Insights
Practical, original writing on how trading software is designed, tested and run. Educational only — none of it is investment advice or a recommendation.
What is algorithmic trading?
A clear definition, how automated execution logic differs from discretionary trading, and what these systems can and cannot do.
TechnologyHow does algorithmic trading software work?
The processing cycle: market data, rule evaluation, signal generation, risk checks, execution and logging — module by module.
DevelopmentHow to convert a trading strategy into software
Turning a discretionary or semi-rules approach into unambiguous, testable conditions a developer can implement.
DevelopmentWhat information does a developer need to code a trading strategy?
A field-by-field checklist: instruments, timeframe, entries, exits, stops, sizing, re-entry, trading hours and risk limits.
BacktestingBacktesting vs live trading: why results differ
Costs, slippage, liquidity, latency, data quality and over-fitting — the gap between a backtest and a live account.
More articles are planned, including risk management in algorithmic trading, what a strategy engine is, broker API integration, slippage, position sizing, maximum drawdown, evaluating trading software, and custom vs off-the-shelf platforms. Each will be published only when it is genuinely useful and original.
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Bring your documented rules and we will map them onto real software.