How to Build a Pons Sniper Bot on Robinhood Chain

A reliable Pons sniper is more than detecting a new token. It needs event detection, token validation, liquidity checks, risk controls, execution, and reconciliation.

Pons token launches create an interesting automation problem on Robinhood Chain.

A simple version of a sniper sounds easy:

New token
   ↓
Detect launch
   ↓
Buy

A real trading system is much more complicated.

You need to know:

• When the launch actually happened
• Which contract emitted the event
• Whether the token is valid
• Which pool is associated with it
• Whether sufficient liquidity exists
• Whether the opportunity passes your filters
• How much capital can be exposed
• Whether the transaction was actually executed
• What happened after execution

That's why I don't think of a Pons sniper as a single bot script.

I think of it as an event-driven trading system.

The architecture I'm building around Pons follows this direction:

Robinhood Chain
      ↓
Pons Contracts
      ↓
TokenLaunched Event
      ↓
Event Detection
      ↓
Token / Pool Validation
      ↓
Filtering
      ↓
Risk
      ↓
Execution
      ↓
Confirmation
      ↓
Reconciliation

The strategy is only one part of the system.

Starting with the Pons SDK

Before building an application such as a sniper, I wanted a reliable contract and event layer.

I've been building a TypeScript Pons SDK around the actual Robinhood Chain contracts.

The SDK currently covers:

• Robinhood Chain connection
• Pons V1/V2 contract interaction
• contract addresses and ABIs
• TokenLaunched event decoding
• chunked event queries
• launch queries
• live RPC integration

The current test baseline is:

30/30 unit tests passing
3/3 live integration tests passing

The live integration tests connect to Robinhood Chain and verify the Pons contract paths against real onchain data.

I also verified decoding against a real TokenLaunched event rather than relying only on synthetic fixtures.

That foundation matters because I don't want every trading application to implement its own blockchain integration.

Build the data layer once.

Reuse it across applications.

1. Detecting new Pons launches

The first component of a sniper is the launch detector.

Conceptually:

Pons Factory
     ↓
TokenLaunched
     ↓
Launch Detector
     ↓
Normalized Launch

The important part is normalizing the raw event.

Instead of making downstream code understand every ABI field, the application should receive something closer to:

interface TokenLaunch {
  version: "v1" | "v2";
  factory: Address;
  token: Address;
  deployer: Address;

  pool?: Address;
  pairToken?: Address;

  blockNumber: bigint;
  transactionHash: Hash;
  logIndex: number;
}

Now the scanner, analytics system, or trading strategy can work with a consistent representation.

That separation becomes important when the application grows.

2. Event detection is not enough

A naive sniper might do:

TokenLaunched
     ↓
BUY

I wouldn't do that.

A launch event is an observation.

It is not automatically a trading signal.

The next step should be validation.

TokenLaunched
      ↓
Is the event valid?
      ↓
Is the token valid?
      ↓
Is the pool known?
      ↓
Is liquidity sufficient?
      ↓
Does it pass our rules?
      ↓
Trading decision

This is where the application layer begins.

3. Token validation

Before considering execution, a sniper can validate basic properties of the launch.

Depending on the strategy, this can include:

• contract address
• factory/version
• deployer
• pool
• pair token
• launch block
• launch transaction
• known protocol contracts
• token metadata
• liquidity state
• trading status

The exact filters should remain configurable.

For example:

interface SniperPolicy {
  minLiquidity: bigint;
  maxPositionSize: bigint;

  allowedFactories: Address[];

  requirePool: boolean;

  maxSignalAgeMs: number;
}

The point is not to hard-code one strategy.

The point is to make the trading decision configurable.

4. Filtering should happen before execution

A useful sniper architecture separates detection from decision.

Launch
  ↓
Detection
  ↓
Validation
  ↓
Filtering
  ↓
Risk
  ↓
Execution

The filter might answer:

Is this launch interesting enough to consider?

Risk answers a different question:

Even if it is interesting, should this account take the trade?

For example:

Candidate
   ↓
Protocol filter
   ↓
Liquidity filter
   ↓
Token filter
   ↓
Wallet/deployer filter
   ↓
Risk limits
   ↓
Approved signal

This makes the system much easier to reason about than one large if statement inside a bot.

5. The sniper needs its own risk layer

A common mistake in automated trading is treating the strategy as the risk system.

They should be separate.

For example:

Strategy
   ↓
"This launch looks interesting"
   ↓
Risk Engine
   ↓
"Can we actually take this trade?"

Risk controls could include:

• maximum position size
• maximum daily exposure
• maximum token exposure
• maximum number of simultaneous positions
• minimum liquidity
• maximum slippage
• gas reserve
• stale-signal protection
• emergency stop
• token deny lists

This becomes especially important when the system moves from research into live execution.

6. Execution should be isolated

The strategy should not directly send blockchain transactions.

Instead:

Sniper Signal
      ↓
Execution Adapter
      ↓
Transaction Builder
      ↓
Signer
      ↓
Robinhood Chain

That separation gives the same strategy several possible modes:

Research
   ↓
Simulation
   ↓
Paper execution
   ↓
Live execution

For an early implementation, I prefer the execution interface to exist before live signing is enabled.

For example:

interface ExecutionAdapter {
  execute(signal: TradeSignal): Promise<ExecutionResult>;
}

Then a research implementation can simply return:

EXECUTION DISABLED

while the rest of the system is tested independently.

