Multiple AVWAP [OmegaTools]The Multiple AVWAP indicator is a sophisticated trading tool designed for professional traders who require precision in volume-weighted price tracking. This indicator allows for the deployment of multiple Anchored Volume Weighted Average Price (AVWAP) calculations simultaneously, offering deep insights into price movements, dynamic support and resistance levels, and trend structures across multiple timeframes.
This indicator caters to both institutional and retail traders by integrating flexible anchoring methods, multi-timeframe adaptability, and enhanced visualization features. It also includes deviation bands for statistical analysis, making it a comprehensive volume-based trading solution.
Key Features & Functionalities
1. Multiple AVWAP Configurations
Users can configure up to four distinct AVWAP calculations to track different market conditions.
Supports various anchoring methods:
Fixed: A traditional AVWAP that starts from a defined historical point.
Perpetual: A rolling VWAP that continuously adjusts over time.
Extension: An extension-based AVWAP that projects from past calculations.
High Volume: Anchors AVWAP to the highest volume bar within a specified period.
None: Option to disable AVWAP calculation if not required.
2. Advanced Deviation Bands
Implements standard deviation bands (1st and 2nd deviation) to provide a statistical measure of price dispersion from the AVWAP.
Serves as a dynamic method for identifying overbought and oversold conditions relative to VWAP pricing.
Deviation bands are customizable in terms of visibility, color, and transparency.
3. Multi-Timeframe Support
Users can assign different timeframes to each AVWAP calculation for macro and micro analysis.
Helps in identifying long-term institutional trading levels alongside short-term intraday trends.
4. Z-Score Normalization Mode
Option to standardize oscillator values based on AVWAP deviations.
Converts price movements into a statistical Z-score, allowing traders to measure price strength in a normalized range.
Helps in detecting extreme price dislocations and mean-reversion opportunities.
5. Customizable Visual & Aesthetic Settings
Fully customizable line colors, transparency, and thickness to enhance clarity.
Users can modify AVWAP and deviation band colors to distinguish between different levels.
Configurable display options to match personal trading preferences.
6. Oscillator Mode for Trend & Momentum Analysis
The indicator converts price deviations into an oscillator format, displaying AVWAP strength and weakness dynamically.
This provides traders with a momentum-based perspective on volume-weighted price movements.
User Guide & Implementation
1. Configuring AVWAPs for Optimal Use
Choose the mode for each AVWAP instance:
Fixed (set historical point)
Perpetual (rolling, continuously updated AVWAP)
Extension (projection from past AVWAP levels)
High Volume (anchored to highest volume bar)
None (disables the AVWAP line)
Adjust the length settings to fine-tune calculation sensitivity.
2. Utilizing Deviation Bands for Market Context
Activate deviation bands to see statistical boundaries of price action.
Monitor +1 / -1 and +2 / -2 standard deviation levels for extended price movements.
Consider price action outside of deviation bands as potential mean-reversion signals.
3. Multi-Timeframe Analysis for Institutional-Level Insights
Assign different timeframes to each AVWAP to compare:
Daily VWAP (institutional trading levels)
Weekly VWAP (swing trading trends)
Intraday VWAPs (short-term momentum shifts)
Helps identify where institutional liquidity is positioned relative to price.
4. Activating the Oscillator for Momentum & Bias Confirmation
The oscillator converts AVWAP deviations into a normalized value.
Use overbought/oversold levels to determine strength and potential reversals.
Combine with other indicators (RSI, MACD) for confluence-based trading decisions.
Trading Applications & Strategies
5. Trend Confirmation & Institutional VWAP Tracking
If price consistently holds above the primary AVWAP, it signals a bullish trend.
If price remains below AVWAP, it indicates selling pressure and a bearish trend.
Monitor retests of AVWAP levels for potential trend continuation or reversal.
6. Dynamic Support & Resistance Levels
AVWAP lines act as dynamic floating support and resistance zones.
Price bouncing off AVWAP suggests continuation, whereas breakdowns indicate a shift in momentum.
Look for confluence with high-volume zones for stronger trade signals.
7. Mean Reversion & Statistical Edge Trading
Prices that deviate beyond +2 or -2 standard deviations often revert toward AVWAP.
Mean reversion traders can fade extended moves and target AVWAP re-tests.
Helps in identifying exhaustion points in trending markets.
8. Institutional Liquidity & Volume Footprints
Institutions often execute large trades near VWAP zones, causing price reactions.
Tracking multi-timeframe AVWAP levels allows traders to anticipate key liquidity areas.
Use higher timeframe AVWAPs as macro support/resistance for swing trading setups.
9. Enhancing Momentum Trading with AVWAP Oscillator
The oscillator provides a momentum-based measure of AVWAP deviations.
Helps in confirming entry and exit timing for trend-following trades.
Useful for pairing with stochastic oscillators, MACD, or RSI to validate trade decisions.
Best Practices & Trading Tips
Use in Conjunction with Volume Analysis: Combine with volume profiles, OBV, or CVD for increased accuracy.
Adjust Timeframes Based on Trading Style: Scalpers can focus on short-term AVWAP, while swing traders benefit from weekly/daily AVWAP tracking.
Backtest Different AVWAP Configurations: Experiment with different anchoring methods and lookback periods to optimize trade performance.
Monitor Institutional Order Flow: Identify key VWAP zones where institutional traders may be active.
Use with Other Technical Indicators: Enhance trading confidence by integrating with moving averages, Bollinger Bands, or Fibonacci retracements.
Final Thoughts & Disclaimer
The Multiple AVWAP indicator provides a comprehensive approach to volume-weighted price tracking, making it ideal for professional traders. While this tool enhances market clarity and trade decision-making, it should be used as part of a well-rounded trading strategy with risk management principles in place.
This indicator is provided for informational and educational purposes only. Trading involves risk, and past performance is not indicative of future results. Always conduct your own analysis and due diligence before executing trades.
OmegaTools - Enhancing Market Clarity with Precision Indicators
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TJR SEEK AND DESTROYTJR SEEK AND DESTROY โ Intraday ICT Trading Tool
Built for day traders, TJR SEEK AND DESTROY combines Smart Money concepts like order blocks, fair value gaps, and liquidity sweeps with structure breaks and daily bias to pinpoint high-probability trades during US market hours (9:30โ16:00). Ideal for scalping or intraday strategies on stocks, futures, or forex.
What Makes It Unique?
Unlike standalone ICT indicators, this script integrates:
Order Blocks with volume and range filters for precise support/resistance zones.
Fair Value Gaps (FVG) to spot pre-market price imbalances.
Break of Structure (BOS) and Liquidity Sweeps for trend and reversal signals.
A 1H MA-based Bias to align trades with the dayโs direction.
BUY/SELL Labels triggered only when bias, BOS, and sweeps align, reducing noise.
How Does It Work?
Order Blocks: Marks zones with high volume (>1.5x 20-period SMA) and low range (<0.5x ATR20) as teal boxesโpotential reversal points.
Fair Value Gap: Compares the prior dayโs close to the current open (pre- or post-9:30), shown as a purple line and label (e.g., "FVG: 0.005").
Pivot Point: Calculates (prevHigh + prevLow + prevClose) / 3 from the prior day, plotted as an orange line for equilibrium.
Break of Structure: Detects crossovers of 5-bar highs/lows (gray lines), marked with red triangles.
Liquidity Sweeps: Tracks breaches of the prior dayโs high/low (yellow lines), marked with yellow triangles.
Daily Bias: Uses 1H close vs. 20-period MA (blue line) for bullish (green background), bearish (red), or neutral (gray) context.
Signals: BUY (green label) when bias is bullish, price breaks up, and sweeps the prior high; SELL (red label) when bias is bearish, price breaks down, and sweeps the prior low.
How to Use It
Setup: Apply to 1Mโ15M charts for US session trading (9:30โ16:00 EST).
Trading:
Wait for a BUY label after a yellow sweep triangle above the prior dayโs high in a green (bullish) background.
Wait for a SELL label after a yellow sweep triangle below the prior dayโs low in a red (bearish) background.
Use order blocks (teal boxes) as support/resistance for stop-loss or take-profit.
Markets: Best for SPY, ES futures, or forex pairs with US session volatility.
Underlying Concepts
Order Blocks: High-volume, low-range bars suggest institutional activity.
FVG: Gaps between close and open indicate imbalance to be filled.
BOS & Sweeps: Price breaking key levels signals momentum or stop-hunting.
Bias: 1H MA filters trades by broader trend.
Chart Setup
Displays order blocks (teal boxes), pivot (orange), open (purple), bias (colored background), BOS/sweeps (triangles), and signals (labels). Keep other indicators off for clarity.
Crypto Market Session Guide with Local TimeMaster the Markets with the Ultimate Trading Session Indicator
Timing is everything in trading. Knowing when liquidity is at its peak and when market sessions overlap can make all the difference in your strategy. This Market Session Guide Indicator helps you navigate the trading day with real-time session tracking, countdown timers, and local time adjustmentsโgiving you a clear edge in the market.
Key Features
Live Session Tracking โ Instantly see which trading session is active: Asian, European, US, or the high-volatility EU-US overlap.
Automatic Local Time Conversion โ No need to convert UTC manuallyโsession times adjust automatically based on your TradingView exchange settings.
Daylight Saving Time Adjustments โ The US market opening and closing times are automatically adjusted for summer and winter shifts.
Countdown Timer for Session Close โ Know exactly when the current session will end so you can time your trades effectively.
Next Market Opening Display โ Always be prepared by knowing which market opens next and at what exact time in your local timezone.
Clear Visual Guide โ A structured table in the top-right of your chart provides all essential session details without cluttering your screen.
How It Works
This indicator tracks the three main trading sessions:
Asian Session (Tokyo, Sydney): 00:00 - 09:00 UTC
European Session (London, Frankfurt): 07:00 - 16:00 UTC
US Session (New York, Chicago): 13:30 - 22:00 UTC (adjusts automatically for Daylight Saving Time)
EU-US Overlap: 12:00 - 16:00 UTC, the most volatile period of the trading day
It also highlights when a session is about to close and when the next one will begin, ensuring you are always aware of liquidity shifts in the market.
Why You Need This Indicator
Optimized for Forex, Crypto, and Indices โ Helps traders align their strategies with the most active market hours.
Ideal for Scalping and Day Trading โ Enter trades during peak volatility to maximize opportunities.
Eliminates Guesswork โ Stop manually tracking time zones and market schedulesโeverything updates dynamically for you.
Upgrade Your Trading Strategy Today
This indicator simplifies market timing, ensuring you're always trading when liquidity and volatility are at their highest. Whether you're trading Forex, Crypto, or Stocks, knowing when markets open and close is essential for making informed decisions.
Try it out, and if you find it useful, consider sharing it with other traders. Your feedback is always welcome!
HTF Candle Volume Thermometer [ChartPrime]The HTF Candle Volume Thermometer is a powerful volume heatmap tool that visualizes higher timeframe candle volume distributions directly on the chart. It helps traders identify key price levels where liquidity is concentrated, allowing for more informed trading decisions.
โฏ KEY FEATURES
Higher Timeframe Volume Mapping
Uses higher timeframe (HTF) candles to create a heatmap of volume distribution within each candle.
Dynamic Volume Heatmap
Colors each HTF candle background green for bullish and red for bearish, with a gradient heat overlay highlighting volume concentration.
Max Volume Point Identification
Marks the level within each HTF candle where the highest volume was recorded, using red for the most significant volume area.
Fully Customizable Display
Users can adjust the HTF timeframe, color settings, and resolution to tailor the indicator to their trading preferences.
Segmented Volume Distribution
Each HTF candle is divided into smaller levels, allowing traders to see volume changes within the range of each candle.
Key Level Detection
Max volume points often act as key support and resistance levels where price is likely to react, helping traders refine their strategies.
โฏ HOW TO USE
Identify Liquidity Zones
Use the max volume levels to determine areas where price is likely to find support or resistance.
Assess Trend Strength
Compare volume distribution between bullish and bearish HTF candles to gauge market momentum.
Optimize Trade Entries & Exits
Look for price reactions at high-volume areas to refine stop-loss and take-profit levels.
Adjust Heatmap Resolution
Customize the resolution setting to get a more detailed or broader view of volume segmentation within HTF candles.
โฏ CONCLUSION
The HTF Candle Volume Thermometer is a must-have tool for traders who want to integrate volume analysis with higher timeframe structures. By visualizing volume heatmaps within each HTF candle, this indicator helps traders pinpoint critical liquidity zones and key price levels.
Volume Metrics & Market CapitalizationThis Pine Script indicator provides a comparative view of volume metrics and market capitalization to help traders analyze relative volume strength in the context of a stockโs overall size.
Key Features:
Volume Formatting:
Converts numerical values into readable units (K for thousand, M for million, B for billion, T for trillion).
