Preparing for a Quant Trading Internship: Programming, Data, and Backtesting

Summary

The document offers preparation advice for an upcoming quantitative trading internship involving research and backtesting across markets. It recommends finding out which programming language the firm uses and gaining familiarity with that environment, since workplace tools may differ from a candidate’s previous experience. It also suggests building a broad understanding of quantitative trading through introductory reading and becoming comfortable working with financial data.

For practical preparation, the answer recommends implementing a simple, documented strategy, such as value, momentum, or a factor strategy, to learn the backtesting process. This exercise can expose common research pitfalls, including look-ahead bias and selecting an appropriate universe of securities. The advice is based on one respondent’s experience at a quantitative equity firm, rather than a universal internship syllabus. Because the role’s asset class and tools are not yet known, the candidate is encouraged to ask the company mentor for relevant papers or background material and tailor preparation accordingly.

Key ideas

• Ask the firm which programming language and tools the internship will use.
• Build a general understanding of quantitative trading with introductory study.
• Practice handling financial data even if the exact asset class is not yet known.
• Backtest a simple documented strategy to learn the research workflow.
• Watch for look-ahead bias and carefully define the securities included in a test.
• Ask the internship mentor for reading material relevant to the live project.

Tags

# Payoff of a Butterfly spread under risk neutral measure is always positive for any t<T


# Payoff of a Butterfly spread under risk neutral measure is always positive for any t<T












In a situation where $$K_3-K_2=K_2-K_1=h>0$$ and $$K_1\le S_t\le K_3$$ where $$S_T=S_t.e^{[(r-\sigma^2/2)(T-t)+\sigma(W_T-W_t)]}$$ (i.e. Stock process follows GBM under the risk neutral measure).

I know the value of the call under the risk neutral measure is: $$f(S_t)= e^{-r(T-t)}*E((S_T-K_1)^+-2(S_T-K_2)^++(S_T-K_3)^+|\mathcal{F_t})$$ How do we know that the value of the payoff of the butterfly spread using calls is positive for any t<T.

## Answer by Gordon (score 4)

https://quant.stackexchange.com/a/70843

Note that \begin{align*} K_2 = \frac{K_1+K_3}{2}. \end{align*} Then \begin{align*} &\ \max(S_T-K_1, \, 0) + \max(S_T-K_3, \, 0) \\ =&\ \max\big(S_T-K_1 + \max(S_T-K_3, \, 0), \, \max(S_T-K_3, \, 0)\big)\\ =&\ \max\big(\max(S_T-K_3 + S_T-K_1, \, S_T-K_1), \, \max(S_T-K_3, \, 0)\big)\\ =&\ \max\big(2S_T-(K_1+K_3), S_T-K_1, S_T-K_3, 0\big)\\ \ge&\ \max\big(2S_T-(K_1+K_3), 0\big)\\ =&\ 2\max(S_T-K_2, \, 0). \end{align*}

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