adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
46458bf5eb
stable/tests/data/test_converter.py

381 lines
13 KiB
Python
Raw Blame History

# pragma pylint: disable=missing-docstring, C0103
import logging
from pathlib import Path
from shutil import copyfile
import numpy as np
import pytest
from freqtrade.configuration.timerange import TimeRange
from freqtrade.data.converter import (convert_ohlcv_format, convert_trades_format,
ohlcv_fill_up_missing_data, ohlcv_to_dataframe,
reduce_dataframe_footprint, trades_dict_to_list,
trades_remove_duplicates, trades_to_ohlcv, trim_dataframe)
from freqtrade.data.history import (get_timerange, load_data, load_pair_history,
validate_backtest_data)
from freqtrade.data.history.idatahandler import IDataHandler
from freqtrade.enums import CandleType
from tests.conftest import generate_test_data, log_has, log_has_re
from tests.data.test_history import _clean_test_file
def test_dataframe_correct_columns(dataframe_1m):
assert dataframe_1m.columns.tolist() == ['date', 'open', 'high', 'low', 'close', 'volume']
def test_ohlcv_to_dataframe(ohlcv_history_list, caplog):
columns = ['date', 'open', 'high', 'low', 'close', 'volume']
caplog.set_level(logging.DEBUG)
# Test file with BV data
dataframe = ohlcv_to_dataframe(ohlcv_history_list, '5m', pair="UNITTEST/BTC",
fill_missing=True)
assert dataframe.columns.tolist() == columns
assert log_has('Converting candle (OHLCV) data to dataframe for pair UNITTEST/BTC.', caplog)
def test_trades_to_ohlcv(ohlcv_history_list, caplog):
caplog.set_level(logging.DEBUG)
with pytest.raises(ValueError, match="Trade-list empty."):
trades_to_ohlcv([], '1m')
trades = [
[1570752011620, "13519807", None, "sell", 0.00141342, 23.0, 0.03250866],
[1570752011620, "13519808", None, "sell", 0.00141266, 54.0, 0.07628364],
[1570752017964, "13519809", None, "sell", 0.00141266, 8.0, 0.01130128]]
df = trades_to_ohlcv(trades, '1m')
assert not df.empty
assert len(df) == 1
assert 'open' in df.columns
assert 'high' in df.columns
assert 'low' in df.columns
assert 'close' in df.columns
assert df.loc[:, 'high'][0] == 0.00141342
assert df.loc[:, 'low'][0] == 0.00141266
def test_ohlcv_fill_up_missing_data(testdatadir, caplog):
data = load_pair_history(datadir=testdatadir,
timeframe='1m',
pair='UNITTEST/BTC',
fill_up_missing=False)
caplog.set_level(logging.DEBUG)
data2 = ohlcv_fill_up_missing_data(data, '1m', 'UNITTEST/BTC')
assert len(data2) > len(data)
# Column names should not change
assert (data.columns == data2.columns).all()
assert log_has_re(f"Missing data fillup for UNITTEST/BTC: before: "
f"{len(data)} - after: {len(data2)}.*", caplog)
# Test fillup actually fixes invalid backtest data
min_date, max_date = get_timerange({'UNITTEST/BTC': data})
assert validate_backtest_data(data, 'UNITTEST/BTC', min_date, max_date, 1)
assert not validate_backtest_data(data2, 'UNITTEST/BTC', min_date, max_date, 1)
def test_ohlcv_fill_up_missing_data2(caplog):
timeframe = '5m'
ticks = [
[
1511686200000, # 8:50:00
8.794e-05, # open
8.948e-05, # high
8.794e-05, # low
8.88e-05, # close
2255, # volume (in quote currency)
],
[
1511686500000, # 8:55:00
8.88e-05,
8.942e-05,
8.88e-05,
8.893e-05,
9911,
],
[
1511687100000, # 9:05:00
8.891e-05,
8.893e-05,
8.875e-05,
8.877e-05,
2251
],
[
1511687400000, # 9:10:00
8.877e-05,
8.883e-05,
8.895e-05,
8.817e-05,
123551
]
]
# Generate test-data without filling missing
data = ohlcv_to_dataframe(ticks, timeframe, pair="UNITTEST/BTC",
fill_missing=False)
assert len(data) == 3
caplog.set_level(logging.DEBUG)
data2 = ohlcv_fill_up_missing_data(data, timeframe, "UNITTEST/BTC")
assert len(data2) == 4
