Normalized DXY+Custom USD Index (DXY+) – Normalized Dollar Strength with Bitcoin, Gold, and Yuan.
This custom USD strength index replicates the structure of the official U.S. Dollar Index (DXY), while expanding it to include modern financial assets such as Bitcoin (BTC), Ethereum (ETH), gold (XAU), and the Chinese yuan (CNY).
Weights for the core fiat currencies (EUR, JPY, GBP, CAD, SEK, CHF) follow the official ICE DXY methodology. Additional components are weighted proportionally based on their estimated global economic influence.
The index is normalized from its initial valid data point, meaning it starts at 100 on the first day all asset inputs are available. From that point forward, it tracks the relative strength of the U.S. dollar against this expanded basket.
This provides a more comprehensive and modernized view of the dollar's strength—not only against traditional fiat currencies, but also in the context of rising decentralized assets and non-Western trade power.
Temel Analiz
H2-25 cuts (bp)This custom TradingView indicator tracks and visualizes the implied pricing of Federal Reserve rate cuts in the market, specifically for the second half of 2025. It does so by comparing the price differences between two specific Fed funds futures contracts: one for June 2025 and one for December 2025. These contracts are traded on the Chicago Board of Trade (CBOT) and are a widely-used market gauge of the expected path of U.S. interest rates.
The indicator calculates the difference between the implied rates for June and December 2025, and then multiplies the result by 100 to express it in basis points (bps). Each 0.01 change in the spread corresponds to a 1-basis point change in expectations for future rate cuts. A positive value indicates that the market is pricing in a higher likelihood of one or more rate cuts in 2025, while a negative value suggests that the market expects the Fed to hold rates steady or even raise them.
The plot represents the difference in implied rate cuts (in basis points) between the two contracts:
June 2025 (ZQM2025): A contract representing the implied Fed funds rate for June 2025.
December 2025 (ZQZ2025): A contract representing the implied Fed funds rate for December 2025.
Custom USD IndexThis is a modernized, expanded version of the U.S. Dollar Index (DXY), designed to provide a more accurate representation of the dollar’s global strength in today’s diversified economy.
Unlike the traditional DXY, which excludes major players like China and entirely omits real-world stores of value, this custom index (DXY+) includes:
Fiat Currencies (78.3% total weight):
EUR, JPY, GBP, CAD, AUD, CHF, and CNY — equally weighted to reflect the global currency landscape.
Gold (17.5%):
Gold (XAUUSD) is included as a traditional reserve asset and inflation hedge, acknowledging its continued monetary relevance.
Cryptocurrencies (2.8% total weight):
Bitcoin (BTC) and Ethereum (ETH) represent the emerging digital monetary layer.
The index rises when the U.S. dollar strengthens relative to this blended basket, and falls when the dollar weakens against it. This is ideal for traders, economists, and macro analysts seeking a more inclusive and up-to-date measure of dollar performance.
Levels & Flow📌 Overview
Levels & Flow is a visual trading tool that combines daily pivot levels with a dynamic EMA ribbon to help traders identify structure, momentum, and key decision zones in the market.
This script is designed for discretionary traders who rely on clean visual cues for intraday and swing trading strategies.
⚙️ Key Features
Daily Pivot, Support, and Resistance Lines
Automatically plots the daily pivot level based on the previous day’s OHLC data, along with calculated support and resistance levels.
Fibonacci Retracement Levels
Two dashed lines above and below the pivot represent the retracement of the pivot-resistance and pivot-support range, forming the boundaries of the “no-trade zone.”
No-Trade Zone (Shaded Box)
A gray shaded box between the two Fibonacci levels to visually mark a high-chop/low-conviction zone.
Trend-Based Candle Coloring (Current Day Only)
Candles are colored green if the close is above the pivot, red if below (only on the current trading day).
Bullish/Bearish Trend Label
A small table in the bottom-right corner displays “Bullish” or “Bearish” depending on whether price is above or below the pivot.
20-EMA Gradient Ribbon
A stack of 20 EMAs, each smoothed and color-coded from blue to green to reflect short- to long-term trend alignment.
Cumulative EMA with Adaptive Weighting
An intelligent moving average line that adjusts weight distribution among the 20 EMAs based on recent predictive accuracy using a learning rate and lookback period.
🧠 How It Works
📍 Levels
The script calculates daily pivot, resistance, and support levels using standard formulas:
Pivot = (High + Low + Close) / 3
Resistance = (2 × Pivot) – Low
Support = (2 × Pivot) – High
These levels update each day and extend 143 bars to the right.
📏 Fib Lines
Fib Up = Pivot + (Resistance – Pivot) × 0.382
Fib Down = Pivot – (Pivot – Support) × 0.382
These lines form the “no-trade zone” box.
📈 EMA Ribbon
20 EMAs starting from the user-defined Base Length, each incremented by 1
Each EMA is smoothed using the Smoothing Period
Color-coded from blue to green for intuitive visual flow
Filled between EMAs to visualize trend strength and alignment
🧠 Cumulative EMA Learning
Each EMA’s historical error is calculated over a Lookback Period
Lower-error EMAs receive higher weight; weights are normalized to sum to 1
The result is a cumulative EMA that adapts based on historical predictive power
🔧 User Inputs
Input
Base EMA Length: Sets the period for the shortest EMA (default: 20)
Smoothing Period: Smooths all EMAs and the cumulative EMA
Lookback for Learning: Number of bars to evaluate EMA prediction accuracy
Learning Rate: Adjusts how quickly weights shift in favor of more accurate EMAs
✅ How to Use It
Use the pivot level to define directional bias.
