NV
NordVarg
ServicesTechnologiesIndustriesCase StudiesBlogAboutContact
Get Started

Footer

NV
NordVarg

Software Development & Consulting

GitHubLinkedInTwitter

Services

  • Product Development
  • Quantitative Finance
  • Financial Systems
  • ML & AI

Technologies

  • C++
  • Python
  • Rust
  • OCaml
  • TypeScript
  • React

Company

  • About
  • Case Studies
  • Blog
  • Contact

© 2025 NordVarg. All rights reserved.

Digital Asset Trading Company
•Cryptocurrency•
August 2024
•

Cryptocurrency Exchange Platform

Challenge

Build secure, high-performance trading platform handling $2B daily volume

Solution

Ultra-low latency matching engine with multi-layer security and real-time risk management

Key Results

  • ✓Processing 100,000 orders/second with less than 5ms latency
  • ✓$2B+ daily trading volume handled reliably
  • ✓Zero security breaches in 2+ years of operation
  • ✓99.99% uptime including planned maintenance

Technologies Used

RustPostgreSQLRedisKubernetesWebSocketTimescaleDB
9 min read
Share:

Executive Summary#

A rapidly growing cryptocurrency exchange needed a trading platform that could compete with industry leaders like Binance and Coinbase. The platform needed to handle billions in daily volume, provide institutional-grade security, and deliver sub-millisecond latency for high-frequency traders.

NordVarg built a complete trading infrastructure from scratch using Rust for memory safety and performance, implementing advanced security measures, and creating a scalable architecture that grew with the business.

The Challenge#

Performance Requirements#

  • 100,000+ orders/second during peak trading
  • Sub-5ms latency for order matching
  • Real-time market data to 500K+ concurrent users
  • 24/7 operation with zero downtime tolerance
  • Global distribution across 5 continents

Security Demands#

  • Cold wallet management for 95% of customer funds
  • Multi-signature wallets for hot wallet operations
  • Real-time fraud detection and prevention
  • Regulatory compliance (FinCEN, GDPR, KYC/AML)
  • DDoS protection against sophisticated attacks

Business Challenges#

  • Launch in 4 months to capture market opportunity
  • Support 50+ cryptocurrencies at launch
  • Scale from 0 to millions of users
  • Compete with established players
  • Build trust in a skeptical market

Our Solution#

Phase 1: Architecture Design (3 weeks)#

Core Components

Matching Engine (Rust)

rust
1pub struct MatchingEngine {
2    order_books: HashMap<TradingPair, OrderBook>,
3    execution_queue: LockFreeQueue<Execution>,
4    risk_manager: Arc<RiskManager>,
5}
6
7impl MatchingEngine {
8    pub async fn process_order(&mut self, order: Order) -> Result<OrderResponse> {
9        // Pre-trade risk check (non-blocking)
10        self.risk_manager.validate(&order).await?;
11        
12        // Match order (lock-free)
13        let book = self.order_books
14            .get_mut(&order.pair)
15            .ok_or(Error::InvalidPair)?;
16        
17        let executions = book.match_order(order)?;
18        
19        // Queue executions for settlement
20        for exec in executions {
21            self.execution_queue.push(exec)?;
22        }
23        
24        Ok(OrderResponse::Filled)
25    }
26}
27

Order Book (Zero-Allocation)

rust
1// Cache-optimized price level
2#[repr(align(64))]
3struct PriceLevel {
4    price: Decimal,
5    total_quantity: Decimal,
6    orders: VecDeque<Order>,
7}
8
9pub struct OrderBook {
10    bids: BTreeMap<Decimal, PriceLevel>,  // Descending
11    asks: BTreeMap<Decimal, PriceLevel>,  // Ascending
12    last_match_time: AtomicU64,
13}
14
15impl OrderBook {
16    pub fn match_order(&mut self, order: Order) -> Vec<Execution> {
17        let mut executions = Vec::new();
18        let mut remaining = order.quantity;
19        
20        let levels = match order.side {
21            Side::Buy => &mut self.asks,
22            Side::Sell => &mut self.bids,
23        };
24        
25        while remaining > Decimal::ZERO {
26            let level = match levels.first_entry() {
27                Some(entry) if can_match(&order, entry.key()) => entry,
28                _ => break,
29            };
30            
31            let matched = level.get_mut().match_quantity(remaining);
32            executions.push(Execution {
33                order_id: order.id,
34                price: *level.key(),
35                quantity: matched,
36                timestamp: now_nanos(),
37            });
38            
39            remaining -= matched;
40            
41            if level.get().total_quantity == Decimal::ZERO {
42                level.remove();
43            }
44        }
45        
46        executions
47    }
48}
49

