When we invest in cryptocurrencies, the development progress and activities of the team are often seen as indicators of potential price increases during bull markets and as a reason to continue holding during bear markets.

But does the team's efforts truly result in greater price gains during bull markets? Are cryptocurrencies with active development teams more resilient to price drops during bear markets?

In this article, we will analyze 10 years of historical data to provide you with the answers.

Since its inception in 2009, Bitcoin has witnessed four distinct market ups and downs for the past 14 years, commonly called bull and bear cycles. These cycles have been influenced by various industry trends, such as the rise of Initial Coin Offerings (ICOs), the proliferation of public blockchain projects, the surge of decentralized finance (DeFi), and the recent wave of Non-Fungible Tokens (NFTs).

To simplify our analysis, we will define the first round of bull market as the period from July 2015 to January 2018. This was followed by the first round of bear market from January 2018 to March 2020. The second round of bull market spanned from March 2020 to May 2021, and the current phase, starting from May 2021, is considered the second round of bear market.

Due to the limited availability of data and the considerable time gap, this article will primarily focus on analyzing the most recent three cycles. The first round of the "ICO" bull market, which occurred between July 2015 and January 2018, is excluded from the rigorous analysis due to its distant timeframe and scarcity of data.

By examining these three cycles in detail, we aim to provide valuable insights into the market dynamics and trends shaping the crypto industry.

In the blockchain industry, most projects use open-source code hosted on GitHub, a platform for sharing and collaborating on code. Therefore, Falcon has identified six specific factors on GitHub that are measurable signs of a team's development activity and progress. These factors include Stars, Forks, Commits, Issues, Pull Requests, and Watchers.

Let's take a closer look at what each factor represents in the table below:

Github data for all the projects in this article are also available on Falcon's products, visit the link:

https://falcon.lucida.fund/en/asset_tracker/73/github?uid=

In our analysis, we examined the price movements of various coins and their corresponding project GitHub data using the six identified factors across three market cycles. After removing any outliers, we obtained a total of 81, 330, and 596 valid token samples for each of the three market cycles.

To help you understand the charts presented below, let's explain the nomenclature that will be used:

GitHub data revealed that it had some mitigating effect on the decline of coin prices. However, the impact was limited due to the small sample size of data available for analysis during that period.

Let's start with the first bear market.

Six GitHub metrics and descriptive information on the token price movement are provided.

During the first round of the bear market, the data for tokens shows a higher level of dispersion, which aligns with the characteristics observed during the early stages of the crypto market's emergence. The standard deviation values for all seven statistics during this period deviate significantly from the average, indicating a wide variation in the previous prices of different tokens and their associated GitHub data. During this period, more established tokens such as Bitcoin and ETH received considerable GitHub attention across all factors. On the other hand, many of the newer tokens had lower GitHub activity and fewer contributions from developers.

Below are the statistics for tokens that experienced a price drop lower than the average drop value (highlighted in bold), along with their corresponding GitHub data for the six factors within this timeframe:

Among them, the gray grid represents tokens that stand out as exceptions to the overall market trend, and we believe that such tokens are more special in nature and need to be analyzed comprehensively in combination with the market situation. Within this group, Binance Exchange stands out. When we looked at its GitHub data across six factors, we found that the star and fork values ranked among the top 10 statistics. However, the commits, issues, pull_requests, and watchers were all remarkably low. This can be explained by the fact that the BNB token was primarily categorized as a "platform coin" and did not possess the attribute of a "public chain" before 2019. Consequently, its code was not open source. During the latter half of 2018, when the market focused on platform coins, BNB experienced a significant rise in value but encountered resistance within the market cycle. For this particular token, we observed a certain correlation between the price and only the star and fork factors among the six GitHub factors.

Out of the tokens that experienced less decline in their coin prices compared to the average, 40% of the tokens with GitHub factors ranked among the top 10 statistics. The rest of the tokens had lower GitHub activity in general. Based on our initial analysis, we can conclude that the GitHub factor has a certain positive impact on reducing the decline in a coin's price during the cycle, although the effect is not significantly large.

Six GitHub metrics and descriptive information on the token price movement are provided.

