WVAD with Gap Compensation**Indicator Name:** WVAD with Gap Compensation
**Purpose:** Enhances the classic Williams Vix Fix (WVAD) by incorporating the impact of price gaps (jump ups/downs) in its calculation.
**Key Features:**
1. **Gap Detection:** Automatically identifies significant gaps (default: >0.5% from prior bar's high/low).
2. **Gap Compensation:** Adjusts the WVAD calculation by adding the gap size to the daily price change.
3. **Dynamic Weighting:** Applies a multiplier (1.2x or 1.5x) to the WVAD value on days with medium/large gaps (based on ATR).
4. **Visualization:**
- Plots the enhanced WVAD line (blue) and optionally the original WVAD (gray circles).
- Marks gap events with colored arrows (green ▲ for gap up, red ▼ for gap down) and connects the gap's impact with dashed lines.
- Includes a zero line for reference.
**Use Cases:**
- Gauges the true strength of money flow by accounting for gaps.
- Identifies potential trend shifts around gap events.
- Filters noise by focusing on significant gaps.
**Parameters:**
- `Accumulation Period`: Number of days to sum WVAD (default: 12).
- `Gap Threshold (%)`: Minimum gap size to trigger compensation (default: 0.5%).
- `Show Original WVAD`: Toggles display of the classic WVAD.
**Version:** Pine Script® v6
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Fear and Greed Index [DunesIsland]The Fear and Greed Index is a sentiment indicator designed to measure the emotions driving the stock market, specifically investor fear and greed. Fear represents pessimism and caution, while greed reflects optimism and risk-taking. This indicator aggregates multiple market metrics to provide a comprehensive view of market sentiment, helping traders and investors gauge whether the market is overly fearful or excessively greedy.How It WorksThe Fear and Greed Index is calculated using four key market indicators, each capturing a different aspect of market sentiment:
Market Momentum (30% weight)
Measures how the S&P 500 (SPX) is performing relative to its 125-day simple moving average (SMA).
A higher value indicates that the market is trading well above its moving average, signaling greed.
Stock Price Strength (20% weight)
Calculates the net number of stocks hitting 52-week highs minus those hitting 52-week lows on the NYSE.
A greater number of net highs suggests strong market breadth and greed.
Put/Call Options (30% weight)
Uses the 5-day average of the put/call ratio.
A lower ratio (more call options being bought) indicates greed, as investors are betting on rising prices.
Market Volatility (20% weight)
Utilizes the VIX index, which measures market volatility.
Lower volatility is associated with greed, as investors are less fearful of large market swings.
Each component is normalized using a z-score over a 252-day lookback period (approximately one trading year) and scaled to a range of 0 to 100. The final Fear and Greed Index is a weighted average of these four components, with the weights specified above.Key FeaturesIndex Range: The index value ranges from 0 to 100:
0–25: Extreme Fear (red)
25–50: Fear (orange)
50–75: Neutral (yellow)
75–100: Greed (green)
Dynamic Plot Color: The plot line changes color based on the index value, visually indicating the current sentiment zone.
Reference Lines: Horizontal lines are plotted at 0, 25, 50, 75, and 100 to represent the different sentiment levels: Extreme Fear, Fear, Neutral, Greed, and Extreme Greed.
How to Interpret
Low Values (0–25): Indicate extreme fear, which may suggest that the market is oversold and could be due for a rebound.
High Values (75–100): Indicate greed, which may signal that the market is overbought and could be at risk of a correction.
Neutral Range (25–75): Suggests a balanced market sentiment, neither overly fearful nor greedy.
This indicator is a valuable tool for contrarian investors, as extreme readings often precede market reversals. However, it should be used in conjunction with other technical and fundamental analysis tools for a well-rounded view of the market.
Ticker Pulse Meter + Fear EKG StrategyDescription
The Ticker Pulse Meter + Fear EKG Strategy is a technical analysis tool designed to identify potential entry and exit points for long positions based on price action relative to historical ranges. It combines two proprietary indicators: the Ticker Pulse Meter (TPM), which measures price positioning within short- and long-term ranges, and the Fear EKG, a VIX-inspired oscillator that detects extreme market conditions. The strategy is non-repainting, ensuring signals are generated only on confirmed bars to avoid false positives. Visual enhancements, such as optional moving averages and Bollinger Bands, provide additional context but are not core to the strategy's logic. This script is suitable for traders seeking a systematic approach to capturing momentum and mean-reversion opportunities.
How It Works
The strategy evaluates price action using two key metrics:
Ticker Pulse Meter (TPM): Measures the current price's position within short- and long-term price ranges to identify momentum or overextension.
Fear EKG: Detects extreme selling pressure (akin to "irrational selling") by analyzing price behavior relative to historical lows, inspired by volatility-based oscillators.
Entry signals are generated when specific conditions align, indicating potential buying opportunities. Exits are triggered based on predefined thresholds or partial position closures to manage risk. The strategy supports customizable lookback periods, thresholds, and exit percentages, allowing flexibility across different markets and timeframes. Visual cues, such as entry/exit dots and a position table, enhance usability, while optional overlays like moving averages and Bollinger Bands provide additional chart context.
Calculation Overview
Price Range Calculations:
Short-Term Range: Uses the lowest low (min_price_short) and highest high (max_price_short) over a user-defined short lookback period (lookback_short, default 50 bars).
Long-Term Range: Uses the lowest low (min_price_long) and highest high (max_price_long) over a user-defined long lookback period (lookback_long, default 200 bars).
Percentage Metrics:
pct_above_short: Percentage of the current close above the short-term range.
pct_above_long: Percentage of the current close above the long-term range.
Combined metrics (pct_above_long_above_short, pct_below_long_below_short) normalize price action for signal generation.
Signal Generation:
Long Entry (TPM): Triggered when pct_above_long_above_short crosses above a user-defined threshold (entryThresholdhigh, default 20) and pct_below_long_below_short is below a low threshold (entryThresholdlow, default 40).
Long Entry (Fear EKG): Triggered when pct_below_long_below_short crosses under an extreme threshold (orangeEntryThreshold, default 95), indicating potential oversold conditions.
Long Exit: Triggered when pct_above_long_above_short crosses under a profit-taking level (profitTake, default 95). Partial exits are supported via a user-defined percentage (exitAmt, default 50%).
Non-Repainting Logic: Signals are calculated using data from the previous bar ( ) and only plotted on confirmed bars (barstate.isconfirmed), ensuring reliability.
Visual Enhancements:
Optional moving averages (SMA, EMA, WMA, VWMA, or SMMA) and Bollinger Bands can be enabled for trend context.
