Real-Time Blockchain Copy-Trading Automation for Prediction Markets
The Problem
A Web3 operation building on Polygon's EVM ecosystem needed production-grade blockchain infrastructure that could monitor on-chain wallet activity in real time and execute automated transactions in response — watching target wallets for prediction market trades and replicating them proportionally. The core challenge was not the trading logic (a simple ratio calculation) but the infrastructure underneath: maintaining persistent, fault-tolerant WebSocket connections to EVM nodes, parsing raw transaction calldata through ABI decoding, managing the full EVM transaction lifecycle (nonce sequencing, EIP-1559 gas estimation, submission, confirmation, revert handling), and doing all of this with a system that holds private keys controlling real funds on a public blockchain where every transaction is irreversible. Previous attempts in Python and JavaScript exposed the gap between scripting against a blockchain and building production infrastructure on one — dropped connections, stuck transactions, imprecise gas estimation, and error handling too brittle for the adversarial reality of decentralized infrastructure. They needed a system built in a memory-safe, high-performance language that could run continuously for weeks on mainnet with real capital.
Why Building Production-Grade On-Chain Automation Infrastructure Is Hard
Building reliable automation on public EVM blockchains combines the systems programming challenges of real-time event processing with the adversarial characteristics of decentralized infrastructure — where RPC providers are unreliable, transactions are irreversible, and the system must be trusted with private keys controlling real funds:
- Persistent WebSocket connections to EVM nodes are inherently unstable — connections drop without notification (sometimes silently, the "zombie connection" problem); the system must detect every disconnection mode, re-subscribe, and reconcile events that occurred during the gap without duplicating actions
- EVM transaction lifecycle management is a state machine with many failure modes — nonce races, EIP-1559 gas estimation, ABI calldata encoding, signing, submission, and confirmation can each fail independently (nonce too low/high, gas too low/high, contract revert, chain reorg), each needing a different recovery strategy
- Mempool monitoring requires processing massive volumes of irrelevant data — subscribing to pending transactions means receiving thousands per second; the system must decode each transaction and filter for target wallets in real time, before the transaction is mined
- Smart contract ABI decoding transforms opaque calldata into structured actions — the system must maintain ABI specifications, decode function selectors, parse parameter types, and handle ABI versioning in a type-safe manner that prevents misinterpretation of on-chain data
- Private key security in a long-running process is a systems programming problem — the key must reside in process memory to sign continuously; the handling requires deterministic memory management, no implicit serialization, and explicit control over when sensitive data is zeroed
- On-chain state reconciliation after any interruption — on crash or restart the system must query on-chain state (receipts, balances, nonce) and reconcile against local state to resume without duplicate transactions or missed events
What We Did
Real-Time Blockchain Event Monitoring
- Built the on-chain event monitoring engine in Rust — persistent WebSocket connections to Polygon RPC providers subscribing to pending transactions and new blocks, filtering the stream in real time for configured target wallets and decoding trade parameters from calldata via the prediction market ABI
- Implemented multi-provider redundancy — connecting to multiple RPC endpoints so that if one drops, rate-limits, or falls behind, the system seamlessly continues from the rest, re-subscribing and reconciling gap events on reconnection
- Developed mempool monitoring for pre-confirmation detection — spotting target wallet activity before it is mined to give the copy-trading logic maximum time to calculate, construct, and submit the replica trade
EVM Transaction Construction & Execution
- Designed the EVM transaction construction pipeline — ABI-encoded calldata, EIP-1559 gas estimation from recent base fees, sequential nonce management that tracks in-flight transactions to prevent gaps or duplicates, and signing via the Rust-native crypto stack
- Implemented proportional trade sizing based on the balance ratio between the operator and target wallets, with gas-cost-aware minimums and portfolio-aware maximums
- Built a mock execution mode (full pipeline without broadcasting) and live execution with full lifecycle management — confirmation monitoring, receipt parsing, transaction replacement on delay, and state updates only after cryptographically verified confirmation
Private Key Security & Blockchain Fault Tolerance
- Engineered secure private key handling using Rust's ownership and type system — a custom SigningKey wrapper that deliberately does not implement Display, Debug, or Clone, with deterministic memory zeroing when the signing context drops
- Implemented seventeen-mode fault tolerance covering every production failure case — zombie connections via heartbeat monitoring, nonce gaps, chain reorgs, provider outages with failover, decoded contract reverts, and gas-driven transaction replacement
- Developed on-chain state reconciliation for crash recovery and graceful shutdown with in-flight transaction awareness — syncing a local state file against on-chain receipts and nonce on restart so the system never double-submits or misses a transaction
Logging, Auditability & Operational Dashboard
- Built a dual logging system — color-coded real-time console output for operational monitoring plus persistent CSV logging of every decision and transaction for analysis, reconciliation, and compliance review
- Implemented a trade journal with a complete record of every copy-trade — triggering event, replication decision, execution result, and portfolio impact — creating an auditable chain from signal to outcome
- Developed performance metrics (execution latency, fill rate, gas efficiency, slippage) and configurable operational parameters adjustable without code changes
Key Results
In Their Words
Trembit built us blockchain infrastructure that runs for weeks without intervention on Polygon mainnet, handling real funds. The mempool monitoring detects wallet activity before confirmation, the transaction pipeline handles nonce and gas without a single stuck transaction, and the fault tolerance has survived every RPC outage we have encountered. Rust was the right call — the system has not crashed once in production.
Their proactive team gets things done as if it were their own project.
What We Learned
Rust's ownership model is a security architecture for systems that hold private keys
In most languages a key loaded into memory can be accidentally copied, logged, or retained in garbage-collected heap memory after signing. In Rust the key is held in a single owned variable with a defined lifetime — available for signing, then deterministically zeroed when the context drops, with no GC retention and no accidental cloning. We also made the SigningKey wrapper omit Display and Debug so it cannot be logged even by a catch-all debug statement. For any system managing keys in long-running processes, the language's memory model is a security decision, not just a performance one.
The difference between a working bot and a production system is entirely in the failure handling
Our first prototype worked for hours then broke when a provider silently stopped sending events. The next iteration handled that but hit a nonce-gap during failover; the next fixed nonces but hit a chain reorganization. Each failure mode was unique and could only be discovered by running real transactions on a live chain. We eventually catalogued seventeen distinct failure modes and their handling strategies. Blockchain infrastructure is adversarial by nature, and the only way to discover the failure modes is extended runtime with real transactions.
Mock trading mode is the operational mode the system runs in most of the time
We built mock mode as a debugging tool, but in practice the operator runs it far more than live mode — evaluating new target wallets, calibrating sizing, and testing config before committing capital. So we redesigned it as a full simulation with a virtual portfolio, simulated P&L, and the same trade journal as live mode. Making mock and live implement the same trait cleanly separated the execution pipeline from the submission layer, making live mode more testable and mock mode more realistic.
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