticks
• Process orders on close. On
• Bar magnifier. Off
• Recalculate after order is filled. Off
• Calc on every tick. Off
• All request.security calls use lookahead = barmerge.lookahead_off
Realism and responsible publication
The strategy is for education and research only. It does not claim any guaranteed edge or future performance. All results in Strategy Tester are hypothetical and depend on the data vendor, costs, and slippage assumptions. Intrabar motion is not modeled inside daily bars so extreme moves and gaps can lead to fills that differ from live trading. The logic is built for standard candles and should not be used on synthetic chart types for execution decisions.
Performance is sensitive to regime structure in the US equity market, which may change over time. The strategy does not protect against single day crash risk inside bars and does not model gap risk explicitly. Past behavior of SPY and the momentum effect does not guarantee future persistence.
Honest limitations and failure modes
• Long sideways regimes with small net change over eighteen months can lead to whipsaw around the zero line.
• Very sharp V shaped reversals after deep declines will often be missed because the model waits for momentum to turn positive again.
• The sample size in a full SPY history is small because regime changes are infrequent, so any test must be interpreted as indicative rather than statistically precise.
• The model is highly dependent on the chosen lookback. Users should test nearby values and validate that behavior is qualitatively stable.
Legal
Education and research only. Not investment advice. You are responsible for your own decisions. Always test on historical data and in simulation with realistic costs before any live use.
Global Liquidity Index LITEGlobal Liquidity Index (GLI LITE) is an indicator that measures global liquidity by combining the balance sheets of major central banks (FED, ECB, PBOC, BOJ) and the M2 money supply of the world’s largest economies (USA, Europe, China, Japan).
Since liquidity directly influences the price of risk assets (BTC, NASDAQ, SPX, etc.), GLI is one of the most important macro signals for identifying market bull/bear regimes.
What the indicator shows:
GLI momentum line (green = liquidity expansion, orange = contraction)
Fast & Slow MA lines that define the liquidity trend
Bull/Bear background coloring
Green → global liquidity is expanding
Red → liquidity is tightening
Correlation between GLI and the asset price (e.g., BTC)
Macro trend panel (Bull / Bear / Neutral)
How to use the indicator:
Bull regime (Fast MA > Slow MA)
Liquidity is expanding and the market has a natural tailwind. Risk assets tend to perform better.
Bear regime (Fast MA < Slow MA)
Liquidity is tightening — higher risk, increased volatility, and more downside pressure.
GLI ↔ Price Correlation
If correlation is high (e.g., > 0.6), GLI can be an excellent leading indicator for price movement.
Stock Relative Strength Rotation Graph🔄 Visualizing Market Rotation & Momentum (Stock RSRG)
This tool visualizes the sector rotation of your watchlist on a single graph. Instead of checking 40 different charts, you can see the entire market cycle in one view. It plots Relative Strength (Trend) vs. Momentum (Velocity) to identify which assets are leading the market and which are lagging.
📜 Credits & Disclaimer
Original Code: Adapted from the open-source " Relative Strength Scatter Plot " by LuxAlgo.
Trademark: This tool is inspired by Relative Rotation Graphs®. Relative Rotation Graphs® is a registered trademark of JOOS Holdings B.V. This script is neither endorsed, nor sponsored, nor affiliated with them.
📊 How It Works (The Math)
The script calculates two metrics for every symbol against a benchmark (Default: SPX):
X-Axis (RS-Ratio): Is the trend stronger than the benchmark? (>100 = Yes)
Y-Axis (RS-Momentum): Is the trend accelerating? (>100 = Yes)
🧩 The 4 Market Quadrants
🟩 Leading (Top-Right): Strong Trend + Accelerating. (Best for holding).
🟦 Improving (Top-Left): Weak Trend + Accelerating. (Best for entries).
⬜ Weakening (Bottom-Right): Strong Trend + Decelerating. (Watch for exits).
🟥 Lagging (Bottom-Left): Weak Trend + Decelerating. (Avoid).
✨ Significant Improvements
This open-source version adds unique features not found in standard rotation scripts:
📝 Quick-Input Engine: Paste up to 40 symbols as a single comma-separated list (e.g., NVDA, AMD, TSLA). No more individual input boxes.