Volume Metrics:
Displays current bar volume, cumulative daily volume, and 30-day average volume.
Market Capitalization Calculation:
Uses the outstanding shares multiplied by closing price to estimate market cap.
Table Display:
Shows all these values in an easy-to-read table in the bottom-right of the chart.
How It Helps Compare Relative Volume to Market Cap
Relative Volume Strength
By comparing current volume and 30-day average volume, traders can quickly gauge if todayโs volume is unusually high or low.
If daily volume exceeds the 30-day average, it suggests increased market interest in the stock.
Market Cap Context
Market cap provides a reference for whether a stock is large-cap, mid-cap, or small-cap, influencing how volume should be interpreted.
A high volume surge in a low market cap stock may indicate stronger momentum compared to the same volume change in a large-cap stock.
Liquidity and Volatility Signals
Comparing volume to market cap helps determine liquidityโstocks with low market cap but high volume may be more volatile.
Example: A small-cap stock with $50M market cap trading $20M daily volume is seeing 40% turnover, a significant indicator of strong movement.
Practical Use Case
Day Traders: Spot stocks experiencing unusual volume surges relative to their market cap, identifying potential breakout or momentum plays.
Swing Traders: Assess if a stock is trading at above-average volume levels, confirming strength in trends.
Investors: Understand liquidity and potential institutional interest in stocks, as larger players typically trade in high market-cap names with sustained volume.
This indicator is a quick-glance tool for identifying high-volume stocks relative to their size, helping traders make more informed decisions on potential opportunities. ๐
Previous Hour High and Low### **๐ท Previous Hour High & Low Indicator โ Description**
#### ๐ **Overview**
The **Previous Hour High & Low Indicator** is designed to help traders identify key levels from the last completed hourly candle. These levels often act as **support and resistance zones**, helping traders make informed decisions about potential breakouts, reversals, and liquidity grabs.
#### ๐ฏ **How It Works**
- At the start of every new hour, the indicator **locks in** the **high and low** from the **previous fully completed hour**.
- It then **draws horizontal lines** on the chart, marking these levels.
- Works **only on intraday timeframes** (e.g., 1m, 5m, 15m, 30m), ensuring clean and relevant levels.
- Updates dynamically **every new hour** without repainting.
#### ๐ **Why Is This Useful?**
โ **Identifies Key Liquidity Zones** โ The market often reacts to previous hour highs/lows, making them useful for stop hunts, liquidity grabs, and order block setups.
โ **Works Well with ICT Concepts** โ If you're trading **ICT kill zones**, these levels can help in finding optimal trade entries.
โ **Helps with Breakout & Rejection Setups** โ Traders can watch for price breaking or rejecting these levels for trade confirmation.
โ **Useful for Scalping & Day Trading** โ Works best for short-term traders looking for intraday movements.
#### โ **Customization Options**
- The high and low levels are color-coded:
๐ต **Previous Hour High (Blue)** โ Acts as potential resistance or breakout point.
๐ด **Previous Hour Low (Red)** โ Acts as potential support or breakdown level.
#### ๐ **Best Timeframes to Use This On**
- **1-minute, 5-minute, 15-minute, 30-minute charts** โ Most effective for intraday trading.
- Avoid using on **hourly or higher timeframes**, as these levels become less relevant.
---
๐ **This indicator is perfect for traders looking to track short-term price reactions at key levels.** Let me know if you want to add alerts, zone shading, or any other enhancements! ๐ฅ
Dynamic Customizable 50% Line & Daily High/Low + True Day OpenA Unique Indicator for Precise Market-Level Analysis
This indicator is a fully integrated solution that automates complex market-level calculations and visualizations, offering traders a tool that goes beyond the functionality of existing open-source alternatives. By seamlessly combining several trading concepts into a single script, it delivers efficiency, accuracy, and customization that cater to both novice and professional traders.
Key Features: A Breakdown of What Makes It Unique
1. Adaptive Daily Highs and Lows
Automatically detects and plots daily high and low levels based on the selected time frame, dynamically updating in real time.
Features session-based adjustments, allowing traders to focus on levels that matter for specific trading sessions (e.g., London, New York).
Fully customizable styling, visibility, and alerts tailored to each traderโs preferences.
How It Works:
The indicator calculates daily high and low levels directly from price data, integrating session-specific time offsets to account for global trading hours. These levels provide traders with clear visual markers for key liquidity zones.
2. Automated ICT 50% Range Line
A pioneering implementation of ICTโs mid-range concept, this feature dynamically calculates and displays the midpoint of the daily range.
Offers traders a visual guide to identify premium and discount zones, aiding in determining market bias and potential trade setups.
How It Works:
The script calculates the range between the dayโs high and low, dividing it by two to generate the midline. This line updates in real-time, ensuring that traders always see the most current premium and discount levels as price action evolves.
3. Dynamic Market Open Levels
Plots session opens (e.g., Asia, London, New York) and the True Day Open to provide actionable reference points for intra-day trading strategies.
Enhances precision in identifying liquidity shifts and aligning trades with institutional price movements.
How It Works:
The indicator uses predefined session times to calculate and display the opening levels for key trading sessions. It dynamically adjusts for time zones, ensuring accuracy regardless of the traderโs location.
4. Custom Watermark for Enhanced Visualization
Includes an optional watermark feature that allows users to display custom text on their charts.
Ideal for personalization, branding, or highlighting session notes without disrupting the clarity of the chart.
Why This Indicator Stands Out
First-to-Market Automation:
While the ICT 50% range line is a widely recognized concept, this is the first script to automate its calculation, combining it with other pivotal trading levels in a single tool.
All-in-One Functionality:
Unlike open-source alternatives that focus on individual features, this script integrates daily highs/lows, mid-range levels, session opens, and customizable watermarks into one cohesive system. The consolidation reduces the need for multiple indicators and ensures a clean, efficient chart setup.
Dynamic Customization:
Every feature can be adjusted to align with a traderโs strategy, time zone, or aesthetic preferences. This level of adaptability is unmatched in existing tools.
Proprietary Logic:
The indicatorโs underlying calculations are built from scratch, leveraging advanced programming techniques to ensure accuracy and reliability. These proprietary methods differentiate it from similar open-source scripts.
How to Use This Indicator
Apply the Indicator:
Add it to your TradingView chart from the library.
Configure Settings:
Use the intuitive settings panel to adjust plotted levels, colors, styles, and visibility. Tailor the indicator to your trading strategy.
Incorporate into Analysis:
Combine the plotted levels with your preferred trading approach to identify liquidity zones, establish market bias, and pinpoint potential reversals or entries.
Stay Focused:
With all key levels automated and updated in real time, traders can focus on execution rather than manual plotting.
Originality and Justification for Closed Source
This script is closed-source due to its unique combination of features and proprietary logic that automates complex trading concepts like the ICT 50% range line and session-specific levels. Open-source alternatives lack this level of integration and customization, making this indicator a valuable and original contribution to the TradingView ecosystem.
What Sets It Apart from Open-Source Scripts?
Unlike open-source tools, this indicator doesnโt just replicate individual featuresโit enhances and integrates them into a seamless, all-in-one solution that offers traders a more efficient and effective way to analyze the market.
Requestโ โ OVERVIEW
This library is a tool for Pine Scriptโข programmers that consolidates access to a wide range of lesser-known data feeds available on TradingView, including metrics from the FRED database, FINRA short sale volume, open interest, and COT data. The functions in this library simplify requests for these data feeds, making them easier to retrieve and use in custom scripts.
โ โ CONCEPTS
Federal Reserve Economic Data (FRED)
FRED (Federal Reserve Economic Data) is a comprehensive online database curated by the Federal Reserve Bank of St. Louis. It provides free access to extensive economic and financial data from U.S. and international sources. FRED includes numerous economic indicators such as GDP, inflation, employment, and interest rates. Additionally, it provides financial market data, regional statistics, and international metrics such as exchange rates and trade balances.
Sourced from reputable organizations, including U.S. government agencies, international institutions, and other public and private entities, FRED enables users to analyze over 825,000 time series, download their data in various formats, and integrate their information into analytical tools and programming workflows.
On TradingView, FRED data is available from ticker identifiers with the "FRED:" prefix. Users can search for FRED symbols in the "Symbol Search" window, and Pine scripts can retrieve data for these symbols via `request.*()` function calls.
FINRA Short Sale Volume
FINRA (the Financial Industry Regulatory Authority) is a non-governmental organization that supervises and regulates U.S. broker-dealers and securities professionals. Its primary aim is to protect investors and ensure integrity and transparency in financial markets.
FINRA's Short Sale Volume data provides detailed information about daily short-selling activity across U.S. equity markets. This data tracks the volume of short sales reported to FINRA's trade reporting facilities (TRFs), including shares sold on FINRA-regulated Alternative Trading Systems (ATSs) and over-the-counter (OTC) markets, offering transparent access to short-selling information not typically available from exchanges. This data helps market participants, researchers, and regulators monitor trends in short-selling and gain insights into bearish sentiment, hedging strategies, and potential market manipulation. Investors often use this data alongside other metrics to assess stock performance, liquidity, and overall trading activity.
It is important to note that FINRA's Short Sale Volume data does not consolidate short sale information from public exchanges and excludes trading activity that is not publicly disseminated.
TradingView provides ticker identifiers for requesting Short Sale Volume data with the format "FINRA:_SHORT_VOLUME", where "" is a supported U.S. equities symbol (e.g., "AAPL").
Open Interest (OI)
Open interest is a cornerstone indicator of market activity and sentiment in derivatives markets such as options or futures. In contrast to volume, which measures the number of contracts opened or closed within a period, OI measures the number of outstanding contracts that are not yet settled. This distinction makes OI a more robust indicator of how money flows through derivatives, offering meaningful insights into liquidity, market interest, and trends. Many traders and investors analyze OI alongside volume and price action to gain an enhanced perspective on market dynamics and reinforce trading decisions.
TradingView offers many ticker identifiers for requesting OI data with the format "_OI", where "" represents a derivative instrument's ticker ID (e.g., "COMEX:GC1!").
Commitment of Traders (COT)
Commitment of Traders data provides an informative weekly breakdown of the aggregate positions held by various market participants, including commercial hedgers, non-commercial speculators, and small traders, in the U.S. derivative markets. Tallied and managed by the Commodity Futures Trading Commission (CFTC) , these reports provide traders and analysts with detailed insight into an asset's open interest and help them assess the actions of various market players. COT data is valuable for gaining a deeper understanding of market dynamics, sentiment, trends, and liquidity, which helps traders develop informed trading strategies.
TradingView has numerous ticker identifiers that provide access to time series containing data for various COT metrics. To learn about COT ticker IDs and how they work, see our LibraryCOT publication.
โ โ USING THE LIBRARY
Common function characteristics
โโข This library's functions construct ticker IDs with valid formats based on their specified parameters, then use them as the `symbol` argument in request.security() to retrieve data from the specified context.
โโข Most of these functions automatically select the timeframe of a data request because the data feeds are not available for all timeframes.
โโข All the functions have two overloads. The first overload of each function uses values with the "simple" qualifier to define the requested context, meaning the context does not change after the first script execution. The second accepts "series" values, meaning it can request data from different contexts across executions.
โโข The `gaps` parameter in most of these functions specifies whether the returned data is `na` when a new value is unavailable for request. By default, its value is `false`, meaning the call returns the last retrieved data when no new data is available.
โโข The `repaint` parameter in applicable functions determines whether the request can fetch the latest unconfirmed values from a higher timeframe on realtime bars, which might repaint after the script restarts. If `false`, the function only returns confirmed higher-timeframe values to avoid repainting. The default value is `true`.
`fred()`
The `fred()` function retrieves the most recent value of a specified series from the Federal Reserve Economic Data (FRED) database. With this function, programmers can easily fetch macroeconomic indicators, such as GDP and unemployment rates, and use them directly in their scripts.
How it works
The function's `fredCode` parameter accepts a "string" representing the unique identifier of a specific FRED series. Examples include "GDP" for the "Gross Domestic Product" series and "UNRATE" for the "Unemployment Rate" series. Over 825,000 codes are available. To access codes for available series, search the FRED website .
The function adds the "FRED:" prefix to the specified `fredCode` to construct a valid FRED ticker ID (e.g., "FRED:GDP"), which it uses in request.security() to retrieve the series data.
Example Usage
This line of code requests the latest value from the Gross Domestic Product series and assigns the returned value to a `gdpValue` variable:
float gdpValue = fred("GDP")
`finraShortSaleVolume()`
The `finraShortSaleVolume()` function retrieves EOD data from a FINRA Short Sale Volume series. Programmers can call this function to retrieve short-selling information for equities listed on supported exchanges, namely NASDAQ, NYSE, and NYSE ARCA.
