def history_view(id):
if not session.get('logged_in'):
return render_template('login.html')
else:
hist = bursData.history_pd(int(id))
market = bursData.market_pd()
symbol = market.loc[market['id'] == str(id)]['symbol'].values[0]
name = market.loc[market['id'] == str(id)]['name'].values[0]
hist['date'] = pd.to_datetime(
hist['date']).apply(lambda x: jdatetime.datetime.fromgregorian(
datetime=x).strftime('%Y-%m-%d'))
Candlestick = go.Candlestick(x=hist['date'],
open=hist['first_price'],
high=hist['max_price'],
low=hist['min_price'],
close=hist['close_price'])
data = [Candlestick]
shapes = {"shapes": []}
graphJSON = json.dumps(data, cls=plotly.utils.PlotlyJSONEncoder)
shapesJSON = json.dumps(shapes, cls=plotly.utils.PlotlyJSONEncoder)
return render_template("history.html",
plot=graphJSON,
layout=shapesJSON,
hist=hist,
symbol=symbol,
name=name,
id=int(id))
def stosk_view(id):
if not session.get('logged_in'):
return render_template('login.html')
else:
market = bursData.market_pd()
symbol = market.loc[market['id'] == str(id)]['symbol'].values[0]
name = market.loc[market['id'] == str(id)]['name'].values[0]
groupid = market.loc[market['id'] == str(id)]['group_id'].values[0]
group_list = market.loc[market['group_id'] == groupid]
day_price_list = bursData.dayprice_pd(int(id))
general = bursData.general_dayinfo_pd(id)
saf = bursData.saf_pd(id)
Candlestick = go.Candlestick(x=day_price_list['time'],
open=day_price_list['open_price'],
high=day_price_list['high_price'],
low=day_price_list['low_price'],
close=day_price_list['close_price'])
data = [Candlestick]
shapes = {"shapes": []}
graphJSON = json.dumps(data, cls=plotly.utils.PlotlyJSONEncoder)
shapesJSON = json.dumps(shapes, cls=plotly.utils.PlotlyJSONEncoder)
return render_template("stock.html",
plot=graphJSON,
layout=shapesJSON,
id=id,
name=name,
symbol=symbol,
group_list=group_list,
saf=saf,
general=general,
day_price_list=day_price_list)
def smart_filter():
if not session.get('logged_in'):
return render_template('login.html')
else:
clientall = pd.merge(bursData.client_all_pd(),
bursData.market_pd(),
on='id')
clientall['sell_ho_tot'] = (
100 * clientall['Sell_N_Volume'].apply(int) /
clientall['volume'].apply(int)).apply(lambda x: '{:.2f}'.format(x))
clientall['buy_ho_tot'] = (
100 * clientall['Buy_N_Volume'].apply(int) /
clientall['volume'].apply(int)).apply(lambda x: '{:.2f}'.format(x))
clientall['sell_ha_tot'] = (
100 * clientall['Sell_I_Volume'].apply(int) /
clientall['volume'].apply(int)).apply(lambda x: '{:.2f}'.format(x))
clientall['buy_ha_tot'] = (
100 * clientall['Buy_I_Volume'].apply(int) /
clientall['volume'].apply(int)).apply(lambda x: '{:.2f}'.format(x))
time = jdatetime.datetime.now().strftime("%H:%M:%S")
clientall = clientall[~clientall['group_id'].apply(int).
isin([68, 69, 59])]
clientall = clientall[[
not any(map(str.isdigit, i)) for i in clientall['symbol']
]]
market = clientall[(clientall['buy_ha_tot'].apply(float) > 70)
& (clientall['sell_ho_tot'].apply(float) > 30)]
for _, id in market['id'].iteritems():
result = pd.merge(bursData.general_dayinfo_pd(id),
bursData.saf_pd(id),
left_index=True,
right_index=True)
market.loc[market['id'] == id,
'buy_volume'] = result['buy_volume'][0]
try:
if int(result['buy_price'][0]) == int(
result['max_traded_price'][0]) and int(
result['buy_volume'][0]) >= 0.5 * int(
market.loc[market['id'] == id]['base_volume']):
market.loc[market['id'] == id, 'saf'] = True
except:
pass
try:
market.loc[market['id'] == id, 'volume_mounth'] = int(
bursData.history_pd(int(id), 30)['volume'].mean())
except:
market.loc[market['id'] == id, 'volume_mounth'] = 0
return render_template("smart_filter.html",
market=clientall,
smartmarket=market,
time=time)
def client_history_view(id):
if not session.get('logged_in'):
return render_template('login.html')
else:
hist = pd.merge(bursData.client_history_pd(int(id)),
bursData.history_pd(int(id)),
on='date')
market = bursData.market_pd()
symbol = market.loc[market['id'] == str(id)]['symbol'].values[0]
name = market.loc[market['id'] == str(id)]['name'].values[0]
hist['date'] = pd.to_datetime(