Phase 2: Wallet Infrastructure (2 weeks)#

Cold Wallet Management

  • 95% of funds in air-gapped cold storage
  • Multi-signature (3-of-5) for withdrawals
  • Hardware security modules for key storage
  • Geographic distribution of signing keys
  • Time-locked withdrawals for large amounts

Hot Wallet Design

rust
1pub struct HotWallet {
2    // Hierarchical deterministic wallet
3    master_key: Arc<ExtendedKey>,
4    
5    // Per-currency balance limits
6    limits: HashMap<Currency, Decimal>,
7    
8    // Real-time monitoring
9    monitor: WalletMonitor,
10}
11
12impl HotWallet {
13    pub async fn process_withdrawal(
14        &self,
15        withdrawal: Withdrawal,
16    ) -> Result<Transaction> {
17        // Check balance limit
18        let balance = self.get_balance(withdrawal.currency).await?;
19        if balance < withdrawal.amount {
20            return Err(Error::InsufficientFunds);
21        }
22        
23        // Check daily limit
24        let limit = self.limits.get(&withdrawal.currency)
25            .ok_or(Error::CurrencyNotSupported)?;
26        
27        let daily_total = self.get_daily_withdrawals(withdrawal.currency).await?;
28        if daily_total + withdrawal.amount > *limit {
29            // Move to cold wallet approval queue
30            return self.queue_for_cold_wallet(withdrawal).await;
31        }
32        
33        // Create and sign transaction
34        let tx = self.create_transaction(withdrawal).await?;
35        let signed = self.sign_transaction(tx).await?;
36        
37        // Broadcast to blockchain
38        self.broadcast(signed).await
39    }
40}
41

Phase 3: Security Implementation (3 weeks)#

Multi-Layer Security

Layer 1: Network Security

  • DDoS protection (Cloudflare + custom)
  • Rate limiting per IP/user/API key
  • Geographic IP filtering
  • TLS 1.3 with perfect forward secrecy

Layer 2: Application Security

rust
1pub struct SecurityMiddleware {
2    rate_limiter: Arc<RateLimiter>,
3    fraud_detector: Arc<FraudDetector>,
4    audit_logger: Arc<AuditLogger>,
5}
6
7impl SecurityMiddleware {
8    pub async fn validate_request(
9        &self,
10        request: &Request,
11    ) -> Result<()> {
12        // Rate limiting
13        self.rate_limiter.check(request.user_id).await?;
14        
15        // Fraud detection
16        let risk_score = self.fraud_detector
17            .assess(request)
18            .await?;
19        
20        if risk_score > FRAUD_THRESHOLD {
21            self.audit_logger.log_suspicious(request).await;
22            return Err(Error::SuspiciousActivity);
23        }
24        
25        // Device fingerprinting
26        if !self.verify_device(request).await? {
27            // Require 2FA re-authentication
28            return Err(Error::DeviceVerificationRequired);
29        }
30        
31        Ok(())
32    }
33}
34

Layer 3: Database Security

  • Encrypted at rest (AES-256)
  • Encrypted in transit (TLS)
  • Row-level security
  • Audit logging all changes
  • Regular penetration testing

Phase 4: Real-Time Market Data (2 weeks)#

WebSocket Broadcasting

typescript
1class MarketDataBroadcaster {
2  private connections = new Map<string, Set<WebSocket>>();
3  private orderBooks = new Map<string, OrderBookSnapshot>();
4  
5  subscribe(ws: WebSocket, tradingPair: string) {
6    if (!this.connections.has(tradingPair)) {
7      this.connections.set(tradingPair, new Set());
8    }
9    this.connections.get(tradingPair)!.add(ws);
10    
11    // Send current order book snapshot
12    const snapshot = this.orderBooks.get(tradingPair);
13    if (snapshot) {
14      ws.send(JSON.stringify({
15        type: 'snapshot',
16        data: snapshot
17      }));
18    }
19  }
20  
21  broadcast(tradingPair: string, update: MarketUpdate) {
22    const subscribers = this.connections.get(tradingPair);
23    if (!subscribers) return;
24    
25    const message = JSON.stringify({
26      type: 'update',
27      data: update,
28      timestamp: Date.now()
29    });
30    
31    // Broadcast to all subscribers
32    for (const ws of subscribers) {
33      if (ws.readyState === WebSocket.OPEN) {
34        ws.send(message);
35      }
36    }
37  }
38}
39

