Open-Closed Principle Applied to Trading Infrastructure: A Case Study

Photo by Markus Spiske on Unsplash

In this post, I want to talk about Open-Closed principle that is widely used and appreciated in the software development world and I want to emphasize the importance of any type of trading system with an actual example that happened during my journey.

So one day, all of a sudden, my bot’s error handler was sending Telegram alerts. I grabbed my laptop and SSH’d into my VPS and I saw that the bot was running, but it wasn’t executing trades, so it was just logging errors about failed API calls.

I checked my exchange balance: unchanged for the past 4 hours, while Bitcoin had moved significantly. I missed three setups that my backtests predicted with high probability, well, with a conservative estimate: approximately $400 in unrealized profits just hit the day.

The cause of the failure surprised me. The bug wasn’t in the new code I deployed. It was in a function that had been operating reliably for three months, and with this post, I will try to explain what went wrong and demonstrate the architectural principle that could have prevented it.

The Architecture Problem: Tight Coupling in Data Retrieval

My trading system was operating successfully on Binance. The implementation was straightforward: a mean reversion strategy I had refined over several months. The system was somewhat profitable.

Then I researched Hyperliquid’s fee structure. Their rates were significantly better for my trade sizes, and they offered certain altcoin pairs with superior liquidity. The decision seemed obvious: run the same strategy across both exchanges to diversify execution venues.

The implementation appeared straightforward. Both exchanges provide REST APIs and both return standardized price data. The modification required only a few conditional statements.

I opened my DataFetcher class and added the new exchange integration:

def get_price(self, symbol):
    if self.source == "binance":
        return self._fetch_binance(symbol)
    elif self.source == "coinbase":  # existing integration, functioning correctly
        return self._fetch_coinbase(symbol)
    elif self.source == "hyperliquid":  # newly added integration
        return self._fetch_hyperliquid(symbol)

I tested the implementation locally. Hyperliquid integration functioned correctly. I executed several dry runs on the VPS. All tests passed. I deployed the changes and configured my monitoring alerts. However, the error logs started piling up after a few hours.

The failure occurred in the Binance integration, not in the newly added Hyperliquid code. The integration that had operated reliably for three months stopped functioning correctly.

During the implementation of Hyperliquid support, I had modified a shared utility function that both Binance and Coinbase integrations depended on. The modification affected symbol normalization logic. Specifically, the change altered how the system transformed symbol formats like “BTC/USDT” versus “BTCUSDT”.

This modification broke the Binance integration’s ability to fetch price data correctly.

The bot continued executing. It simply stopped receiving market data. During the four hours before I detected the issue, the market conditions matched my backtested scenarios precisely. The system should have generated trading signals. Instead, it remained idle due to data retrieval failures.

This incident revealed a fundamental architectural problem. The issue would recur with each subsequent integration.

Identifying the Systemic Issue

Right after rolling back the deployment, I analyzed the underlying architectural problem. This was not an isolated incident. The structure of the code guaranteed similar failures in the future.

Each time I needed to add a new exchange integration, the process required:

• Modifying production code that was actively generating profits

• Introducing risk to existing, validated integrations

• Testing new integrations without proper isolation from existing code

• Deploying changes with potential systemic impact

• Extended monitoring periods due to lack of confidence in changes

Each addition to the core integration system would require opening the same core file and modifying shared logic. This pattern is known as tight coupling. In production trading systems operating with personal capital, tight coupling creates continuous operational risk and sometimes it is not even easy to spot at first.

The Solution: Open-Closed Principle in Trading Systems

The Open-Closed Principle, one of the five SOLID principles of object-oriented design, states: Software entities should be open for extension but closed for modification.

In practical terms for trading infrastructure, this means: new data sources can be added to the system without modifying existing, validated code.

When properly implemented, this principle enables:

• Addition of new exchange integrations without touching production code

• Isolation of each integration for independent testing

• Zero risk of regression in existing integrations

• Uniform interfaces that strategy code can depend on

• Simplified maintenance and debugging

Here is the architectural transformation this principle requires:

Problematic implementation (violates OCP):

class DataFetcher:
    def get_price(self, symbol):
        if exchange == "binance":
            # Binance-specific logic
        elif exchange == "hyperliquid":
            # Hyperliquid-specific logic
        # Each new exchange requires modifying this class

Correct implementation (follows OCP):

class DataFetcher(ABC):  # Abstract base class
    @abstractmethod
    def get_price(self, symbol):
        pass  # Subclasses provide implementations

class BinanceDataFetcher(DataFetcher):
    def get_price(self, symbol):
        # Binance-specific implementation
        # This file becomes immutable once validated

class HyperliquidDataFetcher(DataFetcher):
    def get_price(self, symbol):
        # Hyperliquid-specific implementation
        # Completely separate module
        # Cannot affect Binance implementation

The second approach ensures that adding Hyperliquid requires creating a new module. The Binance module remains unchanged and untouched. Regression in working code becomes architecturally impossible.

Why This Architecture Matters for Individual Trading Systems

The architectural principles discussed here apply with particular force to individual trading operations, though for different reasons than institutional systems.