Watch for price breakouts above resistance or breakdowns below support to consider entry.
Avoid trading inside the shaded zone, where direction is less reliable.
Use the EMA ribbon gradient to confirm short/long alignment.
The cumulative EMA helps define trend with noise reduction.
🧪 Best For
Intraday traders who want to blend structure with flow
Swing traders needing clean daily levels with dynamic confirmation
Anyone looking to avoid choppy zones and improve visual clarity
⚠️ Disclaimer
This script is for educational and informational purposes only. It does not constitute financial advice or a trading recommendation. Always test scripts in simulation or on demo accounts before live use. Use at your own risk.
Bloomberg Financial Conditions Index (Proxy)The Bloomberg Financial Conditions Index (BFCI): A Proxy Implementation
Financial conditions indices (FCIs) have become essential tools for economists, policymakers, and market participants seeking to quantify and monitor the overall state of financial markets. Among these measures, the Bloomberg Financial Conditions Index (BFCI) has emerged as a particularly influential metric. Originally developed by Bloomberg L.P., the BFCI provides a comprehensive assessment of stress or ease in financial markets by aggregating various market-based indicators into a single, standardized value (Hatzius et al., 2010).
The original Bloomberg Financial Conditions Index synthesizes approximately 50 different financial market variables, including money market indicators, bond market spreads, equity market valuations, and volatility measures. These variables are normalized using a Z-score methodology, weighted according to their relative importance to overall financial conditions, and then aggregated to produce a composite index (Carlson et al., 2014). The resulting measure is centered around zero, with positive values indicating accommodative financial conditions and negative values representing tighter conditions relative to historical norms.
As Angelopoulou et al. (2014) note, financial conditions indices like the BFCI serve as forward-looking indicators that can signal potential economic developments before they manifest in traditional macroeconomic data. Research by Adrian et al. (2019) demonstrates that deteriorating financial conditions, as measured by indices such as the BFCI, often precede economic downturns by several months, making these indices valuable tools for predicting changes in economic activity.
Proxy Implementation Approach
The implementation presented in this Pine Script indicator represents a proxy of the original Bloomberg Financial Conditions Index, attempting to capture its essential features while acknowledging several significant constraints. Most critically, while the original BFCI incorporates approximately 50 financial variables, this proxy version utilizes only six key market components due to data accessibility limitations within the TradingView platform.
These components include:
Equity market performance (using SPY as a proxy for S&P 500)
Bond market yields (using TLT as a proxy for 20+ year Treasury yields)
Credit spreads (using the ratio between LQD and HYG as a proxy for investment-grade to high-yield spreads)
Market volatility (using VIX directly)
Short-term liquidity conditions (using SHY relative to equity prices as a proxy)
Each component is transformed into a Z-score based on log returns, weighted according to approximated importance (with weights derived from literature on financial conditions indices by Brave and Butters, 2011), and aggregated into a composite measure.
Differences from the Original BFCI
The methodology employed in this proxy differs from the original BFCI in several important ways. First, the variable selection is necessarily limited compared to Bloomberg's comprehensive approach. Second, the proxy relies on ETFs and publicly available indices rather than direct market rates and spreads used in the original. Third, the weighting scheme, while informed by academic literature, is simplified compared to Bloomberg's proprietary methodology, which may employ more sophisticated statistical techniques such as principal component analysis (Kliesen et al., 2012).
These differences mean that while the proxy BFCI captures the general direction and magnitude of financial conditions, it may not perfectly replicate the precision or sensitivity of the original index. As Aramonte et al. (2013) suggest, simplified proxies of financial conditions indices typically capture broad movements in financial conditions but may miss nuanced shifts in specific market segments that more comprehensive indices detect.
Practical Applications and Limitations
Despite these limitations, research by Arregui et al. (2018) indicates that even simplified financial conditions indices constructed from a limited set of variables can provide valuable signals about market stress and future economic activity. The proxy BFCI implemented here still offers significant insight into the relative ease or tightness of financial conditions, particularly during periods of market stress when correlations among financial variables tend to increase (Rey, 2015).
In practical applications, users should interpret this proxy BFCI as a directional indicator rather than an exact replication of Bloomberg's proprietary index. When the index moves substantially into negative territory, it suggests deteriorating financial conditions that may precede economic weakness. Conversely, strongly positive readings indicate unusually accommodative financial conditions that might support economic expansion but potentially also signal excessive risk-taking behavior in markets (López-Salido et al., 2017).
The visual implementation employs a color gradient system that enhances interpretation, with blue representing neutral conditions, green indicating accommodative conditions, and red signaling tightening conditions—a design choice informed by research on optimal data visualization in financial contexts (Few, 2009).
References
Adrian, T., Boyarchenko, N. and Giannone, D. (2019) 'Vulnerable Growth', American Economic Review, 109(4), pp. 1263-1289.
