minimum variance portfolio using get_fundamentals()

Something I finally got to work, so I thought I'd throw it out to the masses for critique. --Grant

78
Backtest from to with initial capital
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
 Returns 1 Month 3 Month 6 Month 12 Month
 Alpha 1 Month 3 Month 6 Month 12 Month
 Beta 1 Month 3 Month 6 Month 12 Month
 Sharpe 1 Month 3 Month 6 Month 12 Month
 Sortino 1 Month 3 Month 6 Month 12 Month
 Volatility 1 Month 3 Month 6 Month 12 Month
 Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 550775f29534ff0e3a824c55
There was a runtime error.
9 responses

are you sure you are calculating the returns correctly? for me it looks like you are working with price differences (i.e. after taking the diff between consecutive prices, you do not divide by the base price).
furthermore: it looks like the weights do not change from the initial weight (i.e. 0.05). for me it looks like the something is wrong with the scaling the variance/covariance matrix ... as you are calculating them using minute returns, already using the starting weights produces an extremly low variance so that the optimizer seems to stop without actually doing something useful.

just my two cents

Have had that issue with scipy.optimize. You can either adjust the solver's tolerance or scale up the inputs (I prefer the latter). Generally what I do is turn daily variance into annual volatility multiplied by 100 so that small changes in daily variance become obvious to the solver.

I used to compute the return in a ugly way this is much better.

ret = np.diff(prices,axis=0) # returns


How do you organize your codes in Quantopian, whenever I open my algorithms I have a urge to try to organize it, but with a flat list it is kinda hard. Prefix with parameters?

I agree, I don't think np.diff() is computing the returns but only the change in price (not percent change).

Instead you can use the .pct_change() method of a pandas dataframe (as returned by history).

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Or of course the diff of the log-prices.

Thanks for the feedback, all. Here's an update, with:

    prices = history(3*390,'1m','price')
ret = 1000000*prices.pct_change().dropna()
ret = pd.ewma(ret,span=60).as_matrix(context.stocks)


Some mysteries:

• Per Matt's advice, the inputs to the optimizer need to be scaled. This is disturbing, since I would think that there would be normalization internal to the optimizer.
• The leverage jumps up during the backtest, which suggests something is not working as hoped. The portfolio allocation should always sum to 1.
• Is the variance actually being minimized by the optimizer? This could be checked by solving for the minimum analytically (see http://faculty.washington.edu/ezivot/econ424/portfolioTheoryMatrix.pdf), and the code would probably run faster, too.

Grant

78
Backtest from to with initial capital
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
 Returns 1 Month 3 Month 6 Month 12 Month
 Alpha 1 Month 3 Month 6 Month 12 Month
 Beta 1 Month 3 Month 6 Month 12 Month
 Sharpe 1 Month 3 Month 6 Month 12 Month
 Sortino 1 Month 3 Month 6 Month 12 Month
 Volatility 1 Month 3 Month 6 Month 12 Month
 Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 5509460227840e0e4f84d147
There was a runtime error.

Has anyone managed to get this algorithm to work again after the recent upgrade? My attempts are failing...

Here's an updated version. I changed the np.asarray(args) to np.asarray(args[0]) to prevent the creation of a 3D ndarray.

18
Backtest from to with initial capital
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
 Returns 1 Month 3 Month 6 Month 12 Month
 Alpha 1 Month 3 Month 6 Month 12 Month
 Beta 1 Month 3 Month 6 Month 12 Month
 Sharpe 1 Month 3 Month 6 Month 12 Month
 Sortino 1 Month 3 Month 6 Month 12 Month
 Volatility 1 Month 3 Month 6 Month 12 Month
 Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 550e6e6a50096e2abe640cef
There was a runtime error.

Thanks Thomas.