HSI Futures Intraday Mid Point (Day Session)HSI Futures Intraday Mid Point (Day Session), use for determine how strong the intraday market is
Educational
OCM UTXO Whale DistributionOCM UTXO Whale Distribution
🔓 This is a premium indicator. This script detects potential large-scale profit-taking by long-term Bitcoin holders — particularly "whales" — using UTXO (Unspent Transaction Output) data. It analyzes surges in the average value of spent outputs (USD-denominated), smoothed over multiple timeframes to isolate significant deviations.
Combined with price action and volatility filters, the tool flags potential whale exit events: moments when smart money may be distributing into strength. These high-conviction signals are marked directly on the chart, helping traders align with hidden on-chain behaviour that rarely shows up in price alone.
⏰ Timeframes to be used on:
Daily
OCM Realized Cap AccelerationOCM Realized Cap Acceleration
🔓 This is a premium indicator. It monitors the momentum of Bitcoin’s realized cap on a smoothed, daily basis, offering a nuanced view of capital flow dynamics across the network. By analyzing the rate of change in realized value per coin — normalized by circulating supply — it provides an early signal of market sentiment shifts that are not always visible through price alone.
The resulting acceleration metric is color-coded to reflect subtle gradations in bullish or bearish pressure. It serves as a macro-aware oscillator for long-term positioning or for gauging systemic stress and euphoria in the broader Bitcoin market structure.
⏰ Timeframes to be used on:
Daily
Weekly
OCM Logarithmic Deviation TorchOCM Logarithmic Deviation Torch
🔓 This is a premium indicator. The Logarithmic Deviation Torch is a dynamic risk signalling overlay that transforms daily logarithmic price divergence into a normalized probabilistic gauge. By anchoring its logic to long-term trend baselines and structurally adjusting for time via a custom power-scaling method, it identifies asymmetrical extremes in market positioning with visual clarity.
Coloured dots are plotted above price action in real time, representing a graduated risk scale that evolves alongside underlying volatility. This tool is ideal for traders seeking a forward-biased perspective on overextension or compression within a logarithmic return environment — without relying on reactivity or lag-prone indicators.
⏰ Timeframes to be used on:
Daily
Weekly
OCM Z-Field MACDOCM Z-Field MACD
The Z-Field MACD is a custom momentum oscillator that leverages a zero-lag exponential framework to identify pressure gradients within price structure. By tracking the deviation of a smoothed midline from dynamic high and low boundaries, it surfaces key inflection points often obscured by traditional MACD signals.
Colour-coded histogram bars provide immediate visual cues on directional bias and intensity, supporting faster response to emerging momentum shifts. This tool is engineered for traders looking to extract actionable edge from refined trend-resonant signals, without overfitting to short-term noise.
⏰ Timeframes to be used on:
Daily
OCM Puell MultipleOCM Puell Multiple
The Puell Multiple is an on-chain valuation tool that contextualizes Bitcoin miner revenue against a long-term average, offering insight into cyclical market extremes. This dynamic metric highlights shifts in miner profitability that often precede major macro inflection points in Bitcoin’s price history.
By normalizing recent revenue activity and layering adaptive visual elements, the indicator offers a clean, time-sensitive representation of market stress or euphoria. It’s designed for investors seeking to complement technical or macro strategies with deeper on-chain signal awareness—without relying on short-term noise.
⏰ Timeframes to be used on:
Daily
OCM Pi Cycle Top IndicatorOCM Pi Cycle Top Indicator
This overlay indicator is a visual implementation of the Pi Cycle Top strategy, a historically effective method for identifying major Bitcoin market cycle tops. The Pi Cycle Top Indicator has historically been effective in picking out the timing of market cycle highs within 3 days.
It tracks the crossover between two key moving averages:
A 111-day simple moving average (SMA)
A 2x multiple of the 350-day SMA
When the 111-day SMA crosses below the doubled 350-day SMA, the indicator flags a potential market top, marking it on the chart above the current price. This has historically aligned closely with previous Bitcoin macro peaks.