Angelopoulou, E., Balfoussia, H. and Gibson, H. (2014) 'Building a financial conditions index for the euro area and selected euro area countries: what does it tell us about the crisis?', Economic Modelling, 38, pp. 392-403.
Aramonte, S., Rosen, S. and Schindler, J. (2013) 'Assessing and Combining Financial Conditions Indexes', Finance and Economics Discussion Series, Federal Reserve Board, Washington, D.C.
Arregui, N., Elekdag, S., Gelos, G., Lafarguette, R. and Seneviratne, D. (2018) 'Can Countries Manage Their Financial Conditions Amid Globalization?', IMF Working Paper No. 18/15.
Brave, S. and Butters, R. (2011) 'Monitoring financial stability: A financial conditions index approach', Economic Perspectives, Federal Reserve Bank of Chicago, 35(1), pp. 22-43.
Carlson, M., Lewis, K. and Nelson, W. (2014) 'Using policy intervention to identify financial stress', International Journal of Finance & Economics, 19(1), pp. 59-72.
Few, S. (2009) Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press, Oakland, CA.
Hatzius, J., Hooper, P., Mishkin, F., Schoenholtz, K. and Watson, M. (2010) 'Financial Conditions Indexes: A Fresh Look after the Financial Crisis', NBER Working Paper No. 16150.
Kliesen, K., Owyang, M. and Vermann, E. (2012) 'Disentangling Diverse Measures: A Survey of Financial Stress Indexes', Federal Reserve Bank of St. Louis Review, 94(5), pp. 369-397.
López-Salido, D., Stein, J. and Zakrajšek, E. (2017) 'Credit-Market Sentiment and the Business Cycle', The Quarterly Journal of Economics, 132(3), pp. 1373-1426.
Rey, H. (2015) 'Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence', NBER Working Paper No. 21162.
Aggregated Perpetual Futures Open InterestPurpose
Aggregates perpetual futures open interest across Binance, Bybit, and OKX for the base currency of the asset loaded in your tradingview window.
How It Works
Symbol detection: The script grabs syminfo.basecurrency (e.g., “BTC”) from whatever market is on screen.
Ticker mapping: It constructs the three perp-OI feeds that TradingView publishes in the form EXCHANGE:USDT.P_OI
Data request: For each feed it fetches the full OHLC candle (request.security) on the chart’s timeframe. If a venue doesn’t list that perp, the request simply returns na.
Aggregation: The script adds the opens, highs, lows, and closes of all non-na feeds to produce a single aggregated OI candle.
General Notes
The status line shows each venue’s individual OI close.
FVG Candle HighlighterThis indicator highlights only the true Fair Value Gap (FVG) creator candle — the middle candle in a 3-bar FVG formation — with zero clutter.
🔹 Bullish FVG: Candle is colored if price gaps above the high two bars back
🔹 Bearish FVG: Candle is colored if price gaps below the low two bars back
✨ No boxes. No zones. Just pure, visual price-action accuracy.
🔧 Powered by Pine Script v6
🧠 Based on institutional-style FVG logic
🎯 Ideal for Smart Money / ICT / Order Block strategies
Coinbase Premium IndicatorPurpose
Indicates whether a crypto asset listed on a Coinbase spot market is trading at a premium or discount to other spot (Tether) markets.
How It Works
The script takes the base currency for the pair loaded in that TradingView window and searches for its Coinbase spot market. It also maps the base currency to the USDT (Tether) spot markets on Binance, Bybit, and OKX.
The Premium/ Discount is: (coin-btc-usd) - (sum(bnce-btc-usdt, bybt-btc-usdt, okx-btc-usdt))
General Notes
The status line of the Indicator displays the value of the premium/ discount and the market prices of the pair for each constituent exchange.
PER x RangeThis Pine Script calculates the target price of the Nikkei Average based on the EPS (Earnings Per Share) and different PER (Price-to-Earnings Ratio) multiples ranging from 17.5x to 12x, in increments of 0.5x. It then plots these target prices on the chart.
Key Features:
Input EPS: You can manually input the current EPS value of the Nikkei Average (the example uses 2380, but you can replace it with the actual EPS).
PER Multiples Calculation: The script calculates target prices for different PER multiples (17.5x, 17x, 16.5x, ..., down to 12x).
Plotting Target Prices: The calculated target prices (EPS * PER) are plotted on the chart as blue lines, showing you different target price scenarios based on varying PER multiples.
VOID Directional Spike MarkerThis indicator highlights significant directional moves on the $VOID chart (NYSE USI:UVOL − DERIBIT:DVOL ) using simple visual cues:
🔼 Green up arrows when the candle closes significantly higher than it opens
🔽 Red down arrows when the candle closes significantly lower than it opens
Threshold is fully customizable (default: 15,000,000)
Ideal for spotting explosive internal shifts on the 5-minute chart during key market moments
Alerts included for both up and down spikes
Use this to track aggressive buying or selling pressure across NYSE internals and time your entries on NQ, ES, or YM with stronger conviction.
Order Block with BoSHere’s a professional and concise description you can use for publishing your **TradingView script** titled **"Order Block with BoS"**:
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### 📌 **Description for TradingView Publication:**
**"Order Block with Break of Structure (BoS)"** is a powerful price action-based indicator designed to identify potential reversal zones and momentum shifts using **Order Block** detection combined with **Break of Structure (BoS)** confirmation.
