def grid_tester(run_strat, do_plots):
##################Initialise##########################
#Read in data and setup object
# dbpath = "/home/phcostello/Documents/Data/FinanceData.sqlite"
# dbreader = dbr.DBReader(dbpath)
#SP500 = dbreader.readSeries("SP500")
#BA = dbreader.readSeries("BA")
#dim = 'Adj_Close'
#SP500AdCl = SP500[dim]
#BAAdCl = BA[dim]
#dataObj = pd.merge(pd.DataFrame(BAAdCl), pd.DataFrame(SP500AdCl), how='inner',left_index = True, right_index = True)
#dataObj.columns = ['y','x']
#Read data for all pairs
#Note have checked top 10 in and constructed data in
#CheckingSeriesCoint.py
dataObj = pickle.load(open('pickle_jar/DJIA_AdjClose.pkl'))
cointAnalysis = pickle.load(open('pickle_jar/results_DJIA_coint.pkl','rb'))
cointAnalysis = pd.DataFrame(cointAnalysis, columns = ['SeriesX_name','SeriesY_name','Adf_pvalue', 'reg_scaling', 'reg_intercept'])
cointAnalysis = cointAnalysis.sort('Adf_pvalue', ascending=True)
#cointAnalysis.info()
seriesNames = cointAnalysis[['SeriesX_name','SeriesY_name']]
#dataObj.info()
top10 = seriesNames.iloc[0:5]
top10['rank']= range(1, len(top10)+1)
top10.head()
top10 =top10.values.tolist()
#Filter to what we want
#setup logging
logfile = 'logs/GridTester_ma.txt'
logging.basicConfig(filename= logfile, filemode='w', level = logging.ERROR)
##################Make par Table##########################
#Parameter table rows are
#[ pairs_labes, strategy_params, train_ranges, test_ranges ]
#series
series_names = top10
#strat pars
entry_scale = np.arange(1,3.5,0.5)
exit_scale = np.arange(0.0,3.0,0.5)
strat_parameters = [ [sw, lw] for sw in entry_scale for lw in exit_scale if sw >= lw]
#Train - test date pars
dmin = datetime.datetime(2006,1,1)
num_periods = 15
period_delta = datetime.timedelta(182)
#Create range start/end offsets from min data
train_test_ranges = [ [dmin + i*period_delta, #train start
dmin + (i+1)* period_delta, #train end
dmin + (i+1)* period_delta, #test start
dmin + (i+2)* period_delta] #test end
for i in range(0,num_periods)]
#Make table of parameters + dates
parameter_table = [ series + pars + date_ranges for series in series_names for pars in strat_parameters for date_ranges in train_test_ranges]
parameter_table = [ parameter_table[i] + [i] for i in range(0,len(parameter_table))]
colnames =['runtype',\
'series_X',\
'series_Y',\
'rank',\
'entry_scale',\
'exit_scale',\
'train_start',\
'train_end',\
'test_start',\
'test_end',\
'par_rownumber']
##################Make Res Table##########################
#We just define
result_cols = ['returns',\
'volatility',\
'sharpe',\
'sortino']
#Use x will be analyser object which has result functions
#result cols should have same order as function below
result_func = lambda x : [x.get_result().iat[-1,0]/x.get_result().iat[0,0] - 1 ,
x.get_volatility(),
x.sharpe_ratio(),
x.sortino_ratio()]
result_table_cols = colnames + result_cols
##################Test strat on pars in Table##########################
if run_strat:
#open file for output
outfile = open('Results/dummy.csv','wb')
#run simulations
tic = time.clock()
runStrat(parameter_table,dataObj, outfile, result_table_cols, result_func)
toc = time.clock()
print "All strategies took {} seconds to run".format(toc - tic)
outfile.close()
#################$ Plot for one set of parameters ########################
#Setup Data
if do_plots:
this_pars = parameter_table[496]
#Setup Data
#Setup initial portfolio
trade_equity_spread = TradeEquity('spread',
