NIFTY Option Chain Table with Custom CE/PE Price FiltersThis Pine Script creates a powerful and visually organized option chain dashboard for NIFTY Index Options, showing 10 Call Options (CE) and 10 Put Options (PE), with real-time prices updated on a 5-minute chart.
You can filter and view only the most relevant option contracts based on your preferred price ranges, helping you make quick decisions for scalping, intraday, or positional trades.
🔍 How It Works:
You manually select up to 10 Call Option symbols and 10 Put Option symbols from NSE (e.g., NIFTY240530C18000, NIFTY240530P18000, etc.).
Keep that time options this are old options in defalt so there will be a error
The script fetches the real-time close price of each option using the request.security() function.
You define the minimum and maximum price range separately for Calls and Puts.
The script filters out any options that fall outside of your desired price range.
Only a limited number of matching options (as set by you) are displayed in the table for both Calls and Puts.
The table is shown at your preferred location on the chart (Bottom Right, Top Left, etc.).
✅ Features:
🔟 Supports exactly 10 CE and 10 PE options for tracking.
📈 Live price updates pulled directly from the chart timeframe (5-min).
🎯 Custom price filters for CE and PE (separate inputs).
📊 Show only the top X number of contracts that meet your filter criteria.
🧱 Vertical layout with clear headers and color-coded sections (green for Calls, red for Puts).
🎛️ Position the table wherever it's most convenient on your chart.
⚡ Helps you quickly spot low premium or range-bound options during the day.
📌 Use Case:
Ideal for:
Option scalpers and day traders who want to focus only on options within a specific price zone.
Traders who want to monitor multiple strikes simultaneously without clutter.
Users building custom NIFTY strategies based on option premiums.
Indicators and strategies
EMA 9/21/50 + VWAP + MACD + RSI Pro [v7]Updated to make table update on bar close, any time frame. Removed RSI plots
Deviation from 20SMAThis indicator looks to display the variance from the 20SMA relative to the closing candle and the 20SMA. It uses Bollinger Bands to show extreme deviation when price moves in one direction too quickly. The decimal numbers are a representation of the price away from the 20SMA relative to the value of the ticker "(close - sma20) / close". This reduces extremes of nominal value as the price of the ticker gets higher.
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).
📊 Portfolio TrackerPortfolio Tracker
🧠 How This Script Works
This Pine Script generates a dynamic portfolio table in the upper-right corner of your chart. It:
Monitors your positions in: BTC, SOL, ADA, XRP, and XAU (Gold).
Calculates for each asset:
Current value,
Profit/Loss in your currency ,
Percentage change.
Color-coded output:
🟢 Green = Profit
🔴 Red = Loss
Automatically updates every few candles.
Tracks total portfolio value, PnL, and % return.
Triggers custom alerts when:
Total portfolio profit exceeds +5% or +10%.
🛠️ How to Customize It for Your Own Portfolio
🔹 1. Update your personal asset data
Inside the // === INPUTS === section of the code, modify these lines:
btc1_qty = 0.0013
btc1_entry = 72831.80
Repeat for each asset you own:
Replace xxx_qty with your amount.
Replace xxx_entry with your buy price (in your currency).
Make sure the request.security(...) line fetches the correct symbol.
🔹 2. Add more assets (optional)
Duplicate any block like ADA and change the variable names and symbols:
new_qty = ...
new_entry = ...
new_price = request.security("BINANCE:NEWTOKENUSD", timeframe.period, close)
Also include the new asset in:
total_pnl += ...
total_value_now += ...
total_cost += ...
The table.cell(...) block to show it in the table.
Why This Tool Rocks
Tracks all your holdings in one chart panel.
Requires no API or external data feed.
Real-time updates based on TradingView chart prices.
Fully editable and extendable to any other token or asset.
SMA ExtensionsThe SMA Extensions indicator overlays a 200-period Simple Moving Average (SMA) along with its extensions (1.5x, 2x, 2.5x, and 3x) on the price chart. This setup helps traders identify price zones relative to the SMA.
Key Features:
Customizable Settings: Users can adjust the SMA source, length, and colors (default colors: blue, green, yellow, orange, red).
Clear Zone Visualization: The indicator plots each extension as a line, with transparent colored fills between them and below the SMA, visually highlighting price zones.
Dynamic Labels: Labels such as "Very Cheap," "Cheap," "Fair Value," "Expensive," and "Very Expensive" appear only on the last bar. These labels are slightly right-shifted and match the line colors for clarity.