### 🔍 **Key Features:**
* **Order Block Detection**: Highlights bullish and bearish order blocks using precise candle structure logic.
* **Break of Structure (BoS)**: Confirms structural breaks above swing highs or below swing lows to validate potential trend continuation or reversal.
* **Dynamic ATR Filter**: Uses a 14-period ATR with dynamic thresholds to confirm significant moves, filtering out weak breakouts.
* **Visual Aids**:
* Color-coded **boxes** to mark detected Order Blocks.
* **Arrows** at BoS confirmation points when ATR confirms strong momentum.
* Optional **dashed BoS lines** to show where price broke structure.
### ⚙️ **Customizable Inputs**:
* `Swing Length`: Defines the sensitivity of swing high/low detection.
* `Show Break of Structure`: Toggle on/off BoS confirmation lines.
* `Candle Lookback`: Number of historical candles to consider.
This indicator is ideal for traders who incorporate **smart money concepts**, **market structure analysis**, or **institutional order flow** strategies.
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Would you like me to help write the **strategy** version of this or translate the description into another language for international audiences?
Economic Event DatesThis TradingView indicator ("Economic Event Dates") plots significant economic event dates directly on your chart, helping you stay informed about potential market-moving announcements. It includes pre-configured dates for:
* **FOMC Meetings:** Key policy meetings of the Federal Open Market Committee.
* **CPI Releases:** Consumer Price Index data releases, a key measure of inflation.
* **Bitcoin Halvings:** Programmatic reductions in Bitcoin's new supply issuance.
**Features:**
* **Customizable Dates:** Easily input and manage dates for FOMC, CPI, and Halving events for current and future years (2025, 2026, and beyond for Halvings).
* **Visual Cues:** Displays vertical lines on the chart at the precise time of each event.
* **Event Labels:** Shows clear labels (e.g., "FOMC", "CPI", "Halving") for each event line.
* **Color Coding:** Distinct colors for FOMC (blue), CPI (orange), and Halving (purple) events for quick identification.
* **Future Events Focus:** Option to display only upcoming events relative to the current real time.
* **Morning Alerts:** (Optional) Triggers an alert on the morning of a scheduled event, providing a timely reminder.
* **Customizable Appearance:** Adjust line width and toggle label visibility.
**How to Use:**
1. Add the indicator to your TradingView chart.
2. Review and update the input dates for FOMC, CPI, and Halving events in the indicator settings. The script includes placeholders and notes for future dates that may require verification from official sources (e.g., federalreserve.gov, bls.gov).
3. Customize colors, line width, label visibility, and alert preferences as needed.
4. Observe the vertical lines on your chart indicating upcoming economic events.
This tool is designed for traders and investors who want to incorporate awareness of major economic events into their market analysis. Remember to verify future event dates as they are officially announced.
Created by YouNesta
QoQ PAT, Sales & OPM% Labels by GauravThis indicator automatically displays the Quarter-over-Quarter (QoQ) percentage change in Sales, PAT (Profit After Tax), and Operating Profit Margin (OPM%) directly on the price chart.
It fetches quarterly financial data using TradingView’s request.financial() function for:
Sales (TOTAL_REVENUE),
PAT (NET_INCOME),
Operating Profit (OPER_INCOME).
For each earnings update, it calculates:
Sales QoQ %: Growth in sales vs. the previous quarter,
PAT QoQ %: Growth in PAT vs. the previous quarter,
OPM %: Operating Profit Margin = (Operating Profit / Sales) × 100.
This helps traders and investors quickly visualize fundamental growth trends right alongside the candlestick chart, improving fundamental + technical analysis integration.
Long Short dom📊 Long Short dom (VI+) — Custom Vortex Trend Strength Indicator
This indicator is a refined version of the Vortex Indicator (VI) designed to help traders identify trend direction, momentum dominance, and potential long/short opportunities based on VI+ and VI– dynamics.
🔍 What It Shows:
• VI+ (Green Line): Measures upward trend strength.
• VI– (Red Line): Measures downward trend strength.
• Histogram (optional): Displays the difference between VI+ and VI–, helping visualize which side is dominant.
• Background Coloring: Highlights bullish or bearish dominance zones.
• Zero Line: A visual baseline to enhance clarity.
• Highest/Lowest Active Lines: Real-time markers for the strongest directional signals.
⸻
🛠️ Inputs:
• Length: Vortex calculation period (default 14).
• Show Histogram: Enable/disable VI+–VI– difference bars.
• Show Trend Background: Toggle colored zones showing trend dominance.
• Show Below Zero: Decide whether to display values that fall below 0 (for advanced use).
⸻
📈 Strategy Insights:
• When VI+ crosses above VI–, it indicates potential long momentum.
• When VI+ crosses below VI–, it signals possible short pressure.
• The delta histogram (VI+ – VI–) helps you quickly see shifts in momentum strength.
• The background shading provides an intuitive visual cue to assess trend dominance at a glance.
⸻
🚨 Built-in Alerts:
• Bullish Cross: VI+ crosses above VI– → possible entry long.