🎯 Quadrant Filtering: You can now hide specific quadrants (like "Lagging") to clear the noise and focus only on actionable setups.
🐛 Trajectory Trails: Visualizes the historical path of the rotation so you can see the direction of momentum.
🛠️ How to Use
Paste Watchlist: Go to settings and paste your symbols (e.g., US Sectors: XLK, XLF, XLE...).
Find Entries: Look for tails moving from Improving ➔ Leading.
Find Exits: Be cautious when tails move from Leading ➔ Weakening.
Zoom: Use the "Scatter Plot Resolution" setting to zoom in or out if dots are bunched up.
Estrategia Trend Following: 52w/26w BreakoutThis is a classic long-term Trend Following strategy, heavily inspired by the Donchian Channel system and the legendary "Turtle Trading" rules. It is designed to capture major market moves (bull runs) while filtering out short-term market noise and volatility.
This script is ideal for investors and swing traders who prefer a "hands-off" approach, looking to catch large trends rather than day-trading small fluctuations.
How it Works:
1. Entry Condition (The Breakout):
52-Week High: The strategy enters a Long position when the price breaks above the highest high of the last 252 trading days (approx. 1 year).
SuperTrend Filter: An additional filter using the SuperTrend indicator ensures that the breakout is supported by positive momentum, helping to reduce false signals during choppy lateral markets.
2. Exit Condition (The Trailing Stop):
26-Week Low: The strategy ignores short-term corrections. It only closes the position if the price closes below the lowest low of the last 126 trading days (approx. 6 months).
This wide stop allows the trade to "breathe" and stay open during significant pullbacks, ensuring you stay in the trend for as long as possible.
Features & Settings:
Customizable Lookback Periods: You can adjust the Entry (default 252 days) and Exit (default 126 days) periods in the settings menu.
Visual Aids:
Blue Line: Represents the 1-Year High (Entry Threshold).
Red Line: Represents the 6-Month Low (Dynamic Stop Loss).
Channel Shading: Visualizes the trading range between the high and low.
Labels: Clearly marks "BUY" and "EXIT" points on the chart.
Recommended Usage:
Timeframe: Daily (1D). This logic is designed for daily candles.
Assets: Works best on assets with strong trending characteristics (e.g., Bitcoin/Crypto, Tech Stocks, Indices like SPX/NDX, and Commodities).
Patience Required: This strategy generates very few signals. It may stay quiet for months and then hold a position for over a year.
OBV + WaveTrend Volume Scalper [GratefulFutures]This script is a combination script of three different strategies that provides buy and sell signals based on the change of volume with momentum confirmations.
Sources used:
This script relies on the outstanding scripts of the great script writer LazyBear: LazyBear
The following scripts were used in this publication:
1. A modified "On-Balance Volume Oscillator" modified from LazyBear's original script:
2. Wavetrend Oscillator with crosses, Author: LazyBear
3. Squeeze Momentum Oscillator, Author: LazyBear
This script functions based on the following criteria being true:
1. On balance volume oscillator turning from negative to positive (buy) or positive to negative (sell)
2. Squeeze Momentum value is increasing (buy) or decreasing (sell)
3. Wavetrend 1 (wt1) is greater than wavetrend 2 (wt2) (buy)/ Wavetrend 1 (wt1) is less than wavetrend 2 (wt2) (sell)
By combining these factors the indicator is able to signal exactly when net buying turns to net selling (OBV) and when this change is most advantageous to continue based on the momentum and price action of the underlying asset (SQMOMO and Wavetrend).
This allows you to pair volume and price action for a powerful tool to identify where price will reverse or continue providing exceptional entries for short term trades, especially when combined with other aspects such as support and resistance, or volume profile.
How to use:
Simply adjust the settings to your preference and read the given signals as generated.
Settings
There are multiple ways to tune the signals generated. It is set standard for my preferred use on a 1 minute chart.
OBV Oscillator Settings
The first 4 dropdowns in the Inputs section tune the On Balance Volume Oscillator (OBVO) portion of the indicator. You can choose if you want it to calculate based on close, open, high, low, or other value.
