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.
🔶 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.
🔹 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.
🔹 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.
🔹 Dashboard
The bar statistics option enables measuring and presenting trading activity, volatility, and probable liquidations for the last chart bar.
🔶 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 don’t 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.
🔹Liquidation 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 – 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 – 1 / leverage ratio)
the liquidation price (shorts) = entry price * (1 + 1 / leverage ratio)
Example:
Let’s 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 – $1750 = $33250
the liquidation price (short) = $35000 + $1750 = $36750
In this example, short liquidation price is $36750 and long liquidation price is $33250.
🔹How 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 “wiggle-room” 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.
🔶 SETTINGS
🔹Liquidations Meter
Base Price: The option where to set the reference/base price.
🔹Liquidation 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.
🔹Dashboard
Show Bar Statistics: Toggles the visibility of the last bar statistics.
🔹Others
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.
🔶 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
Cari dalam skrip untuk "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.
Steez's Timeframe TableSimple timeframe indicator which can assist with daily bias or draw on liquidity.
Shows all timeframes from 1 minute to 1 day.
Shows close time and if the candle is currently bearish or bullish.
[IX] 15min ORB + Volume Trend DeltaIX 15-Minute ORB + Trend Volume Delta
A powerful intraday tool that combines Opening-Range Breakouts with a real-time volume delta tracker and adaptive trend logic—built for traders who want to stay on the right side of momentum and liquidity.
🔹 WHAT THIS INDICATOR DOES
• Automatically draws the Opening Range (ORB) for London (07:00–07:15 UTC) and New York (13:30–13:45 UTC) sessions.
• Once the ORB is set, it highlights the high and low levels throughout the session.
• Adds key previous levels: yesterday’s High, Low, Open and the most recent 4H High/Low.
• Includes a Trend MA with ±2× ATR(200) bands to define bullish/bearish market conditions.
• Tracks buy vs sell volume within the current trend regime and shows it live on your chart as a percentage delta.
🔹 HOW TO USE IT
Set your chart to 5-minute timeframe (or any intraday timeframe up to the selected ORB window).
When the session begins, the script automatically locks in the ORB zone.
Once the ORB is formed:
• If price breaks and holds above the ORB ➤ focus on longs.
• If price breaks and holds below the ORB ➤ focus on shorts.
Use the Trend Volume Delta as confirmation—strong buy delta supports longs, strong sell delta supports shorts.
Targets and support/resistance can be aligned with previous Daily / 4H levels.
🔹 TREND FILTER & VOLUME DELTA
• The adaptive trend MA uses CMO-based weighting, becoming more responsive during momentum shifts and more stable during chop.
• Trend regime is defined by price closing above or below the ±2× ATR bands.
• While inside a regime (bullish or bearish), the script accumulates volume:
– Bullish = volume from green candles (close > open)
– Bearish = volume from red candles (close < open)
• Displays total buy/sell volume and the percentage delta live in a compact table.
🔹 CUSTOMIZATION OPTIONS
• ORB duration (1–15 min)
• Custom session times for London and New York
• Toggle display for ORBs, previous levels (Daily / 4H), volume delta table, watermark
• Fully adjustable ORB and trend colors
• Trend/Momentum settings, table size and position
🔹 ALERTS INCLUDED
• Price breaking above or below the London or NY ORB
• Alerts only trigger after the ORB is set—perfect for breakout entries
🔹 WHY THIS MATTERS
• ORBs are used by institutional traders to define the day’s first true directional intent
• Trend filter avoids chasing noise by adjusting to volatility
• Volume Delta shows who’s in control—buyers or sellers
• Previous levels give natural reaction points for entries, stops, and targets
🔹 DISCLAIMER
This indicator is for educational use only. Not financial advice. Always do your own research and manage risk responsibly.
🔹 JOIN THE COMMUNITY
Want more free tools like this?
Join the free IXTradingHub Discord to access additional indicators, trading resources, and live discussions.
Or check out our VIP suite if you’re ready to take your trading to the next level and start building consistent profitability.
→ Visit IXTradingHub.com to get started.
Previous Daily High/LowUnderstanding Previous Daily High and Low in Trading
The previous day’s high and low are critical price levels that traders use to identify potential support, resistance, and intraday trading opportunities. These levels represent the highest and lowest prices reached during the prior trading session and often act as reference points for future price action.
Why Are Previous Daily High/Low Important?
Support & Resistance Zones
The previous day’s low often acts as support (buyers defend this level).
The previous day’s high often acts as resistance (sellers defend this level).
Breakout Trading
A move above the previous high suggests bullish momentum.
A move below the previous low suggests bearish momentum.
Mean Reversion Trading
Traders fade moves toward these levels, expecting reversals.
Example: Buying near the previous low in an uptrend.
Institutional Order Flow
Market makers and algos often reference these levels for liquidity.
How to Use Previous Daily High/Low in Trading
1. Breakout Strategy
Long Entry: Price breaks & closes above previous high → bullish continuation.
Short Entry: Price breaks & closes below previous low → bearish continuation.
2. Reversal Strategy
Long at Previous Low: If price pulls back to the prior day’s low in an uptrend.
Short at Previous High: If price rallies to the prior day’s high in a downtrend.
3. Range-Bound Markets
Buy near previous low, sell near previous high if price oscillates between them.
Previous Daily High/LowThe previous day’s high and low are critical price levels that traders use to identify potential support, resistance, and intraday trading opportunities. These levels represent the highest and lowest prices reached during the prior trading session and often act as reference points for future price action.
Why Are Previous Daily High/Low Important?
Support & Resistance Zones
The previous day’s low often acts as support (buyers defend this level).
The previous day’s high often acts as resistance (sellers defend this level).
Breakout Trading
A move above the previous high suggests bullish momentum.
