def handle(self, *args, **options):
last_pt = PredictionTest.objects.filter(type='mock').order_by('-created_on').first()
last_trade = TradeRecommendation.objects.order_by('-created_on').first()
print(last_pt.created_on)
print(last_trade.created_on)
# 7 hours thing is a hack for MST vs UTC timezone issues
is_trader_running = last_trade.created_on > (get_time() - datetime.timedelta(hours=int(7)) - datetime.timedelta(minutes=int(15)))
is_trainer_running = last_pt.created_on > (get_time() - datetime.timedelta(hours=int(7)) - datetime.timedelta(minutes=int(15)))
if not is_trader_running:
self.alert_email("not is_trader_running")
if not is_trainer_running:
self.alert_email("not is_trainer_running")
def run_predictor(self,nn_index):
predictor = self.predictors[nn_index]
config = self.predictor_configs[nn_index]
normalize = config['type'] == 'nn'
prices = predictor.get_latest_prices(normalize=normalize)
prices = prices[(len(prices)-predictor.datasetinputs):(len(prices)+1)]
recommend, nn_price, last_sample, projected_change_pct = predictor.predict(prices)
confidence = predictor.confidence()
if config['type'] == 'nn':
clf = None
made_by = predictor
else:
clf = predictor
made_by = None
print_and_log("(t)({})---- ({} w. {}% conf) ---- price from {} => {}({}% change); ".format(nn_index,recommend, round(confidence,0), round(last_sample,4), round(nn_price,4), int(projected_change_pct * 100.0)))
tr = TradeRecommendation(symbol=config['symbol'],
made_on=str(prices),
made_by=made_by,
clf=clf,
confidence=confidence,
recommendation=recommend,
net_amount = -1 if recommend == 'SELL' else ( 1 if recommend == 'BUY' else 0 ),
created_on_str = str(get_time().strftime('%Y-%m-%d %H:%M')) )
tr.save()
self.trs[nn_index] = tr
return recommend
def run_predictor(self, nn_index):
predictor = self.predictors[nn_index]
config = self.predictor_configs[nn_index]
normalize = config['type'] == 'nn'
prices = predictor.get_latest_prices(normalize=normalize)
prices = prices[(len(prices) - predictor.datasetinputs):(len(prices) +
1)]
recommend, nn_price, last_sample, projected_change_pct = predictor.predict(
prices)
confidence = predictor.confidence()
if config['type'] == 'nn':
clf = None
made_by = predictor
else:
clf = predictor
made_by = None
print_and_log(
"(t)({})---- ({} w. {}% conf) ---- price from {} => {}({}% change); "
.format(nn_index, recommend, round(confidence, 0),
round(last_sample, 4), round(nn_price, 4),
int(projected_change_pct * 100.0)))
tr = TradeRecommendation(symbol=config['symbol'],
made_on=str(prices),
made_by=made_by,
clf=clf,
confidence=confidence,
recommendation=recommend,
net_amount=-1 if recommend == 'SELL' else
(1 if recommend == 'BUY' else 0),
created_on_str=str(
get_time().strftime('%Y-%m-%d %H:%M')))
tr.save()
self.trs[nn_index] = tr
return recommend
def optimize_view(request):
#setup
the_denom=request.GET.get('denom','btc_balance')
denoms = ['usd_balance','btc_balance']
days_ago=request.GET.get('days_ago',False)
hours_ago=request.GET.get('hours_ago',False)
if not hours_ago and not days_ago:
hours_ago=6
if days_ago:
start_time = (timezone.now() - datetime.timedelta(days=int(days_ago)))
elif hours_ago:
start_time = (timezone.now() - datetime.timedelta(hours=int(hours_ago)))
else:
start_time = Balance.objects.order_by('created_on').first().created_on
symbol = 'BTC_ETH'
#get data
data = {}
for t in Trade.objects.filter(symbol=symbol,status='fill').order_by('-created_on').all():
date = datetime.datetime.strftime(t.created_on,'%Y-%m-%d')
if not date in data.keys():
data[date] = { 'buyvol' : [], 'sellvol' : [], 'buy' : [], 'sell': [] }
data[date][t.type].append(t.price)
data[date][t.type+'vol'] = data[date][t.type+'vol'] + [ t.amount ]
bs = Balance.objects.filter(created_on__gte=start_time).all()
last_trade = TradeRecommendation.objects.order_by('-created_on').first()
if last_trade:
trader_last_seen = (last_trade.created_on - datetime.timedelta(hours=int(7))).strftime('%a %H:%M')
is_trader_running = last_trade.created_on > (get_time() - datetime.timedelta(minutes=int(15)))
else:
trader_last_seen = None
is_trader_running = False
i = 0
charts = []
chartnames = []