The indicator is designed for daily timeframes but allows for custom resolution input, making it flexible for backtesting. It also continuously plots both moving averages so traders can visually monitor the crossover dynamics in real-time.
⏰ Timeframes to be used on:
Daily
Weekly
OCM SOPR Z-ScoreOCM SOPR Z-Score
This indicator measures the Spent Output Profit Ratio (SOPR) for Bitcoin, smoothed as a Z-Score. SOPR is a key on-chain metric used to assess market profit-taking behaviour by comparing the price at which coins were sold to the price at which they were acquired. Values above 1 indicate profits being realized, while values below 1 suggest selling at a loss.
The indicator features a heatmap-style colour gradient reflecting the SOPR value intensity, making it easier to visually identify shifts in market sentiment. Subtle background highlights appear when the SOPR crosses upper or lower threshold levels, configurable to highlight overbought or oversold profit-taking extremes. This tool offers a straightforward way to monitor when the market may be topping or bottoming based on realized profit trends.
⏰ Timeframes to be used on:
Daily
Weekly
OCM Net Unrealized Profit/Loss (NUPL)OCM Net Unrealized Profit/Loss (NUPL)
This indicator tracks Bitcoin’s Net Unrealized Profit/Loss (NUPL), a key on-chain metric that reflects the difference between unrealised profits and losses across all market participants. By mapping this ratio, the indicator highlights prevailing market sentiment and investor psychology, segmented into distinct emotional zones ranging from capitulation to euphoria. Each zone is visually distinguished by dynamic colour coding, offering an intuitive way to gauge whether the market is dominated by fear, hope, or greed.
Thresholds for these sentiment zones are configurable, allowing you to tailor sensitivity to different market environments. The plotted NUPL line, combined with reference levels and background highlights for extreme conditions, provides a comprehensive tool to anticipate potential market turning points.
⏰ Timeframes to be used on:
Daily
Weekly
OCM MVRV Z-ScoreOCM MVRV Z-Score
This indicator visualises the Market-Value-to-Realised-Value (MVRV) ratio, a widely used metric to assess Bitcoin market cycles by comparing the market capitalisation against the realised capitalisation. It offers two modes: a standard ratio and a statistically normalised Z-Score variant, enhancing sensitivity to deviations from historical norms. The script applies a gradient colour scheme that dynamically reflects the MVRV value’s relative position within defined overbought and oversold thresholds, allowing you to easily spot cyclical extremes and potential reversal zones.
Critical top and bottom lines are plotted for reference, including an adjustable neutral line, providing further context to Bitcoin's valuation state. This indicator is designed to help you identify periods of market euphoria or distress, making it a robust tool for timing entries and exits within broader market cycles.
⏰ Timeframes to be used on:
Daily
Weekly
OCM 200D MA HeatmapOCM 200D MA Heatmap
This tool visualises Bitcoin’s percentage deviation from its 200-day simple moving average, a long-term reference often associated with deep value zones and cyclical overheats. Each candle is overlaid with a colour-coded dot, with hues shifting according to fixed deviation thresholds. Cooler colours signal periods of market undervaluation, while warmer tones indicate stretched or euphoric price conditions.
The 200D MA line can be optionally displayed, offering a clean view of the long-term trend. Designed as a macro lens for investors and cycle-focused traders, this heatmap distils complex cycle dynamics into an immediate visual signal.
⏰ Timeframes to be used on:
- Daily
- Weekly
Multi-Timeframe RSI AlertsThis Pine Script generates alerts based on the Relative Strength Index (RSI) values across two different timeframes — 30-minute and 5-minute. It's designed to help traders identify momentum shifts for both bullish and bearish scenarios.
Omega Ratio -> The NeW SystemOmega Ratio → The NeW System 🚀
Calculate and visualize a smoothed Omega Ratio to measure upside vs. downside performance relative to a target return.