A position table displays real-time metrics, including open positions, Fear EKG, and Ticker Pulse values.
Background highlights mark periods of high selling pressure.
Entry Rules
Long Entry:
TPM Signal: Occurs when the price shows strength relative to both short- and long-term ranges, as defined by pct_above_long_above_short crossing above entryThresholdhigh and pct_below_long_below_short below entryThresholdlow.
Fear EKG Signal: Triggered by extreme selling pressure, when pct_below_long_below_short crosses under orangeEntryThreshold. This signal is optional and can be toggled via enable_yellow_signals.
Entries are executed only on confirmed bars to prevent repainting.
Exit Rules
Long Exit: Triggered when pct_above_long_above_short crosses under profitTake.
Partial exits are supported, with the strategy closing a user-defined percentage of the position (exitAmt) up to four times per position (exit_count limit).
Exits can be disabled or adjusted via enable_short_signal and exitPercentage settings.
Inputs
Backtest Start Date: Defines the start of the backtesting period (default: Jan 1, 2017).
Lookback Periods: Short (lookback_short, default 50) and long (lookback_long, default 200) periods for range calculations.
Resolution: Timeframe for price data (default: Daily).
Entry/Exit Thresholds:
entryThresholdhigh (default 20): Threshold for TPM entry.
entryThresholdlow (default 40): Secondary condition for TPM entry.
orangeEntryThreshold (default 95): Threshold for Fear EKG entry.
profitTake (default 95): Exit threshold.
exitAmt (default 50%): Percentage of position to exit.
Visual Options: Toggle for moving averages and Bollinger Bands, with customizable types and lengths.
Notes
The strategy is designed to work across various timeframes and assets, with data sourced from user-selected resolutions (i_res).
Alerts are included for long entry and exit signals, facilitating integration with TradingView's alert system.
The script avoids repainting by using confirmed bar data and shifted calculations ( ).
Visual elements (e.g., SMA, Bollinger Bands) are inspired by standard Pine Script practices and are optional, not integral to the core logic.
Usage
Apply the script to a chart, adjust input settings to suit your trading style, and use the visual cues (entry/exit dots, position table) to monitor signals. Enable alerts for real-time notifications.
Designed to work best on Daily timeframe.
Movement WatcherMovement Watcher – Intraday Price Change Alert
This indicator tracks the percentage price movement of a selected symbol (e.g., VIX) from a configurable start time. If the intraday movement crosses a defined threshold (up or down), it triggers a one-time alert per day.
Key Features:
Monitors intraday % change from the specified start time.
Triggers one-time alerts for upper or lower threshold crossings.
Optional end time for monitoring period.
Visual plots and alert markers.
Useful for automated trading via webhook integrations.
This script was designed to work with automated trading tools such as the Trading Automation Toolbox. You can use it to generate alerts based on intraday volatility and route them via webhook for automated strategies.
Cycle Composite 3.6 WeightedThe Cycle Composite is a multi-factor market cycle model designed to classify long-term market behavior into distinct phases using normalized and weighted data inputs.
It combines ten key on-chain, dominance, volatility, sentiment, and trend-following metrics into a single composite output. The goal is to provide a clearer understanding of where the market may stand in the broader cycle (e.g., accumulation, early bull, late bull, or euphoria).
This version (3.4) introduces flexible weighting, trend strength markers, and additional context-aware signals such as risk-on confirmations and altseason flags.
Phases Identified:
The model categorizes the market into one of five zones:
Euphoria (> 85)
Late Bull (70 – 85)
Mid Bull (50 – 70)
Early Bull (30 – 50)
Fear (< 30)
Each phase is determined by a smoothed EMA of the weighted composite score.
Data Sources and Metrics Used (10 total):
BTC Dominance (CRYPTOCAP:BTC.D)
Stablecoin Dominance (USDT + USDC average) (inverted for risk-on)
ETH Dominance (CRYPTOCAP:ETH.D)
BBWP (normalized Bollinger Band Width % over 1-year window)
WVF (Williams VIX Fix for volatility spike detection)
NUPL (Net Unrealized Profit/Loss, external source)
CMF (Chaikin Money Flow, smoothed volume accumulation)
CEX Open Interest (custom input from DAO / external source)
Whale Inflows (custom input from whale exchange transfer data)
Google Trends Average (BTC, Crypto, Altcoin terms)
All inputs are normalized over a 200-bar window and combined via weighted averaging, where each weight is user-configurable.
Additional Features:
Phase Labels: Labels are printed only when a new phase is entered.
Bull Continuation Marker: Triangle up when composite makes higher highs and NUPL increases.
Weakening Marker: Triangle down when composite rolls over in Late Bull and NUPL falls.
Risk-On Signal: Green circle appears when CMF and Google Trends are both rising.
Altseason Flag: Orange diamond appears when dominance of "others.d" exceeds BTC.D and ETH.D and composite is above 50.
Background Shading: Each phase is shaded with a semi-transparent background color.
Timeframe-Aware Display: All markers and signals are shown only on weekly timeframe for clarity.
Intended Use:
This script is intended for educational and macro-trend analysis purposes.
It can be used to:
Identify macro cycle position (accumulation, bull phases, euphoria, etc.)
Spot long-term trend continuation or weakening signals
Add context to price action with external on-chain and sentiment data
Time rotation events such as altseason risk
Disclaimer:
This script does not constitute financial advice.
It is intended for informational and research purposes only.
Users should conduct their own due diligence and analysis before making investment decisions.
5DMA Optional HMA Entry📈 5DMA Optional HMA Entry Signal – Precision-Based Momentum Trigger
Category: Trend-Following / Reversal Timing / Entry Optimization
🔍 Overview:
The 5DMA Optional HMA Entry indicator is a refined price-action entry tool built for traders who rely on clean trend alignment and precise timing. This script identifies breakout-style entry points when price gains upward momentum relative to short-term moving averages — specifically the 5-day Simple Moving Average (5DMA) and an optional Hull Moving Average (HMA).
Whether you're swing trading stocks, scalping ETFs like UVXY or VXX, or looking for pullback recovery entries, this tool helps time your long entries with clarity and flexibility.
⚙️ Core Logic:
Primary Condition (Always On):
🔹 Close must be above the 5DMA – ensuring upward short-term momentum is confirmed.
Optional Condition (Toggled by User):
🔹 Close above the HMA – adds slope-responsive trend filtering for smoother setups. Enable or disable via checkbox.
Bonus Entry Filter (Optional):
🔹 Green Candle Wick Breakout – optional pattern logic that detects bullish momentum when the high pierces above both MAs, with a green body.
