def api_disconnect(request):
# context = {}
# print('Updating values before disconnecting')
# update_values(request)
# Resetting all email alerts to False
reset_email_alerts()
# Resetting the alarms
saved_alarms_stocks = StockData.objects.filter(stock_alarm=True)
for stock in saved_alarms_stocks:
reset_alarms(stock)
print('Stopping the IB API...')
ib_api_wrapper(request,action=STOP_API )
sleep(2)
print('Stopping Yahoo scrapers...')
indices_scrapers(stop=True)
return
def home(request, table_index=1):
user = authenticate(username='******', password='******')
if user is not None:
login(request, user)
context = {}
context['table_index'] = table_index
ib_api_connected = api_connection_status()
context['ib_api_connected'] = ib_api_connected
request.session['table_index'] = table_index
try:
context['email_enabled'] = EmailSupport.objects.all().first().enabled
print(f'EM: {context["email_enabled"]}')
except:
email_support = EmailSupport()
email_support.enabled = False
email_support.save()
stock_ref = StockData.objects.all().last()
# Check if there are any stocks in the DB
if stock_ref:
last_update = stock_ref.stock_date
else:
last_update = False
# Checking current sample_ period
try:
sample_period = stock_ref.sample_period_14
except:
sample_period = None
context['sample_period_14'] = sample_period
current_running_threads = threading.active_count()
print(
f"ACTIVE THREADS: ***************{current_running_threads}****************"
)
stocks = StockData.objects.filter(table_index=table_index)
context['stocks'] = stocks
# VALUES UPDATES
#########################
#TODO: Replace this update with current database data query.
# Updating indexes data every time homepage rendered
# indexes_update_done, indexes_info = indexes_updates(request)
# if indexes_update_done:
# print('Finished updating indexes')
# context.update(indexes_info)
# else:
# messages.error(request, 'Failed updating indexes.')
# logger.error('Failed updating indexes.')
# print('Failed updating indexes.')
# Stock data will be updated at API connect
if request.method == 'POST':
if 'connect_ib_api' in request.POST:
api_connect(request)
stocks = StockData.objects.filter(table_index=table_index)
context['stocks'] = stocks
ib_api_connected = api_connection_status()
context['ib_api_connected'] = ib_api_connected
# Start scraping for indices data
indices_scrapers()
return render(request, 'kadima/home.html', context)
elif 'update_now' in request.POST:
update_values(request)
elif 'disconnect_ib_api' in request.POST:
# print('Stopping the IB API...')
# ib_api_wrapper(request,action=STOP_API )
# sleep(2)
api_disconnect(request)
context['ib_api_connected'] = api_connection_status()
return render(request, 'kadima/home.html', context)
elif 'sort_gap' in request.POST:
print('>> Gap Sorting <<')
request.session['sort_by'] = 'gap_1'
context['sort_by'] = request.session['sort_by']
elif 'sort_week1' in request.POST:
print('>> Week1 Sorting <<')
request.session['sort_by'] = 'week_1'
context['sort_by'] = request.session['sort_by']
elif 'sort_week3' in request.POST:
print('>> Week3 Sorting <<')
request.session['sort_by'] = 'week_3'
context['sort_by'] = request.session['sort_by']
elif 'add_stock' in request.POST:
stocks_to_add = request.POST.get('stock').split(',')
# Checking list of current stocks in the DB
old_stocks = StockData.objects.all()
old_stocks_list = []
for stock in old_stocks:
old_stocks_list.append(stock.id)
for stock in stocks_to_add:
stock = stock.strip()
print(f'Adding stock: {stock}')
context['stock'] = stock
try:
stock_df = fin_data.get_data_yahoo(stock,
start=MAX_PAST,
end=TODAY)
except Exception as e:
messages.error(request, 'Stock does not exists')
# return render(request, 'kadima/home.html', context)
continue
stock_data = StockData()
stock_data.table_index = table_index
# stock_data.stock_date = stock_df.index[-1]
stock_data.stock_displayed_date = date_obj_to_date(
pd.Timestamp("today"), date_format='slash')
stock_data.ticker = stock.upper()
stock_data.stock_price = float("%0.2f" %
stock_df['Close'].iloc[-1])
stock_data.week_1, stock_data.week_1_min, stock_data.week_1_max = week_values(
stock_df, 90)
stock_data.week_2, stock_data.week_2_min, stock_data.week_2_max = week_values(
stock_df, 10)
stock_data.week_3, stock_data.week_3_min, stock_data.week_3_max = week_values(
stock_df, 15)
stock_data.week_5, stock_data.week_5_min, stock_data.week_5_max = week_values(
stock_df, 25)
stock_data.week_1_color = week_color(stock_data.week_1,
week3=True)
stock_data.week_2_color = week_color(stock_data.week_2)
stock_data.week_3_color = week_color(stock_data.week_3,
week3=True)
stock_data.week_5_color = week_color(stock_data.week_5)
prev_close = round(stock_df.loc[stock_df.index[-2]]['Close'],
2)
todays_open = round(stock_df.loc[stock_df.index[-1]]['Open'],
2)
stock_data.prev_close = prev_close
stock_data.todays_open = todays_open
gap_1, gap_1_color = gap_1_check(prev_close, todays_open)
stock_data.gap_1 = gap_1
stock_data.gap_1_color = gap_1_color
# Earning dates
try:
try:
earnings = yf.Ticker(stock).calendar[0][0]
except:
try:
earnings = yf.Ticker(stock).calendar['Value'][0]
except:
print(f'No earnings found for {stock}')
year = earnings.year
month = earnings.month
day = earnings.day
earnings_date = str(f'{day}/{month}/{year}')
earnings_ts = time.mktime(
datetime.datetime.strptime(earnings_date,
"%d/%m/%Y").timetuple())
today_ts = datetime.datetime.timestamp(TODAY)
earnings_dt = datetime.datetime.fromtimestamp(earnings_ts)
today_dt = datetime.datetime.fromtimestamp(today_ts)
if (earnings_dt - today_dt).days <= 7 and (
earnings_dt - today_dt).days >= 0:
stock_data.earnings_warning = "blink-bg"
stock_data.earnings_call_displayed = earnings_date
elif (earnings_dt - today_dt).days < 0:
stock_data.earnings_warning = "PAST"
stock_data.earnings_call_displayed = None
else:
stock_data.earnings_warning = ""
stock_data.earnings_call_displayed = earnings_date
except Exception as e:
messages.error(
request,
'Stock does not have an earnings call date defined.')
