“Show me the incentive and I’ll show you the outcome” – that’s a famous quote from Charlie Munger. These incentives are clearly visible in the market as well. An exchange can set up a specific fee structure to favour certain market participants and, as a result, indirectly influence spreads and liquidity. They can incentivise market makers to provide more liquidity by offering loans based on the value of the liquidity they supply. The possibilities are endless – what matters is understanding which incentives you want to adjust to attract attention and achieve your goals.

After the big airdrop from Hiperliquid, no one wants to be left out. We can clearly see this with Binance Alpha 2.0. Binance Alpha is a pre-listing hub for coins that are still too small for the main Binance Spot exchange but show potential for future listing. With version 2.0, Binance introduced new trading opportunities through auctions, which had not been available before.

We’re not here to discuss the auctions or other features of Binance Alpha. What I want to show is a short story about how incentives, FOMO, and potential future benefits can create strange patterns in the market.

Alpha Points – the key element of this note – are collected by trading on Binance Alpha. You earn points by generating volume or by holding tokens. However, it seems that volume points play the decisive role here. These points can be spent on Alpha events (such as airdrops or TGEs). In the past, coins connected to Binance Launchpad performed quite well, so the incentive is significant. We don’t yet know the potential scale of the benefits, so many traders are eager to collect points in anticipation of a big payoff.

Now to the main point. It’s a simple setup – people need to generate volume to earn points. As a result, we’re seeing massive volume on most Binance Alpha-related coins. At the same time, we’re also seeing unusual patterns that rarely occur under normal market conditions.

Take Bedrock ($BR) as an example. It’s currently the top coin on PancakeSwap, with over $3 billion in trading volume over the past 24 hours. The chart shows trading activity on both sides simultaneously, generating enormous volume (10-minute rolling volume shown below).

Below that, we can observe the difference between the USDT value of buy and sell trades (rolled over 10 blocks). Even though the total volume is around $3 billion, the net difference between buys and sells is minimal. What does this tell us? Someone is executing trades that result in near-zero net exposure.

If you’re trying to understand what’s behind the data – especially when you don’t have any extra context (on-chain data often gives more, while centralized exchanges offer only public info available to everyone) – you need to dig into every detail: trade values, frequency, outliers, market impact distributions, trade sizes, etc. Everything matters when you don’t have the full picture available only to the exchange. Let’s look at trade sizes.

The histogram is far from normal. In most cases, we see an exponential distribution of trade sizes. Here, we don’t. Most trades are clustered around 12–14k, which is high by typical standards. This kind of clustering should raise a flag and prompt deeper analysis. For reference, compare it to the trade sizes of other instruments (here, on the BASE chain).

What’s especially revealing lies deep in the on-chain data. It’s clear that something unusual is going on. It’s very likely that people are trying to earn points as quickly as possible in order to participate in future Binance airdrops. Let’s see whether this hypothesis holds up.

How could someone execute this strategy? Simple – trade in both directions, minimise losses, and collect as many points as possible. It’s actually a clever idea. If it’s true, then we should see most wallets showing nearly identical amounts of coins traded on the buy and sell sides. Let’s take a small sample of the on-chain data.

As we can see, most wallets indeed have a net traded amount close to zero. Let’s quantify how many coins fall within that small range around zero.

To be honest, the result is even stronger than I expected. Over 95% of wallets involved in trading this coin had a near-zero net position (meaning they bought and sold approximately the same amount within the time frame). It seems clear that the aim was to avoid exposure while still generating points.

I also wanted to find out how many points these wallets were targeting. They likely followed some strategy, studied the Binance Alpha documentation, and spotted an opportunity to hit a specific point threshold. Let’s look at the data.

According to my dataset, all wallets (excluding five outliers) generated between 14 and 20 Alpha Points. Nothing more, nothing less. This is likely because there’s no rule that “more points = bigger airdrop.” Traders just need to reach a specific threshold.

I also wondered: what was the cost of generating one Alpha Point? Since they were trading both sides, often within the same block or a few blocks apart, they likely incurred some loss – but how much?

It turns out the cost per Alpha Point averaged around 5 to 10 cents. Not a lot – but we still don’t know what the eventual reward will be.

What I hope to illustrate is this: people will always try to “solve” the system. They aim to do the minimum required to earn the maximum possible reward. Whether you’re building an exchange, a DeFi protocol, or managing a team – it’s your responsibility to design the right incentive structure. Hopefully, this example made that idea crystal clear.

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