Reset Mechanism:
🔁 Signal resets only after price closes back below all active MAs (5DMA and HMA if enabled), reducing noise and avoiding repeated signals during chop.
🧠 Why This Works:
This indicator captures the kind of setups that professional traders look for:
Momentum crossovers without chasing late.
Mean reversion snapbacks that align with fresh bullish moves.
Avoids premature entries by requiring clear structure above moving averages.
Optional HMA filter allows adaptability: turn it off during choppy markets or range conditions, and on during trending environments.
🔔 Features:
✅ Adjustable HMA Length
✅ Enable/Disable HMA Filter
✅ Optional Green Wick Breakout Detection
✅ Visual “Buy” label plotted below qualifying bars
✅ Real-time Alert Conditions for automated trading or manual alerts
🎯 Use Cases:
VIX-based ETFs (e.g., UVXY, VXX): Catch early breakouts aligned with volatility spikes.
Growth Stocks: Time pullback entries during bullish runs.
Futures/Indices: Combine with macro levels for intraday scalps or swing setups.
Overlay on Trend Filters: Combine with RSI, MACD, or VWAP for confirmation.
🛠️ Recommended Settings:
For smooth setups in volatile names, use:
HMA Length: 20
Keep green wick filter ON
For fast momentum trades, disable the HMA filter to act on 5DMA alone.
⭐ Final Thoughts:
This script is built to serve both systematic traders and discretionary scalpers who want actionable signals without noise or lag. The toggleable HMA feature lets you adjust sensitivity depending on market conditions — a key edge in adapting to volatility cycles.
Perfect for those who value clean, non-repainting entries rooted in logical structure.
Project SynthIntroducing Project Synth !
Inspired by Pace of Tape and Cumulative Delta I created Project Synth in order to aggregate volume flow data across multiple marketsfor two primary reasions:
Traditional orderflow tools are not available on Tradingview. My script attempts to bring an original; calculus-based approach to creating not only an alternative for traditional orderflow tools, but also a more accurate one.
In order to detect genuine buying and selling pressure that cannot be easily manipulated. I did this because while I've always enjoyed concept behind both of those tools, I did not think they captured enough data to be useful. By analyzing assets that move together (positive correlation) and assets that move inversely (negative correlation), my system aims to fix the fundamental problems with those indicators and create an objective view of market sentiment based on aggregate orderflow.
Some more detailed explanations (using QQQ and SQQQ as an example):
Inverse Market Dynamics (QQQ vs SQQQ):
In an inverse market like SQQQ, aggressive buyers hit the ask when they expect the underlying (QQQ) to fall, while passive buyers wait on the bid hoping for cheaper inverse exposure. When QQQ rallies, SQQQ sees aggressive selling (people dumping their bearish bets) hitting bids, while passive sellers sit on the ask hoping to exit at better prices. The aggression flows opposite to the underlying market direction.
Why Utilizing Both Markets Provides A More Accurate Delta:
Watching both QQQ and SQQQ gives cross-validation - real buying pressure in QQQ should coincide with selling pressure in SQQQ. If you see buying in QQQ but also buying in SQQQ, that's a conflicting signal suggesting the move might be artificial or driven by other factors. The inverse relationship acts as a confirmation filter, making false signals much harder to generate.
Multiple Markets = Authentic Pressure:
The more unique, important markets you track, the harder it becomes to create fake delta moves. Real institutional buying/selling pressure affects multiple correlated assets simultaneously in predictable patterns - you can't easily manipulate tech stocks, treasury bonds, VIX, and currency pairs all at once to create a false signal. Each additional market acts as a fraud detection layer, ensuring the delta measurement reflects genuine ecosystem-wide buying and selling pressure rather than isolated manipulation or noise.
My Suggestions For Usage:
In order to keep the explanation simple and short for now, I suggest using it just like a cumulative delta indicator. For example: let's say you were watching CME_MINI:ES1! , and you had a resistance level at 6000. When the price reaches your resistance level, you would be looking for a significant divergence between price and Delta. Price : rising, Delta : falling. This means that even though the price was going up, strong and aggressive sellers are jumping in more and more, this can be used as a confirmation tool for a resistance level.
Notes For Moderators, Authors and Users:
Firstly, to the best of my knowledge, I have not been able to find many tools built around the concept of cumulative delta or pace of tape. While I know there are a couple projects, none to the magnitude of synthetically recreating these tools via an algorithm designed around basic calculus principles. While tools like Volume Delta are built in, they do not attempt to capture an accurate picture of aggregated orderflow from what I understand.
Secondly, it needs to be noted that tool aims to create an approximation of buying and selling pressure. To my knowledge it is not possible to create an accurate full picture, at least not within the limitations of Tradingview.
SOFR Spread (proxy: FEDFUNDS - US03MY)📊 SOFR Spread (Proxy: FEDFUNDS - US03MY) – Monitoring USD Money Market Liquidity
In 2008, the spread exhibits a sharp vertical spike, signaling a severe liquidity dislocation: investors rushed into short-term U.S. Treasuries, pushing their yields down dramatically, while the FEDFUNDS rate remained relatively high.
This behavior indicates extreme systemic stress in the interbank lending market, preceding massive Federal Reserve interventions such as rate cuts, emergency liquidity operations, and the launch of quantitative easing (QE).
Description:
This indicator plots the spread between the Effective Federal Funds Rate (FEDFUNDS) and the 3-Month US Treasury Bill yield (US03MY), used here as a proxy for the SOFR spread.
It serves as a simple yet powerful tool to detect liquidity dislocations and stress signals in the US short-term funding markets.
Interpretation:
🔴 Spread > 0.20% → Possible liquidity stress: elevated repo rates, cash shortage, interbank distrust.
🟡 Spread ≈ 0% → Normal market conditions, balanced liquidity.
🟢 Spread < 0% → Excess liquidity: strong demand for T-Bills, “flight to safety”, or distortion due to expansionary monetary policy.
Ideal for:
Monitoring Fed policy impact
Anticipating market-wide liquidity squeezes
Correlation with DXY, SPX, VIX, MOVE Index, and risk sentiment
🧠 Note: As SOFR is not directly available on TradingView, FEDFUNDS is used as a reliable proxy, closely tracking the same trends in most macro conditions.
JPMorgan G7 Volatility IndexThe JPMorgan G7 Volatility Index: Scientific Analysis and Professional Applications
Introduction
The JPMorgan G7 Volatility Index (G7VOL) represents a sophisticated metric for monitoring currency market volatility across major developed economies. This indicator functions as an approximation of JPMorgan's proprietary volatility indices, providing traders and investors with a normalized measurement of cross-currency volatility conditions (Clark, 2019).
