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 2025-06-30 To 2025-07-06
Figure 1 shows the hourly log returns and volatility observed throughout the week, highlighting the relationship between BTC and ETH. Volatility remained relatively stable during the weekdays and declined over the weekend (July 6–7), before a final spike; particularly noticeable in Ethereum, at the end of the week.
This week, we tracked the performance of six miners:
Miners 221, 32, and 200 – consistently among the top-ranked throughout the week
Miners 253, 213, and 76 – each from a different axon, showing strong performance
As usual, performance was evaluated using two key metrics:
CRPS (Continuous Ranked Probability Score) for BTC and ETH forecasts
Leaderboard Scores
Continuous Ranked Probability Score (CRPS). Lower CRPS values indicate better predictive accuracy.
The weekly CRPS values reflect a highly competitive environment among top-tier miners. While there were moments when either group (221/32/200 vs. 253/213/76) outperformed the other, no miner group consistently dominated across all days and volatility regimes. This signals a healthy competitive dynamic in the subnet, one that fosters continuous model improvement and innovation.
Leaderboard Scores. These are exponentially weighted averages of past CRPS values over a 10-day window. Lower scores indicate better performance.
Miners 32, 200, and 221 maintained top-three positions throughout the week. However, the performance gap between them and Miners 76, 213, and 253 narrowed, decreasing from ~40 points to less than ~20 points by week’s end.
This section analyzes summary statistics, including volatility, kurtosis, and skewness to detect potential strategic differences among top miners for both BTC and ETH.
Summary Statistic Definitions:
Volatility: Variability in forecasted returns
Kurtosis: Measures the "tailedness" of the distribution, how likely extreme events are
Skewness: Measures the asymmetry of return distributions (0 = symmetric)
Figure 4 presents these statistics for the four miners across the week.
The figure reveals distinct modeling styles. Miners 32, 200, and 221 tended to produce forecasts with higher volatility and kurtosis, and they showed greater variation in distribution moments (including skewness). Conversely, Miners 253, 213, and 76 used less volatile and more stable distributions.
Interestingly, the latter group closed the performance gap with the frontrunners in leaderboard scores, suggesting that under the specific volatility regime of the week, their more stable approach paid off.
Miners’ Ranking vs. Summary Statistics: All miners who submitted forecasts were ranked by total weekly rewards. Figure 5 shows how summary statistics vary across ranking tiers.
Consistent with previous weeks, miners in the top 10 reward tier produced forecasts that were, on average, more volatile and had more stable kurtosis than those ranked lower.
To assess performance at the group level, we analyzed:
Axon size (number of miners)
Average and total rewards per axon
Axon-level summary statistics (BTC forecasts)
Figure 6 shows the number of miners per axon at the end of the week.
As in the previous weeks, axon 116.202.53.142 had the highest number of registered miners, followed by 138.201.62.165 and 95.216.99.113.
Figure 7 shows the evolution of average (per miner) and total daily rewards for the top 10 axons by average reward.
At the beginning of the week, axon 35.77.6.189 received most of the rewards. However, starting over the weekend, likely due to exploiting lower-volatility periods, miners from axons 95.111.205.93 and 116.202.53.142 began to earn significantly more rewards on average.
Figure 8 compares the top 10 axons (by average rewards) based on key BTC modeling characteristics: average intraday volatility, volatility variability, and average intraday kurtosis.
Except for axon 95.216.99.113 and axon 95.216.2.50, who produced less volatile, less "fat-tailed" forecasts, most axons showed consistent forecasting behavior, with similar volatility and kurtosis levels, both in magnitude and direction.
This week’s results highlight an increasingly dynamic and competitive environment among miners, with no single frontrunner consistently outperforming across all conditions. The shrinking gap in leaderboard scores between the frontrunners and their closest challengers suggests that competition will intensify further in the coming weeks.
Data From 2025-06-30 To 2025-07-06
Figure 1 shows the hourly log returns and volatility observed throughout the week, highlighting the relationship between BTC and ETH. Volatility remained relatively stable during the weekdays and declined over the weekend (July 6–7), before a final spike; particularly noticeable in Ethereum, at the end of the week.
This week, we tracked the performance of six miners:
Miners 221, 32, and 200 – consistently among the top-ranked throughout the week
Miners 253, 213, and 76 – each from a different axon, showing strong performance
As usual, performance was evaluated using two key metrics:
CRPS (Continuous Ranked Probability Score) for BTC and ETH forecasts
Leaderboard Scores
Continuous Ranked Probability Score (CRPS). Lower CRPS values indicate better predictive accuracy.
The weekly CRPS values reflect a highly competitive environment among top-tier miners. While there were moments when either group (221/32/200 vs. 253/213/76) outperformed the other, no miner group consistently dominated across all days and volatility regimes. This signals a healthy competitive dynamic in the subnet, one that fosters continuous model improvement and innovation.
Leaderboard Scores. These are exponentially weighted averages of past CRPS values over a 10-day window. Lower scores indicate better performance.
Miners 32, 200, and 221 maintained top-three positions throughout the week. However, the performance gap between them and Miners 76, 213, and 253 narrowed, decreasing from ~40 points to less than ~20 points by week’s end.
This section analyzes summary statistics, including volatility, kurtosis, and skewness to detect potential strategic differences among top miners for both BTC and ETH.
Summary Statistic Definitions:
Volatility: Variability in forecasted returns
Kurtosis: Measures the "tailedness" of the distribution, how likely extreme events are
Skewness: Measures the asymmetry of return distributions (0 = symmetric)
Figure 4 presents these statistics for the four miners across the week.
The figure reveals distinct modeling styles. Miners 32, 200, and 221 tended to produce forecasts with higher volatility and kurtosis, and they showed greater variation in distribution moments (including skewness). Conversely, Miners 253, 213, and 76 used less volatile and more stable distributions.
Interestingly, the latter group closed the performance gap with the frontrunners in leaderboard scores, suggesting that under the specific volatility regime of the week, their more stable approach paid off.
Miners’ Ranking vs. Summary Statistics: All miners who submitted forecasts were ranked by total weekly rewards. Figure 5 shows how summary statistics vary across ranking tiers.
Consistent with previous weeks, miners in the top 10 reward tier produced forecasts that were, on average, more volatile and had more stable kurtosis than those ranked lower.
To assess performance at the group level, we analyzed:
Axon size (number of miners)
Average and total rewards per axon
Axon-level summary statistics (BTC forecasts)
Figure 6 shows the number of miners per axon at the end of the week.
As in the previous weeks, axon 116.202.53.142 had the highest number of registered miners, followed by 138.201.62.165 and 95.216.99.113.
Figure 7 shows the evolution of average (per miner) and total daily rewards for the top 10 axons by average reward.
At the beginning of the week, axon 35.77.6.189 received most of the rewards. However, starting over the weekend, likely due to exploiting lower-volatility periods, miners from axons 95.111.205.93 and 116.202.53.142 began to earn significantly more rewards on average.
Figure 8 compares the top 10 axons (by average rewards) based on key BTC modeling characteristics: average intraday volatility, volatility variability, and average intraday kurtosis.
Except for axon 95.216.99.113 and axon 95.216.2.50, who produced less volatile, less "fat-tailed" forecasts, most axons showed consistent forecasting behavior, with similar volatility and kurtosis levels, both in magnitude and direction.
This week’s results highlight an increasingly dynamic and competitive environment among miners, with no single frontrunner consistently outperforming across all conditions. The shrinking gap in leaderboard scores between the frontrunners and their closest challengers suggests that competition will intensify further in the coming weeks.
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