print(
f'Stock {stock} does not have an earnings call date defined.'
)
earnings = None
tan_deviation_angle = math.tan(
math.radians(settings.DEVIATION_ANGLE))
# Stock Trend - 30 days sample
a_stock_30, a_macd_30, a_mfi_30, rsi, week1, week2, week3 = trends(
stock, 30)
# a_stock_30 = stock_regression(stock, 30)
stock_data.stock_trend_30 = round(a_stock_30, 2)
# MACD trend
# a_macd_30 = trend_calculator(stock, 'MACD', period=30)
stock_data.macd_trend_30 = round(a_macd_30, 2)
if np.abs(a_macd_30) > tan_deviation_angle:
if (a_stock_30 > 0
and a_macd_30 < 0) or (a_stock_30 < 0
and a_macd_30 > 0):
stock_data.macd_30_clash = True
stock_data.macd_30_color = 'red'
elif (a_stock_30 < 0
and a_macd_30 < 0) or (a_stock_30 > 0
and a_macd_30 > 0):
stock_data.macd_30_clash = False
stock_data.macd_30_color = 'green'
else:
stock_data.macd_30_clash = False
stock_data.macd_30_color = 'green'
# MFI trend
# a_mfi_30 = trend_calculator(stock, 'MFI', period=30)
stock_data.money_flow_trend_30 = round(a_mfi_30, 2)
if np.abs(a_mfi_30) > tan_deviation_angle:
if (a_stock_30 > 0 and a_mfi_30 < 0) or (a_stock_30 < 0
and a_mfi_30 > 0):
stock_data.mfi_30_clash = True
stock_data.mfi_30_color = 'red'
elif (a_stock_30 < 0
and a_mfi_30 < 0) or (a_stock_30 > 0
and a_mfi_30 > 0):
stock_data.mfi_30_clash = False
stock_data.mfi_30_color = 'green'
else:
stock_data.mfi_30_clash = False
stock_data.mfi_30_color = 'green'
# Stock Trend - 14 days sample
a_stock_14, a_macd_14, a_mfi_14, rsi_14, week1, week2, week3 = trends(
stock, 14) # rsi_14 is just for unpacking. it's not user.
# a_stock_14 = stock_regression(stock, 14)
stock_data.stock_trend_14 = round(a_stock_14, 2)
# MACD trend
# a_macd_14 = trend_calculator(stock, 'MACD', period=14)
stock_data.macd_trend_14 = round(a_macd_14, 2)
if np.abs(a_macd_14) > tan_deviation_angle:
if (a_stock_14 > 0
and a_macd_14 < 0) or (a_stock_14 < 0
and a_macd_14 > 0):
stock_data.macd_14_clash = True
stock_data.macd_14_color = 'red'
elif (a_stock_14 < 0
and a_macd_14 < 0) or (a_stock_14 > 0
and a_macd_14 > 0):
stock_data.macd_14_clash = False
stock_data.macd_14_color = 'green'
else:
stock_data.macd_14_clash = False
stock_data.macd_14_color = 'green'
# MFI trend
# a_mfi_14 = trend_calculator(stock, 'MFI', period=14)
stock_data.money_flow_trend_14 = round(a_mfi_14, 2)
if np.abs(a_mfi_30) > tan_deviation_angle:
if (a_stock_14 > 0 and a_mfi_14 < 0) or (a_stock_14 < 0
and a_mfi_14 > 0):
stock_data.mfi_14_clash = True
stock_data.mfi_14_color = 'red'
elif (a_stock_14 < 0
and a_mfi_14 < 0) or (a_stock_14 > 0
and a_mfi_14 > 0):
stock_data.mfi_14_clash = False
stock_data.mfi_14_color = 'green'
else:
stock_data.mfi_14_clash = False
stock_data.mfi_14_color = 'green'
# RSI
# rsi = last_rsi(stock, period=30)
stock_data.rsi = rsi
if rsi > 0 and rsi <= 30:
stock_data.rsi_color = 'red'
elif rsi > 30 and rsi <= 65:
stock_data.rsi_color = 'orange'
elif rsi > 65 and rsi <= 100:
stock_data.rsi_color = 'green'
else:
stock_data.rsi_color = ''
try:
stock_data.save()
except Exception as e:
print(f'ERROR Adding Stock. Reason: {e}')
messages.error(
request,
f'Stock {stock} was not added. Already in the list.')
