CS4646 marketsim

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 the appropriate zip file File:CS4646 spr18 marketsim.zip
• Implement the compute_portvals() function in the file marketsim/marketsim.py.
• The grading script is marketsim/grade_marketsim.py.

Part 1: Basic simulator (90 points)

Your job is to implement your market simulator as a function, compute_portvals() that returns a dataframe with one column. You should implement it within the file marketsim.py. It should adhere to the following API:

def compute_portvals(orders_file = "./orders/orders.csv", start_val = 1000000, commission = 9.95, impact = 0.005):
    # TODO: Your code here
    return portvals

The start date and end date of the simulation are the first and last dates with orders in the orders_file. The arguments are as follows:

• 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)
• commission is the fixed amount in dollars charged for each transaction (both entry and exit)
• impact is the amount the price moves against the trader compared to the historical data at each transaction

Return the result (portvals) as a single-column pandas.DataFrame (column name does not matter), containing the value of the portfolio for each trading day in the first column from start_date to end_date, inclusive.

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 resulting data frame should contain values like this:

2008-12-3 1000000
2008-12-4 1000010
2008-12-5 1000250
...

How it should work

Your code should keep account of how many shares of each stock are in the portfolio on each day and how much cash is available on each day. Note that negative shares and negative cash are possible. Negative shares mean that the portfolio is in a short position for that stock. Negative cash means that you've borrowed money from the broker.

When a BUY order occurs, you should add the appropriate number of shares to the count for that stock and subtract the appropriate cost of the shares from the cash account. The cost should be determined using the adjusted close price for that stock on that day.

When a SELL order occurs, it works in reverse: You should subtract the number of shares from the count and add to the cash account.

Evaluation

We will evaluate your code by calling compute_portvals() with multiple test cases. No other function in your code will be called by us, so do not depend on "main" code being called. Do not depend on global variables.

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: Transaction Costs (10 points)

Note: We strongly encourage you to get the basic simulator working before you implement the transaction cost and market impact components of this project.

Transaction costs are an important consideration for investing strategy. Transaction costs include things like commissions, slippage, market impact and tax considerations. High transaction costs encourage less frequent trading, and accordingly a search for strategies that pay out over longer periods of time rather than just intraday or over several days. For this project we will consider two components of transaction cost: Commissions and market impact:

• Commissions: For each trade that you execute, charge a commission according to the parameter sent. Treat that as a deduction from your cash balance.
• Market impact: For each trade that you execute, assume that the stock price moves against you according to the impact parameter. So if you are buying, assume the price goes up before your purchase. Similarly, if selling, assume the price drops 50 bps before the sale. For simplicity treat the market impact penalty as a deduction from your cash balance.

Both of these penalties should be applied for EACH transaction, for instance, depending on the parameter settings, a complete entry and exit will cost 2 * $9.95, plus 0.5% of the entry price and 0.5% of the exit price.

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_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     997495.775
2011-01-06     997090.775
2011-01-07    1000660.775
2011-01-10    1010125.775
2011-01-11    1008910.775
2011-01-12    1013065.775
2011-01-13    1014940.775
2011-01-14    1019125.775
2011-01-18    1007425.775
2011-01-19    1004725.775
2011-01-20     993036.375

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:

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

Sharpe Ratio of Fund: -1.00015025363
Sharpe Ratio of $SPX: 0.0183389807443

Cumulative Return of Fund: -0.00447059537671
Cumulative Return of $SPX: -0.0224059854302

Standard Deviation of Fund: 0.00678073274458
Standard Deviation of $SPX: 0.0149716091522

Average Daily Return of Fund: -0.000427210193308
Average Daily Return of $SPX: 1.7295909534e-05

Final Portfolio Value: 993036.375

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: 0.997654521878
Sharpe Ratio of $SPX: 0.0183389807443

Cumulative Return of Fund: 0.108872698544
Cumulative Return of $SPX: -0.0224059854302

Standard Deviation of Fund: 0.00730509916835
Standard Deviation of $SPX: 0.0149716091522

Average Daily Return of Fund: 0.000459098655493
Average Daily Return of $SPX: 1.7295909534e-05

Final Portfolio Value: 1106025.8065

orders2.csv

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

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

Sharpe Ratio of Fund: 0.552604907987
Sharpe Ratio of $SPX: 0.0183389807443

Cumulative Return of Fund: 0.0538411196951
Cumulative Return of $SPX: -0.0224059854302

Standard Deviation of Fund: 0.00728172910323
Standard Deviation of $SPX: 0.0149716091522

Average Daily Return of Fund: 0.000253483085898
Average Daily Return of $SPX: 1.7295909534e-05

Final Portfolio Value: 1051088.0915

Hints and Clarifications

• Be sure you are returning a DataFrame! type(foo) will tell you this.
• Fill NaNs in your data as normal. Then every stock should be "trading" every day.
• Don't blindly assume the orders in the orders file are sorted any particular way. Sort them yourself.
• If multiple orders are on the same day, it does not matter which you execute first.
• Assume you are able to trade at the adjusted close (but apply transaction costs as directed).
• Return portfolio values only for trading days.
• Assume the market is open if (and only if) SPY is trading.

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

Out of a total of 100 points:

• Basic simulator: 90 points: 10 test cases: We will test your code against 10 cases (9 points per case) without transaction costs. Points per case are allocated as follows:
  • 2.0: Correct number of days reported in the dataframe (should be the number of trading days between the start date and end date, inclusive).
  • 5.0: Correct portfolio value on the last day +-0.1%
  • 1.0: Correct Sharpe Ratio +-0.1%
  • 1.0: Correct average daily return +-0.1%
• Transaction costs: 10 points: 5 test cases: We will test your code against 5 cases (2 points per case) as follows:
  • Two test cases that evaluate commissions only (impact = 0)
  • Two test cases that evaluate impact only (commission = 0)
  • One test case with non-zero commission and impact.

Required, Allowed & Prohibited

Required:

• Your project must be coded in Python 2.7.x.
• Your code must run on one of the university-provided computers (e.g. buffet02.cc.gatech.edu).
• When utilizing any of the example orders files code must run in less than 10 seconds on one of the university-provided computers.
• To read in stock data, use only the functions in util.py,

Allowed:

• You can develop your code on your personal machine, but it must also run successfully on one of the university provided machines.
• Your code may use standard Python libraries.
• You may use the NumPy, SciPy, matplotlib and Pandas libraries. Be sure you are using the correct versions.
• Code provided by the instructor, or allowed by the instructor to be shared.
• To read in orders files (only) pandas.read_csv() is allowed.

Prohibited:

• Global variables.
• Reading in data by any means other than the functions in util.py for data or pandas.read_csv() for orders.
• Any libraries or modules not listed in the "allowed" section above.
• Any code you did not write yourself (except for the 5 line rule in the "allowed" section).
• Code that takes longer than 10 seconds to run.
• Any Classes (other than Random) that create their own instance variables for later use (e.g., learners like kdtree).