def refreshFundInfo(self,fund_universe,add_managers=[],include_indices=False):
t = date.today()
cur_date = date(t.year,t.month,15)
get = BackStopDataManager()
stats_cache_updater = StatsCacheUpdater(self.cache_path)
file_def = open("def/refresh_fund_def.dat")
report_def = file_def.read()
file_def.close()
if fund_universe != "PeerGroup":
if fund_universe == 1:
file_res = open("def/refresh_inv_res.dat")
report_res = file_res.read()
file_res.close()
elif fund_universe == 2:
file_res = open("def/refresh_focus_res.dat")
report_res = file_res.read()
file_res.close()
elif fund_universe == 3:
file_res = open("def/refresh_inv_focus_res.dat")
report_res = file_res.read()
file_res.close()
info = get.runFundsReport(report_def, report_res, cur_date)
for data in info:
self.fund_index[data[0][1]].DMRR = datetime.strptime(data[1][1],"%m/%d/%Y")
self.fund_index[data[0][1]].IL = data[2][1]
self.fund_index[data[0][1]].Signal = data[3][1]
self.fund_index[data[0][1]].DMRR = stats_cache_updater.getActualDMRR(data[0][1],self.fund_index[data[0][1]].DMRR)
if len(add_managers) > 0:
report_res = """${(report.field1 == """ + str(add_managers[0]) + """)"""
for id in add_managers[1:]:
report_res += """ && (report.field1 == """ + str(id) + """)"""
report_res += """}"""
info = get.runFundsReport(report_def, report_res, cur_date)
for data in info:
self.fund_index[data[0][1]].DMRR = datetime.strptime(data[1][1],"%m/%d/%Y")
self.fund_index[data[0][1]].IL = data[2][1]
self.fund_index[data[0][1]].Signal = data[3][1]
self.fund_index[data[0][1]].DMRR = stats_cache_updater.getActualDMRR(data[0][1],self.fund_index[data[0][1]].DMRR)
if include_indices:
self.MCPFAI_funds = []
file_def = open("def/MCPFAI_def.dat")
file_res = open("def/MCPFAI_res.dat")
report_def = file_def.read()
report_res = file_res.read()
info = get.runFundsReport(report_def, report_res, cur_date)
for data in info:
self.fund_index[data[1][1]].DMRR = datetime.strptime(data[2][1],"%m/%d/%Y")
self.fund_index[data[1][1]].IL = "MCP Fund or Index"
self.fund_index[data[1][1]].Class = "MCP Fund or Index"
self.fund_index[data[1][1]].Signal = data[4][1]
self.fund_index[data[1][1]].DMRR = stats_cache_updater.getActualDMRR(data[1][1],self.fund_index[data[1][1]].DMRR)
self.MCPFAI_funds.append(self.fund_index[data[1][1]])
def __init__(self, cache_path, backstop_only = False):
self.backstop_only = backstop_only
self.cache_path = cache_path
if self.cache_path[len(self.cache_path)-1] != "/":
self.cache_path += "/"
self.bsdm = BackStopDataManager()
self.returns_index = {}
if not self.backstop_only:
self.loadCachedReturns()
else:
self.returns_index = {}
self.returns_index[""] = {}
def __init__(self, cache_path, pg=None):
self.bsdm = BackStopDataManager()
self.cache_path = cache_path
self.pg = pg
if self.cache_path[len(self.cache_path) - 1] != "/":
self.cache_path += "/"
self.checkFolders()
self.loaded = [
False, False, False, False, False, False, False, False, False,
False, False
]
self.funds = {}
self.firms = {}
self.products = {}
def __init__(self,
cache_path=("C:/Documents and Settings/" +
os.getenv("username") + "/cache_root/Stats")):
if cache_path[len(cache_path) -
1] != "/" and cache_path[len(cache_path) - 1] != "\\":
cache_path += "/"
self.cache_path = cache_path
t = date.today()
self.cur_date = date(t.year, t.month, 15)
self.get = BackStopDataManager()
self.months = [
"Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug",
"Sep", "Oct", "Nov"
]
self.checkFolders()
self.loadData()
def __init__(self,cache_path=("C:/Documents and Settings/" + os.getenv("username") + "/cache_root/Stats")):
if cache_path[len(cache_path)-1] != "/" and cache_path[len(cache_path)-1] != "\\":
cache_path += "/"
self.cache_path = cache_path
t = date.today()
self.cur_date = date(t.year,t.month,15)
self.get = BackStopDataManager()
self.months = ["Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep","Oct", "Nov"]
self.checkFolders()
self.loadData()
def __init__(self,cache_path,pg=None):
self.bsdm = BackStopDataManager()
self.cache_path = cache_path
self.pg = pg
if self.cache_path[len(self.cache_path)-1] != "/":
self.cache_path += "/"
self.checkFolders()
self.loaded = [False,False,False,False,False,False,False,False,False,False,False]
self.funds = {}
self.firms = {}
self.products = {}
class MISCacheUpdater:
def __init__(self, cache_path, pg=None):
self.bsdm = BackStopDataManager()
self.cache_path = cache_path
self.pg = pg
if self.cache_path[len(self.cache_path) - 1] != "/":
self.cache_path += "/"
self.checkFolders()
self.loaded = [
False, False, False, False, False, False, False, False, False,
False, False
]
self.funds = {}
self.firms = {}
self.products = {}
def checkFolders(self):
cp = self.cache_path
if not os.access(cp + "reports", os.F_OK):
os.makedirs(cp + "reports")
if not os.access(cp + "firms", os.F_OK):
os.makedirs(cp + "firms")
if not os.access(cp + "funds", os.F_OK):
os.makedirs(cp + "funds")
if not os.access(cp + "products", os.F_OK):
os.makedirs(cp + "products")
def clearCache(self):
cp = self.cache_path
if os.access(cp, os.F_OK):
shutil.rmtree(cp)
self.checkFolders()
def syncCache(self, source_path):
s_time = time.clock()
self.clearCache()
cmd_str = "xcopy \"" + source_path + "\" \"" + self.cache_path[
0:len(self.cache_path) - 1] + "\" /e /i /h /R /Y"
os.system(cmd_str)
print "Copy took", (time.clock() - s_time), "secs"
def update(self, as_of_date=date.today()):
self.clearCache()
self.loadFundData(as_of_date)
self.loadProductData(as_of_date)
self.loadContactsData(as_of_date)
self.loadTransactionData(as_of_date)
self.loadInternalTransactionData(as_of_date)
self.loadMeetingsData(as_of_date)
self.loadExposureData(as_of_date)
self.loadHoldingsData(as_of_date)
self.loadAumData(as_of_date)
self.loadReturnsData(as_of_date)
self.loadStatisticsData(as_of_date)
def loadFundData(self, as_of_date, load_from_cache=False):
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total, 2.0)
cp = self.cache_path
if not load_from_cache:
f_def = open("def/fund_def.dat")
f_res = open("def/fund_res.dat")
if self.pg != None:
self.pg.addMessage("Downloading Fund Data")
self.pg.incSub("running funds report.....")
fund_data = self.bsdm.runFundsReport(f_def.read(), f_res.read(),
as_of_date)
if self.pg != None:
self.pg.incSub("finished funds report.....")
rep_file = open(cp + "reports/fund_rep.cache", 'w')
cPickle.dump(fund_data, rep_file)
rep_file.close()
else:
rep_file = open(cp + "reports/fund_rep.cache")
fund_data = cPickle.load(rep_file)
rep_file.close()
for f in fund_data:
tmp_fund = Fund()
tmp_fund.backstop_id = int(f[0][1])
tmp_fund.name = f[1][1]
tmp_fund.first_investment_date = datetime.strptime(
f[6][1], "%m/%d/%Y")
tmp_fund.incept_date = datetime.strptime(f[3][1], "%m/%d/%Y")
if int(f[30][1]) not in self.firms.keys():
tmp_fund.firm = Firm()
tmp_fund.firm.name = f[31][1]
tmp_fund.firm.backstop_id = int(f[30][1])
self.firms[int(f[30][1])] = tmp_fund.firm
self.funds[tmp_fund.backstop_id] = tmp_fund
self.loaded[0] = True
def loadProductData(self, as_of_date, load_from_cache=False):
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total * .4, 2.0)
cp = self.cache_path
if not load_from_cache:
f_def = open("def/product_def.dat")
f_res = open("def/product_res.dat")
if self.pg != None:
self.pg.addMessage("Downloading Product Data")
self.pg.incSub("running products report.....")
product_rep = self.bsdm.runProductsReport(f_def.read(),
f_res.read(), as_of_date)
product_bal = {}
if self.pg != None:
self.pg.incSub("finished products report.....")
self.pg.startSubProcess(self.pg.cur_sub_proc_total * .6,
len(product_rep))
for p in product_rep:
if self.pg != None:
self.pg.incSub("getting " + p[1][1] + " data")
bal = self.bsdm.getProductBalances(int(p[0][1]), date.today(),
date.today())
product_bal[int(p[0][1])] = float(bal[0][1])
product_data = [product_rep, product_bal]
shutil.rmtree(cp + "products/")
os.mkdir(cp + "products/")
rep_file = open(cp + "reports/product_rep.cache", 'w')
cPickle.dump(product_data, rep_file)
rep_file.close()
else:
rep_file = open(cp + "reports/product_rep.cache")
product_data = cPickle.load(rep_file)
product_bal = product_data[1]
product_rep = product_data[0]
rep_file.close()
for p in product_rep:
if int(p[0][1]) not in self.products.keys():
tmp_product = Product()
tmp_product.backstop_id = int(p[0][1])
tmp_product.name = p[1][1]
tmp_product.balance = product_bal[tmp_product.backstop_id]
if tmp_product.balance > 0:
self.products[int(p[0][1])] = tmp_product
self.loaded[1] = True
def loadContactsData(self, as_of_date):
if not self.loaded[0]:
self.loadFundData(as_of_date, True)
if not self.loaded[1]:
self.loadProductData(as_of_date, True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total, 2.0)
self.pg.addMessage("Downloading Contacts Data.....")
self.pg.incSub("running people/orgs report.....")
cp = self.cache_path
f_def = open("def/people_orgs_def.dat")
f_res = open("def/people_orgs_res.dat")
contact_data = self.bsdm.runPeopleOrgsReport(f_def.read(),
f_res.read(), as_of_date)
if self.pg != None:
self.pg.incSub("finished people/orgs report.....")
rep_file = open(cp + "reports/people_org_rep.cache", 'w')
cPickle.dump(contact_data, rep_file)
rep_file.close()
self.loaded[2] = True
def loadTransactionData(self, as_of_date):
if not self.loaded[0]:
self.loadFundData(as_of_date, True)
if not self.loaded[1]:
self.loadProductData(as_of_date, True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total, 2.0)
self.pg.addMessage("Downloading Transaction Data.....")