• Bearish Cross: VI+ crosses below VI– → possible entry short.
⸻
✅ Ideal For:
• Trend-following strategies
• Identifying long/short bias
• Confirming entries/exits with momentum analysis
⸻
This tool gives you clean, real-time visual insight into trend strength and shift dynamics, empowering smarter trade decisions with clarity and confidence.
Forex Session + Volume Profile [RunRox]📊 Forex Session + Volume Profile is built especially for traders who work with intra-session liquidity concepts or any strategy that needs a clear visual of trading sessions and the liquidity inside them.
Our team created this indicator to give you better session visibility, flexible session styling, and extra tools that help you navigate the market more easily.
📌 Features:
6 fully customizable sessions
Kill Zone (the high-impact trading window)
Volume Profile for each session
POC / VAL / VAH / LVN levels (Point of Control, Value Area Low, Value Area High, Low Volume Node)
PDH / PDL levels (Previous Day High / Low)
PWH / PWL levels (Previous Week High / Low)
NYM level (New York Market level)
Active sessions table
5 style options for each session
All of this gives you the flexibility to set up exactly the layout you need for your trading. Below, you’ll find a more detailed look at each feature.
🗓️ 6 CUSTOMIZABLE SESSION
The indicator includes six sessions that you can fully customize to fit your needs—everything from naming each session and choosing line colors to adjusting opacity, showing the volume profile, or even turning off a session entirely if you don’t need it.
Plus, you can pick different display styles for each session. As shown in the screenshot below, there are five style options you can apply individually to every session.
5 Style Options for Sessions
BOX
AREA
ZONES
LINES
CURVED
These styles can be customized for each session individually to help you highlight the sessions you care about on your chart. Example below
📢 VOLUME PROFILE
We’ve also integrated a Volume Profile into the indicator to pinpoint important levels on the chart. On top of that, we’ve added extra volume-based levels. Below, you’ll find the settings and a visual demo of how it appears on your chart.
To identify optimal entry points, you can use the following key reference levels:
POC (Point of Control)
VAL (Value Area Low)
VAH (Value Area High)
LVN (Low Volume Node)
You can also customize colors and line styles, or hide any levels you don’t need on your chart.
📐 ADDITIONAL LEVELS
You can display the following levels on your chart:
NYM (New York Market)
PDH (Previous Day High)
PDL (Previous Day Low)
PWH (Previous Week High)
PWL (Previous Week Low)
All of these are fully customizable with color selection and the option to extend lines into the next period.
💹 ACTIVE SESSION TABLE
The active sessions table helps you quickly identify the trading times for the sessions you care about. It’s fully customizable, with options to choose border and background colors for the table itself.
🟠 USAGE
This indicator is highly versatile: use it to simply mark trading sessions on your chart, set up the Kill Zone at your chosen time, or identify the context of the previous session by its most traded range levels. All of this makes the indicator an invaluable tool for any trader!
Divergence Macro Sentiment Indicator (DMSI)The Divergence Macro Sentiment Indicator (DMSI)
Think of DMSI as your daily “mood ring” for the markets. It boils down the tug-of-war between growth assets (S&P 500, copper, oil) and safe havens (gold, VIX) into one clear histogram—so you instantly know if the bulls have broad backing or are charging ahead with one foot tied behind.
🔍 What You’re Seeing
Green bars (above zero): Risk-on conviction.
Equities and commodities are rallying while gold and volatility retreat.
Red bars (below zero): Risk-off caution.
Gold or VIX are climbing even as stocks rise—or stocks aren’t fully joined by oil/copper.
Zero line: The line in the sand between “full-steam ahead” and “proceed with care.”
📈 How to Read It
Cross-Zero Signals
Bullish trigger: DMSI flips up through zero after a red stretch → fresh long entries.
Bearish trigger: DMSI tumbles below zero from green territory → tighten stops or go defensive.
Divergence Warnings
If SPX makes new highs but DMSI is rolling over (lower green bars or red), that’s your early red flag—rallies may fizzle.
Strength Confirmation
On pullbacks, only buy dips when DMSI ≥ 0. When DMSI is deeply positive, you can be more aggressive on position size or add leverage.
💡 Trade Guidance & Use Cases
Trend Filter: Only take your S&P or sector-ETF long setups when DMSI is non-negative—avoids hollow rallies.
Macro Pair Trades:
Deep red DMSI: go long gold or gold miners (GLD, GDX).
Strong green DMSI: lean into cyclicals, industrials, even energy names.
Risk Management:
Scale out as DMSI fades into negative territory mid-trade.
Scale in or add to winners when it stays bullish.
Swing Confirmation: Overlay on any oscillator or price-pattern system—accept signals only when the macro tide is flowing in your favour.
🚀 Why It Works
Markets don’t move in a vacuum. When stocks rally but the “real-economy” metals and volatility aren’t cooperating, something’s off under the hood. DMSI catches those cross-asset cracks before price alone can—and gives you an early warning system for smarter entries, tighter risk, and bigger gains when the macro trend really kicks in.