Token data during the second round of bull market is relatively centralized,, indicating increased maturity and growth in the crypto market. The standard deviation statistics for the seven metrics in this period are closer to the average value, and the sample data is more concentrated compared to the statistics from 2018-2020. Taking into account the current market situation, we can observe two important factors. Firstly, the token market in 2020 has become more mature, and tokens that emerged in 2018 have made significant advancements during this period. Their underlying GitHub data has also shown notable improvement. Secondly, as the market continues to evolve, there has been a significant increase in the number of tokens issued during this period. With a larger number of tokens available for analysis, the concentration of data distribution has further intensified.

Here are the statistics for the tokens that experienced greater-than-average price increases (highlighted in black), along with their associated GitHub data factors for that specific period:

Out of the 330 analyzed tokens, 11 of them experienced price increases that were higher than the average. Interestingly, 5 out of the 6 GitHub data factors also exceeded the average for these coins, making up around 45% of the total. This suggests a potential connection between the rise in GitHub data and the increase in token prices. In the third part of this article, we will delve into a detailed analysis of the specific correlation between these two factors.Projects whose token prices decline instead of rising during a bull market tend to exhibit low activity in terms of GitHub development.

Here are some examples of token price outliers where the tokens experienced a decrease in value despite the overall bullish market trend:

Out of the 330 tokens analyzed in this cycle, there are 28 tokens that have shown a different pattern by not following the overall trend of price decline. This observation suggests that these 28 tokens are relatively weaker in performance. Interestingly, around 90% of the GitHub data associated with these tokens is below the average level and is converging towards the minimum value overall.

Six GitHub metrics and descriptive information on the token price movement are provided.

Here is the data for the 20 most popular tokens, including their six other statistics. The tokens above the average STAR factor are highlighted in bold.

As the crypto market continues to evolve, the data for tokens during the second bear market phase becomes more scattered, possibly due to increased fragmentation within the industry. The standard deviation values of the seven statistics in this period differ significantly from the average, indicating a higher level of dispersion in the token data. In 2021, the cryptocurrency market is still experiencing a period of rapid growth, attracting an increasing number of participants. Investors primarily focus on well-established and mature token projects that have garnered significant attention on GitHub, with thousands of followers. However, for newer tokens introduced during this period, they require time to gain familiarity among the public, resulting in lower levels of attention and development.

By examining the statistics of the top 20 tokens ranked according to their STAR data, we can identify certain similarities in the statistical patterns among tokens that surpass the average values in all six GitHub data factors. This suggests a strong correlation between these factors. Furthermore, it is observed that tokens with particularly high rankings in the six GitHub data factors are generally more mature. They were predominantly issued between 2015 and 2018, including well-known tokens like Bitcoin, Ethereum, and Dogecoin.

Outliers (rising in a bear market) :

Out of the 596 tokens analyzed, there are 28 anomalies, and among them, 6 tokens (28%) have above-average GitHub data in multiple factors. From the table, we can infer that an increase in GitHub data has some influence on resisting bearish trends, but its impact is not particularly significant. The strong price advantage of these tokens is mainly determined by factors in other categories.

In the above article, we found that Github's data plays a different role in the bull and bear cycle through a simple statistical analysis.

So, how do we quantify the correlation between Github factors and prices?

The Q-Q plot is a way to visualize data on a graph. It uses a coordinate system with one axis representing the values in the sample and the other axis representing the values expected if the data followed a normal distribution. If the data does follow a normal distribution, the points on the graph will form a straight line that roughly follows the diagonal line. When analyzing data that follows a normal distribution, it's better to use the Pearson correlation coefficient. However, if the data does not follow a normal distribution, it's more appropriate to use the Spearman correlation coefficient.

The results of the six-factor Q-Q diagram of the three intervals are as follows:

Based on the information in the table, we can see that the data points for the six factors (Star, Fork, Commit, Issues, Pull_requests, and Watchers) in the three intervals do not follow a typical pattern. None of these factors seem to have a normal distribution. To understand how these factors relate to the token price, we will use Spearman's coefficient for our correlation analysis. This method allows us to assess the connection between the factors and the token price, even though the data doesn't follow a normal distribution.

Correlation table of six metrics and token price increase:

The five factors derived from GitHub data have a positive impact on the resilience of the coin price during bear markets. Based on the table, the correlation coefficient values of STAR, FORK, ISSUES, PULL_Requests, WATCHERS, and PRICE are all approximately 0.260. These values are statistically significant at the 0.05 level, indicating that all five factors have a positive correlation with the coin price.