How to Use:
Use the color-coded zones to assess whether prices are undervalued or overvalued relative to the SMA.
Adjust the SMA settings to suit different assets or timeframes.
This indicator is designed to provide a clear visual representation of price levels in relation to a standard SMA, making it a versatile tool for both novice and experienced traders.
WaveTrend [LazyBear] with Long/Short LabelsWaveTrend Oscillator with Entry Signals (LONG/SHORT) – Advanced Edition
This indicator is based on the renowned WaveTrend Oscillator by LazyBear, a favorite among professional traders for spotting trend reversals with precision.
🚀 Features:
Original WaveTrend formula with dual-line structure (WT1 & WT2).
Customizable overbought and oversold zones for visual clarity.
Automatic LONG and SHORT signals plotted directly on the chart:
✅ LONG: When WT1 crosses above WT2 below the oversold zone.
❌ SHORT: When WT1 crosses below WT2 above the overbought zone.
Momentum histogram shows strength of market moves.
Fully optimized for Pine Script v5 and lightweight across all timeframes.
🔍 How to use:
Combine with support/resistance levels or candlestick reversal patterns.
Works best on 15min, 1H, or 4H charts.
Suitable for all markets: crypto, stocks, forex, indices.
📊 Ideal for:
Traders seeking clean, reliable entry signals.
Reversal strategies with technical confluence.
Visual confirmation of WaveTrend crossovers without manual interpretation.
💡 Pro Tip: Combine with EMA or RSI filters to further enhance accuracy.
Sunday OpenThis indicator shows the opening price of the daily candle on Sunday .
This price range is a good confirmation for the price reversal. Also, this level is a strong range from which the price gives a good reaction .
This Sunday open line can be used as an additional confirmation to the trading strategy to have a clearer entry point.
Granger Causality Flow IndicatorGranger Causality Flow Indicator (GC Flow)
█ OVERVIEW
The Granger Causality Flow Indicator (GC Flow) attempts to quantify the potential predictive relationship between two user-selected financial instruments (Symbol X and Symbol Y). In essence, it explores whether the past values of one series (e.g., Symbol X) can help explain the current value of another series (e.g., Symbol Y) better than Y's own past values alone.
This indicator provides a "Granger Causality Score" (GC Score) for both directions (X → Y and Y → X). A higher score suggests a stronger statistical linkage where one series may lead or influence the other. The indicator visualizes this "flow" of potential influence through background colors and on-chart text.
Important Note: "Granger Causality" does not imply true economic or fundamental causation. It is a statistical concept indicating predictive power or information flow. This implementation also involves simplifications (notably, using AR(1) models) due to the complexities of full Vector Autoregression (VAR) models in Pine Script®.
█ HOW IT WORKS
The indicator's methodology is based on comparing the performance of Autoregressive (AR) models:
1. Data Preprocessing:
Fetches historical close prices for two user-defined symbols (X and Y).
Optionally applies first-order differencing (`price - price `) to the series. Differencing is a common technique to achieve a proxy for stationarity, which is an underlying assumption for Granger Causality tests. Non-stationary series can lead to spurious correlations.
2. Autoregressive (AR) Models (Simplified to AR(1)):
Due to Pine Script's current limitations for complex multivariate time series models, this indicator uses simplified AR(1) models (where the current value is predicted by its immediately preceding value).
Restricted Model (for Y → Y): Predicts the target series (e.g., Y) using only its own past value (Y ).
`Y = c_R + a_R * Y + residuals_R`
The variance of `residuals_R` (Var_R) is calculated.
Unrestricted Model (Proxy for X → Y): To test if X Granger-causes Y, the indicator examines if the past values of X (X ) can explain the residuals from the restricted model of Y.
`residuals_R = c_UR' + b_UR * X + residuals_UR`
The variance of these final `residuals_UR` (Var_UR) is calculated.
The same process is repeated to test if Y Granger-causes X.
3. Granger Causality (GC) Score Calculation:
The GC Score quantifies the improvement in prediction from adding the other series' past values. It's calculated as:
`GC Score = 1 - (Var_UR / Var_R)`
A score closer to 1 suggests that the "causing" series significantly reduces the unexplained variance of the "target" series (i.e., Var_UR is much smaller than Var_R), indicating stronger Granger causality.
A score near 0 (or capped at 0 if Var_UR >= Var_R) suggests little to no improvement in prediction.
