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How to Build a Robinhood Chain Pons Volume Making Bot

#robinhood #python #tutorial #architecture

Building an automated market-making bot on Robinhood Chain involves much more than sending buy and sell transactions.

A reliable system needs to monitor newly launched tokens, discover their trading pools, calculate a reference price, provide two-sided liquidity, manage inventory, control risk, and track every transaction.

In this tutorial, we'll build the architecture for an educational Pons Market-Making Bot on Robinhood Chain.

The project focuses on:

  • Detecting new Pons token launches
  • Discovering token and pool contracts
  • Monitoring liquidity and price
  • Generating bid/ask quotes
  • Managing inventory
  • Executing controlled trades
  • Protecting against excessive slippage
  • Tracking gas and PnL

The system is intended for education, testing, research, and authorized liquidity-provision environments.

1. Robinhood Chain

Robinhood Chain is an EVM-compatible Layer-2 network.

For mainnet:

Chain ID: 4663
Native token: ETH
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The basic architecture is:

Python
   ↓
web3.py
   ↓
Robinhood Chain RPC
   ↓
Pons Contracts
   ↓
Token / Pool
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Create the project:

mkdir pons-market-maker
cd pons-market-maker

python -m venv .venv
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Activate the environment and install dependencies:

pip install web3 python-dotenv
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Recommended structure:

pons-market-maker/
│
├── bot.py
├── config.py
├── blockchain.py
│
├── pons/
│   ├── factory.py
│   └── pool.py
│
├── market/
│   ├── price.py
│   └── quotes.py
│
├── trading/
│   ├── executor.py
│   ├── risk.py
│   └── inventory.py
│
└── analytics/
    └── pnl.py
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2. Connect to Robinhood Chain

Create .env:

RPC_URL=https://rpc.mainnet.chain.robinhood.com
PRIVATE_KEY=YOUR_PRIVATE_KEY
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Never commit your private key to GitHub.

Then create config.py:

import os
from dotenv import load_dotenv

load_dotenv()

RPC_URL = os.getenv("RPC_URL")
PRIVATE_KEY = os.getenv("PRIVATE_KEY")

CHAIN_ID = 4663
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Connect with web3.py:

from web3 import Web3
from config import RPC_URL

w3 = Web3(
    Web3.HTTPProvider(RPC_URL)
)

if not w3.is_connected():
    raise RuntimeError("RPC connection failed")

print("Chain ID:", w3.eth.chain_id)
print("Block:", w3.eth.block_number)
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Always verify the returned chain ID before executing transactions.

3. Detect Pons Token Launches

The first job of the bot is discovering a token and its associated trading infrastructure.

The general flow is:

Pons Factory
      ↓
Launch Event
      ↓
Token Address
      ↓
Pool / Curve
      ↓
Register Market
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A launch record can be represented as:

launch = {
    "token": token_address,
    "pool": pool_address,
    "factory": factory_address,
    "block": block_number
}
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The exact event ABI and contract addresses should always be obtained from the current Pons documentation before deployment.

Avoid permanently hard-coding assumptions about protocol contracts.

4. Monitor the Market

Once a pool is discovered, the bot monitors relevant on-chain activity.

The market-data layer can track:

Price
Liquidity
Swaps
Reserves
Volume
Block timestamp
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For example:

market = {
    "price": price,
    "liquidity": liquidity,
    "volume": volume,
    "block": block_number
}
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This data becomes the input for the quoting engine.

5. Calculate a Fair Price

A market maker needs a reference price before creating quotes.

A simple implementation could use a moving average:

def moving_average(prices):
    if not prices:
        return 0

    return sum(prices) / len(prices)
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For example:

Recent prices
     ↓
Moving average
     ↓
Reference price
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More advanced implementations can include volatility, liquidity, or external market data.

6. Generate Bid and Ask Prices

Suppose the reference price is:

$0.01200
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and the configured spread is 2.5%.

The bot can calculate:

def generate_quotes(
    fair_price,
    spread
):

    bid = fair_price * (1 - spread)
    ask = fair_price * (1 + spread)

    return bid, ask
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Result:

Fair Price: $0.01200

Bid: $0.01170
Ask: $0.01230
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The spread should adapt to market conditions.

Higher volatility or lower liquidity generally requires more conservative quoting.

7. Inventory Management

Inventory is one of the biggest risks in market making.

Suppose the bot continuously buys tokens:

Inventory
   ↑
   ↑
   ↑
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Eventually it may have too much exposure.

Therefore, quotes should change according to inventory.

Conceptually:

Inventory too high
        ↓
Less aggressive BUY
More competitive SELL
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And:

Inventory too low
        ↓
More competitive BUY
Less aggressive SELL
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A simple model:

def inventory_ratio(
    inventory,
    max_inventory
):
    return inventory / max_inventory
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This ratio can then influence the bid and ask prices.

8. Liquidity and Slippage Protection

Never assume a trade can execute at the displayed price.

