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Said EMA, which was true and felt like a reasonable answer.
Acknowledge that as a software engineer, your focus is on building and maintaining systems that process technical indicators, rather than discretionary trading. Then, discuss the common technical indicators you've encountered in data pipelines or backtesting frameworks, and how you ensure their correct implementation and efficient computation. Finally, tie it back to the role by emphasizing your ability to collaborate with quants and traders to translate their requirements into robust code.
Pro tip: Demonstrate awareness that technical indicators are just features in a larger model, and that data quality and latency often matter more than the specific indicator. Mention that you prioritize reproducibility and testing when implementing indicators, as subtle bugs can lead to significant financial losses.
State that as a software engineer, you don't typically make trading decisions but implement and support the tools that use technical indicators. This sets the right expectation and avoids overstepping.
Mention a few widely used technical indicators such as moving averages (SMA, EMA), RSI, MACD, Bollinger Bands, and volume-based indicators. Explain that these are often used in algorithmic trading strategies.
Describe how you would implement these indicators efficiently, considering factors like data streaming, windowing, and numerical stability. Highlight any experience with libraries or custom code.
Stress the importance of data quality, latency, and rigorous testing (unit tests, backtesting) to ensure indicators are computed correctly and reliably.
Explain how your work enables traders and quants to make informed decisions, and how you collaborate with them to refine requirements and improve system performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I proposed using ewm() which is literally the pandas function for this.
Start by clarifying the EMA definition and parameters (span, adjust, etc.), then implement it using pandas' ewm method or a manual loop. Focus on explaining the recursive formula and how to handle the initial value, while discussing trade-offs between vectorized and iterative approaches.
Pro tip: Demonstrate awareness of pandas' built-in ewm function and explain why a manual implementation might be needed (e.g., custom initialization or performance). Also, mention edge cases like NaN handling and the impact of adjust parameter.
Ask about the expected input (e.g., Series or DataFrame), parameters (span, alpha, adjust), and initialization method for the first EMA value.
State the recursive formula: EMA_t = alpha * price_t + (1 - alpha) * EMA_{t-1}, where alpha = 2/(span+1). Discuss how the initial EMA is typically set (e.g., first price or SMA).
Show how to use pandas' ewm method: series.ewm(span=span, adjust=False).mean(). Explain that adjust=False gives the recursive formula, while adjust=True uses a different weighting.
If needed, outline a manual loop: initialize EMA with first value, then iterate through data applying the formula. Mention performance considerations for large datasets.
Cover handling of NaNs, the effect of adjust parameter, and trade-offs between vectorized (ewm) and iterative approaches in terms of speed and flexibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.