def estimate_bounces(price, plist):
'''
Function to estimate the bounces at 'price'
Parameters
----------
price: float
Price used to identify bounces
plist: PivotList
object
Returns
-------
list List with bounces
'''
hr = HArea(price=price,
pips=hr_extension,
instrument=args.instrument,
granularity=args.granularity)
print("upper: {0} - lower: {1}".format(hr.upper, hr.lower))
inarea_bounces = hr.inarea_bounces(plist=plist)
if len(inarea_bounces) > 1:
inarea_cl = CandleList(inarea_bounces,
instrument=args.instrument,
granularity=args.granularity)
inarea_bounces = inarea_cl.improve_resolution(part='openAsk',
price=price,
min_dist=5)
return inarea_bounces
def test_slice_with_start_end(trend_oanda_object):
candle_list = trend_oanda_object.fetch_candleset()
cl = CandleList(candle_list, instrument='AUD_USD', granularity='D')
startdatetime = datetime.datetime(2017, 12, 20, 22, 0)
endatetime = datetime.datetime(2018, 1, 20, 22, 0)
cl.slice(start=startdatetime, end=endatetime)
def get_first_bounce(self, part='closeAsk'):
'''
Function that uses the the Zigzag algorithm to identify the first bounce (most recent)
of the CounterDoubleTop pattern
Parameters
----------
part: str
Candle part used for the calculation. Default='closeAsk'
Returns
-------
It will set the self.bounce_1st attribute
'''
# get sliced CandleList from datetime defined by self.period1st_bounce period
start_clist = self.clist_period.slice(
start=self.__period1st_bounce_point)
pivotlist = start_clist.get_pivotlist(
th_up=config.CTDBT['threshold_1st_2nd_bounces'],
th_down=-config.CTDBT['threshold_1st_2nd_bounces'])
bounces = pivotlist.plist
arr = np.array(start_clist.clist)
# consider type of trade in order to select peaks or valleys
if self.type == 'short':
bounce_candles = arr[bounces == 1]
elif self.type == 'long':
bounce_candles = arr[bounces == -1]
in_area_list = self.__inarea_bounces(bounce_candles,
part=part,
HRpips=self.HR_pips)
HRpips = self.HR_pips
while len(in_area_list) == 0 and HRpips <= config.CTDBT['max_HRpips']:
HRpips = HRpips + config.CTDBT['step_pips']
in_area_list = self.__inarea_bounces(bounce_candles,
part=part,
HRpips=HRpips)
# keep running the improve_resolution function until a single bounce is obtained
while len(in_area_list) > 1:
inarea_cl = CandleList(in_area_list,
instrument=self.pair,
granularity=self.timeframe)
in_area_list = inarea_cl.improve_resolution(part=part,
price=self.SR)
assert len(in_area_list) == 1, "Exactly one single bounce is needed"
self.bounce_1st = in_area_list[0]
def __initclist(self):
'''
Private function to initialize the CandleList object that goes from self.start
to self.period
This will set the self.clist_period class attribute
'''
delta_period = periodToDelta(self.period, self.timeframe)
delta_1 = periodToDelta(1, self.timeframe)
start = self.start - delta_period # get the start datetime for this CandleList period
end = self.start + delta_1 # increase self.start by one candle to include self.start
oanda = OandaAPI(
url=config.OANDA_API['url'],
instrument=self.pair,
granularity=self.timeframe,
alignmentTimezone=config.OANDA_API['alignmentTimezone'],
dailyAlignment=config.OANDA_API['dailyAlignment'])
oanda.run(start=start.isoformat(), end=end.isoformat(), roll=True)
candle_list = oanda.fetch_candleset(vol_cutoff=0)
cl = CandleList(candle_list,
self.pair,
granularity=self.timeframe,
id=self.id,
type=self.type)
self.clist_period = cl
def fetch_candlelist(self):
'''
This function returns a CandleList object for this Trade
Returns
-------
A CandleList object
'''
oanda = OandaAPI(
url=config.OANDA_API['url'],
instrument=self.pair,
granularity=self.timeframe,
alignmentTimezone=config.OANDA_API['alignmentTimezone'],
start=datetime.datetime.strptime(self.start,
'%Y-%m-%dT%H:%M:%S').isoformat(),
dailyAlignment=config.OANDA_API['dailyAlignment'],
end=datetime.datetime.strptime(self.end,
'%Y-%m-%dT%H:%M:%S').isoformat())
candle_list = oanda.fetch_candleset()
cl = CandleList(candle_list, type=self.type)
return cl
def __init_clist_period(self):
'''
Private function to initialise self.clist_period class attribute
This function process the candlelist going from self.start-self.period to
self.start
'''
warnings.warn("[INFO] Run __init_clist_period")
delta_period = None
delta_1 = None
if self.timeframe == "D":
delta_period = datetime.timedelta(hours=24 * self.period)
delta_1 = datetime.timedelta(hours=24)
else:
fgran = self.timeframe.replace('H', '')
delta_period = datetime.timedelta(hours=int(fgran) * self.period)
delta_1 = datetime.timedelta(hours=int(fgran))
start = self.start - delta_period
end = self.start + delta_1
oanda = OandaAPI(
url=config.OANDA_API['url'],
instrument=self.pair,
granularity=self.timeframe,
alignmentTimezone=config.OANDA_API['alignmentTimezone'],
dailyAlignment=config.OANDA_API['dailyAlignment'])