Theoretical Foundation
Currency volatility is fundamentally defined as "the statistical measure of the dispersion of returns for a given security or market index" (Hull, 2018, p.127). In the context of G7 currencies, this volatility measurement becomes particularly significant due to the economic importance of these nations, which collectively represent more than 50% of global nominal GDP (IMF, 2022).
According to Menkhoff et al. (2012, p.685), "currency volatility serves as a global risk factor that affects expected returns across different asset classes." This finding underscores the importance of monitoring G7 currency volatility as a proxy for global financial conditions.
Methodology
The G7VOL indicator employs a multi-step calculation process:
Individual volatility calculation for seven major currency pairs using standard deviation normalized by price (Lo, 2002)
- Weighted-average combination of these volatilities to form a composite index
- Normalization against historical bands to create a standardized scale
- Visual representation through dynamic coloring that reflects current market conditions
The mathematical foundation follows the volatility calculation methodology proposed by Bollerslev et al. (2018):
Volatility = σ(returns) / price × 100
Where σ represents standard deviation calculated over a specified timeframe, typically 20 periods as recommended by the Bank for International Settlements (BIS, 2020).
Professional Applications
Professional traders and institutional investors employ the G7VOL indicator in several key ways:
1. Risk Management Signaling
According to research by Adrian and Brunnermeier (2016), elevated currency volatility often precedes broader market stress. When the G7VOL breaches its high volatility threshold (typically 1.5 times the 100-period average), portfolio managers frequently reduce risk exposure across asset classes. As noted by Borio (2019, p.17), "currency volatility spikes have historically preceded equity market corrections by 2-7 trading days."
2. Counter-Cyclical Investment Strategy
Low G7 volatility periods (readings below the lower band) tend to coincide with what Shin (2017) describes as "risk-on" environments. Professional investors often use these signals to increase allocations to higher-beta assets and emerging markets. Campbell et al. (2021) found that G7 volatility in the lowest quintile historically preceded emerging market outperformance by an average of 3.7% over subsequent quarters.
3. Regime Identification
The normalized volatility framework enables identification of distinct market regimes:
- Readings above 1.0: Crisis/high volatility regime
- Readings between -0.5 and 0.5: Normal volatility regime
- Readings below -1.0: Unusually calm markets
According to Rey (2015), these regimes have significant implications for global monetary policy transmission mechanisms and cross-border capital flows.
Interpretation and Trading Applications
G7 currency volatility serves as a barometer for global financial conditions due to these currencies' centrality in international trade and reserve status. As noted by Gagnon and Ihrig (2021, p.423), "G7 currency volatility captures both trade-related uncertainty and broader financial market risk appetites."
Professional traders apply this indicator in multiple contexts:
- Leading indicator: Research from the Federal Reserve Board (Powell, 2020) suggests G7 volatility often leads VIX movements by 1-3 days, providing advance warning of broader market volatility.
- Correlation shifts: During periods of elevated G7 volatility, cross-asset correlations typically increase what Brunnermeier and Pedersen (2009) term "correlation breakdown during stress periods." This phenomenon informs portfolio diversification strategies.
- Carry trade timing: Currency carry strategies perform best during low volatility regimes as documented by Lustig et al. (2011). The G7VOL indicator provides objective thresholds for initiating or exiting such positions.
References
Adrian, T. and Brunnermeier, M.K. (2016) 'CoVaR', American Economic Review, 106(7), pp.1705-1741.
Bank for International Settlements (2020) Monitoring Volatility in Foreign Exchange Markets. BIS Quarterly Review, December 2020.
Bollerslev, T., Patton, A.J. and Quaedvlieg, R. (2018) 'Modeling and forecasting (un)reliable realized volatilities', Journal of Econometrics, 204(1), pp.112-130.
Borio, C. (2019) 'Monetary policy in the grip of a pincer movement', BIS Working Papers, No. 706.
Brunnermeier, M.K. and Pedersen, L.H. (2009) 'Market liquidity and funding liquidity', Review of Financial Studies, 22(6), pp.2201-2238.
Campbell, J.Y., Sunderam, A. and Viceira, L.M. (2021) 'Inflation Bets or Deflation Hedges? The Changing Risks of Nominal Bonds', Critical Finance Review, 10(2), pp.303-336.
Clark, J. (2019) 'Currency Volatility and Macro Fundamentals', JPMorgan Global FX Research Quarterly, Fall 2019.
Gagnon, J.E. and Ihrig, J. (2021) 'What drives foreign exchange markets?', International Finance, 24(3), pp.414-428.
Hull, J.C. (2018) Options, Futures, and Other Derivatives. 10th edn. London: Pearson.
International Monetary Fund (2022) World Economic Outlook Database. Washington, DC: IMF.
Lo, A.W. (2002) 'The statistics of Sharpe ratios', Financial Analysts Journal, 58(4), pp.36-52.
Lustig, H., Roussanov, N. and Verdelhan, A. (2011) 'Common risk factors in currency markets', Review of Financial Studies, 24(11), pp.3731-3777.
Menkhoff, L., Sarno, L., Schmeling, M. and Schrimpf, A. (2012) 'Carry trades and global foreign exchange volatility', Journal of Finance, 67(2), pp.681-718.
Powell, J. (2020) Monetary Policy and Price Stability. Speech at Jackson Hole Economic Symposium, August 27, 2020.
Rey, H. (2015) 'Dilemma not trilemma: The global financial cycle and monetary policy independence', NBER Working Paper No. 21162.
Shin, H.S. (2017) 'The bank/capital markets nexus goes global', Bank for International Settlements Speech, January 15, 2017.
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.
RSI Z-Score + TableHow It Works
RSI Calculation
The standard RSI is computed over a user-defined period (default: 14), measuring the strength of recent price movements.
Z-Score Transformation
The RSI is then normalized using the Z-Score formula:
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Z = (RSI - Mean) / Standard Deviation
This highlights whether RSI is unusually high or low compared to its historical behavior.
Smoothing
An optional EMA is applied to the Z-Score for smoother and more reliable signals (default: 10-period smoothing).
Z-Score Table
A real-time value of the RSI Z-Score is displayed in a table in the top-right of the indicator pane.
