Developing an Expression Language for Quantitative Financial Modeling

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mean median first last stddev

rank() zscore()

factor1>factor2

& |rank() percentile()

cross_product()

Factors FiltersClassifiers

(2)

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SQLnumpy

In[3]:

Out[3]:

fromzipline.assetsimportAssetFinderfinder=AssetFinder("sqlite:///data/assets.db")lifetimes=finder.lifetimes(dates=pd.date_range('2001-01-01','2015-10-01'),include_start_date=True,)lifetimes.head(5)

In[4]: daily_count=lifetimes.sum(axis=1)daily_count.plot(title="CompaniesinExistencebyDay");

In[5]: AAPL_prices=pd.read_csv('data_public/AAPL-split.csv',parse_dates=['Date'],index_col='Date',)

defplot_prices(prices):price_plot=prices.plot(title='AAPLPrice',grid=False)price_plot.set_ylabel("Price",rotation='horizontal',labelpad=50)price_plot.vlines(['2014-05-08'],0,700,label="$3.05Dividend",linestyles='dotted',colors='black',)price_plot.vlines(['2014-06-09'],0,700,label="7:1Split",linestyles='--',colors='black',)

price_plot.legend()sns.despine()returnprice_plot

In[6]: plot_prices(AAPL_prices);

In[7]: naive_returns=AAPL_prices.pct_change()naive_returns.plot();

In[8]: frombcolzimportopenfromhumanizeimportnaturalsize

all_prices=open('data/equity_daily_bars.bcolz')min_offset=min(all_prices.attrs['calendar_offset'].itervalues())max_offset=max(all_prices.attrs['calendar_offset'].itervalues())calendar=pd.DatetimeIndex(all_prices.attrs['calendar'])[min_offset:max_offset]

nassets=len(lifetimes.columns)ndates=len(calendar)nfields=len(('id','open','high','low','close','volume','date'))

print"NumberofAssets:%d"%nassetsprint"NumberofDates:%d"%ndatesprint"NaiveDatasetSize:%s"%naturalsize(nassets*ndates*nfields*8)

NumberofAssets:20353NumberofDates:3480NaiveDatasetSize:4.0GB

In[9]: !du-h-d0data/equity_daily_bars.bcolz!du-h-d0data/adjustments.db

299M data/equity_daily_bars.bcolz30M data/adjustments.db

In[9]: !du-h-d0data/equity_daily_bars.bcolz!du-h-d0data/adjustments.db

299M data/equity_daily_bars.bcolz30M data/adjustments.db

In[10]: importpandasaspdfromzipline.utils.tradingcalendarimporttrading_dayfromzipline.pipeline.dataimportUSEquityPricingfromzipline.pipeline.loadersimportUSEquityPricingLoader

loader=USEquityPricingLoader.from_files('data/equity_daily_bars.bcolz','data/adjustments.db')dates=pd.date_range('2014-5-20','2014-06-30',freq=trading_day,tz='UTC',)

In[11]:

Out[11]:

#load_adjusted_array()returnsadictionarymappingcolumnstoinstancesof`AdjustedArray`.(closes,)=loader.load_adjusted_array(columns=[USEquityPricing.close],dates=dates,assets=pd.Int64Index([24,5061]),mask=None,).values()closes

AdjustedArray:

Data:array([[604.4,39.74],[604.55,39.69],[606.28,40.35],...,[90.35,42.02],[90.92,41.73],[91.96,42.24]])

Adjustments:{13:[Float64Multiply(first_row=0,last_row=13,first_col=0,last_col=0,value=0.142860)]}

In[14]:

Out[14]:

dates_iter=iter(dates[4:])window=closes.traverse(5)window

_Float64AdjustedArrayWindowWindowLength:5CurrentBuffer:[[604.439.74][604.5539.69][606.2840.35][607.3340.105][614.1440.12]]RemainingAdjustments:{13:[Float64Multiply(first_row=0,last_row=13,first_col=0,last_col=0,value=0.142860)]}

In[15]: #Thiscellisrunmultipletimestoshowthenumbersscrollingupuntilwehitthesplit.data=next(window)printdataprintnext(dates_iter)

[[604.439.74][604.5539.69][606.2840.35][607.3340.105][614.1440.12]]2014-05-2700:00:00+00:00

float bool

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