def community_dashboard(request, pk):
community = get_object_or_404(Community, pk=pk)
data = get_data.view(pk, 'community')
data_label = ['Community View']
xlabel = "Time"
ylabel = "Number of views"
div_id = "communityview"
communityview = create.data_plot(div_id, 'linechart', data, data_label,
xlabel, ylabel)
articles = article_status.community_articles(pk)
top = article_status.topfive(articles)
article_id = [item[1] for item in top]
view_count = [item[0] for item in top]
article_title = article_status.get_article_name(article_id)
top_id = "topview"
topview = create.data_plot(top_id, 'bargraph', [view_count], ['View'],
'Article Title', 'Number of views',
article_title)
# To show the trending articles in community
data_trending = requests.get(
URL_BASIC +
'logapi/event/article/view/?community-id={!s}&agg_type=terms&agg_field=article-id&limit=5&article-state=publish'
.format(pk),
headers={
'Authorization': "Token " + EVENT_API_TOKEN
}).json()
articles = []
status = 'found'
if 'status code' in list(
data_trending.keys()) and data_trending['status code'] == 200:
articles_keys = []
for item in data_trending['result']:
articles_keys.append(item['key'])
if len(articles_keys) == 0:
status = 'not found'
else:
for key in articles_keys:
try:
article_title = Articles.objects.filter(
id=key).first().title
articles.append({
'article_id': key,
'article_title': article_title
})
except:
continue
else:
status = 'not found'
print("Got Data: {!s}".format(articles))
return render(
request, 'community_dashboard.html', {
'communityview': communityview,
'articles': articles,
'status': status,
'topview': topview
})
def article_dashboard(request, pk):
article = get_object_or_404(Articles, pk=pk)
data = get_data.view(pk, 'article')
data_label = ['Article View']
xlabel = "Time"
ylabel = "Number of Views"
div_id = 'Articleviews'
viewdata = create.data_plot(div_id, 'linechart', data, data_label, xlabel,
ylabel)
print("Got Data: {!s}".format(viewdata))
return render(request, 'article_dashboard.html', {'viewdata': viewdata})
def my_pf(request):
pfs = pf_inst.objects.filter(pf_user=request.user)
if len(pfs) == 0:
form = forms.csv_upload()
return render(request, "csv_upload.html", {'form': form})
#return HttpResponse('<p>no portfolio!</p>')
print(len(pfs))
x = pfs[0].x.split(" ")
y = pfs[0].y.split(" ")
ansx = []
ansy = []
for i in range(0, len(x)):
if x[i] != " " and x[i] != "":
ansx.append(float(x[i]))
if y[i] != " " and y[i] != "":
ansy.append(y[i])
print(ansx)
for i in range(1, len(pfs)):
x = pfs[i].x.split(" ")
print(len(x))
for j in range(0, len(x)):
if x[j] != " " and x[j] != "":
ansx[j] = ansx[j] + float(x[j])
print(ansx)
data = ansx
data.reverse()
ansy.reverse()
val = []
val.append(ansy)
val.append(data)
data_label = ['x', "Portfolio"]
xlabel = "Time"
ylabel = "Portfolio Value"
div_id = "mygraph1"
form = forms.csv_upload()
view = create.data_plot(div_id, 'linechart', val, data_label, xlabel,
ylabel)
return render(request, 'csv_done.html', {
'r1': ansx,
'r2': ansy,
'stockview': view,
'form': form
})
def test_piechart_data_plot(self):
self.assertEqual(
create.data_plot(self.lockto, 'piechart', self.values, self.label),
self.body)
def test_linechart_data_plot(self):
self.assertEqual(
create.data_plot(self.lockto, 'linechart', self.values, self.label,
self.xlabel, self.ylabel), self.body)
def test_bargraph_data_plot(self):
self.assertEqual(
create.data_plot(self.lockto, 'bargraph', self.values, self.label,
self.xlabel, self.ylabel, self.xcat), self.body)
def stock_view_compare(request,pk1,pk2):
# get_stock_data()
if request.user.is_authenticated():
stock_all = Stock.objects.all()
stock_name = []
for item in stock_all:
stock_name.append(str(item))
if request.method == "POST":
form = SearchStockCompare(request.POST)
if form.is_valid():
stockname1 = form.cleaned_data['stock_name1']
stockname2 = form.cleaned_data['stock_name2']
if stockname1 not in stock_name:
not_found = stockname1
error = "Stock of company "+not_found+" does not exist"
return render(request, 'search_stock_notfound_compare.html', {'form': form, 'stock_name': stock_name, 'not_found': error })
elif stockname2 not in stock_name:
not_found = stockname2
error = "Stock of company "+not_found+" does not exist"
return render(request, 'search_stock_notfound_compare.html', {'form': form, 'stock_name': stock_name, 'not_found': error })
elif stockname1 == stockname2:
error = "Stocks of same company are selected"
return render(request, 'search_stock_notfound_compare.html', {'form': form, 'stock_name': stock_name, 'not_found': error })
else:
stock1 = Stock.objects.filter(name = stockname1)
stock2 = Stock.objects.filter(name = stockname2)
# print(stock[0].pk)
return redirect('/stock-compare/'+ str(stock1[0].pk)+'/'+str(stock2[0].pk))
else:
form = SearchStockCompare()
stock1 = get_object_or_404(Stock, pk = pk1)
stock2 = get_object_or_404(Stock, pk = pk2)
ticker1 = stock1.ticker
ticker2 = stock2.ticker
url = get_url(ticker1)
# data1 = np.random.normal(1, 0.001, 100).tolist()
# data2 = np.random.normal(1, 0.001, 100).tolist()
data1 = mainfunc_view(ticker1)
data2 = mainfunc_view(ticker2)
# data = 0
d = datetime.datetime.today()
TRAINDATES=[]
TESTDATES=[]
TRAINDATES.append(d.strftime('%Y-%m-%d'))
# print(TRAINDATES)
for j in range(100):
d -= datetime.timedelta(days=1)
TRAINDATES.append(d.strftime('%Y-%m-%d'))
TRAINDATES.reverse()
val = []
val.append(TRAINDATES)
val.append(data1)
val.append(data2)
data_label = ['x',stock1.name, stock2.name]
xlabel = "Time"
ylabel = "Stock Price"
div_id = "mygraph1"
view = create.data_plot(div_id, 'linechart', val, data_label, xlabel, ylabel)
return render(request, 'search_stock_with_graph_compare.html',{'stockview':view, 'pk':pk1, 'form': form, 'stock_name': stock_name, 'stock': stock1.name})
return render(request, 'search_stock_compare.html', {'form': form, 'stock_name': stock_name })
else:
return redirect('/login/?next=/stock-compare/')
def stock_view(request,pk):
# get_stock_data()
stock_all = Stock.objects.all()
stock_name_actual = []
for item in stock_all:
stock_name_actual .append(str(item))
if request.method == "POST":
form = SearchStock(request.POST)
if form.is_valid():
stockname = form.cleaned_data['stock_name']
if stockname not in stock_name_actual:
not_found = stockname
if request.user.is_authenticated():
color = ['#95b7ed', '#ffe2a0', '#ffbfe6']
data_mostviewed = requests.get(URL_BASIC + 'logapi/user/{!s}/event/stock/view/?limit=3&agg_type=terms&agg_field=stock-id'.format(request.user.id)).json()
stocks=[]
status = 'found'
if 'status code' in list(data_mostviewed.keys()) and data_mostviewed['status code'] == 200:
stocks_keys = []
for item in data_mostviewed['result']:
stocks_keys.append(item['key'])
if len(stocks_keys) == 0:
status = 'not found'
else:
i = 0
for key in stocks_keys:
try:
stock_name = Stock.objects.filter(id=key).first().name
stocks.append({'stock_id':key,'stock_name':stock_name, 'stock_color': color[i]})
i = i + 1
except:
continue
else:
status = 'not found'
print("\n\n\n\n",stocks,"\n\n\n\n")
stocks_recent=[]
recently_viewed = []
recently_viewed = requests.get(URL_BASIC + 'logapi/user/{!s}/event/stock/view/?after=1970-1-1T0:0:0&limit=1000'.format(request.user.id)).json()
status2 = 'found'
if 'status code' in list(recently_viewed.keys()) and recently_viewed['status code'] == 200:
stocks_keys = []
for item in recently_viewed['result']:
stocks_keys.append(item['event']['stock-id'])
if len(stocks_keys) == 0:
status2 = 'not found'
else:
# counts = collections.Counter(stocks_keys)
# stocks_keys = sorted(stocks_keys, key=lambda x: counts[x])
used = set()
stocks_keys = [x for x in stocks_keys if x not in used and (used.add(x) or True)]
if len(stocks_keys)>5:
stocks_keys=stocks_keys[:5]
print(stocks_keys)
i = 0
for key in stocks_keys:
try:
stock_name = Stock.objects.filter(id=key).first().name
stocks_recent.append({'stock_id':key,'stock_name':stock_name, 'stock_color': color[i]})
i=i+1
except:
continue
else:
status2 = 'not found'
stocks_trending=[]
trending = requests.get(URL_BASIC + 'logapi/event/stock/view/?after=1970-1-1T0:0:0&limit=10').json()
status3 = 'found'
if 'status code' in list(trending.keys()) and trending['status code'] == 200:
stocks_keys = []
for item in trending['result']:
stocks_keys.append(item['event']['stock-id'])