The value is clamped between +2 and -2
+2 aligns with strong overbought RSI conditions
-2 aligns with strong oversold RSI conditions
How to Use It
Buy Signal Potential: When the Z-Score drops below -1.5 or -2 → statistically oversold RSI
Sell Signal Potential: When the Z-Score rises above +1.5 or +2 → statistically overbought RSI
Use in Confluence: Combine with price action, trend filters, or other Z-Score indicators (e.g. OBV, VWAP, VIX) for SDCA or mean-reversion strategies
Market Warning Dashboard Enhanced📊 Market Warning Dashboard Enhanced
A powerful macro risk dashboard that tracks and visualizes early signs of market instability across multiple key indicators—presented in a clean, professional layout with a real-time thermometer-style danger gauge.
🔍 Included Macro Signals:
Yield Curve Inversion: 10Y-2Y and 10Y-3M spreads
Credit Spreads: High-yield (HYG) vs Investment Grade (LQD)
Volatility Structure: VIX/VXV ratio
Breadth Estimate: SPY vs 50-day MA (as a proxy)
🔥 Features:
Real-time Danger Score: 0 (Safe) to 100 (Extreme Risk)
Descriptive warnings for each signal
Color-coded thermometer gauge
Alert conditions for each macro risk
Background shifts on rising systemic risk
⚠️ This dashboard can save your portfolio by alerting you to macro trouble before it hits the headlines—ideal for swing traders, long-term investors, and anyone who doesn’t want to get blindsided by systemic risk.
Stochastics + CM Williams VixFix (Simple Buy Signal)📈 Stochastics + CM Williams VixFix (Simple Buy Signal)
This indicator combines two powerful tools to detect potential bottoming opportunities:
✅ Stochastics: Looks for momentum reversals. A signal is triggered when both %K and %D are below the oversold threshold (default: 20), suggesting the asset is deeply oversold.
✅ CM Williams Vix Fix: A volatility-based fear detector. When it spikes above its dynamic threshold, it indicates potential panic selling — often preceding a market bounce.
💡 Buy Signal is generated when:
%K and %D are both below 20
VixFix shows a volatility spike (green condition)
Use this script to identify high-probability reversal setups, especially during market corrections or panic phases.
Risk-On vs Risk-Off Meter (Pro)Risk-On vs Risk-Off Meter (Pro)
This macro-based tool analyzes capital flows across key assets to gauge overall market risk sentiment. It does not use ES, SPY, or stock data directly—making it a powerful confirmation tool for ES traders looking to align with macro forces.
🔹 Core Idea:
Tracks capital rotation between copper/gold, bonds, dollar, crude oil, VIX, and yield spreads to generate a normalized risk score (0–1). This score reflects whether macro money is flowing into risk or safety.
🔹 Use:
Use this indicator as confirmation of directional bias when scalping or day trading ES.
– Green Zone (>0.75): Risk-On environment. Favor long setups.
– Red Zone (<0.45): Risk-Off. Favor short setups or stand aside.
– Yellow Zone: Neutral, use caution.
– Divergence Alerts: Signals when ES price disagrees with macro risk trend—potential reversals or exhaustion zones.
HOT TO USE
– Combine with your existing price action or order flow signals
– Avoid trading against the macro sentiment unless strong setup
– Use divergence as a heads-up for fading or exiting trades
This gives you a macro-informed lens to validate or filter your entries.
Normalized FX Weighted Daily % Change vs DXYThis indicator tracks international liquidity flows by measuring the USD’s relative strength against major currencies—EUR, CNY, JPY, GBP, and CAD. It calculates the weighted percentage change of each pair over a specified interval. A positive reading means the USD is weakening (liquidity flowing out of the US), while a negative reading indicates the USD is strengthening (liquidity flowing in). Additionally, the indicator incorporates the DXY index and VIX, with all components normalized using Z-scores for clear, comparable insights into market dynamics.
Multi-Indicator Trading DashboardMulti-Indicator Trading Dashboard: Comprehensive Analysis and Actionable Signals
This Pine Script indicator, "Multi-Indicator Trading Dashboard," provides a comprehensive overview of key market indicators and generates actionable trading signals, all presented in a clear, easy-to-read table format on your TradingView chart.
Key Features:
Real-time Indicator Analysis: The dashboard displays real-time values and signals for:
RSI (Relative Strength Index): Tracks overbought and oversold conditions.
MACD (Moving Average Convergence Divergence): Identifies trend changes and momentum.
ADX (Average Directional Index): Measures trend strength.
Volatility (ATR-based): Estimates volatility as a percentage, acting as a VIX proxy for single-symbol charts.
Trend Determination: Analyzes 20, 50, and 200-period EMAs to provide a clear trend assessment (Strong Bullish, Cautious Bullish, Cautious Bearish, Strong Bearish).
Combined Trading Signals: Integrates signals from RSI, MACD, ADX, and trend analysis to generate a combined "Buy," "Sell," or "Neutral" action signal.
User-Friendly Table Display: Presents all information in a neatly organized table, positioned at the top-right of your chart.
Visual Chart Overlays: Plots 20, 50, and 200-period EMAs directly on the chart for visual trend confirmation.
Background Color Alerts: Colors the chart's background based on the "Buy" or "Sell" action signal for quick visual cues.
Customizable Inputs: Allows you to adjust key parameters like RSI lengths, MACD settings, ADX thresholds, and EMA periods.
How It Works:
Indicator Calculations: The script calculates RSI, MACD, ADX, and a volatility proxy (ATR) using standard Pine Script functions.
Trend Analysis: It compares 20, 50, and 200-period EMAs to determine the overall trend direction.
Individual Signal Generation: It generates individual "Buy," "Sell," or "Neutral" signals based on RSI, MACD, and ADX values.
Combined Signal Logic: It combines the individual signals and trend analysis, assigning a "Buy" or "Sell" action only when at least two indicators align.
Table Display: It creates a table and populates it with the calculated values, signals, and trend information.
Chart Overlays: It plots the EMAs on the chart and colors the background based on the combined action signal.
Use Cases:
Quick Market Overview: Get a snapshot of key market indicators and trend direction at a glance.
Confirmation Tool: Use the combined signals to confirm your existing trading strategies.
Educational Purpose: Learn how different indicators interact and influence trading decisions.
Automated Alerting: Set up alerts based on the "Buy" or "Sell" action signals.
Customization:
Adjust the input parameters to fine-tune the indicator's sensitivity to your trading style and the specific market you're analyzing.
Disclaimer:
This indicator is for informational and educational purposes only and should not be considered financial advice. Always conduct thorough research and consult with 1 a qualified professional before making any 2 trading decisions.
