Bootcamp 2019 - Welcome!

Bootcamp agenda

Day 1 (1.5 hours) Technology Bootcamp:

Here are my highly-recommended Desktop Applications for students.

Cloud Storages:

Cloud Storages give you the ability to access and sync your files from anywhere. This is extremely convenient if you experience problems with youor own device.

  1. Download Google Drive (Drive File Stream)
    • Unlimited Memory
    • Access to google drive account even after graduation
    • Saves Memory on yoour Laptop/Desktop
    • Access from anywhere
    • File version history
  2. Download OneDrive (Ok option, but not recommended in the long-run).
    • Only up to 1 terabyte of Memory
    • Not as good as Google Drive
    • Pairs well with Microsoft Office
  3. Others: Dropbox, Box
  4. Github

Virtual Sites

Access a UM computer remotely right from your home computer! Follow these instructions to install and run. This can give you access to hundreds of applications for free including many listed below.

Office 365:

Download Office 365

  • Comes free as a umich student and still have access after graduation
  • Access to all Microsoft Office 2016 apps such as Word, Excel, Powerpoint, etc…
  • Can download Office on up to 5 devices

Other Useful apps:

  • Overleaf for LaTex documents
  • Download UM-VPN
  • Download LastPass Password Manager
  • GoodNotes or Notability
  • Adobe Acrobat Pro DC (PDF reader)

Statistical Software:

  1. Download Rstudio
    • Open source
    • Constantly managed
    • No need for UM credentials
  2. Download Matlab
    • Full access while you are a student
  3. Download Stata
    • Get student pricing for Stata, 6 months for $48
  4. Download SAS
    • SAS University Edition

iPad Apps

  • GoodNotes or Notability
  • LastPass
  • Google Drive
  • All the Microsoft Office Apps
  • Handshake
  • Canvas App


Great for finding Job Opportunities exclusive to U of M students.

Build Website

Visit Website Tutorial from Quant Lab.

Interview Questions

Day 2 (6 hours)

  1. Option Pricing Discrete time [Lecture]
  2. Binomial Tree Model [Notebook] [Solution]

Day 3 (4 hours)

  1. Interview Questions
  2. [Matlab Tutorial] [Matlab Notebook]
  3. Python applied to interview questions

Day 4 (3.5 hours)

  1. Option Pricing Continuous Time Black Scholes [Lecture]
  2. Python with stochastic processes brownian motion [Notebook] [Solution]
  3. Discuss the Princeton Quant Trading Conference in Chicago on November 2nd.
  4. Introduction to modeling market risk.

Day 5 (4.5 hours)

  1. Company Reviews
  2. Vetting companies, and reviewing a Handshake/job fair company
  3. Rstudio
  4. Interview Questions
  5. Create python-hackerrank-notebook.ipynb in your Quant-python repo, and solve the balanced brackets problem. Push your changes to your GitHub repo.
  6. Continuing to modeling market risk.
    • Create a portfolio of 100 shares of AMZN stock, and compute 5%-quantile VaR.
    • For the portfolio of 100 shares of AMZN stock, compute 5%-quantile Expected Shortfall (aka Average VaR or Conditional VaR).
    • Begin simulation of 3 stocks (AMZN, GOOG, and AAPL).

Day 6 (3 hours)

  1. Apply to at least one company of your choice on Handshake
  2. VaR [Notebook]
  3. Interview Questions
  4. Complete 3-stock simulation.
  5. Class discussion: Russell 3000 covariance matrix.
  6. Back to modeling market risk.

Part 1

  • Simulation of 3 stocks (AMZN, GOOG, and AAPL) available for download from GitHub.
  • Create a CSV or tab-delimited file similar to your Amazon file, but add columns for the closing prices of Google and Apple.
  • In Python, create a PriceSeries class.
  • In Python, figure out how to read the prices from your file, and create PriceSeries objects for each of the three stocks.
  • Modify your PriceSeries class so that it also caches the log returns.
  • Class discussion: How Monte Carlo (simulation of returns) is done using the RiskMetrics model.
  • Begin building simulation of the 3 stocks in Python.

Part 2

  • Python: Add log returns to price series.
  • Discussion: Multi-factor simulation, and EWMA.
  • Python: Create Scenario class that contains weights for returns.
  • Python: Create Position class with Price Series, and # shares.
  • Python: Create Portfolo class with positions.
  • Python: Generate 100,000 Scenarios, and simulate PnL on portfolio.