The most impactful in the entire settings is going to be the length and smoothing of the OBVO EMA. Making this number lower increasing the sensitivity to changes in volume, making the signals come quicker but is more susceptible to quick fluctuations. A value of between (5-20) is reasonable for the OBVO EMA length. There is a separate smoothing factor titled OBV Smoothing Length and below that, OBV Smoothing Type , a value of (2) is standard with "SMA" for smoothing type with a value of between 2-10 being reasonable. You may also play with these values to see what you like for your trading style.
Wavetrend Settings
The next 3 options are to modify the wavetrend portion of the indicator. I do not modify these from standard, and feel that they work appropriately on all time frames at the following values: n1 length (10), n2 length (20), Wavetrend Signal SMA length (4)
Squeeze Momentum Settings
The following 5 options through the end modify the Squeeze momentum portion of the indicator. The only one that modifies the signals generated is the KC Length , Making this number lower increasing the sensitivity to changes in price action, making the signals come quicker but is more susceptible to quick fluctuations. A value of between (18-25) is reasonable for KC Length .
Style Setting
You may select if you want to see the buy and sell signals. The following 5 options Raw OBV Osc through Squeeze Momentum allow you to see where each specific requirement was met, posted as a vertical line, but for live use it is recommended to turn all of these vertical lines off and only use the buy and sell signals.
Time Frames:
While this script is most effective on shorter time frames (1 minute for scalping and daytrading) it is also viable to use it on longer timeframes, due to the nature of its components being independent of time frame.
Examples of use - (Green and red vertical lines are for visualization purpose and are not part of the script)
SPY 1 Minute (Factory Settings):
SPX 15 minutes (Factory Settings):
Considerations
This script is meant primarily for short term trading, trades on the basis of seconds to minutes primarily. While they can be a good indication of volume lining up with momentum, it is always wise to use them in combination with other factors such as support, resistance, market structure, volume levels, or the many other techniques out there...
As Always... Happy Trading.
-Not_A_Mad_Scientist (GreatfulFutures Trade University)
NQUSB Sector Industry Stocks Strength
A Comprehensive Multi-Industry Performance Comparison Tool
The complete Pine Script code and supporting Python automation scripts are available on GitHub:
GitHub Repository: github.com
Original idea from by www.tradingview.com
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
═══ WHAT'S NEW ═══
4-Level Hierarchical Navigation:
Primary: All 11 NQUSB sectors (NQUSB10, NQUSB15, NQUSB20, etc.)
Secondary (Default): Broad sectors like Technology, Energy
Tertiary: Industry groups within sectors
Quaternary: Individual stocks within industries (37 semiconductors)
Enhanced Stock Coverage:
1,176 total stocks across 129 industries
37 semiconductor stocks
Market-cap weighted selection: 60% tech / 35% others
Range: 1-37 stocks per industry
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
═══ CORE FEATURES ═══
1. Drill-Down/Drill-Up Navigation
View NVDA at different granularity levels:
Quaternary: ● NVDA ranks #3 of 37 semiconductors
Tertiary: ✓ Semiconductors at 85% (strongest in tech hardware)
Secondary: ✓ Tech Hardware at 82% (stronger than software)
Primary: ✓ Technology at 78% (#1 sector overall)
Insight: One indicator, one stock, four perspectives - instantly see if strength is stock-specific, industry-specific, or sector-wide.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
2. Visual Current Stock Identification
Violet Markers - Instant Recognition:
● (dot) marker when current stock is in top N performers
✕ (cross) marker when current stock is below top N
Violet color (#9C27B0) on both symbol and value labels
Example: "NVDA ● ranks #3 of 37"
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
3. Rank Display in Title
Dynamic title shows performance context:
"Semiconductors (RS Rating - 3 Months) | NVDA ranks #3 of 37"
#1 = Best performer, higher number = lower rank
Total adjusts if current stock auto-added
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
4. Auto-Add Current Stock
Always Included:
Current stock automatically added if not in predefined list
Example: Viewing PRSO → "PRSO ranks #37 of 39 ✕"
Works for any stock - from NVDA to obscure small-caps
Violet markers ensure visibility even when ranked low
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
═══ DUAL PERFORMANCE METRICS ═══
RS Rating (Relative Strength):
Normalized strength score 1-99
Compare stocks across different price ranges
Default benchmark: SPX
% Return:
Simple percentage price change
Direct performance comparison
11 Time Periods:
1 Week, 2 Weeks, 1 Month, 2 Months, 3 Months (Default) , 6 Months, 1 Year, YTD, MTD, QTD, Custom (1-500 days)
Result: 22 analytical combinations (2 metrics × 11 periods)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
═══ USE CASES ═══
Sector Rotation Analysis:
Is NVDA's strength semiconductors-specific or tech-wide?
