Get started

Educational

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.
Pine Script® indicator
by EdgeTools
EMA Crossover 9/20//@version=5 indicator("EMA Crossover 9/20", overlay=true) // Define the EMA lengths shortEmaLength = 9 longEmaLength = 20 // Calculate the EMAs shortEma = ta.ema(close, shortEmaLength) longEma = ta.ema(close, longEmaLength) // Plot the EMAs plot(shortEma, color=color.blue, title="EMA 9") plot(longEma, color=color.red, title="EMA 20") // Generate buy and sell signals buySignal = ta.crossover(shortEma, longEma) sellSignal = ta.crossunder(shortEma, longEma) // Plot buy and sell signals on the chart plotshape(buySignal, style=shape.labelup, location=location.belowbar, color=color.green, size=size.small, title="Buy Signal", text="Buy") plotshape(sellSignal, style=shape.labeldown, location=location.abovebar, color=color.red, size=size.small, title="Sell Signal", text="Sell")
Pine Script® indicator
by nky2282
Breakout Pro + ADX/Volume Filter🚀 Breakout Pro: ADX + Volume + RSI Filter High-Probability Breakout System with Trend & Volume Confirmation 🔥 Why This Indicator Stands Out A multi-filter breakout strategy designed for precision: ✅ ADX Trend Filter (Only trade when ADX > 25 = strong trend) ✅ Volume Spike Confirmation (2x avg volume = valid breakout) ✅ Auto Risk-Reward Tools (TP/SL based on breakout range) ✅ Retest Zones (Optimal secondary entries) ⚠️ Needs Upgrade! Help us add RSI momentum filters to reduce false signals! 📌 How It Works Buy Signal (▲ Green Arrow): Breakout above 20-period high Strong ADX + high volume + bullish trend Sell Signal (▼ Red Arrow): Breakout below 20-period low Strong ADX + high volume + bearish trend Retest Levels (Blue/Orange Circles): Potential low-risk re-entry zones 💻 Open-Source Collaboration We’re inviting the TradingView community to enhance this indicator by: pine // TO-DO: Add RSI Momentum Filters // 1. Avoid breakouts when RSI is overbought (>70) or oversold (<30) // 2. RSI divergence confirmation // Contributors will be credited! 🎯 Perfect For Day traders & swing traders (stocks, forex, crypto) Those who trade breakouts with volume confirmation Developers who want to collaborate on open-source tools 📢 Note: This indicator is FREE. If you find it useful, support us with a like & share! 🔗 Tags: #BreakoutTrading #ADX #VolumeSpike #RSI #PineScript #TradingStrategy
Pine Script® indicator
by boring_chan123
Moving Average Convergence Divergenceindicator(title="Moving Average Convergence Divergence", shorttitle="MACD+", timeframe="", timeframe_gaps=true) // === Input Parameters === fast_length = input(title = "Fast Length", defval = 12) slow_length = input(title = "Slow Length", defval = 26) src = input(title = "Source", defval = close) signal_length = input.int(title = "Signal Smoothing", minval = 1, maxval = 50, defval = 9, display = display.data_window) sma_source = input.string(title = "Oscillator MA Type", defval = "EMA", options = , display = display.data_window) sma_signal = input.string(title = "Signal Line MA Type", defval = "EMA", options = , display = display.data_window) // === MACD Calculation === fast_ma = sma_source == "SMA" ? ta.sma(src, fast_length) : ta.ema(src, fast_length) slow_ma = sma_source == "SMA" ? ta.sma(src, slow_length) : ta.ema(src, slow_length) macd = fast_ma - slow_ma signal = sma_signal == "SMA" ? ta.sma(macd, signal_length) : ta.ema(macd, signal_length) hist = macd - signal // === Alerts === alertcondition(hist >= 0 and hist < 0, title = 'Rising to falling', message = 'The MACD histogram switched from a rising to falling state') alertcondition(hist <= 0 and hist > 0, title = 'Falling to rising', message = 'The MACD histogram switched from a falling to rising state') // === Plots === hline(0, "Zero Line", color = color.new(#787B86, 50)) plot(hist, title = "Histogram", style = plot.style_columns, color = (hist >= 0 ? (hist < hist ? #26A69A : #B2DFDB) : (hist < hist ? #FFCDD2 : #FF5252))) // MACD 線顏色根據是否高於 0 自動切換 macd_color = macd >= 0 ? color.green : color.red plot(macd, title = "MACD", color = macd_color) plot(signal, title = "Signal", color = #FF6D00)
Pine Script® indicator
by bonniestock2020
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.
