{"uuid":"9d866469-cd8c-48aa-4673-362549386864","categoryUuid":"My Dashboards","data":{"meta":{"name":"Bitcoin Sharpe Signal NG"},"configs":[{"meta":{"content":"# Collection : Bitcoin Sharpe Signal\n\nGlassnode is proud to introduce the collection of the Bitcoin Sharpe Signal: a groundbreaking approach to automating your trades derived from our proprietary on-chain data and machine learning technology. In this dashboard we will present you the different models, methodlogies and trading heuristic extracted from ML models.  \nThe collection of the Bitcoin Sharpe Signal contains:\n- The original Bitcoin Sharpe Signal (BSS) daily long only model.\n- The Goldilocks model, trading heuristic from the original BSS.\n- The intraday Bitcoin Sharpe Signal, hourly,long-only model.\n- The intraday Bitcoin Sharpe Signal Short, hourly, short-only model.\n- Combining the long and short signals.\n"},"uuid":"17ed1c62-d348-4135-896e-c3ffafe83d60","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"content":"The original Bitcoin Sharpe Signal (BSS) daily long only model"},"uuid":"649e9c90-77f4-4925-aa89-1eb89352c4ef","configType":"text"},{"meta":{"content":"\n\nThe Collection of BSS was meticulously designed to minimize downside risks while capitalizing on the upward trends of Bitcoin, providing traders with a unique edge in the digital asset market.  \n\n**Value and Advantages**\n\nThe Bitcoin Sharpe Signal offers multiple layers of value to traders at various levels of sophistication:\n\n- **Direct Application:** Offering an immediate advantage, the Sharpe Signal offers a straightforward indicator for when to go long on Bitcoin while minimising downside risk. When the indicator surges beyond the 0.5 mark, it has historically been associated with improved risk-adjusted performance in Bitcoin.\n\n- **In-Depth Analytical Engagement:** For traders aiming to refine their strategies further, we will provide a deep dive into the calculations and the model that has shaped the signal. This includes detailed analyses of metric transformations identified as highly correlated with price movements, the trading heuristic based on the strategy developed by the model, and an exhaustive research report. This additional layer of access is designed to empower sophisticated traders with the tools and insights needed to leverage on-chain data in their own research and strategies to its fullest potential."},"uuid":"0547030a-7a1e-4a60-8425-76549d8acad6","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"date":1759276800,"asset":"BTC","since":1325376000,"until":1759276800,"chartType":"timeSeries","metricCode":"signals.BtcSharpeSignal","resolution":"24h"},"uuid":"2e2b3335-cbeb-4901-b8d6-da0935bad9a5","extra":{"name":"Bitcoin Sharpe Signal","zoom":"","price":true,"scale":"lin","lineColor":"#f7931a"},"configType":"metric"},{"meta":{"content":"# Backtesting \u0026 KPIs\n\nBacktesting plays a critical role in verifying the effectiveness of trading strategies. The out-of-sample (OOS) testing method stands out for its objective assessment of a model's predictive capabilities. Unlike in-sample testing, which merely indicates how a model can interpret the data it was trained with, OOS testing crucially evaluates its applicability to novel, unseen data.\n\nThe chart to the right presents the result of the backtest, offering a visual comparison between the Bitcoin Sharpe Signal's performance and the actual price movements of BTC, with colours depicting:\n- The price of BTC, which serves as a benchmark (grey line ⬜️). \n- The out-of-sample and live backtest of the Bitcoin Sharpe Signal (blue line 🟦). It started investing on 2022-05-01.   \n-  The vertical black line represents the inception of live data (2023-09-01)\n- The decision is visualised by the green signal bars 🟩. (BSS surging beyond the 0.5 mark)\n\n### KPIs\n\n![](https://api.glassnode.com/v1/generated/files/image-metrics/bss_statistics.jpg)"},"uuid":"a60cda5b-a28a-47c6-aba9-6367e95b3a9c","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"refUuid":"484a50a8-a8bd-4cfc-7eed-4b2fb991a347"},"uuid":"8bae8e56-35bf-47b0-9754-de4606ed5faf","configType":"workbench"},{"meta":{"content":"## Part I : Which on-chain data drives the model's decision\n\nUnderstanding which on-chain metrics influence trading decisions is crucial. Based on the SHAP analysis on the right side:\n\n- **High value on entities profit** (red dots) tends to impact positively being long on Bitcoin. Low value (blue dots) tends to impact negatively being long on Bitcoin.