QUANTITATIVE SYSTEMS · SC-27
Quantitative Trading & Market Microstructure System
A research stack for order-book events, microstructure signals, backtesting and strategy evaluation.
Starting point
Research becomes misleading when order-book events, execution assumptions and strategy metrics are mixed together.
01 · BUSINESS PROBLEM
A trading signal is meaningless without market context, costs and reproducible replay.
Research becomes misleading when order-book events, execution assumptions and strategy metrics are mixed together.
No replay
Signals cannot be inspected under the same historical event sequence.
Ignored costs
Backtests overstate performance when spread and execution are omitted.
Mixed layers
Data, signal and strategy logic become difficult to isolate.
02 · DECISION LOGIC
From market event to cost-aware strategy decision.
Raw events, derived signals, execution assumptions and evaluation remain separate.
Decision sequence
Each stage answers a different operating question
Store order-book and trade events.
Derive imbalance and microstructure features.
Evaluate with consistent execution assumptions.
Inspect performance after costs.
Decision rule — Research is credible when the same event path can be replayed with the same assumptions.
03 · WHAT CHANGED
A separated stack for data, signal, execution and evaluation.
Market data and strategy research share infrastructure without collapsing into one opaque backtest.
Persist raw events before deriving signals.
Keep feature logic independent from execution logic.
Replay history deterministically.
Report performance net of explicit cost assumptions.
04 · ARCHITECTURE
A modular path from input to decision.
Inputs → preparation → core logic → validation → decision output
SC-27 · SYSTEM ARCHITECTURE
Inputs → preparation → core logic → validation → decision output
Public portfolio implementation
Inputs
Source signals
Capture the operating inputs required by the system. [Python]
Preparation layer
Normalize context and create a stable analytical contract. [TypeScript]
Core system
Core engine
Run the main analytical or automation logic. [Vite]
Decision logic
Apply the rule, model or orchestration logic that changes the decision. [WebSockets]
Validation
Validation
Test outputs against explicit quality criteria. [ClickHouse]
Controls
Keep approvals, thresholds or constraints visible. [Redis]
Decision output
Decision output
Expose the result in a form the user can act on. [Pandas]
Monitoring
Record outcomes, exceptions and evidence for iteration. [NumPy]
Integration boundaries
Python
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
TypeScript
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
Vite
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
05 · EVIDENCE & ECONOMICS
Measure what changes the decision.
Public implementation, inspectable technical proof and decision-focused validation.
Research quality
Replay events
10k
Representative public example.
Signal families
4
Representative public example.
Backtest windows
6
Representative public example.
Backtest coverage
Cost components
3
Representative public example.
Regimes
2
Representative public example.
Reference economics
€1M
Reference scenario
10 bps
Illustrative improvement
€1k
Decision value
06 · TECHNICAL PROOF
Review the code behind the project.
Tools used
BUSINESS CONCLUSION
A quantitative system is useful when it makes assumptions inspectable before it makes performance impressive.
The point is reproducible research, cost-aware evaluation and a clear boundary between market observation and trading action.