if len(stocks_keys) == 0:
status3 = 'not found'
else:
counts = collections.Counter(stocks_keys)
stocks_keys = sorted(stocks_keys, key=lambda x: counts[x])
used = set()
stocks_keys = [x for x in stocks_keys if x not in used and (used.add(x) or True)]
if len(stocks_keys)>5:
stocks_keys=stocks_keys[:5]
print(stocks_keys)
i = 0
for key in stocks_keys:
try:
stock_name = Stock.objects.filter(id=key).first().name
stocks_trending.append({'stock_id':key,'stock_name':stock_name,'stock_color': color[i]})
i = i +1
except:
continue
else:
status3 = 'not found'
return render(request, 'search_stock_notfound.html',{'form': form, 'stock_name': stock_name_actual, 'stocks':stocks, 'stocks_recent': stocks_recent, 'status2':status2, 'status3': status3, 'stocks_trending': stocks_trending, 'not_found': not_found})
else:
return render(request, 'search_stock_notfound.html',{'form': form, 'stock_name': stock_name_actual, 'not_found': not_found})
else:
stock = Stock.objects.filter(name = stockname)
# print(stock[0].pk)
return redirect('/stock-view/'+ str(stock[0].pk))
else:
form = SearchStock()
stock = get_object_or_404(Stock, pk = pk)
ticker = stock.ticker
# url = get_url(ticker)
# data = np.random.normal(1, 0.001, 100).tolist()
# if not data_stock:
# data = mainfunc_view(ticker)
# data_stock = data
# else:
# pass
if not stock.meta:
stock.meta = mainfunc_view(stock.ticker)
stock.save()
data = stock.meta
print("Saved")
elif (datetime.datetime.now(timezone.utc)-stock.updated_at).days != 0:
stock.meta = mainfunc_view(stock.ticker)
stock.save()
data = stock.meta
print("Updated Stock prices")
else:
data = []
print("Exists")
for item in stock.meta:
data.append(json.loads(item))
# num = request.session.get('num')
# if not num:
# num = 1
# print("Set num to 1")
# request.session['num'] = num
# else:
# print("Exists")
# print(num)
# data = 0
d = datetime.datetime.today()
TRAINDATES=[]
TESTDATES=[]
TRAINDATES.append(d.strftime('%Y-%m-%d'))
# print(TRAINDATES)
for j in range(100):
d -= datetime.timedelta(days=1)
TRAINDATES.append(d.strftime('%Y-%m-%d'))
TRAINDATES.reverse()
val = []
val.append(TRAINDATES)
val.append(data)
price = data[-1][0]
prev_price = data[-2][0]
print(price, prev_price)
abs_change = price - prev_price
if prev_price != 0:
cent_change = abs_change/prev_price*100
else:
cent_change = 0
if abs_change<0:
col = 'red'
else:
col = 'green'
data_label = ['x', stock.name]
xlabel = "Time"
ylabel = "Stock Price"
div_id = "mygraph1"
view = create.data_plot(div_id, 'linechart', val, data_label, xlabel, ylabel)
stockurl = "https://www.nasdaq.com/symbol/" + stock.ticker
if round(abs_change,2) == 0:
abs_change = round(abs_change,3)
else:
abs_change = round(abs_change,2)
if round(abs_change,2) == 0:
cent_change = round(cent_change,3)
else:
cent_change = round(cent_change,2)
abs(cent_change)
return render(request, 'stockview.html',{'stockview':view, 'pk':pk, 'form': form, 'stock_name': stock_name_actual, 'stock': stock.name, 'price': price, 'abs_change': abs_change, 'cent_change': cent_change, 'col': col, 'url': stockurl, 'showon': '1' })
def stock_predict(request,pk):
if request.user.is_authenticated():
stock_all = Stock.objects.all()
stock_name = []
for item in stock_all:
stock_name.append(str(item))
if request.method == "POST":
form = SearchStock(request.POST)
if form.is_valid():
stockname = form.cleaned_data['stock_name']
if stockname not in stock_name:
# print(stockname)
not_found = stockname
return render(request, 'search_stock_notfound2.html', {'form': form, 'stock_name': stock_name, 'not_found': not_found })
else:
stock = Stock.objects.filter(name = stockname)
# print(stock[0].pk)
return redirect('/stock-view/'+ str(stock[0].pk))
else:
form = SearchStock()
stock = get_object_or_404(Stock, pk = pk)
ticker = stock.ticker
url = get_url(ticker)
if not stock.meta_predict or (datetime.datetime.now(timezone.utc)-stock.updated_at).days != 0:
K.clear_session()
np.random.seed(7)
# IMPORTING DATASET
dataset=pd.read_csv('https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol='+ticker+'&apikey=WURUTBFP9P5F15BQ&datatype=csv',usecols=[1,2,3,4])
dataset = dataset.reindex(index = dataset.index[::-1])