\n\n- **High value on MVRV** (red dots) tends to impact positively being long on Bitcoin. Low value (blue dots) tends to impact negatively being long on Bitcoin.\n\n- **Low value on SOPR** (blue dots) tends to impact positively being long on Bitcoin, while high value on SOPR (red dots) tends to impact negatively being long on Bitcoin.\n\nTo provide a clearer and more comprehensive analysis for our users, we have decided to focus on a two-dimensional analysis of Entities Profit and STH SOPR. (We discard MVRV as both Entities profit and MVRV having a similar SHAP behaviour). Additionally, we encourage our readers to experiment with different feature combinations and formulate their own trading strategies based on the two-dimensional framework we outline here.\n\n![](https://api.glassnode.com/v1/generated/files/image-metrics/goldilocks_ip_protected.png)\n\n**Conclusion:**   \n\nEntities typically profit during uptrends, indicating a bullish market. When combined with low STH SOPR, which signals seller exhaustion, the market conditions are ripe for a move. Given the bullish indication from the entities in profit, the likely direction is upwards, making it an opportune moment to go long."},"uuid":"d74fb380-670c-469f-b92a-51cc0076dfc1","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"content":"## SHAP: Interpretation of the model\n\nWe utilize [SHAP](https://github.com/shap/shap) (SHapley Additive exPlanations) to quantify the contribution of each on-chain data to the decision of buying Bitcoin. Highlighting in the process the most important features.   \n**On-chain data are ranked by importances** (from the most important one to the less important one to predict daily buying opportunity on Bitcoin).  \n\n![](https://api.glassnode.com/v1/generated/files/image-metrics/shap_ml_long_ip_protected.png)\n\nX-axis: the larger the SHAP value, the higher the impact on predicting a long position on Bitcoin. \nRed dots: indicate high value features, Blue dots: indicate low value features.  \n\n- ** [Entities in profit](https://studio.glassnode.com/metrics?a=BTC\u0026category=\u0026m=entities.ProfitRelative) :** This metric reflects the proportion of network participants currently holding an unrealized profit. An \"entity\" is identified as a collection of addresses that we consider, using Glassnode's unique methodology, to belong to the same participant.\n- **[MVRV](https://studio.glassnode.com/metrics?a=BTC\u0026category=\u0026m=market.Mvrv):** The Market Value to Realized Value (MVRV) ratio contrasts Bitcoin's market capitalization with its realized capitalization. Realized capitalization calculates the value of each coin at the price at which it was last moved or transacted. Essentially, it's an aggregation of the value of each coin at the time it last moved on the blockchain.\n- **[STH SOPR](https://studio.glassnode.com/metrics?a=BTC\u0026category=\u0026m=indicators.SoprLess155):** The Short-Term Holder Spent Output Profit Ratio (SOPR) is a metric  to understand the behavior and profitability of investors who hold their Bitcoin for a short period. SOPR looks at Bitcoin transactions to see if people are selling their Bitcoin for a profit or a loss. It focuses on Bitcoin that has been held for a short time [(under 155 days).](https://insights.glassnode.com/quantifying-bitcoin-hodler-supply/)\n\n*Please be aware that in this study, although we primarily reference the foundational metrics utilized in our model, it's important to understand that each metric underwent transformations prior to their integration into the model. Therefore, whenever we mention a base metric by name, it implicitly includes the transformed features derived from that metric.*"},"uuid":"4faf7653-3952-4caf-8ce3-26b470e68870","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"content":"The Goldilocks model: Trading heuristic from the Original BSS."