However, there is no significant relationship between the COMMIT factor and the price increase in this interval. The correlation coefficient between COMMIT and the price increase or decrease is -0.032, which is close to zero. Additionally, the P-value of 0.776 is greater than the significance level of 0.05. These findings suggest that COMMIT does not have a correlation with the coin price.

The correlation results for STAR, FORK, ISSUES, PULL_Requests, WATCHERS, and PRICE align with our previous analysis. We expected to find a positive effect, and although the correlation is not extremely high, a correlation of 0.260 holds significance for studying token price trends and developing future correlation-based strategies.

However, the correlation results for COMMIT slightly differ from our earlier findings. We believe this discrepancy may be due to the limited amount of sample data available. In the subsequent three intervals, we have collected more data on tokens and will conduct a more detailed analysis to explore the correlation between commit and price.

Correlation table of six metrics and token price increase:

In the second bull market, we observed a notable improvement in the correlation between the six factors (STAR, FORK, COMMIT, ISSUES, PULL_REQUESTS, and WATCHERS) and the coin price. This improvement can be attributed to the increase in the valid sample size, which grew from 81 to 330. The correlation coefficient, measuring the strength of the relationship, reached approximately 0.322 in this interval. This value is significantly higher than the average correlation coefficient of 0.260 in the first interval and is statistically significant at the 0.01 level. Notably, the correlation between factors such as STAR, COMMIT, WATCHERS and PRICE was particularly strong, reaching as high as 0.350. These findings confirm our previous speculation about the negative correlation between commits and price in the first interval. It appears that the limited sample data from the first interval might have been influenced by individual extreme values.

Correlation table of six metrics and token price increase:

In the third interval, the number of valid samples increased to 597, leading to an improvement in the correlation between the factors STAR, FORK, COMMIT, ISSUES, PULL_REQUESTS, WATCHERS, and PRICE compared to the first interval. At a significance level of 0.01, the average correlation coefficient is 0.216 in the third interval. This value is slightly higher than the 0.205 correlation observed in the first bear market but significantly weaker than the desired correlation of 0.322 seen in the second interval.We can conclude that all six factors derived from GitHub data have a positive correlation with coin price increases. However, it's important to note that their predictive power is more pronounced in bull markets. In bear markets, other factors such as trading volume, market sentiment, and price factors have a stronger influence on coin prices. GitHub data, while still valuable as part of the fundamental analysis, plays a relatively limited role in bear markets compared to these broader categories of factors.

Falcon summarized the conclusions drawn from this article are as follows:

1. As the cryptocurrency market grows and the developer ecosystem thrives, Github data is increasingly showing a strong correlation with the price of the currency.

2. From an investment perspective, it is advisable to invest in projects with active GitHub development and avoid projects with inactive GitHub activity.

3. In a bull market, there is a positive correlation between GitHub project activity and the rate of price increase. In a bear market, active GitHub projects tend to exhibit greater resilience against price declines.

4. Github data's correlation with coin prices is significantly higher in bull markets than in bear markets.

Lucida （https://www.lucida.fund/ ） is an industry-leading quantitative hedge fund. Officially entered the Crypto Industry in April 2018, Lucida develops CTA Strategy / Statistical Arbitrage Strategy / the Arbitrage of Option Implied Volatility Strategy, at present, the team manages 30 million dollars, including 14 million dollars of proprietary assets.

Falcon （https://falcon.lucida.fund /）is a Web3 investment infrastructure driven by multi-factor models and assists users in “selecting,” “buying,” “managing,” and “selling” crypto assets. Falcon is a product incubated within LUCIDA in June 2022.

More Information https://linktr.ee/lucida_and_falcon

• 500万行数据盘点Crypto三年大牛市 @LUCIDA

• 5 Million Rows of Data Recap: Investigating The Crypto Market’s 3-Year Bull Run @LUCIDA

• LUCIDA：用多因子模型去选赛道、选币种

• LUCIDA × SnapFingers DAO：三年大牛市，21条头部公链大盘点

• LUCIDA × SnapFingers DAO：21 top public chains in three-year bull market recap

When we invest in cryptocurrencies, the development progress and activities of the team are often seen as indicators of potential price increases during bull markets and as a reason to continue holding during bear markets.

