def test__autochartist(self, mock_get):
"""get autochartist information for an instrument."""
tid = "_v3_forexlabs_autochartist"
td = TestData(responses, tid)
r = labs.Autochartist(params=td.params)
mock_get.register_uri('GET',
"{}/{}".format(api.api_url, r),
text=json.dumps(td.resp))
api.request(r)
self.assertTrue(td.resp == r.response)
def get_signals(client):
print('getting signals')
# params = {}
# r = labs.Autochartist(params=params)
params = {}
r = labs.Autochartist()
client.request(r)
rv = r.response
signals = pd.DataFrame(rv['signals'])
metakeys = signals.loc[0, 'meta'].keys()
datakeys = signals.loc[0, 'data'].keys()
for m in metakeys:
signals[m] = signals.apply(lambda x: x['meta'][m], axis=1)
for d in datakeys:
signals[d] = signals.apply(lambda x: x['data'][d], axis=1)
signals = signals[[
c for c in signals.columns if c not in ['meta', 'data']
]]
scorekeys = signals.loc[0, 'scores'].keys()
for s in scorekeys:
signals[s] = signals.apply(lambda x: x['scores'][s], axis=1)
signals = signals[[c for c in signals.columns if c not in ['scores']]]
historykeys = signals.loc[0, 'historicalstats'].keys()
predictionkeys = signals.loc[0, 'prediction'].keys()
for h in historykeys:
signals[h] = signals.apply(lambda x: x['historicalstats'][h], axis=1)
for p in predictionkeys:
signals[p] = signals.apply(lambda x: x['prediction'][p], axis=1)
signals = signals[[
c for c in signals.columns
if c not in ['historicalstats', 'prediction']
]]
for c in ['pattern', 'symbol', 'hourofday']:
signals[c + '_percent'] = signals.apply(lambda x: x[c]['percent'],
axis=1)
signals = signals[[
c for c in signals.columns
if c not in ['pattern', 'symbol', 'hourofday']
]]
pointskey = signals.loc[0, 'points'].keys()
for p in pointskey:
signals[p] = signals.apply(lambda x: x['points'][p], axis=1)
signals = signals[[c for c in signals.columns if c not in ['points']]]
for c in ['support', 'resistance']:
signals[c + '_y0'] = signals.apply(lambda x: x[c]['y0'], axis=1)
signals[c + '_y1'] = signals.apply(lambda x: x[c]['y1'], axis=1)
signals[c + '_x0'] = signals.apply(lambda x: x[c]['x0'], axis=1)
signals[c + '_x1'] = signals.apply(lambda x: x[c]['x1'], axis=1)
signals = signals[[
c for c in signals.columns if c not in ['support', 'resistance']
]]
time_cols = [
'patternendtime', 'timefrom', 'timeto', 'support_x0', 'support_x1',
'resistance_x0', 'resistance_x1'
]
for c in time_cols:
signals[c] = signals[c].apply(
lambda t: convert_ToLocal(convert_UnixTime(t)))
return signals
def autochartist():
dt = datetime.datetime.now()
utc_time = dt.replace(tzinfo=timezone.utc)
utc_timestamp = int(utc_time.timestamp())
oanda = oandapy.API(environment="practice", access_token=OANDA_API_KEY)
oandaV20 = oandapyV20.API(environment="practice",
access_token=OANDA_API_KEY)
recommended = []
#response = oanda.get_autochartist()
r = labs.Autochartist()
oandaV20.request(r)
response = r.response
signals = response.get("signals")
for x in signals:
inst = x.get("instrument")
instrument = inst.replace('_', '')
recommended.append(instrument)
pricelow = 0.0
pricehigh = 0.0
probability = 0.0
meta = x.get("meta")
if (meta):
#print (meta)
probability = meta.get("probability")
interval = meta.get("interval")
if (interval == 0):
interval = 1440
scoresTotal = 0
quality = 0
scores = meta.get("scores")
if (scores):
quality = scores.get("quality")
clarity = scores.get("clarity")
initialTrend = scores.get("initialtrend")
breakout = scores.get("breakout")
uniformity = scores.get("uniformity")
try:
scoresTotal = quality + clarity + initialTrend + breakout + uniformity
except:
pass
direction = meta.get("direction")
data = x.get("data")
if (data):
prediction = data.get("prediction")
if (prediction):
#print (prediction)
pricelow = prediction.get("pricelow")
pricehigh = prediction.get("pricehigh")
timefrom = prediction.get("timefrom")
timeto = prediction.get("timeto")
print(
"{} direction {} interval {} probability {} quality {} scoresTotal {} pricelow {} pricehigh {} age {}"
.format(instrument, direction, interval, probability, quality,
scoresTotal, pricelow, pricehigh, utc_timestamp - timeto))
if (pricelow > 0.0):
for y in symbol_id:
if (y == instrument):
#if (probability > 65.0 and quality >= 6):
if (probability > 75.0 or quality >= 6):
response = oanda.get_prices(instruments=inst)
prices = response.get("prices")
asking_price = prices[0].get("ask")
print("Current price {}, pricelow {}, pricehigh{}".
format(asking_price, pricelow, pricehigh))
limit = 0
stop = 0
midprice = (pricehigh - pricelow) / 2 + pricelow
if (direction == 1 and midprice > asking_price):
limit = midprice
stop = asking_price - (
(midprice - asking_price) / 2)
elif (direction == -1 and midprice < asking_price):
limit = midprice
stop = asking_price + (
(asking_price - midprice) / 2)
if (limit > 0):
epic_id = getEpicId(y)
deal, oldStop = getDealId(epic_id, direction)
print("Deal is {} oldStop is {} stop is {}".format(
deal, oldStop, stop))
if (deal == "None"):
if (direction == 1
and asking_price < pricelow):
performTradeAbsolute(
epic_id, "BUY", stop, limit, 1)
elif (direction == -1
and asking_price > pricehigh):
performTradeAbsolute(
epic_id, "SELL", stop, limit, 1)
else:
if (direction == 1 and oldStop > stop):
stop = oldStop
elif (direction == -1 and oldStop < stop):
stop = oldStop
if (stop != oldStop):
updatePosition(deal, y, stop, limit)
print("Current time is {}".format(utc_timestamp))
return (recommended)
def ForexlabsAutochartist(access_token, params=None): # check
'Get the ‘autochartist data’.'
r = forexlabs.Autochartist(params=params)
client = API(access_token=access_token)
client.request(r)
return readable_output(Munch(r.response)), Munch(r.response)