def calc_sharpe_ratio_sym(data,
symbol,
lookbackDays,
enddate=dt.today(),
index=None,
silent=False):
#data: Dataframe containing at least date for the required time period for the required symbol and index
#symbol: Symbol for which sharpe ratio is to be calculated
#lookbackDays: # of days to look back from the enddate to calculate sharpe ratio
#enddate: Last day for sharpe ratio calculation
#index: Reference index to be used
#silent: silence print statements
if index == None:
index = getdata.getIndexTicker(
getdata.getData([symbol], getdata.getParamDict('stock exchange')))
#data = getdata.get_history([symbol,index],dt.today()-relativedelta(days=days))
#data = data.drop(['Open','High','Low','Close','Volume'],axis=1)
#data = data.unstack(0).swaplevel(0,1,axis=1).sortlevel(0,axis=1)
data = append_return(data)
r_sym = data.ix[symbol]['return'].ix[(
enddate - relativedelta(days=lookbackDays)):enddate]
r_index = data.ix[index]['return'].ix[(
enddate - relativedelta(days=lookbackDays)):enddate]
r_sym_mean = r_sym.mean()
r_index_mean = r_index.mean()
std_sym_wrt_index = (r_sym - r_index).std()
answer = (r_sym_mean - r_index_mean) / std_sym_wrt_index
if not silent:
print days, "day Sharpe Ratio for", symbol, "=", answer
return answer
def getHistory(self,start_date):
tickers = []
for stock in self.stocks:
ticker = stock.ticker
tickers.append(ticker)
index = getdata.getIndexTicker(getdata.getData([ticker],
getdata.getParamDict('stock exchange')))
if index not in tickers:
tickers.append(index)
return getdata.get_history(tickers,start_date)
def getHistory(self, start_date):
tickers = []
for stock in self.stocks:
ticker = stock.ticker
tickers.append(ticker)
index = getdata.getIndexTicker(
getdata.getData([ticker],
getdata.getParamDict('stock exchange')))
if index not in tickers:
tickers.append(index)
return getdata.get_history(tickers, start_date)
def construct_ranking_table(portfolio,lookbackDays=None,oldTable=None,enddate=dt.today(),index=None,silent=False):
dfarr = []
for stock in portfolio.stocks:
if index == None:
idx = getdata.getIndexTicker(getdata.getData([stock.ticker],getdata.getParamDict('stock exchange')))
else:
idx = index
indexCurrVal = getdata.get_history([idx],
dt.today()-relativedelta(days=5))[-1:]['Close'].values[0]
try:
indexPrevVal = oldTable.ix[stock.ticker]['%Gain Index (Period)']
except:
indexPrevVal = getdata.get_history([idx],
dt.today()-relativedelta(days=lookbackDays))['Close'].values[0]
dftemp = pd.DataFrame(data=[0],index=[0])
#dftemp['Date'] = dt.today()
dftemp['Symbol'] = stock.ticker
dftemp['Name'] = getdata.getData([stock.ticker],"n")
dftemp['Price'] = getdata.get_history([stock.ticker],
dt.today()-relativedelta(days=5))[-1:]['Close'].values[0]
dftemp['Number Owned'] = stock.shares_owned
dftemp['Current Value'] = dftemp['Price']*dftemp['Number Owned']
try:
dftemp['Previous Value'] = oldTable.ix[stock.ticker]['Current Value']
except:
dftemp['Previous Value'] = dftemp['Number Owned']*getdata.get_history([stock.ticker],
dt.today()-relativedelta(days=lookbackDays))['Close'].values[0]
dftemp['%Gain (Period)'] = 100*(dftemp['Current Value'] - dftemp['Previous Value'])/dftemp['Previous Value']
dftemp['%Gain Index (Period)'] = 100*(indexCurrVal - indexPrevVal)/indexPrevVal
dftemp['Index'] = idx
dftemp['GainSt - GainIdx'] = dftemp['%Gain (Period)'] - dftemp['%Gain Index (Period)']
#if dftemp['%Gain Index (Period)'] > dftemp['%Gain (Period)']:
# dftemp['Stock > Index'] = 'N'
#else:
# dftemp['Stock > Index'] = 'Y'
dftemp['Total Investment'] = stock.investment
dftemp['%Total Gain'] = 100*(dftemp['Current Value'] - stock.investment)/stock.investment
dftemp = dftemp.drop(0,axis=1)
dftemp.index = dftemp['Symbol']
dftemp = dftemp.drop('Symbol',axis=1)
dfarr.append(dftemp)
dftoret = pd.concat(dfarr)
#dftoret = dftoret.sort(columns='Stock > Index',ascending=False)
dftoret = dftoret.sort(columns='GainSt - GainIdx',ascending=False)
dftoret = dftoret.drop('GainSt - GainIdx',axis=1)
dftoret['Rank'] = range(1,len(dftoret)+1)
if not silent:
print "Stocks doing better than the market:",dftoret[dftoret['%Gain (Period)']>dftoret['%Gain Index (Period)']].index.values
print "Stocks doing worse than the market:",dftoret[dftoret['%Gain (Period)']<dftoret['%Gain Index (Period)']].index.values
return dftoret
def calc_sharpe_ratio_sym(data,symbol,lookbackDays,enddate=dt.today(),index=None,silent=False):
#data: Dataframe containing at least date for the required time period for the required symbol and index
