MC2-Project-1
Contents
Overview
In this project you will create a basic market simulator that accepts trading orders and keeps track of a portfolio's value and then assesses the performance of that portfolio.
To Do
Part 1: Put all of your code in a file called marketsim.py. Create a function compute_port_val(
python marketsim.py 1000000 orders.csv values.csv
Where the number represents starting cash and orders.csv is a file of orders organized like this:
- Year
- Month
- Day
- Symbol
- BUY or SELL
- Number of Shares
For example:
2008, 12, 3, AAPL, BUY, 130 2008, 12, 8, AAPL, SELL, 130 2008, 12, 5, IBM, BUY, 50
Your simulator should calculate the total value of the portfolio for each day using adjusted closing prices (cash plus value of equities) and print the result to the file values.csv. The contents of the values.csv file should look something like this:
2008, 12, 3, 1000000 2008, 12, 4, 1000010 2008, 12, 5, 1000250 ...
Part 2: Create a portfolio analysis tool, analyze.py, that takes a command line like this:
python analyze.py values.csv \$SPX
The tool should read in the daily values (cumulative portfolio value) from values.csv and plot them. It should use the symbol on the command line as a benchmark for comparison (in this case $SPX). Using this information, analyze.py should:
- Plot the price history over the trading period.
- Your program should also output:
- Standard deviation of daily returns of the total portfolio
- Average daily return of the total portfolio
- Sharpe ratio (Always assume you have 252 trading days in an year. And risk free rate = 0) of the total portfolio
- Cumulative return of the total portfolio
Orders files to run your code on
Grab this zip file to get the input files to run your code against: media:orders-files.zip
Short example to check your code
Here is a very very short example that you can use to check your code. Assuming a 1,000,000 starting cash and the orders file orders-short.csv:
The orders file:
2011,1,05,AAPL,Buy,1500, 2011,1,20,AAPL,Sell,1500,
The daily value of the portfolio (spaces added to help things line up):
2011, 1, 5, 1000000 2011, 1, 6, 999595 2011, 1, 7, 1003165 2011, 1, 10, 1012630 2011, 1, 11, 1011415 2011, 1, 12, 1015570 2011, 1, 13, 1017445 2011, 1, 14, 1021630 2011, 1, 18, 1009930 2011, 1, 19, 1007230 2011, 1, 20, 998035
For reference, here are the adjusted close values for AAPL on the relevant days:
2011-01-05 16:00:00 332.57 2011-01-06 16:00:00 332.30 2011-01-07 16:00:00 334.68 2011-01-10 16:00:00 340.99 2011-01-11 16:00:00 340.18 2011-01-12 16:00:00 342.95 2011-01-13 16:00:00 344.20 2011-01-14 16:00:00 346.99 2011-01-18 16:00:00 339.19 2011-01-19 16:00:00 337.39 2011-01-20 16:00:00 331.26
The full results:
Details of the Performance of the portfolio : Data Range : 2011-01-05 16:00:00 to 2011-01-20 16:00:00 Sharpe Ratio of Fund : -0.449182051041 Sharpe Ratio of $SPX : 0.88647463107 Total Return of Fund : 0.998035 Total Return of $SPX : 1.00289841449 Standard Deviation of Fund : 0.00573613516299 Standard Deviation of $SPX : 0.00492987789459 Average Daily Return of Fund : -0.000162308588036 Average Daily Return of $SPX : 0.000275297459588
More comprehensive examples
We provide an example, orders.csv that you can use to test your code, and compare with others. All of these runs assume a starting portfolio of 1000000 ($1M).
The final value of the portfolio using the sample file is -- 2011,12,20,1133860 Details of the Performance of the portfolio : Data Range : 2011-01-10 16:00:00 to 2011-12-20 16:00:00 Sharpe Ratio of Fund : 1.21540462111 Sharpe Ratio of $SPX : 0.0183391412227 Total Return of Fund : 1.13386 Total Return of $SPX : 0.97759401457 Standard Deviation of Fund : 0.00717514512699 Standard Deviation of $SPX : 0.0149090969828 Average Daily Return of Fund : 0.000549352749569 Average Daily Return of $SPX : 1.72238432443e-05
The other sample file is orders2.csv that you can use to test your code, and compare with others.
The final value of the portfolio using the sample file is -- 2011,12,14, 1078753 Details of the Performance of the portfolio Data Range : 2011-01-14 16:00:00 to 2011-12-14 16:00:00 Sharpe Ratio of Fund : 0.788985460132 Sharpe Ratio of $SPX : -0.177204632551 Total Return of Fund : 1.0787526 Total Return of $SPX : 0.937041848381 Standard Deviation of Fund : 0.00708034136287 Standard Deviation of $SPX : 0.0149914504972 Average Daily Return of Fund : 0.000351902965125 Average Daily Return of $SPX : -0.000167347202139
Implementation suggestions & assumptions
In terms of execution prices, you should assume you get the adjusted close price for the day of the trade.
Here are some hints on how to build it: media:marketsim-guidelines.pdf
What to expect when you turn in your assignment (Coursera)
Once you create the tools described above, you will be asked to run specific orders files through your code and then to run the results through your analyze tool to report on various measures such as Sharpe Ratio and Cumulative Return.
Deliverables for on campus GT students
To do: Run your code for the two files orders.csv and orders2.csv. Generate charts for the two runs.
To turn in:
- The code for your two programs: marketsim.py, analyze.py
- A report, report.pdf that includes:
- The 2 charts for the two orders files.
- Text output of your analysis code.
