Data Interpretation: Is it reliable to use whale movements as trading signals?

Original text: Presto Research

Compiled by: Odaily Planet Daily Golem

Key points:

Whale alerts are popular because large on-chain transactions are often seen as precursors and sell signals for impending token selloffs. To evaluate these claims, Presto Research analyzed the price changes of BTC, ETH, and SOL following large deposits to Binance.

According to the regression analysis, the R-squared values ​​between large transaction deposits and subsequent price changes are low (ranging from 0.0017 to 0.0537). Reducing the data to deposits from VCs and MMs (market makers) improves the R-squared value slightly, but their practical utility as trading signals is still limited. The findings strongly suggest that whale deposits to exchanges lack predictive power as reliable trading signals.

On-chain indicators are effective in other ways, such as analyzing blockchain fundamentals, tracking illicit financial flows, or explaining price fluctuations. Only when investors have more realistic expectations about the capabilities and limitations of these metrics will they serve the industry better.

One of the main differences between cryptoassets and other assets is the public availability of their transaction records, which are stored on distributed ledgers. The transparency of this blockchain has led to the emergence of a variety of tools that take advantage of this unique feature, and these tools are classified into the "on-chain data" category. One such tool is “Whale Alert,” a service that automates notifications of large on-chain crypto transactions. They are popular because large trades are often seen as a precursor to impending selling activity and are therefore considered "sell signals" by traders.

This report assesses the validity of this generally accepted assumption. After a brief overview of popular whale alert services on the market, we will analyze the relationship between large transaction deposits and the prices of BTC, ETH and SOL. Next, we present the results of the analysis and give the main conclusions and recommendations.

Whale Alerts Overview

Whale Alerts refers to services that track and report large crypto transactions. These services emerge as the crypto ecosystem develops, which also reflects market participants’ high recognition of the transparency features of blockchain.

history

The term "whale" became popular as early Bitcoin adopters, miners, and investors (e.g., Satoshi Nakamoto, the Winklevoss Twins, F2Pool, Mt. Gox) accumulated large amounts of Bitcoin. Initially, blockchain enthusiasts monitored large transactions through blockchain browsers such as Blockchain.info and shared this information on forums such as Bitcointalk or Reddit. This data is often used to explain significant fluctuations in Bitcoin prices.

During the 2017 bull market, as the number of whale trades and large transactions increased, there was an urgent need for automated monitoring solutions. In 2018, a European development team launched a tool called "Whale Alert" that can track large-scale crypto transactions on multiple blockchains in real time and send alerts through X, Telegram and the web. The tool quickly gained traction among market participants, becoming the go-to service for those looking for actionable trading signals.

 Source: Whale Alert (@whale_alert)

Basic assumptions

Following the success of Whale Alert, many platforms have emerged over the years offering similar services, as shown below. While many new platforms have added more features to provide context for alerts, the original Whale Alert remains focused on simple, real-time notifications and remains the most popular service, as evidenced by its large following on It can be seen. A common feature of all these services is that they rely on the assumption that large on-chain transactions (especially exchange deposits) are a sign of imminent sell-off behavior.

 Mainstream whale alert services, data sources: Whale Alert, Lookonchain, Glassnode, Santiment, X, Presto Research

Signal validity assessment

Proponents of the Whale Alert service argue that on-chain asset transfers to exchanges often precede liquidations and are therefore effective sell signals. In order to verify this hypothesis, we analyzed the changes in digital asset prices after large deposits entered the exchange. The following figure is the key parameter of the analysis. The hypothesis is that if large trading deposits serve as reliable trading signals, a clear relationship should be observed between deposits and corresponding asset prices.

 Key parameters analyzed, source: Presto Research

Assets, Exchanges, Analysis Periods and Deposit Thresholds

Our analysis focuses on three major crypto assets – BTC, ETH, and SOL – and their USDT prices on Binance between January 1, 2021, and December 27, 2024. This timeframe was chosen to align with the operational duration of the wallet addresses Binance currently uses to aggregate deposits.

