Quantitative Trading

Turn strategy into

A system that can go online and operate

PeriodAbout 1 month

Business LineQuantitative systems

Core StackC++/Rust/Python/Multi-exchange API

RegionChina

Project Background and Challenges

The client is a quantitative team with strategies in the research phase, and needs to move the strategy from the notebook to the real market. It requires support for multi-policy parallelism and low-latency execution, with backtesting and real-world logic consistent with the data.

The challenge lies in consistency between backtesting and live trading—Many quantitative systems suffer from the problem of "backtesting is good, but live trading loses money." Essentially, the backtesting engine and live trading execute using different code paths.

The scope we undertake

Strategy framework, backtesting engine, live trading execution chain, multi-exchange integration, risk control and monitoring, and operation and maintenance alerts.

Scope 1

Strategic framework

Scope 2

Backtesting the engine

Scope 3

Live trading execution link

Scope 4

Multi-exchange integration

Scope 5

Risk control and monitoring

Scope 6

Operations and maintenance alarm

Technology stack

Core

C++ / Rust low-latency execution

Strategy

Python policy framework, hot loading

Exchange

CEX/DEX multi-exchange unified abstraction

Backtesting

Shares the same execution code path as the real disk

Key technical decisions

Decision 01

Backtesting is isomorphic to the real disk

The backtesting engine shares the same order logic with live trading, structurally avoiding losses from inflated backtesting and real trading.

Decision 02

Strategy isolation

Multi-policy isolation runs; a single policy anomaly does not affect other policies, and the fault domain is limited to the policy level.

Decision 03

Real-time risk control

Real-time monitoring of positions, exposures, and loss limits; stop-loss immediately upon trigger, risk control does not rely on strategic self-discipline.

Delivery Success (Desensitization)

<200 ms

End-to-end order delays

10+

Parallel strategy numbers

30+ units

Docking with exchanges

Teams and cycles

The total cycle is about 15 days, involving 4 people, and progressing in four phases:

Strategy and Demand Review

Backtesting and strategy framework

Live trading docking and risk control

Launch and monitoring

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