Market State Momentum OscillatorMarket State Momentum Oscillator (MSMO)
Overview
The MSMO combines three elements in one panel:
Momentum oscillator (gray/blue area with aqua signal line)
Market State filter (green/red background area)
Money Flow Index (orange line)
Works on all markets and all timeframes. Non-repainting at bar close.
Colors and meaning
Gray area: Momentum above 0 (bullish bias)
Blue area: Momentum below 0 (bearish bias)
Aqua line: Signal line smoothing the oscillator
Green background: Market state bullish (price above moving average)
Red background: Market state bearish (price below moving average)
Orange line: Money Flow Index (volume-weighted momentum)
How to use
Always wait for confirmation of the green or red market state before acting.
Trend alignment: Watch the slope of the Weekly and Daily 200 MA and Weekly and Daily 50 MA to understand higher-timeframe trend direction. Trade only in alignment with the broader trend.
Entries:
Long: Green state + gray histogram rising + MFI trending up
Short: Red state + blue histogram falling + MFI trending down
Exits: Histogram crossing back through 0, or state background flips against the position.
Users can add chart alerts on plot crossings if needed.
Inputs
Lengths for oscillator pivot, signal smoothing, state moving average, trend weight, return %, and Money Flow Index. Defaults work for most charts.
Note
Educational use only. Not financial advice.
Tags
trend, oscillator, market state, momentum, money flow, crypto, forex, stocks, indices, futures
Cycle
Dani u nedelji + midnight open @mladja123This indicator breaks the weekly timeframe into cycles and marks the midnight open for each day. It helps traders visualize weekly structure, identify key daily openings, and track market rhythm within the week. Perfect for analyzing trend patterns, swing setups, and session-based strategies.
Killzone za Indexe - @mladja123This indicator highlights the Kill Zones on index charts, showing key market sessions where high-probability price movements are likely to occur. It helps traders identify optimal entry and exit points based on session dynamics and market rhythm, enhancing strategy precision for swing and intraday trading on indices.
Thiru Macro Time CyclesMacro Time Cycles
This indicator plots horizontal lines in a separate pane to highlight key macro timeline windows based on Eastern Time (EST), aiding traders in identifying significant market periods. It includes customizable London and New York trading sessions with adjustable line colors and label visibility.
Key Features:
Displays macro timelines for London (2:45–3:15 AM, 3:45–4:15 AM) and New York AM/PM sessions (7:45–8:15 AM, 8:45–9:15 AM, 9:45–10:15 AM, 10:45–11:15 AM, 11:45 AM–12:15 PM, 12:45–1:15 PM, 1:45–2:15 PM, 2:45–3:15 PM).
Lines are drawn with a fixed width of 3 and can be colored via user inputs.
Labels (e.g., "LO 1", "AM 1") are placed at the bottom of the pane, with options to hide or show them.
Adjustable label alignment (Left, Center, Right) for better chart organization.
Uses a separate pane (overlay = false) to avoid cluttering the price chart.
How to Use:
Add the indicator to your chart via the TradingView interface.
Customize line colors for each macro timeline in the indicator settings.
Toggle "Show Labels" on or off to display or hide labels at the bottom of the pane.
Adjust the "Text Alignment" setting to position labels as preferred.
The indicator automatically adjusts to the chart’s timeframe, ensuring accurate session boundaries.
Notes:
Timezone is fixed to Eastern Time (EST).
Ensure your chart timeframe aligns with the 30-minute macro windows for optimal visibility.
Perfect for traders focusing on London and New York session analysis.
ASI - Meme-CoinsAltcoin Season Indicator (ASI) — Meme Coins (Multi-Timeframe)
Purpose-built for meme coins, which often move off-cycle, with explosive volatility and crowd-driven momentum.
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Why this preset:
Tuned for fast, outsized swings and sharper euphoria/capitulation than standard altcoins.
Prioritizes early trend confirmation and strict overheating exits to help avoid round-trips.
Designed to keep you rational when headlines and social spikes dominate price.
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Usage:
Timeframes: 1D for established memes; 8h for active phases/younger listings; 1h optional for event-driven bursts (expect more noise—confirm with 8h/1D).
Best fit: high-volatility meme coins with sufficient trading activity/liquidity.
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Reading:
Green zone → Entry (credible bottoming / early impulse)
Red zone → Exit (overheating / distribution risk)
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Who is it for?
Intermediate to advanced crypto traders who focus on memes and want a disciplined, visual BUY/EXIT framework that captures big moves while respecting risk.
*(ASI is a timing tool, not financial advice.)*
ASI - Large-CapsAltcoin Season Indicator (ASI) — Large Caps (1D)
Purpose-built for top-tier, established altcoins (typically Top 10–30, ≳ $15B market cap) that have lived through multiple cycles and move differently than small/mid caps.
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Why this preset:
Calibrated for large-cap behavior: longer bases, steadier trends, and fewer whipsaws.
Highlights true bottoming and genuine overheating on the daily chart—without overreacting to short-term noise.