But does the team's efforts truly result in greater price gains during bull markets? Are cryptocurrencies with active development teams more resilient to price drops during bear markets?

In this article, we will analyze 10 years of historical data to provide you with the answers.

Since its inception in 2009, Bitcoin has witnessed four distinct market ups and downs for the past 14 years, commonly called bull and bear cycles. These cycles have been influenced by various industry trends, such as the rise of Initial Coin Offerings (ICOs), the proliferation of public blockchain projects, the surge of decentralized finance (DeFi), and the recent wave of Non-Fungible Tokens (NFTs).

To simplify our analysis, we will define the first round of bull market as the period from July 2015 to January 2018. This was followed by the first round of bear market from January 2018 to March 2020. The second round of bull market spanned from March 2020 to May 2021, and the current phase, starting from May 2021, is considered the second round of bear market.

Due to the limited availability of data and the considerable time gap, this article will primarily focus on analyzing the most recent three cycles. The first round of the "ICO" bull market, which occurred between July 2015 and January 2018, is excluded from the rigorous analysis due to its distant timeframe and scarcity of data.

By examining these three cycles in detail, we aim to provide valuable insights into the market dynamics and trends shaping the crypto industry.

In the blockchain industry, most projects use open-source code hosted on GitHub, a platform for sharing and collaborating on code. Therefore, Falcon has identified six specific factors on GitHub that are measurable signs of a team's development activity and progress. These factors include Stars, Forks, Commits, Issues, Pull Requests, and Watchers.

Let's take a closer look at what each factor represents in the table below:

Github data for all the projects in this article are also available on Falcon's products, visit the link:

https://falcon.lucida.fund/en/asset_tracker/73/github?uid=

In our analysis, we examined the price movements of various coins and their corresponding project GitHub data using the six identified factors across three market cycles. After removing any outliers, we obtained a total of 81, 330, and 596 valid token samples for each of the three market cycles.

To help you understand the charts presented below, let's explain the nomenclature that will be used:

GitHub data revealed that it had some mitigating effect on the decline of coin prices. However, the impact was limited due to the small sample size of data available for analysis during that period.

Let's start with the first bear market.

Six GitHub metrics and descriptive information on the token price movement are provided.

During the first round of the bear market, the data for tokens shows a higher level of dispersion, which aligns with the characteristics observed during the early stages of the crypto market's emergence. The standard deviation values for all seven statistics during this period deviate significantly from the average, indicating a wide variation in the previous prices of different tokens and their associated GitHub data. During this period, more established tokens such as Bitcoin and ETH received considerable GitHub attention across all factors. On the other hand, many of the newer tokens had lower GitHub activity and fewer contributions from developers.

Below are the statistics for tokens that experienced a price drop lower than the average drop value (highlighted in bold), along with their corresponding GitHub data for the six factors within this timeframe:

Among them, the gray grid represents tokens that stand out as exceptions to the overall market trend, and we believe that such tokens are more special in nature and need to be analyzed comprehensively in combination with the market situation. Within this group, Binance Exchange stands out. When we looked at its GitHub data across six factors, we found that the star and fork values ranked among the top 10 statistics. However, the commits, issues, pull_requests, and watchers were all remarkably low. This can be explained by the fact that the BNB token was primarily categorized as a "platform coin" and did not possess the attribute of a "public chain" before 2019. Consequently, its code was not open source. During the latter half of 2018, when the market focused on platform coins, BNB experienced a significant rise in value but encountered resistance within the market cycle. For this particular token, we observed a certain correlation between the price and only the star and fork factors among the six GitHub factors.

Out of the tokens that experienced less decline in their coin prices compared to the average, 40% of the tokens with GitHub factors ranked among the top 10 statistics. The rest of the tokens had lower GitHub activity in general. Based on our initial analysis, we can conclude that the GitHub factor has a certain positive impact on reducing the decline in a coin's price during the cycle, although the effect is not significantly large.

Six GitHub metrics and descriptive information on the token price movement are provided.

