MC2-Project-1
Contents
Overview
In this project you will create a market simulator that accepts trading orders and keeps track of a portfolio's value over time and then assesses the performance of that portfolio.
To Do
Create a file for your code called marketsim.py. Your job is to create a function, your market simulator, compute_portvals() that returns a dataframe with one column. It should adhere to the following API
def compute_portvals(start_date, end_date, ordersfile, startval)
# do stuff
return df_portvals
where start_date and end_date are the first date and last date to track, respectively, ordersfile is the name of a file from which to read orders, and startval is the initial value of the portfolio. The returned result df_portvals is a data frame containing the value of the portfolio for each trading day from start_date to end_date inclusive.
The files containing orders are CSV files 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. The value for each day is cash plus the current value of equities. Return the result in a dataframe that would contain values like this:
2008-12-3 1000000 2008-12-4 1000010 2008-12-5 1000250 ...
We will evaluate your code by calling compute_portvals() with multiple test cases.
For debugging purposes, you should write your own additional helper function to call compute_portvals() with your own test cases. We suggest that you report the following factors
- Plot the price history over the trading period.
- 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
- Ending value of the portfolio
Part 2: Leverage
Your simulator should prohibit trades that would cause portfolio leverage to exceed 2.0. Note that it is OK if the trades are entered and then later, due to stock price changes, leverage exceeds 2.0. Use the following definition of leverage:
leverage = (sum(longs) + sum(abs(shorts)) / (sum(longs) + cash)
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 Cumulative Return of Fund : -0.001965 Cumulative Return of $SPX : 0.00289841449 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 Cumulative Return of Fund : 0.13386 Cumulative 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 Cumulative Return of Fund : 0.0787526 Cumulative Return of $SPX : -0.062958151619 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. Please note that these suggests were for an earlier version of this project in which you were supposed to print results to a file instead of returning them in a data frame.
What to turn in
Be sure to follow these instructions diligently!
Via T-Square, submit as attachment (no zip files; refer to schedule for deadline):
- Your code as marketsim.py (only the function compute_portvals() will be tested)
Unlimited resubmissions are allowed up to the deadline for the project.
Rubric
- 10 test cases: We will test your code against 10 cases (10% per case). Each case will be deemed "correct" if:
- For each day, abs(reference portval - your portval) < $0.01