Ideal when you want clean timing on names that dominate liquidity and follow broader cycle dynamics.
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Usage:
Timeframe: 1D (primary).
Best fit: mature, high-cap projects (Top 10–30; ≳ $15B).
Playbook: Use Large Caps (1D) as your default for majors. If a name becomes more volatile or “mid-cap-like,” you can compare against the Mid Caps (1D) preset; for very young listings, start with Small Caps (8h) until history builds.
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Reading:
Green zone → Entry (credible bottom formation / early uptrend)
Red zone → Exit (overheating / distribution risk)
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Who is it for?
Investors and active traders who want disciplined, visual BUY/EXIT timing on the market’s most established altcoins—capturing the meat of the move while avoiding premature signals.
*(ASI is a timing tool, not financial advice.)*
ASI - Mid-CapsAltcoin-Season Indicator (ASI) - Mid Caps (1D)
Built for established yet still nimble altcoins.
This preset targets projects typically in the ~$200M–$2B market-cap range—assets with solid history but more volatility than top-tier names.
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Why this preset:
Tuned for mid-cap volatility: sensitive enough to catch rotations, restrained enough to avoid noise.
Reads bottoming and overheating phases cleanly on the daily chart.
Versatile across sectors; also works on seasoned small caps that now have sufficient history.
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Usage:
Timeframe: 1D (primary).
Best fit: mid-caps (~$200M–$2B), and small caps with a longer price record.
Playbook: Use Mid Caps (1D) as your go-to once a project has matured beyond the “new listing” phase. If the Default (1D) feels too broad or sluggish for a volatile name, switch to Mid Caps; if a coin is very young, start with Small Caps (8h) and move up to Mid Caps (1D) as history builds.
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Reading:
Green zone → Entry (credible bottoming, start of a new trend)
Red zone → Exit (overheating, distribution risk)
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Who is it for?
Investors and active traders who want disciplined, visual BUY/EXIT timing across a broad mid-cap universe—without overfitting.
*(ASI is a timing tool, not financial advice.)*
ASI - Small-CapsAltcoin Season Indicator (ASI) — Small Caps (8h)
Built for young, fast-moving altcoins with limited price history.
This preset keeps ASI’s core edge—timed entries at real bottoms and timely exits near overheating—but is tuned to read early small-cap structure on the 8-hour chart.
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Why this preset:
Optimized for new listings and low-cap projects with short daily history.
Higher sensitivity to early trend shifts without chasing one-off spikes.
Same clean read as Default: it adapts to the coin and the market phase.
---
Usage:
Timeframe: 8h (primary).
Best fit: newer/smaller projects (e.g., early listings and emerging narratives).
Playbook: If the Default (1D) shows no actionable read on a young coin, switch to Small Caps (8h). As the asset matures and builds sufficient history, transition back to Default (1D).
---
Reading:
Green zone → Entry (credible bottoming, start of a new leg)
Red zone → Exit (overheating, distribution risk)
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Who is it for?
Traders hunting early rotations in small caps who still want disciplined timing and clear visuals.
*(ASI is a timing tool, not financial advice.)*
QLitCycle QuarterlyQLITCYCLE
QLitCycle is an intraday cycle visualization tool that divides each trading day into multiple segments, helping traders identify time-based patterns and recurring market behaviors. By splitting the day into distinct periods, this indicator allows for better analysis of intraday rhythms, cycle alignment, and time-specific market tendencies.
It can be applied to various markets and timeframes, but is most effective on intraday charts where precise time segmentation can reveal valuable insights.
FLD Area - Future Lines of Demarcation by Nibbio996FLD Area v12 - Future Lines of Demarcation
Overview
Advanced FLD (Future Lines of Demarcation) indicator with area visualization for cycle analysis. Projects price levels into the future by half the cycle period, displaying high, low, and median as colored areas.
What are FLDs?
Future Lines of Demarcation are price levels shifted forward in time by approximately half the cycle wavelength. Used in cycle analysis to identify potential support/resistance levels and trend changes.
Key Features
Area visualization between high/low FLD lines with customizable colors
Three bands: Upper, Lower, and Total area with independent transparency
Dynamic labels: Customizable text with period display
Status line integration showing real-time FLD values
Flexible display options: Toggle individual lines, labels, and info displays
Parameters
Period: Cycle length (default: 40)
Colors: Customizable for main, upper, and lower areas
Transparency: Adjustable area opacity (0-100)
Labels: Toggle and customize indicator identification
Display Options: Individual lines, status info, top labels
Usage
Set Period based on your cycle analysis
Customize colors and transparency for chart clarity
Configure labels for identification
Analyze where price interacts with projected FLD areas
Applications
Cycle turning point identification
Dynamic support/resistance levels
Trend analysis with FLD projections
Multi-timeframe cycle analysis
FLD Area v12 by Nibbio996
Pine Script v5 | Overlay Indicator
For educational purposes. Use proper risk management.