Capitulation Volume Detector by @RhinoTradezOverview
Hey traders, want to catch the market when it’s totally losing it? The Capitulation Volume Detector is your go-to buddy for spotting those wild moments when panic selling takes over. Picture this: prices plummet, volume explodes, and everyone’s bailing out—that’s capitulation, and it might just signal a turning point. This script throws a bright marker on your chart whenever the chaos hits, so you can decide if it’s time to jump in or sit tight. Built fresh in Pine Script v6, it’s sleek, customizable, and packs an alert to keep you posted—perfect for stocks, indices like SPY, or even crypto chaos.
Inspired by epic sell-offs like March 2020’s COVID crash, this tool’s here to help you navigate the storm with a smile (and maybe a profit).
What It Does
Capitulation volume is that “everyone’s out!” moment: a steep price drop meets a massive volume surge, hinting that sellers are tapped out. It’s not a guaranteed reversal—sometimes the bleeding continues—but it’s a loud clue that fear’s peaked. Here’s the magic:
Volume Check : Measures current volume against a customizable average (default: 20 bars).
Price Plunge : Tracks the percentage drop from the last close.
Capitulation Cal l: When volume rockets past your threshold (e.g., 2x average) and price tanks (e.g., -5%), you get a red triangle above the bar.
Stay Alert : Fires off a detailed message (e.g., “Volume 300M > 200M, Drop -10%”) so you’re never caught off guard.
Think of it as your market meltdown radar—simple, effective, and ready to roll.
Functionality Breakdown
Volume Surge Spotter :
Uses a 20-bar Simple Moving Average (SMA) of volume as your baseline.
Flags any bar where volume exceeds this average by your chosen multiplier (default: 2x).
Price Drop Detector :
Calculates the percentage change from the prior close.
Triggers when the drop’s bigger than your set limit (default: -5%).
Capitulation Marker:
Combines both signals: high volume + sharp drop = capitulation.
Slaps a red triangle above the bar for instant “whoa, there it is!” vibes.
Real-Time Alerts :
Sends a custom alert with volume and drop details, keeping you in the loop without babysitting the chart.
Customization Options
Tune it to your trading style with these easy settings:
Volume Multiplier (x Avg): Starts at 2.0 (2x average volume). Bump it to 3.0 for only the wildest spikes or dial it to 1.5 for more frequent catches. Range: 1.0-10.0, step 0.1.
Price Drop Threshold (%): Default 5.0 (a -5% drop). Go big with 10.0 for crash-level falls or ease to 3.0 for lighter dips. Range: 1.0-20.0, step 0.1.
Average Volume Period: Default 20 bars. Stretch it to 50 for a broader view or shrink to 10 for quick reactions. Range: 1-100.
Capitulation Marker Color: Red by default—because panic’s loud! Switch it to blue, green, or pink to match your chart’s personality.
How to Use It
Drop It On : Add it to any chart with volume data—SPY daily for market moves, /ES 15-minute for intraday action, or your go-to stock.
Play with Settings : Hit the indicator’s config gear and tweak the multiplier, drop threshold, period, or marker color to fit your vibe.
Set an Alert : Right-click the indicator, add an alert with “Any alert() function call,” and get pinged when capitulation strikes.
Watch the Action : Look for those red triangles on big drop days—pair with your favorite reversal signals for extra oomph.
Pro Tips
Daily Charts : Catch market-wide capitulations like March 23, 2020 (SPY: -10%, 3x volume).
Intraday : Spot flash crashes or sector sell-offs on 15-minute or 5-minute bars.
Context Matters : High volume alone isn’t enough—check the VIX or candlestick patterns (e.g., hammers) to confirm a bottom.
Aggressive Strategy for High IV Market### Strategic background
In a volatile high IV market, prices are volatile and market expectations of future uncertainty are high. This environment provides opportunities for aggressive trading strategies, but also comes with a high level of risk. In pursuit of a high Sharpe ratio (i.e., risk-adjusted return), we need to design a strategy that captures the benefits of market volatility while effectively controlling risk. Based on daily line cycles, I choose a combination of trend tracking and volatility filtering for highly volatile assets such as stocks, futures or cryptocurrencies.
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### Strategy framework
#### Data
- Use daily data, including opening, closing, high and low prices.
- Suitable for highly volatile markets such as technology stocks, cryptocurrencies or volatile index futures.
#### Core indicators
1. ** Trend Indicators ** :
Fast Exponential Moving Average (EMA_fast) : 10-day EMA, used to capture short-term trends.
- Slow Exponential Moving Average (EMA_slow) : 30-day EMA, used to determine the long-term trend.
2. ** Volatility Indicators ** :
Average true Volatility (ATR) : 14-day ATR, used to measure market volatility.
- ATR mean (ATR_mean) : A simple moving average of the 20-day ATR that serves as a volatility benchmark.
- ATR standard deviation (ATR_std) : The standard deviation of the 20-day ATR, which is used to judge extreme changes in volatility.
#### Trading logic
The strategy is based on a trend following approach of double moving averages and filters volatility through ATR indicators, ensuring that trading only in a high-volatility environment is in line with aggressive and high sharpe ratio goals.
---
### Entry and exit conditions
#### Admission conditions
- ** Multiple entry ** :
- EMA_fast Crosses EMA_slow (gold cross), indicating that the short-term trend is turning upward.
-ATR > ATR_mean + 1 * ATR_std indicates that the current volatility is above average and the market is in a state of high volatility.
- ** Short Entry ** :
- EMA_fast Crosses EMA_slow (dead cross) downward, indicating that the short-term trend turns downward.
-ATR > ATR_mean + 1 * ATR_std, confirming high volatility.
#### Appearance conditions
- ** Long show ** :
- EMA_fast Enters the EMA_slow (dead cross) downward, and the trend reverses.
- or ATR < ATR_mean-1 * ATR_std, volatility decreases significantly and the market calms down.
- ** Bear out ** :
- EMA_fast Crosses the EMA_slow (gold cross) on the top, and the trend reverses.
- or ATR < ATR_mean-1 * ATR_std, the volatility is reduced.
---
### Risk management
To control the high risk associated with aggressive strategies, set up the following mechanisms:
1. ** Stop loss ** :
- Long: Entry price - 2 * ATR.
- Short: Entry price + 2 * ATR.
- Dynamic stop loss based on ATR can adapt to market volatility changes.
2. ** Stop profit ** :
- Fixed profit target can be selected (e.g. entry price ± 4 * ATR).
- Or use trailing stop losses to lock in profits following price movements.
3. ** Location Management ** :
- Reduce positions appropriately in times of high volatility, such as dynamically adjusting position size according to ATR, ensuring that the risk of a single trade does not exceed 1%-2% of the account capital.
---
### Strategy features
- ** Aggressiveness ** : By trading only in a high ATR environment, the strategy takes full advantage of market volatility and pursues greater returns.