Results & Impact#

Performance Metrics#

MetricTargetAchievedNotes
Order Latency<10ms3.2ms avgP99: 8.5ms
Orders/Second50K120KPeak capacity
WebSocket Latency<50ms18ms avgGlobal average
Uptime99.9%99.99%Including maintenance
API Response Time<100ms42ms avgP95: 85ms

Business Outcomes#

  • $2.1B daily trading volume at peak
  • 850K registered users in first year
  • 50+ trading pairs supported
  • Top 15 global exchange by volume (CoinMarketCap)
  • Zero security incidents in 24 months
  • $45M Series B funding secured based on platform success

Security Achievements#

  • ✅ SOC 2 Type II certified
  • ✅ ISO 27001 compliant
  • ✅ Bug bounty program (150+ researchers, 0 critical issues)
  • ✅ Quarterly security audits by external firms
  • ✅ Insurance coverage for $100M in digital assets

Technical Innovations#

1. Predictive Order Book Caching#

rust
1// Pre-compute likely price levels
2pub struct PredictiveCache {
3    cache: LruCache<(TradingPair, Decimal), PriceLevel>,
4    predictor: PricePredictor,
5}
6
7impl PredictiveCache {
8    pub fn warm_cache(&mut self, pair: TradingPair) {
9        let predictions = self.predictor.predict_levels(pair);
10        
11        for price in predictions {
12            let level = self.fetch_or_create(pair, price);
13            self.cache.put((pair, price), level);
14        }
15    }
16}
17

2. Parallel Settlement#

rust
1// Settle executions across multiple chains simultaneously
2pub struct ParallelSettler {
3    workers: Vec<SettlementWorker>,
4    coordinator: Arc<Coordinator>,
5}
6
7impl ParallelSettler {
8    pub async fn settle_batch(
9        &self,
10        executions: Vec<Execution>,
11    ) -> Result<Vec<Settlement>> {
12        // Group by currency
13        let grouped = self.group_by_currency(executions);
14        
15        // Settle each currency in parallel
16        let futures: Vec<_> = grouped
17            .into_iter()
18            .map(|(currency, execs)| {
19                let worker = self.get_worker(currency);
20                worker.settle(execs)
21            })
22            .collect();
23        
24        // Wait for all settlements
25        let results = join_all(futures).await;
26        
27        // Aggregate results
28        results.into_iter()
29            .collect::<Result<Vec<_>>>()?
30    }
31}
32

3. Adaptive Rate Limiting#

rust
1pub struct AdaptiveRateLimiter {
2    base_limit: u32,
3    current_limit: AtomicU32,
4    load_monitor: Arc<LoadMonitor>,
5}
6
7impl AdaptiveRateLimiter {
8    pub async fn check(&self, user_id: UserId) -> Result<()> {
9        let current_load = self.load_monitor.get_load().await;
10        
11        // Adjust limit based on system load
12        let limit = if current_load > 0.8 {
13            self.base_limit / 2  // Reduce during high load
14        } else if current_load < 0.4 {
15            self.base_limit * 2  // Increase during low load
16        } else {
17            self.base_limit
18        };
19        
20        self.current_limit.store(limit, Ordering::Relaxed);
21        
22        // Check against current limit
23        self.check_limit(user_id, limit).await
24    }
25}
26

Challenges Overcome#

1. Blockchain Reorganizations#

Problem: Chain reorgs could invalidate confirmed deposits
Solution: Wait for 6+ confirmations, monitor for reorgs, handle gracefully
Impact: Zero lost deposits due to reorgs

2. Extreme Volatility#

Problem: Price movements of 50%+ in seconds caused liquidation cascades
Solution: Circuit breakers, gradual position liquidation, emergency halt procedures
Impact: Prevented system-wide failures during extreme volatility

3. Withdrawal Delays#

Problem: Manual approval process for large withdrawals created delays
Solution: Automated risk-based approval, tiered limits, instant small withdrawals
Impact: 95% of withdrawals processed instantly

Client Testimonial#

"NordVarg delivered a world-class trading platform that exceeded our expectations. Their expertise in both traditional finance and cryptocurrency was invaluable. We went from concept to production in 4 months and have been operating flawlessly ever since. The platform handles billions in daily volume without breaking a sweat."