#symbol: Symbol for which sharpe ratio is to be calculated
#lookbackDays: # of days to look back from the enddate to calculate sharpe ratio
#enddate: Last day for sharpe ratio calculation
#index: Reference index to be used
#silent: silence print statements
if index == None:
index = getdata.getIndexTicker(getdata.getData([symbol],getdata.getParamDict('stock exchange')))
#data = getdata.get_history([symbol,index],dt.today()-relativedelta(days=days))
#data = data.drop(['Open','High','Low','Close','Volume'],axis=1)
#data = data.unstack(0).swaplevel(0,1,axis=1).sortlevel(0,axis=1)
data = append_return(data)
r_sym = data.ix[symbol]['return'].ix[(enddate-relativedelta(days=lookbackDays)):enddate]
r_index = data.ix[index]['return'].ix[(enddate-relativedelta(days=lookbackDays)):enddate]
r_sym_mean = r_sym.mean()
r_index_mean = r_index.mean()
std_sym_wrt_index = (r_sym-r_index).std()
answer = (r_sym_mean-r_index_mean)/std_sym_wrt_index
if not silent:
print days, "day Sharpe Ratio for",symbol, "=",answer
return answer
def construct_ranking_table(portfolio,lookbackDays=None,oldTable=None,enddate=dt.today().date(),index=None,silent=False):
#One,only one and exactly one of lookbackDays and oldTable should be specified.
dfarr = []
for stock in portfolio.stocks:
if index == None:
idx = getdata.getIndexTicker(getdata.getData([stock.ticker],getdata.getParamDict('stock exchange')))
else:
idx = index
indexCurrVal = getdata.get_history([idx],
dt.today()-relativedelta(days=5))[-1:]['Close'].values[0]
try:
indexPrevVal = getdata.get_history([idx],
oldTable.ix[stock.ticker]['Date'])['Close'].values[0]
except:
indexPrevVal = getdata.get_history([idx],
dt.today()-relativedelta(days=lookbackDays))['Close'].values[0]
dftemp = pd.DataFrame(data=[0],index=[0])
dftemp['Symbol'] = stock.ticker
dftemp['Name'] = getdata.getData([stock.ticker],"n")
dftemp['Price'] = getdata.get_history([stock.ticker],
enddate-relativedelta(days=5))[-1:]['Close'].values[0]
dftemp['Number Owned'] = stock.getSharesOwned()
dftemp['Current Value'] = dftemp['Price']*dftemp['Number Owned']
# try:
# dftemp['Previous Value'] = oldTable.ix[stock.ticker]['Current Value']
# except:
# prev_number_owned = dftemp['Number Owned']
# for transaction in stock.transactions:
# if transaction['Date'] > (enddate-relativedelta(days=lookbackDays)):
# if transaction['Type'] == 'Bought':
# prev_number_owned = prev_number_owned - transaction['Number']
# elif transaction['Type'] == 'Sold':
# prev_number_owned = prev_number_owned + transaction['Number']
# dftemp['Previous Value'] = prev_number_owned*getdata.get_history([stock.ticker],
# enddate-relativedelta(days=lookbackDays))['Close'].values[0]
# dftemp['%Gain (Period)'] = 100*(dftemp['Current Value'] - dftemp['Previous Value'])/dftemp['Previous Value']
prev_adj_close = getdata.get_history([stock.ticker],
enddate-relativedelta(days=lookbackDays))['Adj Close'].values[0]
dftemp['%Gain (Period)'] = 100*((getdata.get_history([stock.ticker],
enddate-relativedelta(days=5))[-1:]['Adj Close'].values[0]
- prev_adj_close)/prev_adj_close)
dftemp['%Gain Index (Period)'] = 100*(indexCurrVal - indexPrevVal)/indexPrevVal
dftemp['Index'] = idx
dftemp['GainSt - GainIdx'] = dftemp['%Gain (Period)'] - dftemp['%Gain Index (Period)']
#if dftemp['%Gain Index (Period)'] > dftemp['%Gain (Period)']:
# dftemp['Stock > Index'] = 'N'
#else:
# dftemp['Stock > Index'] = 'Y'
dftemp['Total Investment'] = stock.investment
dftemp['%Total Gain'] = 100*(dftemp['Current Value'] - stock.investment)/stock.investment
dftemp = dftemp.drop(0,axis=1)
dftemp.index = dftemp['Symbol']
dftemp = dftemp.drop('Symbol',axis=1)
dfarr.append(dftemp)
dftoret = pd.concat(dfarr)
#dftoret = dftoret.sort(columns='Stock > Index',ascending=False)
dftoret = dftoret.sort(columns='GainSt - GainIdx',ascending=False)
dftoret = dftoret.drop('GainSt - GainIdx',axis=1)
dftoret['Rank'] = range(1,len(dftoret)+1)
dftoret['Date'] = np.repeat(enddate,dftoret.shape[0])
if not silent:
print "Stocks doing better than the market:",dftoret[dftoret['%Gain (Period)']>dftoret['%Gain Index (Period)']].index.values
print "Stocks doing worse than the market:",dftoret[dftoret['%Gain (Period)']<dftoret['%Gain Index (Period)']].index.values[::-1]
return dftoret
def construct_ranking_table(portfolio,
lookbackDays=None,
oldTable=None,
enddate=dt.today().date(),
index=None,
silent=False):
#One,only one and exactly one of lookbackDays and oldTable should be specified.