self.pg.incSub("running transaction report.....")
cp = self.cache_path
f_def = open("def/port_trans_def.dat")
f_res = open("def/port_trans_res.dat")
trans_data = self.bsdm.runPortfolioTransactionsReport(
f_def.read(), f_res.read(), as_of_date)
if self.pg != None:
self.pg.incSub("finished transaction report.....")
rep_file = open(cp + "reports/trans_rep.cache", 'w')
cPickle.dump(trans_data, rep_file)
rep_file.close()
self.loaded[3] = True
def loadInternalTransactionData(self, as_of_date, product_name="None"):
if not self.loaded[0]:
self.loadFundData(as_of_date, True)
if not self.loaded[1]:
self.loadProductData(as_of_date, True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,
len(self.products.keys()))
cp = self.cache_path
for p in self.products.keys():
if self.pg != None:
self.pg.incSub("getting holdings for " + self.products[p].name)
if product_name == self.products[p].name or product_name == "None":
prod_path = cp + "products/" + self.products[p].name
if not os.access(prod_path + "/", os.F_OK):
os.mkdir(prod_path)
trans_data = {}
holding_ids = self.bsdm.getHoldingsInProduct(p)
for h_id in holding_ids:
tmp = self.bsdm.getHoldingTransactions(
h_id, date(1994, 1, 1), as_of_date)
info = self.bsdm.getHoldingInformation(h_id)
trans_data[h_id] = [tmp, info]
rep_file = open(prod_path + "/transactions.cache", 'w')
cPickle.dump(trans_data, rep_file)
rep_file.close()
self.loaded[4] = True
def loadMeetingsData(self, as_of_date, firm_name="None"):
if not self.loaded[0]:
self.loadFundData(as_of_date, True)
if not self.loaded[1]:
self.loadProductData(as_of_date, True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,
len(self.firms.keys()))
self.pg.addMessage("Downloading Meetings Data.....")
cp = self.cache_path
for f in self.firms.keys():
if self.pg != None:
self.pg.incSub("getting meeting info for " +
self.firms[f].name)
if firm_name == self.firms[f].name or firm_name == "None":
firm_path = cp + "firms/" + self.firms[f].name
if not os.access(firm_path + "/", os.F_OK):
os.mkdir(firm_path)
meeting_data = self.bsdm.getMeetingInfoByBackstopIdandType(
f, "Organization")
rep_file = open(firm_path + "/meeting.cache", 'w')
cPickle.dump(meeting_data, rep_file)
rep_file.close()
self.loaded[5] = True
def loadExposureData(self, as_of_date, fund_name="None"):
if not self.loaded[0]:
self.loadFundData(as_of_date, True)
if not self.loaded[1]:
self.loadProductData(as_of_date, True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,
len(self.funds.keys()))
self.pg.addMessage("Downloading Exposure Data.....")
cp = self.cache_path
for f in self.funds.keys():
if self.pg != None:
self.pg.incSub("getting exposure data for " +
self.funds[f].name)
if fund_name == self.funds[f].name or fund_name == "None":
fund_path = cp + "funds/" + self.funds[f].name
if not os.access(fund_path + "/", os.F_OK):
os.mkdir(fund_path)
exp_data = self.bsdm.getExposureData(
self.funds[f].backstop_id,
self.funds[f].first_investment_date, as_of_date)
final_exp_data = []
for cat in exp_data:
if cat[0] == "PORTFOLIO EXPOSURE" and cat[
1] == "Portfolio Exposure":
final_exp_data.append(cat[2])
rep_file = open(fund_path + "/exposure_data.cache", 'w')
cPickle.dump(final_exp_data, rep_file)
rep_file.close()
self.loaded[6] = True
def loadHoldingsData(self, as_of_date, product_name="None"):
if not self.loaded[0]:
self.loadFundData(as_of_date, True)
if not self.loaded[1]:
self.loadProductData(as_of_date, True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,
len(self.products.keys()))
self.pg.addMessage("Downloading Holdings Data.....")
cp = self.cache_path
for p in self.products.keys():
if self.pg != None:
self.pg.incSub("getting holdings data for " +
self.products[p].name)
if product_name == self.products[p].name or product_name == "None":
fund_path = cp + "products/" + self.products[p].name
if not os.access(fund_path + "/", os.F_OK):
os.mkdir(fund_path)
holding_data = {}
holding_ids = self.bsdm.getHoldingIds(
self.products[p].backstop_id)
for h_id in holding_ids:
info = self.bsdm.getHoldingInformation(h_id)
bal = self.bsdm.getHoldingBalances(h_id, as_of_date,
as_of_date)
holding_data[h_id] = [info, bal]
rep_file = open(fund_path + "/holding_data.cache", 'w')
cPickle.dump(holding_data, rep_file)
rep_file.close()
self.loaded[7] = True
def loadAumData(self, as_of_date, fund_name="None"):
if not self.loaded[0]:
self.loadFundData(as_of_date, True)
if not self.loaded[1]:
self.loadProductData(as_of_date, True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,
len(self.funds.keys()))
self.pg.addMessage("Downloading AUM Data.....")
cp = self.cache_path
for f in self.funds.keys():
if self.pg != None:
self.pg.incSub("getting aum data for " + self.funds[f].name)
if fund_name == self.funds[f].name or fund_name == "None":
fund_path = cp + "funds/" + self.funds[f].name
if not os.access(fund_path + "/", os.F_OK):
os.mkdir(fund_path)
aum_data = self.bsdm.getHedgeFundAums(
self.funds[f].backstop_id, date(1990, 1, 1), as_of_date)
rep_file = open(fund_path + "/aum_data.cache", 'w')
cPickle.dump(aum_data, rep_file)
rep_file.close()
self.loaded[8] = True
def loadReturnsData(self, as_of_date, fund_name="None"):
if not self.loaded[0]:
self.loadFundData(as_of_date, True)
if not self.loaded[1]:
self.loadProductData(as_of_date, True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,
len(self.funds.keys()))
self.pg.addMessage("Downloading Returns Data.....")
cp = self.cache_path
for f in self.funds.keys():
if self.pg != None:
self.pg.incSub("getting returns data for " +
self.funds[f].name)
if fund_name == self.funds[f].name or fund_name == "None":
fund_path = cp + "funds/" + self.funds[f].name
if not os.access(fund_path + "/", os.F_OK):
os.mkdir(fund_path)
return_data = self.bsdm.getHedgeFundReturns(
self.funds[f].backstop_id, self.funds[f].incept_date,
as_of_date)
rep_file = open(fund_path + "/return_data.cache", 'w')
cPickle.dump(return_data, rep_file)
rep_file.close()
self.loaded[9] = True
def loadStatisticsData(self, as_of_date, fund_name="None"):
if not self.loaded[0]:
self.loadFundData(as_of_date, True)
if not self.loaded[1]:
self.loadProductData(as_of_date, True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,
len(self.funds.keys()))
self.pg.addMessage("Downloading statistics Data.....")
cp = self.cache_path
for f in self.funds.keys():
if self.pg != None:
self.pg.incSub("getting statistics data for " +
self.funds[f].name)
if fund_name == self.funds[f].name or fund_name == "None":
fund = self.funds[f]
fund_path = cp + "funds/" + self.funds[f].name
if not os.access(fund_path + "/", os.F_OK):
os.mkdir(fund_path)
stats = StatisticsCalculations(self.bsdm, f,
fund.first_investment_date,
as_of_date)
stats_data = {}
try:
stats_data["Compound Return"] = [stats.CompoundReturn()]
except:
stats_data["Compound Return"] = ["N/A"]
pass
try:
stats_data["Annualized Geometric Return"] = [
stats.AnnualizedGeometricReturn()
]
except:
stats_data["Annualized Geometric Return"] = ["N/A"]
pass
try:
stats_data["Annualized Arithmetic Standard Deviation"] = [
stats.AnnualizedArithmeticStandardDeviation()
]
except:
stats_data["Annualized Arithmetic Standard Deviation"] = [
"N/A"
]
pass
try:
stats_data["Beta"] = [stats.Beta()]
except:
stats_data["Beta"] = ["N/A"]
pass
try:
stats_data["Alpha"] = [stats.AnnualizedAlpha()]
except:
stats_data["Alpha"] = ["N/A"]
pass
try:
stats_data["Correlation Coefficient"] = [
stats.CorrelationCoefficient()
]
except:
stats_data["Correlation Coefficient"] = ["N/A"]
pass
try:
stats_data["RSquared"] = [stats.RSquared()]
except:
stats_data["RSquared"] = ["N/A"]
pass
try:
stats_data["Max Drawdown"] = [stats.MaxDrawDown()]
except:
stats_data["Max Drawdown"] = ["N/A"]
pass
try:
stats_data["Sharpe Ratio"] = [stats.SharpeRatio()]
except:
stats_data["Sharpe Ratio"] = ["N/A"]
pass
try:
stats_data["Annualized Up-Capture"] = [
stats.AnnualizedUpCapture()
]
except:
stats_data["Annualized Up-Capture"] = ["N/A"]
pass
try:
stats_data["Annualized Down-Capture"] = [
stats.AnnualizedDownCapture()
]
except:
stats_data["Annualized Down-Capture"] = ["N/A"]
pass
rep_file = open(fund_path + "/stats_data.cache", 'w')
cPickle.dump(stats_data, rep_file)
rep_file.close()
self.loaded[10] = True
class MISCacheUpdater:
def __init__(self,cache_path,pg=None):
self.bsdm = BackStopDataManager()
self.cache_path = cache_path
self.pg = pg
if self.cache_path[len(self.cache_path)-1] != "/":
self.cache_path += "/"
self.checkFolders()
self.loaded = [False,False,False,False,False,False,False,False,False,False,False]
self.funds = {}
self.firms = {}
self.products = {}
def checkFolders(self):
cp = self.cache_path
if not os.access(cp + "reports", os.F_OK):
os.makedirs(cp + "reports")
if not os.access(cp + "firms", os.F_OK):
os.makedirs(cp + "firms")
if not os.access(cp + "funds", os.F_OK):
os.makedirs(cp + "funds")
if not os.access(cp + "products", os.F_OK):
os.makedirs(cp + "products")
def clearCache(self):
cp = self.cache_path
if os.access(cp, os.F_OK):
shutil.rmtree(cp)
self.checkFolders()
def syncCache(self,source_path):
s_time = time.clock()
self.clearCache()
cmd_str = "xcopy \"" + source_path + "\" \"" + self.cache_path[0:len(self.cache_path)-1] + "\" /e /i /h /R /Y"
os.system(cmd_str)
print "Copy took",(time.clock() - s_time),"secs"
def update(self,as_of_date=date.today()):
self.clearCache()
self.loadFundData(as_of_date)
self.loadProductData(as_of_date)
self.loadContactsData(as_of_date)
self.loadTransactionData(as_of_date)
self.loadInternalTransactionData(as_of_date)
self.loadMeetingsData(as_of_date)
self.loadExposureData(as_of_date)
self.loadHoldingsData(as_of_date)
self.loadAumData(as_of_date)
self.loadReturnsData(as_of_date)
self.loadStatisticsData(as_of_date)
def loadFundData(self,as_of_date,load_from_cache=False):
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,2.0)
cp = self.cache_path
if not load_from_cache:
f_def = open("def/fund_def.dat")
f_res = open("def/fund_res.dat")
if self.pg != None:
self.pg.addMessage("Downloading Fund Data")
self.pg.incSub("running funds report.....")