A move below the previous low suggests bearish momentum.
Mean Reversion Trading
Traders fade moves toward these levels, expecting reversals.
Example: Buying near the previous low in an uptrend.
Institutional Order Flow
Market makers and algos often reference these levels for liquidity.
How to Use Previous Daily High/Low in Trading
1. Breakout Strategy
Long Entry: Price breaks & closes above previous high → bullish continuation.
Short Entry: Price breaks & closes below previous low → bearish continuation.
2. Reversal Strategy
Long at Previous Low: If price pulls back to the prior day’s low in an uptrend.
Short at Previous High: If price rallies to the prior day’s high in a downtrend.
3. Range-Bound Markets
Buy near previous low, sell near previous high if price oscillates between them.
Example Trade Setup
Scenario: Price opens near the previous day’s high.
Bullish Case: A breakout above it targets next resistance.
Bearish Case: Rejection at the high signals a pullback.
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 & Zakrajšek (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 & Zakrajšek, 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 - μ_i) / σ_i
Where X_i,t represents the value of variable i at time t, μ_i is the historical mean, and σ_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 = α × Z_t + (1-α) × EMA_{t-1}
Where α = 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_1×Z_spread,t + w_2×Z_unemployment,t + w_3×Z_consumer,t + w_4×Z_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 = μ_SCMPI - k × σ_SCMPI
Stress Threshold = μ_SCMPI + k × σ_SCMPI
Neutral Line = μ_SCMPI
Where μ_SCMPI and σ_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 ≤ SCMPI ≤ 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 ±2.0 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. & Zakrajšek, 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 2007–2009 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 Pulse💸 In markets where volatility is cheap and structure is noisy, what matters most isn’t just the move — it’s the effort 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 doesn’t try to predict reversals or validate trends. Instead, it reveals the quality of the move in progress: is it fading , driving , exhausting , or hollow ?
🎨 MFP draws from the traditional Money Flow Index (MFI), a volume-enhanced momentum oscillator, but transforms it into a modular “pressure readout” 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 (–2). The result is a responsive diagnostic layer that complements, rather than competes with, tools like ATR and/or On-Balance Volume.
5️⃣ MFP uses a normalized MFI value smoothed over 13 periods and classified into a 5-tier readout of Volume-Driven Conviction :
🍆 Exhaustion Inflow — usually a top or blowoff; not strength, but overdrive (+2)
🥝 Active Inflow — supportive of trend continuation (+1)
🍋 Neutral — chop, coil, or fakeouts (0)
🍑 Selling Intent — weakening structure, possible fade setups (-1)
🍆 Exhaustion Outflow — often signals forced selling or accumulation traps (-2)
🎭 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 — especially on the lower timeframes. Similarly, a –1 reading during a pullback suggests sell-side pressure is building, but a shift to –2 may mean capitulation is already underway. The difference between the two can define whether a move is tradable continuation or strategic exhaustion .
🌊 The MFI ROC (Rate of Change) feature can be toggled to become a volatility-aware pulse monitor beneath the derived MFI tier. Instead of scoring direction or structure, ROC reveals how fast conviction is changing — not just where it’s headed, but how hard it's accelerating or decaying. It measures the raw Δ between the current and previous MFI values, exposing bursts of energy, fading pressure, or transitional churn .
🎢 Visually, ROC appears as a 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 — while a steep wave hints at an emotional overreaction. The ROC value itself provides numeric insight alongside the raw MFI score. A reading of +3.50 implies strong upside momentum in the flow — often supporting trend ignition. A score of –6.00 suggests rapid deceleration or full exhaustion — often preceding reversals or failed breakouts.
・ MFI shows you where the flow is
・ ROC tells you how it’s behaving
😎 This blend reveals not just structure or intent — but also urgency . And in flow-based trading, urgency often precedes outcome.
🧩 Divergence isn’t delay — it’s disagreement . One of the most revealing features of MFP is how it exposes momentum dissonance — 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 signals a breakdown in conviction. The structure may extend — but the effort isn’t there.
・ Price ▲ MFI ▼ → Effortless Markup : Often signals distribution or a grind into liquidity. Without rising MFI, the rally lacks true flow participation — a warning of fragility.
・ Price ▼ MFI ▲ → Absorption or Early Accumulation : Price breaks down, but money keeps flowing in — a hidden bid. Watch for MFI tier shifts or ROC bursts to confirm a reversal.
🏄♂️ These moments don’t require signal overlays or setup hunting. MFP narrates the imbalance. When price breaks structure but flow does not — or vice versa — you’re not seeing trend, you’re seeing disagreement, and that's where edge begins.
💤 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 — it becomes more of a drift indicator than a trigger device. That’s by design: MFP prioritizes pulse, not position. It’s not the fire, it’s the heat.
📎 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 isn’t just technical — it’s flow-based.
🪟 MFP doesn’t speak loudly, but it never whispers without reason. It’s the pulse check before action — the breath of the move before the breakout. While it stays visually minimal on the chart, the true power is in the 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.
🛜 MFP doesn’t chase momentum — it confirms conviction. And in markets defined by noise, that signal isn’t just helpful — it’s 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 AM–11:30 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 (high–low) 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 0–100 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 50–80: 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 absent—or vice versa—providing a more reliable context for potential breakouts or trend continuations.
Indicator Logic
ATR Calculation
Uses Pine Script’s 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 0–100 scale:
normalized value
=(current value−min)(max−min)×100
normalized value= (max−min)(current value−min) ×100
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 (50–80): 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 indicator’s 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 TradingView’s publishing rules, this script is provided with sufficient detail on what it does, how it’s 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.