},"uuid":"dc755bb2-64d9-4ba4-9aee-4f31f25088fa","configType":"text"},{"meta":{"content":"# Part II: Trading heuristics\n\nOur machine learning algorithm enables us to identify the most crucial trading decision thresholds. This allows us to construct rule-based trading strategies with enhanced precision and effectiveness. Please refer to the visualization displayed on the right side.  \n\nThe green concentrated area seems to be the sweet spot of the algorithm, it can be sumarized through the combination of the following trading heuristics:\n\n\n### Entities in profit:\n\n- Threshold: Between 52 and 65.\n- Insights:  A high value in this range suggests a prevailing bull market, making it a favorable environment for investment. However, values above this range hint a potential market overheating, signaling caution.\n\n### Short Term Holder SOPR:\n\n- Threshold: Less than 4%\n- Insights: This low value indicates a market equilibrium where both profit-taking and panic selling subsided. Essentially the market is poised for a move, but the direction isn't specified by this metric alone.\n\n\n## Conclusion:\n\nAccording to the ML model the best buying opportunities seems to be contained in the “Goldilocks zone”:  \n- entities_profit between 52 and 65, AND\n- STH_SOPR below 4%."},"uuid":"0b40f06b-08af-40e9-bf5b-80801b4433a5","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"content":"# Goldilocks Zone\n\n![](https://api.glassnode.com/v1/generated/files/image-metrics/goldilocks_ml_ip_protected.png)  \nDecision boundaries created by the algorithm help us visualise areas of buying opportunities on Bitcoin.  \nGreen dots: Buying opportunity.   \nYellow dots: Flat position.   \nRed dots: Misclassification.  \nGreen zone: Be invested.   \nYellow zone: Stay out of the market.  "},"uuid":"e3875da6-bb36-4727-ba05-4dc461dc8a93","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"content":"# Part III: Live Data and Performance\n\nBased on what we learn from our in-depth machine learning research we create a \"Goldilocks\" signal using the trading heuristic presented above. (based on entities profit and STH SOPR).  \n\n### One the right: Live Data and Goldilock Zone\n\nDisplayed on the right is the real-time version of the scatter plot we reviewed in Part II, enabling us to monitor the market's progression over the last 30 days through the movements of two key variables. Placement within the green Goldilocks zone denotes prime market conditions to be long on Bitcoin.\n\n### Below : Live Performance Tracker\n\nThis custom chart acts as a live tracker for the trading heuristic's performance, compared to the Bitcoin Sharpe Signal.\nIt includes both the out-of-sample backtest and the current live performance according to the Goldilocks zone trading principles. The investment strategy is activated only when both criteria from the Goldilock zone are satisfied; otherwise, the strategy remains inactive.  \n\n- The price of BTC, which serves as a benchmark (grey line ⬜️).  \n- The out-of-sample and live backtest of the Bitcoin Sharpe Signal (blue line 🟦).Out-of-sample starts on 2022-05-10.\n- The out-of-sample and live backtest of the Goldilocks heuristic (orange line 🟧)\n-  The vertical black line represents the inception of live data (2023-09-01)\n- The decision is visualised by the green signal bars 🟩. (BSS surging beyond the 0.5 mark)\n"},"uuid":"88a9232f-4d0e-49cd-a249-43e4202fc4d4","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"content":"# Goldilocks Zone Live Data\n\n![](https://api.glassnode.com/v1/generated/files/image-metrics/ml_momentum_goldilock_zone.jpg)\n"},"uuid":"1a0816ae-98f8-4f50-b95a-f0772d190d92","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"refUuid":"9f9a6209-78aa-4610-69b4-1ed303d50ce8"},"uuid":"488d2038-6479-4ec6-afe2-9e791c4eeee9","configType":"workbench"},{"meta":{"content":"The intraday Bitcoin Sharpe Signal, hourly, long-only model."},"uuid":"61122a9a-2634-402c-82f6-7781d3e7a702","configType":"text"},{"meta":{"content":"The Glassnode Intraday Bitcoin Sharpe enhances the BSS by providing intraday insights into the positioning of the Bitcoin Sharpe signal, thus improving response capabilities for Pro ML package subscribers against market movements.