Business Model Analysis of Trading Bots
Now that we have discussed revenue, let’s break down the business models of trading bots.
Product Features: Purchase, Sale, and Trading Assistance
Purchase
The core of the purchase feature is the token sniping capability. Sniping generally comes in three methods. One is First Block Bundle Sniping (or Block-0 Sniping), which monitors the first transaction of a token and bribes miners to bundle the transaction into the first block. This is the most common sniping method, and its success rate entirely depends on the amount of the bribe.
The other two methods are strategy sniping and liquidity sniping, which monitor specific contract functions (e.g., enabling trading) or increasing liquidity to trigger the snipe. Both usually require users to have some blockchain technical knowledge and may fail due to special settings in the token contract.
Two other relatively important functions of the purchase feature are copy trading and presale sniping. Copy trading involves monitoring specific wallet transactions and executing the same trades. Presale sniping generally only supports presales on platforms like Pinksale.
Banana Gun also has a unique purchase feature called tax limit orders, which only triggers a purchase action when buying or selling taxes reach a target.
Sale
The sale feature is simpler, primarily including limit orders, trailing stop-loss orders, and blacklisted transfers.
Trading Assistance
These are also quite common and standard features, including private nodes, Anti-MEV, Anti-Rug, honeypot detection, etc., mainly related to trading acceleration and loss protection.
Here is a comparison of features among the three major products: Banana Gun, Maestro, and Unibot:
From personal experience, Maestro has the most comprehensive functionality, covering nearly all basic needs users might have. It also has its own Call Channel where various operations can be performed directly, making it very systematic.
Unibot offers a very clear and organized user experience, suitable for beginners. Each step of the process is accompanied by detailed guidance, making it very convenient to use. Additionally, Unibot provides a web application, Unibot X, for PC users. Banana Gun is the simplest, with all the most basic features but at a lower cost.
Revenue Sources
First, a common revenue source for trading TG Bots is transaction fees. As shown in the chart, the typical fee rate ranges from 0.5% to 1% of the transaction total.
The difference lies in how projects with tokens and projects without tokens charge fees.
Projects with Tokens
For these projects, other revenue sources include token trading taxes and symbolic taxes from token unlocks (accounting for 50-60% of income).
Banana Gun’s token charges a 4% trading tax on both buyers and sellers. The tax is distributed as follows: 2% goes to token holders, 1% is kept in the treasury, and the remaining 1% goes to the team. As of October 9, the team’s tax share has reached 166 ETH (approximately $27,000) in just over half a month.
Unibot charges a 5% trading tax and has earned 2,667 ETH (approximately $4.34 million) from the team’s tax share in just over three months.
Tokens serve three main purposes: membership levels, fee discounts, and revenue sharing. Different projects have different combinations, but revenue sharing is generally essential. For Banana Gun token holders, revenue sharing is the only benefit.
Projects without Tokens
For projects without tokens, subscription fees are the primary revenue source.
Subscription fees are divided into two types:
-
Entry Fees: For example, Alphaman, where only paying users can access the bot, but these types of bots usually skip transaction fees.
-
Premium Subscription Fees: For example, Maestro, which charges transaction fees while offering more usage quotas and additional features based on the user’s subscription level.
From the data, paid subscriptions clearly perform better. On one hand, entry fee types significantly lower new user conversion rates. On the other hand, meme coin trading is typically high-frequency, and the revenue from transaction fees often exceeds expectations, making higher fees less of an issue.
Future Directions for TG Bots
Broadly speaking, in the intent-driven trend, trading TG bots seem to be transitional products. They are lightweight and efficient, and with the support of the Telegram ecosystem, they have significant potential for social sharing and usage within communities.
However, the comprehensiveness of TG bot products will always be somewhat lacking compared to fully packaged applications, which have more screen real estate than chat-based TG bots. Trading bots attempt to overcome the issue of smaller interfaces by either leveraging AI to make the bots smarter or using more user-friendly logic to enhance usability.
One approach integrates AI, enriching other intent-driven scenarios by merging GPT-powered LLMs, aiming to build AI agents similar to chatbots in the crypto space. Typical projects include Chain GPT and PAAL AI.
Another approach is more pragmatic, focusing purely on trading. It seeks to enhance product experiences through mobile and web-based applications, deepening various functionalities in trading, and potentially providing superior wallet experiences in the future through AI. Unibot is a typical product using this approach.
In these new trading TG bot experiences, it is clear that the bots are closely aligned with the basic logic of the intent-driven concept, where users express their wishes but want to use services to execute them. The essence of being intent-driven is commonly seen in the Web2 world outside of crypto and lacks specific terminology (the closest might be user-centric).
Finally, Telegram’s vast user base and bot social ecosystem clearly indicate that TG bot trading is just beginning. Time will tell whether they can evolve into specific vertical modules and social portals, leveraging Telegram’s openness to achieve blockchain composability.