Telegram Bot for Crypto Trading: Fast & Secure Like Maestro/Unibot

Telegram Bot for Crypto Trading: How We Make It Faster and Safer

Picture this: a new token on Uniswap V3 adds $200k liquidity. Your bot must buy within 2 seconds, or slippage exceeds 20%. Or you configured a sniper, but it bought a contract with a 99% tax — money lost. Our team builds Telegram bots at the level of Maestro/Banana Gun that solve these problems with transaction simulation in Tenderly, Flashbots Protect, and multi-chain aggregation.

We create full-featured trading protocols with MEV protection, automatic orders, and support for Ethereum, BSC, Arbitrum, Base, and Solana. Each bot is tested on testnets and undergoes smart contract audit. Estimate your project — contact us.

Years of experience in Web3 and dozens of realized trading bots allow us to process transactions worth millions of dollars monthly. In this article, we'll break down the key features that distinguish professional bots from amateur crafts.

How Token Sniping Works

Sniping is the most demanded feature. A new token is deployed on Uniswap, liquidity is added — the bot buys within the first seconds. Let's dive into the key mechanics.

Auto-snipe on new pairs: monitoring Uniswap Factory PairCreated events. When a new pair with matching criteria is detected — automatic purchase.

Launch snipe: the user specifies the token address in advance, the bot prepares the transaction and sends it as soon as liquidity appears.

Anti-honeypot: token verification before purchase:

• Simulate buy + sell: if sell reverts — honeypot
• Check owner functions (mint, blacklist, pause)
• Max wallet/transaction limits
• Tax check: if buy/sell tax > threshold — warning

async def check_token_safety(token_address, amount):
    # Simulate buy transaction
    buy_result = await simulate_swap(WETH, token_address, amount)
    # Simulate immediate sell
    sell_result = await simulate_swap(token_address, WETH, buy_result.amountOut)
    
    # Calculate effective tax
    effective_tax = 1 - (sell_result.amountOut / amount)
    
    return SafetyCheck(
        can_sell=sell_result.success,
        tax=effective_tax,
        warnings=check_contract_functions(token_address)
    )

What Are Limit Orders and How to Implement Them?

DEXes don't have native limit orders — the bot implements them off-chain. The user sets: "buy TOKEN at $0.05, max 0.5 ETH". The bot monitors the price via WebSocket or polling Uniswap price. Upon reaching the target price — automatic purchase.

Trailing stop: the stop moves up with the price. If TOKEN rises from $0.05 to $0.10, trail stop at 15% = stop at $0.085. When it drops to $0.085 — sell.

DCA (Dollar Cost Averaging)

Automatic purchase on a regular basis: /dca BUY TOKEN 0.1 ETH every 6 hours for 7 days. The bot creates a task in the scheduler, every 6 hours it buys 0.1 ETH regardless of price.

How to Protect the Bot from MEV?

Banana Gun built its competitive advantage on MEV protection. Flashbots Protect: sending transactions via https://rpc.flashbots.net. Transactions are visible only to relayers, not in the public mempool — sandwich attack impossible. Auto slippage selection: analysis of pool depth and calculation of minimum slippage ensuring execution. Gas estimation: smart gas price based on current network conditions. More about Flashbots Protect.

Project Token and Revenue Sharing

Unibot and Banana Gun both launched native tokens. Mechanics:

• Revenue share: a percentage (often 40-50%) of protocol fees is distributed to token holders.
• Fee discount: holders pay a lower trading fee.
• Governance: token = voting rights.

Fee structure: 0.5-1% per swap, additional 0.5% for sniper, 5-10% of copy trading profit. At $50M/day turnover, that's $350K/day revenue.

Multi-Chain Support: Why It Matters

Solana requires a separate implementation.

Telegram UI/UX

Inline keyboards for quick actions. When buying — a step-by-step flow: input address, choose amount, confirm with preview, execute, result with link to Etherscan.

Feature Comparison

What's Included in Developing Such a Bot?

