Quantconnect developer to build trading strategy

I need help building a successful trading strategy on QuantConnect. The goal is to achieve a sharp ratio greater than 2 and returns above 100% per annum, with the strategy scripted and backtested on the platform.

Scope of work -

Develop a trading strategy with a sharp ratio greater than

Ensure returns exceed 100% annually. -

Draft and backtest the strategy using QuantConnect platform.

Additional information Build & Backtest a High-Performance QuantConnect Strategy (Python) 1)

Project Overview

I’m looking for an experienced Quant developer to design, code, and backtest a systematic trading strategy on QuantConnect (LEAN) in Python that targets:

Sharpe ratio ≥ 2.0 (net of all modeled costs)

CAGR (annualized return) ≥ 100% over a multi-year backtest

This project includes full research, implementation, backtesting, robustness testing, documentation, and handover. >

Note: I understand these targets are ambitious. Please propose how you’ll approach them (universe, motifs, risk control, and robust validation). The acceptance criteria (below) are what we’ll use to evaluate the final result. 2)

Success Metrics (Net of Costs)

Primary:

Sharpe ratio ≥ 2.0 CAGR ≥ 100%

Risk Guardrails (targets):

Max drawdown ≤ 25%