🔹 Combined ATR + Volume (C-ATR+Vol) 지표 설명
이 인디케이터는 ATR(Average True Range)와 거래량(Volume)을 결합하여 시장의 변동성과 유동성을 동시에 측정하는 지표입니다.
ATR은 가격 변동성의 크기를 나타내며, 거래량은 시장 참여자의 활동 수준을 반영합니다. 보통 높은 ATR은 가격 변동이 크다는 의미이고, 높은 거래량은 시장에서 적극적인 거래가 이루어지고 있음을 나타냅니다.
이 두 지표를 각각 0~100 범위로 정규화한 후, 평균을 구하여 "Combined ATR + Volume (C-ATR+Vol)" 값을 계산합니다.
이를 통해 단순한 가격 변동성뿐만 아니라 거래량까지 고려하여, 더욱 신뢰성 있는 변동성 판단을 할 수 있도록 도와줍니다.
📌 핵심 개념
1️⃣ ATR (Average True Range)란?
시장의 변동성을 측정하는 지표로, 일정 기간 동안의 고점-저점 변동폭을 기반으로 계산됩니다.
ATR이 높을수록 가격 변동이 크며, 낮을수록 횡보장이 지속될 가능성이 큽니다.
하지만 ATR은 방향성을 제공하지 않으며, 단순히 변동성의 크기만을 나타냅니다.
2️⃣ 거래량 (Volume)의 역할
거래량은 시장 참여자의 관심과 유동성을 반영하는 중요한 요소입니다.
높은 거래량은 강한 매수 또는 매도세가 존재함을 의미하며, 낮은 거래량은 시장 참여가 적거나 관심이 줄어들었음을 나타냅니다.
3️⃣ ATR + 거래량의 결합 (C-ATR+Vol)
단순한 ATR 값만으로는 변동성이 커도 거래량이 부족할 수 있으며, 반대로 거래량이 많아도 변동성이 낮을 수 있습니다.
이를 해결하기 위해 ATR과 거래량을 각각 0~100으로 정규화하여 균형 잡힌 변동성 지표를 만들었습니다.
두 지표의 평균값을 계산하여, 가격 변동과 거래량이 동시에 높은지를 측정할 수 있도록 설계되었습니다.
📊 사용법 및 해석
80 이상 → 강한 변동성 구간
가격 변동성이 크고 거래량도 높은 상태
강한 추세가 진행 중이거나 큰 변동이 일어날 가능성이 큼
상승/하락 방향성을 확인한 후 트렌드를 따라가는 전략이 유리
50~80 구간 → 보통 수준의 변동성
가격 움직임이 일정하며, 거래량도 적절한 수준
점진적인 추세 형성이 이루어질 가능성이 있음
시장이 점진적으로 상승 혹은 하락할 가능성이 크므로, 보조지표를 활용하여 매매 타이밍을 결정하는 것이 중요
50 이하 → 낮은 변동성 및 유동성 부족
가격 변동이 적고, 거래량도 낮은 상태
시장이 횡보하거나 조정 기간에 들어갈 가능성이 큼
박스권 매매(지지/저항 활용) 또는 돌파 전략을 고려할 수 있음
💡 활용 방법 및 전략
✅ 1. 트렌드 판단 보조지표로 활용
단독으로 사용하는 것보다는 RSI, MACD, 이동평균선(MA) 등의 지표와 함께 활용하는 것이 효과적입니다.
예를 들어, MACD가 상승 신호를 주고, C-ATR+Vol 값이 80을 초과하면 강한 상승 추세로 해석할 수 있습니다.
✅ 2. 변동성 돌파 전략에 활용
C-ATR+Vol이 80 이상인 구간에서 가격이 특정 저항선을 돌파한다면, 강한 추세의 시작을 의미할 수 있습니다.
반대로, C-ATR+Vol이 50 이하에서 가격이 저항선에 가까워지면 돌파 가능성이 낮아질 수 있습니다.
✅ 3. 시장 참여도와 변동성 확인
단순히 ATR만 높아서는 신뢰하기 어려운 경우가 많습니다. 예를 들어, 급등 후 거래량이 급감하면 상승 지속 가능성이 낮아질 수도 있습니다.
하지만 C-ATR+Vol을 사용하면 거래량이 함께 증가하는지를 확인하여 보다 신뢰할 수 있는 분석이 가능합니다.
🚀 결론
🔹 Combined ATR + Volume (C-ATR+Vol) 인디케이터는 단순한 ATR이 아니라 거래량까지 고려하여 변동성을 측정하는 강력한 도구입니다.
🔹 시장이 큰 움직임을 보일 가능성이 높은 구간을 찾는 데 유용하며, 80 이상일 경우 강한 변동성이 있음을 나타냅니다.
🔹 단독으로 사용하기보다는 보조지표와 함께 활용하여, 트렌드 분석 및 돌파 전략 등에 효과적으로 적용할 수 있습니다.
📌 주의사항
변동성이 크다고 해서 반드시 가격이 급등/급락한다는 보장은 없습니다.
특정한 매매 전략 없이 단순히 이 지표만 보고 매수/매도를 결정하는 것은 위험할 수 있습니다.
시장 상황에 따라 변동성의 의미가 다르게 작용할 수 있으므로, 반드시 다른 보조지표와 함께 활용하는 것이 중요합니다.
🔥 이 지표를 활용하여 시장의 변동성과 거래량을 보다 효과적으로 분석해보세요! 🚀
ZenAlgo - LevelsThis script combines multiple anchored Volume-Weighted Average Price (VWAP) calculations into a single tool, providing a continuous record of past VWAP levels and highlighting when price has tested them. Typically, VWAP indicators show only the current VWAP for a single anchor period, requiring you to either keep re-anchoring manually or juggle multiple instances of different VWAP tools for each timeframe. By contrast, this script automatically tracks both the ongoing VWAP and previously completed VWAP values, along with real-time detection of “tests” (when price crosses a particular VWAP level). It’s especially valuable for traders who want to see how price has interacted with VWAP over several sessions, weeks, or months—without switching between separate indicators or manually setting anchors.
