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 2025-07-14 To 2025-07-20
With the current value of Synth Alpha, last week, Synth paid 20,692 Alpha tokens to miners, equivalent to $79,457. Since March 2025, a total of 427,944 Alpha tokens have been distributed to miners, worth $1,643,307. On a monthly average since March 2025, Synth has paid $345,959 to miners, more than Numerai’s average monthly payout of $205,606.
Last week marked the first full week where Synth miners were tasked with forecasting gold (XAU). Therefore, this commentary also evaluates miners’ performance on gold predictions.
Figure 1 illustrates hourly log returns and volatility for Bitcoin (BTC), Ethereum (ETH), and Gold (XAU) during the week.
We observe large differences in volatility among the three assets. Ethereum was the most volatile, while gold was the least—though it did momentarily reach BTC-level volatility during a spike on July 17. Bitcoin's volatility generally fell between that of Ethereum and gold.
This week, we monitored the performance of six key miners:
Top Performers: Miners 153, 65, and 154
Dropped Out of Top Three: Miners 221, 32, and 129
Axon affiliations:
Miner 221 → axon 35.77.6.189
Miner 32 → axon 3.112.97.164
Miner 154 → axon 95.216.99.113
Miners 65, 129, 153 → axon 116.202.53.142
Performance was evaluated using two main 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.
Across all three assets, CRPS scores were tightly clustered between the two groups. However, ETH scores show that Miners 65 and 153 gained an edge between July 18–19 and during the weekend. In contrast, Miners 32 and 221 were hampered by poor gold forecasts early in the week and higher ETH CRPS scores over the weekend.
Notably, the CRPS score magnitude varied by asset, which is expected due to differences in each asset’s volatility.
Leaderboard Scores. These are exponentially weighted averages of past CRPS values over a 10-day window. Lower scores indicate better performance.
Poor gold performance at the beginning of the week negatively impacted Miners 32 and 221, causing them to drop from the top 2 by Monday, July 14—replaced by Miners 65 and 154. Miner 129 held the top spot for most of the week but dropped out of the top 3 on Sunday, July 21, likely due to weaker ETH performance.
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.
Miners from axon 116.202.53.142 (65, 129, 153) and Miner 154 exhibited consistent modeling: similar volatility, kurtosis near 0, and stable, symmetric skewness.
In contrast, Miners 32 and 221 produced more volatile and fat-tailed forecasts—particularly for gold—with greater skewness variability.
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.
We observed that lower-ranked miners are beginning to match the volatility levels of top-ranked miners for BTC and ETH. For gold, differences are more pronounced.
In terms of kurtosis, miners ranked 10–30 are approaching top-tier performance for BTC, but disparities remain for ETH and XAU.
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, ETH, and XAU forecasts)
Figure 6 shows the number of miners per axon at the end of the week.
Axon 116.202.53.142 had the most 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.
Axon 116.202.53.142 earned the highest total rewards, benefitting from both miner quantity and strong individual performance.
On a per-miner basis:
Early week: Axon 185.150.117.101 led
Midweek: Axon 3.135.57.163
Weekend: Axon 35.77.6.189
116.202.53.142 – 44,143 Alpha ($169,510)
35.77.6.189 – 26,007 Alpha ($99,867)
95.216.99.113 – 23,192 Alpha ($89,058)
186.233.184.223 – 20,834 Alpha ($80,001)
18.183.47.137 – 13,597 Alpha ($52,211)
3.112.97.164 – 13,308 Alpha ($51,104)
95.111.205.93 – 8,456 Alpha ($32,469)
35.78.218.102 – 7,244 Alpha ($27,816)
103.88.234.233 – 6,121 Alpha ($23,505)
160.202.130.77 – 4,347 Alpha ($16,691)
Figure 8 compares the top 10 axons (by average rewards) based on BTC, ETH, and XAU modeling statistics: average intraday volatility, volatility variability, and average intraday kurtosis.
Forecasting styles are converging, particularly for BTC and ETH volatility. Some axons like 185.150.117.10 use low-volatility models, while others like 35.78.218.102 and 170.64.173.44 are more aggressive.
Axon 185.150.117.10 showed significant intraday volatility for BTC and ETH but used flat volatility for gold. It modeled kurtosis for BTC/ETH but ignored it for gold. Axon 116.202.53.142 maintained consistent zero kurtosis across all assets.
This week marked the beginning of performance evaluation for gold forecasts. As observed last week, the gap between top and mid-ranked miners is narrowing, with lower-ranked miners adopting increasingly similar modeling approaches—particularly in volatility.