• Architecture design and stack selection (Solidity, Foundry, ethers.js/viem)
• Smart contract development (Uniswap integration, custom pool handling)
• Backend on Node.js/Python with queues and WebSocket
• MEV protection setup (Flashbots, private mempool)
• Multi-chain support (EVM + Solana)
• Deployment and monitoring (Docker, Tenderly, Grafana)
• Documentation and team training

Case Study: Reducing Sniper Latency

On a recent project, we optimized a sniper bot for an Ethereum memecoin launch. The initial latency was ~2 seconds from detecting the pair to transaction submission, resulting in high slippage (15-25%). We switched from polling to WebSocket subscriptions on the Uniswap Factory contract, used a dedicated node with low latency, and pre-signed transactions to reduce gas estimation overhead. Final latency dropped to 500ms, and slippage averaged 3-5%. The bot executed 40 buys in the first 30 seconds after liquidity addition.

We have years of experience in Web3 and have delivered dozens of trading bots for various networks. Contact us — we'll evaluate your project and offer custom solutions.

Why exchange development requires deep domain expertise

We develop exchanges — not 'chart sites,' but matching engines that process thousands of orders per second without delay, route liquidity between pools, and guarantee that no user gains access to others' funds. Teams that start with the UI and postpone the engine 'for later' end up rewriting everything in six months in 90% of cases.

Order Book vs AMM: where most projects break

Centralized exchanges (CEX) are built around an order book + matching engine. Decentralized exchanges (DEX) either also use an order book (dYdX on StarkEx, Serum/OpenBook on Solana) or an AMM with concentrated liquidity (Uniswap v3/v4, Curve, Balancer). A classic mistake when developing a CEX is implementing the matching engine on top of a relational database with transactions for each match. PostgreSQL handles ~500 RPS without special effort, but at peak loads of 5,000–10,000 orders per second, it turns into a deadlock nightmare. The correct architecture: in-memory order book (Redis Sorted Sets or custom C++/Rust structure), asynchronous writing of matches to PostgreSQL via a queue (Kafka/RabbitMQ), and a separate settlement service that finally updates balances.

For DEX, the most painful problem is sandwich attacks and MEV. A pool with a plain xy=k AMM without slippage protection becomes a target for MEV bots within hours of launch. Uniswap v2 lost hundreds of millions of dollars in user liquidity. Solutions: integration with Flashbots Protect, a commit-reveal scheme for orders, or switching to TWAMM (Time-Weighted AMM) for large trades.

Concentrated liquidity and impermanent loss

Uniswap v3 introduced concentrated liquidity – LPs choose a price range in which to provide liquidity. Capital efficiency increased 4,000x compared to v2 for stable pairs. But implementing this mechanism correctly is non-trivial. The Uniswap v3 liquidity contract uses tick-based accounting: the price space is divided into discrete ticks (tick = log₁.0001(price)), each tick stores accumulated fee growth and liquidity delta. When creating a position, the lower and upper ticks are computed, and the contract recalculates all active positions at each swap. Storage layout is critical here – incorrect variable packing in slots easily adds 40–60% to swap gas cost.

We implemented a Uniswap v3 fork for a client on Polygon with a custom fee tier system. The initial version consumed 180k gas for a swap across 2 ticks. After slot packing of variables in Tick.Info and inlining several internal calls, it dropped to 112k gas. This reduced gas costs by 38% and saved the client substantial costs on fees monthly. The techniques applied are described in the Uniswap v3 Whitepaper and confirmed by our audit experience.

How a matching engine delivers performance

A production-ready matching engine is built according to the following scheme:

• Order ingestion layer – WebSocket gateway (Go or Rust), accepts orders, validates signature, checks balance via Redis, queues them. Latency at this level must be <1ms.
• Matching core – single-threaded event loop (eliminates race conditions without mutexes). In memory, we hold two Sorted Sets for each trading instrument: bids and asks. FIFO matching for limit orders, immediate-or-cancel for market orders. Throughput with a proper Rust implementation – 500k–1M matches per second on a single core.
• Settlement service – reads matches from Kafka, atomically updates balances in PostgreSQL (UPDATE accounts SET balance = balance - $1 WHERE id = $2 AND balance >= $1). Optimistic locking via row versioning.
• Withdrawal pipeline – separate service with cold/hot wallet architecture. The hot wallet holds 5–10% of total deposits, the rest is cold storage with multi-sig (Gnosis Safe or custom HSM). Automatic withdrawals only from hot wallet, large amounts require manual authorization.

