def risk_return(port_rets):
#port_rets=getPortfolioRiskReturn(portfolio,start_date)
x = (port_rets.std() * np.sqrt(252)) * 100
x = x[:-1]
y = (port_rets.mean() * 252) * 100
y = y[:-1]
n = y.index
fig = plt.figure()
fig.set_size_inches(11.7, 8.27)
ax = fig.add_subplot(1, 1, 1)
t = x
print("############x#############")
print(x)
print("############Y#############")
print(y)
ax.scatter(x, y, c=t, cmap='jet')
fmt = '%.2f%%' # Format you want the ticks, e.g. '40%'
xticks = mtick.FormatStrFormatter(fmt)
ax.xaxis.set_major_formatter(xticks)
ax.yaxis.set_major_formatter(xticks)
for i, txt in enumerate(n):
ax.annotate(txt, (x[i], y[i]))
plt.title("Risk / Return of Assets", fontsize=15)
plt.xlabel("Risk", fontsize=10)
plt.ylabel("Return", fontsize=10)
plt.xticks(fontsize=8)
plt.yticks(fontsize=8)
save_json(fig, "risk_return.json")
return fig
def violin(port_rets, start_date):
print(port_rets)
group = 0
port_rets['month'] = pd.DatetimeIndex(port_rets.index).month
port_rets['day'] = pd.DatetimeIndex(port_rets.index).weekday_name
port_rets['month'] = port_rets['month'].apply(
lambda x: calendar.month_abbr[x])
fig, ax = plt.subplots()
# the size of A4 paper
fig.set_size_inches(11.7, 8.27)
if group == "day":
sns.violinplot(x="day",
y="Portfolio Value",
data=port_rets,
palette="Pastel1",
ax=ax)
title = "Daily Returns"
else:
title = "Monthly Returns"
sns.violinplot(x="month",
y="Portfolio Value",
data=port_rets,
palette="Pastel1",
ax=ax)
#Modify x axis labels
sns.despine()
#vals = ax.get_yticks()
#ax.set_yticklabels(['{:.2f}%'.format(x * 100) for x in vals])
#ax.set_xlabel('')
#ax.set_ylabel('')
plt.title(title, fontsize=15)
save_json(fig, "violation.json")
return fig
def correlData(pdata):
cor = pdata.corr()
print("########## cor ##########")
print(cor)
data = cor.values
print("########## data ##########")
print(data)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
fig.set_size_inches(11.7, 8.27)
heatmap = ax.pcolor(data, cmap=plt.cm.coolwarm)
fig.colorbar(heatmap)
ax.set_xticks(np.arange(data.shape[0]) + 0.5, minor=False)
ax.set_yticks(np.arange(data.shape[0]) + 0.5, minor=False)
ax.invert_yaxis()
column_labels = cor.columns
row_labels = cor.index
ax.set_xticklabels(column_labels, rotation=90, fontsize=8)
ax.set_yticklabels(row_labels, fontsize=8)
heatmap.set_clim(-1, 1)
plt.title('Portfolio Correlation', fontsize=15)
plt.tight_layout()
#plt.savefig(root_path + '/Figures/port_correl.png')
call_name = inspect.stack()[1][3]
print("call_name########")
print(call_name)
#if call_name != "diversification":
# plt.show()
save_json(fig, "correlation.json")
return fig
def portfolioOptimization(stocks, st, ed, num_portfolios):
#list of stocks in portfolio
css = """
table
{
border-collapse: collapse;
}
th
{
color: #ffffff;
background-color: #000000;
}
td
{
background-color: #cccccc;
}
table, th, td
{
font-family:Arial, Helvetica, sans-serif;
border: 1px solid black;
text-align: right;
}
"""
stocks = ['AAPL', 'AMZN', 'MSFT', 'ACC']
yf.pdr_override() # <== that's all it takes :-)
#indices= nift200Indices.objects(Ticker__in=["ACC.NS","AMZN.NS"], Date__lte=ed, Date__gte=st)
#=nift200Indices.objects(Ticker ='AAPL.NS')
yf.pdr_override() # <== that's all it takes :-)
end = datetime.date(2018, 5, 27)
begin = datetime.date(2017, 1, 1)
#timestamp format and get apple stock.