fund_data = self.bsdm.runFundsReport(f_def.read(),f_res.read(),as_of_date)
if self.pg != None:
self.pg.incSub("finished funds report.....")
rep_file = open(cp + "reports/fund_rep.cache",'w')
cPickle.dump(fund_data,rep_file)
rep_file.close()
else:
rep_file = open(cp + "reports/fund_rep.cache")
fund_data = cPickle.load(rep_file)
rep_file.close()
for f in fund_data:
tmp_fund = Fund()
tmp_fund.backstop_id = int(f[0][1])
tmp_fund.name = f[1][1]
tmp_fund.first_investment_date = datetime.strptime(f[6][1],"%m/%d/%Y")
tmp_fund.incept_date = datetime.strptime(f[3][1],"%m/%d/%Y")
if int(f[30][1]) not in self.firms.keys():
tmp_fund.firm = Firm()
tmp_fund.firm.name = f[31][1]
tmp_fund.firm.backstop_id = int(f[30][1])
self.firms[int(f[30][1])] = tmp_fund.firm
self.funds[tmp_fund.backstop_id] = tmp_fund
self.loaded[0] = True
def loadProductData(self,as_of_date,load_from_cache=False):
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total*.4,2.0)
cp = self.cache_path
if not load_from_cache:
f_def = open("def/product_def.dat")
f_res = open("def/product_res.dat")
if self.pg != None:
self.pg.addMessage("Downloading Product Data")
self.pg.incSub("running products report.....")
product_rep = self.bsdm.runProductsReport(f_def.read(),f_res.read(),as_of_date)
product_bal = {}
if self.pg != None:
self.pg.incSub("finished products report.....")
self.pg.startSubProcess(self.pg.cur_sub_proc_total*.6,len(product_rep))
for p in product_rep:
if self.pg != None:
self.pg.incSub("getting " + p[1][1] + " data")
bal = self.bsdm.getProductBalances(int(p[0][1]),date.today(),date.today())
product_bal[int(p[0][1])] = float(bal[0][1])
product_data = [product_rep,product_bal]
shutil.rmtree(cp + "products/")
os.mkdir(cp + "products/")
rep_file = open(cp + "reports/product_rep.cache",'w')
cPickle.dump(product_data,rep_file)
rep_file.close()
else:
rep_file = open(cp + "reports/product_rep.cache")
product_data = cPickle.load(rep_file)
product_bal = product_data[1]
product_rep = product_data[0]
rep_file.close()
for p in product_rep:
if int(p[0][1]) not in self.products.keys():
tmp_product = Product()
tmp_product.backstop_id = int(p[0][1])
tmp_product.name = p[1][1]
tmp_product.balance = product_bal[tmp_product.backstop_id]
if tmp_product.balance > 0:
self.products[int(p[0][1])] = tmp_product
self.loaded[1] = True
def loadContactsData(self,as_of_date):
if not self.loaded[0]:
self.loadFundData(as_of_date,True)
if not self.loaded[1]:
self.loadProductData(as_of_date,True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,2.0)
self.pg.addMessage("Downloading Contacts Data.....")
self.pg.incSub("running people/orgs report.....")
cp = self.cache_path
f_def = open("def/people_orgs_def.dat")
f_res = open("def/people_orgs_res.dat")
contact_data = self.bsdm.runPeopleOrgsReport(f_def.read(),f_res.read(),as_of_date)
if self.pg != None:
self.pg.incSub("finished people/orgs report.....")
rep_file = open(cp + "reports/people_org_rep.cache",'w')
cPickle.dump(contact_data,rep_file)
rep_file.close()
self.loaded[2] = True
def loadTransactionData(self,as_of_date):
if not self.loaded[0]:
self.loadFundData(as_of_date,True)
if not self.loaded[1]:
self.loadProductData(as_of_date,True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,2.0)
self.pg.addMessage("Downloading Transaction Data.....")
self.pg.incSub("running transaction report.....")
cp = self.cache_path
f_def = open("def/port_trans_def.dat")
f_res = open("def/port_trans_res.dat")
trans_data = self.bsdm.runPortfolioTransactionsReport(f_def.read(),f_res.read(),as_of_date)
if self.pg != None:
self.pg.incSub("finished transaction report.....")
rep_file = open(cp + "reports/trans_rep.cache",'w')
cPickle.dump(trans_data,rep_file)
rep_file.close()
self.loaded[3] = True
def loadInternalTransactionData(self,as_of_date,product_name="None"):
if not self.loaded[0]:
self.loadFundData(as_of_date,True)
if not self.loaded[1]:
self.loadProductData(as_of_date,True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,len(self.products.keys()))
cp = self.cache_path
for p in self.products.keys():
if self.pg != None:
self.pg.incSub("getting holdings for " + self.products[p].name)
if product_name == self.products[p].name or product_name == "None":
prod_path = cp + "products/" + self.products[p].name
if not os.access(prod_path +"/",os.F_OK):
os.mkdir(prod_path)
trans_data = {}
holding_ids = self.bsdm.getHoldingsInProduct(p)
for h_id in holding_ids:
tmp = self.bsdm.getHoldingTransactions(h_id,date(1994,1,1),as_of_date)
info = self.bsdm.getHoldingInformation(h_id)
trans_data[h_id] = [tmp,info]
rep_file = open(prod_path + "/transactions.cache",'w')
cPickle.dump(trans_data,rep_file)
rep_file.close()
self.loaded[4] = True
def loadMeetingsData(self,as_of_date,firm_name="None"):
if not self.loaded[0]:
self.loadFundData(as_of_date,True)
if not self.loaded[1]:
self.loadProductData(as_of_date,True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,len(self.firms.keys()))
self.pg.addMessage("Downloading Meetings Data.....")
cp = self.cache_path
for f in self.firms.keys():
if self.pg != None:
self.pg.incSub("getting meeting info for " + self.firms[f].name)
if firm_name == self.firms[f].name or firm_name == "None":
firm_path = cp + "firms/" + self.firms[f].name
if not os.access(firm_path +"/",os.F_OK):
os.mkdir(firm_path)
meeting_data = self.bsdm.getMeetingInfoByBackstopIdandType(f, "Organization")
rep_file = open(firm_path + "/meeting.cache",'w')
cPickle.dump(meeting_data,rep_file)
rep_file.close()
self.loaded[5] = True
def loadExposureData(self,as_of_date,fund_name="None"):
if not self.loaded[0]:
self.loadFundData(as_of_date,True)
if not self.loaded[1]:
self.loadProductData(as_of_date,True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,len(self.funds.keys()))
self.pg.addMessage("Downloading Exposure Data.....")
cp = self.cache_path
for f in self.funds.keys():
if self.pg != None:
self.pg.incSub("getting exposure data for " + self.funds[f].name)
if fund_name == self.funds[f].name or fund_name == "None":
fund_path = cp + "funds/" + self.funds[f].name
if not os.access(fund_path +"/",os.F_OK):
os.mkdir(fund_path)
exp_data = self.bsdm.getExposureData(self.funds[f].backstop_id, self.funds[f].first_investment_date, as_of_date)
final_exp_data = []
for cat in exp_data:
if cat[0] == "PORTFOLIO EXPOSURE" and cat[1] == "Portfolio Exposure":
final_exp_data.append(cat[2])
rep_file = open(fund_path + "/exposure_data.cache",'w')
cPickle.dump(final_exp_data,rep_file)
rep_file.close()
self.loaded[6] = True
def loadHoldingsData(self,as_of_date,product_name="None"):
if not self.loaded[0]:
self.loadFundData(as_of_date,True)
if not self.loaded[1]:
self.loadProductData(as_of_date,True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,len(self.products.keys()))
self.pg.addMessage("Downloading Holdings Data.....")
cp = self.cache_path
for p in self.products.keys():
if self.pg != None:
self.pg.incSub("getting holdings data for " + self.products[p].name)
if product_name == self.products[p].name or product_name == "None":
fund_path = cp + "products/" + self.products[p].name
if not os.access(fund_path +"/",os.F_OK):
os.mkdir(fund_path)
holding_data = {}
holding_ids = self.bsdm.getHoldingIds(self.products[p].backstop_id)
for h_id in holding_ids:
info = self.bsdm.getHoldingInformation(h_id)
bal = self.bsdm.getHoldingBalances(h_id,as_of_date,as_of_date)
holding_data[h_id] = [info,bal]
rep_file = open(fund_path + "/holding_data.cache",'w')
cPickle.dump(holding_data,rep_file)
rep_file.close()
self.loaded[7] = True
def loadAumData(self,as_of_date,fund_name="None"):
if not self.loaded[0]:
self.loadFundData(as_of_date,True)
if not self.loaded[1]:
self.loadProductData(as_of_date,True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,len(self.funds.keys()))
self.pg.addMessage("Downloading AUM Data.....")
cp = self.cache_path
for f in self.funds.keys():
if self.pg != None:
self.pg.incSub("getting aum data for " + self.funds[f].name)
if fund_name == self.funds[f].name or fund_name == "None":
fund_path = cp + "funds/" + self.funds[f].name
if not os.access(fund_path +"/",os.F_OK):
os.mkdir(fund_path)
aum_data = self.bsdm.getHedgeFundAums(self.funds[f].backstop_id, date(1990,1,1), as_of_date)
rep_file = open(fund_path + "/aum_data.cache",'w')
cPickle.dump(aum_data,rep_file)
rep_file.close()
self.loaded[8] = True
def loadReturnsData(self,as_of_date,fund_name="None"):
if not self.loaded[0]:
self.loadFundData(as_of_date,True)
if not self.loaded[1]:
self.loadProductData(as_of_date,True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,len(self.funds.keys()))
self.pg.addMessage("Downloading Returns Data.....")