The score is calculated over a rolling `Calculation Window`.
Pine Script® Snippet (Conceptual GC Score Logic):
// Conceptual representation of GC Score calculation
// var_R: Variance of residuals when Y is predicted by Y
// var_UR: Variance of residuals when Y's AR(1) residuals are predicted by X
score = 0.0
if var_R > 1e-9 // Avoid division by zero
score := 1.0 - (var_UR / var_R)
score := score < 0 ? 0 : score // Ensure score is not negative
4. Determining Causal Flow:
The calculated GC Scores for X → Y and Y → X are compared against a user-defined `Significance Threshold for GC Score`.
If GC_X→Y > threshold AND GC_Y→X > threshold: Bidirectional flow.
If GC_X→Y > threshold only: X → Y flow.
If GC_Y→X > threshold only: Y → X flow.
Otherwise: No significant flow.
█ HOW TO USE IT
Interpreting the Visuals:
Background Color:
Green: Indicates X → Y (Symbol 1 potentially leads Symbol 2).
Orange: Indicates Y → X (Symbol 2 potentially leads Symbol 1).
Blue: Indicates Bidirectional influence.
Gray: No significant Granger causality detected based on the threshold.
Data Window Plots: The actual GC Scores for X → Y (blue) and Y → X (red) are plotted and visible in TradingView's Data Window. A dashed gray line shows your `Significance Threshold`.
On-Chart Table (Last Bar): Displays the currently detected causal direction text (e.g., "BTCUSDT → QQQ").
Potential Applications:
Intermarket Analysis: Explore potential lead-lag relationships between different asset classes (e.g., commodities and equities, bonds and currencies).
Pair Trading Components: Identify if one component of a potential pair tends to lead the other.
Confirmation Tool: Use alongside other analyses to see if a move in one asset might foreshadow a move in another.
Considerations:
Symbol Choice: Select symbols that have a plausible economic or market relationship.
Stationarity: Granger Causality tests ideally require stationary time series. The `Use Differencing` option is a simple proxy. True stationarity testing is complex. Non-stationary data can yield misleading results.
Lag Order (p): This indicator is fixed at p=1 due to Pine Script® limitations. In rigorous analysis, selecting the optimal lag order is crucial.
Calculation Window: Shorter windows are more responsive but may be noisier. Longer windows provide smoother scores but lag more.
Significance Threshold: Adjust this based on your desired sensitivity for detecting causal links. There's no universally "correct" threshold; it depends on the context and noise level of the series.
█ INPUTS
Symbol 1 (X): The first symbol in the analysis.
Symbol 2 (Y): The second symbol (considered the target when testing X → Y).
Use Differencing: If true, applies first-order differencing to both series as a proxy for stationarity.
Calculation Window (N): Lookback period for AR model coefficient estimation and variance calculations.
Lag Order (p): Currently fixed at 1. This defines the lag used (e.g., X , Y ) in the AR models.
Significance Threshold for GC Score: A value between 0.01 and 0.99. The calculated GC Score must exceed this to be considered significant.
█ VISUALIZATION
Background Color: Dynamically changes based on the detected Granger causal flow (Green for X → Y, Orange for Y → X, Blue for Bidirectional, Gray for None).
GC Scores (Data Window):
Blue Plot: GC Score for X → Y.
Red Plot: GC Score for Y → X.
Significance Threshold Line: A dashed gray horizontal line plotted at the level of your input threshold.
On-Chart Table: Displayed on the top-right (on the last bar), showing the current causal direction text.
█ ALERTS
The indicator can generate alerts for:
Emergence of X → Y causality.
Emergence of Y → X causality.
General change or cessation of a previously detected causal relationship.
█ IMPORTANT DISCLAIMERS & LIMITATIONS
Correlation vs. Causation: Granger causality measures predictive power, not true underlying economic causation. A strong GC Score doesn't prove one asset *causes* another to move, only that its past values improve predictions.
Stationarity Assumption: While differencing is offered, it's a simplified approach. Non-stationary data can lead to spurious (false) Granger causality detection.
Model Simplification (AR(1)): This script uses AR(1) models for simplicity. Real-world relationships can involve more complex dynamics and higher lag orders. The fixed lag of p=1 is a significant constraint.
Sensitivity to Parameters: Results can be sensitive to the chosen symbols, calculation window, differencing option, and significance threshold.
