DigiFinex26.02.26
AI SCORE 8.5

Remote Quantitative Strategist - High-Frequency Trading

$240K–$480K/month

About the Role

We are seeking a talented Remote Quantitative Strategist to join our dynamic team at DigiFinex. In this role, you will be responsible for statistical analysis of futures and equity markets, developing high-frequency trading models based on your analyses. This is a unique opportunity to work in a fast-paced environment where your insights will directly impact trading strategies.

What You'll Do

  • Conduct statistical analysis of financial markets to develop high-frequency trading models.
  • Provide quantitative research support for various trading strategies, including momentum and options trading.
  • Participate in strategy development, backtesting, optimization, and risk management.
  • Monitor and assess cutting-edge academic research and best practices in finance and artificial intelligence.
  • Conduct quality control and evaluation of research methodologies, proposing new ideas and enhancements.

Requirements

  • Master’s degree or above from a top-tier institution in applied quantitative fields such as physics, mathematics, statistics, or financial engineering.
  • Solid foundation in mathematical statistics and familiarity with finance-related models.
  • Proficient in machine learning and deep learning techniques, with expertise in frameworks like PyTorch or TensorFlow.
  • Excellent programming skills in at least one language: Python, C++, or Java.
  • Experience in quantitative trading, including quantitative development and model validation.

Nice to Have

  • Experience in physics or mathematics competitions.
  • Familiarity with technical implementation and trade execution.

What We Offer

  • Competitive salary ranging from HK$20,000 to HK$40,000 monthly.
  • Remote work flexibility allowing you to work from anywhere in the world.
  • Opportunity to work with cutting-edge technologies and methodologies in finance.
  • Collaborative and innovative team environment.
  • Professional development opportunities to enhance your skills.
Why This Job8.5 of 10

This Remote Quantitative Strategist position at DigiFinex offers a unique opportunity to work on high-frequency trading models with a competitive salary and flexible work environment.

Salary Range
Required
0/1
Optional
0/1
Bonus
0/1

Who Will Succeed Here

Proficient in Python and C++, with hands-on experience in developing quantitative trading algorithms and models, using libraries such as Pandas, NumPy, and TensorFlow for statistical analysis and machine learning.

Self-motivated and disciplined, enabling effective work in a fully remote environment, with strong time management skills to meet the fast-paced demands of high-frequency trading.

Deep understanding of financial markets and trading strategies, with a proven track record of applying advanced statistical methods and machine learning techniques to derive actionable insights that enhance trading performance.

Learning Resources

Statistical Analysis with Pythoncourse

Career Path

Remote Quantitative Strategist - High-Frequency Trading(Now)Lead Quantitative Analyst(1-2 years)Director of Quantitative Research(3-5 years)

Market Overview

Market Size 2024
$12.5B
Annual Growth
15.2%
AI Adoption
65%
Investment in FinTech
+25%
Labour Demand for Quantitative Analysts
+30%
Avg Salary for Quantitative Strategists
$150K

Skills & Requirements

Required
Statistical AnalysisMachine LearningDeep Learning
Growing in Demand
Data VisualizationAdvanced Statistical ModelingReinforcement Learning
Declining
Traditional Statistical Software (e.g., SAS)Excel-based Analysis

Domain Trends

Rise of AI in Trading
AI-driven algorithms are expected to dominate trading strategies, with a 50% increase in AI-based trading systems by 2025.
Increased Focus on Real-time Data Analytics
The demand for real-time analytics solutions in trading has surged, growing by 40% as firms seek to enhance decision-making speed.
Shift towards Cloud-based Trading Platforms
Cloud adoption in trading is projected to grow by 35%, enabling better scalability and access to advanced analytics tools.

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