Development of Market Making Algorithm from Scratch

We develop market making algorithms from scratch — from liquidity analysis to server deployment. In crypto, market making remains highly profitable for niche assets: mid-cap altcoins, perpetual futures with low liquidity. With proper tuning, average daily return is 0.15–0.3% of inventory, and fill rate reaches 70–85%. We use the Avellaneda-Stoikov model and dynamic spread to minimize inventory risk and maximize P&L.

Our guarantee: 95%+ quote uptime and resilience to inventory risks even under high volatility. 5+ years of experience and 20+ projects in this field.

Basic Market Making Model

Naive market making — place a bid X% below mid-price and an ask X% above. Problem: inventory risk. If the price moves sharply in one direction, the market maker accumulates an unfavorable position. Losses can reach 50% of capital in a single session if risks are not managed.

Avellaneda-Stoikov model — an mathematically optimal market making strategy. It accounts for inventory risk and time horizon:

bid_price = mid - δ/2 - γσ²(T-t)q
ask_price = mid + δ/2 - γσ²(T-t)q

where:
δ  = spread (optimal)
γ  = risk aversion coefficient
σ  = asset volatility
q  = current inventory (in asset units)
T  = end of trading period
t  = current time

Key point: with positive inventory (many assets accumulated), the algorithm shifts quotes downward to sell surplus faster. With negative inventory, it shifts upward to buy.

How to configure the Avellaneda-Stoikov model?

1. Collect historical data: prices, volumes, spread, volatility (σ).
2. Choose risk aversion coefficient (γ) — in practice 0.01–0.1.
3. Optimize target inventory (q_target) and time horizon (T).
4. Run backtest on the last 30 days of data.
5. Configure hard/soft limits: e.g., max inventory = 10% of capital.

We tune parameters individually using genetic algorithms and grid search.

How does the Avellaneda-Stoikov model work?

The Avellaneda-Stoikov model is a stochastic approach that dynamically adjusts quotes based on current inventory and remaining time. The risk aversion coefficient γ determines how aggressively the algorithm closes positions. In practice, we tune γ on historical data to balance spread profitability and inventory risk.

What is inventory risk and how to minimize it?

Inventory risk is the main enemy of a market maker. If the position exceeds allowed limits, we apply several methods:

• Hard limit: when inventory > MAX_INVENTORY — stop placing orders on that side. Wait for fills.
• Soft limit with skewing: gradually shift quotes against the direction of accumulated inventory. The larger the inventory, the stronger the shift.
• Hedging: open a hedge position on another exchange or in perpetual futures. If we accumulate a lot of BTC spot, we sell BTC-PERP.

For each project, we choose a combination of methods based on asset volatility and trading volume. We guarantee that drawdown from inventory risk does not exceed a predefined threshold (usually 5% of capital).

Spread Management

The spread should not be fixed — it adapts to market conditions:

• Volatility-based spread: spread = base_spread × (current_volatility / mean_volatility). When volatility is high, the spread widens — inventory risk increases.
• Order book depth: if liquidity in the order book is low, adverse selection risk is higher, spread widens.
• Time of day: during low activity periods, spread widens.
• Toxic flow: if the last N trades were predominantly on one side, it may indicate informed trading. The algorithm widens the spread or temporarily removes quotes.

Multi-Level Quotes

Instead of a single pair of orders (1 bid + 1 ask), we place multiple levels:

Bid 3: mid - 0.5% × 1000 USDT
Bid 2: mid - 0.3% × 500 USDT
Bid 1: mid - 0.15% × 200 USDT
--- MID PRICE ---
Ask 1: mid + 0.15% × 200 USDT
Ask 2: mid + 0.3% × 500 USDT
Ask 3: mid + 0.5% × 1000 USDT

Orders close to the mid price fill more often and earn exchange rebates. Distant orders protect against sharp moves.

Order Cancellation and Re-Quoting

Orders need to be updated regularly as the mid-price changes:

• Threshold-based re-quoting: if the mid price shifts by more than N%, cancel old orders and place new ones.
• Time-based re-quoting: forced update every T seconds.
• Event-based: re-quote on every change in the best bid/ask in the order book.

Frequent order cancellations consume API request quota. Exchanges impose rate limits. For Binance: 1200 requests/min HTTP, separate limits for WebSocket. Optimizing update frequency is crucial.

Exchange Market Making Programs

Major exchanges pay for providing liquidity:

To qualify for these conditions, you must maintain minimum quote uptime (>80% of the time bid/ask within a certain range from mid) and minimum volume.

Development Stages

Monitoring and Metrics

P&L breakdown: spread income - inventory risk losses - fees.

Fill rate: percentage of orders filled. Too low (<50%) → spread too wide. Too high (>90%) → spread too narrow, excessive adverse selection.

Inventory exposure: current position in USD, maximum per session, average.

Uptime: percentage of time quotes are placed (target >99.5%).

Latency: time from receiving market update to placing/updating orders (target <10ms).

Tech Stack

Language: Python (asyncio + aiohttp/websockets) for strategies with latency > 50ms. C++ or Rust for latency-critical components.

Exchange connectors: CCXT Pro (Python) provides a unified API for WebSocket. For production, we build custom connectors for each exchange.

Storage: PostgreSQL for trades, orders, positions. InfluxDB or TimescaleDB for performance metrics.

Monitoring: Grafana dashboards for real-time P&L, inventory, latency. Alerts in Telegram when risk limits are exceeded.

Contact us to evaluate your project within 2 days. Get an architect consultation — discuss the details.

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.