return render(request, 'kadima/home.html', context)
new_stocks = StockData.objects.filter(
table_index=table_index).order_by('week_3')
context['stocks'] = new_stocks
## Updating the streaming IB API data with added stock
new_stocks_api = StockData.objects.all()
new_stocks_list = dict()
for stock in new_stocks_api:
new_stocks_list[stock.id] = stock.ticker
if api_connection_status():
ib_api_wrapper(request,
old_stocks_list,
new_stocks_list,
action=UPADATE_STOCKS)
return render(request, 'kadima/home.html', context)
## Enable email support
elif 'email_enable' in request.POST:
print('>> Enable Email <<')
try:
email_enable = EmailSupport.objects.all().first()
if email_enable.enabled:
email_enable.enabled = False
context['email_enabled'] = False
email_enable.save()
# Resetting all email alerts to False
reset_email_alerts()
else:
email_enable.enabled = True
context['email_enabled'] = True
email_enable.save()
except:
email_enable = EmailSupport()
email_enable.enabled = True
context['email_enabled'] = True
email_enable.save()
# Update sample period for MACD and MFI calculations
elif 'sample_period' in request.POST:
print('>> Sampling <<')
stocks = StockData.objects.all()
for stock in stocks:
if request.POST['sample_period'] == '14':
stock.sample_period_14 = True
stock.save()
context['sample_period_14'] = True
else:
stock.sample_period_14 = False
stock.save()
context['sample_period_14'] = False
# Stock Delete
elif 'delete_stock' in request.POST:
print('>> Deleting <<')
old_stocks = StockData.objects.all()
old_stocks_list = []
for stock in old_stocks:
old_stocks_list.append(stock.id)
# Deleting a stock from DB
delete_stock = StockData.objects.filter(
pk=request.POST["delete_stock"])
delete_stock.delete()
new_stocks = StockData.objects.filter(
table_index=table_index).order_by('week_3')
context['stocks'] = new_stocks
## Updating the streaming IB API data with deleted stock
new_stocks_api = StockData.objects.all()
new_stocks_list = dict()
for stock in new_stocks_api:
new_stocks_list[stock.id] = stock.ticker
if api_connection_status():
ib_api_wrapper(request,
old_stocks_list,
new_stocks_list,
action=UPADATE_STOCKS)
# ib_api_wrapper(request,old_stocks_list=old_stocks_list, updated_stock_list=new_stocks_list, action=UPADATE_STOCKS)
return render(request, 'kadima/home.html', context)
# Save stock to history
elif 'save_stock' in request.POST:
print('>> Saving <<')
stock_id = request.POST['save_stock']
stock_data = StockData.objects.get(id=stock_id)
stock_data.saved_to_history = True
stock_data.save()
history_stock = HistoryStock.objects.create(
table_index=table_index,
ticker=stock_data.ticker,
stock_price=stock_data.stock_price,
week_1=stock_data.week_1,
week_1_color=stock_data.week_1_color,
week_2=stock_data.week_2,
week_2_color=stock_data.week_2_color,
week_3=stock_data.week_3,
week_3_color=stock_data.week_3_color,
week_5=stock_data.week_5,
week_5_color=stock_data.week_5_color,
gap_1=stock_data.gap_1,
gap_1_color=stock_data.gap_1_color,
earnings_call_displayed=stock_data.earnings_call_displayed)
messages.success(
request,
f"Stock was saved. You can review it's data on the History page."
)
stocks = StockData.objects.filter(
table_index=table_index).order_by('week_3')
context['stocks'] = stocks
return render(request, 'kadima/home.html', context)
elif 'alarm_stock' in request.POST:
print('>> Alarm Stock <<')
stock_id = request.POST['alarm_stock']
stock_data = StockData.objects.get(id=stock_id)
stock_data.stock_alarm = True
stock_data.stock_load_price = stock_data.stock_current_price
stock_data.save()
messages.info(
request,
f"Stock {stock_data.ticker} was added to alarm page. You can trigger value alarms on the Alarms page."
