Solal A.

Key Skills

Software

Other

Top Subject Matter

Financial statistics / quantitative trading

Financial education / fintech entrepreneurship

Legal Services & Contract Review

Top Data Types

Text

3D Sensor

Document

Top Task Types

Prompt + Response Writing (SFT)

Freelancer Overview

Information Flow in Crypto Order Books — Financial Statistics (Project). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Bachelor in Engineering, CentraleSupélec (Paris-Saclay University) (2024) and Classes Préparatoires (Intensive Two-Year Training), Lycée Henri IV (2024). AI-training focus includes data types such as Computer Code, Programming, and Text and labeling workflows including Computer Programming, Coding, and Prompt + Response Writing (SFT).

Labeling Experience

Financial Education App — Entrepreneurship Track (Project)

OtherTextPrompt + Response Writing (SFT)

Designed and prototyped a mobile application intended to deliver financial literacy content. Iterated on product features and user flows through founder-led workshops and pitch sessions. Produced and refined instructional materials and product messaging to support the app’s educational objectives. • Conducted iteration cycles based on feedback from workshops and pitch sessions. • Defined user journeys and content/product requirements for financial education. • Refined app concepts and business model components during ideation and presentation. • Translated educational goals into app prototype specifications.

2024

Information Flow in Crypto Order Books — Financial Statistics (Project)

Other

Designed and evaluated a predictive trading strategy using statistical analysis of asynchronous limit order books across multiple cryptocurrencies. Engineered and tested features derived from order-book structure such as bid–ask imbalance and depth shifts to quantify lead–lag effects. Performed backtesting with metrics including Sharpe ratio, hit rate, and net profitability after transaction costs. • Used Python-based data analysis tooling (e.g., NumPy/Pandas/SciPy) and SQL for market data handling. • Applied high-frequency statistical methods to model cross-asset relationships. • Built backtest pipelines to compare strategy performance under transaction costs. • Assessed profitability and risk using standard financial evaluation metrics.

2024

Education

Work History