MC2-Project-1
Contents
Draft version
Note that this is a draft version of this project definition. The key requirements will not change, but we will provide some more information regarding directory structure and so on.
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.
Template
Instructions:
- Download mc2_p1.zip, unzip inside ml4t/
- Copy analysis.py (your solution for MC1-Project-1) to mc2_p1/portfolio/.
- Implement the compute_portvals() function in mc2_p1/marketsim.py.
- To execute, run python -m marketsim from mc2_p1/ directory.
Part 1: Basic simulator (95%)
Open the provided template file: marketsim.py. Your job is to implement 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, orders_file, start_val)
# TODO: Your code here
return df_portvals
where start_date and end_date are the first date and last date to track, respectively, orders_file is the name of a file from which to read orders, and start_val is the starting value of the portfolio (initial cash available). The returned result df_portvals is a dataframe containing the value of the portfolio for each trading day from start_date to end_date, inclusive.
The files containing orders are CSV files with the following columns:
- 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 (5%)
Many brokers allow "leverage" which is to say that you can borrow money from them in order to buy (or sell) more assets. As an example, suppose you deposit $100,000 with your broker; You might then buy $100,000 worth of stocks. At that point you would have a cash position of $0 and a sum of long positions of $100,000. This situation is 1.0 leverage. However, many brokers allow up to 2.0 leverage. So, you could borrow $100,000, to buy more stocks. If you did that, you'd have long positions of $200,000 and a cash position of -$100,000 due to the loan. Here's how to calculate leverage:
leverage = (sum(longs) + sum(abs(shorts)) / ((sum(longs) - sum(abs(shorts)) + cash)
Here are a few examples:
- You deposit $100,000, then short $50K worth of stock and buy $50K worth of stock. You would then have $100K of cash, $50K of longs, -$50K of shorts, so your leverage would be 1.0.
- You deposit $100,000 then short $200K worth of stock. You have $300K of cash and -$200K in shorts. So your leverage is 2.0.
- You deposit $100,000 then buy $50K of stock. Your leverage is 0.5.
Your simulator should prohibit trades that would cause portfolio leverage to exceed 2.0.
FAQs:
- Q: What if the portfolio becomes levered after the trades have been entered?
- A: It is OK if the trades are entered and then later, due to stock price changes, leverage exceeds 2.0.
- Q: Should I allow a partial order to be filled so it gets just right up to 2.0
- A: No reject the order entirely.
- Q: What if the portfolio is levered at 2.0 already, should I accept orders that reduce leverage?
- A: Yes.
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 (an updated mc2_p1.zip with template code will be available soon)
Short example to check your code
Here is a very very short example that you can use to check your code. Starting conditions:
cash = 1,000,000 start_date = 2011-1-05 end_date = 2011-1-20
For the orders file orders-short.csv, the orders are:
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 suggestions 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
- Basic simulator: 10 test cases: We will test your code against 10 cases (9.5% per case). Each case will be deemed "correct" if:
- For each day, abs(reference portval - your portval) < $0.01
- Leverage: 2 test cases (2.5% per case). Each case will be deemed "correct" if:
- For each day, abs(reference portval - your portval) < $0.01