How it works
The `symbol` parameter determines which symbol's short sale volume information is retrieved by the function. If the value is na , the function requests short sale volume data for the chart's symbol. The argument can be the name of the symbol from a supported exchange (e.g., "AAPL") or a ticker ID with an exchange prefix ("NASDAQ:AAPL"). If the `symbol` contains an exchange prefix, it must be one of the following: "NASDAQ", "NYSE", "AMEX", or "BATS".
The function constructs a ticker ID in the format "FINRA:ticker_SHORT_VOLUME", where "ticker" is the symbol name without the exchange prefix (e.g., "AAPL"). It then uses the ticker ID in request.security() to retrieve the available data.
Example Usage
This line of code retrieves short sale volume for the chart's symbol and assigns the result to a `shortVolume` variable:
float shortVolume = finraShortSaleVolume(syminfo.tickerid)
This example requests short sale volume for the "NASDAQ:AAPL" symbol, irrespective of the current chart:
float shortVolume = finraShortSaleVolume("NASDAQ:AAPL")
`openInterestFutures()` and `openInterestCrypto()`
The `openInterestFutures()` function retrieves EOD open interest (OI) data for futures contracts. The `openInterestCrypto()` function provides more granular OI data for cryptocurrency contracts.
How they work
The `openInterestFutures()` function retrieves EOD closing OI information. Its design is focused primarily on retrieving OI data for futures, as only EOD OI data is available for these instruments. If the chart uses an intraday timeframe, the function requests data from the "1D" timeframe. Otherwise, it uses the chart's timeframe.
The `openInterestCrypto()` function retrieves opening, high, low, and closing OI data for a cryptocurrency contract on a specified timeframe. Unlike `openInterest()`, this function can also retrieve granular data from intraday timeframes.
Both functions contain a `symbol` parameter that determines the symbol for which the calls request OI data. The functions construct a valid OI ticker ID from the chosen symbol by appending "_OI" to the end (e.g., "CME:ES1!_OI").
The `openInterestFutures()` function requests and returns a two-element tuple containing the futures instrument's EOD closing OI and a "bool" condition indicating whether OI is rising.
The `openInterestCrypto()` function requests and returns a five-element tuple containing the cryptocurrency contract's opening, high, low, and closing OI, and a "bool" condition indicating whether OI is rising.
Example usage
This code line calls `openInterest()` to retrieve EOD OI and the OI rising condition for a futures symbol on the chart, assigning the values to two variables in a tuple:
= openInterestFutures(syminfo.tickerid)
This line retrieves the EOD OI data for "CME:ES1!", irrespective of the current chart's symbol:
= openInterestFutures("CME:ES1!")
This example uses `openInterestCrypto()` to retrieve OHLC OI data and the OI rising condition for a cryptocurrency contract on the chart, sampled at the chart's timeframe. It assigns the returned values to five variables in a tuple:
= openInterestCrypto(syminfo.tickerid, timeframe.period)
This call retrieves OI OHLC and rising information for "BINANCE:BTCUSDT.P" on the "1D" timeframe:
= openInterestCrypto("BINANCE:BTCUSDT.P", "1D")
`commitmentOfTraders()`
The `commitmentOfTraders()` function retrieves data from the Commitment of Traders (COT) reports published by the Commodity Futures Trading Commission (CFTC). This function significantly simplifies the COT request process, making it easier for programmers to access and utilize the available data.
How It Works
This function's parameters determine different parts of a valid ticker ID for retrieving COT data, offering a streamlined alternative to constructing complex COT ticker IDs manually. The `metricName`, `metricDirection`, and `includeOptions` parameters are required. They specify the name of the reported metric, the direction, and whether it includes information from options contracts.
The function also includes several optional parameters. The `CFTCCode` parameter allows programmers to request data for a specific report code. If unspecified, the function requests data based on the chart symbol's root prefix, base currency, or quoted currency, depending on the `mode` argument. The call can specify the report type ("Legacy", "Disaggregated", or "Financial") and metric type ("All", "Old", or "Other") with the `typeCOT` and `metricType` parameters.
Explore the CFTC website to find valid report codes for specific assets. To find detailed information about the metrics included in the reports and their meanings, see the CFTC's Explanatory Notes .
View the function's documentation below for detailed explanations of its parameters. For in-depth information about COT ticker IDs and more advanced functionality, refer to our previously published COT library .
Available metrics
Different COT report types provide different metrics . The tables below list all available metrics for each type and their applicable directions:
+------------------------------+------------------------+
| Legacy (โCOT) Metric Names | Directions |
+------------------------------+------------------------+
| Open Interest | No direction |
| Noncommercial Positions | Long, Short, Spreading |
| Commercial Positions | Long, Short |
| Total Reportable Positions | Long, Short |
| Nonreportable Positions | Long, Short |
| Traders Total | No direction |
| Traders Noncommercial | Long, Short, Spreading |
| Traders Commercial | Long, Short |
| Traders Total Reportable | Long, Short |
| Concentration Gross โLT 4 TDR | Long, Short |
| Concentration Gross โLT 8 TDR | Long, Short |
| Concentration Net โLT 4 TDR | Long, Short |
| Concentration Net โLT 8 TDR | Long, Short |
+------------------------------+------------------------+
+-----------------------------------+------------------------+
| Disaggregated (COT2) Metric Names | Directions |
+-----------------------------------+------------------------+
| Open Interest | No Direction |
| Producer Merchant Positions | Long, Short |
| Swap Positions | Long, Short, Spreading |
| Managed Money Positions | Long, Short, Spreading |
| Other Reportable Positions | Long, Short, Spreading |
| Total Reportable Positions | Long, Short |
| Nonreportable Positions | Long, Short |
| Traders Total | No Direction |
| Traders Producer Merchant | Long, Short |
| Traders Swap | Long, Short, Spreading |
| Traders Managed Money | Long, Short, Spreading |
| Traders Other Reportable | Long, Short, Spreading |
| Traders Total Reportable | Long, Short |
| Concentration Gross LE 4 TDR | Long, Short |
| Concentration Gross LE 8 TDR | Long, Short |
| Concentration Net LE 4 TDR | Long, Short |
| Concentration Net LE 8 TDR | Long, Short |
+-----------------------------------+------------------------+
+-------------------------------+------------------------+
| Financial (COT3) Metric Names | Directions |
+-------------------------------+------------------------+
| Open Interest | No Direction |
| Dealer Positions | Long, Short, Spreading |
| Asset Manager Positions | Long, Short, Spreading |
| Leveraged Funds Positions | Long, Short, Spreading |
| Other Reportable Positions | Long, Short, Spreading |
| Total Reportable Positions | Long, Short |
| Nonreportable Positions | Long, Short |
| Traders Total | No Direction |
| Traders Dealer | Long, Short, Spreading |
| Traders Asset Manager | Long, Short, Spreading |
| Traders Leveraged Funds | Long, Short, Spreading |
| Traders Other Reportable | Long, Short, Spreading |
| Traders Total Reportable | Long, Short |
| Concentration Gross LE 4 TDR | Long, Short |
| Concentration Gross LE 8 TDR | Long, Short |
| Concentration Net LE 4 TDR | Long, Short |
| Concentration Net LE 8 TDR | Long, Short |
+-------------------------------+------------------------+
Example usage
This code line retrieves "Noncommercial Positions (Long)" data, without options information, from the "Legacy" report for the chart symbol's root, base currency, or quote currency:
float nonCommercialLong = commitmentOfTraders("Noncommercial Positions", "Long", false)
This example retrieves "Managed Money Positions (Short)" data, with options included, from the "Disaggregated" report:
float disaggregatedData = commitmentOfTraders("Managed Money Positions", "Short", true, "", "Disaggregated")
โ โ NOTES
โโข This library uses dynamic requests , allowing dynamic ("series") arguments for the parameters defining the context (ticker ID, timeframe, etc.) of a `request.*()` function call. With this feature, a single `request.*()` call instance can flexibly retrieve data from different feeds across historical executions. Additionally, scripts can use such calls in the local scopes of loops, conditional structures, and even exported library functions, as demonstrated in this script. All scripts coded in Pine Scriptโข v6 have dynamic requests enabled by default. To learn more about the behaviors and limitations of this feature, see the Dynamic requests section of the Pine Scriptโข User Manual.
โโข The library's example code offers a simple demonstration of the exported functions. The script retrieves available data using the function specified by the "Series type" input. The code requests a FRED series or COT (Legacy), FINRA Short Sale Volume, or Open Interest series for the chart's symbol with specific parameters, then plots the retrieved data as a step-line with diamond markers.
Look first. Then leap.
โ โ EXPORTED FUNCTIONS
This library exports the following functions:
fred(fredCode, gaps)
โโRequests a value from a specified Federal Reserve Economic Data (FRED) series. FRED is a comprehensive source that hosts numerous U.S. economic datasets. To explore available FRED datasets and codes, search for specific categories or keywords at fred.stlouisfed.org Calls to this function count toward a script's `request.*()` call limit.
โโParameters:
โโโโ fredCode (series string) : The unique identifier of the FRED series. The function uses the value to create a valid ticker ID for retrieving FRED data in the format `"FRED:fredCode"`. For example, `"GDP"` refers to the "Gross Domestic Product" series ("FRED:GDP"), and `"GFDEBTN"` refers to the "Federal Debt: Total Public Debt" series ("FRED:GFDEBTN").
โโโโ gaps (simple bool) : Optional. If `true`, the function returns a non-na value only when a new value is available from the requested context. If `false`, the function returns the latest retrieved value when new data is unavailable. The default is `false`.
โโReturns: (float) The value from the requested FRED series.
finraShortSaleVolume(symbol, gaps, repaint)
โโRequests FINRA daily short sale volume data for a specified symbol from one of the following exchanges: NASDAQ, NYSE, NYSE ARCA. If the chart uses an intraday timeframe, the function requests data from the "1D" timeframe. Otherwise, it uses the chart's timeframe. Calls to this function count toward a script's `request.*()` call limit.
โโParameters:
โโโโ symbol (series string) : The symbol for which to request short sale volume data. If the specified value contains an exchange prefix, it must be one of the following: "NASDAQ", "NYSE", "AMEX", "BATS".
โโโโ gaps (simple bool) : Optional. If `true`, the function returns a non-na value only when a new value is available from the requested context. If `false`, the function returns the latest retrieved value when new data is unavailable. The default is `false`.
โโโโ repaint (simple bool) : Optional. If `true` and the chart's timeframe is intraday, the value requested on realtime bars may change its time offset after the script restarts its executions. If `false`, the function returns the last confirmed period's values to avoid repainting. The default is `true`.
โโReturns: (float) The short sale volume for the specified symbol or the chart's symbol.
openInterestFutures(symbol, gaps, repaint)
โโRequests EOD open interest (OI) and OI rising information for a valid futures symbol. If the chart uses an intraday timeframe, the function requests data from the "1D" timeframe. Otherwise, it uses the chart's timeframe. Calls to this function count toward a script's `request.*()` call limit.
โโParameters:
โโโโ symbol (series string) : The symbol for which to request open interest data.
โโโโ gaps (simple bool) : Optional. If `true`, the function returns non-na values only when new values are available from the requested context. If `false`, the function returns the latest retrieved values when new data is unavailable. The default is `false`.
โโโโ repaint (simple bool) : Optional. If `true` and the chart's timeframe is intraday, the value requested on realtime bars may change its time offset after the script restarts its executions. If `false`, the function returns the last confirmed period's values to avoid repainting. The default is `true`.
โโReturns: ( ) A tuple containing the following values:
โโโโ- The closing OI value for the symbol.
โโโโ- `true` if the closing OI is above the previous period's value, `false` otherwise.
openInterestCrypto(symbol, timeframe, gaps, repaint)
โโRequests opening, high, low, and closing open interest (OI) data and OI rising information for a valid cryptocurrency contract on a specified timeframe. Calls to this function count toward a script's `request.*()` call limit.
โโParameters:
โโโโ symbol (series string) : The symbol for which to request open interest data.
โโโโ timeframe (series string) : The timeframe of the data request. If the timeframe is lower than the chart's timeframe, it causes a runtime error.
โโโโ gaps (simple bool) : Optional. If `true`, the function returns non-na values only when new values are available from the requested context. If `false`, the function returns the latest retrieved values when new data is unavailable. The default is `false`.
โโโโ repaint (simple bool) : Optional. If `true` and the `timeframe` represents a higher timeframe, the function returns unconfirmed values from the timeframe on realtime bars, which repaint when the script restarts its executions. If `false`, it returns only confirmed higher-timeframe values to avoid repainting. The default is `true`.
โโReturns: ( ) A tuple containing the following values:
โโโโ- The opening, high, low, and closing OI values for the symbol, respectively.