# 3rd candle has been filled
row = data2.loc[2, :]
assert row['volume'] == 0
# close should match close of previous candle
assert row['close'] == data.loc[1, 'close']
assert row['open'] == row['close']
assert row['high'] == row['close']
assert row['low'] == row['close']
# Column names should not change
assert (data.columns == data2.columns).all()
assert log_has_re(f"Missing data fillup for UNITTEST/BTC: before: "
f"{len(data)} - after: {len(data2)}.*", caplog)
def test_ohlcv_drop_incomplete(caplog):
timeframe = '1d'
ticks = [
[
1559750400000, # 2019-06-04
8.794e-05, # open
8.948e-05, # high
8.794e-05, # low
8.88e-05, # close
2255, # volume (in quote currency)
],
[
1559836800000, # 2019-06-05
8.88e-05,
8.942e-05,
8.88e-05,
8.893e-05,
9911,
],
[
1559923200000, # 2019-06-06
8.891e-05,
8.893e-05,
8.875e-05,
8.877e-05,
2251
],
[
1560009600000, # 2019-06-07
8.877e-05,
8.883e-05,
8.895e-05,
8.817e-05,
123551
]
]
caplog.set_level(logging.DEBUG)
data = ohlcv_to_dataframe(ticks, timeframe, pair="UNITTEST/BTC",
fill_missing=False, drop_incomplete=False)
assert len(data) == 4
assert not log_has("Dropping last candle", caplog)
# Drop last candle
data = ohlcv_to_dataframe(ticks, timeframe, pair="UNITTEST/BTC",
fill_missing=False, drop_incomplete=True)
assert len(data) == 3
assert log_has("Dropping last candle", caplog)
def test_trim_dataframe(testdatadir) -> None:
data = load_data(
datadir=testdatadir,
timeframe='1m',
pairs=['UNITTEST/BTC']
)['UNITTEST/BTC']
min_date = int(data.iloc[0]['date'].timestamp())
max_date = int(data.iloc[-1]['date'].timestamp())
data_modify = data.copy()
# Remove first 30 minutes (1800 s)
tr = TimeRange('date', None, min_date + 1800, 0)
data_modify = trim_dataframe(data_modify, tr)
assert not data_modify.equals(data)
assert len(data_modify) < len(data)
assert len(data_modify) == len(data) - 30
assert all(data_modify.iloc[-1] == data.iloc[-1])
assert all(data_modify.iloc[0] == data.iloc[30])
data_modify = data.copy()
tr = TimeRange('date', None, min_date + 1800, 0)
# Remove first 20 candles - ignores min date
data_modify = trim_dataframe(data_modify, tr, startup_candles=20)
assert not data_modify.equals(data)
assert len(data_modify) < len(data)
assert len(data_modify) == len(data) - 20
assert all(data_modify.iloc[-1] == data.iloc[-1])
assert all(data_modify.iloc[0] == data.iloc[20])
data_modify = data.copy()
# Remove last 30 minutes (1800 s)
tr = TimeRange(None, 'date', 0, max_date - 1800)
data_modify = trim_dataframe(data_modify, tr)
assert not data_modify.equals(data)
assert len(data_modify) < len(data)
assert len(data_modify) == len(data) - 30
assert all(data_modify.iloc[0] == data.iloc[0])
assert all(data_modify.iloc[-1] == data.iloc[-31])
data_modify = data.copy()
# Remove first 25 and last 30 minutes (1800 s)
tr = TimeRange('date', 'date', min_date + 1500, max_date - 1800)
data_modify = trim_dataframe(data_modify, tr)
assert not data_modify.equals(data)
assert len(data_modify) < len(data)
assert len(data_modify) == len(data) - 55
# first row matches 25th original row
assert all(data_modify.iloc[0] == data.iloc[25])
def test_trades_remove_duplicates(trades_history):
trades_history1 = trades_history * 3
assert len(trades_history1) == len(trades_history) * 3
res = trades_remove_duplicates(trades_history1)
assert len(res) == len(trades_history)
for i, t in enumerate(res):
assert t == trades_history[i]
def test_trades_dict_to_list(fetch_trades_result):
res = trades_dict_to_list(fetch_trades_result)
assert isinstance(res, list)
assert isinstance(res[0], list)
for i, t in enumerate(res):
assert t[0] == fetch_trades_result[i]['timestamp']