)
stocks = StockData.objects.filter(
table_index=table_index).order_by('week_3')
context['stocks'] = stocks
return render(request, 'kadima/home.html', context)
else:
stocks = StockData.objects.filter(
table_index=table_index).order_by('week_3')
context['stocks'] = stocks
return render(request, 'kadima/home.html', context)
else:
stocks = StockData.objects.filter(
table_index=table_index).order_by('week_3')
context['stocks'] = stocks
return render(request, 'kadima/home.html', context)
return render(request, 'kadima/home.html', context)
def history_all(request):
user = authenticate(username='******', password='******')
if user is not None:
login(request, user)
context = {}
# history_stocks = HistoryStock.objects.filter(table_index=table_index)
history_stocks = HistoryStock.objects.all()
# Calculate sum of all total profits
sum_total_profit = 0
if history_stocks:
for hs in history_stocks:
sum_total_profit += hs.total_profit
context['sum_total_profit'] = sum_total_profit
context['email_enabled'] = EmailSupport.objects.all().first().enabled
context['history_stocks'] = history_stocks
# context['table_index'] = table_index
# request.session['table_index'] = table_index
ib_api_connected = api_connection_status()
context['ib_api_connected'] = ib_api_connected
if request.method == 'POST':
if 'connect_ib_api' in request.POST:
api_connect(request)
ib_api_connected = api_connection_status()
context['ib_api_connected'] = ib_api_connected
# Start scraping for indices data
indices_scrapers()
return render(request, 'kadima/history.html', context)
elif 'disconnect_ib_api' in request.POST:
api_disconnect(request)
# context = {}
ib_api_connected = api_connection_status()
context['ib_api_connected'] = ib_api_connected
return render(request, 'kadima/history.html', context)
elif 'update_stock' in str(request.POST):
stock_id = request.POST.get('update_stock')
stock_data = HistoryStock.objects.get(id=stock_id)
stocks_bought = request.POST.get(f'stocks_bought_{stock_id}')
purchase_price = request.POST.get(
f'stock_purchase_price_{stock_id}')
stocks_sold = request.POST.get(f'stocks_sold_{stock_id}')
selling_price = request.POST.get(f'selling_price_{stock_id}')
if stocks_bought:
stock_data.stocks_bought = float(stocks_bought)
if purchase_price:
stock_data.purchase_price = float(purchase_price)
if stocks_sold:
stock_data.stocks_sold = float(stocks_sold)
if selling_price:
stock_data.selling_price = float(selling_price)
if stocks_bought and purchase_price and stocks_sold and selling_price:
stock_data.profit = round(
(stock_data.stocks_sold * stock_data.selling_price) -
(stock_data.stocks_bought * stock_data.purchase_price), 3)
stock_data.sold_date = str(datetime.datetime.today())
elif stock_data.stocks_bought and stock_data.purchase_price and stocks_sold and selling_price:
stock_data.profit = round(
(stock_data.stocks_sold * stock_data.selling_price) -
(stock_data.stocks_bought * stock_data.purchase_price), 3)
stock_data.sold_date = str(datetime.datetime.today())
elif stock_data.stocks_bought and stock_data.purchase_price and stock_data.stocks_sold and selling_price:
stock_data.profit = round(
(stock_data.stocks_sold * stock_data.selling_price) -
(stock_data.stocks_bought * stock_data.purchase_price), 3)
stock_data.sold_date = str(datetime.datetime.today())
elif stock_data.stocks_bought and stock_data.purchase_price and stocks_sold and stock_data.selling_price:
stock_data.profit = round(
(stock_data.stocks_sold * stock_data.selling_price) -
(stock_data.stocks_bought * stock_data.purchase_price), 3)
stock_data.sold_date = str(datetime.datetime.today())
try:
stock_data.dividends = float(
request.POST.get(f'dividends_{stock_id}'))
except:
pass
if stock_data.profit:
stock_data.total_profit = round(
stock_data.profit + stock_data.dividends, 3)
stock_data.save()
return redirect(request.META['HTTP_REFERER'])
## Enable email support
elif 'email_enable' in request.POST:
print('>> Enable Email <<')
try:
email_enable = EmailSupport.objects.all().first()
if email_enable.enabled:
email_enable.enabled = False
context['email_enabled'] = False
email_enable.save()
# Resetting all email alerts to False
reset_email_alerts()
else:
email_enable.enabled = True
context['email_enabled'] = True
email_enable.save()
except:
email_enable = EmailSupport()
email_enable.enabled = True
context['email_enabled'] = True
email_enable.save()
elif 'delete_stock' in request.POST:
stock_id = request.POST['delete_stock']
# stock_data = StockData.objects.get(id=stock_id)
# stock_data.saved_to_history = False
# stock_data.save()
delete_from_history = HistoryStock.objects.get(id=stock_id)
delete_from_history.delete()
# saved_stocks = StockData.objects.filter(saved_to_history=True)
# history_stocks = HistoryStock.objects.filter(table_index=table_index)
# context['history_stocks'] = history_stocks
# return redirect(request.META['HTTP_REFERER'])
return HttpResponseRedirect(request.path_info)
# Filter history stocks according to dates range
elif 'date_picker' in request.POST:
if request.POST['datetimepicker1']:
start_date_arr = request.POST['datetimepicker1'].split('-')
start_year, start_month, start_day = start_date_arr
start = datetime.datetime(day=int(start_day),
month=int(start_month),
year=int(start_year))
else:
messages.error(request,
'Please pick a start date for the filter.')
return redirect(request.META['HTTP_REFERER'])
if request.POST['datetimepicker2']:
end_date_arr = request.POST['datetimepicker2'].split('-')
end_year, end_month, end_day = end_date_arr
end = datetime.datetime(day=int(end_day),
month=int(end_month),
year=int(end_year))
else:
messages.error(request,
'Please pick a end date for the filter.')