notional=0,
price_series_label='spread')
portfolio_0 = Portfolio("portfolio", cashAmt=100)
portfolio_0.add_trade(trade_equity_spread)
#No more trade types
portfolio_0.fixed_toggle()
print this_pars
strategy, train_data, test_data = setup_strategy(this_pars,dataObj)
data = test_data
strategy.run_strategy(data,portfolio_0)
analyser = ResultsAnalyser(strategy.result,referenceIndex=None)
fig =plt.figure()
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
portfolioVal = analyser.get_result()
print portfolioVal['Value']
start = 0
end = len(portfolioVal)
portfolioVal['Value'].iloc[start:end].plot(ax=ax1)
# trades_at = portfolioVal[portfolioVal['Signal_bs'].isin(['buy','sell'])]
#
# #Putting trade bars on
# for dt in trades_at.index:
# plt.axvline( dt, ymin =0, ymax =1 )
plt.subplot(2,1,1)
pairs_md(data.loc[portfolioVal.index].iloc[start:end],0,0).plot_spreadAndSignals(ax2)
#plt.title(seriesName)
plt.show()
def test_ewma_strat():
#prepare data
import DataHandler.DBReader as dbr
from strategy_tester.trade import TradeEquity
from strategy_tester.Portfolio import Portfolio
dbpath = "/home/phcostello/Documents/Data/FinanceData.sqlite"
dbreader = dbr.DBReader(dbpath)
SP500 = dbreader.readSeries("SP500")
dim = 'Adj_Close'
data = SP500
startDate_train = datetime.date(2012,12,30)
endDate_train = datetime.date(2013,6,30)
dataReduced = data.loc[startDate_train:endDate_train]
#Initialise strategy parameters
strat = EWMA_Trend( MA_win_len=50.0,
std_win_len=50.0,
entryScale= 0,
exitScale= 1)
#Initialize Portfolio
#Generate Signals on Market data
#Run strategy
#Setup portfolio
trade_equity_spread = TradeEquity('Equity',
notional=0,
price_series_label='Adj_Close')
port = Portfolio("portfolio", cashAmt=100)
port.add_trade(trade_equity_spread)
#No more trade types
port.fixed_toggle()
strat.plot_flag=True
strat.run_strategy(market_data = data,
portfolio = port)
# plt.show()
plt.figure()
strat.result['Value'].plot()
# plt.show()
# strat.result.to_csv('Results/pairsmd.csv')
from strategy_tester.ResultsAnalyser import ResultsAnalyser
ra = ResultsAnalyser(data= strat.result) #Sharpe for strat
print ra.sharpe_ratio(), ra.get_cumulative_return().iloc[-1,0]
ra2 = ResultsAnalyser(data = SP500,valueIndex='Adj_Close') #Sharpe for SP500
print ra2.sharpe_ratio(), ra2.get_cumulative_return().iloc[-1,0]
combresdata = pd.merge(strat.result,SP500, how='inner', left_index=True, right_index=True)
ra3 = ResultsAnalyser(data = combresdata,valueIndex='Value',referenceIndex='Adj_Close')
print ra3.sharpe_ratio(useMarketRef=True), ra3.get_cumulative_return(useReference=True).iloc[-1,0]
dbreader = DBReader()
AAPL = dbreader.readSeries('AAPL')
SP500 = dbreader.readSeries('SP500')
print AAPL.info()
print SP500.info()
data = pd.merge(pd.DataFrame(AAPL['Adj_Close']),pd.DataFrame(SP500['Adj_Close']), how='inner', left_index=True, right_index=True)
data_dict = {'AAPL':AAPL , 'SP500':SP500}
panel_series = pd.Panel.from_dict(data_dict,
intersect=True,
orient = 'minor')
print 'panel items', panel_series.items
dim = 'Adj_Close'
df_allseries_dim = panel_series[dim]
dataRed = df_allseries_dim.loc['2011-1-1':'2012-1-1']
rets = dataRed['SP500'].pct_change()
print 'rets std', rets.std()
print rets.head()
#dataRed['SP500'].plot()
plt.show()
ra = ResultsAnalyser(dataRed,valueIndex = 'SP500')#, referenceIndex='SP500')
print "vol", ra.get_volatility(annualising_scalar=1, returns=True)
sharpe = ra.sharpe_ratio(useMarketRef=False)
print sharpe