cp = self.cache_path
for f in self.funds.keys():
if self.pg != None:
self.pg.incSub("getting returns data for " + self.funds[f].name)
if fund_name == self.funds[f].name or fund_name == "None":
fund_path = cp + "funds/" + self.funds[f].name
if not os.access(fund_path +"/",os.F_OK):
os.mkdir(fund_path)
return_data = self.bsdm.getHedgeFundReturns(self.funds[f].backstop_id, self.funds[f].incept_date, as_of_date)
rep_file = open(fund_path + "/return_data.cache",'w')
cPickle.dump(return_data,rep_file)
rep_file.close()
self.loaded[9] = True
def loadStatisticsData(self,as_of_date,fund_name="None"):
if not self.loaded[0]:
self.loadFundData(as_of_date,True)
if not self.loaded[1]:
self.loadProductData(as_of_date,True)
if self.pg != None:
self.pg.startSubProcess(self.pg.cur_sub_proc_total,len(self.funds.keys()))
self.pg.addMessage("Downloading statistics Data.....")
cp = self.cache_path
for f in self.funds.keys():
if self.pg != None:
self.pg.incSub("getting statistics data for " + self.funds[f].name)
if fund_name == self.funds[f].name or fund_name == "None":
fund = self.funds[f]
fund_path = cp + "funds/" + self.funds[f].name
if not os.access(fund_path +"/",os.F_OK):
os.mkdir(fund_path)
stats = StatisticsCalculations(self.bsdm,f,fund.first_investment_date,as_of_date)
stats_data = {}
try:
stats_data["Compound Return"] = [stats.CompoundReturn()]
except:
stats_data["Compound Return"] = ["N/A"]
pass
try:
stats_data["Annualized Geometric Return"] = [stats.AnnualizedGeometricReturn()]
except:
stats_data["Annualized Geometric Return"] = ["N/A"]
pass
try:
stats_data["Annualized Arithmetic Standard Deviation"] = [stats.AnnualizedArithmeticStandardDeviation()]
except:
stats_data["Annualized Arithmetic Standard Deviation"] = ["N/A"]
pass
try:
stats_data["Beta"] = [stats.Beta()]
except:
stats_data["Beta"] = ["N/A"]
pass
try:
stats_data["Alpha"] = [stats.AnnualizedAlpha()]
except:
stats_data["Alpha"] = ["N/A"]
pass
try:
stats_data["Correlation Coefficient"] = [stats.CorrelationCoefficient()]
except:
stats_data["Correlation Coefficient"] = ["N/A"]
pass
try:
stats_data["RSquared"] = [stats.RSquared()]
except:
stats_data["RSquared"] = ["N/A"]
pass
try:
stats_data["Max Drawdown"] = [stats.MaxDrawDown()]
except:
stats_data["Max Drawdown"] = ["N/A"]
pass
try:
stats_data["Sharpe Ratio"] = [stats.SharpeRatio()]
except:
stats_data["Sharpe Ratio"] = ["N/A"]
pass
try:
stats_data["Annualized Up-Capture"] = [stats.AnnualizedUpCapture()]
except:
stats_data["Annualized Up-Capture"] = ["N/A"]
pass
try:
stats_data["Annualized Down-Capture"] = [stats.AnnualizedDownCapture()]
except:
stats_data["Annualized Down-Capture"] = ["N/A"]
pass
rep_file = open(fund_path + "/stats_data.cache",'w')
cPickle.dump(stats_data,rep_file)
rep_file.close()
self.loaded[10] = True
class StatsCalculator:
def __init__(self, cache_path, backstop_only = False):
self.backstop_only = backstop_only
self.cache_path = cache_path
if self.cache_path[len(self.cache_path)-1] != "/":
self.cache_path += "/"
self.bsdm = BackStopDataManager()
self.returns_index = {}
if not self.backstop_only:
self.loadCachedReturns()
else:
self.returns_index = {}
self.returns_index[""] = {}
def loadCachedReturns(self):
if os.access(self.cache_path + "returns/returns.cache",os.F_OK):
tmp_file = open(self.cache_path + "returns/returns.cache")
self.returns_index = cPickle.load(tmp_file)
tmp_file.close()
def calcStatisticsFromList(self,fund_ids, start_date, end_date):
self.benchmark_returns_RMF = []
self.benchmark_returns_RF = []
self.risk_free_returns_RMF = []
self.risk_free_returns_RF = []
stats_index = {}
if self.backstop_only:
tmp_returns = self.bsdm.getHedgeFundReturns(496539, start_date, end_date)
if tmp_returns != False:
for month in tmp_returns:
for item in month:
if item[0] == "amount":
self.benchmark_returns_RMF.append(item[1] + 1)
self.benchmark_returns_RF.append(item[1])
tmp_returns = self.bsdm.getHedgeFundReturns(496605, start_date, end_date)
if tmp_returns != False:
for month in tmp_returns:
for item in month:
if item[0] == "amount":
self.risk_free_returns_RMF.append(item[1] + 1)
self.risk_free_returns_RF.append(item[1])
else:
for y in range(start_date.year,end_date.year+1):
if y == start_date.year and y == end_date.year:
for m in range(start_date.month,end_date.month+1):
self.addMonthOfReturns(496539,y,m,1)
self.addMonthOfReturns(496605,y,m,2)
elif y == start_date.year:
for m in range(start_date.month,13):
self.addMonthOfReturns(496539,y,m,1)
self.addMonthOfReturns(496605,y,m,2)
elif y == end_date.year:
for m in range(1,end_date.month+1):
self.addMonthOfReturns(496539,y,m,1)
self.addMonthOfReturns(496605,y,m,2)
else:
for m in range(1,13):
self.addMonthOfReturns(496539,y,m,1)
self.addMonthOfReturns(496605,y,m,2)
for fund_id in fund_ids:
self.fund_returns_RMF = []
self.fund_returns_RF = []
if self.backstop_only:
tmp_returns = self.bsdm.getHedgeFundReturns(fund_id, start_date, end_date)
if tmp_returns != False:
for month in tmp_returns:
for item in month:
if item[0] == "amount":
self.fund_returns_RMF.append(item[1] + 1)
self.fund_returns_RF.append(item[1])
else:
for y in range(start_date.year,end_date.year+1):
if y == start_date.year and y == end_date.year:
for m in range(start_date.month,end_date.month+1):
self.addMonthOfReturns(fund_id,y,m,3)
elif y == start_date.year:
for m in range(start_date.month,13):
self.addMonthOfReturns(fund_id,y,m,3)
elif y == end_date.year:
for m in range(1,end_date.month+1):
self.addMonthOfReturns(fund_id,y,m,3)
else:
for m in range(1,13):
self.addMonthOfReturns(fund_id,y,m,3)
stats_index[fund_id] = self.gatherStats()
return stats_index
def calcStatistics(self, fund_id, start_date, end_date):
self.fund_returns_RMF = []
self.fund_returns_RF = []
self.benchmark_returns_RMF = []
self.benchmark_returns_RF = []
self.risk_free_returns_RMF = []
self.risk_free_returns_RF = []
if self.backstop_only:
tmp_returns = self.bsdm.getHedgeFundReturns(496539, start_date, end_date)
if tmp_returns != False:
for month in tmp_returns:
for item in month:
if item[0] == "amount":
self.benchmark_returns_RMF.append(item[1] + 1)
self.benchmark_returns_RF.append(item[1])
tmp_returns = self.bsdm.getHedgeFundReturns(496605, start_date, end_date)
if tmp_returns != False:
for month in tmp_returns:
for item in month:
if item[0] == "amount":
self.risk_free_returns_RMF.append(item[1] + 1)
self.risk_free_returns_RF.append(item[1])
tmp_returns = self.bsdm.getHedgeFundReturns(fund_id, start_date, end_date)
if tmp_returns != False:
for month in tmp_returns:
for item in month:
if item[0] == "amount":
self.fund_returns_RMF.append(item[1] + 1)
self.fund_returns_RF.append(item[1])
else:
for y in range(start_date.year,end_date.year+1):
if y == start_date.year and y == end_date.year:
for m in range(start_date.month,end_date.month+1):
self.addMonthOfReturns(496539,y,m,1)
self.addMonthOfReturns(496605,y,m,2)
self.addMonthOfReturns(fund_id,y,m,3)
elif y == start_date.year:
for m in range(start_date.month,13):
self.addMonthOfReturns(496539,y,m,1)
self.addMonthOfReturns(496605,y,m,2)
self.addMonthOfReturns(fund_id,y,m,3)
elif y == end_date.year:
for m in range(1,end_date.month+1):
self.addMonthOfReturns(496539,y,m,1)
self.addMonthOfReturns(496605,y,m,2)
self.addMonthOfReturns(fund_id,y,m,3)
else:
for m in range(1,13):
self.addMonthOfReturns(496539,y,m,1)
self.addMonthOfReturns(496605,y,m,2)
self.addMonthOfReturns(fund_id,y,m,3)
stats = self.gatherStats()
return stats
def gatherStats(self):
stats = []
try:
stats.append(self.AnnualizedGeometricReturn())
except:
stats.append("N/A")
pass
try:
stats.append(self.CompoundReturn())
except:
stats.append("N/A")
pass
try:
stats.append(self.AnnualizedArithmeticStandardDeviation())
except:
stats.append("N/A")
pass
try:
stats.append(self.MaxDrawDown())
except:
stats.append("N/A")
pass
try:
stats.append(self.SecondMaxDrawDown())
except:
stats.append("N/A")
pass
try:
stats.append(self.SharpeRatio())
except:
stats.append("N/A")
pass
try:
stats.append(self.CumulativeUpCapture())
except:
stats.append("N/A")
pass
try:
stats.append(self.CumulativeDownCapture())
except:
stats.append("N/A")
pass
try:
stats.append(self.AnnualizedUpCapture())
except:
stats.append("N/A")
pass
try:
stats.append(self.AnnualizedDownCapture())
except:
stats.append("N/A")
pass
try:
stats.append(self.PercentPeriodsPositve())
except:
stats.append("N/A")
pass
try:
stats.append(self.PercentPeriodsNegative())
except:
stats.append("N/A")
pass
try:
stats.append(self.AverageReturn())
except:
stats.append("N/A")
pass
try:
stats.append(self.HighestSinglePeriodReturn())
except:
stats.append("N/A")
pass
try:
stats.append(self.LowestSinglePeriodReturn())
except:
stats.append("N/A")
pass
try:
stats.append(self.AverageGainInPositivePeriod())
except:
stats.append("N/A")
pass
try:
stats.append(self.AverageLossInLossPeriod())
except:
stats.append("N/A")
pass
try:
stats.append(self.AnnualizedAlpha())
except:
stats.append("N/A")
pass
try:
stats.append(self.Beta())
except:
stats.append("N/A")
pass
try:
stats.append(self.CorrelationCoefficient())
except:
stats.append("N/A")
pass
try:
stats.append(self.RSquared())
except:
stats.append("N/A")
pass
return stats
def addMonthOfReturns(self,fund_id,year,month,type):
from_BS = False
if year not in self.returns_index.keys():
from_BS = True
elif month not in self.returns_index[year].keys():
from_BS = True
elif fund_id not in self.returns_index[year][month].keys():
from_BS = True
if from_BS:
tmp_returns = self.bsdm.getHedgeFundReturns(fund_id, date(year,month,15), date(year,month,15))
if tmp_returns != False:
for month in tmp_returns:
for item in month:
if item[0] == "amount":
if type == 3:
self.fund_returns_RMF.append(item[1] + 1)
self.fund_returns_RF.append(item[1])
elif type == 2:
self.risk_free_returns_RMF.append(item[1] + 1)
self.risk_free_returns_RF.append(item[1])
else:
self.benchmark_returns_RMF.append(item[1] + 1)
self.benchmark_returns_RF.append(item[1])
else:
def_values = False
if fund_id in self.returns_index[year][month].keys():
if "RMF" in self.returns_index[year][month][fund_id].keys():
if type == 3:
self.fund_returns_RMF.append(self.returns_index[year][month][fund_id]["RMF"])
self.fund_returns_RF.append(self.returns_index[year][month][fund_id]["RF"])
elif type == 2:
self.risk_free_returns_RMF.append(self.returns_index[year][month][fund_id]["RMF"])
self.risk_free_returns_RF.append(self.returns_index[year][month][fund_id]["RF"])
else:
self.benchmark_returns_RMF.append(self.returns_index[year][month][fund_id]["RMF"])
self.benchmark_returns_RF.append(self.returns_index[year][month][fund_id]["RF"])
else:
def_values = True
else:
def_values = True
if def_values:
if type == 3:
self.fund_returns_RMF.append(1.0)
self.fund_returns_RF.append(0.0)
elif type == 2:
self.risk_free_returns_RMF.append(1.0)
self.risk_free_returns_RF.append(0.0)
else:
self.benchmark_returns_RMF.append(1.0)
self.benchmark_returns_RF.append(0.0)
def AnnualizedGeometricReturn(self):
if len(self.fund_returns_RMF) < 12:
return self.CompoundReturn()
else:
annual_geo_return = 1.0
for rtrn in self.fund_returns_RMF:
annual_geo_return = annual_geo_return * rtrn
annual_geo_return = (annual_geo_return**(12.0 / len(self.fund_returns_RMF)) - 1)
return annual_geo_return
def AnnGeometricReturn(self,fund_returns_RMF):
annual_geo_return = 1.0
for rtrn in fund_returns_RMF:
annual_geo_return = annual_geo_return * rtrn
annual_geo_return = (annual_geo_return**(12.0 / len(fund_returns_RMF)) - 1)
return annual_geo_return
def AnnualizedArithmeticStandardDeviation(self):
if len(self.fund_returns_RMF) < 12:
return self.ArithmeticStandardDeviation(self.fund_returns_RMF)
else:
return self.ArithmeticStandardDeviation(self.fund_returns_RMF) * 12**.5
def CompoundReturn(self):
compound_return = 1.0
for rtrn in self.fund_returns_RMF:
compound_return = compound_return * rtrn
compound = compound_return - 1
return compound
def CmpndReturn(self, fund_returns_RMF):
compound_return = 1.0
for rtrn in fund_returns_RMF:
compound_return = compound_return * rtrn
compound = compound_return - 1
return compound
def MaxDrawDown(self):
sudo_drawdown, return_multiple = 1.0,1.0
count = 0.0
drawdown_collection, date_collection = [],[]
for rtrn in self.fund_returns_RF:
return_multiple = 1 + rtrn
if rtrn < 0:
sudo_drawdown = sudo_drawdown * return_multiple
if return_multiple > 1:
if return_multiple + sudo_drawdown > 1:
sudo_drawdown = sudo_drawdown * return_multiple
if sudo_drawdown < 1:
drawdown_collection.append(sudo_drawdown)
date_collection.append(count)
if sudo_drawdown > 1:
sudo_drawdown = 1
count = count + 1
if len(drawdown_collection) == 0:
return 0.0
else:
drawdown = (self.Min(drawdown_collection) - 1)
return drawdown
def SecondMaxDrawDown(self):
sudo_drawdown, return_multiple = 1.0,1.0
count = 0
drawdown_collection, date_collection = [],[]
for rtrn in self.fund_returns_RF:
return_multiple = 1 + rtrn
if rtrn < 0:
sudo_drawdown = sudo_drawdown * return_multiple
if return_multiple > 1:
if return_multiple + sudo_drawdown > 1:
sudo_drawdown = sudo_drawdown * return_multiple
if sudo_drawdown < 1:
drawdown_collection.append(sudo_drawdown)
date_collection.append(count)
if sudo_drawdown > 1:
sudo_drawdown = 1
count = count + 1
def getDrawdownRange(drawdown, drawdown_collection, date_collection, fund_returns_RF):
end_pos = drawdown_collection.index(drawdown)
end_pos = date_collection[end_pos]
tmp = 1.0
start_pos = 0
for index in xrange(end_pos,0,-1):
tmp = tmp * (1 + fund_returns_RF[index])
if abs(tmp - drawdown) < 0.000001:
start_pos = index
break
return [start_pos, end_pos]
if len(drawdown_collection) == 0:
return 0.0
maxdraw = self.Min(drawdown_collection)
max_range = getDrawdownRange(maxdraw, drawdown_collection, date_collection, self.fund_returns_RF)
new_drawdown_collection = drawdown_collection[:]
sec_draw = 0.0
for i in range(len(drawdown_collection)):
min = self.Min(new_drawdown_collection)
min_range = getDrawdownRange(min, drawdown_collection, date_collection, self.fund_returns_RF)
if min_range[0] >= max_range[0] and min_range[0] <= max_range[1] or \
min_range[1] >= max_range[0] and min_range[1] <= max_range[1]:
new_drawdown_collection.remove(min)
else:
sec_draw = min
break
if len(new_drawdown_collection) == 0:
return 0.0
else:
return sec_draw - 1
def SharpeRatio(self):
fund_risk_free_delta = []
for point in range(len(self.fund_returns_RMF)):
fund_risk_free_delta.append(self.fund_returns_RMF[point] - self.risk_free_returns_RMF[point] + 1)
sharpe_ratio = self.AnnGeometricReturn(fund_risk_free_delta) / self.AnnualizedArithmeticStandardDeviation()
if sharpe_ratio < 0:
return "N/A"
else:
return sharpe_ratio
def CumulativeUpCapture(self):
benchmark_returns, fund_returns = [],[]
for point in range(len(self.benchmark_returns_RMF)):
if self.benchmark_returns_RMF[point] > 1:
benchmark_returns.append(self.benchmark_returns_RMF[point])
fund_returns.append(self.fund_returns_RMF[point])
cummulative_up_cap = self.CmpndReturn(fund_returns) / self.CmpndReturn(benchmark_returns)
return cummulative_up_cap
def CumulativeDownCapture(self):
benchmark_returns, fund_returns = [],[]
for point in range(len(self.benchmark_returns_RMF)):
if self.benchmark_returns_RMF[point] < 1:
benchmark_returns.append(self.benchmark_returns_RMF[point])
fund_returns.append(self.fund_returns_RMF[point])
cummulative_down_cap = self.CmpndReturn(fund_returns) / self.CmpndReturn(benchmark_returns)
return cummulative_down_cap
def AnnualizedUpCapture(self):
benchmark_positive_returns, fund_returns = [],[]
for point in range(len(self.benchmark_returns_RMF)):
if self.benchmark_returns_RMF[point] > 1:
benchmark_positive_returns.append(self.benchmark_returns_RMF[point])
fund_returns.append(self.fund_returns_RMF[point])
annualized_up_cap = self.AnnGeometricReturn(fund_returns) / self.AnnGeometricReturn(benchmark_positive_returns)
if len(benchmark_positive_returns) < 12:
return self.CumulativeUpCapture()
return annualized_up_cap
def AnnualizedDownCapture(self):
benchmark_negative_returns, fund_returns = [],[]
for point in range(len(self.benchmark_returns_RMF)):
if self.benchmark_returns_RMF[point] < 1:
benchmark_negative_returns.append(self.benchmark_returns_RMF[point])
fund_returns.append(self.fund_returns_RMF[point])
annualized_down_cap = self.AnnGeometricReturn(fund_returns) / self.AnnGeometricReturn(benchmark_negative_returns)
if len(benchmark_negative_returns) < 12:
return self.CumulativeDownCapture()
return annualized_down_cap
def PercentPeriodsPositve(self):
count = 0.0
for rtrn in self.fund_returns_RF:
if rtrn > 0:
count = count + 1
return count / len(self.fund_returns_RF)
def PercentPeriodsNegative(self):
count = 0.0
for rtrn in self.fund_returns_RF:
if rtrn < 0:
count = count + 1
return count / len(self.fund_returns_RF)
def AverageReturn(self):
return self.Average(self.fund_returns_RF)
def HighestSinglePeriodReturn(self):
return self.Max(self.fund_returns_RF)
def LowestSinglePeriodReturn(self):
return self.Min(self.fund_returns_RF)
def AverageGainInPositivePeriod(self):
returns_in_positive = []
for rtrn in self.fund_returns_RF:
if rtrn > 0:
returns_in_positive.append(rtrn)
return self.Average(returns_in_positive)
def AverageLossInLossPeriod(self):
returns_in_negative = []
for rtrn in self.fund_returns_RF:
if rtrn < 0:
returns_in_negative.append(rtrn)
if len(returns_in_negative) == 0:
return 0.0
else:
return self.Average(returns_in_negative)
def Beta(self):
num_sum, den_sum = 0.0,0.0
x_mean = self.Average(self.benchmark_returns_RF)
y_mean = self.Average(self.fund_returns_RF)
for point in range(len(self.fund_returns_RF)):
num_sum = num_sum + (self.fund_returns_RF[point] - y_mean)*(self.benchmark_returns_RF[point]-x_mean)
den_sum = den_sum + (self.benchmark_returns_RF[point] - x_mean)**2
beta = num_sum / den_sum
