Building a Robinhood Bundler Bot

About the Author

Bo$onaX

I write about Robinhood Chain trading bots, pons launchpad infrastructure, token-launch automation, algorithmic trading, Python development, Web3 engineering, and quantitative strategies.

Contact:

Github: https://github.com/n9xdev/Robinhood-Trading-Bot
Telegram: https://t.me/bosonax
Youtube: https://youtu.be/vayW_41kZdo
X: https://x.com/xxniiinxx
Gmail: mailto:dylandevera91928@gmail.com

What You'll Learn

• How to structure a Robinhood bundler bot
• How launch/event detection fits into transaction coordination
• How to separate strategy, risk, signing, and execution
• How pons V1 and V2 affect bot architecture
• How to handle multiple wallets without exposing private keys
• How to build retry, simulation, confirmation, and monitoring layers

Introduction

A Robinhood bundler bot should not be thought of as a magic “one-click sniper.” At the engineering level, it is a transaction-coordination system: detect an on-chain condition, validate it, decide which authorized wallets should participate, construct transactions, sign them independently, broadcast them, and reconcile the resulting state.

That distinction matters on Robinhood Chain because the network is EVM-compatible, meaning standard Ethereum tooling can be used. The current Robinhood Chain documentation lists mainnet chain ID 4663, ETH as the native gas asset, and standard RPC connectivity.

For pons-based automation, the architecture becomes particularly interesting because V1 and V2 use different launch flows. A bot therefore needs protocol-aware detection and validation rather than assuming every launch behaves like a conventional liquidity pool.

Architecture

flowchart TD
    A[Robinhood Chain RPC] --> B[Event / Block Monitor]
    B --> C[Launch Detector]
    C --> D[Protocol Validator]
    D --> E[Strategy Engine]
    E --> F[Risk Engine]

    F --> G[Transaction Planner]
    G --> H[Wallet Signers]

    H --> I[Transaction Broadcaster]
    I --> J[Confirmation Monitor]
    J --> K[State Database]
    K --> L[Position / Wallet Manager]

The important design decision is that planning and signing are separate.

The strategy engine should never directly access a private key. It produces an execution plan such as:

wallet A -> transaction X
wallet B -> transaction Y
wallet C -> transaction Z

The signing layer then determines whether each transaction satisfies the configured policy.

What a Bundler Actually Does

A bundler can coordinate multiple transactions belonging to wallets controlled by the same operator or otherwise explicitly authorized by the operator.

It does not automatically mean that the blockchain will treat those transactions as one atomic operation.

Unless the underlying protocol provides an atomic transaction mechanism, each transaction can succeed or fail independently. A robust bot must therefore maintain per-wallet state:

PLANNED
  ↓
SIGNED
  ↓
SUBMITTED
  ↓
CONFIRMED / REVERTED / UNKNOWN

This is more important than simply broadcasting transactions quickly.

pons V1 vs V2

This is where protocol-specific logic matters.

The pons source repository documents V1 as a CREATE2-based factory that creates a fixed-supply ERC-20, establishes a one-sided Uniswap V3 position, locks the position NFT, and can perform a developer buy in the same transaction.

V2 is structurally different. Its launch begins on a constant-product bonding curve holding the token supply. Once the curve is bought out, the launch graduates into a permanently locked full-range Uniswap V4 pool.

Therefore, a bot designed for pons V2 should not simply search for V1 pool-creation behavior.

For V2, launch detection and trading logic need to understand the curve state first and the post-graduation Uniswap environment afterward.

Python Project Structure

bot/
├── config.py
├── rpc.py
├── events.py
├── detector.py
├── validator.py
├── strategy.py
├── risk.py
├── planner.py
├── signer.py
├── execution.py
├── positions.py
├── monitoring.py
└── main.py

Each component should have one responsibility.

detector.py finds relevant blockchain activity.

validator.py verifies that the observed contract and event match the expected protocol.

strategy.py decides whether the opportunity satisfies the trading rules.

risk.py calculates limits such as maximum capital, maximum exposure, and acceptable slippage.

planner.py creates transactions without signing them.

signer.py handles wallet authorization.

execution.py broadcasts and tracks transactions.

Secure Wallet Management

Never hard-code private keys.

A development configuration might look like:

import os

RPC_URL = os.environ["RPC_URL"]
PRIVATE_KEYS = os.environ["PRIVATE_KEYS"].split(",")

For production, environment variables are preferable to source-code credentials, but a dedicated secret manager is stronger.

A multi-wallet system should also isolate operational wallets from wallets containing unrelated assets.

The signer should enforce policies such as:

def validate_transaction(tx, max_value):
    if tx["value"] > max_value:
        raise ValueError("Transaction exceeds configured limit")

    if tx["to"] is None:
        raise ValueError("Contract creation is not permitted")

The exact validation rules should be determined by the application rather than blindly trusting decoded calldata.

Transaction Planning

A useful bundler separates planning from execution:

from dataclasses import dataclass

@dataclass
class PlannedTransaction:
    wallet: str
    to: str
    value: int
    data: bytes
    nonce: int | None = None

The planner can generate several independent transactions:

plans = [
    PlannedTransaction(wallet=w, to=target, value=value, data=calldata)
    for w in authorized_wallets
]

Before signing, each plan should be checked against:

• target contract
• chain ID
• wallet balance
• nonce
• gas configuration
• transaction value
• calldata
• strategy limits
• token and pool addresses

Do not assume that submitting several transactions simultaneously guarantees ordering or inclusion.