Cross-Asset Risk Appetite IndexCross-Asset Risk Appetite Index (RiskApp) by CWRP combines multiple asset classes into a single risk sentiment signal to help traders and investors detect when the market is in a risk-on or risk-off regime.
It calculates a composite Z-score index based on relative performance between:
SPY / IEF: Equities vs Bonds
HYG / LQD: High Yield vs Investment Grade Credit
CL / GC: Oil vs Gold
VIX / MOVE: Equity vs Bond Market Volatility (inverted)
Each component reflects capital flows toward riskier or safer assets, with dynamic weighting (Equity/Bond: 30%, Credit: 25%, Commodities: 25%, Volatility: 20%) and smoothing applied for a cleaner signal.
How to Read:
Highlighting
Yellow = Risk-On sentiment (market favors risk assets)
Orange = Risk-Off sentiment (flight to safety)
Black Background = Neutral design for emotional detachment
Table
Equity/Bond Z-Score:
Positive (> +1) --> Stocks outperforming bonds --> Risk-On
Negative (< -1) --> Bonds outperforming stocks --> Risk-Off
Credit Spread Z-Score (HYG/LQD):
Positive --> High yield outperforming --> Investors seeking yield
Negative --> Flight to quality --> Credit concerns
Oil/Gold Z-Score:
Positive --> Oil outperforming --> Economic optimism
Negative --> Gold outperforming --> Defensive positioning
Volatility Spread (VIX/MOVE):
Positive --> Equity vol falling relative to bond vol --> Risk stabilizing
Negative --> Equity vol rising --> Caution / Risk-Off
Composite Index:
> +1 --> Strong Risk Appetite
< -1 --> Strong Risk Aversion
Between -1 and +1 --> Neutral regime
Thank you for using the Cross-Asset Risk Appetite Index by CWRP!
I'm open to all critiques and discussion around macro-finance and hope this model adds clarity to your decision-making.
Shift 3M - 30Y Yield Spread🟧 Shift 3M - 30Y Yield Spread
- This indicator visually displays the **inverse of the US Treasury short-long yield spread** (3-month minus 30-year spread reversal signal) in a "price chart-like" form.
- By default, the spread line is shifted by 1 year to help anticipate forward market moves (you can adjust this offset freely).
- Especially customized to be analyzed together with the movements of US indices like the S&P 500, and to help understand broader market cycles.
✅ Description
- Normalizes the spread based on a rolling window length you set (default: 500 bars).
- Both the normalization window and offset (shift) are fully customizable.
- Then, it scales the spread to match your chart’s price range, allowing you to intuitively compare spread movements alongside price action.
- Instantly see the **inverse (reversal) signals of the short-long yield spread**, curve steepening, and how they align with actual price trends.
⚡ By reading macro yield signals, you can **anticipate exactly when a market crash might come or when an explosive rally is about to start**.
⚡ A perfect tool for macro traders and yield curve analysts who want to quickly catch major market turning points!
copyright @invest_hedgeway
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🟧3개월 - 30년 물 장단기 금리차 역수
- 이 인디케이터는 미국 국채 **장단기 금리차 역수**(3개월물 - 30년물 스프레드의 반전 시그널)를 시각적으로 "가격 차트"처럼 표시해 줍니다.
- 기본적으로 스프레드 선은 **1년(365봉) 시프트**되어 있어, 시장을 선행적으로 파악할 수 있도록 설계되었습니다 (값은 자유롭게 조정 가능).
- 특히 S&P500 등 미국 지수 흐름과 함께 분석할 수 있도록 맞춤화되었으며, 시장 사이클을 이해하는 데에도 큰 도움이 됩니다.
✅ 설명
- 지정한 롤링 윈도우 길이(기본: 500봉)를 기준으로 스프레드를 정규화합니다.
- 정규화 길이와 오프셋(시프트) 모두 자유롭게 설정 가능
- 이후 현재 차트의 가격 레인지에 맞게 스케일링해, 가격과 함께 흐름을 직관적으로 비교할 수 있습니다.
- **장단기 금리차의 역전(역수) 시그널**, 커브 스티프닝 등과 실제 가격 움직임의 관계를 한눈에 확인
⚡ 거시 금리 신호를 통해 **언제 폭락이 올지, 언제 폭등이 터질지** 미리 감지할 수 있습니다.
⚡ 시장의 전환점을 빠르게 캐치하고 싶은 매크로 트레이더와 금리 분석가에게 완벽한 도구!
copyright @invest_hedgeway
Chandelier Exit Oscillator [LuxAlgo]The Chandelier Exit Oscillator is a technical analysis tool that provides insights into potential trend reversals, momentum shifts, and trend continuation patterns, helping traders pinpoint optimal exit points for both long and short positions.
By calculating trailing stop levels based on a multiple of the Average True Range (ATR), the oscillator visually indicates when prices move above or below these critical stop levels.
This script uniquely combines the Chandelier Exit indicator with an oscillator format, equipping traders with a versatile tool that leverages ATR-based levels for enhanced trend analysis.
🔶 USAGE
Displaying the Chandelier Exit as an oscillator allows traders to gauge trend momentum and strength, recognize potential reversals, and refine their market insights.