No Statistical Significance Testing (p-values): This indicator uses a direct threshold on the GC Score itself, not a formal statistical test (like an F-test producing p-values) typically found in econometric software.
Use this indicator as an exploratory tool within a broader analytical framework. Do not rely on it as a standalone basis for trading decisions.
█ CREDITS & LICENSE
Author: mastertop ( Twitter: x.com )
Version: 1.0 (Released: 2025-05-08)
This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
© mastertop, 2025
Nowein-Anchored VWAP with 1% Bands Anchored VWAP with ±1% Bands Starting at 9:00 AM
This indicator calculates an Anchored Volume Weighted Average Price (VWAP) starting precisely at 9:00 AM each trading day (customizable). It plots the VWAP line alongside two dynamic bands set at ±1% above and below the VWAP. The bands help visualize potential support and resistance zones relative to the intraday VWAP anchored at market open.
Key Features:
Anchors VWAP calculation to user-defined start time (default 9:00 AM)
Displays VWAP line in orange for easy tracking
Shows upper and lower dashed bands at ±1% of VWAP in green and red, respectively
Bands update dynamically with each new bar throughout the trading day
Designed for intraday charts (1-minute, 5-minute, etc.)
Use this tool to better assess intraday price action around VWAP and identify potential trading opportunities based on price deviations from the anchored VWAP.
SCKC_UT Bot Alerts with Two EMAs, VWAP & Daily CPRTry in higher time Frame for more accuracy.
What It Does?
Generates buy (green upward arrow) and sell (red downward arrow) signals when specific conditions are met, helping traders identify potential entry and exit points.
Core Mechanism: Uses an ATR-based trailing stop to track price volatility and an EMA to confirm trend direction. A buy signal typically occurs when the price crosses above the ATR trailing stop and the EMA confirms an uptrend, while a sell signal triggers when the price falls below the ATR stop with a bearish EMA crossover.
Customization: Traders can adjust parameters like:
Key Value: Controls sensitivity (higher values reduce signal frequency by making the indicator less reactive to price swings).
ATR Period: Determines the lookback period for calculating volatility (default often set to 5 or 10).
EMA Period: Sets the EMA length for trend confirmation.
Custom EMA Zone1. Overview
The Custom EMA Cloud Indicator is a technical analysis tool designed to visually display a dynamic zone (or cloud) between two user-defined EMAs. It supports different EMA lengths and allows users to calculate these EMAs using custom timeframes. This flexibility makes it a powerful tool for identifying trends, key price zones, and potential trade signals.
2. Components of the Indicator
2.1. Exponential Moving Averages (EMAs)
EMA 1 (Faster EMA): Calculated using a shorter period (e.g., 21).
EMA 2 (Slower EMA): Calculated using a longer period (e.g., 50).
Users can customize the periods for both EMAs.
2.2. Timeframe Customization
Each EMA can be calculated using a higher timeframe than the chart’s timeframe (e.g., calculate EMA 50 on a 1-hour chart while viewing on a 5-minute chart).
This feature allows users to incorporate higher timeframe trend context into lower timeframe charts.
2.3. Cloud Zone
The cloud is the shaded area between EMA 1 and EMA 2.
Color Logic:
Light Green: Price opens and closes above both EMAs (bullish momentum).
Light Red: Price opens and closes below both EMAs (bearish momentum).
3. How to Use the Indicator
3.1. Trend Identification
When the entire price action is above the cloud, it signals a probable uptrend.
When the entire price action is below the cloud, it indicates a probable downtrend.
When the price is inside the cloud, it reflects probable market consolidation or indecision.
4. Use Cases in Trading Styles
4.1. Scalping
Use short EMAs (e.g., EMA 5 and EMA 13) on 1-minute or 3-minute charts.
Ideal for quick entries and exits during strong momentum moves.
4.2. Swing Trading
Use longer EMAs (e.g., EMA 21 and EMA 50) on 4-hour or daily charts.
Helps capture trend continuation over multiple days.
4.3. Trend Following
Combine with RSI or MACD to confirm trend strength before entering trades.
Stay in the trade as long as price respects the cloud direction.
5. Advantages
Visual Clarity: Simplifies decision-making with clearly defined zones.
Multi-Timeframe Insight: Offers a higher timeframe trend reference.
Customizable: Fits various strategies through adjustable EMAs and timeframes.
6. Limitations
Lagging Nature: As with all moving averages, there may be lag during fast reversals.
