
Mode Synth (SN50) Subnet Whitepaper and How to Guide
The creation and use of synthetic price data has traditionally been dominated by opaque, well-funded entities such as large financial institutions, centralized technology firms, and regulatory agencies, including JPMorgan Chase, Goldman Sachs, the Federal Reserve, and PayPal. The closed-source nature of these datasets stems from the self-serving priorities of these organizations, which often lack the incentive to democratize data access. This approach exacerbates disparities, creating a wider...

Synth Subnet - Inside Synth’s Accuracy Surge
IntroductionThis report provides a focused assessment of recent progress in the Synth subnet, where miners forecast BTC’s return distribution every day. Over the past month, the subnet has seen a major shift: miners prioritizing volatility accuracy have rapidly climbed the leaderboards, outperforming more complex models that previously held top positions. We analyze the performance of these top miners across multiple metrics, volatility, intraday variation, and kurtosis, using data from June ...

Synth content transitioned to Synth X articles
You can stay up to date with Synth’s ongoing research, performance analysis, and ecosystem updates through our regularly published X articles. All new releases are posted directly on our X account here: https://x.com/SynthdataCo/articles.

Mode Synth (SN50) Subnet Whitepaper and How to Guide
The creation and use of synthetic price data has traditionally been dominated by opaque, well-funded entities such as large financial institutions, centralized technology firms, and regulatory agencies, including JPMorgan Chase, Goldman Sachs, the Federal Reserve, and PayPal. The closed-source nature of these datasets stems from the self-serving priorities of these organizations, which often lack the incentive to democratize data access. This approach exacerbates disparities, creating a wider...

Synth Subnet - Inside Synth’s Accuracy Surge
IntroductionThis report provides a focused assessment of recent progress in the Synth subnet, where miners forecast BTC’s return distribution every day. Over the past month, the subnet has seen a major shift: miners prioritizing volatility accuracy have rapidly climbed the leaderboards, outperforming more complex models that previously held top positions. We analyze the performance of these top miners across multiple metrics, volatility, intraday variation, and kurtosis, using data from June ...

Synth content transitioned to Synth X articles
You can stay up to date with Synth’s ongoing research, performance analysis, and ecosystem updates through our regularly published X articles. All new releases are posted directly on our X account here: https://x.com/SynthdataCo/articles.

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Data from 2025-04-21 To 2025-04-27
During the week of April 21 to April 27, 2025, Bitcoin opened at $85,200 and closed at $93,700, with a weekly low of $85,150 and a high of $95,700. The week experienced significant price movement, driven by a major volatility spike on April 22 between 21:30 and 22:00 UTC. During this period, rumors of a potential tariff deal between China and the U.S. triggered a sharp 2.3% price surge within 30 minutes, pushing Bitcoin from around $91,600 to $93,700. Volatility remained elevated for the next few days, with smaller fluctuations, before stabilizing toward the end of the week as the price settled around $93,700.

At the start of the week, the top miners by leaderboard score were Miners 4, 44, and 100. By the end of the week, Miners 32, 247, and 33 had taken the lead. Below, we analyze their performance using two key metrics: CRPS and Leaderboard Scores.
Lower CRPS values indicate better predictive accuracy.
Early in the week, sudden volatility spikes caused the CRPS values of all miners under analysis to peak around 45,000. Miners 4 and 100, who started with higher scores, saw their CRPS values soar, reflecting their difficulty in adapting to the rapid market shift. Meanwhile, Miners 32, 247, and 33 adjusted more effectively, producing forecasts with lower CRPS values between April 23 and 24. The miners who ended the week at the top of the leaderboard consistently outperformed Miners 44, 4, and 100, especially over the weekend when no significant volatility spikes occurred, leading overall scores to stabilize between 15,000 and 20,000.

Leaderboard scores reflect an exponentially weighted average of past CRPS performance over a 7-day window, with a half-life of 3.5 days. Lower scores are better.
At the start of the week, Miners 4 and 100 led with scores around 600, while Miners 32, 247, and 33 had scores ranging from 1,200 to 1,400. The volatility spike on April 22 caused a significant reshuffling. Miners 4 and 100 saw their scores rise sharply to around 2,000 by April 23 due to their weaker predictive accuracy during the tariff rumor event. In contrast, Miners 32, 247, and 33 improved steadily, with their scores dropping to around 1,000 by April 27, reflecting their ability to adapt to market conditions.

This week’s performance once again highlights the critical need for miners to adapt swiftly to unexpected market events. The tariff deal rumors on April 22 created a challenging environment, and miners who could recalibrate their models quickly—such as Miners 32, 247, and 33—gained a clear advantage. The leaderboard’s emphasis on consistent, long-term performance underscores the importance of robust, adaptive algorithms that can effectively handle both volatile spikes and stable periods. Miners aiming to maintain or achieve top rankings must prioritise flexibility and continuous model refinement to navigate such dynamic market conditions.
Data from 2025-04-21 To 2025-04-27
During the week of April 21 to April 27, 2025, Bitcoin opened at $85,200 and closed at $93,700, with a weekly low of $85,150 and a high of $95,700. The week experienced significant price movement, driven by a major volatility spike on April 22 between 21:30 and 22:00 UTC. During this period, rumors of a potential tariff deal between China and the U.S. triggered a sharp 2.3% price surge within 30 minutes, pushing Bitcoin from around $91,600 to $93,700. Volatility remained elevated for the next few days, with smaller fluctuations, before stabilizing toward the end of the week as the price settled around $93,700.

At the start of the week, the top miners by leaderboard score were Miners 4, 44, and 100. By the end of the week, Miners 32, 247, and 33 had taken the lead. Below, we analyze their performance using two key metrics: CRPS and Leaderboard Scores.
Lower CRPS values indicate better predictive accuracy.
Early in the week, sudden volatility spikes caused the CRPS values of all miners under analysis to peak around 45,000. Miners 4 and 100, who started with higher scores, saw their CRPS values soar, reflecting their difficulty in adapting to the rapid market shift. Meanwhile, Miners 32, 247, and 33 adjusted more effectively, producing forecasts with lower CRPS values between April 23 and 24. The miners who ended the week at the top of the leaderboard consistently outperformed Miners 44, 4, and 100, especially over the weekend when no significant volatility spikes occurred, leading overall scores to stabilize between 15,000 and 20,000.

Leaderboard scores reflect an exponentially weighted average of past CRPS performance over a 7-day window, with a half-life of 3.5 days. Lower scores are better.
At the start of the week, Miners 4 and 100 led with scores around 600, while Miners 32, 247, and 33 had scores ranging from 1,200 to 1,400. The volatility spike on April 22 caused a significant reshuffling. Miners 4 and 100 saw their scores rise sharply to around 2,000 by April 23 due to their weaker predictive accuracy during the tariff rumor event. In contrast, Miners 32, 247, and 33 improved steadily, with their scores dropping to around 1,000 by April 27, reflecting their ability to adapt to market conditions.

This week’s performance once again highlights the critical need for miners to adapt swiftly to unexpected market events. The tariff deal rumors on April 22 created a challenging environment, and miners who could recalibrate their models quickly—such as Miners 32, 247, and 33—gained a clear advantage. The leaderboard’s emphasis on consistent, long-term performance underscores the importance of robust, adaptive algorithms that can effectively handle both volatile spikes and stable periods. Miners aiming to maintain or achieve top rankings must prioritise flexibility and continuous model refinement to navigate such dynamic market conditions.
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