with open(model_save_path, 'rb') as f:
model = pickle.load(f)
logger.info("model loaded from: %s"%model_save_path)
logger.info(model)
raw_data_file = CONFIG.get('Paths','raw_data_path') + '/raw_data.csv'
test_data_file = CONFIG.get('Paths','test_data_path') + '/test_data.npy'
logger.info("raw data file is %s\n, test_data_file is: %s" % (raw_data_file, test_data_file))
# load test data
logger.info("load the test data from %s."%test_data_file)
#filecp = codecs.open(test_data_file, encoding='UTF-8')
#data_test_loaded = np.loadtxt(filecp,delimiter=',')
data_test_loaded = np.load(test_data_file)
logger.info(str(data_test_loaded))
X = data_test_loaded[:,0:int(n)]
y = data_test_loaded[:,-1].reshape((X.shape[0],1))
# data cleaning config
dropFeat = CONFIG.getboolean('data_cleaning','drop_feature')
#dropFeat = CONFIG['data_cleaning']['drop_feature']
#clearning
new_feature_num = CONFIG['data_cleaning']['reduced_col_num']
if dropFeat:
X_dropped = X[:,0:int(new_feature_num)]
#prediction
y_hat = model.predict(X_dropped)
logger.info("Predicted value is: %s"%str(y_hat))
logger.info("True value is: %s"%str(y))
logger.info("Production is finished. Last step.")
def calc_SR(clO, outfile):
'''
Function to calculate S/R lines
Parameters
----------
clO: CandleList object
Used for calculation
outfile : str
Output filename for .png file
Return
------
HAreaList object
'''
PL = clO.get_pivotlist(th_bounces=CONFIG.getfloat('pivots', 'th_bounces'))
## now calculate the price range for calculating the S/R
# add a number of pips to max,min to be sure that we
# also detect the extreme pivots
ul = add_pips2price(clO.data['instrument'], clO.get_highest(),
CONFIG.getint('trade_bot', 'add_pips'))
ll = substract_pips2price(clO.data['instrument'], clO.get_lowest(),
CONFIG.getint('trade_bot', 'add_pips'))
cl_logger.debug("Running calc_SR for estimated range: {0}-{1}".format(
ll, ul))
prices, bounces, score_per_bounce, tot_score = ([] for i in range(4))
# the increment of price in number of pips is double the hr_extension
prev_p = None
##
ll = 0.6444
##
p = float(ll)
while p <= float(ul):
cl_logger.debug("Processing S/R at {0}".format(round(p, 4)))
# get a PivotList for this particular S/R
newPL = PL.inarea_pivots(SR=p)
if len(newPL.plist) == 0:
mean_pivot = 0
else:
mean_pivot = newPL.get_avg_score()
prices.append(round(p, 5))
bounces.append(len(newPL.plist))
tot_score.append(newPL.get_score())
score_per_bounce.append(mean_pivot)
# increment price to following price.
# Because the increment is made in pips
# it does not suffer of the JPY pairs
# issue
p = add_pips2price(clO.data['instrument'], p,
2 * CONFIG.getint('candlelist', 'i_pips'))
if prev_p is None:
prev_p = p
else:
increment_price = round(p - prev_p, 5)
prev_p = p
data = {
'price': prices,
'bounces': bounces,
'scores': score_per_bounce,
'tot_score': tot_score
}
df = pd.DataFrame(data=data)
### establishing bounces threshold as the args.th quantile
# selecting only rows with at least one pivot and tot_score>0,
# so threshold selection considers only these rows
# and selection is not biased when range of prices is wide
dfgt1 = df.loc[(df['bounces'] > 0)]
dfgt2 = df.loc[(df['tot_score'] > 0)]
bounce_th = dfgt1.bounces.quantile(CONFIG.getfloat('trade_bot', 'th'))
score_th = dfgt2.tot_score.quantile(CONFIG.getfloat('trade_bot', 'th'))
print("Selected number of pivot threshold: {0}".format(bounce_th))
print("Selected tot score threshold: {0}".format(round(score_th, 1)))
# selecting records over threshold
dfsel = df.loc[(df['bounces'] > bounce_th) | (df['tot_score'] > score_th)]
# repeat until no overlap between prices
ret = calc_diff(dfsel, increment_price)
dfsel = ret[0]
tog_seen = ret[1]
while tog_seen is True:
ret = calc_diff(dfsel, increment_price)
dfsel = ret[0]
tog_seen = ret[1]
# iterate over DF with selected SR to create a HAreaList
halist = []
for index, row in dfsel.iterrows():
resist = HArea(price=row['price'],
pips=CONFIG.getint('pivots', 'hr_pips'),
instrument=clO.data['instrument'],
granularity=clO.data['granularity'],
no_pivots=row['bounces'],
tot_score=round(row['tot_score'], 5))
halist.append(resist)
halistObj = HAreaList(halist=halist)
# Plot the HAreaList
dt_str = clO.data['candles'][-1]['time'].strftime("%d_%m_%Y_%H_%M")
if CONFIG.getboolean('images', 'plot') is True:
halistObj.plot(clO=clO, outfile=outfile)
cl_logger.info("Run done")
return halistObj