โโโโ- `true` if the closing OI is above the previous period's value, `false` otherwise.
commitmentOfTraders(metricName, metricDirection, includeOptions, CFTCCode, typeCOT, mode, metricType)
โโRequests Commitment of Traders (COT) data with specified parameters. This function provides a simplified way to access CFTC COT data available on TradingView. Calls to this function count toward a script's `request.*()` call limit. For more advanced tools and detailed information about COT data, see TradingView's LibraryCOT library.
โโParameters:
โโโโ metricName (series string) : One of the valid metric names listed in the library's documentation and source code.
โโโโ metricDirection (series string) : Metric direction. Possible values are: "Long", "Short", "Spreading", and "No direction". Consult the library's documentation or code to see which direction values apply to the specified metric.
โโโโ includeOptions (series bool) : If `true`, the COT symbol includes options information. Otherwise, it does not.
โโโโ CFTCCode (series string) : Optional. The CFTC code for the asset. For example, wheat futures (root "ZW") have the code "001602". If one is not specified, the function will attempt to get a valid code for the chart symbol's root, base currency, or main currency.
โโโโ typeCOT (series string) : Optional. The type of report to request. Possible values are: "Legacy", "Disaggregated", "Financial". The default is "Legacy".
โโโโ mode (series string) : Optional. Specifies the information the function extracts from a symbol. Possible modes are:
โโ- "Root": The function extracts the futures symbol's root prefix information (e.g., "ES" for "ESH2020").
โโ- "Base currency": The function extracts the first currency from a currency pair (e.g., "EUR" for "EURUSD").
โโ- "Currency": The function extracts the currency of the symbol's quoted values (e.g., "JPY" for "TSE:9984" or "USDJPY").
โโ- "Auto": The function tries the first three modes (Root -> Base currency -> Currency) until it finds a match.
โโThe default is "Auto". If the specified mode is not available for the symbol, it causes a runtime error.
โโโโ metricType (series string) : Optional. The metric type. Possible values are: "All", "Old", "Other". The default is "All".
โโReturns: (float) The specified Commitment of Traders data series. If no data is available, it causes a runtime error.
Bitcoin Premium [SAKANE]Overview
"Bitcoin Premium " is an indicator designed to analyze the price differences (premiums) of Bitcoin between major exchanges. By using this tool, you can visualize these differences and trends across exchanges, helping you make more informed trading decisions.
Features
1. Premium Calculation and Display
- Calculates and visualizes the price differences between major exchanges like Coinbase, Bitfinex, Upbit, and Binance.
- Premiums are displayed in a histogram format for intuitive analysis.
2. Forex Rate Adjustment
- Prices quoted in KRW (e.g., from Upbit) are converted to USD using real-time KRW/USD forex rates.
3. Moving Average Option
- Displays moving averages (SMA or EMA) of premiums for a clearer view of long-term trends.
4. Customizable Settings
- Toggle the premium display for each exchange on or off.
- Includes label displays to support visual analysis.
What Can It Do for You?
1. Identify Arbitrage Opportunities
By observing price differences (premiums) between exchanges, you can identify arbitrage opportunities.
Example: If Bitcoin is cheaper on Binance and more expensive on Coinbase, you could buy on Binance and sell on Coinbase to capture the price difference.
2. Understand Regional Supply and Demand Trends
Each exchange's premium reflects the supply and demand dynamics of its respective region.
Example: A high premium on Upbit may indicate excess demand or regulatory impacts in the South Korean market.
3. Analyze Liquidity
Price differences often highlight liquidity disparities between exchanges. Markets with lower trading volumes tend to have larger premiums due to price distortions.
4. Evaluate Macroeconomic Impacts
Premium movements may reflect changes in macroeconomic factors, such as exchange rates, regulations, or financial conditions specific to each region.
5. Analyze Trends and Market Sentiment
By tracking premium trends, you can gauge market sentiment and understand regional or exchange-specific behaviors to inform your investment decisions.
6. Support Strategic Trading
This tool is useful for short-term arbitrage strategies as well as long-term evaluations of market health.
Exchange Characteristics and Premium Implications
The meaning of premiums varies by exchange.
- Coinbase (US Market)
Primarily used by investors buying directly with fiat currency (USD). A higher premium often signals bullish sentiment among institutional and retail investors.
- Bitfinex (Global Market)
A trader-focused exchange with active large-scale and leveraged trading. Premiums may reflect liquidity and risk appetite.
- Upbit (South Korean Market)
Priced in KRW, making it subject to forex rates and local market dynamics. High premiums may indicate strong demand or regulatory influences in South Korea.
- Binance (Global Market)
The largest exchange by trading volume. Premiums here are often a reflection of the overall market balance.
Notes
- This indicator is for reference only and does not guarantee trading decisions.
- Please consider the characteristics and conditions of each exchange when using this tool.
First day candle high and low of monthThis script is designed to mark the high and low levels of the first candle of each month on the chart. These levels are often considered significant support and resistance zones, as they can represent key liquidity points in the market.
The idea behind this tool is based on the observation that the low of the first monthly candle can act as a critical support level, especially during a bullish market trend. If the price breaks below this low in a bull market, it may indicate a potential manipulation or stop-loss hunting rather than a genuine shift in trend. Similarly, the high of the first monthly candle may serve as a key resistance level, particularly in consolidating or range-bound markets.
By dynamically plotting these levels, the script provides traders with valuable insights into potential liquidity zones and significant market reactions. It allows for customizable line colors and lengths, making it adaptable to various trading styles and preferences.
This tool is particularly useful for traders who wish to align their strategies with institutional market behaviors, as it highlights areas where liquidity is likely to be targeted. Use it as part of your broader analysis to identify potential trade setups, manage risk effectively, and understand market dynamics more comprehensively.
M2 Money Shift for Bitcoin [SAKANE]M2 Money Shift for Bitcoin was developed to visualize the impact of M2 Money, a macroeconomic indicator, on the Bitcoin market and to support trade analysis.
Bitcoin price fluctuations have a certain correlation with cycles in M2 money supply.In particular, it has been noted that changes in M2 supply can affect the bitcoin price 70 days in advance.Very high correlations have been observed in recent years in particular, making it useful as a supplemental analytical tool for trading.
Support for M2 data from multiple countries
M2 supply data from the U.S., Europe, China, Japan, the U.K., Canada, Australia, and India are integrated and all are displayed in U.S. dollar equivalents.
Slide function
Using the "Slide Days Forward" setting, M2 data can be slid up to 500 days, allowing for flexible analysis that takes into account the time difference from the bitcoin price.
Plotting Total Liquidity
Plot total liquidity (in trillions of dollars) by summing the M2 supply of multiple countries.
How to use
After applying the indicator to the chart, activate the M2 data for the required country from the settings screen. 2.
2. adjust "Slide Days Forward" to analyze the relationship between changes in M2 supply and bitcoin price
3. refer to the Gross Liquidity plot to build a trading strategy that takes into account macroeconomic influences.
Notes.
This indicator is an auxiliary tool for trade analysis and does not guarantee future price trends.
The relationship between M2 supply and bitcoin price depends on many factors and should be used in conjunction with other analysis methods.
OutofOptionsHelperLibraryLibrary "OutofOptionsHelperLibrary"
Helper library for my indicators/strategies
isUp(i)
โโis Up candle
โโParameters:
โโโโ i (int)
โโReturns: bool
isDown(i)
โโis Down candle
โโParameters:
โโโโ i (int)
โโReturns: bool
TF(t)
โโformat time into date/time string
โโParameters:
โโโโ t (int)
โโReturns: string
S(s)
โโformat data to string
โโParameters:
โโโโ s (float)
โโReturns: string
S(s)
โโformat data to string
โโParameters:
โโโโ s (int)
โโReturns: string
S(s)
โโformat data to string
โโParameters:
โโโโ s (bool)
โโReturns: string
barClose(price, up, strict)
โโDetermine if candle closed above/below price
โโParameters:
โโโโ price (float)
โโโโ up (bool)
โโโโ strict (bool) : bool if close over is required or if close at the price is good enough
โโReturns: bool
processSweep(L, price, up, leftB)
โโDetermine how many liquidity sweeps were made
โโParameters:
โโโโ L (array)
โโโโ price (float)
โโโโ up (bool)
โโโโ leftB (int)
โโReturns: int
liquidity
โโFields:
โโโโ price (series float)
โโโโ time (series int)
โโโโ oprice (series float)
โโโโ otime (series int)
โโโโ sweeps (series int)
โโโโ bars_swept (series int)
S&P 100 Option Expiration Week StrategyThe Option Expiration Week Strategy aims to capitalize on increased volatility and trading volume that often occur during the week leading up to the expiration of options on stocks in the S&P 100 index. This period, known as the option expiration week, culminates on the third Friday of each month when stock options typically expire in the U.S. During this week, investors in this strategy take a long position in S&P 100 stocks or an equivalent ETF from the Monday preceding the third Friday, holding until Friday. The strategy capitalizes on potential upward price pressures caused by increased option-related trading activity, rebalancing, and hedging practices.
The phenomenon leveraged by this strategy is well-documented in finance literature. Studies demonstrate that options expiration dates have a significant impact on stock returns, trading volume, and volatility. This effect is driven by various market dynamics, including portfolio rebalancing, delta hedging by option market makers, and the unwinding of positions by institutional investors (Stoll & Whaley, 1987; Ni, Pearson, & Poteshman, 2005). These market activities intensify near option expiration, causing price adjustments that may create short-term profitable opportunities for those aware of these patterns (Roll, Schwartz, & Subrahmanyam, 2009).
The paper by Johnson and So (2013), Returns and Option Activity over the Option-Expiration Week for S&P 100 Stocks, provides empirical evidence supporting this strategy. The study analyzes the impact of option expiration on S&P 100 stocks, showing that these stocks tend to exhibit abnormal returns and increased volume during the expiration week. The authors attribute these patterns to intensified option trading activity, where demand for hedging and arbitrage around options expiration causes temporary price adjustments.
Scientific Explanation
Research has found that option expiration weeks are marked by predictable increases in stock returns and volatility, largely due to the role of options market makers and institutional investors. Option market makers often use delta hedging to manage exposure, which requires frequent buying or selling of the underlying stock to maintain a hedged position. As expiration approaches, their activity can amplify price fluctuations. Additionally, institutional investors often roll over or unwind positions during expiration weeks, creating further demand for underlying stocks (Stoll & Whaley, 1987). This increased demand around expiration week typically leads to temporary stock price increases, offering profitable opportunities for short-term strategies.
Key Research and Bibliography
Johnson, T. C., & So, E. C. (2013). Returns and Option Activity over the Option-Expiration Week for S&P 100 Stocks. Journal of Banking and Finance, 37(11), 4226-4240.
This study specifically examines the S&P 100 stocks and demonstrates that option expiration weeks are associated with abnormal returns and trading volume due to increased activity in the options market.
Stoll, H. R., & Whaley, R. E. (1987). Program Trading and Expiration-Day Effects. Financial Analysts Journal, 43(2), 16-28.
Stoll and Whaley analyze how program trading and portfolio insurance strategies around expiration days impact stock prices, leading to temporary volatility and increased trading volume.
Ni, S. X., Pearson, N. D., & Poteshman, A. M. (2005). Stock Price Clustering on Option Expiration Dates. Journal of Financial Economics, 78(1), 49-87.
This paper investigates how option expiration dates affect stock price clustering and volume, driven by delta hedging and other option-related trading activities.
Roll, R., Schwartz, E., & Subrahmanyam, A. (2009). Options Trading Activity and Firm Valuation. Journal of Financial Markets, 12(3), 519-534.
The authors explore how options trading activity influences firm valuation, finding that higher options volume around expiration dates can lead to temporary price movements in underlying stocks.
Cao, C., & Wei, J. (2010). Option Market Liquidity and Stock Return Volatility. Journal of Financial and Quantitative Analysis, 45(2), 481-507.
This study examines the relationship between options market liquidity and stock return volatility, finding that increased liquidity needs during expiration weeks can heighten volatility, impacting stock returns.
Summary
The Option Expiration Week Strategy utilizes well-researched financial market phenomena related to option expiration. By positioning long in S&P 100 stocks or ETFs during this period, traders can potentially capture abnormal returns driven by option market dynamics. The literature suggests that options-related activitiesโsuch as delta hedging, position rollovers, and portfolio adjustmentsโintensify demand for underlying assets, creating short-term profit opportunities around these key dates.
NYSE, Euronext, and Shanghai Stock Exchange Hours IndicatorNYSE, Euronext, and Shanghai Stock Exchange Hours Indicator
This script is designed to enhance your trading experience by visually marking the opening and closing hours of major global stock exchanges: the New York Stock Exchange (NYSE), Euronext, and Shanghai Stock Exchange. By adding vertical lines and background fills during trading sessions, it helps traders quickly identify these critical periods, potentially informing better trading decisions.
