The Evolution of Algorithmic Trading: Four Generations That Changed Everything
From single-script indicators to AI-orchestrated strategy factories: a technical history of how algorithmic trading infrastructure evolved, and where it's going next.
The Generational Taxonomy
The Scripting Era
Single-script indicators on tick data. No portfolio logic, no risk management.
The Framework Era
Event-driven Python frameworks. Backtesting and multi-strategy support, but entirely hand-coded.
Institutional Infrastructure
Multi-asset orchestration, cloud-scale execution, institutional risk frameworks. The ceiling of manual development.
AI-Assisted Infrastructure
AI assists with research artifacts. Humans define constraints, review evidence, and decide what happens next.
The Paradigm Shift
A fundamental change in the trading model itself. Beyond indicators, beyond candles.
The Bottleneck That Defined Every Generation Before 3.5
Every generation improved what trading systems could do. None of them questioned who would write the code.
Gen 1 traders wrote scripts. Gen 2 developers wrote frameworks. Gen 3 teams wrote institutional-grade infrastructure. The sophistication of the code increased. But the constraint remained constant: a human had to write every line.
This created a coordination bottleneck. Research progress remained bounded by team handoffs, engineering work, and accumulated technical debt.
AI assistance can reduce that coordination burden without removing research standards or accountable judgment.
Gen 1–3 Paradigm
- Human defines logic →
- Human writes code →
- Human reviews/tests
Gen 3.5 Paradigm
- Human defines logic →
- AI assists with code →
- Human reviews/tests
What Gen 3.5 Means in Practice
From Research Intent to an Inspectable Draft
Describe the logic and constraints in natural language, inspect the generated Artifact, then choose whether to run local validation and backtesting. Code generation, backtest completion, statistical validation, and production approval remain separate states.
Your IP Stays on Your Machine
StratCraft validates, compiles, and backtests strategies on your local infrastructure. AI strategy generation is a separate hosted service for Basic accounts; generated C++ returns to the desktop for local execution and evidence persistence.
Signal Factory Model
Traditional frameworks often isolate one strategy at a time. StratCraft keeps candidates, experiments, evidence, and rejection reasons in a connected research record so users can compare alternatives without treating volume as proof.
Built on a Foundation That Gen 3 Cannot Match
Language: C++23
Paradigm: RAII as Causality
Latency: Sub-millisecond execution
Threading: Lock-free where possiblePattern: VS Code Extension Model
Isolation: Each strategy runs sandboxed
Isolation: Explicit plugin lifecycle
AI Target: Plugins are AI generation unitsWhat Comes After 3.5?
Gen 4 is not an incremental improvement. It is a change in the trading model itself. Moving beyond traditional indicators and candle-based price action into a fundamentally different dimension of market interaction.
We are actively building toward this. We won't say more until we can show it.