The deposit threshold is set based on an exchange data analysis. Specifically, based on the limits of US$50 million, US$50 million and US$20 million set by Whale Alert for BTC, ETH and SOL whales respectively, we have lowered the deposit thresholds to US$20 million, US$20 million, respectively. At $8 million, this matches Binance’s 40% share of global spot trading volume.

Entity type

We also specifically analyze deposits of known entities and perform the same analysis on a narrower sample of the data to examine whether deposits of specific types of entities exhibit a stronger relationship with price movements. These entities were identified through Arkham Intelligence and supplemented by our own investigation, as shown below.

 An entity with a known address. Source: Arkham Intelligence, Presto Research

Measure market impact

To assess potential selling pressure on whale deposits, we made the following assumptions:

• Selling pressure manifests itself within a specific time frame after deposits larger than a threshold are confirmed on-chain. We analyzed two time periods: one hour and six hours.
• The Maximum Drawdown (MDD) over a specified interval is used as a measure of the deposit's price impact, if any, effectively filtering out the noise during that period.

result

The analysis results are shown in the following figures:

• Impact of BTC whale deposits (all):

 Source: Binance, Dune Analytics, Presto Research

• Impact of BTC whale deposits (VC and MM only):

 Source: Binance, Dune Analytics, Presto Research

• ETH whale deposit impact (all):

 Source: Binance, Dune Analytics, Presto Research

• Impact of ETH whale deposits (VC and MM only):

 Source: Binance, Dune Analytics, Presto Research

• SOL whale deposit impact (all):

 Source: Binance, Dune Analytics, Presto Research

• Impact of SOL whale deposits (VC and MM only):

 Source: Binance, Dune Analytics, Presto Research

Key takeaways

 Source: Binance, Dune Analytics, Presto Research

The figure above summarizes the results of the above statistics and draws the following three conclusions:

1. Large exchange deposits and price declines are less predictive: R-squared values ​​for all 12 scenarios show extremely weak predictive power, ranging from 0.0017 to 0.0537.
2. VC and MM deposits may be slightly better predictive signals: the R-squared value improves in this part of the data, but this improvement may simply be the result of reduced sample noise and not a true stronger correlation. Furthermore, the absolute value remains low, indicating its limited practical effectiveness as a trading signal.
3. ETH whale deposits mainly come from VCs and MMs: they account for 61% of ETH whale deposits (i.e. 879 of 538), while BTC only accounts for 13% and SOL 32%. This reflects the characteristics of different assets: ETH has a high turnover rate due to its diverse Web3 uses (such as Gas fees, staking, DeFi staking, and Swap media), while BTC is relatively stable as a store of value asset.

in conclusion

It is true that our analysis method has certain limitations, and regression analysis has its inherent limitations, and relying solely on the R-squared value to draw conclusions may sometimes be misleading.

But having said that, this analysis, combined with context and individual observations, strongly suggests that whale deposits to exchanges lack sufficient predictive power to be a reliable trading signal. This also provides us with insights into the wider use of on-chain metrics.

While on-chain indicators are undoubtedly valuable tools, especially for analyzing blockchain fundamentals or tracking illicit financial flows, they can also be useful when interpreting price movements after the fact. However, using them to predict short-term price changes is another matter entirely. Price is a function of supply and demand, and exchange deposits are just one of many factors that influence the supply side, if at all. Price discovery is a complex process that is also affected by fundamentals, market structure, behavioral factors (e.g. sentiment, expectations) and random noise.

In the highly volatile cryptocurrency market, where participants are constantly seeking “foolproof” trading strategies, there will always be an audience attracted to the “magic” of on-chain indicators. While some “overzealous” data providers are eager to exaggerate the promises of their platforms, on-chain metrics can better serve the industry only when investors have realistic expectations about the capabilities and limitations of these tools.