The Timeframe option specifies the timeframe used for calculations, enabling multi-timeframe analysis and allowing traders to align the indicator’s signals with broader or narrower market trends.
The Chandelier Exit Oscillator allows users to select between a Regular or Normalized oscillator type. The Regular option displays raw oscillator values, while the Normalized version smooths values and scales them from 0 to 100.
The Chandelier Exit Overlay allows users to enable or disable the display of Chandelier Exit levels directly on the price chart. When enabled, this overlay plots trailing stop levels for both long and short positions, helping traders visually monitor potential exit points and trend boundaries alongside the price action.
The Trend-based Bar Color feature allows users to color the bars on the price chart according to the current trend direction. This visual differentiation aids in quicker decision-making and provides a clearer understanding of market dynamics.
🔶 SETTINGS
🔹 Chandelier Exit Settings
Timeframe: Sets the timeframe for calculations, allowing multi-timeframe analysis.
ATR Length: Defines the number of bars used for calculating the Average True Range (ATR), which helps in setting Chandelier Exit levels.
ATR Multiplier: Adjusts the sensitivity of the Chandelier Exit lines based on the ATR. Higher values make the indicator more conservative, while lower values make it more responsive.
🔹 Chandelier Exit Oscillator
Chandelier Exit Oscillator: Allows users to choose between a Regular or Normalized oscillator type. The Regular option displays raw oscillator values, while the Normalized version smooths values and scales them from 0 to 100.
Oscillator Smoothing: Controls the level of smoothing applied to the oscillator. Higher smoothing values filter out minor fluctuations.
🔹 Chandelier Exit Overlay
Chandelier Exit Overlay: Enables or disables the display of Chandelier Exit levels directly on the price chart.
Trend-based Bar Colors: Allows users to color bars based on trend direction, enhancing the visual analysis of market direction.
🔶 RELATED SCRIPTS
Market-Structure-Oscillator
Quarterly Cycles [EETrade]The idea of Quarterly Theory is -
Each timeframe is split into 4 "quarters", derived based on logical subdivisions:
- Year: Divided into calendar quarters (Jan-Mar, Apr-Jun, etc.).
- Tertiary (sub-year): Each year quarter is subdivided into 4 parts dynamically based on timestamp deltas.
- Month: Weekly-based logic using Sunday cutoffs and session switch time (18:00 US/Eastern).
- Week: Divided using daily boundaries starting from Sunday 18:00 (based on US futures session logic).
- Day: Split into 4 blocks (Asia, London, AM, PM) using 6-hour segments.
- Session and Macro Quarters: Session is divided further into 4 quarters of 6 hours, then each of those into 15-minute blocks for ultra-granular cycle mapping.
Where we split them into Q1, Q2, Q3 and Q4.
Usually we address
Q1 as accumulation,
Q2 as manipulation
Q3 as Distribution
Q4 as Continuation/Reversal
If we trade Q3 for example, we'd like to use price action mainly from previous Q3s.
Plus there are Semi Cycles which we can utilize
- Q1 with Q3
- Q2 with Q4
- Q3 with Q1
- Q4 with Q2
So we can also use Q1 price action when we are trading Q3
True Open Logic:
The open candle price of the second quarter is the true open for us, it will help us understand if we're on premium or discount area.
Plus this indicator providers a table to dynamically show the premium and discount
We can use this indicator to understand optimal times to trade as we'd like to trade mostly Q3
Multi-Session MarkerMulti-Session Marker is a flexible visual tool for traders who want to highlight up to 10 custom trading sessions directly on their chart’s background.
Custom Sessions: Enter up to 10 time ranges (in HHMM-HHMM format) to mark any market session, news window, or personal focus period.
Visual Clarity: For each session, toggle the highlight on or off and select a unique background color and opacity, making it easy to distinguish active trading windows at a glance.
Universal Time Handling: Session times automatically follow your chart’s time zone—no manual adjustment required.
Efficient and Fast: Utilizes TradingView’s bgcolor() for smooth performance, even on fast timeframes like 1-second charts.
Clean Interface: All session controls are grouped for easy editing in the indicator’s settings panel.
How to use:
In the indicator settings, enter your desired session times (e.g., 0930-1130) for each session you want to highlight.
Toggle “Show Session” and pick a color for each session.
The background will automatically highlight those periods on your chart.
This indicator is ideal for day traders, futures traders, or anyone who wants to visually segment their trading day for better focus and analysis.
Crypto Cycle Projection📈 Crypto Cycle Projection – Indicator Description
This indicator is designed to visually track and forecast repeating price cycles in the crypto market. It highlights a defined time-based cycle starting from a chosen date or the latest bar on the chart. By identifying cycle Start, Midpoint, and End zones, traders can gain insights into timing-based market structure and possible pivot periods.
⚙️ User Settings Explained
Start Point
Start from Last Candle (useLastCandle) – When enabled, the cycle begins from the most recent candle on the chart.
