def dbinit(self):
# Currencies
currencies = {}
currencies["BTC"] = Currency(key_name="BTC",
name="Bitcoin",
symbol="BTC")
currencies["USD"] = Currency(key_name="USD",
name="US Dollar",
symbol=u"$")
currencies["EUR"] = Currency(key_name="EUR", name="Euro", symbol=u"€")
currencies["SEK"] = Currency(key_name="SEK",
name="Swedish Kronor",
symbol="kr")
for currency in currencies:
currencies[currency].put()
# Markets
markets = {}
markets["KapitonSEK"] = Market(key_name="KapitonSEK",
name="Kapiton",
url="http://kapiton.se/",
currency=currencies["SEK"],
fee=0.0135)
markets["MtGoxEUR"] = Market(key_name="MtGoxEUR",
name="MtGox",
url="http://mtgox.com/",
currency=currencies["EUR"],
fee=0.006)
markets["MtGoxUSD"] = Market(key_name="MtGoxUSD",
name="MtGox",
url="http://mtgox.com/",
currency=currencies["USD"],
fee=0.006)
for market in markets:
markets[market].put()
def parse_cyb2(
url='http://www.szse.cn/szseWeb/FrontController.szse?randnum=0.5328349224291742'
):
payload = {
'ACTIONID': 7,
'AJAX': 'AJAX-TRUE',
'CATALOGID': '1898_nm',
'TABKEY': 'tab1',
'txtQueryDate': '2016-01-15',
'REPORT_ACTION': 'reach'
}
res = requests.post(url, data=payload)
print res.text
# read html <table> to list of DataFrame
dfs = pd.read_html(res.text, flavor='lxml')
# dfs = pd.read_html(etree.tostring(r), flavor='bs4')
if len(dfs) >= 1:
df = dfs[0]
print df
tradable_shares = df.iloc[4][1]
total_market = df.iloc[5][1]
volume_money = df.iloc[7][1]
volume = df.iloc[8][1]
pe = df.iloc[10][1]
high_pe = df.iloc[10][3]
value = df.iloc[13][1]
if isinstance(tradable_shares, type(pd.NaT)):
tradable_shares = 0
if type(total_market) == type(pd.NaT):
total_market = 0
if isinstance(volume_money, type(pd.NaT)):
volume_money = 0
if isinstance(volume, type(pd.NaT)):
volume = 0
if isinstance(pe, type(pd.NaT)):
pe = 0
if type(value) != float:
value = 0.0
# 换手率＝成交量÷当日实际流通量
if tradable_shares == 0:
turnover = 0
else:
turnover = float(volume) / float(tradable_shares)
print 'name:{} total_market:{} volume:{} turnover:{} pe:{} value:{}'.format(
name, total_market, volume_money, turnover, pe, value)
market = Market(name,
float(total_market) / 100000000,
float(volume_money) / 100000000, turnover, pe, value)
print market
return market
def scrape(self, limit=None):
soup = BeautifulSoup(self.text, 'lxml')
table = soup.find('table', attrs={'id': TABLE_ID})
coins = []
for row in table.find_all('tr')[1:]:
tds = row.find_all('td')
props = [td.get_text().strip() for td in tds]
icon = tds[1].find('img')
props.append(icon['src'] if icon else '')
coins.append(Coin(props))
if limit and len(coins) == limit:
break
return Market(coins)
def main():
"""
Application that will...
