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freqtrade/tests/optimize/test_optimize_reports.py
Matthias 301d85ef4f test: remove edge tests
2025-06-10 18:20:02 +02:00

688 lines
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import json
import re
import shutil
from datetime import timedelta
from pathlib import Path
from shutil import copyfile
from zipfile import ZipFile
import joblib
import pandas as pd
import pytest
from freqtrade.configuration import TimeRange
from freqtrade.constants import BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data import history
from freqtrade.data.btanalysis import (
get_latest_backtest_filename,
load_backtest_data,
load_backtest_stats,
)
from freqtrade.enums import ExitType
from freqtrade.optimize.optimize_reports import (
generate_backtest_stats,
generate_daily_stats,
generate_pair_metrics,
generate_periodic_breakdown_stats,
generate_strategy_comparison,
generate_trading_stats,
show_sorted_pairlist,
store_backtest_results,
text_table_bt_results,
text_table_strategy,
)
from freqtrade.optimize.optimize_reports.bt_output import text_table_tags
from freqtrade.optimize.optimize_reports.optimize_reports import (
_get_resample_from_period,
calc_streak,
generate_tag_metrics,
)
from freqtrade.resolvers.strategy_resolver import StrategyResolver
from freqtrade.util import dt_ts, format_duration
from freqtrade.util.datetime_helpers import dt_from_ts, dt_utc
from tests.conftest import CURRENT_TEST_STRATEGY, log_has_re
from tests.data.test_history import _clean_test_file
def _backup_file(file: Path, copy_file: bool = False) -> None:
"""
Backup existing file to avoid deleting the user file
:param file: complete path to the file
:param copy_file: keep file in place too.
:return: None
"""
file_swp = str(file) + ".swp"
if file.is_file():
file.rename(file_swp)
if copy_file:
copyfile(file_swp, file)
def test_text_table_bt_results(capsys):
results = pd.DataFrame(
{
"pair": ["ETH/BTC", "ETH/BTC", "ETH/BTC"],
"profit_ratio": [0.1, 0.2, -0.05],
"profit_abs": [0.2, 0.4, -0.1],
"trade_duration": [10, 30, 20],
"close_date": [
dt_utc(2017, 11, 14, 21, 35, 00),
dt_utc(2017, 11, 14, 22, 10, 00),
dt_utc(2017, 11, 14, 22, 43, 00),
],
}
)
pair_results = generate_pair_metrics(
["ETH/BTC"],
stake_currency="BTC",
starting_balance=4,
results=results,
min_date=dt_from_ts(1510688220),
max_date=dt_from_ts(1510700340),
)
text_table_bt_results(pair_results, stake_currency="BTC", title="title")
text = capsys.readouterr().out
re.search(
r".* Pair .* Trades .* Avg Profit % .* Tot Profit BTC .* Tot Profit % .* "
r"Avg Duration .* Win Draw Loss Win% .*",
text,
)
re.search(
r".* ETH/BTC .* 3 .* 8.33 .* 0.50000000 .* 12.50 .* 0:20:00 .* 2 0 1 66.7 .*",
text,
)
re.search(
r".* TOTAL .* 3 .* 8.33 .* 0.50000000 .* 12.50 .* 0:20:00 .* 2 0 1 66.7 .*", text
)
def test_generate_backtest_stats(default_conf, testdatadir, tmp_path):
default_conf.update({"strategy": CURRENT_TEST_STRATEGY})
StrategyResolver.load_strategy(default_conf)
results = {
"DefStrat": {
"results": pd.DataFrame(
{
"pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
"profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
"open_date": [
dt_utc(2017, 11, 14, 19, 32, 00),
dt_utc(2017, 11, 14, 21, 36, 00),
dt_utc(2017, 11, 14, 22, 12, 00),
dt_utc(2017, 11, 14, 22, 44, 00),
],
"close_date": [
dt_utc(2017, 11, 14, 21, 35, 00),
dt_utc(2017, 11, 14, 22, 10, 00),
dt_utc(2017, 11, 14, 22, 43, 00),
dt_utc(2017, 11, 14, 22, 58, 00),
],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
"trade_duration": [123, 34, 31, 14],
"is_open": [False, False, False, True],
"is_short": [False, False, False, False],
"stake_amount": [0.01, 0.01, 0.01, 0.01],
"exit_reason": [
ExitType.ROI.value,