hist['date']).apply(lambda x: jdatetime.datetime.fromgregorian(
datetime=x).strftime('%Y-%m-%d'))
data = [
go.Bar(name='حجم خرید حقوقی',
x=hist['date'],
y=hist['hoghughi_buy_volume']),
go.Bar(name='حجم فروش حقوقی',
x=hist['date'],
y=hist['hoghughi_sell_volume']),
go.Bar(name='حجم خرید حقیقی',
x=hist['date'],
y=hist['haghighi_buy_volume']),
go.Bar(name='حجم فروش حقیقی',
x=hist['date'],
y=hist['haghighi_sell_volume'])
]
graphJSON = json.dumps(data, cls=plotly.utils.PlotlyJSONEncoder)
hist['sell_ho_tot'] = (
100 * hist['hoghughi_sell_volume'].apply(int) /
hist['volume'].apply(int)).apply(lambda x: '{:.2f}'.format(x))
hist['buy_ho_tot'] = (
100 * hist['hoghughi_buy_volume'].apply(int) /
hist['volume'].apply(int)).apply(lambda x: '{:.2f}'.format(x))
return render_template("client_history.html",
plot=graphJSON,
hist=hist,
symbol=symbol,
name=name,
id=int(id))
def alarm():
if not session.get('logged_in'):
return render_template('login.html')
else:
clientall = pd.merge(bursData.client_all_pd(),
bursData.market_pd(),
on='id')
clientall['sell_ho_tot'] = (
100 * clientall['Sell_N_Volume'].apply(int) /
clientall['volume'].apply(int)).apply(lambda x: '{:.2f}'.format(x))
clientall['buy_ho_tot'] = (
100 * clientall['Buy_N_Volume'].apply(int) /
clientall['volume'].apply(int)).apply(lambda x: '{:.2f}'.format(x))
clientall['sell_ha_tot'] = (
100 * clientall['Sell_I_Volume'].apply(int) /
clientall['volume'].apply(int)).apply(lambda x: '{:.2f}'.format(x))
clientall['buy_ha_tot'] = (
100 * clientall['Buy_I_Volume'].apply(int) /
clientall['volume'].apply(int)).apply(lambda x: '{:.2f}'.format(x))
clientall = clientall[~clientall['group_id'].apply(int).
isin([68, 69, 59])]
clientall = clientall[[
not any(map(str.isdigit, i)) for i in clientall['symbol']
]]
market = clientall[(clientall['buy_ha_tot'].apply(float) > 70)
& (clientall['sell_ho_tot'].apply(float) > 30)]
market = market[[
not any(map(str.isdigit, i)) for i in market['symbol']
]]
for _, id in market['id'].iteritems():
try:
market.loc[market['id'] == id, 'volume_mounth'] = int(
bursData.history_pd(int(id), 30)['volume'].mean())
except:
market.loc[market['id'] == id, 'volume_mounth'] = 0
# hist=bursData.history_pd(int(id),30)
# hist['date']=pd.to_datetime(hist['date']).apply(lambda x: jdatetime.datetime.fromgregorian(datetime=x).strftime('%Y-%m-%d'))
# market.loc[market['id'] == id, 'till'] = hist['date'].iloc[29]
# market.loc[market['id'] == id, 'volume_mounth'] = hist['volume'].head(30).sum()/30
# market.loc[market['id'] == id, 'volume_mounth_today'] = (hist['volume'].head(29).sum()+int(market[market['id'] == id]['volume']))/30
return render_template("alarm.html", market=market)
def update_value():
if not session.get('logged_in'):
return render_template('login.html')
else:
if (requests.get('http://www.tsetmc.com/tsev2/data/ClientTypeAll.aspx'
).status_code == 200):
db = TinyDB('./values_IN.json')
date = jdatetime.datetime.now().strftime("%Y-%m-%d")
time = jdatetime.datetime.now().strftime("%H:%M:%S")
clientall = pd.merge(bursData.client_all_pd(),
bursData.market_pd(),
on='id')
tot_Buy_I_Value = (
clientall['Buy_I_Volume'].apply(int) *
clientall['last_price'].apply(int)).sum() / 1000000000000
tot_Buy_N_Value = (
clientall['Buy_N_Volume'].apply(int) *
clientall['last_price'].apply(int)).sum() / 1000000000000
tot_Sell_I_Value = (
clientall['Sell_I_Volume'].apply(int) *
clientall['last_price'].apply(int)).sum() / 1000000000000
tot_Sell_N_Value = (
clientall['Sell_N_Volume'].apply(int) *
clientall['last_price'].apply(int)).sum() / 1000000000000
if (jdatetime.date.today().weekday() < 5):
db.upsert(
{
'date': date,
'time': time,
'tot_Buy_I_Value': tot_Buy_I_Value,
'tot_Buy_N_Value': tot_Buy_N_Value,
'tot_Sell_I_Value': tot_Sell_I_Value,
'tot_Sell_N_Value': tot_Sell_N_Value
},
Query().date == date)
return render_template("values_IN.html",
values_IN=json.loads(json.dumps(db.all())))
def home_page():
if not session.get('logged_in'):
return render_template('login.html')
else:
market = bursData.market_pd()
return render_template("index.html", market=market)