Drill through all 4 levels to find answer
Identify which industry groups are leading/lagging
Finding Hidden Gems:
JPM ranks #3 of 13 in Major Banks
But Financials sector weak overall (68%)
= Relative strength play in weak sector
Cross-Industry Comparison:
129 industries covered
Market-wide scan capability
Find strongest performers across all sectors
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═══ TECHNICAL SPECIFICATIONS ═══
V32 Stats:
Total Industries: 129
Total Stocks: 1,176
File Size: 82,032 bytes (80.1 KB)
Request Limit: 39 max (Semiconductors), 10-16 typical
Granularity Levels: 4 (Primary → Quaternary)
Smart Stock Allocation:
Technology industries: 60% coverage
Other industries: 35% coverage
Market-cap weighted selection
Formula: MIN(39, MAX(5, CEILING(total × percentage)))
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═══ KEY ADVANTAGES ═══
vs. Single Industry Tools:
✓ 129 industries vs 1
✓ Market-wide perspective
✓ Hierarchical navigation
✓ Sector rotation detection
vs. Manual Comparison:
✓ No ETF research needed
✓ Instant visual markers
✓ Automatic ranking
✓ One-click drill-down
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For complete documentation, Python automation scripts, and CSV data files:
github.com
Version: V32
Last Updated: 2025-11-30
Pine Script Version: v5
MFM – Light Context HUD (Minimal)Overview
MFM Light Context HUD is the free version of the Market Framework Model. It gives you a fast and clean view of the current market regime and phase without signals or chart noise. The HUD shows whether the asset is in a bullish or bearish environment and whether it is in a volatile, compression, drift, or neutral phase. This helps you read structure at a glance.
Asset availability
The free version works only on a selected list of five assets.
Supported symbols are
SP:SPX
TVC:GOLD
BINANCE:BTCUSD
BINANCE:ETHUSDT
OANDA:EURUSD
All other assets show a context banner only.
How it works
The free version uses fixed settings based on the original MFM model. It calculates the regime using a higher timeframe RSI ratio and identifies the current phase using simplified momentum conditions. The chart stays clean. Only a small HUD appears in the top corner. Full visual phases, ratio logic, signals, and auto tune are part of the paid version.
The free version shows the phase name only. It does not display colored phase zones on the chart.
Phase meaning
The Market Framework Model uses four structural phases to describe how the market
behaves. These are not signals but context layers that show the underlying environment.
Volatile (Phase 1)
The market is in a fast, unstable or directional environment. Price can move aggressively with
stronger momentum swings.
Compression (Phase 2)
The market is in a contracting state. Momentum slows and volatility decreases. This phase
often appears before expansion, but it does not predict direction.
Drift (Phase 3)
The market moves in a more controlled, persistent manner. Trends are cleaner and volatility
is lower compared to volatile phases.
No phase
No clear structural condition is active.
These phases describe market structure, not trade entries. They help you understand the conditions you are trading in.
Cross asset context
The Market Framework Model reads markets as a multi layer system. The full version includes cross asset analysis to show whether the asset is acting as a leader or lagger relative to its benchmark. The free version uses the same internal benchmark logic for regime detection but does not display the cross asset layer on the chart.
Cross asset structure is a core part of the MFM model and is fully available in the paid version.
Included in this free version
Higher timeframe regime
Current phase name
Clean chart output
Context only
Works on a selected set of assets
Not included
No forecast signals
No ratio leader or lagger logic
No MRM zones
No MPF timing
No auto tune
The full version contains all features of the complete MFM model.
Full version
You can find the full indicator here:
payhip.com
More information
Model details and documentation:
mfm.inratios.com
Momentum Framework Model free HUD indicator User Guide: mfm.inratios.com
Disclaimer
The Market Framework Model (MFM) and all related materials are provided for educational and informational purposes only. Nothing in this publication, the indicator, or any associated charts should be interpreted as financial advice, investment recommendations, or trading signals. All examples, visualizations, and backtests are illustrative and based on historical data. They do not guarantee or imply any future performance. Financial markets involve risk, including the potential loss of capital, and users remain fully responsible for their own decisions. The author and Inratios© make no representations or warranties regarding the accuracy, completeness, or reliability of the information provided. MFM describes structural market context only and should not be used as the sole basis for trading or investment actions.
By using the MFM indicator or any related insights, you agree to these terms.
© 2025 Inratios. Market Framework Model (MFM) is protected via i-Depot (BOIP) – Ref. 155670. No financial advice.
V Stop MTF → STRATEGY Why this strategy works so well (your backtest proves it):
FeatureBenefitMulti-timeframe Volatility StopSmarter trend detection than single TFRepainting controlYou can choose safe non-repainting modeLimbo/breach detectionAvoids whipsaws during HTF conflictsReversing systemAlways in the market → captures all trendsCandle coloring on reversalInstant visual confirmation
Recommended settings that match your +17.33% result:
Symbol: SP:SPX or ES1!
Timeframe: 9min or 15min Heikin-Ashi
HTF: "Multiple Of Current TF" × 3 → gives ~45min on 15min chart
ATR Length: 20
ATR Factor: **2.0
MFM - Light Context HUD (Free)Overview
MFM Light Context HUD is the free version of the Market Framework Model. It gives you a fast and clean view of the current market regime and phase without signals or chart noise. The HUD shows whether the asset is in a bullish or bearish environment and whether it is in a volatile, compression, drift, or neutral phase. This helps you read structure at a glance.
Asset availability
The free version works only on a selected list of five assets.
Supported symbols are
SP:SPX
TVC:GOLD
BINANCE:BTCUSD
BINANCE:ETHUSDT
OANDA:EURUSD
All other assets show a context banner only.
How it works
The free version uses fixed settings based on the original MFM model. It calculates the regime using a higher timeframe RSI ratio and identifies the current phase using simplified momentum conditions. The chart stays clean. Only a small HUD appears in the top corner. Full visual phases, ratio logic, signals, and auto tune are part of the paid version.
The free version shows the phase name only. It does not display colored phase zones on the chart.
Phase meaning
The Market Framework Model uses four structural phases to describe how the market behaves. These are not signals but context layers that show the underlying environment.
Volatile (Phase 1)
The market is in a fast, unstable or directional environment. Price can move aggressively with stronger momentum swings.
Compression (Phase 2)
The market is in a contracting state. Momentum slows and volatility decreases. This phase often appears before expansion, but it does not predict direction.
Drift (Phase 3)
The market moves in a more controlled, persistent manner. Trends are cleaner and volatility is lower compared to volatile phases.
No phase
No clear structural condition is active.
These phases describe market structure, not trade entries. They help you understand the conditions you are trading in.
Cross asset context
The Market Framework Model reads markets as a multi layer system. The full version includes cross asset analysis to show whether the asset is acting as a leader or lagger relative to its benchmark. The free version uses the same internal benchmark logic for regime detection but does not display the cross asset layer on the chart.
Cross asset structure is a core part of the MFM model and is fully available in the paid version.
Included in this free version
Higher timeframe regime
Current phase name
Clean chart output
Context only
Works on a selected set of assets
Not included
No forecast signals
No ratio leader or lagger logic
No MRM zones
No MPF timing
No auto tune
The full version contains all features of the complete MFM model.
Full version
You can find the full indicator here:
payhip.com
More information
Model details and documentation:
mfm.inratios.com
Disclaimer
The Market Framework Model (MFM) and all related materials are provided for educational and informational purposes only. Nothing in this publication, the indicator, or any associated charts should be interpreted as financial advice, investment recommendations, or trading signals. All examples, visualizations, and backtests are illustrative and based on historical data. They do not guarantee or imply any future performance. Financial markets involve risk, including the potential loss of capital, and users remain fully responsible for their own decisions. The author and Inratios© make no representations or warranties regarding the accuracy, completeness, or reliability of the information provided. MFM describes structural market context only and should not be used as the sole basis for trading or investment actions.