Features of This Indicator:
NYSE, Euronext, and Shanghai Stock Exchange Hours: Displays vertical lines at market open and close times for these three exchanges. You can easily switch between showing or hiding the different exchanges to customize the indicator for your needs.
Background Fill: Highlights the trading hours of these exchanges using faint background colors, making it easy to spot when markets are in session. This feature is crucial for timing trades around overlapping trading hours and volume peaks.
Customizable Visuals: Adjust the color, line style (solid, dotted, dashed), and line width to match your preferences, making the indicator both functional and visually aligned with your chart's aesthetics.
How to Use the Indicator:
Add the Indicator to Your Chart: Add the script to your chart from the TradingView script library. Once added, the indicator will automatically plot vertical lines at the opening and closing times of the NYSE, Euronext, and Shanghai Stock Exchange.
Customize Display Settings: Choose which exchanges to display by enabling or disabling the NYSE, Euronext, or Shanghai sessions in the indicator settings. This allows you to focus only on the exchanges that are relevant to your trading strategy.
Adjust Visual Properties: Customize the appearance of the vertical lines and background fill through the settings. Modify the color of each exchange, adjust the line style (solid, dotted, dashed), and control the line thickness to suit your chart preferences. The background fill can also be customized to clearly highlight active trading sessions.
Identify Key Market Hours: Use the vertical lines and background fills to identify the market open and close times. This is particularly useful for understanding how price action changes during specific trading hours or for finding high liquidity periods when multiple markets are open simultaneously.
Adapt Trading Strategies: By knowing when major stock exchanges are open, you can adapt your trading strategy to take advantage of potential price movements, increased volatility, or volume. This can help you avoid low-liquidity times and capitalize on more active trading periods.
This indicator is especially valuable for traders focusing on cross-market dynamics or those interested in understanding how different sessions influence market liquidity and price action. With this tool, you can gain insight into market conditions and adapt your trading strategies accordingly. The clean visual separation of session times helps you maintain context, whether you're trading Forex, stocks, or cryptocurrencies.
Disclaimer: This script is intended for informational and educational purposes only. It does not constitute financial advice or a recommendation to buy or sell any financial instrument. Always conduct your own research and consult with a licensed financial advisor before making any trading decisions. Trading involves risk, and past performance is not indicative of future results.
Liquidations Meter [LuxAlgo]The Liquidation Meter aims to gauge the momentum of the bar, identify the strength of the bulls and bears, and more importantly identify probable exhaustion/reversals by measuring probable liquidations.
๐ถ USAGE
This tool includes many features related to the concept of liquidation. The two core ones are the liquidation meter and liquidation price calculator, highlighted below.
๐น Liquidation Meter
The liquidation meter presents liquidations on the price chart by measuring the highest leverage value of longs and shorts that have been potentially liquidated on the last chart bar, hence allowing traders to:
gauge the momentum of the bar.
identify the strength of the bulls and bears.
identify probable reversal/exhaustion points.
Liquidation of low-leveraged positions can be indicative of exhaustion.
๐น Liquidation Price Calculator
A liquidation price calculator might come in handy when you need to calculate at what price level your leveraged position in Crypto, Forex, Stocks, or any other asset class gets liquidated to add a protective stop to mitigate risk. Monitoring an open position gets easier if the trader can calculate the total risk in order for them to choose the right amount of margin and leverage.
Liquidation price is the distance from the trader's entry price to the price where trader's leveraged position gets liquidated due to a loss. As the leverage is increased, the distance from trader's entry price to the liquidation price shrinks.
While you have one or several trades open you can quickly check their liquidation levels and determine which one of the trades is closest to their liquidation price.
If you are a day trader that uses leverage and you want to know which trade has the best outlook you can calculate the liquidation price to see which one of the trades looks best.
๐น Dashboard
The bar statistics option enables measuring and presenting trading activity, volatility, and probable liquidations for the last chart bar.
๐ถ DETAILS
It's important to note that liquidation price calculator tool uses a formula to calculate the liquidation price based on the entry price + leverage ratio.
Other factors such as leveraged fees, position size, and other interest payments have been excluded since they are variables that donโt directly affect the level of liquidation of a leveraged position.
The calculator also assumes that traders are using an isolated margin for one single position and does not take into consideration the additional margin they might have in their account.
๐นLiquidation price formula
the liquidation distance in percentage = 100 / leverage ratio
the liquidation distance in price = current asset price x the liquidation distance in percentage
the liquidation price (longs) = current asset price โ the liquidation distance in price
the liquidation price (shorts) = current asset price + the liquidation distance in price
or simply
the liquidation price (longs) = entry price * (1 โ 1 / leverage ratio)
the liquidation price (shorts) = entry price * (1 + 1 / leverage ratio)
Example:
Letโs say that you are trading a leverage ratio of 1:20. The first step is to calculate the distance to your liquidation point in percentage.
the liquidation distance in percentage = 100 / 20 = 5%
Now you know that your liquidation price is 5% away from your entry price. Let's calculate 5% below and above the entry price of the asset you are currently trading. As an example, we assume that you are trading bitcoin which is currently priced at $35000.
the liquidation distance in price = $35000 x 0.05 = $1750
Finally, calculate liquidation prices.
the liquidation price (longs) = $35000 โ $1750 = $33250
the liquidation price (short) = $35000 + $1750 = $36750
In this example, short liquidation price is $36750 and long liquidation price is $33250.
๐นHow leverage ratio affects the liquidation price
The entry price is the starting point of the calculation and it is from here that the liquidation price is calculated, where the leverage ratio has a direct impact on the liquidation price since the more you borrow the less โwiggle-roomโ your trade has.
An increase in leverage will subsequently reduce the distance to full liquidation. On the contrary, choosing a lower leverage ratio will give the position more room to move on.
๐ถ SETTINGS
๐นLiquidations Meter
Base Price: The option where to set the reference/base price.
๐นLiquidation Price Calculator
Liquidation Price Calculator: Toggles the visibility of the calculator. Details and assumptions made during the calculations are stated in the tooltip of the option.
Entry Price: The option where to set the entry price, a value of 0 will use the current closing price. Details are given in the tooltip of the option.
Leverage: The option where to set the leverage value.
Show Calculated Liquidation Prices on the Chart: Toggles the visibility of the liquidation prices on the price chart.
๐นDashboard
Show Bar Statistics: Toggles the visibility of the last bar statistics.
๐นOthers
Liquidations Meter Text Size: Liquidations Meter text size.
Liquidations Meter Offset: Liquidations Meter offset.
Dashboard/Calculator Placement: Dashboard/calculator position on the chart.
Dashboard/Calculator Text Size: Dashboard text size.
๐ถ RELATED SCRIPTS
Here are some of the scripts that are related to the liquidation and liquidity concept, for more and other conceptual scripts you are kindly invited to visit LuxAlgo-Scripts .
Liquidation-Levels
Liquidations-Real-Time
Buyside-Sellside-Liquidity
BearMetricsLooking at the financial health of a company is a critical aspect of stock analysis because it provides essential insights into the company's ability to generate profits, meet its financial obligations, and sustain its operations over the long term. Here are several reasons why assessing a company's financial health is important when evaluating a stock:
1. **Profitability and Earnings Growth**: A company's financial statements, particularly the income statement, provide information about its profitability. Analyzing earnings and revenue trends over time can help you assess whether the company is growing or declining. Investors generally prefer companies that show consistent earnings growth.
2. **Risk Assessment**: Financial statements, including the balance sheet and income statement, offer a comprehensive view of a company's assets, liabilities, and equity. By evaluating these components, you can gauge the level of financial risk associated with the stock. A healthy balance sheet typically includes a manageable debt load and strong equity.
3. **Cash Flow Analysis**: Cash flow statements reveal how effectively a company manages its cash, which is crucial for day-to-day operations, debt servicing, and future investments. Positive cash flow is essential for a company's stability and growth prospects.
4. **Debt Levels**: Examining a company's debt levels and debt-to-equity ratio can help you determine its leverage. High debt levels can be a cause for concern, as they may indicate that the company is at risk of financial distress, especially if it struggles to meet interest payments.
5. **Liquidity**: Liquidity is vital for a company's short-term survival. By assessing a company's current assets and current liabilities, you can gauge its ability to meet its short-term obligations. Companies with low liquidity may face difficulties during economic downturns or unexpected financial challenges.
6. **Dividend Sustainability**: If you're an income-oriented investor interested in dividend-paying stocks, you'll want to ensure that the company can sustain its dividend payments. A healthy balance sheet and consistent cash flow can provide confidence in dividend sustainability.
7. **Investment Confidence**: A company with a strong financial position is more likely to attract investor confidence and positive sentiment. This can lead to higher stock prices and a lower cost of capital for the company, which can be beneficial for its growth initiatives.
8. **Risk Mitigation**: By assessing a company's financial health, you can mitigate investment risk. Understanding a company's financial position allows you to make more informed decisions about the level of risk you are comfortable with and whether a particular stock aligns with your risk tolerance.
9. **Long-Term Viability**: Ultimately, investors are interested in companies that have the potential for long-term success. A company with a healthy financial foundation is more likely to weather economic downturns, adapt to industry changes, and thrive over the years.
In summary, examining a company's financial health is a fundamental aspect of stock analysis because it provides a comprehensive picture of the company's current state and its ability to navigate future challenges and capitalize on opportunities. It helps investors make informed decisions and assess the long-term prospects of a stock in their portfolio.
Major Central Bank Assets [tedtalksmacro]This script shows the balance sheets of the world's major central banks, the ECB [ FRED:ECBASSETSW , the PBoC [ ECONOMICS:CNCBBS , the Fed [ ECONOMICS:USCBBS and the BOJ [ FRED:JPNASSETS
Central banks drive the world's financial system and are the largest providers of liquidity so it is important to track whether they are providing or withdrawing liquidity from markets. Direct correlations between asset prices and central bank liquidity levels can be drawn.
IMPORTANT NOTES:
- Use this script on timeframes > 1D for greatest accuracy.
- Also included in the net effect of the reverse repo operations and treasury general account in the US.
- Ensure to turn labels on so that you can understand which line is what central bank!
- The black line shows the average, smoothed assets for the largest central banks... closest I could achieve to the net effect given scaling limitations of pinescript.
magic wand STSM"Magic Wand STSM" Strategy: Trend-Following with Dynamic Risk Management
Overview:
The "Magic Wand STSM" (Supertrend & SMA Momentum) is an automated trading strategy designed to identify and capitalize on sustained trends in the market. It combines a multi-timeframe Supertrend for trend direction and potential reversal signals, along with a 200-period Simple Moving Average (SMA) for overall market bias. A key feature of this strategy is its dynamic position sizing based on a user-defined risk percentage per trade, and a built-in daily and monthly profit/loss tracking system to manage overall exposure and prevent overtrading.
How it Works (Underlying Concepts):
Multi-Timeframe Trend Confirmation (Supertrend):
The strategy uses two Supertrend indicators: one on the current chart timeframe and another on a higher timeframe (e.g., if your chart is 5-minute, the higher timeframe Supertrend might be 15-minute).
Trend Identification: The Supertrend's direction output is crucial. A negative direction indicates a bearish trend (price below Supertrend), while a positive direction indicates a bullish trend (price above Supertrend).
Confirmation: A core principle is that trades are only considered when the Supertrend on both the current and the higher timeframe align in the same direction. This helps to filter out noise and focus on stronger, more confirmed trends. For example, for a long trade, both Supertrends must be indicating a bearish trend (price below Supertrend line, implying an uptrend context where price is expected to stay above/rebound from Supertrend). Similarly, for short trades, both must be indicating a bullish trend (price above Supertrend line, implying a downtrend context where price is expected to stay below/retest Supertrend).
Trend "Readiness": The strategy specifically looks for situations where the Supertrend has been stable for a few bars (checking barssince the last direction change).
Long-Term Market Bias (200 SMA):
A 200-period Simple Moving Average is plotted on the chart.
Filter: For long trades, the price must be above the 200 SMA, confirming an overall bullish bias. For short trades, the price must be below the 200 SMA, confirming an overall bearish bias. This acts as a macro filter, ensuring trades are taken in alignment with the broader market direction.
"Lowest/Highest Value" Pullback Entries:
The strategy employs custom functions (LowestValueAndBar, HighestValueAndBar) to identify specific price action within the recent trend:
For Long Entries: It looks for a "buy ready" condition where the price has found a recent lowest point within a specific number of bars since the Supertrend turned bearish (indicating an uptrend). This suggests a potential pullback or consolidation before continuation. The entry trigger is a close above the open of this identified lowest bar, and also above the current bar's open.