False Signals in Sideways Markets: May produce whipsaws during consolidation
Gold ValuationGold Value Index
The Gold Value Index (GVI) is a macro-driven oscillator that estimates the relative value of gold based on real-time movements in the US Dollar Index (DXY) and the 10-Year US Treasury Yield (US10Y). It helps traders contextualize gold’s price within broader macroeconomic pressure — identifying when gold may be over- or undervalued relative to these key drivers.
How It Works – Macro Inputs:
DXY (US Dollar Index): Typically moves inversely to gold. A rising dollar suggests downward pressure on gold value.
US10Y Yield: Higher yields increase the opportunity cost of holding gold, often leading to weaker gold prices.
Both inputs are Z-score normalized and inverted to reflect their typical negative correlation with gold. When combined, they form a single, scaled index from 0 (undervalued) to 100 (overvalued).
Why Use This Tool?
Gold reacts to macro forces as much as technical ones. The GVI blends these inputs into a clear, visual gauge to:
Anticipate mean-reversion setups.
Avoid emotionally-driven trades in extreme macro conditions.
Enhance timing by understanding gold's macro context.
Important Notes:
Data sources include ICEUS:DXY and TVC:US10Y via TradingView.
Code is protected — this is a private, invite-only script.
Prev Day High/Low - RTH OnlyThis indicator displays the previous day’s Regular Trading Hours (RTH) high and low levels on your chart, but only during the next day’s RTH session — keeping your charts clean while highlighting the most relevant price zones.
🔧 Features:
RTH Timing: 09:30 to 16:00 (New York time)
Auto-Detection: Automatically tracks the high and low of each RTH session
Next-Day Visibility: Lines are only shown during the following day’s RTH session
Minimal Chart Clutter: Levels disappear after the session ends, keeping focus on what's relevant
Clear Labels: Optional “Prev High” and “Prev Low” markers at market open
📊 Use Case:
Perfect for intraday and 0DTE options traders who rely on prior session key levels for:
Breakout trades
Reversal setups
Support/resistance confluence
These levels are often respected by institutions and algo flows, making them highly actionable when approached in the next RTH session.
🧠 Why This Matters:
Many traders overpopulate their charts with persistent support/resistance lines. This script keeps only what’s essential — the previous day’s RTH range — and only when it matters most: the following day’s active market hours.
SOPR with Z-Score Table📊 Glassnode SOPR with Dynamic Z-Score Table
ℹ️ Powered by Glassnode On-Chain Metrics
📈 Description:
This indicator visualizes the Spent Output Profit Ratio (SOPR) for major cryptocurrencies — Bitcoin, Ethereum, and Litecoin — along with a dynamically normalized Z-Score. SOPR is a key on-chain metric that reflects whether coins moved on-chain are being sold at a profit or a loss.
🔍 SOPR is calculated using Glassnode’s entity-adjusted SOPR feed, and a custom SMA is applied to smooth the signal. The normalized Z-Score helps identify market sentiment extremes by scaling SOPR relative to its historical context.
📊 Features:
Selectable cryptocurrency: Bitcoin, Ethereum, or Litecoin
SOPR smoothed by user-defined SMA (default: 10 periods)
Upper & lower bounds (±4%) for SOPR, shown as red/green lines
Background highlighting when SOPR moves outside normal range
Normalized Z-Score scaled between –2 and +2
Live Z-Score display in a compact top-right table
🧮 Calculations:
SOPR data is sourced daily from Glassnode:
Bitcoin: XTVCBTC_SOPR
Ethereum: XTVCETH_SOPR
Litecoin: XTVCLTC_SOPR
Z-Score is calculated as:
SMA of SOPR over zscore_length periods
Standard deviation of SOPR
Z-Score = (SOPR – mean) / standard deviation
Z-Score is clamped between –2 and +2 for visual consistency
🎯 Interpretation:
SOPR > 1 implies coins are sold in profit
SOPR < 1 suggests coins are sold at a loss
When SOPR is significantly above or below its recent range (e.g., +4% or –4%), it may signal overheating or capitulation
The Z-Score contextualizes how extreme the current SOPR is relative to history
📌 Notes:
Best viewed on daily charts
Works across selected assets (BTC, ETH, LTC)
Asian Range + Midpoint + Liquidity Grab AlertsPlot Asian Range 12am to 08am GMT with Midpoint and Liquidity Grab Alerts
Dual Stochastic – %K(14) + %K(5)🔹 This indicator uses two Stochastic Oscillators with different settings to improve signal reliability and timing:
- **%K(14)**: Slow stochastic, used as a **trend filter**
- **%K(5)**: Fast stochastic, used as a **trigger**
A **LONG signal** is shown when both stochastics are in the oversold area (**below 20**) and the fast %K crosses **above** its %D.