for func in [get_trade_chart,get_trade_profitability_chart,get_directional_change_chart,get_performance_comps_chart,get_ticker_price]:
i = i + 1
cht = func(bs,the_denom,symbol,start_time)
charts.append(cht)
chartnames.append(str(func).split()[1].replace('get_','').replace('_chart',''))
options = []
return render_to_response('optimize.html',{
'days_ago' : [1,2,3,4,5,10,15,30],
'hours_ago' : [1,2,3,6,12,24],
'getparams' : getify(request.GET),
'charts' : charts,
'chartnames' : chartnames,
'chartnamesstr' : ",".join(chartnames),
'denoms' : denoms,
'the_denom' : the_denom,
'is_trader_running' : is_trader_running,
'trader_last_seen' : trader_last_seen,
})
def c_chart_view(request):
#setup
symbol=request.GET.get('symbol','BTC_ETH')
i = 0
charts = []
chartnames = []
metas = []
symbols = Price.objects.values('symbol').distinct().order_by('symbol').values_list('symbol',flat=True)
#get data
pts, symbols_that_exist = get_data(request,symbol,'history_classifiertest',ClassifierTest)
if len(pts) == 0:
return render_to_response('notfound.html')
trainer_last_seen = None
try:
last_pt = ClassifierTest.objects.filter(type='mock').order_by('-created_on').first()
is_trainer_running = last_pt.created_on > (get_time() - datetime.timedelta(minutes=int(15)))
trainer_last_seen = (last_pt.created_on- datetime.timedelta(hours=int(7))).strftime('%a %H:%M')
except Exception:
is_trainer_running = False
meta = {
'count' : int(round(pts.count(),0)),
'avg' : round(pts.aggregate(Avg('percent_correct'))['percent_correct__avg'],0),
'median' : round(median_value(pts,'percent_correct'),0),
'max' : round(pts.aggregate(Max('percent_correct'))['percent_correct__max'],0),
'min' : round(pts.aggregate(Min('percent_correct'))['percent_correct__min'],0),
}
#get global chart information
for parameter in ['percent_correct' ,'score']:
i = i + 1
cht = get_line_chart(pts,symbol,parameter)
charts.append(cht)
options = []
chartnames.append("container"+str(i))
metas.append({
'name' : parameter,
'container_class' : 'show',
'class' : "container"+str(i),
'options' : options,
})
# get parameter distribution charts
parameters = ['name','datasetinputs','granularity','minutes_back','timedelta_back_in_granularity_increments','time','prediction_size']
for x_axis in parameters:
i = i + 1
cht = get_scatter_chart(pts,x_axis,symbol)
charts.append(cht)
options_dict = pts.values(x_axis).annotate(Avg('percent_correct')).annotate(Count('pk'))
options = [ (x_axis, obj[x_axis], int(round(obj['percent_correct__avg'],0)), int(round(obj['pk__count'],0)) ) for obj in options_dict ]
options.sort(key=lambda x: x[1])
the_max = max([option[2] for option in options])
for k in range(len(options)):
options[k] = options[k] + (("max" if options[k][2] == the_max else "notmax") + " " + ("warning" if options[k][3] < 5 else "nowarning"),)
chartnames.append("container"+str(i))
metas.append({
'name' : x_axis,
'container_class' : 'show' if len(options) > 1 else 'noshow',
'class' : "container"+str(i),
'options' : options,
})
#Step 3: Send the chart object to the template.
return render_to_response('c_chart.html',{
'pts' : pts.order_by('percent_correct'),
'ticker' : symbol,
'symbols' : symbols,
'meta' : meta,
'days_ago' : [1,2,3,4,5,10,15,30],
'hours_ago' : [1,2,3,6,12,24],
'getparams' : getify(request.GET),
'charts': charts,
'metas' : metas,
'chartnames' : chartnames,
'chartnamesstr' : ",".join(chartnames),
'is_trainer_running' : is_trainer_running,
'trainer_last_seen' : trainer_last_seen,
'symbols_that_exist' : symbols_that_exist,
})
def optimize_view(request):
#setup
the_denom = request.GET.get('denom', 'btc_balance')
denoms = ['usd_balance', 'btc_balance']
days_ago = request.GET.get('days_ago', False)
hours_ago = request.GET.get('hours_ago', False)
if not hours_ago and not days_ago:
hours_ago = 6
if days_ago:
start_time = (timezone.now() - datetime.timedelta(days=int(days_ago)))
elif hours_ago:
start_time = (timezone.now() -
datetime.timedelta(hours=int(hours_ago)))
else:
start_time = Balance.objects.order_by('created_on').first().created_on
symbol = 'BTC_ETH'
#get data
data = {}
for t in Trade.objects.filter(symbol=symbol,
status='fill').order_by('-created_on').all():
date = datetime.datetime.strftime(t.created_on, '%Y-%m-%d')