Manual Date (Year / Month / Day) – If Start from Last Candle is disabled, you can manually set a specific start date for the cycle.
Display Options
- Show Projection (showZone) – Toggles the display of the main cycle projection.
- Show Outer Bars (showOuter) – Adds faded edge bars around the key cycle zones for better visual emphasis.
- Show Previous Cycle (showPreviousCycle) – Adds the prior cycle to the chart, going one full cycle period back from the main start point.
Show Next Cycle (showNextCycle) – Projects one additional cycle forward beyond the current.
Cycle Parameters
Cycle Period (cyclePeriod) – Defines the number of bars in a full cycle (e.g., 60 = 60 bars). This sets the spacing between Start → Midpoint → End.
Each cycle section is color-coded:
Start = White
Midpoint = Yellow
End = Green
These reference lines and zones help you align trades with cycle timing for potential reversals, continuations, or volatility expansions.
Co-author Credit:
Matthew Hyland @ParabolicMatt
Bitcoin Power Law OscillatorThis is the oscillator version of the script. The main body of the script can be found here.
Understanding the Bitcoin Power Law Model
Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship.
By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges.
In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term.
The Bitcoin Power Law has the following function:
Power Law = 10^(a + b * log10(d))
Consisting of the following parameters:
a: Power Law Intercept (default: -17.668).
b: Power Law Slope (default: 5.926).
d: Number of days since a reference point(calculated by counting bars from the reference point with an offset).
Explanation of the a and b parameters:
Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post.
Applying vertical shifts to generate the other lines:
Once the initial power-law is created, additional lines are generated by applying a vertical shift. This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept.
In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest):
Base Power Law Line (no shift)
1-year shifted line
2-year shifted line
3-year shifted line
4-year shifted line
5-year shifted line
The six power law lines:
Bitcoin Power Law Oscillator
This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula: log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula:
normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line)
Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines.
Interpretation of the Bitcoin Power Law Model:
The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.).
By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame.
On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines.
In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples:
Example 1:
In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support at point B(Note: Vertical yearly grid lines have been added for this purpose👍).
Example 2:
In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point D, now aligning with the Base Power Law Line.
Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe :
Example 3:
In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
Bitcoin Power LawThis is the main body version of the script. The Oscillator version can be found here.
Understanding the Bitcoin Power Law Model
Also called the Long-Term Bitcoin Power Law Model. The Bitcoin Power Law model tries to capture and predict Bitcoin's price growth over time. It assumes that Bitcoin's price follows an exponential growth pattern, where the price increases over time according to a mathematical relationship.
By fitting a power law to historical data, the model creates a trend line that represents this growth. It then generates additional parallel lines (support and resistance lines) to show potential price boundaries, helping to visualize where Bitcoin’s price could move within certain ranges.
In simple terms, the model helps us understand Bitcoin's general growth trajectory and provides a framework to visualize how its price could behave over the long term.
The Bitcoin Power Law has the following function:
Power Law = 10^(a + b * log10(d))
Consisting of the following parameters:
a: Power Law Intercept (default: -17.668).
b: Power Law Slope (default: 5.926).
d: Number of days since a reference point(calculated by counting bars from the reference point with an offset).
Explanation of the a and b parameters:
Roughly explained, the optimal values for the a and b parameters are determined through a process of linear regression on a log-log scale (after applying a logarithmic transformation to both the x and y axes). On this log-log scale, the power law relationship becomes linear, making it possible to apply linear regression. The best fit for the regression is then evaluated using metrics like the R-squared value, residual error analysis, and visual inspection. This process can be quite complex and is beyond the scope of this post.
Applying vertical shifts to generate the other lines:
Once the initial power-law is created, additional lines are generated by applying a vertical shift. This shift is achieved by adding a specific number of days (or years in case of this script) to the d-parameter. This creates new lines perfectly parallel to the initial power law with an added vertical shift, maintaining the same slope and intercept.
In the case of this script, shifts are made by adding +365 days, +2 * 365 days, +3 * 365 days, +4 * 365 days, and +5 * 365 days, effectively introducing one to five years of shifts. This results in a total of six Power Law lines, as outlined below (From lowest to highest):
Base Power Law Line (no shift)
1-year shifted line
2-year shifted line
3-year shifted line
4-year shifted line
5-year shifted line
The six power law lines:
Bitcoin Power Law Oscillator
This publication also includes the oscillator version of the Bitcoin Power Law. This version applies a logarithmic transformation to the price, Base Power Law Line, and 5-year shifted line using the formula: log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed Base Power Law Line and 5-year shifted line with the formula:
normalized price = log(close) - log(Base Power Law Line) / log(5-year shifted line) - log(Base Power Law Line)
Finally, the normalized price was multiplied by 5 to map its value between 0 and 5, aligning with the shifted lines.
Interpretation of the Bitcoin Power Law Model:
The shifted Power Law lines provide a framework for predicting Bitcoin's future price movements based on historical trends. These lines are created by applying a vertical shift to the initial Power Law line, with each shifted line representing a future time frame (e.g., 1 year, 2 years, 3 years, etc.).