\n\nThe signal uses a unique ML-based approach with on-chain data to strategically minimize downside risks and capture rising trends in Bitcoin. In the context of enhancing risk-adjusted returns, the model's confidence is visually represented, with green for the highest confidence and orange to red for reduced confidence. A surge beyond the 0.5 mark has historically been associated with improved risk-adjusted performance in Bitcoin.\n"},"uuid":"e4532ad6-6ec7-412f-a26f-3617c947b68a","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"date":1759759200,"asset":"BTC","since":1325376000,"until":1759759200,"chartType":"timeSeries","metricCode":"signals.BtcBssV2","resolution":"1h"},"uuid":"2c169e2e-d6bd-4b0b-8813-5499e3b4075e","extra":{"price":true,"scale":"lin","lineColor":"#f7931a"},"configType":"metric"},{"meta":{"content":"The intraday Bitcoin Sharpe Signal Short , hourly, short-only model."},"uuid":"a63f27c8-0f88-41f1-a15b-cf8dd252228a","configType":"text"},{"meta":{"content":"# Introduction to Bitcoin Sharpe Signal Short\n\nAt Glassnode, we have developed the Bitcoin Sharpe Signal Short to help investors manage risk and improve their risk-adjusted returns. Bitcoin's high volatility makes buy-and-hold positions unsustainable for many investors, who often face drawdowns of 50 to 80% depending on the market cycle. Our goal is to present a data-driven approach to actively manage this risk and navigate Bitcoin's cycles. \nThe Bitcoin Sharpe Signal Short is designed to identify potential market downturns using on-chain data, offering a strategic advantage in both reducing exposure during high-risk periods or taking short positions directly on Bitcoin through derivatives. The signal is available on a daily and hourly resolution.\n\n## Conceptualizing the Bitcoin Sharpe Signal Short\n\nThe Bitcoin Sharpe Signal Short is a machine-learning-based strategy, developed to predict incoming market tensions using on-chain data. This strategy adopts a conservative approach, activating only when the model has a high level of confidence in predicting market turmoil. The Bitcoin Sharpe Signal Short offers a straightforward indicator for when to go short on Bitcoin while minimising downside risk. When the indicator surges beyond the 0.5 mark, it has historically been associated with imminent market downturns.\nThis high confidence threshold ensures that the signal is reliable. By leveraging on-chain data, we enable our investors to benefit from the long-term trend in Bitcoin while managing drawdowns and improving risk-adjusted returns. \n\n## Trading Insights\n\nThrough this dashboard, we aim to understand the mechanisms driving the Bitcoin Sharpe Signal Short, highlighting specific on-chain metrics crucial for spotting potential downturns. Shorting Bitcoin is inherently challenging due to its historical upward bias, despite experiencing downturns and drawdowns. Excelling in shorting requires identifying prime short opportunities and avoiding losses during market upswings. We'll dissect the Bitcoin Sharpe Signal Short model to identify useful on-chain metrics for each scenario and learn how to combine them effectively."},"uuid":"f2f68e10-91c7-4095-bbcd-197d7f1f2257","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"date":1759759200,"asset":"BTC","since":1298678400,"until":1759759200,"chartType":"timeSeries","metricCode":"signals.BtcBssShort","resolution":"1h"},"uuid":"19d66ac3-badd-4185-9c28-b9b8d3cf13b1","extra":{"name":"Bitcoin Sharpe Signal Short","zoom":"Custom","price":true,"scale":"lin","lineColor":"#f7931a"},"configType":"metric"},{"meta":{"content":"# Backtesting\n\nIn this example we backtest out-of-sample **the daily Bitcoin Sharpe Signal Short.**\nThe out-of-sample (OOS) testing method stands out for its objective assessment of a model's predictive capabilities. Unlike in-sample testing, which merely indicates how a model can interpret the data it was trained with, OOS testing crucially evaluates its applicability to novel, unseen data.\n\nThe chart to the right presents the result of the backtest, offering a visual comparison between the Bitcoin Sharpe Signal Short and the actual price movements of BTC, with colours depicting:\n\n- The price of BTC, which serves as a benchmark (grey line ⬜️).  \n- The out-of-sample and live backtest of the Bitcoin Sharpe Signal Short (blue line 🟦). Out-of-sample starts on 2022-01-13.   \n-  The vertical black line represents the inception of live data (2024-06-01)\n- The decision is visualised by the red signal bars 🟥(Bitcoin Sharpe Signal Short surging beyond the 0.5 mark)\n"},"uuid":"335a5c81-0e13-4248-8394-8e0247cc0b75","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"refUuid":"ef46321d-a56f-4bba-6b54-d59107f4e83f"},"uuid":"8e706582-4066-4c41-862d-7ed35fdcf311","configType":"workbench"},{"meta":{"content":"# Useful On-chain metrics to predict risk\n\n### Feature importances\n\nFeature importance is a critical concept in machine learning that measures how much each feature contributes to the model’s predictions. By evaluating the usefulness of specific variables, feature importance helps us understand which data points are most influential in driving the decision of being short. \n \n\n### Interpretability\n\nBy examining the SHAP values (SHapley Additive exPlanations), we can understand how each feature influences the target class. Higher positive SHAP values indicate a stronger impact on the decision to take a short position. Conversely, very negative SHAP values are also important as they signal when not to short during market upswings. This dual-dimensional problem requires accurate predictions both when the market declines and avoiding shorts when the market rises.\n\nAs indicated by the SHAP, profitability of market participants, wallet size and age of market participants seems to be key on-chain indicators for predicting market downturns.\n\nOn-chain metrics that impact the most positively and negatively the SHAP:\n\n- **High value on Hodl Waves 5y-7y** (red dots) tends to impact strongly being short on Bitcoin. Low value (blue dots) tends to impact negatively being short on Bitcoin.\n\n- **High value on MVRV by retail wallet size ** (red dots) tends to impact very negatively being short on Bitcoin.  (very negative SHAP)\n\n- **Low value on Seller Exhaustion ** (blue dots) tends to impact strongly being short on Bitcoin. (high positive SHAP)\n\nBased on the SHAP values analysis, we identified key metrics with predictive power for anticipating market downturns. Notably, three main categories of on-chain data stand out among the model's most significant features.  \n- Firstly, metrics related to the age of market participants, such as hodl_waves 5y 7y, indicate that market veterans often lead market movements.  \n- Secondly, the size of market participants, particularly retail traders (MVRV by wallet size less 0.001), suggests that smaller trades play also a crucial role in being short.  \n- Lastly, the overall profitability of market participants emerges as a critical factor.\n"},"uuid":"c479073e-8abd-4c1d-8413-e6aae2c9986b","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"content":"## SHAP: Interpretation of the model\n\n![](https://api.glassnode.com/v1/generated/files/image-metrics/short_shap_base_metric.png)\n\n\n*Please be aware that in this study, although we primarily reference the foundational metrics utilized in our model, it's important to understand that each metric underwent transformations prior to their integration into the model. Therefore, whenever we mention a base metric by name, it implicitly includes the transformed features derived from that metric.*"},"uuid":"95779ade-a21f-49c0-9ed2-bbdc8820c6f6","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"content":"# Quant Trading Insights\n\nTiming is crucial when it comes to shorting Bitcoin. Our strategy emphasizes the importance of being right about shorting and avoiding shorts during market upswings. The trade-off between true positives and false positives is key here.\n\n### Good Short Opportunities\n\nBy analyzing the dependence plot, we can establish thresholds for trading decisions. The dependence plot reveals a clear pattern in SHAP values based on the seller exhaustion metric. \nWhen the seller exhaustion tumbled below -0.012, the SHAP value increase drastically,this shift indicates that shorting might become a profitable strategy.