Extended Altman Z-Score ModelThe Extended Altman Z-Score Model represents a significant advancement in financial analysis and risk assessment, building upon the foundational work of Altman (1968) while incorporating contemporary data analytics approaches as proposed by Fung (2023). This sophisticated model enhances the traditional bankruptcy prediction framework by integrating additional financial metrics and modern analytical techniques, offering a more comprehensive approach to identifying financially distressed companies.
The model's architecture is built upon two distinct yet complementary scoring systems. The traditional Altman Z-Score components form the foundation, including Working Capital to Total Assets (X1), which measures a company's short-term liquidity and operational efficiency. Retained Earnings to Total Assets (X2) provides insight into the company's historical profitability and reinvestment capacity. EBIT to Total Assets (X3) evaluates operational efficiency and earning power, while Market Value of Equity to Total Liabilities (X4) assesses market perception and leverage. Sales to Total Assets (X5) measures asset utilization efficiency.
These traditional components are enhanced by extended metrics introduced by Fung (2023), which provide additional layers of financial analysis. The Cash Ratio (X6) offers insights into immediate liquidity and financial flexibility. Asset Composition (X7) evaluates the quality and efficiency of asset utilization, particularly in working capital management. The Debt Ratio (X8) provides a comprehensive view of financial leverage and long-term solvency, while the Net Profit Margin (X9) measures overall profitability and operational efficiency.
The scoring system employs a sophisticated formula that combines the traditional Z-Score with weighted additional metrics. The traditional Z-Score is calculated as 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 1.0X5, while the extended components are weighted as follows: 0.5 * X6 + 0.3 * X7 - 0.4 * X8 + 0.6 * X9. This enhanced scoring mechanism provides a more nuanced assessment of a company's financial health, incorporating both traditional bankruptcy prediction metrics and modern financial analysis approaches.
The model categorizes companies into three distinct risk zones, each with specific implications for financial stability and required actions. The Safe Zone (Score > 3.0) indicates strong financial health, with low probability of financial distress and suitability for conservative investment strategies. The Grey Zone (Score between 1.8 and 3.0) suggests moderate risk, requiring careful monitoring and additional fundamental analysis. The Danger Zone (Score < 1.8) signals high risk of financial distress, necessitating immediate attention and potential risk mitigation strategies.
In practical application, the model requires systematic and regular monitoring. Users should track the Extended Score on a quarterly basis, monitoring changes in individual components and comparing results with industry benchmarks. Component analysis should be conducted separately, identifying specific areas of concern and tracking trends in individual metrics. The model's effectiveness is significantly enhanced when used in conjunction with other financial metrics and when considering industry-specific factors and macroeconomic conditions.
The technical implementation in Pine Script v6 provides real-time calculations of both traditional and extended scores, offering visual representation of risk zones, detailed component breakdowns, and warning signals for critical values. The indicator automatically updates with new financial data and provides clear visual cues for different risk levels, making it accessible to both technical and fundamental analysts.
However, as noted by Fung (2023), the model has certain limitations that users should consider. It may not fully account for industry-specific factors, requires regular updates of financial data, and should be used in conjunction with other analysis tools. The model's effectiveness can be enhanced by incorporating industry-specific benchmarks and considering macroeconomic factors that may affect financial performance.
References:
Altman, E.I. (1968) 'Financial ratios, discriminant analysis and the prediction of corporate bankruptcy', The Journal of Finance, 23(4), pp. 589-609.
Li, L., Wang, B., Wu, Y. and Yang, Q., 2020. Identifying poorly performing listed firms using data analytics. Journal of Business Research, 109, pp.1–12. doi.org
Thai Gold BahtIndicator Name: Thai Gold Baht
Short Title: Thai Gold Baht
Purpose
This indicator calculates and visualizes the real-time price of 1 Thai Gold Baht (15.244 grams) based on the global gold price ( XAU/USD ) and the USD/THB exchange rate .
Users can customize gold weight and purity to simulate the local Thai gold market price.
What it does
Retrieves live gold price per troy ounce in USD
Retrieves current USD to Thai Baht exchange rate
Converts the value using user-defined weight and purity
Displays result as a real-time chart
Shows calculation details in the Data Window
Ideal for
Traders tracking Thai gold based on international prices
Analysts comparing local and global bullion markets
Anyone needing a configurable, transparent gold price conversion
Pine Script Functionality
// Uses XAU/USD and USD/THB as inputs
// Calculates 1 Baht Gold (96.5% default purity)
// Outputs the value in THB as a chart line
ชื่ออินดิเคเตอร์: Thai Gold Baht
ชื่อย่อ: Thai Gold Baht
วัตถุประสงค์
อินดิเคเตอร์นี้ใช้คำนวณและแสดงราคาทองคำไทย 1 บาท (15.244 กรัม) แบบเรียลไทม์
โดยอ้างอิงจากราคาทองคำในตลาดโลก ( XAU/USD ) และอัตราแลกเปลี่ยน USD/THB
ผู้ใช้สามารถกำหนดน้ำหนักทองและความบริสุทธิ์เองได้ เพื่อจำลองราคาทองคำในประเทศไทยอย่างแม่นยำ
สิ่งที่อินดิเคเตอร์นี้ทำ
ดึงราคาทองคำแบบเรียลไทม์ต่อทรอยออนซ์ในสกุลเงิน USD
ดึงอัตราแลกเปลี่ยน USD → THB แบบเรียลไทม์
คำนวณราคาจากน้ำหนักและเปอร์เซ็นต์ความบริสุทธิ์ที่ผู้ใช้กำหนด
แสดงผลลัพธ์เป็นกราฟแบบเรียลไทม์ในหน่วยบาทไทย
แสดงรายละเอียดการคำนวณในหน้าต่าง Data Window ของ TradingView
เหมาะสำหรับ
นักเทรดที่ต้องการติดตามราคาทองคำไทยจากราคาทองคำตลาดโลก
นักวิเคราะห์ที่เปรียบเทียบราคาทองคำในประเทศและต่างประเทศ
ผู้ใช้งานที่ต้องการการแปลงราคาทองคำระหว่างประเทศให้โปร่งใสและปรับแต่งได้
การทำงานของ Pine Script
// ใช้ข้อมูล XAU/USD และ USD/THB เป็นอินพุต
// คำนวณราคาทองคำไทย 1 บาท (ความบริสุทธิ์เริ่มต้นที่ 96.5%)
// แสดงผลเป็นเส้นกราฟของราคาทองคำในหน่วยบาทไทย
Volume Change % Display1- Current bar's volume change %
2- Previous bar's volume change %
* Each line uses its own color based on volume rising or falling.
