Computational Investing Part I

As I was taking the Computational Investing Part I course in 2012 I had to work on a set of assignments and for one of them I used PyAlgoTrade.

Homework 1

For this assignment I had to pick 4 stocks, invest a total of $1000000 during 2011, and calculate:

• Final portfolio value
• Anual return
• Average daily return
• Std. dev. of daily returns
• Sharpe ratio

Download the data with the following commands:

python -m "pyalgotrade.tools.quandl" --source-code="WIKI" --table-code="IBM" --from-year=2011 --to-year=2011 --storage=. --force-download --frequency=daily
python -m "pyalgotrade.tools.quandl" --source-code="WIKI" --table-code="AES" --from-year=2011 --to-year=2011 --storage=. --force-download --frequency=daily
python -m "pyalgotrade.tools.quandl" --source-code="WIKI" --table-code="AIG" --from-year=2011 --to-year=2011 --storage=. --force-download --frequency=daily
python -m "pyalgotrade.tools.quandl" --source-code="WIKI" --table-code="ORCL" --from-year=2011 --to-year=2011 --storage=. --force-download --frequency=daily

Although the deliverable was an Excel spreadsheet, I validated the results using this piece of code:

from __future__ import print_function

from pyalgotrade import strategy
from pyalgotrade.barfeed import quandlfeed
from pyalgotrade.stratanalyzer import returns
from pyalgotrade.stratanalyzer import sharpe
from pyalgotrade.utils import stats


class MyStrategy(strategy.BacktestingStrategy):
    def __init__(self, feed):
        super(MyStrategy, self).__init__(feed, 1000000)

        # We wan't to use adjusted close prices instead of close.
        self.setUseAdjustedValues(True)

        # Place the orders to get them processed on the first bar.
        orders = {
            "ibm": 1996,
            "aes": 22565,
            "aig": 5445,
            "orcl": 8582,
        }
        for instrument, quantity in orders.items():
            self.marketOrder(instrument, quantity, onClose=True, allOrNone=True)

    def onBars(self, bars):
        pass

# Load the bar feed from the CSV file
feed = quandlfeed.Feed()
feed.addBarsFromCSV("ibm", "WIKI-IBM-2011-quandl.csv")
feed.addBarsFromCSV("aes", "WIKI-AES-2011-quandl.csv")
feed.addBarsFromCSV("aig", "WIKI-AIG-2011-quandl.csv")
feed.addBarsFromCSV("orcl", "WIKI-ORCL-2011-quandl.csv")

# Evaluate the strategy with the feed's bars.
myStrategy = MyStrategy(feed)

# Attach returns and sharpe ratio analyzers.
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)

# Run the strategy
myStrategy.run()

# Print the results.
print("Final portfolio value: $%.2f" % myStrategy.getResult())
print("Anual return: %.2f %%" % (retAnalyzer.getCumulativeReturns()[-1] * 100))
print("Average daily return: %.2f %%" % (stats.mean(retAnalyzer.getReturns()) * 100))
print("Std. dev. daily return: %.4f" % (stats.stddev(retAnalyzer.getReturns())))
print("Sharpe ratio: %.2f" % (sharpeRatioAnalyzer.getSharpeRatio(0)))

The results were:

Final portfolio value: $876350.83
Anual return: -12.36 %
Average daily return: -0.04 %
Std. dev. daily return: 0.0176
Sharpe ratio: -0.33