Execution and Confirmation

A production executor needs explicit transaction states.

async def execute(plan):
    tx_hash = await broadcast(plan)

    for attempt in range(5):
        receipt = await get_receipt(tx_hash)

        if receipt is not None:
            return receipt

        await backoff(attempt)

    return None

The actual implementation should also handle websocket disconnects, RPC errors, reverted transactions, nonce conflicts, and transactions whose status temporarily cannot be determined.

An important rule is idempotency.

If the process crashes after broadcasting but before writing the transaction hash to the database, restarting the bot must not blindly submit the same transaction again.

Persist the execution state before moving to the next stage.

Risk Engine

A bundler should never let wallet count substitute for risk management.

For example:

def approve_trade(
    estimated_cost,
    max_trade_cost,
    estimated_slippage,
    max_slippage,
):
    if estimated_cost > max_trade_cost:
        return False

    if estimated_slippage > max_slippage:
        return False

    return True

For token-launch automation, additional checks should include contract identity, liquidity availability, price impact, token concentration, suspicious contract behavior, and whether the observed state is stale.

A launch can be legitimate while still being an extremely poor trade.

Hypothetical Execution Flow

Hypothetical example — not measured performance.

Suppose a bot observes a new pons-related on-chain event.

It:

1. receives the event through RPC infrastructure
2. verifies the relevant contract
3. determines whether the launch is V1 or V2
4. reads the current protocol state
5. evaluates strategy conditions
6. checks wallet and portfolio limits
7. creates transactions for authorized wallets
8. validates or simulates them where supported
9. signs them independently
10. broadcasts them
11. tracks each transaction separately
12. records confirmations and resulting balances

If one wallet's transaction fails, the bot should not automatically assume the other transactions failed.

Conversely, it should not assume that successful transactions produced the expected portfolio state until the resulting on-chain state has been reconciled.

Failure Modes

Real systems should expect:

• RPC outages
• websocket disconnections
• duplicate events
• missed events
• stale pool state
• transaction reverts
• insufficient balance
• insufficient liquidity
• nonce conflicts
• malformed calldata
• unexpected contracts
• token impersonation
• malicious tokens
• liquidity risk
• excessive price impact
• infrastructure compromise

The correct response is usually controlled degradation, not aggressive retrying.

For example, retrying an RPC read can be safe. Blindly retrying a transaction after an uncertain broadcast can create duplicate execution.

Performance Engineering

The largest performance improvements generally come from reducing unnecessary work rather than blindly increasing concurrency.

A practical architecture can use:

• websocket/event subscriptions where supported
• HTTP RPC fallback
• local caching
• asynchronous event processing
• persistent execution state
• bounded worker queues
• transaction preconstruction
• dedicated RPC infrastructure

However, simulation and validation add their own latency. A bot therefore needs configurable execution modes:

SAFE
  validate → simulate → sign → broadcast

FAST
  validate → sign → broadcast

The faster mode should never bypass mandatory safety checks.

No architecture can guarantee transaction priority, inclusion, execution price, or profitability.

Testing Strategy

Unit Tests

Test token validation, strategy conditions, position sizing, slippage calculations, and transaction-policy rules independently.

Integration Tests

Run against Robinhood Chain test infrastructure and verify RPC, contract calls, signing, and receipt handling.

Replay Tests

Feed recorded blockchain events into the detector and verify that identical inputs produce deterministic strategy decisions.

Dry-Run Mode

A production bot should have a mode where it constructs and validates transactions without broadcasting them.

Failure Injection

Explicitly test RPC failures, duplicate events, malformed events, reverted transactions, nonce conflicts, insufficient balances, and stale state.

Monitoring

Useful metrics include:

• events detected
• events rejected
• strategy signals
• transactions planned
• transactions signed
• transactions submitted
• transactions reverted
• confirmations
• execution price
• estimated price impact
• realized slippage
• RPC errors
• reconnect count
• event-processing latency

Store enough information to reconstruct why every trade happened.

That audit trail is far more valuable than a simple profit counter.

Advanced Improvements

Once the basic bot is reliable, the architecture can be extended with multi-RPC failover, persistent event offsets, Redis-backed queues, PostgreSQL state, Prometheus metrics, Grafana dashboards, circuit breakers, historical replay, and strategy plugins.

Wallet rotation should only be implemented when there is a legitimate operational reason. It should not be treated as a mechanism for bypassing protocol restrictions or compliance controls.

Frequently Asked Questions

What is a Robinhood bundler bot?

It is an application that coordinates multiple authorized blockchain transactions or wallets through a common strategy and execution system.

Is bundling a native Robinhood Chain feature?

The term “bundler bot” describes an application architecture here. It should not be interpreted as a native Robinhood Chain transaction primitive.

Can a bundler guarantee that all transactions execute?

No. Independent transactions can fail, revert, arrive in different orders, or experience different execution conditions.

Can a pons V2 bot use the same logic as V1?

No. V1 and V2 have materially different launch and liquidity architectures, so protocol-specific detection is required.

Should private keys be stored in the bot?

They should never be committed to source code. Production systems should use isolated wallets and secure secret-management infrastructure.

Does faster transaction submission guarantee a better trade?

No. Faster submission does not guarantee inclusion, execution price, liquidity, or profitability.

Conclusion

The hard part of building a Robinhood bundler bot is not generating multiple transactions. It is maintaining correct state while coordinating detection, strategy, risk, signing, broadcasting, and confirmation across independent wallets.

For pons automation, protocol awareness is especially important because V1 and V2 use different launch architectures. A reliable bot should therefore detect the protocol state first, validate the observed contract and market state, and only then construct an execution plan.

The engineering goal is not “send transactions as fast as possible.”

It is:

detect correctly → validate aggressively → plan deterministically → sign securely → execute independently → reconcile completely.

That architecture is reusable for launch automation, portfolio automation, event-driven trading, and other EVM applications on Robinhood Chain.