Below is a comprehensive explanation of each component, why multiple VWAP lines working together can be more informative than a single line, and how to adjust the script for various markets and trading styles:
Primary VWAP vs. Historical VWAP Lines - Standard VWAP indicators typically focus on the current line only. This script also calculates a primary VWAP, but it “locks in” each completed VWAP value when a new time anchor is detected (e.g., new weekly bar, new monthly bar, new session). As a result, you retain an ongoing history of VWAP lines for every completed anchored period. This is more powerful than manually setting up multiple VWAP tools—one for each desired timeframe—because everything is handled in a single script. You avoid chart clutter and the risk of forgetting to reset your manual VWAP at the correct bar.
Why Combine Multiple Anchored VWAP Lines in One Script? - Viewing several anchored VWAP lines together offers synergy . You see not only the current VWAP but also previous ones from different sessions or months, all within the same chart pane. This synergy becomes apparent if multiple historical VWAP lines cluster near the same price level, indicating a potentially significant zone of volume-based support or resistance. Handling this manually would involve repeatedly setting separate VWAP indicators, each reset at specific points, which is time-consuming and prone to error. In this script, the process is automated: as soon as the anchor changes, a completed VWAP line is stored so you can observe how price eventually reacts to it, repeatedly or not at all.
Automated “Test” Detection - Once a historical VWAP line is set, the script tracks when price crosses it in subsequent bars. If the high and low of a bar span that line, the script marks it in red (both the line and its label). It also keeps a counter of how many times each line has been tested. This method goes beyond a simple visual approach by quantifying the retests. Because all these lines are created and managed in one place, you don’t have to manually label the lines or check them one by one.
Advantages Over Manually Setting Multiple VWAPs
You save screen space: Instead of layering several VWAP indicators, each with unique settings, this single script plots them all on one overlay.
Automation: When a new anchor period begins, the script “closes out” the old VWAP and starts a new one. You never need to remember to reset it manually.
Retest Visualization: The script not only draws each line but also changes color and updates the label automatically if a line gets tested. Doing this by hand would be labor-intensive.
Unified Parameters: All settings (e.g., array size, max distance, test count limit) apply uniformly. You can manage them from one place, instead of configuring multiple separate tools.
Extended Insight with Multiple VWAP Lines
Since VWAP reflects the volume-weighted average price for each chosen period, historical lines can show zones where the market had a fair-value consensus in previous intervals. When the script preserves these lines, you see potential support/resistance areas more distinctly. If, for instance, price continually pivots around an old VWAP line, that may reveal a strong volume-based level. With several older VWAP lines on the chart, you gain an immediate sense of where these volume-derived averages have appeared and how price reacted over time. This wider perspective often proves more revealing than a single “current” VWAP line that does not reflect previous anchor sessions.
Handling of Illiquid Markets and Volume Limitations
VWAP is inherently tied to volume data, so its reliability decreases if volume reporting is missing or if the asset trades with very low liquidity. In such cases, a single large trade might momentarily skew the VWAP, resulting in “false” test signals when the high/low range intersects an abnormal price swing. If you suspect the data is incomplete or the market is unusually thin, it’s wise to confirm the validity of these VWAP lines before using them for any decision-making. Additionally, unusual market conditions—like after-hours trading or sudden high-volatility events—may cause VWAP to shift quickly, setting up multiple lines in a short time.
Key User-Configurable Settings
Hide VWAP on Day timeframe and above : Lets you disable the primary VWAP plot on daily or higher timeframes for a cleaner view.
Anchor Period : Select from Session, Week, Month, Quarter, Year, Decade or Century. Controls how frequently the script resets and preserves the VWAP line.
Offset : Moves the current VWAP line by a specified number of bars if you need a shifted perspective.
Max Array Size : Caps how many past VWAP lines the script will remember. Prevents clutter if you’re charting very long histories.
Max Distance : Defines how far back (in bar index units) a line is kept. If a line’s start bar is older than this threshold, it’s removed, keeping the chart uncluttered.
Max Red Labels : Limits the number of tested (red) VWAP lines that appear. If price tests a large number of old lines, only the newest red labels remain once you hit the set limit.
Workflow Overview
As soon as a new anchor period begins (e.g., a new weekly candle if “Week” is chosen), the script ends the current VWAP and stores that final value in its internal arrays.
It creates a dotted line and label representing the completed VWAP, and keeps track of whether it has been tested or not.
Subsequent bars may then cross that line. If a bar’s high/low includes the line’s value, it’s flagged as tested, labeled red, and a test counter increases.
As new anchored periods come, old lines remain visible—unless they fall outside your maxDistance or you exceed the maximum stored line count.
Real-World Benefits
Combining multiple VWAP lines—ranging, for example, from session-based lines for intraday perspectives to monthly or quarterly lines for broader context—provides a layered view of the volume-based fair price. This can help you quickly spot zones where price repeatedly intersects old VWAPs, potentially highlighting where bulls or bears took action historically. Because this script automates the management of all these lines and flags their retests, it removes a great deal of repetitive manual work that would typically accompany multiple, separate VWAP indicators set to different anchors.
Limitations & Practical Use
As with any volume-related tool, the script depends on reliable volume data. Assets trading on smaller venues or during illiquid periods may produce spurious signals. The script does not signal buy or sell decisions; rather, it helps visually map out where volume-weighted averages from previous periods might still be relevant to market behavior. Always combine the insight from these historical VWAP lines with your existing analytical approach or other technical and fundamental tools you use.
