Data From 2025-05-05 To 2025-05-11

During the week of May 5 to May 11, 2025, Bitcoin opened at $94,300 and closed at $104,150, with a weekly low of $93,400 and a high of $105,000. The week saw several volatility spikes, primarily driven by positive market sentiment and rumors of new tariff deals between the U.S. and other countries, notably the U.K. A significant volatility spike occurred around May 7, between 00:30 and 01:00 UTC, aligning with a sharp price increase as market optimism grew, pushing Bitcoin toward its weekly high of $105,000. Volatility remained elevated through the mid-week before gradually stabilizing as the price settled around $104,150 by the week's end.

At the start of the week, Miners 32, 165, and 103 led the leaderboard. By the end of the week, Miners 136, 133, and 195 had taken the top spots. We analyze their performance using two key metrics: CRPS and Leaderboard Scores.

Continuous Ranked Probability Score (CRPS). Lower CRPS values indicate better predictive accuracy.

The CRPS plot reveals a tight competition among the top miners, with values generally close throughout the week. Very early on, Miners 32, 165, and 103 produced more accurate forecasts, reflected in their lower CRPS values. However, mid-week volatility spikes, particularly around May 7, proved decisive. Miners 136, 133, and 195 adapted more effectively to these market shifts, achieving lower CRPS values between May 8 and 9. By the end of the week, the CRPS scores of all miners under examination stabilized around 2,500 to 3,000.

Leaderboard scores are 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 week's start, Miners 32, 165, and 103 held the top ranks with scores around 70 to 90, while Miners 136, 133, and 195 started with scores between 120 and 150. The mid-week volatility spikes caused a gradual shift. Miners 136, 133, and 195 consistently improved their forecasts, with their leaderboard scores steadily converging toward those of the initial leaders. By May 10 to 11, they overtook the top spots, closing the week with scores around 90 to 110, while Miners 32, 165, and 103 saw their scores rise to around 100 to 110 due to their worse performance during the volatile periods.

In this section, we examine summary statistics derived from the forecasts of all miners during the week to identify what might have given an edge to the top performers over the last 7 days.

To achieve this, we first created an "overall" weekly ranking by summing the reward weights earned by each miner across the week. Next, for each set of forecasts provided by each miner during the week, we calculated the following summary statistics: (a) variability in volatility (i.e., the standard deviation of the standard deviations) of the returns; (b) standard deviations in the hourly standard deviation of the returns; and (c) the average distribution’s kurtosis. While (a) helped gauge how each miner's model captured long-term volatility, (b) was used to observe whether miners adjusted their models to variations across different times of the day; and (c) served to check if miners prepared for "unexpected" events by assigning more weight to the tails of their distributions (which kurtosis measures). After obtaining these three statistics, we divided the miners’ rankings into different bins and analyzed how these statistics correlated with each bin. Results are shown in Figure 4.

As observed, all three plots reveal clear differences in how the top 10 miners modeled Bitcoin price returns compared to all other miners:

• Top miners adapted their volatility estimates more frequently to different market conditions, as shown in the left-most plot;

• Top miners also varied their intra-day volatility more, adjusting their models to different times of the day (an example of how this can be done is detailed in our article "Bitcoin’s Volatility Hack: How Savvy Synth Models Crush the Market");

• Top miners consistently produced distributions with fatter tails on average compared to other miners in the competition (some lower-ranked miners also showed this, but it appears to be more noise than a deliberate attempt to model distribution tails).

This week underscores the importance of adaptability of Synth miners in volatile markets and weeks. The tariff deal rumors and a general sentiment shift triggered significant volatility, challenging miners’ predictive models. Miners 136, 133, and 195 demonstrated superior flexibility, adjusting their forecasts effectively during key volatility spikes, which ultimately propelled them to the top of the leaderboard. The consistent performance of these miners, as reflected in their improving leaderboard scores, highlights the value of robust, adaptive algorithms capable of handling both turbulent and stable market conditions. Miners aiming for top rankings must focus on refining their models to navigate such dynamic environments successfully.

Additionally, the analysis of top miners’ strategies reveals that frequent volatility adjustments, intra-day modeling, and fatter-tailed distributions were key to their success, emphasizing the need for comprehensive modeling approaches in the Synth competition.

