SynthMode 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...
SynthSynth 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 ...
SynthSynth 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.
SynthMode 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...
SynthSynth 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 ...
SynthSynth 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 2025-04-14 To 2025-04-20
During the week of April 14 to April 20, Bitcoin opened at $83,700 and closed at approximately $85,200, fluctuating between a low of $83,100 and a high of $86,400. Overall, volatility was notably lower than in previous weeks. However, brief episodes of heightened price movement persisted.
One of the most notable events occurred on Monday, April 14, between 2 PM and 3 PM UTC, when Bitcoin jumped 1.4% within an hour—from $84,500 to $85,700. This spike coincided with the U.S. stock market opening and the announcement of tariff exemptions for tech companies.
Other volatility events followed—such as Fed Chair Jerome Powell’s speech at the Chicago Economic Club on April 16—but became increasingly muted as the week progressed. The second half of the week was relatively stable, until a final sharp jump on Sunday, April 20, saw the price rise from about $84,000 to over $85,000.
At the start of the week, the top 3 miners by leaderboard score were Miners 91, 196, and 3. By week’s end, leadership had shifted to Miners 44, 4, and 100. Below, we analyze their performance through the lens of two key metrics: CRPS and Leaderboard Scores.
Lower CRPS values indicate better predictive accuracy.
In the more volatile first half of the week, all miners produced relatively similar BTC forecasts, with CRPS values clustering around 35,000 early in the week and improving to about 20,000 by Friday, April 18. During April 15–16, Miners 44, 4, and 100 began to show slightly stronger, more consistent performance than their peers.
As volatility tapered off by April 18, these three miners further distinguished themselves by better adapting to changing market conditions. From April 18–19, their models clearly outperformed others. By April 20, Miners 91, 196, and 3 appeared to have recalibrated their models, regaining performance parity with the top miners.
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.
This smoothed scoring system rewards consistent performance over time rather than short bursts of excellence. Initially, Miners 44, 4, and 100 trailed behind, with scores above 1,800 compared to the 1,600–1,800 range of Miners 91, 196, and 3. However, the consistent accuracy of their models allowed them to quickly rise in the rankings. By April 16, they had taken the lead with scores around 1,400.
Their strong performance continued through the week, pushing their scores down to approximately 600. Meanwhile, the early leaders—Miners 91, 196, and 3—struggled to recover, finishing the week with leaderboard scores more than double those of the top three.
This week’s performance evolution offers valuable lessons for subnet participants:
Miners must strike a delicate balance between quickly adapting to market shocks and maintaining steady, reliable performance over time. The leaderboard’s extended evaluation window means that occasional spikes in accuracy aren’t enough—continuous excellence across varying conditions is essential to earn and hold a top spot.
The rapid leaderboard shifts observed this week underscore the importance of continual model refinement. As the subnet increasingly rewards long-term, high-quality forecasting, miners who want to break into or stay atop the leaderboard must invest in robust, adaptive algorithms capable of navigating diverse market environments without compromising consistency.
Data From 2025-04-14 To 2025-04-20
During the week of April 14 to April 20, Bitcoin opened at $83,700 and closed at approximately $85,200, fluctuating between a low of $83,100 and a high of $86,400. Overall, volatility was notably lower than in previous weeks. However, brief episodes of heightened price movement persisted.
One of the most notable events occurred on Monday, April 14, between 2 PM and 3 PM UTC, when Bitcoin jumped 1.4% within an hour—from $84,500 to $85,700. This spike coincided with the U.S. stock market opening and the announcement of tariff exemptions for tech companies.
Other volatility events followed—such as Fed Chair Jerome Powell’s speech at the Chicago Economic Club on April 16—but became increasingly muted as the week progressed. The second half of the week was relatively stable, until a final sharp jump on Sunday, April 20, saw the price rise from about $84,000 to over $85,000.
At the start of the week, the top 3 miners by leaderboard score were Miners 91, 196, and 3. By week’s end, leadership had shifted to Miners 44, 4, and 100. Below, we analyze their performance through the lens of two key metrics: CRPS and Leaderboard Scores.
Lower CRPS values indicate better predictive accuracy.
In the more volatile first half of the week, all miners produced relatively similar BTC forecasts, with CRPS values clustering around 35,000 early in the week and improving to about 20,000 by Friday, April 18. During April 15–16, Miners 44, 4, and 100 began to show slightly stronger, more consistent performance than their peers.
As volatility tapered off by April 18, these three miners further distinguished themselves by better adapting to changing market conditions. From April 18–19, their models clearly outperformed others. By April 20, Miners 91, 196, and 3 appeared to have recalibrated their models, regaining performance parity with the top miners.
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.
This smoothed scoring system rewards consistent performance over time rather than short bursts of excellence. Initially, Miners 44, 4, and 100 trailed behind, with scores above 1,800 compared to the 1,600–1,800 range of Miners 91, 196, and 3. However, the consistent accuracy of their models allowed them to quickly rise in the rankings. By April 16, they had taken the lead with scores around 1,400.
Their strong performance continued through the week, pushing their scores down to approximately 600. Meanwhile, the early leaders—Miners 91, 196, and 3—struggled to recover, finishing the week with leaderboard scores more than double those of the top three.
This week’s performance evolution offers valuable lessons for subnet participants:
Miners must strike a delicate balance between quickly adapting to market shocks and maintaining steady, reliable performance over time. The leaderboard’s extended evaluation window means that occasional spikes in accuracy aren’t enough—continuous excellence across varying conditions is essential to earn and hold a top spot.
The rapid leaderboard shifts observed this week underscore the importance of continual model refinement. As the subnet increasingly rewards long-term, high-quality forecasting, miners who want to break into or stay atop the leaderboard must invest in robust, adaptive algorithms capable of navigating diverse market environments without compromising consistency.
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