if not date in data.keys():
data[date] = {'buyvol': [], 'sellvol': [], 'buy': [], 'sell': []}
data[date][t.type].append(t.price)
data[date][t.type + 'vol'] = data[date][t.type + 'vol'] + [t.amount]
bs = Balance.objects.filter(created_on__gte=start_time).all()
try:
last_trade = TradeRecommendation.objects.order_by(
'-created_on').first()
trader_last_seen = (
last_trade.created_on -
datetime.timedelta(hours=int(7))).strftime('%a %H:%M')
is_trader_running = last_trade.created_on > (
get_time() - datetime.timedelta(minutes=int(15)))
except Exception:
is_trader_running = False
i = 0
charts = []
chartnames = []
for func in [
get_trade_chart, get_trade_profitability_chart,
get_directional_change_chart, get_performance_comps_chart,
get_ticker_price
]:
i = i + 1
cht = func(bs, the_denom, symbol, start_time)
charts.append(cht)
chartnames.append(
str(func).split()[1].replace('get_', '').replace('_chart', ''))
options = []
return render_to_response(
'optimize.html', {
'days_ago': [1, 2, 3, 4, 5, 10, 15, 30],
'hours_ago': [1, 2, 3, 6, 12, 24],
'getparams': getify(request.GET),
'charts': charts,
'chartnames': chartnames,
'chartnamesstr': ",".join(chartnames),
'denoms': denoms,
'the_denom': the_denom,
'is_trader_running': is_trader_running,
'trader_last_seen': trader_last_seen,
})
def c_chart_view(request):
#setup
symbol = request.GET.get('symbol', 'BTC_ETH')
i = 0
charts = []
chartnames = []
metas = []
symbols = Price.objects.values('symbol').distinct().order_by(
'symbol').values_list('symbol', flat=True)
#get data
pts, symbols_that_exist = get_data(request, symbol,
'history_classifiertest',
ClassifierTest)
if len(pts) == 0:
return render_to_response('notfound.html')
trainer_last_seen = None
try:
last_pt = ClassifierTest.objects.filter(
type='mock').order_by('-created_on').first()
is_trainer_running = last_pt.created_on > (
get_time() - datetime.timedelta(minutes=int(15)))
trainer_last_seen = (
last_pt.created_on -
datetime.timedelta(hours=int(7))).strftime('%a %H:%M')
except Exception:
is_trainer_running = False
meta = {
'count':
int(round(pts.count(), 0)),
'avg':
round(
pts.aggregate(Avg('percent_correct'))['percent_correct__avg'], 0),
'median':
round(median_value(pts, 'percent_correct'), 0),
'max':
round(
pts.aggregate(Max('percent_correct'))['percent_correct__max'], 0),
'min':
round(
pts.aggregate(Min('percent_correct'))['percent_correct__min'], 0),
}
#get global chart information
for parameter in ['percent_correct', 'score']:
i = i + 1
cht = get_line_chart(pts, symbol, parameter)
charts.append(cht)
options = []
chartnames.append("container" + str(i))
metas.append({
'name': parameter,
'container_class': 'show',
'class': "container" + str(i),
'options': options,
})
# get parameter distribution charts
parameters = [
'name', 'datasetinputs', 'granularity', 'minutes_back',
'timedelta_back_in_granularity_increments', 'time', 'prediction_size'
]
for x_axis in parameters:
i = i + 1
cht = get_scatter_chart(pts, x_axis, symbol)
charts.append(cht)
options_dict = pts.values(x_axis).annotate(
Avg('percent_correct')).annotate(Count('pk'))
options = [(x_axis, obj[x_axis],
int(round(obj['percent_correct__avg'],
0)), int(round(obj['pk__count'], 0)))
for obj in options_dict]
options.sort(key=lambda x: x[1])
the_max = max([option[2] for option in options])
for k in range(len(options)):
options[k] = options[k] + (
("max" if options[k][2] == the_max else "notmax") + " " +
("warning" if options[k][3] < 5 else "nowarning"), )
chartnames.append("container" + str(i))
metas.append({
'name': x_axis,
'container_class': 'show' if len(options) > 1 else 'noshow',
'class': "container" + str(i),
'options': options,
})
#Step 3: Send the chart object to the template.
return render_to_response(
'c_chart.html', {
'pts': pts.order_by('percent_correct'),
'ticker': symbol,
'symbols': symbols,
'meta': meta,
'days_ago': [1, 2, 3, 4, 5, 10, 15, 30],
'hours_ago': [1, 2, 3, 6, 12, 24],
'getparams': getify(request.GET),
'charts': charts,
'metas': metas,
'chartnames': chartnames,
'chartnamesstr': ",".join(chartnames),
'is_trainer_running': is_trainer_running,
'trainer_last_seen': trainer_last_seen,
'symbols_that_exist': symbols_that_exist,
})