By using the MFM indicator or any related insights, you agree to these terms.
© 2025 Inratios. Market Framework Model (MFM) is protected via i-Depot (BOIP) – Ref. 155670. No financial advice.
$MTF Fractal Echo DetectorMIL:MTVFR FRACTAL ECHO DETECTOR by Timmy741
The first public multi-timeframe fractal convergence system that actually works.
Market makers don’t move price randomly.
They test the same fractal structure on lower timeframes first → then execute the real move on higher timeframes.
This indicator catches the “echo” — when 3–5 timeframes are printing fractals at almost the exact same price level.
That’s not coincidence. That’s preparation.
FEATURES
• 5 simultaneous timeframes (1min → 4H by default)
• Real Williams Fractal detection (configurable period)
• Dynamic echo tolerance & minimum TF alignment
• Visual S/R zones from every timeframe
• Bullish / Bearish echo convergence signals
• Strength meter (3/5, 4/5, 5/5 TF alignment)
• Zero repainting — uses proper lookahead=off
• Fully Pine v6 typed + optimized
USE CASE
When you see a 4/5 or 5/5 echo:
→ That level is being defended or attacked with intent
→ 80%+ chance the next real move comes from there
→ Trade the breakout or reversal at that exact fractal cluster
Works insane on:
• BTC / ETH (all timeframes)
• Nasdaq / SPX futures
• Forex majors (especially GBP & gold)
• 2025 small-cap rotation setups
100% Open Source • MPL 2.0 • Built by Timmy741 • December 2024
If you know about fractal echoes… you already know.
#fractal #mtf #echo #williamsfractal #multitimeframe #smartmoney #ict #smc #orderflow #convergence #timmy741 #snr #structure
Bitcoin vs. S&P 500 Performance Comparison**Full Description:**
**Overview**
This indicator provides an intuitive visual comparison of Bitcoin's performance versus the S&P 500 by shading the chart background based on relative strength over a rolling lookback period.
**How It Works**
- Calculates percentage returns for both Bitcoin and the S&P 500 (ES1! futures) over a specified lookback period (default: 75 bars)
- Compares the returns and shades the background accordingly:
- **Green/Teal Background**: Bitcoin is outperforming the S&P 500
- **Red/Maroon Background**: S&P 500 is outperforming Bitcoin
- Displays a real-time performance difference label showing the exact percentage spread
**Key Features**
✓ Rolling performance comparison using customizable lookback period (default 75 bars)
✓ Clean visual representation with adjustable transparency
✓ Works on any timeframe (optimized for daily charts)
✓ Real-time performance differential display
✓ Uses ES1! (E-mini S&P 500 continuous futures) for accurate comparison
✓ Fine-tuning adjustment factor for precise calibration
**Use Cases**
- Identify market regimes where Bitcoin outperforms or underperforms traditional equities
- Spot trend changes in relative performance
- Assess risk-on vs risk-off periods
- Compare Bitcoin's momentum against broader market conditions
- Time entries/exits based on relative strength shifts
**Settings**
- **S&P 500 Symbol**: Default ES1! (can be changed to SPX or other indices)
- **Lookback Period**: Number of bars for performance calculation (default: 75)
- **Adjustment Factor**: Fine-tune calibration to match specific data feeds
- **Transparency Controls**: Customize background shading intensity
- **Show/Hide Label**: Toggle performance difference display
**Best Practices**
- Use on daily timeframe for swing trading and position analysis
- Combine with other momentum indicators for confirmation
- Watch for color transitions as potential regime change signals
- Consider using multiple timeframes for comprehensive analysis
**Technical Details**
The indicator calculates rolling percentage returns using the formula: ((Current Price / Price ) - 1) × 100, then compares Bitcoin's return to the S&P 500's return over the same period. The background color dynamically updates based on which asset is showing stronger performance.
Gould 10Y + 4Y patternDescription:
Overview This indicator is a comprehensive tool for macro-market analysis, designed to visualize historical market cycles on your chart. It combines Edson Gould’s famous Decennial Pattern with a Customizable 4-Year Cycle (e.g., 2002 base) to help traders identify long-term trends, potential market bottoms, and strong bullish years.
