Algorithmic Trading Strategist
Job Description
In the microseconds of modern financial markets, algorithmic trading is king. As an Algorithmic Trading Strategist, you will be essential in training AI to master the intricacies of high-frequency trading, execution optimization, and market microstructure, equipping it with the intelligence to execute complex strategies with unparalleled speed and precision.
Key Responsibilities
Develop comprehensive training datasets on various algorithmic trading strategies, including mean reversion, arbitrage, and momentum-based systems.
Create scenarios and provide expert evaluations on market microstructure, order book dynamics, and liquidity analysis.
Evaluate AI outputs related to execution optimization algorithms (e.g., VWAP, TWAP, POV) and their performance metrics.
Assess AI's understanding of high-frequency trading (HFT) techniques, latency optimization, and co-location strategies.
Identify and correct factual inaccuracies or strategic misinterpretations in AI's analysis of market impact, slippage, and adverse selection.
Refine AI's capacity to interpret real-time market data, identify trading opportunities, and manage risk in automated environments.
Ideal Qualifications
Master's degree or Ph.D. in Quantitative Finance, Computer Science, Mathematics, or a related field.
Minimum 6 years of experience in algorithmic trading, quantitative research, or HFT strategy development.
Expert proficiency in programming languages such as Python, C++, or Java, with experience in low-latency systems.
Deep understanding of market microstructure, exchange protocols, and order routing.
Proven track record in developing, backtesting, and deploying algorithmic trading strategies.
Familiarity with financial data APIs (e.g., Interactive Brokers, Alpaca) and backtesting frameworks.
Project Timeline
Start Date: Immediate
Duration: Ongoing, minimum 6 months
• Commitment: 20-30 hours per week
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