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 2025-06-23 To 2025-06-29
Figure 1 presents the hourly log returns and volatility observed throughout the week, highlighting the relationship between BTC and ETH. Volatility generally declined as the week progressed, reaching its lowest levels on Sunday, June 29, before a final spike at the end of the day.
This week, we analyzed six miners: Miners 100, 236, and 238—who started the week at the top of the leaderboard—and Miners 32, 200, and 221, who ended the week in the leading positions.
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.
In the early part of the week, miners submitting “equivalent paths” performed best, benefiting from the high-volatility environment. However, as volatility declined, Miners 32, 200, and 221 made a strong comeback, consistently outperforming Miners 100, 236, and 238—especially over the weekend.
Leaderboard Scores. These are exponentially weighted averages of past CRPS values over a 10-day window. Lower scores indicate better performance.
The leaderboard trends clearly illustrate the decline in performance for Miners 100, 236, and 238. Their use of equivalent paths and lack of adaptability to changes in the volatility regime negatively impacted their results. Conversely, miners able to adapt to changing conditions quickly regained top positions—even after temporarily falling behind.
This section analyzes summary statistics—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 results confirm that Miners 32, 200, and 221 employed a more dynamic approach to modeling volatility, with their estimates varying significantly more than those of Miners 100, 236, and 238. They also produced more symmetrical return distributions, while the others tended to show more negative skewness. In terms of kurtosis, the top-performing miners modeled extreme events more cautiously, resulting in lower kurtosis values overall.
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.
During the high-volatility early part of the week, miners that consistently forecasted high volatility—without adjusting—captured most of the rewards. Their lack of adaptation also resulted in low variability in kurtosis estimates.
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 week, axon 116.202.53.142 had the highest number of registered miners.
Figure 7 shows the evolution of average (per miner) and total daily rewards for the top 10 axons by average reward.
As previously noted, miners generating equivalent high-volatility paths received most of the rewards until the weekend. These miners mainly belonged to axon 186.233.184.223. However, as the volatility regime shifted, their performance declined, and they lost ground to axons with miners who better adapted to changing conditions.
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.
Axons 186.233.184.223 and 160.202.130.77 (which likely belong to the same group and overlap visually in the chart) maintained stable volatility estimates throughout the week, showing nearly zero variation. In contrast, miners from other axons adjusted their volatility estimates downward, following the trends observed in BTC and ETH. Regarding kurtosis, axon 95.111.205.93 stood out for its extremely high values—indicating a modeling approach that overestimated the likelihood of extreme events.
This week once again demonstrated that miners who fail to adapt their forecasts to evolving market and volatility conditions are unlikely to remain on top for long. While static, high-volatility forecasts may perform well in turbulent conditions, they lose effectiveness as volatility moderates. In contrast, adaptive miners—those who adjust their forecasts to reflect changing market realities—are better positioned to sustain high performance in the longer term.
Data From 2025-06-23 To 2025-06-29
Figure 1 presents the hourly log returns and volatility observed throughout the week, highlighting the relationship between BTC and ETH. Volatility generally declined as the week progressed, reaching its lowest levels on Sunday, June 29, before a final spike at the end of the day.
This week, we analyzed six miners: Miners 100, 236, and 238—who started the week at the top of the leaderboard—and Miners 32, 200, and 221, who ended the week in the leading positions.
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.
In the early part of the week, miners submitting “equivalent paths” performed best, benefiting from the high-volatility environment. However, as volatility declined, Miners 32, 200, and 221 made a strong comeback, consistently outperforming Miners 100, 236, and 238—especially over the weekend.
Leaderboard Scores. These are exponentially weighted averages of past CRPS values over a 10-day window. Lower scores indicate better performance.
The leaderboard trends clearly illustrate the decline in performance for Miners 100, 236, and 238. Their use of equivalent paths and lack of adaptability to changes in the volatility regime negatively impacted their results. Conversely, miners able to adapt to changing conditions quickly regained top positions—even after temporarily falling behind.
This section analyzes summary statistics—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 results confirm that Miners 32, 200, and 221 employed a more dynamic approach to modeling volatility, with their estimates varying significantly more than those of Miners 100, 236, and 238. They also produced more symmetrical return distributions, while the others tended to show more negative skewness. In terms of kurtosis, the top-performing miners modeled extreme events more cautiously, resulting in lower kurtosis values overall.
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.
During the high-volatility early part of the week, miners that consistently forecasted high volatility—without adjusting—captured most of the rewards. Their lack of adaptation also resulted in low variability in kurtosis estimates.
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 week, axon 116.202.53.142 had the highest number of registered miners.
Figure 7 shows the evolution of average (per miner) and total daily rewards for the top 10 axons by average reward.
As previously noted, miners generating equivalent high-volatility paths received most of the rewards until the weekend. These miners mainly belonged to axon 186.233.184.223. However, as the volatility regime shifted, their performance declined, and they lost ground to axons with miners who better adapted to changing conditions.
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.
Axons 186.233.184.223 and 160.202.130.77 (which likely belong to the same group and overlap visually in the chart) maintained stable volatility estimates throughout the week, showing nearly zero variation. In contrast, miners from other axons adjusted their volatility estimates downward, following the trends observed in BTC and ETH. Regarding kurtosis, axon 95.111.205.93 stood out for its extremely high values—indicating a modeling approach that overestimated the likelihood of extreme events.
This week once again demonstrated that miners who fail to adapt their forecasts to evolving market and volatility conditions are unlikely to remain on top for long. While static, high-volatility forecasts may perform well in turbulent conditions, they lose effectiveness as volatility moderates. In contrast, adaptive miners—those who adjust their forecasts to reflect changing market realities—are better positioned to sustain high performance in the longer term.
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