def main(dir_path, output_dir):
'''
Run Pipeline of processes on file one by one.
'''
scores = {}
files = os.listdir(dir_path)
maxdelta = 30
delta = range(8, maxdelta)
print('Delta days accounted: ', max(delta))
for file_name in files:
try:
symbol = file_name.split('.')[0]
print(symbol)
datasets = load_dataset(dir_path, file_name)
for dataset in datasets:
columns = dataset.columns
adjclose = columns[-2]
returns = columns[-1]
for dele in delta:
addFeatures(dataset, adjclose, returns, dele)
dataset = dataset.iloc[max(
delta
):, :] # computation of returns and moving means introduces NaN which are nor removed
finance = mergeDataframes(datasets)
high_value = 365
high_value = min(high_value, finance.shape[0] - 1)
lags = range(high_value, 30)
# print('Maximum time lag applied', high_value)
if 'symbol' in finance.columns:
finance.drop('symbol', axis=1, inplace=True)
# print('Size of data frame: ', finance.shape)
# print('Number of NaN after merging: ', count_missing(finance))
finance = finance.interpolate(method='time')
# print('Number of NaN after time interpolation: ', finance.shape[0]*finance.shape[1] - finance.count().sum())
finance = finance.fillna(finance.mean())
# print('Number of NaN after mean interpolation: ', (finance.shape[0]*finance.shape[1] - finance.count().sum()))
finance.columns = [
str(col.replace('&', '_and_')) for col in finance.columns
]
#Move the Open Values behind by one dataset.
finance.open = finance.open.shift(-1)
# print(high_value)
finance = applyTimeLag(finance, [high_value], delta)
# print('Number of NaN after temporal shifting: ', count_missing(finance))
# print('Size of data frame after feature creation: ', finance.shape)
mean_squared_errors, r2_scores = performRegression(
finance, 0.95, symbol, output_dir)
scores[symbol] = [mean_squared_errors, r2_scores]
except Exception, e:
pass
traceback.print_exc()
def main(dir_path, output_dir):
'''
Run Pipeline of processes on file one by one.
'''
scores = {}
files = os.listdir(dir_path)
maxdelta = 30
delta = range(8, maxdelta)
print('Delta days accounted: ', max(delta))
for file_name in files:
try:
symbol = file_name.split('.')[0]
print(symbol)
datasets = load_dataset(dir_path, file_name)
for dataset in datasets:
columns = dataset.columns
adjclose = columns[-2]
returns = columns[-1]
for dele in delta:
addFeatures(dataset, adjclose, returns, dele)
dataset = dataset.iloc[max(delta):,:] # computation of returns and moving means introduces NaN which are nor removed
finance = mergeDataframes(datasets)
high_value = 365
high_value = min(high_value, finance.shape[0] - 1)
lags = range(high_value, 30)
print('Maximum time lag applied', high_value)
if 'symbol' in finance.columns:
finance.drop('symbol', axis=1, inplace=True)
print('Size of data frame: ', finance.shape)
print('Number of NaN after merging: ', count_missing(finance))
finance = finance.interpolate(method='time')
print('Number of NaN after time interpolation: ', finance.shape[0]*finance.shape[1] - finance.count().sum())
finance = finance.fillna(finance.mean())
print('Number of NaN after mean interpolation: ', (finance.shape[0]*finance.shape[1] - finance.count().sum()))
finance.columns = [str(col.replace('&', '_and_')) for col in finance.columns]
#Move the Open Values behind by one dataset.
finance.open = finance.open.shift(-1)
print(high_value)
finance = applyTimeLag(finance, [high_value], delta)
print('Number of NaN after temporal shifting: ', count_missing(finance))
print('Size of data frame after feature creation: ', finance.shape)
mean_squared_errors, r2_scores = performRegression(finance, 0.95, \
symbol, output_dir)
scores[symbol] = [mean_squared_errors, r2_scores]
except Exception, e:
pass
traceback.print_exc()