* Keeps the layout compact and readable.
FA Dashboard: Valuation, Profitability & SolvencyFundamental Analysis Dashboard: A Multi-Dimensional View of Company Quality
This script presents a structured and customizable dashboard for evaluating a company’s fundamentals across three key dimensions: Valuation, Profitability, and Solvency & Liquidity.
Unlike basic fundamental overlays, this dashboard consolidates multiple financial indicators into visual tables that update dynamically and are grouped by category. Each ratio is compared against configurable thresholds, helping traders quickly assess whether a company meets certain value investing criteria. The tables use color-coded checkmarks and fail marks (✔️ / ❌) to visually signal pass/fail evaluations.
▶️ Key Features
Valuation Ratios:
Earnings Yield: EBIT / EV
EV / EBIT and EV / FCF: Enterprise value metrics for profitability
Price-to-Book, Free Cash Flow Yield, PEG Ratio
Profitability Ratios:
Return on Invested Capital (ROIC), ROE, Operating, Net & Gross Margins, Revenue Growth
Solvency & Liquidity Ratios:
Debt to Equity, Debt to EBITDA, Current Ratio, Quick Ratio, Altman Z-Score
Each of these metrics is calculated using request.financial() and can be viewed using either annual (FY) or quarterly (FQ) data, depending on user preference.
🧠 How to Use
Add the script to any stock chart.
Select your preferred data period (FY or FQ).
Adjust thresholds if desired to match your personal investing strategy.
Review the visual dashboard to see which metrics the company passes or fails.
💡 Why It’s Useful
This tool is ideal for traders or long-term investors looking to filter stocks using fundamental criteria. It draws inspiration from principles used by Benjamin Graham, Warren Buffett, and Joel Greenblatt, offering a fast and informative way to screen quality businesses.
This is not a repackaged built-in or autogenerated script. It’s a custom-built, interactive tool tailored for fundamental analysis using official financial data provided via Pine Script’s request.financial().
S&P 500 Top 25 - EPS AnalysisEarnings Surprise Analysis Framework for S&P 500 Components: A Technical Implementation
The "S&P 500 Top 25 - EPS Analysis" indicator represents a sophisticated technical implementation designed to analyze earnings surprises among major market constituents. Earnings surprises, defined as the deviation between actual reported earnings per share (EPS) and analyst estimates, have been consistently documented as significant market-moving events with substantial implications for price discovery and asset valuation (Ball and Brown, 1968; Livnat and Mendenhall, 2006). This implementation provides a comprehensive framework for quantifying and visualizing these deviations across multiple timeframes.
The methodology employs a parameterized approach that allows for dynamic analysis of up to 25 top market capitalization components of the S&P 500 index. As noted by Bartov et al. (2002), large-cap stocks typically demonstrate different earnings response coefficients compared to their smaller counterparts, justifying the focus on market leaders.
The technical infrastructure leverages the TradingView Pine Script language (version 6) to construct a real-time analytical framework that processes both actual and estimated EPS data through the platform's request.earnings() function, consistent with approaches described by Pine (2022) in financial indicator development documentation.
At its core, the indicator calculates three primary metrics: actual EPS, estimated EPS, and earnings surprise (both absolute and percentage values). This calculation methodology aligns with standardized approaches in financial literature (Skinner and Sloan, 2002; Ke and Yu, 2006), where percentage surprise is computed as: (Actual EPS - Estimated EPS) / |Estimated EPS| × 100. The implementation rigorously handles potential division-by-zero scenarios and missing data points through conditional logic gates, ensuring robust performance across varying market conditions.
The visual representation system employs a multi-layered approach consistent with best practices in financial data visualization (Few, 2009; Tufte, 2001).
The indicator presents time-series plots of the four key metrics (actual EPS, estimated EPS, absolute surprise, and percentage surprise) with customizable color-coding that defaults to industry-standard conventions: green for actual figures, blue for estimates, red for absolute surprises, and orange for percentage deviations. As demonstrated by Padilla et al. (2018), appropriate color mapping significantly enhances the interpretability of financial data visualizations, particularly for identifying anomalies and trends.