Token data during the second round of bull market is relatively centralized,, indicating increased maturity and growth in the crypto market. The standard deviation statistics for the seven metrics in this period are closer to the average value, and the sample data is more concentrated compared to the statistics from 2018-2020. Taking into account the current market situation, we can observe two important factors. Firstly, the token market in 2020 has become more mature, and tokens that emerged in 2018 have made significant advancements during this period. Their underlying GitHub data has also shown notable improvement. Secondly, as the market continues to evolve, there has been a significant increase in the number of tokens issued during this period. With a larger number of tokens available for analysis, the concentration of data distribution has further intensified.

Here are the statistics for the tokens that experienced greater-than-average price increases (highlighted in black), along with their associated GitHub data factors for that specific period:

Out of the 330 analyzed tokens, 11 of them experienced price increases that were higher than the average. Interestingly, 5 out of the 6 GitHub data factors also exceeded the average for these coins, making up around 45% of the total. This suggests a potential connection between the rise in GitHub data and the increase in token prices. In the third part of this article, we will delve into a detailed analysis of the specific correlation between these two factors.Projects whose token prices decline instead of rising during a bull market tend to exhibit low activity in terms of GitHub development.

Here are some examples of token price outliers where the tokens experienced a decrease in value despite the overall bullish market trend:

Out of the 330 tokens analyzed in this cycle, there are 28 tokens that have shown a different pattern by not following the overall trend of price decline. This observation suggests that these 28 tokens are relatively weaker in performance. Interestingly, around 90% of the GitHub data associated with these tokens is below the average level and is converging towards the minimum value overall.

Six GitHub metrics and descriptive information on the token price movement are provided.

Here is the data for the 20 most popular tokens, including their six other statistics. The tokens above the average STAR factor are highlighted in bold.

As the crypto market continues to evolve, the data for tokens during the second bear market phase becomes more scattered, possibly due to increased fragmentation within the industry. The standard deviation values of the seven statistics in this period differ significantly from the average, indicating a higher level of dispersion in the token data. In 2021, the cryptocurrency market is still experiencing a period of rapid growth, attracting an increasing number of participants. Investors primarily focus on well-established and mature token projects that have garnered significant attention on GitHub, with thousands of followers. However, for newer tokens introduced during this period, they require time to gain familiarity among the public, resulting in lower levels of attention and development.

By examining the statistics of the top 20 tokens ranked according to their STAR data, we can identify certain similarities in the statistical patterns among tokens that surpass the average values in all six GitHub data factors. This suggests a strong correlation between these factors. Furthermore, it is observed that tokens with particularly high rankings in the six GitHub data factors are generally more mature. They were predominantly issued between 2015 and 2018, including well-known tokens like Bitcoin, Ethereum, and Dogecoin.

Outliers (rising in a bear market) :

Out of the 596 tokens analyzed, there are 28 anomalies, and among them, 6 tokens (28%) have above-average GitHub data in multiple factors. From the table, we can infer that an increase in GitHub data has some influence on resisting bearish trends, but its impact is not particularly significant. The strong price advantage of these tokens is mainly determined by factors in other categories.

In the above article, we found that Github's data plays a different role in the bull and bear cycle through a simple statistical analysis.

So, how do we quantify the correlation between Github factors and prices?

The Q-Q plot is a way to visualize data on a graph. It uses a coordinate system with one axis representing the values in the sample and the other axis representing the values expected if the data followed a normal distribution. If the data does follow a normal distribution, the points on the graph will form a straight line that roughly follows the diagonal line. When analyzing data that follows a normal distribution, it's better to use the Pearson correlation coefficient. However, if the data does not follow a normal distribution, it's more appropriate to use the Spearman correlation coefficient.

The results of the six-factor Q-Q diagram of the three intervals are as follows:

Based on the information in the table, we can see that the data points for the six factors (Star, Fork, Commit, Issues, Pull_requests, and Watchers) in the three intervals do not follow a typical pattern. None of these factors seem to have a normal distribution. To understand how these factors relate to the token price, we will use Spearman's coefficient for our correlation analysis. This method allows us to assess the connection between the factors and the token price, even though the data doesn't follow a normal distribution.

Correlation table of six metrics and token price increase:

The five factors derived from GitHub data have a positive impact on the resilience of the coin price during bear markets. Based on the table, the correlation coefficient values of STAR, FORK, ISSUES, PULL_Requests, WATCHERS, and PRICE are all approximately 0.260. These values are statistically significant at the 0.05 level, indicating that all five factors have a positive correlation with the coin price.

