MC2-Project-1

Updates

09/28/2015: Directory structure/template - please refer to Template section below. Note that order files are available in ./orders/ within mc2_p1. Stock price data is going to be in ../data/ as usual. The included util.py should read data correctly.

09/30/2015: Several changes have been made to the input and output requirements for compute_portvals(), and the format of the CSV order files. The template has been updated accordingly.

• Fixed example output for orders-short.csv, orders.csv and orders2.csv shown on this wiki page.
• Collapsed the separate year, month, day columns in order CSVs into a single Date column in yyyy-mm-dd format so that it is easy to read in directly with pandas.
• Added a header row to CSV files with column names: Date, Symbol, Order, Shares (see details below).
• Changed all Order column values to be UPPERCASE: Buy/Sell -> BUY/SELL
• Clarified input parameters: start_date and end_date will be supplied as strings in yyyy-mm-dd format; however, if you have already implemented compute_portvals() to accept datetime objects, we will evaluate it accordingly when grading.
• Clarified output type: The return value of compute_portvals() should ideally be a pandas.Series (since there is only one column). But you can also return a single-column pandas.DataFrame, and the first column will be extracted for grading (the name of the column does not matter).

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 portvals

where start_date and end_date are the first date and last date to track, respectively (specified as 'yyyy-mm-dd' strings*), 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). Return the result (portvals) as a pandas.Series or a single-column pandas.DataFrame (column name does not matter), containing the value of the portfolio for each trading day from start_date to end_date, inclusive.

• Note: If you have already implemented this project assuming start_date and end_date are datetime objects (as specified in a previous version of the requirements), that is fine - your submission will be graded appropriately if you inform us (instructions on this coming soon!).

The files containing orders are CSV files with the following columns:

• Date (yyyy-mm-dd)
• Symbol (e.g. AAPL, GOOG)
• Order (BUY or SELL)
• Shares (no. of shares to trade)

For example:

Date,Symbol,Order,Shares
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. The result should 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.
• 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
• Standard deviation of daily returns of the total portfolio
• Average daily 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

Example orders files are available in the orders subdirectory.

Short example to check your code

Here is a very very short example that you can use to check your code. Starting conditions:

start_date = '2011-1-05'
end_date = '2011-1-20'
start_val = 1000000

For the orders file orders-short.csv, the orders are:

Date,Symbol,Order,Shares
2011-01-05,AAPL,BUY,1500
2011-01-20,AAPL,SELL,1500

The daily value of the portfolio (spaces added to help things line up):

2011-01-05    1000000
2011-01-06     999595
2011-01-07    1003165
2011-01-10    1012630
2011-01-11    1011415
2011-01-12    1015570
2011-01-13    1017445
2011-01-14    1021630
2011-01-18    1009930
2011-01-19    1007230
2011-01-20     998035

For reference, here are the adjusted close values for AAPL on the relevant days:

              AAPL
2011-01-05  332.57
2011-01-06  332.30
2011-01-07  334.68
2011-01-10  340.99
2011-01-11  340.18
2011-01-12  342.95
2011-01-13  344.20
2011-01-14  346.99
2011-01-18  339.19
2011-01-19  337.39
2011-01-20  331.26

The full results:

Details of the Performance of the portfolio :

Data Range: 2011-01-05 to 2011-01-20

Sharpe Ratio of Fund: -0.446948390642
Sharpe Ratio of $SPX: 0.882168679776

Cumulative Return of Fund: -0.001965
Cumulative Return of $SPX: 0.00289841448894

Standard Deviation of Fund: 0.00634128215394
Standard Deviation of $SPX: 0.00544933521991

Average Daily Return of Fund: -0.000178539446839
Average Daily Return of $SPX: 0.000302827205547

Final Portfolio Value: 998035.0

More comprehensive examples

orders.csv

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).

Data Range: 2011-01-10 to 2011-12-20

Sharpe Ratio of Fund: 1.21540888742
Sharpe Ratio of $SPX: 0.0183389807443

Cumulative Return of Fund: 0.13386
Cumulative Return of $SPX: -0.0224059854302

Standard Deviation of Fund: 0.00720514136323
Standard Deviation of $SPX: 0.0149716091522

Average Daily Return of Fund: 0.000551651296638
Average Daily Return of $SPX: 1.7295909534e-05

Final Portfolio Value: 1133860.0

orders2.csv

The other sample file is orders2.csv that you can use to test your code, and compare with others.

Data Range: 2011-01-14 to 2011-12-14

Sharpe Ratio of Fund: 0.788982285751
Sharpe Ratio of $SPX: -0.177203019906

Cumulative Return of Fund: 0.0787526
Cumulative Return of $SPX: -0.0629581516192

Standard Deviation of Fund: 0.00711102080156
Standard Deviation of $SPX: 0.0150564855724

Average Daily Return of Fund: 0.000353426354584
Average Daily Return of $SPX: -0.000168071648902

Final Portfolio Value: 1078752.6

Implementation suggestions & assumptions

In terms of execution prices, you should assume you get the adjusted close price for the day of the trade.

Here is a video outlining an approach to solving this problem [youtube video]. 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