The implementation includes an advanced background coloring system that highlights periods of significant earnings surprises (exceeding ±3%), a threshold identified by Kinney et al. (2002) as statistically significant for market reactions.
Additionally, the indicator features a dynamic information panel displaying current values, historical maximums and minimums, and sample counts, providing important context for statistical validity assessment.
From an architectural perspective, the implementation employs a modular design that separates data acquisition, processing, and visualization components. This separation of concerns facilitates maintenance and extensibility, aligning with software engineering best practices for financial applications (Johnson et al., 2020).
The indicator processes individual ticker data independently before aggregating results, mitigating potential issues with missing or irregular data reports.
Applications of this indicator extend beyond merely observational analysis. As demonstrated by Chan et al. (1996) and more recently by Chordia and Shivakumar (2006), earnings surprises can be successfully incorporated into systematic trading strategies. The indicator's ability to track surprise percentages across multiple companies simultaneously provides a foundation for sector-wide analysis and potentially improves portfolio management during earnings seasons, when market volatility typically increases (Patell and Wolfson, 1984).
References:
Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers. Journal of Accounting Research, 6(2), 159-178.
Bartov, E., Givoly, D., & Hayn, C. (2002). The rewards to meeting or beating earnings expectations. Journal of Accounting and Economics, 33(2), 173-204.
Bernard, V. L., & Thomas, J. K. (1989). Post-earnings-announcement drift: Delayed price response or risk premium? Journal of Accounting Research, 27, 1-36.
Chan, L. K., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. The Journal of Finance, 51(5), 1681-1713.
Chordia, T., & Shivakumar, L. (2006). Earnings and price momentum. Journal of Financial Economics, 80(3), 627-656.
Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Analytics Press.
Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
Johnson, J. A., Scharfstein, B. S., & Cook, R. G. (2020). Financial software development: Best practices and architectures. Wiley Finance.
Ke, B., & Yu, Y. (2006). The effect of issuing biased earnings forecasts on analysts' access to management and survival. Journal of Accounting Research, 44(5), 965-999.
Kinney, W., Burgstahler, D., & Martin, R. (2002). Earnings surprise "materiality" as measured by stock returns. Journal of Accounting Research, 40(5), 1297-1329.
Livnat, J., & Mendenhall, R. R. (2006). Comparing the post-earnings announcement drift for surprises calculated from analyst and time series forecasts. Journal of Accounting Research, 44(1), 177-205.
Padilla, L., Kay, M., & Hullman, J. (2018). Uncertainty visualization. Handbook of Human-Computer Interaction.
Patell, J. M., & Wolfson, M. A. (1984). The intraday speed of adjustment of stock prices to earnings and dividend announcements. Journal of Financial Economics, 13(2), 223-252.
Skinner, D. J., & Sloan, R. G. (2002). Earnings surprises, growth expectations, and stock returns or don't let an earnings torpedo sink your portfolio. Review of Accounting Studies, 7(2-3), 289-312.
Tufte, E. R. (2001). The visual display of quantitative information (Vol. 2). Graphics Press.
Fakeout Filter📈 Fakeout Filter by ARV
🔍 Overview:
The Fakeout Filter is a smart breakout validation tool designed to help traders avoid false breakouts and focus only on high-probability breakout trades. This indicator combines price action, volume analysis, RSI divergence detection, and OBV trend confirmation to filter out noise and improve your entries.
⚙️ Key Features:
✅ Breakout Detection
Detects when the price closes above a user-defined resistance level.
✅ Volume Spike Confirmation
Confirms breakouts only if there’s a significant increase in volume (customizable via settings).
✅ RSI Bearish Divergence Filter
Warns you of bearish RSI divergence, which often signals fakeouts during breakouts.
✅ OBV Trend Confirmation
Ensures On-Balance Volume (OBV) is rising, aligning volume flow with price movement.
✅ EMA Filter (Trend Confirmation)
Adds a safety filter using Exponential Moving Average (EMA) to ensure price action aligns with the short-term trend.
📌 How to Use:
Set Resistance Level:
In the indicator settings, input a key resistance level (manual input based on your chart analysis).
Watch for Signals:
A green background and “Breakout” label appear when:
Price closes above the resistance.
Volume is significantly higher than average.
OBV is rising.
No bearish RSI divergence is detected.
Price is above the EMA (trend confirmation).
Entry Suggestion:
Consider entering long positions only when the breakout label appears.
For additional confirmation, wait for a retest of the resistance as support before entering.
🔧 Settings:
Resistance Level – Manually set the level you're watching.
Volume Multiplier – Adjusts sensitivity to volume spikes (default: 1.5x average).
RSI Period – RSI used for divergence detection (default: 14).
EMA Period – For trend direction confirmation (default: 21).
✅ Best Use Cases:
Scalpers and intraday traders avoiding fakeouts on 5m–1H timeframes.
Swing traders validating breakout setups.
BTC, ETH, and major altcoins in consolidation or breakout zones.
⚠️ Disclaimer:
This tool is for educational purposes only. Always combine it with your own market analysis and risk management.