oanda.run(start=start.isoformat(), end=end.isoformat(), roll=True)
candle_list = oanda.fetch_candleset(vol_cutoff=0)
cl = CandleList(candle_list,
self.pair,
granularity=self.timeframe,
id=self.id)
warnings.warn("[INFO] Run cl.calc_rsi")
cl.calc_rsi()
warnings.warn("[INFO] Done cl.calc_rsi")
self.clist_period = cl
def __init_clist_trend(self):
'''
Private function to initialise self.clist_trend class attribute
This function process the candlelist going from self.trend_i to self.start
'''
warnings.warn("[INFO] Run __init_clist_trend")
# if trend_i is not defined then calculate it
if hasattr(self, 'trend_i'):
self.trend_i = datetime.datetime.strptime(self.trend_i,
'%Y-%m-%d %H:%M:%S')
else:
self.calc_itrend()
self.__init_clist_trend()
# checking for feats in trend before 1st bounce
oanda = OandaAPI(
url=config.OANDA_API['url'],
instrument=self.pair,
granularity=self.timeframe,
alignmentTimezone=config.OANDA_API['alignmentTimezone'],
dailyAlignment=config.OANDA_API['dailyAlignment'])
oanda.run(start=self.trend_i.isoformat(),
end=self.start.isoformat(),
roll=True)
candle_list = oanda.fetch_candleset(vol_cutoff=0)
cl = CandleList(candle_list,
instrument=self.pair,
granularity=self.timeframe,
id=self.id)
warnings.warn("[INFO] Run cl.calc_rsi")
cl.calc_rsi()
warnings.warn("[INFO] Done cl.calc_rsi")
self.clist_trend = cl
def pl_object2():
'''Returns PivotList object'''
oanda = OandaAPI(url='https://api-fxtrade.oanda.com/v1/candles?',
instrument='AUD_USD',
granularity='D',
alignmentTimezone='Europe/London',
dailyAlignment=22)
oanda.run(start='2016-01-15T22:00:00',
end='2016-08-17T22:00:00',
roll=True)
candle_list = oanda.fetch_candleset()
cl = CandleList(candle_list, instrument='AUD_USD', type='long')
pl = cl.get_pivotlist(outfile='test.png', th_up=0.02, th_down=-0.02)
return pl
def cl_object():
'''Returns CandleList object'''
oanda = OandaAPI(url='https://api-fxtrade.oanda.com/v1/candles?',
instrument='AUD_USD',
granularity='D',
alignmentTimezone='Europe/London',
dailyAlignment=22)
oanda.run(start='2018-01-25T22:00:00',
end='2018-10-12T22:00:00',
roll=True)
candle_list = oanda.fetch_candleset()
cl = CandleList(candle_list, instrument='AUD_USD', type='long')
return cl
def get_second_bounce(self, part='closeAsk'):
'''
Function that uses the the Zigzag algorithm to identify the second bounce
of the CounterDoubleTop pattern
Parameters
----------
part: str
Candle part used for the calculation. Default='closeAsk'
Returns
-------
It will set the self.bounce_2nd attribute
'''
# timepoint cutoff that the defines the period from which the second bounce needs to be
# located
start = self.__get_time4candles(n=self.period2nd_bounce,
anchor_point=self.bounce_1st.time)
# get sliced CandleList from datetime defined by self.period1st_bounce period
start_clist = self.clist_period.slice(start=start,
end=self.bounce_1st.time)
pivotlist = start_clist.get_pivotlist(
th_up=config.CTDBT['threshold_1st_2nd_bounces'],
th_down=-config.CTDBT['threshold_1st_2nd_bounces'])
bounces = pivotlist.plist
arr = np.array(start_clist.clist)
# consider type of trade in order to select peaks or valleys
if self.type == 'short':
bounce_candles = arr[bounces == 1]
elif self.type == 'long':
bounce_candles = arr[bounces == -1]
#if distance between 1st bounce and last detected bounce is less than self.min_dist candles
#then remove last detected bounce progressively
diff = abs(self.bounce_1st.time - bounce_candles[-1].time)
min_distDelta = periodToDelta(ncandles=self.min_dist,
timeframe=self.timeframe)
while diff <= min_distDelta:
bounce_candles = bounce_candles[:-1]
diff = abs(self.bounce_1st.time - bounce_candles[-1].time)
in_area_list = self.__inarea_bounces(
bounce_candles, part=part, HRpips=config.CTDBT['HR_pips_2nd'])
HRpips = config.CTDBT['HR_pips_2nd']
while len(in_area_list) == 0 and HRpips <= config.CTDBT['max_HRpips']:
HRpips = HRpips + config.CTDBT['step_pips']
in_area_list = self.__inarea_bounces(bounce_candles,
part=part,
HRpips=HRpips)
# keep running the improve_resolution function until a single bounce is obtained
while len(in_area_list) > 1:
inarea_cl = CandleList(in_area_list,
instrument=self.pair,
granularity=self.timeframe)
in_area_list = inarea_cl.improve_resolution(part=part,
price=self.SR)
assert len(in_area_list) == 1, "Exactly one single bounce is needed"
self.bounce_2nd = in_area_list[0]
def calc_surr_lengths(b_list):
'''
Function to calculate the number of candles of
segments around each bounce
Parameters
----------
blist: List of candles representing each bounce
Returns
-------
Dict of dicts: Each key will be the datetime of the bounce
and each value will be the number of candles
of the SegmentList before and after the bounce
'''
if len(b_list) == 0:
warnings.warn("No bounces in area. Skipping...")