However, there is no significant relationship between the COMMIT factor and the price increase in this interval. The correlation coefficient between COMMIT and the price increase or decrease is -0.032, which is close to zero. Additionally, the P-value of 0.776 is greater than the significance level of 0.05. These findings suggest that COMMIT does not have a correlation with the coin price.

The correlation results for STAR, FORK, ISSUES, PULL_Requests, WATCHERS, and PRICE align with our previous analysis. We expected to find a positive effect, and although the correlation is not extremely high, a correlation of 0.260 holds significance for studying token price trends and developing future correlation-based strategies.

However, the correlation results for COMMIT slightly differ from our earlier findings. We believe this discrepancy may be due to the limited amount of sample data available. In the subsequent three intervals, we have collected more data on tokens and will conduct a more detailed analysis to explore the correlation between commit and price.

Correlation table of six metrics and token price increase:

In the second bull market, we observed a notable improvement in the correlation between the six factors (STAR, FORK, COMMIT, ISSUES, PULL_REQUESTS, and WATCHERS) and the coin price. This improvement can be attributed to the increase in the valid sample size, which grew from 81 to 330. The correlation coefficient, measuring the strength of the relationship, reached approximately 0.322 in this interval. This value is significantly higher than the average correlation coefficient of 0.260 in the first interval and is statistically significant at the 0.01 level. Notably, the correlation between factors such as STAR, COMMIT, WATCHERS and PRICE was particularly strong, reaching as high as 0.350. These findings confirm our previous speculation about the negative correlation between commits and price in the first interval. It appears that the limited sample data from the first interval might have been influenced by individual extreme values.

Correlation table of six metrics and token price increase:

In the third interval, the number of valid samples increased to 597, leading to an improvement in the correlation between the factors STAR, FORK, COMMIT, ISSUES, PULL_REQUESTS, WATCHERS, and PRICE compared to the first interval. At a significance level of 0.01, the average correlation coefficient is 0.216 in the third interval. This value is slightly higher than the 0.205 correlation observed in the first bear market but significantly weaker than the desired correlation of 0.322 seen in the second interval.We can conclude that all six factors derived from GitHub data have a positive correlation with coin price increases. However, it's important to note that their predictive power is more pronounced in bull markets. In bear markets, other factors such as trading volume, market sentiment, and price factors have a stronger influence on coin prices. GitHub data, while still valuable as part of the fundamental analysis, plays a relatively limited role in bear markets compared to these broader categories of factors.

Falcon summarized the conclusions drawn from this article are as follows:

1. As the cryptocurrency market grows and the developer ecosystem thrives, Github data is increasingly showing a strong correlation with the price of the currency.

2. From an investment perspective, it is advisable to invest in projects with active GitHub development and avoid projects with inactive GitHub activity.

3. In a bull market, there is a positive correlation between GitHub project activity and the rate of price increase. In a bear market, active GitHub projects tend to exhibit greater resilience against price declines.

4. Github data's correlation with coin prices is significantly higher in bull markets than in bear markets.

Lucida （https://www.lucida.fund/ ） is an industry-leading quantitative hedge fund. Officially entered the Crypto Industry in April 2018, Lucida develops CTA Strategy / Statistical Arbitrage Strategy / the Arbitrage of Option Implied Volatility Strategy, at present, the team manages 30 million dollars, including 14 million dollars of proprietary assets.

Falcon （https://falcon.lucida.fund /）is a Web3 investment infrastructure driven by multi-factor models and assists users in “selecting,” “buying,” “managing,” and “selling” crypto assets. Falcon is a product incubated within LUCIDA in June 2022.

More Information https://linktr.ee/lucida_and_falcon

• 500万行数据盘点Crypto三年大牛市 @LUCIDA

• 5 Million Rows of Data Recap: Investigating The Crypto Market’s 3-Year Bull Run @LUCIDA

• LUCIDA：用多因子模型去选赛道、选币种

• LUCIDA × SnapFingers DAO：三年大牛市，21条头部公链大盘点

• LUCIDA × SnapFingers DAO：21 top public chains in three-year bull market recap