# df_start_date = df.date.min()

# df_end_date = df.date.max()

defined_start_date = datetime.strptime(start_date_str, DATE_TIME_FORMAT)

defined_end_date = datetime.strptime(end_date_str, DATE_TIME_FORMAT)

# start_date = df_start_date if defined_start_date < df_start_date else defined_start_date

# end_date = df_end_date if defined_end_date > df_end_date else defined_end_date

start_date = defined_start_date

end_date = defined_end_date

# time_delta = timedelta(hours=interval_hr)

if interval == 'monthly':

time_delta = relativedelta(months=+1)

elif interval == 'weekly':

time_delta = relativedelta(weeks=+1)

elif interval == 'daily':

time_delta = relativedelta(days=+1)

else:

time_delta = relativedelta(months=+1)

slices = []

run = True

while run:

# slice_start_time = end_date - time_delta

slice_end_time = start_date + time_delta

if slice_end_time <= end_date:

slice_date = {

'start': start_date,

'end': slice_end_time

}

slices.append(slice_date)

start_date = slice_end_time

else:

slice_date = {

'start': start_date,

'end': defined_end_date

}

slices.append(slice_date)

run = False

full_dataframe = pd.DataFrame()

for pair in pairs:

print(data_location)

print(config["timeframe"])

print(pair)

candles = load_pair_history(

datadir=data_location,

timeframe=config["timeframe"],

pair=pair,

data_format="hdf5"

)

if len(candles):

# Not sure about AgeFilter

# apply price filter make price 0 to ignore this pair after calculation of quoteVolume

candles.loc[(candles.close < filter_price), 'close'] = 0

column_name = pair

candles[column_name] = candles['volume'] * candles['close']

if full_dataframe.empty:

full_dataframe = candles[['date', column_name]].copy()

else:

full_dataframe = pd.merge(full_dataframe, candles[['date', column_name]].copy(), on='date', how='left')

# print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {data_location}")

# print(full_dataframe.tail(1))

print(full_dataframe.head())

full_dataframe['date'] = full_dataframe['date'].dt.tz_localize(None)

result = {}

for date_slice in date_slices:

df_slice = df[(df.date >= date_slice['start']) & (df.date < date_slice['end'])].copy()

summarised = df_slice.sum()

summarised = summarised[summarised > 0]

summarised = summarised.sort_values(ascending=False)

if len(summarised) > number_assets:

result_pairs_list = list(summarised.index[:number_assets])

else:

result_pairs_list = list(summarised.index)

if len(result_pairs_list) > 0:

result[f'{date_slice["start"].strftime(DATE_FORMAT)}-{date_slice["end"].strftime(DATE_FORMAT)}'] = result_pairs_list

parser = argparse.ArgumentParser()

parser.add_argument("-c", "--config", help="config to parse")

parser.add_argument("-t", "--timerange", nargs='?',

help="timerange as per freqtrade format, e.g. 20210401-, 20210101-20210201, etc")

parser.add_argument("-o", "--outfile", help="path where output the pairlist", type=argparse.FileType('w'))

parser.add_argument("-mp", "--minprice", help="price for price filter")

parser.add_argument("-tf", "--timeframe", help="timeframe of loaded candles data")

parser.add_argument("-na", "--numberassets", help="number of assets to be filtered")

args = parser.parse_args()

# Make this argparseble

# And add blacklist

START_DATE_STR = '20180101 00:00:00'

END_DATE_STR = '20211001 00:00:00'

# For now it shouldn't be less than a day because it's outputs object with timerage suitable for backtesting

# for easy copying eg. 20210501-20210602

INTERVAL_ARR = ['monthly', 'weekly', 'daily']

# INTERVAL_ARR = ['weekly']

# INTERVAL_ARR = ['monthly']

ASSET_FILTER_PRICE_ARR = [0, 0.01, 0.02, 0.05, 0.15, 0.5]

NUMBER_ASSETS_ARR = [30, 45, 60, 75, 90, 105, 120]

# ASSET_FILTER_PRICE_ARR = [0]

# NUMBER_ASSETS_ARR = [90]

start_string = START_DATE_STR.split(' ')[0]

end_string = END_DATE_STR.split(' ')[0]

for asset_filter_price in ASSET_FILTER_PRICE_ARR:

volume_dataframe = process_candles_data(pairs, asset_filter_price)

for interval in INTERVAL_ARR:

date_slices = get_data_slices_dates(volume_dataframe, START_DATE_STR, END_DATE_STR, interval)

for number_assets in NUMBER_ASSETS_ARR:

result_obj = process_date_slices(volume_dataframe, date_slices, number_assets)

# {'timerange': [pairlist]}

print(result_obj)

p_json = json.dumps(result_obj, indent=4)

file_name = f'user_data/pairlists/{STAKE_CURRENCY}/{interval}/{interval}_{number_assets}_{STAKE_CURRENCY}_{str(asset_filter_price).replace(".", ",")}_minprice_{start_string}_{end_string}.json'

os.makedirs(os.path.dirname(file_name), exist_ok=True)

with open(file_name, 'w') as f:

f.write(p_json)

# Save result object as json to --outfile location