return 0
bounce_lengths = {}
delta_period = periodToDelta(100, args.granularity)
for b in b_list:
bounce_lengths[b.time] = {}
# get 50 candles after and before the bounce
start = b.time - delta_period
end = b.time + delta_period
# run api query with new period
oanda.run(start=start.isoformat(), end=end.isoformat(), roll=True)
candle_list = oanda.fetch_candleset()
sub_cl = CandleList(clist=candle_list,
instrument=args.instrument,
granularity=args.instrument)
sub_pl = sub_cl.get_pivotlist(outfile='test_subcl.png',
th_up=0.02,
th_down=-0.02)
slist = sub_pl.slist
mslist = slist.merge_segments(min_n_candles=10,
diff_in_pips=200,
outfile="test_subcl1.png")
diff = None
pr_ms = None
max_pr_ms = None
c_ms = None
for ms in mslist:
if diff is None:
pr_ms = ms
diff = abs(ms.start - b.time)
else:
if abs(ms.start - b.time) < diff:
max_pr_ms = pr_ms
c_ms = ms
diff = abs(ms.start - b.time)
pr_ms = ms
bounce_lengths[b.time] = {
'pre': max_pr_ms.length(),
'after': c_ms.length()
}
return bounce_lengths
granularity=args.granularity,
alignmentTimezone=config.OANDA_API['alignmentTimezone'],
dailyAlignment=config.OANDA_API['dailyAlignment'])
delta_period = periodToDelta(config.SRarea['period'], args.granularity)
startObj = datetime.datetime.strptime(args.start, "%Y-%m-%d %H:%M:%S")
start = startObj - delta_period # get the start datetime for this CandleList period
end = startObj
oanda.run(start=start.isoformat(), end=end.isoformat(), roll=True)
candle_list = oanda.fetch_candleset()
cl = CandleList(clist=candle_list,
instrument=args.instrument,
granularity=args.instrument)
plist = cl.get_pivotlist(
th_down=config.SRarea['th_down'],
th_up=-config.SRarea['th_up'],
outfile="/Users/ernesto/PycharmProjects/FOREX/scripts/test.png")
bounce_dict = {}
hr_extension = 30
increment_price = 0.006 # the increment of price in number of pips is double the hr_extension
for p in np.arange(0.67985, 0.72219, increment_price):
print("Price: {0}".format(p))
inarea_bs = estimate_bounces(price=p, plist=plist)
print("Length: {0}".format(len(inarea_bs)))
alignmentTimezone='Europe/London',
dailyAlignment=22,
start=args.start,
end=args.end)
else:
oanda = OandaAPI(url='https://api-fxtrade.oanda.com/v1/candles?',
instrument=args.instrument,
granularity=args.granularity,
alignmentTimezone='Europe/London',
dailyAlignment=22,
start=args.start)
candle_list = oanda.fetch_candleset()
trade_type = str(args.trade_type)
cl = CandleList(candle_list, type=trade_type)
cl.calc_binary_seq(merge=True)
cl.calc_number_of_0s(norm=True)
cl.calc_number_of_doubles0s(norm=True)
cl.calc_longest_stretch()
for k in cl.seq:
print("\t{0}: {1}".format(k, cl.seq[k]))
print("Number of 0s (merged): %.2f" % cl.number_of_0s['merge'])
print("Double0s (highlow): {0}".format(cl.highlow_double0s))
print("Double0s (openclose): {0}".format(cl.openclose_double0s))
print("Stretch:")
for k in cl.longest_stretch:
print("\t{0}: {1}".format(k, cl.longest_stretch[k]))