Conclusion
This script unifies past and present VWAP lines into one overlay, automatically detecting new anchor resets, storing the final VWAP values, and indicating whenever old lines are retested by price. It offers synergy through the simultaneous display of multiple historical VWAP lines, making it quicker and easier to detect potential support/resistance zones and better reflect changing market volumes over time. You no longer need to manually create, configure, or reset multiple VWAP indicators. Instead, the script handles all aspects of line creation, retest detection, and clutter management, giving you a robust framework to observe how historical VWAP data aligns with current price action.
By understanding the significance of multiple anchored VWAP lines, you can assess market structure from multiple angles in a single view. As always, ensure you confirm the reliability of the volume data for your particular asset and use these lines in conjunction with other analyses to form a well-rounded perspective on current market behavior.
Effective FVG Indicator - ImranDescription:
The Effective FVG Indicator is a technical analysis tool designed to identify Fair Value Gaps (FVGs) in financial markets. FVGs occur when there is a significant gap between the closing price of one session and the opening price of the next session, often indicating a potential reversal point. This indicator uses volume and price movement criteria to confirm and mark these gaps effectively.
Key Features:
Fair Value Gap Detection : Identifies both bullish and bearish FVGs based on significant gaps in price.
Volume Confirmation : Confirms FVGs with high volume, ensuring that the gap is not due to a lack of liquidity.
Price Imbalance : Ensures that the gap is accompanied by a large price movement within the session, indicating strong market sentiment.
Buy/Sell Signals : Marks bullish FVGs with a green "BUY" label below the bar and bearish FVGs with a red "SELL" label above the bar.
Background Highlighting : Highlights the background with a semi-transparent green or red color when a valid FVG is detected, making it easy to spot significant gaps.
Higher Timeframe SeparatorThis script helps visually identify when a higher timeframe candle starts by drawing a vertical line. It also shades the area above or below the opening price, making it easier to track price movement relative to the higher timeframe.
Why It's Useful
If you use multiple timeframes, this indicator provides a clear visual reference for where the price is relative to the higher timeframe. This is much more convenient than constantly switching between charts. You can see in the screenshot below how much clearer the price action becomes when the indicator is enabled:
Additional Benefit
If you trade on a lower timeframe and notice that the number of bars between separators is inconsistent, it means there weren’t enough trades during that period—indicating low liquidity. Illiquid instruments can be riskier to trade. For example, observe how the vertical lines on the left side of the image below are densely packed:
Market Sessions and OverlapsMarket Sessions and Overlaps Indicator
This script, titled " Market Sessions and Overlaps ," provides a detailed visualization of major global trading sessions—Asia, Europe, and New York—along with the periods where these sessions overlap. It is designed to assist traders in understanding session timings and overlaps in their local time zone. Key features include:
Session Visualization: Highlights the Asia, Europe, and New York trading sessions directly on the chart with customizable colors and transparency for better clarity.
Overlap Identification: Marks the overlapping periods between Asia-Europe and Europe-New York sessions, where market activity often intensifies, with distinct candle colors.
Time Zone Support: The script allows users to select their local time zone, ensuring all session times are displayed accurately, no matter the user’s location.
Alerts for Key Events: Includes optional alerts to notify users of session openings, closings, and the start or end of overlap periods.
This indicator serves as a visual tool for tracking session-specific activity and liquidity. It is configurable to match individual preferences, enabling better alignment with trading strategies.
Disclaimer: This script is for informational purposes only and does not provide financial advice. Please consult a licensed financial advisor for personalized trading guidance.
Mean Reversion Pro Strategy [tradeviZion]Mean Reversion Pro Strategy : User Guide
A mean reversion trading strategy for daily timeframe trading.
Introduction
Mean Reversion Pro Strategy is a technical trading system that operates on the daily timeframe. The strategy uses a dual Simple Moving Average (SMA) system combined with price range analysis to identify potential trading opportunities. It can be used on major indices and other markets with sufficient liquidity.
The strategy includes:
Trading System
Fast SMA for entry/exit points (5, 10, 15, 20 periods)
Slow SMA for trend reference (100, 200 periods)
Price range analysis (20% threshold)
Position management rules
Visual Elements
Gradient color indicators
Three themes (Dark/Light/Custom)
ATR-based visuals
Signal zones
Status Table
Current position information
Basic performance metrics
Strategy parameters
Optional messages
📊 Strategy Settings
Main Settings
Trading Mode
Options: Long Only, Short Only, Both
Default: Long Only
Position Size: 10% of equity
Starting Capital: $20,000
Moving Averages
Fast SMA: 5, 10, 15, or 20 periods
Slow SMA: 100 or 200 periods
Default: Fast=5, Slow=100
🎯 Entry and Exit Rules
Long Entry Conditions
All conditions must be met:
Price below Fast SMA
Price below 20% of current bar's range
Price above Slow SMA
No existing position
Short Entry Conditions
All conditions must be met:
Price above Fast SMA
Price above 80% of current bar's range
Price below Slow SMA
No existing position
Exit Rules
Long Positions
Exit when price crosses above Fast SMA
No fixed take-profit levels
No stop-loss (mean reversion approach)
Short Positions
Exit when price crosses below Fast SMA
No fixed take-profit levels
No stop-loss (mean reversion approach)
💼 Risk Management
Position Sizing
Default: 10% of equity per trade
Initial capital: $20,000
Commission: 0.01%
Slippage: 2 points
Maximum one position at a time
Risk Control
Use daily timeframe only
Avoid trading during major news events
Consider market conditions
Monitor overall exposure
📊 Performance Dashboard