By analyzing these shifted lines, users can make predictions about minimum price levels at specific future dates. For example, the 5-year shifted line will act as the main support level for Bitcoin’s price in 5 years, meaning that Bitcoin’s price should not fall below this line, ensuring that Bitcoin will be valued at least at this level by that time. Similarly, the 2-year shifted line will serve as the support line for Bitcoin's price in 2 years, establishing that the price should not drop below this line within that time frame.
On the other hand, the 5-year shifted line also functions as an absolute resistance , meaning Bitcoin's price will not exceed this line prior to the 5-year mark. This provides a prediction that Bitcoin cannot reach certain price levels before a specific date. For example, the price of Bitcoin is unlikely to reach $100,000 before 2021, and it will not exceed this price before the 5-year shifted line becomes relevant. After 2028, however, the price is predicted to never fall below $100,000, thanks to the support established by the shifted lines.
In essence, the shifted Power Law lines offer a way to predict both the minimum price levels that Bitcoin will hit by certain dates and the earliest dates by which certain price points will be reached. These lines help frame Bitcoin's potential future price range, offering insight into long-term price behavior and providing a guide for investors and analysts. Lets examine some examples:
Example 1:
In Example 1 it can be seen that point A on the 5-year shifted line acts as major resistance . Also it can be seen that 5 years later this price level now corresponds to the Base Power Law Line and acts as a major support at point B (Note: Vertical yearly grid lines have been added for this purpose👍).
Example 2:
In Example 2, the price level at point C on the 3-year shifted line becomes a major support three years later at point D, now aligning with the Base Power Law Line.
Finally, let's explore some future price predictions, as this script provides projections on the weekly timeframe :
Example 3:
In Example 3, the Bitcoin Power Law indicates that Bitcoin's price cannot surpass approximately $808K before 2030 as can be seen at point E, while also ensuring it will be at least $224K by then (point F).
Yearly History Calendar-Aligned Price up to 10 Years)Overview
This indicator helps traders compare historical price patterns from the past 10 calendar years with the current price action. It overlays translucent lines (polylines) for each year’s price data on the same calendar dates, providing a visual reference for recurring trends. A dynamic table at the top of the chart summarizes the active years, their price sources, and history retention settings.
Key Features
Historical Projections
Displays price data from the last 10 years (e.g., January 5, 2023 vs. January 5, 2024).
Price Source Selection
Choose from Open, Low, High, Close, or HL2 ((High + Low)/2) for historical alignment.
The selected source is shown in the legend table.
Bulk Control Toggles
Show All Years : Display all 10 years simultaneously.
Keep History for All : Preserve historical lines on year transitions.
Hide History for All : Automatically delete old lines to update with current data.
Individual Year Settings
Toggle visibility for each year (-1 to -10) independently.
Customize color and line width for each year.
Control whether to keep or delete historical lines for specific years.
Visual Alignment Aids
Vertical lines mark yearly transitions for reference.
Polylines are semi-transparent for clarity.
Dynamic Legend Table
Shows active years, their price sources, and history status (On/Off).
Updates automatically when settings change.
How to Use
Configure Settings
Projection Years : Select how many years to display (1–10).
Price Source : Choose Open, Low, High, Close, or HL2 for historical alignment.
History Precision : Set granularity (Daily, 60m, or 15m).
Daily (D) is recommended for long-term analysis (covers 10 years).
60m/15m provides finer precision but may only cover 1–3 years due to data limits.
Adjust Visibility & History
Show Year -X : Enable/disable specific years for comparison.
Keep History for Year -X : Choose whether to retain historical lines or delete them on new year transitions.
Bulk Controls
Show All Years : Display all 10 years at once (overrides individual toggles).
Keep History for All / Hide History for All : Globally enable/disable history retention for all years.
Customize Appearance
Line Width : Adjust polyline thickness for better visibility.
Colors : Assign unique colors to each year for easy identification.
Interpret the Legend Table
The table shows:
Year : Label (e.g., "Year -1").
Source : The selected price type (e.g., "Close", "HL2").
Keep History : Indicates whether lines are preserved (On) or deleted (Off).
Tips for Optimal Use
Use Daily Timeframes for Long-Term Analysis :
Daily (1D) allows 10+ years of data. Smaller timeframes (60m/15m) may have limited historical coverage.
Compare Recurring Patterns :
Look for overlaps between historical polylines and current price to identify potential support/resistance levels.
Customize Colors & Widths :
Use contrasting colors for years you want to highlight. Adjust line widths to avoid clutter.
Leverage Global Toggles :
Enable Show All Years for a quick overview. Use Keep History for All to maintain continuity across transitions.
Example Workflow
Set Up :
Select Projection Years = 5.
Choose Price Source = Close.
Set History Precision = 1D for long-term data.
Customize :
Enable Show Year -1 to Show Year -5.
Assign distinct colors to each year.
Disable Keep History for All to ensure lines update on year transitions.