ExitType.STOP_LOSS.value,
ExitType.ROI.value,
ExitType.FORCE_EXIT.value,
],
}
),
"config": default_conf,
"locks": [],
"final_balance": 1000.02,
"rejected_signals": 20,
"timedout_entry_orders": 0,
"timedout_exit_orders": 0,
"canceled_trade_entries": 0,
"canceled_entry_orders": 0,
"replaced_entry_orders": 0,
"backtest_start_time": dt_ts() // 1000,
"backtest_end_time": dt_ts() // 1000,
"run_id": "123",
}
}
timerange = TimeRange.parse_timerange("1510688220-1510700340")
min_date = dt_from_ts(1510688220)
max_date = dt_from_ts(1510700340)
btdata = history.load_data(
testdatadir, "1m", ["UNITTEST/BTC"], timerange=timerange, fill_up_missing=True
)
stats = generate_backtest_stats(btdata, results, min_date, max_date)
assert isinstance(stats, dict)
assert "strategy" in stats
assert "DefStrat" in stats["strategy"]
assert "strategy_comparison" in stats
strat_stats = stats["strategy"]["DefStrat"]
assert strat_stats["backtest_start"] == min_date.strftime(DATETIME_PRINT_FORMAT)
assert strat_stats["backtest_end"] == max_date.strftime(DATETIME_PRINT_FORMAT)
assert strat_stats["total_trades"] == len(results["DefStrat"]["results"])
# Above sample had no losing trade
assert strat_stats["max_drawdown_account"] == 0.0
# Retry with losing trade
results = {
"DefStrat": {
"results": pd.DataFrame(
{
"pair": ["UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC", "UNITTEST/BTC"],
"profit_ratio": [0.003312, 0.010801, -0.013803, 0.002780],
"profit_abs": [0.000003, 0.000011, -0.000014, 0.000003],
"open_date": [
dt_utc(2017, 11, 14, 19, 32, 00),
dt_utc(2017, 11, 14, 21, 36, 00),
dt_utc(2017, 11, 14, 22, 12, 00),
dt_utc(2017, 11, 14, 22, 44, 00),
],
"close_date": [
dt_utc(2017, 11, 14, 21, 35, 00),
dt_utc(2017, 11, 14, 22, 10, 00),
dt_utc(2017, 11, 14, 22, 43, 00),
dt_utc(2017, 11, 14, 22, 58, 00),
],
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
"close_rate": [0.002546, 0.003014, 0.0032903, 0.003217],
"trade_duration": [123, 34, 31, 14],
"is_open": [False, False, False, True],
"is_short": [False, False, False, False],
"stake_amount": [0.01, 0.01, 0.01, 0.01],
"exit_reason": [
ExitType.ROI.value,
ExitType.ROI.value,
ExitType.STOP_LOSS.value,
ExitType.FORCE_EXIT.value,
],
}
),
"config": default_conf,
"locks": [],
"final_balance": 1000.02,
"rejected_signals": 20,
"timedout_entry_orders": 0,
"timedout_exit_orders": 0,
"canceled_trade_entries": 0,
"canceled_entry_orders": 0,
"replaced_entry_orders": 0,
"backtest_start_time": dt_ts() // 1000,
"backtest_end_time": dt_ts() // 1000,
"run_id": "124",
}
}
stats = generate_backtest_stats(btdata, results, min_date, max_date)
assert isinstance(stats, dict)
assert "strategy" in stats
assert "DefStrat" in stats["strategy"]
assert "strategy_comparison" in stats
strat_stats = stats["strategy"]["DefStrat"]
assert pytest.approx(strat_stats["max_drawdown_account"]) == 1.399999e-08
assert strat_stats["drawdown_start"] == "2017-11-14 22:10:00"
assert strat_stats["drawdown_end"] == "2017-11-14 22:43:00"
assert strat_stats["drawdown_end_ts"] == 1510699380000
assert strat_stats["drawdown_start_ts"] == 1510697400000
assert strat_stats["pairlist"] == ["UNITTEST/BTC"]
# Test storing stats
filename = tmp_path / "btresult.json"
filename_last = tmp_path / LAST_BT_RESULT_FN
_backup_file(filename_last, copy_file=True)
assert not filename.is_file()
default_conf["exportfilename"] = filename
store_backtest_results(default_conf, stats, "2022_01_01_15_05_13")
# get real Filename (it's btresult-<date>.zip)
last_fn = get_latest_backtest_filename(filename_last.parent)
assert re.match(r"btresult-.*\.zip", last_fn)
filename1 = tmp_path / last_fn
assert filename1.is_file()
content = json.dumps(load_backtest_stats(filename1))
assert "max_drawdown_account" in content
assert "strategy" in content