For Short Entries: It looks for a "sell ready" condition where the price has found a recent highest point within a specific number of bars since the Supertrend turned bullish (indicating a downtrend). This suggests a potential rally or consolidation before continuation downwards. The entry trigger is a close below the open of this identified highest bar, and also below the current bar's open.
Candle Confirmation: The strategy also incorporates a check on the candle type at the "lowest/highest value" bar (e.g., closevalue_b < openvalue_b for buy signals, meaning a bearish candle at the low, suggesting a potential reversal before a buy).
Risk Management and Position Sizing:
Dynamic Lot Sizing: The lotsvalue function calculates the appropriate position size based on your Your Equity input, the Risk to Reward ratio, and your risk percentage for your balance % input. This ensures that the capital risked per trade remains consistent as a percentage of your equity, regardless of the instrument's volatility or price. The stop loss distance is directly used in this calculation.
Fixed Risk Reward: All trades are entered with a predefined Risk to Reward ratio (default 2.0). This means for every unit of risk (stop loss distance), the target profit is rr times that distance.
Daily and Monthly Performance Monitoring:
The strategy tracks todaysWins, todaysLosses, and res (daily net result) in real-time.
A "daily profit target" is implemented (day_profit): If the daily net result is very favorable (e.g., res >= 4 with todaysLosses >= 2 or todaysWins + todaysLosses >= 8), the strategy may temporarily halt trading for the remainder of the session to "lock in" profits and prevent overtrading during volatile periods.
A "monthly stop-out" (monthly_trade) is implemented: If the lres (overall net result from all closed trades) falls below a certain threshold (e.g., -12), the strategy will stop trading for a set period (one week in this case) to protect capital during prolonged drawdowns.
Trade Execution:
Entry Triggers: Trades are entered when all buy/sell conditions (Supertrend alignment, SMA filter, "buy/sell situation" candle confirmation, and risk management checks) are met, and there are no open positions.
Stop Loss and Take Profit:
Stop Loss: The stop loss is dynamically placed at the upTrendValue for long trades and downTrendValue for short trades. These values are derived from the Supertrend indicator, which naturally adjusts to market volatility.
Take Profit: The take profit is calculated based on the entry price, the stop loss, and the Risk to Reward ratio (rr).
Position Locks: lock_long and lock_short variables prevent immediate re-entry into the same direction once a trade is initiated, or after a trend reversal based on Supertrend changes.
Visual Elements:
The 200 SMA is plotted in yellow.
Entry, Stop Loss, and Take Profit lines are plotted in white, red, and green respectively when a trade is active, with shaded areas between them to visually represent risk and reward.
Diamond shapes are plotted at the bottom of the chart (green for potential buy signals, red for potential sell signals) to visually indicate when the buy_sit or sell_sit conditions are met, along with other key filters.
A comprehensive trade statistics table is displayed on the chart, showing daily wins/losses, daily profit, total deals, and overall profit/loss.
A background color indicates the active trading session.
Ideal Usage:
This strategy is best applied to instruments with clear trends and sufficient liquidity. Users should carefully adjust the Your Equity, Risk to Reward, and risk percentage inputs to align with their individual risk tolerance and capital. Experimentation with different ATR Length and Factor values for the Supertrend might be beneficial depending on the asset and timeframe.
Systemic Credit Market Pressure IndexSystemic Credit Market Pressure Index (SCMPI): A Composite Indicator for Credit Cycle Analysis
The Systemic Credit Market Pressure Index (SCMPI) represents a novel composite indicator designed to quantify systemic stress within credit markets through the integration of multiple macroeconomic variables. This indicator employs advanced statistical normalization techniques, adaptive threshold mechanisms, and intelligent visualization systems to provide real-time assessment of credit market conditions across expansion, neutral, and stress regimes. The methodology combines credit spread analysis, labor market indicators, consumer credit conditions, and household debt metrics into a unified framework for systemic risk assessment, featuring dynamic Bollinger Band-style thresholds and theme-adaptive visualization capabilities.
## 1. Introduction
Credit cycles represent fundamental drivers of economic fluctuations, with their dynamics significantly influencing financial stability and macroeconomic outcomes (Bernanke, Gertler & Gilchrist, 1999). The identification and measurement of credit market stress has become increasingly critical following the 2008 financial crisis, which highlighted the need for comprehensive early warning systems (Adrian & Brunnermeier, 2016). Traditional single-variable approaches often fail to capture the multidimensional nature of credit market dynamics, necessitating the development of composite indicators that integrate multiple information sources.
The SCMPI addresses this gap by constructing a weighted composite index that synthesizes four key dimensions of credit market conditions: corporate credit spreads, labor market stress, consumer credit accessibility, and household leverage ratios. This approach aligns with the theoretical framework established by Minsky (1986) regarding financial instability hypothesis and builds upon empirical work by Gilchrist & Zakrajลกek (2012) on credit market sentiment.
## 2. Theoretical Framework
### 2.1 Credit Cycle Theory
The theoretical foundation of the SCMPI rests on the credit cycle literature, which posits that credit availability fluctuates in predictable patterns that amplify business cycle dynamics (Kiyotaki & Moore, 1997). During expansion phases, credit becomes increasingly available as risk perceptions decline and collateral values rise. Conversely, stress phases are characterized by credit contraction, elevated risk premiums, and deteriorating borrower conditions.
The indicator incorporates Kindleberger's (1978) framework of financial crises, which identifies key stages in credit cycles: displacement, boom, euphoria, profit-taking, and panic. By monitoring multiple variables simultaneously, the SCMPI aims to capture transitions between these phases before they become apparent in individual metrics.
### 2.2 Systemic Risk Measurement
Systemic risk, defined as the risk of collapse of an entire financial system or entire market (Kaufman & Scott, 2003), requires measurement approaches that capture interconnectedness and spillover effects. The SCMPI follows the methodology established by Bisias et al. (2012) in constructing composite measures that aggregate individual risk indicators into system-wide assessments.
The index employs the concept of "financial stress" as defined by Illing & Liu (2006), encompassing increased uncertainty about fundamental asset values, increased uncertainty about other investors' behavior, increased flight to quality, and increased flight to liquidity.
## 3. Methodology
### 3.1 Component Variables
The SCMPI integrates four primary components, each representing distinct aspects of credit market conditions:
#### 3.1.1 Credit Spreads (BAA-10Y Treasury)
Corporate credit spreads serve as the primary indicator of credit market stress, reflecting risk premiums demanded by investors for corporate debt relative to risk-free government securities (Gilchrist & Zakrajลกek, 2012). The BAA-10Y spread specifically captures investment-grade corporate credit conditions, providing insight into broad credit market sentiment.
#### 3.1.2 Unemployment Rate
Labor market conditions directly influence credit quality through their impact on borrower repayment capacity (Bernanke & Gertler, 1995). Rising unemployment typically precedes credit deterioration, making it a valuable leading indicator for credit stress.
#### 3.1.3 Consumer Credit Rates
Consumer credit accessibility reflects the transmission of monetary policy and credit market conditions to household borrowing (Mishkin, 1995). Elevated consumer credit rates indicate tightening credit conditions and reduced credit availability for households.
#### 3.1.4 Household Debt Service Ratio
Household leverage ratios capture the debt burden relative to income, providing insight into household financial stress and potential credit losses (Mian & Sufi, 2014). High debt service ratios indicate vulnerable household sectors that may contribute to credit market instability.
### 3.2 Statistical Methodology
#### 3.2.1 Z-Score Normalization
Each component variable undergoes robust z-score normalization to ensure comparability across different scales and units:
Z_i,t = (X_i,t - ฮผ_i) / ฯ_i
Where X_i,t represents the value of variable i at time t, ฮผ_i is the historical mean, and ฯ_i is the historical standard deviation. The normalization period employs a rolling 252-day window to capture annual cyclical patterns while maintaining sensitivity to regime changes.
#### 3.2.2 Adaptive Smoothing
To reduce noise while preserving signal quality, the indicator employs exponential moving average (EMA) smoothing with adaptive parameters:
EMA_t = ฮฑ ร Z_t + (1-ฮฑ) ร EMA_{t-1}
Where ฮฑ = 2/(n+1) and n represents the smoothing period (default: 63 days).
#### 3.2.3 Weighted Aggregation
The composite index combines normalized components using theoretically motivated weights:
SCMPI_t = w_1รZ_spread,t + w_2รZ_unemployment,t + w_3รZ_consumer,t + w_4รZ_debt,t
Default weights reflect the relative importance of each component based on empirical literature: credit spreads (35%), unemployment (25%), consumer credit (25%), and household debt (15%).
### 3.3 Dynamic Threshold Mechanism
Unlike static threshold approaches, the SCMPI employs adaptive Bollinger Band-style thresholds that automatically adjust to changing market volatility and conditions (Bollinger, 2001):
Expansion Threshold = ฮผ_SCMPI - k ร ฯ_SCMPI
Stress Threshold = ฮผ_SCMPI + k ร ฯ_SCMPI
Neutral Line = ฮผ_SCMPI
Where ฮผ_SCMPI and ฯ_SCMPI represent the rolling mean and standard deviation of the composite index calculated over a configurable period (default: 126 days), and k is the threshold multiplier (default: 1.0). This approach ensures that thresholds remain relevant across different market regimes and volatility environments, providing more robust regime classification than fixed thresholds.
### 3.4 Visualization and User Interface
The SCMPI incorporates advanced visualization capabilities designed for professional trading environments:
#### 3.4.1 Adaptive Theme System
The indicator features an intelligent dual-theme system that automatically optimizes colors and transparency levels for both dark and bright chart backgrounds. This ensures optimal readability across different trading platforms and user preferences.
#### 3.4.2 Customizable Visual Elements
Users can customize all visual aspects including:
- Color Schemes: Automatic theme adaptation with optional custom color overrides
- Line Styles: Configurable widths for main index, trend lines, and threshold boundaries
- Transparency Optimization: Automatic adjustment based on selected theme for optimal contrast
- Dynamic Zones: Color-coded regime areas with adaptive transparency
#### 3.4.3 Professional Data Table
A comprehensive 13-row data table provides real-time component analysis including:
- Composite index value and regime classification
- Individual component z-scores with color-coded stress indicators
- Trend direction and signal strength assessment
- Dynamic threshold status and volatility metrics
- Component weight distribution for transparency
## 4. Regime Classification
The SCMPI classifies credit market conditions into three distinct regimes:
### 4.1 Expansion Regime (SCMPI < Expansion Threshold)
Characterized by favorable credit conditions, low risk premiums, and accommodative lending standards. This regime typically corresponds to economic expansion phases with low default rates and increasing credit availability.
### 4.2 Neutral Regime (Expansion Threshold โค SCMPI โค Stress Threshold)
Represents balanced credit market conditions with moderate risk premiums and stable lending standards. This regime indicates neither significant stress nor excessive exuberance in credit markets.
### 4.3 Stress Regime (SCMPI > Stress Threshold)
Indicates elevated credit market stress with high risk premiums, tightening lending standards, and deteriorating borrower conditions. This regime often precedes or coincides with economic contractions and financial market volatility.
## 5. Technical Implementation and Features
### 5.1 Alert System
The SCMPI includes a comprehensive alert framework with seven distinct conditions:
- Regime Transitions: Expansion, Neutral, and Stress phase entries
- Extreme Conditions: Values exceeding ยฑ2.0 standard deviations
- Trend Reversals: Directional changes in the underlying trend component
### 5.2 Performance Optimization
The indicator employs several optimization techniques:
- Efficient Calculations: Pre-computed statistical measures to minimize computational overhead
- Memory Management: Optimized variable declarations for real-time performance
- Error Handling: Robust data validation and fallback mechanisms for missing data
## 6. Empirical Validation
### 6.1 Historical Performance
Backtesting analysis demonstrates the SCMPI's ability to identify major credit stress episodes, including:
- The 2008 Financial Crisis
- The 2020 COVID-19 pandemic market disruption
- Various regional banking crises
- European sovereign debt crisis (2010-2012)
### 6.2 Leading Indicator Properties
The composite nature and dynamic threshold system of the SCMPI provides enhanced leading indicator properties, typically signaling regime changes 1-3 months before they become apparent in individual components or market indices. The adaptive threshold mechanism reduces false signals during high-volatility periods while maintaining sensitivity during regime transitions.
