Blog
Practical technology insights
What to Do When a Broker API Rejects an Order
Broker API order rejections are part of real-world trading software. This guide explains rejection categories, logging, user messages, retry rules and escalation so failures are handled safely.
Preventing Duplicate Orders During Network Retries
Network failures can make an automated trading system uncertain about whether an order was actually accepted. Client order IDs, idempotency records and broker reconciliation can help systems retry safely without blindly creating duplicate orders.
How to Track an Order Through Its Full Lifecycle
Understand how trading orders move from submission through pending, open, partial fill, filled, cancelled or rejected states. Learn why accurate lifecycle tracking and reconciliation matter in automated trading software.
REST APIs vs WebSockets in Trading Applications
REST APIs and WebSockets solve different communication problems in trading applications. Understanding their strengths helps businesses choose a practical architecture for market data, orders, positions and recovery.
Broker API Integration: What a Business Should Know
Broker API integration connects trading software with market data and order execution services. This guide explains authentication, instruments, data, orders, limits, testing and operational responsibilities.
Designing Trading Software for Restart Recovery
Trading software must recover safely after crashes, restarts and deployments. This guide explains startup reconciliation for orders, positions, strategy state and missed events.
Avoiding Duplicate Signals in Automated Trading
Duplicate signals can cause automated trading systems to submit repeated entries from the same market event. This guide explains idempotency, candle identifiers, state flags and order reconciliation methods for safer signal processing.
Tick Data vs Candle Data for Trading Automation
Tick data and candle data provide different levels of market information for automated trading systems. This guide compares their granularity, latency, data requirements and practical use cases without assuming that one is always better.
Understanding Strategy Warm-Up Periods
Trading indicators often need historical market data before their values can be evaluated reliably. This guide explains warm-up periods, insufficient historical bars, startup behaviour and how proper data preparation can prevent false strategy decisions.
Single-Symbol vs Multi-Symbol Trading Architecture
Single-symbol and multi-symbol trading systems have different software architecture requirements. This guide compares concurrency, market data, shared risk limits and state isolation so trading software can be designed for its actual scope.
Building a Rule Engine for Trading Strategies
A trading rule engine converts strategy requirements into structured, testable conditions for indicators, confirmations, entries and exits. This guide explains how to design a maintainable rule engine that can support changing strategies without turning the software into one large block of fragile logic.
State Management in Long-Running Trading Bots
Long-running trading bots must remember what they have already processed, which positions they manage and what actions are pending. This guide explains runtime state, persistence, recovery and safeguards that help prevent duplicate or inconsistent actions.
Designing a Multi-Strategy Trading Application
A multi-strategy trading application can run different trading strategies from one platform, but safe architecture requires clear separation of strategy state, capital, orders, logs and failures. This guide explains the key design principles for building scalable custom trading software.
Backtesting vs Paper Trading vs Live Trading
Backtesting, paper trading and live trading are different stages of evaluating a trading strategy or automated system. This guide explains what each stage can reveal, what it cannot prove and why none can guarantee future trading results.
How a Trading Signal Travels from Data to Order
A trading signal passes through several technical stages before an automated order is placed. This guide follows one signal from market data validation and indicator calculation through strategy rules, risk checks, execution and logging.
Event-Driven vs Time-Driven Trading Systems
Event-driven and time-driven trading systems process strategy rules in different ways. This guide explains how each architecture works, their practical differences, suitable strategy types, limitations and important design considerations.
Why Trading Strategy Requirements Must Be Unambiguous
Clear trading strategy requirements are essential when converting a manual strategy into software. This guide explains why vague terms such as strong, near and quickly can cause different interpretations, and how to replace them with measurable conditions.
Algorithmic Trading Software: A Practical Beginner's Guide
Algorithmic trading software uses predefined rules to analyse market conditions, generate signals and automate trade execution. This practical beginner's guide explains its main components, workflow, realistic benefits, limitations and common misconceptions.
How to Convert Manual Trading Rules into an Algorithm
Learn how to convert discretionary trading decisions into clear, measurable rules that a developer can implement. This guide explains how to define entries, exits, timing, position sizing, risk management and exceptions before building an automated trading system.