assert "pairlist" in content
assert filename_last.is_file()
_clean_test_file(filename_last)
filename1.unlink()
def test_store_backtest_results(testdatadir, mocker):
dump_mock = mocker.patch("freqtrade.optimize.optimize_reports.bt_storage.file_dump_json")
zip_mock = mocker.patch("freqtrade.optimize.optimize_reports.bt_storage.ZipFile")
data = {"metadata": {}, "strategy": {}, "strategy_comparison": []}
store_backtest_results(
{"exportfilename": testdatadir, "original_config": {}}, data, "2022_01_01_15_05_13"
)
assert dump_mock.call_count == 2
assert zip_mock.call_count == 1
assert isinstance(dump_mock.call_args_list[0][0][0], Path)
assert str(dump_mock.call_args_list[0][0][0]).startswith(str(testdatadir / "backtest-result"))
dump_mock.reset_mock()
zip_mock.reset_mock()
filename = testdatadir / "testresult.json"
store_backtest_results(
{"exportfilename": filename, "original_config": {}}, data, "2022_01_01_15_05_13"
)
assert dump_mock.call_count == 2
assert zip_mock.call_count == 1
assert isinstance(dump_mock.call_args_list[0][0][0], Path)
# result will be testdatadir / testresult-<timestamp>.json
assert str(dump_mock.call_args_list[0][0][0]).startswith(str(testdatadir / "testresult"))
def test_store_backtest_results_real(tmp_path, caplog):
data = {"metadata": {}, "strategy": {}, "strategy_comparison": []}
config = {
"exportfilename": tmp_path,
"original_config": {},
}
store_backtest_results(
config, data, "2022_01_01_15_05_13", strategy_files={"DefStrat": "NoFile"}
)
assert log_has_re(r"Strategy file .* does not exist\. Skipping\.", caplog)
zip_file = tmp_path / "backtest-result-2022_01_01_15_05_13.zip"
assert zip_file.is_file()
assert (tmp_path / "backtest-result-2022_01_01_15_05_13.meta.json").is_file()
assert not (tmp_path / "backtest-result-2022_01_01_15_05_13_market_change.feather").is_file()
with ZipFile(zip_file, "r") as zipf:
assert "backtest-result-2022_01_01_15_05_13.json" in zipf.namelist()
assert "backtest-result-2022_01_01_15_05_13_market_change.feather" not in zipf.namelist()
assert (tmp_path / LAST_BT_RESULT_FN).is_file()
fn = get_latest_backtest_filename(tmp_path)
assert fn == "backtest-result-2022_01_01_15_05_13.zip"
strategy_test_dir = Path(__file__).parent.parent / "strategy" / "strats"
shutil.copy(strategy_test_dir / "strategy_test_v3.py", tmp_path)
params_file = tmp_path / "strategy_test_v3.json"
with params_file.open("w") as f:
f.write("""{"strategy_name": "TurtleStrategyX5","params":{}}""")
store_backtest_results(
config,
data,
"2024_01_01_15_05_25",
market_change_data=pd.DataFrame(),
strategy_files={"DefStrat": str(tmp_path / "strategy_test_v3.py")},
)
zip_file = tmp_path / "backtest-result-2024_01_01_15_05_25.zip"
assert zip_file.is_file()
assert (tmp_path / "backtest-result-2024_01_01_15_05_25.meta.json").is_file()
assert not (tmp_path / "backtest-result-2024_01_01_15_05_25_market_change.feather").is_file()
with ZipFile(zip_file, "r") as zipf:
assert "backtest-result-2024_01_01_15_05_25.json" in zipf.namelist()
assert "backtest-result-2024_01_01_15_05_25_market_change.feather" in zipf.namelist()
assert "backtest-result-2024_01_01_15_05_25_config.json" in zipf.namelist()
# strategy file is copied to the zip file
assert "backtest-result-2024_01_01_15_05_25_DefStrat.py" in zipf.namelist()
# compare the content of the strategy file
with zipf.open("backtest-result-2024_01_01_15_05_25_DefStrat.py") as strategy_file:
strategy_content = strategy_file.read()
with (strategy_test_dir / "strategy_test_v3.py").open("rb") as original_file:
original_content = original_file.read()
assert strategy_content == original_content
assert "backtest-result-2024_01_01_15_05_25_DefStrat.py" in zipf.namelist()
