2018-11-16 (last meeting of 2018)
Fermi Problem: A classic…you travel back in time to 1 bya (billion years ago)n with a one liter bottle. You fill the bottle with water from the ocean, and dump it out. Then you return to the present day, and buy a liter of water. How many molecules in your purchased liter are from the liter you poured into the ocean?
Brainteaser: you must select a random byte from a data stream of unknown size, but too large to store. How do you do it?
Probability problem: You flip a coin 101 times, and a friend then flips the coin 100 times. What is the probability you flipped more heads?
- This week in the markets. Watch the yield curve. https://pbenson.github.io/docs/quantTechnicalQuestions/quantTechnicalQuestions.pdf
- Yield curve bootstrapping recap. For more information, read Hagan and West.
- Recreational math and computing with numpy. Watch graphing a times table on a circle.
- Warmup: You flip a fair coin 100 times. What is the probability you flip an even number of heads? Prove your answer.
- Fermi Problem: How many tires are sold in the US per year?
- Fermi Problem: How much does the average taxpayer pay in interest on US Debt? US Debt Clock
- Discussion of US Treasury auctions.
- This week in the markets.
- Last time, we learned about pulling US Treasury interest rates into
pandas. Now, we will use that data to build the instantaneous forward rate curve, and generate the yield curve. Start with this template.
2018-11-02 No meeting
- Warmup: You have a coin, and you don’t know if it is fair. How can you use the coin to emulate a fair coin?
- Fermi Problem: How many US dollar coins would you have to lay on the surface area of Manhattan in order to fully cover it?
- This week in the markets. Is it Trump? Or the Fed? FAANG? How about China?
- Quantopian uses numerous data sources, including quandl. Quandl has an API that takes advantange of numpy and pandas. Launch jupyter, and open a copy of the Intro to quandl and iexfinance-template notebook
- Introduction to Quantopian. Familiarization with research notebooks and online help.
- All sample tatistics fromhave sample bias, and we would like to have a window around our statistic with a probability that the actual value of the statistic. As an example, we complete the notebook exercises given in Noise in sample correlations
- Homework: Let $U$ be standard uniform. Given $n$ observations of $U$, what is the distribution of the $k$-th order statistic?
- Homework: Given $X$ with distribution function $F$, show that $F(X)$ is uniform
- Homework: Now that you’ve built a single stock historical simulation in a spreadsheet, build the same thing in Python (problem 2.22 in the primer).
- Historical simulation of multiple stocks.
- Monte Carlo portfolio simulation.
- Pick up where we left off on page 6 of Risk Management Primer
- Advertisement for Cubist Systematic Strategies
If you haven’t already done so, install vscode (Visual Studio Code) with Anaconda. I couldn’t find a way to incrementally install it, so you can simply download and install Anaconda again. At the end of the install, it will ask you if you want to install Visual Studio Code.
OOP with Python using Complex numbers as an example.