# pragma pylint: disable=missing-docstring, C0103
import logging
from shutil import copyfile
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from freqtrade.configuration.timerange import TimeRange
from freqtrade.data.converter import (
convert_ohlcv_format,
convert_trades_format,
convert_trades_to_ohlcv,
ohlcv_fill_up_missing_data,
ohlcv_to_dataframe,
order_book_to_dataframe,
reduce_dataframe_footprint,
trades_df_remove_duplicates,
trades_dict_to_list,
trades_to_ohlcv,
trim_dataframe,
)
from freqtrade.data.history import (
get_timerange,
load_data,
load_pair_history,
validate_backtest_data,
)
from freqtrade.data.history.datahandlers import IDataHandler
from freqtrade.enums import CandleType
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from tests.conftest import generate_test_data, generate_trades_history, 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(trades_history_df, caplog):
caplog.set_level(logging.DEBUG)
with pytest.raises(ValueError, match=r"Trade-list empty\."):
trades_to_ohlcv(pd.DataFrame(columns=trades_history_df.columns), "1m")
df = trades_to_ohlcv(trades_history_df, "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.iloc[0, :]["high"] == 0.019627
assert df.iloc[0, :]["low"] == 0.019626
assert df.iloc[0, :]["date"] == pd.Timestamp("2019-08-14 15:59:00+0000")
df_1h = trades_to_ohlcv(trades_history_df, "1h")
assert len(df_1h) == 1
assert df_1h.iloc[0, :]["high"] == 0.019627
assert df_1h.iloc[0, :]["low"] == 0.019626
assert df_1h.iloc[0, :]["date"] == pd.Timestamp("2019-08-14 15:00:00+0000")
df_1s = trades_to_ohlcv(trades_history_df, "1s")
assert len(df_1s) == 2
assert df_1s.iloc[0, :]["high"] == 0.019627
assert df_1s.iloc[0, :]["low"] == 0.019627
assert df_1s.iloc[0, :]["date"] == pd.Timestamp("2019-08-14 15:59:49+0000")
assert df_1s.iloc[-1, :]["date"] == pd.Timestamp("2019-08-14 15:59:59+0000")
@pytest.mark.parametrize(
"timeframe,rows,days,candles,start,end,weekday",
[
("1s", 20_000, 5, 19522, "2020-01-01 00:00:05", "2020-01-05 23:59:27", None),
("1m", 20_000, 5, 6745, "2020-01-01 00:00:00", "2020-01-05 23:59:00", None),
("5m", 20_000, 5, 1440, "2020-01-01 00:00:00", "2020-01-05 23:55:00", None),
("15m", 20_000, 5, 480, "2020-01-01 00:00:00", "2020-01-05 23:45:00", None),
("1h", 20_000, 5, 120, "2020-01-01 00:00:00", "2020-01-05 23:00:00", None),
("2h", 20_000, 5, 60, "2020-01-01 00:00:00", "2020-01-05 22:00:00", None),
("4h", 20_000, 5, 30, "2020-01-01 00:00:00", "2020-01-05 20:00:00", None),
("8h", 20_000, 5, 15, "2020-01-01 00:00:00", "2020-01-05 16:00:00", None),
("12h", 20_000, 5, 10, "2020-01-01 00:00:00", "2020-01-05 12:00:00", None),
("1d", 20_000, 5, 5, "2020-01-01 00:00:00", "2020-01-05 00:00:00", "Sunday"),
("7d", 20_000, 37, 6, "2020-01-06 00:00:00", "2020-02-10 00:00:00", "Monday"),
("1w", 20_000, 37, 6, "2020-01-06 00:00:00", "2020-02-10 00:00:00", "Monday"),
("1M", 20_000, 74, 3, "2020-01-01 00:00:00", "2020-03-01 00:00:00", None),
("3M", 20_000, 100, 2, "2020-01-01 00:00:00", "2020-04-01 00:00:00", None),
("1y", 20_000, 1000, 3, "2020-01-01 00:00:00", "2022-01-01 00:00:00", None),
],
)
def test_trades_to_ohlcv_multi(timeframe, rows, days, candles, start, end, weekday):
trades_history = generate_trades_history(n_rows=rows, days=days)
df = trades_to_ohlcv(trades_history, timeframe)
assert not df.empty
assert len(df) == candles
assert df.iloc[0, :]["date"] == pd.Timestamp(f"{start}+0000")
assert df.iloc[-1, :]["date"] == pd.Timestamp(f"{end}+0000")
if weekday:
# Weekday is only relevant for daily and weekly candles.
assert df.iloc[-1, :]["date"].day_name() == weekday
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, 1m: before: {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, {timeframe}: before: "
f"{len(data)} - after: {len(data2)}.*",
caplog,
)
@pytest.mark.parametrize(
"timeframe",
["1s", "1m", "5m", "15m", "1h", "2h", "4h", "8h", "12h", "1d", "7d", "1w", "1M", "3M", "1y"],
)
def test_ohlcv_to_dataframe_multi(timeframe):
data = generate_test_data(timeframe, 180)
assert len(data) == 180
df = ohlcv_to_dataframe(data, timeframe, "UNITTEST/USDT")
assert len(df) == len(data) - 1
df1 = ohlcv_to_dataframe(data, timeframe, "UNITTEST/USDT", drop_incomplete=False)
assert len(df1) == len(data)
assert data.equals(df1)
data1 = data.copy()
if timeframe in ("1M", "3M", "1y"):
data1.loc[:, "date"] = data1.loc[:, "date"] + pd.to_timedelta("1W")
else:
# Shift by half a timeframe
timeframe_f = (
timeframe.upper() if timeframe.endswith("d") or timeframe.endswith("w") else timeframe
)
data1.loc[:, "date"] = data1.loc[:, "date"] + (pd.to_timedelta(timeframe_f) / 2)
df2 = ohlcv_to_dataframe(data1, timeframe, "UNITTEST/USDT")
assert len(df2) == len(data) - 1
tfs = timeframe_to_seconds(timeframe)
tfm = timeframe_to_minutes(timeframe)
if 1 <= tfm < 10000:
# minute based resampling does not work on timeframes >= 1 week
ohlcv_dict = {
"open": "first",
"high": "max",
"low": "min",
"close": "last",
"volume": "sum",
}
dfs = data1.resample(f"{tfs}s", on="date").agg(ohlcv_dict).reset_index(drop=False)
dfm = data1.resample(f"{tfm}min", on="date").agg(ohlcv_dict).reset_index(drop=False)
assert dfs.equals(dfm)
assert dfs.equals(df1)
def test_ohlcv_to_dataframe_1M():
# Monthly ticks from 2019-09-01 to 2023-07-01
ticks = [
[1567296000000, 8042.08, 10475.54, 7700.67, 8041.96, 608742.1109999999],
[1569888000000, 8285.31, 10408.48, 7172.76, 9150.0, 2439561.887],
[1572566400000, 9149.88, 9550.0, 6510.19, 7542.93, 4042674.725],
[1575158400000, 7541.08, 7800.0, 6427.0, 7189.0, 4063882.296],
[1577836800000, 7189.43, 9599.0, 6863.44, 9364.51, 5165281.358],
[1580515200000, 9364.5, 10540.0, 8450.0, 8531.98, 4581788.124],
[1583020800000, 8532.5, 9204.0, 3621.81, 6407.1, 10859497.479],
[1585699200000, 6407.1, 9479.77, 6140.0, 8624.76, 11276526.968],
[1588291200000, 8623.61, 10080.0, 7940.0, 9446.43, 12469561.02],
[1590969600000, 9446.49, 10497.25, 8816.4, 9138.87, 6684044.201],
[1593561600000, 9138.88, 11488.0, 8900.0, 11343.68, 5709327.926],
[1596240000000, 11343.67, 12499.42, 10490.0, 11658.11, 6746487.129],
[1598918400000, 11658.11, 12061.07, 9808.58, 10773.0, 6442697.051],
[1601510400000, 10773.0, 14140.0, 10371.03, 13783.73, 7404103.004],
[1604188800000, 13783.73, 19944.0, 13195.0, 19720.0, 12328272.549],
[1606780800000, 19722.09, 29376.7, 17555.0, 28951.68, 10067314.24],
[1609459200000, 28948.19, 42125.51, 27800.0, 33126.21, 12408873.079],
[1612137600000, 33125.11, 58472.14, 32322.47, 45163.36, 8784474.482],
[1614556800000, 45162.64, 61950.0, 44972.49, 58807.24, 9459821.267],
[1617235200000, 58810.99, 64986.11, 46930.43, 57684.16, 7895051.389],
[1619827200000, 57688.29, 59654.0, 28688.0, 37243.38, 16790964.443],
[1622505600000, 37244.36, 41413.0, 28780.01, 35031.39, 23474519.886],
[1625097600000, 35031.39, 48168.6, 29242.24, 41448.11, 16932491.175],
[1627776000000, 41448.1, 50600.0, 37291.0, 47150.32, 13645800.254],