return redirect(request.META['HTTP_REFERER'])
filtered_stocks = HistoryStock.objects.filter(
time_added__range=[start, end])
# Calculate sum of all total profits
sum_total_profit = 0
if history_stocks:
for hs in filtered_stocks:
sum_total_profit += hs.total_profit
context['sum_total_profit'] = sum_total_profit
context['history_stocks'] = filtered_stocks
# return HttpResponseRedirect(request.path_info)
# return render(request, 'kadima/history.html', context)
return render(request, 'kadima/history-all.html', context)
elif 'reset_filter' in request.POST:
# history_stocks = StockData.objects.filter(saved_to_history=True)
# context['history_stocks'] = history_stocks
# return HttpResponseRedirect(request.path_info)
# return render(request, 'kadima/history.html', context)
return redirect(request.META['HTTP_REFERER'])
elif 'export_table' in request.POST:
return redirect('export_data')
else:
# saved_stocks = StockData.objects.filter(saved_to_history=True)
# context['history_stocks'] = saved_stocks
# return render(request, 'kadima/history.html', context)
return redirect(request.META['HTTP_REFERER'])
# return render(request, 'kadima/history.html', context)
return render(request, 'kadima/history-all.html', context)
def home(request, table_index=1):
user = authenticate(username="******", password="******")
if user is not None:
login(request, user)
context = {}
context["table_index"] = table_index
ib_api_connected = api_connection_status()
context["ib_api_connected"] = ib_api_connected
request.session["table_index"] = table_index
try:
context["email_enabled"] = EmailSupport.objects.all().first().enabled
print(f'Email Enabled: {context["email_enabled"]}')
except:
email_support = EmailSupport()
email_support.enabled = False
email_support.save()
stock_ref = StockData.objects.all().last()
# Check if there are any stocks in the DB
if stock_ref:
last_update = stock_ref.stock_date
else:
last_update = False
# Checking current sample_ period
try:
sample_period = stock_ref.sample_period_14
except:
sample_period = None
context["sample_period_14"] = sample_period
current_running_threads = threading.active_count()
print(
f"ACTIVE THREADS: ***************{current_running_threads}****************"
)
stocks = StockData.objects.filter(table_index=table_index)
none_stocks = StockData.objects.filter(
Q(table_index=table_index)
& (Q(gap_1=None) | Q(week_1=None) | Q(week_3=None)
| Q(stock_price=None)))
if len(none_stocks) > 0:
missing_data = True
else:
missing_data = False
# default sorting
try:
print(f"{request.session['sort_by']=}")
context["sort_by"] = request.session["sort_by"]
except:
request.session["sort_by"] = "ticker"
context["sort_by"] = request.session["sort_by"]
context["stocks"] = stocks
# VALUES UPDATES
#########################
# TODO: Replace this update with current database data query.
# Updating indexes data every time homepage rendered
# indexes_update_done, indexes_info = indexes_updates(request)
# if indexes_update_done:
# print('Finished updating indexes')
# context.update(indexes_info)
# else:
# messages.error(request, 'Failed updating indexes.')
# logger.error('Failed updating indexes.')
# print('Failed updating indexes.')
# Stock data will be updated at API connect
if request.method == "POST":
if "connect_ib_api" in request.POST:
api_connect(request)
stocks = StockData.objects.filter(table_index=table_index)
context["stocks"] = stocks
ib_api_connected = api_connection_status()
context["ib_api_connected"] = ib_api_connected
# Start scraping for indices data
indices_scrapers()
return render(request, "kadima/home.html", context)
elif "update_now" in request.POST:
update_values(request)
elif "disconnect_ib_api" in request.POST:
# print('Stopping the IB API...')
# ib_api_wrapper(request,action=STOP_API )
# sleep(2)
api_disconnect(request)
context["ib_api_connected"] = api_connection_status()
return render(request, "kadima/home.html", context)
elif "sort_gap" in request.POST:
print(">> Gap Sorting <<")
if missing_data:
messages.error(
request,
"Table might not sort poperly due to missing stock information. Please remove stocks with None and try again.",
)
return redirect(request.META["HTTP_REFERER"])
else:
request.session["sort_by"] = "gap_1"
context["sort_by"] = "gap_1"
elif "sort_week1" in request.POST:
print(">> Week1 Sorting <<")
if missing_data:
messages.error(
request,
"Table might not sort poperly due to missing stock information. Please remove stocks with None and try again.",
)
return redirect(request.META["HTTP_REFERER"])
else:
request.session["sort_by"] = "week_1"
context["sort_by"] = "week_1"
elif "sort_week3" in request.POST:
print(">> Week3 Sorting <<")
if missing_data:
messages.error(
request,