7. A sniper is a latency pipeline

Once the system is event-driven, latency becomes part of the architecture.

Consider the path:

Token Launch
     ↓
Blockchain Event
     ↓
RPC / WebSocket
     ↓
Event Decoder
     ↓
Launch Normalization
     ↓
Validation
     ↓
Filtering
     ↓
Risk
     ↓
Transaction Build
     ↓
Signing
     ↓
Broadcast
     ↓
Confirmation

Every stage takes time.

A strategy that looks good on paper can become useless if the detection or execution path is too slow.

That is why I prefer measuring the pipeline rather than simply claiming that the bot is “fast.”

Useful measurements include:

event detection latency
decode latency
filter latency
risk evaluation latency
transaction build latency
broadcast latency
confirmation latency

Eventually these can become first-class observability metrics.

8. Confirmation is not reconciliation

Sending a transaction is not the end.

Suppose the sniper submits:

BUY Token X

Several things can happen.

The transaction could:

• succeed
• revert
• remain pending
• execute with different amounts
• partially fill depending on the execution mechanism
• consume more gas than expected
• produce a position different from the local application's expectation

So the system needs a reconciliation layer.

Execution Request
       ↓
Transaction
       ↓
Receipt
       ↓
Onchain State
       ↓
Reconciliation
       ↓
Local Position State

This is one of the differences between a quick trading script and trading infrastructure.

The application needs to know what actually happened.

9. Historical data matters too

A sniper might eventually operate in real time, but historical data is useful before that.

For example:

Historical Pons Events
        ↓
Indexer
        ↓
Persistent State
        ↓
Research
        ↓
Filter Evaluation
        ↓
Strategy Testing

This lets you investigate questions such as:

• How frequently are launches occurring?
• What characteristics do successful candidates have?
• How much liquidity is available?
• How quickly does the relevant state change?
• How often would a filter trigger?
• What would the execution pipeline have seen?

The indexer therefore isn't separate from the trading system.

It becomes part of the foundation underneath it.

10. Scanner and sniper should share infrastructure

This is an important design decision.

I don't want:

Pons Scanner
    ↓
completely separate code

Pons Sniper
    ↓
completely separate code

Instead:

                 Pons Data Layer
                       ↓
              ┌────────┴────────┐
              ↓                 ↓
           Scanner            Sniper
              ↓                 ↓
          Ranking           Execution

The scanner can identify interesting launches.

The sniper can consume the same normalized launch data and apply a different decision process.

That means improvements to the underlying event/data layer benefit both systems.

11. The same foundation can support copy trading

The architecture also connects naturally to the copy-trading system I'm building.

Pons Events
     ↓
SDK / Indexer
     ↓
Normalized State
     ↓
       ┌───────────────┐
       ↓               ↓
    Scanner        Wallet Activity
       ↓               ↓
    Sniper         Copy Trading
       ↓               ↓
       └───────┬───────┘
               ↓
             Risk
               ↓
           Execution

This is the bigger reason I'm building the infrastructure as reusable components.

The goal isn't to create one isolated Pons bot.

The goal is to build infrastructure that can support several trading applications.

12. What I have built so far

The current foundation is progressing roughly like this:

[x] Robinhood Chain connection
[x] Pons V1/V2 contract integration
[x] TokenLaunched decoding
[x] Launch queries
[x] Chunked event queries
[x] 30/30 unit tests
[x] 3/3 live integration tests
[x] Real onchain event verification

→ Historical indexing
→ Persistent state
→ Scanner
→ Wallet intelligence
→ Sniper
→ Copy trading
→ Execution
→ Reconciliation

The important distinction is that the earlier items are implemented and tested, while the later items represent the application roadmap.

I don't want to present an architecture diagram as if every component is already production-ready.

13. Why build this as infrastructure?

A client asking for a sniper bot usually isn't really asking for:

one script that buys a token.

They may eventually want:

• token scanners
• wallet monitoring
• copy trading
• launch detection
• sniping
• bundling
• analytics
• automated execution
• risk controls
• monitoring
• reconciliation

Those applications share a lot of infrastructure.

So I would rather build:

SDK
 ↓
Indexer
 ↓
State
 ↓
Intelligence
 ↓
Risk
 ↓
Execution

and allow different applications to sit on top.

That's more reusable than building a collection of disconnected bots.

The bigger Robinhood Chain stack

The direction I'm working toward is:

                  Robinhood Chain
                        ↓
                  Pons Contracts
                        ↓
                       SDK
                        ↓
                     Indexer
                        ↓
                  Onchain State
                        ↓
               Wallet Intelligence
                        ↓
          ┌─────────────┼─────────────┐
          ↓             ↓             ↓
       Scanner       Copy Trading   Analytics
          ↓             ↓             ↓
       Sniper         Automation    Research
          └─────────────┼─────────────┘
                        ↓
                       Risk
                        ↓
                    Execution
                        ↓
                 Reconciliation

The interesting part isn't simply interacting with a smart contract.

It's building the infrastructure around the interaction.

That's the part that can be reused across multiple trading applications.

Building something similar?

I'm interested in building custom Robinhood Chain trading infrastructure, including:

• Pons scanners
• sniper systems
• copy-trading systems
• wallet intelligence
• onchain indexing
• automated execution
• trading analytics
• custom trading infrastructure

The Pons SDK is the foundation I'm using to explore this architecture, with the goal of turning protocol-specific integrations into reusable trading infrastructure.

The project is being developed as part of my broader Robinhood Chain trading-tools work.