"""
gbce = Market()
# open the market
print("------------------------------------")
print("Global Beverage Corporation Exchange")
print("------------------------------------")
# load sample stock data into the market
for stock in SAMPLE_DATA:
stk = Stock()
setattr(stk, "Stock Symbol", stock[0])
setattr(stk, "Type", stock[1])
setattr(stk, "Last Dividend", stock[2])
setattr(stk, "Fixed Dividend", stock[3])
setattr(stk, "Par Value", stock[4])
gbce.add_stock(stk)
print("Welcome {} to the GBCE".format(getattr(stk, "Stock Symbol")))
sleep(0.8)
# run some trades
for trade_no in range(20):
t = Trade()
setattr(t, "Stock Symbol",
choice(list(map(lambda x: x[0], SAMPLE_DATA))))
setattr(t, "Buy/Sell", choice(['Buy', 'Sell']))
setattr(t, "Quantity",
choice([500, 1000, 2000, 5000, 10000, 20000, 50000]))
setattr(t, "Price", choice(list(range(50, 150))))
gbce.add_trade(t)
sto = gbce.get_stock(getattr(t, "Stock Symbol"))
print("{sale_report} ({all_share})".\
format(sale_report="{time}: {b} {q} shares of {s} for {p}".\
format(b=getattr(t,"Buy/Sell"),
time=t.Timestamp.time().replace(microsecond=0),
q=t.Quantity,p=t.Price,
s=getattr(t,"Stock Symbol")),
all_share="GBCE All-Share: {a:.2f}".\
format(a=gbce.all_share_index())))
sleep(0.75 + choice([0.10, 0.15, 0.25, 0.45]))
# close the market
print("------------------------------------")
def parse_sz_market_common(name, url):
page = parse(url).getroot()
result = etree.tostring(page)
# print '*'*20
# print result
# print '*'*20
r = page.get_element_by_id('REPORTID_tab1')
print '*' * 20
print etree.tostring(r)
print '*' * 20
# read html <table> to list of DataFrame
dfs = pd.read_html(etree.tostring(r), flavor='lxml')
# dfs = pd.read_html(etree.tostring(r), flavor='bs4')
if len(dfs) >= 1:
df = dfs[0]
print df
tradable_shares = df.iloc[4][1]
total_market = df.iloc[5][1]
volume_money = df.iloc[7][1]
volume = df.iloc[8][1]
pe = df.iloc[10][1]
high_pe = df.iloc[10][3]
value = df.iloc[13][1]
if isinstance(tradable_shares, type(pd.NaT)):
tradable_shares = 0
if type(total_market) == type(pd.NaT):
total_market = 0
if isinstance(volume_money, type(pd.NaT)):
volume_money = 0
if isinstance(volume, type(pd.NaT)):
volume = 0
if isinstance(pe, type(pd.NaT)):
pe = 0
if type(value) != float:
value = 0.0
# 换手率＝成交量÷当日实际流通量
if tradable_shares == 0:
turnover = 0
else:
turnover = float(volume) / float(tradable_shares)
print 'name:{} total_market:{} volume:{} turnover:{} pe:{} value:{}'.format(
name, total_market, volume_money, turnover, pe, value)
market = Market(name,
float(total_market) / 100000000,
float(volume_money) / 100000000, turnover, pe, value)
print market
return market
def parse_sh_market():
page = parse(
'http://www.sse.com.cn/market/stockdata/overview/day/').getroot()
result = etree.tostring(page)
print result
r = page.get_element_by_id('dateList')
statistics = r.text_content().split()
for word in statistics:
print word
market = Market(name='sh',
total_market_cap=statistics[1],
volume=float(statistics[8]) / 10000,
turnover=statistics[12],
pe=statistics[14],
date=statistics[2])
print market
return market
def setUp(self):
"""Create test client, add sample data."""
User.query.delete()
Market.query.delete()
SavedArticle.query.delete()
self.client = app.test_client()
m = Market(code="en-CA", name="United States")
db.session.add(m)
db.session.commit()
self.testuser = User(email="*****@*****.**",
username="******",
password="******",
image_url="http://www.picture.com",
market="en-CA")
db.session.add(self.testuser)
db.session.commit()
def parse_sz_market():
page = parse('http://www.szse.cn/main/marketdata/tjsj/jbzb/').getroot()
r = page.get_element_by_id('REPORTID_tab1')
print etree.tostring(r)
# read html <table> to list of DataFrame
dfs = pd.read_html(etree.tostring(r), flavor='lxml')
# print dfs
# print len(dfs)
if len(dfs) >= 1:
df = dfs[0]
print df
total_market = df.iloc[10][1]
volume = df.iloc[12][1]
avg_price = df.iloc[13][1]
pe = df.iloc[14][1]
turnover_rate = df.iloc[15][1]
if type(total_market) == type(pd.NaT):
total_market = 0
if type(volume) == type(pd.NaT):
volume = 0
if type(turnover_rate) == type(pd.NaT):
turnover_rate = 0
if type(pe) == type(pd.NaT):
pe = 0
print 'total_market:{} volume:{} turnover_rate:{} pe:{}'.format(
total_market, volume, turnover_rate, pe)
market = Market('sz',
total_market_cap=float(total_market) / 100000000,
volume=float(volume) / 100000000,
turnover=float(turnover_rate),
pe=float(pe))
print market