st = begin.strftime('%Y-%m-%d')
ed = end.strftime('%Y-%m-%d')
data = web.get_data_yahoo(stocks, st, ed)['Adj Close']
print("####################Data################")
print(data)
#download daily price data for each of the stocks in the portfolio
#data = web.DataReader(stocks,data_source='yahoo',start='01/01/2010')['Adj Close']
data.sort_index(inplace=True)
#convert daily stock prices into daily returns
returns = data.pct_change()
#calculate mean daily return and covariance of daily returns
mean_daily_returns = returns.mean()
cov_matrix = returns.cov()
print("##########cov_matrix########")
print(cov_matrix)
num_portfolios = 100
#set up array to hold results
#We have increased the size of the array to hold the weight values for each stock
results = np.zeros((4 + len(stocks) - 1, num_portfolios))
labels = []
for i in range(num_portfolios):
#select random weights for portfolio holdings
weights = np.array(np.random.random(4))
#rebalance weights to sum to 1
weights /= np.sum(weights)
print("#######WEIGHT##########")
print(weights)
#calculate portfolio return and volatility
portfolio_return = np.sum(mean_daily_returns * weights) * 252
portfolio_std_dev = np.sqrt(
np.dot(weights.T, np.dot(cov_matrix, weights))) * np.sqrt(252)
# print("########### portfolio_return#############")
#print(portfolio_return)
#print("########### portfolio_std_dev#############")
#print(portfolio_std_dev)
#store results in results array
results[0, i] = portfolio_return
results[1, i] = portfolio_std_dev
#store Sharpe Ratio (return / volatility) - risk free rate element excluded for simplicity
results[2, i] = results[0, i] / results[1, i]
#iterate through the weight vector and add data to results array
for j in range(len(weights)):
results[j + 3, i] = weights[j]
print("#########################RESULT##############")
print(results)
fig, ax = plt.subplots()
print("############ results.###### T###########")
print(results.T)
print("############ ARRAY###########")
#convert results array to Pandas DataFrame
results_frame = pd.DataFrame(results.T,
columns=[
'returns', 'standard_deviation',
'sharpe_ratio', stocks[0], stocks[1],
stocks[2], stocks[3]
])
print("################## FRAME###############")
for i in range(num_portfolios):
label = results_frame.ix[[i], :].T
label.columns = ['Statistics']
# .to_html() is unicode; so make leading 'u' go away with str()
labels.append(str(label.to_html()))
print(results_frame)
#locate position of portfolio with highest Sharpe Ratio
#print(results_frame['sharpe_ratio'])
max_sharpe_port = results_frame.iloc[
results_frame['sharpe_ratio'].idxmax()]
#locate positon of portfolio with minimum standard deviation
min_vol_port = results_frame.iloc[
results_frame['standard_deviation'].idxmin()]
#create scatter plot coloured by Sharpe Ratio
plt.xlabel('Volatility')
plt.ylabel('returns')
points = plt.scatter(results_frame.standard_deviation,
results_frame.returns,
c=results_frame.sharpe_ratio,
cmap='RdYlBu')
#label = results_frame
#label.columns = results_frame
# .to_html() is unicode; so make leading 'u' go away with str()
#labels.append(str(label.to_html()))
plt.colorbar()
#plot red star to highlight position of portfolio with highest sharpe_ratio Ratio
plt.scatter(max_sharpe_port[1],
max_sharpe_port[0],
marker=(5, 1, 0),
color='r',
s=1000)
#plot green star to highlight position of minimum variance portfolio
plt.scatter(min_vol_port[1],
min_vol_port[0],
marker=(5, 1, 0),
color='g',
s=1000)
print("#########################standard_deviation###########")
#print(results_frame.standard_deviation)