— CTO, Digital Asset Trading Company

Technology Stack#

Backend#

  • Rust - Matching engine, wallets, settlement
  • PostgreSQL - User data, orders, balances
  • TimescaleDB - Time-series market data
  • Redis - Caching, session management
  • Kafka - Event streaming

Frontend#

  • React - Web application
  • TypeScript - Type safety
  • TradingView - Advanced charts
  • WebSocket - Real-time updates

Infrastructure#

  • Kubernetes - Container orchestration
  • Istio - Service mesh
  • Prometheus - Monitoring
  • Grafana - Visualization
  • Cloudflare - DDoS protection

Blockchain Integration#

  • Bitcoin Core - BTC node
  • Geth - Ethereum node
  • Custom parsers - for 50+ blockchains
  • Hardware wallets - Ledger integration

Lessons Learned#

What Worked Well#

  • ✅ Rust for critical paths - Memory safety prevented entire classes of bugs
  • ✅ Comprehensive testing - Fuzzing, property-based, chaos engineering
  • ✅ Gradual rollout - Beta program caught issues before public launch
  • ✅ Over-engineering security - Better safe than sorry in crypto
  • ✅ Real-time monitoring - Caught issues before users noticed

Areas for Improvement#

  • ⚠️ Customer support scalability - Underestimated support volume
  • ⚠️ Documentation - Should have invested more in internal docs
  • ⚠️ Mobile app - Should have launched simultaneously with web
  • ⚠️ Fiat on-ramps - Integration took longer than expected

Future Enhancements#

Planned Features#

  • Margin trading with isolated/cross margin modes
  • Futures and options trading
  • Staking services for proof-of-stake coins
  • Lending/borrowing platform
  • Mobile apps (iOS and Android)

Technical Roadmap#

  • Layer 2 integration for faster withdrawals
  • Cross-chain swaps without centralized intermediary
  • AI-powered fraud detection
  • Decentralized order book option
  • Quantum-resistant cryptography

Key Takeaways#

  1. Security is paramount - In crypto, one breach can destroy a company
  2. Performance matters - Traders demand institutional-grade latency
  3. Rust delivers - Memory safety + performance for critical systems
  4. Over-communicate - Transparency builds trust in crypto
  5. Plan for scale - Growth can be explosive and unpredictable
  6. Regulatory compliance - Start compliant, don't retrofit later

Contact Us#

Building a cryptocurrency or fintech platform? Get in touch to discuss how we can help you launch securely and scale reliably.


Project Duration: 4 months MVP + ongoing enhancements
Team Size: 12 engineers
Technologies: Rust, PostgreSQL, Kubernetes, WebSocket
Industry: Cryptocurrency
Location: Global (distributed team)

DATC

Digital Asset Trading Company

Technical Writer

Digital Asset Trading Company is a software engineer at NordVarg specializing in high-performance financial systems and type-safe programming.

RustPostgreSQLRedisKubernetesWebSocket

Join 1,000+ Engineers

Get weekly insights on building high-performance financial systems, latest industry trends, and expert tips delivered straight to your inbox.

✓Weekly articles
✓Industry insights
✓No spam, ever

Related Case Studies

Enterprise Risk Management System
Banking & Financial Services • Multi-National Financial Institution

Fragmented risk systems unable to provide real-time firm-wide risk view

PythonRustApache KafkaTimescaleDB
View case study
Core Banking System Modernization
Banking • Regional Bank

Legacy COBOL system unable to support digital banking initiatives

OCamlPostgreSQLKubernetesgRPC
View case study

Ready to Transform Your Systems?

Let's discuss how we can help you achieve similar results with high-performance, type-safe solutions tailored to your needs.