[1630454400000, 47150.32, 52950.0, 39503.58, 43796.57, 10734742.869],
[1633046400000, 43799.49, 67150.0, 43260.01, 61348.61, 9111112.847],
[1635724800000, 61347.14, 69198.7, 53245.0, 56975.0, 7111424.463],
[1638316800000, 56978.06, 59100.0, 40888.89, 46210.56, 8404449.024],
[1640995200000, 46210.57, 48000.0, 32853.83, 38439.04, 11047479.277],
[1643673600000, 38439.04, 45847.5, 34303.7, 43155.0, 10910339.91],
[1646092800000, 43155.0, 48200.0, 37134.0, 45506.0, 10459721.586],
[1648771200000, 45505.9, 47448.0, 37550.0, 37614.5, 8463568.862],
[1651363200000, 37614.4, 40071.7, 26631.0, 31797.8, 14463715.774],
[1654041600000, 31797.9, 31986.1, 17593.2, 19923.5, 20710810.306],
[1656633600000, 19923.3, 24700.0, 18780.1, 23290.1, 20582518.513],
[1659312000000, 23290.1, 25200.0, 19508.0, 20041.5, 17221921.557],
[1661990400000, 20041.4, 22850.0, 18084.3, 19411.7, 21935261.414],
[1664582400000, 19411.6, 21088.0, 17917.8, 20482.0, 16625843.584],
[1667260800000, 20482.1, 21473.7, 15443.2, 17153.3, 18460614.013],
[1669852800000, 17153.4, 18400.0, 16210.0, 16537.6, 9702408.711],
[1672531200000, 16537.5, 23962.7, 16488.0, 23119.4, 14732180.645],
[1675209600000, 23119.5, 25347.6, 21338.0, 23129.6, 15025197.415],
[1677628800000, 23129.7, 29184.8, 19521.6, 28454.9, 23317458.541],
[1680307200000, 28454.8, 31059.0, 26919.3, 29223.0, 14654208.219],
[1682899200000, 29223.0, 29840.0, 25751.0, 27201.1, 13328157.284],
[1685577600000, 27201.1, 31500.0, 24777.0, 30460.2, 14099299.273],
[1688169600000, 30460.2, 31850.0, 28830.0, 29338.8, 8760361.377],
]
data = ohlcv_to_dataframe(
ticks, "1M", pair="UNITTEST/USDT", fill_missing=False, drop_incomplete=False
)
assert len(data) == len(ticks)
assert data.iloc[0]["date"].strftime("%Y-%m-%d") == "2019-09-01"
assert data.iloc[-1]["date"].strftime("%Y-%m-%d") == "2023-07-01"
# Test with filling missing data
data = ohlcv_to_dataframe(
ticks, "1M", pair="UNITTEST/USDT", fill_missing=True, drop_incomplete=False
)
assert len(data) == len(ticks)
assert data.iloc[0]["date"].strftime("%Y-%m-%d") == "2019-09-01"
assert data.iloc[-1]["date"].strftime("%Y-%m-%d") == "2023-07-01"
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_df_remove_duplicates(trades_history_df):
trades_history1 = pd.concat(
[trades_history_df, trades_history_df, trades_history_df]
).reset_index(drop=True)
assert len(trades_history1) == len(trades_history_df) * 3
res = trades_df_remove_duplicates(trades_history1)
assert len(res) == len(trades_history_df)
assert res.equals(trades_history_df)
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, tmp_path):
files = [
{"old": tmp_path / "XRP_ETH-trades.json.gz", "new": tmp_path / "XRP_ETH-trades.json"},
{"old": tmp_path / "XRP_OLD-trades.json.gz", "new": tmp_path / "XRP_OLD-trades.json"},
]
for file in files:
copyfile(testdatadir / file["old"].name, file["old"])
assert not file["new"].exists()
default_conf["datadir"] = tmp_path
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, tmp_path, file_base, candletype):
prependix = "" if candletype == CandleType.SPOT else "futures/"
files_orig = []
files_temp = []
files_new = []
for file in file_base:
file_orig = testdatadir / f"{prependix}{file}.feather"
file_temp = tmp_path / f"{prependix}{file}.feather"
file_new = tmp_path / 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"] = tmp_path
default_conf["candle_types"] = [candletype]
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="feather",