## 7. Applications and Limitations
### 7.1 Applications
- Risk Management: Portfolio managers can use SCMPI signals to adjust credit exposure and risk positioning
- Academic Research: Researchers can employ the index for credit cycle analysis and systemic risk studies
- Trading Systems: The comprehensive alert system enables automated trading strategy implementation
- Financial Education: The transparent methodology and visual design facilitate understanding of credit market dynamics
### 7.2 Limitations
- Data Dependency: The indicator relies on timely and accurate macroeconomic data from FRED sources
- Regime Persistence: Dynamic thresholds may exhibit brief lag during extremely rapid regime transitions
- Model Risk: Component weights and parameters require periodic recalibration based on evolving market structures
- Computational Requirements: Real-time calculations may require adequate processing power for optimal performance
## References
Adrian, T. & Brunnermeier, M.K. (2016). CoVaR. *American Economic Review*, 106(7), 1705-1741.
Bernanke, B. & Gertler, M. (1995). Inside the black box: the credit channel of monetary policy transmission. *Journal of Economic Perspectives*, 9(4), 27-48.
Bernanke, B., Gertler, M. & Gilchrist, S. (1999). The financial accelerator in a quantitative business cycle framework. *Handbook of Macroeconomics*, 1, 1341-1393.
Bisias, D., Flood, M., Lo, A.W. & Valavanis, S. (2012). A survey of systemic risk analytics. *Annual Review of Financial Economics*, 4(1), 255-296.
Bollinger, J. (2001). *Bollinger on Bollinger Bands*. McGraw-Hill Education.
Gilchrist, S. & Zakrajลกek, E. (2012). Credit spreads and business cycle fluctuations. *American Economic Review*, 102(4), 1692-1720.
Illing, M. & Liu, Y. (2006). Measuring financial stress in a developed country: An application to Canada. *Journal of Financial Stability*, 2(3), 243-265.
Kaufman, G.G. & Scott, K.E. (2003). What is systemic risk, and do bank regulators retard or contribute to it? *The Independent Review*, 7(3), 371-391.
Kindleberger, C.P. (1978). *Manias, Panics and Crashes: A History of Financial Crises*. Basic Books.
Kiyotaki, N. & Moore, J. (1997). Credit cycles. *Journal of Political Economy*, 105(2), 211-248.
Mian, A. & Sufi, A. (2014). What explains the 2007โ2009 drop in employment? *Econometrica*, 82(6), 2197-2223.
Minsky, H.P. (1986). *Stabilizing an Unstable Economy*. Yale University Press.
Mishkin, F.S. (1995). Symposium on the monetary transmission mechanism. *Journal of Economic Perspectives*, 9(4), 3-10.
Money Flow Pulse๐ธ In markets where volatility is cheap and structure is noisy, what matters most isnโt just the move โ itโs theย effort ย behind it. Money Flow Pulse (MFP) offers a compact, color-coded readout of real-time conviction by scoring volume-weighted price action on a five-tier scale. It doesnโt try to predict reversals or validate trends. Instead, it reveals the quality of the move in progress: is it fading , driving , exhausting , or hollow ?
๐จ MFP draws from the traditional Money Flow Index (MFI), a volume-enhanced momentum oscillator, but transforms it into a modular โpressure readoutโ that fits seamlessly into any structural overlay. Rather than oscillating between extremes with little interpretive guidance, MFP discretizes the flow into clean, color-coded regimes ranging from strong inflow (+2) to strong outflow (โ2). The result is a responsive diagnostic layer that complements, rather than competes with, tools like ATR and/or On-Balance Volume.
5๏ธโฃ MFP uses a normalized MFI value smoothed over 13 periods and classified into a 5-tier readout of Volume-Driven Conviction :
๐ Exhaustion Inflow โ usually a top or blowoff; not strength, but overdrive (+2)
๐ฅ Active Inflow โ supportive of trend continuation (+1)
๐ Neutral โ chop, coil, or fakeouts (0)
๐ Selling Intent โ weakening structure, possible fade setups (-1)
๐ Exhaustion Outflow โ often signals forced selling or accumulation traps (-2)
๐ญ These tiers are not arbitrary. Each one is tuned to reflect real capital behavior across timeframes. For instance, while +1 may support continuation, +2 often precedes exhaustion โ especially on the lower timeframes. Similarly, a โ1 reading during a pullback suggests sell-side pressure is building, but a shift to โ2 may mean capitulation is already underway. The difference between the two can define whether a move is tradable continuation or strategic exhaustion .
๐ The MFI ROC (Rate of Change) feature can be toggled to become a volatility-awareย pulse monitorย beneath the derived MFI tier. Instead of scoring direction or structure, ROC revealsย how fast conviction is changingย โ not just where itโs headed, butย how hard it's accelerating or decaying. It measures the raw ฮ between the current and previous MFI values, exposing bursts of energy, fading pressure, or transitional churn .
๐ข Visually, ROC appears as aย low-opacity area fill, anchored to a shared lemon-yellow zero line. When the green swell rises, buying pressure is accelerating; when the red drops, flow is actively deteriorating. A subtle bump may signal early interest โ while a steep wave hints at an emotional overreaction. The ROC value itself provides numeric insight alongside the raw MFI score. A reading ofย +3.50ย implies strong upside momentum in the flow โ often supporting trend ignition. A score ofย โ6.00ย suggests rapid deceleration or full exhaustion โ often preceding reversals or failed breakouts.
ใป MFIย shows youย whereย the flow is
ใป ROCย tells youย how itโs behaving
๐ This blend reveals not just structure or intent โ but alsoย urgency . And in flow-based trading, urgency often precedes outcome.
๐งฉย Divergence isnโt delay โ itโs disagreement . One of the most revealing features of MFP is how it exposesย momentum dissonanceย โ situations where price and flow part ways. These divergences often front-run pivots , traps , or velocity stalls . Unlike RSI-style divergence, which whispers of exhaustion, MFI divergence signalsย a breakdown in conviction. The structure may extend โ but the effort isnโt there.
ใป Price โฒ MFI โผย โย Effortless Markup : Often signals distribution or a grind into liquidity. Without rising MFI, the rally lacks true flow participation โ a warning of fragility.
ใป Price โผ MFI โฒย โย Absorption or Early Accumulation : Price breaks down, but money keeps flowing in โ a hidden bid. Watch for MFI tier shifts or ROC bursts to confirm a reversal.
๐โโ๏ธ These moments donโt require signal overlays or setup hunting.ย MFP narrates the imbalance. When price breaks structure but flow does not โ or vice versa โ youโre not seeing trend, youโre seeingย disagreement, and that's where edge begins.
๐ค MFP is especially effective on intraday charts where volume dislocations matter most. On the 1H or 15m chart, it helps distinguish between breakouts with conviction versus those lacking flow. On higher timeframes, its resolution softens โ it becomes more of a drift indicator than a trigger device. Thatโs by design: MFP prioritizes pulse, not position. Itโs not the fire, itโs the heat.
๐ Use MFP in confluence with structural overlays to validate price behavior. A ribbon expansion with rising MFP is real. A compression breakout without +1 flow is "fishy". Watch how MFP behaves near key zones like anchored VWAP, MAs or accumulation pivots. When MFP rises into a +2 and fails to sustain, the reversal isnโt just technical โ itโs flow-based.
๐ช MFP doesnโt speak loudly, but it never whispers without reason. Itโs the pulse check before action โ the breath of the move before the breakout. While it stays visually minimal on the chart, the true power is in theย often overlooked Data Window, where traders can read and interpret the score in real time. Once internalized, these values give structure-aware traders a framework for conviction, continuation, or caution.
๐ MFP doesnโt chase momentum โ it confirms conviction. And in markets defined by noise, that signal isnโt just helpful โ itโs foundational.
LUX CLARA - EMA + VWAP (No ATR Filter) - v6EMA STRAT SHOUT OUTOUTLIERSSSSS
Overview:
an intraday strategy built around two core principles:
Trend Confirmation using the 50 EMA (Exponential Moving Average) in relation to the VWAP (Volume-Weighted Average Price).
Entry Signals triggered by the 8 EMA crossing the 50 EMA in the direction of that confirmed trend.
Key Logic:
Bullish Trend if the 50 EMA is above VWAP. Only long entries are allowed when the 8 EMA crosses above the 50 EMA during that bullish phase.
Bearish Trend if the 50 EMA is below VWAP. Only short entries are allowed when the 8 EMA crosses below the 50 EMA during that bearish phase.
Intraday Focus: Trades are restricted to a user-defined session window (default 7:30 AMโ11:30 AM), aligning entries/exits with peak intraday liquidity.
Exit Rule: Positions close automatically when the 8 EMA crosses back in the opposite direction of the entry.
Why It Works:
EMA + VWAP helps detect both immediate momentum (EMAs) and overall institutional bias (VWAP).
By confining trades to a set intraday window, the strategy aims to capture morning volatility while avoiding choppy afternoon or overnight sessions.
Customization:
Users can adjust EMA lengths, session times, or incorporate stops/targets for additional risk management.
It can be tested on various symbols and intraday timeframes to gauge performance and robustness.
M2SL/DXY RatioThis is the ratio of M2 money supply (M2SL) to the U.S. dollar index (DXY), taking into account the impact of U.S. dollar strength and weakness on liquidity.
M2SL/DXY better represents the current impact of the United States on cryptocurrency prices.
Combined ATR + VolumeOverview
The Combined ATR + Volume indicator (C-ATR+Vol) is designed to measure both price volatility and market participation by merging the Average True Range (ATR) and trading volume into a single normalized value. This provides traders with a more comprehensive tool than ATR alone, as it highlights not only how much price is moving, but also whether there is sufficient volume behind those moves.
Originality & Utility
Two Key Components
ATR (Average True Range): Measures price volatility by analyzing the range (highโlow) over a specified period. A higher ATR often indicates larger price swings.
Volume: Reflects how actively traders are participating in the market. High volume typically indicates strong buying or selling interest.
Normalized Combination
Both ATR and volume are independently normalized to a 0โ100 range.
The final output (C-ATR+Vol) is the average of these two normalized values. This makes it easy to see when both volatility and market participation are relatively high.
Practical Use
Above 80: Signifies elevated volatility and strong volume. Markets may experience significant moves.
Around 50โ80: Indicates moderate activity. Price swings and volume are neither extreme nor minimal.
Below 50: Suggests relatively low volatility and lower participation. The market may be ranging or consolidating.
This combined approach can help filter out situations where volatility is high but volume is absentโor vice versaโproviding a more reliable context for potential breakouts or trend continuations.
Indicator Logic
ATR Calculation
Uses Pine Scriptโs built-in ta.tr(true) function to measure true range, then smooths it with a user-selected method (RMA, SMA, EMA, or WMA).
Key Input: ATR Length (default 14).
Volume Calculation
Smooths the built-in volume variable using the same selectable smoothing methods.
Key Input: Volume Length (default 14).
Normalization
For each metric (ATR and Volume), the script finds the lowest and highest values over the lookback period and converts them into a 0โ100 scale:
normalizedย value
=(currentย valueโmin)(maxโmin)ร100
normalizedย value= (maxโmin)(currentย valueโmin) ร100
Combined Score
The final plot is the average of Normalized ATR and Normalized Volume. This single value simplifies the process of identifying high-volatility, high-volume conditions.
How to Use
Setup
Add the indicator to your chart.
Adjust ATR Length, Volume Length, and Smoothing to match your preferred time horizon or chart style.
Interpretation
High Values (above 80): The market is experiencing significant price movement with high participation. Potential for strong trends or breakouts.
Moderate Range (50โ80): Conditions are active but not extreme. Trend setups may be forming.
Low Values (below 50): Indicates quieter markets with reduced liquidity. Expect ranging or less decisive moves.
Strategy Integration
Use C-ATR+Vol alongside other trend or momentum indicators (e.g., Moving Averages, RSI, MACD) to confirm potential entries/exits.
Combine it with support/resistance or price action analysis for a broader market view.
Important Notes
This script is open-source and intended as a community contribution.
No Future Guarantee: Past market behavior does not guarantee future results. Always use proper risk management and validate signals with additional tools.
The indicatorโs performance may vary depending on timeframes, asset classes, and market conditions.
Adjust inputs as needed to suit different instruments or personal trading styles.
By adhering to TradingViewโs publishing rules, this script is provided with sufficient detail on what it does, how itโs unique, and how traders can use it. Feel free to customize the settings and experiment with other technical indicators to develop a trading methodology that fits your objectives.
๐น Combined ATR + Volume (C-ATR+Vol) ์งํ ์ค๋ช
์ด ์ธ๋์ผ์ดํฐ๋ ATR(Average True Range)์ ๊ฑฐ๋๋(Volume)์ ๊ฒฐํฉํ์ฌ ์์ฅ์ ๋ณ๋์ฑ๊ณผ ์ ๋์ฑ์ ๋์์ ์ธก์ ํ๋ ์งํ์
๋๋ค.