How our exchange development process ensures reliability

Smart contracts and gas optimization

For EVM-based DEX (Ethereum, Arbitrum, Optimism, Polygon), the entire critical path lives in Solidity. Main contracts: Pool, Factory, Router, PositionManager (for v3-like), and Quoter for off-chain calculations. Typical mistakes we see in audits:

Reentrancy via callback. Uniswap v3 uses flash swap with a callback (uniswapV3SwapCallback). If your router lacks a nonReentrant guard and you don't check msg.sender == pool, the contract gets drained via a nested call. This is not hypothetical – several v3 forks lost funds this way.

Oracle manipulation in AMM. If your contract uses the spot price from the pool for collateral calculation, it is front-runnable. Correct: TWAP over 30+ minutes (Uniswap v3 OracleLib) or an external oracle (Chainlink).

Unbounded loops in liquidity range. If a swap crosses many ticks in a row (price impact 80%+), gas may exceed the block limit. Need MAX_TICKS_CROSSED with partial fill and returning the remainder.

For Solana DEX (Anchor framework, Rust), the architecture is fundamentally different: account-based model, Program Derived Addresses (PDA) instead of storage, Cross-Program Invocations instead of internal calls. Solana's throughput (~3,000–4,000 TPS vs 15–30 on Ethereum mainnet) allows building on-chain order books – exactly what Phoenix DEX does.

Liquidity bootstrapping and aggregator integration

Launching a pool is not enough – you need to ensure liquidity at launch. Practical mechanisms:

• Liquidity Bootstrapping Pool (LBP) – initial price is high, asset weights dynamically shift, creating selling pressure and even token distribution. Implemented in Balancer v2.
• Initial Liquidity Offering via Uniswap v3 – adding liquidity in a narrow range around the initial price, then gradually expanding as volume grows. Requires active liquidity management or integration with Arrakis/Gamma.
• Integration with 1inch, Paraswap, Li.Fi – aggregators bring traffic but require standard compliance: the pool must have correct getAmountsOut, support ERC-20 approval/permit, and not have custom transfer hooks that break the aggregator's routing.

Development process and deliverables

Analytics and design begin with choosing the architectural model: CEX with custodial storage, non-custodial DEX, or hybrid (off-chain order book + on-chain settlement, like dYdX v3). This decision determines everything – regulatory load, tech stack, team.

Development proceeds in layers: first smart contracts with full Foundry coverage (fuzzing, invariant testing), then backend services, then integration layer, and finally frontend. Testing includes fork testing on mainnet via Foundry – we reproduce real liquidity conditions, not synthetic ones.

Audit is mandatory before mainnet deployment. For DEX contracts, minimally one firm with manual review (Trail of Bits, Spearbit, Code4rena contest). For CEX custody, audit of key storage processes. We guarantee all contracts undergo formal verification and fuzzing testing (Echidna, Foundry invariant).

Estimated timelines

Cost is calculated individually after a technical briefing: chain selection, throughput requirements, custodial model. Our certified engineers with 10+ years of experience will help you choose the optimal architecture and avoid common pitfalls. Contact our team for a detailed proposal.

Pitfalls to avoid at launch

• Forgetting the price oracle in AMM. Spot price can be manipulated with a flash loan in one transaction. If your lending protocol uses the spot price from its own pool, that's a bug.
• Hot wallet without limits. A CEX without daily limits on automatic withdrawals is an invitation for attackers. Compromising one key should lose at most 10% of total funds.
• Absence of circuit breaker. A 40% price drop in 5 minutes should halt automatic liquidations or withdrawals until manual review. Without this, a cascading liquidation spiral destroys all TVL.
• Incorrect decimal handling. USDC uses 6 decimals, WBTC – 8, most tokens – 18. Mixing without normalization leads to either precision loss or overflow. Solidity has no float; we work with fixed-point using FullMath (mulDiv with overflow protection).

Want to avoid these problems? Get a consultation — we will select the architecture for your project and provide exact timelines. Order exchange development with quality guarantee and ongoing support.