# print df.index
# print df.columns
# print df.values
# print df.describe()
return market
def get_markets(db_session, betfair_api, event):
markets = []
bfl_markets = betfair_api.betting.list_market_catalogue(
filter=bfl.filters.market_filter(
event_ids=[event.betfair_id],
market_type_codes=['WIN'],
),
max_results=25,
market_projection=['MARKET_START_TIME', 'RUNNER_METADATA'])
for bfl_market in bfl_markets:
market = db_session.query(Market).filter(
Market.event_id == event.id,
Market.betfair_id == bfl_market.market_id).one_or_none()
if not market:
print(
f"New market: {bfl_market.market_start_time}: {bfl_market.market_name} ({len(bfl_market.runners)} runners)"
)
market = Market(event_id=event.id,
betfair_id=bfl_market.market_id,
name=bfl_market.market_name,
start_time=bfl_market.market_start_time,
total_matched=bfl_market.total_matched)
db_session.add(market)
markets.append(market)
for bfl_runner in bfl_market.runners:
runner = db_session.query(Runner).filter(
Runner.betfair_id == bfl_runner.selection_id).one_or_none()
if not runner:
runner = Runner(
betfair_id=bfl_runner.selection_id,
name=bfl_runner.runner_name,
)
db_session.add(runner)
market_runner = db_session.query(MarketRunner).filter(
MarketRunner.market_id == market.id,
MarketRunner.runner_id == runner.id).one_or_none()
if not market_runner:
market_runner = MarketRunner(
market_id=market.id,
runner_id=runner.id,
sort_priority=bfl_runner.sort_priority)
jockey_name = bfl_runner.metadata['JOCKEY_NAME']
if jockey_name:
jockey = db_session.query(Jockey).filter(
Jockey.name == jockey_name).one_or_none()
if not jockey:
jockey = Jockey(name=jockey_name, )
db_session.add(jockey)
market_runner.jockey_id = jockey.id
trainer_name = bfl_runner.metadata['TRAINER_NAME']
if trainer_name:
trainer = db_session.query(Trainer).filter(
Trainer.name == trainer_name).one_or_none()
if not trainer:
trainer = Trainer(name=trainer_name, )
db_session.add(trainer)
market_runner.trainer_id = trainer.id
db_session.add(market_runner)
db_session.commit()
return markets
from models import Market
import datetime
from lxml import html
import requests
from models import db
raw = requests.get("https://loanbase.com/stats")
tree = html.fromstring(raw.text)
active_loans = float(
tree.xpath('//div[@class="statistic"]/p//text()')[2].split(" ")[1])
m = Market(lbactive=active_loans, created=datetime.datetime.utcnow())
db.session.add(m)
db.session.commit()
def forecast():
#tam & sas
sales = request.form['sales']
price = request.form['price']
sas = request.form['sas']
growth = request.form['growth']
total = Market(sales, price, sas, growth)
calc_tam = total.tam()
calc_tam_c = '{:,.2f}'.format(calc_tam)
calc_sas = total.sas()
calc_sas_c = '{:,.2f}'.format(calc_sas)
#forecast P&L inputs
salesm1 = request.form['salesm1']
pricem1 = request.form['pricem1']
sgmom = request.form['sgmom']
cmgrowth = request.form['cmgrowth']
pindex = request.form['pindex']
cogs = request.form['cogs']
opex = request.form['opex']
fixed = request.form['fixed']
#forecast P&L class constructor
fpl = Forecastpl(salesm1, pricem1, sgmom, cmgrowth, pindex, cogs, opex,
fixed)
#x chart axis
x = np.arange(1, 13)
#forecast P&L growth and percentages
fpl.sales_growth()
fpl.cm_growth()
fpl.price_growth()
fpl.cogs_pc()
fpl.opex_pc()
fpl.fixed_pc()
#forecast P&L pre-plot calculations
fpl.units_mom()
fpl.price_mom()
fpl_rev = fpl.revenue_profile()
fpl.cm_calc_initial()
fpl.cm_adjusted()
fpl_cmadj = fpl.cm_calc()
fpl.fixed_calc()
fpl_op = fpl.fpl_op_profit()
#forecast P&L cumulative
fpl_rev_cum = np.cumsum(fpl_rev)
fpl_cmadj_cum = np.cumsum(fpl_cmadj)
fpl_op_cum = np.cumsum(fpl_op)
#sas and fp&l comparison
fpl_sas = round(np.sum(fpl_rev, dtype=np.float64), 2)
fpl_sas_c = '{:,.2f}'.format(fpl_sas)
fpl_sas_delta = round(float(fpl_sas - calc_sas), 2)
fpl_sas_delta_c = '{:,.2f}'.format(fpl_sas_delta)
fpl_sas_delta_pc = round(float((fpl_sas / calc_tam) * 100), 2)
#plot parameters
plt.plot(x, fpl_rev_cum, label='revenue')
plt.plot(x, fpl_cmadj_cum, label='cm')
plt.plot(x, fpl_op_cum, label='op profit')
plt.xlabel('month')
plt.ylabel('£')
plt.title('Forecast P&L cumulative ')
plt.legend()
plt.xticks(x)
plt.savefig(img_path)
plt.close()
return render_template('forecast.html',
sales=sales,
price=price,
sas=sas,
growth=growth,
calc_tam_c=calc_tam_c,
calc_sas_c=calc_sas_c,
name='Forecast P&L cumulative',
fpl_sas_c=fpl_sas_c,
fpl_sas_delta_c=fpl_sas_delta_c,
fpl_sas_delta_pc=fpl_sas_delta_pc,
fileName=fileName,
fileExt=fileExt)