# CREATING OWN INDEX FOR FLEXIBILITY
obs = np.arange(1, len(dataset) + 1, 1)
# TAKING DIFFERENT INDICATORS FOR PREDICTION
OHLC_avg = dataset.mean(axis = 1)
HLC_avg = dataset[['high', 'low', 'close']].mean(axis = 1)
close_val = dataset[['close']]
print(close_val)
# # PLOTTING ALL INDICATORS IN ONE PLOT
# plt.plot(obs, OHLC_avg, 'r', label = 'OHLC avg')
# plt.plot(obs, HLC_avg, 'b', label = 'HLC avg')
# plt.plot(obs, close_val, 'g', label = 'Closing price')
# plt.legend(loc = 'upper right')
# plt.show()
# PREPARATION OF TIME SERIES DATASE
OHLC_avg = np.reshape(OHLC_avg.values, (len(OHLC_avg),1)) # 1664
scaler = MinMaxScaler(feature_range=(0, 1))
OHLC_avg = scaler.fit_transform(OHLC_avg)
# TRAIN-TEST SPLIT
train_OHLC = int(len(OHLC_avg) * 0.75)
test_OHLC = len(OHLC_avg) - train_OHLC
train_OHLC, test_OHLC = OHLC_avg[0:train_OHLC,:], OHLC_avg[train_OHLC:len(OHLC_avg),:]
# TIME-SERIES DATASET (FOR TIME T, VALUES FOR TIME T+1)
trainX, trainY = new_dataset(train_OHLC, 1)
testX, testY = new_dataset(test_OHLC, 1)
# RESHAPING TRAIN AND TEST DATA
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
step_size = 1
# LSTM MODEL
model = Sequential()
model.add(LSTM(32, input_shape=(1, step_size), return_sequences = True))
model.add(LSTM(16))
model.add(Dense(1))
model.add(Activation('linear'))
# K.clear_session()
# MODEL COMPILING AND TRAINING
model.compile(loss='mean_squared_error', optimizer='adagrad') # Try SGD, adam, adagrad and compare!!!
model.fit(trainX, trainY, epochs=5, batch_size=1, verbose=2)
print("Model fit done")
# PREDICTION
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# DE-NORMALIZING FOR PLOTTING
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# TRAINING RMSE
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train RMSE: %.2f' % (trainScore))
# TEST RMSE
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test RMSE: %.2f' % (testScore))
# CREATING SIMILAR DATASET TO PLOT TRAINING PREDICTIONS
trainPredictPlot = np.empty_like(OHLC_avg)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[step_size:len(trainPredict)+step_size, :] = trainPredict
# CREATING SIMILAR DATASSET TO PLOT TEST PREDICTIONS
testPredictPlot = np.empty_like(OHLC_avg)
testPredictPlot[:, :] = np.nan
testPredictPlot[len(trainPredict)+(step_size*2)+1:len(OHLC_avg)-1, :] = testPredict
# DE-NORMALIZING MAIN DATASET
OHLC_avg = scaler.inverse_transform(OHLC_avg)
last_val = testPredict[-1]
last_val_scaled = last_val/last_val
next_val = model.predict(np.reshape(last_val_scaled, (1,1,1)))
print ("Last Day Value:", np.asscalar(last_val))
print ("Next Day Value:", np.asscalar(last_val+next_val))
RETURN_Y1=trainPredictPlot[0:75]
RETURN_Y2=testPredictPlot[75:100]
RETURN_NEXTDAYSVALUE = next_val
# print(RETURN_Y1)
# print("\n\n\n")
# print(RETURN_Y2)
return_y1 = []
return_y2 = []
for item in RETURN_Y1:
if np.isnan(item[0]):
pass
else:
return_y1.append(round(item[0],3))
return_y2.append(round(item[0],3))
for item in RETURN_Y2:
if np.isnan(item[0]):
pass
else:
return_y1.append(round(item[0],3))
print(return_y1)
stock.meta_predict = return_y1
stock.save()
data = stock.meta_predict
print("Saved")
else:
data = []
print("Exists")
for item in stock.meta_predict:
data.append(json.loads(item))
# import datetime
d = datetime.datetime.today()
TRAINDATES=[]
TESTDATES=[]
TRAINDATES.append(d.strftime('%Y-%m-%d'))
# print(TRAINDATES)
for j in range(74):
d -= datetime.timedelta(days=1)
TRAINDATES.append(d.strftime('%Y-%m-%d'))
TRAINDATES.reverse()
# print(TRAINDATES)
d = datetime.datetime.today()
for i in range(len(data)-75):
d += datetime.timedelta(1)
TRAINDATES.append(d.strftime('%Y-%m-%d'))
print(len(TRAINDATES),len(data))
data1 = data
data2 = data[:75]
val = []
val.append(TRAINDATES)
val.append(data1)
val.append(data2)
data_label = ['x','Predicted', 'Actual']
xlabel = "Time"
ylabel = "Stock Price"
div_id = "mygraph1"
view = create.data_plot(div_id, 'linechart', val, data_label, xlabel, ylabel)
print("\n\n\n\n",view,"\n\n\n\n")
return render(request, 'predict.html',{'stockview':view, 'pk':pk, 'form': form, 'stock_name': stock_name, 'stock': stock.name})
else:
return redirect('/login/?next=/stock-predict/'+str(pk)+'/')