"Table might not sort poperly due to missing stock information. Please remove stocks with None and try again.",
)
return redirect(request.META["HTTP_REFERER"])
else:
request.session["sort_by"] = "week_3"
context["sort_by"] = "week_3"
elif "add_stock" in request.POST:
try:
stocks_to_add = request.POST.get("stock").split(",")
# Checking list of current stocks in the DB
old_stocks = StockData.objects.all()
old_stocks_list = []
for stock in old_stocks:
old_stocks_list.append(stock.id)
for stock in stocks_to_add:
stock = stock.strip()
print(f"Adding stock: {stock}")
logger.info(f"Adding stock: {stock}")
context["stock"] = stock
try:
stock_df = yf.download(stock, period="1mo")
except Exception as e:
print(f"Failed getting stock. ERROR: {e}")
logger.error(f"Failed getting stock. ERROR: {e}")
messages.error(request, "Stock does not exists")
# return render(request, 'kadima/home.html', context)
continue
stock_data = StockData()
stock_data.table_index = table_index
# stock_data.stock_date = stock_df.index[-1]
stock_data.stock_displayed_date = date_obj_to_date(
pd.Timestamp("today"), date_format="slash")
stock_data.ticker = stock.upper()
stock_data.stock_price = float("%0.2f" %
stock_df["Close"].iloc[-1])
(
stock_data.week_1,
stock_data.week_1_min,
stock_data.week_1_max,
) = week_values(stock_df, 90)
(
stock_data.week_2,
stock_data.week_2_min,
stock_data.week_2_max,
) = week_values(stock_df, 10)
(
stock_data.week_3,
stock_data.week_3_min,
stock_data.week_3_max,
) = week_values(stock_df, 15)
(
stock_data.week_5,
stock_data.week_5_min,
stock_data.week_5_max,
) = week_values(stock_df, 25)
stock_data.week_1_color = week_color(stock_data.week_1,
week3=True)
stock_data.week_2_color = week_color(stock_data.week_2)
stock_data.week_3_color = week_color(stock_data.week_3,
week3=True)
stock_data.week_5_color = week_color(stock_data.week_5)
prev_close = round(
stock_df.loc[stock_df.index[-2]]["Close"], 2)
todays_open = round(
stock_df.loc[stock_df.index[-1]]["Open"], 2)
stock_data.prev_close = prev_close
stock_data.todays_open = todays_open
gap_1, gap_1_color = gap_1_check(prev_close, todays_open)
stock_data.gap_1 = gap_1
stock_data.gap_1_color = gap_1_color
# Earning dates
try:
try:
earnings = yf.Ticker(stock).calendar[0][0]
except:
try:
earnings = yf.Ticker(
stock).calendar["Value"][0]
except:
print(f"No earnings found for {stock}")
year = earnings.year
month = earnings.month
day = earnings.day
earnings_date = str(f"{day}/{month}/{year}")
earnings_ts = time.mktime(
datetime.datetime.strptime(earnings_date,
"%d/%m/%Y").timetuple())
today_ts = datetime.datetime.timestamp(TODAY)
earnings_dt = datetime.datetime.fromtimestamp(
earnings_ts)
today_dt = datetime.datetime.fromtimestamp(today_ts)
if (earnings_dt - today_dt).days <= 7 and (
earnings_dt - today_dt).days >= 0:
stock_data.earnings_warning = "blink-bg"
stock_data.earnings_call_displayed = earnings_date
elif (earnings_dt - today_dt).days < 0:
stock_data.earnings_warning = "PAST"
stock_data.earnings_call_displayed = None
else:
stock_data.earnings_warning = ""
stock_data.earnings_call_displayed = earnings_date
except Exception as e:
messages.error(
request,
"Stock does not have an earnings call date defined.",
)
print(
f"Stock {stock} does not have an earnings call date defined."
)
earnings = None
tan_deviation_angle = math.tan(
math.radians(settings.DEVIATION_ANGLE))
# Stock Trend - 30 days sample
a_stock_30, a_macd_30, a_mfi_30, rsi, week1, week2, week3 = trends(
stock, 30)
# a_stock_30 = stock_regression(stock, 30)
stock_data.stock_trend_30 = round(a_stock_30, 2)
# MACD trend
# a_macd_30 = trend_calculator(stock, 'MACD', period=30)
stock_data.macd_trend_30 = round(a_macd_30, 2)
if np.abs(a_macd_30) > tan_deviation_angle:
if (a_stock_30 > 0
and a_macd_30 < 0) or (a_stock_30 < 0
and a_macd_30 > 0):
stock_data.macd_30_clash = True
stock_data.macd_30_color = "red"
elif (a_stock_30 < 0
and a_macd_30 < 0) or (a_stock_30 > 0
and a_macd_30 > 0):
stock_data.macd_30_clash = False
stock_data.macd_30_color = "green"
else:
stock_data.macd_30_clash = False
stock_data.macd_30_color = "green"
# MFI trend
# a_mfi_30 = trend_calculator(stock, 'MFI', period=30)
stock_data.money_flow_trend_30 = round(a_mfi_30, 2)
if np.abs(a_mfi_30) > tan_deviation_angle:
if (a_stock_30 > 0
and a_mfi_30 < 0) or (a_stock_30 < 0
and a_mfi_30 > 0):
stock_data.mfi_30_clash = True
stock_data.mfi_30_color = "red"
elif (a_stock_30 < 0
and a_mfi_30 < 0) or (a_stock_30 > 0
and a_mfi_30 > 0):
stock_data.mfi_30_clash = False
stock_data.mfi_30_color = "green"
else:
stock_data.mfi_30_clash = False
stock_data.mfi_30_color = "green"
# Stock Trend - 14 days sample
(
a_stock_14,
a_macd_14,
a_mfi_14,
rsi_14,
week1,
week2,
week3,
) = trends(
stock,
14) # rsi_14 is just for unpacking. it's not user.