Pine Script® indicator
by EdgeTools
RSI - SECUNDARIO - mauricioofsousaSecondary RSI – MGO Reading the rhythm behind the price action The Secondary RSI is a specialized oscillator developed as part of the MGO (Matriz Gráficos ON) methodology. It works as a refined strength filter, designed to complement traditional RSI readings by isolating the true internal rhythm of price action and reducing the influence of market noise. While the standard RSI measures price momentum, the Secondary RSI focuses on identifying breaks in oscillatory balance—the moments when the market shifts from accumulation to distribution or from compression to expansion. 🎯 What the Secondary RSI highlights: Internal imbalances in energy between buyers and sellers Micro-divergences not visible on standard RSI Areas of price fatigue or overextension that often precede reversals Confirmation zones for MGO oscillatory events (RPA, RPB, RBA, RBB) 📊 Recommended use: Combine with the Primary RSI for dual-layer validation Use as a noise-reduction tool before entering trends Ideal in medium timeframes (12H / 4H) where oscillatory patterns form clearly 🧠 How it works: The Secondary RSI recalculates the momentum signal using a block-based interpretation (aligned with the MGO structure) instead of simply following raw candle data. It adapts to the periodic nature of price behavior and provides the trader with a more stable and reliable measure of true market strength.
Pine Script® indicator
by mauricio_of_sousa
RSI - PRIMARIO -mauricioofsousa MGO Primary – Matriz Gráficos ON The Blockchain of Trading applied to price behavior The MGO Primary is the foundation of Matriz Gráficos ON — an advanced graphical methodology that transforms market movement into a logical, predictable, and objective sequence, inspired by blockchain architecture and periodic oscillatory phenomena. This indicator replaces emotional candlestick reading with a mathematical interpretation of price blocks, cycles, and frequency. Its mission is to eliminate noise, anticipate reversals, and clearly show where capital is entering or exiting the market. What MGO Primary detects: Oscillatory phenomena that reveal the true behavior of orders in the book: RPA – Breakout of Bullish Pivot RPB – Breakout of Bearish Pivot RBA – Sharp Bullish Breakout RBB – Sharp Bearish Breakout Rhythmic patterns that repeat in medium timeframes (especially on 12H and 4H) Wave and block frequency, highlighting critical entry and exit zones Validation through Primary and Secondary RSI, measuring the real strength behind movements Who is this indicator for: Traders seeking statistical clarity and visual logic Operators who want to escape the subjectivity of candlesticks Anyone who values technical precision with operational discipline Recommended use: Ideal timeframes: 12H (high precision) and 4H (moderate intensity) Recommended assets: indices (e.g., NASDAQ), liquid stocks, and futures Combine with: structured risk management and macro context analysis Real-world performance: The MGO12H achieved a 92% accuracy rate in 2025 on the NASDAQ, outperforming the average performance of major global quantitative strategies, with a net score of over 6,200 points for the year.
Pine Script® indicator
by mauricio_of_sousa
Market Map – AK_TradesMarket Map – AK_Trades A clean, context-driven market structure tool built to enhance the Futures Scalping Signal. 🔹 Dynamic Support & Resistance (auto-adjusting, dashed lines) 🔹 Real-Time Trend Detection with EMA Background 🔹 Breakout Signals using ATR-based filters 🔹 Minimalist, powerful, and clutter-free Disclaimer: This script is for educational and informational purposes only. It is not financial advice. Use at your own risk. The author assumes no responsibility for any trading losses incurred.
Pine Script® indicator
by adeelk714
Multi-Pair MTF Crypto Strategy (Backtest Version)Multi-Pair MTF Crypto Scanner (Smart Long/Short Indicator) This advanced TradingView indicator is designed for crypto traders seeking precise, risk-filtered signals across multiple pairs and timeframes. It combines institutional-grade signal logic with customizable risk management and clean visual labeling. 🔍 Core Features: ✅ Multi-Pair & Multi-Timeframe Scanning Scans assets like BTC, ETH, SOL across timeframes (15m, 1H, 4H) ✅ Buy/Sell Signal Engine Based on EMA 50/200 crossover, RSI, and volume spikes ✅ Dynamic Risk Management Calculates Stop Loss (SL), Take Profit (TP), and Risk-Reward Ratio (RRR) using ATR ✅ RRR Filter Signals only shown if RRR meets your defined minimum (default 1.5) ✅ Confirmation Mode Optional setting to avoid premature signals by requiring bar-close confirmation ✅ Visual Trade Zones Entry, SL, and TP levels plotted directly on chart ✅ Debug Mode Shows labels when trades are rejected due to RRR filters 🧠 Ideal For: Crypto scalpers, swing traders, and algorithmic signal testers Traders focused on high probability entries with defined risk 📣 Alerts: Real-time alerts for qualified BUY and SHORT signals Configurable for automated webhook systems or mobile push Open-source script In true TradingView spirit, the creator of this script has made it open-source, so that traders can review and verify its functionality. Kudos to the author! While you can use it for free, remember that republishing the code is subject to our House Rules. flydreams143 Disclaimer The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.
Pine Script® strategy
by flydreams143