def user_insight_dashboard(request):
if request.user.is_authenticated():
# To show the trending articles in community
data_trending = requests.get(
URL_BASIC +
'logapi/user/{!s}/event/article/view/?after=1970-1-1T0:0:0&limit=1000'
.format(request.user.id),
headers={
'Authorization': "Token " + EVENT_API_TOKEN
}).json()
articles = []
status = 'found'
articles_keys = []
if 'status code' in list(
data_trending.keys()) and data_trending['status code'] == 200:
for item in data_trending['result']:
articles_keys.append(item['event']['article-id'])
if len(articles_keys) == 0:
status = 'not found'
else:
used = set()
articles_keys = [
x for x in articles_keys
if x not in used and (used.add(x) or True)
]
if len(articles_keys) > 5:
articles_keys = articles_keys[:5]
print(articles_keys)
for key in articles_keys:
try:
article_title = Articles.objects.filter(
id=key).first().title
articles.append({
'article_id': key,
'article_title': article_title
})
except:
continue
else:
status = 'not found'
user = request.user
state_list = article_status.extract_state(
article_status.find_articles(user))
res2 = create.data_plot('chart2', 'piechart',
article_status.freq_state(state_list),
article_status.labels(state_list))
user_articles = article_status.find_articles(request.user)
user_toparticles = article_status.topfive(user_articles)
user_article_id = [item[1] for item in user_toparticles]
user_viewcount = [item[0] for item in user_toparticles]
user_article_title = article_status.get_article_name(user_article_id)
usertop_id = 'usertop'
usertopview = create.data_plot(usertop_id, 'bargraph',
[user_viewcount], ['View'],
'Article Title', 'Number of views',
user_article_title)
print("Got Data: {!s}".format(usertopview))
return render(
request, 'user_insight_dashboard.html', {
'articles': articles,
'status': status,
'res2': res2,
'usertopview': usertopview
})
else:
return redirect('/login/?next=/user-insight-dashboard/')
def manually(request):
if request.method == "POST":
comps = []
dates = []
quant = []
comps.append(request.POST['company'])
dates.append(request.POST['date'])
quant.append(request.POST['quantity'])
#if float(quant[0]) < 0:
# return render(request,'invalid.html')
print("comps=")
print(comps)
ansy = []
try:
tickers = ['AAPL', 'BANF', 'CASH', 'FCEL', 'JNJ']
AAPL = pd.read_csv(
'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=AAPL&apikey=WURUTBFP9P5F15BQ&datatype=csv'
)
BANF = pd.read_csv(
'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=BANF&apikey=WURUTBFP9P5F15BQ&datatype=csv'
)
CASH = pd.read_csv(
'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=CASH&apikey=WURUTBFP9P5F15BQ&datatype=csv'
)
FCEL = pd.read_csv(
'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=FCEL&apikey=WURUTBFP9P5F15BQ&datatype=csv'
)
JNJ = pd.read_csv(
'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=JNJ&apikey=WURUTBFP9P5F15BQ&datatype=csv'
)
except:
return render(request, 'trylater.html')
for i in range(0, len(comps)):
if comps[i] == "AAPL":
dates_parsed = dates[i].split("-")
d0 = date(int(dates_parsed[0]), int(dates_parsed[1]),
int(dates_parsed[2]))
ind = 99
try:
for j in range(0, 100):
dtemp = AAPL.at[j, 'timestamp']
dtemp_parsed = dtemp.split("-")
dtemp = date(int(dtemp_parsed[0]),
int(dtemp_parsed[1]),
int(dtemp_parsed[2]))
if dtemp <= d0:
ind = j
break
if i == 0:
ansx = np.zeros(ind + 1)
for l in range(0, ind + 1):
ansy.append(AAPL.at[l, 'timestamp'])
if float(quant[i]) >= 0:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (float(AAPL.at[l, 'close']) *
float(quant[i]))
else:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (float(AAPL.at[ind, 'close']) *
float(quant[i]))
except:
return render(request, 'trylater.html')
elif comps[i] == "BANF":
dates_parsed = dates[i].split("-")
d0 = date(int(dates_parsed[0]), int(dates_parsed[1]),
int(dates_parsed[2]))
ind = 99
try:
for j in range(0, 100):
dtemp = AAPL.at[j, 'timestamp']
dtemp_parsed = dtemp.split("-")
dtemp = date(int(dtemp_parsed[0]),
int(dtemp_parsed[1]),
int(dtemp_parsed[2]))
if dtemp <= d0:
ind = j
break
if i == 0:
ansx = np.zeros(ind + 1)
for l in range(0, ind + 1):
ansy.append(BANF.at[l, 'timestamp'])