- ** High Sharpe ratio potential ** : Trend tracking combined with volatility filtering to avoid ineffective trades during periods of low volatility and improve the ratio of return to risk.
- ** Daily line Cycle ** : Based on daily line data, suitable for traders who operate frequently but are not too complex.
---
### Implementation steps
1. ** Data Preparation ** :
- Get the daily data of the target asset.
- Calculate EMA_fast (10 days), EMA_slow (30 days), ATR (14 days), ATR_mean (20 days), and ATR_std (20 days).
2. ** Signal generation ** :
- Check EMA cross signals and ATR conditions daily to generate long/short signals.
3. ** Execute trades ** :
- Enter according to the signal, set stop loss and profit.
- Monitor exit conditions and close positions in time.
4. ** Backtest and Optimization ** :
- Use historical data to backtest strategies to evaluate Sharpe ratios, maximum retracements, and win rates.
- Optimize parameters such as EMA period and ATR threshold to improve policy performance.
---
### Precautions
- ** Trading costs ** : Highly volatile markets may result in frequent trading, and the impact of fees and slippage on earnings needs to be considered.
- ** Risk Control ** : Aggressive strategies may face large retracements and need to strictly implement stop losses.
- ** Scalability ** : Additional metrics (such as volume or VIX) can be added to enhance strategy robustness, or combined with machine learning to predict trends and volatility.
---
### Summary
This is a trend following strategy based on dual moving averages and ATR, designed for volatile high IV markets. By entering into high volatility and exiting into low volatility, the strategy combines aggressive and risk-adjusted returns for traders seeking a high sharpe ratio. It is recommended to fully backtest before implementation and adjust the parameters according to the specific market.
Put/Call RatioPut/Call Ratio Indicator
This indicator visualizes the Put/Call Ratio for various market symbols, helping traders assess market sentiment and potential reversals. It offers a dropdown menu to select from a range of Put/Call Ratios, including broad equities (CBOE), major indices (SPX, QQQ, IWM, VIX), and individual stocks (TSLA, GOOG, META, AMZN, MSFT, INTC).
The indicator plots the Put/Call Ratio with adjustable moving averages and standard deviation bands to highlight overbought or oversold conditions. A short-term moving average (default: 10 periods) is displayed with trend-based coloring, while longer-term moving averages (defaults: 30 and 200 periods) are calculated but hidden by default. Bands at 1, 1.5, and 2 standard deviations provide context for extreme readings.
Key Overbought/Oversold Signals:
Short-Term Extremes: The 10-day moving average moves beyond 1 standard deviation from the 200-day moving average, signaling potential overbought (above) or oversold (below) conditions. This will be highlighted by red or green background color.
Ratio Extremes: The Put/Call Ratio line itself crosses outside 2 standard deviations from the 200-day moving average, indicating stronger overbought or oversold zones.
Conditional coloring of the ratio line reflects its position relative to the bands, and background shading highlights when the short-term moving average crosses key levels.
Key Features:
Selectable Put/Call Ratio symbols.
Trend-colored moving averages.
Standard deviation bands for volatility analysis.
Dynamic line and background coloring for quick insights.
Usage:
Use this indicator to gauge market sentiment—high ratios may suggest bearish sentiment or oversold conditions, while low ratios may indicate bullish sentiment or overbought conditions. Combine with price action or other tools for confirmation.
Dynamic Volatility Differential Model (DVDM)The Dynamic Volatility Differential Model (DVDM) is a quantitative trading strategy designed to exploit the spread between implied volatility (IV) and historical (realized) volatility (HV). This strategy identifies trading opportunities by dynamically adjusting thresholds based on the standard deviation of the volatility spread. The DVDM is versatile and applicable across various markets, including equity indices, commodities, and derivatives such as the FDAX (DAX Futures).
Key Components of the DVDM:
1. Implied Volatility (IV):
The IV is derived from options markets and reflects the market’s expectation of future price volatility. For instance, the strategy uses volatility indices such as the VIX (S&P 500), VXN (Nasdaq 100), or RVX (Russell 2000), depending on the target market. These indices serve as proxies for market sentiment and risk perception (Whaley, 2000).
2. Historical Volatility (HV):
The HV is computed from the log returns of the underlying asset’s price. It represents the actual volatility observed in the market over a defined lookback period, adjusted to annualized levels using a multiplier of \sqrt{252} for daily data (Hull, 2012).
3. Volatility Spread:
The difference between IV and HV forms the volatility spread, which is a measure of divergence between market expectations and actual market behavior.
4. Dynamic Thresholds:
Unlike static thresholds, the DVDM employs dynamic thresholds derived from the standard deviation of the volatility spread. The thresholds are scaled by a user-defined multiplier, ensuring adaptability to market conditions and volatility regimes (Christoffersen & Jacobs, 2004).
Trading Logic:
1. Long Entry:
A long position is initiated when the volatility spread exceeds the upper dynamic threshold, signaling that implied volatility is significantly higher than realized volatility. This condition suggests potential mean reversion, as markets may correct inflated risk premiums.
2. Short Entry:
A short position is initiated when the volatility spread falls below the lower dynamic threshold, indicating that implied volatility is significantly undervalued relative to realized volatility. This signals the possibility of increased market uncertainty.
3. Exit Conditions:
Positions are closed when the volatility spread crosses the zero line, signifying a normalization of the divergence.
Advantages of the DVDM:
1. Adaptability:
Dynamic thresholds allow the strategy to adjust to changing market conditions, making it suitable for both low-volatility and high-volatility environments.
2. Quantitative Precision:
The use of standard deviation-based thresholds enhances statistical reliability and reduces subjectivity in decision-making.
3. Market Versatility:
The strategy’s reliance on volatility metrics makes it universally applicable across asset classes and markets, ensuring robust performance.
Scientific Relevance:
The strategy builds on empirical research into the predictive power of implied volatility over realized volatility (Poon & Granger, 2003). By leveraging the divergence between these measures, the DVDM aligns with findings that IV often overestimates future volatility, creating opportunities for mean-reversion trades. Furthermore, the inclusion of dynamic thresholds aligns with risk management best practices by adapting to volatility clustering, a well-documented phenomenon in financial markets (Engle, 1982).
References:
1. Christoffersen, P., & Jacobs, K. (2004). The importance of the volatility risk premium for volatility forecasting. Journal of Financial and Quantitative Analysis, 39(2), 375-397.
2. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.