The strategy includes a comprehensive status table displaying:
Strategy Parameters
Current SMA settings
Trading direction
Fast/Slow SMA ratio
Current Status
Active position (Flat/Long/Short)
Current price with color coding
Position status indicators
Performance Metrics
Net Profit (USD and %)
Win Rate with color grading
Profit Factor with thresholds
Maximum Drawdown percentage
Average Trade value
📱 Alert Settings
Entry Alerts
Long Entry (Buy Signal)
Short Entry (Sell Signal)
Exit Alerts
Long Exit (Take Profit)
Short Exit (Take Profit)
Alert Message Format
Strategy name
Signal type and direction
Current price
Fast SMA value
Slow SMA value
💡 Usage Tips
Consider starting with Long Only mode
Begin with default settings
Keep track of your trades
Review results regularly
Adjust settings as needed
Follow your trading plan
⚠️ Disclaimer
This strategy is for educational and informational purposes only. It is not financial advice. Always:
Conduct your own research
Test thoroughly before live trading
Use proper risk management
Consider your trading goals
Monitor market conditions
Never risk more than you can afford to lose
📋 Release Notes
14 January 2025
Added New Fast & Slow SMA Options:
Fibonacci-based periods: 8, 13, 21, 144, 233, 377
Additional period: 50
Complete Fast SMA options now: 5, 8, 10, 13, 15, 20, 21, 34, 50
Complete Slow SMA options now: 100, 144, 200, 233, 377
Bug Fixes:
Fixed Maximum Drawdown calculation in the performance table
Now using strategy.max_drawdown_percent for accurate DD reporting
Previous version showed incorrect DD values
Performance metrics now accurately reflect trading results
Performance Note:
Strategy tested with Fast/Slow SMA 13/377
Test conducted with 10% equity risk allocation
Daily Timeframe
For Beginners - How to Modify SMA Levels:
Find this line in the code:
fastLength = input.int(title="Fast SMA Length", defval=5, options= )
To add a new Fast SMA period: Add the number to the options list, e.g.,
To remove a Fast SMA period: Remove the number from the options list
For Slow SMA, find:
slowLength = input.int(title="Slow SMA Length", defval=100, options= )
Modify the options list the same way
⚠️ Note: Keep the periods that make sense for your trading timeframe
💡 Tip: Test any new combinations thoroughly before live trading
"Trade with Discipline, Manage Risk, Stay Consistent" - tradeviZion
Profitability Visualization with Bid-Ask Spread ApproximationOverview
The " Profitability Visualization with Bid-Ask Spread Approximation " indicator is designed to assist traders in assessing potential profit and loss targets in relation to the current market price or a simulated entry price. It provides flexibility by allowing users to choose between two methods for calculating the offset from the current price:
Bid-Ask Spread Approximation: The indicator attempts to estimate the bid-ask spread by using the highest (high) and lowest (low) prices within a given period (typically the current bar or a user-defined timeframe) as proxies for the ask and bid prices, respectively. This method provides a dynamic offset that adapts to market volatility.
Percentage Offset: Alternatively, users can specify a fixed percentage offset from the current price. This method offers a consistent offset regardless of market conditions.
Key Features
Dual Offset Calculation Methods: Choose between a dynamic bid-ask spread approximation or a fixed percentage offset to tailor the indicator to your trading style and market analysis.
Entry Price Consideration: The indicator can simulate an entry price at the beginning of each trading session (or the first bar on the chart if no sessions are defined). This feature enables a more realistic visualization of potential profit and loss levels based on a hypothetical entry point.
Profit and Loss Targets: When the entry price consideration is enabled, the indicator plots profit target (green) and loss target (red) lines. These lines represent the price levels at which a trade entered at the simulated entry price would achieve a profit or incur a loss equivalent to the calculated offset amount.
Offset Visualization: Regardless of whether the entry price is considered, the indicator always displays upper (aqua) and lower (fuchsia) offset lines. These lines represent the calculated offset levels based on the chosen method (bid-ask approximation or percentage offset).
Customization: Users can adjust the percentage offset, toggle the bid-ask approximation and entry price consideration, and customize the appearance of the lines through the indicator's settings.
Inputs
useBidAskApproximation A boolean (checkbox) input that determines whether to use the bid-ask spread approximation (true) or the percentage offset (false). Default is false.
percentageOffset A float input that allows users to specify the percentage offset to be used when useBidAskApproximation is false. The default value is 0.63.
considerEntryPrice A boolean input that enables the consideration of a simulated entry price for calculating and displaying profit and loss targets. Default is true.
Calculations
Bid-Ask Approximation (if enabled): bidApprox = request.security(syminfo.tickerid, timeframe.period, low) Approximates the bid price using the lowest price (low) of the current period. askApprox = request.security(syminfo.tickerid, timeframe.period, high) Approximates the ask price using the highest price (high) of the current period. spreadApprox = askApprox - bidApprox Calculates the approximate spread.
Offset Amount: offsetAmount = useBidAskApproximation ? spreadApprox / 2 : close * (percentageOffset / 100) Determines the offset amount based on the selected method. If useBidAskApproximation is true, the offset is half of the approximated spread; otherwise, it's the current closing price (close) multiplied by the percentageOffset.
Entry Price (if enabled): var entryPrice = 0.0 Initializes a variable to store the entry price. if considerEntryPrice Checks if entry price consideration is enabled. if barstate.isnew Checks if the current bar is the first bar of a new session. entryPrice := close Sets the entryPrice to the closing price of the first bar of the session.
Profit and Loss Targets (if entry price is considered): profitTarget = entryPrice + offsetAmount Calculates the profit target price level. lossTarget = entryPrice - offsetAmount Calculates the loss target price level.
Plotting
Profit Target Line: Plotted in green (color.green) with a dashed line style (plot.style_linebr) and increased linewidth (linewidth=2) when considerEntryPrice is true.