with zipf.open("backtest-result-2024_01_01_15_05_25_DefStrat.json") as pf:
params_content = pf.read()
with params_file.open("rb") as original_file:
original_content = original_file.read()
assert params_content == original_content
assert (tmp_path / LAST_BT_RESULT_FN).is_file()
# Last file reference should be updated
fn = get_latest_backtest_filename(tmp_path)
assert fn == "backtest-result-2024_01_01_15_05_25.zip"
def test_write_read_backtest_candles(tmp_path):
candle_dict = {"DefStrat": {"UNITTEST/BTC": pd.DataFrame()}}
bt_results = {"metadata": {}, "strategy": {}, "strategy_comparison": []}
mock_conf = {
"exportfilename": tmp_path,
"export": "signals",
"runmode": "backtest",
"original_config": {},
}
# test directory exporting
sample_date = "2022_01_01_15_05_13"
data = {
"signals": candle_dict,
"rejected": {},
"exited": {},
}
store_backtest_results(mock_conf, bt_results, sample_date, analysis_results=data)
stored_file = tmp_path / f"backtest-result-{sample_date}.zip"
signals_pkl = f"backtest-result-{sample_date}_signals.pkl"
rejected_pkl = f"backtest-result-{sample_date}_rejected.pkl"
exited_pkl = f"backtest-result-{sample_date}_exited.pkl"
assert not (tmp_path / signals_pkl).is_file()
assert stored_file.is_file()
with ZipFile(stored_file, "r") as zipf:
assert signals_pkl in zipf.namelist()
assert rejected_pkl in zipf.namelist()
assert exited_pkl in zipf.namelist()
# open and read the file
with zipf.open(signals_pkl) as scp:
pickled_signal_candles = joblib.load(scp)
assert pickled_signal_candles.keys() == candle_dict.keys()
assert pickled_signal_candles["DefStrat"].keys() == pickled_signal_candles["DefStrat"].keys()
assert pickled_signal_candles["DefStrat"]["UNITTEST/BTC"].equals(
pickled_signal_candles["DefStrat"]["UNITTEST/BTC"]
)
_clean_test_file(stored_file)
# test file exporting
filename = tmp_path / "testresult"
mock_conf["exportfilename"] = filename
store_backtest_results(mock_conf, bt_results, sample_date, analysis_results=data)
stored_file = tmp_path / f"testresult-{sample_date}.zip"
signals_pkl = f"testresult-{sample_date}_signals.pkl"
rejected_pkl = f"testresult-{sample_date}_rejected.pkl"
exited_pkl = f"testresult-{sample_date}_exited.pkl"
assert not (tmp_path / signals_pkl).is_file()
assert stored_file.is_file()
with ZipFile(stored_file, "r") as zipf:
assert signals_pkl in zipf.namelist()
assert rejected_pkl in zipf.namelist()
assert exited_pkl in zipf.namelist()
with zipf.open(signals_pkl) as scp:
pickled_signal_candles2 = joblib.load(scp)
assert pickled_signal_candles2.keys() == candle_dict.keys()
assert pickled_signal_candles2["DefStrat"].keys() == pickled_signal_candles2["DefStrat"].keys()
assert pickled_signal_candles2["DefStrat"]["UNITTEST/BTC"].equals(
pickled_signal_candles2["DefStrat"]["UNITTEST/BTC"]
)
_clean_test_file(stored_file)
def test_generate_pair_metrics():
results = pd.DataFrame(
{
"pair": ["ETH/BTC", "ETH/BTC"],
"profit_ratio": [0.1, 0.2],
"profit_abs": [0.2, 0.4],
"trade_duration": [10, 30],
"close_date": [
dt_utc(2017, 11, 14, 21, 35, 00),
dt_utc(2017, 11, 14, 22, 10, 00),
],
"wins": [2, 0],
"draws": [0, 0],
"losses": [0, 0],
}
)
pair_results = generate_pair_metrics(
["ETH/BTC"],
stake_currency="BTC",
starting_balance=2,
results=results,
min_date=dt_from_ts(1510688220),
max_date=dt_from_ts(1510700340),
)
assert isinstance(pair_results, list)
assert len(pair_results) == 2
assert pair_results[-1]["key"] == "TOTAL"
assert (
pytest.approx(pair_results[-1]["profit_mean_pct"]) == pair_results[-1]["profit_mean"] * 100
)
assert pytest.approx(pair_results[-1]["profit_sum_pct"]) == pair_results[-1]["profit_sum"] * 100
def test_generate_daily_stats(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
res = generate_daily_stats(bt_data)
assert isinstance(res, dict)