A **SHORT signal** appears when both are in the overbought area (**above 80**) and the fast %K crosses **below** its %D.
This dual confirmation technique avoids many false signals that occur when relying on a single stochastic.
Background colors highlight agreement zones (green = long zone, red = short zone), while arrows mark actual entry signals.
✅ Ideal for scalping or swing setups
✅ Visual and intuitive
✅ Can be combined with support/resistance or price action
—
Created by `giua64` – Educational tool for technical traders
Part of the **Borsa e Mercati** toolkit
⚠️ This script is for educational purposes only. No investment advice.
Yield Curve Approximation
A yield curve is a graph that plots the yields (interest rates) of bonds with the same credit quality but different maturity dates. It helps investors understand the relationship between short-term and long-term interest rates.
🔹 Types of Yield Curves
1️⃣ Normal Yield Curve – Upward-sloping, indicating economic expansion.
2️⃣ Inverted Yield Curve – Downward-sloping, often a recession warning.
3️⃣ Flat Yield Curve – Suggests economic uncertainty or transition.
The yield curve is widely used to predict economic conditions and interest rate movements. You can learn more about it here. Would you like insights on how traders use the yield curve for investment decisions?
How to Trade Using This?
✅ If the yield curve is steepening (green) → Favor growth stocks, commodities, and high-risk assets.
✅ If the yield curve is flattening or inverting (red) → Consider bonds, defensive sectors, or hedging strategies.
✅ Pair with economic news and interest rate decisions to refine predictions.
Mogambo ScalpingJUST SCALP with the SIGNALS. Make little entries take your profit and enjoy the trades.
JDXBT Monthly VWAPIt calculates the average price for each month, weighted by trading volume, and automatically resets the calculation at the start of each new month. The VWAP line changes colour based on direction: black if rising, fuchsia if falling — helping traders quickly identify monthly price trends with volume context. It’s a useful tool for spotting key levels and momentum shifts on a monthly basis.
Key Candle Re-Entry ZonesTime zone markups for the 1:25 & 9:25 times. This helps build identity for the pre-market and market analysis
Precision LevelsThis open-source Support and Resistance Indicator helps traders plot key price levels where the market may reverse or consolidate. By plotting support and resistance zones based on historical price action, it provides clear visual cues for potential entry and exit points across various timeframes.
Customizable Settings: Adjust visual styles to suit your trading strategy.
Multi-Timeframe Support: View and plot levels from higher timeframes using the monthly and weekly levels.
User-Friendly: Lightweight design with clear plotting for easy integration into any setup.
How It Works:
The indicator plots simple Support and resistance. Zones are labeled monthly, weekly, and daily
Usage:
Apply the indicator to your chart.
Enter a value for each support and resistance level. Drag and Adjust on the chart to your liking.
Use the plotted levels to identify potential reversals, breakouts, or stop-loss placements.
Combine with other tools (e.g., trendlines or oscillators) for confirmation.
Note: This is the open-source version of my previously protected Support and Resistance Indicator. The protected version is flagged and hidden from community and no longer maintained. Feel free to explore and modify the code to fit your needs! For feedback or suggestions, leave a comment below or message me direct.
Anchored VWAP with Bands DebugAnchored VWAP with ±1% Bands Starting at 9:00 AM
This indicator calculates an Anchored Volume Weighted Average Price (VWAP) starting precisely at 9:00 AM each trading day (customizable). It plots the VWAP line alongside two dynamic bands set at ±1% above and below the VWAP. The bands help visualize potential support and resistance zones relative to the intraday VWAP anchored at market open.
Key Features:
Anchors VWAP calculation to user-defined start time (default 9:00 AM)
Displays VWAP line in orange for easy tracking
Shows upper and lower dashed bands at ±1% of VWAP in green and red, respectively
Bands update dynamically with each new bar throughout the trading day
Designed for intraday charts (1-minute, 5-minute, etc.)
Use this tool to better assess intraday price action around VWAP and identify potential trading opportunities based on price deviations from the anchored VWAP.
