FTR Trader | Power Markets Quant, HFT & P&L Impact

Redstone Search; United States; On-site; USD 150,000 - 240,000; Full time

Benefits offered by this job

Competitive compensation

Direct impact on P&L

Cutting-edge technology

High-frequency trading infrastructure

Career progression

Job summary

Redstone Search seeks a skilled FTR Trader or Quantitative Analyst to join a high-performance proprietary trading team in Stamford. The role focuses on developing FTR trading strategies, power market modelling, and congestion analysis to identify opportunities in SPP and ERCOT markets.

You will work with quantitative and fundamental analysts and senior traders to refine predictive analytics, manage risk, and drive portfolio performance in a data-driven, fast-paced environment.

Qualifications

• 4+ years in FTR trading, power trading, or quantitative analytics in power markets.
• Strong understanding of US power grid, nodal markets, congestion, and auction dynamics (SPP & ERCOT preferred).
• Proficiency in Python, R, SQL for data analysis and modelling.
• Analytical, innovative mindset for fast-paced, quantitative trading.

Responsibilities

• Develop and execute FTR trading strategies using power market modelling and congestion analysis.
• Conduct market analysis with quantitative modelling, ML, and statistical arbitrage.
• Collaborate with analysts and senior traders to refine predictive analytics.
• Manage risk exposure, optimise bidding strategies, and improve portfolio performance.

Skills

FTR trading

Power trading

Quant analytics

Python

R

SQL

Market modelling

Machine learning

Job description

Redstone Search seeks a skilled FTR Trader or Quantitative Analyst to join a high-performance proprietary trading team in Stamford. The role focuses on developing FTR trading strategies, power market modelling, and congestion analysis to identify opportunities in SPP and ERCOT markets.

You will work with quantitative and fundamental analysts and senior traders to refine predictive analytics, manage risk, and drive portfolio performance in a data-driven, fast-paced environment.