What is the Omega Ratio? 🤔
The Omega Ratio compares the total gains above a specified target return to the total losses below that target. Unlike other risk metrics that focus on volatility alone, Omega shows you how much reward you’re getting for every unit of shortfall risk. A higher Omega means your upside outweighs downside more attractively.
Indicator Inputs ⚙️
Source 📊: the price series to calculate returns from (e.g. close price).
Calculation Period 📆: number of bars over which returns are compared to the target. Longer periods smooth out fluctuations; shorter periods react faster to changing market conditions.
Target Return per Period (%) 🎯: the minimum return you aim for each bar (e.g. 0.1% per day).
Smoothing Period (EMA) 🔄: how many periods to apply an exponential moving average to the raw Omega Ratio, reducing noise and highlighting the trend.
Strong Threshold 🟢: above this value the line turns green, signaling strong upside vs. downside performance (default: 1.0).
Weak Threshold 🔴: below this value the line turns red, warning that losses outweigh gains relative to your target (default: 0.5).
How the Indicator Works 🧮
Calculate periodic returns by comparing each bar’s price to the previous bar.
Convert your target percentage into a decimal per period.
Accumulate gains above the target by summing every time the return exceeds the target amount.
Accumulate losses below the target by summing the shortfall whenever the return falls short of that target.
Form the raw Omega Ratio by dividing total gains above target by total losses below target. If there are no losses below the target, Omega is undefined (and we handle that gracefully).
Smooth with EMA to filter out spikes and reveal the underlying strength or weakness of the ratio.
Plot & Interpretation 🎨
Dynamic Line Color
🟢 Green when the smoothed Omega Ratio exceeds the Strong Threshold, indicating your asset is delivering more reward above target than risk below it.
🔴 Red when it falls below the Weak Threshold, warning that downside shortfalls dominate.
⚪ Gray between thresholds, suggesting a balanced but unimpressive performance.
Threshold Lines
A dashed green line marks the Strong Threshold.
A dashed red line marks the Weak Threshold.
Pro Tips 💡
An Omega above 1 means you’re gaining more above your target than losing below it—a positive sign.
An Omega below 1 warns that losses are outweighing gains relative to your goal.
Adjust the Target Return to fit your trading style: a higher target demands more “elite” performance, while a low target (even 0%) shows you pure upside vs. downside balance.
Use this indicator to instantly see whether an asset is consistently beating your expectations or struggling to hold ground—helping you make more informed entry and exit decisions.
Sortino Ratio -> The NeW SystemSortino Ratio → The NeW System 🚀
Calculate and visualize an annualized, smoothed Sortino Ratio that focuses on downside volatility.
What is the Sortino Ratio? 🤔
The Sortino Ratio is a risk-adjusted performance metric like the Sharpe Ratio, but it only penalizes returns below a chosen benchmark (usually the risk-free rate). By isolating “bad” volatility—periods when your returns dip under that minimum—it shows you how well an asset rewards you for downside risk alone. 📉
Indicator Inputs ⚙️
Source 📊: the price series to calculate returns from (e.g. close price).
Calculation Period 📆: the number of days/bars used to compute average returns and downside volatility. Longer periods smooth out noise; shorter periods react faster.
Annual Risk-Free Rate (%) 💰: the minimum acceptable yearly return, converted internally to a per-bar rate. In crypto, you might set this to zero.
Smoothing Period (EMA) 🔄: how many periods to apply an exponential moving average to the raw Sortino Ratio, reducing spikes and making trends clearer.
Strong Threshold 🟢: above this level the line turns green, signaling robust downside-risk-adjusted performance.
Weak Threshold 🔴: below this level the line turns red, warning of underperformance relative to downside risk.
How the Indicator Works 🧮
Compute periodic returns by comparing each bar’s price to the prior bar.
Convert annual risk-free rate to a per-bar rate (divide by 365 for daily bars).