\n\n![](https://api.glassnode.com/v1/generated/files/image-metrics/short_shap_sellerex_base.png)\n\n*Please be aware that in this study, although we primarily reference the foundational metrics utilized in our model, it's important to understand that each metric underwent transformations prior to their integration into the model. Therefore, whenever we mention a base metric by name, it implicitly includes the transformed features derived from that metric.\n*\n\n"},"uuid":"9a0198ee-ecc9-4501-aed4-3b58ae18e7f9","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"content":"# Quant Trading Insights\n\n### Avoiding the bear trap:\n\nMVRV by wallet size doesn’t provide clear signals for shorting due to the low SHAP value. However, it is quite informative about when not to short. When the RSI surpasses the 60 mark, the SHAP value decreases sharply, which negatively influences the decision to short.  \nThis implies that a significant rise in retail MVRV suggests increasing momentum among retail investors. Therefore, shorting in this scenario could lead to substantial losses, as indicated by the short model.\n![](https://api.glassnode.com/v1/generated/files/image-metrics/short_dependence_mvrv_base.png)\n\n*Please be aware that in this study, although we primarily reference the foundational metrics utilized in our model, it's important to understand that each metric underwent transformations prior to their integration into the model. Therefore, whenever we mention a base metric by name, it implicitly includes the transformed features derived from that metric.*\n\n"},"uuid":"a9fc8246-ec8b-4833-9790-44a7554692d3","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"content":"# Identifying optimal area for shorting\n\nBased on our findings from the SHAP we can combine metrics to  optimize the good short opportunities and at the same time reduce the risk of bear-trap. We can combine multiple metrics, in order to create our own trading heuristics.\nThe algorithm's decision boundaries highlight the optimal zones for shorting Bitcoin.\n\nGreen dots: Short opportunity, yellow dots: Flat position, red dots: Misclassification\n![](https://api.glassnode.com/v1/generated/files/image-metrics/short_boundaries_mvrv_wallet_base.png).  \n\nThis time, we combine seller exhaustion with hodl waves 3y-5y\n\nThe most favorable area to short BTC is when it falls into the lower quadrant with seller exhaustion under -1.2% and Hodl Waves 3y-5y is below 1.34.\n![](https://api.glassnode.com/v1/generated/files/image-metrics/short_boundaries_sellerx_hodl_base.png)\n"},"uuid":"efcefe4d-f6ac-43e6-bb76-8ddd19f4c6fb","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"content":"# Identifying optimal area for shorting\n\n## Live Data\n\nThe ML algorithm's decision boundaries reflect the SHAP values we reviewed earlier, highlighting that the best time to short BTC is when it falls into the lower quadrant with seller exhaustion under -1.2% and when MVRV by wallet size less than 0.001 is below 60.\n\n![](https://api.glassnode.com/v1/generated/files/image-metrics/ml_short_goldilocks_mvrv.jpg)\n  \n![](https://api.glassnode.com/v1/generated/files/image-metrics/ml_short_goldilocks_hodl.jpg)\n"},"uuid":"3dbda577-b10d-4129-adf4-33aeddbb67d0","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"content":"Combining Bitcoin Sharpe Signal Long \u0026 Short"},"uuid":"06e7aa80-26c5-4264-8666-fe453e21543e","configType":"text"},{"meta":{"content":"# BSS Long/Short Positioning and Performance \n\nOn the right side, you will see the positioning of Intraday BSS Long (y-axis) and BSS Short (x-axis) over the past 30 days. The color trace uses blue for the oldest values (around 30 days ago) and red for the most recent values.\n\n## Out-of-sample and Live Performance\n\nBelow is the backtest for the combined Intraday BSS Long and Short.\n\nWe take a long position when the Intraday BSS Long signal is above 0.5 and the Short signal is below 0.5.  \nWe take a short position when the Short signal is above 0.5 and the Long signal is below 0.5.  \nWe remain flat under all other conditions.  \nThe first vertical black line (September 2023) indicates the start of live data for the Long Signal.  \nThe second black line (June 2024) indicates the start of live data for the Short Signal.