Analyze :
Observe how the 2023 close prices align with 2024’s price action.
Use vertical lines to identify yearly boundaries.
Common Questions
Why are some years missing?
Ensure the chart has sufficient historical data (e.g., daily charts cover 10 years, 60m/15m may only cover 1–3 years).
How do I update the data?
Adjust the Price Source or toggle years/history settings. The legend table updates automatically.
Bitcoin Monthly Seasonality [Alpha Extract]The Bitcoin Monthly Seasonality indicator analyzes historical Bitcoin price performance across different months of the year, enabling traders to identify seasonal patterns and potential trading opportunities. This tool helps traders:
Visualize which months historically perform best and worst for Bitcoin.
Track average returns and win rates for each month of the year.
Identify seasonal patterns to enhance trading strategies.
Compare cumulative or individual monthly performance.
🔶 CALCULATION
The indicator processes historical Bitcoin price data to calculate monthly performance metrics
Monthly Return Calculation
Inputs:
Monthly open and close prices.
User-defined lookback period (1-15 years).
Return Types:
Percentage: (monthEndPrice / monthStartPrice - 1) × 100
Price: monthEndPrice - monthStartPrice
Statistical Measures
Monthly Averages: ◦ Average return for each month calculated from historical data.
Win Rate: ◦ Percentage of positive returns for each month.
Best/Worst Detection: ◦ Identifies months with highest and lowest average returns.
Cumulative Option
Standard View: Shows discrete monthly performance.
Cumulative View: Shows compounding effect of consecutive months.
Example Calculation (Pine Script):
monthReturn = returnType == "Percentage" ?
(monthEndPrice / monthStartPrice - 1) * 100 :
monthEndPrice - monthStartPrice
calcWinRate(arr) =>
winCount = 0
totalCount = array.size(arr)
if totalCount > 0
for i = 0 to totalCount - 1
if array.get(arr, i) > 0
winCount += 1
(winCount / totalCount) * 100
else
0.0
🔶 DETAILS
Visual Features
Monthly Performance Bars: ◦ Color-coded bars (teal for positive, red for negative returns). ◦ Special highlighting for best (yellow) and worst (fuchsia) months.
Optional Trend Line: ◦ Shows continuous performance across months.
Monthly Axis Labels: ◦ Clear month names for easy reference.
Statistics Table: ◦ Comprehensive view of monthly performance metrics. ◦ Color-coded rows based on performance.
Interpretation
Strong Positive Months: Historically bullish periods for Bitcoin.
Strong Negative Months: Historically bearish periods for Bitcoin.
Win Rate Analysis: Higher win rates indicate more consistently positive months.
Pattern Recognition: Identify recurring seasonal patterns across years.
Best/Worst Identification: Quickly spot the historically strongest and weakest months.
🔶 EXAMPLES
The indicator helps identify key seasonal patterns
Bullish Seasons: Visualize historically strong months where Bitcoin tends to perform well, allowing traders to align long positions with favorable seasonality.
Bearish Seasons: Identify historically weak months where Bitcoin tends to underperform, helping traders avoid unfavorable periods or consider short positions.
Seasonal Strategy Development: Create trading strategies that capitalize on recurring monthly patterns, such as entering positions in historically strong months and reducing exposure during weak months.
Year-to-Year Comparison: Assess how current year performance compares to historical seasonal patterns to identify anomalies or confirmation of trends.
🔶 SETTINGS
Customization Options
Lookback Period: Adjust the number of years (1-15) used for historical analysis.
Return Type: Choose between percentage returns or absolute price changes.
Cumulative Option: Toggle between discrete monthly performance or cumulative effect.
Visual Style Options: Bar Display: Enable/disable and customize colors for positive/negative bars, Line Display: Enable/disable and customize colors for trend line, Axes Display: Show/hide reference axes.
Visual Enhancement: Best/Worst Month Highlighting: Toggle special highlighting of extreme months, Custom highlight colors for best and worst performing months.
The Bitcoin Monthly Seasonality indicator provides traders with valuable insights into Bitcoin's historical performance patterns throughout the year, helping to identify potentially favorable and unfavorable trading periods based on seasonal tendencies.
NY Time Cycles# New York Time Cycles Indicator
## Overview
The Time Cycles indicator is a specialized technical analysis tool designed to divide the trading day into distinct time blocks based on New York trading hours. Developed for TradingView, this indicator helps traders identify and analyze market behavior during specific time periods throughout the trading session. The indicator displays six consecutive time blocks, each representing 90-minute segments of the trading day, while tracking price ranges within each block.
## Core Concept
The Time Cycles indicator is built on the premise that different periods during the trading day often exhibit unique market characteristics and behaviors. By segmenting the trading day into standardized 90-minute blocks, traders can:
1. Identify recurring patterns at specific times of day
2. Compare price action across different time blocks
3. Recognize potential support and resistance levels based on the high and low of previous time blocks
4. Develop time-based trading strategies specific to certain market hours
## Time Block Structure
The indicator divides the trading day into six sequential 90-minute blocks based on New York time:
1. **Box 1**: 07:00 - 08:30 ET
2. **Box 2**: 08:30 - 10:00 ET
3. **Box 3**: 10:00 - 11:30 ET
4. **Box 4**: 11:30 - 13:00 ET
5. **Box 5**: 13:00 - 14:30 ET
6. **Box 6**: 14:30 - 16:00 ET
These time blocks cover the core US trading session from pre-market into regular market hours.
