Developing an Emergency Close System for All Bot Positions

"Close everything" — the simplest function in appearance and one of the most critical. In a moment of crisis, when the main engine may hang or the UI becomes unresponsive, this button must work instantly. A 30-second delay during a flash crash can wipe out 5–10% of the portfolio. We have been engineering such mechanisms for years and implemented them for over 15 projects, including high-load DeFi protocols and CEX bots with hundreds of positions.

In one project, during a sharp drop of ETH, the bot's engine froze due to an error in the margin calculation script. The emergency close, implemented as a separate service on asyncio, closed 25 positions in 2 seconds via parallel orders — losses were less than 0.5% instead of a potential 12%. This case shows why isolating the code path is critical.

How We Build the Emergency Close System

Each component is designed with failure in mind. Here are the key requirements:

Speed: emergency close must complete in seconds, not minutes. Achieved by closing all positions simultaneously via asyncio.gather, not a sequential loop.

Reliability: works even if the main trading loop hangs or UI is unavailable. This is a separate, maximally simple code path with no dependencies on the main engine — only an HTTP client to the exchange.

Confirmation: each position must be confirmed closed. If the order is not filled, retry with exponential backoff. Do not stop until all positions are closed or a timeout is reached with an alert in Telegram.

Idempotency: if the button is pressed twice, do not send duplicate orders. Use Redis to store the operation state: on a repeated call, check if an active close is already in progress and return the current status.

Execution Algorithm

1. Get the list of all open positions (REST snapshot)
2. For each position in parallel:
   a. Place a market order of the opposite side
   b. Wait for confirmation fill
   c. If timeout — check status, retry if necessary
3. After N seconds, perform reconciliation:
   - Request current positions from the exchange
   - If anything remains open, repeat for those
4. Send final report: what was closed, at what prices, final P&L

Liquidity Problem During Emergency Close

Emergency close usually happens during high volatility — precisely when liquidity drops. A market order for a large position can cause catastrophic slippage. Solution: for large positions — TWAP execution even during emergency (split into several orders over 30–60 seconds). For small positions — plain market order. The size threshold is configurable.

Comparison of TWAP and Direct Market Order

What to Do If Liquidity Drops to Zero?

In such cases, a plain market order may execute at the worst price in the order book. We add a fallback: if the spread exceeds 5%, the system switches to a limit order with an aggressive price (best bid/ask) and retries every 2 seconds. This protects against slippage while maintaining a chance to close the position.

Comparison of Approaches to Executing Emergency Close

Parallel closing is 3–5 times faster than sequential for 10+ positions.

Why Is It Important to Test Emergency Close Regularly?

The emergency close system is a safety feature that is rarely needed but must work flawlessly when required. Test it regularly in paper trading mode with flash crash simulations. We include automated tests that use historical crisis data and emulate network issues. After our implementation, you can rest assured.

Implementation Process

What's Included in the Work?

• Full architecture and code of emergency close (separate service or integrated module)
• Implementation of parallel execution with asyncio/aiohttp or similar stack
• TWAP execution configuration for large positions
• Reconciliation and alerts on failures
• Documentation (algorithm description, configuration, testing instructions)
• Two hours of post-deployment support

Contact us to discuss your project — we will analyze your stack and propose the optimal solution within one business day.

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.