assert t[1] == fetch_trades_result[i]['id']
assert t[2] == fetch_trades_result[i]['type']
assert t[3] == fetch_trades_result[i]['side']
assert t[4] == fetch_trades_result[i]['price']
assert t[5] == fetch_trades_result[i]['amount']
assert t[6] == fetch_trades_result[i]['cost']
def test_convert_trades_format(default_conf, testdatadir, tmpdir):
tmpdir1 = Path(tmpdir)
files = [{'old': tmpdir1 / "XRP_ETH-trades.json.gz",
'new': tmpdir1 / "XRP_ETH-trades.json"},
{'old': tmpdir1 / "XRP_OLD-trades.json.gz",
'new': tmpdir1 / "XRP_OLD-trades.json"},
]
for file in files:
copyfile(testdatadir / file['old'].name, file['old'])
assert not file['new'].exists()
default_conf['datadir'] = tmpdir1
convert_trades_format(default_conf, convert_from='jsongz',
convert_to='json', erase=False)
for file in files:
assert file['new'].exists()
assert file['old'].exists()
# Remove original file
file['old'].unlink()
# Convert back
convert_trades_format(default_conf, convert_from='json',
convert_to='jsongz', erase=True)
for file in files:
assert file['old'].exists()
assert not file['new'].exists()
_clean_test_file(file['old'])
if file['new'].exists():
file['new'].unlink()
@pytest.mark.parametrize('file_base,candletype', [
(['XRP_ETH-5m', 'XRP_ETH-1m'], CandleType.SPOT),
(['UNITTEST_USDT_USDT-1h-mark', 'XRP_USDT_USDT-1h-mark'], CandleType.MARK),
(['XRP_USDT_USDT-1h-futures'], CandleType.FUTURES),
])
def test_convert_ohlcv_format(default_conf, testdatadir, tmpdir, file_base, candletype):
tmpdir1 = Path(tmpdir)
prependix = '' if candletype == CandleType.SPOT else 'futures/'
files_orig = []
files_temp = []
files_new = []
for file in file_base:
file_orig = testdatadir / f"{prependix}{file}.json"
file_temp = tmpdir1 / f"{prependix}{file}.json"
file_new = tmpdir1 / f"{prependix}{file}.json.gz"
IDataHandler.create_dir_if_needed(file_temp)
copyfile(file_orig, file_temp)
files_orig.append(file_orig)
files_temp.append(file_temp)
files_new.append(file_new)
default_conf['datadir'] = tmpdir1
if candletype == CandleType.SPOT:
default_conf['pairs'] = ['XRP/ETH', 'XRP/USDT', 'UNITTEST/USDT']
else:
default_conf['pairs'] = ['XRP/ETH:ETH', 'XRP/USDT:USDT', 'UNITTEST/USDT:USDT']
default_conf['timeframes'] = ['1m', '5m', '1h']
assert not file_new.exists()
convert_ohlcv_format(
default_conf,
convert_from='json',
convert_to='jsongz',
erase=False,
candle_type=candletype
)
for file in (files_temp + files_new):
assert file.exists()
# Remove original files
for file in (files_temp):
file.unlink()
# Convert back
convert_ohlcv_format(
default_conf,
convert_from='jsongz',
convert_to='json',
erase=True,
candle_type=candletype
)
for file in (files_temp):
assert file.exists()
for file in (files_new):
assert not file.exists()
def test_reduce_dataframe_footprint():
data = generate_test_data('15m', 40)
data['open_copy'] = data['open']
data['close_copy'] = data['close']
data['close_copy'] = data['close']
assert data['open'].dtype == np.float64
assert data['open_copy'].dtype == np.float64
assert data['close_copy'].dtype == np.float64
df2 = reduce_dataframe_footprint(data)
# Does not modify original dataframe
assert data['open'].dtype == np.float64
assert data['open_copy'].dtype == np.float64
assert data['close_copy'].dtype == np.float64
# skips ohlcv columns
assert df2['open'].dtype == np.float64
assert df2['high'].dtype == np.float64
assert df2['low'].dtype == np.float64
assert df2['close'].dtype == np.float64
assert df2['volume'].dtype == np.float64
# Changes dtype of returned dataframe
assert df2['open_copy'].dtype == np.float32
assert df2['close_copy'].dtype == np.float32
Reference in New Issue View Git Blame Copy Permalink