return beta
def Alpha(self):
beta = self.Beta()
x_mean = self.Average(self.benchmark_returns_RF)
y_mean = self.Average(self.fund_returns_RF)
alpha = (y_mean - x_mean * beta) * 100
return alpha
def AnnualizedAlpha(self):
if len(self.fund_returns_RF) < 12:
return self.Alpha()
else:
return self.Alpha()*12.0
def CorrelationCoefficient(self):
num_sum = 0.0
x_mean = self.Average(self.fund_returns_RF)
y_mean = self.Average(self.benchmark_returns_RF)
for point in range(len(self.fund_returns_RF)):
num_sum = num_sum + (self.benchmark_returns_RF[point] - y_mean)*(self.fund_returns_RF[point]-x_mean)
x_stdev = self.ArithmeticStandardDeviation(self.fund_returns_RF)
y_stdev = self.ArithmeticStandardDeviation(self.benchmark_returns_RF)
R = num_sum / ((len(self.fund_returns_RF) -1) * x_stdev * y_stdev)
return R
def RSquared(self):
RSqrd = self.CorrelationCoefficient()**2
return RSqrd
def AlphaGivenBeta(self, beta):
x_mean = self.Average(self.benchmark_returns_RF)
y_mean = self.Average(self.fund_returns_RF)
alpha = (y_mean - x_mean * beta) * 100
return alpha
def RSquaredGivenR(self, R):
return R**2
def ArithmeticStandardDeviation(self, data_set):
mean = 0.0
for rtrn in data_set:
mean = mean + rtrn
mean = mean / len(data_set)
sum_dif_sqrd = 0
for rtrn in data_set:
sum_dif_sqrd = sum_dif_sqrd + (rtrn - mean)**2
stdev = (sum_dif_sqrd / (len(data_set) - 1))**.5
return stdev
def Min(self, data_set):
min = data_set[0]
for point in data_set:
if point < min:
min = point
return min
def Max(self, data_set):
max = data_set[0]
for point in data_set:
if point > max:
max= point
return max
def Average(self, data_set):
sum = 0.0
for point in data_set:
sum = sum + point
return sum / len(data_set)
def FormatPercentage(self, number):
number = number * 100.0
return str("%.2f" % round(number, 2)) + "%"
def FormatNonPercentage(self, number):
return str(round(number, 4))
def DateConversion(self, date): #date object
return date.strftime("%Y-%m-%dT00:00:00-05:00")
class StatsCacheUpdater:
def __init__(self,
cache_path=("C:/Documents and Settings/" +
os.getenv("username") + "/cache_root/Stats")):
if cache_path[len(cache_path) -
1] != "/" and cache_path[len(cache_path) - 1] != "\\":
cache_path += "/"
self.cache_path = cache_path
t = date.today()
self.cur_date = date(t.year, t.month, 15)
self.get = BackStopDataManager()
self.months = [
"Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug",
"Sep", "Oct", "Nov"
]
self.checkFolders()
self.loadData()
def syncCache(self, source_path):
cmd_str = "xcopy \"" + source_path + "\" \"" + self.cache_path[
0:len(self.cache_path) - 1] + "\" /e /i /h"
os.system(cmd_str)
def checkFolders(self):
cp = self.cache_path
if not os.access(cp + "returns", os.F_OK):
os.makedirs(cp + "returns")
if not os.access(cp + "lists", os.F_OK):
os.makedirs(cp + "lists")
if not os.access(cp + "pg_lists", os.F_OK):
os.makedirs(cp + "pg_lists")
def loadData(self):
self.returns_index = {}
self.fund_return_dates = {}
if os.access(self.cache_path + "returns/returns.cache", os.F_OK):
ret_cache_file = open(self.cache_path + "returns/returns.cache")
self.returns_index = cPickle.load(ret_cache_file)
ret_cache_file.close()
self.loadFundReturnDates()
def loadFundReturnDates(self):
for year in self.returns_index.keys():
for month in self.returns_index[year].keys():
for id in self.returns_index[year][month].keys():
if id not in self.fund_return_dates.keys():
self.fund_return_dates[id] = []
if "RMF" in self.returns_index[year][month][id].keys():
if date(year, month,
1) not in self.fund_return_dates[id]:
self.fund_return_dates[id].append(
date(year, month, 1))
def updateReturns(self, as_of_date=date.today()):
if self.equalOrAfter(as_of_date, date.today()):
as_of_date = date.today()
if not os.access(self.cache_path + "lists/active_funds.cache",
os.F_OK):
self.updateLists()
tmp_file = open(self.cache_path + "lists/active_funds.cache")
active_funds = cPickle.load(tmp_file)
tmp_file.close()
benchmarks = [496539, 496605]
returns = {}
for b_id in benchmarks:
tmp_returns = self.get.getHedgeFundReturns(b_id, as_of_date,
as_of_date)
if tmp_returns != False:
returns[b_id] = {}
for month in tmp_returns:
if str(month[5][1]) != "not_existent":
returns[b_id]["RMF"] = month[0][1] + 1
returns[b_id]["RF"] = month[0][1]
for fund_var in range(0, len(active_funds)):
if fund_var % 25 == 0:
print 100.0 * float(fund_var) / float(len(active_funds)), "%"
fund = active_funds[fund_var]
tmp_returns = self.get.getHedgeFundReturns(fund.ID, as_of_date,
as_of_date)
if tmp_returns != False:
returns[fund.ID] = {}
for month in tmp_returns:
if str(month[5][1]) != "not_existent":
returns[fund.ID]["RMF"] = month[0][1] + 1
returns[fund.ID]["RF"] = month[0][1]
if not as_of_date.year in self.returns_index.keys():
self.returns_index[as_of_date.year] = {}
self.returns_index[as_of_date.year][as_of_date.month] = returns
self.loadFundReturnDates()
ret_file = open(self.cache_path + "returns/returns.cache", 'w')
cPickle.dump(self.returns_index, ret_file)
ret_file.close()
def updatePGList(self):
ids = self.get.getPeerGroupIds()
id_name_map = dict()
for id in ids:
if "{Stats}" in id[1]:
id_name_map[id[0]] = id[1]
file = open(
self.cache_path + "pg_lists/cache-peer_group_id_name_map.cache",
"w")
cPickle.dump(id_name_map, file)
file.close()
pg_member_ids = {}
for pg_id in id_name_map.keys():
info = self.get.getPeerGroupMemberIds(pg_id, date.today())
id_list = []
for i in info:
id_list.append(i[0])
pg_member_ids[pg_id] = id_list
file = open(
self.cache_path + "pg_lists/cache-peer_group_member_id.cache", "w")
cPickle.dump(pg_member_ids, file)
file.close()
def updateMCPFAIList(self):
MCPFAI = []
file_def = open("def/MCPFAI_def.dat")
file_res = open("def/MCPFAI_res.dat")
report_def = file_def.read()
report_res = file_res.read()
info = self.get.runFundsReport(report_def, report_res, date.today())
for data in info:
fund = Fund()
fund.Name = data[0][1]
fund.ID = data[1][1]
fund.DMRR = datetime.strptime(data[2][1], "%m/%d/%Y")
fund.incept_date = datetime.strptime(data[3][1], "%m/%d/%Y")
fund.IL = "MCP Fund or Index"
fund.Class = "MCP Fund or Index"
fund.DMRR = self.getActualDMRR(fund.ID, fund.DMRR)
MCPFAI.append(fund)
tmp_file = open(self.cache_path + "lists/MCPFAI_funds.cache", 'w')
cPickle.dump(MCPFAI, tmp_file)
tmp_file.close()
def updateLists(self):
self.updatePGList()
self.updateMCPFAIList()
self.updateFundList()
def updateFundList(self):
file_def = open("def/all_funds_def.dat")
report_def = file_def.read()
report_res = "${true}"
t = date.today()
info = self.get.runFundsReport(report_def, report_res, self.cur_date)
funds = []
active_funds = []
fund_index = {}
invested_funds = []
focus_list_funds = []
inv_and_focus_funds = []
ex_focus_funds = []
ex_invested_funds = []
ex_inv_and_focus_funds = []
for data in info:
if data[2][1] != 'None':
fund = Fund()
fund.Name = data[0][1]
fund.ID = data[1][1]
fund.DMRR = datetime.strptime(data[2][1], "%m/%d/%Y")
fund.DMRR = self.getActualDMRR(fund.ID, fund.DMRR)
fund.IL = data[4][1]
fund.StrengthRating = data[5][1]
fund.Class = data[6][1]
fund.prime_strat = data[7][1]
fund.sub_strat1 = data[8][1]
fund.sub_strat2 = data[9][1]
fund.city = data[10][1]
tmp_state = self.getState(data[12][1])
if tmp_state == "None":
fund.state = data[11][1]
else:
fund.state = tmp_state
fund.postal_code = data[12][1]
fund.incept_date = datetime.strptime(data[13][1], "%m/%d/%Y")
fund.part_of_pmg = str.lower(str(data[14][1])).strip()
if fund.part_of_pmg == "yes":
fund.part_of_pmg = True
else:
fund.part_of_pmg = False
funds.append(fund)
for fund in funds:
fund.ListName = fund.Name + " (MRR:" + self.dateFormat(
fund.DMRR, "LIST") + ")"
fund.ListName += "(Int:" + fund.IL + ")"
fund.ListName += "(Str:" + fund.StrengthRating + ")"
fund.ListName += "(Cls:" + fund.Class + ")"
fund.ListName += "(Strat:" + fund.prime_strat + ")"
fund.ListName += "(Sub1:" + fund.sub_strat1 + ")"
fund.ListName += "(Sub2:" + fund.sub_strat2 + ")"
fund.ListName += "(City:" + fund.city + ")"
fund.ListName += "(State:" + fund.state + ")"
fund.ListName += "(Zip:" + fund.postal_code + ")"
fund_index[fund.ID] = fund
if self.equalOrAfter(
fund.DMRR,
datetime(self.cur_date.year - 1, self.cur_date.month,
self.cur_date.day)):
active_funds.append(fund)
if fund.IL == "Invested" and fund.part_of_pmg:
inv_and_focus_funds.append(fund)
invested_funds.append(fund)
ex_focus_funds.append(fund)
elif fund.IL == "Focus List" and fund.part_of_pmg:
inv_and_focus_funds.append(fund)
focus_list_funds.append(fund)
ex_invested_funds.append
else:
ex_invested_funds.append(fund)
ex_focus_funds.append(fund)
ex_inv_and_focus_funds.append(fund)
invested_funds = sorted(invested_funds, key=lambda Fund: Fund.Name)
focus_list_funds = sorted(focus_list_funds, key=lambda Fund: Fund.Name)
inv_and_focus_funds = sorted(inv_and_focus_funds,
key=lambda Fund: Fund.Name)
ex_focus_funds = sorted(ex_focus_funds, key=lambda Fund: Fund.Name)