dfarr = []
for stock in portfolio.stocks:
if index == None:
idx = getdata.getIndexTicker(
getdata.getData([stock.ticker],
getdata.getParamDict('stock exchange')))
else:
idx = index
indexCurrVal = getdata.get_history(
[idx],
dt.today() - relativedelta(days=5))[-1:]['Close'].values[0]
try:
indexPrevVal = getdata.get_history(
[idx], oldTable.ix[stock.ticker]['Date'])['Close'].values[0]
except:
indexPrevVal = getdata.get_history(
[idx],
dt.today() -
relativedelta(days=lookbackDays))['Close'].values[0]
dftemp = pd.DataFrame(data=[0], index=[0])
dftemp['Symbol'] = stock.ticker
dftemp['Name'] = getdata.getData([stock.ticker], "n")
dftemp['Price'] = getdata.get_history(
[stock.ticker],
enddate - relativedelta(days=5))[-1:]['Close'].values[0]
dftemp['Number Owned'] = stock.getSharesOwned()
dftemp['Current Value'] = dftemp['Price'] * dftemp['Number Owned']
# try:
# dftemp['Previous Value'] = oldTable.ix[stock.ticker]['Current Value']
# except:
# prev_number_owned = dftemp['Number Owned']
# for transaction in stock.transactions:
# if transaction['Date'] > (enddate-relativedelta(days=lookbackDays)):
# if transaction['Type'] == 'Bought':
# prev_number_owned = prev_number_owned - transaction['Number']
# elif transaction['Type'] == 'Sold':
# prev_number_owned = prev_number_owned + transaction['Number']
# dftemp['Previous Value'] = prev_number_owned*getdata.get_history([stock.ticker],
# enddate-relativedelta(days=lookbackDays))['Close'].values[0]
# dftemp['%Gain (Period)'] = 100*(dftemp['Current Value'] - dftemp['Previous Value'])/dftemp['Previous Value']
prev_adj_close = getdata.get_history(
[stock.ticker],
enddate - relativedelta(days=lookbackDays))['Adj Close'].values[0]
dftemp['%Gain (Period)'] = 100 * (
(getdata.get_history([stock.ticker], enddate - relativedelta(
days=5))[-1:]['Adj Close'].values[0] - prev_adj_close) /
prev_adj_close)
dftemp['%Gain Index (Period)'] = 100 * (indexCurrVal -
indexPrevVal) / indexPrevVal
dftemp['Index'] = idx
dftemp['GainSt - GainIdx'] = dftemp['%Gain (Period)'] - dftemp[
'%Gain Index (Period)']
#if dftemp['%Gain Index (Period)'] > dftemp['%Gain (Period)']:
# dftemp['Stock > Index'] = 'N'
#else:
# dftemp['Stock > Index'] = 'Y'
dftemp['Total Investment'] = stock.investment
dftemp['%Total Gain'] = 100 * (dftemp['Current Value'] -
stock.investment) / stock.investment
dftemp = dftemp.drop(0, axis=1)
dftemp.index = dftemp['Symbol']
dftemp = dftemp.drop('Symbol', axis=1)
dfarr.append(dftemp)
dftoret = pd.concat(dfarr)
#dftoret = dftoret.sort(columns='Stock > Index',ascending=False)
dftoret = dftoret.sort(columns='GainSt - GainIdx', ascending=False)
dftoret = dftoret.drop('GainSt - GainIdx', axis=1)
dftoret['Rank'] = range(1, len(dftoret) + 1)
dftoret['Date'] = np.repeat(enddate, dftoret.shape[0])
if not silent:
print "Stocks doing better than the market:", dftoret[
dftoret['%Gain (Period)'] >
dftoret['%Gain Index (Period)']].index.values
print "Stocks doing worse than the market:", dftoret[
dftoret['%Gain (Period)'] <
dftoret['%Gain Index (Period)']].index.values[::-1]
return dftoret