# a_stock_14 = stock_regression(stock, 14)
stock_data.stock_trend_14 = round(a_stock_14, 2)
# MACD trend
# a_macd_14 = trend_calculator(stock, 'MACD', period=14)
stock_data.macd_trend_14 = round(a_macd_14, 2)
if np.abs(a_macd_14) > tan_deviation_angle:
if (a_stock_14 > 0
and a_macd_14 < 0) or (a_stock_14 < 0
and a_macd_14 > 0):
stock_data.macd_14_clash = True
stock_data.macd_14_color = "red"
elif (a_stock_14 < 0
and a_macd_14 < 0) or (a_stock_14 > 0
and a_macd_14 > 0):
stock_data.macd_14_clash = False
stock_data.macd_14_color = "green"
else:
stock_data.macd_14_clash = False
stock_data.macd_14_color = "green"
# MFI trend
# a_mfi_14 = trend_calculator(stock, 'MFI', period=14)
stock_data.money_flow_trend_14 = round(a_mfi_14, 2)
if np.abs(a_mfi_30) > tan_deviation_angle:
if (a_stock_14 > 0
and a_mfi_14 < 0) or (a_stock_14 < 0
and a_mfi_14 > 0):
stock_data.mfi_14_clash = True
stock_data.mfi_14_color = "red"
elif (a_stock_14 < 0
and a_mfi_14 < 0) or (a_stock_14 > 0
and a_mfi_14 > 0):
stock_data.mfi_14_clash = False
stock_data.mfi_14_color = "green"
else:
stock_data.mfi_14_clash = False
stock_data.mfi_14_color = "green"
# RSI
# rsi = last_rsi(stock, period=30)
stock_data.rsi = rsi
if rsi > 0 and rsi <= 30:
stock_data.rsi_color = "red"
elif rsi > 30 and rsi <= 65:
stock_data.rsi_color = "orange"
elif rsi > 65 and rsi <= 100:
stock_data.rsi_color = "green"
else:
stock_data.rsi_color = ""
try:
stock_data.save()
except Exception as e:
print(f"ERROR Adding Stock. Reason: {e}")
messages.error(
request,
f"Stock {stock} was not added. Already in the list.",
)
return render(request, "kadima/home.html", context)
new_stocks = StockData.objects.filter(
table_index=table_index).order_by("week_3")
context["stocks"] = new_stocks
## Updating the streaming IB API data with added stock
new_stocks_api = StockData.objects.all()
new_stocks_list = dict()
for stock in new_stocks_api:
new_stocks_list[stock.id] = stock.ticker
if api_connection_status():
ib_api_wrapper(request,
old_stocks_list,
new_stocks_list,
action=UPADATE_STOCKS)
except Exception as e:
print(f"Failed adding stock. ERROR: {e}")
logger.error(f"Failed adding stock. ERROR: {e}")
return render(request, "kadima/home.html", context)
## Enable email support
elif "email_enable" in request.POST:
print(">> Enable Email <<")
try:
email_enable = EmailSupport.objects.all().first()
if email_enable.enabled:
email_enable.enabled = False
context["email_enabled"] = False
email_enable.save()
# Resetting all email alerts to False
reset_email_alerts()
else:
email_enable.enabled = True
context["email_enabled"] = True
email_enable.save()
except:
email_enable = EmailSupport()
email_enable.enabled = True
context["email_enabled"] = True
email_enable.save()
# Update sample period for MACD and MFI calculations
elif "sample_period" in request.POST:
print(">> Sampling <<")
stocks = StockData.objects.all()
for stock in stocks:
if request.POST["sample_period"] == "14":
stock.sample_period_14 = True
stock.save()
context["sample_period_14"] = True
else:
stock.sample_period_14 = False
stock.save()
context["sample_period_14"] = False
# Stock Delete
elif "delete_stock" in request.POST:
print(">> Deleting <<")
old_stocks = StockData.objects.all()
old_stocks_list = []
for stock in old_stocks:
old_stocks_list.append(stock.id)
# Deleting a stock from DB
delete_stock = StockData.objects.filter(
pk=request.POST["delete_stock"])
delete_stock.delete()
new_stocks = StockData.objects.filter(
table_index=table_index).order_by("week_3")
context["stocks"] = new_stocks
## Updating the streaming IB API data with deleted stock
new_stocks_api = StockData.objects.all()
new_stocks_list = dict()
for stock in new_stocks_api:
new_stocks_list[stock.id] = stock.ticker
if api_connection_status():
ib_api_wrapper(request,
old_stocks_list,
new_stocks_list,
action=UPADATE_STOCKS)
# ib_api_wrapper(request,old_stocks_list=old_stocks_list, updated_stock_list=new_stocks_list, action=UPADATE_STOCKS)
return render(request, "kadima/home.html", context)
# Save stock to history
elif "save_stock" in request.POST:
print(">> Saving <<")
stock_id = request.POST["save_stock"]
stock_data = StockData.objects.get(id=stock_id)
stock_data.saved_to_history = True
stock_data.save()
history_stock = HistoryStock.objects.create(
table_index=table_index,
ticker=stock_data.ticker,
stock_price=stock_data.stock_price,
week_1=stock_data.week_1,
week_1_color=stock_data.week_1_color,
week_2=stock_data.week_2,
week_2_color=stock_data.week_2_color,
week_3=stock_data.week_3,
week_3_color=stock_data.week_3_color,
week_5=stock_data.week_5,
week_5_color=stock_data.week_5_color,
gap_1=stock_data.gap_1,
gap_1_color=stock_data.gap_1_color,