Production Risk Management

When operating a personal trading system, there is no rollback infrastructure, no QA team, and typically no staging environment. Deploying changes to production code means accepting direct exposure to potential failures. Market conditions do not pause for debugging sessions. The Open-Closed Principle eliminates this class of risk by preventing modifications to validated code.

Data Source Proliferation

Trading systems inevitably accumulate data sources. Initial development typically begins with a single exchange. Then additional exchanges are added for fee optimization. Historical CSV files become necessary for backtesting. Paper trading environments are required before live deployment. DEX integrations become relevant. Without proper abstraction, each addition increases system complexity nonlinearly.

Code Reuse Between Backtesting and Live Trading

Strategy implementations should operate identically whether consuming live market data or historical records. The data source should be completely transparent to strategy logic. This requires a unified interface that abstracts away implementation details. The Open-Closed Principle provides this abstraction naturally.

Solo Developer Constraints

Individual developers lack the resources for comprehensive testing across all integration points. Changes to shared code require testing every dependent system. With proper abstraction, new integrations can be tested in complete isolation. Existing integrations require no retesting because they remain unchanged.

This architectural approach transforms “adding features breaks things” into “adding features is safe by design.”

The Underlying Pattern: Interface Standardization

Exchange APIs share fundamental similarities at the conceptual level:

• All exchanges provide symbol identifiers (BTC/USDT, ETH/USD)

• All exchanges return price data (bid, ask, last price)

• All exchanges offer historical candlestick data (OHLCV)

• All exchanges support similar order types

However, implementation details vary significantly:

• Symbol format conventions differ (”BTC/USDT” vs “BTCUSDT” vs “BTC-USDT”)

• Rate-limiting policies are exchange-specific

• Authentication mechanisms vary

• Response formats lack standardization

• Error handling requires custom implementations per exchange

The architectural solution is an abstraction layer that captures conceptual similarities while isolating implementation differences. The abstract base class defines a unified interface. Each exchange provides a concrete implementation of that interface.

Strategy code interacts exclusively with the interface, never with exchange-specific implementations. This is the fundamental pattern underlying libraries like ccxt, though understanding the design principle enables you to apply it beyond pre-built libraries.

Implementation Workflow Transformation

The Open-Closed Principle fundamentally changes the development process:

Previous workflow:

1. Modify core DataFetcher class to add exchange support

2. Test new exchange functionality

3. Retest all existing exchanges to verify no regressions

4. Deploy with uncertainty about the impact on production code

5. Extended monitoring period due to systemic risk

New workflow:

1. Create new HyperliquidDataFetcher class implementing the interface

2. Test Hyperliquid implementation in isolation with paper trading

3. No retesting of existing exchanges required (they are unchanged)

4. Deploy with confidence (existing code is untouched)

5. Swap data source in strategy configuration when validated

Additional benefits:

• Backtesting requires creating CSVDataFetcher that reads historical files. Strategy code remains identical.

• Paper trading requires creating MockDataFetcher that returns simulated prices. Zero code changes in strategies.

• Multi-exchange arbitrage becomes straightforward: loop through a list of fetchers, compare prices.

• Each data source can be developed, tested, and debugged independently.

The transformation is not merely organizational. It represents a shift from “adding features creates risk” to “adding features is architecturally safe.”

Technical Debt Accumulation Patterns

The consequences of poor architecture are not immediately apparent. They accumulate gradually, then manifest suddenly.

Conditional branching approach:

• First exchange (Binance): 50 lines of code, functions correctly

• Second exchange (Coinbase): 50 additional lines (total: 100), still manageable

• Third exchange (Hyperliquid): 50 additional lines (total: 150), increasing complexity

This appears linear. However, cognitive overhead increases nonlinearly:

• Understanding requires reading all conditional branches

• Testing one modification requires validating all branches

• Debugging failures requires analyzing multiple exchange interactions

• Adding exchange N increases concern about breaking exchanges 1 through N-1

• Development velocity decreases as risk aversion increases

Abstraction-based approach:

• First exchange: 100 lines (additional upfront design cost)

• Second exchange: 60 lines (total: 160, but isolated)

• Third exchange: 60 lines (total: 220, complexity remains constant)

The initial investment is higher. However, for a solo developer:

• Each exchange exists in a separate module

• Work on Hyperliquid requires zero knowledge of Binance internals

• Testing is isolated to the specific implementation

• Refactoring one integration cannot affect others

• Adding exchanges 4, 5, and 6 maintains constant complexity

By the third or fourth exchange integration, the abstraction approach typically provides 5-10x improvement in development velocity and debugging time. For solo developers, this directly translates to reduced operational burden.

The Real Lesson

I’m telling you about trading bots because that’s what I build. But this principle applies everywhere:

• Building a portfolio tracker that supports multiple brokers? Same pattern.

• Creating a data pipeline that reads from multiple sources? Same pattern.

• Making a multi-cloud deployment tool? Same pattern.

Anywhere you have “multiple things that are similar but different,” you need this abstraction layer. The hard part is recognizing when you need it BEFORE you’ve built the mess.

Paid subscribers can find the Google Colab notebook link below that I prepared for you guys, together with some other great examples and educational content for a trading bot platform and quantitative finance.