ex_invested_funds = sorted(ex_invested_funds,
key=lambda Fund: Fund.Name)
ex_inv_and_focus_funds = sorted(ex_inv_and_focus_funds,
key=lambda Fund: Fund.Name)
active_funds = sorted(active_funds, key=lambda Fund: Fund.Name)
funds = sorted(funds, key=lambda Fund: Fund.Name)
file = open(self.cache_path + "lists/" + "all_funds.cache", "w")
cPickle.dump(funds, file)
file.close()
file = open(self.cache_path + "lists/" + "active_funds.cache", "w")
cPickle.dump(active_funds, file)
file.close()
file = open(self.cache_path + "lists/invested_funds.cache", "w")
cPickle.dump(invested_funds, file)
file.close()
file = open(self.cache_path + "lists/focus_list_funds.cache", "w")
cPickle.dump(focus_list_funds, file)
file.close()
file = open(self.cache_path + "lists/inv_and_focus_funds.cache", "w")
cPickle.dump(inv_and_focus_funds, file)
file.close()
file = open(self.cache_path + "lists/ex-focus.cache", "w")
cPickle.dump(ex_focus_funds, file)
file.close()
file = open(self.cache_path + "lists/ex-invested.cache", "w")
cPickle.dump(ex_invested_funds, file)
file.close()
file = open(self.cache_path + "lists/ex-invested_and_focus.cache", "w")
cPickle.dump(ex_inv_and_focus_funds, file)
file.close()
file = open(self.cache_path + "lists/all_fund_index.cache", "w")
cPickle.dump(fund_index, file)
file.close()
def getActualDMRR(self, id, dmrr):
tmp_dmrr = dmrr
if id in self.fund_return_dates.keys():
dates = self.fund_return_dates[id]
elif str(id) in self.fund_return_dates.keys():
dates = self.fund_return_dates[str(id)]
elif int(str(id)) in self.fund_return_dates.keys():
dates = self.fund_return_dates[int(str(id))]
elif unicode(str(id)) in self.fund_return_dates.keys():
dates = self.fund_return_dates[unicode(str(id))]
else:
dates = []
print "dates = ", dates
if len(dates) > 0:
dates = sorted(dates, reverse=True)
if self.equalOrAfter(dates[0], dmrr):
tmp_dmrr = dates[0]
return tmp_dmrr
def equalOrAfter(self, question, bench):
if question.year > bench.year or question.year == bench.year and question.month >= bench.month:
return True
else:
return False
def prevMonthYear(self, offset, cur_date=datetime.now()):
year = 0
if cur_date.month == 12:
year = cur_date.year + 1
else:
year = cur_date.year
return year - offset
def prevMonth(self, cur_date=datetime.now()):
month = 0
if cur_date.month == 1:
month = 11
else:
month = cur_date.month - 2
return month
def getEndOfMonth(self, month, year):
num_days = 0
if month == 4 or month == 6 or month == 9 or month == 11:
num_days = 30
elif month == 2:
if calendar.isleap(year):
num_days = 29
else:
num_days = 28
else:
num_days = 31
return num_days
def dateFormat(self, cur_date, type="DEFAULT"):
formatted_str = ""
if type == "LIST":
formatted_str = self.months[cur_date.month % 12] + "'" + str(
cur_date.year)[2:]
elif type == "DEFAULT":
year = cur_date.year - 2000
if year < 10:
year = "0" + str(year)
else:
year = str(year)
formatted_str = months[cur_date.month % 12] + "-" + year
return formatted_str
def getState(self, zip_code):
prefix = str(zip_code)[0:3]
try:
prefix = int(prefix)
except:
prefix = -1
if prefix >= 995:
state = "Alaska"
elif prefix >= 980:
state = "Washington"
elif prefix >= 970:
state = "Oregon"
elif prefix >= 967:
state = "Hawaii"
elif prefix >= 962:
state = "None"
elif prefix >= 900:
state = "California"
elif prefix >= 889:
state = "Nevada"
elif prefix == 885:
state = "Texas"
elif prefix >= 870:
state = "New Mexico"
elif prefix >= 850:
state = "Arizona"
elif prefix >= 840:
state = "Utah"
elif prefix >= 832:
state = "Idaho"
elif prefix >= 820:
state = "Wyoming"
elif prefix >= 800:
state = "Colorado"
elif prefix >= 750:
state = "Texas"
elif prefix >= 730:
state = "Oklahoma"
elif prefix >= 716:
state = "Arkansas"
elif prefix >= 700:
state = "Lousiana"
elif prefix >= 680:
state = "Nebraska"
elif prefix >= 660:
state = "Kansas"
elif prefix >= 630:
state = "Missouri"
elif prefix >= 600:
state = "Illinois"
elif prefix >= 590:
state = "Montana"
elif prefix >= 580:
state = "North Dakota"
elif prefix >= 570:
state = "South Dakota"
elif prefix >= 569:
state = "Washington D.C."
elif prefix >= 550:
state = "Minnesota"
elif prefix >= 530:
state = "Wisconsin"
elif prefix >= 500:
state = "Iowa"
elif prefix >= 480:
state = "Michigan"
elif prefix >= 460:
state = "Indiana"
elif prefix >= 430:
state = "Ohio"
elif prefix >= 400:
state = "Kentucky"
elif prefix >= 398:
state = "Georgia"
elif prefix >= 386:
state = "Mississippi"
elif prefix >= 370:
state = "Tennessee"
elif prefix >= 350:
state = "Alabama"
elif prefix >= 320:
state = "Florida"
elif prefix >= 300:
state = "Georgia"
elif prefix >= 290:
state = "South Carolina"
elif prefix >= 270:
state = "North Carolina"
elif prefix >= 247:
state = "West Virginia"
elif prefix >= 220:
state = "Virginia"
elif prefix >= 206:
state = "Maryland"
elif prefix >= 202:
state = "Fed. Government"
elif prefix >= 201:
state = "Virginia"
elif prefix >= 200:
state = "Washington D.C."
elif prefix >= 197:
state = "Delaware"
elif prefix >= 150:
state = "Pennsylvania"
elif prefix >= 100:
state = "New York"
elif prefix >= 90:
state = "None"
elif prefix >= 70:
state = "New Jersey"
elif prefix >= 60:
state = "Connecticut"
elif prefix >= 50:
state = "Vermont"
elif prefix >= 39:
state = "Maine"
elif prefix >= 30:
state = "New Hampshire"
elif prefix >= 28:
state = "Rhode Island"
elif prefix >= 10:
state = "Massachusetts"
elif prefix >= 6:
state = "Puerto Rico"
else:
state = "None"
return state
class StatsCacheUpdater:
def __init__(self,cache_path=("C:/Documents and Settings/" + os.getenv("username") + "/cache_root/Stats")):
if cache_path[len(cache_path)-1] != "/" and cache_path[len(cache_path)-1] != "\\":
cache_path += "/"
self.cache_path = cache_path
t = date.today()
self.cur_date = date(t.year,t.month,15)
self.get = BackStopDataManager()
self.months = ["Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep","Oct", "Nov"]
self.checkFolders()
self.loadData()
def syncCache(self,source_path):
cmd_str = "xcopy \"" + source_path + "\" \"" + self.cache_path[0:len(self.cache_path)-1] + "\" /e /i /h"
os.system(cmd_str)
def checkFolders(self):
cp = self.cache_path
if not os.access(cp + "returns", os.F_OK):
os.makedirs(cp + "returns")
if not os.access(cp + "lists", os.F_OK):
os.makedirs(cp + "lists")
if not os.access(cp + "pg_lists", os.F_OK):
os.makedirs(cp + "pg_lists")
def loadData(self):
self.returns_index = {}
self.fund_return_dates = {}
if os.access(self.cache_path + "returns/returns.cache",os.F_OK):
ret_cache_file = open(self.cache_path + "returns/returns.cache")
self.returns_index = cPickle.load(ret_cache_file)
ret_cache_file.close()
self.loadFundReturnDates()
def loadFundReturnDates(self):
for year in self.returns_index.keys():
for month in self.returns_index[year].keys():
for id in self.returns_index[year][month].keys():
if id not in self.fund_return_dates.keys():
self.fund_return_dates[id] = []
if "RMF" in self.returns_index[year][month][id].keys():
if date(year,month,1) not in self.fund_return_dates[id]:
self.fund_return_dates[id].append(date(year,month,1))
def updateReturns(self,as_of_date=date.today()):
if self.equalOrAfter(as_of_date,date.today()):
as_of_date = date.today()
if not os.access(self.cache_path + "lists/active_funds.cache",os.F_OK):
self.updateLists()
tmp_file = open(self.cache_path + "lists/active_funds.cache")
active_funds = cPickle.load(tmp_file)
tmp_file.close()
benchmarks = [496539,496605]
returns = {}
for b_id in benchmarks:
tmp_returns = self.get.getHedgeFundReturns(b_id, as_of_date, as_of_date)
if tmp_returns != False:
returns[b_id] = {}
for month in tmp_returns:
if str(month[5][1]) != "not_existent":
returns[b_id]["RMF"] = month[0][1] + 1
returns[b_id]["RF"] = month[0][1]
for fund_var in range(0,len(active_funds)):
if fund_var % 25 == 0:
print 100.0 * float(fund_var)/float(len(active_funds)), "%"
fund = active_funds[fund_var]
tmp_returns = self.get.getHedgeFundReturns(fund.ID, as_of_date, as_of_date)
if tmp_returns != False:
returns[fund.ID] = {}
for month in tmp_returns:
if str(month[5][1]) != "not_existent":
returns[fund.ID]["RMF"] = month[0][1] + 1
returns[fund.ID]["RF"] = month[0][1]
if not as_of_date.year in self.returns_index.keys():
self.returns_index[as_of_date.year] = {}
self.returns_index[as_of_date.year][as_of_date.month] = returns
self.loadFundReturnDates()
ret_file = open(self.cache_path + "returns/returns.cache",'w')
cPickle.dump(self.returns_index, ret_file)
ret_file.close()
def updatePGList(self):
ids = self.get.getPeerGroupIds()
id_name_map = dict()
for id in ids:
if "{Stats}" in id[1]:
id_name_map[id[0]] = id[1]
file = open(self.cache_path + "pg_lists/cache-peer_group_id_name_map.cache", "w")
cPickle.dump(id_name_map,file)
file.close()
pg_member_ids = {}
for pg_id in id_name_map.keys():
info = self.get.getPeerGroupMemberIds(pg_id,date.today())
id_list = []
