01
Strategy Design
Rule-based trading logic is designed to make decision paths explicit, testable, and easier to refine.
- Translate discretionary observations into structured entry and exit conditions.
- Define signal generation concepts that can be observed and evaluated consistently.
- Reduce ambiguity so performance can be analyzed at the rule level rather than by intuition alone.
02
Backtesting Methodology
Backtesting is treated as a discipline, not a marketing exercise.
- Use multi-year historical data to observe strategy behavior across different volatility and trend regimes.
- Actively avoid overfitting by keeping rule complexity constrained and interpreting results conservatively.
- Track performance metrics such as Sharpe ratio, drawdown, and consistency rather than focusing on headline returns alone.
03
Risk Management
Risk controls are part of the system design from the start, not an afterthought.
- Position sizing is tied to exposure discipline and expected strategy variance.
- Stop-loss logic is applied to limit uncontrolled downside during adverse moves.
- Capital allocation is managed at the portfolio level to prevent concentration risk.
04
Execution Architecture
Execution infrastructure is built to respond reliably in real time.
- Event-driven system design keeps data intake, signal evaluation, and order execution modular.
- Real-time market data processing supports timely strategy decisions and monitoring.
- API-based execution links system logic directly with exchange environments while preserving control visibility.
05
System Stack
The stack is selected for speed of implementation, operational flexibility, and system clarity.
- Python for research workflows, orchestration, and rapid iteration.
- Go for performance-oriented components and service reliability.
- Docker for consistent deployment environments across local and cloud workflows.
- Linux and WSL for development ergonomics and production-aligned system behavior.