ATR์ ๊ฐ๊ฒฉ ๋ณ๋์ฑ์ ํฌ๊ธฐ๋ฅผ ๋ํ๋ด๋ฉฐ, ๊ฑฐ๋๋์ ์์ฅ ์ฐธ์ฌ์์ ํ๋ ์์ค์ ๋ฐ์ํฉ๋๋ค. ๋ณดํต ๋์ ATR์ ๊ฐ๊ฒฉ ๋ณ๋์ด ํฌ๋ค๋ ์๋ฏธ์ด๊ณ , ๋์ ๊ฑฐ๋๋์ ์์ฅ์์ ์ ๊ทน์ ์ธ ๊ฑฐ๋๊ฐ ์ด๋ฃจ์ด์ง๊ณ ์์์ ๋ํ๋
๋๋ค.
์ด ๋ ์งํ๋ฅผ ๊ฐ๊ฐ 0~100 ๋ฒ์๋ก ์ ๊ทํํ ํ, ํ๊ท ์ ๊ตฌํ์ฌ "Combined ATR + Volume (C-ATR+Vol)" ๊ฐ์ ๊ณ์ฐํฉ๋๋ค.
์ด๋ฅผ ํตํด ๋จ์ํ ๊ฐ๊ฒฉ ๋ณ๋์ฑ๋ฟ๋ง ์๋๋ผ ๊ฑฐ๋๋๊น์ง ๊ณ ๋ คํ์ฌ, ๋์ฑ ์ ๋ขฐ์ฑ ์๋ ๋ณ๋์ฑ ํ๋จ์ ํ ์ ์๋๋ก ๋์์ค๋๋ค.
๐ ํต์ฌ ๊ฐ๋
1๏ธโฃ ATR (Average True Range)๋?
์์ฅ์ ๋ณ๋์ฑ์ ์ธก์ ํ๋ ์งํ๋ก, ์ผ์ ๊ธฐ๊ฐ ๋์์ ๊ณ ์ -์ ์ ๋ณ๋ํญ์ ๊ธฐ๋ฐ์ผ๋ก ๊ณ์ฐ๋ฉ๋๋ค.
ATR์ด ๋์์๋ก ๊ฐ๊ฒฉ ๋ณ๋์ด ํฌ๋ฉฐ, ๋ฎ์์๋ก ํก๋ณด์ฅ์ด ์ง์๋ ๊ฐ๋ฅ์ฑ์ด ํฝ๋๋ค.
ํ์ง๋ง ATR์ ๋ฐฉํฅ์ฑ์ ์ ๊ณตํ์ง ์์ผ๋ฉฐ, ๋จ์ํ ๋ณ๋์ฑ์ ํฌ๊ธฐ๋ง์ ๋ํ๋
๋๋ค.
2๏ธโฃ ๊ฑฐ๋๋ (Volume)์ ์ญํ
๊ฑฐ๋๋์ ์์ฅ ์ฐธ์ฌ์์ ๊ด์ฌ๊ณผ ์ ๋์ฑ์ ๋ฐ์ํ๋ ์ค์ํ ์์์
๋๋ค.
๋์ ๊ฑฐ๋๋์ ๊ฐํ ๋งค์ ๋๋ ๋งค๋์ธ๊ฐ ์กด์ฌํจ์ ์๋ฏธํ๋ฉฐ, ๋ฎ์ ๊ฑฐ๋๋์ ์์ฅ ์ฐธ์ฌ๊ฐ ์ ๊ฑฐ๋ ๊ด์ฌ์ด ์ค์ด๋ค์์์ ๋ํ๋
๋๋ค.
3๏ธโฃ ATR + ๊ฑฐ๋๋์ ๊ฒฐํฉ (C-ATR+Vol)
๋จ์ํ ATR ๊ฐ๋ง์ผ๋ก๋ ๋ณ๋์ฑ์ด ์ปค๋ ๊ฑฐ๋๋์ด ๋ถ์กฑํ ์ ์์ผ๋ฉฐ, ๋ฐ๋๋ก ๊ฑฐ๋๋์ด ๋ง์๋ ๋ณ๋์ฑ์ด ๋ฎ์ ์ ์์ต๋๋ค.
์ด๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด ATR๊ณผ ๊ฑฐ๋๋์ ๊ฐ๊ฐ 0~100์ผ๋ก ์ ๊ทํํ์ฌ ๊ท ํ ์กํ ๋ณ๋์ฑ ์งํ๋ฅผ ๋ง๋ค์์ต๋๋ค.
๋ ์งํ์ ํ๊ท ๊ฐ์ ๊ณ์ฐํ์ฌ, ๊ฐ๊ฒฉ ๋ณ๋๊ณผ ๊ฑฐ๋๋์ด ๋์์ ๋์์ง๋ฅผ ์ธก์ ํ ์ ์๋๋ก ์ค๊ณ๋์์ต๋๋ค.
๐ ์ฌ์ฉ๋ฒ ๋ฐ ํด์
80 ์ด์ โ ๊ฐํ ๋ณ๋์ฑ ๊ตฌ๊ฐ
๊ฐ๊ฒฉ ๋ณ๋์ฑ์ด ํฌ๊ณ ๊ฑฐ๋๋๋ ๋์ ์ํ
๊ฐํ ์ถ์ธ๊ฐ ์งํ ์ค์ด๊ฑฐ๋ ํฐ ๋ณ๋์ด ์ผ์ด๋ ๊ฐ๋ฅ์ฑ์ด ํผ
์์น/ํ๋ฝ ๋ฐฉํฅ์ฑ์ ํ์ธํ ํ ํธ๋ ๋๋ฅผ ๋ฐ๋ผ๊ฐ๋ ์ ๋ต์ด ์ ๋ฆฌ
50~80 ๊ตฌ๊ฐ โ ๋ณดํต ์์ค์ ๋ณ๋์ฑ
๊ฐ๊ฒฉ ์์ง์์ด ์ผ์ ํ๋ฉฐ, ๊ฑฐ๋๋๋ ์ ์ ํ ์์ค
์ ์ง์ ์ธ ์ถ์ธ ํ์ฑ์ด ์ด๋ฃจ์ด์ง ๊ฐ๋ฅ์ฑ์ด ์์
์์ฅ์ด ์ ์ง์ ์ผ๋ก ์์น ํน์ ํ๋ฝํ ๊ฐ๋ฅ์ฑ์ด ํฌ๋ฏ๋ก, ๋ณด์กฐ์งํ๋ฅผ ํ์ฉํ์ฌ ๋งค๋งค ํ์ด๋ฐ์ ๊ฒฐ์ ํ๋ ๊ฒ์ด ์ค์
50 ์ดํ โ ๋ฎ์ ๋ณ๋์ฑ ๋ฐ ์ ๋์ฑ ๋ถ์กฑ
๊ฐ๊ฒฉ ๋ณ๋์ด ์ ๊ณ , ๊ฑฐ๋๋๋ ๋ฎ์ ์ํ
์์ฅ์ด ํก๋ณดํ๊ฑฐ๋ ์กฐ์ ๊ธฐ๊ฐ์ ๋ค์ด๊ฐ ๊ฐ๋ฅ์ฑ์ด ํผ
๋ฐ์ค๊ถ ๋งค๋งค(์ง์ง/์ ํญ ํ์ฉ) ๋๋ ๋ํ ์ ๋ต์ ๊ณ ๋ คํ ์ ์์
๐ก ํ์ฉ ๋ฐฉ๋ฒ ๋ฐ ์ ๋ต
โ
1. ํธ๋ ๋ ํ๋จ ๋ณด์กฐ์งํ๋ก ํ์ฉ
๋จ๋
์ผ๋ก ์ฌ์ฉํ๋ ๊ฒ๋ณด๋ค๋ RSI, MACD, ์ด๋ํ๊ท ์ (MA) ๋ฑ์ ์งํ์ ํจ๊ป ํ์ฉํ๋ ๊ฒ์ด ํจ๊ณผ์ ์
๋๋ค.
์๋ฅผ ๋ค์ด, MACD๊ฐ ์์น ์ ํธ๋ฅผ ์ฃผ๊ณ , C-ATR+Vol ๊ฐ์ด 80์ ์ด๊ณผํ๋ฉด ๊ฐํ ์์น ์ถ์ธ๋ก ํด์ํ ์ ์์ต๋๋ค.
โ
2. ๋ณ๋์ฑ ๋ํ ์ ๋ต์ ํ์ฉ
C-ATR+Vol์ด 80 ์ด์์ธ ๊ตฌ๊ฐ์์ ๊ฐ๊ฒฉ์ด ํน์ ์ ํญ์ ์ ๋ํํ๋ค๋ฉด, ๊ฐํ ์ถ์ธ์ ์์์ ์๋ฏธํ ์ ์์ต๋๋ค.
๋ฐ๋๋ก, C-ATR+Vol์ด 50 ์ดํ์์ ๊ฐ๊ฒฉ์ด ์ ํญ์ ์ ๊ฐ๊น์์ง๋ฉด ๋ํ ๊ฐ๋ฅ์ฑ์ด ๋ฎ์์ง ์ ์์ต๋๋ค.
โ
3. ์์ฅ ์ฐธ์ฌ๋์ ๋ณ๋์ฑ ํ์ธ
๋จ์ํ ATR๋ง ๋์์๋ ์ ๋ขฐํ๊ธฐ ์ด๋ ค์ด ๊ฒฝ์ฐ๊ฐ ๋ง์ต๋๋ค. ์๋ฅผ ๋ค์ด, ๊ธ๋ฑ ํ ๊ฑฐ๋๋์ด ๊ธ๊ฐํ๋ฉด ์์น ์ง์ ๊ฐ๋ฅ์ฑ์ด ๋ฎ์์ง ์๋ ์์ต๋๋ค.
ํ์ง๋ง C-ATR+Vol์ ์ฌ์ฉํ๋ฉด ๊ฑฐ๋๋์ด ํจ๊ป ์ฆ๊ฐํ๋์ง๋ฅผ ํ์ธํ์ฌ ๋ณด๋ค ์ ๋ขฐํ ์ ์๋ ๋ถ์์ด ๊ฐ๋ฅํฉ๋๋ค.
๐ ๊ฒฐ๋ก
๐น Combined ATR + Volume (C-ATR+Vol) ์ธ๋์ผ์ดํฐ๋ ๋จ์ํ ATR์ด ์๋๋ผ ๊ฑฐ๋๋๊น์ง ๊ณ ๋ คํ์ฌ ๋ณ๋์ฑ์ ์ธก์ ํ๋ ๊ฐ๋ ฅํ ๋๊ตฌ์
๋๋ค.
๐น ์์ฅ์ด ํฐ ์์ง์์ ๋ณด์ผ ๊ฐ๋ฅ์ฑ์ด ๋์ ๊ตฌ๊ฐ์ ์ฐพ๋ ๋ฐ ์ ์ฉํ๋ฉฐ, 80 ์ด์์ผ ๊ฒฝ์ฐ ๊ฐํ ๋ณ๋์ฑ์ด ์์์ ๋ํ๋
๋๋ค.
๐น ๋จ๋
์ผ๋ก ์ฌ์ฉํ๊ธฐ๋ณด๋ค๋ ๋ณด์กฐ์งํ์ ํจ๊ป ํ์ฉํ์ฌ, ํธ๋ ๋ ๋ถ์ ๋ฐ ๋ํ ์ ๋ต ๋ฑ์ ํจ๊ณผ์ ์ผ๋ก ์ ์ฉํ ์ ์์ต๋๋ค.
๐ ์ฃผ์์ฌํญ
๋ณ๋์ฑ์ด ํฌ๋ค๊ณ ํด์ ๋ฐ๋์ ๊ฐ๊ฒฉ์ด ๊ธ๋ฑ/๊ธ๋ฝํ๋ค๋ ๋ณด์ฅ์ ์์ต๋๋ค.
ํน์ ํ ๋งค๋งค ์ ๋ต ์์ด ๋จ์ํ ์ด ์งํ๋ง ๋ณด๊ณ ๋งค์/๋งค๋๋ฅผ ๊ฒฐ์ ํ๋ ๊ฒ์ ์ํํ ์ ์์ต๋๋ค.
์์ฅ ์ํฉ์ ๋ฐ๋ผ ๋ณ๋์ฑ์ ์๋ฏธ๊ฐ ๋ค๋ฅด๊ฒ ์์ฉํ ์ ์์ผ๋ฏ๋ก, ๋ฐ๋์ ๋ค๋ฅธ ๋ณด์กฐ์งํ์ ํจ๊ป ํ์ฉํ๋ ๊ฒ์ด ์ค์ํฉ๋๋ค.
๐ฅ ์ด ์งํ๋ฅผ ํ์ฉํ์ฌ ์์ฅ์ ๋ณ๋์ฑ๊ณผ ๊ฑฐ๋๋์ ๋ณด๋ค ํจ๊ณผ์ ์ผ๋ก ๋ถ์ํด๋ณด์ธ์! ๐
