In this post I’m going to breakdown the different parts of a system trading bot that I built to execute trend following and mean reversion strategies.
There’s a short video here if you prefer a quick walkthrough: https://youtube.com/shorts/3tWl4ETbqTw
Building a robust and profitable system trading bot requires careful planning, solid architecture, and a deep understanding of the market. Whether you’re automating crypto trades or working with traditional equities, a good bot is far more than just code, it’s a carefully balanced engine made of multiple moving parts.
Let’s take a look at the individual parts…
1. Data Collection
The foundation of any trading system is reliable data. Before any decisions are made, your bot needs access to raw market information, including:
- Price data Historical and real-time OHLCV (Open, High, Low, Close, Volume).
- Trade data Ticks, fills, and transaction records.
- Order book data Depth charts, bid/ask spreads, and market liquidity snapshots.
Most exchanges (centralised and decentralised) offer REST or WebSocket APIs for this purpose. The goal is to collect high-resolution, time-synced data that can fuel everything from trend detection to statistical modelling.
Pro tip: Normalise and store this data locally in a time-series database to minimise latency and allow for backtesting.
2. Analysis
Raw data is just noise until it’s structured, analyzed, and made actionable. This stage involves:
- Feature engineering Create indicators like RSI, MACD, Bollinger Bands, or custom signals derived from the order book.
- Statistical modelling Use historical correlations, mean reversion, or volatility forecasts.
- Signal generation Translate analysis into binary or weighted trade signals (e.g, Buy, Sell, Hold).
Your analysis layer should prioritise speed and modularity-signals must be calculated and acted upon in milliseconds, especially in high-frequency trading environments.
3. Risk Management
This is where most bots fail-and where professional systems shine. A bot without proper risk controls is a ticking time bomb.
- Position sizing Use techniques like Kelly Criterion, fixed fractional sizing, or volatility based allocation.
- Strategy allocation Spread capital across multiple uncorrelated strategies to reduce portfolio risk.
- Stop losses and trailing stops Automatically close out losing trades based on defined criteria.
- Max drawdown limits Set hard caps to prevent total account blow up.
Every position should be opened with a risk budget in mind, and every strategy should be subject to portfolio level constraints.
4. Strategies
This is the heart of the bot, the intelligence that drives profitability. A successful bot often contains multiple strategies running simultaneously, such as:
- Trend following Catch directional moves based on momentum.
- Mean reversion Exploit price deviations from historical averages.
- Arbitrage Take advantage of price differences between exchanges or pairs.
- Market making Provide liquidity by placing both bids and asks to earn the spread.
Strategies should be dynamic, data driven, and continuously evaluated for performance. They can be rule based or use machine learning, but they must adapt as market conditions change.
Machine learning doesn’t mean link up the ChatGPT API to your strategy and hope for the best. LLM’s are text based and not designed to predict numerical sequences. Something like Tensorflow from Google is a better suited tool for the job.
5. Execution
Execution logic turns a trade signal into a real-world order. The challenge is minimizing slippage and fees while maximizing fill probability.
- Limit orders Preferred for reducing fees but may not fill in fast moving markets.
- Market orders Ensure fills but can suffer from slippage in thin order books and higher fees for taking liquidity.
- Smart order routing In DEX environments, route through multiple pools (e.g, Uniswap, SushiSwap) to find the best price.
- Latency optimization For low time frame strategies it might be necessary to use local servers located as close to the exchange nodes as possible, low latency dedicated API servers which are often available if you are doing high volume and locally hosted RPC nodes to gain an advantage over the competition.
Execution must be adaptive if volatility spikes, your limit order strategy may need to switch to aggressive market orders.
6. Exchanges
A trading bot must interact with one or more exchanges, and each integration comes with unique challenges:
- Centralized Exchanges (CEXs)
- Examples Binance, Coinbase, Kraken
- Features REST and WebSocket APIs, order types, KYC/AML compliance
- Challenges Rate limits, downtime, API inconsistencies
- Decentralized Exchanges (DEXs)
- Examples Uniswap, Curve, 1inch
- Features On-chain transactions, smart contract interactions
- Challenges Gas costs, slippage, MEV risks
A robust bot should include an exchange abstraction layer, allowing strategy and execution logic to remain agnostic to the underlying venue.
7. Analytics
What gets measured gets optimized. Analytics help you understand performance, detect bugs, and improve over time.
- Performance metrics P&L, Sharpe ratio, max drawdown, win rate
- Strategy attribution Understand which strategies are contributing to gains or losses
- Latency and slippage tracking Find inefficiencies in execution
- Trade logs and visualization Help with debugging and post trade analysis
These insights drive the next cycle of refinement and ensure the bot continues to evolve.
Building a system trading bot is as much an engineering challenge as it is a trading one. Each component data, analysis, risk, strategy, execution, exchange integration, and analytics must be tightly integrated and continuously monitored.
Done right, a bot can trade thousands of times a day without emotion, fatigue, or error. Done wrong, it can lose everything in seconds.
Whether you’re starting small or building institutional grade infrastructure, treat each layer as critical.
The market is always changing. Your bot should be too.
