F2scores = ["F2(8,20)","F2(15,40)"]
sheets = pd.ExcelFile(DATA_DIR+fn).sheet_names
#print sheets
df = pd.ExcelFile(DATA_DIR+fn).parse(sheets[0],index_col=0,parse_dates=True)
print "number of observations retrieved:",len(df)
stock_name = fn.split(" ")[0]
Stock_Names.append(stock_name)
print stock_name
to_feed_anOld_habbit(DF,df,Stocks,stock_name)
Date_Stock = {} #keys are dates, values are stocks
df,shift,xVar,yVar,F2scores = transform_df_ASneeded(df,F2scores,shift,stock_name)
fig_fn = out_dir+ stock_name+"_behaviour.jpg"
Plotting.stacked_TimeSeries(df,stock_name,["ClosePrice","EWPoolClose","ccRelRet","barraBeta"],"Behaviour of "+stock_name,fig_fn)
#g_xVar = 'barraBeta'
#fig_fn = out_dir+ stock_name+"_Prices_vs_"+g_xVar+".jpg"
#Plotting.stacked_ScatterPlots(df,stock_name,["ccRawRet","ccPoolRet"],g_xVar,stock_name,fig_fn)
#now go thru the segments of dates
if Dates != []:
for d in range(len(Dates)-1):
date1 = Dates[d]
date2 = Dates[d+1]
curr_df = df[date1.strftime('%d/%m/%Y'):date2.strftime('%d/%m/%Y')]
stock = STOCK(stock_name)
stock.df = curr_df
#utils.simple_operations_on_DataFrame(curr_df,stock)