3. Hull, J. C. (2012). Options, Futures, and Other Derivatives. Pearson Education.
4. Poon, S. H., & Granger, C. W. J. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41(2), 478-539.
5. Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
This strategy leverages quantitative techniques and statistical rigor to provide a systematic approach to volatility trading, making it a valuable tool for professional traders and quantitative analysts.
Tomas' Financial Conditions Z Score"The indicator is a composite z-score comprised of the following four components (equally-weighted):
Credit spreads - ICE BofA High Yield Option Adjusted Spread (BAMLH0A0HYM2) and ICE BofA Corporate Index Option Adjusted Spread (BAMLC0A0CM)
Volatility indexes - VIX (S&P 500 implied volatility) and MOVE (US Treasury bond implied volatility)
I've got it set to a 160-day lookback period, which I think is roughly the best setting after some tinkering.
When the z-score is above zero, it throws a red signal - and when the z-score is below zero, it throws a green signal.
This indicator is a follow-on from the "traffic light financial conditions indicator" that I wrote a thread about a couple of months ago.
I moved on from that previous indicator because it is based on the Federal Reserve's NFCI, which is regularly revised, but I didn't take that into account at the time.
So not a great real-time indicator, if the signal can be subsequently revised in the opposite direction weeks later.
This new indicator is based on real-time market data, so there's no revisions, and it also updates daily, as opposed to weekly for the NFCI"
Black-Scholes option price model & delta hedge strategyBlack-Scholes Option Pricing Model Strategy
The strategy is based on the Black-Scholes option pricing model and allows the calculation of option prices, various option metrics (the Greeks), and the creation of synthetic positions through delta hedging.
ATTENTION!
Trading derivative financial instruments involves high risks. The author of the strategy is not responsible for your financial results! The strategy is not self-sufficient for generating profit! It is created exclusively for constructing a synthetic derivative financial instrument. Also, there might be errors in the script, so use it at your own risk! I would appreciate it if you point out any mistakes in the comments! I would be even more grateful if you send the corrected code!
Application Scope
This strategy can be used for delta hedging short positions in sold options. For example, suppose you sold a call option on Bitcoin on the Deribit exchange with a strike price of $60,000 and an expiration date of September 27, 2024. Using this script, you can create a delta hedge to protect against the risk of loss in the option position if the price of Bitcoin rises.
Another example: Suppose you use staking of altcoins in your strategies, for which options are not available. By using this strategy, you can hedge the risk of a price drop (Put option). In this case, you won't lose money if the underlying asset price increases, unlike with a short futures position.
Another example: You received an airdrop, but your tokens will not be fully unlocked soon. Using this script, you can fully hedge your position and preserve their dollar value by the time the tokens are fully unlocked. And you won't fear the underlying asset price increasing, as the loss in the event of a price rise is limited to the option premium you will pay if you rebalance the portfolio.
Of course, this script can also be used for simple directional trading of momentum and mean reversion strategies!
Key Features and Input Parameters
1. Option settings:
- Style of option: "European vanilla", "Binary", "Asian geometric".
- Type of option: "Call" (bet on the rise) or "Put" (bet on the fall).
- Strike price: the option contract price.
- Expiration: the expiry date and time of the option contract.
2. Market statistic settings:
- Type of price source: open, high, low, close, hl2, hlc3, ohlc4, hlcc4 (using hl2, hlc3, ohlc4, hlcc4 allows smoothing the price in more volatile series).
- Risk-free return symbol: the risk-free rate for the market where the underlying asset is traded. For the cryptocurrency market, the return on the funding rate arbitrage strategy is accepted (a special function is written for its calculation based on the Premium Price).
- Volatility calculation model: realized (standard deviation over a moving period), implied (e.g., DVOL or VIX), or custom (you can specify a specific number in the field below). For the cryptocurrency market, the calculation of implied volatility is implemented based on the product of the realized volatility ratio of the considered asset and Bitcoin to the Bitcoin implied volatility index.
- User implied volatility: fixed implied volatility (used if "Custom" is selected in the "Volatility Calculation Method").
3. Display settings:
- Choose metric: what to display on the indicator scale – the price of the underlying asset, the option price, volatility, or Greeks (all are available).
- Measure: bps (basis points), percent. This parameter allows choosing the unit of measurement for the displayed metric (for all except the Greeks).
4. Trading settings:
- Hedge model: None (do not trade, default), Simple (just open a position for the full volume when the strike price is crossed), Synthetic option (creating a synthetic option based on the Black-Scholes model).
- Position side: Long, Short.
- Position size: the number of units of the underlying asset needed to create the option.
- Strategy start time: the moment in time after which the strategy will start working to create a synthetic option.
- Delta hedge interval: the interval in minutes for rebalancing the portfolio. For example, a value of 5 corresponds to rebalancing the portfolio every 5 minutes.
Post scriptum
My strategy based on the SegaRKO model. Many thanks to the author! Unfortunately, I don't have enough reputation points to include a link to the author in the description. You can find the original model via the link in the code, as well as through the search indicators on the charts by entering the name: "Black-Scholes Option Pricing Model". I have significantly improved the model: the calculation of volatility, risk-free rate and time value of the option have been reworked. The code performance has also been significantly optimized. And the most significant change is the execution, with which you can now trade using this script.
Proxy Financial Stress Index StrategyThis strategy is based on a Proxy Financial Stress Index constructed using several key financial indicators. The strategy goes long when the financial stress index crosses below a user-defined threshold, signaling a potential reduction in market stress. Once a position is opened, it is held for a predetermined number of bars (periods), after which it is automatically closed.
The financial stress index is composed of several normalized indicators, each representing different market aspects:
VIX - Market volatility.
US 10-Year Treasury Yield - Bond market.
Dollar Index (DXY) - Currency market.
S&P 500 Index - Stock market.
EUR/USD - Currency exchange rate.
High-Yield Corporate Bond ETF (HYG) - Corporate bond market.
Each component is normalized using a Z-score (based on the user-defined moving average and standard deviation lengths) and weighted according to user inputs. The aggregated index reflects overall market stress.
The strategy enters a long position when the stress index crosses below a specified threshold from above, indicating reduced financial stress. The position is held for a defined holding period before being closed automatically.
Scientific References:
The concept of a financial stress index is derived from research that combines multiple financial variables to measure systemic risks in the financial markets. Key research includes:
The Financial Stress Index developed by various Federal Reserve banks, including the Cleveland Financial Stress Index (CFSI)
Bank of America Merrill Lynch Option Volatility Estimate (MOVE) Index as a measure of interest rate volatility, which correlates with financial stress
These indices are widely used in economic research to gauge financial instability and help in policy decisions. They track real-time fluctuations in various markets and are often used to anticipate economic downturns or periods of high financial risk.