## Visual Representation
Each time block is represented on the chart as a visual box that:
- Spans the exact time period of the block (horizontally)
- Extends from the highest high to the lowest low recorded during that time period (vertically)
- Is displayed with customizable colors and transparency levels
- Automatically builds in real-time as price action develops
Additionally, the indicator draws dashed projection lines that:
- Display the high and low of the most recently completed time block
- Extend forward in time (for up to 24 hours)
- Help traders identify potential support and resistance levels
## Technical Implementation
The indicator employs several key technical features:
1. **Time Detection**: Accurately identifies the current New York time to place each box in the correct time period
2. **Dynamic Box Creation**: Initializes and updates boxes in real-time as price action develops
3. **Range Tracking**: Continuously monitors and adjusts the high and low of each active time block
4. **Projection Lines**: Creates horizontal dashed lines projecting the high and low of the most recently completed time block
5. **Daily Reset**: Automatically resets all boxes and lines at the start of each new trading day
6. **Customization**: Allows users to set custom colors and transparency levels for each time block
This Time Cycles indicator provides traders with a structured framework for analyzing intraday market movements based on specific time periods. By understanding how the market typically behaves during each 90-minute block, traders can develop more targeted strategies and potentially identify higher-probability trading opportunities throughout the trading day.
My-Indicator - Global Liquidity & Money Supply M2 + Time OffsetThis script is designed to visualize a global liquidity and money supply index by combining data from various regions and, optionally, central bank activity. Visualizing this data on a chart allows you to see how central banks are intervening in the financial system and how the total amount of money in the economy is changing. Let’s take a look at how it works:
Central Bank Liquidity
Shows the actions of central banks (e.g. FED, ECB) providing short-term cash to commercial banks. If you see spikes or a steady increase in these indicators, it may suggest that liquidity is being increased through intervention, which often stimulates the market.
Money Supply
M2 money supply is a monetary aggregate that includes M1 (cash and current deposits) plus savings deposits, small term deposits, and other financial instruments that, while not as liquid as M1, can be quickly converted into cash. As a result, M2 provides a broader picture of the available money in the economy, which is useful for analyzing market conditions and potential economic trends.
How does it help investors?
It allows you to quickly see when central banks are injecting additional liquidity, which could signal higher prices.
It allows you to see trends in the money supply, which informs potential changes in inflation and the economic cycle.
Combining both sets of data provides a more complete picture – both in the short and long term – which makes it easier to predict upcoming price movements.
This allows investors to better respond to changes in central bank policy and broader monetary trends, increasing their chances of making better investment decisions.
Data Collection
The script retrieves money supply data for key markets such as the USA (USM2), Europe (EUM2), China (CNM2), and Japan (JPM2). It also offers additional money supply series for other markets—like Canada (CAM2), Great Britain (GBM2), Russia (RUM2), Brazil (BRM2), Mexico (MXM2), and New Zealand (NZM2)—with extra options (e.g., Australia, India, Korea, Indonesia, Malaysia, Sweden) disabled by default. Moreover, you can enable data for central bank liquidity (such as FED, RRP, TGA, ECB, PBC, BOJ, and other central banks), which are also disabled by default.
Index Calculation
The indicator calculates the index by adding together all the enabled money supply series (and the central bank data if activated) and then scales the sum by dividing it by 1,000,000,000,000 (one trillion). This scaling makes the resulting values more manageable and easier to read on the chart.
Time Offset Feature
A key feature of the script is the time offset. With the input parameter "Time Offset (days)", the user can shift the plotted index line by a specific number of days. The script converts the given offset in days into a number of bars based on the current chart's timeframe. This allows you to adjust for the delay between liquidity changes and their effect on asset prices.
Overall, the indicator plots a line on your chart representing the global liquidity and money supply index, allowing you to visually monitor trends and better understand how liquidity and central bank actions may influence market movements.
What makes this script different from others?
Every supported market—both major regions (USA, Eurozone, China, Japan, etc.) and additional ones—is available. You can toggle each series on or off, so you can view only Money Supply data, only Central Bank Liquidity, or any custom combination.
Separated Data Groups. Inputs are organized into clear groups (“Money Supply”, “Other Money Supply”, “Central Bank Liquidity”), making it easy to focus on just the data you need without clutter.
True Day‑Based Offset. This script converts your chosen “Time Offset (days)” into actual days regardless of timeframe. Whether you’re on a 5‑minute or daily chart, the index is always shifted by exactly the number of days you specify.
Bitcoin Polynomial Regression ModelThis is the main version of the script. Click here for the Oscillator part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines. The Oscillator version can be found here.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
