DEV Community: Alex Kamnier Crypto Arbitrage & AI Developer

Architecting a Multi-Chain Arbitrage Engine: Handling Latency and MEV in 2026

Alex Kamnier Crypto Arbitrage & AI Developer — Wed, 20 May 2026 17:12:29 +0000

Introduction
The landscape of decentralized finance is shifting toward high-frequency interaction. While many retail tools focus on simple UI-based trading, the real edge lies in understanding how to interact with AMM mempools and order books directly at the protocol level.

I have been developing an open-source framework called Cortex AI — a modular engine designed to bridge the gap between high-level AI-driven reasoning and low-level transaction execution across Solana, TON, and EVM chains.

The Engineering Challenge
Building a cross-chain arbitrage bot is not just about price comparison; it is about managing the "latency gap." If your node synchronization is lagging by even 50ms, the price discovery loop on platforms like Raydium or STON.fi will have already shifted, turning a profitable opportunity into a failed trade.

Core Architecture

To maintain modularity, I structured the engine around an asynchronous broker pattern. This allows the system to handle price telemetry and order execution in separate, non-blocking threads.

Here is a snippet of the execution logic handling MEV-protected bundle submission:

`Python
import asyncio

class ArbitrageExecutor:
    """
    Handles MEV-protected bundle routing to bypass hostile mempools.
    """
    def __init__(self, node_provider):
        self.node = node_provider

    async def execute_trade(self, opportunity_data):
        try:
            # Constructing a pre-execution bundle
            bundle = self.construct_bundle(opportunity_data)

            # Using private RPC routing to minimize front-running risk
            result = await self.node.submit_private_tx(bundle)

            if result.get('status') == 'confirmed':
                return True
        except Exception as e:
            print(f"Execution Error: {e}")
        return False`

Overcoming MEV & Front-Running
One of the biggest hurdles in arbitrage is transaction bundling. Public mempools are hostile environments. By utilizing private RPC routing and pre-execution state simulation, the engine ensures that a trade is only submitted if the spread remains valid at the exact moment of block inclusion.

Why I Open-Sourced the Core
I believe that quantitative trading infrastructure should be transparent and accessible to developers. You can explore the full implementation, strategy modules, and infrastructure setup in the repository below:

👉 Link to Repository: Cortex AI Arbitrage Engine

Check out the Wiki for a deep dive into the MEV-Shield implementation and custom RPC-routing configurations.

What’s Next?
I am currently experimenting with local LangChain agents to adjust slippage tolerance based on real-time macro-volatility indices. If you are interested in HFT architecture or multi-chain liquidity, feel free to dive into the codebase, and let’s discuss the implementation in the repo issues!

How I Built a Real-Time Whale Mirroring Bot for Polymarket CLOB

Alex Kamnier Crypto Arbitrage & AI Developer — Tue, 12 May 2026 12:14:30 +0000

Recently, I’ve been exploring the Polymarket CLOB (Central Limit Order Book). While most traders use the web interface, the real alpha is hidden in the API. I decided to build a tool that tracks "Whale" movements and mirrors their trades automatically.

The Challenge

The main issue with Polymarket's API is handling the rate limits while maintaining a high-speed WebSocket connection. If you're too slow, the spread eats your profit. If you're too fast, you get a 429 Too Many Requests error.

The Solution: Cortex Framework

I developed a modular framework called Cortex to handle these issues. It uses an asynchronous architecture to monitor order books without hitting the rate limits too hard.

Here is a snippet of how the mirroring logic calculates the effective execution price:

def calculate_execution_price(order_book, side, target_volume):
    orders = order_book['bids' if side == 'sell' else 'asks']
    total_filled, total_cost = 0, 0

    for price, size in orders:
        needed = target_volume - total_filled
        if needed <= 0: break

        fill = min(float(size), needed)
        total_filled += fill
        total_cost += fill * float(price)

    return total_cost / total_filled if total_filled > 0 else None

Full Source Code
I've decided to make the core of this mirroring module open-source so the community can build upon it. You can find the full implementation, including the Wiki and setup guides, on GitHub:

👉 Polymarket Copy Trading Bot on GitHub

What's next?
Currently, I'm working on integrating a Solana-based liquidity bridge to allow faster rebalancing between chains.

If you have any questions about the CLOB API or the strategy logic, feel free to drop a comment below or join our discussion in the repository!

Solving Liquidity Fragmentation: Building a Cross-Chain Monitoring Engine in Python 2026

Alex Kamnier Crypto Arbitrage & AI Developer — Fri, 08 May 2026 14:54:39 +0000

The 2026 crypto landscape is more fragmented than ever. With liquidity split between TON, Solana, and EVM L2s, the real challenge for developers isn't just trading—it's synchronized data monitoring.

In this post, I want to break down the logic of high-speed liquidity tracking and how to handle latency spikes when monitoring DEX pools like STON.fi or Jupiter.

The Latency Challenge
When you are building an automated system, every millisecond in the WebSocket stream counts. A typical issue is "stale data" where the price in your local state doesn't match the on-chain reality due to RPC delays.

Basic Monitoring Snippet (Python)
Here is a simplified logic for a multi-threaded monitor that I’ve been implementing in my core engine:

`Python
import asyncio
import time

class LiquidityMonitor:
    def __init__(self, chain_id, rpc_url):
        self.chain_id = chain_id
        self.rpc = rpc_url
        self.latency_threshold = 0.05  # 50ms target

    async def fetch_pool_data(self):
        # Logic for real-time price fetching
        start_time = time.perf_counter()
        # Simulation of an RPC call
        await asyncio.sleep(0.02) 
        end_time = time.perf_counter()

        execution_time = end_time - start_time
        if execution_time > self.latency_threshold:
            print(f"[{self.chain_id}] Warning: Latency spike detected: {execution_time:.4f}s")
        return execution_time

async def main():
    monitor = LiquidityMonitor("Solana", "https://api.mainnet-beta.solana.com")
    await monitor.fetch_pool_data()

if __name__ == "__main__":
    asyncio.run(main())`

Why MEV Protection Matters
In 2026, simply finding a gap isn't enough. Without a proper "Shield" module, your transactions will get front-run by institutional searchers. In my project, Cortex AI, I’ve shifted towards using Jito-bundles on Solana and private RPC endpoints to ensure that our execution remains invisible to the public mempool.

Conclusion
The future of automated trading lies in robust infrastructure, not just "magic" algorithms. If you're interested in the full architecture of such systems, I am documenting the entire development process of the Cortex AI crypto arbitrage bot—it's an open-source framework designed for this exact type of cross-chain analysis.

You can find the technical documentation and the full source code by searching for "Cortex AI crypto arbitrage bot" on GitHub or Google. I'd love to hear your thoughts on handling TON network latency in the comments!