\n"},"uuid":"069107eb-0dcf-4da1-a536-95cf569a4fa6","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"content":"# BSS Long \u0026 Short Positioning\n\n![](https://api.glassnode.com/v1/generated/files/image-metrics/ml_bss_long_short.jpg)"},"uuid":"a383d66d-022c-4fc6-aacd-e1dbe64cf113","extra":{"backgroundColor":"#ffffff"},"configType":"content"},{"meta":{"refUuid":"4815c81b-7db5-4877-681a-10451ae152f5"},"uuid":"3c92fada-06aa-4c15-8340-41d29d79af06","configType":"workbench"}],"layouts":[{"h":6,"i":"17ed1c62-d348-4135-896e-c3ffafe83d60","w":12,"x":0,"y":0,"minH":1,"minW":3,"moved":false,"static":false},{"h":1,"i":"649e9c90-77f4-4925-aa89-1eb89352c4ef","w":12,"x":0,"y":6,"maxH":1,"minH":1,"minW":3,"moved":false,"static":false,"isResizable":false},{"h":9,"i":"0547030a-7a1e-4a60-8425-76549d8acad6","w":4,"x":0,"y":7,"minH":1,"minW":3,"moved":false,"static":false},{"h":9,"i":"2e2b3335-cbeb-4901-b8d6-da0935bad9a5","w":8,"x":4,"y":7,"minH":1,"minW":3,"moved":false,"static":false},{"h":15,"i":"a60cda5b-a28a-47c6-aba9-6367e95b3a9c","w":4,"x":0,"y":16,"minH":1,"minW":3,"moved":false,"static":false},{"h":15,"i":"8bae8e56-35bf-47b0-9754-de4606ed5faf","w":8,"x":4,"y":16,"minH":1,"minW":3,"moved":false,"static":false},{"h":16,"i":"d74fb380-670c-469f-b92a-51cc0076dfc1","w":5,"x":0,"y":32,"minH":1,"minW":3,"moved":false,"static":false},{"h":16,"i":"4faf7653-3952-4caf-8ce3-26b470e68870","w":7,"x":5,"y":32,"minH":1,"minW":3,"moved":false,"static":false},{"h":1,"i":"dc755bb2-64d9-4ba4-9aee-4f31f25088fa","w":12,"x":0,"y":31,"maxH":1,"minH":1,"minW":3,"moved":false,"static":false,"isResizable":false},{"h":14,"i":"0b40f06b-08af-40e9-bf5b-80801b4433a5","w":5,"x":0,"y":48,"minH":1,"minW":3,"moved":false,"static":false},{"h":14,"i":"e3875da6-bb36-4727-ba05-4dc461dc8a93","w":7,"x":5,"y":48,"minH":1,"minW":3,"moved":false,"static":false},{"h":11,"i":"88a9232f-4d0e-49cd-a249-43e4202fc4d4","w":5,"x":0,"y":62,"minH":1,"minW":3,"moved":false,"static":false},{"h":11,"i":"1a0816ae-98f8-4f50-b95a-f0772d190d92","w":7,"x":5,"y":62,"minH":1,"minW":3,"moved":false,"static":false},{"h":11,"i":"488d2038-6479-4ec6-afe2-9e791c4eeee9","w":12,"x":0,"y":73,"minH":1,"minW":3,"moved":false,"static":false},{"h":1,"i":"61122a9a-2634-402c-82f6-7781d3e7a702","w":12,"x":0,"y":84,"maxH":1,"minH":1,"minW":3,"moved":false,"static":false,"isResizable":false},{"h":9,"i":"e4532ad6-6ec7-412f-a26f-3617c947b68a","w":4,"x":0,"y":85,"minH":1,"minW":3,"moved":false,"static":false},{"h":9,"i":"2c169e2e-d6bd-4b0b-8813-5499e3b4075e","w":8,"x":4,"y":85,"minH":1,"minW":3,"moved":false,"static":false},{"h":1,"i":"a63f27c8-0f88-41f1-a15b-cf8dd252228a","w":12,"x":0,"y":94,"maxH":1,"minH":1,"minW":3,"moved":false,"static":false,"isResizable":false},{"h":12,"i":"f2f68e10-91c7-4095-bbcd-197d7f1f2257","w":4,"x":0,"y":95,"minH":1,"minW":3,"moved":false,"static":false},{"h":12,"i":"19d66ac3-badd-4185-9c28-b9b8d3cf13b1","w":8,"x":4,"y":95,"minH":1,"minW":3,"moved":false,"static":false},{"h":9,"i":"335a5c81-0e13-4248-8394-8e0247cc0b75","w":4,"x":0,"y":107,"minH":1,"minW":3,"moved":false,"static":false},{"h":11,"i":"8e706582-4066-4c41-862d-7ed35fdcf311","w":8,"x":4,"y":107,"minH":1,"minW":3,"moved":false,"static":false},{"h":15,"i":"c479073e-8abd-4c1d-8413-e6aae2c9986b","w":4,"x":0,"y":116,"minH":1,"minW":3,"moved":false,"static":false},{"h":13,"i":"95779ade-a21f-49c0-9ed2-bbdc8820c6f6","w":8,"x":4,"y":118,"minH":1,"minW":3,"moved":false,"static":false},{"h":17,"i":"9a0198ee-ecc9-4501-aed4-3b58ae18e7f9","w":5,"x":0,"y":131,"minH":1,"minW":3,"moved":false,"static":false},{"h":17,"i":"a9fc8246-ec8b-4833-9790-44a7554692d3","w":7,"x":5,"y":131,"minH":1,"minW":3,"moved":false,"static":false},{"h":23,"i":"efcefe4d-f6ac-43e6-bb76-8ddd19f4c6fb","w":5,"x":0,"y":148,"minH":1,"minW":3,"moved":false,"static":false},{"h":23,"i":"3dbda577-b10d-4129-adf4-33aeddbb67d0","w":7,"x":5,"y":148,"minH":1,"minW":3,"moved":false,"static":false},{"h":1,"i":"06e7aa80-26c5-4264-8666-fe453e21543e","w":12,"x":0,"y":171,"maxH":1,"minH":1,"minW":3,"moved":false,"static":false,"isResizable":false},{"h":10,"i":"069107eb-0dcf-4da1-a536-95cf569a4fa6","w":6,"x":0,"y":172,"minH":1,"minW":3,"moved":false,"static":false},{"h":10,"i":"a383d66d-022c-4fc6-aacd-e1dbe64cf113","w":6,"x":6,"y":172,"minH":1,"minW":3,"moved":false,"static":false},{"h":8,"i":"3c92fada-06aa-4c15-8340-41d29d79af06","w":12,"x":0,"y":182,"minH":1,"minW":3,"moved":false,"static":false}]}}