Calculate average return over the chosen period.
Measure downside deviation by squaring only the shortfalls below the risk-free rate, averaging them, and then taking the square root.
Form the raw Sortino Ratio by subtracting the per-bar risk-free rate from the average return, dividing by downside deviation, and annualizing. If downside deviation is zero, it defaults to zero to avoid errors.
Smooth with EMA to filter noise and highlight the underlying trend.
Plot & Interpretation 🎨
Line Color
🟢 Green when the smoothed Sortino Ratio ≥ Strong Threshold (strong downside-risk-adjusted returns).
🔴 Red when ≤ Weak Threshold (weak or negative performance).
⚪ Gray between thresholds (neutral zone).
Threshold Lines
Dashed green line at the Strong Threshold.
Dashed red line at the Weak Threshold.
Pro Tips 💡
A Sortino Ratio around 1 means returns match downside risk on a 1:1 basis—generally acceptable -> Long Term.
Below 0 indicates returns haven’t beaten your minimum acceptable rate.
Above 2 signals excellent downside-risk-adjusted performance—even in volatile markets like crypto, values slightly below 2 can still be strong -> Long Term.
Use this system to spot when an asset’s returns aren’t just high, but safely high—helping you trade with confidence and minimize nasty drawdowns! 🎯
Sharpe Ratio -> The NeW SystemSharpe Ratio → The NeW System 📈
Calculate and visualize an annualized, smoothed Sharpe Ratio based on daily returns.
What is the Sharpe Ratio? 🤔
The Sharpe Ratio measures risk-adjusted return by dividing the average return by its volatility. A higher Sharpe means you’re earning more reward per unit of risk. In crypto, we assume a 0% risk-free rate.
Indicator Inputs ⚙️
Source
The price series to use (default: close).
Sharpe Rolling Period
Number of days for the rolling average and volatility calculation.
Smoothing Period (EMA)
How many periods to smooth the raw Sharpe with an exponential moving average.
Strong Threshold 🔥
Sharpe ≥ this value shows a “strong” signal in green.
Weak Threshold ❄️
Sharpe ≤ this value shows a “weak” signal in red.
How It Works 🧮
Daily Returns – Calculate the percentage change in price from one day to the next.
Rolling Average – Smooth those daily returns over the chosen Sharpe period.
Rolling Volatility – Compute the standard deviation of daily returns over the same period.
Raw Sharpe – Divide the rolling average by the rolling volatility (with zero-volatility guard).
EMA Smoothing – Apply an EMA to the raw Sharpe to reduce noise.
Annualization – Multiply the smoothed daily Sharpe by √365 to get a yearlyized figure.
Plot & Interpretation 🎨
Line Color
🟢 Green when annualized Sharpe ≥ Strong Threshold (strong risk-adjusted performance)
🔴 Red when annualized Sharpe ≤ Weak Threshold (weak or negative performance)
⚪ Gray when between the thresholds (neutral zone)
Threshold Lines
A dashed green line marks the Strong Threshold.
A dashed red line marks the Weak Threshold.
Pro Tips 💡
A Sharpe around 1 is generally acceptable -> Long term.
Below 0 means you’re losing per unit of risk on average.
Above 2 is excellent—although in crypto, slightly lower values can still signal strength -> Long term.
Use this system to spot when an asset’s risk-adjusted returns are heating up (🔥) or cooling off (❄️), so you can time your trades more effectively!
EPT-DB:EMA Trend Table + Stoch RSIThe script attached is a simple table that tells you some directions with 9/20 EMA crosses.
If the the 1hour,2hour,4hour are all one direction, trades on any time frame below will only display buy or sell with those as a measure of confluence.
If you would like help making your own trading dashboard, let me know.
I have also attached RSI, those will flash green and red on their respective oversold levels.
EMA Trend Strength MeterThis indicator will leverage the EMA as basis to indentify value of Strength of trend
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.