This tool is ideal for long-term investors and analysts looking for cyclical confluence on monthly or yearly timeframes (e.g., SPX, NDX).
Key Concepts
Edson Gould’s Decennial Pattern (10-Year Cycle)
Based on the theory that the stock market follows a psychological cycle determined by the last digit of the year.
5 (Strongest Bull): Historically the strongest performance years.
7 (Panic/Crash): Years often associated with market panic or crashes.
2 (Bottom/Buy): Years that often mark major lows.
Custom 4-Year Cycle (Target Year Strategy)
Identify recurring 4-year opportunities based on a user-defined base year.
Default Setting (Base 2002): Highlights years like 2002, 2006, 2010, 2014, 2018, 2022... which have historically been significant market bottoms or excellent buying opportunities.
When a "Target Year" arrives, the indicator highlights the background and displays a distinct Green "Target Year" Label.
Features
Real-time Dashboard: A table in the top-right corner displays the current year's status for both the 10-Year and 4-Year cycles, including a countdown to the next target year.
Dynamic Labels: Automatically marks every year on the chart with its Decennial status (e.g., "Strong Bull (5)", "Panic (7)").
Visual Highlighting:
Target Years: Distinct green background and labels for easy identification of the 4-year cycle.
Significant Decennial Years: Special small markers for years ending in 5 and 7.
Fully Customizable: You can change the base year for the 4-year cycle, toggle the dashboard, and adjust colors via the settings menu.
How to Use
Apply this indicator to high-timeframe charts (Weekly or Monthly) of major indices like S&P 500 or Nasdaq.
Look for confluence between the 10-Year Pattern (e.g., Year 6 - Bullish) and the 4-Year Cycle (Target Year) to confirm long-term bias.
Disclaimer This tool is for educational and research purposes only based on historical cycle theories. Past performance is not indicative of future results. Always manage your risk.
Global Liquidity Score
Global Liquidity Score – Simple Risk-On / Risk-Off Gauge
This indicator measures overall market liquidity conditions using a single, normalized score.
It takes several macro and crypto variables, standardizes each one (z-score), and combines them into one clear Liquidity Score Line.
You only follow one line (your pink/white line).
The background color shows the current liquidity regime.
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What the indicator measures
The algorithm looks at four major liquidity sources:
1. USD Liquidity (tightening or easing)
• DXY (strong dollar = tighter global liquidity)
• US10Y yield (higher yields = liquidity drain)
2. Risk Sentiment (risk-on vs risk-off)
• VIX index (volatility)
• S&P 500 index (SPX)
3. Credit Market Strength
• High-yield ETFs: HYG, JNK
• Investment-grade corporate credit: LQD
Stronger credit = easier liquidity.
Weaker credit = tightening risk.
4. Internal Crypto Liquidity
• USDT dominance (higher = risk-off in crypto)
• Bitcoin price
• TOTAL2 (crypto market cap excluding BTC)
These are all converted into z-scores and combined into one metric:
Total Liquidity Score =
USD Block + Risk Block − Credit Block − 0.5 × Crypto Block
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How to read the colors
The indicator uses background colors to show the liquidity regime:
Color Meaning
Dark Red Severe liquidity tightening / strong risk-off
Red Mild-to-moderate tightening
Green Liquidity easing / soft risk-on
Dark Green Strong easing, high liquidity / risk-on
Your pink/white line = the final liquidity score.
You only need to follow that single line.
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How to interpret the score
📉 Positive score → Liquidity Tightening (Risk-Off)
• USD stronger
• Yields rising
• Volatility rising
• Credit markets weakening
• Crypto rotating to stablecoins
📈 Negative score → Liquidity Easing (Risk-On)
• USD weakening
• Yields falling
• Stocks rising
• Volatility low
• Credit markets strong
• Crypto beta assets outperform
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What this indicator is NOT
This is not a price predictor.
It does not follow BTC directly.
It tells you liquidity conditions, not immediate price direction.
It answers the macro question:
“Is liquidity flowing INTO the market or OUT of the market?”
If liquidity is tightening (red), crypto rallies are harder to sustain.
If liquidity is easing (green), crypto rallies have more fuel.
