How We Built a High-Frequency Trading Bot: Architecture Deep-Dive

How We Built a High-Frequency Trading Bot: Architecture Deep-Dive

A client came to us with a straightforward brief: build a crypto trading bot that executes a momentum strategy on Solana DEXes, handles 500+ trades per day, and never loses more than 3% of portfolio value on a bad day. What sounds simple on paper is a system with at least seven moving parts that all need to work reliably — at the same time, under market stress, with real money on the line. This is a breakdown of how we built it, what tradeoffs we made, and what we'd do differently.

System Architecture Overview

Before writing a single line of code, we mapped out the full data flow. The system has five layers, each with a single responsibility:

Market Data Feeds

↓

Strategy Engine

↓

Risk Manager ←→ Kill Switch

↓

Order Executor

↓

Monitoring & Alerting

This separation matters. When a bug appears in production — and it will — you need to know immediately which layer failed. A monolithic bot where market data, strategy logic, and order execution are entangled is impossible to debug under pressure.

Data Layer: Real-Time Market Data

The data layer is the foundation. Bad data means bad decisions, and bad decisions with automated execution means losses before you can intervene.

WebSocket connections. We connect directly to Solana DEX WebSocket feeds (Jupiter, Raydium) for order book updates and trade events. HTTP polling introduces latency that makes real-time strategies unreliable. WebSockets give us sub-100ms event propagation.

Data normalization. Different DEXes return data in different formats. We built a normalization layer that converts all incoming events to a unified internal schema before they touch the strategy engine. This means the strategy is exchange-agnostic — adding a new data source means writing one adapter, not modifying strategy logic.

Handling connection drops. WebSocket connections drop. Always. Our data layer implements exponential backoff reconnection, a secondary data source that activates on primary failure, and a staleness detector that halts trading if no updates have been received in N seconds. The strategy should never execute on stale data.

Latency monitoring. We track end-to-end latency from market event to strategy evaluation on every tick. If latency spikes above our threshold (200ms for this strategy), the system flags it. Consistent latency spikes often precede node congestion on Solana — a useful early warning.

Strategy Engine

The strategy engine consumes normalized market data and produces trade signals. We kept this layer intentionally simple and stateless.

Stateless vs Stateful strategies. A stateless strategy evaluates each tick independently based only on current market state. A stateful strategy maintains internal memory — moving averages, trailing stops, cross-session positions. Stateless strategies are easier to test and reason about. For this project, the core signal generation is stateless; position tracking is handled separately by the Risk Manager.

The momentum strategy itself. We can't share the exact parameters (that's the client's IP), but the pattern is: detect price momentum above a threshold, confirm with volume, check spread, execute. The strategy function takes a market snapshot and returns a signal enum: BUY | SELL | HOLD. No side effects, fully unit-testable.

Backtesting framework. Before live deployment, every strategy change goes through backtesting against 6 months of historical data. We built a backtester that replays historical ticks through the exact same strategy function used in production. This matters: many backtesting frameworks simulate trades at a different level of abstraction than live execution, introducing backtest-to-live gaps. Ours doesn't.

One hard lesson: backtesting on daily candles is almost meaningless for HFT strategies. We backtest on tick data. The difference in results is significant.

Risk Management System

This is the most important layer in the system. A bug in the strategy engine loses you one trade. A bug in risk management can lose you everything.

Position size limits. Maximum position size per trade is capped as a percentage of total portfolio. This is hardcoded, not configurable at runtime, and cannot be overridden by strategy signals.

Daily drawdown circuit breaker. If total P&L for the day drops below -3% (the client's requirement), all trading halts automatically. The bot sends a Telegram alert and waits for manual restart. The system does not auto-resume — resuming after a drawdown requires a human decision.

Per-trade stop-loss. Every open position has a stop-loss attached at entry. If the price moves against the position by X%, the order exits immediately regardless of strategy signal. Stop-losses execute via the same order execution path as normal trades, so they're subject to the same slippage — we account for this in position sizing.

The kill switch. A Telegram bot (separate from the trading bot) accepts a single command from authorized users: /halt. On receipt, the system cancels all open orders, closes all positions at market, and disables new order execution. We tested this under simulated high-load conditions before deploying to mainnet. The kill switch must work when everything else is broken.

Order Execution Layer

On Solana, order execution is not just "send a transaction." Solana's mempool dynamics, compute unit limits, and priority fee mechanics mean naive execution will regularly fail or get front-run.

Transaction construction. We use versioned transactions with address lookup tables to fit complex DEX swaps within Solana's compute unit limits. Transactions include a priority fee calculated dynamically based on recent network congestion data.

Retry logic. Solana transactions fail. Not rarely — often during congestion. Our execution layer implements a retry strategy with exponential backoff, but with a hard expiry: if a transaction hasn't confirmed within N seconds, we treat it as failed and do not retry. The strategy engine is notified and can re-evaluate. Retrying indefinitely on a fast-moving market is a good way to execute a trade that should have been cancelled.

Slippage management. For each trade, we calculate the acceptable slippage range before submitting and include it in the transaction parameters. If on-chain execution would result in worse slippage than our tolerance, the transaction fails on-chain rather than executing at an unfavorable price. This is a property of Solana's DEX programs, not something we implement ourselves — but you need to set it correctly.

Confirmation tracking. We track every transaction from submission to confirmed status. Unconfirmed transactions after a threshold trigger alerts. This data feeds into our monitoring dashboard.

Monitoring and Alerting

A trading bot without monitoring is a liability. You need to know immediately when something is wrong — before the risk management system is the only thing standing between you and losses.

P&L tracking in real time. We push P&L updates to a dashboard every 60 seconds. Running P&L, position-level breakdown, daily high/low, slippage cost — everything visible without opening a code editor.

Operational metrics. Latency percentiles, transaction success rate, order rejection rate, WebSocket reconnection frequency. These are leading indicators: if the WebSocket reconnects 10 times in an hour, something is wrong with infrastructure or network before it manifests as trading problems.

Anomaly detection. We built simple threshold-based anomaly detection: if win rate drops below historical baseline for 30+ consecutive trades, if average slippage increases 3x from baseline, if order execution time spikes — Telegram alert goes out to the client. Sophisticated ML-based anomaly detection wasn't necessary here; rule-based thresholds caught every real issue during the first 3 months of live operation.

Telegram alerting. All alerts go to a dedicated Telegram group with the client. Critical alerts (kill switch triggered, drawdown limit hit, system error) send immediately. Informational alerts (daily P&L summary, reconnection events) are batched and sent every 4 hours.

Performance Results

After 3 months of live operation (anonymized data):

Strategy executed 18,000+ trades. Daily drawdown limit was triggered twice — both during periods of unusual Solana network congestion that made execution unreliable. Both times, the manual review confirmed the circuit breaker decision was correct. System uptime: 99.7%. Average end-to-end latency (market event to order submitted): 87ms.

The client's primary benchmark was risk-adjusted return. Meeting the 3% max drawdown constraint while maintaining positive expected value required more iteration on risk parameters than on strategy logic — which is typical. Strategies are relatively easy to generate. Risk management that keeps them alive long enough to be profitable is the hard part.