assert round(res["backtest_best_day"], 4) == 0.1796
assert round(res["backtest_worst_day"], 4) == -0.1468
assert res["winning_days"] == 19
assert res["draw_days"] == 0
assert res["losing_days"] == 2
# Select empty dataframe!
res = generate_daily_stats(bt_data.loc[bt_data["open_date"] == "2000-01-01", :])
assert isinstance(res, dict)
assert round(res["backtest_best_day"], 4) == 0.0
assert res["winning_days"] == 0
assert res["draw_days"] == 0
assert res["losing_days"] == 0
def test_generate_trading_stats(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
res = generate_trading_stats(bt_data)
assert isinstance(res, dict)
assert res["winner_holding_avg"] == format_duration(timedelta(seconds=1440))
assert res["loser_holding_avg"] == format_duration(timedelta(days=1, seconds=21420))
assert "wins" in res
assert "losses" in res
assert "draws" in res
# Select empty dataframe!
res = generate_trading_stats(bt_data.loc[bt_data["open_date"] == "2000-01-01", :])
assert res["wins"] == 0
assert res["losses"] == 0
def test_calc_streak(testdatadir):
df = pd.DataFrame(
{
"profit_ratio": [0.05, -0.02, -0.03, -0.05, 0.01, 0.02, 0.03, 0.04, -0.02, -0.03],
}
)
# 4 consecutive wins, 3 consecutive losses
res = calc_streak(df)
assert res == (4, 3)
assert isinstance(res[0], int)
assert isinstance(res[1], int)
# invert situation
df1 = df.copy()
df1["profit_ratio"] = df1["profit_ratio"] * -1
assert calc_streak(df1) == (3, 4)
df_empty = pd.DataFrame(
{
"profit_ratio": [],
}
)
assert df_empty.empty
assert calc_streak(df_empty) == (0, 0)
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
assert calc_streak(bt_data) == (7, 18)
def test_text_table_exit_reason(capsys):
results = pd.DataFrame(
{
"pair": ["ETH/BTC", "ETH/BTC", "ETH/BTC"],
"profit_ratio": [0.1, 0.2, -0.1],
"profit_abs": [0.2, 0.4, -0.2],
"trade_duration": [10, 30, 10],
"close_date": [
dt_utc(2017, 11, 14, 21, 35, 00),
dt_utc(2017, 11, 14, 22, 10, 00),
dt_utc(2017, 11, 14, 22, 43, 00),
],
"wins": [2, 0, 0],
"draws": [0, 0, 0],
"losses": [0, 0, 1],
"exit_reason": [ExitType.ROI.value, ExitType.ROI.value, ExitType.STOP_LOSS.value],
}
)
exit_reason_stats = generate_tag_metrics(
"exit_reason",
starting_balance=22,
results=results,
min_date=dt_from_ts(1510688220),
max_date=dt_from_ts(1510700340),
skip_nan=False,
)
text_table_tags("exit_tag", exit_reason_stats, "BTC")
text = capsys.readouterr().out
assert re.search(
r".* Exit Reason .* Exits .* Avg Profit % .* Tot Profit BTC .* Tot Profit % .* "
r"Avg Duration .* Win Draw Loss Win% .*",
text,
)
assert re.search(
r".* roi .* 2 .* 15.0 .* 0.60000000 .* 2.73 .* 0:20:00 .* 2 0 0 100 .*",
text,
)
assert re.search(
r".* stop_loss .* 1 .* -10.0 .* -0.20000000 .* -0.91 .* 0:10:00 .* 0 0 1 0 .*",
text,
)
assert re.search(
r".* TOTAL .* 3 .* 6.67 .* 0.40000000 .* 1.82 .* 0:17:00 .* 2 0 1 66.7 .*", text
)
def test_generate_sell_reason_stats():
results = pd.DataFrame(
{
"pair": ["ETH/BTC", "ETH/BTC", "ETH/BTC"],
"profit_ratio": [0.1, 0.2, -0.1],
"profit_abs": [0.2, 0.4, -0.2],
"trade_duration": [10, 30, 10],
"close_date": [
dt_utc(2017, 11, 14, 21, 35, 00),
dt_utc(2017, 11, 14, 22, 10, 00),
dt_utc(2017, 11, 14, 22, 43, 00),
],
"wins": [2, 0, 0],
"draws": [0, 0, 0],
"losses": [0, 0, 1],