Data From 2025-07-14 To 2025-07-20
With the current value of Synth Alpha, last week, Synth paid 20,692 Alpha tokens to miners, equivalent to $79,457. Since March 2025, a total of 427,944 Alpha tokens have been distributed to miners, worth $1,643,307. On a monthly average since March 2025, Synth has paid $345,959 to miners, more than Numerai’s average monthly payout of $205,606.
Last week marked the first full week where Synth miners were tasked with forecasting gold (XAU). Therefore, this commentary also evaluates miners’ performance on gold predictions.
Figure 1 illustrates hourly log returns and volatility for Bitcoin (BTC), Ethereum (ETH), and Gold (XAU) during the week.
We observe large differences in volatility among the three assets. Ethereum was the most volatile, while gold was the least—though it did momentarily reach BTC-level volatility during a spike on July 17. Bitcoin's volatility generally fell between that of Ethereum and gold.
This week, we monitored the performance of six key miners:
Top Performers: Miners 153, 65, and 154
Dropped Out of Top Three: Miners 221, 32, and 129
Axon affiliations:
Miner 221 → axon 35.77.6.189
Miner 32 → axon 3.112.97.164
Miner 154 → axon 95.216.99.113
Miners 65, 129, 153 → axon 116.202.53.142
Performance was evaluated using two main 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.
Across all three assets, CRPS scores were tightly clustered between the two groups. However, ETH scores show that Miners 65 and 153 gained an edge between July 18–19 and during the weekend. In contrast, Miners 32 and 221 were hampered by poor gold forecasts early in the week and higher ETH CRPS scores over the weekend.
Notably, the CRPS score magnitude varied by asset, which is expected due to differences in each asset’s volatility.
Leaderboard Scores. These are exponentially weighted averages of past CRPS values over a 10-day window. Lower scores indicate better performance.
Poor gold performance at the beginning of the week negatively impacted Miners 32 and 221, causing them to drop from the top 2 by Monday, July 14—replaced by Miners 65 and 154. Miner 129 held the top spot for most of the week but dropped out of the top 3 on Sunday, July 21, likely due to weaker ETH performance.
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.
Miners from axon 116.202.53.142 (65, 129, 153) and Miner 154 exhibited consistent modeling: similar volatility, kurtosis near 0, and stable, symmetric skewness.
In contrast, Miners 32 and 221 produced more volatile and fat-tailed forecasts—particularly for gold—with greater skewness variability.
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.
We observed that lower-ranked miners are beginning to match the volatility levels of top-ranked miners for BTC and ETH. For gold, differences are more pronounced.
In terms of kurtosis, miners ranked 10–30 are approaching top-tier performance for BTC, but disparities remain for ETH and XAU.
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, ETH, and XAU forecasts)
Figure 6 shows the number of miners per axon at the end of the week.
Axon 116.202.53.142 had the most 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.
Axon 116.202.53.142 earned the highest total rewards, benefitting from both miner quantity and strong individual performance.
On a per-miner basis:
Early week: Axon 185.150.117.101 led
Midweek: Axon 3.135.57.163
Weekend: Axon 35.77.6.189
116.202.53.142 – 44,143 Alpha ($169,510)
35.77.6.189 – 26,007 Alpha ($99,867)
95.216.99.113 – 23,192 Alpha ($89,058)
186.233.184.223 – 20,834 Alpha ($80,001)
18.183.47.137 – 13,597 Alpha ($52,211)
3.112.97.164 – 13,308 Alpha ($51,104)
95.111.205.93 – 8,456 Alpha ($32,469)
35.78.218.102 – 7,244 Alpha ($27,816)
103.88.234.233 – 6,121 Alpha ($23,505)
160.202.130.77 – 4,347 Alpha ($16,691)
Figure 8 compares the top 10 axons (by average rewards) based on BTC, ETH, and XAU modeling statistics: average intraday volatility, volatility variability, and average intraday kurtosis.
Forecasting styles are converging, particularly for BTC and ETH volatility. Some axons like 185.150.117.10 use low-volatility models, while others like 35.78.218.102 and 170.64.173.44 are more aggressive.
Axon 185.150.117.10 showed significant intraday volatility for BTC and ETH but used flat volatility for gold. It modeled kurtosis for BTC/ETH but ignored it for gold. Axon 116.202.53.142 maintained consistent zero kurtosis across all assets.
This week marked the beginning of performance evaluation for gold forecasts. As observed last week, the gap between top and mid-ranked miners is narrowing, with lower-ranked miners adopting increasingly similar modeling approaches—particularly in volatility.
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