print("#########################return###########")
# print(results_frame.return)
print("#########################sharpe_ratio###########")
#print(results_frame.sharpe)
ax.grid(color='lightgray', alpha=0.7)
#fig1 = plt.gcf()
print("#############FIG###########")
#print(fig1)
byte_file = io.BytesIO()
tooltip = plugins.PointHTMLTooltip(points,
labels,
voffset=10,
hoffset=10,
css=css)
plugins.connect(fig, tooltip)
# tooltip = plugins.PointHTMLTooltip(points[0], labels,
# voffset=10, hoffset=10, css=css)
#plugins.connect(fig, tooltip)
#print(fig_to_html(fig))
# display_d3(fig1)
# fig1.savefig(byte_file, format='png')
#image_file= byte_file.getvalue()
save_json(fig, "test.json")
return fig_to_html(fig)
def portfolioOptimization(portfolio, st,ed,num_portfolios):
#list of stocks in portfolio
css = """
table
{
border-collapse: collapse;
}
th
{
color: #ffffff;
background-color: #000000;
}
td
{
background-color: #cccccc;
}
table, th, td
{
font-family:Arial, Helvetica, sans-serif;
border: 1px solid black;
text-align: right;
}
"""
stocks = portfolio.Ticker_List
#yf.pdr_override() # <== that's all it takes :-)
#indices= nift200Indices.objects(Ticker__in=["ACC.NS","AMZN.NS"], Date__lte=ed, Date__gte=st)
#=nift200Indices.objects(Ticker ='AAPL.NS')
#yf.pdr_override() # <== that's all it takes :-)
begin = datetime.date(2017,1,1)
#timestamp format and get apple stock.
start_date=begin.strftime('%Y-%m-%d')
#end_date=end.strftime('%Y-%m-%d')
end_date = dt.date.today()
#data = web.get_data_yahoo(stocks,st,ed)['Adj Close']
i=0
for sysm in stocks:
if portfolio.Company_Type =="nifty50":
objnf50=nift50Indices.objects.filter(Date__gte=start_date, Date__lte=end_date,Ticker=sysm).values_list('Adj_Close','Date')
elif portfolio.Company_Type =="nifty100":
objnf50=nift100Indices.objects.filter(Date__gte=start_date, Date__lte=end_date,Ticker=sysm).values_list('Adj_Close','Date')
elif portfolio.Company_Type =="nifty200":
objnf50=nift200Indices.objects.filter(Date__gte=start_date, Date__lte=end_date,Ticker=sysm).values_list('Adj_Close','Date')
else:
objnf50=nift500Indices.objects.filter(Date__gte=start_date, Date__lte=end_date,Ticker=sysm).values_list('Adj_Close','Date')
if i==0:
data=pd.DataFrame(list(objnf50),columns=[sysm,'Date'])
data=data.set_index('Date')
else:
data_frame =pd.DataFrame(list(objnf50),columns=[sysm,'Date'])
data_frame=data_frame.set_index('Date')
data = pd.merge(
data, data_frame, right_index=True, left_index=True, how='outer')
i+=1
#temp_data = data.iloc[:,0:len(data.columns)].apply(pd.to_numeric)
#download daily price data for each of the stocks in the portfolio
#data = web.DataReader(stocks,data_source='yahoo',start='01/01/2010')['Adj Close']
#print("########## data ##########")
#print(portfolio.Portfolio_Name)
#print(data)
data.sort_index(inplace=True)
#convert daily stock prices into daily returns
returns = data.pct_change()
#print("#########returns#########")
returns = returns.dropna(how='any')
#print(returns)
#calculate mean daily return and covariance of daily returns
mean_daily_returns = returns.mean()
cov_matrix = returns.astype(float).cov()
#print("#########DATA##########")
#print(data)
#print("##########cov_matrix##########")
#print(cov_matrix)
num_portfolios = 500
#set up array to hold results
#We have increased the size of the array to hold the weight values for each stock
results = np.zeros((4+len(stocks)-1,num_portfolios))
labels = []
for i in range(num_portfolios):
#select random weights for portfolio holdings
weights = np.array(np.random.random(len(stocks)))
#rebalance weights to sum to 1
