378 lines
12 KiB
Python
378 lines
12 KiB
Python
# 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-1h-mark', 'XRP_USDT-1h-mark'], CandleType.MARK),
|
|
(['XRP_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
|
|
default_conf['pairs'] = ['XRP_ETH', 'XRP_USDT', 'UNITTEST_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
|