convert_to="jsongz",
erase=False,
)
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="feather",
erase=True,
)
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
def test_convert_trades_to_ohlcv(testdatadir, tmp_path, caplog):
pair = "XRP/ETH"
file1 = tmp_path / "XRP_ETH-1m.feather"
file5 = tmp_path / "XRP_ETH-5m.feather"
filetrades = tmp_path / "XRP_ETH-trades.json.gz"
copyfile(testdatadir / file1.name, file1)
copyfile(testdatadir / file5.name, file5)
copyfile(testdatadir / filetrades.name, filetrades)
# Compare downloaded dataset with converted dataset
dfbak_1m = load_pair_history(datadir=tmp_path, timeframe="1m", pair=pair)
dfbak_5m = load_pair_history(datadir=tmp_path, timeframe="5m", pair=pair)
tr = TimeRange.parse_timerange("20191011-20191012")
convert_trades_to_ohlcv(
[pair],
timeframes=["1m", "5m"],
data_format_trades="jsongz",
datadir=tmp_path,
timerange=tr,
erase=True,
data_format_ohlcv="feather",
candle_type=CandleType.SPOT,
)
assert log_has("Deleting existing data for pair XRP/ETH, interval 1m.", caplog)
# Load new data
df_1m = load_pair_history(datadir=tmp_path, timeframe="1m", pair=pair)
df_5m = load_pair_history(datadir=tmp_path, timeframe="5m", pair=pair)
assert_frame_equal(dfbak_1m, df_1m, check_exact=True)
assert_frame_equal(dfbak_5m, df_5m, check_exact=True)
msg = "Could not convert NoDatapair to OHLCV."
assert not log_has(msg, caplog)
convert_trades_to_ohlcv(
["NoDatapair"],
timeframes=["1m", "5m"],
data_format_trades="jsongz",
datadir=tmp_path,
timerange=tr,
erase=True,
data_format_ohlcv="feather",
candle_type=CandleType.SPOT,
)
assert log_has(msg, caplog)
def test_order_book_to_dataframe():
bids = [
[100.0, 5.0],
[99.5, 3.0],
[99.0, 2.0],
]
asks = [
[100.5, 4.0],
[101.0, 6.0],
[101.5, 1.0],
]
result = order_book_to_dataframe(bids, asks)
assert isinstance(result, pd.DataFrame)
expected_columns = ["b_sum", "b_size", "bids", "asks", "a_size", "a_sum"]
assert result.columns.tolist() == expected_columns
assert len(result) == max(len(bids), len(asks))
assert result["bids"].tolist() == [100.0, 99.5, 99.0]
assert result["b_size"].tolist() == [5.0, 3.0, 2.0]
assert result["b_sum"].tolist() == [5.0, 8.0, 10.0]
assert result["asks"].tolist() == [100.5, 101.0, 101.5]
assert result["a_size"].tolist() == [4.0, 6.0, 1.0]
assert result["a_sum"].tolist() == [4.0, 10.0, 11.0]
def test_order_book_to_dataframe_empty():
bids = []
asks = []
result = order_book_to_dataframe(bids, asks)
assert isinstance(result, pd.DataFrame)
expected_columns = ["b_sum", "b_size", "bids", "asks", "a_size", "a_sum"]
assert result.columns.tolist() == expected_columns
# Empty input should result in empty dataframe
assert len(result) == 0
def test_order_book_to_dataframe_unequal_lengths():
bids = [
[100.0, 5.0],
[99.5, 3.0],
[99.0, 2.0],
[98.5, 1.0],
]
asks = [
[100.5, 4.0],
[101.0, 6.0],
]
result = order_book_to_dataframe(bids, asks)
assert len(result) == max(len(bids), len(asks))
assert len(result) == 4
assert result["bids"].tolist() == [100.0, 99.5, 99.0, 98.5]
assert result["b_size"].tolist() == [5.0, 3.0, 2.0, 1.0]
assert result["b_sum"].tolist() == [5.0, 8.0, 10.0, 11.0]
assert result["asks"].tolist()[:2] == [100.5, 101.0]
# NA for missing asks
assert pd.isna(result["asks"].iloc[2])
assert pd.isna(result["asks"].iloc[3])
assert result["a_size"].tolist()[:2] == [4.0, 6.0]
assert result["a_sum"].tolist()[:2] == [4.0, 10.0]