Data From 2025-05-05 To 2025-05-11

During the week of May 5 to May 11, 2025, Bitcoin opened at $94,300 and closed at $104,150, with a weekly low of $93,400 and a high of $105,000. The week saw several volatility spikes, primarily driven by positive market sentiment and rumors of new tariff deals between the U.S. and other countries, notably the U.K. A significant volatility spike occurred around May 7, between 00:30 and 01:00 UTC, aligning with a sharp price increase as market optimism grew, pushing Bitcoin toward its weekly high of $105,000. Volatility remained elevated through the mid-week before gradually stabilizing as the price settled around $104,150 by the week's end.

At the start of the week, Miners 32, 165, and 103 led the leaderboard. By the end of the week, Miners 136, 133, and 195 had taken the top spots. We analyze their performance using two key metrics: CRPS and Leaderboard Scores.

Continuous Ranked Probability Score (CRPS). Lower CRPS values indicate better predictive accuracy.

The CRPS plot reveals a tight competition among the top miners, with values generally close throughout the week. Very early on, Miners 32, 165, and 103 produced more accurate forecasts, reflected in their lower CRPS values. However, mid-week volatility spikes, particularly around May 7, proved decisive. Miners 136, 133, and 195 adapted more effectively to these market shifts, achieving lower CRPS values between May 8 and 9. By the end of the week, the CRPS scores of all miners under examination stabilized around 2,500 to 3,000.

Leaderboard scores are 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 week's start, Miners 32, 165, and 103 held the top ranks with scores around 70 to 90, while Miners 136, 133, and 195 started with scores between 120 and 150. The mid-week volatility spikes caused a gradual shift. Miners 136, 133, and 195 consistently improved their forecasts, with their leaderboard scores steadily converging toward those of the initial leaders. By May 10 to 11, they overtook the top spots, closing the week with scores around 90 to 110, while Miners 32, 165, and 103 saw their scores rise to around 100 to 110 due to their worse performance during the volatile periods.

In this section, we examine summary statistics derived from the forecasts of all miners during the week to identify what might have given an edge to the top performers over the last 7 days.

To achieve this, we first created an "overall" weekly ranking by summing the reward weights earned by each miner across the week. Next, for each set of forecasts provided by each miner during the week, we calculated the following summary statistics: (a) variability in volatility (i.e., the standard deviation of the standard deviations) of the returns; (b) standard deviations in the hourly standard deviation of the returns; and (c) the average distribution’s kurtosis. While (a) helped gauge how each miner's model captured long-term volatility, (b) was used to observe whether miners adjusted their models to variations across different times of the day; and (c) served to check if miners prepared for "unexpected" events by assigning more weight to the tails of their distributions (which kurtosis measures). After obtaining these three statistics, we divided the miners’ rankings into different bins and analyzed how these statistics correlated with each bin. Results are shown in Figure 4.

As observed, all three plots reveal clear differences in how the top 10 miners modeled Bitcoin price returns compared to all other miners:

• Top miners adapted their volatility estimates more frequently to different market conditions, as shown in the left-most plot;

• Top miners also varied their intra-day volatility more, adjusting their models to different times of the day (an example of how this can be done is detailed in our article "Bitcoin’s Volatility Hack: How Savvy Synth Models Crush the Market");

• Top miners consistently produced distributions with fatter tails on average compared to other miners in the competition (some lower-ranked miners also showed this, but it appears to be more noise than a deliberate attempt to model distribution tails).

This week underscores the importance of adaptability of Synth miners in volatile markets and weeks. The tariff deal rumors and a general sentiment shift triggered significant volatility, challenging miners’ predictive models. Miners 136, 133, and 195 demonstrated superior flexibility, adjusting their forecasts effectively during key volatility spikes, which ultimately propelled them to the top of the leaderboard. The consistent performance of these miners, as reflected in their improving leaderboard scores, highlights the value of robust, adaptive algorithms capable of handling both turbulent and stable market conditions. Miners aiming for top rankings must focus on refining their models to navigate such dynamic environments successfully.

Additionally, the analysis of top miners’ strategies reveals that frequent volatility adjustments, intra-day modeling, and fatter-tailed distributions were key to their success, emphasizing the need for comprehensive modeling approaches in the Synth competition.