"exit_reason": [ExitType.ROI.value, ExitType.ROI.value, ExitType.STOP_LOSS.value],
}
)
exit_reason_stats = generate_tag_metrics(
"exit_reason",
starting_balance=22,
results=results,
min_date=dt_from_ts(1510688220),
max_date=dt_from_ts(1510700340),
skip_nan=False,
)
roi_result = exit_reason_stats[0]
assert roi_result["key"] == "roi"
assert roi_result["trades"] == 2
assert pytest.approx(roi_result["profit_mean"]) == 0.15
assert roi_result["profit_mean_pct"] == round(roi_result["profit_mean"] * 100, 2)
assert pytest.approx(roi_result["profit_mean"]) == 0.15
assert roi_result["profit_mean_pct"] == round(roi_result["profit_mean"] * 100, 2)
stop_result = exit_reason_stats[1]
assert stop_result["key"] == "stop_loss"
assert stop_result["trades"] == 1
assert pytest.approx(stop_result["profit_mean"]) == -0.1
assert stop_result["profit_mean_pct"] == round(stop_result["profit_mean"] * 100, 2)
assert pytest.approx(stop_result["profit_mean"]) == -0.1
assert stop_result["profit_mean_pct"] == round(stop_result["profit_mean"] * 100, 2)
def test_text_table_strategy(testdatadir, capsys):
filename = testdatadir / "backtest_results/backtest-result_multistrat.json"
bt_res_data = load_backtest_stats(filename)
bt_res_data_comparison = bt_res_data.pop("strategy_comparison")
strategy_results = generate_strategy_comparison(bt_stats=bt_res_data["strategy"])
assert strategy_results == bt_res_data_comparison
text_table_strategy(strategy_results, "BTC", "STRATEGY SUMMARY")
captured = capsys.readouterr()
text = captured.out
assert re.search(
r".* Strategy .* Trades .* Avg Profit % .* Tot Profit BTC .* Tot Profit % .* "
r"Avg Duration .* Win Draw Loss Win% .* Drawdown .*",
text,
)
assert re.search(
r".*StrategyTestV2 .* 179 .* 0.08 .* 0.02608550 .* "
r"260.85 .* 3:40:00 .* 170 0 9 95.0 .* 0.00308222 BTC 8.67%.*",
text,
)
assert re.search(
r".*TestStrategy .* 179 .* 0.08 .* 0.02608550 .* "
r"260.85 .* 3:40:00 .* 170 0 9 95.0 .* 0.00308222 BTC 8.67%.*",
text,
)
def test_generate_periodic_breakdown_stats(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename).to_dict(orient="records")
res = generate_periodic_breakdown_stats(bt_data, "day")
assert isinstance(res, list)
assert len(res) == 21
day = res[0]
assert "date" in day
assert "draws" in day
assert "losses" in day
assert "wins" in day
assert "profit_abs" in day
# Select empty dataframe!
res = generate_periodic_breakdown_stats([], "day")
assert res == []
def test__get_resample_from_period():
assert _get_resample_from_period("day") == "1d"
assert _get_resample_from_period("week") == "1W-MON"
assert _get_resample_from_period("month") == "1ME"
with pytest.raises(ValueError, match=r"Period noooo is not supported."):
_get_resample_from_period("noooo")
for period in BACKTEST_BREAKDOWNS:
assert isinstance(_get_resample_from_period(period), str)
def test_show_sorted_pairlist(testdatadir, default_conf, capsys):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_stats(filename)
default_conf["backtest_show_pair_list"] = True
show_sorted_pairlist(default_conf, bt_data)
out, _err = capsys.readouterr()
assert "Pairs for Strategy StrategyTestV3: \n[" in out
assert "TOTAL" not in out
assert '"ETH/BTC", // ' in out
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