for i in info:
id_list.append(i[0])
pg_member_ids[pg_id] = id_list
file = open(self.cache_path + "pg_lists/cache-peer_group_member_id.cache", "w")
cPickle.dump(pg_member_ids,file)
file.close()
def updateMCPFAIList(self):
MCPFAI = []
file_def = open("def/MCPFAI_def.dat")
file_res = open("def/MCPFAI_res.dat")
report_def = file_def.read()
report_res = file_res.read()
info = self.get.runFundsReport(report_def, report_res, date.today())
for data in info:
fund = Fund()
fund.Name = data[0][1]
fund.ID = data[1][1]
fund.DMRR = datetime.strptime(data[2][1],"%m/%d/%Y")
fund.incept_date = datetime.strptime(data[3][1],"%m/%d/%Y")
fund.IL = "MCP Fund or Index"
fund.Class = "MCP Fund or Index"
fund.DMRR = self.getActualDMRR(fund.ID, fund.DMRR)
MCPFAI.append(fund)
tmp_file = open(self.cache_path + "lists/MCPFAI_funds.cache",'w')
cPickle.dump(MCPFAI, tmp_file)
tmp_file.close()
def updateLists(self):
self.updatePGList()
self.updateMCPFAIList()
self.updateFundList()
def updateFundList(self):
file_def = open("def/all_funds_def.dat")
report_def = file_def.read()
report_res = "${true}"
t = date.today()
info = self.get.runFundsReport(report_def, report_res, self.cur_date)
funds = []
active_funds = []
fund_index = {}
invested_funds = []
focus_list_funds = []
inv_and_focus_funds = []
ex_focus_funds = []
ex_invested_funds = []
ex_inv_and_focus_funds = []
for data in info:
if data[2][1] != 'None':
fund = Fund()
fund.Name = data[0][1]
fund.ID = data[1][1]
fund.DMRR = datetime.strptime(data[2][1],"%m/%d/%Y")
fund.DMRR = self.getActualDMRR(fund.ID, fund.DMRR)
fund.IL = data[4][1]
fund.StrengthRating = data[5][1]
fund.Class = data[6][1]
fund.prime_strat = data[7][1]
fund.sub_strat1 = data[8][1]
fund.sub_strat2 = data[9][1]
fund.city = data[10][1]
tmp_state = self.getState(data[12][1])
if tmp_state == "None":
fund.state = data[11][1]
else:
fund.state = tmp_state
fund.postal_code = data[12][1]
fund.incept_date = datetime.strptime(data[13][1],"%m/%d/%Y")
fund.part_of_pmg = str.lower(str(data[14][1])).strip()
if fund.part_of_pmg == "yes":
fund.part_of_pmg = True
else:
fund.part_of_pmg = False
funds.append(fund)
for fund in funds:
fund.ListName = fund.Name + " (MRR:" + self.dateFormat(fund.DMRR,"LIST") + ")"
fund.ListName += "(Int:" + fund.IL + ")"
fund.ListName += "(Str:" + fund.StrengthRating + ")"
fund.ListName += "(Cls:" + fund.Class + ")"
fund.ListName += "(Strat:" + fund.prime_strat + ")"
fund.ListName += "(Sub1:" + fund.sub_strat1 + ")"
fund.ListName += "(Sub2:" + fund.sub_strat2 + ")"
fund.ListName += "(City:" + fund.city +")"
fund.ListName += "(State:" + fund.state + ")"
fund.ListName += "(Zip:" + fund.postal_code + ")"
fund_index[fund.ID] = fund
if self.equalOrAfter(fund.DMRR,datetime(self.cur_date.year-1,self.cur_date.month,self.cur_date.day)):
active_funds.append(fund)
if fund.IL == "Invested" and fund.part_of_pmg:
inv_and_focus_funds.append(fund)
invested_funds.append(fund)
ex_focus_funds.append(fund)
elif fund.IL == "Focus List" and fund.part_of_pmg:
inv_and_focus_funds.append(fund)
focus_list_funds.append(fund)
ex_invested_funds.append
else:
ex_invested_funds.append(fund)
ex_focus_funds.append(fund)
ex_inv_and_focus_funds.append(fund)
invested_funds = sorted(invested_funds, key=lambda Fund:Fund.Name)
focus_list_funds = sorted(focus_list_funds, key=lambda Fund:Fund.Name)
inv_and_focus_funds = sorted(inv_and_focus_funds, key=lambda Fund:Fund.Name)
ex_focus_funds = sorted(ex_focus_funds, key=lambda Fund:Fund.Name)
ex_invested_funds = sorted(ex_invested_funds, key=lambda Fund:Fund.Name)
ex_inv_and_focus_funds = sorted(ex_inv_and_focus_funds, key=lambda Fund:Fund.Name)
active_funds = sorted(active_funds, key=lambda Fund:Fund.Name)
funds = sorted(funds, key=lambda Fund:Fund.Name)
file = open(self.cache_path + "lists/" + "all_funds.cache", "w")
cPickle.dump(funds,file)
file.close()
file = open(self.cache_path + "lists/" + "active_funds.cache", "w")
cPickle.dump(active_funds,file)
file.close()
file = open(self.cache_path + "lists/invested_funds.cache", "w")
cPickle.dump(invested_funds,file)
file.close()
file = open(self.cache_path + "lists/focus_list_funds.cache", "w")
cPickle.dump(focus_list_funds,file)
file.close()
file = open(self.cache_path + "lists/inv_and_focus_funds.cache", "w")
cPickle.dump(inv_and_focus_funds,file)
file.close()
file = open(self.cache_path + "lists/ex-focus.cache", "w")
cPickle.dump(ex_focus_funds,file)
file.close()
file = open(self.cache_path + "lists/ex-invested.cache", "w")
cPickle.dump(ex_invested_funds,file)
file.close()
file = open(self.cache_path + "lists/ex-invested_and_focus.cache", "w")
cPickle.dump(ex_inv_and_focus_funds,file)
file.close()
file = open(self.cache_path + "lists/all_fund_index.cache", "w")
cPickle.dump(fund_index,file)
file.close()
def getActualDMRR(self,id,dmrr):
tmp_dmrr = dmrr
if id in self.fund_return_dates.keys():
dates = self.fund_return_dates[id]
elif str(id) in self.fund_return_dates.keys():
dates = self.fund_return_dates[str(id)]
elif int(str(id)) in self.fund_return_dates.keys():
dates = self.fund_return_dates[int(str(id))]
elif unicode(str(id)) in self.fund_return_dates.keys():
dates = self.fund_return_dates[unicode(str(id))]
else:
dates = []
print "dates = ", dates
if len(dates) > 0:
dates = sorted(dates,reverse=True)
if self.equalOrAfter(dates[0],dmrr):
tmp_dmrr = dates[0]
return tmp_dmrr
def equalOrAfter(self,question, bench):
if question.year > bench.year or question.year == bench.year and question.month >= bench.month:
return True
else:
return False
def prevMonthYear(self,offset,cur_date=datetime.now()):
year = 0
if cur_date.month==12:
year = cur_date.year + 1
else:
year = cur_date.year
return year - offset
def prevMonth(self,cur_date=datetime.now()):
month = 0
if cur_date.month==1:
month=11
else:
month = cur_date.month - 2
return month
def getEndOfMonth(self,month,year):
num_days = 0
if month == 4 or month == 6 or month == 9 or month == 11:
num_days = 30
elif month == 2:
if calendar.isleap(year):
num_days = 29
else:
num_days = 28
else:
num_days = 31
return num_days
def dateFormat(self,cur_date,type="DEFAULT"):
formatted_str = ""
if type == "LIST":
formatted_str = self.months[cur_date.month%12] + "'" + str(cur_date.year)[2:]
elif type == "DEFAULT":
year = cur_date.year - 2000
if year < 10:
year = "0" + str(year)
else:
year = str(year)
formatted_str = months[cur_date.month%12] + "-" + year
return formatted_str
def getState(self,zip_code):
prefix = str(zip_code)[0:3]
try:
prefix = int(prefix)
except:
prefix = -1
if prefix >= 995:
state = "Alaska"
elif prefix >= 980:
state = "Washington"
elif prefix >= 970:
state = "Oregon"
elif prefix >= 967:
state = "Hawaii"
elif prefix >= 962:
state = "None"
elif prefix >= 900:
state = "California"
elif prefix >= 889:
state = "Nevada"
elif prefix == 885:
state = "Texas"
elif prefix >= 870:
state = "New Mexico"
elif prefix >= 850:
state = "Arizona"
elif prefix >= 840:
state = "Utah"
elif prefix >= 832:
state = "Idaho"
elif prefix >= 820:
state = "Wyoming"
elif prefix >= 800:
state = "Colorado"
elif prefix >= 750:
state = "Texas"
elif prefix >= 730:
state = "Oklahoma"
elif prefix >= 716:
state = "Arkansas"
elif prefix >= 700:
state = "Lousiana"
elif prefix >= 680:
state = "Nebraska"
elif prefix >= 660:
state = "Kansas"
elif prefix >= 630:
state = "Missouri"
elif prefix >= 600:
state = "Illinois"
elif prefix >= 590:
state = "Montana"
elif prefix >= 580:
state = "North Dakota"
elif prefix >= 570:
state = "South Dakota"
elif prefix >= 569:
state = "Washington D.C."
elif prefix >= 550:
state = "Minnesota"
elif prefix >= 530:
state = "Wisconsin"
elif prefix >= 500:
state = "Iowa"
elif prefix >= 480:
state = "Michigan"
elif prefix >= 460:
state = "Indiana"
elif prefix >= 430:
state = "Ohio"
elif prefix >= 400:
state = "Kentucky"
elif prefix >= 398:
state = "Georgia"
elif prefix >= 386:
state = "Mississippi"
elif prefix >= 370:
state = "Tennessee"
elif prefix >= 350:
state = "Alabama"
elif prefix >= 320:
state = "Florida"
elif prefix >= 300:
state = "Georgia"
elif prefix >= 290:
state = "South Carolina"
elif prefix >= 270:
state = "North Carolina"
elif prefix >= 247:
state = "West Virginia"
elif prefix >= 220:
state = "Virginia"
elif prefix >= 206:
state = "Maryland"
elif prefix >= 202:
state = "Fed. Government"
elif prefix >= 201:
state = "Virginia"
elif prefix >= 200:
state = "Washington D.C."
elif prefix >= 197:
state = "Delaware"
elif prefix >= 150:
state = "Pennsylvania"
elif prefix >= 100:
state = "New York"
elif prefix >= 90:
state = "None"
elif prefix >= 70:
state = "New Jersey"
elif prefix >= 60:
state = "Connecticut"
elif prefix >= 50:
state = "Vermont"
elif prefix >= 39:
state = "Maine"
elif prefix >= 30:
state = "New Hampshire"
elif prefix >= 28:
state = "Rhode Island"
elif prefix >= 10:
state = "Massachusetts"
elif prefix >= 6:
state = "Puerto Rico"
else:
state = "None"
return state
