def positions_json():
# Get all transactions and cost details
# This serves the main page
dfdyn, piedata = positions_dynamic()
dfdyn = dfdyn.to_dict(orient='index')
json_dict = {
'positions': dfdyn,
'piechart': piedata,
'user': fx_rate(),
'btc': price_data_rt("BTC") * fx_rate()['fx_rate']
}
return simplejson.dumps(json_dict, ignore_nan=True)
def navchart():
data = generatenav()
navchart = data[["NAV_fx"]]
# dates need to be in Epoch time for Highcharts
navchart.index = (navchart.index - datetime(1970, 1, 1)).total_seconds()
navchart.index = navchart.index * 1000
navchart.index = navchart.index.astype(np.int64)
navchart = navchart.to_dict()
navchart = navchart["NAV_fx"]
port_value_chart = data[[
"PORT_cash_value_fx", "PORT_fx_pos", "PORT_ac_CFs_fx"
]]
port_value_chart["ac_pnl_fx"] = (port_value_chart["PORT_fx_pos"] -
port_value_chart["PORT_ac_CFs_fx"])
# dates need to be in Epoch time for Highcharts
port_value_chart.index = (port_value_chart.index -
datetime(1970, 1, 1)).total_seconds()
port_value_chart.index = port_value_chart.index * 1000
port_value_chart.index = port_value_chart.index.astype(np.int64)
port_value_chart = port_value_chart.to_dict()
return render_template("warden/warden_navchart.html",
title="NAV Historical Chart",
navchart=navchart,
port_value_chart=port_value_chart,
fx=FX,
current_user=fx_rate(),
donated=donate_check())
def portstats():
meta = {}
# Looking to generate the following data here and return as JSON
# for AJAX query on front page:
# Start date, End Date, Start NAV, End NAV, Returns (1d, 1wk, 1mo, 1yr,
# YTD), average daily return. Best day, worse day. Std dev of daily ret,
# Higher NAV, Lower NAV + dates. Higher Port Value (date).
data = generatenav()
meta["start_date"] = (data.index.min()).date().strftime("%B %d, %Y")
meta["end_date"] = data.index.max().date().strftime("%B %d, %Y")
meta["start_nav"] = data["NAV_fx"][0]
meta["end_nav"] = data["NAV_fx"][-1].astype(float)
meta["max_nav"] = data["NAV_fx"].max().astype(float)
meta["max_nav_date"] = data[
data["NAV_fx"] == data["NAV_fx"].max()].index.strftime("%B %d, %Y")[0]
meta["min_nav"] = data["NAV_fx"].min().astype(float)
meta["min_nav_date"] = data[
data["NAV_fx"] == data["NAV_fx"].min()].index.strftime("%B %d, %Y")[0]
meta["end_portvalue"] = data["PORT_fx_pos"][-1].astype(float)
meta["end_portvalue_usd"] = meta["end_portvalue"] / fx_rate()['fx_rate']
meta["max_portvalue"] = data["PORT_fx_pos"].max().astype(float)
meta["max_port_date"] = data[data["PORT_fx_pos"] == data["PORT_fx_pos"].
max()].index.strftime("%B %d, %Y")[0]
meta["min_portvalue"] = round(data["PORT_fx_pos"].min(), 0)
meta["min_port_date"] = data[data["PORT_fx_pos"] == data["PORT_fx_pos"].
min()].index.strftime("%B %d, %Y")[0]
meta["return_SI"] = (meta["end_nav"] / meta["start_nav"]) - 1
# Temporary fix for an issue with portfolios that are just too new
# Create a function to handle this
try:
meta["return_1d"] = (meta["end_nav"] / data["NAV_fx"][-2]) - 1
except IndexError:
meta["return_1d"] = "-"
try:
meta["return_1wk"] = (meta["end_nav"] / data["NAV_fx"][-7]) - 1
except IndexError:
meta["return_1wk"] = "-"
try:
meta["return_30d"] = (meta["end_nav"] / data["NAV_fx"][-30]) - 1
except IndexError:
meta["return_30d"] = "-"
try:
meta["return_90d"] = (meta["end_nav"] / data["NAV_fx"][-90]) - 1
except IndexError:
meta["return_90d"] = "-"
try:
meta["return_ATH"] = (meta["end_nav"] / meta["max_nav"]) - 1
except IndexError:
meta["return_ATH"] = "-"
try:
yr_ago = pd.to_datetime(datetime.today() - relativedelta(years=1))
yr_ago_NAV = data.NAV_fx[data.index.get_loc(yr_ago, method="nearest")]
meta["return_1yr"] = meta["end_nav"] / yr_ago_NAV - 1
except IndexError:
meta["return_1yr"] = "-"
# Create data for summa"age
meta["fx"] = FX
meta["daily"] = {}
for days in range(1, 8):
meta["daily"][days] = {}
meta["daily"][days]["date"] = data.index[days * -1].date().strftime(
"%A <br> %m/%d")
meta["daily"][days]["nav"] = data["NAV_fx"][days * -1]
meta["daily"][days]["nav_prev"] = data["NAV_fx"][(days + 1) * -1]
meta["daily"][days]["perc_chg"] = (meta["daily"][days]["nav"] /
meta["daily"][days]["nav_prev"]) - 1
meta["daily"][days]["port"] = data["PORT_fx_pos"][days * -1]
meta["daily"][days]["port_prev"] = data["PORT_fx_pos"][(days + 1) * -1]
meta["daily"][days]["port_chg"] = (meta["daily"][days]["port"] -
meta["daily"][days]["port_prev"])
# Removes Numpy type from json - returns int instead
def convert(o):
if isinstance(o, np.int64):
return int(o)
else:
return (o)
# create chart data for a small NAV chart
return simplejson.dumps(meta, ignore_nan=True, default=convert)
def realtime_btc():
fx_details = fx_rate()
fx_r = {'cross': fx_details['symbol'], 'fx_rate': fx_details['fx_rate']}
fx_r['btc_usd'] = price_data_rt("BTC")
fx_r['btc_fx'] = fx_r['btc_usd'] * fx_r['fx_rate']
return json.dumps(fx_r)