We are looking for a specialist in Solana security programs to conduct an audit of the smart contract before deployment on the mainnet. Contract stack: Rust + Anchor framework SPL Token (transfer/storage of tokens in PDA) Mechanics: timing rounds, escrow of bets, resolver of final price, calculation and distribution of payouts, claim instructions, emergency pause + refund, fee configuration through admin authority Check for common vulnerabilities in Solana/Anchor: missing ownership/signer checks, account validation, integer overflow, PDA seeds collisions, reentrancy patterns, authority key privileges Analysis of payout distribution logic (rounding, edge cases with multiple participants) Verification of emergency mechanisms (pause, refund) — whether they can be bypassed or abused Report with classification of findings by criticality (Critical/High/Medium/Low) and recommendations Re-check after fixes (re-audit fixes) Requirements for the performer: Proven experience in auditing or developing Solana programs (Rust/Anchor) — please provide links to GitHub, previous audits, or examples of found vulnerabilities Understanding of SPL Token and PDA-escrow patterns Experience with static analysis tools (Soteria, Sec3, cargo-audit) will be a plus In your response, please indicate: Experience specifically with Solana (not EVM) — specific projects Estimated cost and timeline for a contract of this scope Report format (example, if available)
Proposals are currently absent
Proposals are currently absent
Current freelance projects in the category Cryptocurrency & Blockchain
What we are calculating in the project: Realized PnL, ROI, and Win Rate for cryptocurrency wallets - how profitable the wallet traded a certain token over the selected period. Based on what data: the history of on-chain transactions of the wallet (swaps, token transfers) + the market price of the token at the time of each transaction. The main data source is Moralis: two calls during the initial loading of the wallet - native ETH transfers and all ERC-20 token transfers. What we are comparing with: Nansen.io - we take it as a benchmark, comparing our calculated metrics with what Nansen shows for the same wallets over the same period. Problem: our figures significantly differ from Nansen, and we do not fully understand the rules by which some actions of the wallet should be classified for PnL purposes. We need to fix the calculation of Realized PnL, ROI, and Win Rate to match Nansen. In your application, please write: - experience with similar tasks - experience with Nansen - experience with Moralis - experience with DeFiLlama - experience with articles on crypto transactions - approximate cost and timeline for the fix
It is important to start - TODAY I need a person who has experience in writing trading bots on DEXs (needed on the BSC network) with an understanding of transaction costs, gas, etc. I want to test several trading strategies on a real trading agent I need to quickly develop an autonomous trading agent that uses the Trust Wallet and CoinMarketCap APIs to execute trades on the BNB Chain (BSC) based on 3–5 embedded strategies. Technology stack (mandatory) - Trust Wallet Agent Kit (TWAK) — for self-custodial signing and executing transactions (https://portal.trustwallet.com) - CoinMarketCap AI Agent Hub — for obtaining market data and signals (https://coinmarketcap.com/api/agent) - BNB AI Agent SDK — for quick integration (https://github.com/bnb-chain/bnbagent-sdk) Functional requirements Data (CMC AI Agent Hub) - Data retrieval via MCP or x402 (https://coinmarketcap.com/api/agent) - Use of ready-made Skills for RSI, MACD, Fear & Greed, volumes Execution (Trust Wallet Agent Kit) - Local signing of transactions — keys remain with the user (https://portal.trustwallet.com) - Autonomous mode: the agent signs and sends trades by itself - Limitations: drawdown, daily limit, stop-loss, token whitelist - Use of x402 for payment of data/computations Strategies - Implement 3–5 described strategies with the ability to switch between them - Examples: combination of funding rates + Fear & Greed, DCA based on sentiments with filters Technical requirements - Language: Python (preferred) or TypeScript/JavaScript - Tools: TWAK CLI, CMC CLI, BNB AI Agent SDK Who can take this on?