if float(quant[i]) >= 0:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (float(BANF.at[l, 'close']) *
float(quant[i]))
else:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (float(BANF.at[ind, 'close']) *
float(quant[i]))
except:
return render(request, 'trylater.html')
elif comps[i] == "CASH":
dates_parsed = dates[i].split("-")
d0 = date(int(dates_parsed[0]), int(dates_parsed[1]),
int(dates_parsed[2]))
ind = 99
try:
for j in range(0, 100):
dtemp = CASH.at[j, 'timestamp']
dtemp_parsed = dtemp.split("-")
dtemp = date(int(dtemp_parsed[0]),
int(dtemp_parsed[1]),
int(dtemp_parsed[2]))
if dtemp <= d0:
ind = j
break
if i == 0:
ansx = np.zeros(ind + 1)
for l in range(0, ind + 1):
ansy.append(CASH.at[l, 'timestamp'])
if float(quant[i]) >= 0:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (float(CASH.at[l, 'close']) *
float(quant[i]))
else:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (float(CASH.at[ind, 'close']) *
float(quant[i]))
except:
return render(request, 'trylater.html')
elif comps[i] == "FCEL":
dates_parsed = dates[i].split("-")
d0 = date(int(dates_parsed[0]), int(dates_parsed[1]),
int(dates_parsed[2]))
ind = 99
try:
for j in range(0, 100):
dtemp = AAPL.at[j, 'timestamp']
dtemp_parsed = dtemp.split("-")
dtemp = date(int(dtemp_parsed[0]),
int(dtemp_parsed[1]),
int(dtemp_parsed[2]))
if dtemp <= d0:
ind = j
break
if i == 0:
ansx = np.zeros(ind + 1)
for l in range(0, ind + 1):
ansy.append(FCEL.at[l, 'timestamp'])
if float(quant[i]) >= 0:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (float(FCEL.at[l, 'close']) *
float(quant[i]))
else:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (float(FCEL.at[ind, 'close']) *
float(quant[i]))
except:
return render(request, 'trylater.html')
elif comps[i] == "JNJ":
dates_parsed = dates[i].split("-")
d0 = date(int(dates_parsed[0]), int(dates_parsed[1]),
int(dates_parsed[2]))
ind = 99
try:
for j in range(0, 100):
dtemp = JNJ.at[j, 'timestamp']
dtemp_parsed = dtemp.split("-")
dtemp = date(int(dtemp_parsed[0]),
int(dtemp_parsed[1]),
int(dtemp_parsed[2]))
if dtemp <= d0:
ind = j
break
if i == 0:
ansx = np.zeros(ind + 1)
for l in range(0, ind + 1):
ansy.append(JNJ.at[l, 'timestamp'])
if float(quant[i]) >= 0:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (float(JNJ.at[l, 'close']) *
float(quant[i]))
else:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (float(JNJ.at[ind, 'close']) *
float(quant[i]))
except:
return render(request, 'trylater.html')
print("ansy=")
print(ansx)
print(ansy)
pf = pf_inst()
pf.company = comps[0]
pf.idy = np.random.uniform()
pf.pf_user = request.user
x = ""
y = ""
for i in range(0, len(ansx)):
x = x + str(ansx[i]) + " "
y = y + ansy[i] + " "
pf.x = x
pf.y = y
pf.save()
data = ansx.tolist()
ansy.reverse()
data.reverse()
val = []
val.append(ansy)
val.append(data)
data_label = ['x', "Portfolio"]
xlabel = "Time"
ylabel = "Portfolio Value"
div_id = "mygraph1"
form = forms.csv_upload()
view = create.data_plot(div_id, 'linechart', val, data_label,
xlabel, ylabel)
return render(request, 'csv_done.html', {
'r1': ansx,
'r2': ansy,
'stockview': view,
'form': form
})
# return render(request,'csv_done.html', {'r1': ansx, 'r2':ansy})
else:
return render(request, "add_portfolio.html")
def portfolio(request):
if request.method == "POST":
form = forms.csv_upload(request.POST, request.FILES)
if form.is_valid():
namee = (find(request.user, "portfolio/"))
if namee != "nulll":
os.remove(namee)
for filename in request.FILES:
request.FILES[filename].name = str(request.user)
form = forms.csv_upload(request.POST, request.FILES)
form.save()
for filename in request.FILES:
name = request.FILES[filename].name
with open('portfolio/' + name) as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
comps = []
dates = []
quant = []
ansy = []
try:
for row in csv_reader:
comps.append(row[0])
if row[0] != "AAPL" and row[0] != "BANF" and row[
0] != "CASH" and row[0] != "FCEL" and row[
0] != "JNJ":
return render(request, 'invalid.html')
dates.append(row[1])
dates_parsed = row[1].split("-")
dtemp = date(int(dates_parsed[0]),
int(dates_parsed[1]),
int(dates_parsed[2]))
if dtemp > date.today() or dtemp < date(2010, 1, 1):
print("in the date")
return render(request, 'invalid.html')
quant.append(float(row[2]))
if float(row[2]) > 1000000 or float(row[2]) < -1000000:
print("quanti")
return render(request, 'invalid.html')
except:
return render(request, 'invalid.html')
try:
tickers = ['AAPL', 'BANF', 'CASH', 'FCEL', 'JNJ']
AAPL = pd.read_csv(
'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=AAPL&apikey=WURUTBFP9P5F15BQ&datatype=csv'
)
BANF = pd.read_csv(
'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=BANF&apikey=WURUTBFP9P5F15BQ&datatype=csv'
)
CASH = pd.read_csv(
'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=CASH&apikey=WURUTBFP9P5F15BQ&datatype=csv'
)
FCEL = pd.read_csv(
'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=FCEL&apikey=WURUTBFP9P5F15BQ&datatype=csv'
)
JNJ = pd.read_csv(
'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=JNJ&apikey=WURUTBFP9P5F15BQ&datatype=csv'
)
except:
return render(request, 'trylater.html')
for i in range(0, len(comps)):
dates_parsed = dates[i].split("-")
try:
d0 = date(int(dates_parsed[0]), int(dates_parsed[1]),
int(dates_parsed[2]))
except:
return render(request, 'invalid.html')
if i == 0:
maxd = d0 - d0
#dtemp=date.today()
try:
dtemp = AAPL.at[0, 'timestamp']
dtemp_parsed = dtemp.split("-")
dtemp = date(int(dtemp_parsed[0]),
int(dtemp_parsed[1]),
int(dtemp_parsed[2]))
except:
return render(request, 'trylater.html')
if dtemp - d0 > maxd:
maxd = dtemp - d0
#dtemp=date.today()
dtemp = AAPL.at[0, 'timestamp']
dtemp_parsed = dtemp.split("-")
dtemp = date(int(dtemp_parsed[0]), int(dtemp_parsed[1]),
int(dtemp_parsed[2]))
d0 = dtemp - maxd
ind = 99
for j in range(0, 100):
dtemp = AAPL.at[j, 'timestamp']
dtemp_parsed = dtemp.split("-")
dtemp = date(int(dtemp_parsed[0]), int(dtemp_parsed[1]),
int(dtemp_parsed[2]))
if dtemp < d0:
ind = j - 1
break
#dtemp=dtemp-1
ansx = np.zeros(ind + 1)
for l in range(0, ind + 1):
ansy.append(AAPL.at[l, 'timestamp'])
for i in range(0, len(comps)):
if comps[i] == "AAPL":
dates_parsed = dates[i].split("-")
try:
d0 = date(int(dates_parsed[0]),
int(dates_parsed[1]),
int(dates_parsed[2]))
except:
return render(request, 'invalid.html')
ind = 99
for j in range(0, 100):
dtemp = AAPL.at[j, 'timestamp']
dtemp_parsed = dtemp.split("-")
dtemp = date(int(dtemp_parsed[0]),
int(dtemp_parsed[1]),
int(dtemp_parsed[2]))
if dtemp < d0:
ind = j - 1
break
#if i==0:
# ansx=np.zeros(ind+1)
# for l in range(0,ind+1):
# ansy.append(AAPL.at[l,'timestamp'])
try:
a = float(quant[i])
#if float(quant[i])<0:
# return render(request, 'invalid.html')
except:
return render(request, 'invalid.html')
for l in range(0, ind + 1):
try:
if float(quant[i]) >= 0:
ansx[l] = ansx[l] + (float(
AAPL.at[l, 'close']) * float(quant[i]))
else:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (
float(AAPL.at[ind, 'close']) *
float(quant[i]))
except:
return render(request, 'trylater.html')
elif comps[i] == "BANF":
dates_parsed = dates[i].split("-")
try:
d0 = date(int(dates_parsed[0]),
int(dates_parsed[1]),
int(dates_parsed[2]))
except:
return render(request, 'invalid.html')
try:
ind = 99
for j in range(0, 100):
dtemp = BANF.at[j, 'timestamp']
dtemp_parsed = dtemp.split("-")
dtemp = date(int(dtemp_parsed[0]),
int(dtemp_parsed[1]),
int(dtemp_parsed[2]))
if dtemp < d0:
ind = j - 1
break
#if i==0:
# ansx=np.zeros(ind+1)
# for l in range(0,ind+1):
# ansy.append(BANF.at[l,'timestamp'])
try:
a = quant[i]
#if float(quant[i])<0:
# return render(request, 'invalid.html')
except:
return render(request, 'invalid.html')
if float(quant[i]) >= 0:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (float(
BANF.at[l, 'close']) * float(quant[i]))
else:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (
float(BANF.at[ind, 'close']) *
float(quant[i]))
except:
return render(request, 'trylater.html')
elif comps[i] == "CASH":
dates_parsed = dates[i].split("-")
try:
d0 = date(int(dates_parsed[0]),
int(dates_parsed[1]),
int(dates_parsed[2]))
except:
return render(request, 'invalid.html')
try:
ind = 99
for j in range(0, 100):
dtemp = CASH.at[j, 'timestamp']
dtemp_parsed = dtemp.split("-")
dtemp = date(int(dtemp_parsed[0]),
int(dtemp_parsed[1]),
int(dtemp_parsed[2]))
if dtemp < d0:
ind = j - 1
break
#if i==0:
# ansx=np.zeros(ind+1)
# for l in range(0,ind+1):
# ansy.append(CASH.at[l,'timestamp'])
try:
a = float(quant[i])
# if float(quant[i])<0:
# return render(request, 'invalid.html')
except:
return render(request, 'invalid.html')
if float(quant[i]) >= 0:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (float(
CASH.at[l, 'close']) * float(quant[i]))
else:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (
float(CASH.at[ind, 'close']) *
float(quant[i]))
except:
return render(request, 'trylater.html')
elif comps[i] == "FCEL":
dates_parsed = dates[i].split("-")
try:
d0 = date(int(dates_parsed[0]),
int(dates_parsed[1]),
int(dates_parsed[2]))
except:
return render(request, 'invalid.html')
try:
ind = 99
for j in range(0, 100):
dtemp = FCEL.at[j, 'timestamp']
dtemp_parsed = dtemp.split("-")
dtemp = date(int(dtemp_parsed[0]),
int(dtemp_parsed[1]),
int(dtemp_parsed[2]))
if dtemp < d0:
ind = j - 1
break
#if i==0:
# ansx=np.zeros(ind+1)
# for l in range(0,ind+1):
# ansy.append(FCEL.at[l,'timestamp'])
try:
a = float(quant[i])
# if float(quant[i])<0:
# return render(request, 'invalid.html')
except:
return render(request, 'invalid.html')
if float(quant[i]) >= 0:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (float(
FCEL.at[l, 'close']) * float(quant[i]))
else:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (
float(FCEL.at[ind, 'close']) *
float(quant[i]))
except:
return render(request, 'trylater.html')
elif comps[i] == "JNJ":
dates_parsed = dates[i].split("-")
try:
d0 = date(int(dates_parsed[0]),
int(dates_parsed[1]),
int(dates_parsed[2]))
except:
return render(request, 'invalid.html')
try:
ind = 99
for j in range(0, 100):
dtemp = JNJ.at[j, 'timestamp']
dtemp_parsed = dtemp.split("-")
dtemp = date(int(dtemp_parsed[0]),
int(dtemp_parsed[1]),
int(dtemp_parsed[2]))
if dtemp < d0:
ind = j - 1
break
try:
a = float(quant[i])
#if float(quant[i])<0:
# return render(request, 'invalid.html')
except:
return render(request, 'invalid.html')
#if i==0:
# ansx=np.zeros(ind+1)
# for l in range(0,ind+1):
# ansy.append(JNJ.at[l,'timestamp'])
if float(quant[i]) >= 0:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (float(
JNJ.at[l, 'close']) * float(quant[i]))
else:
for l in range(0, ind + 1):
ansx[l] = ansx[l] + (
float(JNJ.at[ind, 'close']) *
float(quant[i]))
except:
return render(request, 'trylater.html')
print("ansy=")
print(ansx)
print(ansy)
form = forms.csv_upload()
data = ansx.tolist()
data.reverse()
print(data)
val = []
ansy.reverse()
print(ansy)
val.append(ansy)
val.append(data)
data_label = ['x', 'Portfolio']
xlabel = "Time"
ylabel = "Portfolio Value"
div_id = "mygraph1"
view = create.data_plot(div_id, 'linechart', val, data_label,
xlabel, ylabel)
print(view)
return render(request, 'csv_done.html', {
'r1': ansx,
'r2': ansy,
'stockview': view,
'form': form
})
# return render(request,'csv_done.html', {'r1': ansx, 'r2':ansy, 'form':form})
else:
return render(request, 'invalid.html')
else:
pfs = pf_inst.objects.filter(pf_user=request.user)
if len(pfs) == 0:
form = forms.csv_upload()
return render(request, "csv_upload.html", {'form': form})
#return HttpResponse('<p>no portfolio!</p>')
print(len(pfs))
maxlen = 0
ind = 0
for i in range(0, len(pfs)):
x = pfs[i]
x = pfs[i].x.split(" ")
if maxlen < len(x):
maxlen = len(x)
ind = i
x = pfs[ind].x.split(" ")
y = pfs[ind].y.split(" ")
ansx = []
ansy = []
for i in range(0, len(x)):
if x[i] != " " and x[i] != "":
ansx.append(float(x[i]))
if y[i] != " " and y[i] != "":
ansy.append(y[i])
print(ansx)
for i in range(0, len(pfs)):
if i == ind:
continue
x = pfs[i].x.split(" ")
print(len(x))
for j in range(0, len(x)):
if x[j] != " " and x[j] != "":
ansx[j] = ansx[j] + float(x[j])
print(ansx)
data = ansx
data.reverse()
ansy.reverse()
val = []
val.append(ansy)
val.append(data)
data_label = ['x', "Portfolio"]
xlabel = "Time"
ylabel = "Portfolio Value"
div_id = "mygraph1"
form = forms.csv_upload()
view = create.data_plot(div_id, 'linechart', val, data_label, xlabel,
ylabel)
return render(request, 'csv_done.html', {
'r1': ansx,
'r2': ansy,
'stockview': view,
'form': form
})