weights /= np.sum(weights)
#calculate portfolio return and volatility
portfolio_return = np.sum(mean_daily_returns * weights) * 252
portfolio_std_dev = np.sqrt(np.dot(weights.T,np.dot(cov_matrix, weights))) * np.sqrt(252)
# print("########### portfolio_return#############")
#print(portfolio_return)
#print("########### portfolio_std_dev#############")
#print(portfolio_std_dev)
#store results in results array
results[0,i] = portfolio_return
results[1,i] = portfolio_std_dev
#store Sharpe Ratio (return / volatility) - risk free rate element excluded for simplicity
results[2,i] = results[0,i] / results[1,i]
#iterate through the weight vector and add data to results array
for j in range(len(weights)):
results[j+3,i] = weights[j]
fig, ax = plt.subplots()
columnst=[]
columnst.append("returns")
columnst.append("standard_deviation")
columnst.append("sharpe_ratio")
for st in stocks:
columnst.append(st)
#convert results array to Pandas DataFrame
#print("###########columst#########")
#print(columnst)
results_frame = pd.DataFrame(results.T,columns=columnst)
for i in range(num_portfolios):
label = results_frame.ix[[i], :].T
label.columns = ['Statistics']
# .to_html() is unicode; so make leading 'u' go away with str()
labels.append(str(label.to_html()))
#locate position of portfolio with highest Sharpe Ratio
#print(results_frame['sharpe_ratio'])
max_sharpe_port = results_frame.iloc[results_frame['sharpe_ratio'].idxmax()]
#locate positon of portfolio with minimum standard deviation
min_vol_port = results_frame.iloc[results_frame['standard_deviation'].idxmin()]
#create scatter plot coloured by Sharpe Ratio
plt.xlabel('Volatility')
plt.ylabel('returns')
points = plt.scatter(results_frame.standard_deviation,results_frame.returns,c=results_frame.sharpe_ratio,cmap='RdYlBu')
#label = results_frame
#label.columns = results_frame
# .to_html() is unicode; so make leading 'u' go away with str()
#labels.append(str(label.to_html()))
plt.colorbar()
#plot red star to highlight position of portfolio with highest sharpe_ratio Ratio
pointsst1=plt.scatter(max_sharpe_port[1],max_sharpe_port[0],marker=(5,1,0),color='r',s=1000)
#plot green star to highlight position of minimum variance portfolio
pointsst2=plt.scatter(min_vol_port[1],min_vol_port[0],marker=(5,1,0),color='g',s=1000)
#print(results_frame.standard_deviation)
# print(results_frame.return)
#print(results_frame.sharpe)
ax.grid(color='lightgray', alpha=0.7)
#fig1 = plt.gcf()
#print(fig1)
byte_file = io.BytesIO()
point1= []
pointsst1label = max_sharpe_port.ix[0:]
pointsst1label=pointsst1label.to_frame()
pointsst1label.columns = ['Statistics']
print("######## pointsst1label######")
print(pointsst1label)
point1.append(str(pointsst1label.to_html()))
point2=[]
pointsst2label = min_vol_port.ix[0:]
pointsst2label=pointsst2label.to_frame()
pointsst2label.columns = ['Statistics']
point2.append(str(pointsst2label.to_html()))
tooltip = plugins.PointHTMLTooltip(points, labels,voffset=10, hoffset=10, css=css)
tooltipstr1 = plugins.PointHTMLTooltip(pointsst1, point1,voffset=10, hoffset=10, css=css)
tooltipstr2 = plugins.PointHTMLTooltip(pointsst2, point2,voffset=10, hoffset=10, css=css)
plugins.connect(fig, tooltip)
plugins.connect(fig, tooltipstr1)
plugins.connect(fig, tooltipstr2)
fig.set_size_inches(11.7, 8.27)
# tooltip = plugins.PointHTMLTooltip(points[0], labels,
# voffset=10, hoffset=10, css=css)
#plugins.connect(fig, tooltip)
#print(fig_to_html(fig))
# display_d3(fig1)
# fig1.savefig(byte_file, format='png')
#image_file= byte_file.getvalue()
save_json(fig,"heatmap.json")
return fig