earnings_call_displayed=stock_data.earnings_call_displayed,
)
messages.success(
request,
f"Stock was saved. You can review it's data on the History page.",
)
stocks = StockData.objects.filter(
table_index=table_index).order_by("week_3")
context["stocks"] = stocks
return render(request, "kadima/home.html", context)
elif "alarm_stock" in request.POST:
print(">> Alarm Stock <<")
stock_id = request.POST["alarm_stock"]
stock_data = StockData.objects.get(id=stock_id)
stock_data.stock_alarm = True
stock_data.stock_load_price = stock_data.stock_current_price
stock_data.save()
messages.info(
request,
f"Stock {stock_data.ticker} was added to alarm page. You can trigger value alarms on the Alarms page.",
)
stocks = StockData.objects.filter(
table_index=table_index).order_by("week_3")
context["stocks"] = stocks
return render(request, "kadima/home.html", context)
else:
stocks = StockData.objects.filter(
table_index=table_index).order_by("week_3")
context["stocks"] = stocks
return render(request, "kadima/home.html", context)
else:
stocks = StockData.objects.filter(
table_index=table_index).order_by("week_3")
context["stocks"] = stocks
return render(request, "kadima/home.html", context)
return render(request, "kadima/home.html", context)
def history(request, table_index=1):
context = {}
saved_stocks = StockData.objects.filter(saved_to_history=True, table_index=table_index)
context['email_enabled'] = EmailSupport.objects.all().first().enabled
context['stocks'] = saved_stocks
context['table_index'] = table_index
request.session['table_index'] = table_index
ib_api_connected = api_connection_status()
context['ib_api_connected'] = ib_api_connected
date_picker = DateForm()
if request.method == 'POST':
if 'connect_ib_api' in request.POST:
api_connect(request)
ib_api_connected = api_connection_status()
context['ib_api_connected'] = ib_api_connected
# Start scraping for indices data
indices_scrapers()
return render(request, 'kadima/history.html', context)
elif 'disconnect_ib_api' in request.POST:
api_disconnect(request)
# context = {}
ib_api_connected = api_connection_status()
context['ib_api_connected'] = ib_api_connected
return render(request, 'kadima/history.html', context)
elif 'update_stock' in str(request.POST):
for k in request.POST.keys():
if 'update_stock' in k:
stock_id = k.split('_')[-1]
else:
print(f'ERROR:failed getting stock ID')
logger.error(f'ERROR:failed getting stock ID')
stock_data = StockData.objects.get(id=stock_id)
stock_data.stocks_bought = int(request.POST.get(f'stocks_bought_{stock_id}'))
stock_data.purchase_price = float(request.POST.get(f'stock_purchase_price_{stock_id}'))
stock_data.stocks_sold = int(request.POST.get(f'stocks_sold_{stock_id}'))
stock_data.selling_price = float(request.POST.get(f'selling_price_{stock_id}'))
stock_data.profit = (stock_data.stocks_sold * stock_data.selling_price) - (stock_data.stocks_bought * stock_data.purchase_price)
try:
stock_data.dividends = float(request.POST.get(f'dividends_{stock_id}'))
except:
pass
stock_data.total_profit = stock_data.profit + stock_data.dividends
stock_data.save()
context['stocks'] = saved_stocks
## Enable email support
elif 'email_enable' in request.POST:
print('>> Enable Email <<')
try:
email_enable = EmailSupport.objects.all().first()
if email_enable.enabled:
email_enable.enabled = False
context['email_enabled'] = False
email_enable.save()
# Resetting all email alerts to False
reset_email_alerts()
else:
email_enable.enabled = True
context['email_enabled'] = True
email_enable.save()
except:
email_enable = EmailSupport()
email_enable.enabled = True
context['email_enabled'] = True
email_enable.save()
elif 'delete_stock' in request.POST:
stock_id = request.POST['delete_stock']
stock_data = StockData.objects.get(id=stock_id)
stock_data.saved_to_history = False
stock_data.save()
saved_stocks = StockData.objects.filter(saved_to_history=True)
context['stocks'] = saved_stocks
return HttpResponseRedirect(request.path_info)
# Filter history stocks according to dates range
elif 'date_picker' in request.POST:
start_date = request.POST['datetimepicker1']
end_date = request.POST['datetimepicker2']
start = date_obj_to_date(start_date,'dash')
end = date_obj_to_date(end_date,'dash')
print(f"START: ***************{start}****************")
filtered_stocks = StockData.objects.filter(saved_to_history=True, stock_date_range=[start, end])
context['stocks'] = filtered_stocks
# return HttpResponseRedirect(request.path_info)
return render(request, 'kadima/history.html', context)
else:
saved_stocks = StockData.objects.filter(saved_to_history=True)
context['stocks'] = saved_stocks
return render(request, 'kadima/history.html', context)
return render(request, 'kadima/history.html', context)