Building a Crypto Trading Bot from Scratch - 1: Why Architecture Matters
I’ve been fascinated by algorithmic trading for years. The idea of writing code that makes trading decisions based on data and logic, rather than emotion and gut feeling, always appealed to me. So I decided to build my own cryptocurrency trading bot from scratch—not using some pre-built platform or template, but actually architecting the whole system myself.
This is the first post in a series where I’ll document the entire journey of building a production-ready trading bot. I want to share what I’m learning, the mistakes I’m making, and the design decisions that actually matter when you’re building something that will trade with real money.
Why Build Your Own?
Before we dive into architecture, let me address the obvious question: why build your own trading bot when there are so many platforms and libraries out there?
For me, it came down to three things:
Control: I want to understand every line of code that’s making decisions with my capital. Black boxes make me nervous.
Learning: There’s no better way to understand trading systems than building one from the ground up.
Flexibility: Pre-built platforms are great until you want to do something they don’t support. Building your own means no limitations.
If you’re here reading this, you probably have similar motivations.
The Vision
Here’s what I set out to build:
Exchange-agnostic: Should work with Binance, Coinbase, Kraken, or any exchange I want
Multi-strategy: Run multiple trading strategies simultaneously on different pairs and timeframes
Transparent: Every signal, every trade, every decision should be logged and explainable
Safe: Risk management built in from day one, not bolted on later
Testable: Must be able to backtest strategies on historical data before risking real money
That last point is crucial. I’ve read too many horror stories about bots that worked great in theory but blew up accounts in practice. Backtesting isn’t perfect, but it’s better than trading blind.
The Architecture That Makes It Possible
When I started sketching out the system, I kept coming back to one principle: separation of concerns. A trading system has several distinct responsibilities, and mixing them together is a recipe for bugs and technical debt.
Here’s the high-level architecture I settled on:
Strategy Engine → Signal Generator → Trade Executor
↓ ↓ ↓
Indicators Risk Manager Position Manager
↓ ↓ ↓
Data Manager
↓
Exchange Connector (ccxt)
Let me break down what each component does and why it exists:
Strategy Engine
The Strategy Engine is the orchestrator. Its job is to:
Load and manage multiple strategy instances
Coordinate data flow to each strategy
Manage strategy lifecycle (start, stop, pause)
Each strategy runs independently on its configured symbol and timeframe. The engine doesn’t care what logic your strategy uses—it just makes sure your strategy gets the data it needs and captures the signals it generates.
Signal Generator
This is where your trading logic lives. The Signal Generator:
Analyzes market data using your strategy’s rules
Produces BUY/SELL/HOLD signals
Attaches metadata explaining why each signal was generated
That last point is huge. Every signal includes a human-readable reason and the full state of all indicators at the time. If your bot makes a bad trade, you can go back and see exactly why it made that decision.
Trade Executor
The Trade Executor bridges the gap between signals and actual orders. It:
Validates signals against risk rules
Calculates position sizes
Places orders on the exchange
Handles failures and retries
Crucially, the Trade Executor doesn’t know or care about your strategy logic. It just takes validated signals and executes them.
Risk Manager
This is your safety net. The Risk Manager enforces limits at three levels:
Position-level: Stop loss, take profit, position size limits Strategy-level: Max consecutive losses, confidence thresholds Account-level: Max daily loss, max total exposure, drawdown limits
All risk checks happen before trades are executed. If a signal violates any risk rule, it gets rejected before an order touches the exchange.
Position Manager
The Position Manager tracks everything you have open:
Current positions and their entry prices
Unrealized and realized P&L
Stop loss and take profit levels
It also handles position exits when SL/TP levels are hit or when your strategy generates a closing signal.
Data Manager
This is your central data hub. The Data Manager:
Fetches historical price data
Streams real-time price updates
Caches data efficiently
Stores trades and signals for later analysis
By centralizing data access, we avoid having every strategy make redundant API calls to the exchange.
Exchange Connector
This is the abstraction layer that makes the system exchange-agnostic. Instead of your code calling Binance APIs directly, it calls the Exchange Connector, which then talks to whatever exchange you’ve configured.
Want to switch from Binance to Kraken? Just swap the connector. Your strategy code doesn’t change.
Why This Architecture Works
You might be thinking this seems like a lot of components for a “simple” trading bot. And you’re right—it is more complex than just writing a script that checks if price crosses an EMA and then buys or sells.
But here’s what this architecture gives you:
1. Testability
Each component can be tested in isolation. I can test my risk manager without touching an exchange. I can test my strategies with mock data. This is crucial for building confidence before going live.
2. Extensibility
Want to add a new strategy? Write a class that inherits from BaseStrategy. Want to support a new exchange? Implement the ExchangeBase interface. The core system doesn’t need to change.
3. Maintainability
When something breaks (and it will), the component boundaries make it easy to isolate the problem. Is it a data issue? A strategy bug? An exchange error? The architecture makes this clear.
4. Safety
By having dedicated risk management and validation layers, we ensure that no trade happens without passing through safety checks. This isn’t optional—it’s baked into the architecture.
The Exchange-Agnostic Design
Let me dig deeper into one of the most important design decisions: exchange abstraction.
Early on, I could have just used the Binance API directly. It would have been faster to get something working. But that would have locked me into Binance forever. What if they change their API? What if I want to trade on multiple exchanges? What if Binance isn’t available in my region anymore?
Instead, I defined an abstract interface that any exchange must implement:
