Discretionary Trading vs Quantitative Trading
Developers should learn discretionary trading when building or integrating trading platforms, financial analysis tools, or algorithmic trading systems that require human oversight or hybrid approaches meets developers should learn quantitative trading to apply programming, data science, and machine learning skills in finance, enabling careers in fintech, hedge funds, or proprietary trading firms. Here's our take.
Discretionary Trading
Developers should learn discretionary trading when building or integrating trading platforms, financial analysis tools, or algorithmic trading systems that require human oversight or hybrid approaches
Discretionary Trading
Nice PickDevelopers should learn discretionary trading when building or integrating trading platforms, financial analysis tools, or algorithmic trading systems that require human oversight or hybrid approaches
Pros
- +It's particularly useful in scenarios involving complex market events, regulatory compliance checks, or when developing user interfaces for professional traders who rely on discretionary decision-making
- +Related to: algorithmic-trading, technical-analysis
Cons
- -Specific tradeoffs depend on your use case
Quantitative Trading
Developers should learn quantitative trading to apply programming, data science, and machine learning skills in finance, enabling careers in fintech, hedge funds, or proprietary trading firms
Pros
- +It's particularly valuable for building automated trading systems, analyzing market data, and optimizing portfolios, with use cases including arbitrage, market-making, and risk management
- +Related to: python, machine-learning
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use Discretionary Trading if: You want it's particularly useful in scenarios involving complex market events, regulatory compliance checks, or when developing user interfaces for professional traders who rely on discretionary decision-making and can live with specific tradeoffs depend on your use case.
Use Quantitative Trading if: You prioritize it's particularly valuable for building automated trading systems, analyzing market data, and optimizing portfolios, with use cases including arbitrage, market-making, and risk management over what Discretionary Trading offers.
Developers should learn discretionary trading when building or integrating trading platforms, financial analysis tools, or algorithmic trading systems that require human oversight or hybrid approaches
Disagree with our pick? nice@nicepick.dev