Before execution:

Expected Price
      ↓
Liquidity Check
      ↓
Price Impact
      ↓
Slippage Check
      ↓
Risk Check
      ↓
Execute
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Example configuration:

MAX_SLIPPAGE = 0.02
MAX_TRADE_SIZE = 500
MAX_POSITION = 10000
MAX_DAILY_LOSS = 100
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If any limit is exceeded, reject the trade.

def risk_check(
    position,
    trade_size,
    daily_loss
):

    if position + trade_size > MAX_POSITION:
        return False

    if trade_size > MAX_TRADE_SIZE:
        return False

    if daily_loss >= MAX_DAILY_LOSS:
        return False

    return True
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A separate risk layer makes the system much safer.

9. Execute and Confirm Transactions

After a quote passes the risk checks, the execution engine can construct and sign the transaction.

The process should be:

Quote
 ↓
Risk Check
 ↓
Build Transaction
 ↓
Sign
 ↓
Broadcast
 ↓
Wait for Receipt
 ↓
Reconcile
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With web3.py:

receipt = w3.eth.wait_for_transaction_receipt(
    tx_hash
)

if receipt.status == 1:
    print("Trade confirmed")
else:
    print("Transaction reverted")
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Never count a transaction as a successful trade merely because it was broadcast.

10. Track PnL and Execution

Every trade should be recorded.

Useful metrics include:

Trade count
Buy volume
Sell volume
Average execution price
Slippage
Gas cost
Inventory
Realized PnL
Unrealized PnL
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For example:

trade = {
    "tx_hash": tx_hash.hex(),
    "side": side,
    "amount": amount,
    "price": price,
    "gas_used": receipt.gasUsed,
    "status": receipt.status
}
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This allows the bot to compare its expected execution with the actual on-chain result.

11. Complete Architecture

The final system looks like:

                  Pons
                   │
                   ▼
            Launch Detector
                   │
                   ▼
            Pool Discovery
                   │
                   ▼
             Market Monitor
                   │
          ┌────────┴────────┐
          ▼                 ▼
       Price             Liquidity
          │                 │
          └────────┬────────┘
                   ▼
              Quote Engine
                   │
                   ▼
            Inventory Model
                   │
                   ▼
              Risk Engine
                   │
                   ▼
             Trade Executor
                   │
                   ▼
          Robinhood Chain
                   │
                   ▼
             Reconciliation
                   │
                   ▼
              PnL / Analytics
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The important principle is to keep market data, strategy, execution, risk, and accounting separate.

12. Testing Strategy

Before using real funds:

Unit Tests
    ↓
Local Simulation
    ↓
Testnet
    ↓
Paper Trading
    ↓
Small Authorized Deployment
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Test each component separately:

✓ RPC connection
✓ Launch detection
✓ Pool discovery
✓ Price calculation
✓ Quote generation
✓ Inventory limits
✓ Slippage protection
✓ Transaction confirmation
✓ PnL calculation
✓ Emergency shutdown
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A market-making bot should also have a kill switch that stops execution when loss, inventory, gas, RPC, or transaction-failure limits are exceeded.

13. GitHub

The broader Robinhood Trading Bot System repository contains research and implementation examples for automated trading infrastructure on Robinhood Chain.

You can explore it here:

GitHub:
https://github.com/Benjam1nCup/Robinhood-Trading-Bot-System

The repository can be used as a starting point for experimenting with token monitoring, automated execution, liquidity strategies, and on-chain analytics.

Conclusion

A Pons Market-Making Bot can be summarized as five major layers:

Launch Detection
       ↓
Market Monitoring
       ↓
Quote Generation
       ↓
Risk-Controlled Execution
       ↓
PnL & Reconciliation
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The goal isn't simply to create transactions.

A reliable market-making system needs to understand price, liquidity, inventory, slippage, gas, execution quality, and risk.

Start with testnet and paper trading, verify every transaction on-chain, and only move to real capital after the complete system has been tested.

If you're researching Robinhood Chain trading infrastructure, the project is available on GitHub:

GitHub logo Benjam1nCup / Robinhood-Trading-Bot-System

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An open-source and Strong Strategy collection of Robinhood Chain trading bot and Robinhood Chain sniper bot and Robinhood Chain copy trading bot in Python for high-performance automated on-chain trading.

Robinhood bot dashboard

This repository is primarily intended for educational and research purposes. It includes strategy concepts, implementation approaches, and selected performance screenshots to help developers understand how different automated trading strategies can be designed and tested on Robinhood Chain.

Robinhood Chain is an Ethereum-compatible Layer-2 blockchain built with Arbitrum technology. The mainnet uses Chain ID 4663, ETH as the native gas token, and provides EVM-compatible infrastructure for developers building on-chain applications and trading systems.

The repository does not provide a complete production-ready trading bot source code. Instead, it provides strategy descriptions and research materials that you can use as a foundation for developing your own system.

If you…


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For questions, collaboration, or development discussions:

Telegram: https://t.me/BenjaminCup

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