An evolving research platform for testing trading ideas as repeatable software experiments. The system is being designed to keep data ingestion, features, signals, risk rules and execution adapters separate, so each layer can change without rewriting the whole workflow.
In development
Python · Automation · Applied ML
Algorithmic Trading System
A modular research environment that brings market data, feature engineering, signal generation, backtesting and paper trading into one workflow.
PythonPandasscikit-learnREST APIsSQL
Architecture view
Research-to-execution architecture
A planned modular flow that keeps market research, portfolio rules and execution concerns independently testable.
01Market dataIngest · normalize
02FeaturesTime-safe transforms
03Signal engineRules · ML models
04Risk gateSize · limits · costs
05ExecutionBacktest · paper
01 / ContextWhat I built
What I built
and why.
Trading prototypes can easily mix data preparation, strategy logic and execution assumptions in one script. That makes experiments difficult to reproduce and can hide look-ahead bias, unrealistic costs or inconsistent position sizing.
The current architecture treats each research run as a traceable pipeline. Market data is normalized first, features are generated without future information, signals pass through a dedicated risk gate, and the same interface is intended to support both historical backtests and paper-trading experiments.
02 / Engineering
Key decisions
The choices that shaped the system—not only the technologies that appear in it.
Separate research from execution
Strategy logic produces intent; portfolio and execution layers decide whether and how that intent becomes an order.
Make assumptions explicit
Costs, position sizing and timing rules belong in configuration so results can be reproduced and challenged.
Log the whole experiment
Parameters, data windows and evaluation outputs are tracked together instead of being scattered across notebooks.
Technical toolkit
Built with
PythonPandasscikit-learnREST APIsSQL
03 / Reflection
What the project taught me
- Clean module boundaries are especially valuable when the research question changes often.
- Risk and execution assumptions can matter as much as the raw signal.
- A useful backtest should be easy to inspect, reproduce and disprove.
This page uses a system diagram to document the current technical structure. Product screenshots and experiment outputs can be added here as each project evolves.