Loss Target Line: Plotted in red (color.red) with a dashed line style (plot.style_linebr) and increased linewidth (linewidth=2) when considerEntryPrice is true.
Upper Offset Line: Always plotted in aqua (color.aqua) to show the offset level above the current price.
Lower Offset Line: Always plotted in fuchsia (color.fuchsia) to show the offset level below the current price.
Limitations
Approximation: The bid-ask spread approximation is based on high and low prices and may not perfectly reflect the actual bid-ask spread of a specific broker, especially during periods of high volatility or low liquidity.
Simplified Entry: The entry price simulation is basic and assumes entry at the beginning of each session. It does not account for specific entry signals or order types.
No Order Execution: This indicator is purely for visualization and does not execute any trades.
Data Discrepancies: The high and low values used for approximation might not always align with real-time bid and ask prices due to differences in data aggregation and timing between TradingView and various brokers.
Disclaimer
This indicator is for educational and informational purposes only and should not be considered financial advice. Trading involves substantial risk, and past performance is not indicative of future results. Always conduct thorough research and consider your own risk tolerance before making any trading decisions. It is recommended to combine this indicator with other technical analysis tools and a well-defined trading strategy.
DAILY Supertrend + EMA Crossover with RSI FilterThis strategy is a technical trading approach that combines multiple indicators—Supertrend, Exponential Moving Averages (EMAs), and the Relative Strength Index (RSI)—to identify and manage trades.
Core Components:
1. Exponential Moving Averages (EMAs):
Two EMAs, one with a shorter period (fast) and one with a longer period (slow), are calculated. The idea is to spot when the faster EMA crosses above or below the slower EMA. A fast EMA crossing above the slow EMA often suggests upward momentum, while crossing below suggests downward momentum.
2. Supertrend Indicator:
The Supertrend uses Average True Range (ATR) to establish dynamic support and resistance lines. These lines shift above or below price depending on the prevailing trend. When price is above the Supertrend line, the trend is considered bullish; when below, it’s considered bearish. This helps ensure that the strategy trades only in the direction of the overall trend rather than against it.
3. RSI Filter:
The RSI measures momentum. It helps avoid buying into markets that are already overbought or selling into markets that are oversold. For example, when going long (buying), the strategy only proceeds if the RSI is not too high, and when going short (selling), it only proceeds if the RSI is not too low. This filter is meant to improve the quality of the trades by reducing the chance of entering right before a reversal.
4. Time Filters:
The strategy only triggers entries during user-specified date and time ranges. This is useful if one wants to limit trading activity to certain trading sessions or periods with higher market liquidity.
5. Risk Management via ATR-based Stops and Targets:
Both stop loss and take profit levels are set as multiples of the ATR. ATR measures volatility, so when volatility is higher, both stops and profit targets adjust to give the trade more breathing room. Conversely, when volatility is low, stops and targets tighten. This dynamic approach helps maintain consistent risk management regardless of market conditions.
Overall Logic Flow:
- First, the market conditions are analyzed through EMAs, Supertrend, and RSI.
- When a buy (long) condition is met—meaning the fast EMA crosses above the slow EMA, the trend is bullish according to Supertrend, and RSI is below the specified “overbought” threshold—the strategy initiates or adds to a long position.
- Similarly, when a sell (short) condition is met—meaning the fast EMA crosses below the slow EMA, the trend is bearish, and RSI is above the specified “oversold” threshold—it initiates or adds to a short position.
- Each position is protected by an automatically calculated stop loss and a take profit level based on ATR multiples.
Intended Result:
By blending trend detection, momentum filtering, and volatility-adjusted risk management, the strategy aims to capture moves in the primary trend direction while avoiding entries at excessively stretched prices. Allowing multiple entries can potentially amplify gains in strong trends but also increases exposure, which traders should consider in their risk management approach.
In essence, this strategy tries to ride established trends as indicated by the Supertrend and EMAs, filter out poor-quality entries using RSI, and dynamically manage trade risk through ATR-based stops and targets.
Alternative Price [OmegaTools]The Alternative Price script is a sophisticated and flexible indicator designed to redefine how traders visualize and interpret price data. By offering multiple unique charting modes, robust customization options, and advanced features, this tool provides a comprehensive alternative to traditional price charts. It is particularly useful for identifying market trends, detecting patterns, and simplifying complex data into actionable insights.
This script is highly versatile, allowing users to choose from five distinct charting modes: Candles, Line, Channel, Renko, and Bubbles. Each mode serves a unique purpose and presents price information in an innovative way. When using this script, it is strongly recommended to hide the platform’s default price candles or chart data. Doing so will eliminate redundancy and provide a clearer and more focused view of the alternative price visualization.
The Candles mode offers a traditional candlestick charting style but with added flexibility. Users can choose to enable smoothed opens or smoothed closes, which adjust the way the open and close prices are calculated. When smoothed opens are enabled, the opening price is computed as the average of the actual open price and the closing prices of the previous two bars. This creates a more gradual representation of price transitions, particularly useful in markets prone to sudden spikes or irregularities. Similarly, smoothed closes modify the closing price by averaging it with the previous close, the high-low midpoint, and an exponential moving average of the high-low-close mean. This technique filters out noise, making trends and price momentum easier to identify.
In the Line mode, the script displays a simple line chart that connects the smoothed closing prices. This mode is ideal for traders who prefer minimalism or need to focus on the overall trend without the distraction of individual bar details. The Channel mode builds upon this by plotting additional lines representing the highs and lows of each bar. The resulting visualization resembles a price corridor that helps identify support and resistance zones or price compression areas.
The Renko mode introduces a more advanced and noise-filtering method of visualizing price movements. Renko charts, constructed using the ATR (Average True Range) as a baseline, display blocks that represent a specific price range. The script dynamically calculates the size of these blocks based on ATR, with separate thresholds for upward and downward movements. This makes Renko mode particularly effective for identifying sustained trends while ignoring minor price fluctuations. Additionally, the open and close values of Renko blocks can be smoothed to further refine the visualization.
The Bubbles mode represents price activity using circles or bubbles whose size corresponds to relative volume. This mode provides a quick and intuitive way to assess market participation at different price levels. Larger bubbles indicate higher trading volumes, while smaller bubbles highlight periods of lower activity. This visualization is particularly valuable in understanding the relationship between price movements and market liquidity.
The coloring of candles and other chart elements is a core feature of this script. Users can select between two color modes: Normal and Volume. In Normal mode, bullish candles are displayed in the user-defined bullish color, while bearish candles use the bearish color. Neutral elements, such as midpoints or undecided price movements, are shaded with a neutral color. In Volume mode, the candle colors are dynamically adjusted based on trading volume. A gradient color scale is applied, where the intensity of the bullish or bearish colors reflects the volume for that particular bar. This feature allows traders to visually identify periods of heightened activity and associate them with specific price movements.
Engulfing patterns, a popular technical analysis tool, are automatically detected and marked on the chart when the corresponding setting is enabled. The script identifies long engulfing patterns, where the current bar's range completely encompasses the previous bar’s range and indicates a potential bullish reversal. Similarly, short engulfing patterns are identified where the current bar fully engulfs the previous bar in the opposite direction, suggesting a bearish reversal. These patterns are visually highlighted with circular markers to draw the trader’s attention.
Each feature and mode is highly customizable. The colors for bullish, bearish, and neutral movements can be personalized, and the thresholds for patterns or smoothing can be fine-tuned to match specific trading strategies. The script's ability to toggle between various modes makes it adaptable to different market conditions and analysis preferences.
In summary, the Alternative Price script is a comprehensive tool that redefines the way traders view price charts. By offering multiple visualization modes, customizable features, and advanced detection algorithms, it provides a powerful way to uncover market trends, volume relationships, and significant patterns. The recommendation to hide default chart elements ensures that the focus remains on this innovative tool, enhancing its usability and clarity. This script empowers traders to gain deeper insights into market behavior and make informed trading decisions, all while maintaining a clean and visually appealing chart layout.
Keep in mind that some of the modes of this indicator might not reflect the actual closing price of the underlying asset, before opening a trade, check carefully the actual price!
Merged Conditional Horizontal Lines with TogglesThe ranges that have blue highs & orange lows have been broken out of & may get re-tested as "support".
Prefer this candle range to be an expansion with neutral wicks.
The ranges that have red highs and green lows have generated interest (inside-bars) in the market, where the first end will get turtle souped and the second will be the draw on liquidity.
Prefer this candle range has long wick(s).
This patch allows you to toggle either range off.
FVG Price & Volume Graph [LuxAlgo]The FVG Price & Volume Graph tool plot recently detected fair value gaps relative to the volume traded within their area during their formation. This allows us to effectively visualize significant fair value gaps caused by high liquidity.
The indicator also returns levels from the fair value gaps areas average with the highest associated volume.
Do note that the indicator can consider the chart's visible range when being computed, which will recalculate the indicator when the chart's visible range changes.
🔶 USAGE
Fair Value Gaps (FVG) are core price action concepts occurring when the disparity between supply and demand is significant. Price has a tendency to come back to those areas and mitigating them, that is filling them.
The provided tools allow for effective visualization of both FVG's area's height as well as the volume originating from their creation, which is defined by the total traded volume located within the FVG during its creation. FVG's with more associated volume are displayed to the rightmost of the chart.
Users can determine the amount of most recent FVG's to display from the "Display Amount" setting. Disabling the "Consider Mitigation" setting will return mitigated FVGs in the plot, which can be useful to know where most FVGs were located.
We can use the area average of the FVGs with the most associated volume as potential support/resistance levels. Users can extend more FVG's averages by increasing the "Highest Volume Averages" setting.
🔹 Visualizing Volume/Price Relationships of FVG's
A linear regression is fit between FVG's areas average and their associated volume, with this linear regression helping us see where FVG's with specific volume might be located in the future based on existing FVG's.
Note that FVG's do not tend to exhibit linear relationships with their associated volume, the provided linear regression can give a general sense of tendency, but nothing necessarily accurate.
🔶 DETAILS
🔹 Intrabar Data TF
Given a formation of three candles causing an FVG, the volume traded within that FVG area is obtained by looking at the lower timeframe intrabar candles located within the intermediary candle of the formation. The volume of the intrabar candles located within the FVG areas is added up to obtain the associated volume of the FVG.
Using a lower "Intrabar Data TF" allows obtaining more precise volume results, at the cost of computation time and data availability (if there is a high difference between the "Intrabar Data TF" and the chart TF then less FVG can have their associated volume calculated due to Tradingview limitations).
🔹 Display
Users have access to multiple graphical settings affecting how the indicator is displayed.
The "Graph Resolution" setting determines the length of the X axis, with higher values returning more precise results on the location of FVGs over the X axis. Users can also control the number of labels displayed on the X-axis using the numerical input to the right of "Show X-Axis Labels".
Additionally, users can color FVG areas using a gradient relative to the size of the area, or the volume associated with the FVG.
🔶 SETTINGS
Display Amount: Amount of most recent FVGs to display.
Highest Volume Averages: Amount of FVG averages levels with the highest volume to display and extend.
Consider Mitigation: Only display unmitigated FVGs.
Filter FVGs Outside Visible Range: Only display FVGs areas that are located within the user chart visible range.
Intrabar Data TF: Timeframe used to obtain intrabar data. Should be lower than the user chart timeframe.
